Variations in cardiovascular disease under-diagnosis in England: national cross-sectional spatial analysisReport as inadecuate

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BMC Cardiovascular Disorders

, 11:12

First Online: 17 March 2011Received: 31 August 2010Accepted: 17 March 2011


BackgroundThere is under-diagnosis of cardiovascular disease CVD in the English population, despite financial incentives to encourage general practices to register new cases. We compared the modelled expected and diagnosed observed prevalence of three cardiovascular conditions- coronary heart disease CHD, hypertension and stroke- at local level, their geographical variation, and population and healthcare predictors which might influence diagnosis.

MethodsCross-sectional observational study in all English local authorities 351 and general practices 8,372 comparing model-based expected prevalence with diagnosed prevalence on practice disease registers. Spatial analyses were used to identify geographic clusters and variation in regression relationships.

ResultsA total of 9,682,176 patients were on practice CHD, stroke and transient ischaemic attack, and hypertension registers. There was wide spatial variation in observed: expected prevalence ratios for all three diseases, with less than five per cent of expected cases diagnosed in some areas. London and the surrounding area showed statistically significant discrepancies in observed: expected prevalence ratios, with observed prevalence much lower than the epidemiological models predicted. The addition of general practitioner supply as a variable yielded stronger regression results for all three conditions.

ConclusionsDespite almost universal access to free primary healthcare, there may be significant and highly variable under-diagnosis of CVD across England, which can be partially explained by persistent inequity in GP supply. Disease management studies should consider the possible impact of under-diagnosis on population health outcomes. Compared to classical regression modelling, spatial analytic techniques can provide additional information on risk factors for under-diagnosis, and can suggest where healthcare resources may be most needed.

Electronic supplementary materialThe online version of this article doi:10.1186-1471-2261-11-12 contains supplementary material, which is available to authorized users.

Michael Soljak, Edgar Samarasundera, Tejal Indulkar, Hannah Walford and Azeem Majeed contributed equally to this work.

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Author: Michael Soljak - Edgar Samarasundera - Tejal Indulkar - Hannah Walford - Azeem Majeed


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