Socioeconomic inequalities in maternal health care utilization in Ghana
Bibliographic record
Abstract
BACKGROUND: Improving maternal and child health remains a public health priority in Ghana. Despite efforts made towards universal coverage, there are still challenges with access to and utilization of maternal health care. This study examined socioeconomic inequalities in maternal health care utilization related to pregnancy and identified factors that account for these inequalities. METHODS: We used data from three rounds of the Ghana Demographic and Health Surveys (2003, 2008 and 2014). Two health care utilization measures were used; (i) four or more antenatal care (ANC) visits and (ii) delivery by trained attendants (DTA). We first constructed the concentration curve (CC) and estimated concentration indices (CI) to examine the trend in inequality. Secondly, the CI was decomposed to estimate the contribution of various factors to inequality in these outcomes. RESULTS: The CCs show that utilization of at least four ANC visits and DTA were concentrated among women from wealthier households. However, the trends show the levels of inequality decreased in 2014. The CI of at least four ANC visits was 0.30 in 2003 and 0.18 in 2014. Similarly, the CIs for DTA was 0.60 in 2003 and 0.42 in 2014. The decomposition results show that access to National Health Insurance Scheme (NHIS) and women's education levels were the most important contributors to the reduction in inequality in maternal health care utilization. CONCLUSIONS: The findings highlight the importance of the NHIS and formal education in bridging the socioeconomic gap in maternal health care utilization.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".