Magnitude and trends in socio-economic and geographic inequality in access to birth by cesarean section in Tanzania: evidence from five rounds of Tanzania demographic and health surveys (1996–2015)
Bibliographic record
Abstract
Abstract Background Majority of maternal deaths are avoidable through quality obstetric care such as Cesarean Section (CS). However, in low-and middle-income countries, many women are still dying due to lack of obstetric services. Tanzania is one of the African countries where maternal mortality is high. However, there is paucity of evidence related to the magnitude and trends of disparities in CS utilization in the country. This study examined both the magnitude and trends in socio-economic and geographic inequalities in access to birth by CS. Methods Data were extracted from the Tanzania Demographic and Health Surveys (TDHSs) (1996–2015) and analyzed using the World Health Organization’s (WHO) Health Equity Assessment Toolkit (HEAT) software. First, access to birth by CS was disaggregated by four equity stratifiers: wealth index, education, residence and region. Second, we measured the inequality through summary measures, namely Difference (D), Ratio (R), Slope Index of Inequality (SII) and Relative Index of Inequality (RII). A 95% confidence interval was constructed for point estimates to measure statistical significance. Results The results showed variations in access to birth by CS across socioeconomic, urban-rural and regional subgroups in Tanzania from 1996 to 2015. Among the poorest subgroups, there was a 1.38 percentage points increase in CS coverage between 1996 and 2015 whereas approximately 11 percentage points increase was found among the richest subgroups within same period of time. The coverage of CS increased by nearly 1 percentage point, 3 percentage points and 9 percentage points among non-educated, those who had primary education and secondary or higher education, respectively over the last 19 years. The increase in coverage among rural residents was 2 percentage points and nearly 8 percentage points among urban residents over the last 19 years. Substantial disparity in CS coverage was recorded in all the studied surveys. For instance, in the most recent survey, pro-rich (RII = 15.55, 95% UI; 10.44, 20.66, SII = 15.8, 95% UI; 13.70, 17.91), pro-educated (RII = 13.71, 95% UI; 9.04, 18.38, SII = 16.04, 95% UI; 13.58, 18.49), pro-urban ( R = 3.18, 95% UI; 2.36, 3.99), and subnational (D = 16.25, 95% UI; 10.02, 22.48) absolute and relative inequalities were observed. Conclusion The findings showed that over the last 19 years, women who were uneducated, poorest/poor, living in rural settings and from regions such as Zanzibar South, appeared to utilize CS services less in Tanzania. Therefore, such subpopulations need to be the central focus of policies and programmes implemmentation to improve CS services coverage and enhance equity-based CS services utilization.
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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 | 0.002 | 0.000 |
| 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 | 0.000 |
| 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".