ArticleRisk management and healthcare policy2025
The Impact of Individual Mandate and Income on Private Health Insurance Enrollment: A State-Level Analysis on Individual Behavior Change.
Article in Risk management and healthcare policy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Health insurance, vital for improved health outcomes and reduced financial burdens, faced challenges within the market system due to adverse selection and the risk of a death spiral. This led to the Affordable Care Act (ACA) introducing the Individual Mandate in 2014. After the penalty under the Individual Mandate had been repealed, several states have kept regulative measures to encourage individuals to keep health insurance. Methods: This study analyzed the impact of individual mandates in four states and D.C. on private health insurance enrollment using pooled cross-sectional data from the Integrated Public Use Microdata Series (IPUMS) USA. The dataset included 4,524,753 non-elderly individuals (ages 19 to 64) from 2019 to 2021. A logistic regression model was employed to estimate the likelihood of having private health insurance, with the individual mandate as a state-level binary variable and income as a continuous predictor. Interaction effects between income and the mandate were examined. To further interpret the results, marginal effects were calculated to quantify the impact of the individual mandate and income on the probability of private health insurance enrollment. Results: The study revealed a positive association between the individual mandate regulation and increased private health insurance enrollment. Additionally, income exhibited a positive influence on enrollment in private health insurance coverage. Notably, when considering the moderating effect of income, the mandate had a more pronounced impact on lower-income individuals, resulting in a 0.885% decrease in the probability of having private health insurance for every 10k increase in individual income. Conclusion: The findings highlight that individuals in the mandated states are more likely to have private health insurance, addressing adverse selection issues and stabilizing the health insurance market. However, the mandate disproportionately affects lower-income individuals, suggesting the need for additional financial assistance to sustain enrollment. Policymakers should consider complementary support programs, such as subsidies and cost-sharing reductions, to mitigate financial burdens and ensure broader coverage. Maintaining an individual mandate alongside targeted financial policies can contribute to a more equitable and sustainable healthcare system.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.