Introduction
Linked administrative data are increasingly being used to evaluate health-service use because they comprehensively capture interactions with the health system. However analysis of these data are complex and require advanced strategies.
Methods
We evaluated the impact of changes in regularity of general practitioner contact on diabetes related hospitalisation using whole of population, person-level linked primary care, hospital, Electoral Roll and death records. The data were unbalanced (individuals could exit and enter multiple times), over-dispersed and contained a high proportion of zeros. Other challenges included changes in availability of tests (ascertainment bias), the likelihood of prior health service use influencing the dependent variable (initial conditions, simultaneity/reverse causality bias) and likely correlation of observed and unobserved variables.
Results
Models which included separate components for zero and non-zero outcomes, were required for these data. Mundlak variables (group-means of time-varying variables) were used to relax the assumption in the random-effects estimator that the observed variables were uncorrelated with the unobserved ones. Prior health service use was adjusted for using 4-year lags of GP contact and one-year lag of hospitalisation. Ascertainment bias was addressed using the number of years available for identification for each person as a covariate. AIC/BIC values were used to identify the best model.
Conclusion
Availability of linked data, together with increases in computing power, has vastly increased its potential for use. This has also increased the complexity of analyses being undertaken necessitating recognizing and addressing problems, such as endogeneity, that arise due to the observational nature of the studies undertaken.