Welcome!
To use the personalized features of this site, please log in or register.
If you have forgotten your username or password, we can help.
|
 |
A Method for Modeling Utilization Data from Multiple Sources: Application in a Study of Linkage to Primary Care
| |
|
A Method for Modeling Utilization Data from Multiple Sources: Application in a Study of Linkage to Primary Care Nicholas J. Horton1  , Richard Saitz2, 3 , Nan M. Laird4 and Jeffrey H. Samet5, 6 | (1) | Department of Mathematics, Smith College, Northampton, MA, USA |
| (2) | Clinical Addiction Research and Education (CARE) Unit; Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA; |
| (3) | Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA |
| (4) | Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA |
| (5) | Clinical Addiction Research and Education (CARE) Unit; Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA; |
| (6) | Department of Social and Behavioral Sciences, Boston University School of Public Health, Boston, MA, USA |
Abstract In many studies designed to measure health outcomes, information about subject utilization of health services is often obtained from multiple sources (or informants). Key methodological challenges in analyzing such data concern how they should best be represented and interpreted in statistical models. In the HELP (Health Evaluation and Linkage to Primary care) study, subjects without primary medical care undergoing alcohol or drug detoxification enrolled into a randomized controlled trial of a health evaluation intervention to link them with primary care. The outcome of interest was attendance at a primary care appointment (linkage to primary care) after discharge from the detoxification unit. Both self-report and administrative sources of linkage were collected. We apply methodology developed by Fitzmaurice et al. (American Journal of Epidemiology, 1995) to fit a single regression that allows inclusion of all multiple-source outcomes in a single multivariate regression analysis. This model allows testing for source differences in outcome, and estimation of different source effects where necessary, and includes data from subjects with partially observed source observations. These methods were applied to the analysis of the HELP study using correlated survival regression models to assess the magnitude and significance of the relationship between predictor variables and linkage. multiple sources - multiple informants - generalized estimating equations - service utilization
Fulltext Preview (Small, Large)
 References secured to subscribers.
|
|
|
|
|
|