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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; 45:754-761. [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] [MESH Headings] [Grants] [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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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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3
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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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4
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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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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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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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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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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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Fernández-Caballero M, Martinez MF, Oristrell G, Palmer N, Santamaría A. Off-pump technique and replacement therapy for coronary artery bypass surgery in a patient with hemophilia B. J Thromb Thrombolysis 2019; 48:299-302. [PMID: 31152365 DOI: 10.1007/s11239-019-01888-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Antithrombotic treatment and perioperative management in patients with hemophilia remains a challenge. As life expectancy in these patients is increasing, a concern about cardiovascular diseases is emerging. Herein we present the case of a 68 year-old patient with mild hemophilia B and multivessel coronary disease who underwent coronary artery bypass grafting (CABG) surgery. Off-pump surgery with continuous infusion FIX treatment was performed successfully with stable factor IX levels, and no bleeding or thrombotic complications. There is a paucity of cases reported regarding management of CABG in this population. To our knowledge, this is the first patient with mild hemophilia B that underwent CABG surgery with off-pump technique, that seems to be a secure and effective procedure.
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Affiliation(s)
- M Fernández-Caballero
- Hemophilia and Thrombosis Unit, Department of Hematology, Vall d'Hebron University Hospital, Barcelona, Spain.
| | - M F Martinez
- Hemophilia and Thrombosis Unit, Department of Hematology, Vall d'Hebron University Hospital, Barcelona, Spain
| | - G Oristrell
- Department of Cardiology, Vall d'Hebron University Hospital, Barcelona, Spain
| | - N Palmer
- Department of Cardiac Surgery, Vall d'Hebron University Hospital, Barcelona, Spain
| | - A Santamaría
- Hemophilia and Thrombosis Unit, Department of Hematology, Vall d'Hebron University Hospital, Barcelona, Spain
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Arelovich HM, Lagrange S, Torre R, Martinez MF, Laborde HE. Feeding value of whole raw soya beans as a protein supplement for beef cattle consuming low-quality forages. J Anim Physiol Anim Nutr (Berl) 2018; 102:e421-e430. [PMID: 28608536 DOI: 10.1111/jpn.12761] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2017] [Accepted: 05/04/2017] [Indexed: 11/28/2022]
Abstract
Experiments (Exp) I and II were conducted to compare raw whole soya beans (WSB), roasted (rWSB) or other protein sources as supplements of low-quality forages fed ad libitum to beef cattle, upon DM intake (DMI), ruminal and blood parameters, and animal performance. Exp I: treatments for wheat straw fed to four ruminally cannulated steers were (i) Control-WS: no supplement; (ii) WSB-WS: whole soya beans; (iii) rWSB-WS: roasted WSB; and (iv) SBM-WS: soybean meal-wheat midds mixture; all fed at 1.4 kg DM/day. Exp II: 12 steers grazed deferred grain sorghum (DS) receiving these treatments: (i) Control-DS: no supplement; (ii) WSB-DS: 1.26 kg DM/day whole soya beans; and (iii) SFM-DS: 1.35 kg DM/day of sunflower meal. In Exp I, WS DMI resulted 47, 52 and 41% greater for WSB-WS, rWSB-WS and SBM-WS, respectively, than Control-WS (p < .05). In Exp II, the DMI of DS was unaffected by supplementation; a substitution of DS by supplement was found for WSB-DS (p < .05); however, total diet and digestible DMI increased with supplementation (p < .05). Rumen pH in Exp I remained unaffected by supplementation, but N-NH3 as well as blood urea-N in Exp II increased (p < .05). In Exp II, average daily weight gains improved similarly with both supplements compared with Control-DS. Additionally, feed-to-gain ratio decreased (p < .05), being lower for WSB-DS (8.3) vs. SFM-DS (9.9). Roasting effects of WSB as a supplement for low-quality forages were not detected, and all protein sources increased total diet DMI and forage utilization. Only moderate cattle weight gains could be expected for unsupplemented DS.
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Affiliation(s)
- H M Arelovich
- Departamento de Agronomía, Universidad Nacional del Sur, Bahía Blanca, Argentina
- Centro de Recursos Naturales Renovables de la Zona Semiárida, CERZOS - CONICET, Bahía Blanca, Argentina
- Comisión de Investigaciones Científicas de la Provincia de Buenos Aires - CIC, Bahía Blanca, Argentina
| | - S Lagrange
- Instituto Nacional de Tecnología Agropecuaria INTA EEA Bordenave, Bordenave, Argentina
| | - R Torre
- Instituto Nacional de Tecnología Agropecuaria INTA EEA Bordenave, Bordenave, Argentina
| | - M F Martinez
- Departamento de Agronomía, Universidad Nacional del Sur, Bahía Blanca, Argentina
| | - H E Laborde
- Departamento de Agronomía, Universidad Nacional del Sur, Bahía Blanca, Argentina
- Centro de Recursos Naturales Renovables de la Zona Semiárida, CERZOS - CONICET, Bahía Blanca, Argentina
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McRae KM, Schultz M, Mackintosh CG, Shackell GH, Martinez MF, Knowler KJ, Williams M, Ho C, Elmes SN, McEwan JC. Ovine rumen papillae biopsy via oral endoscopy; a rapid and repeatable method for serial sampling. N Z Vet J 2016; 64:174-8. [PMID: 26642120 PMCID: PMC4867882 DOI: 10.1080/00480169.2015.1121845] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
AIMS: To explore and validate the utility of rumen endoscopy for collection of rumen papillae for gene expression measurement. METHODS: Four adult Coopworth ewes were fasted for either 4 or 24 hours. Animals were sedated, placed in a dorsally recumbent position at 45 degrees with the head upright, and an endoscope inserted via a tube inserted into the mouth. Biopsies of rumen papillae were taken from the ventral surface of the rumen atrium under visual guidance. Two biopsies were collected from one of the animals that had been fasted for 4 hours, and three from one of the animals that had been fasted for 24 hours. Video of the rumen atrium and reticulum was also collected. The animals recovered uneventfully. Biopsies were subsequently used for extraction and sequencing of mRNA. RESULTS: The ventral surface of the rumen atrium was accessible after 4 hours off pasture, but a larger region was accessible after 24 hours of fasting. Sedation allowed access for endoscope use for around 5 to 10 minutes after which increased saliva flow was noted. Rumen papillae biopsies were easily collected, with samples from a variety of sites collected in the ∼10 minute time window. High quality RNA was obtained for stranded mRNA sequencing. Of the resulting reads, 69–70% mapped uniquely to version 3.1 of the ovine genome, and 48–49% to a known gene. The rumen mRNA profiles were consistent with a previously reported study. CONCLUSIONS: This method for obtaining rumenal tissue was found to be rapid and resulted in no apparent short or long term effects on the animal. High quality RNA was successfully extracted and amplified from the rumen papillae biopsies, indicating that this technique could be used for future gene expression studies. The use of rumen endoscopy could be extended to collection of a variety of rumen and reticulum anatomical measurements and deposition and retrieval of small sensors from the rumen. Rumen endoscopy offers an attractive and cost effective approach to repeated rumen biopsies compared with serial slaughter or use of cannulated animals.
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Affiliation(s)
- K M McRae
- a AgResearch , Invermay Agricultural Centre , Private Bag 50034, Mosgiel , 9053 , New Zealand
| | - M Schultz
- b Dunedin School of Medicine, Department of Medicine , University of Otago , PO Box 56, Dunedin , 9054 , New Zealand
| | - C G Mackintosh
- a AgResearch , Invermay Agricultural Centre , Private Bag 50034, Mosgiel , 9053 , New Zealand
| | - G H Shackell
- a AgResearch , Invermay Agricultural Centre , Private Bag 50034, Mosgiel , 9053 , New Zealand
| | - M F Martinez
- a AgResearch , Invermay Agricultural Centre , Private Bag 50034, Mosgiel , 9053 , New Zealand
| | - K J Knowler
- a AgResearch , Invermay Agricultural Centre , Private Bag 50034, Mosgiel , 9053 , New Zealand
| | - M Williams
- b Dunedin School of Medicine, Department of Medicine , University of Otago , PO Box 56, Dunedin , 9054 , New Zealand
| | - C Ho
- b Dunedin School of Medicine, Department of Medicine , University of Otago , PO Box 56, Dunedin , 9054 , New Zealand
| | - S N Elmes
- a AgResearch , Invermay Agricultural Centre , Private Bag 50034, Mosgiel , 9053 , New Zealand
| | - J C McEwan
- a AgResearch , Invermay Agricultural Centre , Private Bag 50034, Mosgiel , 9053 , New Zealand
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Martinez MF, Tutt D, Quirke LD, Tattersfield G, Juengel JL. Development of a GnRH-PGF2α-progesterone-based synchronization protocol with eCG for inducing single and double ovulations in beef cattle. J Anim Sci 2015; 92:4935-48. [PMID: 25349343 DOI: 10.2527/jas.2013-7512] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Experiments were designed to investigate the effect of different doses and timing of an eCG treatment given during GnRH-based synchronization protocols on follicular dynamics and fertility in cattle. In Exp. 1, Angus heifers (n = 50) received a 7-d Ovsynch + progesterone protocol (on d 0, GnRH and progesterone insert were administered; on d 7, progesterone insert was removed and PGF2α was injected; and on d 9.5, GnRH was injected 56 h after progesterone removal) with eCG (0, 300, 500, 700, or 1,000 IU) administered on d 7. In Exp. 2, Angus cows (n = 27) received the same protocol as Exp. 1 and were assigned randomly to receive 0 or 400 IU eCG i.m. on d 2 or 7. In Exp. 3, Angus cows (n = 18) received a 6-d Ovsynch + progesterone protocol and were randomly assigned to receive 0 or 800 IU eCG on d 3 of the protocol (Exp. 3a). A pilot field trial was also performed using the same treatments in suckled Angus-cross cows (n = 72; Exp. 3b). In Exp. 4, beef heifers (n = 200) were assigned randomly to the same treatments as in Exp. 3, but the second GnRH was not given, with Holstein bulls introduced on d 6. In Exp. 5, Angus cows (n = 12) received the same treatment as in Exp. 3, but were not inseminated. Progesterone concentrations were assessed in plasma collected during the estrous cycle following synchronization. Ultrasonography was used to monitor ovarian dynamics and to diagnose pregnancy. In Exp. 1, the mean number of ovulations was affected (P < 0.02) by the dose of eCG and the stage of follicular development when administered. Treatment with eCG on d 2 tended (P < 0.08) to extend the interval from PGF2α to ovulation, but was not successful in inducing double ovulations. In contrast, eCG on d 3 increased (P < 0.01) the number of cows with double ovulation when administered i.m. and increased (P < 0.04) pregnancy rate in single ovulating heifers after bull breeding (68.0 vs. 53.1%). This treatment also elevated progesterone concentrations during the estrous cycle following synchronization. Thus, the mechanism by which administration of eCG on d 3 of the synchronization increased pregnancy rates may be through supporting development of a healthy follicle and subsequent corpus luteum capable of secreting increased concentrations of progesterone during early pregnancy. In conclusion, strategic administration of eCG during a synchronization protocol can be used to improve reproductive performance through increased pregnancy rates in single ovulating animals as well as the induction of twin ovulations for twinning.
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Affiliation(s)
- M F Martinez
- AgResearch Invermay Agricultural Centre, Dunedin 9013, New Zealand
| | - D Tutt
- AgResearch Invermay Agricultural Centre, Dunedin 9013, New Zealand
| | - L D Quirke
- AgResearch Invermay Agricultural Centre, Dunedin 9013, New Zealand
| | - G Tattersfield
- Integrated Foods Ltd., 266 Childers Road, Gisborne 4010, New Zealand
| | - J L Juengel
- AgResearch Invermay Agricultural Centre, Dunedin 9013, New Zealand
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Martinez MF, de Nava G, Demmers KJ, Tutt D, Rodriguez Sabarrós M, Smaill B, Corti M, Juengel J. Intravaginal progesterone devices in synchronization protocols for artificial insemination in beef heifers. Reprod Domest Anim 2011; 47:230-7. [PMID: 21883507 DOI: 10.1111/j.1439-0531.2011.01833.x] [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: 11/26/2022]
Abstract
Two experiments were designed to investigate the administration of intravaginal progesterone in protocols for oestrus and ovulation synchronization in beef heifers. In Experiment 1, cyclic Black Angus heifers (n = 20) received an Ovsynch protocol and were randomly assigned to receive (CIDR-Ovsynch) or not (Ovsynch) a progesterone device between Days 0 and 7. Treatment with a controlled internal drug release (CIDR) device significantly increased the size of the dominant follicle prior to ovulation (12.8 ± 0.4 CIDR-Ovsynch vs 11.4 ± 0.4 Ovsynch) (p < 0.02). Plasma progesterone concentrations throughout the experiment were affected by the interaction between group and day effects (p < 0.004). In Experiment 2, cyclic Polled Hereford heifers (n = 382) were randomly assigned to one of the six treatment groups (3 × 2 factorial design) to receive a CIDR, a used bovine intravaginal device (DIB), or a medroxiprogesterone acetate (MAP) sponge and GnRH analogues (lecirelin or buserelin). All heifers received oestradiol benzoate plus one of the devices on Day 0 and PGF on Day 7 pm (device withdrawal). Heifers were detected in oestrus 36 h after PGF and inseminated 8-12 h later, while the remainder received GnRH 48 h after PGF and were inseminated on Day 10 (60 h). The number of heifers detected in oestrus on Day 8 and conception rate to AI on Day 9 were higher (p < 0.01) in the used-DIB than in the CIDR or MAP groups, while the opposite occurred with the pregnancy rate to FTAI on Day 10 (p < 0.01). There was no effect of progesterone source, GnRH analogue or their interaction on overall pregnancy rates (64.9%). Progesterone treatment of heifers during an Ovsynch protocol resulted in a larger pre-ovulatory follicle in beef heifers. Progesterone content of intravaginal devices in synchronization protocols is important for the timing of AI, as the use of low-progesterone devices can shorten the interval to oestrus.
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Affiliation(s)
- M F Martinez
- AgResearch Limited, Invermay Agricultural Centre, Mosgiel, New Zealand.
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Ledezma ML, Martinez MF, Nadela L, Rasgon SA. 165 Glycemic Control in Spanish Speaking Patients with Type II Diabetes Mellitus Using a Group Visit Model. Am J Kidney Dis 2011. [DOI: 10.1053/j.ajkd.2011.02.168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Palasz AT, Rodriguez-Martinez H, Beltran-Breña P, Perez-Garnelo S, Martinez MF, Gutierrez-Adan A, De la Fuente J. Effects of hyaluronan, BSA, and serum on bovine embryo in vitro development, ultrastructure, and gene expression patterns. Mol Reprod Dev 2006; 73:1503-11. [PMID: 16902955 DOI: 10.1002/mrd.20516] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Effects of hyaluronan (HA), BSA, and FCS on in vitro development, ultrastructure, and mRNA transcription of four developmentally important genes: apoptosis (Bax), oxidative stress (SOX), growth factor (IGF-II), and cell-to-cell adhesion (Ecad) were examined. Two biological origin HA, Hylartil and Hyonate and one produced by fermentation (f-HA) MAP-5 were tested. Embryos were cultured in SOF medium with 0.4% BSA or with 0.4% BSA and 10% FCS. HA was added 96 hr post insemination (pi) to half of the embryos from each culture group. Embryo development was not affected by either HA preparation, however, hatching rates were higher in Hyalartil and MAP-5 than in control and Hyonate (P < 0.05). There was no effect of HA on number of blastocysts developed in SOF + BSA. However, more blastocysts developed in SOF + BSA + f-HA than in SOF + BSA + FCS or with BSA + FCS + f-HA. HA added to SOF + BSA, increased level of expression of epidermal growth factor (EGF)-II and decreased the levels of expression of BAX, SOX, and Ecad (P < 0.05). Presence of FCS increased the levels of SOX and decreased the level of IGF-II (P < 0.05) and the addition of f-HA to SOF containing FCS showed no effect on the level of transcription of any analyzed genes. The fine structure of embryos cultured with f-HA irrespective of protein sources used was clearly improved. In summary, f-HA added 96 hr pi to SOF supplemented with BSA but not FCS improved development, molecular composition and fine structure of bovine embryos.
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Affiliation(s)
- A T Palasz
- Departamento de Reproducción Animal y Conservación de Recursos Zoogenéticos, INIA, Madrid, Spain.
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Colazo MG, Kastelic JP, Mainar-Jaime RC, Gavaga QA, Whittaker PR, Small JA, Martinez MF, Wilde RE, Veira DM, Mapletoft RJ. Resynchronization of previously timed-inseminated beef heifers with progestins. Theriogenology 2006; 65:557-72. [PMID: 16039702 DOI: 10.1016/j.theriogenology.2005.06.001] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.6] [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: 11/30/2004] [Revised: 05/31/2005] [Accepted: 06/06/2005] [Indexed: 11/15/2022]
Abstract
The objective was to determine the efficacy of a previously used CIDR or melengestrol acetate (MGA; 0.5mg/head/day) for resynchronization of estrus in beef heifers not pregnant to timed-AI (TAI). In three experiments and a field trial, heifers were reinseminated 6-12 h after first detection of estrus. Pregnancy diagnosis was done from approximately 25-43 days after either TAI or reinsemination. In Experiment 1, 79 heifers received a once-used CIDR from 13 to 20 days after TAI and 80 heifers were untreated controls. For these two groups, there were 34 and 35 heifers, respectively, not pregnant to TAI; median +/- S.E. intervals from TAI to onset of estrus were 22 +/- 0.2 days versus 20 +/- 0.6 days (P < 0.001); estrus rates were 70.6% versus 85.7% (P = 0.1); conception rates were 62.5% versus 76.7% (P < 0.3); and pregnancy rates were 44.1% versus 65.7% (P = 0.07), for CIDR and untreated (control) groups, respectively. In Experiment 2, heifers (n = 651) were TAI (Day 0) and 13 days later randomly assigned to one of seven groups (n = 93 per group) to receive a once-used CIDR (three groups; Days 13-20), MGA (three groups; Days 13-19), or no treatment (control group). Groups given a CIDR or MGA also received: no further treatment (CIDR or MGA alone); 1.5mg estradiol-17beta (E-17beta) and 50 mg progesterone (P4) in 2 mL canola oil on Day 13; or E-17beta and P4 on Day 13 and 0.5 mg E-17beta on Day 21 (24 h after CIDR removal or 48 h after the last feeding of MGA). Pregnancy rate to TAI was lowest (P < 0.05) for the group given a CIDR plus E-17beta and P4 on Day 13 and E-17beta on Day 21. Variability in return to estrus was greater (P < 0.001) in the control and MGA groups than in CIDR groups. Conception and pregnancy rates in heifers given a CIDR (65.1 and 61.4%) were higher (P<0.01) than those fed MGA (49.6 and 40.4%), but not different from controls (62.2 and 54.9%, respectively). In Experiment 3, 616 heifers received a once- or twice-used CIDR for 7 days, beginning 13+/-1 days after TAI, with or without a concurrent injection of 150 mg of P4 (2 x 2 factorial design). Pregnancy rate to TAI was 47.2%. In heifers that returned to estrus, there was no significant difference between a once- or twice-used CIDR for rates of estrus (68.8%, P < 0.3), conception (65.9%, P < 0.6) and pregnancy (45.3%, P < 0.8). Injecting progesterone at CIDR insertion increased the median interval from CIDR removal to onset of estrus (P < 0.05) and reduced rates of estrus (63.8% versus 73.8%, P<0.05), conception (60.5% versus 70.6%, P = 0.1) and pregnancy (38.6% versus 52.2%, P < 0.02). In a field trial, 983 heifers received a once-used CIDR for 7 days, beginning 13 +/- 1 days after TAI. Pregnancy rate to TAI was 55.2%. The median (and mode) of the interval from CIDR removal to estrus was 2.5 days. Estrus, conception and pregnancy rates were 78.2, 70.3 and 55.0% (overall pregnancy rate to TAI and rebreeding, 78.7%). In summary, a once- or twice-used CIDR for 7 days, starting 13 +/- 1 days after TAI resulted in the majority of nonpregnant heifers detected in estrus over a 4-day interval, with acceptable conception rates; however, injecting progesterone at CIDR insertion significantly reduced both estrus and pregnancy rates, and estradiol treatment after CIDR removal was associated with a decreased pregnancy rate to TAI. Fertility was higher in heifers resynchronized with a once-used CIDR than with MGA.
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Affiliation(s)
- M G Colazo
- WCVM, University of Saskatchewan, Saskatoon, SK, Canada S7N 5B4
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Martinez MF, Kastelic JP, Adams GP, Mapletoft RJ. The use of a progesterone-releasing device (CIDR-B) or melengestrol acetate with GnRH, LH, or estradiol benzoate for fixed-time AI in beef heifers. J Anim Sci 2002; 80:1746-51. [PMID: 12162641 DOI: 10.2527/2002.8071746x] [Citation(s) in RCA: 51] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The objective of this experiment was to compare two progestins and three treatments for synchronizing follicular wave emergence and ovulation in protocols for fixed-time AI in beef heifers. On d 0 (beginning of the experiment), Angus and Angus-Simmental cross beef heifers at random stages of the estrous cycle either received a CIDR-B device (n = 257) or were started on 0.5 mg x anima(-1) x d(-1) melengestrol acetate (MGA; n = 246) and were randomly assigned to receive i.m. injections of 100 microg GnRH, 12.5 mg porcine LH (pLH), or 2 mg estradiol benzoate (EB) and 50 mg progesterone (P4). The last feeding of MGA was given on d 6 and on d 7, CIDR-B devices were removed and all heifers received 500 microg cloprostenol (PG). Consistent with their treatment groups on d 0, heifers were given either 100 microg GnRH or 12.5 mg pLH 48 h after PG (and were concurrently inseminated) or 1 mg EB 24 h after PG and were inseminated 28 h later (52 h after PGF). Estrus rate (combined for both progestins) in heifers receiving EB (92.0%) was greater (P < 0.05) than that in heifers receiving GnRH and pLH (combined) and a CIDR-B device (62.9%) or MGA (34.3%). Although the mean interval from PG treatment to estrus did not differ among groups (overall, 47.8 h; P = 0.85), it was less variable (P < 0.01) in MGA-fed heifers (SD = 2.5 h) than in CIDR-B-treated heifers (SD = 8.1 h). Pregnancy rates (determined by ultrasonography approximately 30 d after AI) did not differ (P = 0.30) among the six treatment groups (average, 58.0%; range, 52.5 to 65.0%). Although fixed-time AI was done, pregnancy rates were greater in heifers detected in estrus than in those not detected in estrus (62.6 vs 51.9%; P < 0.05). In conclusion, GnRH, pLH, or EB treatment in combination with a CIDR-B device or MGA effectively synchronized ovulation-for fixed-time AI, resulting in acceptable pregnancy rates in beef heifers.
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Affiliation(s)
- M F Martinez
- Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, Canada
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Martinez MF, Kastelic JP, Adams GP, Mapletoft RJ. The use of GnRH or estradiol to facilitate fixed-time insemination in an MGA-based synchronization regimen in beef cattle. Anim Reprod Sci 2001; 67:221-9. [PMID: 11530268 DOI: 10.1016/s0378-4320(01)00128-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Two experiments were conducted to compare pregnancy rates when GnRH or estradiol were given to synchronize ovarian follicular wave emergence and ovulation in an MGA-based estrus synchronization program. Crossbred beef cattle were fed melengestrol acetate (MGA, 0.5 mg per day) for 7 days (designated days 0-6, without regard to stage of the estrous cycle) and given cloprostenol (PGF; 500 microg intramuscular (im)) on day 7. In Experiment 1, lactating beef cows (n=140) and pubertal heifers (n=40) were randomly allocated to three groups to receive 100 microg gonadorelin (GnRH), 5 mg estradiol-17beta and 100 mg progesterone (E+P) in canola oil or no treatment (control) on day 0. All cattle were observed for estrus every 12 h from 36 to 96 h after PGF. Cattle in the GnRH group that were detected in estrus 36 or 48 h after PGF were inseminated 12 h later; the remainder were given 100 microg GnRH im 72 h after PGF and concurrently inseminated. Cattle in the E+P group were randomly assigned to receive either 0.5 or 1.0 mg estradiol benzoate (EB) in 2 ml canola oil im 24 h after PGF and were inseminated 30 h later. Cattle in the control group were inseminated 12 h after the first detection of estrus; if not in estrus by 72 h after PGF, they were given 100 microg GnRH im and concurrently inseminated. In the absence of significant differences, all data for heifers and for cows were combined and the 0.5 and 1.0 mg EB groups were combined into a single estradiol group. Estrus rates were 57.6, 57.4 and 60.0% for the GnRH, E+P and control groups, respectively (P=0.95). The mean (+/-S.D.) interval from PGF treatment to estrus was shorter (P<0.001) and less variable (P<0.001) in the E+P group (49.0+/-6.1 h) than in either the GnRH (64.2+/-15.9 h) or control (66.3+/-13.3 h) groups. Overall pregnancy rates were higher (P<0.005) in the GnRH (57.6%) and E+P (55.7%) groups than in the control group (30.0%) as were pregnancy rates to fixed-time AI (47.5, 55.7 and 28.3%, respectively). In Experiment 2, 122 crossbred beef heifers were given either 100 microg GnRH or 2 mg EB and 50 mg progesterone in oil on day 0 and subsequently received either 100 microg GnRH 36 h after PGF and inseminated 14 h later or 1 mg EB im 24 h after PGF and inseminated 28 h later in a 2 x 2 factorial design. Pregnancy rates were not significantly different among groups (41.9, 32.2, 33.3 and 36.7% in GnRH/GnRH, GnRH/EB, EB/GnRH and EB/EB groups, respectively). In conclusion, GnRH or estradiol given to synchronize ovarian follicular wave emergence and ovulation in an MGA-based synchronization regimen resulted in acceptable pregnancy rates to fixed-time insemination.
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Affiliation(s)
- M F Martinez
- Department of Large Animal Clinical Sciences, Western College of Veterinary Medicine, University of Saskatchewan, 52 Campus Drive, Saskatoon, Sask., S7N 5B4, Canada
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Martinez MF, Adams GP, Kastelic JP, Bergfel DR, Mapletoft RJ. Induction of follicular wave emergence for estrus synchronization and artificial insemination in heifers. Theriogenology 2000; 54:757-69. [PMID: 11101036 DOI: 10.1016/s0093-691x(00)00388-5] [Citation(s) in RCA: 74] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The objective was to synchronize follicular wave emergence among cattle for synchronization of estrus and ovulation, and to determine pregnancy rate after AI at observed estrus. At random stages of the estrous cycle, a controlled internal drug release device (CIDR-B) was inserted intravaginally (Day 0) in 67 cross-bred beef heifers, and they were randomly allocated to receive either no further treatment (Control; n = 18); 5 mg of estradiol-17beta and 100 mg of progesterone im (E/P; n = 16); 100 microg im of GnRH (GnRH; n = 16); or transvaginal ultrasound-guided follicular ablation of all follicles > or = 5 mm (FA; n = 17). All heifers received a luteolytic dose of PGF (repeated 12 h later), and CIDR-B were removed on Days 9, 8, 6 or 5, in Control, E/P, GnRH or FA groups, respectively, so the dominant follicle of the induced wave was exposed to exogenous progesterone for a similar period of time in each group. Mean (+/- SEM) intervals (and range, in days) from treatment to follicular wave emergence in these groups were 3.5 +/- 0.6 (-2 to 8), 3.4 +/- 0.1 (3 to 4), 1.5 +/- 0.3 (-1 to 4), and 1.0 +/- 0.1 (0 to 2), respectively. Although the interval was longest (P<0.01) in the E/P and Control groups, it was least variable (P<0.01) in the E/P and FA groups. Intervals (and range, in days) from CIDR-B removal (and first PGF treatment) to estrus were 2.3 +/- 0.2 (1.5 to 4.5), 2.2 +/- 0.2 (1.5 to 3.0), 2.1 +/- 0.1,(1.5 to 3.5), and 2.5 +/- 0.1 (2.0 to 3.5), and to ovulation were 3.5 +/- 0.2 (2.5 to 5.5), 3.4 +/- 0.1 (3.0 to 4.5), 3.5 +/- 0.1 (2.5 to 4.5), and 3.8 +/- 0.1 (3.0 to 4.5), for Control, E/P, GnRH and FA groups, respectively (ns). The proportion of heifers displaying estrus was higher in the Control than in the FA group (94% versus 65%, P<0.05) and intermediate in EP and GnRH groups (87% and 75%). Heifers were inseminated approximately 12 h prior to ovulation (based on estrous behavior and ultrasound examinations). Pregnancy rates were 78%, 80%, 69% and 65% for Control, E/P, GnRH and FA groups, respectively (P=0.73). Results support the hypothesis that synchronous follicular wave emergence results in synchronous follicle development and, following progesterone removal, synchronous estrus and ovulation with high pregnancy rates to AI. The synchrony of estrus and ovulation in the E/P, GnRH and FA groups suggest that these treatments, in combination with CIDR-B, could be adapted to fixed-time insemination programs.
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Affiliation(s)
- M F Martinez
- Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, Canada
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Abstract
A study was designed to compare superovulatory responses in cattle when gonadotropin treatment followed 1 of 3 different treatments to synchronize follicular wave emergence. Animals at unknown stages of the estrous cycle were randomly assigned to 3 groups: ablation of the 2 largest follicles per pair of ovaries (n = 21); ablation of all follicles > or = 5 mm (n = 19); or intramuscular administration of 5 mg estradiol-17beta plus 100 mg progesterone (n = 23). All animals were given a CIDR-B intravaginally at the time of the respective treatments. Gonadotropin treatment, initiated 1 d after follicle ablation or 4 d after estradiol plus progesterone treatment, in the respective groups, consisted of 200 mg of pFSH divided in decreasing doses twice daily over 4 d. Cloprostenol (500 microg) was given at 48 and 60 h after the first pFSH treatment; CIDR-B devices were removed at the time of the second cloprostenol treatment. Ovarian ultrasonography was done on the days of CIDR-B insertion, first gonadotropin treatment, and at 36 and 72 h after CIDR-B removal. Cattle were inseminated twice, at 60 and 72 h after the first injection of cloprostenol. Ovarian and ova/embryo data were collected at slaughter 5, 6 or 7 d after insemination. No differences were detected among groups in the number of follicles > or = 8 mm at the time of first insemination (20.4 +/- 1.7 vs 16.6 +/- 2.0 vs 19.9 +/- 2.3; P > 0.05). At slaughter, no differences were detected among groups in the numbers of CL (23.3 +/- 1.9 vs 17.9 +/- 1.9 vs 20.1 +/- 2.6; P < 0.05), unovulated follicles > or = 8 mm (2.2 +/- 0.5 vs 2.1 +/- 0.3 vs 3.7 +/- 0.9; P < 0.05), ova/embryos (11.0 +/- 1.4 vs 12.2 +/- 1.3 vs 8.5 +/- 1.3; P < 0.05), fertilized ova (9.4 +/- 1.3 vs 10.1 +/- 1.2 vs 7.5 +/- 1.1; P < 0.05) or transferable embryos (8.2 +/- 1.2 vs 8.4 +/- 1.3 vs 6.5 +/- 0.9; P < 0.05). Variation in the numbers of CL (P = 0.1) and unovulated follicles > or = 8 mm (P < 0.01) was lower in the ablation groups than in the steroid-treated group. Results suggest that follicle ablation is as effective as estradiol plus progesterone in synchronizing follicular wave emergence for superstimulation in cattle, and that ablation of the 2 largest follicles is as efficacious as ablating all follicles > or = 5 mm.
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Affiliation(s)
- M I Baracaldo
- Department of Herd Medicine and Theriogenology, WCVM, University of Saskatchewan, Saskatoon, Canada
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Martinez MF, Adams GP, Bergfelt DR, Kastelic JP, Mapletoft RJ. Effect of LH or GnRH on the dominant follicle of the first follicular wave in beef heifers. Anim Reprod Sci 1999; 57:23-33. [PMID: 10565437 DOI: 10.1016/s0378-4320(99)00057-3] [Citation(s) in RCA: 114] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
A study was designed to characterise ovarian follicular dynamics in heifers treated with porcine luteinizing hormone (pLH) or gonadotropin releasing hormone (GnRH) on days 3, 6 or 9 (ovulation = day 0), corresponding to the growing, early-static, and late-static phases of the first follicular wave. Following ovulation, 65 beef heifers were assigned, by replicate, to the following seven treatment groups: 25 mg im of pLH on days 3, 6 or 9 (n = 9 per group); 100 microg im of GnRH on days 3, 6 or 9 (n = 9 per group); or controls (no treatment; n = 11). Ovulation occurred within 36 h in 67%, 100% and 67% of heifers treated with pLH and in 89%, 56% and 22% of heifers treated with GnRH on days 3, 6 or 9, respectively (treatment-by-day interaction, P < 0.09). Combined for all treatment days, ovulation rates were 78% and 56% in pLH- and GnRH-treated groups, respectively (P < 0.09). Overall, mean day (+/- SD) of emergence of the second follicular wave in heifers that ovulated was different from that in controls or in heifers that did not ovulate (P < 0.05). Mean (+/- SD) day of emergence of the second wave occurred earlier (day 5.6+/-1.2; P < 0.05) in heifers that ovulated after treatment on day 3 (n = 14) than in controls (day 8.7+/-1.6; n = 11); however, wave emergence in all heifers treated on day 6 (day 8.1+/-0.5; n = 18) did not differ from controls, regardless of whether or not ovulation occurred. In the heifers that ovulated in response to treatment on day 9 (n = 8), the emergence of the second follicular wave was delayed (day 10.9+/-0.4; P < 0.05). The day of emergence of the second wave in the 14 treated heifers that failed to ovulate, irrespective of the day of treatment (day 8.9+/-1.4) did not differ from control heifers. The emergence of the second wave was more synchronous in day 6 heifers (regardless of whether they ovulated) and in day 9 heifers that ovulated compared to control heifers (P < 0.05). Results did not support the hypothesis that the administration of pLH or GnRH at known stages of the follicular wave in cycling heifers would consistently induce ovulation or atresia and, thereby, induce emergence of a new follicular wave at a predictable interval. New wave emergence was induced consistently (1.3 days post-treatment) only in those animals that ovulated in response to treatment. However, 22% of LH-treated heifers and 44% of GnRH-treated heifers failed to ovulate. Treatments did not induce atresia of the dominant follicle or alter the interval to new wave emergence in animals that did not ovulate in response to treatment.
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Affiliation(s)
- M F Martinez
- Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, Canada
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Santos TM, Johnston DA, Azevedo V, Ridgers IL, Martinez MF, Marotta GB, Santos RL, Fonseca SJ, Ortega JM, Rabelo EM, Saber M, Ahmed HM, Romeih MH, Franco GR, Rollinson D, Pena SD. Analysis of the gene expression profile of Schistosoma mansoni cercariae using the expressed sequence tag approach. Mol Biochem Parasitol 1999; 103:79-97. [PMID: 10514083 DOI: 10.1016/s0166-6851(99)00100-0] [Citation(s) in RCA: 35] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
ESTs constitute rapid and informative tools with which to study gene-expression profiles of the diverse stages of the schistosome life cycle. Following a comprehensive EST study of adult worms, analysis has now targeted the cercaria, the parasite larval form responsible for infection of the vertebrate host. Two Schistosoma mansoni cercarial cDNA libraries were examined and partial sequence obtained from 957 randomly selected clones. On the basis of database searches, 551 (57.6%) ESTs generated had no homologs in the public databases whilst 308 (32.2%) were putatively identified, totaling 859 informative ESTs. The remaining 98 (10.2%) were uninformative ESTs (ribosomal RNA and non-coding mitochondrial sequences). By clustering analysis we have identified 453 different genes. The most common sequences in both libraries represented Sm8 calcium binding protein (8% of ESTs), fructose-1,6-bisphosphate aldolase, glyceraldehyde-3-phosphate dehydrogenase, cytochrome oxidase subunit 1, ATP guanidine kinase and triose phosphate isomerase. One hundred and nineteen identified genes were sorted into 11 functional categories, with genes associated with energy metabolism being the most abundant (13%) and diverse. The diversity and abundance of genes associated with the transcription/translation machinery and with regulatory/signaling functions were also marked. A paramyosin transcript was identified, indicating that this gene is not exclusively expressed in adult worms and sporocysts (as had been suggested previously). The possible physiological relevance to cercariae of the presence of transcripts with homology to calcium binding proteins of the EF-hand superfamily, Gq-coupled rhodopsin photoreceptor, rod phosphodiesterase 8 subunit and peripheral-type benzodiazepine receptor is discussed.
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Affiliation(s)
- T M Santos
- Departamento de Bioquímica e Imunologia, ICB-UFMG, Belo Horizonte, MG, Brazil
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Frappaz D, Philip T, Philip I, Biron P, Bouffet E, Favrot M, Veley B, Martinez MF, Lacroze M, Dutou L. [Massive chemotherapy and purged bone marrow autograft in severe neuroblastoma. Preliminary results apropos of 5 cases]. Pediatrie 1985; 40:539-51. [PMID: 3913896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Massive chemotherapy with in vitro purged autologous bone marrow transplantation has benefited from better understanding of massive chemotherapy for solid tumors, and better search for bone marrow involvement; it may now be used for treatment of poor prognosis neuroblastomas. Authors report preliminary results of 5 cases (4 IV and 1 p III b stage of TNM classification) completely treated in one institution (Centre Léon-Bérard, Lyon, France). Three patients are in complete remission, and 2 have relapsed (median follow up: 18 months). Authors comment on these results.
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