Prevalence and clustering of cardiovascular disease risk factors in rural Nepalese population aged 40–80years

Prevalence and clustering of cardiovascular disease risk factors in rural Nepalese population... Background: Cardiovascular diseases (CVD) are the main cause of mortality in low- and middle-income countries like Nepal. Different risk factors usually cluster and interact multiplicatively to increase the risk of developing acute cardiovascular events; however, information related to clustering of CVD risk factors is scarce in Nepal. Therefore, we aimed to determine the prevalence of CVD risk factors with a focus on their clustering pattern in a rural Nepalese population. Methods: A community-based cross-sectional study was conducted among residents aged 40 to 80 years in Lamjung District of Nepal in 2014. A clustered sampling technique was used in steps. At first, four out of 18 wards were chosen at random. Then, one person per household was selected randomly (n = 388). WHO STEPS questionnaires (version 2.2) were used to collect data. Chi-square and independent t-test were used to test significance at the level of p <0.05. Results: A total 345 samples with complete data were analyzed. Smoking [24.1% (95% CI: 19.5–28.6)], harmful use of alcohol [10.7% (7.4–13.9)], insufficient intake of fruit and vegetable [72% (67.1–76.6)], low physical activity [10.1% (6.9–13.2)], overweight and obesity [59.4% (54.2–64.5)], hypertension [42.9% (37.6–48.1)], diabetes [16.2% (14.0–18.3)], and dyslipidemia [56.0% (53.0–58.7)] were common risk factors among the study population. Overall, 98.2% had at least one risk factor, while 2.0% exhibited six risk factors. Overall, more than a half (63.4%) of participants had at least three risk factors (male: 69.4%, female: 58.5%). Age [OR: 2.3 (95% CI: 1.13–4.72)] and caste/ethnicity [2.0 (95% CI: 1.28–3.43)] were significantly associated with clustering of at least three risk factors. Conclusions: Cardiovascular risk factors and their clustering were common in the rural population of Nepal. Therefore, comprehensive interventions against all risk factors should be immediately planned and implemented to reduce the future burden of CVD in the rural population of Nepal. Keywords: Cardiovascular diseases, Risk factors, Clustering of CVD risk factors, Smoking, Hypertension, Diabetes mellitus, Dyslipidemia, Rural population, Nepal Background Heart Disease and Cerebrovascular Diseases) were 152 Cardiovascular diseases (CVD) are the number one and 82 per 100,000 population respectively in 2008 [2]. cause of mortality globally. In 2015, global estimated CVD were the second most common (40.0%) noncom- deaths caused by CVD were 17.7 million. More than municable diseases among indoor patients of the three-quaters of these deaths occur in low- and non-specialist hospitals of Nepal in 2010 [3]. Moreover, middle-income countries [1]. In Nepal, the estimated 13.8% of industrial workers of Nepal were diagnosed age-standardized death rates caused by CVD (Ischemic with CVD in 2016 [4]. Based on attributable deaths globally, common CVD risk factors are high blood pressure (to which 13.0% of global * Correspondence: drmkkhanal@gmail.com deaths is attributed), tobacco use (9.0%), diabetes (6.0%), Bangladesh Institute of Health Sciences (BIHS), Dhaka, Bangladesh Ministry of Health and Population (MoHP), Kathmandu, Nepal physical inactivity (6.0%), overweight/obesity (5.0%), Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Khanal et al. BMC Public Health (2018) 18:677 Page 2 of 13 cholesterol (4.5%), harmful use of alcohol (4.0%), and low disability, bed-ridden patients, and pregnant women were fruit and vegetable intake (3.0%) [2, 5, 6]. In Nepal, hyper- excluded from the study. tension was the most prevalent risk factor for CVD which ranged from 26.0% to 38.9% [7–11] during the last 3 years. Sample size and sampling Moreover, diabetes mellitus was seen in 8.4% of Nepalese The estimated sample size was calculated based on the population [12]. STEPS survey of Nepal in 2013 detected prevalence of current smoking (19.0%) reported by nation- hypercholesterol in 23.0%, smoking in 19.0%, overweight in wide study of Aryal et al. [7] at 5% of allowable error. 21.0%, raised blood glucose in 4.0%, physical inactivity in After adjusting for finite population correction and design 3.0%, and harmful use of alcohol in 2.0% [7]. These behav- effect (1.5), minimum required sample size was 331. With ioral and metabolic risk factors usually cluster together, an expectation of 15% non-response rate, the sample size interact, and multiply so that the total risk of developing was inflated to 390. The study used a clustered sampling acute cardiovascular events is increased [13, 14]. In Asia, technique in three stages. The study considered the ward for instance, almost 44.0% of Chinese adult population had as the primary cluster which is the geographic and admin- clustered at least two cardiovascular risk factors [15]. More- istrative unit of the communities. Culturally, economically, over, in Nepal, more than 60.0% had a minimum of two geographically, and socially diverse populations were res- clustered risk factors [7]. Evidence shows that about 58.0% iding in each ward, and these features were similar be- decline in CVD mortality has been attributed to reductions tween wards [21]. The wards were selected based on per in the population levels of these risk factors [16, 17]. probability proportion to size (PPS). The sample size for A recent study from China reported that low-income each cluster was determined based upon their population areas had a higher prevalence of total CVD compared to size. In the first stage, out of 18 wards, four were selected. high-income areas [18]. In addition, cardiovascular health Secondly, systematic random sampling technique was is poor in rural communities and disproportionally affects used to select the required number of households from elderly populations [19]. Risk factors and their clustering each ward. Every sixth household was considered for the phenomenon should be explored to plan and organize in- study, starting from an arbitrary point. Finally, we used dividual and population-based interventions in rural com- age and gender of the eligible participants of that house- munities focusing on elderly population. However, there is hold to place in selection grid of the Kish table so that we no data available on status and clustering of all cardiovas- would interview only an eligible participant from each cular risk factors in the elderly rural population in Nepal. household [22]. Therefore, we aimed to determine the prevalence of CVD risk factors with a focus on their clustering in rural Nepal- Data collection ese population aged 40–80 years. Data was collected by comprehensively trained health workers (two Auxiliary Nurse Midwifes, one Auxiliary Methods Health Worker, and two third-year medical students) Study design, settings, and participants using standard WHO STEPS instrument version 2.2 The current article is a part of the study titled “Risk predic- [22]. We adopted all the core questionnaires as well as tion of cardiovascular disease in selected rural communities few of the extended questionnaires in our final tool. The of Nepal”, details of the study are available elsewhere [20]. tool was translated into Nepali by applying the forward Briefly, the original study was a community-based and backward methodology and then pretested to 20 cross-sectional study carried out in two rural villages (Bho- participants before the final implementation for piloting. tewodar and Sunderbazar) of Lamjung district (about Information on socio-demographic condition, behavioral 200 km west from capital city Kathmandu) of Nepal from risk factors, such as tobacco use, alcohol use, fruit and October to November in 2014. The study area had 3869 vegetable intake, physical activity, history of blood pres- households where 14,275 people were residing. Of these, sure, and diabetes were obtained by asking related ques- about one-quater (26.0%) were aged 40 to 80 years in the tionnaires. For instance, we asked the question “do you study area. Indigenous people like Gurung, Tamang, Magar, currently smoke any tobacco products, such as ciga- and Newar were major inhabitants in addition to Bramhan rettes, cigars or pipes” to acquire information on current and Kshetri in the study site. Most of them were involved tobacco use. Alcohol consumption habit was ascertained in agricultural work, livestock activities, grocery shops, and asking “have you consumed an alcoholic drink within locally available jobs [21]. There were two sub-health posts the past 30 days”. If yes, we used show cards to estimate and two fifteen-bed hospital in the communities. Any per- an average amount of standard drinks per day. Similarly, son who was residing for more than 6 months, aged 40– for assessing fruit and vegetable intake, we asked the 80 years, and without a history of self-reported angina, question, “on how many days do you eat fruit and vege- myocardial infarction, stroke, intermittent claudication table in a typical week”. If they responded one or more were included in the study. Those with an intelectual days, we displayed show cards to calculate average Khanal et al. BMC Public Health (2018) 18:677 Page 3 of 13 numbers of fruit and vegetable servings per day. Height, than five servings (400 g) per day [22]. Physical activities weight, waist circumference, and blood pressure were were measured in metabolic equivalents of task (METs) measured precisely using standard methods as described minutes per week. Low level physical activities (physically in WHO STEPs manual [22]. Height and weight were inactive) was defined as less than 600 MET-minutes per measured over a light cloth and without foot bearings week of physical activities [22]. using the portable weighing machine and stadiometer, respectively. The non-elastic measuring tape was used to Anthropometric measurements (STEP II) measure waist circumference at the level of the umbil- Weight was divided by square of height (in meters) to icus (midway between last rib and anterior superior iliac calculate Body Mass Index (BMI) of participants. The spine) during normal respiration. Doctor’s aneroid BMI was classified as underweight (< 18.5 Kg/m ), nor- 2 2 sphygmomanometer with an appropriate sized cuff and a mal (18.5–24.9 Kg/m ), overweight (≥25–29.9 Kg/m ), stethoscope were utilized over the unclothed left arm to and obese (≥30 Kg/m )[25]. Raised waist circumference measure blood pressure. Three blood pressure readings (central obesity) was defined as the raised level if it was were obtained, the first after at least 15 min, second and more than 88 cm in women and more than 102 cm in third after at least five-minute intervals. Finally, the partic- men [26]. For blood pressure, an average of the last two ipants were invited in the next morning, after overnight readings was used in the final analysis. Hypertension fasting, to a nearby hospital or community centre, where, was defined as average systolic blood pressure skilled phlebotomist withdrew venous blood to measure (SBP) ≥ 140 mmHg and/or average diastolic blood fasting blood sugar and lipid profile. Participants without pressure (DBP) ≥ 90 mmHg and/or history of taking known diabetes were then requested to drink 75 g of an- antihypertensive medication in the last 2 weeks [27]. hydrous glucose mixed with 250 ml of water within five minutes. Another one milliliter of venous blood was also Biochemical variables (STEP III) collected two hours after the oral glucose intake for oral Diabetes mellitus was defined by the presence of fasting glucose tolerance test. Blood sugar and lipid profile were blood sugar ≥126 mg/dl (milligram/deciliter) and/or post measured using a semi-automated machine of Erba prandial blood sugar ≥200 mg/dl and/or intake of any Mannheim (Germany), chem. 5 v3 model in the labora- anti-diabetic drugs [28]. Dyslipidemia was defined as pres- tory of a nearby community hospital. ence of at least one of the following; raised total choles- terol (> 200 mg/dl), raised triglyceride (> 150 mg/dl), Definition of variables raised low-density lipoprotein (> 130 mg/dl), decreased Demographic and socio-economic variables high-density lipoprotein (< 40 mg/dl in male and < 50 mg/ Age was taken in complete years. Education was catego- dl in female), and/or use of antilipidemic drug [29]. rized into illiterate and no formal education (< grade 1), primary (grade 1–5) and more than or equal to secondary Clustering of risk factors (≥ grade 6). Caste was divided into upper caste (Bramhan The clustering of modifiable CVD risk factors were and Kshetri), Janajati (Gurung, Tamang, Newar, Magar, assessed based on the presence of eight major risk fac- and Thakali) and Dalit (low caste). Information related to tors; current smoking, harmful use of alcohol, low fruit occupation was subdivided into employed, self-employed and vegetable intake, low physical activity, overweight or (operating own business), unemployed (student or obesity, hypertension, diabetes, and dyslipidemia. house-maker or non-paid workers), and retired. Poor was defined as any participant whose family income per per- Statistical analysis son per year was less than 18,428 rupees ($184.2) [23]. Data was compiled and edited to maintain consistency be- fore entering into Epidata version 3.1. Duplications were Variables related to behavior (STEP I) removed and exported into SPSS V.16.0 for further ana- Current smoking was defined as those who were smok- lysis. Simple descriptive statistics were used for ing cigarettes and those who quit less than 1 month be- socio-demographic characteristics. Prevalence of risk fac- fore data collection. Similarly, those who were chewing tors was computed in percentage with 95% confidence tobacco in the last 30 days were defined as current interval (CI). In the next step, age and sex adjusted preva- smokeless tobacco users [22]. The total amount of alco- lence of CVD risk factors was computed using WHO hol intake was calculated in a number of the standard world standard population distribution between 2000 and drink (10 g of pure ethanol). Average consumption of al- 2025 considering sex weight of 0.5 [30]. Clustering of risk cohol of at least one (women) or two (men) standard factors was presented as a percentage. Mean and standard drinks per day over last 30 days was defined as the deviations (SD) were reported for normally distributed harmful use of alcohol [24]. Insufficient intake of fruit and continuous variables. Median with interquartile range was vegetable was considered if the participants consumed less reported for non-normally distributed continuous variable. Khanal et al. BMC Public Health (2018) 18:677 Page 4 of 13 Correlation between different risk factors was determined (± 12.5) mg/dl, and 86.3 (± 33.6) mg/dl, respectively. using Pearson’s correlation coefficients. Socio-demographic Standard drinks of alcohol per day (p = < 0.001), waist variables were entered into simple and multiple logistic re- circumference (p = < 0.001), diastolic blood pressure gression models to determine crude and adjusted odds ra- (p = 0.008), total cholesterol (p = 0.009), and triglyceride tios, respectively. Chi-square and independent t-tests were (p = 0.009) were significantly different between men and used to compare categorical and continuous variables, re- women (Table 1). spectively. Mann-Whitney U test was used to test for The overall prevalence of current smoking was 24.1% non-normally distributed variables. All tests were two tail (95% CI: 19.5–28.6) with a higher proportion in men test and p < 0.05 was considered statistically significant. (29.2%) compared to women (20.0%). About one-fifth (19.0%) were using smokeless tobacco (men: 29.6%, women: 11.0%). Overall, 10.7% (95% CI: 7.4–13.9) were Ethical consideration drinking a harmful dose of alcohol (men: 21.4%, women: Ethical Review Board of Nepal Health Research Council 2.3%). Similarly, 10.1% (95% CI: 6.9–13.2) were physically reviewed and approved the study protocol. Before data inactive. Women were less physically active (12.6%) com- collection, data enumerator informed all the eligible par- pared to men (7.1%). Additionally, 72.0% (95% CI: ticipants about the study objectives, data collection pro- 67.1–76.6) were consuming less than five servings of fruit cedures, benefits and risks of the study, confidentiality, and vegetable daily (men: 76.6%, women: 68.0%) (Table 2). and anticipated use of the results. Then, the enumera- Overweight and obesity was observed among 59.4% tors (health professionals) obtained written consent or (95% CI: 54.2–64.5) of participants while 31.3% (95% CI: thumb impression (if unable to write) using a consent 26.4–36.1) of participants had central obesity. Over- form in Nepali language. weight and obesity was higher in males (61.0%) than in females (58%), whereas central obesity was higher in Results women (55.0%) compared to men (14.3%). The overall Socio-demographic characteristics prevalence of hypertension was 42.9% (95% CI: Overall, 345 samples with complete data were analysed 37.6–48.1) where more men (46.8%) compared to excluding 43 participants who did not participate in bio- women (39.8%) had raised blood pressure (Table 2). chemical assessment. Socio-demographic characteristics Diabetes mellitus was determined in 16.2% (95% CI: of the study subjects are presented elsewhere [20]. In 14.0–18.3) of total study subjects. Out of all, dyslipid- brief, more than half of the participants were females emia was revealed in 56.0% (95% CI: 53.0–58.7) of par- (55.4%), aged 53.5 ± 10.1 years, and 40% of them were ticipants. Dyslipidemia was higher in women (59.7%) aged 40–49 years. About half of the participants (53.6%) compared to men (52.0%). When analysed separately, were uneducated and from Janajati (55.5%) population. 29.9% (95% CI: 27.2–32.5) had raised high-density lipo- The majority were unemployed (58.0%). Almost all par- protein, 27.2% (95% CI: 24.6–29.7) had elevated trigly- ticipants were married (91.3%) and poor (97.1%). Out of ceride, 17.1% (95% CI: 14.9–19.2) had increased level of participants who did not attend the biochemical assess- total cholesterol and 10.4% (95% CI: 8.6–12.1) had raised ment, mean age was 52 ± 10.7 years, 55.8% were female, low-density lipoprotein (Table 2). 55.8% did not have formal education, 51.2% were Jana- jati, 67.4% were unemployed. These characteristics were not significantly different from the participants who had Distribution of cardiovascular disease risk factors completed the study. The proportion of current smoking (p = 0.04), harmful use of alcohol (p < 0.001), raised waist circumference Prevalence of cardiovascular disease risk factors (p = 0.001), raised triglycerides (p = 0.007), and decreased On average, participants were smoking at least two ciga- high-density lipoprotein (p = 0.001) were significantly dif- rettes per day. Additionally, participants were consuming ferent between men and women (Table 2). at least one standard drink per day. Overall, the median in- The proportion of current smoking, less fruit and take of fruit and vegetable was four servings per day, and vegetable intake, hypertension, and diabetes increased median physical activity was 3360 METs-minutes per week. with increased age. In opposition, the proportion of Mean body mass index was 25.9 (± 4.2) Kg/m and mean harmful use of alcohol, physical inactivity, overweight/ waist circumference was 88.8 (± 11.7) cm. The average sys- obesity, and dyslipidemia decreased with increased age. tolic and diastolic blood pressure were 124.5 (± 18.7) mm However, only overweight and obesity were significantly Hg and 88.8 (± 10.8) mm Hg, respectively. The mean fast- associated with age (p = 0.002) (Table 2). ing blood sugar, total cholesterol, triglyceride, high-density The harmful use of alcohol was significantly associated lipoprotein, and low-density lipoprotein were 92.8 (± 34.1) with the level of education (p = 0.003), caste (p = 0.02), mg/dl, 165.1 (± 34.9) mg/dl, 129.9 (± 69.2) mg/dl, 53.2 and economic status (p = 0.046). It was highest among Khanal et al. BMC Public Health (2018) 18:677 Page 5 of 13 Table 1 Distribution of cardiovascular disease risk factors of study subjects by gender Risk factors Both sexes Men Women P value Mean (SD) Mean (SD) Mean (SD) Average cigar per day 1.98 (5.2) 2.2 (5.7) 1.8 (4.7) 0.478 Standard drinks of alcohol/day 0.8 (2.5) 1.6 (3.5) 0.09 (0.64) < 0.001 Fruit/vegetable (servings/day) 4.0 (2.0) 4 (1.0) 4 (2.0) 0.101 Metabolic equivalent - minute/week 3360.0 (3576.0) 3150.0 (3600.0) 3600.0 (3525.0) 0.121 Body Mass Index (Kg/m ) 25.9 (4.2) 25.9 (3.4) 25.9 (4.6) 0.996 Waist circumference (cm) 88.8 (11.7) 91.9 (9.9) 86.3 (12.3) < 0.001 Systolic Blood Pressure (mmHg) 124.5 (18.7) 126.8 (18.7) 122.6 (18.4) 0.039 Diastolic Blood pressure (mmHg) 79.9 (10.8) 81.6 (11.6) 78.5 (9.9) 0.008 Fasting Blood Sugar (mg/dl) 92.8 (34.1) 91.7 (23.8) 93.6 (40.5) 0.61 Total cholesterol (mg/dl) 165.1 (34.9) 170.6 (34.4) 160.7 (34.8) 0.009 Triglyceride (mg/dl) 129.9 (69.2) 138.7 (76.6) 119.2 (61.4) 0.009 High-density Lipoprotein (mg/dl) 53.2 (12.5) 53 (12.3) 53.3 (12.7) 0.8 Low-density Lipoprotein (mg/dl) 86.3 (33.6) 89.8 (33.5) 83.4 (33.5) 0.03 median Interquartile range, SD Standard Deviation participants who had at least secondary level of educa- was observed among aproximately one-third of the par- tion (11.0%) and who were Dalit (28.6%) (Table 3). ticipants (30.4%). Clustering was higher among males Overweight and obesity was associated with ethni- (36.3%) compared to females (25.6%) (Fig. 1). About city (p = 0.007) with the highest prevalence among 2.0% (male: 3.2%, female: 1%) had up to six risk factors. Dalit (66.7%) participants. Waist circumference was We studied the socio-economic determinants of cluster- associated with the level of education (p = 0.001) and ing of at least three risk factors, as after this clustering occupation (p < 0.001). It was highest among was suddenly dropped down. In univariate logistic re- uneducated (39.5%) and unemployed (38.5%). Hyper- gression; gender, age, and caste/ethnicity were signifi- tension was associated with occupation (p = 0.071), cantly associated with clustering of at least three risk and retired participants had highest prevalence factors. However, after adjusting for all other (59.3%) (Table 3). socio-demographic factors, age and caste/ethnicity were Prevalence of diabetes mellitus was significantly differ- significantly associated with clustering of risk factors (at ent among different levels of education (p = 0.02). It was least three). Odds of clustering of at least three risk fac- highest among those with at least secondary level educa- tors was 2.3 times (95% CI: 1.13–4.72) in the age group tion (24.4%). Raised total cholesterol (p = 0.03) was sig- of 60–69 years compared to 40–49 years of age. Dalits nificantly different among different ethnic groups. had two times (95% CI: 1.28–3.43) more chance of risk Similarly, the raised triglyceride was associated with the factor clustering compared to upper caste residents level of education (p = 0.0 1). High-density lipoprotein (Table 5). was associated with the level of education (p = 0.002) and the occupation (p = 0.006) (Table 3). Discussion This study determined the prevalence of cardiovascular dis- Correlation between cardiovascular disease risk factors ease risk factors along with their clustering phenomenon in Correlations between different risk factors were weak. a rural population of Nepal. Prevalence of each cardiovas- Stronger correlations were observed between smoking and cular risk factor was high and a maximum of six risk factors alcohol intake, diastolic blood pressure and body mass was clustered in some study participants. index along with waist circumference and triglyceride Our current study revealed that approximately (Table 4). one-quater (24.1%) were smoking cigarettes in a rural population of Nepal. Our finding was consistent with the Clustering of cardiovascular disease risk factors tobacco smoking prevalence reported in other studies in Almost all of the participants (98.2%) had at least one Nepal. For instance, the proportion of current smoker was risk factor. In addition, 86% of total participants had 29.0% in STEPS survey 2013 of Nepal among aged 40– clustering of at least two risk factors. Overall, 63.4% of 69 years [7] and 28.6% in rural Sindhuli [31]. However, the participants had at least three risk factors (male: 69.4%, prevalence of smoking found in our study was higher female: 58.5%). Clustering of at least four risk factors compared to that found in the capital city Kathmandu in Khanal et al. BMC Public Health (2018) 18:677 Page 6 of 13 Table 2 Prevalence of cardiovascular disease risk factors stratified by gender and age Characteristics Current Harmful use Fruit/Vegetable Inadequate Overweight Hypertension Diabetes TC (> 200 mg/dl) TG(> 150 mg/dl) HDL (M < 40, LDL(> 130 mg/dl) Dyslipidemia Smoking of alcohol < 5 servings Physical activity and obesity mellitus F < 50 mg/dl) Men and Women 40–49 17.5 9.5 66.4 14.6 64.2 36.5 15.3 19 26.3 32.1 11.7 57.9 50–59 25.7 14.3 76.2 6.7 59.0 42.9 15.1 19 29.5 30.5 9.5 58.1 60–69 31.0 9.9 71.8 8.5 64.8 52.1 15.5 15.5 28.2 23.9 12.7 56.3 70–79 31.3 6.3 81.3 6.3 28.1 50 15.6 6.2 21.9 31.2 3.1 40.6 Total 24.1 10.7 71.9 10.1 59.4 42.9 16.2 17.1 27.2 29.9 10.4 55.9 95% CI 19.5– 7.4–13.9 67.1–76.6 6.9–13.2 54.2–64.5 37.6–48.1 14.0– 14.9–19.2 24.6–29.7 27.2–32.5 8.6–12.1 53.0–58.7 28.6 18.3 Age sex 24.9 10.4 71.5 9.9 58.6 43.2 15.9 16.8 27.5 29.3 10.1 55.4 adjusted prevalence Men 40–49 26.2 20.0 75.4 9.2 56.9 44.6 16.9 24.6 35.4 13.8 18.5 55.4 50–59 26.1 28.3 78.3 2.2 69.6 52.2 17.4 17.4 34.8 10.9 8.7 50.0 60–69 37.5 18.8 71.9 12.5 65.6 46.9 15.6 15.6 34.4 18.8 12.5 56.3 70–79 36.4 9.1 90.9 0.0 36.4 36.4 9.1 9.1 27.3 0.0 0.0 27.3 Total 29.2 21.4 76.6 7.1 61.0 46.8 16.2 19.5 34.4 13.0 13.0 51.9 Women 40–49 9.7 0.0 58.3 19.4 70.8 29.2 13.9 13.9 18.1 48.6 5.6 59.7 50–59 25.4 3.1 74.6 10.2 50.8 35.6 18.6 20.3 25.4 45.8 10.2 64.4 60–69 25.6 4.8 71.8 5.1 64.1 56.4 15.4 15.4 23.1 28.2 12.8 56.4 70–79 28.6 4.2 76.2 9.5 23.8 57.1 19.0 4.8 19 47.6 4.8 47.6 Total 19.9 2.3 68.1 12.6 58.1 39.8 16.2 15.2 21.5 43.5 8.4 59.2 P value (men 0.044 < 0.001 0.079 0.097 0.58 0.194 0.99 0.29 0.007 0.001 0.16 0.18 and women) P value (age 0.105 0.56 0.506 0.224 0.002 0.144 0.94 0.33 0.84 0.66 0.47 0.33 groups) TC Total cholesterol, TG Triglyceride, HDL High-density Lipoprotein, LDL Low-density Lipoprotein Khanal et al. BMC Public Health (2018) 18:677 Page 7 of 13 Table 3 Distribution of cardiovascular disease risk factors by socio-economic characteristics Characteristics Current Harmful use Fruit/Vegetable Physical Overweight Hypertension Diabetes TC (> 200 mg/dl) TG(> 150 mg/dl) HDL (M < 40, LDL(> 130 mg/dl) Dyslipidemia Smoking of alcohol < 5 servings inactivity and obesity mellitus F < 50 mg/dl) Level of education No formal 25.9 6.5 73.0 10.8 54.6 41.6 15.1 15.7 23.8 37.8 10.8 56.8 education Primary 25.7 21.4 75.7 8.6 67.1 50 8.6 12.9 21.4 18.6 7.1 47.1 ≥ Secondary 18.9 11.1 66.7 10 63.3 40 24.4 23.3 38.9 22.2 12.2 61.1 P value 0.41 0.003 0.401 0.868 0.129 0.392 0.02 0.16 0.01 0.002 0.56 0.19 Caste/ ethnicity Upper cast 21.8 8.3 72.2 11.3 48.9 39.8 13.5 21.8 28.6 27.1 12 59.4 Dalit 42.9 28.6 76.2 9.5 66.7 52.4 19.0 0.0 14.3 19.0 4.8 33.3 Janajati 23.6 10.5 71.2 9.4 66.0 44.0 17.8 15.7 27.7 33.0 9.9 56.0 P value 0.108 0.02 0.886 0.858 0.007 0.505 0.55 0.03 0.38 0.27 0.56 0.08 Occupation Employ 22.5 12.5 67.5 7.5 52.5 30.0 22.5 22.5 42.5 17.5 10.0 65.0 Self employed 16.7 12.8 75.6 14.1 65.4 48.7 7.7 19.2 29.5 29.5 11.5 53.8 Unemployed 26.0 8.0 72.5 10.5 57.0 41.0 17.0 14.5 23.0 35.5 9.5 55.5 Retired 33.3 22.2 63.0 0.0 70.4 59.3 25.9 22.2 29.6 7.4 14.8 51.9 P value 0.254 0.124 0.567 0.193 0.287 0.071 0.06 0.47 0.07 0.006 0.83 0.64 Economic status Above poverty line 10.0 30.0 70.0 10.0 50.0 50.0 30.0 20.0 20.0 20.0 10.0 40.0 Poor 24.5 10.1 71.9 10.1 59.7 42.7 31.3 17 27.5 30.1 10.4 56.4 P value 0.291 0.046 0.893 0.988 0.538 0.645 0.58 0.8 0.6 0.49 0.96 0.3 TC Total cholesterol, TG Triglyceride, HDL High-density Lipoprotein, LDL Low-density Lipoprotein Khanal et al. BMC Public Health (2018) 18:677 Page 8 of 13 Table 4 Correlation between different cardiovascular disease risk factors CG AL FV MET BMI WC SBP DBP FBS TC TG HDL LDL CG AL .286 FV −.045 −.087 MET .018 .023 .016 BMI −.096 −.049 .077 −.029 b a WC -.121 −.007 .055 −.090 .569 b b a SBP −.010 .117 −.025 .028 .118 .162 a a a DBP −.078 .065 −.050 −.065 .201 .219 .769 FBS −.013 .011 −.037 −.075 .022 .032 .018 .037 TC .010 .100 .037 −.022 −.035 .001 .033 .030 .098 a b b a a TG −.038 −.026 −.012 −.048 .096 .181 .133 .125 .198 .250 a b a HDL .078 .173 .040 .012 −.034 −.101 −.005 −.098 .136 .195 −.075 a b a LDL −.004 .050 .029 −.008 −.064 −.036 −.019 .016 −.030 .863 -.123 -.140 CG Average no. of cigarettes/day, AL Average standard drinks/day, FV Total servings of fruit and vegetable/day, METs Total METs-Minutes/week, BMI Body Mass Index, WC Waist circumference, SBP Systolic Blood Pressure, DBP Diastolic Blood pressure, FBS Fasting Blood Sugar, TC Total cholesterol, TG Triglyceride, HDL High- density Lipoprotein, LDL Low-density Lipoprotein Correlation is significant at the 0.01 level Correlation is significant at the 0.05 level Fig. 1 Distribution of clustering of cardiovascular disease risk factors by gender Khanal et al. BMC Public Health (2018) 18:677 Page 9 of 13 Table 5 Socio-demographic factors associated with clustering of cardiovascular risk factors three or more Characteristics Crude odds ratio (95% CI) Adjusted odds ratio (95% CI) Sex Female Ref Ref Male 1.60 (1.02–2.51) 1.53 (0.80–2.93) Age 40–49 Ref Ref 50–59 1.27 (0.75–2.14) 1.39 (0.79–2.45) a a 60–69 1.89 (1.01–3.53) 2.31 (1.13–4.72) 70–79 0.88 (0.40–1.83) 0.94 (0.38–2.31) Level of Education No formal education Ref Ref Primary 0.78 (0.46–1.33) 0.80 (0.36–1.77) ≥ Secondary 0.95 (0.49–1.85) 0.87 (0.42–1.81) Caste/Ethnicity a a Upper cast Ref Ref Janajati 0.55 (0.35–0.88) 2.03 (0.71–5.82) Dalit 1.14 (0.42–3.09) 2.09 (1.28–3.43) Marital Status Married Ref Ref Widow or Widower 0.85 (0.38–1.89) 0.71 (0.29–1.74) Occupation Employ Ref Ref Self employed 1.34 (0.60–3.00) 1.09 (0.46–2.62) Unemployed 0.93 (0.46–1.89) 0.80 (0.32–2.02) Retired 1.19 (0.43–3.34) 0.70 (0.22–2.16) Economic status Higher(> NRs 8498) Ref Ref Poor (≤ NRs 8498) 1.16 (0.32–4.20) 1.22 (0.31–4.75) significant at the 0.05 level urban settings (20.0%) [10] and rural settings (21.7%) when compared with the result of a study representing the [10, 32] and the sub-metropolitan city Pokhara (17.0%) [8] whole country (99.0%) [7] and a study from surveillance of Nepal. In all of these studies, more males compared to site in Bhaktapur (97.9%) of Nepal [34]. Variation according females were smoking cigarettes. These studies revealed to age, race/ethnicity, income, education, and availability of the severity of smoking in Nepal. fruit and vegetable are established socio-demographic fac- One in ten study participants was drinking a harmful tors for low fruit and vegetable intake [35, 36]. dose of alcohol in the current study. Similar result was Ten percent of our study participants were engaged in observed in a recent study conducted in Pokhara (12.0%) a low level of physical activities. This prevalence was in of Nepal [8]. However, our finding was higher than the re- line with the findings from Pokhara of Nepal (7.0%) and port of STEPs survey of 2013 (2.7%) for the 45 to 69 years world health survey for physical activities (8.0%) [8, 37]. of age group [7]. Most of the current study subjects were However, current prevalence was less than the findings of indigenous and Dalit caste. Such ethnic people were of capital city Kathmandu (40.2%) [10], Eastern region more likely to drink alcohol and brew alcohol at home (37.6%) [38], and Bhaktapur (male: 18.0% and female: [33], which might be the reason for higher prevalence. 22.0%) of Nepal [39]. Work and travel related activities The burden of low fruit and vegetable intake was high in are the major domains for physical activities in Nepal Nepal. The current study determined that about [39]. These domains are more pronounced in rural two-thirds (72.0%) were not consuming five servings of areas, and might be the reason for the lower prevalence fruit and vegetable per day. This prevalence was lower of physical inactivity in our study. Khanal et al. BMC Public Health (2018) 18:677 Page 10 of 13 About six in ten participants (59.0%) were overweight city Kathmandu, which determined 73.3% had dyslipidemia and obese in our study population. This prevalence was [45]. Our study revealed that prevalent risk factors namely comparable with the result of Western Nepal (60.7%) [38]. waist circumference, systolic blood pressure, and fasting However, it was three-times greater than the findings of a blood sugar were correlated with triglyceride level. Simi- stepwise survey of risk factors in Nepal (21.0%) [7]. The larly, alcohol intake and fasting blood sugar were positively higher prevalence of overweight and obesity may be related correlated with the reduction of serum high-density lipo- to the higher proportion of participants who smoke protein. All of these may have been responsible for such cigarette, chew tobacco, drink alcohol, and indulge in less high prevalence of dyslipidemia. physical activities in the current study population. Almost all of the study population had at least one risk Evidences support that alcohol energy intake may be re- factor. The finding of our study was consistent with the sponsible for weight gain if not counterbalanced, for in- STEPS survey 2013 of Nepal which revealed 99.6% of par- stance, by physical activities [40]. However, further studies ticipants had at least one risk factor [7]. When compared would be needed to confirm and explain our findings. with the studies from other Asian countries, clustering of at Two-fifths of participants (43.0%) had hypertension in least two risk factors was observed in 44% of Chinese our study. The prevalence of hypertension in the current population [15] and 76% of cases in Bangladesh [46]. study (38.9%) was comparable with above-mentioned Comparably, the current study displayed 86% of study STEPS survey (47.0%) for the respective age of more than participants had a minimum of two risk factors clus- 45 years [7]. However, hypertension was greater in our tered together. The current finding of clustering of study compared to populations from Eastern Nepal three to five risk factors (63.4%) was higher compared (34.0%) [38], municipalities of Kathmandu (32.5%) [10], to STEPS survey 2013 (30.0%) for the age group 45 Pokhara-Lekhnath (28.0%) [8], Dhulikhel (27.7%) [9], and to 69 years [7]. Our study included the harmful use Surkhet (38.9%) [11] of Nepal. Alcohol consumption, body of alcohol, diabetes, and dyslipidemia when analyzing mass index, total cholesterol, and triglyceride were corre- the clustering phenomenon, which the STEPS survey lated to systolic and diastolic blood pressure in our study. did not. This could explain the disparity between the The high prevalence of these risk factors in our study findings. might explain the higher rates of hypertension. When different risk factors act together, the effect will The prevalence of diabetes mellitus was 16.2% in our be multiplicative and raises the risk of CVD more than study. This prevalence was higher than the reported preva- the summation of risk factors [13, 14]. For instance, one lences from Eastern Nepal for the age group 40 to 80 years study reported that incidence of ischemic heart disease (11.5%) [38], STEPS survey 2013 (9.0%) for age group 45– rises from 0 to 40% as the number of risk factors con- 69 years [7], and rural population of Sunsari (9.0%) [41]. glomerate from zero to five among diabetic patients [47]. One study conducted in Kathmandu valley reported that Moreover, annual medical expenditure increases with the 25.9% of participants aged 60 years and older had diabetes rise in clustering of risk factors [48]. In our study greatest mellitus [42]. That means elderly age group has the higher clustering was observed among 60–69 years of age group chance of diabetes [43]. A high proportion of diabetes in and Dalit participant. Therefore, it could be pronounced our study could be a result of the high prevalence of trigly- that preventive strategies should be focused on individuals ceride that was correlated with fasting blood sugar. Another who have more risk factors and especially elderly popula- reason for a higher burden of diabetes in our study may be tion who are aged 60–69 years and are Dalit. becauseofthe useofanoralglucose tolerancetestfor It is important to emphasize health education programs those who did not have known diabetes. that warn about the behavioral risk factors of CVD. In Our study revealed more than half (56.0%) of participants addition, early detection and treatment of intermediary risk of the rural population had dyslipidemia. This overall dys- factors (hypertension, diabetes, dyslipidemia and over- lipidemia was accounted by 17.0% of elevated total choles- weight) are required to minimize the future burden of terol, 27.0% of raised triglyceride, 30.0% decreased CVD [2]. Health promotion approaches could be delivered high-density lipoprotein, and 10.0% of raised low-density through various approaches: health workforce, trained lipoprotein among all the study subjects. When compared community volunteers of the current health system, school with above-mentioned STEPS survey, our study population health programs or media campaigns [49–51] collaborating had less prevalence of dyslipidemia. For instance, among with all stakeholders. Such community-based programs participants of age group 45 to 69 years of age, 33% had have already been implemented in developed and develop- raised cholesterol, 35% had elevated triglyceride, and 24% ing countries [50, 52]. Package of Essential Noncommunic- hadelevatedlow-density lipoproteininthe STEPSsurvey able Disease (PEN) interventions could be implemented to [44]. Furthermore, the proportion of dyslipidemia in our prevent cardiovascular diseases as well, so that the commu- study was less compared to another study from nity could utilize current health care delivery system [53]. non-diabetes participants of the urban area of the capital One study from Northern India revealed that the primary Khanal et al. BMC Public Health (2018) 18:677 Page 11 of 13 health care setting was a feasible setting for CVD risk man- data management. Finally, we would like to acknowledge the study team and study participants. agement even in rural areas [52]. Our study had several limitations. The study was con- Funding ducted in a selected rural community where a large popula- This research work (M.Phil thesis) was funded by the ‘Norad’s Programme for Master Studies (NOMA)’ grant of the University of Oslo, Norway. The funding tion of Janajati and women were residing. Our sample size body did not have any role in the study design and collection, analysis, and was not large enough to make it a representative sample of interpretation of data and in writing the manuscript. all the risk factors. As we have included participants aged 40 to 80 years, we could not report the status of risk factor Availability of data and materials The datasets used and/or analyzed during the current study available from among the younger population. The current study might the corresponding author on reasonable request. have underestimated the cardiovascular risk factors, as it ex- cluded those who had known heart diseases, stroke, or inter- Authors’ contributions MKK conceptualized the main thesis, trained data collectors, curated the mittent claudication. The prevalence of risk factors might data, performed formal analysis, interpreted the result, wrote the original have been underestimated as about 11.0% of total partici- draft, and finalized it; MSAMA supervised the quality of the main thesis, pants did not involve in biochemical assessment, though critically reviewed, and edited the manuscript; MZ investigated the project, involved in data curation, interpreted the results, and critically reviewed the socio-demographic characteristics of responders and manuscript; PCB and PB involved in project administration, acquisition, non-responders were not significantly different. Despite compilation, analysis, and interpretation of data, reviewed and edited the these limitations, our study had several strengths. This is the orginal draft; RD involed in project administration and investigation, analysed the result, reviewed the draft and provided intellectual comments on the first study conducted among elderly rural population of manuscript; SD and AS allocated the resources, oversaw the acquisition of Nepaltoexplore alltraditional risk factors of CVD including data, revised the main thesis, and the manuscript critically. All authors read biochemical assessment. We used trained enumerators to and approved the final manuscript. acquire data related to the participants at their own home. Ethics approval and consent to participate We performed the oral glucose tolerance test to confirm Ethical Review Committee of Nepal Health Research Council reviewed and diabetes along with the fasting blood sugar. Therefore, find- approved the study. Enumerators informed about study objectives, data collection procedures, benefits and risks of the study, confidentiality, and ings could be cautiously generalized to other population. anticipated use of the results to all eligible participants. Then, the trained enumerators (health professionals) obtained written consent or thumb impression (if unable to write) using a consent form in Nepali language. Conclusions Cardiovascular risk factors and their clustering were com- Competing interests moninthe ruralpopulationofNepal.Almostall of the par- The authors declare that they have no competing interests. ticipants displayed on risk factor, with the majority of the participants presenting with one or clusters of two-three risk Publisher’sNote factors. When different risk factors aggregate together, their Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. effect would be multiplicative to develop cardiovascular dis- eases. Therefore, comprehensive intervention to address Author details multiple risk factors should be immediately planned and im- Bangladesh Institute of Health Sciences (BIHS), Dhaka, Bangladesh. Department of Community Medicine, Bangladesh Institute of Health plemented to reduce the risk factors and future burden of Sciences (BIHS), Dhaka, Bangladesh. Padmakanya Campus, Tribhuvan cardiovascular diseases in a rural population of Nepal. University (TU), Kathmandu, Nepal. Institute of Medicine (IOM), Tribhuvan University, Kathmandu, Nepal. Ministry of Health and Population (MoHP), Abbreviations Kathmandu, Nepal. College of Health and Biomedicine, Victoria University, BMI: Body mass index; CVD: Cardiovascular diseases; DBP: Diastolic blood Melbourne, Australia. pressure; HDL: High-density lipoprotein; LDL: Low-density lipoprotein; METs: Metabolic equivalents of task; NCD: Non-communicable diseases; Received: 27 September 2017 Accepted: 24 May 2018 SBP: Systolic blood pressure; STEPs: Stepwise approach to surveillance Acknowledgements References This paper is based on the thesis submitted to Bangladesh Institute of Health 1. Cardiovascular Diseases (CVDs) Fact Sheet; 2017. Available at [http://www. Sciences (BIHS) as a partial fulfillment of M.Phil degree in Noncommunicable who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)]. Diseases under the University of Dhaka. The authors want to express their Accessed 14 Aug 2017. sincere gratitude and appreciation to Mr. Matthew Bourke, PhD student, 2. WHO. Global Atlas on Cardiovascular Disease Prev Control. Geneva: WHO; 2011. 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Prevalence and clustering of cardiovascular disease risk factors in rural Nepalese population aged 40–80years

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Medicine & Public Health; Public Health; Medicine/Public Health, general; Epidemiology; Environmental Health; Biostatistics; Vaccine
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Abstract

Background: Cardiovascular diseases (CVD) are the main cause of mortality in low- and middle-income countries like Nepal. Different risk factors usually cluster and interact multiplicatively to increase the risk of developing acute cardiovascular events; however, information related to clustering of CVD risk factors is scarce in Nepal. Therefore, we aimed to determine the prevalence of CVD risk factors with a focus on their clustering pattern in a rural Nepalese population. Methods: A community-based cross-sectional study was conducted among residents aged 40 to 80 years in Lamjung District of Nepal in 2014. A clustered sampling technique was used in steps. At first, four out of 18 wards were chosen at random. Then, one person per household was selected randomly (n = 388). WHO STEPS questionnaires (version 2.2) were used to collect data. Chi-square and independent t-test were used to test significance at the level of p <0.05. Results: A total 345 samples with complete data were analyzed. Smoking [24.1% (95% CI: 19.5–28.6)], harmful use of alcohol [10.7% (7.4–13.9)], insufficient intake of fruit and vegetable [72% (67.1–76.6)], low physical activity [10.1% (6.9–13.2)], overweight and obesity [59.4% (54.2–64.5)], hypertension [42.9% (37.6–48.1)], diabetes [16.2% (14.0–18.3)], and dyslipidemia [56.0% (53.0–58.7)] were common risk factors among the study population. Overall, 98.2% had at least one risk factor, while 2.0% exhibited six risk factors. Overall, more than a half (63.4%) of participants had at least three risk factors (male: 69.4%, female: 58.5%). Age [OR: 2.3 (95% CI: 1.13–4.72)] and caste/ethnicity [2.0 (95% CI: 1.28–3.43)] were significantly associated with clustering of at least three risk factors. Conclusions: Cardiovascular risk factors and their clustering were common in the rural population of Nepal. Therefore, comprehensive interventions against all risk factors should be immediately planned and implemented to reduce the future burden of CVD in the rural population of Nepal. Keywords: Cardiovascular diseases, Risk factors, Clustering of CVD risk factors, Smoking, Hypertension, Diabetes mellitus, Dyslipidemia, Rural population, Nepal Background Heart Disease and Cerebrovascular Diseases) were 152 Cardiovascular diseases (CVD) are the number one and 82 per 100,000 population respectively in 2008 [2]. cause of mortality globally. In 2015, global estimated CVD were the second most common (40.0%) noncom- deaths caused by CVD were 17.7 million. More than municable diseases among indoor patients of the three-quaters of these deaths occur in low- and non-specialist hospitals of Nepal in 2010 [3]. Moreover, middle-income countries [1]. In Nepal, the estimated 13.8% of industrial workers of Nepal were diagnosed age-standardized death rates caused by CVD (Ischemic with CVD in 2016 [4]. Based on attributable deaths globally, common CVD risk factors are high blood pressure (to which 13.0% of global * Correspondence: drmkkhanal@gmail.com deaths is attributed), tobacco use (9.0%), diabetes (6.0%), Bangladesh Institute of Health Sciences (BIHS), Dhaka, Bangladesh Ministry of Health and Population (MoHP), Kathmandu, Nepal physical inactivity (6.0%), overweight/obesity (5.0%), Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Khanal et al. BMC Public Health (2018) 18:677 Page 2 of 13 cholesterol (4.5%), harmful use of alcohol (4.0%), and low disability, bed-ridden patients, and pregnant women were fruit and vegetable intake (3.0%) [2, 5, 6]. In Nepal, hyper- excluded from the study. tension was the most prevalent risk factor for CVD which ranged from 26.0% to 38.9% [7–11] during the last 3 years. Sample size and sampling Moreover, diabetes mellitus was seen in 8.4% of Nepalese The estimated sample size was calculated based on the population [12]. STEPS survey of Nepal in 2013 detected prevalence of current smoking (19.0%) reported by nation- hypercholesterol in 23.0%, smoking in 19.0%, overweight in wide study of Aryal et al. [7] at 5% of allowable error. 21.0%, raised blood glucose in 4.0%, physical inactivity in After adjusting for finite population correction and design 3.0%, and harmful use of alcohol in 2.0% [7]. These behav- effect (1.5), minimum required sample size was 331. With ioral and metabolic risk factors usually cluster together, an expectation of 15% non-response rate, the sample size interact, and multiply so that the total risk of developing was inflated to 390. The study used a clustered sampling acute cardiovascular events is increased [13, 14]. In Asia, technique in three stages. The study considered the ward for instance, almost 44.0% of Chinese adult population had as the primary cluster which is the geographic and admin- clustered at least two cardiovascular risk factors [15]. More- istrative unit of the communities. Culturally, economically, over, in Nepal, more than 60.0% had a minimum of two geographically, and socially diverse populations were res- clustered risk factors [7]. Evidence shows that about 58.0% iding in each ward, and these features were similar be- decline in CVD mortality has been attributed to reductions tween wards [21]. The wards were selected based on per in the population levels of these risk factors [16, 17]. probability proportion to size (PPS). The sample size for A recent study from China reported that low-income each cluster was determined based upon their population areas had a higher prevalence of total CVD compared to size. In the first stage, out of 18 wards, four were selected. high-income areas [18]. In addition, cardiovascular health Secondly, systematic random sampling technique was is poor in rural communities and disproportionally affects used to select the required number of households from elderly populations [19]. Risk factors and their clustering each ward. Every sixth household was considered for the phenomenon should be explored to plan and organize in- study, starting from an arbitrary point. Finally, we used dividual and population-based interventions in rural com- age and gender of the eligible participants of that house- munities focusing on elderly population. However, there is hold to place in selection grid of the Kish table so that we no data available on status and clustering of all cardiovas- would interview only an eligible participant from each cular risk factors in the elderly rural population in Nepal. household [22]. Therefore, we aimed to determine the prevalence of CVD risk factors with a focus on their clustering in rural Nepal- Data collection ese population aged 40–80 years. Data was collected by comprehensively trained health workers (two Auxiliary Nurse Midwifes, one Auxiliary Methods Health Worker, and two third-year medical students) Study design, settings, and participants using standard WHO STEPS instrument version 2.2 The current article is a part of the study titled “Risk predic- [22]. We adopted all the core questionnaires as well as tion of cardiovascular disease in selected rural communities few of the extended questionnaires in our final tool. The of Nepal”, details of the study are available elsewhere [20]. tool was translated into Nepali by applying the forward Briefly, the original study was a community-based and backward methodology and then pretested to 20 cross-sectional study carried out in two rural villages (Bho- participants before the final implementation for piloting. tewodar and Sunderbazar) of Lamjung district (about Information on socio-demographic condition, behavioral 200 km west from capital city Kathmandu) of Nepal from risk factors, such as tobacco use, alcohol use, fruit and October to November in 2014. The study area had 3869 vegetable intake, physical activity, history of blood pres- households where 14,275 people were residing. Of these, sure, and diabetes were obtained by asking related ques- about one-quater (26.0%) were aged 40 to 80 years in the tionnaires. For instance, we asked the question “do you study area. Indigenous people like Gurung, Tamang, Magar, currently smoke any tobacco products, such as ciga- and Newar were major inhabitants in addition to Bramhan rettes, cigars or pipes” to acquire information on current and Kshetri in the study site. Most of them were involved tobacco use. Alcohol consumption habit was ascertained in agricultural work, livestock activities, grocery shops, and asking “have you consumed an alcoholic drink within locally available jobs [21]. There were two sub-health posts the past 30 days”. If yes, we used show cards to estimate and two fifteen-bed hospital in the communities. Any per- an average amount of standard drinks per day. Similarly, son who was residing for more than 6 months, aged 40– for assessing fruit and vegetable intake, we asked the 80 years, and without a history of self-reported angina, question, “on how many days do you eat fruit and vege- myocardial infarction, stroke, intermittent claudication table in a typical week”. If they responded one or more were included in the study. Those with an intelectual days, we displayed show cards to calculate average Khanal et al. BMC Public Health (2018) 18:677 Page 3 of 13 numbers of fruit and vegetable servings per day. Height, than five servings (400 g) per day [22]. Physical activities weight, waist circumference, and blood pressure were were measured in metabolic equivalents of task (METs) measured precisely using standard methods as described minutes per week. Low level physical activities (physically in WHO STEPs manual [22]. Height and weight were inactive) was defined as less than 600 MET-minutes per measured over a light cloth and without foot bearings week of physical activities [22]. using the portable weighing machine and stadiometer, respectively. The non-elastic measuring tape was used to Anthropometric measurements (STEP II) measure waist circumference at the level of the umbil- Weight was divided by square of height (in meters) to icus (midway between last rib and anterior superior iliac calculate Body Mass Index (BMI) of participants. The spine) during normal respiration. Doctor’s aneroid BMI was classified as underweight (< 18.5 Kg/m ), nor- 2 2 sphygmomanometer with an appropriate sized cuff and a mal (18.5–24.9 Kg/m ), overweight (≥25–29.9 Kg/m ), stethoscope were utilized over the unclothed left arm to and obese (≥30 Kg/m )[25]. Raised waist circumference measure blood pressure. Three blood pressure readings (central obesity) was defined as the raised level if it was were obtained, the first after at least 15 min, second and more than 88 cm in women and more than 102 cm in third after at least five-minute intervals. Finally, the partic- men [26]. For blood pressure, an average of the last two ipants were invited in the next morning, after overnight readings was used in the final analysis. Hypertension fasting, to a nearby hospital or community centre, where, was defined as average systolic blood pressure skilled phlebotomist withdrew venous blood to measure (SBP) ≥ 140 mmHg and/or average diastolic blood fasting blood sugar and lipid profile. Participants without pressure (DBP) ≥ 90 mmHg and/or history of taking known diabetes were then requested to drink 75 g of an- antihypertensive medication in the last 2 weeks [27]. hydrous glucose mixed with 250 ml of water within five minutes. Another one milliliter of venous blood was also Biochemical variables (STEP III) collected two hours after the oral glucose intake for oral Diabetes mellitus was defined by the presence of fasting glucose tolerance test. Blood sugar and lipid profile were blood sugar ≥126 mg/dl (milligram/deciliter) and/or post measured using a semi-automated machine of Erba prandial blood sugar ≥200 mg/dl and/or intake of any Mannheim (Germany), chem. 5 v3 model in the labora- anti-diabetic drugs [28]. Dyslipidemia was defined as pres- tory of a nearby community hospital. ence of at least one of the following; raised total choles- terol (> 200 mg/dl), raised triglyceride (> 150 mg/dl), Definition of variables raised low-density lipoprotein (> 130 mg/dl), decreased Demographic and socio-economic variables high-density lipoprotein (< 40 mg/dl in male and < 50 mg/ Age was taken in complete years. Education was catego- dl in female), and/or use of antilipidemic drug [29]. rized into illiterate and no formal education (< grade 1), primary (grade 1–5) and more than or equal to secondary Clustering of risk factors (≥ grade 6). Caste was divided into upper caste (Bramhan The clustering of modifiable CVD risk factors were and Kshetri), Janajati (Gurung, Tamang, Newar, Magar, assessed based on the presence of eight major risk fac- and Thakali) and Dalit (low caste). Information related to tors; current smoking, harmful use of alcohol, low fruit occupation was subdivided into employed, self-employed and vegetable intake, low physical activity, overweight or (operating own business), unemployed (student or obesity, hypertension, diabetes, and dyslipidemia. house-maker or non-paid workers), and retired. Poor was defined as any participant whose family income per per- Statistical analysis son per year was less than 18,428 rupees ($184.2) [23]. Data was compiled and edited to maintain consistency be- fore entering into Epidata version 3.1. Duplications were Variables related to behavior (STEP I) removed and exported into SPSS V.16.0 for further ana- Current smoking was defined as those who were smok- lysis. Simple descriptive statistics were used for ing cigarettes and those who quit less than 1 month be- socio-demographic characteristics. Prevalence of risk fac- fore data collection. Similarly, those who were chewing tors was computed in percentage with 95% confidence tobacco in the last 30 days were defined as current interval (CI). In the next step, age and sex adjusted preva- smokeless tobacco users [22]. The total amount of alco- lence of CVD risk factors was computed using WHO hol intake was calculated in a number of the standard world standard population distribution between 2000 and drink (10 g of pure ethanol). Average consumption of al- 2025 considering sex weight of 0.5 [30]. Clustering of risk cohol of at least one (women) or two (men) standard factors was presented as a percentage. Mean and standard drinks per day over last 30 days was defined as the deviations (SD) were reported for normally distributed harmful use of alcohol [24]. Insufficient intake of fruit and continuous variables. Median with interquartile range was vegetable was considered if the participants consumed less reported for non-normally distributed continuous variable. Khanal et al. BMC Public Health (2018) 18:677 Page 4 of 13 Correlation between different risk factors was determined (± 12.5) mg/dl, and 86.3 (± 33.6) mg/dl, respectively. using Pearson’s correlation coefficients. Socio-demographic Standard drinks of alcohol per day (p = < 0.001), waist variables were entered into simple and multiple logistic re- circumference (p = < 0.001), diastolic blood pressure gression models to determine crude and adjusted odds ra- (p = 0.008), total cholesterol (p = 0.009), and triglyceride tios, respectively. Chi-square and independent t-tests were (p = 0.009) were significantly different between men and used to compare categorical and continuous variables, re- women (Table 1). spectively. Mann-Whitney U test was used to test for The overall prevalence of current smoking was 24.1% non-normally distributed variables. All tests were two tail (95% CI: 19.5–28.6) with a higher proportion in men test and p < 0.05 was considered statistically significant. (29.2%) compared to women (20.0%). About one-fifth (19.0%) were using smokeless tobacco (men: 29.6%, women: 11.0%). Overall, 10.7% (95% CI: 7.4–13.9) were Ethical consideration drinking a harmful dose of alcohol (men: 21.4%, women: Ethical Review Board of Nepal Health Research Council 2.3%). Similarly, 10.1% (95% CI: 6.9–13.2) were physically reviewed and approved the study protocol. Before data inactive. Women were less physically active (12.6%) com- collection, data enumerator informed all the eligible par- pared to men (7.1%). Additionally, 72.0% (95% CI: ticipants about the study objectives, data collection pro- 67.1–76.6) were consuming less than five servings of fruit cedures, benefits and risks of the study, confidentiality, and vegetable daily (men: 76.6%, women: 68.0%) (Table 2). and anticipated use of the results. Then, the enumera- Overweight and obesity was observed among 59.4% tors (health professionals) obtained written consent or (95% CI: 54.2–64.5) of participants while 31.3% (95% CI: thumb impression (if unable to write) using a consent 26.4–36.1) of participants had central obesity. Over- form in Nepali language. weight and obesity was higher in males (61.0%) than in females (58%), whereas central obesity was higher in Results women (55.0%) compared to men (14.3%). The overall Socio-demographic characteristics prevalence of hypertension was 42.9% (95% CI: Overall, 345 samples with complete data were analysed 37.6–48.1) where more men (46.8%) compared to excluding 43 participants who did not participate in bio- women (39.8%) had raised blood pressure (Table 2). chemical assessment. Socio-demographic characteristics Diabetes mellitus was determined in 16.2% (95% CI: of the study subjects are presented elsewhere [20]. In 14.0–18.3) of total study subjects. Out of all, dyslipid- brief, more than half of the participants were females emia was revealed in 56.0% (95% CI: 53.0–58.7) of par- (55.4%), aged 53.5 ± 10.1 years, and 40% of them were ticipants. Dyslipidemia was higher in women (59.7%) aged 40–49 years. About half of the participants (53.6%) compared to men (52.0%). When analysed separately, were uneducated and from Janajati (55.5%) population. 29.9% (95% CI: 27.2–32.5) had raised high-density lipo- The majority were unemployed (58.0%). Almost all par- protein, 27.2% (95% CI: 24.6–29.7) had elevated trigly- ticipants were married (91.3%) and poor (97.1%). Out of ceride, 17.1% (95% CI: 14.9–19.2) had increased level of participants who did not attend the biochemical assess- total cholesterol and 10.4% (95% CI: 8.6–12.1) had raised ment, mean age was 52 ± 10.7 years, 55.8% were female, low-density lipoprotein (Table 2). 55.8% did not have formal education, 51.2% were Jana- jati, 67.4% were unemployed. These characteristics were not significantly different from the participants who had Distribution of cardiovascular disease risk factors completed the study. The proportion of current smoking (p = 0.04), harmful use of alcohol (p < 0.001), raised waist circumference Prevalence of cardiovascular disease risk factors (p = 0.001), raised triglycerides (p = 0.007), and decreased On average, participants were smoking at least two ciga- high-density lipoprotein (p = 0.001) were significantly dif- rettes per day. Additionally, participants were consuming ferent between men and women (Table 2). at least one standard drink per day. Overall, the median in- The proportion of current smoking, less fruit and take of fruit and vegetable was four servings per day, and vegetable intake, hypertension, and diabetes increased median physical activity was 3360 METs-minutes per week. with increased age. In opposition, the proportion of Mean body mass index was 25.9 (± 4.2) Kg/m and mean harmful use of alcohol, physical inactivity, overweight/ waist circumference was 88.8 (± 11.7) cm. The average sys- obesity, and dyslipidemia decreased with increased age. tolic and diastolic blood pressure were 124.5 (± 18.7) mm However, only overweight and obesity were significantly Hg and 88.8 (± 10.8) mm Hg, respectively. The mean fast- associated with age (p = 0.002) (Table 2). ing blood sugar, total cholesterol, triglyceride, high-density The harmful use of alcohol was significantly associated lipoprotein, and low-density lipoprotein were 92.8 (± 34.1) with the level of education (p = 0.003), caste (p = 0.02), mg/dl, 165.1 (± 34.9) mg/dl, 129.9 (± 69.2) mg/dl, 53.2 and economic status (p = 0.046). It was highest among Khanal et al. BMC Public Health (2018) 18:677 Page 5 of 13 Table 1 Distribution of cardiovascular disease risk factors of study subjects by gender Risk factors Both sexes Men Women P value Mean (SD) Mean (SD) Mean (SD) Average cigar per day 1.98 (5.2) 2.2 (5.7) 1.8 (4.7) 0.478 Standard drinks of alcohol/day 0.8 (2.5) 1.6 (3.5) 0.09 (0.64) < 0.001 Fruit/vegetable (servings/day) 4.0 (2.0) 4 (1.0) 4 (2.0) 0.101 Metabolic equivalent - minute/week 3360.0 (3576.0) 3150.0 (3600.0) 3600.0 (3525.0) 0.121 Body Mass Index (Kg/m ) 25.9 (4.2) 25.9 (3.4) 25.9 (4.6) 0.996 Waist circumference (cm) 88.8 (11.7) 91.9 (9.9) 86.3 (12.3) < 0.001 Systolic Blood Pressure (mmHg) 124.5 (18.7) 126.8 (18.7) 122.6 (18.4) 0.039 Diastolic Blood pressure (mmHg) 79.9 (10.8) 81.6 (11.6) 78.5 (9.9) 0.008 Fasting Blood Sugar (mg/dl) 92.8 (34.1) 91.7 (23.8) 93.6 (40.5) 0.61 Total cholesterol (mg/dl) 165.1 (34.9) 170.6 (34.4) 160.7 (34.8) 0.009 Triglyceride (mg/dl) 129.9 (69.2) 138.7 (76.6) 119.2 (61.4) 0.009 High-density Lipoprotein (mg/dl) 53.2 (12.5) 53 (12.3) 53.3 (12.7) 0.8 Low-density Lipoprotein (mg/dl) 86.3 (33.6) 89.8 (33.5) 83.4 (33.5) 0.03 median Interquartile range, SD Standard Deviation participants who had at least secondary level of educa- was observed among aproximately one-third of the par- tion (11.0%) and who were Dalit (28.6%) (Table 3). ticipants (30.4%). Clustering was higher among males Overweight and obesity was associated with ethni- (36.3%) compared to females (25.6%) (Fig. 1). About city (p = 0.007) with the highest prevalence among 2.0% (male: 3.2%, female: 1%) had up to six risk factors. Dalit (66.7%) participants. Waist circumference was We studied the socio-economic determinants of cluster- associated with the level of education (p = 0.001) and ing of at least three risk factors, as after this clustering occupation (p < 0.001). It was highest among was suddenly dropped down. In univariate logistic re- uneducated (39.5%) and unemployed (38.5%). Hyper- gression; gender, age, and caste/ethnicity were signifi- tension was associated with occupation (p = 0.071), cantly associated with clustering of at least three risk and retired participants had highest prevalence factors. However, after adjusting for all other (59.3%) (Table 3). socio-demographic factors, age and caste/ethnicity were Prevalence of diabetes mellitus was significantly differ- significantly associated with clustering of risk factors (at ent among different levels of education (p = 0.02). It was least three). Odds of clustering of at least three risk fac- highest among those with at least secondary level educa- tors was 2.3 times (95% CI: 1.13–4.72) in the age group tion (24.4%). Raised total cholesterol (p = 0.03) was sig- of 60–69 years compared to 40–49 years of age. Dalits nificantly different among different ethnic groups. had two times (95% CI: 1.28–3.43) more chance of risk Similarly, the raised triglyceride was associated with the factor clustering compared to upper caste residents level of education (p = 0.0 1). High-density lipoprotein (Table 5). was associated with the level of education (p = 0.002) and the occupation (p = 0.006) (Table 3). Discussion This study determined the prevalence of cardiovascular dis- Correlation between cardiovascular disease risk factors ease risk factors along with their clustering phenomenon in Correlations between different risk factors were weak. a rural population of Nepal. Prevalence of each cardiovas- Stronger correlations were observed between smoking and cular risk factor was high and a maximum of six risk factors alcohol intake, diastolic blood pressure and body mass was clustered in some study participants. index along with waist circumference and triglyceride Our current study revealed that approximately (Table 4). one-quater (24.1%) were smoking cigarettes in a rural population of Nepal. Our finding was consistent with the Clustering of cardiovascular disease risk factors tobacco smoking prevalence reported in other studies in Almost all of the participants (98.2%) had at least one Nepal. For instance, the proportion of current smoker was risk factor. In addition, 86% of total participants had 29.0% in STEPS survey 2013 of Nepal among aged 40– clustering of at least two risk factors. Overall, 63.4% of 69 years [7] and 28.6% in rural Sindhuli [31]. However, the participants had at least three risk factors (male: 69.4%, prevalence of smoking found in our study was higher female: 58.5%). Clustering of at least four risk factors compared to that found in the capital city Kathmandu in Khanal et al. BMC Public Health (2018) 18:677 Page 6 of 13 Table 2 Prevalence of cardiovascular disease risk factors stratified by gender and age Characteristics Current Harmful use Fruit/Vegetable Inadequate Overweight Hypertension Diabetes TC (> 200 mg/dl) TG(> 150 mg/dl) HDL (M < 40, LDL(> 130 mg/dl) Dyslipidemia Smoking of alcohol < 5 servings Physical activity and obesity mellitus F < 50 mg/dl) Men and Women 40–49 17.5 9.5 66.4 14.6 64.2 36.5 15.3 19 26.3 32.1 11.7 57.9 50–59 25.7 14.3 76.2 6.7 59.0 42.9 15.1 19 29.5 30.5 9.5 58.1 60–69 31.0 9.9 71.8 8.5 64.8 52.1 15.5 15.5 28.2 23.9 12.7 56.3 70–79 31.3 6.3 81.3 6.3 28.1 50 15.6 6.2 21.9 31.2 3.1 40.6 Total 24.1 10.7 71.9 10.1 59.4 42.9 16.2 17.1 27.2 29.9 10.4 55.9 95% CI 19.5– 7.4–13.9 67.1–76.6 6.9–13.2 54.2–64.5 37.6–48.1 14.0– 14.9–19.2 24.6–29.7 27.2–32.5 8.6–12.1 53.0–58.7 28.6 18.3 Age sex 24.9 10.4 71.5 9.9 58.6 43.2 15.9 16.8 27.5 29.3 10.1 55.4 adjusted prevalence Men 40–49 26.2 20.0 75.4 9.2 56.9 44.6 16.9 24.6 35.4 13.8 18.5 55.4 50–59 26.1 28.3 78.3 2.2 69.6 52.2 17.4 17.4 34.8 10.9 8.7 50.0 60–69 37.5 18.8 71.9 12.5 65.6 46.9 15.6 15.6 34.4 18.8 12.5 56.3 70–79 36.4 9.1 90.9 0.0 36.4 36.4 9.1 9.1 27.3 0.0 0.0 27.3 Total 29.2 21.4 76.6 7.1 61.0 46.8 16.2 19.5 34.4 13.0 13.0 51.9 Women 40–49 9.7 0.0 58.3 19.4 70.8 29.2 13.9 13.9 18.1 48.6 5.6 59.7 50–59 25.4 3.1 74.6 10.2 50.8 35.6 18.6 20.3 25.4 45.8 10.2 64.4 60–69 25.6 4.8 71.8 5.1 64.1 56.4 15.4 15.4 23.1 28.2 12.8 56.4 70–79 28.6 4.2 76.2 9.5 23.8 57.1 19.0 4.8 19 47.6 4.8 47.6 Total 19.9 2.3 68.1 12.6 58.1 39.8 16.2 15.2 21.5 43.5 8.4 59.2 P value (men 0.044 < 0.001 0.079 0.097 0.58 0.194 0.99 0.29 0.007 0.001 0.16 0.18 and women) P value (age 0.105 0.56 0.506 0.224 0.002 0.144 0.94 0.33 0.84 0.66 0.47 0.33 groups) TC Total cholesterol, TG Triglyceride, HDL High-density Lipoprotein, LDL Low-density Lipoprotein Khanal et al. BMC Public Health (2018) 18:677 Page 7 of 13 Table 3 Distribution of cardiovascular disease risk factors by socio-economic characteristics Characteristics Current Harmful use Fruit/Vegetable Physical Overweight Hypertension Diabetes TC (> 200 mg/dl) TG(> 150 mg/dl) HDL (M < 40, LDL(> 130 mg/dl) Dyslipidemia Smoking of alcohol < 5 servings inactivity and obesity mellitus F < 50 mg/dl) Level of education No formal 25.9 6.5 73.0 10.8 54.6 41.6 15.1 15.7 23.8 37.8 10.8 56.8 education Primary 25.7 21.4 75.7 8.6 67.1 50 8.6 12.9 21.4 18.6 7.1 47.1 ≥ Secondary 18.9 11.1 66.7 10 63.3 40 24.4 23.3 38.9 22.2 12.2 61.1 P value 0.41 0.003 0.401 0.868 0.129 0.392 0.02 0.16 0.01 0.002 0.56 0.19 Caste/ ethnicity Upper cast 21.8 8.3 72.2 11.3 48.9 39.8 13.5 21.8 28.6 27.1 12 59.4 Dalit 42.9 28.6 76.2 9.5 66.7 52.4 19.0 0.0 14.3 19.0 4.8 33.3 Janajati 23.6 10.5 71.2 9.4 66.0 44.0 17.8 15.7 27.7 33.0 9.9 56.0 P value 0.108 0.02 0.886 0.858 0.007 0.505 0.55 0.03 0.38 0.27 0.56 0.08 Occupation Employ 22.5 12.5 67.5 7.5 52.5 30.0 22.5 22.5 42.5 17.5 10.0 65.0 Self employed 16.7 12.8 75.6 14.1 65.4 48.7 7.7 19.2 29.5 29.5 11.5 53.8 Unemployed 26.0 8.0 72.5 10.5 57.0 41.0 17.0 14.5 23.0 35.5 9.5 55.5 Retired 33.3 22.2 63.0 0.0 70.4 59.3 25.9 22.2 29.6 7.4 14.8 51.9 P value 0.254 0.124 0.567 0.193 0.287 0.071 0.06 0.47 0.07 0.006 0.83 0.64 Economic status Above poverty line 10.0 30.0 70.0 10.0 50.0 50.0 30.0 20.0 20.0 20.0 10.0 40.0 Poor 24.5 10.1 71.9 10.1 59.7 42.7 31.3 17 27.5 30.1 10.4 56.4 P value 0.291 0.046 0.893 0.988 0.538 0.645 0.58 0.8 0.6 0.49 0.96 0.3 TC Total cholesterol, TG Triglyceride, HDL High-density Lipoprotein, LDL Low-density Lipoprotein Khanal et al. BMC Public Health (2018) 18:677 Page 8 of 13 Table 4 Correlation between different cardiovascular disease risk factors CG AL FV MET BMI WC SBP DBP FBS TC TG HDL LDL CG AL .286 FV −.045 −.087 MET .018 .023 .016 BMI −.096 −.049 .077 −.029 b a WC -.121 −.007 .055 −.090 .569 b b a SBP −.010 .117 −.025 .028 .118 .162 a a a DBP −.078 .065 −.050 −.065 .201 .219 .769 FBS −.013 .011 −.037 −.075 .022 .032 .018 .037 TC .010 .100 .037 −.022 −.035 .001 .033 .030 .098 a b b a a TG −.038 −.026 −.012 −.048 .096 .181 .133 .125 .198 .250 a b a HDL .078 .173 .040 .012 −.034 −.101 −.005 −.098 .136 .195 −.075 a b a LDL −.004 .050 .029 −.008 −.064 −.036 −.019 .016 −.030 .863 -.123 -.140 CG Average no. of cigarettes/day, AL Average standard drinks/day, FV Total servings of fruit and vegetable/day, METs Total METs-Minutes/week, BMI Body Mass Index, WC Waist circumference, SBP Systolic Blood Pressure, DBP Diastolic Blood pressure, FBS Fasting Blood Sugar, TC Total cholesterol, TG Triglyceride, HDL High- density Lipoprotein, LDL Low-density Lipoprotein Correlation is significant at the 0.01 level Correlation is significant at the 0.05 level Fig. 1 Distribution of clustering of cardiovascular disease risk factors by gender Khanal et al. BMC Public Health (2018) 18:677 Page 9 of 13 Table 5 Socio-demographic factors associated with clustering of cardiovascular risk factors three or more Characteristics Crude odds ratio (95% CI) Adjusted odds ratio (95% CI) Sex Female Ref Ref Male 1.60 (1.02–2.51) 1.53 (0.80–2.93) Age 40–49 Ref Ref 50–59 1.27 (0.75–2.14) 1.39 (0.79–2.45) a a 60–69 1.89 (1.01–3.53) 2.31 (1.13–4.72) 70–79 0.88 (0.40–1.83) 0.94 (0.38–2.31) Level of Education No formal education Ref Ref Primary 0.78 (0.46–1.33) 0.80 (0.36–1.77) ≥ Secondary 0.95 (0.49–1.85) 0.87 (0.42–1.81) Caste/Ethnicity a a Upper cast Ref Ref Janajati 0.55 (0.35–0.88) 2.03 (0.71–5.82) Dalit 1.14 (0.42–3.09) 2.09 (1.28–3.43) Marital Status Married Ref Ref Widow or Widower 0.85 (0.38–1.89) 0.71 (0.29–1.74) Occupation Employ Ref Ref Self employed 1.34 (0.60–3.00) 1.09 (0.46–2.62) Unemployed 0.93 (0.46–1.89) 0.80 (0.32–2.02) Retired 1.19 (0.43–3.34) 0.70 (0.22–2.16) Economic status Higher(> NRs 8498) Ref Ref Poor (≤ NRs 8498) 1.16 (0.32–4.20) 1.22 (0.31–4.75) significant at the 0.05 level urban settings (20.0%) [10] and rural settings (21.7%) when compared with the result of a study representing the [10, 32] and the sub-metropolitan city Pokhara (17.0%) [8] whole country (99.0%) [7] and a study from surveillance of Nepal. In all of these studies, more males compared to site in Bhaktapur (97.9%) of Nepal [34]. Variation according females were smoking cigarettes. These studies revealed to age, race/ethnicity, income, education, and availability of the severity of smoking in Nepal. fruit and vegetable are established socio-demographic fac- One in ten study participants was drinking a harmful tors for low fruit and vegetable intake [35, 36]. dose of alcohol in the current study. Similar result was Ten percent of our study participants were engaged in observed in a recent study conducted in Pokhara (12.0%) a low level of physical activities. This prevalence was in of Nepal [8]. However, our finding was higher than the re- line with the findings from Pokhara of Nepal (7.0%) and port of STEPs survey of 2013 (2.7%) for the 45 to 69 years world health survey for physical activities (8.0%) [8, 37]. of age group [7]. Most of the current study subjects were However, current prevalence was less than the findings of indigenous and Dalit caste. Such ethnic people were of capital city Kathmandu (40.2%) [10], Eastern region more likely to drink alcohol and brew alcohol at home (37.6%) [38], and Bhaktapur (male: 18.0% and female: [33], which might be the reason for higher prevalence. 22.0%) of Nepal [39]. Work and travel related activities The burden of low fruit and vegetable intake was high in are the major domains for physical activities in Nepal Nepal. The current study determined that about [39]. These domains are more pronounced in rural two-thirds (72.0%) were not consuming five servings of areas, and might be the reason for the lower prevalence fruit and vegetable per day. This prevalence was lower of physical inactivity in our study. Khanal et al. BMC Public Health (2018) 18:677 Page 10 of 13 About six in ten participants (59.0%) were overweight city Kathmandu, which determined 73.3% had dyslipidemia and obese in our study population. This prevalence was [45]. Our study revealed that prevalent risk factors namely comparable with the result of Western Nepal (60.7%) [38]. waist circumference, systolic blood pressure, and fasting However, it was three-times greater than the findings of a blood sugar were correlated with triglyceride level. Simi- stepwise survey of risk factors in Nepal (21.0%) [7]. The larly, alcohol intake and fasting blood sugar were positively higher prevalence of overweight and obesity may be related correlated with the reduction of serum high-density lipo- to the higher proportion of participants who smoke protein. All of these may have been responsible for such cigarette, chew tobacco, drink alcohol, and indulge in less high prevalence of dyslipidemia. physical activities in the current study population. Almost all of the study population had at least one risk Evidences support that alcohol energy intake may be re- factor. The finding of our study was consistent with the sponsible for weight gain if not counterbalanced, for in- STEPS survey 2013 of Nepal which revealed 99.6% of par- stance, by physical activities [40]. However, further studies ticipants had at least one risk factor [7]. When compared would be needed to confirm and explain our findings. with the studies from other Asian countries, clustering of at Two-fifths of participants (43.0%) had hypertension in least two risk factors was observed in 44% of Chinese our study. The prevalence of hypertension in the current population [15] and 76% of cases in Bangladesh [46]. study (38.9%) was comparable with above-mentioned Comparably, the current study displayed 86% of study STEPS survey (47.0%) for the respective age of more than participants had a minimum of two risk factors clus- 45 years [7]. However, hypertension was greater in our tered together. The current finding of clustering of study compared to populations from Eastern Nepal three to five risk factors (63.4%) was higher compared (34.0%) [38], municipalities of Kathmandu (32.5%) [10], to STEPS survey 2013 (30.0%) for the age group 45 Pokhara-Lekhnath (28.0%) [8], Dhulikhel (27.7%) [9], and to 69 years [7]. Our study included the harmful use Surkhet (38.9%) [11] of Nepal. Alcohol consumption, body of alcohol, diabetes, and dyslipidemia when analyzing mass index, total cholesterol, and triglyceride were corre- the clustering phenomenon, which the STEPS survey lated to systolic and diastolic blood pressure in our study. did not. This could explain the disparity between the The high prevalence of these risk factors in our study findings. might explain the higher rates of hypertension. When different risk factors act together, the effect will The prevalence of diabetes mellitus was 16.2% in our be multiplicative and raises the risk of CVD more than study. This prevalence was higher than the reported preva- the summation of risk factors [13, 14]. For instance, one lences from Eastern Nepal for the age group 40 to 80 years study reported that incidence of ischemic heart disease (11.5%) [38], STEPS survey 2013 (9.0%) for age group 45– rises from 0 to 40% as the number of risk factors con- 69 years [7], and rural population of Sunsari (9.0%) [41]. glomerate from zero to five among diabetic patients [47]. One study conducted in Kathmandu valley reported that Moreover, annual medical expenditure increases with the 25.9% of participants aged 60 years and older had diabetes rise in clustering of risk factors [48]. In our study greatest mellitus [42]. That means elderly age group has the higher clustering was observed among 60–69 years of age group chance of diabetes [43]. A high proportion of diabetes in and Dalit participant. Therefore, it could be pronounced our study could be a result of the high prevalence of trigly- that preventive strategies should be focused on individuals ceride that was correlated with fasting blood sugar. Another who have more risk factors and especially elderly popula- reason for a higher burden of diabetes in our study may be tion who are aged 60–69 years and are Dalit. becauseofthe useofanoralglucose tolerancetestfor It is important to emphasize health education programs those who did not have known diabetes. that warn about the behavioral risk factors of CVD. In Our study revealed more than half (56.0%) of participants addition, early detection and treatment of intermediary risk of the rural population had dyslipidemia. This overall dys- factors (hypertension, diabetes, dyslipidemia and over- lipidemia was accounted by 17.0% of elevated total choles- weight) are required to minimize the future burden of terol, 27.0% of raised triglyceride, 30.0% decreased CVD [2]. Health promotion approaches could be delivered high-density lipoprotein, and 10.0% of raised low-density through various approaches: health workforce, trained lipoprotein among all the study subjects. When compared community volunteers of the current health system, school with above-mentioned STEPS survey, our study population health programs or media campaigns [49–51] collaborating had less prevalence of dyslipidemia. For instance, among with all stakeholders. Such community-based programs participants of age group 45 to 69 years of age, 33% had have already been implemented in developed and develop- raised cholesterol, 35% had elevated triglyceride, and 24% ing countries [50, 52]. Package of Essential Noncommunic- hadelevatedlow-density lipoproteininthe STEPSsurvey able Disease (PEN) interventions could be implemented to [44]. Furthermore, the proportion of dyslipidemia in our prevent cardiovascular diseases as well, so that the commu- study was less compared to another study from nity could utilize current health care delivery system [53]. non-diabetes participants of the urban area of the capital One study from Northern India revealed that the primary Khanal et al. BMC Public Health (2018) 18:677 Page 11 of 13 health care setting was a feasible setting for CVD risk man- data management. Finally, we would like to acknowledge the study team and study participants. agement even in rural areas [52]. Our study had several limitations. The study was con- Funding ducted in a selected rural community where a large popula- This research work (M.Phil thesis) was funded by the ‘Norad’s Programme for Master Studies (NOMA)’ grant of the University of Oslo, Norway. The funding tion of Janajati and women were residing. Our sample size body did not have any role in the study design and collection, analysis, and was not large enough to make it a representative sample of interpretation of data and in writing the manuscript. all the risk factors. As we have included participants aged 40 to 80 years, we could not report the status of risk factor Availability of data and materials The datasets used and/or analyzed during the current study available from among the younger population. The current study might the corresponding author on reasonable request. have underestimated the cardiovascular risk factors, as it ex- cluded those who had known heart diseases, stroke, or inter- Authors’ contributions MKK conceptualized the main thesis, trained data collectors, curated the mittent claudication. The prevalence of risk factors might data, performed formal analysis, interpreted the result, wrote the original have been underestimated as about 11.0% of total partici- draft, and finalized it; MSAMA supervised the quality of the main thesis, pants did not involve in biochemical assessment, though critically reviewed, and edited the manuscript; MZ investigated the project, involved in data curation, interpreted the results, and critically reviewed the socio-demographic characteristics of responders and manuscript; PCB and PB involved in project administration, acquisition, non-responders were not significantly different. Despite compilation, analysis, and interpretation of data, reviewed and edited the these limitations, our study had several strengths. This is the orginal draft; RD involed in project administration and investigation, analysed the result, reviewed the draft and provided intellectual comments on the first study conducted among elderly rural population of manuscript; SD and AS allocated the resources, oversaw the acquisition of Nepaltoexplore alltraditional risk factors of CVD including data, revised the main thesis, and the manuscript critically. All authors read biochemical assessment. We used trained enumerators to and approved the final manuscript. acquire data related to the participants at their own home. Ethics approval and consent to participate We performed the oral glucose tolerance test to confirm Ethical Review Committee of Nepal Health Research Council reviewed and diabetes along with the fasting blood sugar. Therefore, find- approved the study. Enumerators informed about study objectives, data collection procedures, benefits and risks of the study, confidentiality, and ings could be cautiously generalized to other population. anticipated use of the results to all eligible participants. Then, the trained enumerators (health professionals) obtained written consent or thumb impression (if unable to write) using a consent form in Nepali language. Conclusions Cardiovascular risk factors and their clustering were com- Competing interests moninthe ruralpopulationofNepal.Almostall of the par- The authors declare that they have no competing interests. ticipants displayed on risk factor, with the majority of the participants presenting with one or clusters of two-three risk Publisher’sNote factors. When different risk factors aggregate together, their Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. effect would be multiplicative to develop cardiovascular dis- eases. Therefore, comprehensive intervention to address Author details multiple risk factors should be immediately planned and im- Bangladesh Institute of Health Sciences (BIHS), Dhaka, Bangladesh. Department of Community Medicine, Bangladesh Institute of Health plemented to reduce the risk factors and future burden of Sciences (BIHS), Dhaka, Bangladesh. Padmakanya Campus, Tribhuvan cardiovascular diseases in a rural population of Nepal. University (TU), Kathmandu, Nepal. Institute of Medicine (IOM), Tribhuvan University, Kathmandu, Nepal. Ministry of Health and Population (MoHP), Abbreviations Kathmandu, Nepal. College of Health and Biomedicine, Victoria University, BMI: Body mass index; CVD: Cardiovascular diseases; DBP: Diastolic blood Melbourne, Australia. pressure; HDL: High-density lipoprotein; LDL: Low-density lipoprotein; METs: Metabolic equivalents of task; NCD: Non-communicable diseases; Received: 27 September 2017 Accepted: 24 May 2018 SBP: Systolic blood pressure; STEPs: Stepwise approach to surveillance Acknowledgements References This paper is based on the thesis submitted to Bangladesh Institute of Health 1. Cardiovascular Diseases (CVDs) Fact Sheet; 2017. Available at [http://www. Sciences (BIHS) as a partial fulfillment of M.Phil degree in Noncommunicable who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)]. Diseases under the University of Dhaka. The authors want to express their Accessed 14 Aug 2017. sincere gratitude and appreciation to Mr. Matthew Bourke, PhD student, 2. WHO. Global Atlas on Cardiovascular Disease Prev Control. Geneva: WHO; 2011. 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Journal

BMC Public HealthSpringer Journals

Published: May 31, 2018

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