Effective Strategies for Increasing the Uptake of Modern Methods of Family Planning in South Asia: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Background Family planning (FP) interventions have improved the use of modern contraceptives, yet a high unmet need for contraception still exists in South Asia. This systematic review of existing research was conducted to identify effective FP interventions that led to an increase in the uptake of modern methods of contraception in South Asia.Methods Five electronic databases were searched for relevant studies published between January 2000 and May 2021. Experimental studies which reported data on the impact of FP interventions on modern contraceptive use among women of reproductive age (15–49 years) in the South Asian region were included. A random-effects Inverse Variance weighted model was employed to pool the adjusted odds ratio (OR) on modern contraceptive use and unmet need for contraception. In addition, we computed subgroup meta-estimates based on intervention type and the urban-rural divide.Results Among 513 studies identified, 21 met the inclusion criteria. The overall pooled odds ratio for modern contraceptive use was significantly higher (OR 1.51; 95% CI 1.35–1.70; heterogeneity; I2 = 81%) for FP interventions with a significant reduction in unmet need for contraception (OR 0.86; 95% CI 0.78–0.94, I2 = 50%). The subgroup analysis revealed demand-generation (OR 1.61; 95% CI 1.32–1.96), health system integrated (OR 1.53; 95% CI 1.07–2.20), and franchised FP clinic interventions (OR 1.32; 95% CI 1.21–1.44) had promoted the modern contraceptive uptake. Further, FP interventions implemented in urban settings showed a higher increase in modern contraceptive use (OR 1.73; 95% CI 1.44–2.07) compared to rural settings (OR 1.46; 95% CI 1.28–1.66). Given the considerable heterogeneity observed across studies and low degree of certainty indicated by GRADE summary for the primary outcome, caution is advised when interpreting the results.Conclusion The review collated experimentally evaluated FP interventions that increased modern contraception use and reduced the unmet need in South Asia. The demand generation interventions were found to be the most effective interventions in increasing the uptake of modern contraceptive methods. Furthermore, the urban environment provides a conducive environment for interventions to improve contraceptive usage. However, further studies should assess which aspects were most effective on attitudes towards contraception, selection of more effective methods, and contraceptive behaviors.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.034 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".