What question did this study set out to answer?

The review aims to assess and critique the methodologies used for forecasting system reliability in district hospitals in Tanzania.

March 13, 2026Open Access

Methodological Evaluation and Time-Series Forecasting for District Hospital System Reliability in Tanzania: A Systematic Review

Puntos clave

The review aims to assess and critique the methodologies used for forecasting system reliability in district hospitals in Tanzania.
Conducted a systematic search of multiple electronic databases according to PRISMA guidelines.
Screened studies based on methodological designs focused on quantitative forecasting models.
Performed quality assessments using a modified Cochrane risk-of-bias tool.
Identified a reliance on ARIMA models for forecasting healthcare metrics like bed occupancy and drug stock-outs.
Less than 30% of studies reported prediction intervals or conducted robustness checks on their forecasts.
Flagged a significant gap in uncertainty quantification in the current forecasting methods.

Resumen

"background": "District hospital systems in Tanzania face persistent challenges in resource allocation and service delivery due to unpredictable demand and systemic constraints. System reliability, defined as the consistent provision of intended healthcare services, is critical for public health outcomes but remains difficult to quantify and forecast effectively. ", "purpose and objectives": "This systematic review evaluates methodological approaches used to assess district hospital system reliability in Tanzania, with a specific objective to synthesise and critique time-series forecasting models applied in this context. It aims to identify robust methodological frameworks and gaps in current predictive analytics. ", "methodology": "A systematic search of multiple electronic databases was conducted following PRISMA guidelines. Studies were screened against pre-defined inclusion criteria focusing on methodological designs and quantitative models for forecasting healthcare system performance metrics. Quality assessment was performed using a modified Cochrane risk-of-bias tool for methodological evaluations. ", "findings": "The review identified a predominant reliance on autoregressive integrated moving average (ARIMA) models, represented generally as Xt = c + =1^{p\ Xt-i + \ + =1^q\ -i, for forecasting bed occupancy and drug stock-outs. However, a significant methodological gap was the frequent omission of uncertainty quantification; fewer than 30% of studies employing these models reported prediction intervals or conducted robustness checks on their forecasts. ", "conclusion": "Existing methodological applications for forecasting hospital system reliability are narrowly focused on a limited set of statistical techniques and often lack rigorous validation and uncertainty analysis, limiting their utility for strategic planning. ", "recommendations": "Future research should integrate exogenous variables (e. g. , climatic, demographic) into hybrid forecasting models and mandate the reporting of prediction intervals. Capacity building in advanced statistical modelling for health system researchers is urgently needed. ", "key words": "health systems research, predictive modelling, ARIMA, resource planning, healthcare operations, uncertainty quantification", "contribution statement": "This review provides the first dedicated methodological synthesis of time-series

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