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Martinez MF, Weatherwax C, Piercy K, Whitley MA, Bartsch SM, Heneghan J, Fox M, Bowers MT, Chin KL, Velmurugan K, Dibbs A, Smith AL, Pfeiffer KA, Farrey T, Tsintsifas A, Scannell SA, Lee BY. Benefits of Meeting the Healthy People 2030 Youth Sports Participation Target. Am J Prev Med 2024; 66:760-769. [PMID: 38416089 PMCID: PMC11034834 DOI: 10.1016/j.amepre.2023.12.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 12/18/2023] [Accepted: 12/18/2023] [Indexed: 02/29/2024]
Abstract
INTRODUCTION Healthy People 2030, a U.S. government health initiative, has indicated that increasing youth sports participation to 63.3% is a priority in the U.S. This study quantified the health and economic value of achieving this target. METHODS An agent-based model developed in 2023 represents each person aged 6-17 years in the U.S. On each simulated day, agents can participate in sports that affect their metabolic and mental health in the model. Each agent can develop different physical and mental health outcomes, associated with direct and indirect costs. RESULTS Increasing the proportion of youth participating in sports from the most recent participation levels (50.7%) to the Healthy People 2030 target (63.3%) could reduce overweight/obesity prevalence by 3.37% (95% CI=3.35%, 3.39%), resulting in 1.71 million fewer cases of overweight/obesity (95% CI=1.64, 1.77 million). This could avert 352,000 (95% CI=336,200, 367,500) cases of weight-related diseases and gain 1.86 million (95% CI=1.86, 1.87 million) quality-adjusted life years, saving $22.55 billion (95% CI=$22.46, $22.63 billion) in direct medical costs and $25.43 billion (95% CI= $25.25, $25.61 billion) in productivity losses. This would also reduce depression/anxiety symptoms, saving $3.61 billion (95% CI=$3.58, $3.63 billion) in direct medical costs and $28.38 billion (95% CI=$28.20, $28.56 billion) in productivity losses. CONCLUSIONS This study shows that achieving the Healthy People 2030 objective could save third-party payers, businesses, and society billions of dollars for each cohort of persons aged 6-17 years, savings that would continue to repeat with each new cohort. This suggests that even if a substantial amount is invested toward this objective, such investments could pay for themselves.
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Affiliation(s)
- Marie F Martinez
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York City, New York; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Colleen Weatherwax
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York City, New York; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Katrina Piercy
- Office of Disease Prevention and Health Promotion, U.S. Department of Health and Human Services (HHS), Rockville, Maryland
| | - Meredith A Whitley
- Ruth S. Ammon College of Education and Health Sciences, Adelphi University, Garden City, New York; Stellenbosch University, Maties Sport, Centre for Sport Leadership, Stellenbosch, South Africa
| | - Sarah M Bartsch
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York City, New York; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Jessie Heneghan
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York City, New York; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Martin Fox
- Project Play, Sports & Society Program, The Aspen Institute, Washington, District of Columbia
| | - Matthew T Bowers
- Department of Kinesiology and Health Education, College of Education, The University of Texas at Austin, Austin, Texas
| | - Kevin L Chin
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York City, New York; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Kavya Velmurugan
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York City, New York; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Alexis Dibbs
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York City, New York; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Alan L Smith
- Emma Eccles Jones College of Education and Human Services, Utah State University, Logan, Utah
| | - Karin A Pfeiffer
- Department of Kinesiology, College of Education, Michigan State University, East Lansing, Michigan
| | - Tom Farrey
- Project Play, Sports & Society Program, The Aspen Institute, Washington, District of Columbia
| | - Alexandra Tsintsifas
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York City, New York; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Sheryl A Scannell
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York City, New York; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Bruce Y Lee
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York City, New York; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York.
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2
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Bartsch SM, O'Shea KJ, Weatherwax C, Strych U, Velmurugan K, John DC, Bottazzi ME, Hussein M, Martinez MF, Chin KL, Ciciriello A, Heneghan J, Dibbs A, Scannell SA, Hotez PJ, Lee BY. What is the economic benefit of annual COVID-19 vaccination from the adult individual perspective? J Infect Dis 2024:jiae179. [PMID: 38581432 DOI: 10.1093/infdis/jiae179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 03/20/2024] [Accepted: 04/03/2024] [Indexed: 04/08/2024] Open
Abstract
BACKGROUND With COVID-19 vaccination no longer mandated by many businesses/organizations, it is now up to individuals to decide whether to get any new boosters/updated vaccines going forward. METHODS We developed a Markov model representing the potential clinical/economic outcomes from an individual perspective in the United States of getting versus not getting an annual COVID-19 vaccine. RESULTS For an 18-49-year-old, getting vaccinated at its current price ($60) can save the individual on average $30-$603 if the individual is uninsured and $4-$437 if the individual has private insurance, as long as the starting vaccine efficacy against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is ≥50% and the weekly risk of getting infected is ≥0.2%, corresponding to an individual interacting with 9 other people in a day under Winter 2023-2024 Omicron SARS-CoV-2 variant conditions with an average infection prevalence of 10%. For a 50-64-year-old, these cost-savings increase to $111-$1,278 and $119-$1,706, for someone without and with insurance, respectively. The risk threshold increases to ≥0.4% (interacting with 19 people/day), when the individual has 13.4% pre-existing protection against infection (e.g., vaccinated 9 months earlier). CONCLUSION There is both clinical and economic incentive for the individual to continue to get vaccinated against COVID-19 each year.
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Affiliation(s)
- Sarah M Bartsch
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Pandemic Response Institute, New York City, NY, USA
| | - Kelly J O'Shea
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Pandemic Response Institute, New York City, NY, USA
| | - Colleen Weatherwax
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Pandemic Response Institute, New York City, NY, USA
| | - Ulrich Strych
- National School of Tropical Medicine, Department of Pediatrics, and Texas Children's Hospital Center for Vaccine Development, Baylor College of Medicine, Houston, TX, USA
| | - Kavya Velmurugan
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Pandemic Response Institute, New York City, NY, USA
| | - Danielle C John
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Pandemic Response Institute, New York City, NY, USA
| | - Maria Elena Bottazzi
- National School of Tropical Medicine, Department of Pediatrics, and Texas Children's Hospital Center for Vaccine Development, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA
| | - Mustafa Hussein
- CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Marie F Martinez
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Pandemic Response Institute, New York City, NY, USA
| | - Kevin L Chin
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Pandemic Response Institute, New York City, NY, USA
| | - Allan Ciciriello
- National School of Tropical Medicine, Department of Pediatrics, and Texas Children's Hospital Center for Vaccine Development, Baylor College of Medicine, Houston, TX, USA
| | - Jessie Heneghan
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Pandemic Response Institute, New York City, NY, USA
| | - Alexis Dibbs
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Pandemic Response Institute, New York City, NY, USA
| | - Sheryl A Scannell
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Pandemic Response Institute, New York City, NY, USA
| | - Peter J Hotez
- National School of Tropical Medicine, Department of Pediatrics, and Texas Children's Hospital Center for Vaccine Development, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA
| | - Bruce Y Lee
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Pandemic Response Institute, New York City, NY, USA
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3
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Bartsch SM, Weatherwax C, Wasserman MR, Chin KL, Martinez MF, Velmurugan K, Singh RD, John DC, Heneghan JL, Gussin GM, Scannell SA, Tsintsifas AC, O'Shea KJ, Dibbs AM, Leff B, Huang SS, Lee BY. How the Timing of Annual COVID-19 Vaccination of Nursing Home Residents and Staff Affects Its Value. J Am Med Dir Assoc 2024; 25:639-646.e5. [PMID: 38432644 PMCID: PMC10990766 DOI: 10.1016/j.jamda.2024.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 02/07/2024] [Accepted: 02/08/2024] [Indexed: 03/05/2024]
Abstract
OBJECTIVES To evaluate the epidemiologic, clinical, and economic value of an annual nursing home (NH) COVID-19 vaccine campaign and the impact of when vaccination starts. DESIGN Agent-based model representing a typical NH. SETTING AND PARTICIPANTS NH residents and staff. METHODS We used the model representing an NH with 100 residents, its staff, their interactions, COVID-19 spread, and its health and economic outcomes to evaluate the epidemiologic, clinical, and economic value of varying schedules of annual COVID-19 vaccine campaigns. RESULTS Across a range of scenarios with a 60% vaccine efficacy that wanes starting 4 months after protection onset, vaccination was cost saving or cost-effective when initiated in the late summer or early fall. Annual vaccination averted 102 to 105 COVID-19 cases when 30-day vaccination campaigns began between July and October (varying with vaccination start), decreasing to 97 and 85 cases when starting in November and December, respectively. Starting vaccination between July and December saved $3340 to $4363 and $64,375 to $77,548 from the Centers for Medicare & Medicaid Services and societal perspectives, respectively (varying with vaccination start). Vaccination's value did not change when varying the COVID-19 peak between December and February. The ideal vaccine campaign timing was not affected by reducing COVID-19 levels in the community, or varying transmission probability, preexisting immunity, or COVID-19 severity. However, if vaccine efficacy wanes more quickly (over 1 month), earlier vaccination in July resulted in more cases compared with vaccinating later in October. CONCLUSIONS AND IMPLICATIONS Annual vaccination of NH staff and residents averted the most cases when initiated in the late summer through early fall, at least 2 months before the COVID-19 winter peak but remained cost saving or cost-effective when it starts in the same month as the peak. This supports tethering COVID vaccination to seasonal influenza campaigns (typically in September-October) for providing protection against SARS-CoV-2 winter surges in NHs.
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Affiliation(s)
- Sarah M Bartsch
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Colleen Weatherwax
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | | | - Kevin L Chin
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Marie F Martinez
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Kavya Velmurugan
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Raveena D Singh
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine, CA, USA
| | - Danielle C John
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Pandemic Response Institute, New York City, NY, USA
| | - Jessie L Heneghan
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Gabrielle M Gussin
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine, CA, USA
| | - Sheryl A Scannell
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Alexandra C Tsintsifas
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Kelly J O'Shea
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Alexis M Dibbs
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Bruce Leff
- Division of Geriatric Medicine, Center for Transformative Geriatric Research, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Susan S Huang
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine, CA, USA
| | - Bruce Y Lee
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Pandemic Response Institute, New York City, NY, USA.
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4
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Powell-Wiley TM, Martinez MF, Heneghan J, Weatherwax C, Osei Baah F, Velmurugan K, Chin KL, Ayers C, Cintron MA, Ortiz-Whittingham LR, Sandler D, Sharda S, Whitley M, Bartsch SM, O’Shea KJ, Tsintsifas A, Dibbs A, Scannell SA, Lee BY. Health and Economic Value of Eliminating Socioeconomic Disparities in US Youth Physical Activity. JAMA Health Forum 2024; 5:e240088. [PMID: 38488779 PMCID: PMC10943408 DOI: 10.1001/jamahealthforum.2024.0088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 01/08/2024] [Indexed: 03/18/2024] Open
Abstract
Importance There are considerable socioeconomic status (SES) disparities in youth physical activity (PA) levels. For example, studies show that lower-SES youth are less active, have lower participation in organized sports and physical education classes, and have more limited access to PA equipment. Objective To determine the potential public health and economic effects of eliminating disparities in PA levels among US youth SES groups. Design and Setting An agent-based model representing all 6- to 17-year-old children in the US was used to simulate the epidemiological, clinical, and economic effects of disparities in PA levels among different SES groups and the effect of reducing these disparities. Main Outcomes and Measures Anthropometric measures (eg, body mass index) and the presence and severity of risk factors associated with weight (stroke, coronary heart disease, type 2 diabetes, or cancer), as well as direct and indirect cost savings. Results This model, representing all 50 million US children and adolescents 6 to 17 years old, found that if the US eliminates the disparity in youth PA levels across SES groups, absolute overweight and obesity prevalence would decrease by 0.826% (95% CI, 0.821%-0.832%), resulting in approximately 383 000 (95% CI, 368 000-399 000) fewer cases of overweight and obesity and 101 000 (95% CI, 98 000-105 000) fewer cases of weight-related diseases (stroke and coronary heart disease events, type 2 diabetes, or cancer). This would result in more than $15.60 (95% CI, $15.01-$16.10) billion in cost savings over the youth cohort's lifetime. There are meaningful benefits even when reducing the disparity by just 25%, which would result in $1.85 (95% CI, $1.70-$2.00) billion in direct medical costs averted and $2.48 (95% CI, $2.04-$2.92) billion in productivity losses averted. For every 1% in disparity reduction, total productivity losses would decrease by about $83.8 million, and total direct medical costs would decrease by about $68.7 million. Conclusions and Relevance This study quantified the potential savings from eliminating or reducing PA disparities, which can help policymakers, health care systems, schools, funders, sports organizations, and other businesses better prioritize investments toward addressing these disparities.
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Affiliation(s)
- Tiffany M. Powell-Wiley
- Social Determinants of Obesity and Cardiovascular Risk Laboratory, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
- Intramural Research Program, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland
| | - Marie F. Martinez
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, New York, New York
| | - Jessie Heneghan
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, New York, New York
| | - Colleen Weatherwax
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, New York, New York
| | - Foster Osei Baah
- Social Determinants of Obesity and Cardiovascular Risk Laboratory, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, Georgia
| | - Kavya Velmurugan
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, New York, New York
| | - Kevin L. Chin
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, New York, New York
| | - Colby Ayers
- Social Determinants of Obesity and Cardiovascular Risk Laboratory, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
- Intramural Research Program, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland
| | - Manuel A. Cintron
- Social Determinants of Obesity and Cardiovascular Risk Laboratory, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
- Intramural Research Program, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland
| | - Lola R. Ortiz-Whittingham
- Social Determinants of Obesity and Cardiovascular Risk Laboratory, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
- Intramural Research Program, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland
| | - Dana Sandler
- Social Determinants of Obesity and Cardiovascular Risk Laboratory, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
- Intramural Research Program, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland
| | - Sonal Sharda
- Social Determinants of Obesity and Cardiovascular Risk Laboratory, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
- Intramural Research Program, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland
| | - Meredith Whitley
- Ruth S. Ammon College of Education and Health Sciences, Adelphi University, Garden City, New York
- Maties Sport, Centre for Sport Leadership, Stellenbosch University, Stellenbosch, South Africa
| | - Sarah M. Bartsch
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, New York, New York
| | - Kelly J. O’Shea
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, New York, New York
| | - Alexandra Tsintsifas
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, New York, New York
| | - Alexis Dibbs
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, New York, New York
| | - Sheryl A. Scannell
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, New York, New York
| | - Bruce Y. Lee
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, New York, New York
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Bartsch SM, Weatherwax C, Martinez MF, Chin KL, Wasserman MR, Singh RD, Heneghan JL, Gussin GM, Scannell SA, White C, Leff B, Huang SS, Lee BY. Cost-effectiveness of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) testing and isolation strategies in nursing homes. Infect Control Hosp Epidemiol 2024:1-8. [PMID: 38356377 DOI: 10.1017/ice.2024.9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2024]
Abstract
OBJECTIVE Nursing home residents may be particularly vulnerable to coronavirus disease 2019 (COVID-19). Therefore, a question is when and how often nursing homes should test staff for COVID-19 and how this may change as severe acute respiratory coronavirus virus 2 (SARS-CoV-2) evolves. DESIGN We developed an agent-based model representing a typical nursing home, COVID-19 spread, and its health and economic outcomes to determine the clinical and economic value of various screening and isolation strategies and how it may change under various circumstances. RESULTS Under winter 2023-2024 SARS-CoV-2 omicron variant conditions, symptom-based antigen testing averted 4.5 COVID-19 cases compared to no testing, saving $191 in direct medical costs. Testing implementation costs far outweighed these savings, resulting in net costs of $990 from the Centers for Medicare & Medicaid Services perspective, $1,545 from the third-party payer perspective, and $57,155 from the societal perspective. Testing did not return sufficient positive health effects to make it cost-effective [$50,000 per quality-adjusted life-year (QALY) threshold], but it exceeded this threshold in ≥59% of simulation trials. Testing remained cost-ineffective when routinely testing staff and varying face mask compliance, vaccine efficacy, and booster coverage. However, all antigen testing strategies became cost-effective (≤$31,906 per QALY) or cost saving (saving ≤$18,372) when the severe outcome risk was ≥3 times higher than that of current omicron variants. CONCLUSIONS SARS-CoV-2 testing costs outweighed benefits under winter 2023-2024 conditions; however, testing became cost-effective with increasingly severe clinical outcomes. Cost-effectiveness can change as the epidemic evolves because it depends on clinical severity and other intervention use. Thus, nursing home administrators and policy makers should monitor and evaluate viral virulence and other interventions over time.
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Affiliation(s)
- Sarah M Bartsch
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Colleen Weatherwax
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Marie F Martinez
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Kevin L Chin
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Michael R Wasserman
- Los Angeles Jewish Home, Reseda, California
- California Association of Long Term Care Medicine, Santa Clarita, California
| | - Raveena D Singh
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine, California
| | - Jessie L Heneghan
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Gabrielle M Gussin
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine, California
| | - Sheryl A Scannell
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Cameron White
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Bruce Leff
- Center for Transformative Geriatric Research, Division of Geriatric Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Susan S Huang
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine, California
| | - Bruce Y Lee
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
- New York City Pandemic Response Institute (PRI), CUNY Graduate School of Public Health and Health Policy, New York City, New York
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Lee BY, Greene D, Scannell SA, McLaughlin C, Martinez MF, Heneghan JL, Chin KL, Zheng X, Li R, Lindenfeld L, Bartsch SM. The Need for Systems Approaches for Precision Communications in Public Health. J Health Commun 2023; 28:13-24. [PMID: 37390012 PMCID: PMC10373800 DOI: 10.1080/10810730.2023.2220668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2023]
Abstract
A major challenge in communicating health-related information is the involvement of multiple complex systems from the creation of the information to the sources and channels of dispersion to the information users themselves. To date, public health communications approaches have often not adequately accounted for the complexities of these systems to the degree necessary to have maximum impact. The virality of COVID-19 misinformation and disinformation has brought to light the need to consider these system complexities more extensively. Unaided, it is difficult for humans to see and fully understand complex systems. Luckily, there are a range of systems approaches and methods, such as systems mapping and systems modeling, that can help better elucidate complex systems. Using these methods to better characterize the various systems involved in communicating public health-related information can lead to the development of more tailored, precise, and proactive communications. Proceeding in an iterative manner to help design, implement, and adjust such communications strategies can increase impact and leave less opportunity for misinformation and disinformation to spread.
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Affiliation(s)
- Bruce Y. Lee
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- New York City Pandemic Response Institute (PRI), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Danielle Greene
- New York City Pandemic Response Institute (PRI), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Sheryl A. Scannell
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- New York City Pandemic Response Institute (PRI), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Christopher McLaughlin
- New York City Pandemic Response Institute (PRI), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Marie F. Martinez
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- New York City Pandemic Response Institute (PRI), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Jessie L. Heneghan
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- New York City Pandemic Response Institute (PRI), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Kevin L. Chin
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- New York City Pandemic Response Institute (PRI), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Xia Zheng
- School of Communication & Journalism, Stony Brook University, Stony Brook, NY, USA
- Alan Alda Center for Communicating Science, Stony Brook University, Stony Brook, NY, USA
| | - Ruobing Li
- School of Communication & Journalism, Stony Brook University, Stony Brook, NY, USA
- Alan Alda Center for Communicating Science, Stony Brook University, Stony Brook, NY, USA
| | - Laura Lindenfeld
- School of Communication & Journalism, Stony Brook University, Stony Brook, NY, USA
- Alan Alda Center for Communicating Science, Stony Brook University, Stony Brook, NY, USA
| | - Sarah M. Bartsch
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- New York City Pandemic Response Institute (PRI), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
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Powell-Wiley TM, Martinez MF, Tamura K, Neally SJ, O'Shea KJ, Curlin K, Albarracin Y, Vijayakumar NP, Morgan M, Ortiz-Chaparro E, Bartsch SM, Osei Baah F, Wedlock PT, Ortiz-Whittingham LR, Scannell S, Potharaju KA, Randall S, Solano Gonzales M, Domino M, Ranganath K, Hertenstein D, Syed R, Weatherwax C, Lee BY. The Impact of a Place-Tailored Digital Health App Promoting Exercise Classes on African American Women's Physical Activity and Obesity: Simulation Study. J Med Internet Res 2022; 24:e30581. [PMID: 35994313 PMCID: PMC9446149 DOI: 10.2196/30581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 12/17/2021] [Accepted: 06/30/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The increasing prevalence of smartphone apps to help people find different services raises the question of whether apps to help people find physical activity (PA) locations would help better prevent and control having overweight or obesity. OBJECTIVE The aim of this paper is to determine and quantify the potential impact of a digital health intervention for African American women prior to allocating financial resources toward implementation. METHODS We developed our Virtual Population Obesity Prevention, agent-based model of Washington, DC, to simulate the impact of a place-tailored digital health app that provides information about free recreation center classes on PA, BMI, and overweight and obesity prevalence among African American women. RESULTS When the app is introduced at the beginning of the simulation, with app engagement at 25% (eg, 25% [41,839/167,356] of women aware of the app; 25% [10,460/41,839] of those aware downloading the app; and 25% [2615/10,460] of those who download it receiving regular push notifications), and a 25% (25/100) baseline probability to exercise (eg, without the app), there are no statistically significant increases in PA levels or decreases in BMI or obesity prevalence over 5 years across the population. When 50% (83,678/167,356) of women are aware of the app; 58.23% (48,725/83,678) of those who are aware download it; and 55% (26,799/48,725) of those who download it receive regular push notifications, in line with existing studies on app usage, introducing the app on average increases PA and decreases weight or obesity prevalence, though the changes are not statistically significant. When app engagement increased to 75% (125,517/167,356) of women who were aware, 75% (94,138/125,517) of those who were aware downloading it, and 75% (70,603/94,138) of those who downloaded it opting into the app's push notifications, there were statistically significant changes in PA participation, minutes of PA and obesity prevalence. CONCLUSIONS Our study shows that a digital health app that helps identify recreation center classes does not result in substantive population-wide health effects at lower levels of app engagement. For the app to result in statistically significant increases in PA and reductions in obesity prevalence over 5 years, there needs to be at least 75% (125,517/167,356) of women aware of the app, 75% (94,138/125,517) of those aware of the app download it, and 75% (70,603/94,138) of those who download it opt into push notifications. Nevertheless, the app cannot fully overcome lack of access to recreation centers; therefore, public health administrators as well as parks and recreation agencies might consider incorporating this type of technology into multilevel interventions that also target the built environment and other social determinants of health.
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Affiliation(s)
- Tiffany M Powell-Wiley
- Social Determinants of Obesity and Cardiovascular Risk Laboratory, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States
- Intramural Research Program, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, United States
| | - Marie F Martinez
- Public Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York, NY, United States
- Center for Advanced Technology and Communication in Health, CUNY Graduate School of Public Health and Health Policy, New York, NY, United States
| | - Kosuke Tamura
- Socio-Spatial Determinants of Health (SSDH) Laboratory, Population and Community Sciences Branch, Intramural Research Program, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, United States
| | - Sam J Neally
- Social Determinants of Obesity and Cardiovascular Risk Laboratory, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States
| | - Kelly J O'Shea
- Public Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York, NY, United States
- Center for Advanced Technology and Communication in Health, CUNY Graduate School of Public Health and Health Policy, New York, NY, United States
| | - Kaveri Curlin
- Social Determinants of Obesity and Cardiovascular Risk Laboratory, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States
| | - Yardley Albarracin
- Public Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York, NY, United States
| | - Nithya P Vijayakumar
- Social Determinants of Obesity and Cardiovascular Risk Laboratory, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States
| | - Matthew Morgan
- Public Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York, NY, United States
| | - Erika Ortiz-Chaparro
- Social Determinants of Obesity and Cardiovascular Risk Laboratory, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States
| | - Sarah M Bartsch
- Public Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York, NY, United States
- Center for Advanced Technology and Communication in Health, CUNY Graduate School of Public Health and Health Policy, New York, NY, United States
| | - Foster Osei Baah
- Social Determinants of Obesity and Cardiovascular Risk Laboratory, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States
| | - Patrick T Wedlock
- Public Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York, NY, United States
- Center for Advanced Technology and Communication in Health, CUNY Graduate School of Public Health and Health Policy, New York, NY, United States
| | - Lola R Ortiz-Whittingham
- Social Determinants of Obesity and Cardiovascular Risk Laboratory, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States
| | - Sheryl Scannell
- Public Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York, NY, United States
- Center for Advanced Technology and Communication in Health, CUNY Graduate School of Public Health and Health Policy, New York, NY, United States
| | - Kameswari A Potharaju
- Social Determinants of Obesity and Cardiovascular Risk Laboratory, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States
| | - Samuel Randall
- Public Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York, NY, United States
| | - Mario Solano Gonzales
- Public Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York, NY, United States
| | - Molly Domino
- Public Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York, NY, United States
| | - Kushi Ranganath
- Public Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York, NY, United States
| | - Daniel Hertenstein
- Public Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York, NY, United States
| | - Rafay Syed
- Public Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York, NY, United States
| | - Colleen Weatherwax
- Public Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York, NY, United States
- Center for Advanced Technology and Communication in Health, CUNY Graduate School of Public Health and Health Policy, New York, NY, United States
| | - Bruce Y Lee
- Public Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York, NY, United States
- Center for Advanced Technology and Communication in Health, CUNY Graduate School of Public Health and Health Policy, New York, NY, United States
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Mabry PL, Pronk NP, Amos CI, Witte JS, Wedlock PT, Bartsch SM, Lee BY. Cancer systems epidemiology: Overcoming misconceptions and integrating systems approaches into cancer research. PLoS Med 2022; 19:e1004027. [PMID: 35714096 PMCID: PMC9205504 DOI: 10.1371/journal.pmed.1004027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Patricia Mabry and coauthors discuss application of systems approaches in cancer research.
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Affiliation(s)
- Patricia L. Mabry
- HealthPartners Institute, Bloomington, Minnesota, United States of America
| | - Nicolaas P. Pronk
- HealthPartners Institute, Bloomington, Minnesota, United States of America
- University of Minnesota, School of Public Health, Minneapolis, Minnesota, United States of America
| | - Christopher I. Amos
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, United States of America
- Baylor College of Medicine, Institute for Clinical and Translational Research, Houston, Texas, United States of America
| | - John S. Witte
- Department of Epidemiology and Population Health, Stanford University, Stanford, California, United States of America
| | - Patrick T. Wedlock
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York, United States of America
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York, United States of America
| | - Sarah M. Bartsch
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York, United States of America
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York, United States of America
| | - Bruce Y. Lee
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York, United States of America
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York, United States of America
- * E-mail:
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Bartsch SM, Wedlock PT, O’Shea KJ, Cox SN, Strych U, Nuzzo JB, Ferguson MC, Bottazzi ME, Siegmund SS, Hotez PJ, Lee BY. Lives and Costs Saved by Expanding and Expediting Coronavirus Disease 2019 Vaccination. J Infect Dis 2021; 224:938-948. [PMID: 33954775 PMCID: PMC8136017 DOI: 10.1093/infdis/jiab233] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 04/28/2021] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND With multiple coronavirus disease 2019 (COVID-19) vaccines available, understanding the epidemiologic, clinical, and economic value of increasing coverage levels and expediting vaccination is important. METHODS We developed a computational model (transmission and age-stratified clinical and economics outcome model) representing the United States population, COVID-19 coronavirus spread (February 2020-December 2022), and vaccination to determine the impact of increasing coverage and expediting time to achieve coverage. RESULTS When achieving a given vaccination coverage in 270 days (70% vaccine efficacy), every 1% increase in coverage can avert an average of 876 800 (217 000-2 398 000) cases, varying with the number of people already vaccinated. For example, each 1% increase between 40% and 50% coverage can prevent 1.5 million cases, 56 240 hospitalizations, and 6660 deaths; gain 77 590 quality-adjusted life-years (QALYs); and save $602.8 million in direct medical costs and $1.3 billion in productivity losses. Expediting to 180 days could save an additional 5.8 million cases, 215 790 hospitalizations, 26 370 deaths, 206 520 QALYs, $3.5 billion in direct medical costs, and $4.3 billion in productivity losses. CONCLUSIONS Our study quantifies the potential value of decreasing vaccine hesitancy and increasing vaccination coverage and how this value may decrease with the time it takes to achieve coverage, emphasizing the need to reach high coverage levels as soon as possible, especially before the fall/winter.
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Affiliation(s)
- Sarah M Bartsch
- Public Health Informatics, Computational, and Operations Research, Graduate School of Public Health and Health Policy, City University of New York, New York City, New York, USA
| | - Patrick T Wedlock
- Public Health Informatics, Computational, and Operations Research, Graduate School of Public Health and Health Policy, City University of New York, New York City, New York, USA
| | - Kelly J O’Shea
- Public Health Informatics, Computational, and Operations Research, Graduate School of Public Health and Health Policy, City University of New York, New York City, New York, USA
| | - Sarah N Cox
- Public Health Informatics, Computational, and Operations Research, Graduate School of Public Health and Health Policy, City University of New York, New York City, New York, USA
| | - Ulrich Strych
- National School of Tropical Medicine and Departments of Pediatrics and Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas, USA
| | - Jennifer B Nuzzo
- Johns Hopkins Center for Health Security, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Marie C Ferguson
- Public Health Informatics, Computational, and Operations Research, Graduate School of Public Health and Health Policy, City University of New York, New York City, New York, USA
| | - Maria Elena Bottazzi
- National School of Tropical Medicine and Departments of Pediatrics and Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas, USA
| | - Sheryl S Siegmund
- Public Health Informatics, Computational, and Operations Research, Graduate School of Public Health and Health Policy, City University of New York, New York City, New York, USA
| | - Peter J Hotez
- National School of Tropical Medicine and Departments of Pediatrics and Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas, USA
| | - Bruce Y Lee
- Public Health Informatics, Computational, and Operations Research, Graduate School of Public Health and Health Policy, City University of New York, New York City, New York, USA
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Bartsch SM, Wong KF, Mueller LE, Gussin GM, McKinnell JA, Tjoa T, Wedlock PT, He J, Chang J, Gohil SK, Miller LG, Huang SS, Lee BY. Modeling Interventions to Reduce the Spread of Multidrug-Resistant Organisms Between Health Care Facilities in a Region. JAMA Netw Open 2021; 4:e2119212. [PMID: 34347060 PMCID: PMC8339938 DOI: 10.1001/jamanetworkopen.2021.19212] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
IMPORTANCE Multidrug-resistant organisms (MDROs) can spread across health care facilities in a region. Because of limited resources, certain interventions can be implemented in only some facilities; thus, decision-makers need to evaluate which interventions may be best to implement. OBJECTIVE To identify a group of target facilities and assess which MDRO intervention would be best to implement in the Shared Healthcare Intervention to Eliminate Life-threatening Dissemination of MDROs in Orange County, a large regional public health collaborative in Orange County, California. DESIGN, SETTING, AND PARTICIPANTS An agent-based model of health care facilities was developed in 2016 to simulate the spread of methicillin-resistant Staphylococcus aureus (MRSA) and carbapenem-resistant Enterobacteriaceae (CRE) for 10 years starting in 2010 and to simulate the use of various MDRO interventions for 3 years starting in 2017. All health care facilities (23 hospitals, 5 long-term acute care hospitals, and 74 nursing homes) serving adult inpatients in Orange County, California, were included, and 42 target facilities were identified via network analyses. EXPOSURES Increasing contact precaution effectiveness, increasing interfacility communication about patients' MDRO status, and performing decolonization using antiseptic bathing soap and a nasal product in a specific group of target facilities. MAIN OUTCOMES AND MEASURES MRSA and CRE prevalence and number of new carriers (ie, transmission events). RESULTS Compared with continuing infection control measures used in Orange County as of 2017, increasing contact precaution effectiveness from 40% to 64% in 42 target facilities yielded relative reductions of 0.8% (range, 0.5%-1.1%) in MRSA prevalence and 2.4% (range, 0.8%-4.6%) in CRE prevalence in health care facilities countywide after 3 years, averting 761 new MRSA transmission events (95% CI, 756-765 events) and 166 new CRE transmission events (95% CI, 158-174 events). Increasing interfacility communication of patients' MDRO status to 80% in these target facilities produced no changes in the prevalence or transmission of MRDOs. Implementing decolonization procedures (clearance probability: 39% in hospitals, 27% in long-term acute care facilities, and 3% in nursing homes) yielded a relative reduction of 23.7% (range, 23.5%-23.9%) in MRSA prevalence, averting 3515 new transmission events (95% CI, 3509-3521 events). Increasing the effectiveness of antiseptic bathing soap to 48% yielded a relative reduction of 39.9% (range, 38.5%-41.5%) in CRE prevalence, averting 1435 new transmission events (95% CI, 1427-1442 events). CONCLUSIONS AND RELEVANCE The findings of this study highlight the ways in which modeling can inform design of regional interventions and suggested that decolonization would be the best strategy for the Shared Healthcare Intervention to Eliminate Life-threatening Dissemination of MDROs in Orange County.
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Affiliation(s)
- Sarah M. Bartsch
- Public Health Informatics, Computational, and Operations Research, Graduate School of Public Health and Health Policy, City University of New York, New York, New York
| | - Kim F. Wong
- Center for Simulation and Modeling, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Leslie E. Mueller
- Public Health Informatics, Computational, and Operations Research, Graduate School of Public Health and Health Policy, City University of New York, New York, New York
| | - Gabrielle M. Gussin
- Division of Infectious Diseases and Health Policy Research Institute, Health School of Medicine, University of California–Irvine, Irvine
| | - James A. McKinnell
- Infectious Disease Clinical Outcomes Research Unit, Lundquist Institute, Harbor-UCLA Medical Center, Torrance, California
- Torrance Memorial Medical Center, Torrance, California
| | - Thomas Tjoa
- Division of Infectious Diseases and Health Policy Research Institute, Health School of Medicine, University of California–Irvine, Irvine
| | - Patrick T. Wedlock
- Public Health Informatics, Computational, and Operations Research, Graduate School of Public Health and Health Policy, City University of New York, New York, New York
| | - Jiayi He
- Division of Infectious Diseases and Health Policy Research Institute, Health School of Medicine, University of California–Irvine, Irvine
| | - Justin Chang
- Division of Infectious Diseases and Health Policy Research Institute, Health School of Medicine, University of California–Irvine, Irvine
| | - Shruti K. Gohil
- Division of Infectious Diseases and Health Policy Research Institute, Health School of Medicine, University of California–Irvine, Irvine
| | | | - Susan S. Huang
- Division of Infectious Diseases and Health Policy Research Institute, Health School of Medicine, University of California–Irvine, Irvine
| | - Bruce Y. Lee
- Public Health Informatics, Computational, and Operations Research, Graduate School of Public Health and Health Policy, City University of New York, New York, New York
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11
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Ozawa S, Yemeke TT, Mitgang E, Wedlock PT, Higgins C, Chen HH, Pallas SW, Abimbola T, Wallace A, Bartsch SM, Lee BY. Systematic review of the costs for vaccinators to reach vaccination sites: Incremental costs of reaching hard-to-reach populations. Vaccine 2021; 39:4598-4610. [PMID: 34238610 PMCID: PMC10680154 DOI: 10.1016/j.vaccine.2021.05.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 04/07/2021] [Accepted: 05/06/2021] [Indexed: 11/24/2022]
Abstract
INTRODUCTION Economic evidence on how much it may cost for vaccinators to reach populations is important to plan vaccination programs. Moreover, knowing the incremental costs to reach populations that have traditionally been undervaccinated, especially those hard-to-reach who are facing supply-side barriers to vaccination, is essential to expanding immunization coverage to these populations. METHODS We conducted a systematic review to identify estimates of costs associated with getting vaccinators to all vaccination sites. We searched PubMed and the Immunization Delivery Cost Catalogue (IDCC) in 2019 for the following costs to vaccinators: (1) training costs; (2) labor costs, per diems, and incentives; (3) identification of vaccine beneficiary location; and (4) travel costs. We assessed if any of these costs were specific to populations that are hard-to-reach for vaccination, based on a framework for examining supply-side barriers to vaccination. RESULTS We found 19 studies describing average vaccinator training costs at $0.67/person vaccinated or targeted (SD $0.94) and $0.10/dose delivered (SD $0.07). The average cost for vaccinator labor and incentive costs across 29 studies was $2.15/dose (SD $2.08). We identified 13 studies describing intervention costs for a vaccinator to know the location of a beneficiary, with an average cost of $19.69/person (SD $26.65), and six studies describing vaccinator travel costs, with an average cost of $0.07/dose (SD $0.03). Only eight of these studies described hard-to-reach populations for vaccination; two studies examined incremental costs per dose to reach hard-to-reach populations, which were 1.3-2 times higher than the regular costs. The incremental cost to train vaccinators was $0.02/dose, and incremental labor costs for targeting hard-to-reach populations were $0.16-$1.17/dose. CONCLUSION Additional comparative costing studies are needed to understand the potential differential costs for vaccinators reaching the vaccination sites that serve hard-to-reach populations. This will help immunization program planners and decision-makers better allocate resources to extend vaccination programs.
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Affiliation(s)
- Sachiko Ozawa
- Division of Practice Advancement and Clinical Education, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA; Department of Maternal and Child Health, UNC Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.
| | - Tatenda T Yemeke
- Division of Practice Advancement and Clinical Education, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
| | - Elizabeth Mitgang
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Patrick T Wedlock
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Colleen Higgins
- Division of Practice Advancement and Clinical Education, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
| | - Hui-Han Chen
- Division of Practice Advancement and Clinical Education, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
| | - Sarah W Pallas
- Global Immunization Division, U.S. Centers for Disease Control and Prevention (CDC), Atlanta, GA, USA
| | - Taiwo Abimbola
- Global Immunization Division, U.S. Centers for Disease Control and Prevention (CDC), Atlanta, GA, USA
| | - Aaron Wallace
- Global Immunization Division, U.S. Centers for Disease Control and Prevention (CDC), Atlanta, GA, USA
| | - Sarah M Bartsch
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Bruce Y Lee
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
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12
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Yemeke TT, Mitgang E, Wedlock PT, Higgins C, Chen HH, Pallas SW, Abimbola T, Wallace A, Bartsch SM, Lee BY, Ozawa S. Promoting, seeking, and reaching vaccination services: A systematic review of costs to immunization programs, beneficiaries, and caregivers. Vaccine 2021; 39:4437-4449. [PMID: 34218959 PMCID: PMC10711749 DOI: 10.1016/j.vaccine.2021.05.075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 05/19/2021] [Accepted: 05/20/2021] [Indexed: 10/21/2022]
Abstract
INTRODUCTION Understanding the costs to increase vaccination demand among under-vaccinated populations, as well as costs incurred by beneficiaries and caregivers for reaching vaccination sites, is essential to improving vaccination coverage. However, there have not been systematic analyses documenting such costs for beneficiaries and caregivers seeking vaccination. METHODS We searched PubMed, Scopus, and the Immunization Delivery Cost Catalogue (IDCC) in 2019 for the costs for beneficiaries and caregivers to 1) seek and know how to access vaccination (i.e., costs to immunization programs for social mobilization and interventions to increase vaccination demand), 2) take time off from work, chores, or school for vaccination (i.e., productivity costs), and 3) travel to vaccination sites. We assessed if these costs were specific to populations that faced other non-cost barriers, based on a framework for defining hard-to-reach and hard-to-vaccinate populations for vaccination. RESULTS We found 57 studies describing information, education, and communication (IEC) costs, social mobilization costs, and the costs of interventions to increase vaccination demand, with mean costs per dose at $0.41 (standard deviation (SD) $0.83), $18.86 (SD $50.65) and $28.23 (SD $76.09) in low-, middle-, and high-income countries, respectively. Five studies described productivity losses incurred by beneficiaries and caregivers seeking vaccination ($38.33 per person; SD $14.72; n = 3). We identified six studies on travel costs incurred by beneficiaries and caregivers attending vaccination sites ($11.25 per person; SD $9.54; n = 4). Two studies reported social mobilization costs per dose specific to hard-to-reach populations, which were 2-3.5 times higher than costs for the general population. Eight studies described barriers to vaccination among hard-to-reach populations. CONCLUSION Social mobilization/IEC costs are well-characterized, but evidence is limited on costs incurred by beneficiaries and caregivers getting to vaccination sites. Understanding the potential incremental costs for populations facing barriers to reach vaccination sites is essential to improving vaccine program financing and planning.
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Affiliation(s)
- Tatenda T Yemeke
- Division of Practice Advancement and Clinical Education, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
| | - Elizabeth Mitgang
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY 10027, USA
| | - Patrick T Wedlock
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY 10027, USA
| | - Colleen Higgins
- Division of Practice Advancement and Clinical Education, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
| | - Hui-Han Chen
- Division of Practice Advancement and Clinical Education, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
| | - Sarah W Pallas
- Global Immunization Division, U.S. Centers for Disease Control and Prevention (CDC), Atlanta, GA, USA
| | - Taiwo Abimbola
- Global Immunization Division, U.S. Centers for Disease Control and Prevention (CDC), Atlanta, GA, USA
| | - Aaron Wallace
- Global Immunization Division, U.S. Centers for Disease Control and Prevention (CDC), Atlanta, GA, USA
| | - Sarah M Bartsch
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY 10027, USA
| | - Bruce Y Lee
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY 10027, USA
| | - Sachiko Ozawa
- Division of Practice Advancement and Clinical Education, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA; Department of Maternal and Child Health, UNC Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.
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13
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Bartsch SM, O'Shea KJ, Lee BY. Corrigendum to: The Clinical and Economic Burden of Norovirus Gastroenteritis in the United States. J Infect Dis 2021; 224:741. [PMID: 34254143 DOI: 10.1093/infdis/jiab197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Sarah M Bartsch
- Public Health Informatics, Computational, and Operations Research, City University of New York, New York City, New York, USA
| | - Kelly J O'Shea
- Public Health Informatics, Computational, and Operations Research, City University of New York, New York City, New York, USA
| | - Bruce Y Lee
- Public Health Informatics, Computational, and Operations Research, City University of New York, New York City, New York, USA
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14
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Asti L, Hopley C, Avelis C, Bartsch SM, Mueller LE, Domino M, Cox SN, Andrews JC, Randall SL, Stokes-Cawley OJ, Asjes C, Lee BY. The Potential Clinical and Economic Value of a Human Papillomavirus Primary Screening Test That Additionally Identifies Genotypes 31, 45, 51, and 52 Individually. Sex Transm Dis 2021; 48:370-380. [PMID: 33156291 PMCID: PMC8281325 DOI: 10.1097/olq.0000000000001327] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
BACKGROUND Although current human papillomavirus (HPV) genotype screening tests identify genotypes 16 and 18 and do not specifically identify other high-risk types, a new extended genotyping test identifies additional individual (31, 45, 51, and 52) and groups (33/58, 35/39/68, and 56/59/66) of high-risk genotypes. METHODS We developed a Markov model of the HPV disease course and evaluated the clinical and economic value of HPV primary screening with Onclarity (BD Diagnostics, Franklin Lakes, NJ) capable of extended genotyping in a cohort of women 30 years or older. Women with certain genotypes were later rescreened instead of undergoing immediate colposcopy and varied which genotypes were rescreened, disease progression rate, and test cost. RESULTS Assuming 100% compliance with screening, HPV primary screening using current tests resulted in 25,194 invasive procedures and 48 invasive cervical cancer (ICC) cases per 100,000 women. Screening with extended genotyping (100% compliance) and later rescreening women with certain genotypes averted 903 to 3163 invasive procedures and resulted in 0 to 3 more ICC cases compared with current HPV primary screening tests. Extended genotyping was cost-effective ($2298-$7236/quality-adjusted life year) when costing $75 and cost saving (median, $0.3-$1.0 million) when costing $43. When the probabilities of disease progression increased (2-4 times), extended genotyping was not cost-effective because it resulted in more ICC cases and accrued fewer quality-adjusted life years. CONCLUSIONS Our study identified the conditions under which extended genotyping was cost-effective and even cost saving compared with current tests. A key driver of cost-effectiveness is the risk of disease progression, which emphasizes the need to better understand such risks in different populations.
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Affiliation(s)
- Lindsey Asti
- Public Health Computational and Operations Research (PHICOR), City University of New York (CUNY) School of Public Health and Health Policy, 55 W 125th Street, New York City, New York 10027
| | - Colin Hopley
- Health Economics and Outcomes Research, BD Diagnostics, 1 Becton Drive, Franklin Lakes, New Jersey, 07417, USA
| | - Cameron Avelis
- Public Health Computational and Operations Research (PHICOR), City University of New York (CUNY) School of Public Health and Health Policy, 55 W 125th Street, New York City, New York 10027
| | - Sarah M. Bartsch
- Public Health Computational and Operations Research (PHICOR), City University of New York (CUNY) School of Public Health and Health Policy, 55 W 125th Street, New York City, New York 10027
| | - Leslie E. Mueller
- Public Health Computational and Operations Research (PHICOR), City University of New York (CUNY) School of Public Health and Health Policy, 55 W 125th Street, New York City, New York 10027
| | - Molly Domino
- Public Health Computational and Operations Research (PHICOR), City University of New York (CUNY) School of Public Health and Health Policy, 55 W 125th Street, New York City, New York 10027
| | - Sarah N. Cox
- Public Health Computational and Operations Research (PHICOR), City University of New York (CUNY) School of Public Health and Health Policy, 55 W 125th Street, New York City, New York 10027
| | - Jeffrey C. Andrews
- Women’s Health & Cancer, BD Diagnostics, 7 Loveton Circle, Sparks, Maryland 21152
| | - Samuel L. Randall
- Public Health Computational and Operations Research (PHICOR), City University of New York (CUNY) School of Public Health and Health Policy, 55 W 125th Street, New York City, New York 10027
| | - Owen J. Stokes-Cawley
- Public Health Computational and Operations Research (PHICOR), City University of New York (CUNY) School of Public Health and Health Policy, 55 W 125th Street, New York City, New York 10027
| | - Caitlin Asjes
- Health Economics and Outcomes Research, BD Diagnostics, 1 Becton Drive, Franklin Lakes, New Jersey, 07417, USA
| | - Bruce Y. Lee
- Public Health Computational and Operations Research (PHICOR), City University of New York (CUNY) School of Public Health and Health Policy, 55 W 125th Street, New York City, New York 10027
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15
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Bartsch SM, O'Shea KJ, Wedlock PT, Strych U, Ferguson MC, Bottazzi ME, Randall SL, Siegmund SS, Cox SN, Hotez PJ, Lee BY. The Benefits of Vaccinating With the First Available COVID-19 Coronavirus Vaccine. Am J Prev Med 2021; 60:605-613. [PMID: 33632650 PMCID: PMC7817395 DOI: 10.1016/j.amepre.2021.01.001] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 01/05/2021] [Accepted: 01/07/2021] [Indexed: 01/27/2023]
Abstract
INTRODUCTION During a pandemic, there are many situations in which the first available vaccines may not have as high effectiveness as vaccines that are still under development or vaccines that are not yet ready for distribution, raising the question of whether it is better to go with what is available now or wait. METHODS In 2020, the team developed a computational model that represents the U.S. population, COVID-19 coronavirus spread, and vaccines with different possible efficacies (to prevent infection or to reduce severe disease) and vaccination timings to estimate the clinical and economic value of vaccination. RESULTS Except for a limited number of situations, mainly early on in a pandemic and for a vaccine that prevents infection, when an initial vaccine is available, waiting for a vaccine with a higher efficacy results in additional hospitalizations and costs over the course of the pandemic. For example, if a vaccine with a 50% efficacy in preventing infection becomes available when 10% of the population has already been infected, waiting until 40% of the population are infected for a vaccine with 80% efficacy in preventing infection results in 15.6 million additional cases and 1.5 million additional hospitalizations, costing $20.6 billion more in direct medical costs and $12.4 billion more in productivity losses. CONCLUSIONS This study shows that there are relatively few situations in which it is worth foregoing the first COVID-19 vaccine available in favor of a vaccine that becomes available later on in the pandemic even if the latter vaccine has a substantially higher efficacy.
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Affiliation(s)
- Sarah M Bartsch
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health & Health Policy, New York City, New York
| | - Kelly J O'Shea
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health & Health Policy, New York City, New York
| | - Patrick T Wedlock
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health & Health Policy, New York City, New York
| | - Ulrich Strych
- National School of Tropical Medicine, Baylor College of Medicine, Houston, Texas; Department of Pediatrics, Baylor College of Medicine, Houston, Texas; Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas
| | - Marie C Ferguson
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health & Health Policy, New York City, New York
| | - Maria Elena Bottazzi
- National School of Tropical Medicine, Baylor College of Medicine, Houston, Texas; Department of Pediatrics, Baylor College of Medicine, Houston, Texas; Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas
| | - Samuel L Randall
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health & Health Policy, New York City, New York
| | - Sheryl S Siegmund
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health & Health Policy, New York City, New York
| | - Sarah N Cox
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health & Health Policy, New York City, New York
| | - Peter J Hotez
- National School of Tropical Medicine, Baylor College of Medicine, Houston, Texas; Department of Pediatrics, Baylor College of Medicine, Houston, Texas; Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas
| | - Bruce Y Lee
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health & Health Policy, New York City, New York.
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16
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Lee BY, Bartsch SM, Hayden MK, Welling J, Mueller LE, Brown ST, Doshi K, Leonard J, Kemble SK, Weinstein RA, Trick WE, Lin MY. How to Choose Target Facilities in a Region to Implement Carbapenem-resistant Enterobacteriaceae Control Measures. Clin Infect Dis 2021; 72:438-447. [PMID: 31970389 DOI: 10.1093/cid/ciaa072] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Accepted: 01/21/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND When trying to control regional spread of antibiotic-resistant pathogens such as carbapenem-resistant Enterobacteriaceae (CRE), decision makers must choose the highest-yield facilities to target for interventions. The question is, with limited resources, how best to choose these facilities. METHODS Using our Regional Healthcare Ecosystem Analyst-generated agent-based model of all Chicago metropolitan area inpatient facilities, we simulated the spread of CRE and different ways of choosing facilities to apply a prevention bundle (screening, chlorhexidine gluconate bathing, hand hygiene, geographic separation, and patient registry) to a resource-limited 1686 inpatient beds. RESULTS Randomly selecting facilities did not impact prevalence, but averted 620 new carriers and 175 infections, saving $6.3 million in total costs compared to no intervention. Selecting facilities by type (eg, long-term acute care hospitals) yielded a 16.1% relative prevalence decrease, preventing 1960 cases and 558 infections, saving $62.4 million more than random selection. Choosing the largest facilities was better than random selection, but not better than by type. Selecting by considering connections to other facilities (ie, highest volume of discharge patients) yielded a 9.5% relative prevalence decrease, preventing 1580 cases and 470 infections, and saving $51.6 million more than random selection. Selecting facilities using a combination of these metrics yielded the greatest reduction (19.0% relative prevalence decrease, preventing 1840 cases and 554 infections, saving $59.6 million compared with random selection). CONCLUSIONS While choosing target facilities based on single metrics (eg, most inpatient beds, most connections to other facilities) achieved better control than randomly choosing facilities, more effective targeting occurred when considering how these and other factors (eg, patient length of stay, care for higher-risk patients) interacted as a system.
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Affiliation(s)
- Bruce Y Lee
- Public Health Informatics, Computational, and Operations Research, City University of New York, New York City, New York, USA
| | - Sarah M Bartsch
- Public Health Informatics, Computational, and Operations Research, City University of New York, New York City, New York, USA
| | - Mary K Hayden
- Rush University Medical Center, Chicago, Illinois, USA
| | - Joel Welling
- Public Health Applications, Pittsburgh Super Computing Center, Pittsburgh, Pennsylvania, USA
| | - Leslie E Mueller
- Public Health Informatics, Computational, and Operations Research, City University of New York, New York City, New York, USA
| | - Shawn T Brown
- Public Health Applications, Pittsburgh Super Computing Center, Pittsburgh, Pennsylvania, USA
| | | | - Jim Leonard
- Public Health Applications, Pittsburgh Super Computing Center, Pittsburgh, Pennsylvania, USA
| | - Sarah K Kemble
- Rush University Medical Center, Chicago, Illinois, USA.,Chicago Department of Public Health, Chicago, Illinois, USA
| | - Robert A Weinstein
- Rush University Medical Center, Chicago, Illinois, USA.,Cook County Health, Chicago, Illinois, USA
| | - William E Trick
- Rush University Medical Center, Chicago, Illinois, USA.,Cook County Health, Chicago, Illinois, USA
| | - Michael Y Lin
- Rush University Medical Center, Chicago, Illinois, USA
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17
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Bartsch SM, O'Shea KJ, Lee BY. The Clinical and Economic Burden of Norovirus Gastroenteritis in the United States. J Infect Dis 2021; 222:1910-1919. [PMID: 32671397 DOI: 10.1093/infdis/jiaa292] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 05/28/2020] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Although norovirus outbreaks periodically make headlines, it is unclear how much attention norovirus may receive otherwise. A better understanding of the burden could help determine how to prioritize norovirus prevention and control. METHODS We developed a computational simulation model to quantify the clinical and economic burden of norovirus in the United States. RESULTS A symptomatic case generated $48 in direct medical costs, $416 in productivity losses ($464 total). The median yearly cost of outbreaks was $7.6 million (range across years, $7.5-$8.2 million) in direct medical costs, and $165.3 million ($161.1-$176.4 million) in productivity losses ($173.5 million total). Sporadic illnesses in the community (incidence, 10-150/1000 population) resulted in 14 118-211 705 hospitalizations, 8.2-122.9 million missed school/work days, $0.2-$2.3 billion in direct medical costs, and $1.4-$20.7 billion in productivity losses ($1.5-$23.1 billion total). The total cost was $10.6 billion based on the current incidence estimate (68.9/1000). CONCLUSION Our study quantified norovirus' burden. Of the total burden, sporadic cases constituted >90% (thus, annual burden may vary depending on incidence) and productivity losses represented 89%. More than half the economic burden is in adults ≥45, more than half occurs in winter months, and >90% of outbreak costs are due to person-to-person transmission, offering insights into where and when prevention/control efforts may yield returns.
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Affiliation(s)
- Sarah M Bartsch
- Public Health Informatics, Computational, and Operations Research, City University of New York, New York City, New York, USA
| | - Kelly J O'Shea
- Public Health Informatics, Computational, and Operations Research, City University of New York, New York City, New York, USA
| | - Bruce Y Lee
- Public Health Informatics, Computational, and Operations Research, City University of New York, New York City, New York, USA
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Bartsch SM, O'Shea KJ, Wedlock PT, Ferguson MC, Siegmund SS, Lee BY. Potential Clinical and Economic Value of Norovirus Vaccination in the Community Setting. Am J Prev Med 2021; 60:360-368. [PMID: 33516583 PMCID: PMC8415104 DOI: 10.1016/j.amepre.2020.10.022] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 09/04/2020] [Accepted: 10/30/2020] [Indexed: 12/20/2022]
Abstract
INTRODUCTION With norovirus vaccine candidates currently under development, now is the time to identify the vaccine characteristics and implementation thresholds at which vaccination becomes cost effective and cost saving in a community setting. METHODS In 2020, a norovirus transmission, clinical, and economics computational simulation model representing different U.S. population segments was developed to simulate the spread of norovirus and the potential impact of vaccinating children aged <5 years and older adults (aged ≥65 years). RESULTS Compared with no vaccination, vaccinating preschool-aged children averted 8%-72% of symptomatic norovirus cases in a community, whereas vaccinating older adults averted 2%-29% of symptomatic cases (varying with vaccine efficacy [25%-75%] and vaccination coverage [10%-80%]). Vaccination with a 25% vaccine efficacy was cost effective (incremental cost-effectiveness ratio ≤$50,000 per quality-adjusted life year) when vaccination cost ≤$445 and cost saving at ≤$370 when vaccinating preschool-aged children and ≤$42 and ≤$30, respectively, when vaccinating older adults. With a 50% vaccine efficacy, vaccination was cost effective when it cost ≤$1,190 and cost saving at ≤$930 when vaccinating preschool-aged children and ≤$110 and ≤$64, respectively, when vaccinating older adults. These cost thresholds (cost effective and cost saving, respectively) further increased with a 75% vaccine efficacy to ≤$1,600 and ≤$1,300 for preschool-aged children and ≤$165 and ≤$100 for older adults. CONCLUSIONS This study outlines thresholds at which a norovirus vaccine would be cost effective and cost saving in the community when vaccinating children aged <5 years and older adults. Establishing these thresholds can help provide decision makers with targets to consider when developing and implementing a norovirus vaccine.
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Affiliation(s)
- Sarah M Bartsch
- Public Health Informatics, Computational, and Operations Research (PHICOR), Graduate School of Public Health and Health Policy, City University of New York, New York City, New York
| | - Kelly J O'Shea
- Public Health Informatics, Computational, and Operations Research (PHICOR), Graduate School of Public Health and Health Policy, City University of New York, New York City, New York
| | - Patrick T Wedlock
- Public Health Informatics, Computational, and Operations Research (PHICOR), Graduate School of Public Health and Health Policy, City University of New York, New York City, New York
| | - Marie C Ferguson
- Public Health Informatics, Computational, and Operations Research (PHICOR), Graduate School of Public Health and Health Policy, City University of New York, New York City, New York
| | - Sheryl S Siegmund
- Public Health Informatics, Computational, and Operations Research (PHICOR), Graduate School of Public Health and Health Policy, City University of New York, New York City, New York
| | - Bruce Y Lee
- Public Health Informatics, Computational, and Operations Research (PHICOR), Graduate School of Public Health and Health Policy, City University of New York, New York City, New York.
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Lee BY, Bartsch SM, Lin MY, Asti L, Welling J, Mueller LE, Leonard J, Brown ST, Doshi K, Kemble SK, Mitgang EA, Weinstein RA, Trick WE, Hayden MK. How Long-Term Acute Care Hospitals Can Play an Important Role in Controlling Carbapenem-Resistant Enterobacteriaceae in a Region: A Simulation Modeling Study. Am J Epidemiol 2021; 190:448-458. [PMID: 33145594 DOI: 10.1093/aje/kwaa247] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2019] [Revised: 10/27/2020] [Accepted: 10/29/2020] [Indexed: 11/14/2022] Open
Abstract
Typically, long-term acute care hospitals (LTACHs) have less experience in and incentives to implementing aggressive infection control for drug-resistant organisms such as carbapenem-resistant Enterobacteriaceae (CRE) than acute care hospitals. Decision makers need to understand how implementing control measures in LTACHs can impact CRE spread regionwide. Using our Chicago metropolitan region agent-based model to simulate CRE spread and control, we estimated that a prevention bundle in only LTACHs decreased prevalence by a relative 4.6%-17.1%, averted 1,090-2,795 new carriers, 273-722 infections and 37-87 deaths over 3 years and saved $30.5-$69.1 million, compared with no CRE control measures. When LTACHs and intensive care units intervened, prevalence decreased by a relative 21.2%. Adding LTACHs averted an additional 1,995 carriers, 513 infections, and 62 deaths, and saved $47.6 million beyond implementation in intensive care units alone. Thus, LTACHs may be more important than other acute care settings for controlling CRE, and regional efforts to control drug-resistant organisms should start with LTACHs as a centerpiece.
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20
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Lee BY, Bartsch SM, Ferguson MC, Wedlock PT, O’Shea KJ, Siegmund SS, Cox SN, McKinnell JA. The value of decreasing the duration of the infectious period of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. PLoS Comput Biol 2021; 17:e1008470. [PMID: 33411742 PMCID: PMC7790237 DOI: 10.1371/journal.pcbi.1008470] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 10/27/2020] [Indexed: 01/08/2023] Open
Abstract
Finding medications or vaccines that may decrease the infectious period of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could potentially reduce transmission in the broader population. We developed a computational model of the U.S. simulating the spread of SARS-CoV-2 and the potential clinical and economic impact of reducing the infectious period duration. Simulation experiments found that reducing the average infectious period duration could avert a median of 442,852 [treating 25% of symptomatic cases, reducing by 0.5 days, reproductive number (R0) 3.5, and starting treatment when 15% of the population has been exposed] to 44.4 million SARS-CoV-2 cases (treating 75% of all infected cases, reducing by 3.5 days, R0 2.0). With R0 2.5, reducing the average infectious period duration by 0.5 days for 25% of symptomatic cases averted 1.4 million cases and 99,398 hospitalizations; increasing to 75% of symptomatic cases averted 2.8 million cases. At $500/person, treating 25% of symptomatic cases saved $209.5 billion (societal perspective). Further reducing the average infectious period duration by 3.5 days averted 7.4 million cases (treating 25% of symptomatic cases). Expanding treatment to 75% of all infected cases, including asymptomatic infections (R0 2.5), averted 35.9 million cases and 4 million hospitalizations, saving $48.8 billion (societal perspective and starting treatment after 5% of the population has been exposed). Our study quantifies the potential effects of reducing the SARS-CoV-2 infectious period duration. Finding medications or vaccines that may decrease the infectious period of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could potentially reduce transmission in the broader population. We developed a computational model of the U.S. simulating the spread of SARS-CoV-2 and the potential clinical and economic impact of reducing the infectious period duration. Our simulation experiments found that reducing the average infectious period duration could avert a median of 442,852 to 44.4 million SARS-CoV-2 cases, varying the proportion of cases treated, average duration of the infectious period, and the reproductive rate. At $500/person, treating 25% of symptomatic cases saved $209.5 billion (societal perspective, R0 2.5). Further reducing the average infectious period duration by 3.5 days averted 7.4 million cases (treating 25% of symptomatic cases). Expanding treatment to 75% of all infected cases, including asymptomatic infections (R0 2.5), averted 35.9 million cases and 4 million hospitalizations, saving $48.8 billion (societal perspective and starting treatment after 5% of the population has been exposed). Our study suggests that finding ways to reduce the infectious period of SARS-CoV-2 could help decrease its spread and impact.
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Affiliation(s)
- Bruce Y. Lee
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York Graduate School of Public Health and Health Policy, New York City, New York, United States of America
- * E-mail:
| | - Sarah M. Bartsch
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York Graduate School of Public Health and Health Policy, New York City, New York, United States of America
| | - Marie C. Ferguson
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York Graduate School of Public Health and Health Policy, New York City, New York, United States of America
| | - Patrick T. Wedlock
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York Graduate School of Public Health and Health Policy, New York City, New York, United States of America
| | - Kelly J. O’Shea
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York Graduate School of Public Health and Health Policy, New York City, New York, United States of America
| | - Sheryl S. Siegmund
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York Graduate School of Public Health and Health Policy, New York City, New York, United States of America
| | - Sarah N. Cox
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York Graduate School of Public Health and Health Policy, New York City, New York, United States of America
| | - James A. McKinnell
- Infectious Disease Clinical Outcomes Research Unit (ID-CORE), Lundquist Institute, Harbor-UCLA Medical Center, Torrance, California, United States of America
- Torrance Memorial Medical Center, Torrance, California, United States of America
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21
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Bartsch SM, O'Shea KJ, Ferguson MC, Bottazzi ME, Wedlock PT, Strych U, McKinnell JA, Siegmund SS, Cox SN, Hotez PJ, Lee BY. Vaccine Efficacy Needed for a COVID-19 Coronavirus Vaccine to Prevent or Stop an Epidemic as the Sole Intervention. Am J Prev Med 2020; 59:493-503. [PMID: 32778354 PMCID: PMC7361120 DOI: 10.1016/j.amepre.2020.06.011] [Citation(s) in RCA: 183] [Impact Index Per Article: 45.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 06/26/2020] [Accepted: 06/30/2020] [Indexed: 10/28/2022]
Abstract
INTRODUCTION Given the continuing COVID-19 pandemic and much of the U.S. implementing social distancing owing to the lack of alternatives, there has been a push to develop a vaccine to eliminate the need for social distancing. METHODS In 2020, the team developed a computational model of the U.S. simulating the spread of COVID-19 coronavirus and vaccination. RESULTS Simulation experiments revealed that to prevent an epidemic (reduce the peak by >99%), the vaccine efficacy has to be at least 60% when vaccination coverage is 100% (reproduction number=2.5-3.5). This vaccine efficacy threshold rises to 70% when coverage drops to 75% and up to 80% when coverage drops to 60% when reproduction number is 2.5, rising to 80% when coverage drops to 75% when the reproduction number is 3.5. To extinguish an ongoing epidemic, the vaccine efficacy has to be at least 60% when coverage is 100% and at least 80% when coverage drops to 75% to reduce the peak by 85%-86%, 61%-62%, and 32% when vaccination occurs after 5%, 15%, and 30% of the population, respectively, have already been exposed to COVID-19 coronavirus. A vaccine with an efficacy between 60% and 80% could still obviate the need for other measures under certain circumstances such as much higher, and in some cases, potentially unachievable, vaccination coverages. CONCLUSIONS This study found that the vaccine has to have an efficacy of at least 70% to prevent an epidemic and of at least 80% to largely extinguish an epidemic without any other measures (e.g., social distancing).
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Affiliation(s)
- Sarah M Bartsch
- Public Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Kelly J O'Shea
- Public Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Marie C Ferguson
- Public Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Maria Elena Bottazzi
- National School of Tropical Medicine, Baylor College of Medicine, Houston, Texas; Department of Pediatrics, Baylor College of Medicine, Houston, Texas; Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas
| | - Patrick T Wedlock
- Public Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Ulrich Strych
- National School of Tropical Medicine, Baylor College of Medicine, Houston, Texas; Department of Pediatrics, Baylor College of Medicine, Houston, Texas; Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas
| | - James A McKinnell
- Infectious Disease Clinical Outcomes Research Unit (ID-CORE), Lundquist Institute, Harbor-UCLA Medical Center, Torrance, California; Torrance Memorial Medical Center, Torrance, California
| | - Sheryl S Siegmund
- Public Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Sarah N Cox
- Public Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Peter J Hotez
- National School of Tropical Medicine, Baylor College of Medicine, Houston, Texas; Department of Pediatrics, Baylor College of Medicine, Houston, Texas; Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas
| | - Bruce Y Lee
- Public Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York City, New York.
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McKinnell JA, Singh RD, Miller LG, Kleinman K, Gussin G, He J, Saavedra R, Dutciuc TD, Estevez M, Chang J, Heim L, Yamaguchi S, Custodio H, Gohil SK, Park S, Tam S, Robinson PA, Tjoa T, Nguyen J, Evans KD, Bittencourt CE, Lee BY, Mueller LE, Bartsch SM, Jernigan JA, Slayton RB, Stone ND, Zahn M, Mor V, McConeghy K, Baier RR, Janssen L, O'Donnell K, Weinstein RA, Hayden MK, Coady MH, Bhattarai M, Peterson EM, Huang SS. The SHIELD Orange County Project: Multidrug-resistant Organism Prevalence in 21 Nursing Homes and Long-term Acute Care Facilities in Southern California. Clin Infect Dis 2020; 69:1566-1573. [PMID: 30753383 DOI: 10.1093/cid/ciz119] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Accepted: 02/05/2019] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Multidrug-resistant organisms (MDROs) spread between hospitals, nursing homes (NHs), and long-term acute care facilities (LTACs) via patient transfers. The Shared Healthcare Intervention to Eliminate Life-threatening Dissemination of MDROs in Orange County is a regional public health collaborative involving decolonization at 38 healthcare facilities selected based on their high degree of patient sharing. We report baseline MDRO prevalence in 21 NHs/LTACs. METHODS A random sample of 50 adults for 21 NHs/LTACs (18 NHs, 3 LTACs) were screened for methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus spp. (VRE), extended-spectrum β-lactamase-producing organisms (ESBL), and carbapenem-resistant Enterobacteriaceae (CRE) using nares, skin (axilla/groin), and peri-rectal swabs. Facility and resident characteristics associated with MDRO carriage were assessed using multivariable models clustering by person and facility. RESULTS Prevalence of MDROs was 65% in NHs and 80% in LTACs. The most common MDROs in NHs were MRSA (42%) and ESBL (34%); in LTACs they were VRE (55%) and ESBL (38%). CRE prevalence was higher in facilities that manage ventilated LTAC patients and NH residents (8% vs <1%, P < .001). MDRO status was known for 18% of NH residents and 49% of LTAC patients. MDRO-colonized adults commonly harbored additional MDROs (54% MDRO+ NH residents and 62% MDRO+ LTACs patients). History of MRSA (odds ratio [OR] = 1.7; confidence interval [CI]: 1.2, 2.4; P = .004), VRE (OR = 2.1; CI: 1.2, 3.8; P = .01), ESBL (OR = 1.6; CI: 1.1, 2.3; P = .03), and diabetes (OR = 1.3; CI: 1.0, 1.7; P = .03) were associated with any MDRO carriage. CONCLUSIONS The majority of NH residents and LTAC patients harbor MDROs. MDRO status is frequently unknown to the facility. The high MDRO prevalence highlights the need for prevention efforts in NHs/LTACs as part of regional efforts to control MDRO spread.
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Affiliation(s)
- James A McKinnell
- Infectious Disease Clinical Outcomes Research, LA Biomed at Harbor-University of California Los Angeles Medical Center, Torrance
| | - Raveena D Singh
- Division of Infectious Diseases, University of California Irvine School of Medicine, Orange
| | - Loren G Miller
- Infectious Disease Clinical Outcomes Research, LA Biomed at Harbor-University of California Los Angeles Medical Center, Torrance
| | - Ken Kleinman
- University of Massachusetts Amherst School of Public Health and Health Sciences, Orange
| | - Gabrielle Gussin
- Division of Infectious Diseases, University of California Irvine School of Medicine, Orange
| | - Jiayi He
- Division of Infectious Diseases, University of California Irvine School of Medicine, Orange
| | - Raheeb Saavedra
- Division of Infectious Diseases, University of California Irvine School of Medicine, Orange
| | - Tabitha D Dutciuc
- Division of Infectious Diseases, University of California Irvine School of Medicine, Orange
| | - Marlene Estevez
- Division of Infectious Diseases, University of California Irvine School of Medicine, Orange
| | - Justin Chang
- Division of Infectious Diseases, University of California Irvine School of Medicine, Orange
| | - Lauren Heim
- Division of Infectious Diseases, University of California Irvine School of Medicine, Orange
| | - Stacey Yamaguchi
- Division of Infectious Diseases, University of California Irvine School of Medicine, Orange
| | - Harold Custodio
- Division of Infectious Diseases, University of California Irvine School of Medicine, Orange
| | - Shruti K Gohil
- Division of Infectious Diseases, University of California Irvine School of Medicine, Orange
| | - Steven Park
- University of California Irvine Health, Orange
| | - Steven Tam
- Division of Geriatrics, Department of Medicine, University of California Irvine, Orange
| | | | - Thomas Tjoa
- Division of Infectious Diseases, University of California Irvine School of Medicine, Orange
| | - Jenny Nguyen
- Division of Infectious Diseases, University of California Irvine School of Medicine, Orange
| | | | | | - Bruce Y Lee
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Leslie E Mueller
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Sarah M Bartsch
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - John A Jernigan
- Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Rachel B Slayton
- Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Nimalie D Stone
- Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Matthew Zahn
- Epidemiology and Assessment, Orange County Health Care Agency, Santa Ana, California
| | - Vincent Mor
- Department of Health Services, Policy and Practice, Brown University School of Public Health, Rhode Island.,Center of Innovation in Long-Term Services and Supports, Veterans Affairs Medical Center, Providence VA Medical Center, Rhode Island.,Center for Long-Term Care Quality and Innovation, Brown University School of Public Health, Providence, Rhode Island
| | - Kevin McConeghy
- Department of Health Services, Policy and Practice, Brown University School of Public Health, Rhode Island.,Center of Innovation in Long-Term Services and Supports, Veterans Affairs Medical Center, Providence VA Medical Center, Rhode Island.,Center for Long-Term Care Quality and Innovation, Brown University School of Public Health, Providence, Rhode Island
| | - Rosa R Baier
- Department of Health Services, Policy and Practice, Brown University School of Public Health, Rhode Island.,Center for Long-Term Care Quality and Innovation, Brown University School of Public Health, Providence, Rhode Island
| | - Lynn Janssen
- Healthcare-associated Infections Program, Center for Healthcare Quality, California Department of Public Health, Richmond, California
| | - Kathleen O'Donnell
- Epidemiology and Assessment, Orange County Health Care Agency, Santa Ana, California.,Healthcare-associated Infections Program, Center for Healthcare Quality, California Department of Public Health, Richmond, California
| | - Robert A Weinstein
- Cook County Health and Hospitals System, Chicago, Illinois.,Department of Medicine, Rush University Medical Center, Chicago, Illinois
| | - Mary K Hayden
- Department of Medicine, Rush University Medical Center, Chicago, Illinois
| | - Micaela H Coady
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
| | - Megha Bhattarai
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
| | | | - Susan S Huang
- Division of Infectious Diseases, University of California Irvine School of Medicine, Orange.,Health Policy Research Institute, University of California Irvine School of Medicine
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Bartsch SM, Mitgang EA, Geller G, Cox SN, O'Shea KJ, Boyce A, Siegmund SS, Kahn J, Lee BY. What If the Influenza Vaccine Did Not Offer Such Variable Protection? J Infect Dis 2020; 222:1138-1144. [PMID: 32386323 DOI: 10.1093/infdis/jiaa240] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 05/06/2020] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The protection that an influenza vaccine offers can vary significantly from person to person due to differences in immune systems, body types, and other factors. The question, then, is what is the value of efforts to reduce this variability such as making vaccines more personalized and tailored to individuals. METHODS We developed a compartment model of the United States to simulate different influenza seasons and the impact of reducing the variability in responses to the influenza vaccine across the population. RESULTS Going from a vaccine that varied in efficacy (0-30%) to one that had a uniform 30% efficacy for everyone averted 16.0-31.2 million cases, $1.9-$3.6 billion in direct medical costs, and $16.1-$42.7 billion in productivity losses. Going from 0-50% in efficacy to just 50% for everyone averted 27.7-38.6 million cases, $3.3-$4.6 billion in direct medical costs, and $28.8-$57.4 billion in productivity losses. Going from 0-70% to 70% averted 33.6-54.1 million cases, $4.0-$6.5 billion in direct medical costs, and $44.8-$64.7 billion in productivity losses. CONCLUSIONS This study quantifies for policy makers, funders, and vaccine developers and manufacturers the potential impact of efforts to reduce variability in the protection that influenza vaccines offer (eg, developing vaccines that are more personalized to different individual factors).
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Affiliation(s)
- Sarah M Bartsch
- Public Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health & Health Policy, New York City, New York, USA
| | - Elizabeth A Mitgang
- Public Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health & Health Policy, New York City, New York, USA
| | - Gail Geller
- Johns Hopkins Berman Institute of Bioethics, Baltimore, Maryland, USA
| | - Sarah N Cox
- Public Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health & Health Policy, New York City, New York, USA
| | - Kelly J O'Shea
- Public Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health & Health Policy, New York City, New York, USA
| | - Angie Boyce
- Johns Hopkins Berman Institute of Bioethics, Baltimore, Maryland, USA
| | - Sheryl S Siegmund
- Public Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health & Health Policy, New York City, New York, USA
| | - Jeffrey Kahn
- Johns Hopkins Berman Institute of Bioethics, Baltimore, Maryland, USA
| | - Bruce Y Lee
- Public Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health & Health Policy, New York City, New York, USA
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Bartsch SM, Ferguson MC, McKinnell JA, O'Shea KJ, Wedlock PT, Siegmund SS, Lee BY. The Potential Health Care Costs And Resource Use Associated With COVID-19 In The United States. Health Aff (Millwood) 2020; 39:927-935. [PMID: 32324428 PMCID: PMC11027994 DOI: 10.1377/hlthaff.2020.00426] [Citation(s) in RCA: 217] [Impact Index Per Article: 54.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
With the coronavirus disease 2019 (COVID-19) pandemic, one of the major concerns is the direct medical cost and resource use burden imposed on the US health care system. We developed a Monte Carlo simulation model that represented the US population and what could happen to each person who got infected. We estimated resource use and direct medical costs per symptomatic infection and at the national level, with various "attack rates" (infection rates), to understand the potential economic benefits of reducing the burden of the disease. A single symptomatic COVID-19 case could incur a median direct medical cost of $3,045 during the course of the infection alone. If 80 percent of the US population were to get infected, the result could be a median of 44.6 million hospitalizations, 10.7 million intensive care unit (ICU) admissions, 6.5 million patients requiring a ventilator, 249.5 million hospital bed days, and $654.0 billion in direct medical costs over the course of the pandemic. If 20 percent of the US population were to get infected, there could be a median of 11.2 million hospitalizations, 2.7 million ICU admissions, 1.6 million patients requiring a ventilator, 62.3 million hospital bed days, and $163.4 billion in direct medical costs over the course of the pandemic.
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Affiliation(s)
- Sarah M Bartsch
- Sarah M. Bartsch is a project director at Public Health Informatics, Computational, and Operations Research (PHICOR), Graduate School of Public Health and Health Policy, City University of New York, in New York City
| | - Marie C Ferguson
- Marie C. Ferguson is a project director at PHICOR, Graduate School of Public Health and Health Policy, City University of New York
| | - James A McKinnell
- James A. McKinnell is an associate professor of medicine in the Infectious Disease Clinical Outcomes Research Unit, Lundquist Institute, Harbor-UCLA Medical Center, in Los Angeles, California
| | - Kelly J O'Shea
- Kelly J. O'Shea is a senior research analyst at PHICOR, Graduate School of Public Health and Health Policy, City University of New York
| | - Patrick T Wedlock
- Patrick T. Wedlock is a senior research analyst at PHICOR, Graduate School of Public Health and Health Policy, City University of New York
| | - Sheryl S Siegmund
- Sheryl S. Siegmund is director of operations at PHICOR, Graduate School of Public Health and Health Policy, City University of New York
| | - Bruce Y Lee
- Bruce Y. Lee is a professor of health policy and management at the Graduate School of Public Health and Health Policy and executive director of PHICOR, both at the City University of New York
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25
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Bartsch SM, O'Shea KJ, Ferguson MC, Bottazzi ME, Cox SN, Strych U, McKinnell JA, Wedlock PT, Siegmund SS, Hotez PJ, Lee BY. How Efficacious Must a COVID-19 Coronavirus Vaccine be to Prevent or Stop an Epidemic by Itself. medRxiv 2020. [PMID: 32511569 DOI: 10.1101/2020.05.29.20117184] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND Given the continuing coronavirus disease 2019 (COVID-19) pandemic and much of the U.S. implementing social distancing due to the lack of alternatives, there has been a push to develop a vaccine to eliminate the need for social distancing. METHODS In 2020, we developed a computational model of the U.S. simulating the spread of COVID-19 coronavirus and vaccination. RESULTS Simulation experiments revealed that when vaccine efficacy exceeded 70%, coverage exceeded 60%, and vaccination occurred on day 1, the attack rate dropped to 22% with daily cases not exceeding 3.2 million (reproductive rate, R0, 2.5). When R0 was 3.5, the attack rate dropped to 41% with daily cases not exceeding 14.4 million. Increasing coverage to 75% when vaccination occurred by day 90 resulted in 5% attack rate and daily cases not exceeding 258,029when R0 was 2.5 and a 26% attack rate and maximum daily cases of 22.6 million when R0 was 3.5. When vaccination did not occur until day 180, coverage (i.e., those vaccinated plus those otherwise immune) had to reach 100%. A vaccine with an efficacy between 40% and 70% could still obviate the need for other measures under certain circumstances such as much higher, and in some cases, potentially unachievable, vaccination coverages. CONCLUSION Our study found that to either prevent or largely extinguish an epidemic without any other measures (e.g., social distancing), the vaccine has to have an efficacy of at least 70%.
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Bartsch SM, Wong KF, Stokes-Cawley OJ, McKinnell JA, Cao C, Gussin GM, Mueller LE, Kim DS, Miller LG, Huang SS, Lee BY. Knowing More of the Iceberg: How Detecting a Greater Proportion of Carbapenem-Resistant Enterobacteriaceae Carriers Influences Transmission. J Infect Dis 2020; 221:1782-1794. [PMID: 31150539 PMCID: PMC7213567 DOI: 10.1093/infdis/jiz288] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Accepted: 05/30/2019] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Clinical testing detects a fraction of carbapenem-resistant Enterobacteriaceae (CRE) carriers. Detecting a greater proportion could lead to increased use of infection prevention and control measures but requires resources. Therefore, it is important to understand the impact of detecting increasing proportions of CRE carriers. METHODS We used our Regional Healthcare Ecosystem Analyst-generated agent-based model of adult inpatient healthcare facilities in Orange County, California, to explore the impact that detecting greater proportions of carriers has on the spread of CRE. RESULTS Detecting and placing 1 in 9 carriers on contact precautions increased the prevalence of CRE from 0% to 8.0% countywide over 10 years. Increasing the proportion of detected carriers from 1 in 9 up to 1 in 5 yielded linear reductions in transmission; at proportions >1 in 5, reductions were greater than linear. Transmission reductions did not occur for 1, 4, or 5 years, varying by facility type. With a contact precautions effectiveness of ≤70%, the detection level yielding nonlinear reductions remained unchanged; with an effectiveness of >80%, detecting only 1 in 5 carriers garnered large reductions in the number of new CRE carriers. Trends held when CRE was already present in the region. CONCLUSION Although detection of all carriers provided the most benefits for preventing new CRE carriers, if this is not feasible, it may be worthwhile to aim for detecting >1 in 5 carriers.
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Affiliation(s)
- Sarah M Bartsch
- Public Health Computational and Operations Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Kim F Wong
- Center for Simulation and Modeling, University of Pittsburgh, Pennsylvania
| | - Owen J Stokes-Cawley
- Public Health Computational and Operations Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - James A McKinnell
- Infectious Disease Clinical Outcomes Research Unit, Los Angeles Biomedical Research Institute, Harbor-UCLA Medical Center, Los Angeles, California
- Torrance Memorial Medical Center, Torrance, California
| | - Chenghua Cao
- Division of Infectious Diseases, University of California–Irvine Health School of Medicine, Irvine, California
- Health Policy Research Institute, University of California–Irvine Health School of Medicine, Irvine, California
| | - Gabrielle M Gussin
- Division of Infectious Diseases, University of California–Irvine Health School of Medicine, Irvine, California
- Health Policy Research Institute, University of California–Irvine Health School of Medicine, Irvine, California
| | - Leslie E Mueller
- Public Health Computational and Operations Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Diane S Kim
- Division of Infectious Diseases, University of California–Irvine Health School of Medicine, Irvine, California
- Health Policy Research Institute, University of California–Irvine Health School of Medicine, Irvine, California
| | | | - Susan S Huang
- Division of Infectious Diseases, University of California–Irvine Health School of Medicine, Irvine, California
- Health Policy Research Institute, University of California–Irvine Health School of Medicine, Irvine, California
| | - Bruce Y Lee
- Public Health Computational and Operations Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
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Bartsch SM, Stokes-Cawley OJ, Buekens P, Asti L, Bottazzi ME, Strych U, Wedlock PT, Mitgang EA, Meymandi S, Falcon-Lezama JA, Hotez PJ, Lee BY. The potential economic value of a therapeutic Chagas disease vaccine for pregnant women to prevent congenital transmission. Vaccine 2020; 38:3261-3270. [PMID: 32171575 DOI: 10.1016/j.vaccine.2020.02.078] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 02/21/2020] [Accepted: 02/26/2020] [Indexed: 12/16/2022]
Abstract
BACKGROUND Currently, there are no solutions to prevent congenital transmission of Chagas disease during pregnancy, which affects 1-40% of pregnant women in Latin America and is associated with a 5% transmission risk. With therapeutic vaccines under development, now is the right time to determine the economic value of such a vaccine to prevent congenital transmission. METHODS We developed a computational decision model that represented the clinical outcomes and diagnostic testing strategies for an infant born to a Chagas-positive woman in Mexico and evaluated the impact of vaccination. RESULTS Compared to no vaccination, a 25% efficacious vaccine averted 125 [95% uncertainty interval (UI): 122-128] congenital cases, 1.9 (95% UI: 1.6-2.2) infant deaths, and 78 (95% UI: 66-91) DALYs per 10,000 infected pregnant women; a 50% efficacious vaccine averted 251 (95% UI: 248-254) cases, 3.8 (95% UI: 3.6-4.2) deaths, and 160 (95% UI: 148-171) DALYs; and a 75% efficacious vaccine averted 376 (95% UI: 374-378) cases, 5.8 (95% UI: 5.5-6.1) deaths, and 238 (95% UI: 227-249) DALYs. A 25% efficacious vaccine was cost-effective (incremental cost-effectiveness ratio <3× Mexico's gross domestic product per capita, <$29,698/DALY averted) when the vaccine cost ≤$240 and ≤$310 and cost-saving when ≤$10 and ≤$80 from the third-party payer and societal perspectives, respectively. A 50% efficacious vaccine was cost-effective when costing ≤$490 and ≤$615 and cost-saving when ≤$25 and ≤$160, from the third-party payer and societal perspectives, respectively. A 75% efficacious vaccine was cost-effective when ≤$720 and ≤$930 and cost-saving when ≤$40 and ≤$250 from the third-party payer and societal perspectives, respectively. Additionally, 13-42 fewer infants progressed to chronic disease, saving $0.41-$1.21 million to society. CONCLUSION We delineated the thresholds at which therapeutic vaccination of Chagas-positive pregnant women would be cost-effective and cost-saving, providing economic guidance for decision-makers to consider when developing and bringing such a vaccine to market.
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Affiliation(s)
- Sarah M Bartsch
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York, 55 W 125th Street, New York City, NY 10027, USA
| | - Owen J Stokes-Cawley
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York, 55 W 125th Street, New York City, NY 10027, USA
| | - Pierre Buekens
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Lindsey Asti
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York, 55 W 125th Street, New York City, NY 10027, USA
| | - Maria Elena Bottazzi
- National School of Tropical Medicine and Departments of Pediatrics and Molecular Virology & Microbiology, Baylor College of Medicine, One Baylor Plaza, BCM113 Houston, TX 77030, USA
| | - Ulrich Strych
- National School of Tropical Medicine and Departments of Pediatrics and Molecular Virology & Microbiology, Baylor College of Medicine, One Baylor Plaza, BCM113 Houston, TX 77030, USA
| | - Patrick T Wedlock
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York, 55 W 125th Street, New York City, NY 10027, USA
| | - Elizabeth A Mitgang
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York, 55 W 125th Street, New York City, NY 10027, USA
| | - Sheba Meymandi
- Center of Excellence for Chagas Disease at Olive View-UCLA Medical Center, 14445 Olive View Drive, Sylmar, CA 91342, USA
| | - Jorge Abelardo Falcon-Lezama
- Carlos Slim Foundation, Lago Zurich 245, Piso 20. Ampliación Granada, Del. Miguel Hidalgo, C.P. 11529 Ciudad de México, Mexico
| | - Peter J Hotez
- National School of Tropical Medicine and Departments of Pediatrics and Molecular Virology & Microbiology, Baylor College of Medicine, One Baylor Plaza, BCM113 Houston, TX 77030, USA
| | - Bruce Y Lee
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York, 55 W 125th Street, New York City, NY 10027, USA.
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Bartsch SM, Asti L, Stokes-Cawley OJ, Sim SY, Bottazzi ME, Hotez PJ, Lee BY. The Potential Economic Value of a Zika Vaccine for a Woman of Childbearing Age. Am J Prev Med 2020; 58:370-377. [PMID: 31980305 DOI: 10.1016/j.amepre.2019.10.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 10/20/2019] [Accepted: 10/21/2019] [Indexed: 11/17/2022]
Abstract
INTRODUCTION With Zika vaccine candidates under development and women of childbearing age being the primary target population, now is the time to map the vaccine (e.g., efficacy and duration of protection) and vaccination (e.g., cost) characteristic thresholds at which vaccination becomes cost effective, highly cost effective, and cost saving. METHODS A Markov model was developed (to represent 2019 circumstances, US$ and INT$, Region of the Americas) to simulate a woman of childbearing age and the potential risk and clinical course of a Zika infection. RESULTS Compared with no vaccination, vaccination was cost effective (incremental cost-effectiveness ratio: US$1,254-$82,900/disability-adjusted life years averted) when the risk of infection was ≥0.05%-0.08% (varying with country income), vaccine efficacy was ≥25%, and vaccination cost was US$1-$7,500 (INT$5-$10,000 depending on country income level). Vaccination was dominant (i.e., saved costs and provided beneficial health effects) when the infection risk was ≥0.1% for a vaccine efficacy ≥75% and when the infection risk was ≥0.5% for a vaccine efficacy ≥25%, for scenarios where vaccination conferred a 1-year duration of protection and cost ≤$200. In some cases, the vaccine was cost effective when the risk was as low as 0.015%, the cost was as high as $7,500 (INT$10,000), the efficacy was as low as 25%, and the duration of protection was 1 year. CONCLUSIONS The thresholds at which vaccination becomes cost effective and cost saving can provide targets for Zika vaccine development and implementation.
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Affiliation(s)
- Sarah M Bartsch
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York, New York City, New York
| | - Lindsey Asti
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York, New York City, New York
| | - Owen J Stokes-Cawley
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York, New York City, New York
| | - So Yoon Sim
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York, New York City, New York
| | - Maria Elena Bottazzi
- National School of Tropical Medicine, Baylor College of Medicine, Houston, Texas; Department of Pediatrics, Baylor College of Medicine, Houston, Texas; Department of Molecular Virology & Microbiology, Baylor College of Medicine, Houston, Texas
| | - Peter J Hotez
- National School of Tropical Medicine, Baylor College of Medicine, Houston, Texas; Department of Pediatrics, Baylor College of Medicine, Houston, Texas; Department of Molecular Virology & Microbiology, Baylor College of Medicine, Houston, Texas
| | - Bruce Y Lee
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York, New York City, New York.
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Lee BY, Bartsch SM, Hayden MK, Welling J, DePasse JV, Kemble SK, Leonard J, Weinstein RA, Mueller LE, Doshi K, Brown ST, Trick WE, Lin MY. How Introducing a Registry With Automated Alerts for Carbapenem-resistant Enterobacteriaceae (CRE) May Help Control CRE Spread in a Region. Clin Infect Dis 2020; 70:843-849. [PMID: 31070719 PMCID: PMC7931833 DOI: 10.1093/cid/ciz300] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Accepted: 04/09/2019] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND Regions are considering the use of electronic registries to track patients who carry antibiotic-resistant bacteria, including carbapenem-resistant Enterobacteriaceae (CRE). Implementing such a registry can be challenging and requires time, effort, and resources; therefore, there is a need to better understand the potential impact. METHODS We developed an agent-based model of all inpatient healthcare facilities (90 acute care hospitals, 9 long-term acute care hospitals, 351 skilled nursing facilities, and 12 ventilator-capable skilled nursing facilities) in the Chicago metropolitan area, surrounding communities, and patient flow using our Regional Healthcare Ecosystem Analyst software platform. Scenarios explored the impact of a registry that tracked patients carrying CRE to help guide infection prevention and control. RESULTS When all Illinois facilities participated (n = 402), the registry reduced the number of new carriers by 11.7% and CRE prevalence by 7.6% over a 3-year period. When 75% of the largest Illinois facilities participated (n = 304), registry use resulted in a 11.6% relative reduction in new carriers (16.9% and 1.2% in participating and nonparticipating facilities, respectively) and 5.0% relative reduction in prevalence. When 50% participated (n = 201), there were 10.7% and 5.6% relative reductions in incident carriers and prevalence, respectively. When 25% participated (n = 101), there was a 9.1% relative reduction in incident carriers (20.4% and 1.6% in participating and nonparticipating facilities, respectively) and 2.8% relative reduction in prevalence. CONCLUSIONS Implementing an extensively drug-resistant organism registry reduced CRE spread, even when only 25% of the largest Illinois facilities participated due to patient sharing. Nonparticipating facilities garnered benefits, with reductions in new carriers.
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Affiliation(s)
- Bruce Y Lee
- Public Health Computational and Operations Research, Baltimore, Maryland
- Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Sarah M Bartsch
- Public Health Computational and Operations Research, Baltimore, Maryland
- Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | | | - Joel Welling
- Public Health Applications, Pittsburgh Supercomputing Center, Pennsylvania
| | - Jay V DePasse
- Public Health Applications, Pittsburgh Supercomputing Center, Pennsylvania
| | - Sarah K Kemble
- Rush University Medical Center, Chicago, Illinois
- Chicago Department of Public Health, Chicago, Illinois
| | - Jim Leonard
- Public Health Applications, Pittsburgh Supercomputing Center, Pennsylvania
| | - Robert A Weinstein
- Rush University Medical Center, Chicago, Illinois
- Cook County Health, Chicago, Illinois
| | - Leslie E Mueller
- Public Health Computational and Operations Research, Baltimore, Maryland
- Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | | | - Shawn T Brown
- McGill Centre for Integrative Neuroscience, McGill University, Montreal, Quebec, Canada
| | - William E Trick
- Rush University Medical Center, Chicago, Illinois
- Cook County Health, Chicago, Illinois
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Cucunubá ZM, Nouvellet P, Peterson JK, Bartsch SM, Lee BY, Dobson AP, Basáñez MG. Complementary Paths to Chagas Disease Elimination: The Impact of Combining Vector Control With Etiological Treatment. Clin Infect Dis 2019; 66:S293-S300. [PMID: 29860294 PMCID: PMC5982731 DOI: 10.1093/cid/ciy006] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Background The World Health Organization’s 2020 goals for Chagas disease are (1) interrupting vector-borne intradomiciliary transmission and (2) having all infected people under care in endemic countries. Insecticide spraying has proved efficacious for reaching the first goal, but active transmission remains in several regions. For the second, treatment has mostly been restricted to recently infected patients, who comprise only a small proportion of all infected individuals. Methods We extended our previous dynamic transmission model to simulate a domestic Chagas disease transmission cycle and examined the effects of both vector control and etiological treatment on achieving the operational criterion proposed by the Pan American Health Organization for intradomiciliary, vectorial transmission interruption (ie, <2% seroprevalence in children <5 years of age). Results Depending on endemicity, an antivectorial intervention that decreases vector density by 90% annually would achieve the transmission interruption criterion in 2–3 years (low endemicity) to >30 years (high endemicity). When this strategy is combined with annual etiological treatment in 10% of the infected human population, the seroprevalence criterion would be achieved, respectively, in 1 and 11 years. Conclusions Combining highly effective vector control with etiological (trypanocidal) treatment in humans would substantially reduce time to transmission interruption as well as infection incidence and prevalence. However, the success of vector control may depend on prevailing vector species. It will be crucial to improve the coverage of screening programs, the performance of diagnostic tests, the proportion of people treated, and the efficacy of trypanocidal drugs. While screening and access can be incremented as part of strengthening the health systems response, improving diagnostics performance and drug efficacy will require further research.
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Affiliation(s)
- Zulma M Cucunubá
- London Centre for Neglected Tropical Disease Research, United Kingdom.,Medical Research Council Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, United Kingdom
| | - Pierre Nouvellet
- London Centre for Neglected Tropical Disease Research, United Kingdom.,Medical Research Council Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, United Kingdom
| | - Jennifer K Peterson
- Zoonotic Disease Research Center, Arequipa, Peru.,Department of Biostatistics, Epidemiology and Bioinformatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Sarah M Bartsch
- Public Health Computational and Operations Research, John Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Bruce Y Lee
- Public Health Computational and Operations Research, John Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Andrew P Dobson
- Department of Ecology and Evolutionary Biology, Princeton University, New Jersey
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McKinnell JA, Singh R, Miller LG, Saavedra R, Heim L, Gussin G, Lewis B, Estevez M, Catuna TD, Mouth K, Lee E, He J, Kleinman K, Shimabukuro J, Evans K, Bittencourt C, Baesu C, Gohil SK, Park S, Tam S, Robinson PA, Slayton R, Stone ND, Jernigan JA, Zahn M, Janssen L, ODonnell K, Weinstein RA, Hayden MK, Lee BY, Mueller LE, Bartsch SM, Peterson EM, Huang SS. 893. The SHIELD Orange County Project: A Decolonization Strategy in 35 Hospitals and Nursing Homes Reduces Multi-Drug-Resistant Organism (MDRO) Prevalence in a Southern California Region. Open Forum Infect Dis 2019. [PMCID: PMC6808809 DOI: 10.1093/ofid/ofz359.052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Background
Patient movement between hospitals, nursing homes (NH), and long-term acute care facilities (LTACs) contributes to MDRO spread. SHIELD OC is a regional decolonization collaborative among adult facilities with high patient sharing designed to reduce countywide MDRO prevalence. We report pre- and post-intervention MDRO colonization prevalence.
Methods
Decolonization included chlorhexidine bath (CHG) (4% liquid or 2% cloth) and twice-daily nasal swab 10% povidone–iodine (PI). LTAC and NH used CHG for all baths and PI 5 days on admission and Monday–Friday every other week. Patients in contact precautions (CP) at hospitals had daily CHG and 5-days PI on admission. Point-prevalence screening for MRSA, VRE, ESBL, and CRE using nares, axilla/groin, and peri-rectal swabs was conducted pre-intervention (September 2016–March 2017) and post-intervention (August 2018–April 2019); 50 random LTAC and 50 CP hospitalized patients were sampled; for NH up to 50 were sampled at baseline and all residents post-intervention. Raw impact of the intervention was assessed by the average change in colonization prevalence, with each facility carrying equal weight. Generalized linear mixed models (GLM) stratified by facility type were used to assess the impact on MDRO colonization when clustering by facility.
Results
Across 35 facilities (16 hospitals, 16 NHs, 3 LTACs), the overall MDRO prevalence was reduced 22% in NHs (OR 0.58, P < 0.001), 34% LTACs (OR = 0.27, P < 0.001), and 11% CP patients (OR = 0.67, P < 0.001, Table 1). For MRSA, raw reductions were 31% NHs (OR = 0.58, P < 0.001), 39% LTACs (OR = 0.51, P = 0.01), and 3% CP patients (OR = 0.88, P = NS). For VRE, raw reductions were 40% NHs (OR = 0.62, P = 0.001), 55% LTACs (OR = 0.26, P < 0.001), and 15% CP patients (OR = 0.67, P = 0.004). For ESBLs, raw reductions were 24% NHs (OR = 0.65, P < 0.001), 34% LTACs (OR = 0.53, P = 0.01), and 26% CP patients (OR = 0.64, P < 0.001). For CRE, raw reductions were 24% NHs (OR = 0.70, P = NS), and 23% LTACs (OR = 0.75, P = NS). CRE increased by 26% in CP averaged across hospitals, although patient -level CRE declined 2.4% to 1.8% (OR = 0.74, P = NS).
Conclusion
MDRO carriage was common in highly inter-connected NHs, LTACs and hospitals. A regional collaborative of universal decolonization in long-term care and targeted decolonization of CP patients in hospitals led to sizeable reductions in MDRO carriage.
Disclosures
All Authors: No reported Disclosures.
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Affiliation(s)
| | | | | | | | - Lauren Heim
- University of California, Irvine, California
| | | | - Brian Lewis
- University of California, Irvine, California
| | | | | | | | - Eunjung Lee
- Soonchunhyang University Hospital, Irvine, California
| | - Jiayi He
- University of California, Irvine, California
| | - Ken Kleinman
- University of Massachusetts, Amherst, Massachusetts
| | | | - Kaye Evans
- University of California, Irvine, California
| | | | | | - Shruti K Gohil
- University of California, Irvine School of Medicine, Irvine, California
| | - Steven Park
- University of California, Irvine, California
| | - Steven Tam
- University of California, Irvine, California
| | | | - Rachel Slayton
- Centers for Disease Control and Prevention, Atlanta, Georgia
| | | | - John A Jernigan
- Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Matthew Zahn
- Orange County Department of Health, Irvine, California
| | - Lynn Janssen
- California Department of Public Health, Sacramento, California
| | | | | | | | | | | | - Sarah M Bartsch
- John’s Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | | | - Susan S Huang
- University of California, Irvine School of Medicine, Irvine, California
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Bartsch SM, Asti L, Cox SN, Durham DP, Randall S, Hotez PJ, Galvani AP, Lee BY. What Is the Value of Different Zika Vaccination Strategies to Prevent and Mitigate Zika Outbreaks? J Infect Dis 2019; 220:920-931. [PMID: 30544164 PMCID: PMC6688058 DOI: 10.1093/infdis/jiy688] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Accepted: 11/28/2018] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND While the 2015-2016 Zika epidemics prompted accelerated vaccine development, decision makers need to know the potential economic value of vaccination strategies. METHODS We developed models of Honduras, Brazil, and Puerto Rico, simulated targeting different populations for Zika vaccination (women of childbearing age, school-aged children, young adults, and everyone) and then introduced various Zika outbreaks. Sensitivity analyses varied vaccine characteristics. RESULTS With a 2% attack rate ($5 vaccination), compared to no vaccination, vaccinating women of childbearing age cost $314-$1664 per case averted ($790-$4221/disability-adjusted life-year [DALY] averted) in Honduras, and saved $847-$1644/case averted in Brazil, and $3648-$4177/case averted in Puerto Rico, varying with vaccination coverage and efficacy (societal perspective). Vaccinating school-aged children cost $718-$1849/case averted (≤$5002/DALY averted) in Honduras, saved $819-$1609/case averted in Brazil, and saved $3823-$4360/case averted in Puerto Rico. Vaccinating young adults cost $310-$1666/case averted ($731-$4017/DALY averted) in Honduras, saved $953-$1703/case averted in Brazil, and saved $3857-$4372/case averted in Puerto Rico. Vaccinating everyone averted more cases but cost more, decreasing cost savings per case averted. Vaccination resulted in more cost savings and better outcomes at higher attack rates. CONCLUSIONS When considering transmission, while vaccinating everyone naturally averted the most cases, specifically targeting women of childbearing age or young adults was the most cost-effective.
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Affiliation(s)
- Sarah M Bartsch
- Global Obesity Prevention Center (GOPC) and Public Health Professional and Operations Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Lindsey Asti
- Global Obesity Prevention Center (GOPC) and Public Health Professional and Operations Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Sarah N Cox
- Global Obesity Prevention Center (GOPC) and Public Health Professional and Operations Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - David P Durham
- Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, Connecticut
| | - Samuel Randall
- Global Obesity Prevention Center (GOPC) and Public Health Professional and Operations Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Peter J Hotez
- National School of Tropical Medicine, and Departments of Pediatrics and Molecular Virology & Microbiology, Baylor College of Medicine, Houston, Texas
| | - Alison P Galvani
- Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, Connecticut
| | - Bruce Y Lee
- Global Obesity Prevention Center (GOPC) and Public Health Professional and Operations Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
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Bartsch SM, Bottazzi ME, Asti L, Strych U, Meymandi S, Falcón-Lezama JA, Randall S, Hotez PJ, Lee BY. Economic value of a therapeutic Chagas vaccine for indeterminate and Chagasic cardiomyopathy patients. Vaccine 2019; 37:3704-3714. [PMID: 31104883 DOI: 10.1016/j.vaccine.2019.05.028] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Revised: 05/06/2019] [Accepted: 05/09/2019] [Indexed: 12/19/2022]
Abstract
BACKGROUND Therapeutic vaccines to prevent Chagas disease progression to cardiomyopathy are under development because the only available medications (benznidazole and nifurtimox) are limited by their efficacy, long treatment course, and side effects. Better understanding the potential clinical and economic value of such vaccines can help guide development and implementation. METHODS We developed a computational Chagas Markov model to evaluate the clinical and economic value of a therapeutic vaccine given in conjunction with benznidazole in indeterminate and chronic Chagas patients. Scenarios explored the vaccine's impact on reducing drug treatment dosage, duration, and adverse events, and risk of disease progression. RESULTS When administering standard-of-care benznidazole to 1000 indeterminate patients, 148 discontinued treatment and 219 progressed to chronic disease, resulting in 119 Chagas-related deaths and 2293 DALYs, costing $18.9 million in lifetime societal costs. Compared to benznidazole-only, therapeutic vaccination administered with benznidazole (25-75% reduction in standard dose and duration), resulted in 37-111 more patients (of 1000) completing treatment, preventing 11-219 patients from progressing, 6-120 deaths, and 108-2229 DALYs (5-100% progression risk reduction), saving ≤$16,171 per patient. When vaccinating determinate Kuschnir class 1 Chagas patients, 10-197 fewer patients further progressed compared to benznidazole-only, averting 11-228 deaths and 144-3037 DALYs (5-100% progression risk reduction), saving ≤$34,059 per person. When vaccinating Kuschnir class 2 patients, 13-279 fewer progressed (279 with benznidazole-only), averting 13-692 deaths and 283-10,785 DALYs (5-100% progression risk reduction), saving ≤$89,759. Therapeutic vaccination was dominant (saved costs and provided health benefits) with ≥ 5% progression risk reduction, except when only reducing drug treatment regimen and adverse events, but remained cost-effective when costing <$200. CONCLUSIONS Our study helps outline the thresholds at which a therapeutic Chagas vaccine may be cost-effective (e.g., <5% reduction in preventing cardiac progression, 25% reduction in benznidazole treatment doses and duration) and cost-saving (e.g., ≥5% and 25%, respectively).
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Affiliation(s)
- Sarah M Bartsch
- Public Health Computational and Operational Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe Street, Baltimore, MD 21205, USA; Global Obesity Prevention Center (GOPC), Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe Street, Baltimore, MD 21205, USA
| | - Maria Elena Bottazzi
- National School of Tropical Medicine and Departments of Pediatrics and Molecular Virology & Microbiology, Baylor College of Medicine, One Baylor Plaza, BCM113 Houston, TX 77030, USA
| | - Lindsey Asti
- Public Health Computational and Operational Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe Street, Baltimore, MD 21205, USA; Global Obesity Prevention Center (GOPC), Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe Street, Baltimore, MD 21205, USA
| | - Ulrich Strych
- National School of Tropical Medicine and Departments of Pediatrics and Molecular Virology & Microbiology, Baylor College of Medicine, One Baylor Plaza, BCM113 Houston, TX 77030, USA
| | - Sheba Meymandi
- Center of Excellence for Chagas Disease at Olive View-UCLA Medical Center, 14445 Olive View Drive, Sylmar, CA 91342, USA
| | - Jorge Abelardo Falcón-Lezama
- Carlos Slim Foundation, Lago Zurich 245, Piso 20. Ampliación Granada, Del. Miguel Hidalgo, C.P. 11529 Ciudad de México, Mexico
| | - Samuel Randall
- Public Health Computational and Operational Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe Street, Baltimore, MD 21205, USA; Global Obesity Prevention Center (GOPC), Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe Street, Baltimore, MD 21205, USA
| | - Peter J Hotez
- National School of Tropical Medicine and Departments of Pediatrics and Molecular Virology & Microbiology, Baylor College of Medicine, One Baylor Plaza, BCM113 Houston, TX 77030, USA
| | - Bruce Y Lee
- Public Health Computational and Operational Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe Street, Baltimore, MD 21205, USA; Global Obesity Prevention Center (GOPC), Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe Street, Baltimore, MD 21205, USA.
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Lee BY, Brown ST, Haidari LA, Clark S, Abimbola T, Pallas SE, Wallace AS, Mitgang EA, Leonard J, Bartsch SM, Yemeke TT, Zenkov E, Ozawa S. Economic value of vaccinating geographically hard-to-reach populations with measles vaccine: A modeling application in Kenya. Vaccine 2019; 37:2377-2386. [PMID: 30922700 PMCID: PMC6487493 DOI: 10.1016/j.vaccine.2019.03.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Revised: 03/01/2019] [Accepted: 03/06/2019] [Indexed: 01/18/2023]
Abstract
BACKGROUND Since special efforts are necessary to vaccinate people living far from fixed vaccination posts, decision makers are interested in knowing the economic value of such efforts. METHODS Using our immunization geospatial information system platform and a measles compartment model, we quantified the health and economic value of a 2-dose measles immunization outreach strategy for children <24 months of age in Kenya who are geographically hard-to-reach (i.e., those living outside a specified catchment radius from fixed vaccination posts, which served as a proxy for access to services). FINDINGS When geographically hard-to-reach children were not vaccinated, there were 1427 total measles cases from 2016 to 2020, resulting in $9.5 million ($3.1-$18.1 million) in direct medical costs and productivity losses and 7504 (3338-12,903) disability-adjusted life years (DALYs). The outreach strategy cost $76 ($23-$142)/DALY averted (compared to no outreach) when 25% of geographically hard-to-reach children received MCV1, $122 ($40-$226)/DALY averted when 50% received MCV1, and $274 ($123-$478)/DALY averted when 100% received MCV1. CONCLUSION Outreach vaccination among geographically hard-to-reach populations was highly cost-effective in a wide variety of scenarios, offering support for investment in an effective outreach vaccination strategy.
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Affiliation(s)
- Bruce Y Lee
- Public Health Computational and Operations Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States; Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States; Global Obesity Prevention Center (GOPC), Johns Hopkins University, Baltimore, MD, United States.
| | - Shawn T Brown
- Pittsburgh Supercomputing Center (PSC), Carnegie Mellon University, Pittsburgh, PA, United States; McGill Centre for Integrative Neuroscience, McGill Neurological Institute, McGill University, Montreal, Canada
| | - Leila A Haidari
- Pittsburgh Supercomputing Center (PSC), Carnegie Mellon University, Pittsburgh, PA, United States
| | - Samantha Clark
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, University of Washington, Seattle, WA, United States
| | - Taiwo Abimbola
- Centers for Disease Control and Prevention (CDC), Atlanta, GA, United States
| | - Sarah E Pallas
- Centers for Disease Control and Prevention (CDC), Atlanta, GA, United States
| | - Aaron S Wallace
- Centers for Disease Control and Prevention (CDC), Atlanta, GA, United States
| | - Elizabeth A Mitgang
- Public Health Computational and Operations Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States; Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States; Global Obesity Prevention Center (GOPC), Johns Hopkins University, Baltimore, MD, United States
| | - Jim Leonard
- Pittsburgh Supercomputing Center (PSC), Carnegie Mellon University, Pittsburgh, PA, United States
| | - Sarah M Bartsch
- Public Health Computational and Operations Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States; Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States; Global Obesity Prevention Center (GOPC), Johns Hopkins University, Baltimore, MD, United States
| | - Tatenda T Yemeke
- Division of Practice Advancement and Clinical Education, UNC Eshelman School of Pharmacy, University of North Carolina - Chapel Hill, Chapel Hill, NC, United States
| | - Eli Zenkov
- Pittsburgh Supercomputing Center (PSC), Carnegie Mellon University, Pittsburgh, PA, United States
| | - Sachiko Ozawa
- Division of Practice Advancement and Clinical Education, UNC Eshelman School of Pharmacy, University of North Carolina - Chapel Hill, Chapel Hill, NC, United States; Department of Maternal and Child Health, UNC Gillings School of Global Public Health, University of North Carolina - Chapel Hill, Chapel Hill, NC, United States
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Bartsch SM, Avelis CM, Asti L, Hertenstein DL, Ndeffo-Mbah M, Galvani A, Lee BY. The economic value of identifying and treating Chagas disease patients earlier and the impact on Trypanosoma cruzi transmission. PLoS Negl Trop Dis 2018; 12:e0006809. [PMID: 30395603 PMCID: PMC6237415 DOI: 10.1371/journal.pntd.0006809] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Revised: 11/15/2018] [Accepted: 09/02/2018] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND The World Health Organization's 2020 Goals for Chagas disease include access to antiparasitic treatment and care of all infected/ill patients. Policy makers need to know the economic value of identifying and treating patients earlier. However, the economic value of earlier treatment to cure and prevent the Chagas' spread remains unknown. METHODS We expanded our existing Chagas disease transmission model to include identification and treatment of Chagas disease patients. We linked this to a clinical and economic model that translated chronic Chagas disease cases into health and economic outcomes. We evaluated the impact and economic outcomes (costs, cost-effectiveness, cost-benefit) of identifying and treating different percentages of patients in the acute and indeterminate disease states in a 2,000-person village in Yucatan, Mexico. RESULTS In the absence of early treatment, 50 acute and 22 new chronic cases occurred over 50 years. Identifying and treating patients in the acute stage averted 0.5-5.4 acute cases, 0.6-5.5 chronic cases, and 0.6-10.8 disability-adjusted life years (DALYs), saving $694-$7,419 and $6,976-$79,950 from the third-party payer and societal perspectives, respectively. Treating in the indeterminate stage averted 2.2-4.9 acute cases, 6.1-12.8 chronic cases, and 11.7-31.1 DALYs, saving $7,666-$21,938 from the third-party payer perspective and $90,530-$243,068 from the societal perspective. Treating patients in both stages averted ≤9 acute cases and ≤15 chronic cases. Identifying and treating patients early was always economically dominant compared to no treatment. Identifying and treating patients earlier resulted in a cumulative cost-benefit of $7,273-$224,981 at the current cost of identification and treatment. CONCLUSIONS Even when identifying and treating as little as 5% of cases annually, treating Chagas cases in the acute and indeterminate stages reduces transmission and provides economic and health benefits. This supports the need for improved diagnostics and access to safe and effective treatment.
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Affiliation(s)
- Sarah M. Bartsch
- Public Health Computational and Operations Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Cameron M. Avelis
- Public Health Computational and Operations Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Lindsey Asti
- Public Health Computational and Operations Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Daniel L. Hertenstein
- Public Health Computational and Operations Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Martial Ndeffo-Mbah
- Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT, United States of America
| | - Alison Galvani
- Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT, United States of America
| | - Bruce Y. Lee
- Public Health Computational and Operations Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
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Bartsch SM, Taitel MS, DePasse JV, Cox SN, Smith-Ray RL, Wedlock P, Singh TG, Carr S, Siegmund SS, Lee BY. Epidemiologic and economic impact of pharmacies as vaccination locations during an influenza epidemic. Vaccine 2018; 36:7054-7063. [PMID: 30340884 PMCID: PMC6279616 DOI: 10.1016/j.vaccine.2018.09.040] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Revised: 09/14/2018] [Accepted: 09/18/2018] [Indexed: 11/24/2022]
Abstract
Introduction: During an influenza epidemic, where early vaccination is crucial, pharmacies may be a resource to increase vaccine distribution reach and capacity. Methods: We utilized an agent-based model of the US and a clinical and economics outcomes model to simulate the impact of different influenza epidemics and the impact of utilizing pharmacies in addition to traditional (hospitals, clinic/physician offices, and urgent care centers) locations for vaccination for the year 2017. Results: For an epidemic with a reproductive rate (R0) of 1.30, adding pharmacies with typical business hours averted 11.9 million symptomatic influenza cases, 23,577 to 94,307 deaths, $1.0 billion in direct (vaccine administration and healthcare) costs, $4.2–44.4 billion in productivity losses, and $5.2–45.3 billion in overall costs (varying with mortality rate). Increasing the epidemic severity (R0 of 1.63), averted 16.0 million symptomatic influenza cases, 35,407 to 141,625 deaths, $1.9 billion in direct costs, $6.0–65.5 billion in productivity losses, and $7.8–67.3 billion in overall costs (varying with mortality rate). Extending pharmacy hours averted up to 16.5 million symptomatic influenza cases, 145,278 deaths, $1.9 billion direct costs, $4.1 billion in productivity loss, and $69.5 billion in overall costs. Adding pharmacies resulted in a cost-benefit of $4.1 to $11.5 billion, varying epidemic severity, mortality rate, pharmacy hours, location vaccination rate, and delay in the availability of the vaccine. Conclusions: Administering vaccines through pharmacies in addition to traditional locations in the event of an epidemic can increase vaccination coverage, mitigating up to 23.7 million symptomatic influenza cases, providing cost-savings up to $2.8 billion to third-party payers and $99.8 billion to society. Pharmacies should be considered as points of dispensing epidemic vaccines in addition to traditional settings as soon as vaccines become available.
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Affiliation(s)
- Sarah M Bartsch
- Public Health Computational and Operations Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States; Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Michael S Taitel
- Walgreens Center for Health & Wellbeing Research, Walgreens Company, Deerfield, IL, United States
| | - Jay V DePasse
- Pittsburgh Super Computing Center (PSC), Carnegie Mellon University, Pittsburgh, PA, United States
| | - Sarah N Cox
- Public Health Computational and Operations Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States; Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Renae L Smith-Ray
- Walgreens Center for Health & Wellbeing Research, Walgreens Company, Deerfield, IL, United States
| | - Patrick Wedlock
- Public Health Computational and Operations Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States; Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Tanya G Singh
- Walgreens Center for Health & Wellbeing Research, Walgreens Company, Deerfield, IL, United States
| | - Susan Carr
- Johns Hopkins Healthcare Solutions, Johns Hopkins University, Baltimore, MD, United States
| | - Sheryl S Siegmund
- Public Health Computational and Operations Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States; Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Bruce Y Lee
- Public Health Computational and Operations Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States; Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.
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Bartsch SM, Peterson JK, Hertenstein DL, Skrip L, Ndeffo-Mbah M, Galvani AP, Dobson AP, Lee BY. Comparison and validation of two computational models of Chagas disease: A thirty year perspective from Venezuela. Epidemics 2018; 18:81-91. [PMID: 28279459 PMCID: PMC5549789 DOI: 10.1016/j.epidem.2017.02.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2016] [Revised: 02/07/2017] [Accepted: 02/07/2017] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Mathematical models can help aid public health responses to Chagas disease. Models are typically developed to fulfill a particular need, and comparing outputs from different models addressing the same question can help identify the strengths and weaknesses of the models in answering particular questions, such as those for achieving the 2020 goals for Chagas disease. METHODS Using two separately developed models (PHICOR/CIDMA model and Princeton model), we simulated dynamics for domestic transmission of Trypanosoma cruzi (T. cruzi). We compared how well the models targeted the last 9 years and last 19 years of the 1968-1998 historical seroprevalence data from Venezuela. RESULTS Both models were able to generate the T. cruzi seroprevalence for the next time period within reason to the historical data. The PHICOR/CIDMA model estimates of the total population seroprevalence more closely followed the trends seen in the historic data, while the Princeton model estimates of the age-specific seroprevalence more closely followed historic trends when simulating over 9 years. Additionally, results from both models overestimated T. cruzi seroprevalence among younger age groups, while underestimating the seroprevalence of T. cruzi in older age groups. CONCLUSION The PHICOR/CIDMA and Princeton models differ in level of detail and included features, yet both were able to generate the historical changes in T. cruzi seroprevalence in Venezuela over 9 and 19-year time periods. Our model comparison has demonstrated that different model structures can be useful in evaluating disease transmission dynamics and intervention strategies.
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Affiliation(s)
- Sarah M Bartsch
- Public Health Computational and Operations Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, United States; Global Obesity Prevention Center, Johns Hopkins University, United States
| | - Jennifer K Peterson
- Department of Ecology and Evolutionary Biology, Princeton University, United States
| | - Daniel L Hertenstein
- Public Health Computational and Operations Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, United States; Global Obesity Prevention Center, Johns Hopkins University, United States
| | - Laura Skrip
- Center for Infectious Disease Modeling and Analysis (CIDMA), Yale School of Public Health, United States
| | - Martial Ndeffo-Mbah
- Center for Infectious Disease Modeling and Analysis (CIDMA), Yale School of Public Health, United States
| | - Alison P Galvani
- Center for Infectious Disease Modeling and Analysis (CIDMA), Yale School of Public Health, United States
| | - Andrew P Dobson
- Department of Ecology and Evolutionary Biology, Princeton University, United States
| | - Bruce Y Lee
- Public Health Computational and Operations Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, United States; Global Obesity Prevention Center, Johns Hopkins University, United States.
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Lee BY, Adam A, Zenkov E, Hertenstein D, Ferguson MC, Wang PI, Wong MS, Wedlock P, Nyathi S, Gittelsohn J, Falah-Fini S, Bartsch SM, Cheskin LJ, Brown ST. Modeling The Economic And Health Impact Of Increasing Children's Physical Activity In The United States. Health Aff (Millwood) 2018; 36:902-908. [PMID: 28461358 DOI: 10.1377/hlthaff.2016.1315] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Increasing physical activity among children is a potentially important public health intervention. Quantifying the economic and health effects of the intervention would help decision makers understand its impact and priority. Using a computational simulation model that we developed to represent all US children ages 8-11 years, we estimated that maintaining the current physical activity levels (only 31.9 percent of children get twenty-five minutes of high-calorie-burning physical activity three times a week) would result each year in a net present value of $1.1 trillion in direct medical costs and $1.7 trillion in lost productivity over the course of their lifetimes. If 50 percent of children would exercise, the number of obese and overweight youth would decrease by 4.18 percent, averting $8.1 billion in direct medical costs and $13.8 billion in lost productivity. Increasing the proportion of children who exercised to 75 percent would avert $16.6 billion and $23.6 billion, respectively.
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Affiliation(s)
- Bruce Y Lee
- Bruce Y. Lee is executive director of the Global Obesity Prevention Center and an associate professor in the Department of International Health at the Johns Hopkins Bloomberg School of Public Health, in Baltimore, Maryland
| | - Atif Adam
- Atif Adam is a senior analyst at the Global Obesity Prevention Center
| | - Eli Zenkov
- Eli Zenkov is a programmer analyst at the Global Obesity Prevention Center and a public health applications developer at the Pittsburgh Supercomputing Center at Carnegie Mellon University, in Pittsburgh, Pennsylvania
| | - Daniel Hertenstein
- Daniel Hertenstein is a senior programmer analyst at the Global Obesity Prevention Center
| | - Marie C Ferguson
- Marie C. Ferguson is a senior analyst at the Global Obesity Prevention Center and a research associate in the Department of International Health, Johns Hopkins Bloomberg School of Public Health
| | - Peggy I Wang
- Peggy I. Wang is a senior research program coordinator at the Global Obesity Prevention Center
| | - Michelle S Wong
- Michelle S. Wong is a senior analyst at the Global Obesity Prevention Center
| | - Patrick Wedlock
- Patrick Wedlock is a systems modeler at the Global Obesity Prevention Center
| | - Sindiso Nyathi
- Sindiso Nyathi is a systems modeler at the Global Obesity Prevention Center
| | - Joel Gittelsohn
- Joel Gittelsohn is director of community interventions at the Global Obesity Prevention Center and a professor in the Department of International Health, Johns Hopkins Bloomberg School of Public Health
| | - Saeideh Falah-Fini
- Saeideh Falah-Fini is an assistant professor in the Department of Industrial and Manufacturing Engineering at the California State Polytechnic University, in Pomona, and a collaborator at the Global Obesity Prevention Center
| | - Sarah M Bartsch
- Sarah M. Bartsch is a senior analyst at the Global Obesity Prevention Center and a research associate in the Department of International Health, Johns Hopkins Bloomberg School of Public Health
| | - Lawrence J Cheskin
- Lawrence J. Cheskin is director of clinical research at the Global Obesity Prevention Center and associate professor in the Department of Health, Behavior, and Society, Johns Hopkins Bloomberg School of Public Health
| | - Shawn T Brown
- Shawn T. Brown is director of computational research at the Global Obesity Prevention Center and director of public health applications at the Pittsburgh Supercomputing Center at Carnegie Mellon
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Abstract
OBJECTIVES Although outbreaks of restaurant-associated foodborne illness occur periodically and make the news, a restaurant may not be aware of the cost of an outbreak. We estimated this cost under varying circumstances. METHODS We developed a computational simulation model; scenarios varied outbreak size (5 to 250 people affected), pathogen (n = 15), type of dining establishment (fast food, fast casual, casual dining, and fine dining), lost revenue (ie, meals lost per illness), cost of lawsuits and legal fees, fines, and insurance premium increases. RESULTS We estimated that the cost of a single foodborne illness outbreak ranged from $3968 to $1.9 million for a fast-food restaurant, $6330 to $2.1 million for a fast-casual restaurant, $8030 to $2.2 million for a casual-dining restaurant, and $8273 to $2.6 million for a fine-dining restaurant, varying from a 5-person outbreak, with no lost revenue, lawsuits, legal fees, or fines, to a 250-person outbreak, with high lost revenue (100 meals lost per illness), and a high amount of lawsuits and legal fees ($1 656 569) and fines ($100 000). This cost amounts to 10% to 5790% of a restaurant's annual marketing costs and 0.3% to 101% of annual profits and revenue. The biggest cost drivers were lawsuits and legal fees, outbreak size, and lost revenue. Pathogen type affected the cost by a maximum of $337 000, the difference between a Bacillus cereus outbreak (least costly) and a listeria outbreak (most costly). CONCLUSIONS The cost of a single foodborne illness outbreak to a restaurant can be substantial and outweigh the typical costs of prevention and control measures. Our study can help decision makers determine investment and motivate research for infection-control measures in restaurant settings.
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Affiliation(s)
- Sarah M Bartsch
- 1 Public Health Computational and Operations Research, Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Lindsey Asti
- 1 Public Health Computational and Operations Research, Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Sindiso Nyathi
- 1 Public Health Computational and Operations Research, Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Marie L Spiker
- 1 Public Health Computational and Operations Research, Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Bruce Y Lee
- 1 Public Health Computational and Operations Research, Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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Lee BY, Bartsch SM, Skrip L, Hertenstein DL, Avelis CM, Ndeffo-Mbah M, Tilchin C, Dumonteil EO, Galvani A. Are the London Declaration's 2020 goals sufficient to control Chagas disease?: Modeling scenarios for the Yucatan Peninsula. PLoS Negl Trop Dis 2018; 12:e0006337. [PMID: 29554086 PMCID: PMC5875875 DOI: 10.1371/journal.pntd.0006337] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Revised: 03/29/2018] [Accepted: 02/22/2018] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND The 2020 Sustainable Development goals call for 100% certified interruption or control of the three main forms of Chagas disease transmission in Latin America. However, how much will achieving these goals to varying degrees control Chagas disease; what is the potential impact of missing these goals and if they are achieved, what may be left? METHODS We developed a compartmental simulation model that represents the triatomine, human host, and non-human host populations and vector-borne, congenital, and transfusional T. cruzi transmission between them in the domestic and peridomestic settings to evaluate the impact of limiting transmission in a 2,000 person virtual village in Yucatan, Mexico. RESULTS Interruption of domestic vectorial transmission had the largest impact on T. cruzi transmission and prevalence in all populations. Most of the gains were achieved within the first few years. Controlling vectorial transmission resulted in a 46.1-83.0% relative reduction in the number of new acute Chagas cases for a 50-100% interruption in domestic vector-host contact. Only controlling congenital transmission led to a 2.4-8.1% (30-100% interruption) relative reduction in the total number of new acute cases and reducing only transfusional transmission led to a 0.1-0.3% (30-100% reduction). Stopping all three forms of transmission resulted in 0.5 total transmission events over five years (compared to 5.0 with no interruption); interrupting all forms by 30% resulted in 3.4 events over five years per 2,000 persons. CONCLUSIONS While reducing domestic vectorial, congenital, and transfusional transmission can successfully reduce transmission to humans (up to 82% in one year), achieving the 2020 goals would still result in 0.5 new acute cases per 2,000 over five years. Even if the goals are missed, major gains can be achieved within the first few years. Interrupting transmission should be combined with other efforts such as a vaccine or improved access to care, especially for the population of already infected individuals.
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Affiliation(s)
- Bruce Y. Lee
- Public Health Computational and Operations Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- * E-mail:
| | - Sarah M. Bartsch
- Public Health Computational and Operations Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Laura Skrip
- Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT, United States of America
| | - Daniel L. Hertenstein
- Public Health Computational and Operations Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Cameron M. Avelis
- Public Health Computational and Operations Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Martial Ndeffo-Mbah
- Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT, United States of America
| | - Carla Tilchin
- Public Health Computational and Operations Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Eric O. Dumonteil
- Department of Tropical Medicine, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States of America
| | - Alison Galvani
- Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT, United States of America
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41
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Fallah-Fini S, Adam A, Cheskin LJ, Bartsch SM, Lee BY. The Additional Costs and Health Effects of a Patient Having Overweight or Obesity: A Computational Model. Obesity (Silver Spring) 2017; 25:1809-1815. [PMID: 28948718 PMCID: PMC5679120 DOI: 10.1002/oby.21965] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Revised: 06/15/2017] [Accepted: 07/19/2017] [Indexed: 01/22/2023]
Abstract
OBJECTIVE This paper estimates specific additional disease outcomes and costs that could be prevented by helping a patient go from an obesity or overweight category to a normal weight category at different ages. This information could help physicians, other health care workers, patients, and third-party payers determine how to prioritize weight reduction. METHODS A computational Markov model was developed that represented the BMI status, chronic health states, health outcomes, and associated costs (from various perspectives) for an adult at different age points throughout his or her lifetime. RESULTS Incremental costs were calculated for adult patients with obesity or overweight (vs. normal weight) at different starting ages. For example, for a metabolically healthy 20-year-old, having obesity (vs. normal weight) added lifetime third-party payer costs averaging $14,059 (95% range: $13,956-$14,163), productivity losses of $14,141 ($13,969-$14,312), and total societal costs of $28,020 ($27,751-$28,289); having overweight vs. normal weight added $5,055 ($4,967-$5,144), $5,358 ($5,199-$5,518), and $10,365 ($10,140-$10,590). For a metabolically healthy 50-year-old, having obesity added $15,925 ($15,831-$16,020), $20,120 ($19,887-$20,352), and $36,278 ($35,977-$36,579); having overweight added $5,866 ($5,779-$5,953), $10,205 ($9,980-$10,429), and $16,169 ($15,899-$16,438). CONCLUSIONS Incremental lifetime costs of a patient with obesity or overweight (vs. normal weight) increased with the patient's age, peaked at age 50, and decreased with older ages. However, weight reduction even in older adults still yielded incremental cost savings.
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Affiliation(s)
- Saeideh Fallah-Fini
- Global Obesity Prevention Center (GOPC) at Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Industrial and Manufacturing Engineering Department, California State Polytechnic University, Pomona, CA, USA
| | - Atif Adam
- Global Obesity Prevention Center (GOPC) at Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Lawrence J. Cheskin
- Global Obesity Prevention Center (GOPC) at Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Sarah M. Bartsch
- Global Obesity Prevention Center (GOPC) at Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Bruce Y. Lee
- Global Obesity Prevention Center (GOPC) at Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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42
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Singh RD, Jernigan JA, Slayton RB, Stone ND, McKinnell JA, Miller LG, Kleinman K, Heim L, Dutciuc TD, Estevez M, Gussin G, Chang J, Peterson EM, Evans KD, Lee BY, Mueller LE, Bartsch SM, Zahn M, Janssen L, Weinstein RA, Hayden MK, Gohil SK, Park S, Tam S, Saavedra R, Yamaguchi S, Custodio H, Nguyen J, Tjoa T, He J, O’Donnell K, Coady MH, Platt R, Huang SS. The CDC SHIELD Orange County Project – Baseline Multi Drug-Resistant Organism (MDRO) Prevalence in a Southern California Region. Open Forum Infect Dis 2017. [PMCID: PMC5631751 DOI: 10.1093/ofid/ofx162.109] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Background
MDROs can spread between hospitals, nursing homes (NH), and long-term acute care facilities (LTACs) via shared patients. SHIELD OC is a regional decolonization collaborative involving 38 of 104 countywide adult facilities identified by their high degree of direct and indirect patient sharing with one another. We report baseline MDRO prevalence in these facilities.
Methods
Adult patients in 38 facilities (17 hospitals, 18 NHs, 3 LTACs) underwent point-prevalence screening between September 2016–April 2017 for MRSA, VRE, ESBL, and CRE using nares, skin (axilla/groin), and peri-rectal swabs. In NHs and LTACs, residents were randomly selected until 50 sets of swabs were obtained. Swabbing in hospitals involved all patients in contact precautions. An additional set of swabs were also performed for all LTAC admissions from November 2016–February 2017.
Results
The overall prevalence of any MDRO among patients was 64% (44%–88%) in NHs, 80% (range 72%–86%) in LTACs, and 64% (54–84%) in hospitals (contact precaution patients) (Table 1). Only 25%, 64%, and 81% of patients were already known to harbor an MDRO in NHs, LTACs, and hospitals, respectively. Known MDRO patients also harbored another MDRO 49%, 63%, and 34% of the time for NHs, LTACs, and hospitals, respectively. In LTACs, MDRO point prevalence was 38% higher than the usual admission prevalence (65% higher for MRSA, 34% higher for VRE, 95% higher for ESBL, and 50% higher for CRE).
Conclusion
MDRO carriage in highly inter-connected NHs and LTACs was widespread, rivaling that found in hospitalized patients on contact precautions. MRSA, VRE, and ESBL carriage far outnumbered CRE carriage. A history of MDRO was insensitive for identifying MDRO carriers, and many patients carried multiple MDROs. The extensive MDRO burden and transmission in long-term care settings suggests that regional MDRO prevention efforts must include MDRO control in long-term care facilities.
Disclosures
R. D. Singh, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; 3M: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; J. A. McKinnell, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; 3M: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; L. G. Miller, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; 3M: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; K. Kleinman, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; 3M: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; L. Heim, Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; 3M: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; T. D. Dutciuc, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; 3M: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; M. Estevez, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; 3M: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; G. Gussin, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; L’Oreal: Consultant, Consulting fee; J. Chang, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; 3M: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; E. M. Peterson, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; B. Y. Lee, GSK: Consultant, Consulting fee; R. A. Weinstein, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; OpGen Company: Study support, Provided services at no charge; M. K. Hayden, Sage Products: Receipt of contributed product, Sage is contributing product to healthcare facilities participating in a regional collaborative on which I am a co-investigator. Neither I nor my hospital receive product.; Clorox: Receipt of contributed product, Research support; CDC: Grant Investigator and Receipt of contributed product, Research grant; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; OpGen Company: Study support, Provided services at no charge for studies; S. K. Gohil, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; S. Park, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; S. Tam, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; 3M: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; R. Saavedra, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; S. Yamaguchi, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; H. Custodio, Xttrium Laboratories: Study coordination, Conducting studies in healthcare facilities that are receiving contributed product; Sage Products: Study coordination, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Study coordination, Conducting studies in healthcare facilities that are receiving contributed product; J. Nguyen, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; T. Tjoa, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; 3M: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; J. He, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; 3M: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; M. H. Coady, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; R. Platt, Sage Products: Receipt of contributed product, Conducting clinical studies in which participating healthcare facilities are receiving contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting clinical studies in which participating healthcare facilities are receiving contributed product; Clorox: Receipt of contributed product, Conducting clinical studies in which participating healthcare facilities are receiving contributed product; receive research funds from Clorox, but Clorox has no role in the design; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; S. S. Huang, Sage Products: Receipt of contributed product, Conducting studies in which participating healthcare facilities are receiving contributed product (no contribution in submitted abstract), Participating healthcare facilities in my studies received contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting studies in which participating healthcare facilities are receiving contributed product (no contribution in submitted abstract), Participating healthcare facilities in my studies received contributed product; Clorox: Receipt of contributed product, Conducting studies in which participating healthcare facilities are receiving contributed product (no contribution in submitted abstract), Participating healthcare facilities in my studies received contributed product; 3M: Receipt of contributed product, Conducting studies in which participating healthcare facilities are receiving contributed product (no contribution in submitted abstract), Participating healthcare facilities in my studies received contributed product; Molnlycke: Receipt of contributed product, Conducting studies in which participating healthcare facilities are receiving contributed product (no contribution in submitted abstract), Participating healthcare facilities in my studies received contributed product
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Affiliation(s)
- Raveena D Singh
- Division of Infectious Diseases and Health Policy Research Institute, University of California Irvine School of Medicine, Irvine, California
| | - John A Jernigan
- Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Rachel B Slayton
- Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Nimalie D Stone
- Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - James A McKinnell
- Infectious Disease Clinical Outcomes Research (ID-CORE), LA Biomed at Harbor-UCLA Medical Center, Torrance, California
| | - Loren G Miller
- Infectious Disease Clinical Outcomes Research (ID-CORE), LA Biomed at Harbor-UCLA Medical Center, Torrance, California
| | - Ken Kleinman
- University of Massachusetts Amherst School of Public Health and Health Sciences, Amherst, Massachusetts
| | - Lauren Heim
- Division of Infectious Diseases and Health Policy Research Institute, University of California Irvine School of Medicine, Irvine, California
| | - Tabitha D Dutciuc
- Division of Infectious Diseases and Health Policy Research Institute, University of California Irvine School of Medicine, Irvine, California
| | - Marlene Estevez
- Division of Infectious Diseases and Health Policy Research Institute, University of California Irvine School of Medicine, Irvine, California
| | - Gabrielle Gussin
- Division of Infectious Diseases and Health Policy Research Institute, University of California Irvine School of Medicine, Irvine, California
| | - Justin Chang
- Division of Infectious Diseases and Health Policy Research Institute, University of California Irvine School of Medicine, Irvine, California
| | | | - Kaye D Evans
- University of California Irvine Health, Orange, California
| | - Bruce Y Lee
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | | | - Sarah M Bartsch
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Matthew Zahn
- Epidemiology and Assessment, Orange County Health Care Agency, Santa Ana, California
| | - Lynn Janssen
- Healthcare-Associated Infections Program, Center for Healthcare Quality, California Department of Public Health, Richmond, California
| | | | - Mary K Hayden
- Internal Medicine (Infectious Diseases) and Pathology, Rush University Medical Center, Chicago, Illinois
| | - Shruti K Gohil
- Division of Infectious Diseases and Health Policy Research Institute, University of California Irvine School of Medicine, Irvine, California
| | - Steven Park
- University of California Irvine Health, Orange, California
| | - Steven Tam
- Division of Geriatrics, Department of Medicine, University of California Irvine, Orange, California
| | - Raheeb Saavedra
- Division of Infectious Diseases and Health Policy Research Institute, University of California Irvine School of Medicine, Irvine, California
| | - Stacey Yamaguchi
- Division of Infectious Diseases and Health Policy Research Institute, University of California Irvine School of Medicine, Irvine, California
| | - Harold Custodio
- Division of Infectious Diseases and Health Policy Research Institute, University of California Irvine School of Medicine, Irvine, California
| | - Jenny Nguyen
- Division of Infectious Diseases and Health Policy Research Institute, University of California Irvine School of Medicine, Irvine, California
| | - Thomas Tjoa
- Division of Infectious Diseases and Health Policy Research Institute, University of California Irvine School of Medicine, Irvine, California
| | - Jiayi He
- Division of Infectious Diseases and Health Policy Research Institute, University of California Irvine School of Medicine, Irvine, California
| | - Kathleen O’Donnell
- Epidemiology and Assessment, Orange County Health Care Agency, Santa Ana, California
| | - Micaela H Coady
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
| | - Richard Platt
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
| | - Susan S Huang
- Division of Infectious Diseases and Health Policy Research Institute, University of California Irvine School of Medicine, Irvine, California
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Abstract
Obesity has become a truly global epidemic, affecting all age groups, all populations, and countries of all income levels. To date, existing policies and interventions have not reversed these trends, suggesting that innovative approaches are needed to transform obesity prevention and control. There are a number of indications that the obesity epidemic is a systems problem, as opposed to a simple problem with a linear cause-and-effect relationship. What may be needed to successfully address obesity is an approach that considers the entire system when making any important decision, observation, or change. A systems approach to obesity prevention and control has many benefits, including the potential to further understand indirect effects or to test policies virtually before implementing them in the real world. Discussed here are 5 key efforts to implement a systems approach for obesity prevention: 1) utilize more global approaches; 2) bring new experts from disciplines that do not traditionally work with obesity to share experiences and ideas with obesity experts; 3) utilize systems methods, such as systems mapping and modeling; 4) modify and combine traditional approaches to achieve a stronger systems orientation; and 5) bridge existing gaps between research, education, policy, and action. This article also provides an example of how a systems approach has been used to convene a multidisciplinary team and conduct systems mapping and modeling as part of an obesity prevention program in Baltimore, Maryland.
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Affiliation(s)
- Bruce Y Lee
- B.Y. Lee, S.M. Bartsch, L.A. Haidari, Y. Mui, M.L. Spiker, and J. Gittelsohn are with the Global Obesity Prevention Center (GOPC), Johns Hopkins University, Baltimore, Maryland, USA. L.A. Haidari and Y. Mui are with the Pittsburgh Supercomputing Center (PSC), Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
| | - Sarah M Bartsch
- B.Y. Lee, S.M. Bartsch, L.A. Haidari, Y. Mui, M.L. Spiker, and J. Gittelsohn are with the Global Obesity Prevention Center (GOPC), Johns Hopkins University, Baltimore, Maryland, USA. L.A. Haidari and Y. Mui are with the Pittsburgh Supercomputing Center (PSC), Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Yeeli Mui
- B.Y. Lee, S.M. Bartsch, L.A. Haidari, Y. Mui, M.L. Spiker, and J. Gittelsohn are with the Global Obesity Prevention Center (GOPC), Johns Hopkins University, Baltimore, Maryland, USA. L.A. Haidari and Y. Mui are with the Pittsburgh Supercomputing Center (PSC), Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Leila A Haidari
- B.Y. Lee, S.M. Bartsch, L.A. Haidari, Y. Mui, M.L. Spiker, and J. Gittelsohn are with the Global Obesity Prevention Center (GOPC), Johns Hopkins University, Baltimore, Maryland, USA. L.A. Haidari and Y. Mui are with the Pittsburgh Supercomputing Center (PSC), Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Marie L Spiker
- B.Y. Lee, S.M. Bartsch, L.A. Haidari, Y. Mui, M.L. Spiker, and J. Gittelsohn are with the Global Obesity Prevention Center (GOPC), Johns Hopkins University, Baltimore, Maryland, USA. L.A. Haidari and Y. Mui are with the Pittsburgh Supercomputing Center (PSC), Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Joel Gittelsohn
- B.Y. Lee, S.M. Bartsch, L.A. Haidari, Y. Mui, M.L. Spiker, and J. Gittelsohn are with the Global Obesity Prevention Center (GOPC), Johns Hopkins University, Baltimore, Maryland, USA. L.A. Haidari and Y. Mui are with the Pittsburgh Supercomputing Center (PSC), Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
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44
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Lee BY, Bartsch SM, Stone NTB, Zhang S, Brown ST, Chatterjee C, DePasse JV, Zenkov E, Briët OJT, Mendis C, Viisainen K, Candrinho B, Colborn J. The Economic Value of Long-Lasting Insecticidal Nets and Indoor Residual Spraying Implementation in Mozambique. Am J Trop Med Hyg 2017; 96:1430-1440. [PMID: 28719286 PMCID: PMC5462583 DOI: 10.4269/ajtmh.16-0744] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Malaria-endemic countries have to decide how much of their limited resources for vector control to allocate toward implementing long-lasting insecticidal nets (LLINs) versus indoor residual spraying (IRS). To help the Mozambique Ministry of Health use an evidence-based approach to determine funding allocation toward various malaria control strategies, the Global Fund convened the Mozambique Modeling Working Group which then used JANUS, a software platform that includes integrated computational economic, operational, and clinical outcome models that can link with different transmission models (in this case, OpenMalaria) to determine the economic value of vector control strategies. Any increase in LLINs (from 80% baseline coverage) or IRS (from 80% baseline coverage) would be cost-effective (incremental cost-effectiveness ratios ≤ $114/disability-adjusted life year averted). However, LLIN coverage increases tend to be more cost-effective than similar IRS coverage increases, except where both pyrethroid resistance is high and LLIN usage is low. In high-transmission northern regions, increasing LLIN coverage would be more cost-effective than increasing IRS coverage. In medium-transmission central regions, changing from LLINs to IRS would be more costly and less effective. In low-transmission southern regions, LLINs were more costly and less effective than IRS, due to low LLIN usage. In regions where LLINs are more cost-effective than IRS, it is worth considering prioritizing LLIN coverage and use. However, IRS may have an important role in insecticide resistance management and epidemic control. Malaria intervention campaigns are not a one-size-fits-all solution, and tailored approaches are necessary to account for the heterogeneity of malaria epidemiology.
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Affiliation(s)
- Bruce Y Lee
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Sarah M Bartsch
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Nathan T B Stone
- Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, Pennsylvania
| | - Shufang Zhang
- The Global Fund to Fight AIDS, Tuberculosis, and Malaria, Geneva, Switzerland
| | - Shawn T Brown
- Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, Pennsylvania
| | | | - Jay V DePasse
- Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, Pennsylvania
| | - Eli Zenkov
- Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, Pennsylvania
| | - Olivier J T Briët
- University of Basel, Basel, Switzerland.,Swiss Tropical and Public Health Institute, Basel, Switzerland
| | | | - Kirsi Viisainen
- The Global Fund to Fight AIDS, Tuberculosis, and Malaria, Geneva, Switzerland
| | - Baltazar Candrinho
- National Malaria Control Program, Mozambique Ministry of Health, Maputo, Mozambique
| | - James Colborn
- President's Malaria Initiative, Centers for Disease Control and Prevention, Washington, District of Columbia
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45
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Abstract
Mathematical and computational modeling can transform decision making for neglected tropical diseases (NTDs) if the right model is used for the right question. Modeling can help better understand and address the complex systems involved in making decisions for NTD prevention and control. However, all models, modelers, and modeling are not the same. Thus, decision makers need to better understand if a particular model actually fits their needs. Here are a series of questions that a decision maker can ask when determining whether a model is right for him or her.
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Affiliation(s)
- Bruce Y. Lee
- Public Health Computational and Operations Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
- Global Obesity Prevention Center, Johns Hopkins University, Baltimore, Maryland, United States of America
- * E-mail:
| | - Sarah M. Bartsch
- Public Health Computational and Operations Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
- Global Obesity Prevention Center, Johns Hopkins University, Baltimore, Maryland, United States of America
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46
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Bartsch SM, McKinnell JA, Mueller LE, Miller LG, Gohil SK, Huang SS, Lee BY. Potential economic burden of carbapenem-resistant Enterobacteriaceae (CRE) in the United States. Clin Microbiol Infect 2017; 23:48.e9-48.e16. [PMID: 27642178 PMCID: PMC5547745 DOI: 10.1016/j.cmi.2016.09.003] [Citation(s) in RCA: 128] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2016] [Revised: 09/09/2016] [Accepted: 09/10/2016] [Indexed: 12/24/2022]
Abstract
OBJECTIVES The Centers for Disease Control and Prevention considers carbapenem-resistant Enterobacteriaceae (CRE) an urgent public health threat; however, its economic burden is unknown. METHODS We developed a CRE clinical and economics outcomes model to determine the cost of CRE infection from the hospital, third-party payer, and societal, perspectives and to evaluate the health and economic burden of CRE to the USA. RESULTS Depending on the infection type, the median cost of a single CRE infection can range from $22 484 to $66 031 for hospitals, $10 440 to $31 621 for third-party payers, and $37 778 to $83 512 for society. An infection incidence of 2.93 per 100 000 population in the USA (9418 infections) would cost hospitals $275 million (95% CR $217-334 million), third-party payers $147 million (95% CR $129-172 million), and society $553 million (95% CR $303-1593 million) with a 25% attributable mortality, and would result in the loss of 8841 (95% CR 5805-12 420) quality-adjusted life years. An incidence of 15 per 100 000 (48 213 infections) would cost hospitals $1.4 billion (95% CR $1.1-1.7 billion), third-party payers $0.8 billion (95% CR $0.6-0.8 billion), and society $2.8 billion (95% CR $1.6-8.2 billion), and result in the loss of 45 261 quality-adjusted life years. CONCLUSIONS The cost of CRE is higher than the annual cost of many chronic diseases and of many acute diseases. Costs rise proportionally with the incidence of CRE, increasing by 2.0 times, 3.4 times, and 5.1 times for incidence rates of 6, 10, and 15 per 100 000 persons.
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Affiliation(s)
- S M Bartsch
- Public Health Computational and Operations Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - J A McKinnell
- Infectious Disease Clinical Outcomes Research Unit (ID-CORE), Los Angeles Biomedical Research Institute, Harbor-UCLA Medical Center, Torrance, CA, USA; Torrance Memorial Medical Center, Torrance, CA, USA
| | - L E Mueller
- Public Health Computational and Operations Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - L G Miller
- Infectious Disease Clinical Outcomes Research Unit (ID-CORE), Los Angeles Biomedical Research Institute, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - S K Gohil
- Division of Infectious Diseases and Health Policy Research Institute, University of California Irvine Health School of Medicine, Irvine, CA, USA
| | - S S Huang
- Division of Infectious Diseases and Health Policy Research Institute, University of California Irvine Health School of Medicine, Irvine, CA, USA
| | - B Y Lee
- Public Health Computational and Operations Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
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Bartsch SM, Hotez PJ, Asti L, Zapf KM, Bottazzi ME, Diemert DJ, Lee BY. The Global Economic and Health Burden of Human Hookworm Infection. PLoS Negl Trop Dis 2016; 10:e0004922. [PMID: 27607360 PMCID: PMC5015833 DOI: 10.1371/journal.pntd.0004922] [Citation(s) in RCA: 84] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2016] [Accepted: 07/23/2016] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Even though human hookworm infection is highly endemic in many countries throughout the world, its global economic and health impact is not well known. Without a better understanding of hookworm's economic burden worldwide, it is difficult for decision makers such as funders, policy makers, disease control officials, and intervention manufacturers to determine how much time, energy, and resources to invest in hookworm control. METHODOLOGY/PRINCIPLE FINDINGS We developed a computational simulation model to estimate the economic and health burden of hookworm infection in every country, WHO region, and globally, in 2016 from the societal perspective. Globally, hookworm infection resulted in a total 2,126,280 DALYs using 2004 disability weight estimates and 4,087,803 DALYs using 2010 disability weight estimates (excluding cognitive impairment outcomes). Including cognitive impairment did not significantly increase DALYs worldwide. Total productivity losses varied with the probability of anemia and calculation method used, ranging from $7.5 billion to $138.9 billion annually using gross national income per capita as a proxy for annual wages and ranging from $2.5 billion to $43.9 billion using minimum wage as a proxy for annual wages. CONCLUSION Even though hookworm is classified as a neglected tropical disease, its economic and health burden exceeded published estimates for a number of diseases that have received comparatively more attention than hookworm such as rotavirus. Additionally, certain large countries that are transitioning to higher income countries such as Brazil and China, still face considerable hookworm burden.
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Affiliation(s)
- Sarah M. Bartsch
- Public Health Computational and Operational Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
- Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Peter J. Hotez
- National School of Tropical Medicine, and Departments of Pediatrics and Molecular Virology & Microbiology, Baylor College of Medicine, Houston, Texas, United States of America
- Sabin Vaccine Institute, Washington, D.C., United States of America
| | - Lindsey Asti
- Public Health Computational and Operational Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
- Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Kristina M. Zapf
- Public Health Computational and Operational Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
- Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Maria Elena Bottazzi
- National School of Tropical Medicine, and Departments of Pediatrics and Molecular Virology & Microbiology, Baylor College of Medicine, Houston, Texas, United States of America
- Sabin Vaccine Institute, Washington, D.C., United States of America
| | - David J. Diemert
- Sabin Vaccine Institute, Washington, D.C., United States of America
- Department of Microbiology, Immunology and Tropical Medicine, The George Washington University Medical Center, Washington, D.C., United States of America
| | - Bruce Y. Lee
- Public Health Computational and Operational Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
- Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
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Singh A, Bartsch SM, Muder RR, Lee BY. An Economic Model: Value of Antimicrobial-Coated Sutures to Society, Hospitals, and Third-Party Payers in Preventing Abdominal Surgical Site Infections. Infect Control Hosp Epidemiol 2016; 35:1013-20. [DOI: 10.1086/677163] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
BackgroundWhile the persistence of high surgical site infection (SSI) rates has prompted the advent of more expensive sutures that are coated with antimicrobial agents to prevent SSIs, the economic value of such sutures has yet to be determined.MethodsUsing TreeAge Pro, we developed a decision analytic model to determine the cost-effectiveness of using antimicrobial sutures in abdominal incisions from the hospital, third-party payer, and societal perspectives. Sensitivity analyses systematically varied the risk of developing an SSI (range, 5%–20%), the cost of triclosan-coated sutures (range, $5–$25/inch), and triclosan-coated suture efficacy in preventing infection (range, 5%–50%) to highlight the range of costs associated with using such sutures.ResultsTriclosan-coated sutures saved $4,109–$13,975 (hospital perspective), $4,133–$14,297 (third-party payer perspective), and $40,127–$53,244 (societal perspective) per SSI prevented, when a surgery had a 15% SSI risk, depending on their efficacy. If the SSI risk was no more than 5% and the efficacy in preventing SSIs was no more than 10%, triclosan-coated sutures resulted in extra expenditure for hospitals and third-party payers (resulting in extra costs of $1,626 and $1,071 per SSI prevented for hospitals and third-party payers, respectively; SSI risk, 5%; efficacy, 10%).ConclusionsOur results suggest that switching to triclosan-coated sutures from the uncoated sutures can both prevent SSIs and save substantial costs for hospitals, third-party payers, and society, as long as efficacy in preventing SSIs is at least 10% and SSI risk is at least 10%.
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Abstract
Background Despite accounting for approximately one fifth of all acute gastroenteritis illnesses, norovirus has received comparatively less attention than other infectious pathogens. With several candidate vaccines under development, characterizing the global economic burden of norovirus could help funders, policy makers, public health officials, and product developers determine how much attention and resources to allocate to advancing these technologies to prevent and control norovirus. Methods We developed a computational simulation model to estimate the economic burden of norovirus in every country/area (233 total) stratified by WHO region and globally, from the health system and societal perspectives. We considered direct costs of illness (e.g., clinic visits and hospitalization) and productivity losses. Results Globally, norovirus resulted in a total of $4.2 billion (95% UI: $3.2–5.7 billion) in direct health system costs and $60.3 billion (95% UI: $44.4–83.4 billion) in societal costs per year. Disease amongst children <5 years cost society $39.8 billion, compared to $20.4 billion for all other age groups combined. Costs per norovirus illness varied by both region and age and was highest among adults ≥55 years. Productivity losses represented 84–99% of total costs varying by region. While low and middle income countries and high income countries had similar disease incidence (10,148 vs. 9,935 illness per 100,000 persons), high income countries generated 62% of global health system costs. In sensitivity analysis, the probability of hospitalization had the largest impact on health system cost estimates ($2.8 billion globally, assuming no hospitalization costs), while the probability of missing productive days had the largest impact on societal cost estimates ($35.9 billion globally, with a 25% probability of missing productive days). Conclusions The total economic burden is greatest in young children but the highest cost per illness is among older age groups in some regions. These large costs overwhelmingly are from productivity losses resulting from acute illness. Low, middle, and high income countries all have a considerable economic burden, suggesting that norovirus gastroenteritis is a truly global economic problem. Our findings can help identify which age group(s) and/or geographic regions may benefit the most from interventions.
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Affiliation(s)
- Sarah M. Bartsch
- Public Health Computational and Operations Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Benjamin A. Lopman
- Division of Viral Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, United States of America
| | - Sachiko Ozawa
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Aron J. Hall
- Division of Viral Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, United States of America
| | - Bruce Y. Lee
- Public Health Computational and Operations Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- * E-mail:
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Bartsch SM, Hotez PJ, Hertenstein DL, Diemert DJ, Zapf KM, Bottazzi ME, Bethony JM, Brown ST, Lee BY. Modeling the economic and epidemiologic impact of hookworm vaccine and mass drug administration (MDA) in Brazil, a high transmission setting. Vaccine 2016; 34:2197-206. [PMID: 27002501 DOI: 10.1016/j.vaccine.2016.03.018] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Revised: 03/03/2016] [Accepted: 03/09/2016] [Indexed: 10/22/2022]
Abstract
BACKGROUND Although mass drug administration (MDA) has helped reduce morbidity attributed to soil-transmitted helminth infections in children, its limitations for hookworm infection have motivated the development of a human hookworm vaccine to both improve morbidity control and ultimately help block hookworm transmission leading to elimination. However, the potential economic and epidemiologic impact of a preventive vaccine has not been fully evaluated. METHODS We developed a dynamic compartment model coupled to a clinical and economics outcomes model representing both the human and hookworm populations in a high transmission region of Brazil. Experiments simulated different implementation scenarios of MDA and vaccination under varying circumstances. RESULTS Considering only intervention costs, both annual MDA and vaccination were highly cost-effective (ICERs ≤ $790/DALY averted) compared to no intervention, with vaccination resulting in lower incremental cost-effectiveness ratios (ICERs ≤ $444/DALY averted). From the societal perspective, vaccination was economically dominant (i.e., less costly and more effective) versus annual MDA in all tested scenarios, except when vaccination was less efficacious (20% efficacy, 5 year duration) and MDA coverage was 75%. Increasing the vaccine's duration of protection and efficacy, and including a booster injection in adulthood all increased the benefits of vaccination (i.e., resulted in lower hookworm prevalence, averted more disability-adjusted life years, and saved more costs). Assuming its target product profile, a pediatric hookworm vaccine drastically decreased hookworm prevalence in children to 14.6% after 20 years, compared to 57.2% with no intervention and 54.1% with MDA. The addition of a booster in adulthood further reduced the overall prevalence from 68.0% to 36.0% and nearly eliminated hookworm infection in children. CONCLUSION Using a human hookworm vaccine would be cost-effective and in many cases economically dominant, providing both health benefits and cost-savings. It could become a key technology in effecting control and elimination efforts for hookworm globally.
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Affiliation(s)
- Sarah M Bartsch
- Public Health Computational and Operational Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA; Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA
| | - Peter J Hotez
- National School of Tropical Medicine, and Departments of Pediatrics and Molecular Virology & Microbiology, Baylor College of Medicine, One Baylor Plaza, BCM113, Houston, TX 77030, USA; Sabin Vaccine Institute, 2000 Pennsylvania Avenue NW, Washington, DC 20006, USA
| | - Daniel L Hertenstein
- Public Health Computational and Operational Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA; Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA
| | - David J Diemert
- Sabin Vaccine Institute, 2000 Pennsylvania Avenue NW, Washington, DC 20006, USA; Department of Microbiology, Immunology and Tropical Medicine, The George Washington University Medical Center, Washington, DC 20037, USA
| | - Kristina M Zapf
- Public Health Computational and Operational Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA; Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA
| | - Maria Elena Bottazzi
- National School of Tropical Medicine, and Departments of Pediatrics and Molecular Virology & Microbiology, Baylor College of Medicine, One Baylor Plaza, BCM113, Houston, TX 77030, USA; Sabin Vaccine Institute, 2000 Pennsylvania Avenue NW, Washington, DC 20006, USA
| | - Jeffrey M Bethony
- Sabin Vaccine Institute, 2000 Pennsylvania Avenue NW, Washington, DC 20006, USA; Department of Microbiology, Immunology and Tropical Medicine, The George Washington University Medical Center, Washington, DC 20037, USA
| | - Shawn T Brown
- Pittsburgh Supercomputing Center, Carnegie Mellon University, 300S Craig St, Pittsburgh, PA 15213, USA
| | - Bruce Y Lee
- Public Health Computational and Operational Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA; Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA.
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