Correlates of antenatal care utilization among women of reproductive age in sub-Saharan Africa: evidence from multinomial analysis of demographic and health surveys (2010–2018) from 31 countries
Bibliographic record
Abstract
BACKGROUND: Despite a global reduction of about 38% in maternal mortality rate between 2000 and 2017, sub-Saharan Africa is still experiencing high mortality among women. Access to high quality care before, during and after childbirth has been described as one of the effective means of reducing such mortality. In the sub-region, only 52% of women receive at least four antenatal visits. This study examined the factors influencing antenatal care utilization in sub-Saharan Africa. METHODS: Data from Demographic and Health Surveys (DHS) of 31 countries involving 235,207 women age 15-49 years who had given birth to children within 5 years of the surveys were used in the study. Multinomial logistic regression model was applied in the analysis. RESULTS: About 13% of women in sub-Saharan Africa did not utilize antenatal care while 35 and 53% respectively partially and adequately utilized the service. Adequate utilization of antenatal care was highest among women age 25-34 years (53.9%), with secondary or higher education (71.3%) and from the richest households (54.4%). The odds of adequate antenatal care utilization increased for women who are educated up to secondary or higher education level, from richest households, working, living in urban areas, exposed to media and did not experience problem getting to health facility or obtaining permission to visit health facility. CONCLUSIONS: This study has revealed information not only on women who did not utilize antenatal care but also on women who partially and adequately utilized the service. The study concluded that the correlates of antenatal care utilization in sub-Saharan Africa include socioeconomic and demographic factors, getting permission to visit health facility, unwillingness to visit health facility alone and problem encountered in reaching the health facility.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".