Health insurance coverage and poverty status of postpartum women in the United States in 2019: an ACS-PUMS population-based cross-sectional study
Bibliographic record
Abstract
Abstract Background A quarter of United States (US) postpartum women still report unmet health care needs and health care unaffordability. We aimed to study associations between receipt of health insurance coverage and poverty status/receipt of government financial support and determine coverage gaps overall and by social factors among US postpartum women in poverty. Methods This study design is a cross-sectional study using secondary data. We included women who gave birth within the last 12 months from 2019 American Community Survey Public Use Microdata Sample. Poverty was defined as having an income-to-poverty ratio of less than 100%. We explored Medicaid/government medical assistance gaps among women in poverty. To examine the associations between Medicaid/government medical assistance (exposures) and poverty/government financial support (outcomes), we used age-, race-, and multivariable-adjusted logistic regression models. We also evaluated the associations of state, race, citizenship status, or language other than English spoken at home (exposures) with receipt of Medicaid/government medical assistance (outcomes) among women in poverty through multivariable-adjusted logistic regression. Results It was notable that 35.6% of US postpartum women in poverty did not have Medicaid/government medical assistance and only a small proportion received public assistance income (9.8%)/supplementary security income (3.1%). Women with Medicaid/government medical assistance, compared with those without the coverage, had statistically significantly higher odds of poverty [adjusted odds ratio (aOR): 3.15, 95% confidence interval (95% CI): 2.85–3.48], having public assistance income (aOR: 24.52 [95% CI: 17.31–34.73]), or having supplementary security income (aOR: 4.22 [95% CI: 2.81–6.36]). Also, among postpartum women in poverty, women in states that had not expanded Medicaid, those of Asian or other race, non-US citizens, and those speaking another language had statistically significantly higher odds of not receiving Medicaid/government medical assistance [aORs (95% CIs): 2.93 (2.55–3.37); 1.30 (1.04–1.63); 3.65 (3.05–4.38); and 2.08 (1.86–2.32), respectively]. Conclusions Our results showed that the receipt of Medicaid/government medical assistance is significantly associated with poverty and having government financial support. However, postpartum women in poverty still had Medicaid/government medical assistance gaps, especially those who lived in states that had not expanded Medicaid, those of Asian or other races, non-US citizens, and other language speakers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".