Diabetes Prevalence and Associated Risk Factors among Adults in Rural Herat Province, Afghanistan: A Community-Based Cross-Sectional Study

Diabetes Prevalence and Associated Risk Factors among Adults in Rural Herat Province, Afghanistan: A Community-Based Cross-Sectional Study | 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 Diabetes Prevalence and Associated Risk Factors among Adults in Rural Herat Province, Afghanistan: A Community-Based Cross-Sectional Study Abdul Saboor Karim Suri, Khwaja Mir Islam Saeed, Said Naim Allemy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7948496/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Mar, 2026 Read the published version in BMC Public Health → Version 1 posted 12 You are reading this latest preprint version Abstract Background Diabetes mellitus is a major public health concern in low- and middle-income countries, particularly in fragile health systems such as Afghanistan. Limited community-based evidence exists on the burden and correlates of diabetes among rural populations. This study aimed to estimate the prevalence of diabetes and identify its associated risk factors among adults living in rural areas of Herat province, Afghanistan, using the WHO Stepwise approach to noncommunicable disease (NCD) risk factor surveillance. Methods A community-based cross-sectional survey was conducted from January to December 2024 among adults aged ≥ 18 years residing in rural areas of Herat province. A multistage cluster sampling technique was applied following the WHO STEPS methodology. Data were collected on sociodemographic characteristics, behavioral and metabolic risk factors using standardized questionnaires, physical measurements, and fasting blood samples. Diabetes was defined as fasting plasma glucose ≥ 126 mg/dL or self-reported use of antidiabetic medication. Both bivariate and multivariable logistic regression analyses were performed to determine factors associated with diabetes, with adjusted odds ratios (AOR) and 95% confidence intervals (CI) reported. Results A total of 609 participants were included in the analysis (mean age: 40.6 ± 15.8 years; 51.9% were female). The overall prevalence of diabetes was 7.6% (95% CI, 5.6–9.9). In bivariate analyses, several factors—namely older age, central obesity, and hypertension—were significantly associated with diabetes. After adjusting for potential confounders in the multivariable logistic regression model, only hypertension remained independently associated with diabetes (AOR = 2.52; 95% CI: 1.37–4.63). Conclusions The prevalence of diabetes among adults in rural Herat province is considerable, highlighting an urgent need for community-level screening and preventive interventions targeting modifiable risk factors, particularly hypertension. Strengthening primary health care and integrating NCD surveillance in rural Afghanistan are essential to reduce the growing burden of diabetes. Diabetes mellitus Hypertension Risk factors Rural population non-communicable diseases Afghanistan WHO STEPS Introduction Diabetes mellitus (DM) represents one of the fastest-growing noncommunicable diseases (NCDs) globally, posing a substantial threat to health systems, economies, and human development. According to the latest estimates from the International Diabetes Federation (IDF), approximately 537 million adults were living with diabetes in 2021, and this number is projected to rise to 783 million by 2045, with the largest relative increases expected in low- and middle-income countries (LMICs) [ 1 , 2 ]. The condition contributes significantly to premature mortality, reduced quality of life, and increased healthcare expenditures worldwide [ 3 ]. In South Asia, the prevalence of diabetes has reached alarming levels, driven by rapid urbanization, dietary changes, obesity, and physical inactivity [ 4 – 7 ]. Despite these global and regional concerns, Afghanistan remains among the least studied countries regarding the epidemiology of diabetes and related risk factors. The country faces multiple public health challenges, including limited resources, ongoing humanitarian crises, and fragile health infrastructure [ 8 ]. National surveillance systems for NCDs are still in the early stages of implementation, and population-based data, especially from rural areas, are scarce. Most available reports are limited to facility-based or small-scale studies conducted in urban settings, which do not reflect the true burden of diabetes across the broader Afghan population [ 9 – 11 ]. This lack of comprehensive data hampers the ability of policymakers to design and implement effective NCD prevention and control strategies. The WHO STEPwise approach (STEPS) provides a standardized framework for monitoring key NCD risk factors, including diabetes, through community-based surveys [ 12 ]. This methodology enables countries with limited health information systems, such as Afghanistan, to generate nationally and regionally representative data for evidence-based policy-making [ 13 ]. Implementing the STEPS approach in rural Afghan communities is particularly critical because more than 70% of the population resides in rural areas, where access to health services and awareness of NCD prevention remain very limited [ 14 , 15 ]. Moreover, rural residents may have distinct behavioral and metabolic risk profiles compared to urban populations due to differences in lifestyle, nutrition, and socioeconomic status [ 16 ]. Previous studies from neighboring countries, including Pakistan and Iran, have reported a rising prevalence of diabetes and its risk factors even among rural populations [ 17 , 18 ]. However, similar large-scale community-based evidence from Afghanistan is almost non-existent. Understanding the prevalence and determinants of diabetes in these underserved rural communities is essential for developing cost-effective screening programs, allocating health resources, and integrating diabetes care into primary healthcare services, as recommended in the WHO Package of Essential NCD Interventions (PEN) for primary healthcare in low-resource settings [ 40 ] Therefore, this study was conducted to estimate the prevalence of diabetes and to identify its associated risk factors among adults aged 18 years and above living in rural areas of Herat province, Afghanistan, using the WHO STEPwise approach to NCD risk factor surveillance. By providing reliable community-based data, the study aims to fill a critical gap in national evidence and support policymakers and health authorities in designing targeted interventions to reduce the growing burden of diabetes in Afghanistan’s rural settings. Methods Study design and setting A community-based cross-sectional survey using the World Health Organization (WHO) STEPwise approach to noncommunicable disease (NCD) risk factor surveillance was conducted between January and December 2024 in rural areas of Herat province, western Afghanistan. Herat is one of the largest provinces of the country, with a predominantly rural population that relies mainly on agriculture and small-scale trading activities. The WHO STEPS methodology was chosen to ensure comparability with national and international NCD surveillance studies [ 12 , 13 ]. Study population and eligibility criteria The target population consisted of adults aged 18 years and above who had been living in the selected rural communities of Herat province for at least six months. Individuals who were pregnant, critically ill, or unable to communicate during the interview were excluded. Sample size determination The required sample size was calculated using the Cochran formula[ 19 ], assuming a 95% confidence level, 5% margin of error, and a design effect of 1.5, based on the most recent demographic estimates from the National Statistics and Information Authority (NSIA) of Afghanistan. An additional 10% was added to account for potential non-response. Accordingly, 643 individuals were initially selected. Of these, 24 samples were lost due to refusal to provide blood samples or laboratory errors, resulting in a final analytical sample of 609 participants. Sampling procedure A multistage cluster sampling technique was applied in accordance with the WHO STEPS methodology [ 12 ]. Villages were first selected as primary sampling units using probability proportional to size, and households within each cluster were then selected systematically. One eligible adult from each household was randomly chosen for participation using the Kish method[ 20 , 21 ]. Data collection procedure Data collection followed the three standardized STEPS: Step 1 (Questionnaire): Socio-demographic and behavioral data, including age, sex, education, occupation, tobacco use, diet, and physical activity, were collected using the standardized WHO STEPS questionnaire, This translated version is the officially nationally validated and adapted Dari instrument utilized in previous Afghanistan WHO STEPS surveys[ 10 , 11 ]. which had been culturally adapted and translated into Dari for use in Afghanistan. Trained interviewers administered the questionnaire through face-to-face interviews after receiving three days of intensive training. Step 2 (Physical measurements): Anthropometric and blood pressure measurements were obtained using calibrated instruments. Height and weight were measured using a mechanical Frölix analog scale, and body mass index (BMI) was computed as weight (kg) divided by height squared (m²). Blood pressure was measured twice using a Yuwell aneroid sphygmomanometer (Two-Shelling model) after participants had rested for at least five minutes in a seated position. The average of two readings was recorded. Step 3 (Biochemical measurements): Fasting blood samples were collected following at least eight hours of fasting. Serum glucose levels were analyzed in a certified laboratory using a Photometer 5010 biochemical analyzer (Germany). Definition of variables Diabetes mellitus was defined as fasting plasma glucose (FPG) ≥ 126 mg/dL (7.0 mmol/L) or current use of antidiabetic medication, following WHO criteria [ 22 ]. Hypertension was defined as systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg or current use of antihypertensive medication [ 23 ]. Overweight and obesity were classified according to WHO BMI categories: overweight (25.0–29.9 kg/m²) and obesity (≥ 30 kg/m²) [ 38 ]. Central obesity was defined as a waist circumference ≥ 94 cm for men and ≥ 80 cm for women [ 39 ]. Low vegetable intake was defined as consuming vegetables fewer than five days per week, based on WHO dietary recommendations [ 26 ]. Data analysis Data were entered and analyzed using IBM SPSS Statistics version 26. Descriptive statistics (means, standard deviations, and proportions) were used to summarize participants’ characteristics. The prevalence of diabetes and 95% confidence intervals (CI) were calculated. Associations between diabetes and independent variables were examined using binary logistic regression. Variables with p < 0.20 in the bivariate analysis were included in the multivariable logistic regression model to identify independent predictors[ 24 ]. Adjusted odds ratios (AOR) and corresponding 95% CI were reported. Statistical significance was set at p < 0.05. Ethical considerations The study was approved by the Institutional Review Board of the Ministry of Public Health of Afghanistan (Approval Code: A.1023.429). Written informed consent was obtained from all participants before data collection. Confidentiality and anonymity were strictly maintained throughout the study. For participants identified as illiterate, the informed consent form was read verbatim in the local language (Dari) by a trained interviewer in the presence of a competent and impartial witness. Following the verbal affirmation of understanding by both the participant and the witness, the participant affixed their thumbprint to the consent form as documented proof of their voluntary participation. Results Descriptive Analysis: Out of the total study sample of 609 participants, 316 (51.88%) were females. The mean age of the participants was 40.60 years (SD: 15.79), while the median age was 38.00 years with an interquartile range (IQR) of 27.00–50.00 years. Approximately 175 participants (28.73%) were in the 25–35-year age group. In terms of ethnicity, 291 (47.78%) were Pashtuns and 284 (46.63%) were Tajiks. Overall, 579 participants (95.07%) were married, of whom 49 (8.04%) were widowed. The prevalence of raised blood sugar (≥ 126 mg/dl) was observed in 39 participants (6.40%), while an additional seven were already under treatment. Accordingly, the overall prevalence of diabetes mellitus was 46 (7.55%). Bivariable Analysis: The results of bivariate analysis of sociodemographic characteristics of participants are described in Table 1 . Table 1 Bivariate Analysis of demographic characteristics of the study participants of diabetes mellitus, Herat Afghanistan Variables Categories Non-diabetes Diabetes Total OR CI 95% LL CI 95% UL N % N % N % Age < 35 268 97.45 7 2.55 275 45.16 Ref 1 1 35–44 110 94.02 7 5.98 117 19.21 2.436 0.835 7.109 45–54 86 80.37 21 19.63 107 17.57 9.349 3.842 22.748 55+ 99 90.00 11 10.00 110 18.06 4.254 1.604 11.281 Gender Male 272 92.83 21 7.17 293 48.11 Ref Female 291 92.09 25 7.91 316 51.89 1.113 0.609 2.034 Level of Education Non-Educated 414 90.99 41 9.01 455 74.71 Ref 1 1 Educated 149 96.75 5 3.25 154 25.29 0.339 0.131 0.874 Job Official Work 32 100.00 0 0.00 32 5.25 0 0 0 Physical Work 189 91.30 18 8.70 207 33.99 2.095 0.944 4.649 Unemployed/Unable 105 86.07 17 13.93 122 20.03 3.562 1.577 8.047 Housewife 220 95.65 10 4.35 230 37.77 Ref 1 1 Monthly Income in AFN Less than 10,000 98 90.74 10 9.26 108 17.73 Ref 1 1 More than 10,000 144 88.34 19 11.66 163 26.77 0.613 0.240 1.562 Marital Status Never Married 28 96.55 1 3.45 29 4.76 Ref 1 1 Married 534 92.23 45 7.77 579 95.07 2.36 0.314 17.747 As seen, the age groups of 45–54 years and more than 55 years were significantly associated with diabetes with odds ratio of 9.35 and 95%CI of 3.84–22.75, and OR of 4.25 and 95% CI of 1.60-11.28 respectively. Furthermore, being educated or non-educated was also significantly associated with being diagnosed with diabetes with OR of 0.34 and 95%CI of 0.13–0.87. Similarly, those who were unemployed or unable to work had higher odds of being diagnosed with diabetes as compared to their counterparts with OR of 3.56 and 95% CI of 1.58–8.03. Table 2 Bivariate analysis of behavioral risk factors for diabetes among the study participants, Herat Afghanistan Variables Categories Nondiabetic Diabetic Total OR 95%CI-LL 95%CI-UL N % N % N % Smoking Cigarettes Yes 39 92.86 3 7.14 42 6.90 Ref 1 1 No 524 92.42 43 7.58 567 93.10 1.067 0.317 3.595 Mouth Snuff Status Yes 127 89.44 15 10.56 142 23.32 Ref 1 1 No 436 93.36 31 6.64 467 76.68 0.602 0.315 1.15 Fruit or vegetable serving (days per week) ≥ 3 102 92.73 8 7.27 110 18.06 Ref 1 1 < 3 461 92.38 38 7.62 499 81.94 1.051 0.476 2.32 Type of Kitchen Oil Liquid 26 81.25 6 18.75 32 5.25 Ref 1 1 Solid 507 92.86 39 7.14 546 89.66 6.923 0.782 61.315 Both 30 96.77 1 3.23 31 89.66 2.308 0.306 17.375 Vigorous Physical Activity Yes 64 88.89 8 11.11 72 11.82 Ref 1 1 No 499 92.92 38 7.08 537 88.18 0.609 0.272 1.363 Moderate Physical Activity Yes 309 93.92 20 6.08 329 54.02 Ref 1 1 No 254 90.71 26 9.29 280 45.98 1.581 0.863 2.899 Pedaling or bicycle for 10 Minutes per day Yes 178 90.36 19 9.64 197 32.35 Ref 1 1 No 83 93.26 6 6.74 89 14.61 0.677 0.261 1.758 Reclining/siting (hours per day) < 3 326 94.49 19 5.51 345 56.65 Ref 1 1 ≥ 3 237 89.77 27 10.23 264 43.35 1.955 1.062 3.599 Similarly, the results of bivariate analysis of behavioral risk factors with diabetes have been reflected in Table 2 . As depicted in this table prevalence of smoking was 6.90% (42/609) and 23.32% (142/609) were using mouth snuff as tobacco products. Just 110 (18.06%) were consuming ≥ 3 days per week fruits or vegetables. On average the participants consumed 1.69 days per week fruits and 2.45 days per week vegetables. Those who were sitting or reclining ≥ 3 hours per day had significantly higher odds of diabetes with OR of 1.96 and 95%CI of 1.06–3.60. The results of bivariate analysis of metabolic factors and diabetes are shown in Table 3 . Table 3 Bivariate Analysis of metabolic risk factors for diabetes mellitus among study participants, Herat Afghanistan Variables Categories Nondiabetic Diabetic Total OR 95%CI-LL 95%CI-UL N % N % N % General Obesity Non-obese 517 92.82 40 7.18 557 91.46 Ref 1 1 Obese 46 88.46 6 11.54 52 8.54 1.686 0.679 4.186 Central Obesity (excluding Pregnancy) No 356 94.43 21 5.57 377 61.90 Ref 1 1 Yes 207 89.22 25 10.78 232 38.10 2.047 1.118 3.749 Blood Pressure (including under treatment) Hypotensive 412 94.50 24 5.50 436 71.59 Ref 1 1 Hypertensive 151 87.28 22 12.72 173 28.41 2.501 1.362 4.593 Total Cholesterol < 190 mg/dL 530 92.82 41 7.18 571 93.76 Ref 1 1 ≥ 190 mg/dL 33 86.84 5 13.16 38 6.24 1.959 0.726 5.286 LDL < 100 mg/dL 521 92.87 40 7.13 561 92.12 Ref 1 1 ≥ 100 mg/dL 42 87.50 6 12.50 48 7.88 1.861 0.746 4.64 HDL (borderline 40 mg/dL for male and 50mg/dL for female) < 40 and 50mg/dL 240 92.66 19 7.34 259 42.53 Ref 1 1 ≥ 40 and 50mg/dL 323 92.29 27 7.71 350 57.47 1.056 0.574 1.944 Triglycerides < 150 mg/dL 310 92.54 25 7.46 335 55.01 Ref 1 1 ≥ 150 mg/dL 253 92.34 21 7.66 274 44.99 1.029 0.563 1.882 If we review Table 3 , it is depicted that the prevalence of general and abdominal obesity is 52 (8.54%) and 232 (38.10%). Likewise, 173 (28.41%) had high blood pressure. The proportions of blood lipids are 38 (6.24%) for total cholesterol, 48 (7.88%) for low density lipoprotein (LDL), 350 (57.47%) for high density lipoprotein (HDL) and 274 (44.99%) for triglycerides. There were significant associations between central obesity (OR = 2.05; 95%CI:1.12–3.75) and blood pressure (Or-2.50;95%CI:1.36–4.60). Multivariate Analysis: After conduction of descriptive and bivariate analysis, we conducted multivariate analysis using logistic regression to account for confounding and to identify variables independently associated with diabetes. Finally, after running the model, we found that only blood pressure (AOR = 2.52; 95% CI: 1.37–4.63) had an independent association with diabetes Discussion Summary of Key Findings In this community-based cross-sectional study, the prevalence of diabetes mellitus (DM) was found to be 7.6%. In the unadjusted (bivariate) analyses, several factors—such as older age, lower educational attainment, central obesity, and hypertension—were significantly associated with diabetes. However, after adjustment for potential confounders in the multivariable logistic regression model, hypertension was the only factor that remained independently associated with diabetes. Comparison with National, Regional, and Global Evidence The prevalence identified in this study is lower than national estimates. The 2024 Afghanistan STEPS-based survey reported diabetes in 11.1% of adults and impaired fasting glucose in 10.3% [ 11 ], while a meta-analysis estimated a pooled national prevalence of approximately 12% [ 12 ], confirming the ongoing epidemiological transition toward higher NCD burdens [ 13 ]. Subnational studies also demonstrate variability: 13.2% in Kabul [ 14 ], 9.9% in urban Herat [ 15 ], and 9.7% in rural Andkhoy [ 16 ]. Our estimate is consistent with these rural findings and supports the urban–rural gradient, whereby urbanization, sedentary behavior, and dietary westernization increase diabetes risk. Regionally, diabetes prevalence remains substantially higher in South Asia. A 2024 systematic review from Pakistan found prevalence ranging from 16% to 25% [ 25 ], and a 2025 meta-analysis estimated 19.1% for diabetes and 14.4% for prediabetes across 42 studies [ 26 ]. In India, the 2023 ICMR–INDIAB survey reported prevalence between 9% and 20% across states [ 27 ]. A 2025 regional synthesis further indicated that most South Asian nations now exceed 10–15% adult prevalence [ 28 ]. Globally, the International Diabetes Federation (IDF) estimated a 10.5% adult prevalence in 2021, projected to rise to 12.2% by 2045, with the steepest increases expected in low- and middle-income countries [ 2 ]. Although rural Afghanistan currently shows lower prevalence, this trend is unlikely to persist without active prevention and early detection strategies. Interpretation of Determinants The observed associations between diabetes, central obesity, and hypertension align with global evidence linking visceral adiposity and metabolic syndrome to insulin resistance [ 29 , 30 ]. Similar suppression effects have been reported in other South Asian datasets [ 29 , 31 ]. Hypertension remains one of the most consistent predictors of diabetes in regional and international studies [ 31 ], reinforcing the shared pathophysiological mechanisms—such as endothelial dysfunction, inflammation, and insulin resistance—that connect vascular and metabolic disease processes. Although dietary factors like low vegetable intake did not remain an independent predictor in our final model, their importance is well-established, consistent with meta-analyses showing that diets rich in vegetables and fruits protect against type 2 diabetes through antioxidant and anti-inflammatory mechanisms [ 32 , 33 ]. Shifts toward refined carbohydrates and high-fat diets in South Asia have been strongly linked to increased diabetes incidence [ 34 ]. Educational attainment and employment status were inversely associated with diabetes, likely due to improved health literacy, awareness, and access to care among higher socioeconomic groups [ 30 , 35 ]. As anticipated, older age emerged as the strongest non-modifiable determinant, highlighting the cumulative impact of metabolic risk exposure. Possible Explanations for Observed Differences The relatively lower prevalence in rural Herat may reflect higher physical activity levels from agricultural work, traditional dietary habits, and lower obesity rates compared with urban areas. However, underdiagnosis remains probable. The use of fasting plasma glucose (FPG) alone can miss up to one-third of diabetes cases that HbA1c or oral glucose tolerance tests (OGTT) would detect [ 36 ]. Limited diagnostic capacity, lack of routine screening, and variable laboratory standards across studies also contribute to potential underestimation. Consequently, the true burden of diabetes in rural Afghanistan may be higher than reported. Policy and Public Health Implications Integrating diabetes management into Afghanistan’s broader NCD control framework is essential. The clustering of metabolic and socioeconomic risk factors highlights the need for community-based, multi-sectoral interventions. Community Health Workers (CHWs)—a cornerstone of Afghanistan’s primary health system—can play a vital role in screening, early referral, and lifestyle education. Evidence from neighboring South Asian countries demonstrates that CHW-led lifestyle interventions can effectively reduce diabetes risk and are cost-efficient in low-resource contexts [ 28 ]. At the policy level, institutionalizing routine WHO STEPS surveillance with biochemical components is critical to monitor national and provincial trends [ 22 ]. Nutrition-sensitive policies, including local vegetable promotion, subsidies for healthy foods, and restriction of trans fats, should be prioritized [ 34 ]. Cross-border collaboration with Pakistan and India can strengthen regional strategies for diabetes prevention. Aligning national health initiatives with Sustainable Development Goal (SDG) 3.4—which calls for a one-third reduction in premature NCD mortality by 2030—offers a global framework for action [ 37 ]. Strengths and Limitations This study is among the few population-based investigations of diabetes in rural Afghanistan. Strengths include probability-based cluster sampling, adherence to the standardized WHO STEPS protocol, and the use of laboratory-confirmed fasting glucose. The inclusion of behavioral, socioeconomic, and clinical variables enhanced internal validity and provided a multidimensional assessment of risk. However, the study’s cross-sectional nature precludes causal inference. The exclusion of urban participants limits generalizability, and self-reported data may have introduced recall bias. The use of FPG alone might underestimate prevalence, and small subgroup sizes limited the ability to detect weaker associations. Despite these limitations, the study provides valuable baseline evidence to guide national diabetes surveillance and prevention efforts. Conclusion In summary, hypertension was the only independent risk factor for diabetes in this rural Afghan population, while central obesity and dietary factors showed associations in unadjusted analyses. These findings illustrate the intertwined burden of vascular and metabolic disorders and emphasize the need for community-based, integrated hypertension and diabetes screening and prevention programs tailored to rural Afghanistan. Declarations Ethical Considerations The study protocol was reviewed and approved by the Institutional Review Board (IRB) of the Ministry of Public Health, Afghanistan (Approval Code: A.1023.429, dated 25 October 2023). All procedures performed in this study involving human participants were conducted in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Written informed consent was obtained from all participants prior to data collection. Participants were fully informed about the purpose of the study, assured of the confidentiality and anonymity of their information, and informed of their right to withdraw from participation at any time without any negative consequences. The researchers are committed to sharing the results of this study with the Ministry of Public Health prior to any public dissemination, as required by the IRB approval conditions. Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare no competing interests. Funding This study was financially supported solely by Jami University, Herat, Afghanistan. The funder had no role in study design, data collection, analysis, interpretation, or manuscript writing. Authors’ contributions Abdul Saboor Karim Suri (ASKS): Conceptualization, study design, data collection, data entry into Excel files, and writing the original draft. Khwaja Mir Islam Saeed (KMIS): Formal analysis, data interpretation, and writing—review & editing. Said Naim Allemy (SNA): Writing—review & editing. Corresponding author : Abdul Saboor Karim Suri, MD Candidate Department of Curative Medicine, Faculty of Medicine, Jami University, Herat, Afghanistan Email: [email protected] Tel: +93 795257474 ORCID: 0009-0003-0290-2470 All authors read and approved the final version of the manuscript prior to submission. Acknowledgements The authors gratefully acknowledge the Ministry of Public Health of Afghanistan, provincial health authorities of Herat, and all participants for their invaluable contributions. We also thank local health workers, community leaders, and field staff for facilitating data collection. Their collaboration was essential for the success of this study. 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Anjana RM, Unnikrishnan R, Deepa M, Pradeepa R, Tandon N, Das AK, ICMR–INDIAB Collaborative Study Group, et al. Metabolic non-communicable disease health report of India: the ICMR-INDIAB national cross-sectional study (ICMR-INDIAB-17). Lancet Diabetes Endocrinol. 2023;11:474–89. 10.1016/S2213-8587(23)00119-5 . Ali M, Alam MM, Rifat MA, Simi SM, Sarwar S, Amin MR, Saha S. Prevalence of diabetes and prediabetes in South Asian countries: a systematic review and meta-analysis. Discover Public Health. 2025;22:39. 10.1186/s12982-025-00426-8 . Ranasinghe P, Jayawardena R, Katulanda P, Sheriff MHR, Constantine GR. Rising trends of diabetes in South Asia: a systematic review and meta-analysis. Diabetes Metab Syndr. 2024;18(5):103160. 10.1016/j.dsx.2024.103160 . Andretti B, Atanasova P, Verdun Z, Wellappuli NT, Pradeepa R, Vasudevan S, Tyagi A, Ahsan A, Hossain MM, Shamim AA, et al. Income-based inequalities in risk factors of NCDs and inequities of preventive care services amongst 202,682 adults: a cross-sectional study of South Asia Biobank. BMC Med. 2025;23:504. 10.1186/s12916-025-04308-3 . Raza Mohammad, Bansod DW. Hypertension in India: a gender-based study of prevalence and associated risk factors. BMC Public Health. 2024;24:2681. 10.1186/s12889-024-20097-5 . Halvorsen RE, Elvestad M, Molin M, Aune D. Fruit and vegetable consumption and the risk of type 2 diabetes: a systematic review and dose–response meta-analysis of prospective studies. BMJ Nutr Prev Health. 2021;0:e000218. 10.1136/bmjnph-2020-000218 . Banjarnahor RL, Javadi Arjmand E, Onni AT, Thomassen LM, Perillo M, Balakrishna R, et al. Umbrella review of systematic reviews and meta-analyses on consumption of different food groups and risk of type 2 diabetes mellitus and metabolic syndrome. J Nutr. 2025;155(5):1285–97. 10.1016/j.tjnut.2025.03.021 . Giosuè A, Calabrese I, Riccardi G, Vaccaro O, Vitale M. Consumption of different animal-based foods and risk of type 2 diabetes: an umbrella review of meta-analyses of prospective studies. Diabetes Res Clin Pract. 2022;191:110071doi. 10.1016/j.diabres.2022.110071 . Qin GQ, Chen L, Zheng J, Wu XM, Li Y, Yang K, et al. Effect of passive smoking exposure on risk of type 2 diabetes: a systematic review and meta-analysis of prospective cohort studies. Front Endocrinol (Lausanne). 2023;14:1195354. 10.3389/fendo.2023.1195354 . Butler AE, English E, Kilpatrick ES, Östlundh L, Chemaitelly HS, Abu-Raddad LJ, et al. Diagnosing type 2 diabetes using Hemoglobin A1c: a systematic review and meta-analysis of the diagnostic cutpoint based on microvascular complications. Acta Diabetol. 2021;58(3):279–300. 10.1007/s00592-020-01606-5 . World Health Organization. Noncommunicable diseases progress monitor 2022. Geneva: World Health Organization; 2022. World Health Organization. Obesity: preventing and managing the global epidemic. Report of a WHO consultation. WHO Technical Report Series 894. Geneva: World Health Organization. 2000. Available from: WHO Library (IRIS) — https://apps.who.int/iris/handle/10665/42330 Alberti KGMM, Zimmet P, Shaw J. The metabolic syndrome–a new worldwide definition. Lancet. 2005;366(9491):1059–62. 10.1016/S0140-6736(05)67402-8 . World Health Organization. Package of essential noncommunicable (PEN) disease interventions for primary health care in low-resource settings. Geneva: World Health Organization. 2010. Available from: https://apps.who.int/iris/handle/10665/44260 Additional Declarations No competing interests reported. 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16:23:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1334083,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7948496/v1/e67a89c5-2c40-46ba-9546-8f5cf937bf68.pdf"},{"id":96279146,"identity":"4c97674f-cda8-4964-ade6-22dde06d1d66","added_by":"auto","created_at":"2025-11-19 10:51:08","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":28915,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEChecklistBMCPublicHealthHeratStudy.docx","url":"https://assets-eu.researchsquare.com/files/rs-7948496/v1/c39ced8291c5cba13e16b1d3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diabetes Prevalence and Associated Risk Factors among Adults in Rural Herat Province, Afghanistan: A Community-Based Cross-Sectional Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiabetes mellitus (DM) represents one of the fastest-growing noncommunicable diseases (NCDs) globally, posing a substantial threat to health systems, economies, and human development. According to the latest estimates from the International Diabetes Federation (IDF), approximately 537\u0026nbsp;million adults were living with diabetes in 2021, and this number is projected to rise to 783\u0026nbsp;million by 2045, with the largest relative increases expected in low- and middle-income countries (LMICs) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The condition contributes significantly to premature mortality, reduced quality of life, and increased healthcare expenditures worldwide [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In South Asia, the prevalence of diabetes has reached alarming levels, driven by rapid urbanization, dietary changes, obesity, and physical inactivity [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite these global and regional concerns, Afghanistan remains among the least studied countries regarding the epidemiology of diabetes and related risk factors. The country faces multiple public health challenges, including limited resources, ongoing humanitarian crises, and fragile health infrastructure [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. National surveillance systems for NCDs are still in the early stages of implementation, and population-based data, especially from rural areas, are scarce. Most available reports are limited to facility-based or small-scale studies conducted in urban settings, which do not reflect the true burden of diabetes across the broader Afghan population [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This lack of comprehensive data hampers the ability of policymakers to design and implement effective NCD prevention and control strategies.\u003c/p\u003e\u003cp\u003eThe WHO STEPwise approach (STEPS) provides a standardized framework for monitoring key NCD risk factors, including diabetes, through community-based surveys [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This methodology enables countries with limited health information systems, such as Afghanistan, to generate nationally and regionally representative data for evidence-based policy-making [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Implementing the STEPS approach in rural Afghan communities is particularly critical because more than 70% of the population resides in rural areas, where access to health services and awareness of NCD prevention remain very limited [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Moreover, rural residents may have distinct behavioral and metabolic risk profiles compared to urban populations due to differences in lifestyle, nutrition, and socioeconomic status [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrevious studies from neighboring countries, including Pakistan and Iran, have reported a rising prevalence of diabetes and its risk factors even among rural populations [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, similar large-scale community-based evidence from Afghanistan is almost non-existent. Understanding the prevalence and determinants of diabetes in these underserved rural communities is essential for developing cost-effective screening programs, allocating health resources, and integrating diabetes care into primary healthcare services, as recommended in the WHO Package of Essential NCD Interventions (PEN) for primary healthcare in low-resource settings [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eTherefore, this study was conducted to estimate the prevalence of diabetes and to identify its associated risk factors among adults aged 18 years and above living in rural areas of Herat province, Afghanistan, using the WHO STEPwise approach to NCD risk factor surveillance. By providing reliable community-based data, the study aims to fill a critical gap in national evidence and support policymakers and health authorities in designing targeted interventions to reduce the growing burden of diabetes in Afghanistan\u0026rsquo;s rural settings.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and setting\u003c/h2\u003e\u003cp\u003eA community-based cross-sectional survey using the World Health Organization (WHO) STEPwise approach to noncommunicable disease (NCD) risk factor surveillance was conducted between January and December 2024 in rural areas of Herat province, western Afghanistan. Herat is one of the largest provinces of the country, with a predominantly rural population that relies mainly on agriculture and small-scale trading activities. The WHO STEPS methodology was chosen to ensure comparability with national and international NCD surveillance studies [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy population and eligibility criteria\u003c/h3\u003e\n\u003cp\u003eThe target population consisted of adults aged 18 years and above who had been living in the selected rural communities of Herat province for at least six months. Individuals who were pregnant, critically ill, or unable to communicate during the interview were excluded.\u003c/p\u003e\n\u003ch3\u003eSample size determination\u003c/h3\u003e\n\u003cp\u003eThe required sample size was calculated using the Cochran formula[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], assuming a 95% confidence level, 5% margin of error, and a design effect of 1.5, based on the most recent demographic estimates from the National Statistics and Information Authority (NSIA) of Afghanistan. An additional 10% was added to account for potential non-response. Accordingly, 643 individuals were initially selected. Of these, 24 samples were lost due to refusal to provide blood samples or laboratory errors, resulting in a final analytical sample of 609 participants.\u003c/p\u003e\n\u003ch3\u003eSampling procedure\u003c/h3\u003e\n\u003cp\u003eA multistage cluster sampling technique was applied in accordance with the WHO STEPS methodology [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Villages were first selected as primary sampling units using probability proportional to size, and households within each cluster were then selected systematically. One eligible adult from each household was randomly chosen for participation using the Kish method[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eData collection procedure\u003c/h3\u003e\n\u003cp\u003eData collection followed the three standardized STEPS:\u003c/p\u003e\u003cp\u003eStep 1 (Questionnaire): Socio-demographic and behavioral data, including age, sex, education, occupation, tobacco use, diet, and physical activity, were collected using the standardized WHO STEPS questionnaire, This translated version is the officially nationally validated and adapted Dari instrument utilized in previous Afghanistan WHO STEPS surveys[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. which had been culturally adapted and translated into Dari for use in Afghanistan. Trained interviewers administered the questionnaire through face-to-face interviews after receiving three days of intensive training.\u003c/p\u003e\u003cp\u003eStep 2 (Physical measurements): Anthropometric and blood pressure measurements were obtained using calibrated instruments. Height and weight were measured using a mechanical Fr\u0026ouml;lix analog scale, and body mass index (BMI) was computed as weight (kg) divided by height squared (m\u0026sup2;). Blood pressure was measured twice using a Yuwell aneroid sphygmomanometer (Two-Shelling model) after participants had rested for at least five minutes in a seated position. The average of two readings was recorded.\u003c/p\u003e\u003cp\u003eStep 3 (Biochemical measurements): Fasting blood samples were collected following at least eight hours of fasting. Serum glucose levels were analyzed in a certified laboratory using a Photometer 5010 biochemical analyzer (Germany).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eDefinition of variables\u003c/h2\u003e\u003cp\u003eDiabetes mellitus was defined as fasting plasma glucose (FPG)\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL (7.0 mmol/L) or current use of antidiabetic medication, following WHO criteria [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHypertension was defined as systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg and/or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg or current use of antihypertensive medication [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOverweight and obesity were classified according to WHO BMI categories: overweight (25.0\u0026ndash;29.9 kg/m\u0026sup2;) and obesity (\u0026ge;\u0026thinsp;30 kg/m\u0026sup2;) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCentral obesity was defined as a waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;94 cm for men and \u0026ge;\u0026thinsp;80 cm for women [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLow vegetable intake was defined as consuming vegetables fewer than five days per week, based on WHO dietary recommendations [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eData were entered and analyzed using IBM SPSS Statistics version 26. Descriptive statistics (means, standard deviations, and proportions) were used to summarize participants\u0026rsquo; characteristics. The prevalence of diabetes and 95% confidence intervals (CI) were calculated. Associations between diabetes and independent variables were examined using binary logistic regression. Variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.20 in the bivariate analysis were included in the multivariable logistic regression model to identify independent predictors[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Adjusted odds ratios (AOR) and corresponding 95% CI were reported. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEthical considerations\u003c/h3\u003e\n\u003cp\u003e The study was approved by the Institutional Review Board of the Ministry of Public Health of Afghanistan (Approval Code: A.1023.429). Written informed consent was obtained from all participants before data collection. Confidentiality and anonymity were strictly maintained throughout the study.\u003c/p\u003e\u003cp\u003e For participants identified as illiterate, the informed consent form was read verbatim in the local language (Dari) by a trained interviewer in the presence of a competent and impartial witness. Following the verbal affirmation of understanding by both the participant and the witness, the participant affixed their thumbprint to the consent form as documented proof of their voluntary participation.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eDescriptive Analysis:\u003c/h2\u003e\u003cp\u003eOut of the total study sample of 609 participants, 316 (51.88%) were females. The mean age of the participants was 40.60 years (SD: 15.79), while the median age was 38.00 years with an interquartile range (IQR) of 27.00\u0026ndash;50.00 years. Approximately 175 participants (28.73%) were in the 25\u0026ndash;35-year age group. In terms of ethnicity, 291 (47.78%) were Pashtuns and 284 (46.63%) were Tajiks. Overall, 579 participants (95.07%) were married, of whom 49 (8.04%) were widowed. The prevalence of raised blood sugar (\u0026ge;\u0026thinsp;126 mg/dl) was observed in 39 participants (6.40%), while an additional seven were already under treatment. Accordingly, the overall prevalence of diabetes mellitus was 46 (7.55%).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eBivariable Analysis:\u003c/h2\u003e\u003cp\u003eThe results of bivariate analysis of sociodemographic characteristics of participants are described in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eBivariate Analysis of demographic characteristics of the study participants of diabetes mellitus, Herat Afghanistan\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCategories\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eNon-diabetes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCI 95% LL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCI 95% UL\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e97.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e45.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\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=\"c2\"\u003e\u003cp\u003e35\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e19.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7.109\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=\"c2\"\u003e\u003cp\u003e45\u0026ndash;54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e17.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e9.349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e3.842\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e22.748\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=\"c2\"\u003e\u003cp\u003e55+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.604\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e11.281\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\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=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e272\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e48.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e51.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.609\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2.034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLevel of Education\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-Educated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e414\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e74.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\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=\"c2\"\u003e\u003cp\u003eEducated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e25.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.874\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eJob\u003c/b\u003e\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=\"c2\"\u003e\u003cp\u003eOfficial Work\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\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=\"c2\"\u003e\u003cp\u003ePhysical Work\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e33.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.944\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e4.649\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=\"c2\"\u003e\u003cp\u003eUnemployed/Unable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.577\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e8.047\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=\"c2\"\u003e\u003cp\u003eHousewife\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e37.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMonthly Income in AFN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLess than 10,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e17.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\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=\"c2\"\u003e\u003cp\u003eMore than 10,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e26.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.562\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\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=\"c2\"\u003e\u003cp\u003eNever Married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\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=\"c2\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e534\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e95.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e17.747\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAs seen, the age groups of 45\u0026ndash;54 years and more than 55 years were significantly associated with diabetes with odds ratio of 9.35 and 95%CI of 3.84\u0026ndash;22.75, and OR of 4.25 and 95% CI of 1.60-11.28 respectively. Furthermore, being educated or non-educated was also significantly associated with being diagnosed with diabetes with OR of 0.34 and 95%CI of 0.13\u0026ndash;0.87. Similarly, those who were unemployed or unable to work had higher odds of being diagnosed with diabetes as compared to their counterparts with OR of 3.56 and 95% CI of 1.58\u0026ndash;8.03.\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\u003eBivariate analysis of behavioral risk factors for diabetes among the study participants, Herat Afghanistan\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategories\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eNondiabetic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eDiabetic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e95%CI-LL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e95%CI-UL\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003eSmoking Cigarettes\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\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=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e524\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e93.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.317\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e3.595\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMouth Snuff Status\u003c/b\u003e\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=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e23.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\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=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e76.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFruit or vegetable serving (days per week)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\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=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e81.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eType of Kitchen Oil\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLiquid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\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=\"c2\"\u003e\u003cp\u003eSolid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e507\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e546\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e89.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.923\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e61.315\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=\"c2\"\u003e\u003cp\u003eBoth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e89.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.308\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e17.375\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVigorous Physical Activity\u003c/b\u003e\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=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\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=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e537\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e88.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.609\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.272\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.363\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModerate Physical Activity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e329\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e54.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\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=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e45.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.581\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2.899\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePedaling or bicycle for 10 Minutes per day\u003c/b\u003e\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=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e32.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\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=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e14.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.677\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.758\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eReclining/siting (hours per day)\u003c/b\u003e\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=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e326\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e56.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\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=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e43.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e3.599\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSimilarly, the results of bivariate analysis of behavioral risk factors with diabetes have been reflected in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. As depicted in this table prevalence of smoking was 6.90% (42/609) and 23.32% (142/609) were using mouth snuff as tobacco products. Just 110 (18.06%) were consuming\u0026thinsp;\u0026ge;\u0026thinsp;3 days per week fruits or vegetables. On average the participants consumed 1.69 days per week fruits and 2.45 days per week vegetables. Those who were sitting or reclining\u0026thinsp;\u0026ge;\u0026thinsp;3 hours per day had significantly higher odds of diabetes with OR of 1.96 and 95%CI of 1.06\u0026ndash;3.60.\u003c/p\u003e\u003cp\u003eThe results of bivariate analysis of metabolic factors and diabetes are shown in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\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\u003eBivariate Analysis of metabolic risk factors for diabetes mellitus among study participants, Herat Afghanistan\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategories\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eNondiabetic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eDiabetic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e95%CI-LL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e95%CI-UL\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGeneral Obesity\u003c/b\u003e\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=\"c2\"\u003e\u003cp\u003eNon-obese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e557\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e91.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\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=\"c2\"\u003e\u003cp\u003eObese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.686\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.679\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e4.186\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCentral Obesity (excluding Pregnancy)\u003c/b\u003e\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=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e377\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e61.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\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=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e38.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e3.749\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBlood Pressure (including under treatment)\u003c/b\u003e\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=\"c2\"\u003e\u003cp\u003eHypotensive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e71.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\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=\"c2\"\u003e\u003cp\u003eHypertensive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e28.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e4.593\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal Cholesterol\u003c/b\u003e\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=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;190 mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e530\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e93.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\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=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;190 mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.959\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.726\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e5.286\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLDL\u003c/b\u003e\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=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;100 mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e92.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\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=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;100 mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.746\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e4.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHDL (borderline 40 mg/dL for male and 50mg/dL for female)\u003c/b\u003e\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=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;40 and 50mg/dL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e42.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\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=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;40 and 50mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e323\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e57.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.574\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.944\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTriglycerides\u003c/b\u003e\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=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;150 mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e335\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e55.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\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=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;150 mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e44.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.882\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIf we review Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, it is depicted that the prevalence of general and abdominal obesity is 52 (8.54%) and 232 (38.10%). Likewise, 173 (28.41%) had high blood pressure. The proportions of blood lipids are 38 (6.24%) for total cholesterol, 48 (7.88%) for low density lipoprotein (LDL), 350 (57.47%) for high density lipoprotein (HDL) and 274 (44.99%) for triglycerides. There were significant associations between central obesity (OR\u0026thinsp;=\u0026thinsp;2.05; 95%CI:1.12\u0026ndash;3.75) and blood pressure (Or-2.50;95%CI:1.36\u0026ndash;4.60).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eMultivariate Analysis:\u003c/h2\u003e\u003cp\u003eAfter conduction of descriptive and bivariate analysis, we conducted multivariate analysis using logistic regression to account for confounding and to identify variables independently associated with diabetes. Finally, after running the model, we found that only blood pressure (AOR\u0026thinsp;=\u0026thinsp;2.52; 95% CI: 1.37\u0026ndash;4.63) had an independent association with diabetes\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eSummary of Key Findings\u003c/h2\u003e\u003cp\u003eIn this community-based cross-sectional study, the prevalence of diabetes mellitus (DM) was found to be 7.6%. In the unadjusted (bivariate) analyses, several factors\u0026mdash;such as older age, lower educational attainment, central obesity, and hypertension\u0026mdash;were significantly associated with diabetes. However, after adjustment for potential confounders in the multivariable logistic regression model, hypertension was the only factor that remained independently associated with diabetes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eComparison with National, Regional, and Global Evidence\u003c/h2\u003e\u003cp\u003eThe prevalence identified in this study is lower than national estimates. The 2024 Afghanistan STEPS-based survey reported diabetes in 11.1% of adults and impaired fasting glucose in 10.3% [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], while a meta-analysis estimated a pooled national prevalence of approximately 12% [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], confirming the ongoing epidemiological transition toward higher NCD burdens [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Subnational studies also demonstrate variability: 13.2% in Kabul [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], 9.9% in urban Herat [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and 9.7% in rural Andkhoy [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Our estimate is consistent with these rural findings and supports the urban\u0026ndash;rural gradient, whereby urbanization, sedentary behavior, and dietary westernization increase diabetes risk.\u003c/p\u003e\u003cp\u003eRegionally, diabetes prevalence remains substantially higher in South Asia. A 2024 systematic review from Pakistan found prevalence ranging from 16% to 25% [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and a 2025 meta-analysis estimated 19.1% for diabetes and 14.4% for prediabetes across 42 studies [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In India, the 2023 ICMR\u0026ndash;INDIAB survey reported prevalence between 9% and 20% across states [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A 2025 regional synthesis further indicated that most South Asian nations now exceed 10\u0026ndash;15% adult prevalence [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Globally, the International Diabetes Federation (IDF) estimated a 10.5% adult prevalence in 2021, projected to rise to 12.2% by 2045, with the steepest increases expected in low- and middle-income countries [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Although rural Afghanistan currently shows lower prevalence, this trend is unlikely to persist without active prevention and early detection strategies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eInterpretation of Determinants\u003c/h2\u003e\u003cp\u003eThe observed associations between diabetes, central obesity, and hypertension align with global evidence linking visceral adiposity and metabolic syndrome to insulin resistance [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Similar suppression effects have been reported in other South Asian datasets [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Hypertension remains one of the most consistent predictors of diabetes in regional and international studies [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], reinforcing the shared pathophysiological mechanisms\u0026mdash;such as endothelial dysfunction, inflammation, and insulin resistance\u0026mdash;that connect vascular and metabolic disease processes. Although dietary factors like low vegetable intake did not remain an independent predictor in our final model, their importance is well-established, consistent with meta-analyses showing that diets rich in vegetables and fruits protect against type 2 diabetes through antioxidant and anti-inflammatory mechanisms [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Shifts toward refined carbohydrates and high-fat diets in South Asia have been strongly linked to increased diabetes incidence [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Educational attainment and employment status were inversely associated with diabetes, likely due to improved health literacy, awareness, and access to care among higher socioeconomic groups [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. As anticipated, older age emerged as the strongest non-modifiable determinant, highlighting the cumulative impact of metabolic risk exposure.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003ePossible Explanations for Observed Differences\u003c/h2\u003e\u003cp\u003eThe relatively lower prevalence in rural Herat may reflect higher physical activity levels from agricultural work, traditional dietary habits, and lower obesity rates compared with urban areas. However, underdiagnosis remains probable. The use of fasting plasma glucose (FPG) alone can miss up to one-third of diabetes cases that HbA1c or oral glucose tolerance tests (OGTT) would detect [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Limited diagnostic capacity, lack of routine screening, and variable laboratory standards across studies also contribute to potential underestimation. Consequently, the true burden of diabetes in rural Afghanistan may be higher than reported.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003ePolicy and Public Health Implications\u003c/h2\u003e\u003cp\u003eIntegrating diabetes management into Afghanistan\u0026rsquo;s broader NCD control framework is essential. The clustering of metabolic and socioeconomic risk factors highlights the need for community-based, multi-sectoral interventions. Community Health Workers (CHWs)\u0026mdash;a cornerstone of Afghanistan\u0026rsquo;s primary health system\u0026mdash;can play a vital role in screening, early referral, and lifestyle education. Evidence from neighboring South Asian countries demonstrates that CHW-led lifestyle interventions can effectively reduce diabetes risk and are cost-efficient in low-resource contexts [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAt the policy level, institutionalizing routine WHO STEPS surveillance with biochemical components is critical to monitor national and provincial trends [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Nutrition-sensitive policies, including local vegetable promotion, subsidies for healthy foods, and restriction of trans fats, should be prioritized [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Cross-border collaboration with Pakistan and India can strengthen regional strategies for diabetes prevention. Aligning national health initiatives with Sustainable Development Goal (SDG) 3.4\u0026mdash;which calls for a one-third reduction in premature NCD mortality by 2030\u0026mdash;offers a global framework for action [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and Limitations\u003c/h2\u003e\u003cp\u003eThis study is among the few population-based investigations of diabetes in rural Afghanistan. Strengths include probability-based cluster sampling, adherence to the standardized WHO STEPS protocol, and the use of laboratory-confirmed fasting glucose. The inclusion of behavioral, socioeconomic, and clinical variables enhanced internal validity and provided a multidimensional assessment of risk.\u003c/p\u003e\u003cp\u003eHowever, the study\u0026rsquo;s cross-sectional nature precludes causal inference. The exclusion of urban participants limits generalizability, and self-reported data may have introduced recall bias. The use of FPG alone might underestimate prevalence, and small subgroup sizes limited the ability to detect weaker associations. Despite these limitations, the study provides valuable baseline evidence to guide national diabetes surveillance and prevention efforts.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, hypertension was the only independent risk factor for diabetes in this rural Afghan population, while central obesity and dietary factors showed associations in unadjusted analyses. These findings illustrate the intertwined burden of vascular and metabolic disorders and emphasize the need for community-based, integrated hypertension and diabetes screening and prevention programs tailored to rural Afghanistan.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was reviewed and approved by the Institutional Review Board (IRB) of the Ministry of Public Health, Afghanistan (Approval Code: A.1023.429, dated 25 October 2023). All procedures performed in this study involving human participants were conducted in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all participants prior to data collection. Participants were fully informed about the purpose of the study, assured of the confidentiality and anonymity of their information, and informed of their right to withdraw from participation at any time without any negative consequences.\u003c/p\u003e\n\u003cp\u003eThe researchers are committed to sharing the results of this study with the Ministry of Public Health prior to any public dissemination, as required by the IRB approval conditions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was financially supported solely by Jami University, Herat, Afghanistan. The funder had no role in study design, data collection, analysis, interpretation, or manuscript writing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbdul Saboor Karim Suri (ASKS): Conceptualization, study design, data collection, data entry into Excel files, and writing the original draft.\u003c/p\u003e\n\u003cp\u003eKhwaja Mir Islam Saeed (KMIS): Formal analysis, data interpretation, and writing\u0026mdash;review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eSaid Naim Allemy (SNA): Writing\u0026mdash;review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e: Abdul Saboor Karim Suri, MD Candidate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDepartment of Curative Medicine, Faculty of Medicine, Jami University, Herat, Afghanistan\u003c/p\u003e\n\u003cp\u003eEmail: [email protected]\u003c/p\u003e\n\u003cp\u003eTel: +93 795257474\u003c/p\u003e\n\u003cp\u003eORCID: 0009-0003-0290-2470\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final version of the manuscript prior to submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the Ministry of Public Health of Afghanistan, provincial health authorities of Herat, and all participants for their invaluable contributions. We also thank local health workers, community leaders, and field staff for facilitating data collection. Their collaboration was essential for the success of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eInternational Diabetes Federation. IDF Diabetes Atlas. 11th ed. Brussels: International Diabetes Federation. 2025.Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://diabetesatlas.org/resources/idf-diabetes-atlas-2025/\u003c/span\u003e\u003cspan address=\"https://diabetesatlas.org/resources/idf-diabetes-atlas-2025/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF Diabetes Atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. 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Geneva: World Health Organization. 2010. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://apps.who.int/iris/handle/10665/44260\u003c/span\u003e\u003cspan address=\"https://apps.who.int/iris/handle/10665/44260\" targettype=\"URL\" 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":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Diabetes mellitus, Hypertension, Risk factors, Rural population, non-communicable diseases, Afghanistan, WHO STEPS","lastPublishedDoi":"10.21203/rs.3.rs-7948496/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7948496/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eDiabetes mellitus is a major public health concern in low- and middle-income countries, particularly in fragile health systems such as Afghanistan. Limited community-based evidence exists on the burden and correlates of diabetes among rural populations. This study aimed to estimate the prevalence of diabetes and identify its associated risk factors among adults living in rural areas of Herat province, Afghanistan, using the WHO Stepwise approach to noncommunicable disease (NCD) risk factor surveillance.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA community-based cross-sectional survey was conducted from January to December 2024 among adults aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years residing in rural areas of Herat province. A multistage cluster sampling technique was applied following the WHO STEPS methodology. Data were collected on sociodemographic characteristics, behavioral and metabolic risk factors using standardized questionnaires, physical measurements, and fasting blood samples. Diabetes was defined as fasting plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL or self-reported use of antidiabetic medication. Both bivariate and multivariable logistic regression analyses were performed to determine factors associated with diabetes, with adjusted odds ratios (AOR) and 95% confidence intervals (CI) reported.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 609 participants were included in the analysis (mean age: 40.6\u0026thinsp;\u0026plusmn;\u0026thinsp;15.8 years; 51.9% were female). The overall prevalence of diabetes was 7.6% (95% CI, 5.6\u0026ndash;9.9). In bivariate analyses, several factors\u0026mdash;namely older age, central obesity, and hypertension\u0026mdash;were significantly associated with diabetes. After adjusting for potential confounders in the multivariable logistic regression model, only hypertension remained independently associated with diabetes (AOR\u0026thinsp;=\u0026thinsp;2.52; 95% CI: 1.37\u0026ndash;4.63).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe prevalence of diabetes among adults in rural Herat province is considerable, highlighting an urgent need for community-level screening and preventive interventions targeting modifiable risk factors, particularly hypertension. Strengthening primary health care and integrating NCD surveillance in rural Afghanistan are essential to reduce the growing burden of diabetes.\u003c/p\u003e","manuscriptTitle":"Diabetes Prevalence and Associated Risk Factors among Adults in Rural Herat Province, Afghanistan: A Community-Based Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-19 10:51:03","doi":"10.21203/rs.3.rs-7948496/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-02T07:24:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-01T09:11:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-28T11:46:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69077522242987076082810188103825019953","date":"2025-11-15T13:50:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"108263603045488644154483872656042979390","date":"2025-11-10T15:59:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"263053343186130894777075096276001955589","date":"2025-11-10T13:09:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"164245380585442623296475768895652025490","date":"2025-11-10T10:55:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-10T10:38:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-08T10:12:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-31T08:50:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-31T08:31:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-10-31T08:27:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a9bd5b3f-9da0-473f-9638-cb20696fc2c9","owner":[],"postedDate":"November 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T16:20:39+00:00","versionOfRecord":{"articleIdentity":"rs-7948496","link":"https://doi.org/10.1186/s12889-026-27103-y","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2026-03-24 16:08:36","publishedOnDateReadable":"March 24th, 2026"},"versionCreatedAt":"2025-11-19 10:51:03","video":"","vorDoi":"10.1186/s12889-026-27103-y","vorDoiUrl":"https://doi.org/10.1186/s12889-026-27103-y","workflowStages":[]},"version":"v1","identity":"rs-7948496","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7948496","identity":"rs-7948496","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}