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CVD risk fluctuates in India by region
CVD risk in India varied across states and sociodemographic groups, according to a study published in PLOS Medicine.
“While a major investment in CVD and risk factor prevention, screening and treatment is needed across India, this study provides important new insights on the distribution of CVD risk to effectively target health system resources for CVD management to those most at risk and most in need,” Pascal Geldsetzer, MD, PhD, MPH, postdoctoral research fellow in the department of global health and population at Harvard T.H. Chan School of Public Health, and colleagues wrote.
Researchers analyzed data from 797,540 patients from India aged 30 to 74 years from the District Level Household Survey-4 and the second update of the Annual Health Survey. Both surveys were conducted between 2012 and 2014. Questionnaires were completed to collect anthropometric, clinical and biomarker measurements, in addition to hypertension, diabetes and smoking history. BMI, blood glucose and BP were measured at baseline and 12 to 18 months after completion of the questionnaire.
The outcome of interest was the 10-year CVD risk, which was calculated using the Framingham Risk Score. Secondary analyses included risk calculations using Harvard-National Health and Nutrition Examination Survey, Globorisk and WHO-International Society for Hypertension scores.
Throughout India, the mean CVD risk varied from 13.2% in Jharkhand (95% CI, 12.7-13.6) to 19.5% in Kerala (95% CI, 19.1-19.9). Areas with the highest risk for CVD included South India, northeastern states, the three most northern states and West Bengal.
There was a positive association between CVD risk and district-level wealth, which was also seen for urbanization. In both sexes, household wealth and urban areas were positively associated with CVD risk, although the associations were stronger in women.
Patients with poorer household wealth and lived in rural areas were more likely to smoke. High blood glucose, BMI and systolic BP were positively linked to urban location and household wealth.
Compared with women, men had higher mean systolic BP (126.9 mm Hg; 95% CI, 126.7-127.1; vs. 124.3 mm Hg; 95% CI, 124.1-124.5) and smoking prevalence (26.2%; 95% CI, 25.7-26.7; vs. 1.8%; 95% CI, 1.7-1.9).
“Such investments in targeted CVD care programs as well as relevant health policy measures are urgently needed — particularly in states with a high CVD risk — if India is to minimize CVD’s adverse consequences for health, well-being, financial risk protection and economic growth,” Geldsetzer and colleagues wrote. “Given the size and projected growth of India’s population, the determination and effectiveness of the country’s measures to prevent and treat CVD over the coming years will have an important bearing on the achievement of the [Sustainable Development Goals] at the global level.” – by Darlene Dobkowski
Disclosures: The authors report no relevant financial disclosures.
Perspective
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The investigators should be applauded for evaluating CV risk on a granular level in India. Previous risk factor surveys have looked at CV risk factors in isolation by describing the rates of risk factors such as elevated BP, tobacco use or diabetes prevalence. Instead, these investigators pooled demographic health surveys to create state-level estimates of predicted CV risk.
However, the authors note that the models that are used have not been validated in India, and thus, the absolute levels of predicted risk may differ from the true levels of future risk.
These data are useful because they describe the relative differences in predicted CV risk across India. States and union territories in India have a six-fold variation of predicted risk based upon age-adjusted models: women in Assam have the lowest predicted CV risk and men in Kerala have the highest predicted risk.
These data are not able to directly account for competing risks, but some signals are present. For example, individuals who are in lower socioeconomic position often bear the burden of diseases that affect younger individuals, whether they be infectious diseases or maternal conditions. Thus, some individuals in these states may not have the “opportunity” to develop CVD, which is more commonly a disease in midlife and older, which may partially explain some of the differences in predicted risk between groups with higher vs. lower socioeconomic position.
Further, the details of what drives risk in each group is important. For example, these and other data from India demonstrate that individuals who are some a lower socioeconomic position, whether it be an urban setting or rural setting, are more likely to use tobacco. Thus, different tools to reduce CV risk may be needed based upon the drivers of that risk.
Placing these findings in the context of reducing or ideally eliminating inequities is also needed. For example, individuals of higher socioeconomic position may have higher predicted relative risk compared with individuals from lower socioeconomic position, yet strategies to reduce risk should be focused on helping those in greatest need.