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Discovery of temporal and disease association patterns in condition-specific hospital utilization rates

Haimovich, Julian S ; Venkatesh, Arjun K ; Shojaee, Abbas ; Coppi, Andreas ; Warner, Frederick ; Li, Shu-Xia ; Krumholz, Harlan M Shaman, Jeffrey

PloS one, 2017-03, Vol.12 (3), p.e0172049-e0172049 [Periódico revisado por pares]

United States: Public Library of Science

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Título:
Discovery of temporal and disease association patterns in condition-specific hospital utilization rates
Autor: Haimovich, Julian S ; Venkatesh, Arjun K ; Shojaee, Abbas ; Coppi, Andreas ; Warner, Frederick ; Li, Shu-Xia ; Krumholz, Harlan M
Shaman, Jeffrey
Assuntos: Acute Disease - classification ; Acute Disease - epidemiology ; Administrative Claims, Healthcare - statistics & numerical data ; Air pollution ; Alcohol ; Analysis ; Bacterial infections ; Behavior disorders ; Bioinformatics ; Bipolar disorder ; Cardiac arrhythmia ; Care and treatment ; Classification ; Climate ; Cluster Analysis ; Clustering ; Coefficient of variation ; Data mining ; Data processing ; Databases, Factual ; Demographic aspects ; Discharge ; Earth Sciences ; Emergency medical care ; Emotional disorders ; Epidemiology ; Etiology ; Female ; Hospitalization ; Hospitalization - trends ; Hospitals ; Humans ; Infections ; Internal medicine ; Linearity ; Loess ; Mathematical analysis ; Medical care ; Medical care utilization ; Medical research ; Medicine ; Medicine and Health Sciences ; Mental disorders ; Models, Statistical ; New York - epidemiology ; Outdoor air quality ; Ovarian cancer ; Ovarian carcinoma ; Ovarian Neoplasms - epidemiology ; Patient Discharge - trends ; People and places ; Periodicity ; Regression analysis ; Schizophrenia ; Schizophrenia - epidemiology ; Seasonal variations ; Seasonal variations (Diseases) ; Seasons ; Spectral analysis ; Statistical analysis ; Studies ; Time series ; Tuberculosis ; Tuberculosis, Pulmonary - epidemiology ; Utilization ; Vector quantization
É parte de: PloS one, 2017-03, Vol.12 (3), p.e0172049-e0172049
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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Conceptualization: HMK AKV AS JSH.Data curation: JSH AS.Formal analysis: AS AC FW JSH SXL.Funding acquisition: HMK JSH AKV.Methodology: HMK AKV AS JSH AC FW.Resources: HMK.Software: JSH AS.Supervision: HMK AS AKV.Validation: JSH AS AC FW.Visualization: JSH AS.Writing – original draft: JSH AKV SXL HMK.Writing – review & editing: JSH AKV SXL HMK AC FW.
Competing Interests: Dr. Krumholz is a recipient of research agreements from Medtronic and from Johnson & Johnson (Janssen), through Yale University, to develop methods of clinical trial data sharing; is the recipient of a grant from the Food and Drug Administration and Medtronic, through Yale University, to develop methods for post-market surveillance of medical devices; works under contract with the Centers for Medicare & Medicaid Services to develop and maintain performance measures; chairs a cardiac scientific advisory board for UnitedHealth; is a participant/participant representative of the IBM Watson Health Life Sciences Board; and is the founder of Hugo, a personal health information platform. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
Descrição: 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.
Editor: United States: Public Library of Science
Idioma: Inglês

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Discovery of temporal and disease association patterns in condition-specific hospital utilization rates

Haimovich, Julian S ; Venkatesh, Arjun K ; Shojaee, Abbas ; Coppi, Andreas ; Warner, Frederick ; Li, Shu-Xia ; Krumholz, Harlan M Shaman, Jeffrey

United States: Public Library of Science

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