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Seasonality is an important aspect of disease manifestation as well as a clue to the etiology of disease. Consistent seasonal behaviour suggests the possibility of predictable behaviour. While individual diseases are extensively studied, no studies have systematically evaluated the seasonality and predictability of hospital admissions using health services data. Using time series analysis, seasonality in the occurrence of healthcare events was examined, including seasonal fluctuations in hospital admissions, starting initially with discrete disease categories including asthma, falls and aortic aneurysms. Subsequently, it was hypothesized and confirmed that the hospitalizations in the system considered in totality also demonstrated consistent seasonal effects (Crighton et al. 2003). Recognizing the shortcomings of several test statistics, the R2 Autoreg, interpreted similarly to a correlation coefficient, was created to measure the magnitude of seasonal effects (value of 1 = perfect seasonality; 0 = no seasonality) (Moineddin et al. 2003). More than 6.5 million admissions representing 52 distinct disease categories over an 11-year timeframe were examined (Upshur et al. 2005). Hospital admissions were identified by ICD-9 code from the Canadian Institute for Health Information Discharge Abstract Database. Discharge diagnoses were ranked by frequency, converted to rates per 100,000 population and assessed for statistical evidence of seasonality. The R2 Autoreg values ranged from a high of 0.95 (bronchiolitis) to a low of 0.11 (infantile cataract). Fourteen categories showed evidence of strong seasonality (R2 Autoreg greater than 0.7), 28 categories showed evidence of moderate seasonality (R2 Autoreg between 0.40 and 0.69) and 10 categories showed evidence of weak seasonality (R2 Autoreg less than 0.40). Table 1 shows the complete admission series, ranked by R2 Autoreg values, and also indicates the number of predicted values falling outside the 95% confidence interval. As expected, many of the categories falling in the strong seasonality group were related to respiratory diseases, such as bronchiolitis, croup and pneumonia, which are strongly correlated with the presence of viral pathogens, such as influenza viruses and respiratory syncytial virus. However, the highly seasonal behaviour of chronic disease, such as osteoarthritis, and surgical conditions, such as appendicitis and uterine fibroids, was unexpected. Further analysis indicated that reduced surgical volumes drive the seasonality during the summer. Of note, conditions believed to be seasonal, such as bipolar disorder and gastrointestinal bleeding, showed no evidence of seasonality. The first 148 months of data were used to create predictive models for the final 12 months of data. The predictive models performed well: 96.5% of the predictions fell within To Everything There Is a Season: Hospitalizations in Ontario Demonstrate Strong Evidence of Seasonality and Predictability Ross Upshur

ICES Report: To Everything There Is a Season: Hospitalizations in Ontario Demonstrate Strong Evidence of Seasonality and Predictability