Inequities in utilization of prenatal care: a population-based study in the Canadian province of Manitoba
Bibliographic record
Abstract
BACKGROUND: Ensuring high quality and equitable maternity services is important to promote positive pregnancy outcomes. Despite a universal health care system, previous research shows neighborhood-level inequities in utilization of prenatal care in Manitoba, Canada. The purpose of this population-based retrospective cohort study was to describe prenatal care utilization among women giving birth in Manitoba, and to determine individual-level factors associated with inadequate prenatal care. METHODS: We studied women giving birth in Manitoba from 2004/05-2008/09 using data from a repository of de-identified administrative databases at the Manitoba Centre for Health Policy. The proportion of women receiving inadequate prenatal care was calculated using a utilization index. Multivariable logistic regressions were used to identify factors associated with inadequate prenatal care for the population, and for a subset with more detailed risk information. RESULTS: Overall, 11.5% of women in Manitoba received inadequate, 51.0% intermediate, 33.3% adequate, and 4.1% intensive prenatal care (N = 68,132). Factors associated with inadequate prenatal care in the population-based model (N = 64,166) included northern or rural residence, young maternal age (at current and first birth), lone parent, parity 4 or more, short inter-pregnancy interval, receiving income assistance, and living in a low-income neighborhood. Medical conditions such as multiple birth, hypertensive disorders, antepartum hemorrhage, diabetes, and prenatal psychological distress were associated with lower odds of inadequate prenatal care. In the subset model (N = 55,048), the previous factors remained significant, with additional factors being maternal education less than high school, social isolation, and prenatal smoking, alcohol, and/or illicit drug use. CONCLUSION: The rate of inadequate prenatal care in Manitoba ranged from 10.5-12.5%, and increased significantly over the study period. Factors associated with inadequate prenatal care included geographic, demographic, socioeconomic, and pregnancy-related factors. Rates of inadequate prenatal care varied across geographic regions, indicating persistent inequities in use of prenatal care. Inadequate prenatal care was associated with several individual indicators of social disadvantage, such as low income, education less than high school, and social isolation. These findings can inform policy makers and program planners about regions and populations most at-risk for inadequate prenatal care and assist with development of initiatives to reduce inequities in utilization of prenatal care.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".