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Predictors of the quality of cardiovascular prevention – a multilevel cross-sectional study
AIM: To attempt to develop a model of predictors for quality of the process of cardiovascular prevention in patients at high risk of cardiovascular disease (CVD). METHODS: We formed a random sample of patients from a stratified sample of 36 family practice registers of patients at high risk of CVD w...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Croatian Medical Schools
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3243324/ https://www.ncbi.nlm.nih.gov/pubmed/22180271 http://dx.doi.org/10.3325/cmj.2011.52.718 |
Sumario: | AIM: To attempt to develop a model of predictors for quality of the process of cardiovascular prevention in patients at high risk of cardiovascular disease (CVD). METHODS: We formed a random sample of patients from a stratified sample of 36 family practice registers of patients at high risk of CVD without diabetes and without established CVD. Data were gathered by chart audit and questionnaires about patient and practice characteristics. We defined the process of care as a dependent variable by principle component analysis and tested the relationship of the process with several independent variables (family physicians’, patients’, and practice characteristics). To study the effects of independent variables (predictors) on the process of care we carried out multilevel regression analysis with the patients constituting the lower level and nested within the family physician/practice (the second level). RESULTS: Multilevel regression analysis included 645 patients from 36 practices (74.1% from the final sample). Patients’ characteristics that predicted the higher-quality process of CVD prevention were younger age (t = -4.94, 95% confidence interval [CI] -0.018 to -0.008) and lower socioeconomic status (t = -2.18, 95%CI -0.195 to -0.010). Practice characteristics that predicted the higher-quality process of CVD prevention were smaller practice size (t = 2.83, 95% CI 0.063 to 1.166), a good information system for CVD prevention (t = 3.15, 95% CI 0.030 to 0.282), and the organization of education on CVD prevention (t = 3.19, 95%CI 0.043 to 0.380). CONCLUSION: This study shows that the quality of cardiovascular prevention could be measured as a composite outcome and future studies should further develop this approach and test the impact of several practice/patient characteristics on the quality of CVD prevention with the international data. |
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