Global Behavioral Risk Factor Surveillance


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Competing interests: The authors have declared that no competing interests exist. Chronic disease such as cardiovascular diseases, diabetes and cancers claim millions of deaths each year all over the world [1]. A relatively small number of modifiable behavioral risk factors BRFs , including tobacco use, excessive drinking, physical inactivity, unhealthy diet, and overweight, contribute majorly to the development of these chronic diseases [1] — [3].

Concrete evidence indicates that risk of adverse health outcomes increases with more number of unhealthy behaviors [4]. Having 2 BRFs advances risk of mortality approximately more than 6 years compared with having no BRFs [5] and adherence to healthy behaviors was associated with lower mortality and lower risk of chronic diseases [6]. In addition, previous studies from the U. From an intervention perspective, alternative approaches for program design could be utilized on the basis of the identification and strength of unidimensionality [9].

Expanding epidemiologic researches from mere documenting prevalence, distribution and correlates of single BRF to studying the clustering and correlations of multiple BRFs and their potential determinants may provide more valuable information for further chronic disease control and prevention. It has been well investigated how risk behaviors covary, cluster, distribute and interact with related correlates among adolescents and adults in developed countries [8] , [10] — [12].

Chronic diseases have become the leading causes of mortality in China [13]. China's economy widely acclaimed as a miracle brought over drastic societal shifts which exerted considerable influence on lifestyle-health-related behaviors in recent decades. The observed increase of detrimental impacts associated with BRFs has led to many researches investigating the epidemiology on individual behavioral risks factors and their potential correlates in China [14] — [19].

There are, however, few studies in China focusing on the clustering and correlations of multiple BRFs.

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In addition, the disparity in income distribution and socioeconomic status is growing in parallel with the daunting health care inequity in China, which may add to the increasing burden of chronic diseases [13] , [20]. Population based programs to prevent risky behaviors may be an effective resolution. Examining and identifying characteristics of population with multiple BRFs and their potential socioeconomic determinants could lead to more targeted intervention programs. Details of establishment, history and good representativeness of DSPs system to the country were published elsewhere [21] , [22].

Only persons who had lived in their current residence for 6 months or longer in past 12 months were eligible to participate. A replacement method, which required similar household structure of the substitute to the originally sampled family, was adopted in the survey when interviewers could not reach the sampled subject after three attempts so as to guarantee adequate sample size.

The overall sample replacement rate was 9. A total of 51, people were designed to be selected to participate in the study; 51, persons completed the study except for 1 surveillance point persons and 4 villages persons which failed to conduct the survey. After exclusion of persons with miscoded sampling information, persons for whom demographic and social-economic information is missing and for whom data on BRFs and chronic conditions were missing, 49, persons 23, men, 25, women were included in the analyses.

Demographic characters and socioeconomic status: Demographic characters included age 15—17, 18—24, 25—34, 35—44, 45—54, 55—69 years , gender, ethnicity the Han nationality, minority. BRFs: Participants were invited to answer questions on their current status of tobacco use, alcohol drinking, diet pattern, physical activity. We also measured the actual height and weight of all respondents, from which body mass index BMI was computed.

BMI was used to define individuals who were overweight or obesity. According to the Dietary Guidelines for Chinese Residents [23] , we defined excessive drinking as consuming 25 g pure alcohol or more per drinking day for men and 15 g or more for women. For the daily quantity consumed, respondents who ever drank alcohol in the last 12 months before the survey were required to report types strong spirits, less strong spirits, yellow rice wine, wine and beer and quantity bottle for beer, liang or 50 g for other alcoholic beverages of alcoholic beverages consumed in a typical drinking day.

The volume of beer bottle was set at ml, which was most common in China. The usual daily quantity was calculated by multiplying quantity or bottles of alcoholic beverages by the percentage of ethanol contents. If a drinker consumed two or more types of alcoholic beverages in a day, then all pure alcohol was summed. We denoted those who had a low level of activity as physical inactivity according to the criteria of Global Physical Activity Questionnaire [24] , [25].

As Dietary Guidelines for Chinese Residents recommends consuming both vegetables and fruit daily [23] , we defined Insufficient intake of fruit and vegetable as not consuming fruit and vegetable every day in the past 12 months. We included overweight or obesity as a substitute BRF by reason of lacking information on calories consumption although being overweight or obese is not a behavior.

Chronic Conditions: We defined individuals as having chronic conditions if they reported that a physician or health professional told them had any of the following conditions: myocardial infarction, chronic obstructive pulmonary disease, stroke, high blood pressure, diabetes mellitus and cancer. In this study, we weighted all calculations to obtain DSPs-representative results on the basis of the surveillance sampling scheme, with post-stratification adjustments for age and gender using Chinese population estimates from National Bureau of Statistics of China.

First, the overall distributions of selected participants' characteristics demographic, geographic location, socioeconomic and health attributes were examined.

We then presented data on prevalence of each BRF and distribution of number of BRFs range, 0—5 , as well as the prevalence of all possible clustering patterns of the five BRFs. We also used binary logistic regressions, to examine the bivariate co-variations among five BRFs. Next, we used mean number of BRFs as indicator to reflect their clustering within individuals. For each demographic, geographic, socioeconomic and health covariates, the mean numbers of BRFs and corresponding confidence intervals CIs were examined.

Finally, ordinal logistic regression ordinal number of BRFs was the dependent variable was adopted to explore the independent effects of demographic and socioeconomic covariates on BRFs' clustering with adjustment for all covariates. We carried out all statistical analyses using SAS version 9. Written informed consent was obtained from each participant before data collection. The majority of the respondents were between 35 and 44 years old The most common education level was junior high school graduate or some senior high school The weighted distribution of participants reflected the demographics of the national population Table 1.

The most common BRF was insufficient intake of fruit and vegetable, which was reported by Among individuals with four risk factors, the most prevalent clustering pattern was smoking, excessive drinking, physical inactivity and insufficient intake of fruit and vegetable 2. Similarly, among those with three risk factors, the group of smoking, excessive drinking and insufficient intake of fruit and vegetable was by far the most common 5.

The combination of physical inactivity and insufficient intake of fruit and vegetable topped among two-factor patterns Overweight or obesity and insufficient intake of fruit and vegetable overlapped for 7. Next, we examined 10 pairs of bivariate co-variations among the five BRFs with adjustment for possible confounders using binary logistic regressions Table 4. Six of the ten pairs were positively associated, two were inversely associated, and the other two were not significantly associated. The strongest relationship existed between smoking and excessive alcohol drinking.

Also, we found insufficient fruit and vegetable intake was associated with three other BRFs, and overweight or obesity was also associated with two other BRFs positively. Furthermore, inactivity was not found statistically associated with heavy drinking and smoking.

We also examined the extent to which BRFs clustered within the study population and how this clustering varied by social-demographic factors Table 5. The mean number of BRFs among all participants was 1. Individuals aged 45—54 years had more BRFs mean number: 1. Individuals living in rural areas mean number: 1. The mean number of BRFs generally fell with increasing education level, although an inflection point was observed at the next to the lowest level. Individuals who were single or never married mean number: 1.

Higher annual household income was associated with fewer BRFs. Reporting chronic disease was associated with a higher number of BRFs. The number of BRFs did not vary by ethnicity. Finally, we explored the independent effects of social-demographic variables on BRF clustering using an ordinal multivariate logistic model Table 5. Seven of the variables were found to be associated with multiple BRFs. Among the demographic characteristics, individuals aged 45—54 years were 3. Women were less likely to have multiple BRFs were men.

Among the social and health conditions characteristics, having a lower yearly family income; living in a rural area; having a lower education level; being separated, divorced or widowed; and reporting chronic disease all increased the likelihood of having more BRFs.

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We used a large, nationally representative sample to examine the co-variations of five BRFs and their clustering within a population of Chinese aged 15—69 years. We found that Certain BRFs e. The BRFs were more likely to cluster in older individuals, men, persons living in rural areas and those with lower SES.

As far as we known, Chinese do not consider fruit to be a dietary necessity, and most still cannot afford to consume both vegetables and fruit daily. The most common risk behavior for chronic disease among Chinese aged 15—69 years was insufficient intake of fruit and vegetable However, smoking Over-reporting or underreporting of individual BRFs may exist, since all our data except BMI were collected by self-reporting.

However, after accounting for nuanced differences in risk behavior definitions, the overall prevalence estimates of health behaviors in this study are fundamentally similar to those reported in other recent studies among Chinese adults [14] , [15] , [17] — [19]. Lifestyle related risk behaviors usually persist along with adulthood once they are established, and are difficult to change without appropriate intervention [27] — [29]. Multiple lines of evidence indicate that adherence to healthy behaviors is associated with lower mortality and lower risk of chronic diseases [4] , [8].

In our study, Evident correlations between BRFs were observed in this study, and some were positive and others were negative. Some of these relationships were directionally similar to those found in other countries and cultures. For example, positive associations between insufficient fruit and vegetable intake and heavy drinking, inactivity, and smoking have been reported in The U. The finding that smokers drink more and have a lower BMI than do non-smokers is similar to previous studies from both Western countries and China [31] — [35].

However, other relationships we found were by no way the same as those observed outside China. Chinese cuisine traditions differs from those in the West in that Chinese usually cook vegetables with oil, which may increase energy intake and the likelihood of overweight or obesity. The link between high fruit and vegetable consumption and overweight or obesity was previously found in Shi et al.

Since many BRFs vary concurrently, the effects of an isolated behavior-specific modification program may be counteracted by interconnections with other BRFs. For instance, in China, both drinking and offering cigarettes to friends are widely considered to be effective ways of enhancing social relationships [14] , [39]. Many people trying to cease smoking would probably re-indulge in smoking after excessive drinking had weakened their willpower, especially when friends offer cigarettes.


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As a result, an intervention program focus only on reducing smoking might yield little. Interventions to reduce smoking and heavy drinking simultaneously might therefore be mutually reinforcing. Although a spate of effective interventions have been developed to address individual BRFs [29] , [40] , Cecchini and colleagues suggest that a combined package of multiple interventions across the full range of risk factors for chronic disease could generate substantially larger health gains than would a single intervention, often with a favorable cost-effective profile [41].

As persons at higher risk for one BRF are, for most part, also at higher risk for others, the integrated or comprehensive implementation of public health measures to tackle risk behaviors would offer a better opportunity to address rapidly the emerging problems of chronic disease, although the cost-effectiveness of such interventions in China needs to be examined further. This provided critical penetration for the design of targeted public health prevention programs. Numerous previous studies and risk reduction programs have considered adolescents to be a priority since childhood and adolescence are critical periods for behavior formation during which risk behaviors are more modifiable than in adulthood [27].

However, BRFs tend to aggregate within families, and parents' lifestyle strongly affects the development of health-related behaviors in children and adolescents [42] , [43]. In addition, the middle-aged population usually plays a backbone role in social and economic development, so they should not be ignored in terms of interventions aimed at reducing their risk behaviors. One possible cause of the enormous disparity in BRFs between genders is that BRFs such as smoking and heavy drinking are, so far as we know, widely regarded as a sign of masculinity in China, though it needs further confirmation.

Less access to primary care and health education for individuals living in rural areas might be the main driving force of the BRFs gap between urban and rural areas. On the other hand, health inequalities, which mainly stem from chronic disease, have become truly daunting in China as the social determinants of health have become more inequitable [13] , [20]. Considering the confirmed causal relationships between lifestyle-related BRFs and chronic diseases, our research corroborates the finding from an English study that health behaviors may account for a substantial portion of the social inequality in health [44].

Improving and modifying the risk behaviors for those with lower SES may therefore help reverse the growing disparities in mortality and prevalence of chronic disease. However, though less prevalent, risk behaviors were still common among persons with higher SES. A broad prevention strategy, involving all adults and reaching all communities, is needed if resources permit. Comparison of surveillance data on metropolitan and rural health.

Diabetes in southern Australia as an example; D. Did they use it? Beyond the collection of surveillance information; J. Ottoson, D. Harmonising local health survey data. Morabia, et al. Analysis, interpretation, and use of complex social and behavioural surveillance data. Looking back in order to go forward; D. McQueen, L. Gauger Elsner. Mega country health promotion network surveillance initiative.

Global Behavioral Risk Factor Surveillance

Strengthening the capacity of the world's most populous countries to monitor non-communicable disease behavioural risk factors; K. Douglas, et al. Epidemiological surveillance system in Latin America and the Caribbean. Perspectives, challenges, and solutions; L. Creating a synthetic behavioural risk factor index to assess trends in surveillance data.

An index of risk for cardio-vascular disease as an example; S. Campostrini, D. Perspectives on building infrastructure, comparing data, and using surveillance data in developing countries; D. McQueen, et al. Non-communicable disease surveillance in Latin America and the Caribbean. Advances supported by the Pan American health organization; S. Corber, et al.

Perspectives on global risk factor surveillance. Lessons learned and challenges ahead; D. See All Customer Reviews. Shop Textbooks.

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