High-Resolution District Level Contraceptive Prevalence in Pakistan Using a Bayesian Small Area Estimation Approach

IntroductionNational surveys in Pakistan are typically representative only at national or provincial levels, leaving large uncertainties in district-level contraceptive prevalence. This obscures local heterogeneity and limits data-driven program planning. Administrative data, although more frequent and detailed, are often underused due to reporting and measurement challenges. This study develops a multi-source small area estimation (SAE) framework to generate district-level estimates of contraceptive prevalence rate (CPR) and modern contraceptive prevalence rate (mCPR) using routine commodities data. MethodsA two-stage Bayesian SAE model was constructed to integrate survey, supply, and census data. In Stage 1, contraceptive dispensation data from the Contraceptive Logistics Management Information System (cLMIS) were converted into inferred users, normalized to married women of reproductive age (MWRA) from the 2023 Census, and scaled to provincial CPR benchmarks from the Pakistan Social and Living Standards Measurement Survey (PSLM). In Stage 2, a bivariate hierarchical Bayesian model jointly estimated CPR and mCPR, accounting for measurement error and borrowing statistical strength from socioeconomic and demographic covariates. Convergence and model stability were assessed through standard diagnostics (R-hat, ESS, BFMI, divergence checks). ResultsDistrict-level estimates were produced for 121 districts. CPR ranged from 9% to 46% and mCPR from 6% to 35%. Aggregated provincial estimates were consistent with PSLM benchmarks (within {+/-} 0.6 percentage points). Comparison with published district studies showed mean absolute deviations around 4 percentage points. ConclusionThe Bayesian SAE framework generates statistically coherent, high-resolution contraceptive prevalence estimates, substantially improving visibility into geographic inequities in Pakistans family planning landscape. These granular metrics offer policymakers an actionable basis for prioritizing underserved districts and tailoring context-sensitive interventions.