Examining barriers to healthcare access and utilization of antenatal care services: evidence from demographic health surveys in sub-Saharan Africa
Bibliographic record
Abstract
Abstract Background Antenatal care utilization is one of the means for reducing the high maternal mortality rates in sub-Saharan Africa. This study examined the association between barriers to healthcare access and implementation of the 2016 WHO antenatal care services model among pregnant women seeking antenatal care in selected countries in sub-Saharan Africa. Methods This study considered only Demographic and Health Survey data collected in 2018 in sub-Saharan Africa. Hence, the Demographic and Health Survey data of four countries in sub-Saharan Africa (Nigeria, Mali, Guinea and Zambia) were used. A sample of 6761 from Nigeria, 1973 from Mali, 1690 from Guinea and 1570 from Zambia was considered. Antenatal care visits, categorized as < 8 visits or ≥8 visits, and time of the first antenatal care visit, categorized as ≤3 months or > 3 months (as per the WHO recommendations) were the outcome variables for this study. Both descriptive statistics and ordinal logistic regression were used to analyze the data. Crude odds ratios (cOR) and adjusted odds ratios (aOR) and p -values < 0.05 were used for the interpretation of results. Results With timing of antenatal care visits, getting money needed for treatment (aOR = 1.38, 95% CI = 1.03–1.92) influenced early timing of antenatal care visits in Mali whereas getting permission to visit the health facility (aOR = 1.62, 95% CI = 1.15–2.33) motivated women to have early timing of antenatal care visits in Guinea. We found that women who considered getting money needed for treatment as not a big problem in Nigeria were more likely to have the recommended number of antenatal care visits (aOR = 1.38, 95% CI= 1.11–1.73). On the contrary, in Guinea, Zambia and Mali, getting permission to visit health facilities, getting money for treatment, distance to the health facility and not wanting to go alone were not barriers to having ≥ 8 antenatal care visits. Conclusion Our study has emphasized the role played by barriers to healthcare access in antenatal care utilization across sub-Saharan African countries. There is the need for governmental and non-governmental organizations to ensure that policies geared towards improving the quality of antenatal care and promoting good interaction between health care seekers and health care providers are integrated within the health system.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".