Inequalities in demand satisfied with modern methods of family planning among women aged 15–49 years: a secondary data analysis of Demographic and Health Surveys of six South Asian countries
Bibliographic record
Abstract
OBJECTIVE: To estimate educational and wealth inequalities in demand satisfied with modern methods of family planning (mDFPS). DESIGN: A secondary data analyses of Demographic and Health Surveys. SETTING: Six South Asian countries, Afghanistan (2015), Bangladesh (2014), India (2015-2016), Maldives (2016-2017), Nepal (2016) and Pakistan (2017-2018). PARTICIPANTS: Women aged 15-49 years. Primary and secondary outcome measures mDFPS was defined as married women aged 15-49 years or their partners, who desired no child, no additional children or to postpone the next pregnancy and who are currently using any modern contraceptive method. We estimated weighted and age-standardised estimates of mDFPS. We calculated the slope index of inequality (SII) and relative index of inequality (RII) as the measures of socioeconomic inequalities. RESULTS: A total of 782 639 women were surveyed. The response rate was 84.0% and above. The prevalence of mDFPS was below 50% in Maldives (22.8%, 95% CI 20.7 to 25.0), Pakistan (42.0%, 95% CI 39.9 to 44.0) and Afghanistan (39.1%, 95% CI 36.9 to 41.3), whereas Bangladesh had achieved 76% (75.8%, 95% CI 74.2 to 77.3). Both wealth and educational inequalities varied in magnitude and direction between the countries. Except in Nepal and Bangladesh, mDFPS wealth inequalities showed a trend of increasing mDFPS as we moved towards richer, and richest wealth quintiles that is, pro-poor (RII (0.5 to 0.9); SII (-4.9 to -23.0)). In India and Nepal, higher versus no education was in favour of no education (higher mDFPS among not educated women) (RII 1.1 and 1.4; SII 4.1 and 15.3, respectively) and reverse in other countries ((RII (0.4 to 0.8); SII (-10.5 to -30.3)). Afghanistan, Maldives and Pakistan fared badly in both educational and wealth inequalities among the countries. CONCLUSIONS: South Asia region still has a long way ahead towards achieving universal access to mDFPS. Diverse patterns of socioeconomic inequalities between the countries call for national governments and international development agencies to target the population subgroups for improving the mDFPS coverage.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".