An Event Count Model for Studying Health Services Utilization

Dados Bibliográficos

ID9099657
AutoresChunhuei Chi (0000-0002-9203-5352, Oregon State University, autor correspondente)
Ano1998
Volume36
Fascículo12
Páginas1639-1659
Data de publicação1998-12-01
Peer ReviewedSim
Open AccessNão
TipoARTICLE
PeriódicoMedical Care (JOURNAL)
Identificadores do periódicoISSN: 0025-7079 • E-ISSN: 1537-1948
EditoraOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/00005650-199812000-00003
PMID9860054
OpenAlexW2009521601
IdiomaEN
Citações recebidas3
Referências citadas21

OBJECTIVES: The purpose of this article is to first conceptualize health services utilization behavior as event counts. Based on this concept and behavioral theory, the author presents the Generalized Event Count model as an alternative modeling tool for studying health services utilization. This model is theory driven and is consistent with behavioral assumptions. METHODS: In presenting the Generalized Event Count model, the author first examines its model assumptions to see whether they conform to elements of behavior theory, assumptions of health services utilization, and the distribution assumption of the nature of the data. To demonstrate the Generalized Event Count model, the author applied this model to an empirical ambulatory care utilization data set from a 1988 household interview of Chinese-Americans in Boston's inner-city community. RESULTS: The Generalized Event Count model analysis suggested that the regular source of medical care and the use of Chinese medicine were strong predictors of physician visits for this population. Further, the Generalized Event Count model was able to test that most of the ambulatory visits within an individual were correlated. CONCLUSIONS: Compared with other models, the Generalized Event Count model is more consistent with health services utilization behavioral assumptions. Moreover, it makes an efficient use of information from the utilization data for model estimation. This model has the potential of having broad applications in studying various types of health services utilization, especially for analyzing cross-sectional utilization data

Event (particle physics) · Computer Science · Health disparities and outcomes · Healthcare Systems and Reforms · Intergenerational Family Dynamics and Caregiving