Social determinants of health and disparities in prenatal care utilization during the Great Recession period 2005-2010
Bibliographic record
Abstract
BACKGROUND: Early, regular prenatal care utilization is an important strategy for improving maternal and infant health outcomes. The purpose of this study is to better understand contributing factors to disparate prenatal care utilization outcomes among women of different racial/ethnic and social status groups before, during, and after the Great Recession (December 2007-June 2009). METHODS: Data from 678,235 Washington (WA) and Florida (FL) birth certificates were linked to community and state characteristic data to carry out cross-sectional pooled time series analyses with institutional review board approval for human subjects' research. Predictors of on-time as compared to late or non-entry to prenatal care utilization (late/no prenatal care utilization) were identified and compared among pregnant women. Also explored was a simulated triadic relationship among time (within recession-related periods), social characteristics, and prenatal care utilization by clustering individual predictors into three scenarios representing low, average, and high degrees of social disadvantage. RESULTS: Individual and community indicators of need (e.g., maternal Medicaid enrollment, unemployment rate) increased during the Recession. Associations between late/no prenatal care utilization and individual-level characteristics (including disparate associations among race/ethnicity groups) did not shift greatly with young maternal age and having less than a high school education remaining the largest contributors to late/no prenatal care utilization. In contrast, individual maternal enrollment in a supplemental nutrition program for women, infants, and children (WIC) exhibited a protective association against late/no prenatal care utilization. The magnitude of association between community-level partisan voting patterns and expenditures on some maternal child health programs increased in non-beneficial directions. Simulated scenarios show a high combined impact on prenatal care utilization among women who have multiple disadvantages. CONCLUSIONS: Our findings provide a compelling picture of the important roles that individual characteristics-particularly low education and young age-play in late/no prenatal care utilization among pregnant women. Targeted outreach to individuals with high disadvantage characteristics, particularly those with multiple disadvantages, may help to increase first trimester entry to utilization of prenatal care. Finally, WIC may have played a valuable role in reducing late/no prenatal care utilization, and its effectiveness during the Great Recession as a policy-based approach to reducing late/no prenatal care utilization should be further explored.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".