Relationship Between Health Insurance Status and Frequency of Routine Medical Checkups
Bibliographic record
Abstract
BACKGROUND: Routine medical checkups are essential for early disease detection and prevention. However, disparities in utilization persist across sociodemographic groups, particularly in relation to health insurance coverage in the US population. OBJECTIVE: This study aims to examine the relationship between health insurance status and recent routine medical checkups among US adults, using nationally representative survey data. METHODS: This cross-sectional study analyzed data from the 2019 Behavioral Risk Factor Surveillance System (BRFSS) (n = 329,549; weighted population = 198,183,089). Descriptive statistics, chi-square tests, and survey-weighted logistic regression were employed to examine the associations between recent checkup status and various variables, including insurance coverage, age, sex, education, income, and race/ethnicity. RESULTS: Individuals with health insurance had nearly four times the odds of having had a recent checkup compared to those without insurance (OR = 3.90, 95% CI: 3.69-4.12). Female sex, older age, and Hispanic or Black race/ethnicity were also positively associated with recent checkups. Conversely, lower income and educational attainment were linked to reduced utilization. CONCLUSION: Health insurance coverage is a strong predictor of routine healthcare utilization. Expanding access to insurance may substantially improve the uptake of preventive services, particularly among underserved populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".