Trends, seasonal variations and forecasting of chronic respiratory disease morbidity in charcoal producing areas, northwest Ethiopia: time series analysis

ObjectiveThis study analyzed the trend, seasonal variations and forecasting of chronic respiratory disease morbidity in charcoal producing areas, northwest Ethiopia, aiming to provide evidences in planning, designing strategies, and decision-makings for preparedness and resource allocation to prevent CRD and reduce public health burden in the future.Materials and methodsThe trend, seasonal variation, and forecasting for CRD were estimated using data collected from the three zones of Amhara region annual reports of DHIS2 records. Smoothing decomposition analysis was employed to demonstrate the trend and seasonal component of CRD. The ARIMA (2, 1, 2) (0, 0, 0) model was used to forecast CRD morbidity. The model's fitness was checked based on Bayesian information criteria. The stationarity of the data was assessed with a line chart and statistically with the Ljung-Box Q-test. SPSS version 27 was utilized for statistical analysis.ResultsThe annual morbidity rate of CRD has shown an increasing trend in both sexes over a seven-year period among people aged 15 years and older. Seasonal variation in CRD morbidity was observed. The smoothing decomposition analysis depicted that the seasonal component was attributed to 44.47% and 19.16% of excess CRD cases in the period between September to November, and June to August, respectively. A substantial difference among the three zones of the Amhara region in CRD morbidity rate was noted, with the highest observed in the Awi zone. Forecasting with the ARIMA model revealed that CRD-related morbidity will continue to increase from 2020 to 2030.ConclusionThe study revealed that the CRD morbidity rate has shown an increasing trend from 2013 to 2019. Seasonal variation in the CRD morbidity rate was observed, with the highest peak from September to November. The morbidity attributed to CRD will continue to increase for the next ten years (2020–2030). Therefore, this study could potentially play a groundbreaking role. Further study is warranted to understand the risk factors and facility readiness through a further understanding of seasonality and future trends.