Work

Health system resilience: quantifying the dynamic impact of environmental shocks on health service utilization using an interrupted time series and time-series forecasting approach in Western Province, Zambia

Year: 2026

Type: article

Abstract: Background: In low- and lower-middle-income nations, advancements toward Universal Health Coverage (UHC) are progressively jeopardized by the combined effects of acute shocks, such as floods and pandemics, alongside chronic health system stressors. Although the disruptive effects of individual conditions are well understood, a significant gap exists in the empirical quantification of their synergistic interactions over time. This study employs an innovative multi-model time-series methodology to dynamically forecast health service utilization and assess the impacts of major shocks in Western Province (WP), Zambia.Methods: We conducted a longitudinal ecological study utilizing 73 months of routine health data collected from 62 healthcare facilities, spanning October 2017 to September 2023. Initially, a comparative analysis of four time-series models—ARIMA, SARIMA, ETS, and Prophet—was performed to determine the most accurate forecasting approach. Subsequently, a Hierarchical Time Series (HTS) model was employed to generate reconciled forecasts. Finally, an Interrupted Time Series (ITS) analysis was implemented to quantify the impacts of three distinct shocks: a drought in 2019, the COVID-19 pandemic in 2021, and a complex double-peak flood event in 2023.Findings: The Prophet model was identified as the most accurate forecasting tool, achieving a mean absolute percentage error (MAPE) of 7·22%. The ITS analysis demonstrated that each shock distinctly affected health service utilization. The drought was associated with a significant immediate decline in utilization (level change=-0·105, p<0·001), followed by a recovery trend (trend change=0·019, p<0·001). The COVID-19 pandemic resulted in an immediate increase (level change=0·069, p<0·001) followed by a negative trend change (trend change=-0·005, p<0·001). The 2023 flood event showed no immediate impact but was associated with a significant positive trend change (trend change=0·019, p<0·001).Interpretation: This study provides innovative empirical evidence demonstrating that the interaction between environmental shocks and health system stressors dynamically affects health service utilization. The findings underscore the significance of adopting multi-model predictive strategies to produce early warnings and to inform targeted, data-driven interventions to strengthen health system resilience.

Source: The Journal of Climate Change and Health

DOI: 10.1016/j.joclim.2026.100672