ArticleBMJ open2014
Undiagnosed diabetes from cross-sectional GP practice data: an approach to identify communities with high likelihood of undiagnosed diabetes.
Article in BMJ open, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Combining Geospatial Analysis with Dementia Risk Utilising General Practice Data: A Systematic Review.The journal of prevention of Alzheimer's disease · 2018Pooled it
- Spillover effects from a type 2 diabetes integrated model of care in 22,706 Australians: an open cohort stepped wedge trial.BMC endocrine disorders · 2024Article
- Cost-Utility Analysis of Dapagliflozin Compared to Sulfonylureas for Type 2 Diabetes as Second-Line Treatment in Indian Healthcare Payer's Perspective.ClinicoEconomics and outcomes research : CEOR · 2021Article
- Development of the Australian neighborhood social fragmentation index and its association with spatial variation in depression across communities.Social psychiatry and psychiatric epidemiology · 2019Article
- Practical utility of general practice data capture and spatial analysis for understanding COPD and asthma.BMC health services research · 2018Article
- Prevalence of diabetes in Australia: insights from the Fremantle Diabetes Study Phase II.Internal medicine journal · 2018Article
- Awareness of Pre-diabetes or Diabetes and Associated Factors in People With Psychosis.Schizophrenia bulletin · 2016Article
- Impact of geography on the control of type 2 diabetes mellitus: a review of geocoded clinical data from general practice.BMJ open · 2015Article
- Postmortem diagnosis of diabetes mellitus and its complications.Croatian medical journal · 2015Review
- Community cardiovascular disease risk from cross-sectional general practice clinical data: a spatial analysis.Preventing chronic disease · 2015Article
- A taxonomy of hospitals based on partnerships for population health management.Health care management reviewArticle
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7 authors.
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Abstract
objectivesTo estimate undiagnosed diabetes prevalence from general practitioner (GP) practice data and identify areas with high levels of undiagnosed and diagnosed diabetes.
designData from the North-West Adelaide Health Survey (NWAHS) were used to develop a model which predicts total diabetes at a small area. This model was then applied to cross-sectional data from general practices to predict the total level of expected diabetes. The difference between total expected and already diagnosed diabetes was defined as undiagnosed diabetes prevalence and was estimated for each small area. The patterns of diagnosed and undiagnosed diabetes were mapped to highlight the areas of high prevalence.
settingNorth-West Adelaide, Australia.
participantsThis study used two population samples-one from the de-identified GP practice data (n=9327 active patients, aged 18 years and over) and another from NWAHS (n=4056, aged 18 years and over).
main outcome measuresTotal diabetes prevalence, diagnosed and undiagnosed diabetes prevalence at GP practice and Statistical Area Level 1.
resultsOverall, it was estimated that there was one case of undiagnosed diabetes for every 3-4 diagnosed cases among the 9327 active patients analysed. The highest prevalence of diagnosed diabetes was seen in areas of lower socioeconomic status. However, the prevalence of undiagnosed diabetes was substantially higher in the least disadvantaged areas.
conclusionsThe method can be used to estimate population prevalence of diabetes from general practices wherever these data are available. This approach both flags the possibility that undiagnosed diabetes may be a problem of less disadvantaged social groups, and provides a tool to identify areas with high levels of unmet need for diabetes care which would enable policy makers to apply geographic targeting of effective interventions.
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