Cargando…

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

INTRODUCTION: Cardiovascular disease (CVD) continues to be a leading cause of illness and death among adults worldwide. The objective of this study was to calculate a CVD risk score from general practice (GP) clinical records and assess spatial variations of CVD risk in communities. METHODS: We used...

Descripción completa

Detalles Bibliográficos
Autores principales: Bagheri, Nasser, Gilmour, Bridget, McRae, Ian, Konings, Paul, Dawda, Paresh, Del Fante, Peter, van Weel, Chris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Centers for Disease Control and Prevention 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4344355/
https://www.ncbi.nlm.nih.gov/pubmed/25719216
http://dx.doi.org/10.5888/pcd12.140379
_version_ 1782359410890244096
author Bagheri, Nasser
Gilmour, Bridget
McRae, Ian
Konings, Paul
Dawda, Paresh
Del Fante, Peter
van Weel, Chris
author_facet Bagheri, Nasser
Gilmour, Bridget
McRae, Ian
Konings, Paul
Dawda, Paresh
Del Fante, Peter
van Weel, Chris
author_sort Bagheri, Nasser
collection PubMed
description INTRODUCTION: Cardiovascular disease (CVD) continues to be a leading cause of illness and death among adults worldwide. The objective of this study was to calculate a CVD risk score from general practice (GP) clinical records and assess spatial variations of CVD risk in communities. METHODS: We used GP clinical data for 4,740 men and women aged 30 to 74 years with no history of CVD. A 10-year absolute CVD risk score was calculated based on the Framingham risk equation. The individual risk scores were aggregated within each Statistical Area Level One (SA1) to predict the level of CVD risk in that area. Finally, the pattern of CVD risk was visualized to highlight communities with high and low risk of CVD. RESULTS: The overall 10-year risk of CVD in our sample population was 14.6% (95% confidence interval [CI], 14.3%–14.9%). Of the 4,740 patients in our study, 26.7% were at high risk, 29.8% were at moderate risk, and 43.5% were at low risk for CVD over 10 years. The proportion of patients at high risk for CVD was significantly higher in the communities of low socioeconomic status. CONCLUSION: This study illustrates methods to further explore prevalence, location, and correlates of CVD to identify communities of high levels of unmet need for cardiovascular care and to enable geographic targeting of effective interventions for enhancing early and timely detection and management of CVD in those communities.
format Online
Article
Text
id pubmed-4344355
institution National Center for Biotechnology Information
language English
publishDate 2015
publisher Centers for Disease Control and Prevention
record_format MEDLINE/PubMed
spelling pubmed-43443552015-03-06 Community Cardiovascular Disease Risk From Cross-Sectional General Practice Clinical Data: A Spatial Analysis Bagheri, Nasser Gilmour, Bridget McRae, Ian Konings, Paul Dawda, Paresh Del Fante, Peter van Weel, Chris Prev Chronic Dis Original Research INTRODUCTION: Cardiovascular disease (CVD) continues to be a leading cause of illness and death among adults worldwide. The objective of this study was to calculate a CVD risk score from general practice (GP) clinical records and assess spatial variations of CVD risk in communities. METHODS: We used GP clinical data for 4,740 men and women aged 30 to 74 years with no history of CVD. A 10-year absolute CVD risk score was calculated based on the Framingham risk equation. The individual risk scores were aggregated within each Statistical Area Level One (SA1) to predict the level of CVD risk in that area. Finally, the pattern of CVD risk was visualized to highlight communities with high and low risk of CVD. RESULTS: The overall 10-year risk of CVD in our sample population was 14.6% (95% confidence interval [CI], 14.3%–14.9%). Of the 4,740 patients in our study, 26.7% were at high risk, 29.8% were at moderate risk, and 43.5% were at low risk for CVD over 10 years. The proportion of patients at high risk for CVD was significantly higher in the communities of low socioeconomic status. CONCLUSION: This study illustrates methods to further explore prevalence, location, and correlates of CVD to identify communities of high levels of unmet need for cardiovascular care and to enable geographic targeting of effective interventions for enhancing early and timely detection and management of CVD in those communities. Centers for Disease Control and Prevention 2015-02-26 /pmc/articles/PMC4344355/ /pubmed/25719216 http://dx.doi.org/10.5888/pcd12.140379 Text en https://creativecommons.org/licenses/by/4.0/This is a publication of the U.S. Government. This publication is in the public domain and is therefore without copyright. All text from this work may be reprinted freely. Use of these materials should be properly cited.
spellingShingle Original Research
Bagheri, Nasser
Gilmour, Bridget
McRae, Ian
Konings, Paul
Dawda, Paresh
Del Fante, Peter
van Weel, Chris
Community Cardiovascular Disease Risk From Cross-Sectional General Practice Clinical Data: A Spatial Analysis
title Community Cardiovascular Disease Risk From Cross-Sectional General Practice Clinical Data: A Spatial Analysis
title_full Community Cardiovascular Disease Risk From Cross-Sectional General Practice Clinical Data: A Spatial Analysis
title_fullStr Community Cardiovascular Disease Risk From Cross-Sectional General Practice Clinical Data: A Spatial Analysis
title_full_unstemmed Community Cardiovascular Disease Risk From Cross-Sectional General Practice Clinical Data: A Spatial Analysis
title_short Community Cardiovascular Disease Risk From Cross-Sectional General Practice Clinical Data: A Spatial Analysis
title_sort community cardiovascular disease risk from cross-sectional general practice clinical data: a spatial analysis
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4344355/
https://www.ncbi.nlm.nih.gov/pubmed/25719216
http://dx.doi.org/10.5888/pcd12.140379
work_keys_str_mv AT bagherinasser communitycardiovasculardiseaseriskfromcrosssectionalgeneralpracticeclinicaldataaspatialanalysis
AT gilmourbridget communitycardiovasculardiseaseriskfromcrosssectionalgeneralpracticeclinicaldataaspatialanalysis
AT mcraeian communitycardiovasculardiseaseriskfromcrosssectionalgeneralpracticeclinicaldataaspatialanalysis
AT koningspaul communitycardiovasculardiseaseriskfromcrosssectionalgeneralpracticeclinicaldataaspatialanalysis
AT dawdaparesh communitycardiovasculardiseaseriskfromcrosssectionalgeneralpracticeclinicaldataaspatialanalysis
AT delfantepeter communitycardiovasculardiseaseriskfromcrosssectionalgeneralpracticeclinicaldataaspatialanalysis
AT vanweelchris communitycardiovasculardiseaseriskfromcrosssectionalgeneralpracticeclinicaldataaspatialanalysis