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J Prim Health Care. 2011 Dec 1;3(4):262-8.

Can the prevalence of diagnosed diabetes be estimated from linked national health records? The validity of a method applied in New Zealand.

Author information

1
Department of Epidemiology and Biostatistics, School of Population Health, The University of Auckland, Auckland, New Zealand. sithor@woosh.co.nz

Abstract

INTRODUCTION:

With projected global increases in the prevalence of Type 2 diabetes, the health sector requires timely assessments of the prevalence of this disease to monitor trends, plan services, and measure the efficacy of prevention programmes.

AIM:

To assess the validity of a method to estimate the prevalence of diagnosed diabetes from linked national health records.

METHODS:

We measured the agreement between a diabetes diagnosis (using combined national lists of drug dispensing, outpatient attendance, laboratory tests (HbA1c) and hospital diagnoses) and a primary care diabetes diagnosis in a (PREDICTâ„¢) cohort of 53,911 adult New Zealanders. The completeness of the diagnosis of diabetes in the cohort was estimated using capture-recapture methods.

RESULTS:

The primary care cohort had a high prevalence of recorded diabetes (20.9%, 11,266/53,911), similar to our derived prevalence of 20.1%. Of the participants with a diagnosis of diabetes, 89% (10,182/11,266) had a similar derived diagnosis, indicating that only about one in 10 people with a primary care diagnosis had not been either admitted to hospital, seen at outpatient clinics, prescribed diabetes drugs or undertaken regular HbA1c tests. The capture-recapture prevalence of diagnosed diabetes in this cohort was 23.7% indicating that primary care diagnoses in the cohort were about 90% complete.

DISCUSSION:

A method for estimating the prevalence of diagnosed diabetes from national health data shows high-level agreement with primary care records. Linked health data can provide an efficient method for estimating the prevalence of diagnosed diabetes in regions where such records are individually linked.

Comment in

PMID:
22132378
[Indexed for MEDLINE]

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