Validating self-report of diabetes use by participants in the 45 and up study: a record linkage study

Abstract

BackgroundPrevalence studies usually depend on self-report of disease status in survey data or administrative data collections and may over- or under-estimate disease prevalence. The establishment of a linked data collection provided an opportunity to explore the accuracy and completeness of capture of information about diabetes in survey and administrative data collections.MethodsBaseline questionnaire data at recruitment to the 45 and Up Study was obtained for 266,848 adults aged 45 years and over sampled from New South Wales, Australia in 2006–2009, and linked to administrative data about hospitalisation from the Admitted Patient Data Collection (APDC) for 2000–2009, claims for medical services (MBS) and pharmaceuticals (PBS) from Medicare Australia data for 2004–2009. Diabetes status was determined from response to a question ‘Has a doctor EVER told you that you have diabetes’ (n = 23,981) and augmented by examination of free text fields about diagnosis (n = 119) or use of insulin (n = 58). These data were used to identify the sub-group with type 1 diabetes. We explored the agreement between self-report of diabetes, identification of diabetes diagnostic codes in APDC data, claims for glycosylated haemoglobin (HbA1c) in MBS data, and claims for dispensed medication (oral hyperglycaemic agents and insulin) in PBS data.ResultsMost participants with diabetes were identified in APDC data if admitted to hospital (79.3%), in MBS data with at least one claim for HbA1c testing (84.7%; 73.4% if 2 tests claimed) or in PBS data through claim for diabetes medication (71.4%). Using these alternate data collections as an imperfect ‘gold standard’ we calculated sensitivities of 83.7% for APDC, 63.9% (80.5% for two tests) for MBS, and 96.6% for PBS data and specificities of 97.7%, 98.4% and 97.1% respectively. The lower sensitivity for HbA1c may reflect the use of this test to screen for diabetes suggesting that it is less useful in identifying people with diabetes without additional information. Kappa values were 0.80, 0.70 and 0.80 for APDC, MBS and PBS respectively reflecting the large population sample under consideration. Compared to APDC, there was poor agreement about identifying type 1 diabetes status.ConclusionsSelf-report of diagnosis augmented with free text data indicating diabetes as a chronic condition and/or use of insulin among medications used was able to identify participants with diabetes with high sensitivity and specificity compared to available administrative data collections.

Risk factors of incident type 2-diabetes mellitus over a 3-year follow-up: Results from a large Australian sample

“…The limitations of the study included the short follow-up time (three years) and the use of self-reported measures of risk factors and the diabetes outcome measure. However, most risk factors were measured using validated instruments and the self-reported measure of physiciandiagnosed diabetes was found to have high sensitivity and specificity when compared against linked administrative data [13]. Due to the lack of blood samples or other biological measures, the percentage of undiagnosed diabetes could be high, which could partially explain the much lower incidence of T2DM of the current study as compared with AusDiab and the Blue Mountains Eye Study.…”

“…Incident T2DM was defined as not having diabetes at baseline and a diagnosis of T2DM between baseline and follow-up. A previous study from the same cohort validated the self-report of diabetes against linked administrative data on hospitalization, medical services, and pharmaceuticals and found that reported diagnosis of diabetes in the 45 and Up study had high sensitivity and specificity [13].…”

National Patterns in Diabetes Screening: Data from the National Health and Nutrition Examination Survey (NHANES) 2005–2012

“…Further, numerous studies suggest that people diagnosed with diabetes are aware of their condition and can report it accurately. [23][24][25] Regarding the accuracy of recalling diabetes screening, data are limited. However, for cholesterol screening, which is analogous in its mechanism of screening (blood draw, sometimes fasting), the available evidence indicates that screening recall (both of it being performed and of the results) is fairly reliable, 26,27 although some data indicate that people tend to underreport such screening.…”

Optimising the use of linked administrative data for infectious diseases research in Australia

“…Primary care data available through the Australian Government's universal health insurer, Medicare, has not been used to assess the burden of infectious diseases at the lower end of the disease severity scale but has been successfully used for diabetes research. 7 These national datasets are available for linkage through approved integrating authorities: the Australian Bureau of Statistics, the Australian Institute of Health and Welfare and the Australian Institute of Family Studies. In recent times, access to, and linkages between, state and national data have been hampered by lengthy approval processes.…”