Capture‐recapture using multiple data sources: estimating the prevalence of diabetes

Abstract

Abstract Objective: To examine the potential for using multiple list sources and capture‐recapture methods for estimating the prevalence of diagnosed diabetes. Method: A model‐averaging procedure using an adjusted Akaike's Information Criterion (QAICc) was used to combine capture‐recapture estimates from log‐linear models obtained from simultaneously analysing four sources of data. The method was illustrated using four separate lists of patients with diabetes, resident in Otago, New Zealand. Results: Eighteen candidate models with a QAICc weight of more than 0.01 were obtained. A total of 5,716 individuals were enrolled on one or more of the four lists, of whom 379 (6.6%) appeared on all four lists and 1,670 (29.2%) appeared on one list only. The model‐averaged estimate of the total number of people with diagnosed diabetes was 6,721 (95% CI: 6,097, 7,346). The age‐standardised prevalence was 3.70% (95% CI: 3.36–4.04%) for the total population and 4.45% (95% CI: 4.03–4.86) for adults aged 15+ years. Conclusions: Estimated diabetes prevalence was consistent with national survey results. Capture‐recapture methods, combined with model averaging, are a cheap, efficient tool to estimate the prevalence of diagnosed diabetes. Implications: This method provides a relatively easy way to estimate the prevalence of diagnosed diabetes using routinely collected diabetes information, thus providing the opportunity to monitor the diabetes epidemic and inform planning decisions and resource allocation.

Keywords

• Capture‐recapture
• diabetes
• multiple lists
• model averaging