African Operations Research (Business/Math crossover) | 15 December 2009

Time-Series Forecasting Model for Evaluating Clinical Outcomes in Nigerian Community Health Centres Systems: A Methodological Assessment

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DOI: https://doi.org/10.5281/zenodo.18899318

Abstract

Community health centers in Nigeria face challenges in monitoring clinical outcomes due to limited data collection and analysis methods. A mixed-method approach was employed, including quantitative data analysis using a SARIMA (Seasonal AutoRegressive Integrated Moving Average) model to forecast future trends and qualitative interviews with healthcare providers to assess model applicability. The SARIMA model demonstrated an R² value of 0.85 in forecasting monthly outpatient visits, indicating moderate predictive accuracy; however, variability in data collection methods across centers led to discrepancies in forecasts. While the time-series model showed promise for clinical outcome prediction, further validation and standardisation are required to enhance its utility in diverse settings. Standardise data collection protocols and conduct additional empirical tests before implementing the model in different Nigerian health centers. SARIMA, Time-Series Forecasting, Clinical Outcomes, Community Health Centers, Nigeria Treatment effect was estimated with $\text{logit}(p<em>i)=\beta</em>0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.