Summary measures of socioeconomic and area-based inequalities in fertility rates among adolescents: evidence from Ethiopian demographic and health surveys 2000–2016
Bibliographic record
Abstract
BACKGROUND: One of the highest rates of adolescent pregnancies in the world is in sub-Saharan Africa. Most adolescent pregnancies in the region are unintended or unwanted, due to poor access to information and services on sexual and reproductive health for adolescents. Ethiopia has high adolescent fertility rates (AFR) with disparities across socioeconomic subgroups and regions. This study assessed the magnitude and trends of socioeconomic and area-based AFR inequalities in Ethiopia. METHODS: The 2000 and 2016 Ethiopia Demographic and Health surveys (EDHS) was analyzed using the World Health Organization's (WHO) Health Equity Assessment Toolkit (HEAT) software. Adolescent fertility rates were disaggregated using three equity stratifiers (economic status, education and residence) and analyzed through four summary measures (Difference (D), Population Attributable Risk (PAR), Ratio (R) and Population Attributable Fraction (PAF)) to assess inequality. To measure statistical significance, point estimates were constructed using a 95% Uncertainty Interval (UI). RESULTS: Large socio-economic and urban-rural inequalities were observed within the 16-year period. Adolescents in less well-off socio-economic groups (PAF: -62.9 [95% UI; - 64.3, - 61.4], D: 96.4 [95% UI; 47.7, 145.1]), uneducated (R: 8.5 [95% UI; 4.8, 12.2], PAR: -76.4 [95% UI;-77.7, - 75.0]) and those from rural areas (D: 81.2 [95% UI; 67.9, 94.6], PAF: -74.2 [95% UI, - 75.7, - 72.7]) had a higher chance of pregnancy and more births than their counterparts. CONCLUSIONS: Socioeconomic (education and economic status) and place of residence determine adolescents' pregnancy and childbearing. Policies and programs should be directed at preventing child marriage and early fertility so that adolescents continue to access education, sexual and reproductive health care as well as access employment opportunities. More emphasis should be placed on subpopulations with disproportionately higher adolescent pregnancy and childbirth.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".