Addressing population health and health inequalities: the role of fundamental causes
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Objectives. As a case study of the impact of universal versus targeted interventions on population health and health inequalities, we used simulations to examine (1) whether universal or targeted manipulations of collective efficacy better reduced population-level rates and racial/ethnic inequalities in violent victimization; and (2) whether experiments reduced disparities without addressing fundamental causes. Methods. We applied agent-based simulation techniques to the specific example of an intervention on neighborhood collective efficacy to reduce population-level rates and racial/ethnic inequalities in violent victimization. The agent population consisted of 4000 individuals aged 18 years and older with sociodemographic characteristics assigned to match distributions of the adult population in New York City according to the 2000 US Census. Results. Universal experiments reduced rates of victimization more than targeted experiments. However, neither experiment reduced inequalities. To reduce inequalities, it was necessary to eliminate racial/ethnic residential segregation. Conclusions. These simulations support the use of universal intervention but suggest that it is not possible to address inequalities in health without first addressing fundamental causes.
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Agent-based models have been used to test interventions to enhance neighborhood collective efficacy to reduce racial and ethnic disparities in violent victimization and multicomponent interventions to improve diarrheal disease control.
“enhancing neighborhood collective efficacy to reduce racial and ethnic disparities in violent victimization (Cerda et al., 2014), and multicomponent interventions to improve diarrheal disease control (Mellor et al., 2012)”
From Syndemics: a theory in search of data or data in search of a theory? (2018) · cites this paper as Cerda et al., 2014
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- Authors
- Cerdá, M., Tracy, M., Ahern, J., & Galea, S.
- Affiliations
- Columbia University (US)
- Venue
- American Journal of Public Health
- Publisher
- American Public Health Association
- Published
- 2014-09
- Volume
- 104
- Issue
- S4
- Pages
- S609-S619
- ISSN
- 0090-0036 (ISSN-L)
- 1541-0048
- PMC
- PMC4126171
- OpenAlex
- W2088575466
- Cited by
- 73
- References
- 98
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 5 · 2023: 4 · 2022: 6 · 2021: 6 · 2020: 4 · 2019: 8 · 2018: 12 · 2017: 9 · 2016: 7 · 2015: 7
- FWCI
- 9.79
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- American Journal of Public Health (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institutes of Health
- National Institute on Alcohol Abuse and Alcoholism (R21AA021909)
- National Institute on Drug Abuse (K01DA030449)
- National Institute of Child Health and Human Development
- National Center for Chronic Disease Prevention and Health Promotion (DP2HD080350)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (DP2HD080350)
- Topics
- Social Sciences › Health › Health disparities and outcomes
- Health Sciences › Health Professions › General Health Professions › Public Health Policies and Education
- Social Sciences › Sociology and Political Science › Crime Patterns and Interventions
- Keywords
- population health, health disparities, fundamental causes, collective efficacy, neighborhood interventions, violent victimization, racial and ethnic disparities, residential segregation, agent-based modeling
- MeSH terms
- Aging, Computer Simulation, Ethnicity, Humans, Models, Theoretical, Residence Characteristics, Socioeconomic Factors, Stress Disorders, Post-Traumatic, Crime Victims, Racial Groups, Health Status Disparities
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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