|BRIEF RESEARCH ARTICLE
|Year : 2015 | Volume
| Issue : 1 | Page : 61-64
Anthropometric and behavioral risk factor for non-communicable diseases: A cluster survey from rural Wardha
Assistant Professor, Department of Community Medicine, Dhanalakshmi Srinivasan Medical College and Hospital, Tamil Nadu, India
|Date of Web Publication||9-Mar-2015|
Assistant Professor, D-12, Staff Quarters, Dhanalakshmi Srinivasan Medical College and Hospital Campus, Siruvachur, Perambalur, Tamil Nadu
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Monitoring of risk factors for non-communicable diseases (NCDs) over a period of time would be useful to make an indirect assessment of the actual disease burden. A cross-sectional survey was done among males aged 15-64 years, to study the prevalence of anthropometric and behavioral risk factors of NCDs. Information was collected on the sociodemographical factors, tobacco use, alcohol intake, diet, salt consumption, and physical activity, using a predesigned and pretested interview schedule. Anthropometric measurements were taken. A study found that prevalence of current smoking and use of smokeless tobacco was 14.2 and 54.9%, respectively. Alcohol intake was present in 22.7% of the study population. Per capita salt consumption per day was 14.6 g. A sedentary lifestyle was present among 19% of the men. Prevalence of overweight and obesity was 8.8% and 9.5%, respectively. Our finding suggested that greater surveillance of the NCD risk factors should be initiated as early as possible, in parallel with surveillance for communicable diseases.
Keywords: Behavioral, Cluster survey, Non-communicable disease, Risk factor
|How to cite this article:|
Kumar R. Anthropometric and behavioral risk factor for non-communicable diseases: A cluster survey from rural Wardha. Indian J Public Health 2015;59:61-4
|How to cite this URL:|
Kumar R. Anthropometric and behavioral risk factor for non-communicable diseases: A cluster survey from rural Wardha. Indian J Public Health [serial online] 2015 [cited 2020 Nov 27];59:61-4. Available from: https://www.ijph.in/text.asp?2015/59/1/61/152868
India is in the midst of an epidemiological transition, with increasing importance being given to non-communicable diseases. The current epidemic of non-communicable diseases in India is attributed to increased longevity and lifestyle changes, resulting from urbanization. , The increasing prevalence of non-communicable disease risk factors in rural areas has important public health implications, as, notwithstanding the rapid urbanization, two-thirds of India's one billion population still lives in rural areas.  Rural populations have limited access to healthcare and can ill afford to pay the high treatment costs associated with chronic conditions.
Targeting the risk factors for non-communicable diseases is recognized as an essential preventive strategy. Several surveys have examined the prevalence of risk factors for non-communicable diseases in urban India, but data from rural India are sparse. ,,
The Government of India has also included surveillance of the risk factors of non-communicable diseases (NCDs) in the Integrated Disease Surveillance Project.  Monitoring these risk factors over a period of time would be useful to make an indirect assessment of the actual disease burden.
The present study addresses this challenge in the rural population of the Wardha district of Maharashtra. The objective of the study was to study the prevalence of anthropometric and behavioral risk factors of NCDs.
The study was conducted in the field practice area of the Rural Health and Training Centre (RHTC), Bhidi, Wardha District, Maharashtra state, India. The study subjects were males in the age group of 15-64 years. The minimum sample size required for the study was 193, assuming the prevalence of 50%, allowable error 10%, and an alpha error of 5%. The design effect was taken as two, as cluster sampling was used for sampling. The village was selected as a cluster unit. Clusters were selected by a probability proportionate to the population size (PPS) method. In each cluster, 10 houses were selected by methodology, as adopted by the World Health Organization (WHO), for estimation of the immunization coverage by cluster sampling. In each selected house, one adult male was randomly selected for our study. A total of 300 subjects were selected for the study by (30 × 10) cluster-sampling. Five study subjects were excluded from the analysis owing to incomplete data forms. The data was collected by final year MBBS students, who were trained in data collection by the investigator in a four-hour session, prior to the study. Students visited the houses in pairs and interviewed one of the randomly selected eligible members in the family.
The WHO stepwise tool was used and the behavioral risk factor Questionnaire was suitably modified and translated into the local language.  Some portions in the WHO STEPS questionnaire such as biochemical investigations have been removed for the present study. Ten modified questionnaires were pilot tested by an investigator prior to the start of the data collection, to check for the reliability and validity of the questionnaire. Information was collected on the sociodemographic factors, tobacco use, alcohol intake, diet, salt consumption, and physical activity, by using the predesigned and pretested interview schedule. The participants were asked about their current tobacco use in any form (smoked or chewed on a daily basis, in the previous six months), and regular consumption of alcohol (on ≥ 10 days a month, in the previous six months). For calculating salt consumption, the subjects were asked, "How long does a packet of one kilogram salt last in your house?" Then, per capita salt consumption per day was calculated as: Per capita salt consumption per day (in grams).
Physical activity of a participant was determined by the different kinds of physical activities each person participated in, the average time spent on this activity per day, and the total time spent by the person per week in performing each activity. The physical activity status was determined at four levels according to the degree of manual labor involved: Sedentary, light, moderate, and vigorous activity, and then a Metabolic Equivalent (MET) was assigned to each. The METs for different types of activities were taken as, vigorous ( ≥ 6 METs), moderate (3-5.9 METs), light (1.5-2.9 METs), and sedentary (1-1.4). Physical activity was assessed in terms of the MET-hours, which combined the contribution of multiple types of physical activity. The MET-hours per week were calculated as the sum of the MET level of each activity multiplied by the duration of the activity performed in a week. The physical activity was categorized by quartiles of the number of MET hours per week: Sedentary activity < 40; light activity = 40-83, moderate activity = 83- 103, and vigorous activity > 103 MET-hours per week. Anthropometric measurements were taken as per the standard WHO protocol.  The body mass index (BMI) was calculated as Weight (in kg) / Height  (in m). The nutritional status of adult males had been classified as thin (BMI < 18.5 kg/m  ); normal (BMI between 18.5 and 22.9 kg/m  ), and overweight (BMI 23.0-24.9 kg/m  ), and obese (≥ 25.0 kg/m  ).  The waist circumference (WC) of the study subjects had been classified as < 101 cm and > 101 cm, as a waist circumference of > 101 among males was associated with a substantially high risk of metabolic and cardiovascular complication. Ethical clearance was obtained from the Institutional Ethical Committee before conducting this study. Informed consent was obtained from the study participants before starting the interview. All the data collection sheets were checked for completeness by the investigator prior to data entry into the software. Incomplete data sheets (five in number) were not included for analysis. The data was entered and analyzed using the EPI INFO version 6 software. The results were presented as a percentage or mean. The standard error (SE) and confidence interval (CI) were also calculated.
The mean age of the study population was 38.4 years. The average year of schooling was 8.4 years. The population living below the poverty line was 44.75%. Most of the study population belonged to other backward classes (54%), followed by scheduled caste (SC), and scheduled tribe (ST), of 20 and 12% percent, respectively. The majority (75%) were agricultural workers.
The percentage of respondents, who had never tried smoking was 25.4%. The percentage of the current smokers was 14.2%, of which 5.4% were cigarette smokers and 8.8% smoked the beedi. A large percent (22.7%) of the subjects reported that presently they consumed alcohol [Table 1].
|Table 1: Distribution of the study population by behavioral and anthropometric risk factors|
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Fruit intake of the study population was low, as the average number of times fruits were consumed in a week was only 1.43. The average number of servings of vegetables eaten in a typical week was 12.37. Per capita salt consumption per day was high (14.6 gm). Consumption of junk food and fatty food was low. All study subjects reported using unsaturated fat as the main cooking oil, mainly soyabean oil [Table 2].
A majority of the study subjects were moderate or strenuous workers (81%). Still a significant population (19.0%) was engaged in sedentary or light physical activity [Table 1].
A WC >101 cm was found in 3.4% of the subjects, making them more prone to cardiovascular and metabolic complications. Overweight and obesity was also a problem in the rural area with a prevalence of 9.5 and 8.8%, respectively [Table 1].
Tobacco consumption either in the smoking or chewing form is still a problem in the rural areas, particularly in a sense that such people, being less educated, may not have the social etiquette, thus exposing vulnerable people to tobacco consumption indirectly (passive smoking). In the present study, the percentage of current smokers was 14.2%.
In spite of a legal ban on alcohol consumption in the study area, the present study found that alcohol was consumed by 22.7% of the adult males. This indicated that accessibility was not a problem for alcohol, in spite of legislative enforcement. Thus we still had to pay more attention to behavior change strategies.
Contrary to the general belief, physical inactivity is an emerging cause of concern in the rural areas of India. In the present study, a sedentary lifestyle (mild and light physical activity) was present in 19% of the rural males. Physical activity measurement at the community level is difficult with the existing instruments, and therefore, the results need to be interpreted with caution.
Consumption of vegetables and fruits is extremely low in the study area, with multiple factors contributing to it, leading to undernutrition, particularly micronutrient deficiencies and their consequences. However, the urban lifestyle such as eating junk food and fatty foods had still not affected the rural people in the present study. The present study found a higher salt consumption (14.6 gm/capita/day) by individuals in our study, as against a norm of <5 gm/capita/day, which exposes the community to the risk of hypertension and its consequences.
In the present study, 3.4% of the subjects had a WC > 101 cm, making them more prone to cardiovascular and metabolic complication. Overweight and obesity was also a problem in the rural area, with a prevalence of 9.5 and 8.8%, respectively.
One of the limitations of our study was that we had not studied the females for practical purposes, however, in our setup, the prevalence of risk factors, such as, high dietary fat, addiction, and others were unlikely to be higher in females as compared to males, particularly keeping in mind the sociocultural practices and various interventions carried out by the government, medical colleges, and other agencies, with a special focus on females. Moreover, inclusion of trans-fat consumption, in the form of deep fried food (like pakoras etc.), local bakery biscuits, cakes, and extra saturated fat in the form of ghee, butter, and vanaspati, in the questionnaire, would have been more relevant.
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[Table 1], [Table 2]
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