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Identifying temporal variation in hospitalization rates may provide insights about disease patterns and thereby inform research, policy, and clinical care. However, the majority of medical conditions have not been studied for their potential seasonal variation. The objective of this study was to apply a data-driven approach to characterize temporal variation in condition-specific hospitalizations. Using a dataset of 34 million inpatient discharges gathered from hospitals in New York State from 2008–2011, we grouped all discharges into 263 clinical conditions based on the principal discharge diagnosis using Clinical Classification Software in order to mitigate the limitation that administrative claims data reflect clinical conditions to varying specificity. After applying Seasonal-Trend Decomposition by LOESS, we estimated the periodicity of the seasonal component using spectral analysis and applied harmonic regression to calculate the amplitude and phase of the condition’s seasonal utilization pattern. We also introduced four new indices of temporal variation: mean oscillation width, seasonal coefficient, trend coefficient, and linearity of the trend. Finally, K-means clustering was used to group conditions across these four indices to identify common temporal variation patterns. Of all 263 clinical conditions considered, 164 demonstrated statistically significant seasonality. Notably, we identified conditions for which seasonal variation has not been previously described such as ovarian cancer, tuberculosis, and schizophrenia. Clustering analysis yielded three distinct groups of conditions based on multiple measures of seasonal variation. Our study was limited to New York State and results may not directly apply to other regions with distinct climates and health burden. A substantial proportion of medical conditions, larger than previously described, exhibit seasonal variation in hospital utilization. Moreover, the application of clustering tools yields groups of clinically heterogeneous conditions with similar seasonal phenotypes. Further investigation is necessary to uncover common etiologies underlying these shared seasonal phenotypes.

Background Heart failure as recognized and treated in typical practice may represent a complex condition that defies discrete categorizations. To illuminate this complexity, we examined treatment strategies for patients hospitalized and treated for decompensated heart failure. We focused on the receipt of medications appropriate for other acute conditions associated with shortness of breath including acute asthma, pneumonia, and exacerbated chronic obstructive pulmonary disease. Methods and Results Using Premier Perspective®, we studied adults hospitalized with a principal discharge diagnosis of heart failure and evidence of acute heart failure treatment from 2009-2010 at 370 US hospitals. We determined treatment with acute respiratory therapies during the initial 2 days of hospitalization and daily during hospital days 3-5. We also calculated adjusted odds of in-hospital death, admission to the intensive care unit, and late intubation (intubation after hospital day 2). Among 164,494 heart failure hospitalizations, 53% received acute respiratory therapies during the first 2 hospital days: 37% received short-acting inhaled bronchodilators, 33% received antibiotics, and 10% received high-dose corticosteroids. Of these 87,319 hospitalizations, over 60% continued receiving respiratory therapies after hospital day 2. Respiratory treatment was more frequent among the 60,690 hospitalizations with chronic lung disease. Treatment with acute respiratory therapy during the first 2 hospital days was associated with higher adjusted odds of all adverse outcomes. Conclusions Acute respiratory therapy is administered to more than half of patients hospitalized with and treated for decompensated heart failure. Heart failure is therefore regularly treated as a broader cardiopulmonary syndrome rather than as a singular cardiac condition.