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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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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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Gussin GM, McKinnell JA, Singh RD, Miller LG, Kleinman K, Saavedra R, Tjoa T, Gohil SK, Catuna TD, Heim LT, Chang J, Estevez M, He J, O’Donnell K, Zahn M, Lee E, Berman C, Nguyen J, Agrawal S, Ashbaugh I, Nedelcu C, Robinson PA, Tam S, Park S, Evans KD, Shimabukuro JA, Lee BY, Fonda E, Jernigan JA, Slayton RB, Stone ND, Janssen L, Weinstein RA, Hayden MK, Lin MY, Peterson EM, Bittencourt CE, Huang SS. Reducing Hospitalizations and Multidrug-Resistant Organisms via Regional Decolonization in Hospitals and Nursing Homes. JAMA 2024:2817010. [PMID: 38557703 PMCID: PMC10985619 DOI: 10.1001/jama.2024.2759] [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: 11/16/2023] [Accepted: 02/16/2024] [Indexed: 04/04/2024]
Abstract
Importance Infections due to multidrug-resistant organisms (MDROs) are associated with increased morbidity, mortality, length of hospitalization, and health care costs. Regional interventions may be advantageous in mitigating MDROs and associated infections. Objective To evaluate whether implementation of a decolonization collaborative is associated with reduced regional MDRO prevalence, incident clinical cultures, infection-related hospitalizations, costs, and deaths. Design, Setting, and Participants This quality improvement study was conducted from July 1, 2017, to July 31, 2019, across 35 health care facilities in Orange County, California. Exposures Chlorhexidine bathing and nasal iodophor antisepsis for residents in long-term care and hospitalized patients in contact precautions (CP). Main Outcomes and Measures Baseline and end of intervention MDRO point prevalence among participating facilities; incident MDRO (nonscreening) clinical cultures among participating and nonparticipating facilities; and infection-related hospitalizations and associated costs and deaths among residents in participating and nonparticipating nursing homes (NHs). Results Thirty-five facilities (16 hospitals, 16 NHs, 3 long-term acute care hospitals [LTACHs]) adopted the intervention. Comparing decolonization with baseline periods among participating facilities, the mean (SD) MDRO prevalence decreased from 63.9% (12.2%) to 49.9% (11.3%) among NHs, from 80.0% (7.2%) to 53.3% (13.3%) among LTACHs (odds ratio [OR] for NHs and LTACHs, 0.48; 95% CI, 0.40-0.57), and from 64.1% (8.5%) to 55.4% (13.8%) (OR, 0.75; 95% CI, 0.60-0.93) among hospitalized patients in CP. When comparing decolonization with baseline among NHs, the mean (SD) monthly incident MDRO clinical cultures changed from 2.7 (1.9) to 1.7 (1.1) among participating NHs, from 1.7 (1.4) to 1.5 (1.1) among nonparticipating NHs (group × period interaction reduction, 30.4%; 95% CI, 16.4%-42.1%), from 25.5 (18.6) to 25.0 (15.9) among participating hospitals, from 12.5 (10.1) to 14.3 (10.2) among nonparticipating hospitals (group × period interaction reduction, 12.9%; 95% CI, 3.3%-21.5%), and from 14.8 (8.6) to 8.2 (6.1) among LTACHs (all facilities participating; 22.5% reduction; 95% CI, 4.4%-37.1%). For NHs, the rate of infection-related hospitalizations per 1000 resident-days changed from 2.31 during baseline to 1.94 during intervention among participating NHs, and from 1.90 to 2.03 among nonparticipating NHs (group × period interaction reduction, 26.7%; 95% CI, 19.0%-34.5%). Associated hospitalization costs per 1000 resident-days changed from $64 651 to $55 149 among participating NHs and from $55 151 to $59 327 among nonparticipating NHs (group × period interaction reduction, 26.8%; 95% CI, 26.7%-26.9%). Associated hospitalization deaths per 1000 resident-days changed from 0.29 to 0.25 among participating NHs and from 0.23 to 0.24 among nonparticipating NHs (group × period interaction reduction, 23.7%; 95% CI, 4.5%-43.0%). Conclusions and Relevance A regional collaborative involving universal decolonization in long-term care facilities and targeted decolonization among hospital patients in CP was associated with lower MDRO carriage, infections, hospitalizations, costs, and deaths.
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Affiliation(s)
- Gabrielle M. Gussin
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - James A. McKinnell
- Division of Infectious Diseases, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, California
| | - Raveena D. Singh
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - Loren G. Miller
- Division of Infectious Diseases, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, California
| | - Ken Kleinman
- Program in Biostatistics, University of Massachusetts Amherst School of Public Health and Health Sciences, Amherst
| | - Raheeb Saavedra
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - Thomas Tjoa
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - Shruti K. Gohil
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - Tabitha D. Catuna
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - Lauren T. Heim
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - Justin Chang
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - Marlene Estevez
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - Jiayi He
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - Kathleen O’Donnell
- Healthcare-Associated Infections Program, Center for Healthcare Quality, California Department of Public Health, Richmond
| | - Matthew Zahn
- Epidemiology and Assessment, Orange County Health Care Agency, Santa Ana, California
| | - Eunjung Lee
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
- Division of Infectious Diseases, Department of Internal Medicine, Soonchunhyang University Seoul Hospital, Seoul, Republic of Korea
| | - Chase Berman
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - Jenny Nguyen
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - Shalini Agrawal
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - Isabel Ashbaugh
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - Christine Nedelcu
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - Philip A. Robinson
- Division of Infectious Diseases, Hoag Hospital, Newport Beach, California
| | - Steven Tam
- Division of Geriatric Medicine and Gerontology, University of California Irvine Health, Orange
| | - Steven Park
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - Kaye D. Evans
- Clinical Microbiology Laboratory, University of California Irvine Health, Orange
| | - Julie A. Shimabukuro
- Clinical Microbiology Laboratory, University of California Irvine Health, Orange
| | - Bruce Y. Lee
- PHICOR (Public Health Informatics Computational Operations Research), Department of Health Policy and Management, City University of New York Graduate School of Public Health, New York
| | | | - 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
| | - Lynn Janssen
- Healthcare-Associated Infections Program, Center for Healthcare Quality, California Department of Public Health, Richmond
| | - Robert A. Weinstein
- Division of Infectious Diseases, Department of Medicine, Rush University Medical Center, Chicago, Illinois
- Department of Medicine, Cook County Health and Hospitals System, Chicago, Illinois
| | - Mary K. Hayden
- Division of Infectious Diseases, Department of Medicine, Rush University Medical Center, Chicago, Illinois
| | - Michael Y. Lin
- Division of Infectious Diseases, Department of Medicine, Rush University Medical Center, Chicago, Illinois
| | - Ellena M. Peterson
- Department of Pathology and Laboratory Medicine, University of California Irvine Health, Orange
| | - Cassiana E. Bittencourt
- Department of Pathology and Laboratory Medicine, University of California Irvine Health, Orange
| | - Susan S. Huang
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
- Department of Epidemiology and Infection Prevention, University of California Irvine Health, Orange
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5
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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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6
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Pronk NP, Lee BY. Qualitative systems mapping in promoting physical activity and cardiorespiratory fitness: Perspectives and recommendations. Prog Cardiovasc Dis 2024; 83:43-48. [PMID: 38431224 DOI: 10.1016/j.pcad.2024.02.013] [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] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Accepted: 02/25/2024] [Indexed: 03/05/2024]
Abstract
The purpose of this report is to provide a perspective on the use of qualitative systems mapping, provide examples of physical activity (PA) systems maps, discuss the role of PA systems mapping in the context of iterative learning to derive breakthrough interventions, and provide actionable recommendations for future work. Systems mapping methods and applications for PA are emerging in the scientific literature in the study of complex health issues and can be used as a prelude to mathematical/computational modeling where important factors and relationships can be elucidated, data needs can be prioritized and guided, interventions can be tested and (co)designed, and metrics and evaluations can be developed. Examples are discussed that describe systems mapping based on Group Model Building or literature reviews. Systems maps are highly informative, illustrate multiple components to address PA and physical inactivity issues, and make compelling arguments against single intervention action. No studies were identified in the literature scan that considered cardiorespiratory fitness the focal point of a systems maps. Recommendations for future research and education are presented and it is concluded that systems mapping represents a valuable yet underutilized tool for visualizing the complexity of PA promotion.
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Affiliation(s)
- Nicolaas P Pronk
- HealthPartners Institute, 8170 33(rd) Avenue South, Bloomington, MN 55425, USA; Department of Health Policy and Management, University of Minnesota, 420 Delaware St SE, Minneapolis, MN 55455, USA.
| | - Bruce Y Lee
- Center for Advanced Technology and Communication in Health (CATCH) and PIHCOR, Graduate School of Public Health and Health Policy, City University of New York (CUNY), New York, NY, USA
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7
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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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8
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Hotez PJ, Bottazzi ME, Kaye PM, Lee BY, Puchner KP. Neglected tropical disease vaccines: hookworm, leishmaniasis, and schistosomiasis. Vaccine 2023; 41 Suppl 2:S176-S179. [PMID: 38407985 PMCID: PMC10713477 DOI: 10.1016/j.vaccine.2023.04.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 03/29/2023] [Accepted: 04/06/2023] [Indexed: 02/28/2024]
Affiliation(s)
- Peter J Hotez
- Texas Children's Hospital Center for Vaccine Development, Departments of Pediatrics and Molecular Virology and Microbiology, National School of Tropical Medicine, Baylor College of Medicine, Houston TX, USA.
| | - Maria Elena Bottazzi
- Texas Children's Hospital Center for Vaccine Development, Departments of Pediatrics and Molecular Virology and Microbiology, National School of Tropical Medicine, Baylor College of Medicine, Houston TX, USA
| | - Paul M Kaye
- York Biomedical Research Institute, Hull York Medical School, University of York, Heslington York, UK
| | - Bruce Y Lee
- Center for Advanced Technology and Communication in Health (CATCH), Public Health Informatics Computational and Operations Research (PHICOR), and Department of Health Policy and Management, City University of New York, School of Public Health, New York, NY, USA
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9
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Livings MS, Bruine de Bruin W, Wilson JP, Lee BY, Xu M, Frazzini A, Chandra S, Weber K, Babboni M, de la Haye K. Food Insecurity Is Under-reported in Surveys That Ask About the Past Year. Am J Prev Med 2023; 65:657-666. [PMID: 37028568 DOI: 10.1016/j.amepre.2023.03.022] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 03/23/2023] [Accepted: 03/26/2023] [Indexed: 04/09/2023]
Abstract
INTRODUCTION Food insecurity affects one in ten Americans in a typical year; recent U.S. Department of Agriculture data show that this food insecurity rate was stable from 2019 to 2021. However, data from Los Angeles County and other U.S. regions show that food insecurity spiked during the early months of the COVID-19 pandemic. One reason for this discrepancy may be that food insecurity measures assess experiences over different time frames. This study investigated the discrepancies in food insecurity rates by comparing past-week and past-year food insecurity measures and explored the role of recall bias. METHODS Data were obtained from a representative survey panel of Los Angeles adults (N=1,135). Participants were surveyed about past-week food insecurity eleven times throughout 2021 and once about past-year food insecurity in December 2021. Data were analyzed in 2022. RESULTS Of the participants who reported past-week food insecurity at any time in 2021, only two thirds also reported past-year food insecurity in December 2021, suggesting that one third of participants under-reported past-year food insecurity. Logistic regression models indicated that three characteristics were significantly associated with under-reporting of past-year food insecurity: having reported past-week food insecurity at fewer survey waves, not reporting recent past-week food insecurity, and having a relatively high household income. CONCLUSIONS These results suggest substantial under-reporting of past-year food insecurity, related to recall bias and social factors. Measuring food insecurity at multiple points throughout the year may help to improve the accuracy of reporting and public health surveillance of this issue.
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Affiliation(s)
- Michelle S Livings
- Spatial Sciences Institute, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, California
| | - Wändi Bruine de Bruin
- Department of Psychology, Dana and David Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, California; Sol Price School of Public Policy, University of Southern California, Los Angeles, California; Leonard D. Schaeffer Center for Health Policy & Economics, University of Southern California, Los Angeles, California; Dornsife Center for Economic and Social Research, University of Southern California, Los Angeles, California
| | - John P Wilson
- Spatial Sciences Institute, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, California; Department of Sociology, Dana and David Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, California; Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California; Department of Civil & Environmental Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California; Department of Computer Science, Viterbi School of Engineering, University of Southern California, Los Angeles, California; School of Architecture, University of Southern California, Los Angeles, California
| | - Bruce Y Lee
- Graduate School of Public Health & Health Policy, City University of New York, New York, New York; Center for Advanced Technology and Communication in Health, City University of New York, New York, New York
| | - Mengya Xu
- Spatial Sciences Institute, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, California
| | - Alison Frazzini
- Los Angeles County Chief Sustainability Office, Los Angeles, California
| | - Swati Chandra
- Los Angeles County Food Equity Roundtable, Los Angeles, California
| | - Kate Weber
- USC Dornsife Public Exchange, University of Southern California, Los Angeles, California
| | - Marianna Babboni
- USC Dornsife Public Exchange, University of Southern California, Los Angeles, California
| | - Kayla de la Haye
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California.
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10
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Estrada-Magbanua WM, Huang TTK, Lounsbury DW, Zito P, Iftikhar P, El-Bassel N, Gilbert L, Wu E, Lee BY, Mateu-Gelabert P, S. Sabounchi N. Application of group model building in implementation research: A systematic review of the public health and healthcare literature. PLoS One 2023; 18:e0284765. [PMID: 37590193 PMCID: PMC10434911 DOI: 10.1371/journal.pone.0284765] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 04/09/2023] [Indexed: 08/19/2023] Open
Abstract
BACKGROUND Group model building is a process of engaging stakeholders in a participatory modeling process to elicit their perceptions of a problem and explore concepts regarding the origin, contributing factors, and potential solutions or interventions to a complex issue. Recently, it has emerged as a novel method for tackling complex, long-standing public health issues that traditional intervention models and frameworks cannot fully address. However, the extent to which group model building has resulted in the adoption of evidence-based practices, interventions, and policies for public health remains largely unstudied. The goal of this systematic review was to examine the public health and healthcare applications of GMB in the literature and outline how it has been used to foster implementation and dissemination of evidence-based interventions. METHODS We searched PubMed, Web of Science, and other databases through August 2022 for studies related to public health or health care where GMB was cited as a main methodology. We did not eliminate studies based on language, location, or date of publication. Three reviewers independently extracted data on GMB session characteristics, model attributes, and dissemination formats and content. RESULTS Seventy-two studies were included in the final review. Majority of GMB activities were in the fields of nutrition (n = 19, 26.4%), health care administration (n = 15, 20.8%), and environmental health (n = 12, 16.7%), and were conducted in the United States (n = 29, 40.3%) and Australia (n = 7, 9.7%). Twenty-three (31.9%) studies reported that GMB influenced implementation through policy change, intervention development, and community action plans; less than a third reported dissemination of the model outside journal publication. GMB was reported to have increased insight, facilitated consensus, and fostered communication among stakeholders. CONCLUSIONS GMB is associated with tangible benefits to participants, including increased community engagement and development of systems solutions. Transdisciplinary stakeholder involvement and more rigorous evaluation and dissemination of GMB activities are recommended.
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Affiliation(s)
- Weanne Myrrh Estrada-Magbanua
- Center for Systems and Community Design and NYU-CUNY Prevention Research Center, CUNY Graduate School of Public Health and Health Policy, New York, NY, United States of America
| | - Terry T.-K. Huang
- Center for Systems and Community Design and NYU-CUNY Prevention Research Center, CUNY Graduate School of Public Health and Health Policy, New York, NY, United States of America
| | - David W. Lounsbury
- Division of Health Behavior Research and Implementation Science, Albert Einstein College of Medicine, New York, NY, United States of America
| | - Priscila Zito
- Center for Systems and Community Design and NYU-CUNY Prevention Research Center, CUNY Graduate School of Public Health and Health Policy, New York, NY, United States of America
| | - Pulwasha Iftikhar
- Center for Systems and Community Design and NYU-CUNY Prevention Research Center, CUNY Graduate School of Public Health and Health Policy, New York, NY, United States of America
| | - Nabila El-Bassel
- Social Intervention Group, School of Social Work, Columbia University, New York, NY, United States of America
| | - Louisa Gilbert
- Social Intervention Group, School of Social Work, Columbia University, New York, NY, United States of America
| | - Elwin Wu
- Social Intervention Group, School of Social Work, Columbia University, New York, NY, United States of America
| | - Bruce Y. Lee
- Center for Systems and Community Design and NYU-CUNY Prevention Research Center, CUNY Graduate School of Public Health and Health Policy, New York, NY, United States of America
| | - Pedro Mateu-Gelabert
- Center for Systems and Community Design and NYU-CUNY Prevention Research Center, CUNY Graduate School of Public Health and Health Policy, New York, NY, United States of America
| | - Nasim S. Sabounchi
- Center for Systems and Community Design and NYU-CUNY Prevention Research Center, CUNY Graduate School of Public Health and Health Policy, New York, NY, United States of America
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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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Spiker ML, Welling J, Hertenstein D, Mishra S, Mishra K, Hurley KM, Neff RA, Fanzo J, Lee BY. When increasing vegetable production may worsen food availability gaps: A simulation model in India. Food Policy 2023; 116:102416. [PMID: 37234381 PMCID: PMC10206406 DOI: 10.1016/j.foodpol.2023.102416] [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] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 10/28/2022] [Accepted: 01/11/2023] [Indexed: 05/28/2023]
Abstract
Translating agricultural productivity into food availability depends on food supply chains. Agricultural policy and research efforts promote increased horticultural crop production and yields, but the ability of low-resource food supply chains to handle increased volumes of perishable crops is not well understood. This study developed and used a discrete event simulation model to assess the impact of increased production of potato, onion, tomato, brinjal (eggplant), and cabbage on vegetable supply chains in Odisha, India. Odisha serves as an exemplar of vegetable supply chain challenges in many low-resource settings. Model results demonstrated that in response to increasing vegetable production 1.25-5x baseline amounts, demand fulfillment at the retail level fluctuated by + 3% to -4% from baseline; in other words, any improvements in vegetable availability for consumers were disproportionately low compared to the magnitude of increased production, and in some cases increased production worsened demand fulfillment. Increasing vegetable production led to disproportionately high rates of postharvest loss: for brinjal, for example, doubling agricultural production led to a 3% increase in demand fulfillment and a 19% increase in supply chain losses. The majority of postharvest losses occurred as vegetables accumulated and expired during wholesale-to-wholesale trade. In order to avoid inadvertently exacerbating postharvest losses, efforts to address food security through agriculture need to ensure that low-resource supply chains can handle increased productivity. Supply chain improvements should consider the constraints of different types of perishable vegetables, and they may need to go beyond structural improvements to include networks of communication and trade.
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Affiliation(s)
- Marie L. Spiker
- Nutritional Sciences Program and Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, United States
- Global Obesity Prevention Center (GOPC), Johns Hopkins University, Baltimore, MD, United States1
| | - Joel Welling
- Pittsburgh Supercomputing Center, Pittsburgh, PA, United States
| | - Daniel Hertenstein
- Global Obesity Prevention Center (GOPC), Johns Hopkins University, Baltimore, MD, United States1
| | | | | | - Kristen M. Hurley
- Johns Hopkins Bloomberg School of Public Health, Department of International Health, Baltimore, MD, United States
| | - Roni A. Neff
- Johns Hopkins Bloomberg School of Public Health, Department of Environmental Health and Engineering, Baltimore, MD, United States
- Johns Hopkins Bloomberg School of Public Health, Center for a Livable Future, Baltimore, MD, United States
| | - Jess Fanzo
- Johns Hopkins Bloomberg School of Public Health, Department of International Health, Baltimore, MD, United States
- Johns Hopkins University, Berman Institute of Bioethics, Baltimore, MD, United States
- Johns Hopkins University, School of Advanced International Studies, Washington, DC, United States
| | - Bruce Y. Lee
- PHICOR (Public Health Informatics, Computational, and Operations Research), City University of New York Graduate School of Public Health & Health Policy (CUNY SPH), New York City, NY, United States
- CATCH (Center for Advanced Technology and Communication in Health), City University of New York Graduate School of Public Health & Health Policy (CUNY SPH), New York City, NY, United States
- AIMINGS (Artificial Intelligence, Modeling, and Informatics for Nutrition Guidance and Systems) Center, City University of New York Graduate School of Public Health & Health Policy (CUNY SPH), New York City, NY, United States
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13
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Lee BY, Ordovás JM, Parks EJ, Anderson CAM, Barabási AL, Clinton SK, de la Haye K, Duffy VB, Franks PW, Ginexi EM, Hammond KJ, Hanlon EC, Hittle M, Ho E, Horn AL, Isaacson RS, Mabry PL, Malone S, Martin CK, Mattei J, Meydani SN, Nelson LM, Neuhouser ML, Parent B, Pronk NP, Roche HM, Saria S, Scheer FAJL, Segal E, Sevick MA, Spector TD, Van Horn L, Varady KA, Voruganti VS, Martinez MF. Research gaps and opportunities in precision nutrition: an NIH workshop report. Am J Clin Nutr 2022; 116:1877-1900. [PMID: 36055772 PMCID: PMC9761773 DOI: 10.1093/ajcn/nqac237] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [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: 10/11/2021] [Revised: 04/06/2022] [Accepted: 08/30/2022] [Indexed: 02/01/2023] Open
Abstract
Precision nutrition is an emerging concept that aims to develop nutrition recommendations tailored to different people's circumstances and biological characteristics. Responses to dietary change and the resulting health outcomes from consuming different diets may vary significantly between people based on interactions between their genetic backgrounds, physiology, microbiome, underlying health status, behaviors, social influences, and environmental exposures. On 11-12 January 2021, the National Institutes of Health convened a workshop entitled "Precision Nutrition: Research Gaps and Opportunities" to bring together experts to discuss the issues involved in better understanding and addressing precision nutrition. The workshop proceeded in 3 parts: part I covered many aspects of genetics and physiology that mediate the links between nutrient intake and health conditions such as cardiovascular disease, Alzheimer disease, and cancer; part II reviewed potential contributors to interindividual variability in dietary exposures and responses such as baseline nutritional status, circadian rhythm/sleep, environmental exposures, sensory properties of food, stress, inflammation, and the social determinants of health; part III presented the need for systems approaches, with new methods and technologies that can facilitate the study and implementation of precision nutrition, and workforce development needed to create a new generation of researchers. The workshop concluded that much research will be needed before more precise nutrition recommendations can be achieved. This includes better understanding and accounting for variables such as age, sex, ethnicity, medical history, genetics, and social and environmental factors. The advent of new methods and technologies and the availability of considerably more data bring tremendous opportunity. However, the field must proceed with appropriate levels of caution and make sure the factors listed above are all considered, and systems approaches and methods are incorporated. It will be important to develop and train an expanded workforce with the goal of reducing health disparities and improving precision nutritional advice for all Americans.
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Affiliation(s)
- Bruce Y Lee
- Health Policy and Management, City University of New York Graduate School of Public Health and Health Policy, New York, NY, USA
| | - José M Ordovás
- USDA-Human Nutrition Research Center on Aging at Tufts University, Boston, MA, USA
| | - Elizabeth J Parks
- Nutrition and Exercise Physiology, University of Missouri School of Medicine, MO, USA
| | | | - Albert-László Barabási
- Network Science Institute and Department of Physics, Northeastern University, Boston, MA, USA
| | | | - Kayla de la Haye
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Valerie B Duffy
- Allied Health Sciences, University of Connecticut, Storrs, CT, USA
| | - Paul W Franks
- Novo Nordisk Foundation, Hellerup, Denmark, Copenhagen, Denmark, and Lund University Diabetes Center, Sweden
- The Lund University Diabetes Center, Malmo, SwedenInsert Affiliation Text Here
| | - Elizabeth M Ginexi
- National Institutes of Health, Office of Behavioral and Social Sciences Research, Bethesda, MD, USA
| | - Kristian J Hammond
- Computer Science, Northwestern University McCormick School of Engineering, IL, USA
| | - Erin C Hanlon
- Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Michael Hittle
- Epidemiology and Clinical Research, Stanford University, Stanford, CA, USA
| | - Emily Ho
- Public Health and Human Sciences, Linus Pauling Institute, Oregon State University, Corvallis, OR, USA
| | - Abigail L Horn
- Information Sciences Institute, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | | | | | - Susan Malone
- Rory Meyers College of Nursing, New York University, New York, NY, USA
| | - Corby K Martin
- Ingestive Behavior Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA
| | - Josiemer Mattei
- Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Simin Nikbin Meydani
- USDA-Human Nutrition Research Center on Aging at Tufts University, Boston, MA, USA
| | - Lorene M Nelson
- Epidemiology and Population Health, Stanford University, Stanford, CA, USA
| | | | - Brendan Parent
- Grossman School of Medicine, New York University, New York, NY, USA
| | | | - Helen M Roche
- UCD Conway Institute, School of Public Health, Physiotherapy, and Sports Science, University College Dublin, Dublin, Ireland
| | - Suchi Saria
- Johns Hopkins University, Baltimore, MD, USA
| | - Frank A J L Scheer
- Brigham and Women's Hospital, Boston, MA, USA
- Medicine and Neurology, Harvard Medical School, Boston, MA, USA
| | - Eran Segal
- Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel
| | - Mary Ann Sevick
- Grossman School of Medicine, New York University, New York, NY, USA
| | - Tim D Spector
- Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - Linda Van Horn
- Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Krista A Varady
- Kinesiology and Nutrition, University of Illinois at Chicago, Chicago, IL, USA
| | - Venkata Saroja Voruganti
- Nutrition and Nutrition Research Institute, Gillings School of Public Health, The University of North Carolina, Chapel Hill, NC, USA
| | - Marie F Martinez
- Health Policy and Management, City University of New York Graduate School of Public Health and Health Policy, New York, NY, USA
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14
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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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15
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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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16
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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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18
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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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19
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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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20
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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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21
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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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22
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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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23
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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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24
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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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Lee BY, Ferguson MC, Cox SN, Phan PH. Big Data and Systems Methods: The Next Frontier to Tackling the Global Obesity Epidemic. Obesity (Silver Spring) 2021; 29:263-264. [PMID: 33421308 PMCID: PMC8409058 DOI: 10.1002/oby.23062] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 09/04/2020] [Accepted: 09/09/2020] [Indexed: 11/06/2022]
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, New York, USA
- Johns Hopkins Carey Business School, Baltimore, Maryland, USA
| | - 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, New York, USA
| | - 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, New York, USA
| | - Phillip H. Phan
- Johns Hopkins Carey Business School, Baltimore, Maryland, USA
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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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Ferguson MC, O'Shea KJ, Hammer LD, Hertenstein DL, Syed RM, Nyathi S, Gonzales MS, Domino M, S Siegmund S, Randall S, Wedlock P, Adam A, Lee BY. Can following formula-feeding recommendations still result in infants who are overweight or have obesity? Pediatr Res 2020; 88:661-667. [PMID: 32179869 PMCID: PMC7492437 DOI: 10.1038/s41390-020-0844-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 02/04/2020] [Accepted: 02/08/2020] [Indexed: 12/13/2022]
Abstract
BACKGROUND Studies show that by 3 months, over half of US infants receive formula, and guidelines play a key role in formula feeding. The question then is, what might happen if caregivers follow guidelines and, more specifically, are there situations where following guidelines can result in infants who are overweight/have obesity? METHODS We used our "Virtual Infant" agent-based model representing infant-caregiver pairs that allowed caregivers to feed infants each day according to guidelines put forth by Johns Hopkins Medicine (JHM), Children's Hospital of Philadelphia (CHOP), Children's Hospital of the King's Daughters (CHKD), and Women, Infants, and Children (WIC). The model simulated the resulting development of the infants from birth to 6 months. The two sets of guidelines vary in their recommendations, and do not provide studies that support amounts at given ages. RESULTS Simulations identified several scenarios where caregivers followed JHM/CHOP/CHKD and WIC guidelines, but infants still became overweight/with obesity by 6 months. For JHM/CHOP/CHKD guidelines, this occurred even when caregivers adjusted feeding based on infant's weight. For WIC guidelines, when caregivers adjusted formula amounts, infants maintained healthy weight. CONCLUSIONS WIC guidelines may be a good starting point for caregivers who adjust as their infant grows, but the minimum amounts for JHM/CHKD/CHOP recommendations may be too high. IMPACT Our virtual infant simulation study answers the question: can caregivers follow current formula-feeding guidelines and still end up with an infant who is overweight or has obesity? Our study identified several situations in which unhealthy weight gain and/or weight loss could result from following established formula-feeding recommendations. Our study also suggests that the minimum recommended amount of daily formula feeding should be lower for JHM/CHOP/CHKD guidelines to give caregivers more flexibility in adjusting daily feeding levels in response to infant weight. WIC guidelines may be a good starting point for caregivers who adjust as their infant grows. In order to understand how to adjust guidelines, we can use computational simulation models, which serve as "virtual laboratories" to help overcome the logistical and ethical issues of clinical trials.
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Affiliation(s)
- Marie C Ferguson
- PHICOR (Public Health Informatics, Computational and Operations Research), City University of New York (previously at Johns Hopkins University, Baltimore, MD), New York, NY, USA
| | - Kelly J O'Shea
- PHICOR (Public Health Informatics, Computational and Operations Research), City University of New York (previously at Johns Hopkins University, Baltimore, MD), New York, NY, USA
| | | | - Daniel L Hertenstein
- PHICOR (Public Health Informatics, Computational and Operations Research), City University of New York (previously at Johns Hopkins University, Baltimore, MD), New York, NY, USA
| | - Rafay M Syed
- PHICOR (Public Health Informatics, Computational and Operations Research), City University of New York (previously at Johns Hopkins University, Baltimore, MD), New York, NY, USA
| | - Sindiso Nyathi
- PHICOR (Public Health Informatics, Computational and Operations Research), City University of New York (previously at Johns Hopkins University, Baltimore, MD), New York, NY, USA
| | - Mario Solano Gonzales
- PHICOR (Public Health Informatics, Computational and Operations Research), City University of New York (previously at Johns Hopkins University, Baltimore, MD), New York, NY, USA
| | - Molly Domino
- PHICOR (Public Health Informatics, Computational and Operations Research), City University of New York (previously at Johns Hopkins University, Baltimore, MD), New York, NY, USA
| | - Sheryl S Siegmund
- PHICOR (Public Health Informatics, Computational and Operations Research), City University of New York (previously at Johns Hopkins University, Baltimore, MD), New York, NY, USA
| | - Samuel Randall
- PHICOR (Public Health Informatics, Computational and Operations Research), City University of New York (previously at Johns Hopkins University, Baltimore, MD), New York, NY, USA
| | - Patrick Wedlock
- PHICOR (Public Health Informatics, Computational and Operations Research), City University of New York (previously at Johns Hopkins University, Baltimore, MD), New York, NY, USA
| | - Atif Adam
- PHICOR (Public Health Informatics, Computational and Operations Research), City University of New York (previously at Johns Hopkins University, Baltimore, MD), New York, NY, USA
| | - Bruce Y Lee
- PHICOR (Public Health Informatics, Computational and Operations Research), City University of New York (previously at Johns Hopkins University, Baltimore, MD), New York, NY, USA.
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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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Scott-Sheldon LAJ, Hedges LV, Cyr C, Young-Hyman D, Khan LK, Magnus M, King H, Arteaga S, Cawley J, Economos CD, Haire-Joshu D, Hunter CM, Lee BY, Kumanyika SK, Ritchie LD, Robinson TN, Schwartz MB. Childhood Obesity Evidence Base Project: A Systematic Review and Meta-Analysis of a New Taxonomy of Intervention Components to Improve Weight Status in Children 2-5 Years of Age, 2005-2019. Child Obes 2020; 16:S221-S248. [PMID: 32936038 PMCID: PMC7482126 DOI: 10.1089/chi.2020.0139] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Objective: To evaluate the efficacy of childhood obesity interventions and conduct a taxonomy of intervention components that are most effective in changing obesity-related health outcomes in children 2-5 years of age. Methods: Comprehensive searches located 51 studies from 18,335 unique records. Eligible studies: (1) assessed children aged 2-5, living in the United States; (2) evaluated an intervention to improve weight status; (3) identified a same-aged comparison group; (4) measured BMI; and (5) were available between January 2005 and August 2019. Coders extracted study, sample, and intervention characteristics. Effect sizes [ESs; and 95% confidence intervals (CIs)] were calculated by using random-effects models. Meta-regression was used to determine which intervention components explain variability in ESs. Results: Included were 51 studies evaluating 58 interventions (N = 29,085; mean age = 4 years; 50% girls). Relative to controls, children receiving an intervention had a lower BMI at the end of the intervention (g = 0.10, 95% CI = 0.02-0.18; k = 55) and at the last follow-up (g = 0.17, 95% CI = 0.04-0.30; k = 14; range = 18-143 weeks). Three intervention components moderated efficacy: engage caregivers in praise/encouragement for positive health-related behavior; provide education about the importance of screen time reduction to caregivers; and engage pediatricians/health care providers. Conclusions: Early childhood obesity interventions are effective in reducing BMI in preschool children. Our findings suggest that facilitating caregiver education about the importance of screen time reduction may be an important strategy in reducing early childhood obesity.
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Affiliation(s)
- Lori A J Scott-Sheldon
- Centers for Behavioral and Preventive Medicine, The Miriam Hospital, Providence, RI, USA
- Department of Psychiatry and Human Behavior, Alpert School of Medicine, Brown University, Providence, RI, USA
| | - Larry V Hedges
- Department of Statistics, Northwestern University, Evanston, IL, USA
| | - Chris Cyr
- Impact Genome Project, Mission Measurement, Chicago, IL, USA
| | - Deborah Young-Hyman
- Office of Behavioral and Social Sciences, Office of the Director, National Institutes of Health, Bethesda, MD, USA
| | - Laura Kettel Khan
- Division of Nutrition, Physical Activity, and Obesity, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | | | - Heather King
- Impact Genome Project, Mission Measurement, Chicago, IL, USA
| | - Sonia Arteaga
- Office of the Director, National Institutes of Health, National Institutes of Health, Bethesda, MD, USA
| | - John Cawley
- Department of Policy Analysis and Management, Cornell University, Ithaca, NY, USA
- Department of Economics, Cornell University, Ithaca, NY, USA
| | - Christina D Economos
- Division of Nutrition Interventions, Communication, and Behavior Change, Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
| | - Debra Haire-Joshu
- Center for Obesity Prevention and Policy Research, Brown School, Washington University, Saint Louis, MO, USA
| | - Christine M Hunter
- Office of Behavioral and Social Sciences, Office of the Director, National Institutes of Health, Bethesda, MD, USA
| | - Bruce Y Lee
- CUNY Graduate School of Public Health and Policy, New York, NY, USA
| | - Shiriki K Kumanyika
- Department of Community Health and Prevention, Dornsife School of Public Health, Drexel University, Philadelphia, PA, USA
| | - Lorrene D Ritchie
- Nutrition Policy Institute, University of California, Division of Agriculture and Natural Resources, Berkeley, CA, USA
| | - Thomas N Robinson
- Departments of Pediatrics and Medicine, Stanford Solutions Science Lab, Stanford University, Stanford, CA, USA
| | - Marlene B Schwartz
- Department of Human Development and Family Studies, University of Connecticut, Hartford, CT, USA
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King H, Magnus M, Hedges LV, Cyr C, Young-Hyman D, Kettel Khan L, Scott-Sheldon LAJ, Saul JA, Arteaga S, Cawley J, Economos CD, Haire-Joshu D, Hunter CM, Lee BY, Kumanyika SK, Ritchie LD, Robinson TN, Schwartz MB. Childhood Obesity Evidence Base Project: Methods for Taxonomy Development for Application in Taxonomic Meta-Analysis. Child Obes 2020; 16:S27-S220. [PMID: 32936039 PMCID: PMC7482109 DOI: 10.1089/chi.2020.0138] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Meta-analysis has been used to examine the effectiveness of childhood obesity prevention efforts, yet traditional conventional meta-analytic methods restrict the kinds of studies included, and either narrowly define mechanisms and agents of change, or examine the effectiveness of whole interventions as opposed to the specific actions that comprise interventions. Taxonomic meta-analytic methods widen the aperture of what can be included in a meta-analysis data set, allowing for inclusion of many types of interventions and study designs. The National Collaborative on Childhood Obesity Research Childhood Obesity Evidence Base (COEB) project focuses on interventions intended to prevent childhood obesity in children 2-5 years old who have an outcome measure of BMI. The COEB created taxonomies, anchored in the Social Ecological Model, which catalog specific outcomes, intervention components, intended recipients, and contexts of policies, initiatives, and interventions conducted at the individual, interpersonal, organizational, community, and societal level. Taxonomies were created by discovery from the literature itself using grounded theory. This article describes the process used for a novel taxonomic meta-analysis of childhood obesity prevention studies between the years 2010 and 2019. This method can be applied to other areas of research, including obesity prevention in additional populations.
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Affiliation(s)
- Heather King
- Impact Genome Project, Mission Measurement, Chicago, IL, USA
| | | | - Larry V Hedges
- Department of Statistics, Northwestern University, Evanston, IL, USA
| | - Chris Cyr
- Impact Genome Project, Mission Measurement, Chicago, IL, USA
| | - Deborah Young-Hyman
- Office of Behavioral and Social Sciences, Office of the Director, National Institutes of Health, Bethesda, MD, USA
| | - Laura Kettel Khan
- Division of Nutrition, Physical Activity, and Obesity, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Lori A J Scott-Sheldon
- Centers for Behavioral and Preventive Medicine, The Miriam Hospital, Providence, RI, USA
- Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Jason A Saul
- Center for Impact Sciences, Harris School of Public Policy, University of Chicago, Chicago, IL, USA
| | - Sonia Arteaga
- Office of the Director, National Institutes of Health, Bethesda, MD, USA
| | - John Cawley
- Department of Policy Analysis and Management and Cornell University, Ithaca, NY, USA
- Department of Economics, Cornell University, Ithaca, NY, USA
| | - Christina D Economos
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
| | - Debra Haire-Joshu
- Center for Obesity Prevention and Policy Research, Brown School Washington University, Saint Louis, MO, USA
| | - Christine M Hunter
- Office of Behavioral and Social Sciences, Office of the Director, National Institutes of Health, Bethesda, MD, USA
| | - Bruce Y Lee
- CUNY Graduate School of Public Health and Policy, New York, NY, USA
| | - Shiriki K Kumanyika
- Department of Community Health and Prevention, Dornsife School of Public Health, Drexel University, Philadelphia, PA, USA
| | - Lorrene D Ritchie
- Nutrition Policy Institute, University of California Agriculture and Natural Resources, Berkeley, CA, USA
| | - Thomas N Robinson
- Stanford Solutions Science Lab, Stanford University, Stanford, CA, USA
| | - Marlene B Schwartz
- Department of Human Development and Family Studies, University of Connecticut, Hartford, CT, USA
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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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Ferguson MC, Morgan MJ, O’Shea KJ, Winch L, Siegmund SS, Gonzales MS, Randall S, Hertenstein D, Montague V, Woodberry A, Cassatt T, Lee BY. Using Simulation Modeling to Guide the Design of the Girl Scouts Fierce & Fit Program. Obesity (Silver Spring) 2020; 28:1317-1324. [PMID: 32378341 PMCID: PMC7311310 DOI: 10.1002/oby.22827] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 03/07/2020] [Accepted: 03/28/2020] [Indexed: 01/29/2023]
Abstract
OBJECTIVE The study aim was to help the Girl Scouts of Central Maryland evaluate, quantify, and potentially modify the Girl Scouts Fierce & Fit program. METHODS From 2018 to 2019, our Public Health Informatics, Computational, and Operations Research team developed a computational simulation model representing the 250 adolescent girls participating in the Fierce & Fit program and how their diets and physical activity affected their BMI and subsequent outcomes, including costs. RESULTS Changing the Fierce & Fit program from a 6-week program meeting twice a week, with 5 minutes of physical activity each session, to a 12-week program meeting twice a week with 30 minutes of physical activity saved an additional $84,828 ($80,130-$89,526) in lifetime direct medical costs, $81,365 ($76,528-$86,184) in lifetime productivity losses, and 7.85 (7.38-8.31) quality-adjusted life-years. The cost-benefit of implementing this program was $95,943. Based on these results, the Girl Scouts of Central Maryland then implemented these changes in the program. CONCLUSIONS This is an example of using computational modeling to help evaluate and revise the design of a program aimed at increasing physical activity among girls.
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Affiliation(s)
- Marie C. Ferguson
- PHICOR (Public Health Informatics, Computational and Operations Research), City University of New York Graduate School of Public Health and Health Policy, New York, New York, (formerly at Johns Hopkins University, Baltimore, MD)
| | - Matthew J. Morgan
- PHICOR (Public Health Informatics, Computational and Operations Research), City University of New York Graduate School of Public Health and Health Policy, New York, New York, (formerly at Johns Hopkins University, Baltimore, MD)
| | - Kelly J. O’Shea
- PHICOR (Public Health Informatics, Computational and Operations Research), City University of New York Graduate School of Public Health and Health Policy, New York, New York, (formerly at Johns Hopkins University, Baltimore, MD)
| | - Lucas Winch
- PHICOR (Public Health Informatics, Computational and Operations Research), City University of New York Graduate School of Public Health and Health Policy, New York, New York, (formerly at Johns Hopkins University, Baltimore, MD)
| | - Sheryl S. Siegmund
- PHICOR (Public Health Informatics, Computational and Operations Research), City University of New York Graduate School of Public Health and Health Policy, New York, New York, (formerly at Johns Hopkins University, Baltimore, MD)
| | - Mario Solano Gonzales
- PHICOR (Public Health Informatics, Computational and Operations Research), City University of New York Graduate School of Public Health and Health Policy, New York, New York, (formerly at Johns Hopkins University, Baltimore, MD)
| | - Samuel Randall
- PHICOR (Public Health Informatics, Computational and Operations Research), City University of New York Graduate School of Public Health and Health Policy, New York, New York, (formerly at Johns Hopkins University, Baltimore, MD)
| | - Daniel Hertenstein
- PHICOR (Public Health Informatics, Computational and Operations Research), City University of New York Graduate School of Public Health and Health Policy, New York, New York, (formerly at Johns Hopkins University, Baltimore, MD)
| | | | | | | | - Bruce Y. Lee
- PHICOR (Public Health Informatics, Computational and Operations Research), City University of New York Graduate School of Public Health and Health Policy, New York, New York, (formerly at Johns Hopkins University, Baltimore, MD)
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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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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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Lee BY, Wedlock PT, Mitgang EA, Cox SN, Haidari LA, Das MK, Dutta S, Kapuria B, Brown ST. How coping can hide larger systems problems: the routine immunisation supply chain in Bihar, India. BMJ Glob Health 2019; 4:e001609. [PMID: 31565408 PMCID: PMC6747917 DOI: 10.1136/bmjgh-2019-001609] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [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: 04/02/2019] [Revised: 08/06/2019] [Accepted: 08/10/2019] [Indexed: 01/01/2023] Open
Abstract
Introduction Coping occurs when health system personnel must make additional, often undocumented efforts to compensate for existing system and management deficiencies. While such efforts may be done with good intentions, few studies evaluate the broader impact of coping. Methods We developed a computational simulation model of Bihar, India’s routine immunisation supply chain where coping (ie, making additional vaccine shipments above stated policy) occurs. We simulated the impact of coping by allowing extra trips to occur as needed up to one time per day and then limiting coping to two times per week and three times per month before completely eliminating coping. Results Coping as needed resulted in 3754 extra vaccine shipments over stated policy resulting in 56% total vaccine availability and INR 2.52 logistics cost per dose administered. Limiting vaccine shipments to two times per week reduced shipments by 1224 trips, resulting in a 7% vaccine availability decrease to 49% and an 8% logistics cost per dose administered increase to INR 2.73. Limiting shipments to three times per month reduced vaccine shipments by 2635 trips, which decreased vaccine availability by 19% to 37% and increased logistics costs per dose administered by 34% to INR 3.38. Completely eliminating coping further reduced shipments by 1119 trips, decreasing total vaccine availability an additional 24% to 13% and increasing logistics cost per dose administered by 169% to INR 9.08. Conclusion Our results show how coping can hide major system design deficiencies and how restricting coping can improve problem diagnosis and potentially lead to enhanced system design.
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Affiliation(s)
- Bruce Y Lee
- Global Obesity Prevention Center (GOPC), Johns Hopkins University, Baltimore, Maryland, USA.,Public Health Informatics, Computational, and Operations Research (PHICOR), Baltimore, Maryland and New York City, New York, USA
| | - Patrick T Wedlock
- Global Obesity Prevention Center (GOPC), Johns Hopkins University, Baltimore, Maryland, USA.,Public Health Informatics, Computational, and Operations Research (PHICOR), Baltimore, Maryland and New York City, New York, USA
| | - Elizabeth A Mitgang
- Global Obesity Prevention Center (GOPC), Johns Hopkins University, Baltimore, Maryland, USA.,Public Health Informatics, Computational, and Operations Research (PHICOR), Baltimore, Maryland and New York City, New York, USA
| | - Sarah N Cox
- Global Obesity Prevention Center (GOPC), Johns Hopkins University, Baltimore, Maryland, USA.,Public Health Informatics, Computational, and Operations Research (PHICOR), Baltimore, Maryland and New York City, New York, USA
| | - Leila A Haidari
- Public Health Informatics, Computational, and Operations Research (PHICOR), Baltimore, Maryland and New York City, New York, USA.,HERMES Logistics Team, Pittsburgh, Pennsylvania and Baltimore, Maryland, USA
| | | | | | | | - Shawn T Brown
- HERMES Logistics Team, Pittsburgh, Pennsylvania and Baltimore, Maryland, USA.,McGill Center for Integrative Neuroscience, McGill University, Montreal, Quebec, Canada
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Ozawa S, Yemeke TT, Evans DR, Pallas SE, Wallace AS, Lee BY. Defining hard-to-reach populations for vaccination. Vaccine 2019; 37:5525-5534. [PMID: 31400910 PMCID: PMC10414189 DOI: 10.1016/j.vaccine.2019.06.081] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [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: 02/22/2019] [Revised: 06/24/2019] [Accepted: 06/25/2019] [Indexed: 12/29/2022]
Abstract
Extending the benefits of vaccination to everyone who is eligible requires an understanding of which populations current vaccination efforts have struggled to reach. A clear definition of "hard-to-reach" populations - also known as high-risk or marginalized populations, or reaching the last mile - is essential for estimating the size of target groups, sharing lessons learned based on consistent definitions, and allocating resources appropriately. A literature review was conducted to determine what formal definitions of hard-to-reach populations exist and how they are being used, and to propose definitions to consider for future use. Overall, we found that (1) there is a need to distinguish populations that are hard to reach versus hard to vaccinate, and (2) the existing literature poorly defined these populations and clear criteria or thresholds for classifying them were missing. Based on this review, we propose that hard-to-reach populations be defined as those facing supply-side barriers to vaccination due to geography by distance or terrain, transient or nomadic movement, healthcare provider discrimination, lack of healthcare provider recommendations, inadequate vaccination systems, war and conflict, home births or other home-bound mobility limitations, or legal restrictions. Although multiple mechanisms may apply to the same population, supply-side barriers should be distinguished from demand-side barriers. Hard-to-vaccinate populations are defined as those who are reachable but difficult to vaccinate due to distrust, religious beliefs, lack of awareness of vaccine benefits and recommendations, poverty or low socioeconomic status, lack of time to access available vaccination services, or gender-based discrimination. Further work is needed to better define hard-to-reach populations and delineate them from populations that may be hard to vaccinate due to complex refusal reasons, improve measurement of the size and importance of their impact, and examine interventions related to overcoming barriers for each mechanism. This will enable policy makers, governments, donors, and the vaccine community to better plan interventions and allocate necessary resources to remove existing barriers to vaccination.
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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
| | | | - Sarah E Pallas
- Global Immunization Division, U.S. Centers for Disease Control and Prevention (CDC), Atlanta, GA, USA
| | - Aaron S Wallace
- Global Immunization Division, U.S. Centers for Disease Control and Prevention (CDC), Atlanta, GA, USA
| | - Bruce Y Lee
- Public Health Computational and Operations Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Global Obesity Prevention Center (GOPC), Johns Hopkins University, Baltimore, MD, USA
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Ferguson MC, O'Shea KJ, Hammer LD, Hertenstein DL, Schwartz NJ, Winch LE, Siegmund SS, Lee BY. The Impact of Following Solid Food Feeding Guides on BMI Among Infants: A Simulation Study. Am J Prev Med 2019; 57:355-364. [PMID: 31353163 PMCID: PMC6871772 DOI: 10.1016/j.amepre.2019.04.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2018] [Revised: 04/08/2019] [Accepted: 04/09/2019] [Indexed: 11/29/2022]
Abstract
INTRODUCTION There are several recommendations advising caregivers when and how to introduce solid food to infants. These complementary feeding guides vary in terms of the recommendations for timing and portions. The objective of this study is to determine the impact of following different guidelines on weight trajectories of infants. METHODS In 2018, the study team developed a computational simulation model to capture feeding behaviors, activity levels, metabolism, and body size of infants from 6 months to 1 year. Daily food intake of virtual infants based on feeding recommendations translated to changes in body weight. Next, simulations tested the impact of the following complementary feeding recommendations that provided amount, type, and timing of foods: Children's Hospital of Philadelphia, Johns Hopkins Medicine, Enfamil, and Similac. RESULTS When virtual caregivers fed infants according to the four different guides, none of the simulated situations resulted in normal weight at 12 months when infants were also being breastfed along average observed patterns. Reducing breast milk portions in half while caregivers fed infants according to complementary feeding guidelines resulted in overweight BMIs between 9 and 11 months for Children's Hospital of Philadelphia, Johns Hopkins Medicine, and Enfamil guidelines. Cutting breast milk portions in half also led to infants reaching unhealthy underweight BMI percentiles between 7 and 11 months for female and male infants when caregivers followed Children's Hospital of Philadelphia, Johns Hopkins Medicine, and Similac guidelines. CONCLUSIONS This study identified situations in which infants could reach unhealthy weights, even while following complementary feeding guidelines, suggesting that current recommended portion sizes should be tightened.
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Affiliation(s)
- Marie C Ferguson
- Global Obesity Prevention Center (GOPC) at Johns Hopkins, Baltimore, Maryland; Public Health Computational and Operations Research (PHICOR), Baltimore, Maryland
| | - Kelly J O'Shea
- Global Obesity Prevention Center (GOPC) at Johns Hopkins, Baltimore, Maryland; Public Health Computational and Operations Research (PHICOR), Baltimore, Maryland
| | | | - Daniel L Hertenstein
- Global Obesity Prevention Center (GOPC) at Johns Hopkins, Baltimore, Maryland; Public Health Computational and Operations Research (PHICOR), Baltimore, Maryland
| | - Nathaniel J Schwartz
- Global Obesity Prevention Center (GOPC) at Johns Hopkins, Baltimore, Maryland; Public Health Computational and Operations Research (PHICOR), Baltimore, Maryland
| | - Lucas E Winch
- Global Obesity Prevention Center (GOPC) at Johns Hopkins, Baltimore, Maryland; Public Health Computational and Operations Research (PHICOR), Baltimore, Maryland
| | - Sheryl S Siegmund
- Global Obesity Prevention Center (GOPC) at Johns Hopkins, Baltimore, Maryland; Public Health Computational and Operations Research (PHICOR), Baltimore, Maryland
| | - Bruce Y Lee
- Global Obesity Prevention Center (GOPC) at Johns Hopkins, Baltimore, Maryland; Public Health Computational and Operations Research (PHICOR), Baltimore, Maryland.
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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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Kim WJ, Gupta V, Nishimura M, Makita H, Idolor L, Roa C, Loh LC, Ong CK, Wang JS, Boonsawat W, Gunasekera KD, Madegedara D, Kuo HP, Wang CH, Wang C, Yang T, Lin YX, Ko FWS, Hui DSC, Lan LTT, Vu QTT, Bhome AB, Ng A, Seo JB, Lee BY, Lee JS, Oh YM, Lee SD. Identification of chronic obstructive pulmonary disease subgroups in 13 Asian cities. Int J Tuberc Lung Dis 2019; 22:820-826. [PMID: 29914609 DOI: 10.5588/ijtld.17.0524] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Chronic obstructive pulmonary disease (COPD) is a heterogeneous condition that can differ in its clinical manifestation, structural changes and response to treatment. OBJECTIVE To identify subgroups of COPD with distinct phenotypes, evaluate the distribution of phenotypes in four related regions and calculate the 1-year change in lung function and quality of life according to subgroup. METHODS Using clinical characteristics, we performed factor analysis and hierarchical cluster analysis in a cohort of 1676 COPD patients from 13 Asian cities. We compared the 1-year change in forced expiratory volume in one second (FEV1), modified Medical Research Council dyspnoea scale score, St George's Respiratory Questionnaire (SGRQ) score and exacerbations according to subgroup derived from cluster analysis. RESULTS Factor analysis revealed that body mass index, Charlson comorbidity index, SGRQ total score and FEV1 were principal factors. Using these four factors, cluster analysis identified three distinct subgroups with differing disease severity and symptoms. Among the three subgroups, patients in subgroup 2 (severe disease and more symptoms) had the most frequent exacerbations, most rapid FEV1 decline and greatest decline in SGRQ total score. CONCLUSION Three subgroups with differing severities and symptoms were identified in Asian COPD subjects.
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Affiliation(s)
- W J Kim
- Department of Internal Medicine, Kangwon National University, Chuncheon, Korea
| | - V Gupta
- Department of Internal Medicine, Kangwon National University, Chuncheon, Korea, Adesh Institute of Medical Sciences and Research, Bathinda, India
| | - M Nishimura
- Division of Respiratory Medicine, Department of Internal Medicine, Hokkaido University Hospital, Sapporo, Japan
| | - H Makita
- Division of Respiratory Medicine, Department of Internal Medicine, Hokkaido University Hospital, Sapporo, Japan
| | - L Idolor
- Section of Respiratory Services and Physical Therapy and Rehabilitation Lung Center of the Philippines, Quezon City
| | - C Roa
- College of Medicine and Philippine General Hospital, University of the Philippines, Manila, The Philippines
| | - L-C Loh
- Department of Medicine, Penang Medical College, Penang, Malaysia
| | - C-K Ong
- Department of Medicine, Penang Medical College, Penang, Malaysia
| | - J-S Wang
- Taipei Medical University, Taipei, Taiwan
| | - W Boonsawat
- Department of Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - K D Gunasekera
- Central Chest Clinic, National Hospital of Sri Lanka, Colombo
| | - D Madegedara
- Respiratory Disease Treatment Unit, Teaching Hospital Kandy, Kandy, Sri Lanka
| | - H-P Kuo
- Department of Thoracic Medicine, Chang Gung Memorial Hospital, Taipei, Taiwan
| | - C-H Wang
- Department of Thoracic Medicine, Chang Gung Memorial Hospital, Taipei, Taiwan
| | - C Wang
- Department of Respiratory and Critical Care Medicine, Beijing China-Japan Friendship Hospital, Beijing
| | - T Yang
- Department of Respiratory and Critical Care Medicine, Beijing China-Japan Friendship Hospital, Beijing
| | - Y-X Lin
- Beijing Institute of Respiratory Medicine, Department of Respiratory and Critical Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing
| | - F W S Ko
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - D S C Hui
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - L T T Lan
- Respiratory Care Center, University Medical Center, Ho Chi Minh City, Viet Nam
| | - Q T T Vu
- Respiratory Care Center, University Medical Center, Ho Chi Minh City, Viet Nam
| | - A B Bhome
- Indian Coalition of Obstructive Lung Diseases Network, Pune, Maharashtra, India
| | - A Ng
- Department of Respiratory and Critical Care Medicine, Tan Tock Seng Hospital, Singapore
| | - J B Seo
- Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul
| | - B Y Lee
- Division of Allergy and Respiratory Diseases, Soon Chun Hyang University Hospital, Seoul
| | - J S Lee
- Department of Pulmonary and Critical Care Medicine and Clinical Research Center for Chronic Obstructive Airway Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Y-M Oh
- Department of Pulmonary and Critical Care Medicine and Clinical Research Center for Chronic Obstructive Airway Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - S-D Lee
- Department of Pulmonary and Critical Care Medicine and Clinical Research Center for Chronic Obstructive Airway Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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Matza LS, Kim KJ, Yu H, Belden KA, Chen AF, Kurd M, Lee BY, Webb J. Health state utilities associated with post-surgical Staphylococcus aureus infections. Eur J Health Econ 2019; 20:819-827. [PMID: 30887157 PMCID: PMC6652168 DOI: 10.1007/s10198-019-01036-3] [Citation(s) in RCA: 15] [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] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Accepted: 02/08/2019] [Indexed: 05/12/2023]
Abstract
INTRODUCTION Surgical site infections (SSIs) are among the most common and potentially serious complications after surgery. Staphylococcus aureus is a virulent pathogen frequently identified as a cause of SSI. As vaccines and other infection control measures are developed to reduce SSI risk, cost-utility analyses (CUA) of these interventions are needed to inform resource allocation decisions. A recent systematic review found that available SSI utilities are of "questionable quality." Therefore, the purpose of this study was to estimate the disutility (i.e., utility decrease) associated with SSIs. METHODS In time trade-off interviews, general population participants in the UK (London, Edinburgh) valued health states drafted based on literature and clinician interviews. Health states described either joint or spine surgery, with or without an SSI. The utility difference between otherwise identical health states with and without the SSI represented the disutility associated with the SSI. RESULTS A total of 201 participants completed interviews (50.2% female; mean age = 46.2 years). Mean (SD) utilities of health states describing joint and spine surgery without infections were 0.79 (0.23) and 0.78 (0.23). Disutilities of SSIs ranged from - 0.03 to - 0.32, depending on severity of the infection and subsequent medical interventions. All differences between corresponding health with and without SSIs were statistically significant (all p < 0.001). CONCLUSION The preference-based SSI disutilities derived in this study may be used to represent mild and serious SSIs in CUAs assessing and comparing the value of vaccinations that may reduce the risk of SSIs.
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Affiliation(s)
- Louis S. Matza
- Evidera, 7101 Wisconsin Avenue, Suite 1400, Bethesda, MD 20814 USA
| | - Katherine J. Kim
- Evidera, 7101 Wisconsin Avenue, Suite 1400, Bethesda, MD 20814 USA
| | - Holly Yu
- Pfizer Inc, Collegeville, PA USA
| | - Katherine A. Belden
- Sydney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA USA
| | - Antonia F. Chen
- Department of Orthopaedics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
| | - Mark Kurd
- Department of Orthopedic Surgery Sidney Kimmel Medical College, Thomas Jefferson University The Rothman Institute, Philadelphia, PA USA
| | - Bruce Y. Lee
- Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD USA
| | - Jason Webb
- Avon Orthopaedic Centre, Southmead Hospital, Bristol, UK
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50
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Young IS, Ebbeling C, Selvin E, Lee BY. Obesity-Addressing a Challenge for Public Health and Laboratory Medicine. Clin Chem 2019; 64:1-3. [PMID: 29295831 DOI: 10.1373/clinchem.2017.284000] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Accepted: 11/06/2017] [Indexed: 11/06/2022]
Affiliation(s)
- Ian S Young
- Centre for Public Health, Queen's University Belfast, UK;
| | - Cara Ebbeling
- New Balance Foundation Obesity Prevention Center, Boston Children's Hosptial, Boston, MA
| | - Elizabeth Selvin
- Department of Epidemiology and the Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Bruce Y Lee
- Global Obesity Prevention Center (GOPC), Johns Hopkins, Baltimore, MD
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