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Martinez MF, Weatherwax C, Piercy K, Whitley MA, Bartsch SM, Heneghan J, Fox M, Bowers MT, Chin KL, Velmurugan K, Dibbs A, Smith AL, Pfeiffer KA, Farrey T, Tsintsifas A, Scannell SA, Lee BY. Benefits of Meeting the Healthy People 2030 Youth Sports Participation Target. Am J Prev Med 2024; 66:760-769. [PMID: 38416089 PMCID: PMC11034834 DOI: 10.1016/j.amepre.2023.12.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 12/18/2023] [Accepted: 12/18/2023] [Indexed: 02/29/2024]
Abstract
INTRODUCTION Healthy People 2030, a U.S. government health initiative, has indicated that increasing youth sports participation to 63.3% is a priority in the U.S. This study quantified the health and economic value of achieving this target. METHODS An agent-based model developed in 2023 represents each person aged 6-17 years in the U.S. On each simulated day, agents can participate in sports that affect their metabolic and mental health in the model. Each agent can develop different physical and mental health outcomes, associated with direct and indirect costs. RESULTS Increasing the proportion of youth participating in sports from the most recent participation levels (50.7%) to the Healthy People 2030 target (63.3%) could reduce overweight/obesity prevalence by 3.37% (95% CI=3.35%, 3.39%), resulting in 1.71 million fewer cases of overweight/obesity (95% CI=1.64, 1.77 million). This could avert 352,000 (95% CI=336,200, 367,500) cases of weight-related diseases and gain 1.86 million (95% CI=1.86, 1.87 million) quality-adjusted life years, saving $22.55 billion (95% CI=$22.46, $22.63 billion) in direct medical costs and $25.43 billion (95% CI= $25.25, $25.61 billion) in productivity losses. This would also reduce depression/anxiety symptoms, saving $3.61 billion (95% CI=$3.58, $3.63 billion) in direct medical costs and $28.38 billion (95% CI=$28.20, $28.56 billion) in productivity losses. CONCLUSIONS This study shows that achieving the Healthy People 2030 objective could save third-party payers, businesses, and society billions of dollars for each cohort of persons aged 6-17 years, savings that would continue to repeat with each new cohort. This suggests that even if a substantial amount is invested toward this objective, such investments could pay for themselves.
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Affiliation(s)
- Marie F Martinez
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York City, New York; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Colleen Weatherwax
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York City, New York; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Katrina Piercy
- Office of Disease Prevention and Health Promotion, U.S. Department of Health and Human Services (HHS), Rockville, Maryland
| | - Meredith A Whitley
- Ruth S. Ammon College of Education and Health Sciences, Adelphi University, Garden City, New York; Stellenbosch University, Maties Sport, Centre for Sport Leadership, Stellenbosch, South Africa
| | - Sarah M Bartsch
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York City, New York; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Jessie Heneghan
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York City, New York; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Martin Fox
- Project Play, Sports & Society Program, The Aspen Institute, Washington, District of Columbia
| | - Matthew T Bowers
- Department of Kinesiology and Health Education, College of Education, The University of Texas at Austin, Austin, Texas
| | - Kevin L Chin
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York City, New York; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Kavya Velmurugan
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York City, New York; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Alexis Dibbs
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York City, New York; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Alan L Smith
- Emma Eccles Jones College of Education and Human Services, Utah State University, Logan, Utah
| | - Karin A Pfeiffer
- Department of Kinesiology, College of Education, Michigan State University, East Lansing, Michigan
| | - Tom Farrey
- Project Play, Sports & Society Program, The Aspen Institute, Washington, District of Columbia
| | - Alexandra Tsintsifas
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York City, New York; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Sheryl A Scannell
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York City, New York; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Bruce Y Lee
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York City, New York; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York.
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2
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Bartsch SM, O'Shea KJ, Weatherwax C, Strych U, Velmurugan K, John DC, Bottazzi ME, Hussein M, Martinez MF, Chin KL, Ciciriello A, Heneghan J, Dibbs A, Scannell SA, Hotez PJ, Lee BY. What is the economic benefit of annual COVID-19 vaccination from the adult individual perspective? J Infect Dis 2024:jiae179. [PMID: 38581432 DOI: 10.1093/infdis/jiae179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 03/20/2024] [Accepted: 04/03/2024] [Indexed: 04/08/2024] Open
Abstract
BACKGROUND With COVID-19 vaccination no longer mandated by many businesses/organizations, it is now up to individuals to decide whether to get any new boosters/updated vaccines going forward. METHODS We developed a Markov model representing the potential clinical/economic outcomes from an individual perspective in the United States of getting versus not getting an annual COVID-19 vaccine. RESULTS For an 18-49-year-old, getting vaccinated at its current price ($60) can save the individual on average $30-$603 if the individual is uninsured and $4-$437 if the individual has private insurance, as long as the starting vaccine efficacy against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is ≥50% and the weekly risk of getting infected is ≥0.2%, corresponding to an individual interacting with 9 other people in a day under Winter 2023-2024 Omicron SARS-CoV-2 variant conditions with an average infection prevalence of 10%. For a 50-64-year-old, these cost-savings increase to $111-$1,278 and $119-$1,706, for someone without and with insurance, respectively. The risk threshold increases to ≥0.4% (interacting with 19 people/day), when the individual has 13.4% pre-existing protection against infection (e.g., vaccinated 9 months earlier). CONCLUSION There is both clinical and economic incentive for the individual to continue to get vaccinated against COVID-19 each year.
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Affiliation(s)
- Sarah M Bartsch
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Pandemic Response Institute, New York City, NY, USA
| | - Kelly J O'Shea
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Pandemic Response Institute, New York City, NY, USA
| | - Colleen Weatherwax
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Pandemic Response Institute, New York City, NY, USA
| | - Ulrich Strych
- National School of Tropical Medicine, Department of Pediatrics, and Texas Children's Hospital Center for Vaccine Development, Baylor College of Medicine, Houston, TX, USA
| | - Kavya Velmurugan
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Pandemic Response Institute, New York City, NY, USA
| | - Danielle C John
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Pandemic Response Institute, New York City, NY, USA
| | - Maria Elena Bottazzi
- National School of Tropical Medicine, Department of Pediatrics, and Texas Children's Hospital Center for Vaccine Development, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA
| | - Mustafa Hussein
- CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Marie F Martinez
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Pandemic Response Institute, New York City, NY, USA
| | - Kevin L Chin
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Pandemic Response Institute, New York City, NY, USA
| | - Allan Ciciriello
- National School of Tropical Medicine, Department of Pediatrics, and Texas Children's Hospital Center for Vaccine Development, Baylor College of Medicine, Houston, TX, USA
| | - Jessie Heneghan
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Pandemic Response Institute, New York City, NY, USA
| | - Alexis Dibbs
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Pandemic Response Institute, New York City, NY, USA
| | - Sheryl A Scannell
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Pandemic Response Institute, New York City, NY, USA
| | - Peter J Hotez
- National School of Tropical Medicine, Department of Pediatrics, and Texas Children's Hospital Center for Vaccine Development, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA
| | - Bruce Y Lee
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Pandemic Response Institute, New York City, NY, USA
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3
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Bartsch SM, Weatherwax C, Wasserman MR, Chin KL, Martinez MF, Velmurugan K, Singh RD, John DC, Heneghan JL, Gussin GM, Scannell SA, Tsintsifas AC, O'Shea KJ, Dibbs AM, Leff B, Huang SS, Lee BY. How the Timing of Annual COVID-19 Vaccination of Nursing Home Residents and Staff Affects Its Value. J Am Med Dir Assoc 2024; 25:639-646.e5. [PMID: 38432644 PMCID: PMC10990766 DOI: 10.1016/j.jamda.2024.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 02/07/2024] [Accepted: 02/08/2024] [Indexed: 03/05/2024]
Abstract
OBJECTIVES To evaluate the epidemiologic, clinical, and economic value of an annual nursing home (NH) COVID-19 vaccine campaign and the impact of when vaccination starts. DESIGN Agent-based model representing a typical NH. SETTING AND PARTICIPANTS NH residents and staff. METHODS We used the model representing an NH with 100 residents, its staff, their interactions, COVID-19 spread, and its health and economic outcomes to evaluate the epidemiologic, clinical, and economic value of varying schedules of annual COVID-19 vaccine campaigns. RESULTS Across a range of scenarios with a 60% vaccine efficacy that wanes starting 4 months after protection onset, vaccination was cost saving or cost-effective when initiated in the late summer or early fall. Annual vaccination averted 102 to 105 COVID-19 cases when 30-day vaccination campaigns began between July and October (varying with vaccination start), decreasing to 97 and 85 cases when starting in November and December, respectively. Starting vaccination between July and December saved $3340 to $4363 and $64,375 to $77,548 from the Centers for Medicare & Medicaid Services and societal perspectives, respectively (varying with vaccination start). Vaccination's value did not change when varying the COVID-19 peak between December and February. The ideal vaccine campaign timing was not affected by reducing COVID-19 levels in the community, or varying transmission probability, preexisting immunity, or COVID-19 severity. However, if vaccine efficacy wanes more quickly (over 1 month), earlier vaccination in July resulted in more cases compared with vaccinating later in October. CONCLUSIONS AND IMPLICATIONS Annual vaccination of NH staff and residents averted the most cases when initiated in the late summer through early fall, at least 2 months before the COVID-19 winter peak but remained cost saving or cost-effective when it starts in the same month as the peak. This supports tethering COVID vaccination to seasonal influenza campaigns (typically in September-October) for providing protection against SARS-CoV-2 winter surges in NHs.
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Affiliation(s)
- Sarah M Bartsch
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Colleen Weatherwax
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | | | - Kevin L Chin
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Marie F Martinez
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Kavya Velmurugan
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Raveena D Singh
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine, CA, USA
| | - Danielle C John
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Pandemic Response Institute, New York City, NY, USA
| | - Jessie L Heneghan
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Gabrielle M Gussin
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine, CA, USA
| | - Sheryl A Scannell
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Alexandra C Tsintsifas
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Kelly J O'Shea
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Alexis M Dibbs
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Bruce Leff
- Division of Geriatric Medicine, Center for Transformative Geriatric Research, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Susan S Huang
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine, CA, USA
| | - Bruce Y Lee
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Pandemic Response Institute, New York City, NY, USA.
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4
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Powell-Wiley TM, Martinez MF, Heneghan J, Weatherwax C, Osei Baah F, Velmurugan K, Chin KL, Ayers C, Cintron MA, Ortiz-Whittingham LR, Sandler D, Sharda S, Whitley M, Bartsch SM, O’Shea KJ, Tsintsifas A, Dibbs A, Scannell SA, Lee BY. Health and Economic Value of Eliminating Socioeconomic Disparities in US Youth Physical Activity. JAMA Health Forum 2024; 5:e240088. [PMID: 38488779 PMCID: PMC10943408 DOI: 10.1001/jamahealthforum.2024.0088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 01/08/2024] [Indexed: 03/18/2024] Open
Abstract
Importance There are considerable socioeconomic status (SES) disparities in youth physical activity (PA) levels. For example, studies show that lower-SES youth are less active, have lower participation in organized sports and physical education classes, and have more limited access to PA equipment. Objective To determine the potential public health and economic effects of eliminating disparities in PA levels among US youth SES groups. Design and Setting An agent-based model representing all 6- to 17-year-old children in the US was used to simulate the epidemiological, clinical, and economic effects of disparities in PA levels among different SES groups and the effect of reducing these disparities. Main Outcomes and Measures Anthropometric measures (eg, body mass index) and the presence and severity of risk factors associated with weight (stroke, coronary heart disease, type 2 diabetes, or cancer), as well as direct and indirect cost savings. Results This model, representing all 50 million US children and adolescents 6 to 17 years old, found that if the US eliminates the disparity in youth PA levels across SES groups, absolute overweight and obesity prevalence would decrease by 0.826% (95% CI, 0.821%-0.832%), resulting in approximately 383 000 (95% CI, 368 000-399 000) fewer cases of overweight and obesity and 101 000 (95% CI, 98 000-105 000) fewer cases of weight-related diseases (stroke and coronary heart disease events, type 2 diabetes, or cancer). This would result in more than $15.60 (95% CI, $15.01-$16.10) billion in cost savings over the youth cohort's lifetime. There are meaningful benefits even when reducing the disparity by just 25%, which would result in $1.85 (95% CI, $1.70-$2.00) billion in direct medical costs averted and $2.48 (95% CI, $2.04-$2.92) billion in productivity losses averted. For every 1% in disparity reduction, total productivity losses would decrease by about $83.8 million, and total direct medical costs would decrease by about $68.7 million. Conclusions and Relevance This study quantified the potential savings from eliminating or reducing PA disparities, which can help policymakers, health care systems, schools, funders, sports organizations, and other businesses better prioritize investments toward addressing these disparities.
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Affiliation(s)
- Tiffany M. Powell-Wiley
- Social Determinants of Obesity and Cardiovascular Risk Laboratory, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
- Intramural Research Program, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland
| | - Marie F. Martinez
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, New York, New York
| | - Jessie Heneghan
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, New York, New York
| | - Colleen Weatherwax
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, New York, New York
| | - Foster Osei Baah
- Social Determinants of Obesity and Cardiovascular Risk Laboratory, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, Georgia
| | - Kavya Velmurugan
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, New York, New York
| | - Kevin L. Chin
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, New York, New York
| | - Colby Ayers
- Social Determinants of Obesity and Cardiovascular Risk Laboratory, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
- Intramural Research Program, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland
| | - Manuel A. Cintron
- Social Determinants of Obesity and Cardiovascular Risk Laboratory, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
- Intramural Research Program, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland
| | - Lola R. Ortiz-Whittingham
- Social Determinants of Obesity and Cardiovascular Risk Laboratory, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
- Intramural Research Program, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland
| | - Dana Sandler
- Social Determinants of Obesity and Cardiovascular Risk Laboratory, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
- Intramural Research Program, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland
| | - Sonal Sharda
- Social Determinants of Obesity and Cardiovascular Risk Laboratory, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
- Intramural Research Program, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland
| | - Meredith Whitley
- Ruth S. Ammon College of Education and Health Sciences, Adelphi University, Garden City, New York
- Maties Sport, Centre for Sport Leadership, Stellenbosch University, Stellenbosch, South Africa
| | - Sarah M. Bartsch
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, New York, New York
| | - Kelly J. O’Shea
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, New York, New York
| | - Alexandra Tsintsifas
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, New York, New York
| | - Alexis Dibbs
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, New York, New York
| | - Sheryl A. Scannell
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, New York, New York
| | - Bruce Y. Lee
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, New York, New York
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5
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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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Galera-Laporta L, Kikuchi K, Weatherwax C, Lam JY, Chae Moon E, Theodorakis EA, Garcia-Ojalvo J, Suel GM. Electrochemical potential enables dormant spores to integrate environmental signals. Biophys J 2023; 122:538a. [PMID: 36784785 DOI: 10.1016/j.bpj.2022.11.2850] [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: 02/12/2023] Open
Affiliation(s)
| | - Kaito Kikuchi
- University of California San Diego, La Jolla, CA, USA
| | | | - Jamie Y Lam
- University of California San Diego, La Jolla, CA, USA
| | - Eun Chae Moon
- University of California San Diego, La Jolla, CA, USA
| | | | | | - Gurol M Suel
- University of California San Diego, La Jolla, CA, USA
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7
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Kikuchi K, Galera-Laporta L, Weatherwax C, Lam JY, Moon EC, Theodorakis EA, Garcia-Ojalvo J, Süel GM. Electrochemical potential enables dormant spores to integrate environmental signals. Science 2022; 378:43-49. [PMID: 36201591 PMCID: PMC10593254 DOI: 10.1126/science.abl7484] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
The dormant state of bacterial spores is generally thought to be devoid of biological activity. We show that despite continued dormancy, spores can integrate environmental signals over time through a preexisting electrochemical potential. Specifically, we studied thousands of individual Bacillus subtilis spores that remain dormant when exposed to transient nutrient pulses. Guided by a mathematical model of bacterial electrophysiology, we modulated the decision to exit dormancy by genetically and chemically targeting potassium ion flux. We confirmed that short nutrient pulses result in step-like changes in the electrochemical potential of persistent spores. During dormancy, spores thus gradually release their stored electrochemical potential to integrate extracellular information over time. These findings reveal a decision-making mechanism that operates in physiologically inactive cells.
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Affiliation(s)
- Kaito Kikuchi
- Molecular Biology Section, Division of Biological Sciences, University of California San Diego; La Jolla, CA 92093, USA
| | - Leticia Galera-Laporta
- Molecular Biology Section, Division of Biological Sciences, University of California San Diego; La Jolla, CA 92093, USA
| | - Colleen Weatherwax
- Molecular Biology Section, Division of Biological Sciences, University of California San Diego; La Jolla, CA 92093, USA
| | - Jamie Y Lam
- Department of Chemistry and Biochemistry, University of California San Diego; La Jolla, CA 92093, USA
| | - Eun Chae Moon
- Molecular Biology Section, Division of Biological Sciences, University of California San Diego; La Jolla, CA 92093, USA
| | - Emmanuel A Theodorakis
- Department of Chemistry and Biochemistry, University of California San Diego; La Jolla, CA 92093, USA
| | - Jordi Garcia-Ojalvo
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra; 08003 Barcelona, Spain
- Senior author
| | - Gürol M Süel
- Molecular Biology Section, Division of Biological Sciences, University of California San Diego; La Jolla, CA 92093, USA
- San Diego Center for Systems Biology, University of California San Diego; La Jolla, CA 92093-0380, USA
- Center for Microbiome Innovation, University of California San Diego; La Jolla, CA 92093-0380, USA
- Senior author
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8
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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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9
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Yang CY, Bialecka-Fornal M, Weatherwax C, Larkin JW, Prindle A, Liu J, Garcia-Ojalvo J, Süel GM. Encoding Membrane-Potential-Based Memory within a Microbial Community. Cell Syst 2020; 10:417-423.e3. [PMID: 32343961 PMCID: PMC7286314 DOI: 10.1016/j.cels.2020.04.002] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [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: 12/16/2019] [Revised: 02/13/2020] [Accepted: 04/02/2020] [Indexed: 12/29/2022]
Abstract
Cellular membrane potential plays a key role in the formation and retrieval of memories in the metazoan brain, but it remains unclear whether such memory can also be encoded in simpler organisms like bacteria. Here, we show that single-cell-level memory patterns can be imprinted in bacterial biofilms by light-induced changes in the membrane potential. We demonstrate that transient optical perturbations generate a persistent and robust potassium-channel-mediated change in the membrane potential of bacteria within the biofilm. The light-exposed cells respond in an anti-phase manner, relative to unexposed cells, to both natural and induced oscillations in extracellular ion concentrations. This anti-phase response, which persists for hours following the transient optical stimulus, enables a direct single-cell resolution visualization of spatial memory patterns within the biofilm. The ability to encode robust and persistent membrane-potential-based memory patterns could enable computations within prokaryotic communities and suggests a parallel between neurons and bacteria.
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Affiliation(s)
- Chih-Yu Yang
- Division of Biological Sciences, University of California, San Diego, Pacific Hall Room 2225B, Mail Code 0347, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Maja Bialecka-Fornal
- Division of Biological Sciences, University of California, San Diego, Pacific Hall Room 2225B, Mail Code 0347, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Colleen Weatherwax
- Division of Biological Sciences, University of California, San Diego, Pacific Hall Room 2225B, Mail Code 0347, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Joseph W Larkin
- Division of Biological Sciences, University of California, San Diego, Pacific Hall Room 2225B, Mail Code 0347, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Arthur Prindle
- Division of Biological Sciences, University of California, San Diego, Pacific Hall Room 2225B, Mail Code 0347, 9500 Gilman Drive, La Jolla, CA 92093, USA; Department of Biochemistry and Molecular Genetics, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Jintao Liu
- Division of Biological Sciences, University of California, San Diego, Pacific Hall Room 2225B, Mail Code 0347, 9500 Gilman Drive, La Jolla, CA 92093, USA; Center for Infectious Diseases Research and Tsinghua-Peking Center for Life Sciences, School of Medicine, Tsinghua University, Beijing 100084, People's Republic of China
| | - Jordi Garcia-Ojalvo
- Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona Biomedical Research Park, Barcelona 08003, Spain
| | - Gürol M Süel
- Division of Biological Sciences, University of California, San Diego, Pacific Hall Room 2225B, Mail Code 0347, 9500 Gilman Drive, La Jolla, CA 92093, USA; San Diego Center for Systems Biology, University of California, San Diego, La Jolla, CA 92093, USA; Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093-0380, USA.
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10
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Yang CY, Bialecka-Fornal M, Weatherwax C, Larkin J, Prindle A, Liu J, Garcia-Ojalvo J, Suel GM. Encoding Spatial Memory within a Bacterial Biofilm Community. Biophys J 2020. [DOI: 10.1016/j.bpj.2019.11.3295] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
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11
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Eisenstein E, Li J, Walsh ST, Bera A, Bakalarz J, Weatherwax C, Pierce B, Parsons J. Elementary Steps in Plant‐Pathogen Recognition Revealed by Enhancing the Stability and Structure of Domains of Plant Innate Immune Receptors. FASEB J 2019. [DOI: 10.1096/fasebj.2019.33.1_supplement.779.8] [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: 11/11/2022]
Affiliation(s)
- Edward Eisenstein
- IBBR, BioengineeringUniversity of MarylandRockvilleMD
- Fischell Department of BioengineeringUniversity of MarylandRockvilleMD
| | - Jia Li
- IBBR, BioengineeringUniversity of MarylandRockvilleMD
| | | | - Asim Bera
- IBBR, BioengineeringUniversity of MarylandRockvilleMD
| | | | | | - Brian Pierce
- IBBR, BioengineeringUniversity of MarylandRockvilleMD
| | - James Parsons
- IBBR, BioengineeringUniversity of MarylandRockvilleMD
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