Chronic non-communicable diseases (NCDs) are the leading causes of morbidity and mortality in most low- and middle-income countries (LMIC) [1]. Recent estimates demonstrate that nearly 80% of NCDs deaths occur in LMIC and about three fourth of global NCD-related deaths take place after the age of 60 [2]. Behavioral risk factors such as tobacco use, unhealthy diet, insufficient physical activity and the harmful use of alcohol are known and modifiable contributors to a number of NCDs and health mediators [3,4]. Additionally, with over half of the global population in urban areas, risk factors associated with urbanization such as diet, obesity, hypertension, and a decrease in physical activity will all have significant impacts on the health of the population [5]. Self-report activity data document a pattern of increased inactivity with advancing age [6,7]. As part of the English Longitudinal Study on Ageing, Shankar and colleagues found evidence of clustering of health-related behaviors in older adults [8]. Some epidemiological evidence also suggests multiple risk factors were common in rural Africa [9]. Independently or in combination, these risk factors present an opportunity for interventions to reduce future health burdens in ageing populations in LMIC.

The development of a national risk factor profile for NCDs provides key information required for planning prevention and control activities and could also help to predict the future burden of disease. Reliable and comparable analysis of risks to health is especially important for preventing or modifying disease and injury. However, until recently, analysis of health risks were limited by inconsistent methodologies, dated assumptions and/or variations in assessment criteria for evidence on prevalence, causality and hazard size - all of which limited the ability to produce comparable data to estimate population health status [10].

This study used data from the six countries that implemented the World Health Organization’s Study on global AGEing and adult health (SAGE) Wave 1. The purpose of this paper is to describe the distribution of main risk factors for NCDs by socioeconomic status (SES) within and across countries to better understand the levels of modifiable NCD risk factors for adults aged 50 years and older, and whether these risk factors show age, sex, rural/urban, wealth and country-specific differences.

A total of 38,670 individuals aged 50 and older participated in the SAGE survey. Individuals who couldn’t completed or partially completed interview or with missing sociodemographic variables were excluded from the analyses. Finally, A total of 34,114 individuals aged 50 and older in the six countries were considered in this analysis. China has the largest sample (N=13,157), and Mexico (N=2,318) the smallest sample. The socio-demographic characteristics for each country are shown in Table 1. The demographic and socioeconomic characteristics of the older population differed widely across the six countries, the proportion of women is higher than men except in the Ghana, which consisted of 52.4% men and 47.6% women. The 50–59 age groups were the highest proportion in all countries. India remained largely a rural society, with more than two-thirds residing in rural areas; in contrast, the majority of older Mexicans, Russians, and South Africans lived in urban areas. Ghana and India had the lowest educational level among the SAGE countries, with over 54% and 51%, respectively, of the older population having no formal education. In contrast, only 0.5% of older Russians had no formal education, and nearly one in five had a college degree or higher.Table 1

Percent distribution of respondent sociodemographic characteristics, by country and multi-country pooled data, SAGE Wave 1

The ranking of all seven NCD risk factors for each country is shown in Figure 1: central obesity, inadequate vegetable fruit intake and hypertension are the three most common risk factors across all six countries, except in India where current daily tobacco use pushed hypertension to fourth among all seven NCD risk factors. In India, the prevalence of inadequate vegetable fruit intake and current daily smoker are the highest among all the six countries. In contrast, the rate of hypertension (33%) in India is the lowest. The prevalence of obesity in Mexico, Russia and South Africa are markedly higher than that in China, India and Ghana.Figure 1

Ranking of selected risk factors among adults aged 50 years and older across six countries.

This is, to our knowledge, the first population-based comparative paper of NCD risk factors specifically designed for older adults residing in LMIC. Participating SAGE countries, China, India, the Russian Federation and South Africa are part of the BRICS countries. Being the biggest countries in the world, China and India together constitute about 38% of the world’s population aged 50 years and older [20]. According to the World Bank, GDP per capita was highest in the Russian Federation, Mexico and South Africa, followed by China, India and Ghana was lowest among all the six countries in 2010. This study reports the prevalence of seven common risk factors for NCDs and demonstrated differences in prevalence across six countries as well as variations within countries. The data were collected using a standard protocol to ensure the comparability of data, the same equipment were used to measure weight, height, waist and hip circumferences, and blood pressure to minimize a possible bias in the measurements.

We found that central obesity, inadequate vegetable fruit intake and hypertension are the most common risk factors for NCDs across all six countries except India, where current daily tobacco use replaced hypertension. The highest burden of hypertension was found in South Africa and the Russian Federation, with 78% and 69%, respectively, followed by China, Ghana and Mexico, all over 50%. These figures seem to be higher than previously found among older adult populations in Africa (rural Malawi, Rwanda and Tanzania (36.6–41.0%) [9], 42.4% of women in Accra, Ghana [21] and East Asia, China (24.2–64.9%) [22,23], and Taiwan (31.1–38.0%) [24]. In addition, the prevalence and awareness of hypertension in urban and rural dwellers in SAGE Wave 1 show marked differences, especially those on treatment and with adequate control by age and urban or rural residence. Individuals not diagnosed but with high blood pressure on measurement (higher in rural settings) are as much of a concern as those who know they have hypertension and are still hypertensive on measurement (much higher in urban settings). In these six countries, only 4–14% were receiving effective treatment [25].

The results of this study also show that older adults from upper-middle income countries such as Mexico, Russian Federation and South Africa are more likely than those from low or lower-middle income countries such like China, India and Ghana to be obese. South Africa has the highest prevalence of obesity (45.2%), even higher than Europeans aged 50 years and older [26], especially among those aged 60–69 years (50%) and among urban dwellers (47%). Over nutrition play an important role and determinants include female gender, low physical activity and chronic conditions [27]. Obesity seems less of a concern for old adults in China and India for now compared to other four countries, although obesity has increased 4-fold in the last 2 decades in china [28]. Like the pattern of prevalence of obesity, the prevalence of low physical activity was also highest in South Africa and Mexico. As the association between physical inactivity and obesity is well recognized [29,30], low physical activity was a very important factor contributing to obesity in these two countries, but was not found in the Russian Federation in this study, where it has lower prevalence of low physical activity compared to Mexico but has higher prevalence of obesity, thus indicating that other health behavior such as alcohol consumption and/or socioeconomic factors related to nationality are influencing obesity [31].

Tobacco use is serious health-damaging behavior in China [32,33], this study seems to have again confirmed. We also found prevalence of current daily tobacco use among older Chinese men was close to the GATS with self-reported prevalence of 58.9% and 40.2% among adults aged 55–64 years and 65 years plus, respectively [34]. Tobacco use is also very prevalent in India, with almost half of Indian are current daily smoker in this study, It is worth noting that smokeless tobacco use is particularly prevalent in India, which is different from other five countries. However, there is evidence that smokeless tobacco use plays a role in oral cancer in south-central Asia [35]. About 52% of oral cancers in India are attributable to the use of smokeless tobacco products [36]. No evidence show rates of smoking are decreasing in LMIC. Suggesting health policy, planning and programmes of tobacco control should promote implementation of effective strategies [37].

Analysis of the simultaneous occurrence of more than one risk factor indicates that people aged 50 years and older across six countries engage in a number of risk factors that put them at high risk of NCDs, however, we found that these selected risk factors occurred much more frequently in upper-middle income countries than in low-middle income countries. This difference may reflect the fact that compared with older adults in upper middle-income countries, older adults in lower middle-income countries are more likely to have had lower levels of exposure to NCD-risk factors associated with urban living (such as smoking, sedentary lifestyles and processed foods) [38].

We also found the pattern of associations between income and risk factors for NCDs vary among countries. The association of income with smoking has been reported before in other studies on Western societies [39-41]. We found that the pattern of tobacco use association with household wealth differed between low-middle income countries and upper-middle income countries in this study. Wealth showed a strong relationship with current daily smoking in low-middle income countries such as Ghana and India, but it does not show any specific trend with income in upper-middle income countries such as Mexico, the Russian Federation and South Africa. Previous studies have shown that education is more strongly related to smoking than income is in most countries within the European Union [42], considering education and economic status are closely related in developing countries, so this may in part explain this difference between upper-middle income countries and low-middle income countries. There is a lot of controversy on association between income and obesity, numerous studies show that low-income and obesity are linked in many high income nations [43]. But results of this study show inverse pattern of association between income and obesity, that increasing income increased the risk of obesity. There is also still little difference between upper-middle income countries and low-middle income countries. The prevalence of obesity reach their peak among older adults in the fourth income quintile in South Africa, Mexico, Russia Federal and China, but occurred in the highest income quintile in Ghana and India. This implies that the burden of obesity is shifting toward the low SES and can no longer be considered a disease of the socioeconomic elite in LMIC [44]. We observe what appears to be the first inkling of the transition in South Africa, Mexico, the Russian Federation and China. It implies policymakers in developing countries and even low-income countries should prepare in advance to address this transition over the next several decades [45-48]

Some limitation must be taken into account in this study. First, there are different response rate across six countries,from 51% in Mexico to 93% in China. The low response rate was potential selection bias to this study. The main reason for household non-response was inability to locate the selected household, or the household refusing to participate even before a roster could be obtained. Second, a limitation to this study is the use of self-report for part of risk factors for NCDs. It can lead to recall bias, although self-reported method widely applied in population study and other studies have illustrated the reliability and validity of self-report for behaviors such as cigarette smoking, alcohol consumption, and physical activity [49,50]. Finally, SAGE wave 1 is a cross-sectional study, which determines that we could not examined the changes in prevalence of these risk factors for NCDs over time, fortunately, SAGE Second and third waves of data collection will be 2013 and 2015. It will provide an opportunity to track these changes.

In conclusion, this study estimated the prevalence rates of common risk factors for NCDs and showed the pattern of these risk factors in six main LMIC. The baseline information on the magnitude of the problem of risk factors provided by this study can help countries and health policymakers to set up interventions addressing the global non communicable disease epidemic. Understanding the relationship of risk factors pattern and burden of NCDs in LMIC presents an important challenge for further research.