Pathways to Healing: Determinants of Time-to-Repair in Obstetric Fistula Using an Exponentiated-Survival Model

Background: Obstetric fistula is preventable, yet many women face prolonged waits before definitive repair. We sought to identify which care pathway factors are most strongly associated with earlier repair and to provide a practical, interpretable survival tool that distinguishes the majority of repair times from the smaller group with prolonged delays.

Methodology: We analysed 257 women with obstetric fistula, measuring weeks to repair and treating unrepaired cases as right‑censored. We developed and fitted a parametric proportional‑hazards survival model (Lehmann Type II (exponentiated‑survival)) with an intuitive baseline that explicitly separates the majority of cases from the long tail, yielding closed‑form survival, hazard, and quantile functions. Simulations were used to stress‑test small‑sample performance typical of single‑centre cohorts. Covariates in the adjusted model were education, antenatal follow-up, delivery mode, labour duration (<2, 2-4, >4 days), and stature (<150 cm against ≥150 cm). Model adequacy was summarised with AIC/BIC and subgroup survival/hazard profiles.

Results: Two pathway factors were dominant. Formal education (HR = 2.87; 95% CI 1.93-4.26) and antenatal care (HR = 2.57; 1.71-3.88) were each associated with a higher instantaneous probability of repair, i.e., earlier repairs and fewer long waits. In contrast, a caesarean section compared to N/V was associated with a lower repair hazard (HR = 0.51; 0.34-0.79), consistent with more complex cases requiring specialist capacity. Labour duration and short stature were not independently strong after adjustment. Simulations confirmed estimator stability in modest cohorts and illustrated how parameter shifts move probability away from the long-wait tail.

Conclusion: Strengthening education‑linked navigation and antenatal engagement should shift repairs earlier and reduce backlogs, while C/S‑related cases warrant specialist triage and theatre skills to prevent accumulation in the long‑wait tail. Because the model’s parameter values and closed‑form outputs translate directly into time‑to‑percentage‑repaired forecasts, it is well suited for scheduling surgical camps, managing bed turnover, and tracking backlog risk in fistula programmes.