The Evolution of Comorbidities in Hospital Administrative Databases: A 15-Year Analysis

Alberto Freitas, Isabel Garcia Lema and Altamiro Costa-Pereira
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Alberto Freitas: Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine of the University of Porto (FMUP), Porto, Portugal and CINTESIS - Center for Health Technology and Services Research, Portugal
Isabel Garcia Lema: Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine of the University of Porto (FMUP), Porto, Portugal and CINTESIS - Center for Health Technology and Services Research, Portugal
Altamiro Costa-Pereira: Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine of the University of Porto (FMUP), Porto, Portugal and CINTESIS - Center for Health Technology and Services Research, Portugal

International Journal of Reliable and Quality E-Healthcare (IJRQEH), 2017, vol. 6, issue 2, 29-39

Abstract: The analysis of inpatient comorbidities is important for hospital management, epidemiological studies, and health services research and planning. This paper aims to study the evolution of coded comorbidities in a nationwide administrative database. Specifically, data from Portuguese hospitals over the period 2000-2014 was used. Secondary diagnoses, coded with ICD-9-CM, were used to identify comorbidities in 9,613,563 inpatient episodes, using both the Elixhauser and the Charslon/Deyo methods. A description of comorbidities evolution over years, including an analysis of the associated principal diagnosis, was carried out. Results clearly evidence a positive association between the number of secondary diagnoses and coded comorbidities. It can be argued that the increased number of comorbidities over time is mostly related to an increase in the quality of coded data, and not so much to an increase in the severity of treated patients. Data analysts, researchers and decision makers should be alert to possible data quality bias, such as completeness, when using administrative databases.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jrqeh0:v:6:y:2017:i:2:p:29-39

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