Ten-year risk assessment for cardiovascular disease & associated factors among adult Indians (aged 40-69 yr): Insights from the National Noncommunicable Disease Monitoring Survey (NNMS)

Material & Methods

This study was conducted at ICMR-National Centre for Disease Informatics and Research (ICMR-NCDIR, Bengaluru, India). The survey received approval from the ICMR-NCDIR Institutional Ethics Committee (IEC) and respective survey implementing agencies IECs. Informed consent was obtained from subjects aged 18-69 yr.

Results

The NNMS survey included 10659 adult participants aged 18-69 yr from 12,000 households, of which 4552 adults were aged between 40 and 69 yr. Among them, 72 had a missing value for blood pressure or anthropometry measurements, and hence 4480 were finally included in this study (Fig. 1).

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Fig. 1.

Distribution of survey population and response rates.

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10-year CVD risk

In the total sample, 84.9 per cent had a very low to low (<10%) 10-year risk of CVD, while 14.4 per cent had moderate risk (10 to <20%) and 0.7 per cent had high to very high risk (≥20%) of CVD (Table II). Hence, a total of 15.1 per cent of participants in the age group of 40-69 yr had an elevated 10-year risk (≥10%). Almost half the individuals in the age group 60-69 yr had an elevated risk (Fig. 2). However, the prevalence of an elevated CVD risk was lower in younger age groups, with 99.6 per cent of individuals aged 40-49 yr experiencing a very low to low CVD risk. On examining the gender pattern, the proportion of men with a CVD risk ≥10 per cent was double that of women (men: 19.8%, women: 10%). The percentage distribution of different CVD risk categories according to the levels of risk factors considered for CVD risk estimation is provided in Figure 3.

Table II. Distribution of non-laboratory-based cardiovascular disease risk according to background sample characteristics

Variables	Very low to low (<10%)	Moderate (10% to <20%)	High to very high (≥20%)	P value (Chi2)
	n=3803 (84.9%), n(%)	n=645 (14.4%), n(%)	n=32 (0.7%), n(%)
Place of residence
Rural	2495 (86.2)	387 (13.4)	13 (0.4)	0.01
Urban	1308 (82.5)	258 (16.3)	19 (1.2)
Educational status
No formal education	1565 (84.6)	275 (14.9)	10 (0.6)	0.58
1-5 yr	671 (84.1)	123 (15.4)	4 (0.5)
6-8 yr	434 (84.4)	71 (13.9)	10 (1.7)
8-12 yr	832 (86.3)	125 (13)	7 (0.7)
Degree & higher	301 (85.2)	51 (14.4)	1 (0.4)
Occupation status
Employed	2343 (86.9)	337 (12.5)	15 (0.6)	<0.0001
Unemployed	186 (53.5)	146 (42)	16 (4.5)
Homemaker	1271 (88.7)	161 (11.3)	1 (0)
Wealth index quartiles
First quartile (<0-25% of WI)	1264 (84.5)	220 (14.7)	12 (0.8)	0.96
Second quartile (25-50% of WI)	974 (85)	166 (14.6)	6 (0.5)
Third quartile (50-75% of WI)	817 (85.3)	134 (14)	7 (0.7)
Fourth quartile (>75% of WI)	748 (85)	125 (14.2)	7 (0.8)
Marital Status
Currently married	1853 (84.9)	308 (14.1)	21 (1)	0.33
Others	1950 (84.8)	338 (14.7)	11 (0.5)
Current smokeless tobacco user
No	2808 (84.5)	488 (14.7)	26 (0.8)	0.41
Yes	995 (86)	157 (13.6)	6 (0.5)
Current alcohol user
No	3228 (85.7)	514 (13.6)	26 (0.7)	0.02
Yes	575 (80.7)	131 (18.5)	6 (0.8)
Exposure to second hand smoke
No	2078 (85.6)	329 (13.6)	19 (0.8)	0.37
Yes	1725 (84)	316 (15.4)	13 (0.6)
Physical activity
Sufficient	2268 (87.6)	309 (12)	11 (0.4)	<0.0001
Insufficient	1535 (81.1)	336 (17.8)	21 (1.1)
Fasting blood glucose
Normal	3025 (86.6)	456 (13)	12 (0.4)	<0.0001
Raised	511 (75.5)	147 (21.7)	18 (2.7)
Central obesity
No	2305 (86.6)	343 (12.9)	14 (0.5)	0.01
Yes	1498 (82.4)	302 (16.6)	17 (0.9)

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Fig. 2.

Distribution of non-laboratory-based cardiovascular disease (CVD) risk among survey adults. BMI, body mass index.

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Fig. 3.

Percentage distribution of different CVD risk categories according to the levels of risk factors.

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More individuals from urban areas of residence had an elevated risk of CVD, where 17.5 per cent had a risk ≥10% compared to only 13.8 per cent in rural populations. There was no significant difference in CVD risk between different educational attainment statuses or between wealth index quartiles. However, the unemployed group had a significantly higher risk of CVD than the employed or homemaker populations. While 87-88 per cent of the latter two groups had a low risk of CVD, only 54 per cent of the unemployed population had a low risk.

Discussion

This study determined the 10-year risk of fatal or non-fatal cardiovascular events in an older adult Indian population using the non-laboratory-based WHO charts and categorized the population into three groups: very low to low risk, moderate risk and high to very high risk. Almost 85 per cent of the study population, aged 40-69 yr, was observed to have a CVD risk <10 per cent. To the best of our knowledge, this is the first study estimating the CVD risk in India using the new non-lab charts. However, a limited number of studies have studied the CVD risk in India using the previous WHO/ISH charts, which use diabetic status and/or serum cholesterol levels. They have observed a varied prevalence of the study population having low CVD risk (<10%), ranging from 50 to 86 per cent^20-24.

All these studies were done using a geographically localized population, and hence, a national estimate of CVD risk is missing in India. A study by Madhu et al²⁴ in a rural setting in South India observed that 40 per cent of the rural population and half of the tribal population had elevated CVD risk of CVD²⁴. The reason for the higher risk could be due to the fact that 35 per cent of the population is aged 60 and that a very high prevalence of tobacco usage (90%) was observed among the tribal population. In another study, a similar proportion (44.4%) of people in rural North India were seen to have an elevated risk of CVD in another study²¹. However, the sample size was very small (n=133) and almost half the population was elderly (aged 60 yr and above), increasing the overall CVD risk of the study population.

Two other studies in rural settings in South India observed results similar to the findings of our study. In rural Puducherry, it was estimated that 86 per cent of the study population had a low risk of CVD²³, and the corresponding estimate was 80 per cent in a village in Tamil Nadu²⁰. Deori et al²² noted that 78.5 per cent had low CVD risk in rural Lucknow when the CVD risk chart that included cholesterol levels was used, and this dropped to 76.8 per cent when the chart without cholesterol was used. In a multinational study, 79.2, 87.2 and 94.9per cent of the population studied had low CVD risk in Pakistan, Nepal and Sri Lanka, respectively²⁵.

India falls within the WHO epidemiological region, sharing similarities with Nepal’s 87.2 per cent low CVD risk estimate. Another Nepalese study, employing the WHO/ISH chart, reported 86.4 per cent with low CVD risk²⁶. A singular regional study conducted in Bangladesh using the updated WHO non-laboratory chart revealed a higher CVD risk (72.5%) than our findings. Notably, their population skewed older (≥60), with 10 per cent aged 70 and above, all exhibiting elevated CVD risk²⁷. In contrast, our study focused on individuals aged 40-69 yr, with half falling within the younger age group (40-49 yr). Additionally, our population exhibited a lower prevalence of smoking (25%) compared to their study, resulting in a comparatively lower CVD risk.

Our findings also showed that urban residents have a higher prevalence of elevated risk than rural residents (13.8 vs. 17.5%). The higher prevalence of CVD risk factors and consequently, CVD events in urban areas than rural areas is well documented in India^28-30. Factors such as sedentary lifestyle, low physical activity, altered dietary habits, higher prevalence of obesity, etc., are commonly quoted as reasons for higher CVD risk in urban regions^28,31. A higher proportion of men had an elevated risk of CVD than women, which is consistent with previous findings^20,23,27. Further, as expected, the risk increased with age, as age is one of the predictive factors for CVD risk.

Among the factors examined for association with elevated CVD risk, place of residence was significant, as expected. However, it was significant only for women, suggesting a greater urban disadvantage for women. There is no significant difference in CVD risk experienced by men living in urban or rural areas. The differing findings between men and women can be further explored by the contributions of sociodemographic, behavioural and metabolic risk factors. A graded pattern of decreasing CVD risk with increasing education was evident in the Bangladeshi population as well²⁷. Education can be a factor in determining the awareness levels of individuals, thus leading to better adoption of preventive lifestyle habits. Further, the well-established metabolic risk factors for CVD, such as raised blood glucose and central obesity, had a significant association with elevated CVD risk.

The findings of this study reveal a notable discrepancy between educational status and employment concerning their association with CVD risk. While no significant difference was observed in CVD risk across various educational attainment levels or wealth index quartiles, the unemployed group demonstrated a significantly higher risk compared to the employed or homemaker populations. This disparity suggests that factors related to employment status may play a more substantial role in influencing CVD risk in this population than educational attainment or wealth. Sub-analysis was performed to identify the disparity between educational status and unemployment. This revealed that the majority of the unemployed males and females belong to the age group of 60-69 yr (72.5 and 57.8%, respectively), due to which CVD risk was significantly high among the unemployed. Further investigation is warranted to understand the specific dynamics contributing to this difference and to inform targeted interventions for CVD prevention. Strategic initiatives play a crucial role in helping India progress towards meeting the targets outlined by the WHO and Sustainable Development Goals in the context of CVD, as the country is currently lagging behind in achieving these objectives³².

This study adds to the knowledge of the increasing burden of CVD and its risk factors in India. The novel contribution of this paper lies in identifying unexpected disparities in CVD risk among different socioeconomic groups in the Indian population using nationally representative data. Traditionally, higher educational attainment and wealth have been associated with lower CVD risk. However, this study reveals a notable absence of significant differences in risk across various educational and wealth levels. Contrary to expectations, the unemployed group exhibited a significantly higher CVD risk compared to both the employed and homemaker populations. This unexpected finding underscores the complexity of socioeconomic determinants of health, indicating that factors beyond education and wealth, such as employment status, play a crucial role in shaping CVD risk.

This risk stratification method is very useful for efficiently channelling limited resources in resource-poor primary care settings³³. The use of a non-laboratory-based chart for risk stratification reduces the overdependence on laboratory investigations for CVD risk estimation. Individuals with elevated risk, according to the non-laboratory-based chart, can be subjected to further investigation, reducing resource utilization. However, the non-lab-based chart has been observed to underestimate the proportion with >10 per cent CVD risk as it excludes the additional CVD risk due to elevated blood glucose⁹. While ethnicity can influence cardiovascular risk, utilizing a region-specific tool may be more feasible in resource-limited settings and offer broader generalizability. However, researchers should consider conducting further analyses if ethnicity significantly impacts cardiovascular risk in the population of interest. The results haveto be read with caution as the sample in the study was relatively younger and included only up to age 69, which could have underestimated the CVD risk of the population. Finally, factors such as diet, family history and past medical history were not considered for risk estimation, so the results have to be interpreted with caution for assessing the individual level of CVD risk.

Overall, the population’s moderate to very high 10-year CVD risk was 15 per cent higher in urban areas. More men than women faced elevated CVD risk. The non-laboratory chart aids individual and policy-level risk estimation. It’s beneficial for efficient policymaking and resource allocation, addressing India’s growing CVD burden. Further research is needed on the positive association of secondhand smoke with CVD risk in India. Future studies should focus on local CVD risk estimation and validate non-laboratory charts against laboratory-based methods.