ArticleHealth services research2022
Difference-in-differences for categorical outcomes.
Article in Health services research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A causal framework for evaluating drivers of policy effect heterogeneity using difference-in-differences.Health services & outcomes research methodology · 2026Article
- [High commitment despite high task load: work-related evaluation in the implementation of a virtual multidisciplinary limb board].Unfallchirurgie (Heidelberg, Germany) · 2025Article
- Time on Your Side: Aggregating Data in Difference-In-Differences Studies.Health services research · 2025Article
- Medicaid: Increased Patient Access To MOUD In Residential Treatment Associated With Facility Openings And Closures, 2012-22.Health affairs (Project Hope) · 2025Article
- Improving the Care of Severe, Open Fractures and Postoperative Infections of the Lower Extremities: Protocol for an Interdisciplinary Treatment Approach.JMIR research protocols · 2024Article
- Difference-in-differences for categorical outcomes.Health services research · 2022Article
- Medicaid expansion is associated with increased 1-year survival for primary malignant brain tumors.Neuro-oncology advancesArticle
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Authors and funding
4 authors.
Funding
Abstract
objectiveTo discuss and develop difference-in-difference estimators for categorical outcomes and apply them to estimate the effect of the Affordable Care Act's Medicaid expansion on insurance coverage. DATA SOURCES: Secondary analysis of Survey on Income and Program Participation (SIPP) data on health insurance coverage types before (January 2013) and after (December 2015) Medicaid expansion in 39 US states (19 expansion and 20 non-expansion). STUDY
designWe develop difference-in-difference methods for repeated measures (panel data) of categorical outcomes. We discuss scale-dependence of DID assumptions for marginal and transition effect estimates and specify a new target estimand: the difference between outcome category transitions under treatment versus no treatment. We establish causal assumptions about transitions that are sufficient to identify this and a marginal target estimand. We contrast the marginal estimands identified by the transition approach versus an additive assumption only about marginal evolution. We apply both the marginal and transition approaches to estimate the effects of Medicaid expansion on health insurance coverage types (employer-sponsored; other private, non-group; public; and uninsured). DATA EXTRACTION: We analyzed 16,027 individual survey responses from people aged 18-62 years in the 2014 SIPP panel. PRINCIPAL
findingsWe show that the two identifying assumptions are equivalent (on the scale of the marginals) if either the baseline marginal distributions are identical or the marginals are constant in both groups. Applying our transitions approach to the SIPP data, we estimate a differential increase in transitions from uninsured to public coverage and differential decreases in transitions from uninsured to private, non-group coverage and in remaining uninsured.
conclusionsBy comparing the assumption that marginals are evolving in parallel to an assumption about transitions across outcome values, we illustrate the scale-dependence of difference-in-differences. Our application shows that studying transitions can illuminate nuances obscured by changes in the marginals.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.