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Treffer: Rigorous policy measurement: causal inference challenges and opportunities.

Title:
Rigorous policy measurement: causal inference challenges and opportunities.
Authors:
Schnake-Mahl A; Urban Health Collaborative, Drexel Dornsife School of Public Health, Philadelphia, PA, United States.; Department of Health Management and Policy, Drexel Dornsife School of Public Health, Philadelphia, PA, United States., Diez Roux AV; Urban Health Collaborative, Drexel Dornsife School of Public Health, Philadelphia, PA, United States.; Department of Epidemiology and Biostatistics, Drexel Dornsife School of Public Health, Philadelphia, PA, United States., Bilal U; Urban Health Collaborative, Drexel Dornsife School of Public Health, Philadelphia, PA, United States.; Department of Epidemiology and Biostatistics, Drexel Dornsife School of Public Health, Philadelphia, PA, United States., Schwartz GL; Urban Health Collaborative, Drexel Dornsife School of Public Health, Philadelphia, PA, United States.; Department of Health Management and Policy, Drexel Dornsife School of Public Health, Philadelphia, PA, United States., Burris S; Center for Public Health Law Research, Beasley School of Law, Temple University, Philadelphia, PA, United States.
Source:
American journal of epidemiology [Am J Epidemiol] 2025 Nov 04; Vol. 194 (11), pp. 3099-3105.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Oxford University Press Country of Publication: United States NLM ID: 7910653 Publication Model: Print Cited Medium: Internet ISSN: 1476-6256 (Electronic) Linking ISSN: 00029262 NLM ISO Abbreviation: Am J Epidemiol Subsets: MEDLINE
Imprint Name(s):
Publication: Cary, NC : Oxford University Press
Original Publication: Baltimore, School of Hygiene and Public Health of Johns Hopkins Univ.
MeSH Terms:
Causality* , Health Policy*/legislation & jurisprudence , Epidemiology*, Humans ; Bias ; Epidemiologic Methods ; Health Status Disparities
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Grant Information:
K01 AI168579 United States AI NIAID NIH HHS; U54 CA267735 United States CA NCI NIH HHS; U54CA267735-03 Drexel FIRST (Faculty Institutional Recruitment for Sustainable Transformation) Program, National Institutes of Health; K01AI168579 National Institutes of Allergy and Infectious Diseases
Contributed Indexing:
Keywords: causal inference; legal epidemiology; policy; policy evaluation; social epidemiology
Entry Date(s):
Date Created: 20250107 Date Completed: 20251120 Latest Revision: 20260111
Update Code:
20260111
PubMed Central ID:
PMC12782659
DOI:
10.1093/aje/kwae468
PMID:
39763145
Database:
MEDLINE

Weitere Informationen

Epidemiologists are increasingly asking questions about the effects of policies on health and health disparities, generally using quasi-experimental methods. Researchers have developed a burgeoning body of rigorous methodological work focused on addressing potential inference challenges arising from modeling choices, study design, data availability, and common sources of bias in policy evaluations using observational data. However, epidemiologists have paid less attention to the measurement and operationalization of policy exposures. The field of legal epidemiology offers rigorous, formalized methods to address challenges in measuring policy, yet disciplinary divides have impeded the communication of these approaches from lawyers to epidemiologists. In this article, we use terminology familiar to epidemiologists to describe the field of legal epidemiology and how challenges in measuring policy exposures can compromise causal inference, with a particular focus on addressing information bias and consistency assumptions. Laws and regulations can address or enforce structural inequities, and understanding challenges to their characterization and measurement can enhance epidemiologic research on their health and health equity effects. This article is part of a Special Collection on Methods in Social Epidemiology.
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