How Has Early Marriage, a Critical Social Determinant of Child Stunting and Wasting, Changed over a Decade in South Asia? Trends, Inequities and Drivers, 2005 to 2018
Bibliographic record
Abstract
In South Asia, many women are married before their 18th birthday and give birth soon after. Delaying marriage is an attractive nutrition policy target as previous research shows that early marriage (EM) is associated with poor child growth outcomes, operating through many pathways. We sought to describe the prevalence, trends, inequities and predictors of EM in South Asia. We used Demographic and Health Survey data available in the last 15 years for 7 South Asian countries: Afghanistan (AF; 2015), Bangladesh (BG; 2007, 2014), India (IN; 2006, 2016), Maldives (MV; 2009, 2017), Nepal (NP; 2005, 2016), and Pakistan (PK; 2007, 2018). EM was defined as the percentage of women aged 20–24 years who were married before 18 years of age. Our analyses included 133,680 women. The prevalence and absolute burden in terms of number of individuals affected were estimated for each survey round. Relative trends were examined using average annual rate of reduction (AARR). Inequities were examined by geography, wealth, place of residence, and education. Regression decomposition was used to examine the contribution of improvements in wealth and education to EM reductions. The most recent rounds of data show that EM is common in BG (69%), AF (52%), NP (52%), IN (41%), and PK (37%) but not MV (4%). IN accounts for 68% of the regional burden, with 21.9 million women married early in 2016. The fastest reductions in EM have occurred in IN (59% to 41% over 10 years, an AARR of −3.8% per year), PK (−2.8% per year), and BG (−1.5% per year). EM prevalence varies subnationally, e.g., from 5% to 52% for states within IN in 2016. Equity analysis shows that EM disproportionately burdens women who are poor, uneducated, and live in rural areas. Progress in narrowing these inequalities has been slow in the past decade. When examining predictors of EM, completion of secondary school was associated with a 20% (PK) to 36% (NP) lower EM prevalence. Decomposition analysis shows that improvements in wealth and education alone predicted between 46% (PK) and 96% (NP) of the actual EM reduction. EM remains highly prevalent in South Asia and trends indicate an enduring problem. The nutrition community should invest in building linkages with researchers and practitioners to further understand and address this important social determinant of poor child growth. A4NH/IFPRI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| 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".