Barriers to Voting and Access to Health Insurance Among US Adults: A Cross-Sectional Study
Bibliographic record
Abstract
Background: Many states in the United States (US) have introduced barriers to impede voting among individuals from socio-economically disadvantaged groups. This may reduce representation thereby decreasing access to lifesaving goods, such as health insurance. Methods: We used cross-sectional data from 242,727 adults in the 50 states and District of Columbia participating in the US 2017 Behavioral Risk Factor Surveillance System (BRFSS). To quantify access to voting, the Cost of Voting Index (COVI), a global measure of barriers to voting within a state during a US election was used. Multilevel modeling was used to determine whether barriers to voting were associated with health insurance status after adjusting for individual- and state-level covariates. Analyses were stratified by racial/ethnic identity, household income, and age group. Findings: A one standard deviation (SD) increase in COVI score was associated with an overall increased odds of being uninsured (OR=1.25; 95% CI=1.22, 1.28). This association was also present for Non-Hispanic Black (OR=1.18; 95% CI=1.13,1.22), Hispanic (1.18; 95% CI=1.15,1.21), and Asian (OR=1.45;95%CI=1.27,1.66), and other Non-Hispanic (OR=1.12, 95% CI=1.06, 1.18) US adults, but not for White Non-Hispanic and Native US adults. Likewise, a one SD increase in COVI among adults from low-income households was associated with an increased odds of being uninsured (OR=1.32; 95% CI=1.26,1.38) but there was no association among individuals with incomes greater than $75,000. This association was similar for younger US adults (OR=1.22; 95%CI=1.20,1.24) but not among those aged 45 to 64. Interpretation: Groups commonly targeted by voting restriction laws-those with low incomes, who are racial minorities, and who are young-are also less likely to be insured in states with more voting restrictions. However, those who are wealthier, white or older are no more likely to be uninsured irrespective of the level of voting restrictions. Funding: Pabayo is a Tier II Canada Research Chair.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".