Socioeconomic and geographic variations in antenatal care coverage in Angola: further analysis of the 2015 demographic and health survey
Bibliographic record
Abstract
BACKGROUND: In African countries, including Angola, antenatal care (ANC) coverage is suboptimal and maternal mortality is still high due to pregnancy and childbirth-related complications. There is evidence of disparities in the uptake of ANC services, however, little is known about both the socio-economic and geographic-based disparity in the use of ANC services in Angola. The aim of this study was to assess the extent of socio-economic, urban-rural and subnational inequality in ANC coverage in Angola. METHODS: We analyzed data from the 2015 Angola Demographic and Health Survey (ADHS) using the World Health Organization (WHO) Health Equity Assessment Toolkit (HEAT) software. The analysis consisted of disaggregated ANC coverage rates using four equity stratifiers (economic status, education, residence, and region) and four summary measures (Difference, Population Attributable Risk, Ratio and Population Attributable Fraction). To measure statistical significance, an uncertainty interval (UI) of 95% was constructed around point estimates. RESULTS: The study showed both absolute and relative inequalities in coverage of ANC services in Angola. More specifically, inequality favored women who were rich (D = 54.2, 95% UI; 49.59, 58.70, PAF = 43.5, 95% UI; 40.12, 46.92), educated (PAR = 19.9, 95% UI; 18.14, 21.64, R = 2.14, 95% UI; 1.96, 2.32), living in regions such as Luanda (D = 51.7, 95% UI; 43.56, 59.85, R = 2.64, 95% UI; 2.01, 3.26) and residing in urban dwellings (PAF = 20, 95% UI; 17.70, 22.38, PAR = 12.3, 95% UI; 10.88, 13.75). CONCLUSION: The uptake of ANC services were lower among poor, uneducated, and rural residents as well as women from the Cuanza Sul region. Government policy makers must consider vulnerable subpopulations when designing needed interventions to improve ANC coverage in Angola to achieve the 2030 Sustainable Development Goal of reducing global maternal mortality ratio to 70 deaths per 100,000 live births.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".