Social and behavioural risk factors in the prevention and management of cardiovascular disease in Kerala, India: a catchment area population survey
AuthorDevassy, SM; Webber, M; Scaria, L; Thiyagarajan, JA; Fendt-Newlin, M; Joubert, J; Benny, AM; Nannatt, A; Joubert, L
Source TitleBMC Cardiovascular Disorders
University of Melbourne Author/sJoubert, Lynette
Document TypeJournal Article
CitationsDevassy, S. M., Webber, M., Scaria, L., Thiyagarajan, J. A., Fendt-Newlin, M., Joubert, J., Benny, A. M., Nannatt, A. & Joubert, L. (2020). Social and behavioural risk factors in the prevention and management of cardiovascular disease in Kerala, India: a catchment area population survey. BMC CARDIOVASCULAR DISORDERS, 20 (1), https://doi.org/10.1186/s12872-020-01595-x.
Access StatusOpen Access
BACKGROUND: Cardiovascular disease (CVD) is the leading cause of mortality in India. Social and behavioural factors are strongly interrelated in the prevention and control of CVD. The ability to make lifestyle changes to control hypertension and diabetes (major risk factors for CVD) is determined by factors such as education, gender, caste, poverty, and urbanicity. This study aimed to improve our understanding of the inter-relationship of social and behavioural factors in the management of elevated serum glucose and high blood pressure and co-morbid mental health conditions. METHODS: A population-based catchment area cross sectional survey was conducted in Kerala, India. Data were collected from residents aged over 30 years (n = 997) using standardized tools and clinical measures. We performed latent class analysis incrementally to extract homogeneous latent classes of individuals based on their responses to social and behavioural risk factors in the survey. Using structural equation models, we assessed the mediating effect of depression and anxiety, and social or behavioural risk factors, on management of high blood pressure and raised serum glucose levels. RESULTS: The prevalence of high blood pressure and blood glucose in the sample was 33 and 26% respectively. Latent class analysis found three clusters of risk factors. One had a predominance of behavioural characteristics, another of social risk factors and the third was a low risk group. Age, female sex, and marital status had an effect on high blood pressure and high glucose, though were mediated by mental health, social and behavioural risk factors. CONCLUSIONS: Interventions to improve the management of risk factors for CVD need to address social risk factors and be sensitive to the needs of population sub-groups that may require additional support to access health services. An integration of social and health services may be required to achieve this.
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