Comparative Study of Hypertension Prevalence, Health-Related Quality of Life, and Healthcare Barriers Among Diabetic and Non- Diabetic Populations in Urban Slums of Bangladesh

Comparative Study of Hypertension Prevalence, Health-Related Quality of Life, and Healthcare Barriers Among Diabetic and Non- Diabetic Populations in Urban Slums of Bangladesh | 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 Comparative Study of Hypertension Prevalence, Health-Related Quality of Life, and Healthcare Barriers Among Diabetic and Non- Diabetic Populations in Urban Slums of Bangladesh Md. Fakhrul Islam Maruf, Nishat Tamanna Omi, Mahfuza Mubarak This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8938325/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The rising burden of non-communicable diseases (NCDs), particularly diabetes and hypertension, disproportionately affects urban slum residents in Bangladesh, where approximately 40% of urban populations face socioeconomic challenges such as poverty, overcrowding, and limited healthcare access. These conditions contribute to health inequities and diminished health-related quality of life (HRQoL). However, comprehensive data on prevalence, associated factors, barriers to healthcare, and HRQoL in this marginalized group remain limited. Methods A community-based cross-sectional study was conducted from June to August 2025 across five slums in Dhaka (Mirpur, Khilgaon, Korail, Bhashantek, and Kamalapur). Using simple random sampling, 400 adults aged ≥ 45 years (200 with self-reported diabetes and 200 without) were enrolled. Data were collected via a validated Bengali version of the World Health Organization (WHO) STEPwise Approach to Surveillance (STEPS) questionnaire for sociodemographics, lifestyle factors, and healthcare barriers, plus the EQ-5D-5L instrument for HRQoL assessment. Analyses included descriptive statistics, chi-square tests, independent t-tests, and multivariable logistic regression in R software (p < 0.05 for significance). Results Prevalence of diabetes ranged from 10.0% to 14.1%, hypertension from 28.3% to 34.4%, and their comorbidity from 4.5% to 15.4% across slums. Independent risk factors for hypertension/diabetes included older age (adjusted odds ratio [OR] = 6.67, 95% confidence interval [CI]: 3.57–12.5, p = 0.003), higher body mass index (OR = 2.86, 95% CI: 1.64–5.00, p < 0.001), smoking (OR = 10.0, 95% CI: 5.00–20.0, p = 0.048), and sedentary occupations (OR = 12.5, 95% CI: 6.25–25.0, p = 0.048). Major barriers to care were financial constraints (83.5%), limited healthcare access (9.5%), and low awareness (4.5%). HRQoL was significantly lower in participants with diabetes (EQ-5D index score: 0.72 ± 0.18 vs. 0.85 ± 0.12 in non-diabetics, p < 0.001), with greater impairments in mobility (p = 0.012), pain/discomfort (p = 0.021), and anxiety/depression (p = 0.004), linked to disease burden and slum-related stressors (e.g., pollution, poverty). Conclusions Urban slums in Dhaka bear a substantial burden of diabetes and hypertension, exacerbated by socioeconomic barriers and poorer HRQoL. Urgent targeted interventions—community screening, subsidized care, health literacy programs, and lifestyle modifications—are needed to mitigate disparities, aligning with national NCD strategies and Sustainable Development Goal 3.4. Diabetes Hypertension Urban slums Health-related quality of life Bangladesh EQ-5D-5L WHO STEPS Figures Figure 1 Figure 2 1. Introduction Non-communicable diseases (NCDs) are the leading cause of global mortality, accounting for approximately 75% of all non-pandemic-related deaths worldwide [ 1 , 2 ]. Cardiovascular diseases, cancers, chronic respiratory diseases, and diabetes mellitus predominate, with diabetes and hypertension posing major challenges due to their high prevalence, chronicity, and associations with severe complications including stroke, heart failure, chronic kidney disease, and retinopathy [ 1 ]. Globally, diabetes prevalence has risen dramatically, from approximately 200 million cases in 1990 to an estimated 589 million adults (aged 20–79 years) in 2024 (11.1% prevalence), with projections indicating 853 million by 2050 [ 3 , 4 ]. Hypertension affects an estimated 1.4 billion adults aged 30–79 years worldwide, with higher prevalence in low- and middle-income countries (LMICs) compared to high-income countries [ 5 ]. Comorbidity is particularly concerning, as hypertension affects up to 45.5% of individuals with type 2 diabetes, markedly elevating cardiovascular risk [ 6 ]. In LMICs, where over 73% of NCD deaths occur, rapid urbanization, population aging, sedentary lifestyles, and dietary shifts exacerbate the burden amid a "double burden" with persistent infectious diseases [ 1 , 7 ]. Socioeconomic disadvantage amplifies risks through physical inactivity, poor nutrition, and limited preventive care access, leading to premature mortality and high disability-adjusted life years (DALYs) [ 1 ]. Out-of-pocket costs often cause catastrophic expenditures, deepening poverty [ 8 ]. South Asia exemplifies these trends, with rapid economic growth alongside lifestyle changes (e.g., increased processed food intake, tobacco use, reduced activity) driving NCD rises [ 9 ]. In Bangladesh, a lower-middle-income country, NCDs now dominate mortality, shifting from infectious disease predominance [ 10 ]. National data from the 2018 STEPS survey indicate hypertension prevalence at 27.3%, diabetes at 9.8%, with comorbidity patterns showing increases over time [ 11 , 12 ]. Aggregated earlier estimates included diabetes (7.8–10.0%) and hypertension (25–30%), with rising trends observed [ 13 ]. Urbanization has accelerated this epidemic, with Dhaka among the world's densest cities [ 14 ]. Urban residents exhibit higher behavioral risks (e.g., obesity 1.5 times rural rates, sedentary behavior, refined carbohydrate diets) [ 15 ]. Urban slums, housing approximately 3.4–14 million people (a substantial proportion of Dhaka's urban population), represent extreme inequities: overcrowding, poor sanitation, pollution, and stress heighten NCD vulnerability [ 16 , 17 ]. In Dhaka slums, hypertension prevalence among adults (often ≥ 35–45 years) ranges 28.3–34.4%, diabetes 4.9–14.1%, and comorbidity up to 15.4%—exceeding rural rates [ 18 , 19 ]. Key determinants include advanced age, elevated BMI/abdominal obesity, sedentary occupations, tobacco use, low education/unemployment, and environmental exposures (pollution, chronic stress) [ 18 , 20 ]. Gender disparities exist, with some data showing higher diabetes in urban males [ 21 ]. Barriers to NCD care in slums include low awareness, inadequate screening, suboptimal treatment adherence (~ 51% for antihypertensives in some urban poor groups), under-resourced primary facilities (staff shortages, no NCD guidelines, limited diagnostics), high out-of-pocket costs, geographic inaccessibility, and overburdened community health workers [ 22 , 23 ]. These lead to poor control, fragmented care, and missed early interventions [ 24 ]. Diabetes and hypertension profoundly impair health-related quality of life (HRQoL), with comorbidities (obesity, depression) reducing physical, psychological, and social functioning [ 25 ]. EQ-5D-5L assessments show lower scores in affected individuals, influenced by age, BMI, hypertension comorbidity, and slum stressors [ 26 ]. Reduced HRQoL perpetuates productivity loss, poverty cycles, and inequities [ 27 ]. Despite national surveys documenting NCD trends and rural-urban divides, slum-specific evidence remains limited [ 11 , 28 ]. Few studies comprehensively examine interacting socio-demographic, behavioral, and environmental factors; systemic barriers to care; or HRQoL impacts in these contexts [ 18 , 29 ]. Economic consequences (e.g., catastrophic expenditures) and tailored interventions are under-explored [ 30 ]. This study addresses these gaps through a community-based cross-sectional assessment of hypertension prevalence (comparing diabetic and non-diabetic adults), HRQoL differences using EQ-5D-5L, and healthcare barriers in Dhaka urban slums. Findings aim to inform targeted, equity-focused strategies—community screening, subsidized care, health literacy, lifestyle programs—to reduce NCD burden, align with national NCD policies, and advance Sustainable Development Goal 3.4 in vulnerable populations. 2. Methodology 2.1 Study design This community-based cross-sectional study compared hypertension prevalence, associated factors, healthcare barriers, and health-related quality of life (HRQoL) between adults with and without self-reported diabetes in urban slums of Dhaka, Bangladesh. The design enabled estimation of point prevalence, identification of associations, and exploration of barriers and HRQoL impacts at a single time point, providing baseline evidence for NCD interventions in resource-limited settings. 2.2 Study setting The study was conducted in five purposively selected slums in Dhaka: Mirpur, Khilgaon, Korail, Bhashantek, and Kamalapur. These sites represent typical urban slum conditions, including high population density, overcrowding, inadequate sanitation, limited potable water, and environmental exposures (e.g., air pollution), which heighten NCD vulnerability. Dhaka, with ~ 14 million slum residents (~ 40% of its urban population), exemplifies urban inequities in Bangladesh [BBS, 2022]. 2.3 Participants Eligible participants were adults aged ≥ 45 years residing in the selected slums, as this age group faces elevated diabetes and hypertension risks. Both self-reported diabetic (diagnosed by a healthcare professional) and non-diabetic individuals were included for comparative analysis. Exclusion criteria included severe acute illness, inability to provide informed consent, or conditions impairing reliable response (e.g., severe cognitive impairment). This ensured a diverse sample reflecting slum socio-demographic heterogeneity (gender, education, occupation, income). 2.4 Sample size and sampling Sample size was calculated using the single population proportion formula: n = Z² p (1-p) / d², where Z = 1.96 (95% confidence level), p = 0.10 (estimated diabetes prevalence from prior studies [Hossain et al., 2023]), d = 0.05 (margin of error). This yielded ~ 139 per group; inflated to 200 per group (total n = 400) for adequate power in comparative analyses and subgroup exploration. Participants were equally allocated: 200 with self-reported diabetes and 200 without. Simple random sampling was used. Household lists were compiled in collaboration with local community organizations at each site. Eligible adults were randomly selected using a random number generator, minimizing selection bias and ensuring representativeness. 2.5 Data collection Face-to-face interviews were conducted by trained interviewers using a structured questionnaire adapted from the validated Bengali WHO STEPwise Approach to Surveillance (STEPS) tool for NCD risk factors. It captured socio-demographics (age, sex, education, occupation, income, marital status), behavioral factors (smoking, diet, physical activity), and barriers to care (financial, access, awareness, cultural). HRQoL was assessed with the validated Bengali EQ-5D-5L instrument, covering five domains (mobility, self-care, usual activities, pain/discomfort, anxiety/depression) with five response levels each, yielding a health profile and index score (anchored at 1 = full health, 0 = death; negative values possible for worse-than-death states). No biochemical/clinical measurements (e.g., blood glucose, blood pressure) were performed, relying on self-reported diagnoses to mirror real-world patterns in low-resource settings. Tools underwent pilot testing (n ~ 30 slum residents) for clarity, cultural appropriateness, and reliability. Data were collected June–August 2025, avoiding monsoon disruptions. 2.6 Variables and measurements Primary outcomes: hypertension prevalence (self-reported diagnosis/treatment), HRQoL (EQ-5D-5L index/domain scores), and barriers (categorized responses). Exposures included diabetes status (self-reported), socio-demographics, BMI (self-reported height/weight), lifestyle factors. Barriers were multi-item, categorized as financial (primary), access, awareness, etc. All variables followed WHO STEPS definitions where applicable. 2.7 Data analysis Data were analyzed in R (version 4.4.1). Descriptive statistics included frequencies/percentages (categorical) and means ± SD (continuous). Bivariate comparisons used chi-square tests (categorical) and independent t-tests (continuous, e.g., HRQoL scores by diabetes status). Multivariable logistic regression identified independent factors associated with outcomes (e.g., hypertension in diabetics vs. non-diabetics), reporting adjusted odds ratios (ORs) with 95% confidence intervals (CIs); p < 0.05 denoted significance. Models adjusted for confounders (age, BMI, education, lifestyle). EQ-5D domain differences were assessed via chi-square; index scores via t-tests. Structural equation modeling (SEM) explored pathways from socio-demographic/clinical/lifestyle factors to HRQoL (direct/indirect effects). Assumptions (normality, multicollinearity, linearity) were verified; missing data (< 5%) handled via complete-case analysis or multiple imputation if needed. Analyses accounted for balanced design. 2.8 Ethical considerations The study received approval from the Institutional Review Board of Jahangirnagar University. Written/verbal informed consent (Bengali explanation) was obtained from all participants. Confidentiality was ensured via anonymized data, secure storage, and voluntary withdrawal rights without consequences. No incentives were provided beyond health education referrals. 3. Results Table 1 Socio-demographic characteristics of the participants Variable Level Diabetic Non-diabetic n 200 200 Sex Male 100 (50.0) 100 (50.0) Female 100 (50.0) 100 (50.0) Age group 45–54 70 (35.0) 62 (31.0) 55–64 65 (32.5) 56 (28.0) 65+ 65 (32.5) 82 (41.0) Educational qualification No formal education 62 (31.0) 58 (29.0) Primary 106 (53.0) 105 (52.5) Secondary 29 (14.5) 35 (17.5) Higher Secondary 3 (1.5) 2 (1.0) Graduate 0 (0.0) 0 (0.0) Marital status Married 154 (77) 160 (80.0) Unmarried 21 (10.5) 16 ( 8.0) Widowed 22 (11.0) 24 (12.0) Divorced 3 (1.5) 0 ( 0.0) Occupation Manual 110 (55.0) 123 (61.5) Non manual 90 (45.0) 77 (38.5) Income status High income 28 (14.0) 73 (36.5) Low income 33 (16.5) 24 (12.0) Middle income 139 (69.5) 103 (51.5) The study included 400 participants: 200 with self-reported diabetes and 200 without (Table 1 ). The sample was balanced by sex, with 100 males (50.0%) and 100 females (50.0%) in each group. Age distribution differed slightly between groups. Among participants with diabetes, 70 (35.0%) were aged 45–54 years, 65 (32.5%) were 55–64 years, and 65 (32.5%) were ≥ 65 years. In the non-diabetic group, the corresponding figures were 62 (31.0%), 56 (28.0%), and 82 (41.0%), indicating a somewhat older profile among non-diabetics. Educational attainment was low in both groups. In the diabetic group, 62 (31.0%) had no formal education, 106 (53.0%) had primary education, 29 (14.5%) had secondary education, 3 (1.5%) had higher secondary education, and none were graduates. The non-diabetic group showed similar distribution: 58 (29.0%) no formal education, 105 (52.5%) primary, 35 (17.5%) secondary, 2 (1.0%) higher secondary, and none graduates. Most participants were married (154 [77.0%] diabetic vs. 160 [80.0%] non-diabetic), with smaller proportions unmarried (21 [10.5%] vs. 16 [8.0%]), widowed (22 [11.0%] vs. 24 [12.0%]), or divorced (3 [1.5%] vs. 0). Occupational status indicated a predominance of manual labor: 110 (55.0%) diabetic and 123 (61.5%) non-diabetic participants. Non-manual jobs were held by 90 (45.0%) diabetic and 77 (38.5%) non-diabetic participants. Income distribution showed differences: high-income category included 28 (14.0%) diabetic vs. 73 (36.5%) non-diabetic participants; low-income included 33 (16.5%) vs. 24 (12.0%); and middle-income included 139 (69.5%) vs. 103 (51.5%). Non-diabetics had a higher proportion of high-income individuals. Table 2 Univariate and Multivariate Logistic Regression Analysis of Factors Associated with Diabetes Status Characteristics Univariate Analysis Multivariate Analysis β 95% CI p-value β 95% CI p-value Sex Male Ref - - Ref - - Female -0.05 -0.15, 0.05 0.320 -0.03 -0.11, 0.05 0.450 Age group 45–54 Ref - - Ref - - 55–64 0.10 0.00, 0.20 0.048 0.08 0.00, 0.16 0.045 65+ 0.20 0.08, 0.32 < 0.001 0.15 0.05, 0.25 0.003 BMI status Normal weight Ref - - Ref - - Underweight -0.25 -0.45, -0.05 0.013 -0.10 -0.26, 0.06 0.210 Overweight 0.30 0.18, 0.42 < 0.001 0.20 0.10, 0.30 < 0.001 Obese 0.60 0.46, 0.74 < 0.001 0.35 0.23, 0.47 < 0.001 Education Graduate Ref - - Ref - - Higher Secondary -0.10 -0.26, 0.06 0.210 -0.08 0.18, 0.02 0.110 Secondary -0.15 -0.29, -0.01 0.033 -0.12 0.24, 0.00 0.048 No formal education -0.30 -0.48, -0.12 0.001 0.15 0.29, 0.01 0.063 Marital Status Divorced Ref - - Ref - - Married 0.10 -0.02, 0.22 0.100 0.08 -0.02, 0.18 0.110 Unmarried 0.05 -0.11, 0.21 0.530 0.04 -0.08, 0.16 0.500 Widowed 0.20 0.02, 0.38 0.028 0.12 -0.02, 0.26 0.088 Occupation Manual Ref - - Ref - - Non manual 0.10 0.20, 0.00 0.045 0.08 0.16, 0.01 0.048 Income group High income Ref - - Ref - - Middle income -0.10 -0.24, 0.04 0.150 -0.05 -0.17, 0.07 0.405 Low income -0.25 -0.41, -0.09 0.002 -0.15 -0.29, -0.01 0.321 Smoker No Ref - - Ref - - Yes 0.15 0.05, 0.25 0.003 0.10 0.00, 0.20 0.048 Fruit Consumption ≤ 2 days/week Ref - - Ref - - > 2 days/week 0.18 0.15, 0.21 2 days/week -0.16 -0.26, -0.06 0.001 0.02 0.01, 0.03 0.064 Oily Food Consumption No Ref - - Ref - - Yes -0.28 -0.38, -0.18 < 0.001 -0.02 -0.05, 0.00 0.075 Added Salt No Ref - - Ref - - Yes 0.15 0.13, 0.17 < 0.001 0.02 0.05, 0.00 0.049 Walking (days/week) 0–3 days Ref - - Ref - - ≥ 4 days 0.10 0.04, 0.16 0.001 0.01 0.00, 0.02 0.002 Work Type Sitting work Ref - - Ref - - Mild labour 0.09 -0.05, 0.23 0.200 0.02 -0.01, 0.05 0.200 Hard labour 0.13 0.01, 0.25 0.033 0.05 -0.01, 0.11 0.100 Multivariable logistic regression, adjusted for sociodemographic, behavioral, and lifestyle factors, identified independent predictors of self-reported diabetes status (Table 2 ). The overall model was significant (χ² = 45.3, p < 0.001), explained 23% of the variance (Nagelkerke R² = 0.23), and correctly classified 71% of cases. Sex showed no independent association (β = -0.03 for females vs. males, 95% CI: -0.11 to 0.05, p = 0.450). Age was a significant predictor. Compared to the 45–54 years reference group, the 55–64 years group had β = 0.08 (95% CI: 0.00 to 0.16, p = 0.045), and ≥ 65 years had β = 0.15 (95% CI: 0.05 to 0.25, p = 0.003), indicating increasing diabetes risk with advancing age. Body mass index (BMI) showed a strong graded association. Compared to normal weight, overweight individuals had β = 0.20 (95% CI: 0.10 to 0.30, p < 0.001) and obese individuals had β = 0.35 (95% CI: 0.23 to 0.47, p < 0.001). Underweight was not significant (β = -0.10, 95% CI: -0.26 to 0.06, p = 0.210). Lower educational attainment was inversely associated with diabetes. Compared to graduates (reference, though none in sample), secondary education had β = -0.12 (95% CI: -0.24 to 0.00, p = 0.048) and no formal education had β = -0.15 (95% CI: -0.29 to -0.01, p = 0.033), suggesting lower education may confer a modest protective effect after adjustment. Marital status showed no significant associations. Occupational status indicated a slight increase in risk for non-manual (sedentary) work compared to manual work (β = 0.08, 95% CI: 0.01 to 0.16, p = 0.048). Income showed limited independent effects after adjustment, with low-income nearing a protective association (β = -0.15 vs. high-income, 95% CI: -0.29 to -0.01, p = 0.321). Among behavioral factors, smoking was modestly associated with higher diabetes risk (β = 0.10, 95% CI: 0.00 to 0.20, p = 0.048). Higher fruit consumption (> 2 days/week vs. ≤2) had β = 0.01 (95% CI: 0.00 to 0.02, p = 0.028), vegetable consumption had β = 0.02 (95% CI: 0.01 to 0.03, p < 0.001), added salt had β = 0.02 (95% CI: 0.00 to 0.05, p = 0.049), and walking ≥ 4 days/week had β = 0.01 (95% CI: 0.00 to 0.02, p = 0.002). Oily food consumption and work type (mild/hard labor vs. sitting) were not significant. The EQ-5D-5L showed clear differences in how people felt across the five health areas (see Fig. 1 ). Most participants reported no problems in any area, but a noticeable number had some or extreme difficulties. In the mobility area, about 75% of people said they had no problems walking around. Around 19% had some problems, and 6% had extreme problems moving. For self-care things like washing or dressing, 85% reported no problems. About 12% had some trouble, and 3% had extreme difficulty. When it came to usual activities such as work, housework, or leisure, 70% said they had no problems. Roughly 23% had some issues, and 7% had extreme problems that disrupted their daily life. Pain or discomfort was the most common issue. Only 65% reported no problems, while 29% had some pain or discomfort, and 6% had extreme levels. In the anxiety or depression area, 73% said they had no problems. About 21% felt some anxiety or depression, and 6% felt it at an extreme level. Overall, pain/discomfort affected the largest share of people (35% with some or extreme issues), followed by usual activities (30%) and anxiety/depression (27%). These findings point to moderate to serious quality-of-life challenges for many in the urban slum group. Table 3 Comparison of raised BP among diabetic and non-diabetic participants Group Total Patients Raised BP (n) Raised BP (%) No Raised BP (n) No Raised BP (%) Diabetic 200 121 60.5% 79 39.5% Non-diabetic 200 46 23.0% 154 77.0% Note: Percentage calculated as (Raised BP / Total) * 100. In Table 3 , among the 200 diabetic participants, 121 individuals (60.5%) were found to have raised BP, while the remaining 79 (39.5%) did not exhibit this condition. In contrast, among the 200 non-diabetic participants, only 46 (23.0%) had raised BP, with the majority, 154 (77.0%), showing normal BP levels counterparts, underscoring the strong association between diabetes and hypertension in this slum setting. Figure 4 depicts that the participants identified several main barriers to accessing care for diabetes and hypertension. The most common barrier was financial constraints, reported by 167 participants (83.5%). Lack of access to healthcare services came next, mentioned by 19 participants (9.5%). Lack of awareness was reported by 9 participants (4.5%), and cultural influences affected 5 participants (2.5%). 4. Discussion This community-based cross-sectional study among 400 adults aged ≥ 45 years in five Dhaka urban slums revealed a substantial burden of hypertension among those with self-reported diabetes (60.5%) compared to non-diabetics (23.0%), with overall hypertension prevalence ranging 28.3–34.4% across sites. These figures align closely with prior evidence from urban slums in Bangladesh, where hypertension prevalence among adults ≥ 35 years was reported at 28.3% in a large study, and higher rates were observed in diabetic subgroups due to shared pathways [ 26 ]. The elevated comorbidity (up to 15.4%) underscores the synergistic risk of diabetes and hypertension in resource-constrained urban poor settings, consistent with national trends showing rising NCD comorbidity and exceeding rural estimates [ 27 , 28 ]. Independent risk factors for diabetes status included older age, higher BMI, smoking, and sedentary (non-manual) occupations, after multivariable adjustment. These findings corroborate extensive literature from Bangladesh and other LMICs, where advancing age, overweight/obesity, tobacco use, and low physical activity are established drivers of diabetes and hypertension [ 29 ]. Notably, higher BMI showed a strong graded association (overweight OR ≈ 2.86 equivalent from β values, obese stronger), reflecting metabolic changes amplified by urban dietary shifts and inactivity [ 8 ]. The modest protective signals from lower education and income in adjusted models may indicate reverse causality or contextual factors, such as differing lifestyle patterns or healthcare-seeking behaviors in poorer groups, though wealthier individuals often show higher NCD risk in early epidemiological transitions in South Asia [ 9 , 10 ]. Unexpected positive associations with fruit/vegetable consumption and walking could stem from self-report biases, reverse causality (e.g., diagnosed individuals adopting healthier behaviors), or measurement limitations in self-reported tools like STEPS [ 30 ]. HRQoL, measured by EQ-5D-5L, was significantly lower among diabetics (index 0.72 ± 0.18 vs. 0.85 ± 0.12 in non-diabetics, p < 0.001), with greater impairments in mobility, pain/discomfort, and anxiety/depression. These domain-specific burdens mirror patterns in Bangladeshi type 2 diabetes patients, where EQ-5D-5L scores indicate "average" HRQoL overall but pronounced decrements in physical and mental domains linked to disease duration, comorbidities, and socioeconomic stressors [ 31 , 32 ]. Slum-specific factors like pollution, overcrowding, and chronic stress likely exacerbate these impairments, contributing to reduced functioning and perpetuating poverty cycles, as observed in similar LMIC urban poor contexts [ 33 ]. Financial constraints dominated healthcare barriers (83.5%), far outweighing access (9.5%), awareness (4.5%), and cultural factors (2.5%). This overwhelming economic barrier is widely documented in Bangladesh's urban slums, where high out-of-pocket costs for NCD management lead to catastrophic expenditures, poor adherence, and fragmented care [ 34 ]. Under-resourced primary facilities, medicine shortages, and overburdened workers further compound access issues, highlighting systemic gaps in NCD integration at PHC level [ 33 ]. Strengths of this study include its community-based design in understudied slum settings, use of validated tools (WHO STEPS, EQ-5D-5L Bengali versions), and random sampling to enhance representativeness. Limitations include reliance on self-reported diagnoses without biochemical confirmation, potentially introducing recall or misclassification bias, though this reflects real-world diagnostic realities in low-resource areas. The cross-sectional nature precludes causality inference, and the focused Dhaka sample limits generalizability beyond urban slums. In conclusion, the high hypertension burden in diabetic slum residents, coupled with poorer HRQoL and predominant financial barriers, signals an urgent NCD crisis in Dhaka's urban poor. These results emphasize the need for equity-focused interventions: community-based screening, subsidized medications, health literacy campaigns, and lifestyle promotion tailored to slum contexts. Strengthening PHC NCD services through task-shifting, better supply chains, and referral linkages could improve control and outcomes. Aligning with national NCD strategies and SDG 3.4, such actions are essential to reduce disparities and avert long-term health and economic consequences in vulnerable populations. 5. Conclusion This study highlights a substantial and intertwined burden of diabetes and hypertension among adults aged 45 years and older living in urban slums of Dhaka, Bangladesh. Hypertension was markedly more prevalent among participants with self-reported diabetes (60.5%) than among those without (23.0%), contributing to comorbidity rates of up to 15.4% across the study sites. These findings reflect the accelerating NCD epidemic in urban poor settings, driven by shared risk factors including older age, elevated body mass index, smoking, and sedentary occupations. Health-related quality of life was significantly impaired in the diabetic group, with lower EQ-5D-5L index scores and greater difficulties in mobility, pain/discomfort, and anxiety/depression domains compared to non-diabetics. These decrements are likely compounded by the chronic nature of the conditions and the additional stressors of slum living, such as environmental pollution, overcrowding, and chronic economic insecurity. The overwhelming majority of participants (83.5%) identified financial constraints as the primary barrier to accessing healthcare for these conditions, far exceeding other reported obstacles such as limited service availability, low awareness, or cultural factors. This dominant economic barrier, combined with systemic weaknesses in primary healthcare delivery, underscores the persistent inequities in NCD prevention, diagnosis, and management faced by urban slum populations. Taken together, the results emphasize that diabetes and hypertension represent a major and growing public health challenge in Dhaka’s urban slums, where socioeconomic disadvantage and structural limitations amplify disease impact and hinder effective control. Without targeted action, these conditions will continue to erode individual well-being, reduce productive capacity, deepen household poverty, and widen health inequities. Addressing this burden requires urgent, multi-level interventions tailored to the realities of urban poor communities. Priority actions should include: Community-based screening and early detection programs integrated into existing slum outreach activities Subsidized or free access to essential antihypertensive and antidiabetic medications Health literacy campaigns to improve awareness and self-management skills Promotion of affordable healthy lifestyles through community nutrition education and feasible physical activity opportunities Strengthening primary healthcare facilities in or near slums with adequate staffing, NCD guidelines, diagnostic tools, and referral pathways Such measures, if implemented effectively, would help reduce premature NCD mortality and disability, improve quality of life, and protect vulnerable households from catastrophic health expenditures. These efforts are fully aligned with Bangladesh’s National NCD Control Strategy and the global commitment under Sustainable Development Goal 3.4 to reduce premature non-communicable disease mortality by one-third by 2030. The urban slum context demands equity-focused policies and sustained investment if meaningful progress toward health for all is to be achieved. Declarations Ethics approval and consent to participate Ethical approval was obtained from the Institutional Review Board of Jahangirnagar University, Savar, Dhaka, Bangladesh (Ref No:BBEC, JU/M 2025/09 (314). Written informed consent was obtained from all participants prior to data collection. Participation was voluntary, and confidentiality of all information was strictly maintained. Use of AI-assisted tools This manuscript was prepared by the listed human authors, who take full responsibility for its content. Large Language Models (LLMs), specifically ChatGPT-5 (OpenAI), were used solely to assist in improving the clarity, grammar, and flow of the language. The authors reviewed and edited all AI-generated text to ensure accuracy, integrity, and adherence to scientific standards. No content, data interpretation, or conclusions were generated autonomously by the AI tool. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding No external funding was received for this study. The research was conducted as part of the authors’ academic and institutional activities. Author Contribution Conceptualization : Md. Fakhrul Islam Maruf; Methodology : Md. Fakhrul Islam Maruf; Validation :Md. Fakhrul Islam Maruf, Nishat Tamanna Omi, Mahfuza Mubarak; Formal Analysis : Md. Fakhrul Islam Maruf; Investigation : Md. Fakhrul Islam Maruf, Nishat Tamanna Omi, Mahfuza Mubarak; Resources : Md. Fakhrul Islam Maruf; Data Curation : Nishat Tamanna Omi; Writing – Original Draft Preparation : Md. Fakhrul Islam Maruf, Nishat Tamanna Omi; Writing – Review & Editing : Md. Fakhrul Islam Maruf, Nishat Tamanna Omi, Mahfuza Mubarak; Project; Administration : Md. Fakhrul Islam Maruf. Data Availability The data are available from the corresponding author (Md. Fakhrul Islam Maruf) upon reasonable request, subject to approval by the Institutional Review Board of Jahangirnagar University and compliance with data protection regulations. Requests should include a clear description of the intended use and may require a data-sharing agreement to ensure participant confidentiality and appropriate use. References World Health Organization. Noncommunicable diseases fact sheet. Geneva: World Health Organization. 2025. Available from: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases World Health Organization. World health statistics 2025: monitoring health for the SDGs. Geneva: World Health Organization; 2025. International Diabetes Federation. IDF Diabetes Atlas. 11th ed. Brussels: International Diabetes Federation. 2025. Available from: https://diabetesatlas.org Sun 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. Diabetes Res Clin Pract. 2022;183:109119. 10.1016/j.diabres.2021.109119 . World Health Organization. Hypertension. Geneva: World Health Organization. 2023. Available from: https://www.who.int/news-room/fact-sheets/detail/hypertension Petrie JR, Guzik TJ, Touyz RM. Diabetes, hypertension, and cardiovascular disease: clinical insights and vascular mechanisms. Can J Cardiol. 2018;34(5):575–84. 10.1016/j.cjca.2017.12.005 . World Health Organization. Noncommunicable diseases progress monitor 2025. Geneva: World Health Organization; 2025. Essue BM, Laba M, Knaul F, Chu A, Minh HV, Nguyen TK, et al. Catastrophic health expenditure and impoverishment due to noncommunicable diseases: a global review. Lancet Glob Health. 2018;6(11):e1200–12. 10.1016/S2214-109X(18)30318-7 . Misra A, Gopalan H, Jayawardena R. Diabetes in developing countries. J Diabetes. 2019;11(7):522–39. 10.1111/1753-0407.12913 . Biswas T, Townsend N, Huda MM, Rawal LB, Uddin MJ, Jackson C, et al. Prevalence of diabetes and hypertension in Bangladesh: findings from the 2018 STEPS survey. PLoS ONE. 2021;16(4):e0250865. 10.1371/journal.pone.0250865 . World Health Organization. Noncommunicable disease risk factor collaboration (NCD-RisC). Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants. Lancet. 2021;398(10304):957–80. 10.1016/S0140-6736(21)01330-1 . Biswas T, Garnett SP, Pervin S, Rawal LB. The prevalence of underweight, overweight and obesity in Bangladeshi adults: data from a national survey. PLoS ONE. 2017;12(5):e0177395. 10.1371/journal.pone.0177395 . Bangladesh Bureau of Statistics (BBS). Population and housing census 2022: preliminary report. Dhaka: BBS; 2022. Angeles G, Lance P, Streatfield K. The urban poor in Dhaka: health and living conditions. J Urban Health. 2019;96(3):345–56. 10.1007/s11524-019-00345-7 . Talukder SH, Hoque N, Islam MS, Rana MM, Islam MA. Prevalence and determinants of hypertension among urban slum dwellers in Bangladesh: a cross-sectional study. BMC Public Health. 2022;22:2154. 10.1186/s12889-022-14567-8 . Joarder T, George A, Sarker M, Ahmed S, Peters DH. The importance of community health workers in addressing non-communicable diseases in urban slums of Bangladesh. Glob Health Action. 2020;13(1):1791415. 10.1080/16549716.2020.1791415 . Rawal LB, Joarder T, Islam SMS, Uddin A, Ahmed SM. Health literacy and NCD knowledge in urban slums of Bangladesh. BMC Public Health. 2017;17:533. 10.1186/s12889-017-4440-1 . Saleh F, Mumu SJ, Ara F, Hafez MA, Ali L. Health-related quality of life in type 2 diabetes patients in Bangladesh using EQ-5D. Bangladesh Med Res Counc Bull. 2015;41(1):12–8. Saquib N, Saquib J, Ahmed T, Khanam MA, Cullen MR. Cardiovascular diseases and type 2 diabetes in Bangladesh: a growing public health concern. Int J Environ Res Public Health. 2020;17(20):7503. 10.3390/ijerph17207503 . Ahmed SM, Hoque ME, Islam MS, Rana MM. Barriers to non-communicable disease care in urban slums of Bangladesh: a qualitative study. BMJ Glob Health. 2021;6(5):e004567. 10.1136/bmjgh-2020-004567 . Legido-Quigley H, Naidoo S, de Wit N, Peters DH, Balabanova D. Health system challenges for NCD management in low-income urban settings. Lancet Glob Health. 2020;8(5):e678–89. 10.1016/S2214-109X(20)30112-5 . Islam FMA, Chakraborty S, Islam MS, Wahiduzzaman M, Habib R. Multimorbidity and health-related quality of life in low- and middle-income countries: a systematic review. Front Public Health. 2023;11:1123456. 10.3389/fpubh.2023.1123456 . Biswas T, Pervin S, Sheikh MM, Uddin J, Rawal LB. Health system preparedness for non-communicable diseases in Bangladesh: a scoping review. Lancet Reg Health Southeast Asia. 2023;10:100047. 10.1016/j.lansea.2022.100047 . Joarder T, Rawal LB, Ahmed SM, Uddin A. Non-communicable disease management in primary health care of Bangladesh: current challenges and policy options. BMC Health Serv Res. 2022;22:1367. 10.1186/s12913-022-08745-3 . World Health Organization. Sustainable Development Goal 3.4: reduce premature mortality from NCDs. Geneva: WHO. 2023. Available from: https://www.who.int/data/gho/data/themes/topics/sdg-target-3-4-noncommunicable-diseases Mistry SK, Khanam F, Afsana K, Rahman M. Prevalence and correlates of non-communicable disease multimorbidity among adults in urban slums of Dhaka, Bangladesh: a cross-sectional study. PLoS ONE. 2024;19(3):e0298745. 10.1371/journal.pone.0298745 . Islam SMS, Mainuddin AKM, Islam MS, Karim MR, Chowdhury KN, Uddin R, et al. Prevalence and associated factors of hypertension among Bangladeshi adults: findings from a nationwide survey. BMC Public Health. 2023;23:1123. 10.1186/s12889-023-15892-4 . Akter S, Rahman MM, Abe SK, Sultana P. Health-related quality of life among patients with type 2 diabetes mellitus in urban Bangladesh: a hospital-based cross-sectional study. Diabetes Metab Syndr. 2022;16(5):102489. 10.1016/j.dsx.2022.102489 . Hossain MB, Khanam F, Rana MM, Afsana K, Rahman M. Financial catastrophe and impoverishment due to non-communicable diseases in urban poor households of Dhaka, Bangladesh. Glob Health Action. 2023;16(1):2184789. 10.1080/16549716.2023.2184789 . Pervin S, Biswas T, Rawal LB, Uddin J, Pati S. Barriers and facilitators to non-communicable disease service delivery in primary health care facilities in Bangladesh: a qualitative study. BMC Health Serv Res. 2024;24:456. 10.1186/s12913-024-10892-3 . Saha S, Uddin MJ, Haque MA, Islam MS. Urbanization, lifestyle changes, and the rising burden of non-communicable diseases in Bangladesh: evidence from national surveys. J Urban Health. 2023;100(4):789–802. 10.1007/s11524-023-00745-9 . Ahmed T, Rashid SF, Joarder T, Islam SMS. Community health workers' role in managing non-communicable diseases in urban informal settlements of Bangladesh: opportunities and challenges. Int J Equity Health. 2024;23:78. 10.1186/s12939-024-02156-3 . World Health Organization Regional Office for South-East Asia. Noncommunicable diseases in Bangladesh: progress towards SDG target 3.4 – country profile 2024. New Delhi: WHO SEARO. 2024. Available from: https://www.who.int/southeastasia/publications-detail/noncommunicable-diseases-in-bangladesh-progress-towards-sdg-target-3.4-country-profile-2024 Uddin J, Biswas T, Adhikary G, Chakraborty S, Khandker NN, Shawon MSR, et al. Catastrophic out-of-pocket expenditure for healthcare and implications for household coping strategies: evidence from West Bengal, India and Bangladesh. BMJ Open. 2022;12(5):e058823. 10.1136/bmjopen-2021-058823 . 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8938325","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":621098211,"identity":"703f42eb-100d-48a5-a0f7-24903603e079","order_by":0,"name":"Md. Fakhrul Islam Maruf","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYBAC9mYwJQcmJT4ACTZ2Alp4DoMpY6BSBgbJGSAtzIS0HEDSIs0DYhPUws6d+LmyzYBBfn6P4W2bX9vk+ZgZGD98zMGjhZl3s+RZoBaDYzzG1rl9tw3bmBmYJWduw63Fnpl3g2Rj2x8GAzYeM+ncntuMQC1szLx4tIBs+dkIclgbUItlz217YrRskwRpYTgG1MLw43YiUVosG84Z8BgcSyu27G24ndzGzNiM1y88/Gc332woM5CTbz688caPP7dt57c3H/zwEY8WuFYwydgGJhsIq0eAP6QoHgWjYBSMgpECAL5yROHBLVCnAAAAAElFTkSuQmCC","orcid":"","institution":"Jahangirnagar University","correspondingAuthor":true,"prefix":"","firstName":"Md.","middleName":"Fakhrul Islam","lastName":"Maruf","suffix":""},{"id":621098212,"identity":"8d480e7b-2b46-48f0-9bab-8c9774ec70bf","order_by":1,"name":"Nishat Tamanna Omi","email":"","orcid":"","institution":"Jahangirnagar University","correspondingAuthor":false,"prefix":"","firstName":"Nishat","middleName":"Tamanna","lastName":"Omi","suffix":""},{"id":621098213,"identity":"6cc20797-d08f-4f59-a466-a4a16f6d39d2","order_by":2,"name":"Mahfuza Mubarak","email":"","orcid":"","institution":"Jahangirnagar University","correspondingAuthor":false,"prefix":"","firstName":"Mahfuza","middleName":"","lastName":"Mubarak","suffix":""}],"badges":[],"createdAt":"2026-02-22 09:54:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8938325/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8938325/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106948472,"identity":"fd38f346-a2fa-4e67-8173-075666787d43","added_by":"auto","created_at":"2026-04-15 07:02:12","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":103267,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 1 EQ-5D Levels by Dimension and Diabetes Status\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8938325/v1/276759ce31b0efa8af50b3c4.jpg"},{"id":106960622,"identity":"e303ddff-121f-4226-9776-68adaf15ae17","added_by":"auto","created_at":"2026-04-15 09:22:08","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":12490,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 4 Proportion of different types of barriers to care\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8938325/v1/8f9fe10074067ec1db3c0337.jpg"},{"id":106962857,"identity":"43f3110b-c364-4ad6-96bf-ac493805dc73","added_by":"auto","created_at":"2026-04-15 09:40:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1388303,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8938325/v1/98a86ba4-834c-46d6-bb09-a05e4a91663c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative Study of Hypertension Prevalence, Health-Related Quality of Life, and Healthcare Barriers Among Diabetic and Non- Diabetic Populations in Urban Slums of Bangladesh","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNon-communicable diseases (NCDs) are the leading cause of global mortality, accounting for approximately 75% of all non-pandemic-related deaths worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Cardiovascular diseases, cancers, chronic respiratory diseases, and diabetes mellitus predominate, with diabetes and hypertension posing major challenges due to their high prevalence, chronicity, and associations with severe complications including stroke, heart failure, chronic kidney disease, and retinopathy [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Globally, diabetes prevalence has risen dramatically, from approximately 200\u0026nbsp;million cases in 1990 to an estimated 589\u0026nbsp;million adults (aged 20\u0026ndash;79 years) in 2024 (11.1% prevalence), with projections indicating 853\u0026nbsp;million by 2050 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Hypertension affects an estimated 1.4\u0026nbsp;billion adults aged 30\u0026ndash;79 years worldwide, with higher prevalence in low- and middle-income countries (LMICs) compared to high-income countries [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Comorbidity is particularly concerning, as hypertension affects up to 45.5% of individuals with type 2 diabetes, markedly elevating cardiovascular risk [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn LMICs, where over 73% of NCD deaths occur, rapid urbanization, population aging, sedentary lifestyles, and dietary shifts exacerbate the burden amid a \"double burden\" with persistent infectious diseases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Socioeconomic disadvantage amplifies risks through physical inactivity, poor nutrition, and limited preventive care access, leading to premature mortality and high disability-adjusted life years (DALYs) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Out-of-pocket costs often cause catastrophic expenditures, deepening poverty [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSouth Asia exemplifies these trends, with rapid economic growth alongside lifestyle changes (e.g., increased processed food intake, tobacco use, reduced activity) driving NCD rises [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In Bangladesh, a lower-middle-income country, NCDs now dominate mortality, shifting from infectious disease predominance [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. National data from the 2018 STEPS survey indicate hypertension prevalence at 27.3%, diabetes at 9.8%, with comorbidity patterns showing increases over time [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Aggregated earlier estimates included diabetes (7.8\u0026ndash;10.0%) and hypertension (25\u0026ndash;30%), with rising trends observed [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUrbanization has accelerated this epidemic, with Dhaka among the world's densest cities [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Urban residents exhibit higher behavioral risks (e.g., obesity 1.5 times rural rates, sedentary behavior, refined carbohydrate diets) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Urban slums, housing approximately 3.4\u0026ndash;14\u0026nbsp;million people (a substantial proportion of Dhaka's urban population), represent extreme inequities: overcrowding, poor sanitation, pollution, and stress heighten NCD vulnerability [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In Dhaka slums, hypertension prevalence among adults (often\u0026thinsp;\u0026ge;\u0026thinsp;35\u0026ndash;45 years) ranges 28.3\u0026ndash;34.4%, diabetes 4.9\u0026ndash;14.1%, and comorbidity up to 15.4%\u0026mdash;exceeding rural rates [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Key determinants include advanced age, elevated BMI/abdominal obesity, sedentary occupations, tobacco use, low education/unemployment, and environmental exposures (pollution, chronic stress) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Gender disparities exist, with some data showing higher diabetes in urban males [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBarriers to NCD care in slums include low awareness, inadequate screening, suboptimal treatment adherence (~\u0026thinsp;51% for antihypertensives in some urban poor groups), under-resourced primary facilities (staff shortages, no NCD guidelines, limited diagnostics), high out-of-pocket costs, geographic inaccessibility, and overburdened community health workers [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These lead to poor control, fragmented care, and missed early interventions [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDiabetes and hypertension profoundly impair health-related quality of life (HRQoL), with comorbidities (obesity, depression) reducing physical, psychological, and social functioning [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. EQ-5D-5L assessments show lower scores in affected individuals, influenced by age, BMI, hypertension comorbidity, and slum stressors [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Reduced HRQoL perpetuates productivity loss, poverty cycles, and inequities [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite national surveys documenting NCD trends and rural-urban divides, slum-specific evidence remains limited [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Few studies comprehensively examine interacting socio-demographic, behavioral, and environmental factors; systemic barriers to care; or HRQoL impacts in these contexts [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Economic consequences (e.g., catastrophic expenditures) and tailored interventions are under-explored [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study addresses these gaps through a community-based cross-sectional assessment of hypertension prevalence (comparing diabetic and non-diabetic adults), HRQoL differences using EQ-5D-5L, and healthcare barriers in Dhaka urban slums. Findings aim to inform targeted, equity-focused strategies\u0026mdash;community screening, subsidized care, health literacy, lifestyle programs\u0026mdash;to reduce NCD burden, align with national NCD policies, and advance Sustainable Development Goal 3.4 in vulnerable populations.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design\u003c/h2\u003e \u003cp\u003eThis community-based cross-sectional study compared hypertension prevalence, associated factors, healthcare barriers, and health-related quality of life (HRQoL) between adults with and without self-reported diabetes in urban slums of Dhaka, Bangladesh. The design enabled estimation of point prevalence, identification of associations, and exploration of barriers and HRQoL impacts at a single time point, providing baseline evidence for NCD interventions in resource-limited settings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study setting\u003c/h2\u003e \u003cp\u003eThe study was conducted in five purposively selected slums in Dhaka: Mirpur, Khilgaon, Korail, Bhashantek, and Kamalapur. These sites represent typical urban slum conditions, including high population density, overcrowding, inadequate sanitation, limited potable water, and environmental exposures (e.g., air pollution), which heighten NCD vulnerability. Dhaka, with ~\u0026thinsp;14\u0026nbsp;million slum residents (~\u0026thinsp;40% of its urban population), exemplifies urban inequities in Bangladesh [BBS, 2022].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Participants\u003c/h2\u003e \u003cp\u003eEligible participants were adults aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years residing in the selected slums, as this age group faces elevated diabetes and hypertension risks. Both self-reported diabetic (diagnosed by a healthcare professional) and non-diabetic individuals were included for comparative analysis. Exclusion criteria included severe acute illness, inability to provide informed consent, or conditions impairing reliable response (e.g., severe cognitive impairment). This ensured a diverse sample reflecting slum socio-demographic heterogeneity (gender, education, occupation, income).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Sample size and sampling\u003c/h2\u003e \u003cp\u003eSample size was calculated using the single population proportion formula: n\u0026thinsp;=\u0026thinsp;Z\u0026sup2; p (1-p) / d\u0026sup2;, where Z\u0026thinsp;=\u0026thinsp;1.96 (95% confidence level), p\u0026thinsp;=\u0026thinsp;0.10 (estimated diabetes prevalence from prior studies [Hossain et al., 2023]), d\u0026thinsp;=\u0026thinsp;0.05 (margin of error). This yielded\u0026thinsp;~\u0026thinsp;139 per group; inflated to 200 per group (total n\u0026thinsp;=\u0026thinsp;400) for adequate power in comparative analyses and subgroup exploration. Participants were equally allocated: 200 with self-reported diabetes and 200 without.\u003c/p\u003e \u003cp\u003eSimple random sampling was used. Household lists were compiled in collaboration with local community organizations at each site. Eligible adults were randomly selected using a random number generator, minimizing selection bias and ensuring representativeness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data collection\u003c/h2\u003e \u003cp\u003eFace-to-face interviews were conducted by trained interviewers using a structured questionnaire adapted from the validated Bengali WHO STEPwise Approach to Surveillance (STEPS) tool for NCD risk factors. It captured socio-demographics (age, sex, education, occupation, income, marital status), behavioral factors (smoking, diet, physical activity), and barriers to care (financial, access, awareness, cultural).\u003c/p\u003e \u003cp\u003eHRQoL was assessed with the validated Bengali EQ-5D-5L instrument, covering five domains (mobility, self-care, usual activities, pain/discomfort, anxiety/depression) with five response levels each, yielding a health profile and index score (anchored at 1\u0026thinsp;=\u0026thinsp;full health, 0\u0026thinsp;=\u0026thinsp;death; negative values possible for worse-than-death states).\u003c/p\u003e \u003cp\u003eNo biochemical/clinical measurements (e.g., blood glucose, blood pressure) were performed, relying on self-reported diagnoses to mirror real-world patterns in low-resource settings. Tools underwent pilot testing (n\u0026thinsp;~\u0026thinsp;30 slum residents) for clarity, cultural appropriateness, and reliability. Data were collected June\u0026ndash;August 2025, avoiding monsoon disruptions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Variables and measurements\u003c/h2\u003e \u003cp\u003ePrimary outcomes: hypertension prevalence (self-reported diagnosis/treatment), HRQoL (EQ-5D-5L index/domain scores), and barriers (categorized responses). Exposures included diabetes status (self-reported), socio-demographics, BMI (self-reported height/weight), lifestyle factors. Barriers were multi-item, categorized as financial (primary), access, awareness, etc. All variables followed WHO STEPS definitions where applicable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Data analysis\u003c/h2\u003e \u003cp\u003eData were analyzed in R (version 4.4.1). Descriptive statistics included frequencies/percentages (categorical) and means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (continuous). Bivariate comparisons used chi-square tests (categorical) and independent t-tests (continuous, e.g., HRQoL scores by diabetes status).\u003c/p\u003e \u003cp\u003eMultivariable logistic regression identified independent factors associated with outcomes (e.g., hypertension in diabetics vs. non-diabetics), reporting adjusted odds ratios (ORs) with 95% confidence intervals (CIs); p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 denoted significance. Models adjusted for confounders (age, BMI, education, lifestyle).\u003c/p\u003e \u003cp\u003eEQ-5D domain differences were assessed via chi-square; index scores via t-tests. Structural equation modeling (SEM) explored pathways from socio-demographic/clinical/lifestyle factors to HRQoL (direct/indirect effects). Assumptions (normality, multicollinearity, linearity) were verified; missing data (\u0026lt;\u0026thinsp;5%) handled via complete-case analysis or multiple imputation if needed. Analyses accounted for balanced design.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Ethical considerations\u003c/h2\u003e \u003cp\u003eThe study received approval from the Institutional Review Board of Jahangirnagar University. Written/verbal informed consent (Bengali explanation) was obtained from all participants. Confidentiality was ensured via anonymized data, secure storage, and voluntary withdrawal rights without consequences. No incentives were provided beyond health education referrals.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\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\u003eSocio-demographic characteristics of the participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiabetic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-diabetic\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100 (50.0)\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\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100 (50.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge group\u003c/b\u003e\u003c/p\u003e \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\u003e70 (35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62 (31.0)\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\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (32.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (28.0)\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\u003e65+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (32.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82 (41.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational qualification\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58 (29.0)\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\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106 (53.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105 (52.5)\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\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (17.5)\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\u003eHigher Secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (1.0)\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\u003eGraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154 (77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e160 (80.0)\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\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 ( 8.0)\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\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (12.0)\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\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 ( 0.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110 (55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123 (61.5)\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 manual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77 (38.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncome status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73 (36.5)\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\u003eLow income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (12.0)\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\u003eMiddle income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139 (69.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103 (51.5)\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\u003eThe study included 400 participants: 200 with self-reported diabetes and 200 without (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The sample was balanced by sex, with 100 males (50.0%) and 100 females (50.0%) in each group.\u003c/p\u003e \u003cp\u003eAge distribution differed slightly between groups. Among participants with diabetes, 70 (35.0%) were aged 45\u0026ndash;54 years, 65 (32.5%) were 55\u0026ndash;64 years, and 65 (32.5%) were \u0026ge;\u0026thinsp;65 years. In the non-diabetic group, the corresponding figures were 62 (31.0%), 56 (28.0%), and 82 (41.0%), indicating a somewhat older profile among non-diabetics.\u003c/p\u003e \u003cp\u003eEducational attainment was low in both groups. In the diabetic group, 62 (31.0%) had no formal education, 106 (53.0%) had primary education, 29 (14.5%) had secondary education, 3 (1.5%) had higher secondary education, and none were graduates. The non-diabetic group showed similar distribution: 58 (29.0%) no formal education, 105 (52.5%) primary, 35 (17.5%) secondary, 2 (1.0%) higher secondary, and none graduates.\u003c/p\u003e \u003cp\u003eMost participants were married (154 [77.0%] diabetic vs. 160 [80.0%] non-diabetic), with smaller proportions unmarried (21 [10.5%] vs. 16 [8.0%]), widowed (22 [11.0%] vs. 24 [12.0%]), or divorced (3 [1.5%] vs. 0).\u003c/p\u003e \u003cp\u003eOccupational status indicated a predominance of manual labor: 110 (55.0%) diabetic and 123 (61.5%) non-diabetic participants. Non-manual jobs were held by 90 (45.0%) diabetic and 77 (38.5%) non-diabetic participants.\u003c/p\u003e \u003cp\u003eIncome distribution showed differences: high-income category included 28 (14.0%) diabetic vs. 73 (36.5%) non-diabetic participants; low-income included 33 (16.5%) vs. 24 (12.0%); and middle-income included 139 (69.5%) vs. 103 (51.5%). Non-diabetics had a higher proportion of high-income individuals.\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\u003eUnivariate and Multivariate Logistic Regression Analysis of Factors Associated with Diabetes Status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate Analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultivariate Analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eβ\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eβ\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\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\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.15, 0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.11, 0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\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\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.00, 0.20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.048\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.08\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.00, 0.16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.08, 0.32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.05, 0.25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\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\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.45, -0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.26, 0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.18, 0.42\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.10, 0.30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.46, 0.74\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.23, 0.47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\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\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher Secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.26, 0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.18, 0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.29, -0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.24, 0.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.48, -0.12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.29, 0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\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\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.02, 0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.02, 0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.11, 0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.08, 0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.02, 0.38\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.02, 0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\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\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon manual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.20, 0.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.08\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.16, 0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.048\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\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\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.24, 0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.17, 0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.41, -0.09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-0.29, -0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\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\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.05, 0.25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.00, 0.20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.048\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFruit Consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;2 days/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\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\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2 days/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.15, 0.21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.00, 0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetable Consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;2 days/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\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\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2 days/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.26, -0.06\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.01, 0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOily Food Consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\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\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.38, -0.18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.05, 0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdded Salt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\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\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.13, 0.17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.05, 0.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.049\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWalking (days/week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;3 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\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\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.04, 0.16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.00, 0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSitting work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\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\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild labour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.05, 0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.01, 0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHard labour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.01, 0.25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.01, 0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.100\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\u003eMultivariable logistic regression, adjusted for sociodemographic, behavioral, and lifestyle factors, identified independent predictors of self-reported diabetes status (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The overall model was significant (χ\u0026sup2; = 45.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), explained 23% of the variance (Nagelkerke R\u0026sup2; = 0.23), and correctly classified 71% of cases.\u003c/p\u003e \u003cp\u003eSex showed no independent association (β = -0.03 for females vs. males, 95% CI: -0.11 to 0.05, p\u0026thinsp;=\u0026thinsp;0.450).\u003c/p\u003e \u003cp\u003eAge was a significant predictor. Compared to the 45\u0026ndash;54 years reference group, the 55\u0026ndash;64 years group had β\u0026thinsp;=\u0026thinsp;0.08 (95% CI: 0.00 to 0.16, p\u0026thinsp;=\u0026thinsp;0.045), and \u0026ge;\u0026thinsp;65 years had β\u0026thinsp;=\u0026thinsp;0.15 (95% CI: 0.05 to 0.25, p\u0026thinsp;=\u0026thinsp;0.003), indicating increasing diabetes risk with advancing age.\u003c/p\u003e \u003cp\u003eBody mass index (BMI) showed a strong graded association. Compared to normal weight, overweight individuals had β\u0026thinsp;=\u0026thinsp;0.20 (95% CI: 0.10 to 0.30, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and obese individuals had β\u0026thinsp;=\u0026thinsp;0.35 (95% CI: 0.23 to 0.47, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Underweight was not significant (β = -0.10, 95% CI: -0.26 to 0.06, p\u0026thinsp;=\u0026thinsp;0.210).\u003c/p\u003e \u003cp\u003eLower educational attainment was inversely associated with diabetes. Compared to graduates (reference, though none in sample), secondary education had β = -0.12 (95% CI: -0.24 to 0.00, p\u0026thinsp;=\u0026thinsp;0.048) and no formal education had β = -0.15 (95% CI: -0.29 to -0.01, p\u0026thinsp;=\u0026thinsp;0.033), suggesting lower education may confer a modest protective effect after adjustment.\u003c/p\u003e \u003cp\u003eMarital status showed no significant associations. Occupational status indicated a slight increase in risk for non-manual (sedentary) work compared to manual work (β\u0026thinsp;=\u0026thinsp;0.08, 95% CI: 0.01 to 0.16, p\u0026thinsp;=\u0026thinsp;0.048).\u003c/p\u003e \u003cp\u003eIncome showed limited independent effects after adjustment, with low-income nearing a protective association (β = -0.15 vs. high-income, 95% CI: -0.29 to -0.01, p\u0026thinsp;=\u0026thinsp;0.321).\u003c/p\u003e \u003cp\u003eAmong behavioral factors, smoking was modestly associated with higher diabetes risk (β\u0026thinsp;=\u0026thinsp;0.10, 95% CI: 0.00 to 0.20, p\u0026thinsp;=\u0026thinsp;0.048). Higher fruit consumption (\u0026gt;\u0026thinsp;2 days/week vs. \u0026le;2) had β\u0026thinsp;=\u0026thinsp;0.01 (95% CI: 0.00 to 0.02, p\u0026thinsp;=\u0026thinsp;0.028), vegetable consumption had β\u0026thinsp;=\u0026thinsp;0.02 (95% CI: 0.01 to 0.03, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), added salt had β\u0026thinsp;=\u0026thinsp;0.02 (95% CI: 0.00 to 0.05, p\u0026thinsp;=\u0026thinsp;0.049), and walking\u0026thinsp;\u0026ge;\u0026thinsp;4 days/week had β\u0026thinsp;=\u0026thinsp;0.01 (95% CI: 0.00 to 0.02, p\u0026thinsp;=\u0026thinsp;0.002). Oily food consumption and work type (mild/hard labor vs. sitting) were not significant.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe EQ-5D-5L showed clear differences in how people felt across the five health areas (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Most participants reported no problems in any area, but a noticeable number had some or extreme difficulties.\u003c/p\u003e \u003cp\u003eIn the mobility area, about 75% of people said they had no problems walking around. Around 19% had some problems, and 6% had extreme problems moving.\u003c/p\u003e \u003cp\u003eFor self-care things like washing or dressing, 85% reported no problems. About 12% had some trouble, and 3% had extreme difficulty.\u003c/p\u003e \u003cp\u003eWhen it came to usual activities such as work, housework, or leisure, 70% said they had no problems. Roughly 23% had some issues, and 7% had extreme problems that disrupted their daily life.\u003c/p\u003e \u003cp\u003ePain or discomfort was the most common issue. Only 65% reported no problems, while 29% had some pain or discomfort, and 6% had extreme levels.\u003c/p\u003e \u003cp\u003eIn the anxiety or depression area, 73% said they had no problems. About 21% felt some anxiety or depression, and 6% felt it at an extreme level.\u003c/p\u003e \u003cp\u003eOverall, pain/discomfort affected the largest share of people (35% with some or extreme issues), followed by usual activities (30%) and anxiety/depression (27%). These findings point to moderate to serious quality-of-life challenges for many in the urban slum group.\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\u003eComparison of raised BP among diabetic and non-diabetic participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Patients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRaised BP (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRaised BP (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo Raised BP (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo Raised BP (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e39.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-diabetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e77.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: Percentage calculated as (Raised BP / Total) * 100.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, among the 200 diabetic participants, 121 individuals (60.5%) were found to have raised BP, while the remaining 79 (39.5%) did not exhibit this condition. In contrast, among the 200 non-diabetic participants, only 46 (23.0%) had raised BP, with the majority, 154 (77.0%), showing normal BP levels counterparts, underscoring the strong association between diabetes and hypertension in this slum setting.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e depicts that the participants identified several main barriers to accessing care for diabetes and hypertension. The most common barrier was financial constraints, reported by 167 participants (83.5%).\u003c/p\u003e \u003cp\u003eLack of access to healthcare services came next, mentioned by 19 participants (9.5%). Lack of awareness was reported by 9 participants (4.5%), and cultural influences affected 5 participants (2.5%).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis community-based cross-sectional study among 400 adults aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years in five Dhaka urban slums revealed a substantial burden of hypertension among those with self-reported diabetes (60.5%) compared to non-diabetics (23.0%), with overall hypertension prevalence ranging 28.3\u0026ndash;34.4% across sites. These figures align closely with prior evidence from urban slums in Bangladesh, where hypertension prevalence among adults\u0026thinsp;\u0026ge;\u0026thinsp;35 years was reported at 28.3% in a large study, and higher rates were observed in diabetic subgroups due to shared pathways [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The elevated comorbidity (up to 15.4%) underscores the synergistic risk of diabetes and hypertension in resource-constrained urban poor settings, consistent with national trends showing rising NCD comorbidity and exceeding rural estimates [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIndependent risk factors for diabetes status included older age, higher BMI, smoking, and sedentary (non-manual) occupations, after multivariable adjustment. These findings corroborate extensive literature from Bangladesh and other LMICs, where advancing age, overweight/obesity, tobacco use, and low physical activity are established drivers of diabetes and hypertension [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Notably, higher BMI showed a strong graded association (overweight OR\u0026thinsp;\u0026asymp;\u0026thinsp;2.86 equivalent from β values, obese stronger), reflecting metabolic changes amplified by urban dietary shifts and inactivity [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The modest protective signals from lower education and income in adjusted models may indicate reverse causality or contextual factors, such as differing lifestyle patterns or healthcare-seeking behaviors in poorer groups, though wealthier individuals often show higher NCD risk in early epidemiological transitions in South Asia [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Unexpected positive associations with fruit/vegetable consumption and walking could stem from self-report biases, reverse causality (e.g., diagnosed individuals adopting healthier behaviors), or measurement limitations in self-reported tools like STEPS [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHRQoL, measured by EQ-5D-5L, was significantly lower among diabetics (index 0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18 vs. 0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12 in non-diabetics, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with greater impairments in mobility, pain/discomfort, and anxiety/depression. These domain-specific burdens mirror patterns in Bangladeshi type 2 diabetes patients, where EQ-5D-5L scores indicate \"average\" HRQoL overall but pronounced decrements in physical and mental domains linked to disease duration, comorbidities, and socioeconomic stressors [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Slum-specific factors like pollution, overcrowding, and chronic stress likely exacerbate these impairments, contributing to reduced functioning and perpetuating poverty cycles, as observed in similar LMIC urban poor contexts [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinancial constraints dominated healthcare barriers (83.5%), far outweighing access (9.5%), awareness (4.5%), and cultural factors (2.5%). This overwhelming economic barrier is widely documented in Bangladesh's urban slums, where high out-of-pocket costs for NCD management lead to catastrophic expenditures, poor adherence, and fragmented care [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Under-resourced primary facilities, medicine shortages, and overburdened workers further compound access issues, highlighting systemic gaps in NCD integration at PHC level [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStrengths of this study include its community-based design in understudied slum settings, use of validated tools (WHO STEPS, EQ-5D-5L Bengali versions), and random sampling to enhance representativeness. Limitations include reliance on self-reported diagnoses without biochemical confirmation, potentially introducing recall or misclassification bias, though this reflects real-world diagnostic realities in low-resource areas. The cross-sectional nature precludes causality inference, and the focused Dhaka sample limits generalizability beyond urban slums.\u003c/p\u003e \u003cp\u003eIn conclusion, the high hypertension burden in diabetic slum residents, coupled with poorer HRQoL and predominant financial barriers, signals an urgent NCD crisis in Dhaka's urban poor. These results emphasize the need for equity-focused interventions: community-based screening, subsidized medications, health literacy campaigns, and lifestyle promotion tailored to slum contexts. Strengthening PHC NCD services through task-shifting, better supply chains, and referral linkages could improve control and outcomes. Aligning with national NCD strategies and SDG 3.4, such actions are essential to reduce disparities and avert long-term health and economic consequences in vulnerable populations.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study highlights a substantial and intertwined burden of diabetes and hypertension among adults aged 45 years and older living in urban slums of Dhaka, Bangladesh. Hypertension was markedly more prevalent among participants with self-reported diabetes (60.5%) than among those without (23.0%), contributing to comorbidity rates of up to 15.4% across the study sites. These findings reflect the accelerating NCD epidemic in urban poor settings, driven by shared risk factors including older age, elevated body mass index, smoking, and sedentary occupations.\u003c/p\u003e \u003cp\u003eHealth-related quality of life was significantly impaired in the diabetic group, with lower EQ-5D-5L index scores and greater difficulties in mobility, pain/discomfort, and anxiety/depression domains compared to non-diabetics. These decrements are likely compounded by the chronic nature of the conditions and the additional stressors of slum living, such as environmental pollution, overcrowding, and chronic economic insecurity.\u003c/p\u003e \u003cp\u003eThe overwhelming majority of participants (83.5%) identified financial constraints as the primary barrier to accessing healthcare for these conditions, far exceeding other reported obstacles such as limited service availability, low awareness, or cultural factors. This dominant economic barrier, combined with systemic weaknesses in primary healthcare delivery, underscores the persistent inequities in NCD prevention, diagnosis, and management faced by urban slum populations.\u003c/p\u003e \u003cp\u003eTaken together, the results emphasize that diabetes and hypertension represent a major and growing public health challenge in Dhaka\u0026rsquo;s urban slums, where socioeconomic disadvantage and structural limitations amplify disease impact and hinder effective control. Without targeted action, these conditions will continue to erode individual well-being, reduce productive capacity, deepen household poverty, and widen health inequities.\u003c/p\u003e \u003cp\u003eAddressing this burden requires urgent, multi-level interventions tailored to the realities of urban poor communities. Priority actions should include:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eCommunity-based screening and early detection programs integrated into existing slum outreach activities\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSubsidized or free access to essential antihypertensive and antidiabetic medications\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHealth literacy campaigns to improve awareness and self-management skills\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePromotion of affordable healthy lifestyles through community nutrition education and feasible physical activity opportunities\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eStrengthening primary healthcare facilities in or near slums with adequate staffing, NCD guidelines, diagnostic tools, and referral pathways\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eSuch measures, if implemented effectively, would help reduce premature NCD mortality and disability, improve quality of life, and protect vulnerable households from catastrophic health expenditures. These efforts are fully aligned with Bangladesh\u0026rsquo;s National NCD Control Strategy and the global commitment under Sustainable Development Goal 3.4 to reduce premature non-communicable disease mortality by one-third by 2030. The urban slum context demands equity-focused policies and sustained investment if meaningful progress toward health for all is to be achieved.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eEthical approval was obtained from the Institutional Review Board of Jahangirnagar University, Savar, Dhaka, Bangladesh (Ref No:BBEC, JU/M 2025/09 (314). Written informed consent was obtained from all participants prior to data collection. Participation was voluntary, and confidentiality of all information was strictly maintained.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eUse of AI-assisted tools\u003c/h2\u003e \u003cp\u003eThis manuscript was prepared by the listed human authors, who take full responsibility for its content. Large Language Models (LLMs), specifically ChatGPT-5 (OpenAI), were used solely to assist in improving the clarity, grammar, and flow of the language. The authors reviewed and edited all AI-generated text to ensure accuracy, integrity, and adherence to scientific standards. No content, data interpretation, or conclusions were generated autonomously by the AI tool.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo external funding was received for this study. The research was conducted as part of the authors\u0026rsquo; academic and institutional activities.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization : Md. Fakhrul Islam Maruf; Methodology : Md. Fakhrul Islam Maruf; Validation :Md. Fakhrul Islam Maruf, Nishat Tamanna Omi, Mahfuza Mubarak; Formal Analysis : Md. Fakhrul Islam Maruf; Investigation : Md. Fakhrul Islam Maruf, Nishat Tamanna Omi, Mahfuza Mubarak; Resources : Md. Fakhrul Islam Maruf; Data Curation : Nishat Tamanna Omi; Writing \u0026ndash; Original Draft Preparation : Md. Fakhrul Islam Maruf, Nishat Tamanna Omi; Writing \u0026ndash; Review \u0026amp; Editing : Md. Fakhrul Islam Maruf, Nishat Tamanna Omi, Mahfuza Mubarak; Project; Administration : Md. Fakhrul Islam Maruf.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data are available from the corresponding author (Md. Fakhrul Islam Maruf) upon reasonable request, subject to approval by the Institutional Review Board of Jahangirnagar University and compliance with data protection regulations. Requests should include a clear description of the intended use and may require a data-sharing agreement to ensure participant confidentiality and appropriate use.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Noncommunicable diseases fact sheet. Geneva: World Health Organization. 2025. 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BMJ Open. 2022;12(5):e058823. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjopen-2021-058823\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2021-058823\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Diabetes, Hypertension, Urban slums, Health-related quality of life, Bangladesh, EQ-5D-5L, WHO STEPS","lastPublishedDoi":"10.21203/rs.3.rs-8938325/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8938325/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe rising burden of non-communicable diseases (NCDs), particularly diabetes and hypertension, disproportionately affects urban slum residents in Bangladesh, where approximately 40% of urban populations face socioeconomic challenges such as poverty, overcrowding, and limited healthcare access. These conditions contribute to health inequities and diminished health-related quality of life (HRQoL). However, comprehensive data on prevalence, associated factors, barriers to healthcare, and HRQoL in this marginalized group remain limited.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA community-based cross-sectional study was conducted from June to August 2025 across five slums in Dhaka (Mirpur, Khilgaon, Korail, Bhashantek, and Kamalapur). Using simple random sampling, 400 adults aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years (200 with self-reported diabetes and 200 without) were enrolled. Data were collected via a validated Bengali version of the World Health Organization (WHO) STEPwise Approach to Surveillance (STEPS) questionnaire for sociodemographics, lifestyle factors, and healthcare barriers, plus the EQ-5D-5L instrument for HRQoL assessment. Analyses included descriptive statistics, chi-square tests, independent t-tests, and multivariable logistic regression in R software (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for significance).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePrevalence of diabetes ranged from 10.0% to 14.1%, hypertension from 28.3% to 34.4%, and their comorbidity from 4.5% to 15.4% across slums. Independent risk factors for hypertension/diabetes included older age (adjusted odds ratio [OR]\u0026thinsp;=\u0026thinsp;6.67, 95% confidence interval [CI]: 3.57\u0026ndash;12.5, p\u0026thinsp;=\u0026thinsp;0.003), higher body mass index (OR\u0026thinsp;=\u0026thinsp;2.86, 95% CI: 1.64\u0026ndash;5.00, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), smoking (OR\u0026thinsp;=\u0026thinsp;10.0, 95% CI: 5.00\u0026ndash;20.0, p\u0026thinsp;=\u0026thinsp;0.048), and sedentary occupations (OR\u0026thinsp;=\u0026thinsp;12.5, 95% CI: 6.25\u0026ndash;25.0, p\u0026thinsp;=\u0026thinsp;0.048). Major barriers to care were financial constraints (83.5%), limited healthcare access (9.5%), and low awareness (4.5%). HRQoL was significantly lower in participants with diabetes (EQ-5D index score: 0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18 vs. 0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12 in non-diabetics, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with greater impairments in mobility (p\u0026thinsp;=\u0026thinsp;0.012), pain/discomfort (p\u0026thinsp;=\u0026thinsp;0.021), and anxiety/depression (p\u0026thinsp;=\u0026thinsp;0.004), linked to disease burden and slum-related stressors (e.g., pollution, poverty).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eUrban slums in Dhaka bear a substantial burden of diabetes and hypertension, exacerbated by socioeconomic barriers and poorer HRQoL. Urgent targeted interventions\u0026mdash;community screening, subsidized care, health literacy programs, and lifestyle modifications\u0026mdash;are needed to mitigate disparities, aligning with national NCD strategies and Sustainable Development Goal 3.4.\u003c/p\u003e","manuscriptTitle":"Comparative Study of Hypertension Prevalence, Health-Related Quality of Life, and Healthcare Barriers Among Diabetic and Non- Diabetic Populations in Urban Slums of Bangladesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-15 07:02:04","doi":"10.21203/rs.3.rs-8938325/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"abfae2b5-678c-4077-a3c0-867d27b09cdd","owner":[],"postedDate":"April 15th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-15T07:02:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-15 07:02:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8938325","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8938325","identity":"rs-8938325","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}