The importance of historical residential address information in longitudinal studies using administrative health data

When information on changes in address or migration of people to or from a study jurisdiction is unavailable in longitudinal studies, issues relating to loss-to-follow-up and misclassification bias may result. This study investigated how estimations of associations between general practitioner (GP) contact and hospital use were affected by incomplete address and migration data.

This was a retrospective population-based cohort study of Western Australians from 1990 to 2004. Linked administrative data including mortality records, hospital admissions, primary care and Electoral Roll records were used. Regularity of GP contact, based on the variance of the number of days between GP visits, was calculated for each person-year. Outcomes were the number and costs (A$2014) of diabetes-related hospital admissions in the following year. Models were estimated separately for cohorts where (i) postcode was ascertained at study commencement and held constant, and (ii) postcode and residency within Western Australia were updated with each change of address recorded on the Electoral Roll over the study period.

Updating address data reduced total person-years by 11% and changed the distribution of covariates. Estimations of associations between patterns of GP contact and number of hospitalizations changed; the incidence rate ratios measuring the relationship with the most regular GP contact (baseline of those with <2 GP visits) changed from 0.81 [95% confidence interval (CI) 0.66-1.00] to 0.42 (95% CI 0.33-0.53) after updating postcode information. Impacts on cost models were smaller, though still statistically significant.

Longitudinal studies using administrative data may report biased results if they ignore address changes and migration. Researchers should attempt to link to these data wherever possible, or choose study designs which these issues are less likely to affect. Custodians should be aware that such data can be vital to high quality research.