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Predictors of the quality of cardiovascular prevention--a multilevel cross-sectional study
Davorina Petek1, Anuska Ferligoj, Rok Platinovsek
1Department of Family Medicine, Faculty of Medicine, University of Ljubljana, Poljanski nasip 58, Ljubljana, Slovenia. davorina.petek@gmail.com
Insights
Predictors of high-quality cardiovascular disease (CVD) prevention include younger patient age and lower socioeconomic status. Smaller practices with good information systems and organized education also improve CVD prevention quality.
Area of Science:
- Cardiology
- Public Health
- Health Services Research
Background:
- Cardiovascular disease (CVD) prevention is crucial for high-risk individuals.
- Identifying predictors of quality care is essential for improving patient outcomes.
Purpose of the Study:
- To develop a predictive model for the quality of cardiovascular prevention processes in high-risk patients.
- To identify patient and practice characteristics influencing CVD prevention quality.
Main Methods:
- Multilevel regression analysis was used to examine patient and practice characteristics.
- Data were collected via chart audits and questionnaires from 645 patients across 36 family practices.
- The process of care was defined as a dependent variable using principal component analysis.
Main Results:
- Younger patient age and lower socioeconomic status were associated with higher quality CVD prevention.
- Smaller practice size, effective information systems, and organized CVD prevention education predicted better quality care.
- Multilevel regression analysis identified key predictors at both patient and practice levels.
Conclusions:
- Cardiovascular prevention quality can be measured as a composite outcome.
- Further research should refine this measurement and explore international data.
- Practice and patient characteristics significantly impact the quality of CVD prevention.
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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