Abstract: Health and demographic surveillance systems (HDSSs) provide a foundation for characterizing and defining priorities and strategies for improving population health. The Child Health and Mortality Prevention Surveillance (CHAMPS) project aims to inform policy to prevent child deaths through generating causes of death from surveillance data combined with innovative diagnostic and laboratory methods. Six of the 7 sites that constitute the CHAMPS network have active HDSSs: Mozambique, Mali, Ethiopia, Kenya, Bangladesh, and South Africa; the seventh, in Sierra Leone, is in the early planning stages. This article describes the network of CHAMPS HDSSs and their role in the CHAMPS project. To generate actionable health and demographic data to prevent child deaths, the network depends on reliable demographic surveillance, and the HDSSs play this crucial role.

Abstract: Adolescents with depression engage in unhealthy eating habits and irregular sleep patterns and are often at an increased risk for weight‐related problems. Improvement in these lifestyle behaviours may help to prevent depression, but knowledge about the associations between depression, sleep, eating habits and body weight among adolescents in India is limited. This cross‐sectional study investigated the prevalence of depression and its association with sleep patterns, eating habits and body weight status among a convenience sample of 527 adolescents, ages 10–17 years in Mumbai, India. Participants completed a survey on sleep patterns such as sleep duration, daytime sleepiness and sleep problems and eating habits such as frequency of breakfast consumption, eating family meals and eating out. Depression was assessed using the Patient Health Questionnaire modified for Adolescents (PHQ‐A). Anthropometric measurements were also taken. Within this sample, 25% had moderate to severe depression (PHQ‐A ≥ 10) and 46% reported sleeping less than 6 h 〉 thrice a week. Adolescents with moderate to severe depression had significantly higher body mass index than those with minimal depression (26.2 ± 6.6 vs. 20.2 ± 4.8 kg/m 2 ). The odds of having clinically significant depression (PHQ‐A ≥ 10) was 4.5 times higher in adolescents who had family meals ≤ once a week, 1.6 times higher among those who were sleeping 〈 6 h and 2.3 times higher among participants having trouble falling to sleep more than thrice a week. The findings indicated that a significant proportion of adolescents had depression symptoms; improving sleep and eating habits may present potential targets for interventions.

Abstract: To develop and evaluate a Nutrition Transition-FFQ (NT-FFQ) to measure nutrition transition among adolescents in South India. Design We developed an interviewer-administered NT-FFQ comprising a 125-item semi-quantitative FFQ and a twenty-seven-item eating behaviour survey. The reproducibility and validity of the NT-FFQ were assessed using Spearman correlations, intra-class correlation coefficients (ICC), and levels of agreement using Bland–Altman and cross-classification over 2 months (NT-FFQ1 and NT-FFQ2). Validity of foods was evaluated against three 24-h dietary recalls (24-HR). Face validity of eating behaviours was evaluated through semi-structured cognitive interviews. The reproducibility of eating behaviours was assessed using weighted kappa ( κ w ) and cross-classification analyses. Setting Vijayapura, India. Subjects A representative sample of 198 adolescents aged 14–18 years. Results Reproducibility of NT-FFQ: Spearman correlations ranged from 0·33 (pulses) to 0·80 (red meat) and ICC from 0·05 (fruits) to 1·00 (tea). On average, concordance (agreement) was 60 % and discordance was 7 % for food groups. For eating behaviours, κ w ranged from 0·24 (eating snacks while watching television) to 0·67 (eating lunch at home) with a mean of 0·40. Validity of NT-FFQ: Spearman correlations ranged from 0·11 (fried traditional foods) to 0·70 (tea) and ICC ranged from 0·02 (healthy global foods) to 1·00 (grains). The concordance and discordance were 48 % and 8 %, respectively. Bland–Altman plots showed acceptable agreement between NT-FFQ2 and 24-HR. The eating behaviours had acceptable face validity. Conclusions The NT-FFQ has good reproducibility and acceptable validity for food intake and eating behaviours. The NT-FFQ can quantify the nutrition transition among Indian adolescents.

Abstract: To describe adolescents’ eating patterns of traditional, global/non-local and mixed foods, and the factors that may influence food consumption, access and preferences, in a globalizing city. Design A representative sample of school-going adolescents completed a cross-sectional survey including an FFQ designed to identify traditional and global foods. Student’s t test and ordinal logistic regression were used to examine weekly food intake, including differences between boys and girls and between adolescents attending private and public schools. Setting Vijayapura city, Karnataka State, India. Subjects Adolescents ( n 399) aged 13–16 years. Results Compared with dietary guidelines, adolescents consumed fruit, green leafy vegetables, non-green leafy vegetables and dairy less frequently than recommended and consumed energy-dense foods more frequently than recommended. Traditional but expensive foods (fruits, dairy, homemade sweets and added fat) were more frequently consumed by private-school students, generally from wealthier, more connected families, than by public-school students; the latter more frequently consumed both traditional (tea, coffee, eggs) and mixed foods (snack and street foods; P ≤0·05). Girls reported more frequent consumption of global/non-local packaged and ready-to-eat foods, non-green leafy vegetables and added fat than boys ( P ≤0·05). Boys reported more frequent consumption of eggs and street foods than girls ( P ≤0·05). Conclusions Adolescents’ eating patterns in a globalizing city reflect a combination of global/non-local and traditional foods, access and preferences. As global foods continue to appear in low- and middle-income countries, understanding dietary patterns and preferences can inform efforts to promote diversity and healthfulness of foods.

Abstract: Emerging research suggests changes in dietary patterns among adolescents during the global nutrition transition, but little is known about the nature of the nutrition transition among adolescents residing in regions that are in different stages of exposure to global markets. Objectives To describe and compare the nutrition transition among adolescents in new versus established urban centers in South India. Methods Responses to the validated Nutrition Transition‐Food Frequency Questionnaire by adolescents ages 13–18 years in two regions in South India were used to assess and compare their diets with a focus on evidence of transitioning diet patterns. The dietary data were drawn from a representative sample of adolescents from the new urban region (population 〉 5,000) of Vijayapura (n=198) and from a convenience sample of adolescents from the established urban city (population 〉 100,000) of Bengaluru (n=192) in Karnataka, South India. The validated Nutrition Transition‐Diet score (range 0–10), an index of nutrition transition developed in our previous work, was used to evaluate and compare the nutrition transition among adolescents in the new versus established urban centers. Characteristics and diets of adolescents in the highest quartile (≥75 percentile) of the Nutrition Transition‐Diet Score, indicating more transitioned diets, were compared with those of adolescents with less transitioned diets using chi‐sq tests and Students t tests. Results The mean Nutrition Transition‐Diet Score (range 0–10) of adolescents was 6.1 ± 1.2 (range 2–9) in Bengaluru (urban region) and 5.6 ± 1.2 (range 1–8) in Vijayapura (urbanizing region); it was significantly higher among those in highest quartile versus lower quartile groups in both cities (Bangalore 7.3 ± 0.5 vs, 5.4 ± 0.8, p ≤0.001; Vijayapura 7.1 ± 0.3 versus 5.1 ± 0.9, p ≤0.001). The Nutrition Transition‐Diet Score was higher among non‐vegetarians than vegetarians (6.1 ± 1.1 versus. 5.4 ± 1.1, p ≤ 0.05) and among private school pupils than public school pupils (6.4 ± 1.0 versus. 5.8 ± 1.2, p ≤ 0.01) in Bengaluru. A higher proportion of adolescents in the established urban center as compared to the new urban center consumed processed foods and fried foods daily (intake ≥ 30 g/d; processed foods 74.5% vs. 43.9% and fried foods 91.7% versus 83.3%). On the contrary, a higher proportion of adolescents in the new urban center than the established urban center consumed bread and bread products (intake ≥ 30 g/d; 47.5% versus 30.7%). Adolescents in both cities had saturated fat intake ≥ 10% of total calories (100% vs. 99.5%) but total fat intake was 〈 30% of total calories. Conclusion The nutrition transition among adolescents is more advanced in the established urban center than the new urban center in South India. Support or Funding Information Nida I. Shaikh was supported by the award number 1‐R25 TW009337‐01 funded by the Fogarty International Center at National Institutes of Health Proportion of adolescents across each component of the pre‐defined Nutrition Transition‐Diet Score in new versus established urban center in South India Nutrition Transition‐Diet Score Bengaluru (established urban center)n=192 Vijayapura (new urban center)n=198 Components Criteria for score 1 1 Criteria for score 0 2 Score 1 3 Score 0 3 Score 1 3 Score 0 3 1. Processed foods ≥ 30 g/d 〈 30 g/d 74.5 25.5 43.9 56.1 2. Fried foods ≥ 30 g/d 〈 30 g/d 91.7 8.3 83.3 16.7 3. Sugar‐sweetened beverages 〉 0 ml/d 0 ml/d 96.9 3.1 94.9 5.1 4. Dairy ≥ 500 ml/d 〈 500 ml/d 4.7 95.3 7.1 92.9 5. Fruits and vegetables ≤ 400 g/d 〉 400 g/d 86.5 13.5 88.9 11.1 6. Breads ≥ 30 g/d 〈 30 g/d 25.5 74.5 47.5 52.5 7. Added sugar ≥ 30 g/d 〈 30 g/d 32.3 67.7 1.0 99.0 8. Fat, total ≥ 30% of total calories 〈 30% of total calories 0 100.0 0 100.0 9. Saturated fat ≥ 10% of total calories 〈 10% of total calories 100.0 0 99.5 0.5 10. Sodium ≥ 2300 mg/d 〈 2300 mg/d 93.8 6.2 91.9 8.1 Score 1 indicates presence of the Nutrition Transition dietary behavior. Score 0 indicates absence of the Nutrition Transition dietary behavior. All values are percentages. Dietary Guidelines of India and the literature on the nutrition transition used to develop the predefined Nutrition Transition‐Diet Score. Differences in food intake according to the highest vs. lower pre‐defined Nutrition Transition‐Diet Score among adolescents in new versus established urban centers in South India. Fond group (g/d) 1 Bengaluru (established urban center) Vijayapura (new urban center) Overalln=l92 Highest quartile 2n=63 Lower quartilen=129 Overalln=198 Highest quartilen=47 Lower quartilen=15l Energy‐dense foods Bread 3 22.3 (22.1) 35.4 (24.1) 14.7 (16.9) *** 37.0 (38.4) 76.6 (40.2) 24.6 (28.2) *** Global foods, unhealthy 4 45.6 (48.5) 45.7 (40.8) 45.6 (52.5) 28.4 (39.7) 54.3 (52.2) 19.4 (29.7) *** Global foods, healthy 5 17.8 (34.9) 27.2 (45.7) 12.4 (25.5) ** 1.3 (6.1) 2.4 (10.0) 0.9 (3.9) Processed foods 6 60.0 (36.4) 72.6 (31.3) 52.3 (37.2) ** 33.7 (28.8) 62.5 (25.4) 23.7 (22.5) *** Snacks, non‐fried 7 18.1 (17.6) 23.8 (20.9) 14.8 (14.5) ** 38.1 (36.6) 48.8 (44.1) 34.4 (33.0) * Snacks, fried 8 93.1 (73.2) 99.6 (58.5) 89.3 (80.5) 79.5 (78.9) 137.4 (103.3) 59.4 (56.3) *** Fried traditional food 9 24.8 (26.7) 28.7 (28.1) 22.5 (25.8) 23.7 (19.7) 35.1 (25.2) 19.7 (15.6) *** Sweets & desserts 62.9 (44.6) 72.1 (45.5) 57.7 (43.4) * 66.7 (55.7) 98.6 (59.0) 55.6 (50.2) *** Animal‐source foods Red meat 22.7 (44.9) 21.1 (39.4) 23.4 (47.9) 4.5 (6.7) 6.1 (11.1) 4.0 (9.8) Lean meat 40.6 (43.7) 47.6 (49.2) 36.6 (39.8) 12.1 (24.7) 18.6 (35.6) 9.9 (19.2) Eggs 21.6 (20.4) 22.1 (17.1) 21.4 (22.2) 18.0 (25.6) 27.8 (37.2) 14.6 (19.1) * Dairy 209.7 (147.8) 261.0 (162.3) 180.2 (130.3) ** 175.5 (176.9) 249.9 (194.6) 149.7 (163.3) ** Beverages Soda & energy drinks 23.0 (30.8) 25.4 (25.9) 21.7 (33.3) 17.3 (31.0) 35.1 (42.2) 11.2 (23.1) ** Fruit juice 38.7 (41.7) 50.7 (49.7) 31.9 (34.8) ** 62.1 (67.1) 102.0 (79.8) 48.2 (56.1) *** Tea & coffee 84.0 (97.5) 95.9 (108.4) 76.4 (90.4) 158.9 (113.8) 72.7 (40.7) 149.4 (109.9) * Traditional foods Fruits 130.2 (85.0) 146.7 (81.7) 120.7 (85.8) * 109.6 (83.5) 126.5 (76.8) 103.8 (85.2) Vegetables 112.4 (82.0) 104.9 (57.4) 116.7 (93.2) 115.1 (104.3) 139.0 (137.1) 106.8 (89.2) Pulse & nuts 226.1 (145.6) 233.7 (163.5) 221.6 (134.9) 264.7 (140.9) 301.1 (132.3) 251.7 (141.9) * Grains 492.3 (241.8) 487.8 (232.9) 494.8 (247.6) 552.0 (267.5) 588.6 (256.3) 504.6 (255.5) *** Added sugar 24.7 (19.8) 35.6 (21.8) 18.5 (15.5) *** 4.5 (6.7) 5.9 (7.9) 4.0 (6.2) Ghee 1.9 (3.8) 1.8 (3.3) 2.0 (4.0) 1.3 (2.7) 1.7 (3.7) 1.2 (2.2) Dietary data were collected in Vijayapura in November 2013–January 2014 and in Bengaluru in November 2014–January 2015. p 〈 0.05, p 〈 0.05, p 〈 0.001. T‐tests for differences in food group intake among adolescents with high and low Nutrition Transition Diet Scores in Vijayapura and Bengaluru. Values are mean (SD) of food group intake in grams/day. For the predefined Nutrition Transition Diet Score, the ‘highest’ group indicates participants in the lop quartile (≥75th percentile) and ‘lower’ group indicates those in the remaining quartilcs. Dietary data were from adolescents' responses to the validated Nutrition Transition‐FFQ in Vijayapura and Bengaluru in Karnataka. India. Breads include white bread, brown bread, and paav/bun. Unhealthy global foods include vegetarian burger, non‐vegetarian burger, vegetarian pizza, chicken pizza, vegetarian puff, egg puff, pancake, and pasta. Healthy global foods include oats, multigrain biscuit, and breakfast cereal. Processed foods include Toffee (candy), chocolate, instant noodles, and cream and non‐cream biscuits. Non‐fried snacks include popcorn, pav bhaji, chooda, and khaari/rusk/butter. Fried snacks include Potato chips, packaged, finger chips, frozen paratha, frozen cutlet, samosa, wada pav, dahi wada, medhu wada, sago (sabudana) wada, chaats, bhajji, potato bonda, and kachori. Fried traditional foods include puri, paratha, puranpoli, and papad.

Abstract: Substantial evidence in western settings support strong associations between current depression and increased risk of current smoking and alcohol use, even among patients with type 2 diabetes. Little is known about the nature of these associations in other settings including India. Objective To evaluate the relationship between severity of depression and alcohol and tobacco use among adults with type 2 diabetes in India. Methods Subjects were 404 adults aged 35–74 years with depression and type 2 diabetes participating in the ongoing INTegrating DEPrEessioN and Diabetes TreatmENT (INDEPENDENT) trial aimed at testing the integration of a depression‐focused intervention into existing diabetes care in India. From the trial's administered survey at baseline, responses to PHQ‐9 (extent of depressive symptoms), AUDIT‐c (alcohol use in past year), tobacco use (smoking and/or chewable form), and demographic and socioeconomic characteristics were drawn and described. Depression severity was dichotomized as moderate and severe (PHQ‐9 score: 10–14 vs. 15–22), respectively. Alcohol consumers and non‐consumers were defined using AUDIT‐c scores (≥1 vs. 0), respectively. Students t test and chi‐square tests were used to describe characteristics of subjects according to depression severity status. Multivariate logistic regression was conducted to identify the relationship between alcohol and tobacco use (exposure as a single and/or dual habit) and severity of depression (outcome), adjusting for covariates. Results Overall, 75% had moderate depression and 25% had severe depression (PHQ‐9 range: 10–22, mean PHQ‐9 score: 12.1 ± 1.4 vs. 16.7 ± 1.7, p 〈 0.001), 6.7% used alcohol in the past year, 6.7% smoked and/or chewed tobacco, and 2.7% used both alcohol and tobacco. Severity of depression was significantly associated with gender (severe depression: 69.3% female vs. 30.7% male; moderate depression: 55.8% female and 44.2% male) and marital status (p 〈 0.05). Those with moderate depression had significantly (p 〈 0.05) more members in the household (4.7 ± 2.9 vs. 4.0 ± 2.0), had been chewing tobacco longer (1.0 ± 5.4 years vs. 0.3 ± 1.7 years) and were taller (158.8 ± 8.9 cm vs. 156.2 ± 8.6 cm) than those with severe depression; who had significantly higher LDL cholesterol (109.4 ± 37.5 mg/dl vs. 98.0 ± 37.9 mg/dl, p 〈 0.05). Holding all covariates constant, the odds of having severe depression than moderate depression was over 3 times higher among alcohol consumers than non‐consumers (OR 3.12, 95% CI: 1.05, 9.28), was 12% lower for the presence of each additional member in the household (OR 0.88, 95% CI: 0.77, 0.99), and was 3% lesser for each 1 mm Hg increase in systolic blood pressure (OR 0.97, 95% CI: 0.95, 0.99). Conclusion Severity of depression was associated with use of alcohol but not tobacco (smoking and/or chewable form) among adults with type 2 diabetes in India. Support or Funding Information This project was supported by National Institutes of Health (NIH) Research Training Grant R25 TW009337, funded by the Fogarty International Center, the NIH Office of the Director, and the National Institute of Mental Health (NIMH R01 MH100390). Characteristics of adults with moderate versus severe depression: the INDEPENDENT study (n=404) Characteristic 1 Overall (n=404) Moderate depression 2 (n=303) Severe depression 3 (n=101) Age (years) 52.7 ± 8.6 52.5 ± 8.4 53.0 ± 9.1 Male, % 40.8 44.2 30.7 * Education, % Literate 92.6 91.8 93.4 Illiterate 7.4 8.2 6.6 Occupation, 4 % Employed 40.8 35.6 46.4 * Not employed 59.2 64.4 53.6 Monthly household income (INR), 5 % Low ( 〈 30,000 INR or 〈 449.1 USD) 77.5 79.3 75.5 High ( 〉 30,001 INR or 〉 449.1 USD) 22.5 20.7 24.5 Marital status, % Married 85.4 77.2 88.1 ** Other (single/widowed/separated/divorced) 14.6 22.8 11.9 Number of household members 4.6 ± 2.7 4.7 ± 2.9 4.0 ± 2.0 ** Height, cm 158.2 ± 8.6 158.8 ± 8.9 156.2 ± 8.6 ** Weight, kg 67.7 ± 12.8 67.7 ± 12.3 67.6 ± 14.4 BMI 26.9 ± 4.7 26.7 ± 4.3 27.6 ± 5.6 Waist Circumference, cm 94.7 ± 12.3 94.4 ± 12.1 95.6 ± 13.0 HbAlc 9.1 ± 1.9 9.1 ± 2.0 9.2 ± 1.8 Fasting blood glucose mg/dl 182.0 ± 72.4 181.7 ± 72.9 183.0 ± 71.0 Triglycerides, mg/dl 160.8 ± 83.3 161.6 ± 88.4 158.3 ± 67.4 Total cholesterol, mg/dl 173.9 ± 44.1 171.6 ± 42.8 180.9 ± 47.5 HDL cholesterol, mg/dl 41.9 ± 11.8 41.8 ± 12.1 42.1 ± 10.7 LDL cholesterol, mg/dl 100.9 ± 38.1 98.0 ± 37.9 109.4 ± 37.5 ** Systolic Blood Pressure (mm Hg) 132.2 ± 16.3 133.0 ± 15.8 129.8 ± 17.5 Diastolic Blood Pressure (mm Hg) 80.3 ± 10.1 80.2 ± 10.4 80.3 ± 9.4 Alcohol use in past year (AUDIT‐c score) 0.17 ± 0.7 0.14 ± 0.6 0.25 ± 1.0 Alcohol use, % 6.7 6.3 7.9 Tobacco use, smoking form, % 3.0 3.6 1.0 Years smoked tobacco 1.0 ± 5.4 1.1 ± 5.4 0.8 ± 5.3 Number of cigarettes smoked/day 0.4 ± 2.2 0.5 ± 2.4 0.2 ± 1.1 Tobacco use, chewable form, % 3.7 4.0 3.0 Years chewed tobacco 0.8 ± 4.8 1.0 ± 5.4 0.3 ± 1.7 * Number of tobacco products chewed/day 0.2 ± 1.2 0.1 ± 1.2 0.2 ± 1.2 Tobacco use (smoking and/or chewable form) 6.7 7.6 4.0 Alcohol and tobacco use, % 2.7 3.3 1.0 Drug use, % 0 0 0 PHQ‐9 score 13.2 ± 2.5 12.1 ± 1.4 16.7 ± 1.7 *** Difficulty/severity of depression, % Not difficult at all 41.1 40.6 42.6 * Somewhat difficult 52.5 54.8 45.5 Very difficult 6.4 4.6 11.9 p ≤ 0.05, p ≤ 0.01, p ≤ 0.001. Mean ± SD. Baseline data from INDEPENDENT Study collected between 2015–2016. Moderate depression was defined as PHQ‐9 score between 10–14. Severe depression was defined as PHQ‐9 score between 15–22. Occupation was defined as employed (including professionals and skilled, semi‐skilled, and unskilled workers) or not‐employed (including housewives and those that are retired or unemployed). INR, Indian Rupees; USD, US Dollars. 1 USD=66.8 INR as of November 2016. Multivariate logistic regression [OR and 95% CI] describing relationship between severity of depression and alcohol and/or tobacco use among adults: the INDEPENDENT study (n=404) Variables Model 1Outcome: DepressionExposure: Alcohol use Model 1a [Model 1 adjusted only for age and gender] Model 1b [Model 1 adjusted for all covariates] Model 2Outcome: DepressionExposure: Tobacco use Model 2a [Model 2 adjusted only for age and gender] Model 2b [Model 2 adjusted for all covariates] Model 3Outcome: DepressionExposures: Alcohol and Tobacco use Model 3a [Model 3 adjusted only for age and gender] Model 3b [Model 3 adjusted for all covariates] Exposure(s) Alcohol consumer Yes 1.29 (0.55, 3.03) 2.20 (0.85, 5.64) 2.33 (0.85, 6.41) ‐ ‐ ‐ 1.74 (0.68, 4.42) 2.86 (1.04, 7.88) * 3.12 (1.05, 9.28) * No (ref) 1.0 1.0 1.0 1.0 1.0 1.0 Tobacco user Yes ‐ 0.50 (0.17, 1.48) 0.61 (0.20, 1.82) 0.59 (0.18, 1.92) 0.40 (0.12, 1.29) 0.43 (0.13, 1.39) 0.40 (0.12, 1.41) No (ref) ‐ ‐ ‐ 1.0 1.0 1.0 1.0 1.0 1.0 Covariates Age group 35–54 years (ref) ‐ NS 1 NS ‐ NS NS ‐ NS NS 55–74 years Gender Male ‐ 0.48 (0.28, 0.81) ** NS ‐ 0.58 (0.36, 0.94) * NS ‐ 0.49 (0.29, 0.82) * NS Female (ref) 1.0 1.0 1.0 Education Literate ‐ ‐ NS ‐ ‐ NS ‐ ‐ NS Illiterate (ref) Occupation Employed ‐ ‐ NS ‐ ‐ NS ‐ ‐ NS Not‐employed (ref) Monthly household income High (≥30,000 INR) ‐ ‐ NS ‐ ‐ NS ‐ ‐ NS Low ( 〈 30,000 INR) (ref) Marital status Married ‐ ‐ NS ‐ ‐ NS ‐ ‐ NS Other (ref) Number of members in the household ‐ ‐ 0.88 (0.78, 0.99) * ‐ ‐ 0.88 (0.78, 0.99) * ‐ ‐ 0.88 (0.77, 0.99) * BMI ‐ ‐ NS ‐ ‐ NS ‐ ‐ NS Waist circumference , cm ‐ ‐ NS ‐ ‐ NS ‐ ‐ NS HbA1c ‐ ‐ NS ‐ ‐ NS ‐ ‐ NS Triglycerides , mg/dl ‐ ‐ NS ‐ ‐ NS ‐ ‐ NS Total cholesterol , mg/dl ‐ ‐ NS ‐ ‐ NS ‐ ‐ NS HDL cholesterol , mg/dl ‐ ‐ NS ‐ ‐ NS ‐ ‐ NS LDL cholesterol , mg/dl ‐ ‐ NS ‐ ‐ NS ‐ ‐ NS Systolic Mood pressure , mm Hg ‐ ‐ 0.98 (0.95, 0.99) * ‐ ‐ 0.97 (0.95, 0.99) * ‐ ‐ 0.97 (0.95, 0.99) * Diastolic blood pressure , mm Hg ‐ ‐ NS ‐ ‐ NS ‐ ‐ NS p ≤ 0.05, p ≤ 0.01, *** p ≤ 0.001; NS, not significant at p 〈 0.05; IN, Indian Rupees; 1 USD=66.8 INR as of November 2016.