Sub-regional disparities in the use of antenatal care service in Mauritania: findings from nationally representative demographic and health surveys (2011–2015)
Bibliographic record
Abstract
BACKGROUND: Skilled antenatal care (ANC) has been identified as a proven intervention to reducing maternal deaths. Despite improvements in maternal health outcomes globally, some countries are signaling increased disparities in ANC services among disadvantaged sub-groups. Mauritania is one of sub-Saharan countries in Africa with a high maternal mortality ratio. Little is known about the inequalities in the country's antenatal care services. This study examined both the magnitude and change from 2011 to 2015 in socioeconomic and geographic-related disparities in the utilization of at least four antenatal care visits in Mauritania. METHODS: Using the World Health Organization's Health Equity Assessment Toolkit (HEAT) software, data from the 2011 and 2015 Mauritania Multiple Indicator Cluster Surveys (MICS) were analyzed. The inequality analysis consisted of disaggregated rates of antenatal care utilization using four equity stratifiers (economic status, education, residence, and region) and four summary measures (Difference, Population attributable risk, Ratio and Population attributable fraction). A 95% Uncertainty Interval was constructed around point estimates to measure statistical significance. RESULTS: Substantial absolute and relative socioeconomic and geographic related disparities in attending four or more ANC visits (ANC4+ utilization) were observed favoring women who were richest/rich (PAR = 19.5, 95% UI; 16.53, 22.43), educated (PAF = 7.3 95% UI; 3.34, 11.26), urban residents (D = 19, 95% UI; 14.50, 23.51) and those living in regions such as Nouakchott (R = 2.1, 95% UI; 1.59, 2.56). While education-related disparities decreased, wealth-driven and regional disparities remained constant over the 4 years of the study period. Urban-rural inequalities were constant except with the PAR measure, which showed an increasing pattern. CONCLUSION: A disproportionately lower ANC4+ utilization was observed among women who were poor, uneducated, living in rural areas and regions such as Guidimagha. As a result, policymakers need to design interventions that will enable disadvantaged subpopulations to benefit from ANC4+ utilization to meet the Sustainable Development Goal (SDG) of reducing the maternal mortality ratio (MMR) to 140/100, 000 live births by 2030.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".