Gill, Geoff, Ismail, A. A., Beeching, Nicholas ORCID: https://orcid.org/0000-0002-7019-8791, Macfarlane, S. B. J. and Bellis, M. A. (2003) 'Hidden diabetes in the UK: use of capture-recapture methods to estimate total prevalence of diabetes mellitus in an urban population'. Journal of the Royal Society of Medicine, Vol 96, Issue 7, pp. 328-332.
Full text not available from this repository.Abstract
An early requirement of the UK's Diabetes National Service Framework is enumeration of the total affected population. Existing estimates tend to be based on incomplete lists. In a study conducted over one year in North Liverpool, we compared crude prevalence rates for type 1 and type 2 diabetes with estimates obtained by capture-recapture (CR) analysis of multiple incomplete patient lists, to assess the extent of unascertained but diagnosed cases. Patient databases were constructed from six sources-a hospital diabetes centre; general practitioner registers; hospital admissions with a diagnosis of diabetes; a hospital diabetic retinal clinic; a research list of patients with diabetes admitted with stroke; and a local children's hospital. Log linear modelling was used to estimate missing cases, hence total prevalence. The crude prevalence of diabetes was 1.5% (95% confidence interval [CI] 1.41, 1.52), compared with a CR-adjusted rate of 3.1 % (CI 3.03, 3.19). Age-banded CR-adjusted prevalence was always higher in males than in females and the difference became more pronounced with increasing age. Among males, CR-adjusted prevalence rose from 0.4% at age 10-19 years to 18.3% at 80+ years; in females the corresponding figures were 0.4% and 9.3%. The gap between crude and CR-estimated prevalence points to a rate of hidden diabetes' that has substantial implications for future diabetes care.
Item Type: | Article |
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Subjects: | WK Endocrine System > WK 810 Diabetes mellitus |
Digital Object Identifer (DOI): | https://doi.org/10.1258/jrsm.96.7.328 |
Depositing User: | Martin Chapman |
Date Deposited: | 29 Jan 2013 15:03 |
Last Modified: | 22 Nov 2024 08:00 |
URI: | https://archive.lstmed.ac.uk/id/eprint/2552 |
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