Community Cardiovascular Disease Risk From Cross-Sectional General Practice Clinical Data: A Spatial Analysis

Bagheri, Nasser; Gilmour, Bridget; McRae, Ian; Konings, Paul; Dawda, Paresh; Fante, Peter Del; Weel, Chris van

doi:10.5888/pcd12.140379

Cited by 25 publications

(19 citation statements)

References 20 publications

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“…Our findings are consistent with previous findings that incidence and prevalence of CVD is higher in people of low compared to high socioeconomic position. [29][30][31][32] The observed inverse association between socioeconomic position and treatment in people with prior CVD were contrary to our expectation that, consistent with findings of inequalities in care 33 , people with a low socioeconomic position would be less likely to receive treatment than those with a high socioeconomic position.…”

Section: Discussioncontrasting

confidence: 97%

Socioeconomic variation in absolute cardiovascular disease risk and treatment in the Australian population

Paige

Welsh

Agostino

et al. 2018

Preventive Medicine

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Cardiovascular disease (CVD), preventable through appropriate management of absolute CVD risk, disproportionately affects socioeconomically disadvantaged individuals. The aim of this study was to estimate absolute and relative socioeconomic inequalities in absolute CVD risk and treatment in the Australian population using cross-sectional representative data on 4751 people aged 45-74 from the 2011-12 Australian Health Survey. Poisson regression was used to calculate prevalence differences (PD) and ratios (PR) for prior CVD, high 5-year absolute risk of a primary CVD event and guideline-recommended medication use, in relation to socioeconomic position (SEP, measured by education). After adjusting for age and sex, the prevalence of high absolute risk of a primary CVD event among those of low, intermediate and high SEP was 12.6%, 10.9% and 7.7% (PD, low vs. high = 5.0 [95% CI: 2.3, 7.7], PR = 1.6 [1.2, 2.2]) and for prior CVD was 10.7%, 9.1% and 6.7% (PD = 4.0 [1.4, 6.6], PR = 1.6 [1.1, 2.2]). The proportions using preventive medication use among those with high primary risk were 21.3%, 19.5% and 29.4% for low, intermediate and high SEP and for prior CVD, were 37.8%, 35.7% and 17.7% (PD = 20.1 [9.7, 30.5], PR = 2.1 [1.3, 3.5]). Proportions at high primary risk and not using medications among those of low, intermediate and high SEP were 10.6%, 8.8% and 4.7% and with prior CVD and not using medications were 8.5%, 6.3% and 4.1%. Findings indicate substantial potential to prevent CVD and reduce inequalities through appropriate management of high absolute risk in the population.

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Section: Discussioncontrasting

confidence: 97%

Socioeconomic variation in absolute cardiovascular disease risk and treatment in the Australian population

Paige

Welsh

Agostino

et al. 2018

Preventive Medicine

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“…96 Geomapping, the identification of geographic “hot spots” of individuals at high risk for CVD, may be utilized in the future to target communities that would benefit from aggressive community-based interventions. 97 Geomapping has demonstrated promising early results in Sweden for identification of at-risk populations for diabetes and could be adapted to provide increased screening and treatment services for low-SES communities with a high prevalence of CVD risk. 98…”

Section: Interventions To Improve Health Behaviors and Risk Factorsmentioning

confidence: 99%

Socioeconomic Status and Cardiovascular Outcomes

Schultz¹,

Kelli

Lisko³

et al. 2018

Circulation

957

638

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Socioeconomic status (SES) has a measurable and significant effect on cardiovascular health. Biological, behavioral, and psychosocial risk factors prevalent in disadvantaged individuals accentuate the link between SES and cardiovascular disease (CVD). Four measures have been consistently associated with CVD in high-income countries: income level, educational attainment, employment status, and neighborhood socioeconomic factors. In addition, disparities based on sex have been shown in several studies. Interventions targeting patients with low SES have predominantly focused on modification of traditional CVD risk factors. Promising approaches are emerging that can be implemented on an individual, community, or population basis to reduce disparities in outcomes. Structured physical activity has demonstrated effectiveness in low-SES populations, and geomapping may be used to identify targets for large-scale programs. Task shifting, the redistribution of healthcare management from physician to nonphysician providers in an effort to improve access to health care, may have a role in select areas. Integration of SES into the traditional CVD risk prediction models may allow improved management of individuals with high risk, but cultural and regional differences in SES make generalized implementation challenging. Future research is required to better understand the underlying mechanisms of CVD risk that affect individuals of low SES and to determine effective interventions for patients with high risk. We review the current state of knowledge on the impact of SES on the incidence, treatment, and outcomes of CVD in high-income societies and suggest future research directions aimed at the elimination of these adverse factors, and the integration of measures of SES into the customization of cardiovascular treatment.

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“…A study by Noble et al (2011) examined the feasibility of mapping T2D risk at the community level in a London (UK) district. In Australia, several small studies have 25 used general practice data to identify and map areas of undiagnosed T2D (Bagheri et al 2014), diagnosed T2D (Jiwa et al 2015) and cardiovascular disease (CVD) risk (Bagheri et al 2015) and, in the USA, a larger study by Gabert et al (2016) examined diagnosed T2D. However, this unique and rich source 30 of routinely collected data in general practice is underutilised for the purposes of T2D risk prediction, geospatial analyses and community-based interventions.…”

mentioning

confidence: 99%

Identifying hotspots of type 2 diabetes risk using general practice data and geospatial analysis: an approach to inform policy and practice

Bagheri

Konings

Wangdi

et al. 2020

Aust. J. Prim. Health

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The prevalence of type 2 diabetes (T2D) is increasing worldwide and there is a need to identify communities with a high-risk profile and to develop appropriate primary care interventions. This study aimed to predict future T2D risk and identify community-level geographic variations using general practices data. The Australian T2D risk assessment (AUSDRISK) tool was used to calculate the individual T2D risk scores using 55693 clinical records from 16 general practices in west Adelaide, South Australia, Australia. Spatial clusters and potential ‘hotspots’ of T2D risk were examined using Local Moran’s I and the Getis-Ord Gi* techniques. Further, the correlation between T2D risk and the socioeconomic status of communities were mapped. Individual risk scores were categorised into three groups: low risk (34.0% of participants), moderate risk (35.2% of participants) and high risk (30.8% of participants). Spatial analysis showed heterogeneity in T2D risk across communities, with significant clusters in the central part of the study area. These study results suggest that routinely collected data from general practices offer a rich source of data that may be a useful and efficient approach for identifying T2D hotspots across communities. Mapping aggregated T2D risk offers a novel approach to identifying areas of unmet need.

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Community Cardiovascular Disease Risk From Cross-Sectional General Practice Clinical Data: A Spatial Analysis

Cited by 25 publications

References 20 publications

Socioeconomic variation in absolute cardiovascular disease risk and treatment in the Australian population

Socioeconomic variation in absolute cardiovascular disease risk and treatment in the Australian population

Socioeconomic Status and Cardiovascular Outcomes

Identifying hotspots of type 2 diabetes risk using general practice data and geospatial analysis: an approach to inform policy and practice

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