Skip to main content
Log in

Modeling Long-Term Budgetary Impacts of Prevention: An Overview of Meta-analyses of Relationships Between Key Health Outcomes Across the Life-Course

  • Original Paper
  • Published:
Journal of Prevention Aims and scope Submit manuscript


Budget analysis entities often cannot capture the full downstream impacts of investments in prevention services, programs, and interventions. This study describes and applies an approach to synthesizing existing literature to more fully account for these effects. This study reviewed meta-analyses in PubMed published between Jan 1, 2010 and Dec 31, 2019. The initial search included meta-analyses on the association between health risk factors, including maternal behavioral health, intimate partner violence, child maltreatment, depression, and obesity, with a later health condition. Through a snowball sampling-type approach, the endpoints of the meta-analyses identified became search terms for a subsequent search, until each health risk was connected to one of the ten costliest health conditions. These results were synthesized to create a path model connecting the health risks to the high-cost health conditions in a cascade. Thirty-seven meta-analyses were included. They connected early-life health risk factors with six high-cost health conditions: hypertension, diabetes, asthma and chronic obstructive pulmonary disorder, mental disorders, heart conditions, and trauma-related disorders. If confounders could be controlled for and causality inferred, the cascading associations could be used to more fully account for downstream impacts of preventive interventions. This would support budget analysis entities to better include potential savings from investments in chronic disease prevention and promote greater implementation at scale.

This is a preview of subscription content, log in via an institution to check access.

Access this article

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Fig. 1
Fig. 2

Similar content being viewed by others


  • Agency for Healthcare Research and Quality. (n.d.). PQMP measures. AHRQ. Retrieved October 12, 2019, from

  • Averett, S., & Wang, Y. (2013). The effects of earned income tax credit payment expansion on maternal smoking. Health Economics, 22(11), 1344–1359.

    Article  PubMed  Google Scholar 

  • Baumgardner, J. R., Bilheimer, L. T., Booth, M. B., Carrington, W. J., Duchovny, N. J., & Werble, E. C. (2012). Cigarette taxes and the federal budget—report from the CBO. New England Journal of Medicine, 367(22), 2068–2070.

    Article  CAS  PubMed  Google Scholar 

  • Blueprints for Healthy Youth Development. (n.d.). (2019). Program search. Blueprints for Healthy Youth Development. Retrieved October 16, from

  • Borenstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2011). Introduction to meta-analysis. John Wiley & Sons.

    Google Scholar 

  • Brownell, M. D., Chartier, M. J., Nickel, N. C., Chateau, D., Martens, P. J., Sarkar, J., Burland, E., Jutte, D. P., Taylor, C., Santos, R. G., & Katz, A. (2016). Unconditional prenatal income supplement and birth outcomes. Pediatrics.

    Article  PubMed  Google Scholar 

  • Chambers, D. A., Glasgow, R. E., & Stange, K. C. (2013). The dynamic sustainability framework: Addressing the paradox of sustainment amid ongoing change. Implementation Science, 8(1), 1–11.

    Article  Google Scholar 

  • Clayborne, Z. M., Varin, M., & Colman, I. (2019). Systematic review and meta-analysis: Adolescent depression and long-term psychosocial outcomes. Journal of the American Academy of Child & Adolescent Psychiatry, 58(1), 72–79.

    Article  Google Scholar 

  • Congressional Budget Office (2020). How CBO analyzes approaches to improve health through disease prevention.

  • Counts, N. Z., Billioux, A., & Perrin, J. M. (2020). Short-term and long-term returns for states implementing pediatric alternative payment models. JAMA Pediatrics, 174(5), 403–404.

    Article  PubMed  Google Scholar 

  • Crowley, D. M., Dodge, K. A., Barnett, W. S., Corso, P., Duffy, S., Graham, P., Greenberg, M., Haskins, R., Hill, L., Jones, D. E., & Karoly, L. A. (2018). Standards of evidence for conducting and reporting economic evaluations in prevention science. Prevention Science, 19(3), 366–390.

    Article  PubMed  PubMed Central  Google Scholar 

  • Duchovny, N., Molloy, E., Housman, L., & Werble, E. (2015). Estimating the effects of federal policies targeting obesity: Challenges and research needs. The Congressional Budget Office.

  • Elmendorf, D. W. (2015). “Dynamic scoring”: Why and how to include macroeconomic effects in budget estimates for legislative proposals. Brookings Papers on Economic Activity, 2015(2), 91–149.

    Article  Google Scholar 

  • Feinberg, M. E., Jones, D. E., Roettger, M. E., Hostetler, M. L., Sakuma, K. L., Paul, I. M., & Ehrenthal, D. B. (2016). Preventive effects on birth outcomes: Buffering impact of maternal stress, depression, and anxiety. Maternal and Child Health Journal, 20(1), 56–65.

    Article  PubMed  Google Scholar 

  • Feinberg, M. E., Roettger, M. E., Jones, D. E., Paul, I. M., & Kan, M. L. (2015). Effects of a psychosocial couple-based prevention program on adverse birth outcomes. Maternal and Child Health Journal, 19(1), 102–111.

    Article  PubMed  PubMed Central  Google Scholar 

  • Gottfredson, D. C., Cook, T. D., Gardner, F. E., Gorman-Smith, D., Howe, G. W., Sandler, I. N., & Zafft, K. M. (2015). Standards of evidence for efficacy, effectiveness, and scale-up research in prevention science: Next generation. Prevention Science, 16(7), 893–926.

    Article  PubMed  PubMed Central  Google Scholar 

  • Grant, R. L. (2014). Converting an odds ratio to a range of plausible relative risks for better communication of research findings. BMJ.

    Article  PubMed  Google Scholar 

  • Halfon, N., & Forrest, C. B. (2018). The emerging theoretical framework of life course health development. Handbook of Life Course Health Development.

    Article  Google Scholar 

  • Halfon, N., Larson, K., & Slusser, W. (2013). Associations between obesity and comorbid mental health, developmental, and physical health conditions in a nationally representative sample of US children aged 10 to 17. Academic Pediatrics, 13(1), 6–13.

    Article  PubMed  Google Scholar 

  • Harris, N. B. (2020). Screening for adverse childhood experiences. JAMA, 324(17), 1788–1789.

    Article  PubMed  Google Scholar 

  • Hawkins, J. D., Jenson, J. M., Catalano, R., Fraser, M. W., Botvin, G. J., Shapiro, V., Brown, C. H., Beardslee, W., Brent, D., Leslie, L. K., & Rotheram-Borus, M. J. (2016). Unleashing the power of prevention. American Journal of Medical Research, 3(1), 39.

    Article  Google Scholar 

  • Heckman, J. J., & Corbin, C. O. (2016). Capabilities and skills. Journal of Human Development and Capabilities, 17(3), 342–359.

    Article  PubMed  PubMed Central  Google Scholar 

  • Heckman, J. J., & Raut, L. K. (2016). Intergenerational long-term effects of preschool-structural estimates from a discrete dynamic programming model. Journal of Econometrics, 191(1), 164–175.

    Article  PubMed  Google Scholar 

  • Hughes, K., Ford, K., Bellis, M. A., Glendinning, F., Harrison, E., & Passmore, J. (2021). Health and financial costs of adverse childhood experiences in 28 European countries: A systematic review and meta-analysis. The Lancet Public Health, 6(11), e848–e857.

    Article  PubMed  PubMed Central  Google Scholar 

  • Kline, R. B. (2015). Principles and practice of structural equation modeling (Fourth Edition).

  • Köhler, C. A., Evangelou, E., Stubbs, B., Solmi, M., Veronese, N., Belbasis, L., Bortolato, B., Melo, M. C., Coelho, C. A., Fernandes, B. S., & Olfson, M. (2018). Mapping risk factors for depression across the lifespan: An umbrella review of evidence from meta-analyses and Mendelian randomization studies. Journal of Psychiatric Research, 103, 189–207.

    Article  PubMed  Google Scholar 

  • Lecy, J. D., Mergel, I. A., & Schmitz, H. P. (2014). Networks in public administration: Current scholarship in review. Public Management Review, 16(5), 643–665.

    Article  Google Scholar 

  • Lederer, D. J., Bell, S. C., Branson, R. D., Chalmers, J. D., Marshall, R., Maslove, D. M., Ost, D. E., Punjabi, N. M., Schatz, M., Smyth, A. R., & Stewart, P. W. (2019). Control of confounding and reporting of results in causal inference studies. Guidance for authors from editors of respiratory, sleep, and critical care journals. Annals of the American Thoracic Society, 16(1), 22–28.

    Article  PubMed  Google Scholar 

  • Levy, S. (2017). Spending money to make money: CBO scoring of secondary effects. Yale Law Journal, 127, 936.

    Google Scholar 

  • Marshall, D. A., Burgos-Liz, L., IJzerman, M. J., Crown, W., Padula, W. V., Wong, P. K., & Osgood, N. D. (2015). Selecting a dynamic simulation modeling method for health care delivery research—Part 2: Report of the ISPOR dynamic simulation modeling emerging good practices task force. Value in Health, 18(2), 147–160.

    Article  PubMed  Google Scholar 

  • McDaid, D., Park, A. L., & Wahlbeck, K. (2019). The economic case for the prevention of mental illness. Annual Review of Public Health, 40, 373–389.

    Article  PubMed  Google Scholar 

  • Merrick, M. T., Ford, D. C., Ports, K. A., Guinn, A. S., Chen, J., Klevens, J., & Mercy, J. A. (2019). Vital signs: Estimated proportion of adult health problems attributable to adverse childhood experiences and implications for prevention—25 States, 2015–2017. Morbidity and Mortality Weekly Report, 68(44), 999.

    Article  PubMed  PubMed Central  Google Scholar 

  • Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. Annals of Internal Medicine, 151, 264–269.

    Article  PubMed  Google Scholar 

  • National Academies of Sciences, Engineering, and Medicine. (2019). Fostering healthy mental, emotional, and behavioral development in children and youth: A national agenda.

  • O’Connor, E., Senger, C. A., Henninger, M. L., Coppola, E., & Gaynes, B. N. (2019). Interventions to prevent perinatal depression: Evidence report and systematic review for the US preventive services task force. JAMA, 321(6), 588–601.

    Article  PubMed  Google Scholar 

  • Parekh, A. K. (2019). Prevention first: Policymaking for a healthier America. JHU Press.

    Book  Google Scholar 

  • Petruccelli, K., Davis, J., & Berman, T. (2019). Adverse childhood experiences and associated health outcomes: A systematic review and meta-analysis. Child Abuse & Neglect, 97, 104127.

    Article  Google Scholar 

  • Polanin, J. R., & Snilstveit, B. (2016). Converting between effect sizes. Campbell Systematic Reviews, 12(1), 1–13.

    Article  Google Scholar 

  • Ramos, G., Ponting, C., Labao, J. P., & Sobowale, K. (2021). Considerations of diversity, equity, and inclusion in mental health apps: A scoping review of evaluation frameworks. Behaviour Research and Therapy, 147, 103990.

    Article  PubMed  Google Scholar 

  • Shackleton, R. (2018). Estimating and projecting potential output using CBO's forecasting growth model. Congressional Budget Office. Working Paper 2018–03.

  • Smith, J. D., Berkel, C., Rudo-Stern, J., Montaño, Z., St George, S. M., Prado, G., Mauricio, A., Chiapa, M., Bruening, M. M., & Dishion, T. J. (2018a). The family check-Up 4 health (FCU4Health): Applying implementation science frameworks to the process of adapting an evidence-based parenting program for prevention of pediatric obesity and excess weight gain in primary Care. Frontiers in Public Health, 6, 293.

    Article  PubMed  PubMed Central  Google Scholar 

  • Smith, J. D., Egan, K. N., Montaño, Z., Dawson-McClure, S., Jake-Schoffman, D. E., Larson, M., & St. George, S. M. (2018b). A developmental cascade perspective of paediatric obesity: A conceptual model and scoping review. Health Psychology Review, 12(3), 271–293.

    Article  PubMed  PubMed Central  Google Scholar 

  • Smith, J. D., Montaño, Z., Dishion, T. J., Shaw, D. S., & Wilson, M. N. (2015). Preventing weight gain and obesity: Indirect effects of the family check-up in early childhood. Prevention Science, 16(3), 408–419.

    Article  PubMed  PubMed Central  Google Scholar 

  • Soni, A. (2011). Top 10 Most Costly Conditions among Men and Women, 2008: Estimates for the US Civilian noninstitutionalized adult population, Age 18 and older. statistical brief No. 331. Agency for Healthcare Research and Quality, Rockville, MD.

  • Steuerle, E., & Rennane, S. (2013). United States: Pioneer in fiscal surveillance. In Restoring public debt sustainability: The role of independent fiscal institutions (pp. 99–120).

  • St. George, S. M., Agosto, Y., Rojas, L. M., Soares, M., Bahamon, M., Prado, G., & Smith, J. D. (2020). A developmental cascade perspective of paediatric obesity: A systematic review of preventive interventions from infancy through late adolescence. Obesity Reviews, 21(2), e12939.

    Article  PubMed  Google Scholar 

  • US Preventive Services Task Force. (n.d.). Recommendation topics. Recommendation Topics | United States Preventive Services Taskforce. Retrieved October 14, 2019, from

  • Van Pinxteren, M. M., Pluymaekers, M., & Lemmink, J. G. (2020). Human-like communication in conversational agents: A literature review and research agenda. Journal of Service Management, 31(2), 203–225.

    Article  Google Scholar 

  • Vanlandingham, G. R., & Drake, E. K. (2012). Results first: Using evidence-based policy models in state policymaking. Public Performance & Management Review, 35(3), 550–563.

    Article  Google Scholar 

  • Washington State Institute for Public Policy. (2019). Benefit-Cost Technical Documentation. Washington State Institute for Public Policy.

Download references


JDS was supported by the CIRCL-Chicago Implementation Research Center, National Heart, Lung, and Blood Institute (UG3HL154297; Smith, Kho, & Davis) and the United States Department of Agriculture (2018-68001-27550; Smith & Berkel). MEF was supported by the National Institute for Child health and Development (HD099295; Feinberg).

Author information

Authors and Affiliations



All authors contributed to the conceptualization, analysis plan, interpretation of findings, and revision of the manuscript. Mr. Counts led the data collection and analysis, as well as the initial drafting of the manuscript. Dr. Lee led the statistical analyses.

Corresponding author

Correspondence to Nathaniel Z. Counts.

Ethics declarations

Conflict of Interest

The authors did not declare any conflicts of interest.

Ethical approval

This study did not require ethical approval.

Informed consent

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 32 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Counts, N.Z., Feinberg, M.E., Lee, Jk. et al. Modeling Long-Term Budgetary Impacts of Prevention: An Overview of Meta-analyses of Relationships Between Key Health Outcomes Across the Life-Course. J of Prevention 45, 177–192 (2024).

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: