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Aim: Our aim was to evaluate the implementation process of a comprehensive cardiovascular disease prevention program in general practice, to enhance understanding of influencing factors to implementation success and sustainability, and to learn how to overcome barriers., Background: Cardiovascular disease and its risk factors are the world's leading cause of mortality, yet can be prevented by addressing unhealthy lifestyle behavior. Nevertheless, the transition toward a prevention-oriented primary health care remains limited. A better understanding of factors facilitating or hindering implementation success and sustainability of prevention programs, and how barriers may be addressed, is needed. This work is part of Horizon 2020 project 'SPICES', which aims to implement validated preventive interventions in vulnerable populations., Methods: We conducted a qualitative process evaluation with participatory action research approach of implementation in five general practices. Data were collected through 38 semi-structured individual and small group interviews with seven physicians, 11 nurses, one manager and one nursing assistant, conducted before, during, and after the implementation period. We applied adaptive framework analysis guided by RE-AIM Qualitative Evaluation for Systematic Translation (RE-AIM QuEST) and Consolidated Framework for Implementation Research (CFIR)., Findings: Multiple facilitators and barriers affected reach of vulnerable target populations: adoption by primary health care providers, implementation and fidelity and intention to maintain the program into routine practice. In addition, our study revealed concrete actions, linked to implementation strategies, that can be undertaken to address identified barriers. Prioritization of prevention in general practice vision, ownership, and shared responsibility of all team members, compatibility with existing work processes and systems, expanding nurse's roles and upskilling competence profiles, supportive financial and regulatory frameworks, and a strong community - health care link are crucial to increase implementation success and long-term maintenance of prevention programs. COVID-19 was a major barrier to the implementation. RE-AIM QuEST, CFIR, and participatory strategies are useful to guide implementation of prevention programs in primary health care.

Despite advances in the healthcare system, cardiovascular diseases (CVDs) are still an important public health problem with disparities in the burden within and between countries. Studies among the adult population documented that socioeconomic and environmental factors play a role in the incidence and progression of CVDs. However, evidence is scarce on the socioeconomic determinants and the interplay with behavioral risks among older adults. Therefore, we identified socioeconomic and behavioral determinants of CVDs among older adults. Our sample consisted of 14,322 people aged 50 years and above from Belgium and France who responded to the waves 4, 5, 6 and/or 7 of the Survey of Health Ageing and Retirement in Europe. The effect of determinants on the occurrence of CVD was examined using a Generalized Estimating Equation (GEE) approach for binary longitudinal data. The overall rate of heart attack was 8.3%, which is 7.6% in Belgium and 9.1% in France. Whereas, 2.6% and 2.3% in Belgium and France, respectively, had experienced stroke. In the multivariable GEE model, older age [AOR: 1.057, 95%CI: 1.055-1.060], living in large cities [AOR: 1.14, 95%CI: 1.07-1.18], and retirement [AOR: 1.21, 95%CI: 1.16-1.31] were associated with higher risk of CVD. Furthermore, higher level of education [AOR: 0.82, 95%CI: 0.79-0.90], upper wealth quantile [AOR: 0.82, 95%CI: 0.76-0.86] and having social support [AOR: 0.81, 95%CI: 0.77-0.84] significantly lowers the odds of having CVD. A higher hand grip strength was also significantly associated with lower risk of CVD [AOR: 0.987, 95%CI: 0.984-0.990]. This study demonstrated that older adults who do not have social support, live in big cities, belong to the lowest wealth quantile, and have a low level of education have a higher likelihood of CVD. Therefore, community-based interventions aimed at reducing cardiovascular risks need to give more emphasis to high-risk retired older adults with lower education, no social support and those who live in large cities., Competing Interests: The authors have declared that no competing interests exist.

Aims: Accurate prediction of a person's risk of cardiovascular disease (CVD) is vital to initiate appropriate intervention. The non-laboratory INTERHEART risk score (NL-IHRS) is among the tools to estimate future risk of CVD. However, measurement disparities of the tool across contexts are not well documented. Thus, we investigated variation in NL-IHRS and components in selected sub-Saharan African and European countries. Methods and results: We used data from a multi-country study involving 9309 participants, i.e. 4941 in Europe, 3371 in South Africa, and 997 in Uganda. Disparities in total NL-IHRS score, specific subcomponents, subcategories, and their contribution to the total score were investigated. The variation in the adjusted total and component scores was compared across contexts using analysis of variance. The adjusted mean NL-IHRS was higher in South Africa (10.2) and Europe (10.0) compared to Uganda (8.2), and the difference was statistically significant (P < 0.001). The prevalence and per cent contribution of diabetes mellitus and high blood pressure were lowest in Uganda. Score contribution of non-modifiable factors was lower in Uganda and South Africa, entailing 11.5% and 8.0% of the total score, respectively. Contribution of behavioural factors to the total score was highest in both sub-Saharan African countries. In particular, adjusted scores related to unhealthy dietary patterns were highest in South Africa (3.21) compared to Uganda (1.66) and Europe (1.09). Whereas, contribution of metabolic factors was highest in Europe (30.6%) compared with Uganda (20.8%) and South Africa (22.6%). Conclusion: The total risk score, subcomponents, categories, and their contribution to total score greatly vary across contexts, which could be due to disparities in risk burden and/or self-reporting bias in resource-limited settings. Therefore, primary preventive initiatives should identify risk factor burden across contexts and intervention activities need to be customized accordingly. Furthermore, contextualizing the risk assessment tool and evaluating its usefulness in different settings are recommended. Graphical Abstract [ABSTRACT FROM AUTHOR]