Can administrative health utilisation data provide an accurate diabetes prevalence estimate for a geographical region?. (May 2018)

• Record Type: Journal Article
• Authors: Chan, Wing Cheuk; Papaconstantinou, Dean; Lee, Mildred; Telfer, Kendra; Jo, Emmanuel; Drury, Paul L.; Tobias, Martin
• Is Part Of: Diabetes research and clinical practice. Volume 139(2018)
• Page Start: 59
• Page End: 71
• Publication Date: 2018-05
• Subjects: Diabetes mellitus -- Prevalence -- Health services utilisation -- Administrative data -- Epidemiology -- Comparative studies; Diabetes -- Periodicals; Diabetes Mellitus -- Periodicals; 616.462
• DOI: 10.1016/j.diabres.2018.02.028
• Languages: English
• ISSNs: 0168-8227

Abstract

Highlights: Administrative health service utilisation data can be used to estimate diabetes prevalence at a population level. The study optimises an algorithm to estimate diabetes prevalence using administrative data. At an individual level, the use of diabetes-related services may not be a good proxy of a clinical diagnosis of diabetes. This study highlights the potential value of a register constructed from both laboratory results and administrative data. Abstract: Aim: To validate the New Zealand Ministry of Health (MoH) Virtual Diabetes Register (VDR) using longitudinal laboratory results and to develop an improved algorithm for estimating diabetes prevalence at a population level. Methods: The assigned diabetes status of individuals based on the 2014 version of the MoH VDR is compared to the diabetes status based on the laboratory results stored in the Auckland regional laboratory result repository (TestSafe) using the New Zealand diabetes diagnostic criteria. The existing VDR algorithm is refined by reviewing the sensitivity and positive predictive value of the each of the VDR algorithm rules individually and as a combination. Results: The diabetes prevalence estimate based on the original 2014 MoH VDR was 17% higher (n = 108, 505) than the corresponding TestSafe prevalence estimate (n = 92, 707). Compared to the diabetes prevalence based on TestSafe, the original VDR has a sensitivity of 89%, specificity of 96%, positive predictive value of 76% and negative predictive value of 98%. The modified VDR algorithm has improved the positive predictive value by 6.1% and the specificity by 1.4% with modest reductions in sensitivity of 2.2% and negative predictive value of 0.3%. At an aggregated level the overall diabetes prevalence estimated by the modified VDR is 5.7% higher than the corresponding estimate based on TestSafe. Conclusion: The Ministry of Health Virtual Diabetes Register algorithm has been refined to provide a more accurate diabetes prevalence estimate at a population level. The comparison highlights the potential value of a national population long term condition register constructed from both laboratory results and administrative data.