Prevalence and Predictors of Type 2 Diabetes Mellitus in a Rural Community of Haryana, India

Prevalence and Predictors of Type 2 Diabetes Mellitus in a Rural Community of Haryana, India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prevalence and Predictors of Type 2 Diabetes Mellitus in a Rural Community of Haryana, India Geetika Singh, Nidhi Gupta, Neha Singla, Sunita Vashist This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9230995/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Background: Diabetes mellitus is a pressing global health concern contributing substantially to morbidity and premature mortality. In India, this burden is steadily rising in rural communities which are undergoing rapid epidemiological transition. Aims & Objectives: To determine the prevalence of diabetes and to identify the associated socio-demographic, behavioural and biological risk factors in a rural community of Haryana. Methodology: A community-based cross-sectional study was conducted among 300 adults aged 30-70 years residing in Mankrola village, Gurugram. Participants were selected through systematic random sampling. Data was collected using a pre-tested structured questionnaire comprising of socio-demographic profile, anthropometric and clinical assessments, random blood glucose estimation and Perceived Stress Scale (PSS-10). Multivariate logistic regression was performed to identify the independent predictors. Results: The prevalence of diabetes and pre-diabetes was found to be 12.7% and 17.3% respectively. Rates were higher among females (14.2%) than males (10.3%) and increased progressively with age. Significant predictors included upper middle socioeconomic status (AOR = 3.009, p = 0.036), overweight (AOR 4.35; p=0.044), physical inactivity (AOR 5.50; p=0.003), high oil/fat intake (AOR 7.85; p 0.05) Conclusion: Diabetes and pre-diabetes are considerably prevalent in rural Haryana driven largely by modifiable risk factors. Strengthening community-based prevention, early detection and lifestyle modifications are imperative to mitigate the rural diabetes epidemic. Diabetes DM pre-diabetes prevalence risk factors predictors rural community Figures Figure 1 Figure 2 Introduction Diabetes mellitus is a group of metabolic disorders characterized by chronic hyperglycaemia accompanied by abnormalities in the metabolism of carbohydrates, fats and proteins caused by impaired insulin secretion, insulin function or a combination of both. Diabetes mellitus is a major global health challenge and one of the leading causes of morbidity, disability, and premature mortality especially in Low and Middle-Income Countries (LMICs). According to the International Diabetes Federation (IDF), around 537 million adults aged 20–79 years worldwide were living with diabetes in 2021, and this number is projected to rise to 643 million by 2030 and 783 million by 2045. 1 India has become the global epicentre of diabetes with approximately 101 million people currently living with the disease and another 136 million having prediabetes. 2 The Global Burden of Disease Study (2019) further highlights the impact of diabetes by identifying it as the seventh leading contributor to disability-adjusted life years (DALYs) in India. 3 Contributing factors include rapid urbanization, sedentary lifestyles, increased consumption of high-calorie processed foods, escalating obesity status and an inherent genetic predisposition among the Indian population. At the state level, Haryana shows a similarly alarming trend. As per the National Family Health Survey-5 (2019–21), 12.1% of women and 15.7% of men aged 15 years and above in Haryana had elevated blood sugar levels or were on medication for diabetes. 4 Though urban areas report a higher prevalence, the rural population is increasingly affected due to shifting dietary patterns, reduced physical activity and rising overweight/obesity levels. The risk factors for diabetes are well-documented and encompasses modifiable factors such as obesity, physical inactivity, poor dietary practices, tobacco use, alcohol consumption and comorbidities like hypertension and dyslipidaemia. Non-modifiable risk factors include advancing age, family history and a genetic predisposition which significantly contribute to the disease susceptibility. 5 Furthermore, socio-economic determinants like poor income, lower education status and limited access to healthcare services affect both the incidence and outcomes of diabetes. In addition, stress has been established to be an etiological link as well as an outcome in diabetes mellitus owing to the prolonged need for medication adherence and sustained lifestyle changes. 6 Despite ongoing nationwide initiatives such as the National Programme for Prevention and Control of Non-Communicable Diseases (NP-NCD) and the expansion of Ayushman Bharat Health and Wellness Centres, gaps persist in early detection, regular monitoring and effective diabetes management particularly in semi-urban and rural areas. Therefore, the present study was undertaken to assess the prevalence of diabetes and examine its association with various sociodemographic, behavioural and biological determinants in a rural area of district Gurugram, Haryana. Materials & Methods This was a community based cross-sectional study which was conducted in the year 2025 over a period of three months in Mankrola village, the rural field practice area under the Department of Community Medicine, Faculty of Medicine and Health Sciences (FMHS), SGT University, Gurugram, Haryana. The study population included all adults aged 30–70 years who were permanent residents of the study area. All existing cases of diabetes identified during the survey were also recruited. Those found to be pregnant, migrants (residing in the area for less than six months), severely ill or bedridden patients were excluded from the study. Sample size was calculated using the standard formula for single proportion (n = Z²pq/d²). A prevalence (p) of 15.5% 7 for Diabetes was used with an absolute precision of 4.1% (d) at 95% confidence level. The minimum required sample size was 299 which was rounded off to 300 participants. The Mankrola village comprised of approximately 600 households. To obtain the required 300 participants, systematic random sampling was applied. The sampling interval (k) was calculated as 600/300 = 2. A random starting point (either the 1st or 2nd household) was chosen by lottery and thereafter every 2nd household was visited till desired sample size was reached. From each selected household, one eligible adult aged 30–70 years was chosen. In households with more than one eligible adult, a simple random method was used to select one participant. In cases where no eligible individual was available or the household was locked at the time of the visit, the next house in sequence was surveyed. Data were collected using a pre-designed, pre-tested, structured interview schedule adapted from the WHO STEPwise approach to noncommunicable disease risk factor surveillance 8 . The questionnaire consisted of four main sections: socio-demographic profile and behavioral characteristics, physical measurements, random blood glucose estimation and assessment of perceived stress. Socio-demographic details such as age, gender, education, occupation, marital status, and household income were recorded. Behavioural risk factors included tobacco and alcohol use, physical activity and dietary patterns, including salt and oil intake and daily consumption of fruits and vegetables which were assessed using the 24-hour recall method. Physical and clinical assessments included anthropometric measurements such as height (measured using a stadiometer), weight (using a digital scale), waist and hip circumferences (using a measuring tape) and recording of blood pressure (using a digital BP instrument). Biochemical testing involved the estimation of random blood glucose using a standard digital glucometer in which a finger prick was made using a sterile lancet on the left ring finger to obtain the sample. Perceived stress was assessed using the Perceived Stress Scale (PSS-10) which is a validated tool widely used to measure perceived stress levels over the past month. Individual scores on the PSS can range from 0 to 40 with higher scores indicating higher perceived stress. 8 Operational definitions 9 – 14 Diabetes: Defined as either: A random plasma glucose level ≥ 200 mg/dL in the presence of classic symptoms of hyperglycaemia (polyuria, polydipsia, unexplained weight loss) or a documented prescription/diagnosis of diabetes mellitus by a registered medical practitioner. Body mass index (BMI): calculated using the formula weight in kilograms divided by height in meters squared (kg/m²). BMI was classified using WHO cut-offs: 25-29.9 kg/m² for overweight and ≥ 30 kg/m² for obesity. Waist-hip ratio (WHR): calculated and categorized based on WHO cut-offs- ≥ 0.9 for men and ≥ 0.85 for women. Stress: PSS-10 scores were calculated and categorized as low (0–13), moderate (14–26) and high (27–40) levels of stress. Addictions- Alcohol Usage: Current alcohol use was defined as those who had consumed alcohol in the last 1 year. Past: More than one year back; Never: Not used ever. Tobacco usage: Current tobacco use was defined as those who smoked and/or consumed smokeless tobacco in the past 30 days. Past: More than one year back; Never: Not used ever. Vegetables/fruits intake: Low consumption of fruits and vegetables defined at less than five servings per day. (One serving of vegetables was defined as either one cup of raw leafy vegetables or half a cup of cooked vegetables. Similarly, one serving of fruit was considered equivalent to one medium-sized whole fruit or half a cup of chopped fruit) Physical Activity: Low physical activity was defined as < 150 minutes of moderate physical activity per week. Statistical Analysis Data was entered and analysed using the Statistical Package for Social Sciences (SPSS) version 26. Descriptive statistics such as means, standard deviations and percentages were used to summarize the data. To assess the association between independent variables and the dependent variable, crude odds ratios with 95% confidence intervals (CI) were calculated. Multivariate logistic regression was performed using covariates as independent variables and Diabetes as the outcome to identify the significant predictors of diabetes. A p-value of less than 0.05 was considered statistically significant. Model fit was evaluated using the Hosmer–Lemeshow goodness-of-fit test. The study was approved by the Institutional Ethics Committee. Prior to participation, each subject was explicitly explained about the purpose of the study by the investigator and written informed consent was obtained from all participants. Results: Socio-demographic characteristics Out of total 300 participants, majority 78(26%) belonged to the 30–40 years age group followed by 71(23.67%) in the age group of 40–50 years. More than half 183 (61%) were females while the remaining 117 (39%) were males. The mean age of the study population was 50.31 ± 12.76 years, with males having a mean age of 50.15 ± 12.88 years and females 49.92 ± 12.80 years. Around three-fourths i.e. 225(75%) were currently married while 36 (12%) were found to be unmarried. Most of the participants 286 (95.33%) were Hindu by religion. 80 (27%) were educated upto senior secondary level and above whereas 69 (23%) participants were found to be illiterate. Half of the participants 150 (50%) lived in a joint family while 118 (39.33%) belonged to nuclear family. 137 (45.67%) of participants were home makers while a quarter 76(25.33%) were unemployed. According to Modified BG Prasad socio-economic status scale, most of the participants 136(45.33%) belonged to upper class followed by 117(39%) in upper middle class. The overall prevalence of Diabetes was found to be 38(12.67%) while 52(17.33%) of participants were pre-diabetics. (Fig. 1 ) Figure 2 illustrates the age-wise distribution of glycaemic status stratified by sex. Overall, among males (n = 117), 12 (10.3%) were diabetic, 6 (5.1%) were pre-diabetic and 99 (84.6%) were normoglycemic. Among females (n = 183), 26 (14.2%) were diabetic, 46 (25.1%) were pre-diabetic, and 111 (60.7%) were normoglycemic. Majority of younger participants (30–40 years) were normoglycemic, comprising of 40 males (93.0%) and 26 females (66.7%). Prediabetes was more frequently observed among middle-aged females with 15 (28.3%) in the 40 to 50 years age group and 24 (49.0%) in the 50–60 year group. The prevalence of diabetes increased progressively with age in both sexes, with the highest proportion among males aged ≥ 70 years (6; 54.5%) and females aged 60–70 years (9; 20.5%). Table 1 demonstrates the clinical and anthropometric characteristics of diabetic participants by gender. The overall mean random blood glucose level was 140 ± 47 mg/dl, with males recording 137 ± 45 mg/dl and females 141 ± 47 mg/dl. The mean systolic blood pressure was 124 ± 9 mmHg (males: 125 ± 9 mmHg; females: 124 ± 8 mmHg), while the mean diastolic blood pressure was 81 ± 6 mmHg (males: 81 ± 6 mmHg; females: 81 ± 5 mmHg). Stress scores were almost similar across genders (males: 18.51 ± 3.98; females: 18.63 ± 3.88; overall: 18.57 ± 3.93). The average body weight was 67.50 ± 10.97 kg (males: 68.17 ± 10.94 kg; females: 67.45 ± 11.02 kg), and the mean height was 1.62 ± 0.11 m. The mean body mass index (BMI) was 25.76 ± 4.21 kg/m² overall, with males and females showing values of 25.84 ± 4.28 kg/m² and 25.88 ± 4.31 kg/m² respectively. The mean waist–hip ratio was 0.91 ± 0.06 in females and 0.92 ± 0.05 in males. Table 1 Gender-wise comparison of means of clinical and anthropometric characteristics among diabetic participants (N = 38) Characteristics Male (N = 12) Female (N = 26) Total (N = 38) Random Blood glucose (mg/dl) 137 ± 45 141 ± 47 140 ± 47 SBP*(mmHg) 125 ± 9 124 ± 8 124 ± 9 DBP* (mmHg) 81 ± 6 81 ± 5 81 ± 6 Stress Score 18.51 ± 3.98 18.63 ± 3.88 18.57 ± 3.93 Weight (kg) 68.17 ± 10.94 67.45 ± 11.02 67.50 ± 10.97 Height (metres) 1.63 ± 0.11 1.62 ± 0.11 1.62 ± 0.11 BMI (kg/m 2 ) 25.84 ± 4.28 25.88 ± 4.31 25.76 ± 4.21 Waist hip ratio 0.92 ± 0.05 0.91 ± 0.06 0.91 ± 0.06 *SBP - Systolic Blood Pressure *DBP - Diastolic Blood Pressure Table 2 presents the multivariate logistic regression analysis of the socio-demographic determinants associated with Diabetes. Compared to participants aged ≥ 70 years (reference category), those aged 30–40 years (AOR = 0.008; 95% CI: 0.001–0.061), 40–50 years (AOR = 0.024; 95% CI: 0.004–0.156), and 50–60 years (AOR = 0.030; 95% CI: 0.005–0.193) had significantly lower odds of diabetes (p < 0.001). The 60–70 years group did not show a statistically significant association (p = 0.073). Table 2 Multivariate logistic regression analysis of the socio-demographic determinants associated with Diabetes Variables Category Status Sig. AOR 95% CI Diabetic Non-Diabetic Lower Upper Age groups (in yrs) 30–40 3(7.89%) 75(28.63%) .000 .008 .001 .061 40–50 5(13.16%) 66(25.19%) .000 .024 .004 .156 50–60 6(15.79%) 59(22.52%) .000 .030 .005 .193 60–70 14(36.84%) 45(17.18%) .073 .285 .072 1.125 >=70 10(26.32%) 17(6.49%) * 1 * * Gender Male 12(31.58%) 105(40.08%) .314 .516 .142 1.874 Female 26(68.42%) 157(59.92%) * 1 * * Marital status Unmarried 5(13.16%) 31(11.83%) .207 2.647 .583 12.011 Separated 1(2.63%) 15(5.73%) .121 .097 .005 1.847 Widowed (Widower) 4(10.53%) 19(7.25%) .992 1.007 .241 4.215 Married 28(73.68%) 197(75.19%) * 1 * * Religion Sikh - 3(1.15%) .999 .000 .000 Christian 1(2.63%) 1(0.38%) .444 3.928 .118 130.514 Muslim 2(5.26%) 7(2.67%) .127 4.762 .643 35.283 Hindu 35(92.11%) 251(95.80%) * 1 * * Education Illiterate 9(23.68%) 60(22.90%) .011 .123 .025 .614 Just literate 5(13.16%) 29(11.07%) .115 .266 .051 1.378 Primary 2(5.26%) 22(8.40%) .059 .144 .019 1.077 Middle 3(7.89%) 33(12.60%) .166 .287 .049 1.677 High school 11(28.95%) 46(17.56%) .909 1.073 .320 3.601 Senior Secondary and above 8(21.05%) 72(27.48%) * 1 * * Type of Family Nuclear 11(28.95%) 107(40.84%) .512 1.764 .323 9.630 Joint 24(63.16%) 126(48.09%) .079 4.093 .851 19.685 Three Generation 3(7.89%) 29(11.07%) * 1 * * Occupation Employed 7(18.42%) 80(30.53%) .993 1.006 .261 3.876 Unemployed 13(34.21%) 63(24.05%) .605 1.464 .345 6.217 Housewife 18(47.37%) 119(45.42%) * 1 * * Socio-economic status Lower class 0 1(0.38%) 1.00 .000 .000 - Lower middle class 0 7(2.67%) .999 .000 .000 - Middle class 2(5.26%) 37(14.12%) .979 1.025 .166 6.344 Upper middle class 20(52.63%) 97(37.02%) .036 3.009 1.076 8.415 Upper class 16(42.11%) 120(45.80%) * 1 * * *- Reference category • Omnibus Chi-square = 62.019, df = 24, p < 0.001 • Nagelkerke R² = 0.351 (35.1% of the variance explained) • Hosmer-Lemeshow test: p = 0.458 → model fits well • Classification accuracy = 89.3% Participants aged 30–40 years had 99.2% lower odds of diabetes (AOR = 0.008, p < 0.001) and those aged 50–60 years had 97.0% lower odds (AOR = 0.030, p < 0.001), compared to participants aged ≥ 70 years. Illiterate participants had 87.7% lower odds of diabetes (AOR = 0.123, p = 0.011) and those with primary education had 85.6% lower odds (AOR = 0.144, p = 0.059) relative to individuals with senior secondary education or above. Socio-economic status also showed a significant association, with participants from the upper middle class having three times higher odds of diabetes (AOR = 3.009, p = 0.036) compared to those from the upper class. Participants living in joint families had nearly four times higher odds of diabetes (AOR = 4.093, p = 0.079), although this association was not statistically significant. No statistically significant associations were observed for gender, marital status, religion, or occupation after adjustment. Table 3 summarizes the prevalence of diabetes mellitus (DM) across behavioural and biological characteristics from multivariate analysis. Lower vegetable intake (< 400 g/day) was associated with increased risk, with higher intake (≥ 400 g/day) showing a protective effect (AOR = 0.240, p = 0.005). Participants reporting higher daily oil and fat consumption (≥ 25 g/day) were at markedly greater risk (AOR = 7.848, p < 0.001). Conversely, participants consuming ≥ 5 g salt/day had significantly lower odds of diabetes (AOR = 0.207, p = 0.011) compared to those consuming < 5 g/day. Alcohol consumers had significantly lower odds of diabetes compared to non-consumers (AOR = 0.203, p = 0.037). Furthermore, physical inactivity (< 150 minutes/week) remained a strong predictor (AOR = 5.502, p = 0.003). Overweight individuals (BMI 25–29.9 kg/m²) had higher odds of diabetes (AOR = 4.345, p = 0.044). Other variables, including smoking, tobacco use, perceived stress, blood pressure, waist–hip ratio did not show statistically significant associations. Table 3 Multivariate logistic regression analysis of the behavioural, dietary and biological factors associated with diabetes Variables Category Status Sig. AOR 95% CI Diabetic Non-Diabetic Lower Upper Vegetable Intake (g/day) =400g/day 13(34.21%) 41(15.65%) * 1 * * Fruit intake (g/day) =100g/day 38(100%) 251(95.80%) * 1 * * Daily oil & fat intake >=25g/day 26(68.42%) 105(40.08%) .000 7.848 2.977 20.685 =5gm/day 32(84.21%) 225(85.88%) .011 .207 .062 .698 < 5gm/day 6(15.79%) 37(14.12%) * 1 * * Perceived stress Stress 34(89.47%) 232(88.55%) .826 1.164 .301 4.496 Normal 4(10.53%) 30(11.45%) * 1 * * Alcohol Consumption Yes 3(7.89%) 52(19.85%) .037 .203 .045 .909 No 35(92.11%) 210(80.15%) * 1 * * Smoking Smokers 12(31.58%) 83(31.68%) .145 2.107 .774 5.731 Non Smokers 26(68.42%) 179(68.32%) * 1 * * Tobacco Consumption (Khaini, Gutka) Consumers 3(7.89%) 39(14.89%) .559 .638 .142 2.874 Non consumers 35(92.11%) 223(85.11%) * 1 * * Blood Pressure Raised 37(97.37%) 235(89.69%) .234 3.662 .431 31.089 Normal 1(2.63%) 27(10.31%) * 1 * * Physical Activity =150min/week 6(15.79%) 111(42.37%) * 1 * * Waist Hip Ratio Male > = 0.9/female > = 0.85 30(78.95%) 193(73.66%) .258 1.747 .665 4.592 Male < 0.9/female < 0.85 8(21.05%) 69(26.34%) * 1 * * BMI (kg/m 2 ) =30 3(7.89%) 40(15.27%) * 1 * * *- Reference category • Omnibus Chi-square = 52.552, df = 14, p < 0.001 • Nagelkerke R² = 0.302 (30.2% of the variance explained) • Hosmer-Lemeshow test: p = 0.730 → model fits well • Classification accuracy = 89.7% Discussion According to the present study, the overall prevalence of diabetes mellitus was observed to be high (13%). This was higher than previous studies in Haryana by Jangra A et al. where the prevalence was 9.2% and the study in Uttar Pradesh by Singh PS et al. (8.03%). 15,16 Another study from South India by Nithesh KK et al. showed that 8.1% of adults were diagnosed as Type 2 Diabetics. 17 However, the recent state wide STEPS Survey conducted in two northern states of India reported an overall prevalence of 14.3% and 15.1% in Punjab and Haryana, respectively which is slightly higher as compared to the current study. 7 These differences could be attributed to variations in lifestyle, sociodemographic background, genetic predisposition and use of varying diagnostic methods or criteria for diabetes in different studies. The prevalence of pre-diabetes in the present study was approximately 17%, which is notably higher than that reported in previous studies where 10.04% and 14.3% of participants were classified as pre-diabetic respectively. 16 , 18 A large ICMR-INDIAB multi-state survey covering 31 states in India also documented a comparable prevalence of 15.3%. 5 These findings highlight that pre-diabetes remains high across settings and warrants immediate public health attention, given its well-documented progression to diabetes. Prevalence was higher in female population (14.2%) as compared to males (10.3%) which is congruent to other studies conducted in different regions of India. 15 , 16 , 19 This difference may be explained by higher rates of overweight and physical inactivity among women as well as possible lifestyle influences and hormonal transitions, particularly after menopause. These results underline the need for gender-focused screening and preventive interventions. As anticipated, our study demonstrates a positive association between diabetes mellitus and advancing age. The highest proportion of diabetes was observed among males aged ≥ 70 years (54.5%) and females aged 60–70 years (20.5%). Similar trend was reported by other studies where DM increased with age and the association was found to be statistically significant. 17 , 20 , 21 This may be explained by the age-related β-cell decline, reduced insulin sensitivity and cumulative impact of lifestyle influences, underscoring the need for targeted screening and structured health education in older adults. Multivariate logistic regression analysis identified multiple sociodemographic, lifestyle and clinical predictors of diabetes mellitus. Advancing age emerged as the strongest risk factor. Younger age groups exhibited substantially lower odds which in accordance with various international and national studies. In China, more than half of diabetes cases occurred in adults ≥ 60 years while in Nepal those aged 60 and above had nearly fivefold higher odds (AOR 4.7). 22,23 Similar findings were documented by a country wide National NCD Monitoring Survey in India (NNMS, 2022) where adults aged 50–69 years had nearly ninefold higher odds (AOR 8.89, p < 0.001) of having diabetes. 24 Regarding educational status, illiterates and those with only primary education showed lower odds of having Diabetes than the more educated groups which is in contrast to other studies in China and India where higher diabetes prevalence was reported in less literate groups. 21 , 22 , 25 This may reflect the importance of traditional diets and higher physical activity among less educated participants, thus buffering against the diabetes risk. Interestingly, socio-economic status emerged as a risk modifier with individuals from upper middle-class families showing approximately threefold higher odds of diabetes (AOR = 3.009, p = 0.036). This is consistent with global evidence from China, Nepal and Gambia where urban and affluent participants carried a greater risk and with surveys in India which also demonstrated a higher diabetes prevalence in richer quintiles. These findings could be due to the westernisation of lifestyles, sedentary occupations and dietary excesses in the higher SES groups. 22 – 26 Although dietary patterns are known to play a significant role in diabetes risk, only a limited number of studies have examined this aspect in depth. In the present study, a high intake of oil or fat (≥ 25 g/day) was associated with nearly an eightfold higher risk (AOR 7.85), whereas adequate vegetable consumption showed a protective effect. However, a cross-sectional survey from India reported contrasting findings, where the association between higher level of diabetes and daily consumption of green vegetables and fruits was noted. 25 Similarly, in our study, alcohol and salt intake appeared to have inverse associations with diabetes. Given the well-established adverse metabolic effects of excessive alcohol and sodium consumption, these observed associations may reflect reverse causality, residual confounding or measurement error inherent to self-reported dietary data within a cross-sectional design. The current study highlights that perceived stress (PSS-10 score ≥ 14) was higher among individuals with diabetes. This is consistent with the findings of Siddharthan et al. who reported perceived stress in 39.3% of diabetic inpatients in South India. Similarly, Saha et al. observed that nearly 39.1% had high perceived stress (mean PSS score = 28 ± 3.47) and stress levels showed a positive though nonsignificant correlation which is same as our study. 6 , 27 These findings reveal that psychological stress is common among Indian diabetics which often worsens metabolic control, thus reinforcing the need to integrate stress management into diabetes care programs. Furthermore, in our study, physical inactivity was strongly associated with diabetes (AOR 5.5, p = 0.003). This aligns with findings from the NNMS survey which also reported a slightly higher risk among adults engaging in insufficient physical activity (AOR 1.08) though the association was not statistically significant. 24 Overweight significantly predicted diabetes (AOR 4.35) as excess adiposity promotes release of inflammatory cytokines and reduces adiponectin together causing insulin resistance. This is consistent with global evidence that obesity remains a key determinant of diabetes across diverse populations. 22 – 26 Strengths & Limitations Firstly, a major strength of this study was its community-based design and usage of systematic random sampling which ensured an adequate representation of the rural population. Secondly, the inclusion of sociodemographic and behavioural variables along with clinical and anthropometric assessments allowed for a comprehensive evaluation of the determinants of diabetes. Thirdly, utilisation of standardized tools such as the Perceived Stress Scale (PSS-10) and the clearly defined operational definitions further enhanced the reliability & validity of the measurements. However, the study had few limitations too. Firstly, the causal relationships between risk factors and diabetes cannot be established due to cross-sectional design of the study. Secondly, blood glucose estimation was based on a single random measurement using a glucometer which may have underestimated or overestimated the true prevalence compared to the laboratory based fasting glucose or HbA1c testing. Thirdly, the dietary intake and behavioural risk factors were assessed through self-reported data making them susceptible to recall and reporting bias. Finally, the study was confined to a single rural village in Haryana which may limit the generalizability of findings to other regions with differing socio- demographic or lifestyle contexts. Conclusion The study reveals a high burden of diabetes and pre-diabetes in rural Haryana, driven mainly by modifiable risk factors such as overweight, physical inactivity and unhealthy diets. Despite being perceived as an urban disease, rural populations are equally vulnerable due to lifestyle transitions. Hence, urgent, context-specific prevention strategies are needed focusing on community health education, sustained behaviour change, healthier diets, physical activity and gender-sensitive approaches. Systematic screening of high-risk groups and integration of stress management into primary care can enhance control efforts. Strengthening NP-NCD implementation and optimizing Health and Wellness Centres for early detection, lifestyle modification and continuity of care are critical to curb progression and reduce long-term health system burden. Declarations Acknowledgement: We sincerely express our gratitude to all the participants who generously gave their time and shared their information without which this study would not have been possible. We are also grateful to the Medical Social Workers for their diligent assistance in the assessment and data collection processes. Conflicts of interest statement: None declared Financial support and sponsorship: This research was supported by an intramural research grant from SGT University. Contribution contributions: GS: Conceptualization, Methodology, Resources, Data Acquisition, Writing—Original Draft Preparation, Review & Editing; NG: Methodology, Resources, Writing—Original Draft Preparation & Editing; NS: Methodology, Data & Statistical analysis; SV: Conceptualization, Supervision, Manuscript Review & Editing. Data availability statement: The data that support the findings of this study are available from the corresponding author upon request. Ethical Declarations Ethical approval: The study was approved by the Institutional Ethics Committee, Faculty of Medicine & Health Sciences, SGT University, Gurugram, Haryana via letter no IEC/FMHS/F/2023-08 dated 08th November, 2024 Consent to participate: Prior to the start of the study, written informed consent was obtained for participation in the study and patient’s data was used only for research and educational purposes. The study procedures adhered to the guidelines of the Declaration of Helsinki 1964 and its subsequent revisions. Statement on approval from all contributors: The manuscript has been read and approved by all the authors, the requirements for authorship as stated earlier in this document have been met and each author believes that the manuscript represents honest work. Consent for publication: The authors affirm that human research participants provided informed consent for publication. Competing interests: The authors declare no competing interests. Clinical trial number: Not applicable References International Diabetes Federation. IDF Diabetes Atlas, 10th ed. Brussels: International Diabetes Federation. 2021. Available from: https://diabetesatlas.org Unnikrishnan R, Anjana RM, Mohan V. The Indian Council of Medical Research–India Diabetes (ICMR–INDIAB) Study: A national diabetes study in India. Lancet Diabetes Endocrinol. 2023;11(7):470–80. 10.1016/S2213-8587(23)00106-0 . India State-Level Disease Burden Initiative Collaborators. The burden of diabetes and hyperglycemia in India: The Global Burden of Disease Study 2019. Lancet Diabetes Endocrinol. 2020;8(12):e1235–50. 10.1016/S2213-8587(20)30314-6 . International Institute for Population Sciences (IIPS) and Ministry of Health and Family Welfare (MoHFW). National Family Health Survey (NFHS-5), 2019–21: Haryana. Mumbai: IIPS; 2021. Anjana RM, Unnikrishnan R, Deepa M, Pradeepa R, Tandon N, Das AK, et al. 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Clin Diabetes. 2016;34(1):3–21. 10.2337/diaclin.34.1.3 . World Health Organization. Obesity: Preventing and managing the global epidemic. Report of a WHO consultation. Geneva: World Health Organization. 2000. (WHO Technical Report Series, No. 894). Sruthi KG, John SM, David SM. Assessment of obesity in the Indian setting: A clinical review. Clin Epidemiol Glob Health. 2023;23:101348. 10.1016/j.cegh.2023.101348 . Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav. 1983;24(4):385–96. 10.2307/2136404 . WHO/FAO, Diet. Nutrition and the Prevention of Chronic Diseases. Geneva: WHO/FAO; 2003. U.S. Centers for Disease Control and Prevention. What you can do to meet physical activity recommendations [Internet]. 2024 Apr [cited 2025 Aug 9]. Available from: https://www.cdc.gov/physical-activity-basics/guidelines/index.html Jangra A, Malik JS, Singh S, Sharma N. Diabetes in rural Haryana, India: a population-based study. J Med Allied Sci. 2019;9(2):48–54. 10.5455/jmas.24046 . Singh PS, Sharma H, Zafar KS, Singh PK, Yadav SK, Gautam RK, et al. Prevalence of type 2 diabetes mellitus in rural population of India: A study from Western Uttar Pradesh. Int J Res Med Sci. 2017;5(4):1363–7. 10.18203/2320-6012.ijrms20171263 . Nithesh KK, Katkuri S, Ramyacharitha I. A study to assess prevalence of diabetes mellitus and its associated risk factors among adult residents of rural Khammam. Int J Community Med Public Health. 2018;5(4):1360–5. 10.18203/2394-6040.ijcmph20181208 . Achuth KS, Subramanian M, Pradeep C. Prevalence of pre-diabetes and its associated risk factors among people in a rural field practice area of Vydehi Institute of Medical Sciences and Research Centre, Bangalore. Int J Community Med Public Health. 2023;10(4):1675–80. 10.18203/2394-6040.ijcmph20231118 . Singh M, Kishore S, Jain B, Aggarwal P, Verma SK. Prevalence of diabetes mellitus and its associated risk factors. Indian J Community Health. 2020;32(1):97–100. Pradhan N, Sachdeva A, Goel T, Arora S, Barua S. Prevalence of diabetes mellitus in rural population of Mullana, district Ambala, Haryana, India. Int J Res Med Sci. 2018;6(4):1248–51. 10.18203/2320-6012.ijrms20181428 . Shriraam V, Mahadevan S, Seshadri KG, Aravind SR, Chinnasami B, Srivastava A, et al. Prevalence and risk factors of diabetes in tribal populations in India: A community-based study. Indian J Endocrinol Metab. 2021;25(4):313–9. 10.4103/ijem.IJEM_517_20 . Li Y, Xu F, Ye T, Yang S, Li L, Wang H, et al. Prevalence of diabetes mellitus and its risk factors among middle-aged and elderly residents in a Chinese community: A cross-sectional study. BMJ Open. 2022;12(4):e049754. 10.1136/bmjopen-2021-049754 . Shrestha N, Mishra SR, Ghimire S, Gyawali B, Mehata S, Pradhan PMS, et al. Prevalence of diabetes and associated risk factors in Nepal: Findings from a nationwide cross-sectional STEPS survey. BMJ Open. 2022;12(7):e060750. 10.1136/bmjopen-2021-060750 . Mathur P, Leburu S, Kulothungan V, et al. Prevalence, awareness, treatment and control of diabetes in India from the countrywide National NCD Monitoring Survey. Front Public Health. 2022;10:748157. 10.3389/fpubh.2022.748157 . Sujata TR. Unequal burden of equal risk factors of diabetes between different gender in India: A cross-sectional analysis. Sci Rep. 2021;11:22653. 10.1038/s41598-021-02012-9 . Jobe M, Bah S, Touray S, Jallow A, Sanyang M, Samateh AL, et al. Prevalence of diabetes and its risk factors in The Gambia: A nationally representative cross-sectional survey. Lancet Glob Health. 2024;12(1):e55–65. 10.1016/S2214-109X(23)00508-9 . Saha A, Chakraborty D, Saha S. Assessment of level of stress and glycemic control among type 2 diabetes mellitus patients: An observational study. Int J Sci Stud. 2023;11(2):72–7. 10.17354/ijss/2023/117 . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9230995","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":615169253,"identity":"b5e4a364-ee91-4562-a348-9627136e2bb2","order_by":0,"name":"Geetika Singh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYDACZijNDyISCkjQIiHZANJiQIJlEgYHQBQxWuTbeQ+//FJhU2d8fnXihwcGDPL8YgfwazE4zJdmLXMmTcLsxtvNEkCHGc6cnUBACzOPmbFk22GglrMbQFoSDG4T0CLfDNLy77+E8Yyzm38QpYXhMI/xw48NByQM+Hu3EWeLwWEeM2aGY8mSM27wbrNIMJAg7Bf5/jPGH3/U2PHz95/dfPNHhY08vzQhhzEwsEnzgCgJsEoJgspBgPnjDxDFf4Ao1aNgFIyCUTACAQDOJ0Gyxqb23AAAAABJRU5ErkJggg==","orcid":"","institution":"FMHS, SGT UNIVERSITY","correspondingAuthor":true,"prefix":"","firstName":"Geetika","middleName":"","lastName":"Singh","suffix":""},{"id":615169254,"identity":"01394e95-dbb8-4810-94cc-963b1b97049c","order_by":1,"name":"Nidhi Gupta","email":"","orcid":"","institution":"FMHS, SGT UNIVERSITY","correspondingAuthor":false,"prefix":"","firstName":"Nidhi","middleName":"","lastName":"Gupta","suffix":""},{"id":615169255,"identity":"d8830c59-0047-4c50-8feb-4fa102848ad9","order_by":2,"name":"Neha Singla","email":"","orcid":"","institution":"Tutor cum Statistician, FMHS, SGT UNIVERSITY","correspondingAuthor":false,"prefix":"","firstName":"Neha","middleName":"","lastName":"Singla","suffix":""},{"id":615169256,"identity":"ba00301a-23b1-4ca4-828f-b64a96f762ee","order_by":3,"name":"Sunita Vashist","email":"","orcid":"","institution":"FMHS, SGT UNIVERSITY","correspondingAuthor":false,"prefix":"","firstName":"Sunita","middleName":"","lastName":"Vashist","suffix":""}],"badges":[],"createdAt":"2026-03-26 07:56:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9230995/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9230995/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106093531,"identity":"abae0d22-effe-4a84-81d6-ec1b3af10b84","added_by":"auto","created_at":"2026-04-03 11:37:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55696,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrevalence of Diabetes and Pre-diabetes among study participants (N=300)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9230995/v1/cb5f951596851a7b28887f4c.png"},{"id":106094398,"identity":"d28940f7-8e52-4a4f-bd89-045baecccdde","added_by":"auto","created_at":"2026-04-03 11:42:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":70038,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAge and gender wise distribution of blood glucose levels among the study participants\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9230995/v1/00956b5f5e7e23479d52afdd.png"},{"id":106095794,"identity":"fea959c3-1de6-4bc6-ad9e-dbfcc513e1f0","added_by":"auto","created_at":"2026-04-03 11:51:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1429235,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9230995/v1/fcf069ab-354d-4ad1-9030-e82322e167b0.pdf"},{"id":105985321,"identity":"e5453c58-c086-4412-adbf-daaed68a1603","added_by":"auto","created_at":"2026-04-02 07:23:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":125711,"visible":true,"origin":"","legend":"","description":"","filename":"IEC.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9230995/v1/2b90789405f3d0b197882ae5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence and Predictors of Type 2 Diabetes Mellitus in a Rural Community of Haryana, India","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiabetes mellitus is a group of metabolic disorders characterized by chronic hyperglycaemia accompanied by abnormalities in the metabolism of carbohydrates, fats and proteins caused by impaired insulin secretion, insulin function or a combination of both. Diabetes mellitus is a major global health challenge and one of the leading causes of morbidity, disability, and premature mortality especially in Low and Middle-Income Countries (LMICs). According to the International Diabetes Federation (IDF), around 537\u0026nbsp;million adults aged 20\u0026ndash;79 years worldwide were living with diabetes in 2021, and this number is projected to rise to 643\u0026nbsp;million by 2030 and 783\u0026nbsp;million by 2045.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIndia has become the global epicentre of diabetes with approximately 101\u0026nbsp;million people currently living with the disease and another 136\u0026nbsp;million having prediabetes.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e The Global Burden of Disease Study (2019) further highlights the impact of diabetes by identifying it as the seventh leading contributor to disability-adjusted life years (DALYs) in India.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Contributing factors include rapid urbanization, sedentary lifestyles, increased consumption of high-calorie processed foods, escalating obesity status and an inherent genetic predisposition among the Indian population.\u003c/p\u003e \u003cp\u003eAt the state level, Haryana shows a similarly alarming trend. As per the National Family Health Survey-5 (2019\u0026ndash;21), 12.1% of women and 15.7% of men aged 15 years and above in Haryana had elevated blood sugar levels or were on medication for diabetes.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Though urban areas report a higher prevalence, the rural population is increasingly affected due to shifting dietary patterns, reduced physical activity and rising overweight/obesity levels.\u003c/p\u003e \u003cp\u003eThe risk factors for diabetes are well-documented and encompasses modifiable factors such as obesity, physical inactivity, poor dietary practices, tobacco use, alcohol consumption and comorbidities like hypertension and dyslipidaemia. Non-modifiable risk factors include advancing age, family history and a genetic predisposition which significantly contribute to the disease susceptibility.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Furthermore, socio-economic determinants like poor income, lower education status and limited access to healthcare services affect both the incidence and outcomes of diabetes. In addition, stress has been established to be an etiological link as well as an outcome in diabetes mellitus owing to the prolonged need for medication adherence and sustained lifestyle changes.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDespite ongoing nationwide initiatives such as the National Programme for Prevention and Control of Non-Communicable Diseases (NP-NCD) and the expansion of Ayushman Bharat Health and Wellness Centres, gaps persist in early detection, regular monitoring and effective diabetes management particularly in semi-urban and rural areas. Therefore, the present study was undertaken to assess the prevalence of diabetes and examine its association with various sociodemographic, behavioural and biological determinants in a rural area of district Gurugram, Haryana.\u003c/p\u003e"},{"header":"Materials \u0026 Methods","content":"\u003cp\u003eThis was a community based cross-sectional study which was conducted in the year 2025 over a period of three months in Mankrola village, the rural field practice area under the Department of Community Medicine, Faculty of Medicine and Health Sciences (FMHS), SGT University, Gurugram, Haryana. The study population included all adults aged 30\u0026ndash;70 years who were permanent residents of the study area. All existing cases of diabetes identified during the survey were also recruited. Those found to be pregnant, migrants (residing in the area for less than six months), severely ill or bedridden patients were excluded from the study.\u003c/p\u003e \u003cp\u003eSample size was calculated using the standard formula for single proportion (n\u0026thinsp;=\u0026thinsp;Z\u0026sup2;pq/d\u0026sup2;). A prevalence (p) of 15.5%\u003csup\u003e7\u003c/sup\u003e for Diabetes was used with an absolute precision of 4.1% (d) at 95% confidence level. The minimum required sample size was 299 which was rounded off to 300 participants.\u003c/p\u003e \u003cp\u003eThe Mankrola village comprised of approximately 600 households. To obtain the required 300 participants, systematic random sampling was applied. The sampling interval (k) was calculated as 600/300\u0026thinsp;=\u0026thinsp;2. A random starting point (either the 1st or 2nd household) was chosen by lottery and thereafter every 2nd household was visited till desired sample size was reached. From each selected household, one eligible adult aged 30\u0026ndash;70 years was chosen. In households with more than one eligible adult, a simple random method was used to select one participant. In cases where no eligible individual was available or the household was locked at the time of the visit, the next house in sequence was surveyed.\u003c/p\u003e \u003cp\u003eData were collected using a pre-designed, pre-tested, structured interview schedule adapted from the WHO STEPwise approach to noncommunicable disease risk factor surveillance\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The questionnaire consisted of four main sections: socio-demographic profile and behavioral characteristics, physical measurements, random blood glucose estimation and assessment of perceived stress. Socio-demographic details such as age, gender, education, occupation, marital status, and household income were recorded. Behavioural risk factors included tobacco and alcohol use, physical activity and dietary patterns, including salt and oil intake and daily consumption of fruits and vegetables which were assessed using the 24-hour recall method.\u003c/p\u003e \u003cp\u003ePhysical and clinical assessments included anthropometric measurements such as height (measured using a stadiometer), weight (using a digital scale), waist and hip circumferences (using a measuring tape) and recording of blood pressure (using a digital BP instrument). Biochemical testing involved the estimation of random blood glucose using a standard digital glucometer in which a finger prick was made using a sterile lancet on the left ring finger to obtain the sample. Perceived stress was assessed using the Perceived Stress Scale (PSS-10) which is a validated tool widely used to measure perceived stress levels over the past month. Individual scores on the PSS can range from 0 to 40 with higher scores indicating higher perceived stress.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eOperational definitions\u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e\u003c/h2\u003e \u003cp\u003eDiabetes: Defined as either: A random plasma glucose level\u0026thinsp;\u0026ge;\u0026thinsp;200 mg/dL in the presence of classic symptoms of hyperglycaemia (polyuria, polydipsia, unexplained weight loss) or a documented prescription/diagnosis of diabetes mellitus by a registered medical practitioner.\u003c/p\u003e \u003cp\u003eBody mass index (BMI): calculated using the formula weight in kilograms divided by height in meters squared (kg/m\u0026sup2;). BMI was classified using WHO cut-offs: 25-29.9 kg/m\u0026sup2; for overweight and \u0026ge;\u0026thinsp;30 kg/m\u0026sup2; for obesity.\u003c/p\u003e \u003cp\u003eWaist-hip ratio (WHR): calculated and categorized based on WHO cut-offs- \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e0.9 for men and \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;0.85 for women.\u003c/p\u003e \u003cp\u003eStress: PSS-10 scores were calculated and categorized as low (0\u0026ndash;13), moderate (14\u0026ndash;26) and high (27\u0026ndash;40) levels of stress.\u003c/p\u003e \u003cp\u003eAddictions- Alcohol Usage: Current alcohol use was defined as those who had consumed alcohol in the last 1 year. Past: More than one year back; Never: Not used ever. Tobacco usage: Current tobacco use was defined as those who smoked and/or consumed smokeless tobacco in the past 30 days. Past: More than one year back; Never: Not used ever.\u003c/p\u003e \u003cp\u003eVegetables/fruits intake: Low consumption of fruits and vegetables defined at less than five servings per day. (One serving of vegetables was defined as either one cup of raw leafy vegetables or half a cup of cooked vegetables. Similarly, one serving of fruit was considered equivalent to one medium-sized whole fruit or half a cup of chopped fruit)\u003c/p\u003e \u003cp\u003ePhysical Activity: Low physical activity was defined as \u0026lt;\u0026thinsp;150 minutes of moderate physical activity per week.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStatistical Analysis\u003c/strong\u003e \u003cp\u003eData was entered and analysed using the Statistical Package for Social Sciences (SPSS) version 26. Descriptive statistics such as means, standard deviations and percentages were used to summarize the data. To assess the association between independent variables and the dependent variable, crude odds ratios with 95% confidence intervals (CI) were calculated. Multivariate logistic regression was performed using covariates as independent variables and Diabetes as the outcome to identify the significant predictors of diabetes. A p-value of less than 0.05 was considered statistically significant. Model fit was evaluated using the Hosmer\u0026ndash;Lemeshow goodness-of-fit test.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe study was approved by the Institutional Ethics Committee. Prior to participation, each subject was explicitly explained about the purpose of the study by the investigator and written informed consent was obtained from all participants.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results:","content":"\u003cp\u003e \u003cstrong\u003eSocio-demographic characteristics\u003c/strong\u003e \u003cp\u003eOut of total 300 participants, majority 78(26%) belonged to the 30\u0026ndash;40 years age group followed by 71(23.67%) in the age group of 40\u0026ndash;50 years. More than half 183 (61%) were females while the remaining 117 (39%) were males. The mean age of the study population was 50.31\u0026thinsp;\u0026plusmn;\u0026thinsp;12.76 years, with males having a mean age of 50.15\u0026thinsp;\u0026plusmn;\u0026thinsp;12.88 years and females 49.92\u0026thinsp;\u0026plusmn;\u0026thinsp;12.80 years. Around three-fourths i.e. 225(75%) were currently married while 36 (12%) were found to be unmarried. Most of the participants 286 (95.33%) were Hindu by religion. 80 (27%) were educated upto senior secondary level and above whereas 69 (23%) participants were found to be illiterate. Half of the participants 150 (50%) lived in a joint family while 118 (39.33%) belonged to nuclear family. 137 (45.67%) of participants were home makers while a quarter 76(25.33%) were unemployed. According to Modified BG Prasad socio-economic status scale, most of the participants 136(45.33%) belonged to upper class followed by 117(39%) in upper middle class.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe overall prevalence of Diabetes was found to be 38(12.67%) while 52(17.33%) of participants were pre-diabetics. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the age-wise distribution of glycaemic status stratified by sex. Overall, among males (n\u0026thinsp;=\u0026thinsp;117), 12 (10.3%) were diabetic, 6 (5.1%) were pre-diabetic and 99 (84.6%) were normoglycemic. Among females (n\u0026thinsp;=\u0026thinsp;183), 26 (14.2%) were diabetic, 46 (25.1%) were pre-diabetic, and 111 (60.7%) were normoglycemic. Majority of younger participants (30\u0026ndash;40 years) were normoglycemic, comprising of 40 males (93.0%) and 26 females (66.7%). Prediabetes was more frequently observed among middle-aged females with 15 (28.3%) in the 40 to 50 years age group and 24 (49.0%) in the 50\u0026ndash;60 year group. The prevalence of diabetes increased progressively with age in both sexes, with the highest proportion among males aged\u0026thinsp;\u0026ge;\u0026thinsp;70 years (6; 54.5%) and females aged 60\u0026ndash;70 years (9; 20.5%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e demonstrates the clinical and anthropometric characteristics of diabetic participants by gender. The overall mean random blood glucose level was 140\u0026thinsp;\u0026plusmn;\u0026thinsp;47 mg/dl, with males recording 137\u0026thinsp;\u0026plusmn;\u0026thinsp;45 mg/dl and females 141\u0026thinsp;\u0026plusmn;\u0026thinsp;47 mg/dl. The mean systolic blood pressure was 124\u0026thinsp;\u0026plusmn;\u0026thinsp;9 mmHg (males: 125\u0026thinsp;\u0026plusmn;\u0026thinsp;9 mmHg; females: 124\u0026thinsp;\u0026plusmn;\u0026thinsp;8 mmHg), while the mean diastolic blood pressure was 81\u0026thinsp;\u0026plusmn;\u0026thinsp;6 mmHg (males: 81\u0026thinsp;\u0026plusmn;\u0026thinsp;6 mmHg; females: 81\u0026thinsp;\u0026plusmn;\u0026thinsp;5 mmHg). Stress scores were almost similar across genders (males: 18.51\u0026thinsp;\u0026plusmn;\u0026thinsp;3.98; females: 18.63\u0026thinsp;\u0026plusmn;\u0026thinsp;3.88; overall: 18.57\u0026thinsp;\u0026plusmn;\u0026thinsp;3.93). The average body weight was 67.50\u0026thinsp;\u0026plusmn;\u0026thinsp;10.97 kg (males: 68.17\u0026thinsp;\u0026plusmn;\u0026thinsp;10.94 kg; females: 67.45\u0026thinsp;\u0026plusmn;\u0026thinsp;11.02 kg), and the mean height was 1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11 m. The mean body mass index (BMI) was 25.76\u0026thinsp;\u0026plusmn;\u0026thinsp;4.21 kg/m\u0026sup2; overall, with males and females showing values of 25.84\u0026thinsp;\u0026plusmn;\u0026thinsp;4.28 kg/m\u0026sup2; and 25.88\u0026thinsp;\u0026plusmn;\u0026thinsp;4.31 kg/m\u0026sup2; respectively. The mean waist\u0026ndash;hip ratio was 0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 in females and 0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 in males.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGender-wise comparison of means of clinical and anthropometric characteristics among diabetic participants (N\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale (N\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale (N\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal (N\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom Blood glucose (mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e137\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e141\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e140\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP*(mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e125\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e124\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e124\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP* (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e81\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e81\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e81\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStress Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e18.51\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;3.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e18.63\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;3.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e18.57\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;3.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e68.17\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;10.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e67.45\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;11.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e67.50\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;10.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (metres)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.63\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.62\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.62\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e25.84\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e25.88\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e25.76\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;4.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist hip ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.92\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.91\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.91\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003e*SBP - Systolic Blood Pressure\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003e*DBP - Diastolic Blood Pressure\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the multivariate logistic regression analysis of the socio-demographic determinants associated with Diabetes. Compared to participants aged\u0026thinsp;\u0026ge;\u0026thinsp;70 years (reference category), those aged 30\u0026ndash;40 years (AOR\u0026thinsp;=\u0026thinsp;0.008; 95% CI: 0.001\u0026ndash;0.061), 40\u0026ndash;50 years (AOR\u0026thinsp;=\u0026thinsp;0.024; 95% CI: 0.004\u0026ndash;0.156), and 50\u0026ndash;60 years (AOR\u0026thinsp;=\u0026thinsp;0.030; 95% CI: 0.005\u0026ndash;0.193) had significantly lower odds of diabetes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The 60\u0026ndash;70 years group did not show a statistically significant association (p\u0026thinsp;=\u0026thinsp;0.073).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate logistic regression analysis of the socio-demographic determinants associated with Diabetes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eStatus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiabetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-Diabetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eAge groups (in yrs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(7.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75(28.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(13.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66(25.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(15.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59(22.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u0026ndash;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(36.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45(17.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;=70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(26.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17(6.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(31.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105(40.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.874\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(68.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e157(59.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(13.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31(11.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeparated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(2.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15(5.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.847\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWidowed (Widower)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(10.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(7.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(73.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e197(75.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eReligion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSikh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(1.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChristian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(2.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(0.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e130.514\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(5.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(2.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35.283\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(92.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e251(95.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(23.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60(22.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.614\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJust literate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(13.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29(11.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(5.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22(8.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(7.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33(12.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.677\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(28.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46(17.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.601\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSenior Secondary and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(21.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72(27.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eType of Family\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNuclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(28.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107(40.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.630\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJoint\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(63.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126(48.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19.685\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThree Generation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(7.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29(11.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(18.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80(30.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.876\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(34.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63(24.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(47.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e119(45.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eSocio-economic status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(0.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower middle class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(2.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(5.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37(14.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.344\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003cp\u003emiddle class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(52.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97(37.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.036\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.415\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUpper class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(42.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120(45.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003e*- Reference category\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u0026bull; \u003cb\u003eOmnibus Chi-square\u0026thinsp;=\u0026thinsp;62.019, df\u0026thinsp;=\u0026thinsp;24, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u0026bull; \u003cb\u003eNagelkerke R\u0026sup2; = 0.351 (35.1% of the variance explained)\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u0026bull; \u003cb\u003eHosmer-Lemeshow test: p\u0026thinsp;=\u0026thinsp;0.458 \u0026rarr; model fits well\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u0026bull; \u003cb\u003eClassification accuracy\u0026thinsp;=\u0026thinsp;89.3%\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eParticipants aged 30\u0026ndash;40 years had 99.2% lower odds of diabetes (AOR\u0026thinsp;=\u0026thinsp;0.008, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and those aged 50\u0026ndash;60 years had 97.0% lower odds (AOR\u0026thinsp;=\u0026thinsp;0.030, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), compared to participants aged\u0026thinsp;\u0026ge;\u0026thinsp;70 years. Illiterate participants had 87.7% lower odds of diabetes (AOR\u0026thinsp;=\u0026thinsp;0.123, p\u0026thinsp;=\u0026thinsp;0.011) and those with primary education had 85.6% lower odds (AOR\u0026thinsp;=\u0026thinsp;0.144, p\u0026thinsp;=\u0026thinsp;0.059) relative to individuals with senior secondary education or above. Socio-economic status also showed a significant association, with participants from the upper middle class having three times higher odds of diabetes (AOR\u0026thinsp;=\u0026thinsp;3.009, p\u0026thinsp;=\u0026thinsp;0.036) compared to those from the upper class. Participants living in joint families had nearly four times higher odds of diabetes (AOR\u0026thinsp;=\u0026thinsp;4.093, p\u0026thinsp;=\u0026thinsp;0.079), although this association was not statistically significant. No statistically significant associations were observed for gender, marital status, religion, or occupation after adjustment.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the prevalence of diabetes mellitus (DM) across behavioural and biological characteristics from multivariate analysis. Lower vegetable intake (\u0026lt;\u0026thinsp;400 g/day) was associated with increased risk, with higher intake (\u0026ge;\u0026thinsp;400 g/day) showing a protective effect (AOR\u0026thinsp;=\u0026thinsp;0.240, p\u0026thinsp;=\u0026thinsp;0.005). Participants reporting higher daily oil and fat consumption (\u0026ge;\u0026thinsp;25 g/day) were at markedly greater risk (AOR\u0026thinsp;=\u0026thinsp;7.848, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, participants consuming\u0026thinsp;\u0026ge;\u0026thinsp;5 g salt/day had significantly lower odds of diabetes (AOR\u0026thinsp;=\u0026thinsp;0.207, p\u0026thinsp;=\u0026thinsp;0.011) compared to those consuming\u0026thinsp;\u0026lt;\u0026thinsp;5 g/day. Alcohol consumers had significantly lower odds of diabetes compared to non-consumers (AOR\u0026thinsp;=\u0026thinsp;0.203, p\u0026thinsp;=\u0026thinsp;0.037). Furthermore, physical inactivity (\u0026lt;\u0026thinsp;150 minutes/week) remained a strong predictor (AOR\u0026thinsp;=\u0026thinsp;5.502, p\u0026thinsp;=\u0026thinsp;0.003). Overweight individuals (BMI 25\u0026ndash;29.9 kg/m\u0026sup2;) had higher odds of diabetes (AOR\u0026thinsp;=\u0026thinsp;4.345, p\u0026thinsp;=\u0026thinsp;0.044). Other variables, including smoking, tobacco use, perceived stress, blood pressure, waist\u0026ndash;hip ratio did not show statistically significant associations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate logistic regression analysis of the behavioural, dietary and biological factors associated with diabetes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eStatus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiabetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-Diabetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVegetable Intake (g/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;400g/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25(65.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e221(84.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;=400g/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(34.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41(15.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFruit intake (g/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;100g/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(4.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;=100g/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e251(95.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDaily oil \u0026amp; fat intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;=25g/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(68.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105(40.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.685\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;25g/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(31.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e157(59.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSalt Consumption per day(in gm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;=5gm/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32(84.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e225(85.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.698\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5gm/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(15.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37(14.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePerceived stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34(89.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e232(88.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.496\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(10.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30(11.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAlcohol Consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(7.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52(19.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.037\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.909\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(92.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e210(80.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmokers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(31.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83(31.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.731\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon Smokers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(68.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e179(68.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTobacco Consumption (Khaini, Gutka)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConsumers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(7.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39(14.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.874\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon consumers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(92.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e223(85.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBlood Pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaised\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37(97.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e235(89.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(2.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27(10.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePhysical Activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;150min/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32(84.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e151(57.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16.665\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;=150min/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(15.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111(42.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWaist Hip Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.9/female\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(78.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e193(73.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.592\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u0026thinsp;\u0026lt;\u0026thinsp;0.9/female\u0026thinsp;\u0026lt;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(21.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69(26.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(1.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.5\u0026ndash;24.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(44.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e128(48.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.390\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25-29.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(47.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89(33.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.044\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;=30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(7.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40(15.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003e*- Reference category\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u0026bull; \u003cb\u003eOmnibus Chi-square\u0026thinsp;=\u0026thinsp;52.552, df\u0026thinsp;=\u0026thinsp;14, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u0026bull; \u003cb\u003eNagelkerke R\u0026sup2; = 0.302 (30.2% of the variance explained)\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u0026bull; \u003cb\u003eHosmer-Lemeshow test: p\u0026thinsp;=\u0026thinsp;0.730 \u0026rarr; model fits well\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u0026bull; \u003cb\u003eClassification accuracy\u0026thinsp;=\u0026thinsp;89.7%\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAccording to the present study, the overall prevalence of diabetes mellitus was observed to be high (13%). This was higher than previous studies in Haryana by Jangra A et al. where the prevalence was 9.2% and the study in Uttar Pradesh by Singh PS et al. (8.03%).\u003csup\u003e15,16\u003c/sup\u003e Another study from South India by Nithesh KK et al. showed that 8.1% of adults were diagnosed as Type 2 Diabetics.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e However, the recent state wide STEPS Survey conducted in two northern states of India reported an overall prevalence of 14.3% and 15.1% in Punjab and Haryana, respectively which is slightly higher as compared to the current study.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003eThese differences could be attributed to variations in lifestyle, sociodemographic background, genetic predisposition and use of varying diagnostic methods or criteria for diabetes in different studies.\u003c/p\u003e \u003cp\u003eThe prevalence of pre-diabetes in the present study was approximately 17%, which is notably higher than that reported in previous studies where 10.04% and 14.3% of participants were classified as pre-diabetic respectively.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e A large ICMR-INDIAB multi-state survey covering 31 states in India also documented a comparable prevalence of 15.3%.\u003csup\u003e5\u003c/sup\u003e These findings highlight that pre-diabetes remains high across settings and warrants immediate public health attention, given its well-documented progression to diabetes.\u003c/p\u003e \u003cp\u003ePrevalence was higher in female population (14.2%) as compared to males (10.3%) which is congruent to other studies conducted in different regions of India.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e This difference may be explained by higher rates of overweight and physical inactivity among women as well as possible lifestyle influences and hormonal transitions, particularly after menopause. These results underline the need for gender-focused screening and preventive interventions.\u003c/p\u003e \u003cp\u003eAs anticipated, our study demonstrates a positive association between diabetes mellitus and advancing age. The highest proportion of diabetes was observed among males aged\u0026thinsp;\u0026ge;\u0026thinsp;70 years (54.5%) and females aged 60\u0026ndash;70 years (20.5%). Similar trend was reported by other studies where DM increased with age and the association was found to be statistically significant.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e This may be explained by the age-related β-cell decline, reduced insulin sensitivity and cumulative impact of lifestyle influences, underscoring the need for targeted screening and structured health education in older adults.\u003c/p\u003e \u003cp\u003eMultivariate logistic regression analysis identified multiple sociodemographic, lifestyle and clinical predictors of diabetes mellitus. Advancing age emerged as the strongest risk factor. Younger age groups exhibited substantially lower odds which in accordance with various international and national studies. In China, more than half of diabetes cases occurred in adults\u0026thinsp;\u0026ge;\u0026thinsp;60 years while in Nepal those aged 60 and above had nearly fivefold higher odds (AOR 4.7).\u003csup\u003e22,23\u003c/sup\u003e Similar findings were documented by a country wide National NCD Monitoring Survey in India (NNMS, 2022) where adults aged 50\u0026ndash;69 years had nearly ninefold higher odds (AOR 8.89, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) of having diabetes.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eRegarding educational status, illiterates and those with only primary education showed lower odds of having Diabetes than the more educated groups which is in contrast to other studies in China and India where higher diabetes prevalence was reported in less literate groups.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e This may reflect the importance of traditional diets and higher physical activity among less educated participants, thus buffering against the diabetes risk. Interestingly, socio-economic status emerged as a risk modifier with individuals from upper middle-class families showing approximately threefold higher odds of diabetes (AOR\u0026thinsp;=\u0026thinsp;3.009, p\u0026thinsp;=\u0026thinsp;0.036). This is consistent with global evidence from China, Nepal and Gambia where urban and affluent participants carried a greater risk and with surveys in India which also demonstrated a higher diabetes prevalence in richer quintiles. These findings could be due to the westernisation of lifestyles, sedentary occupations and dietary excesses in the higher SES groups.\u003csup\u003e\u003cspan additionalcitationids=\"CR23 CR24 CR25\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAlthough dietary patterns are known to play a significant role in diabetes risk, only a limited number of studies have examined this aspect in depth. In the present study, a high intake of oil or fat (\u0026ge;\u0026thinsp;25 g/day) was associated with nearly an eightfold higher risk (AOR 7.85), whereas adequate vegetable consumption showed a protective effect. However, a cross-sectional survey from India reported contrasting findings, where the association between higher level of diabetes and daily consumption of green vegetables and fruits was noted.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Similarly, in our study, alcohol and salt intake appeared to have inverse associations with diabetes. Given the well-established adverse metabolic effects of excessive alcohol and sodium consumption, these observed associations may reflect reverse causality, residual confounding or measurement error inherent to self-reported dietary data within a cross-sectional design.\u003c/p\u003e \u003cp\u003eThe current study highlights that perceived stress (PSS-10 score\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;14) was higher among individuals with diabetes. This is consistent with the findings of Siddharthan et al. who reported perceived stress in 39.3% of diabetic inpatients in South India. Similarly, Saha et al. observed that nearly 39.1% had high perceived stress (mean PSS score\u0026thinsp;=\u0026thinsp;28\u0026thinsp;\u0026plusmn;\u0026thinsp;3.47) and stress levels showed a positive though nonsignificant correlation which is same as our study.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e These findings reveal that psychological stress is common among Indian diabetics which often worsens metabolic control, thus reinforcing the need to integrate stress management into diabetes care programs.\u003c/p\u003e \u003cp\u003eFurthermore, in our study, physical inactivity was strongly associated with diabetes (AOR 5.5, p\u0026thinsp;=\u0026thinsp;0.003). This aligns with findings from the NNMS survey which also reported a slightly higher risk among adults engaging in insufficient physical activity (AOR 1.08) though the association was not statistically significant.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Overweight significantly predicted diabetes (AOR 4.35) as excess adiposity promotes release of inflammatory cytokines and reduces adiponectin together causing insulin resistance. This is consistent with global evidence that obesity remains a key determinant of diabetes across diverse populations.\u003csup\u003e\u003cspan additionalcitationids=\"CR23 CR24 CR25\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e"},{"header":"Strengths \u0026 Limitations","content":"\u003cp\u003eFirstly, a major strength of this study was its community-based design and usage of systematic random sampling which ensured an adequate representation of the rural population. Secondly, the inclusion of sociodemographic and behavioural variables along with clinical and anthropometric assessments allowed for a comprehensive evaluation of the determinants of diabetes. Thirdly, utilisation of standardized tools such as the Perceived Stress Scale (PSS-10) and the clearly defined operational definitions further enhanced the reliability \u0026amp; validity of the measurements.\u003c/p\u003e \u003cp\u003eHowever, the study had few limitations too. Firstly, the causal relationships between risk factors and diabetes cannot be established due to cross-sectional design of the study. Secondly, blood glucose estimation was based on a single random measurement using a glucometer which may have underestimated or overestimated the true prevalence compared to the laboratory based fasting glucose or HbA1c testing. Thirdly, the dietary intake and behavioural risk factors were assessed through self-reported data making them susceptible to recall and reporting bias. Finally, the study was confined to a single rural village in Haryana which may limit the generalizability of findings to other regions with differing socio- demographic or lifestyle contexts.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study reveals a high burden of diabetes and pre-diabetes in rural Haryana, driven mainly by modifiable risk factors such as overweight, physical inactivity and unhealthy diets. Despite being perceived as an urban disease, rural populations are equally vulnerable due to lifestyle transitions. Hence, urgent, context-specific prevention strategies are needed focusing on community health education, sustained behaviour change, healthier diets, physical activity and gender-sensitive approaches.\u003c/p\u003e \u003cp\u003eSystematic screening of high-risk groups and integration of stress management into primary care can enhance control efforts. Strengthening NP-NCD implementation and optimizing Health and Wellness Centres for early detection, lifestyle modification and continuity of care are critical to curb progression and reduce long-term health system burden.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u003c/strong\u003e We sincerely express our gratitude to all the participants who generously gave their time and shared their information without which this study would not have been possible. We are also grateful to the Medical Social Workers for their diligent assistance in the assessment and data collection processes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest statement:\u003c/strong\u003e None declared\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial support and sponsorship:\u003c/strong\u003e This research was supported by an intramural research grant from SGT University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContribution contributions:\u003c/strong\u003e GS: Conceptualization, Methodology, Resources, Data Acquisition, Writing—Original Draft Preparation, Review \u0026amp; Editing; NG: Methodology, Resources, Writing—Original Draft Preparation \u0026amp; Editing; NS: Methodology, Data \u0026amp; Statistical analysis; SV: Conceptualization, Supervision, Manuscript Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u003c/strong\u003e The data that support the findings of this study are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval: The study was approved by the Institutional Ethics Committee, Faculty of Medicine \u0026amp; Health Sciences, SGT University, Gurugram, Haryana via letter no IEC/FMHS/F/2023-08 dated 08th November, 2024\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u003c/strong\u003e Prior to the start of the study, written informed consent was obtained for participation in the study and patient’s data was used only for research and educational purposes. The study procedures adhered to the guidelines of the Declaration of Helsinki 1964 and its subsequent revisions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement on approval from all contributors:\u003c/strong\u003e The manuscript has been read and approved by all the authors, the requirements for authorship as stated earlier in this document have been met and each author believes that the manuscript represents honest work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eThe authors affirm that human research participants provided informed consent for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e Not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eInternational Diabetes Federation. 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Assessment of level of stress and glycemic control among type 2 diabetes mellitus patients: An observational study. Int J Sci Stud. 2023;11(2):72\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.17354/ijss/2023/117\u003c/span\u003e\u003cspan address=\"10.17354/ijss/2023/117\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Diabetes, DM, pre-diabetes, prevalence, risk factors, predictors, rural, community","lastPublishedDoi":"10.21203/rs.3.rs-9230995/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9230995/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Diabetes mellitus is a pressing global health concern contributing substantially to morbidity and premature mortality. In India, this burden is steadily rising in rural communities which are undergoing rapid epidemiological transition. \u003cstrong\u003eAims \u0026amp; Objectives:\u003c/strong\u003e To determine the prevalence of diabetes and to identify the associated socio-demographic, behavioural and biological risk factors in a rural community of Haryana.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology: \u003c/strong\u003eA community-based cross-sectional study was conducted among 300 adults aged 30-70 years residing in Mankrola village, Gurugram. Participants were selected through systematic random sampling. Data was collected using a pre-tested structured questionnaire comprising of socio-demographic profile, anthropometric and clinical assessments, random blood glucose estimation and Perceived Stress Scale (PSS-10). Multivariate logistic regression was performed to identify the independent predictors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The prevalence of diabetes and pre-diabetes was found to be 12.7% and 17.3% respectively. Rates were higher among females (14.2%) than males (10.3%) and increased progressively with age. Significant predictors included upper middle socioeconomic status (AOR = 3.009, p = 0.036), overweight (AOR 4.35; p=0.044), physical inactivity (AOR 5.50; p=0.003), high oil/fat intake (AOR 7.85; p\u0026lt;0.001) and inadequate vegetable consumption (AOR 4.17; p=0.005). Mean perceived stress scores were high (18.57\u003cu\u003e+\u003c/u\u003e3.93) among diabetics although not statistically significant (AOR 1.16; p\u0026gt; 0.05) \u003cstrong\u003eConclusion: \u003c/strong\u003eDiabetes and pre-diabetes are considerably prevalent in rural Haryana driven largely by modifiable risk factors. Strengthening community-based prevention, early detection and lifestyle modifications are imperative to mitigate the rural diabetes epidemic.\u003c/p\u003e","manuscriptTitle":"Prevalence and Predictors of Type 2 Diabetes Mellitus in a Rural Community of Haryana, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-02 07:22:58","doi":"10.21203/rs.3.rs-9230995/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-15T19:10:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-09T18:05:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T15:06:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"27403716071263000386964096047644601894","date":"2026-05-05T13:00:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"175201970737439251498501301113712498889","date":"2026-05-02T03:38:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278776596777355732772865079156837442640","date":"2026-04-30T17:44:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87370985671488513980140572303710607946","date":"2026-04-27T18:32:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-27T12:43:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-09T05:57:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-31T09:50:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-31T09:49:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2026-03-26T07:37:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"744e7761-aaf6-425c-982d-3491bc02447a","owner":[],"postedDate":"April 2nd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-15T19:10:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-09T18:05:36+00:00","index":61,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T15:06:10+00:00","index":60,"fulltext":""},{"type":"reviewerAgreed","content":"27403716071263000386964096047644601894","date":"2026-05-05T13:00:42+00:00","index":58,"fulltext":""},{"type":"reviewerAgreed","content":"175201970737439251498501301113712498889","date":"2026-05-02T03:38:05+00:00","index":56,"fulltext":""},{"type":"reviewerAgreed","content":"278776596777355732772865079156837442640","date":"2026-04-30T17:44:16+00:00","index":55,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T19:23:36+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-02 07:22:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9230995","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9230995","identity":"rs-9230995","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}