Utilization of long-acting contraceptive methods and associated factor among women of reproductive age in East Africa: A multi-level analysis of recent Demographic and Health Surveys
Bibliographic record
Abstract
BACKGROUND: Comprehensive family planning is essential for reproductive health, allowing individuals to make informed choices about family size and enhancing maternal and child health. Long-acting contraceptives (LACs) are known for their high efficacy and consistent use. This study examines the prevalence and determinants of LAC utilization among women of reproductive-age in 11 East African countries. METHODS: Secondary data from Demographic and Health Surveys (2015-2022) across 11 East African nations were pooled and analyzed. The study sample included 144,414 women aged 15-49. Bivariate and multivariate regression analyses were conducted using Stata 17 to explore factors associated with LAC utilization. Results are presented as adjusted odds ratios (AOR), with statistical significance at p < 0.05. RESULTS: The prevalence of LAC utilization among reproductive-age women in East Africa is 14.87%. Women from middle-income households are 13% more likely to utilize LACs compared to those from poor households (AOR = 1.13, 95% CI: 1.08-1.18), while those from wealthy households are 15% more likely (AOR = 1.15, 95% CI: 1.11-1.20). Married women and those living with a partner are 42% more likely to utilize LACs than unmarried women (AOR = 1.42, 95% CI: 1.33-1.52). Educational attainment significantly impacts LAC utilization: women with primary education are 52% more likely (AOR = 1.52, 95% CI: 1.44-1.60), those with secondary education are 63% more likely (AOR = 1.63, 95% CI: 1.53-1.73), and women with higher education are twice as likely to utilize LACs (AOR = 2.00, 95% CI: 1.84-2.17). CONCLUSIONS: LAC utilization remains relatively low in the studied East African countries. Factors positively associated with higher LAC utilization include being married, higher income, educational attainment, employment, and media exposure. Additionally, women with more than three children and those from countries with lower illiteracy and poverty rates also have higher LAC utilization. Conversely, a history of abortion, fewer health fieldworker visits, and later initiation of sexual activity and childbirth are associated with lower LAC utilization. These findings emphasize the need for targeted interventions to overcome barriers and promote LAC access and utilization.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".