Diabetes risk profiling in rural Odisha using the Indian Diabetes Risk Score and thematic analysis of patient perspectives a concurrent mixed methods community based cross sectional study

Diabetes risk profiling in rural Odisha using the Indian Diabetes Risk Score and thematic analysis of patient perspectives a concurrent mixed methods community based cross sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Diabetes risk profiling in rural Odisha using the Indian Diabetes Risk Score and thematic analysis of patient perspectives a concurrent mixed methods community based cross sectional study Rameswar Behera, Prashansa Dash, Pradeep Kumar Panda, Soumya Krishna Guin, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9184255/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Background Non-communicable diseases, particularly diabetes mellitus, represent a growing public health burden in rural India. Limited community-level data exist for Ganjam district, Odisha. Methods A concurrent mixed-methods, community-based cross-sectional study was conducted from May to June 2023 in Jagannath Prasad block, Ganjam district. A total of 341 adults aged ≥ 25 years were recruited via multistage Probability Proportional to Size (PPS) sampling. Diabetes risk was assessed using the validated Indian Diabetes Risk Score (IDRS). Binary logistic regression identified independent predictors of high risk. In-depth interviews (n = 8) with participants living with diabetes were analysed using Braun and Clarke's thematic analysis. Findings were integrated through data triangulation. Results Overall, 52.5% of participants were classified as high risk (IDRS ≥ 60). Multivariable logistic regression identified sedentary lifestyle (OR: 4.17; 95% CI: 2.08–8.35), age ≥ 40 years (OR: 3.22; 95% CI: 1.88–5.05), family history (OR: 2.81; 95% CI: 1.56–5.05), and elevated waist circumference (OR: 2.54; 95% CI: 1.49–4.32) as significant independent predictors (Nagelkerke R² = 0.31). Thematic analysis generated five themes: patient understanding, management challenges, self-care burden, illness perceptions, and social support. Conclusions High diabetes risk in rural Ganjam is driven by both modifiable lifestyle factors and psychosocial determinants. IDRS-based community screening, family-centred self-care education, and targeted healthcare access interventions are urgently needed. Diabetes risk factors Indian Diabetes Risk Score mixed-methods binary logistic regression thematic analysis data triangulation rural India non-communicable diseases 1. Introduction Non-communicable diseases (NCDs) have emerged as the foremost cause of premature mortality in the twenty-first century, accounting for approximately 41 million deaths annually and representing 71% of all global deaths. Of particular concern is premature NCD mortality — defined as death before the age of 70 years — which claims 17 million lives each year, with 86% of these deaths occurring in low- and middle-income countries [ 1 ]. Cardiovascular diseases and cancers collectively account for approximately 17.9 million deaths annually, while diabetes mellitus and chronic respiratory diseases contribute a further 6.2 million deaths, together comprising over 80% of premature NCD mortality worldwide [ 2 ]. These figures underscore the disproportionate burden borne by LMICs, where limited health infrastructure and high rates of undiagnosed disease compound the epidemiological challenge. Diabetes mellitus has reached epidemic proportions globally. According to the IDF Diabetes Atlas, an estimated 537 million adults aged 20–79 years were living with diabetes in 2021, with projections of 643 million by 2030 and 783 million by 2045 [ 3 ]. India alone accounts for approximately 73 million adults with diabetes, nearly half of whom remain undiagnosed and untreated — a diagnostic gap that represents both a clinical challenge and a public health emergency [ 4 ]. Furthermore, approximately 240 million people worldwide have undiagnosed diabetes, with the South-East Asia region disproportionately affected [ 5 ]. India has responded to this burden through the National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Disease and Stroke (NPCDCS), which incorporates population-based screening initiatives using frontline healthcare workers [ 6 ]. Under the Ayushman Bharat–Health and Wellness Centres framework, the National Health Mission has further expanded NCD screening capacity at the sub-centre level [ 7 ]. The escalating global burden of cardiovascular diseases and associated risk factors further underscores the urgency of such population-level screening initiatives [ 8 ]. Despite these policy investments, substantial gaps in early detection persist, particularly in rural communities where healthcare access is constrained by geography, workforce, and socioeconomic factors. A key instrument supporting community-level screening is the Indian Diabetes Risk Score (IDRS), a validated, low-cost tool comprising four components: age, abdominal waist circumference, physical activity level, and family history of diabetes — with two modifiable and two non-modifiable components [ 9 ]. The IDRS provides a practical and affordable estimate of future diabetes risk without requiring laboratory investigations, making it particularly suitable for resource-limited settings [ 10 ]. Its discriminatory performance has been validated across diverse Indian populations [ 11 ]. The epidemiological profile of diabetes in India reflects stark urban–rural and regional disparities. The ICMR–INDIAB study reported an overall adult diabetes prevalence of 7.3% across 15 Indian states, with urban prevalence (10.6%–11.2%) substantially higher than rural rates (4.9%–5.4%) [ 12 ]. Within Odisha, the estimated adult prevalence is 6.8%, rising to 22.2% among adults aged 60 years and above [ 13 ]. These figures highlight an ageing rural population at growing risk, yet evidence on diabetes risk factors specifically within Ganjam district of southern Odisha remains limited. Ganjam is a predominantly rural district characterised by agrarian livelihoods, limited access to tertiary healthcare, and sociodemographic factors — including low female educational attainment and sedentary domestic occupations — that may independently elevate diabetes risk. Effective diabetes management in community settings is not solely a biomedical challenge; it is deeply embedded in social, cultural, and economic contexts. Understanding why individuals fail to seek timely screening, adhere to treatment, or adopt preventive behaviours requires qualitative insight that epidemiological surveys alone cannot provide. A mixed-methods approach — integrating quantitative risk assessment with qualitative inquiry — is therefore essential for generating actionable, contextualised evidence [ 14 ]. This study aimed to: (i) assess the prevalence and distribution of diabetes risk factors among adults in Jagannath Prasad block, Ganjam district, using the IDRS; (ii) identify independent predictors of high diabetes risk using binary logistic regression; (iii) explore the lived experiences and management challenges of individuals living with diabetes through in-depth interviews; and (iv) integrate quantitative and qualitative findings through data triangulation to generate a comprehensive understanding of diabetes risk and management in this rural community. 2. Materials and Methods 2.1. Study design This study employed a concurrent mixed-methods design, integrating a quantitative cross-sectional survey with qualitative in-depth interviews. Both strands carried equal weight and were conducted simultaneously (May–June 2023). Findings were integrated at the interpretation stage using a data triangulation protocol examining convergence, complementarity, and discordance between strands. 2.2. Study setting The study was conducted in Jagannath Prasad block, Ganjam district, Odisha, India — a predominantly rural area comprising four Primary Health Centres (PHCs) and 23 sub-centres. The block's population depends largely on rain-fed agriculture and seasonal labour, with limited access to specialist healthcare. 2.3. Sample size Sample size was calculated using the single-proportion formula: n = Z² × p(1 − p) / d², where Z = 1.96 (95% confidence level), p = 0.16 (estimated diabetes prevalence in Odisha [ 12 ]), and d = 0.05, yielding 206. Adjusted for 10% non-response and design effect of 1.5, giving 230; expanded to 341 to accommodate data attrition. 2.4. Sampling strategy Multistage PPS sampling: Stage 1 — two of four PHCs randomly selected; Stage 2 — two sub-centres per PHC (n = 4); Stage 3 — villages merged to ≥ 1,000 population, two per sub-centre randomly selected (n = 8 villages). Within villages, households were systematically enumerated from the bus stop; one eligible adult per household selected via random number table. 2.5. Inclusion and exclusion criteria Inclusion Adults aged ≥ 25 years, residing in study villages, of either sex, willing to provide written informed consent. Exclusion Pregnant women, severely ill or bedridden individuals, and those declining participation. 2.6. Quantitative data collection and IDRS instrument A structured questionnaire was administered via the Open Data Kit (ODK) application on smartphones [ 15 ]. The survey instrument captured sociodemographic characteristics and the four IDRS components. The IDRS assigns scores for: age (0 for < 35 years; 20 for 35–49 years; 30 for ≥ 50 years); physical activity (0 for vigorous; 20 for moderate; 30 for sedentary); waist circumference (0–20 points, sex-specific thresholds); and family history of diabetes (0 for none; 10 for one parent/sibling; 20 for both parents). Total scores range from 0 to 100; participants are classified as low risk (< 30), moderate risk (30–50), or high risk (≥ 60), as validated by Mohan et al. [ 9 ]. Waist circumference was measured with a non-elastic measuring tape at the mid-point between the lowest rib and the iliac crest. Written informed consent was obtained from all participants prior to data collection. 2.7. Quantitative data analysis Data exported from ODK were analysed using IBM SPSS Statistics (Version 25) [ 16 ]. Descriptive statistics summarised sociodemographic and IDRS variable distributions. Chi-square tests assessed associations between categorical variables and IDRS risk categories. Binary logistic regression — conducted in two steps (unadjusted univariable, then adjusted multivariable) — identified independent predictors of high diabetes risk (IDRS ≥ 60). Model fit was assessed using the Hosmer–Lemeshow goodness-of-fit test, Nagelkerke R², and overall classification accuracy. Results are reported as odds ratios (ORs) with 95% confidence intervals (CIs); p < 0.05 was considered statistically significant. 2.8. Qualitative data collection and analysis Eight participants currently living with diabetes were purposively selected to maximise variation in sex, age, marital status, and diabetes duration. In-depth interviews (IDIs) were conducted using a semi-structured topic guide covering diabetes understanding, management experiences, self-care practices, illness perceptions, and social support. Interviews were audio-recorded with written consent, transcribed verbatim, and translated from Odia to English by a bilingual researcher. Data saturation was confirmed by the eighth interview. Transcripts were analysed in MAXQDA (Version 19) [ 17 ] using Braun and Clarke's six-phase thematic analysis framework. Analytical rigour was ensured through a reflexivity journal, peer debriefing, and negative case analysis. 2.9. Mixed-methods integration Integration was performed at the interpretation stage using a data triangulation matrix. Each major quantitative association was systematically cross-examined against corresponding qualitative themes to determine convergence, complementarity, or discordance (Table 5 ). 2.10. Ethical approval Ethical approval was obtained from the Institute Research and Ethics Committee, AIPH University, Bhubaneswar, and the Research and Ethics Committee, Department of Health and Family Welfare, Government of Odisha. The study complied with the Declaration of Helsinki. All participants provided written informed consent. 3. Results 3.1. Baseline characteristics of study participants Table 1 presents the baseline sociodemographic and clinical characteristics of all 341 participants. The majority were female (58.9%), aged ≥ 40 years (56.0%), married (87.1%), with mean age 43.2 ± 11.8 years. Agriculture and domestic occupations predominated. Regarding IDRS risk distribution: 52.5% were classified as high risk (IDRS ≥ 60), 33.7% as moderate risk, and 13.8% as low risk. Table 1. Baseline sociodemographic and clinical characteristics of study participants (N = 341) Characteristic n (%) Mean ± SD Range Sociodemographic variables Total participants 341 (100.0) — — Sex Male 140 (41.1) — — Female 201 (58.9) — — Age group (years) 25–39 150 (44.0) — 25–39 ≥ 40 191 (56.0) — 40–80 Mean age (years) — 43.2 ± 11.8 25–80 Marital status Married 297 (87.1) — — Unmarried 33 (9.7) — — Widowed 14 (4.1) — — Educational attainment No formal education 68 (19.9) — — Primary (up to Class 5) 84 (24.6) — — Secondary (Class 6–10) 96 (28.2) — — Higher secondary and above 93 (27.3) — — Occupation Agriculture / daily wage labour 154 (45.2) — — Homemaker 112 (32.8) — — Salaried / professional 48 (14.1) — — Business / self-employed 27 (7.9) — — IDRS component variables Waist circumference Mean waist – males (cm) — 82.4 ± 9.1 62–114 Mean waist – females (cm) — 79.8 ± 10.3 58–108 Physical activity level Vigorous 76 (22.3) — — Moderate 163 (47.8) — — Sedentary 52 (15.2) — — Family history of diabetes No family history 288 (84.5) — — One parent or sibling 51 (14.9) — — Both parents 2 (0.6) — — IDRS risk category (outcome) Low risk (IDRS < 30) 47 (13.8) — — Moderate risk (IDRS 30–50) 115 (33.7) — — High risk (IDRS ≥ 60) 179 (52.5) — — SD = standard deviation; IDRS = Indian Diabetes Risk Score. Data presented as n (%) for categorical variables and mean ± SD for continuous variables. IDRS risk categories per Mohan et al. [9]: low risk (score < 30); moderate risk (score 30–50); high risk (score ≥ 60). Maximum possible IDRS score = 100. 3.2. Association between participant characteristics, IDRS components, and diabetes risk category Table 2 presents associations between sociodemographic variables (Section A) and IDRS component variables (Section B) with diabetes risk classification. Among participants aged ≥ 40 years, 65.9% were classified as high risk compared with 35.3% of those aged 25–39 (p < 0.001). Females (59.4%) showed higher high-risk prevalence than males (29.3%). Among widowed participants, 85.7% were classified as high risk. High-risk prevalence among females reached 100% with waist ≥ 90 cm. Among sedentary participants, 78.8% were classified as high risk. Positive family history was strongly associated with high-risk classification (74.5% with one affected relative; p < 0.001). Table 2. Association between participant characteristics, IDRS components, and diabetes risk category (N = 341) Variable Low Risk n (%) Moderate Risk n (%) High Risk n (%) p-value A. Sociodemographic characteristics Age group (years) 25–39 36 (24.0) 61 (40.7) 53 (35.3) < 0.001 ≥ 40 11 (5.8) 54 (28.3) 126 (65.9) Sex Male 27 (19.3) 72 (51.4) 41 (29.3) < 0.001 Female 20 (9.9) 62 (30.7) 120 (59.4) Marital status Married 35 (11.8) 106 (35.7) 156 (52.5) < 0.001 Unmarried 12 (36.4) 14 (42.4) 7 (21.2) Widowed 0 (0.0) 2 (14.3) 12 (85.7) B. IDRS component variables Waist circumference – females < 70 cm 16 (21.6) 35 (47.3) 23 (31.1) < 0.001 70–79 cm 4 (4.9) 37 (45.1) 41 (50.0) 80–89 cm 0 (0.0) 10 (44.4) 13 (55.3) ≥ 90 cm 0 (0.0) 0 (0.0) 16 (100.0) Physical activity Vigorous 28 (36.8) 35 (46.1) 13 (17.1) < 0.001 Moderate 17 (10.4) 82 (50.3) 64 (39.3) Sedentary 2 (3.8) 9 (17.3) 41 (78.8) Family history of diabetes No history 44 (15.3) 103 (35.8) 141 (49.0) < 0.001 One parent/sibling 3 (5.9) 10 (19.6) 38 (74.5) Both parents 0 (0.0) 2 (25.0) 6 (75.0) *** p < 0.001 (Chi-square test). Risk categories: low = IDRS < 30; moderate = IDRS 30–50; high = IDRS ≥ 60. Section A: sociodemographic variables; Section B: IDRS component variables. 3.3. Binary logistic regression and model fit statistics Table 3 presents the full logistic regression model (unadjusted and adjusted) with model fit statistics. In the adjusted model, four variables independently predicted high diabetes risk: sedentary lifestyle (OR: 4.17; 95% CI: 2.08–8.35; p < 0.001), age ≥ 40 years (OR: 3.22; 95% CI: 1.88–5.05; p < 0.001), family history of diabetes (OR: 2.81; 95% CI: 1.56–5.05; p = 0.001), and elevated waist circumference (OR: 2.54; 95% CI: 1.49–4.32; p = 0.001). Male sex was inversely associated with high risk (OR: 0.32; 95% CI: 0.14–0.76; p = 0.008). Socioeconomic variables were not statistically significant. The adjusted model demonstrated Nagelkerke R² = 0.31, Hosmer–Lemeshow p = 0.61, and 74.8% overall classification accuracy. Table 3. Binary logistic regression analysis of predictors of high diabetes risk (IDRS ≥ 60) and model fit statistics Variable B SE Wald χ² df OR 95% CI p-value Nagelkerke R² Step 1: Unadjusted (univariable) model Age ≥ 40 years 1.23 0.21 30.96 1 3.42 2.14–5.47 < 0.001 Sex (male vs. female) -1.14 0.43 7.02 1 0.32 0.14–0.76 0.009 Sedentary lifestyle 1.43 0.36 15.76 1 4.17 2.08–8.35 < 0.001 High waist circumference 0.93 0.27 11.88 1 2.54 1.49–4.32 0.001 Family history of diabetes 1.03 0.30 11.82 1 2.81 1.56–5.05 0.001 Step 2: Adjusted (multivariable) final model Age ≥ 40 years 1.17 0.25 21.91 1 3.22 1.88–5.05 < 0.001 0.31 Sex (male vs. female) -1.14 0.43 7.05 1 0.32 0.14–0.76 0.008 Sedentary lifestyle 1.43 0.36 15.82 1 4.17 2.08–8.35 < 0.001 High waist circumference 0.93 0.27 11.70 1 2.54 1.49–4.32 0.001 Family history of diabetes 1.03 0.30 11.80 1 2.81 1.56–5.05 0.001 Constant -2.31 0.44 27.51 1 — — < 0.001 Model fit statistics −2 Log likelihood Step 1: 394.2 Step 2: 341.7 Cox & Snell R² 0.23 0.28 Nagelkerke R² 0.27 0.31 Hosmer–Lemeshow p-value 0.43 0.61 Overall classification accuracy 71.5% 74.8% B = regression coefficient; SE = standard error; Wald χ² = Wald chi-square statistic; OR = odds ratio; CI = confidence interval. Reference categories: age 25–39 years; female sex; active physical activity; normal waist circumference; no family history of diabetes. Hosmer–Lemeshow p > 0.05 indicates adequate model fit. Nagelkerke R² reflects proportion of outcome variance explained. 3.4. Qualitative findings: thematic analysis Thematic analysis of eight in-depth interviews generated five overarching themes with ten sub-themes (Table 4), developed inductively from participant data and confirmed through peer debriefing and negative case analysis. Table 4. Qualitative thematic framework: themes, sub-themes, representative quotations, and analytical interpretations Theme Sub-theme Representative quotation (translated) Analytical interpretation 1. Patient understanding Biomedical knowledge deficit "I thought sugar disease only happens to fat people; I never thought it could happen to me." (Female, 52) Limited understanding of diabetes aetiology and personal susceptibility reflects low health literacy. Misconceptions about medication "I stopped my medicine when I felt fine; I did not know the danger." (Male, 45) Asymptomatic periods trigger treatment discontinuation, reflecting a gap between perceived and actual disease severity. 2. Management challenges Healthcare access barriers "The hospital is 15 km away; I cannot go every month to collect tablets." (Female, 60) Geographical distance and transport costs are primary structural barriers to consistent medication adherence. Financial burden "Sometimes I buy food or buy medicine — I cannot afford both." (Male, 55) Competing expenditure priorities create critical trade-offs that undermine glycaemic control. 3. Self-care burden Dietary restriction fatigue "I am tired of eating the same things every day. My family eats rice; I must watch." (Female, 48) Chronic dietary vigilance within rice-dominant food cultures generates self-care fatigue and social isolation. Psychological distress "I feel like a burden on my children. I must take medicine for life." (Male, 63) Chronic illness-related guilt reflects unmet psychosocial support needs and potential depressive comorbidity. 4. Illness perceptions Fatalistic attribution "It is God's wish. My father had it — it is in our blood." (Male, 58) Fatalistic and hereditary explanatory models reduce perceived self-efficacy and motivation for behavioural change. Stigma and concealment "I do not tell neighbours. They will think I eat too much." (Female, 44) Stigma-driven diagnostic concealment impedes community support-seeking and peer-based self-management. 5. Social support Family as enabler "My daughter reminds me to take tablets and prepares my food separately." (Male, 70) Family-mediated medication reminders and dietary preparation substantially improve self-management adherence. Social isolation as barrier "I live alone since my husband died. Nobody is there to help me cook." (Female, 67) Social isolation among widowed older women constitutes a high-risk profile requiring targeted community outreach. Participant identifiers: sex and age in parentheses. All quotations translated from Odia. Analysis conducted in MAXQDA Version 19 using Braun and Clarke's six-phase thematic analysis framework. 3.5. Mixed-methods integration: data triangulation Table 5 presents the triangulation matrix. Of five integration points: three demonstrated convergent findings (both strands confirming and extending the same conclusion), one yielded complementary evidence (qualitative strand adding a dimension absent from the quantitative estimate), and one was discordant — the quantitative regression found no significant socioeconomic associations, while qualitative participants clearly described financial hardship as a major barrier to adherence. Table 5. Data triangulation matrix: integration of quantitative and qualitative findings Quantitative finding Qualitative finding Point of integration Triangulation outcome 78.8% of sedentary participants at high IDRS risk; OR 4.17 (95% CI 2.08–8.35) Domestic roles, lack of safe spaces, and fatigue cited as drivers of physical inactivity (Themes 3, 4) Qualitative strand contextualises why sedentary behaviour is the strongest quantitative predictor Confirmed and extended: both strands converge; qualitative findings add structural and gender-based depth 65.9% of participants aged ≥ 40 years at high risk; OR 3.22 Older participants expressed fatalism and lower motivation for lifestyle modification (Theme 4) Qualitative data explains why older adults may resist preventive advice despite high quantitative risk scores Confirmed and extended: fatalism identified as a psychosocial mediator of age-related risk Family history: OR 2.81; 74.5% with one affected relative in high-risk category Family members serve dual roles as genetic risk carriers and active enablers of self-care (Theme 5) Qualitative strand reveals the protective dimension of family absent from the quantitative OR estimate Complementary: qualitative findings add nuance not captured in the quantitative estimate No significant socioeconomic associations in logistic regression Financial barriers (food vs. medicine trade-offs) cited as a major management obstacle (Theme 2) Quantitative survey failed to detect individual-level financial hardship captured in qualitative inquiry Discordant: warrants stratified socioeconomic analysis in future studies Females at higher risk: 59.4% high risk; OR for male sex 0.32 Female participants described stigma, dietary isolation, and sedentary domestic roles as compounding barriers (Themes 3, 4) Qualitative strand maps social pathways underlying the sex differential in quantitative risk distribution Confirmed and extended: both strands confirm female vulnerability; qualitative findings explain the mechanisms Confirmed and extended = both strands reach the same conclusion with qualitative findings adding explanatory depth. Complementary = strands address different dimensions of the same construct. Discordant = strands yield contradictory conclusions, warranting further investigation. 4. Discussion This concurrent mixed-methods study provides important new evidence on the burden of diabetes risk factors and the lived experience of diabetes management in rural Ganjam district, Odisha. The finding that 65.9% of participants aged ≥ 40 years were classified as high risk by the IDRS substantially exceeds rates reported in comparable Indian studies — 33.4% in Meerut [18] and 31.5% in Andhra Pradesh [19] — and likely reflects the older demographic profile of the study population, high rates of sedentary behaviour, and limited access to preventive healthcare in rural southern Odisha. Sedentary lifestyle emerged as the strongest independent predictor of high diabetes risk (OR: 4.17), consistent with Sri PKSSU et al. (2022), who identified physical inactivity in 71.7% of high-risk individuals across Andhra Pradesh [19]. The adjusted model's Nagelkerke R² of 0.31 and 74.8% classification accuracy confirm the IDRS as a valid and discriminating screening instrument in this setting — a finding aligned with Dudeja et al. (2017), who demonstrated that 97% of confirmed undiagnosed diabetes cases in an urban slum had IDRS scores ≥ 60 [11]. The inverse association between male sex and high diabetes risk (OR: 0.32) likely reflects greater physical activity among males in agricultural labour, alongside sex differences in abdominal fat distribution. Female participants consistently showed higher waist circumference scores, a pattern reported by Prashant et al. (2022) [20]. The qualitative data further contextualises this disparity: female participants described sedentary domestic roles, stigma-related dietary isolation, and restricted access to physical activity as compounding barriers. The qualitative findings reveal that biological risk factors are substantially compounded by structural, psychosocial, and cultural determinants. Financial barriers creating trade-offs between medication and food — directly captured in participant narratives — represent a critical modifiable barrier that quantitative survey instruments failed to detect. This discordance between quantitative and qualitative strands is a methodologically important finding: it demonstrates that mixed-methods integration yields insights that neither approach produces independently. Fatalistic illness perceptions and stigma-driven diagnostic concealment identified in Themes 4 and 5 mirror patterns described by Bukhsh et al. (2020) in Pakistan [21] and Naithani et al. (2006) in the United Kingdom [22], suggesting cross-cultural transferability in South Asian contexts. The high-risk prevalence observed in this study is further contextualised by longitudinal data from urban south India documenting rising incidence of diabetes and pre-diabetes [23], as well as studies validating IDRS utility among medical students in northern India [24] and urban slum populations [25], collectively reinforcing the need for expanded community-based screening across all demographic groups in India. 4.1. Strengths and limitations Strengths include the concurrent mixed-methods design with equal strand weighting; validated IDRS instrument; multistage PPS sampling; full two-step logistic regression reporting with model fit statistics; Braun and Clarke's structured thematic framework; and formal data triangulation. Limitations include the cross-sectional quantitative design precluding causal inference; single-block restriction limiting generalisability; potential social desirability bias in physical activity self-reporting; absence of biochemical confirmation of diabetes status; and a qualitative sample of eight participants, which, while sufficient for thematic saturation, limits transferability. 5. Conclusion This mixed-methods study demonstrates a high prevalence of diabetes risk factors in rural Ganjam district, Odisha. Sedentary lifestyle, advancing age, abdominal obesity, and family history of diabetes are the primary independent predictors of high IDRS risk. Qualitative inquiry reveals that effective diabetes prevention and management are constrained by structural barriers, limited health literacy, psychosocial burden, illness stigma, and inadequate social support — determinants that quantitative data alone cannot fully capture. Data triangulation confirms convergence across key associations while revealing important discordances that highlight the added value of mixed-methods inquiry. Targeted IDRS-based community screening, family-centred diabetes education, stigma-reduction programmes, and policies addressing financial barriers to medication adherence are urgently needed to reduce the undiagnosed diabetes burden in rural Odisha. 6. Recommendations Scale up IDRS-based mass screening at primary healthcare centres in Ganjam district, targeting adults aged ≥ 40 years, females, and individuals with sedentary lifestyles. Deploy ASHA/ANM-led community awareness programmes addressing modifiable risk factors using culturally appropriate and gender-sensitive messaging. Integrate psychosocial support and stigma-reduction components into existing diabetes education sessions at Ayushman Bharat–Health and Wellness Centres. Establish family-engagement modules and peer support networks to improve self-care adherence, with particular focus on socially isolated older women. Address financial barriers through subsidised medicine schemes and community-based nutritional support programmes. Conduct prospective cohort studies with biochemical confirmation of diabetes status and socioeconomic stratification to establish causal pathways and evaluate intervention effectiveness. Declarations Financial Support and Sponsorship This research received no specific funding from any public, commercial, or not-for-profit agency. Funding Declaration This study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. No funding was received for the conduct of this study or preparation of this article. Clinical Trial Number Clinical trial number: not applicable. Conflicts of Interest The authors declare no conflicts of interest. Ethical approval Ethical approval was obtained from the Institute Research and Ethics Committee, AIPH University, Bhubaneswar (Ref. No. AIPH/IREC/2023/04), and the Research and Ethics Committee, Department of Health and Family Welfare, Government of Odisha. The study was conducted in compliance with the Declaration of Helsinki. Consent to participate Written informed consent to participate was obtained from all participants prior to data collection. For qualitative in-depth interviews, additional written consent for audio recording was obtained from each participant. Consent to publish Informed consent to publish, including consent to publish anonymised quotations and data derived from their participation, was obtained from all participants as part of the written consent process conducted prior to data collection. Data Availability Statement All the data used in this study will be provided on request to the corresponding author. References GBD 2019 Diseases and Injuries Collaborators. 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Health care delivery model in India with relevance to diabetes care. Heliyon. 2022;8(10):e10904. Mwencha M, Rosen JE, Sogun H, Osborn S, Perrone LA, Lambert W. Increasing access to health worker support tools: a descriptive assessment of a USAID mHealth programme in sub-Saharan Africa. Glob Health Sci Pract. 2014;2(4):471–84. IBM Corp. IBM SPSS statistics for Windows [software]. Version 25.0. Armonk. (NY): IBM Corp; 2017. Verbi Software. MAXQDA: software for qualitative data analysis [software]. Version 19. Berlin: Verbi Software; 2019. Anand K, Jain S, Chopra H, Kumar A, Singh G. Indian Diabetes Risk Score (IDRS): an effective tool to screen undiagnosed diabetes. Indian J Community Health. 2022;34(1):130–5. Sri PKSSU, Rao BT, Naidu SA, Mokshanand K, Lakshmi NN, Keerthi MD. Indian Diabetic Risk Score as a screening tool for assessment of diabetes in urban and rural areas in Andhra Pradesh. Int J Community Med Public Health. 2022;9(12):4417–24. Kokiwar PR. Assessment of diabetes risk using Indian Diabetes Risk Score among medical students at a medical college in Telangana, India. J Diabetol. 2022;12:542–7. Bukhsh A, Goh BH, Zimbudzi E, Lo C, Zoungas S, Chan KG, et al. Type 2 diabetes patients' perspectives, experiences, and barriers toward diabetes-related self-care: a qualitative study from Pakistan. Front Endocrinol (Lausanne). 2020;11:534873. Naithani S, Gulliford M, Morgan M. Patients' perceptions and experiences of 'continuity of care' in diabetes. Health Expect. 2006;9(2):118–29. Mohan V, Deepa M, Anjana RM, Lanthorn H, Deepa R. Incidence of diabetes and pre-diabetes in a selected urban south Indian population (CUPS-19). J Assoc Physicians India. 2008;56:152–7. Singh MM, Mangla V, Pangtey R, Garg S. Risk assessment of diabetes using the Indian Diabetes Risk Score: a study on young medical students from northern India. Indian J Endocrinol Metab. 2019;23(1):86–90. Nittoori S, Wilson V. Risk of type 2 diabetes mellitus among urban slum population using Indian Diabetes Risk Score. Indian J Med Res. 2020;152(3):308–11. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 17 May, 2026 Reviews received at journal 13 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviews received at journal 27 Apr, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers invited by journal 20 Apr, 2026 Editor assigned by journal 09 Apr, 2026 Submission checks completed at journal 06 Apr, 2026 First submitted to journal 06 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-9184255","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":629648840,"identity":"86b83a2e-f6cc-4d3a-97f4-bc3e9d6fe2f0","order_by":0,"name":"Rameswar Behera","email":"","orcid":"","institution":"AIPH University","correspondingAuthor":false,"prefix":"","firstName":"Rameswar","middleName":"","lastName":"Behera","suffix":""},{"id":629648842,"identity":"51ac5207-7232-41f4-880a-024b9a19c13a","order_by":1,"name":"Prashansa Dash","email":"","orcid":"","institution":"AIPH University","correspondingAuthor":false,"prefix":"","firstName":"Prashansa","middleName":"","lastName":"Dash","suffix":""},{"id":629648845,"identity":"2ff15aa2-8ca0-4367-a685-56b3ce21e600","order_by":2,"name":"Pradeep Kumar Panda","email":"","orcid":"","institution":"AIPH University","correspondingAuthor":false,"prefix":"","firstName":"Pradeep","middleName":"Kumar","lastName":"Panda","suffix":""},{"id":629648846,"identity":"6d123fb5-ae5a-46f5-bff9-505d11021db4","order_by":3,"name":"Soumya Krishna Guin","email":"","orcid":"","institution":"AIPH University","correspondingAuthor":false,"prefix":"","firstName":"Soumya","middleName":"Krishna","lastName":"Guin","suffix":""},{"id":629648847,"identity":"f3766ebf-7362-4756-a8a8-7af8a117942e","order_by":4,"name":"Paramjot Panda","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYFCDA/zPP3wA0mzsxGvhYWOcAdLCTIoWZh4Qg5AWc/azDz/d3GGXx3f87LHHNr+2yfMxMzB++JiDW4tlT7qxdO6Z5GLJM3npxrl9tw3bmBmYJWduw63F4EAag3RuG3PihgMJBtK5PbcZgVrYmHnxaTn/jPl3blt94obzDwykLXtu2xPWciONDWjL4cQNN3LMpBl+3E4kqMVyxjM269wzxxNn3niWbNjbcDu5jZmxGa9fzPnTmG/n7qhO7DuffPDBjz+3bee3Nx/88BGfw0AEYwOUx9iGwiVGC8MfvIpHwSgYBaNghAIA4EtWtaHSzPQAAAAASUVORK5CYII=","orcid":"","institution":"AIPH University","correspondingAuthor":true,"prefix":"","firstName":"Paramjot","middleName":"","lastName":"Panda","suffix":""}],"badges":[],"createdAt":"2026-03-21 08:09:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9184255/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9184255/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108803842,"identity":"97ad5593-3bed-4d5b-bccb-f40f0781fb08","added_by":"auto","created_at":"2026-05-08 15:09:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":452643,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9184255/v1/7017d20c-abb3-459c-be4d-2be9533c2b25.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diabetes risk profiling in rural Odisha using the Indian Diabetes Risk Score and thematic analysis of patient perspectives a concurrent mixed methods community based cross sectional study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNon-communicable diseases (NCDs) have emerged as the foremost cause of premature mortality in the twenty-first century, accounting for approximately 41\u0026nbsp;million deaths annually and representing 71% of all global deaths. Of particular concern is premature NCD mortality \u0026mdash; defined as death before the age of 70 years \u0026mdash; which claims 17\u0026nbsp;million lives each year, with 86% of these deaths occurring in low- and middle-income countries [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Cardiovascular diseases and cancers collectively account for approximately 17.9\u0026nbsp;million deaths annually, while diabetes mellitus and chronic respiratory diseases contribute a further 6.2\u0026nbsp;million deaths, together comprising over 80% of premature NCD mortality worldwide [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These figures underscore the disproportionate burden borne by LMICs, where limited health infrastructure and high rates of undiagnosed disease compound the epidemiological challenge.\u003c/p\u003e \u003cp\u003eDiabetes mellitus has reached epidemic proportions globally. According to the IDF Diabetes Atlas, an estimated 537\u0026nbsp;million adults aged 20\u0026ndash;79 years were living with diabetes in 2021, with projections of 643\u0026nbsp;million by 2030 and 783\u0026nbsp;million by 2045 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. India alone accounts for approximately 73\u0026nbsp;million adults with diabetes, nearly half of whom remain undiagnosed and untreated \u0026mdash; a diagnostic gap that represents both a clinical challenge and a public health emergency [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Furthermore, approximately 240\u0026nbsp;million people worldwide have undiagnosed diabetes, with the South-East Asia region disproportionately affected [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIndia has responded to this burden through the National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Disease and Stroke (NPCDCS), which incorporates population-based screening initiatives using frontline healthcare workers [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Under the Ayushman Bharat\u0026ndash;Health and Wellness Centres framework, the National Health Mission has further expanded NCD screening capacity at the sub-centre level [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The escalating global burden of cardiovascular diseases and associated risk factors further underscores the urgency of such population-level screening initiatives [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Despite these policy investments, substantial gaps in early detection persist, particularly in rural communities where healthcare access is constrained by geography, workforce, and socioeconomic factors.\u003c/p\u003e \u003cp\u003eA key instrument supporting community-level screening is the Indian Diabetes Risk Score (IDRS), a validated, low-cost tool comprising four components: age, abdominal waist circumference, physical activity level, and family history of diabetes \u0026mdash; with two modifiable and two non-modifiable components [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The IDRS provides a practical and affordable estimate of future diabetes risk without requiring laboratory investigations, making it particularly suitable for resource-limited settings [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Its discriminatory performance has been validated across diverse Indian populations [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe epidemiological profile of diabetes in India reflects stark urban\u0026ndash;rural and regional disparities. The ICMR\u0026ndash;INDIAB study reported an overall adult diabetes prevalence of 7.3% across 15 Indian states, with urban prevalence (10.6%\u0026ndash;11.2%) substantially higher than rural rates (4.9%\u0026ndash;5.4%) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Within Odisha, the estimated adult prevalence is 6.8%, rising to 22.2% among adults aged 60 years and above [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These figures highlight an ageing rural population at growing risk, yet evidence on diabetes risk factors specifically within Ganjam district of southern Odisha remains limited. Ganjam is a predominantly rural district characterised by agrarian livelihoods, limited access to tertiary healthcare, and sociodemographic factors \u0026mdash; including low female educational attainment and sedentary domestic occupations \u0026mdash; that may independently elevate diabetes risk.\u003c/p\u003e \u003cp\u003eEffective diabetes management in community settings is not solely a biomedical challenge; it is deeply embedded in social, cultural, and economic contexts. Understanding why individuals fail to seek timely screening, adhere to treatment, or adopt preventive behaviours requires qualitative insight that epidemiological surveys alone cannot provide. A mixed-methods approach \u0026mdash; integrating quantitative risk assessment with qualitative inquiry \u0026mdash; is therefore essential for generating actionable, contextualised evidence [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study aimed to: (i) assess the prevalence and distribution of diabetes risk factors among adults in Jagannath Prasad block, Ganjam district, using the IDRS; (ii) identify independent predictors of high diabetes risk using binary logistic regression; (iii) explore the lived experiences and management challenges of individuals living with diabetes through in-depth interviews; and (iv) integrate quantitative and qualitative findings through data triangulation to generate a comprehensive understanding of diabetes risk and management in this rural community.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study design\u003c/h2\u003e \u003cp\u003eThis study employed a concurrent mixed-methods design, integrating a quantitative cross-sectional survey with qualitative in-depth interviews. Both strands carried equal weight and were conducted simultaneously (May\u0026ndash;June 2023). Findings were integrated at the interpretation stage using a data triangulation protocol examining convergence, complementarity, and discordance between strands.\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 Jagannath Prasad block, Ganjam district, Odisha, India \u0026mdash; a predominantly rural area comprising four Primary Health Centres (PHCs) and 23 sub-centres. The block's population depends largely on rain-fed agriculture and seasonal labour, with limited access to specialist healthcare.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Sample size\u003c/h2\u003e \u003cp\u003eSample size was calculated using the single-proportion formula: n\u0026thinsp;=\u0026thinsp;Z\u0026sup2; \u0026times; p(1\u0026thinsp;\u0026minus;\u0026thinsp;p) / d\u0026sup2;, where Z\u0026thinsp;=\u0026thinsp;1.96 (95% confidence level), p\u0026thinsp;=\u0026thinsp;0.16 (estimated diabetes prevalence in Odisha [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]), and d\u0026thinsp;=\u0026thinsp;0.05, yielding 206. Adjusted for 10% non-response and design effect of 1.5, giving 230; expanded to 341 to accommodate data attrition.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Sampling strategy\u003c/h2\u003e \u003cp\u003eMultistage PPS sampling: Stage 1 \u0026mdash; two of four PHCs randomly selected; Stage 2 \u0026mdash; two sub-centres per PHC (n\u0026thinsp;=\u0026thinsp;4); Stage 3 \u0026mdash; villages merged to \u0026ge;\u0026thinsp;1,000 population, two per sub-centre randomly selected (n\u0026thinsp;=\u0026thinsp;8 villages). Within villages, households were systematically enumerated from the bus stop; one eligible adult per household selected via random number table.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Inclusion and exclusion criteria\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eInclusion\u003c/strong\u003e \u003cp\u003eAdults aged\u0026thinsp;\u0026ge;\u0026thinsp;25 years, residing in study villages, of either sex, willing to provide written informed consent.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eExclusion\u003c/strong\u003e \u003cp\u003ePregnant women, severely ill or bedridden individuals, and those declining participation.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Quantitative data collection and IDRS instrument\u003c/h2\u003e \u003cp\u003eA structured questionnaire was administered via the Open Data Kit (ODK) application on smartphones [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The survey instrument captured sociodemographic characteristics and the four IDRS components. The IDRS assigns scores for: age (0 for \u0026lt;\u0026thinsp;35 years; 20 for 35\u0026ndash;49 years; 30 for \u0026ge;\u0026thinsp;50 years); physical activity (0 for vigorous; 20 for moderate; 30 for sedentary); waist circumference (0\u0026ndash;20 points, sex-specific thresholds); and family history of diabetes (0 for none; 10 for one parent/sibling; 20 for both parents). Total scores range from 0 to 100; participants are classified as low risk (\u0026lt;\u0026thinsp;30), moderate risk (30\u0026ndash;50), or high risk (\u0026ge;\u0026thinsp;60), as validated by Mohan et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Waist circumference was measured with a non-elastic measuring tape at the mid-point between the lowest rib and the iliac crest. Written informed consent was obtained from all participants prior to data collection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Quantitative data analysis\u003c/h2\u003e \u003cp\u003eData exported from ODK were analysed using IBM SPSS Statistics (Version 25) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Descriptive statistics summarised sociodemographic and IDRS variable distributions. Chi-square tests assessed associations between categorical variables and IDRS risk categories. Binary logistic regression \u0026mdash; conducted in two steps (unadjusted univariable, then adjusted multivariable) \u0026mdash; identified independent predictors of high diabetes risk (IDRS\u0026thinsp;\u0026ge;\u0026thinsp;60). Model fit was assessed using the Hosmer\u0026ndash;Lemeshow goodness-of-fit test, Nagelkerke R\u0026sup2;, and overall classification accuracy. Results are reported as odds ratios (ORs) with 95% confidence intervals (CIs); p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Qualitative data collection and analysis\u003c/h2\u003e \u003cp\u003eEight participants currently living with diabetes were purposively selected to maximise variation in sex, age, marital status, and diabetes duration. In-depth interviews (IDIs) were conducted using a semi-structured topic guide covering diabetes understanding, management experiences, self-care practices, illness perceptions, and social support. Interviews were audio-recorded with written consent, transcribed verbatim, and translated from Odia to English by a bilingual researcher. Data saturation was confirmed by the eighth interview. Transcripts were analysed in MAXQDA (Version 19) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] using Braun and Clarke's six-phase thematic analysis framework. Analytical rigour was ensured through a reflexivity journal, peer debriefing, and negative case analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9. Mixed-methods integration\u003c/h2\u003e \u003cp\u003eIntegration was performed at the interpretation stage using a data triangulation matrix. Each major quantitative association was systematically cross-examined against corresponding qualitative themes to determine convergence, complementarity, or discordance (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \n\u003ch2\u003e2.10. Ethical approval\u003c/h2\u003e\n\u003cp\u003eEthical approval was obtained from the Institute Research and Ethics Committee, AIPH University, Bhubaneswar, and the Research and Ethics Committee, Department of Health and Family Welfare, Government of Odisha. The study complied with the Declaration of Helsinki. All participants provided written informed consent.\u003c/p\u003e"},{"header":"3. Results","content":"\u003ch2\u003e3.1. Baseline characteristics of study participants\u003c/h2\u003e\n\u003cp\u003eTable 1 presents the baseline sociodemographic and clinical characteristics of all 341 participants. The majority were female (58.9%), aged ≥ 40 years (56.0%), married (87.1%), with mean age 43.2 ± 11.8 years. Agriculture and domestic occupations predominated. Regarding IDRS risk distribution: 52.5% were classified as high risk (IDRS ≥ 60), 33.7% as moderate risk, and 13.8% as low risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Baseline sociodemographic and clinical characteristics of study participants (N = 341)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean ± SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSociodemographic variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal participants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e341 (100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e140 (41.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e201 (58.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25–39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e150 (44.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25–39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e≥ 40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e191 (56.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40–80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMean age (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43.2 ± 11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25–80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e297 (87.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational attainment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo formal education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e68 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrimary (up to Class 5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e84 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSecondary (Class 6–10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e96 (28.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigher secondary and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e93 (27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAgriculture / daily wage labour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e154 (45.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHomemaker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e112 (32.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSalaried / professional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48 (14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBusiness / self-employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27 (7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIDRS component variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaist circumference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMean waist – males (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e82.4 ± 9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62–114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMean waist – females (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79.8 ± 10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58–108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical activity level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVigorous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e76 (22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e163 (47.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSedentary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52 (15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily history of diabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo family history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e288 (84.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOne parent or sibling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51 (14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBoth parents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIDRS risk category (outcome)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLow risk (IDRS \u0026lt; 30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModerate risk (IDRS 30–50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e115 (33.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh risk (IDRS ≥ 60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e179 (52.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eSD = standard deviation; IDRS = Indian Diabetes Risk Score. Data presented as n (%) for categorical variables and mean ± SD for continuous variables. IDRS risk categories per Mohan et al. [9]: low risk (score \u0026lt; 30); moderate risk (score 30–50); high risk (score ≥ 60). Maximum possible IDRS score = 100.\u003c/em\u003e\u003c/p\u003e\n\u003ch2\u003e3.2. Association between participant characteristics, IDRS components, and diabetes risk category\u003c/h2\u003e\n\u003cp\u003eTable 2 presents associations between sociodemographic variables (Section A) and IDRS component variables (Section B) with diabetes risk classification. Among participants aged ≥ 40 years, 65.9% were classified as high risk compared with 35.3% of those aged 25–39 (p \u0026lt; 0.001). Females (59.4%) showed higher high-risk prevalence than males (29.3%). Among widowed participants, 85.7% were classified as high risk. High-risk prevalence among females reached 100% with waist ≥ 90 cm. Among sedentary participants, 78.8% were classified as high risk. Positive family history was strongly associated with high-risk classification (74.5% with one affected relative; p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Association between participant characteristics, IDRS components, and diabetes risk category (N = 341)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow Risk n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate Risk n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh Risk n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eA. Sociodemographic characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25–39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36 (24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61 (40.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53 (35.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e≥ 40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54 (28.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e126 (65.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27 (19.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72 (51.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41 (29.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62 (30.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e120 (59.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e106 (35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e156 (52.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12 (36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (42.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 (21.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12 (85.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eB. IDRS component variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaist circumference – females\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 70 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35 (47.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23 (31.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e70–79 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37 (45.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e80–89 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13 (55.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e≥ 90 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVigorous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28 (36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35 (46.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e82 (50.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64 (39.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSedentary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9 (17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41 (78.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily history of diabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e44 (15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e103 (35.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e141 (49.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOne parent/sibling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38 (74.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBoth parents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e*** p \u0026lt; 0.001 (Chi-square test). Risk categories: low = IDRS \u0026lt; 30; moderate = IDRS 30–50; high = IDRS ≥ 60. Section A: sociodemographic variables; Section B: IDRS component variables.\u003c/em\u003e\u003c/p\u003e\n\u003ch2\u003e3.3. Binary logistic regression and model fit statistics\u003c/h2\u003e\n\u003cp\u003eTable 3 presents the full logistic regression model (unadjusted and adjusted) with model fit statistics. In the adjusted model, four variables independently predicted high diabetes risk: sedentary lifestyle (OR: 4.17; 95% CI: 2.08–8.35; p \u0026lt; 0.001), age ≥ 40 years (OR: 3.22; 95% CI: 1.88–5.05; p \u0026lt; 0.001), family history of diabetes (OR: 2.81; 95% CI: 1.56–5.05; p = 0.001), and elevated waist circumference (OR: 2.54; 95% CI: 1.49–4.32; p = 0.001). Male sex was inversely associated with high risk (OR: 0.32; 95% CI: 0.14–0.76; p = 0.008). Socioeconomic variables were not statistically significant. The adjusted model demonstrated Nagelkerke R² = 0.31, Hosmer–Lemeshow p = 0.61, and 74.8% overall classification accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Binary logistic regression analysis of predictors of high diabetes risk (IDRS ≥ 60) and model fit statistics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWald χ²\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003edf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNagelkerke R²\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStep 1: Unadjusted (univariable) model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge ≥ 40 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.14–5.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSex (male vs. female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.14–0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSedentary lifestyle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.08–8.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh waist circumference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.49–4.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFamily history of diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.56–5.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStep 2: Adjusted (multivariable) final model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge ≥ 40 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.88–5.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSex (male vs. female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.14–0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSedentary lifestyle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.08–8.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh waist circumference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.49–4.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFamily history of diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.56–5.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel fit statistics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e−2 Log likelihood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eStep 1: 394.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eStep 2: 341.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCox \u0026amp; Snell R²\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNagelkerke R²\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHosmer–Lemeshow p-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOverall classification accuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e71.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e74.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eB = regression coefficient; SE = standard error; Wald χ² = Wald chi-square statistic; OR = odds ratio; CI = confidence interval. Reference categories: age 25–39 years; female sex; active physical activity; normal waist circumference; no family history of diabetes. Hosmer–Lemeshow p \u0026gt; 0.05 indicates adequate model fit. Nagelkerke R² reflects proportion of outcome variance explained.\u003c/em\u003e\u003c/p\u003e\n\u003ch2\u003e3.4. Qualitative findings: thematic analysis\u003c/h2\u003e\n\u003cp\u003eThematic analysis of eight in-depth interviews generated five overarching themes with ten sub-themes (Table 4), developed inductively from participant data and confirmed through peer debriefing and negative case analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Qualitative thematic framework: themes, sub-themes, representative quotations, and analytical interpretations\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTheme\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSub-theme\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRepresentative quotation (translated)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnalytical interpretation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1. Patient understanding\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBiomedical knowledge deficit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\"I thought sugar disease only happens to fat people; I never thought it could happen to me.\" (Female, 52)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLimited understanding of diabetes aetiology and personal susceptibility reflects low health literacy.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMisconceptions about medication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\"I stopped my medicine when I felt fine; I did not know the danger.\" (Male, 45)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAsymptomatic periods trigger treatment discontinuation, reflecting a gap between perceived and actual disease severity.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2. Management challenges\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHealthcare access barriers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\"The hospital is 15 km away; I cannot go every month to collect tablets.\" (Female, 60)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGeographical distance and transport costs are primary structural barriers to consistent medication adherence.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFinancial burden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\"Sometimes I buy food or buy medicine — I cannot afford both.\" (Male, 55)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCompeting expenditure priorities create critical trade-offs that undermine glycaemic control.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3. Self-care burden\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDietary restriction fatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\"I am tired of eating the same things every day. My family eats rice; I must watch.\" (Female, 48)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChronic dietary vigilance within rice-dominant food cultures generates self-care fatigue and social isolation.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePsychological distress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\"I feel like a burden on my children. I must take medicine for life.\" (Male, 63)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChronic illness-related guilt reflects unmet psychosocial support needs and potential depressive comorbidity.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4. Illness perceptions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFatalistic attribution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\"It is God's wish. My father had it — it is in our blood.\" (Male, 58)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFatalistic and hereditary explanatory models reduce perceived self-efficacy and motivation for behavioural change.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStigma and concealment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\"I do not tell neighbours. They will think I eat too much.\" (Female, 44)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStigma-driven diagnostic concealment impedes community support-seeking and peer-based self-management.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e5. Social support\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFamily as enabler\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\"My daughter reminds me to take tablets and prepares my food separately.\" (Male, 70)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFamily-mediated medication reminders and dietary preparation substantially improve self-management adherence.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSocial isolation as barrier\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\"I live alone since my husband died. Nobody is there to help me cook.\" (Female, 67)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSocial isolation among widowed older women constitutes a high-risk profile requiring targeted community outreach.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eParticipant identifiers: sex and age in parentheses. All quotations translated from Odia. Analysis conducted in MAXQDA Version 19 using Braun and Clarke's six-phase thematic analysis framework.\u003c/em\u003e\u003c/p\u003e\n\u003ch2\u003e3.5. Mixed-methods integration: data triangulation\u003c/h2\u003e\n\u003cp\u003eTable 5 presents the triangulation matrix. Of five integration points: three demonstrated convergent findings (both strands confirming and extending the same conclusion), one yielded complementary evidence (qualitative strand adding a dimension absent from the quantitative estimate), and one was discordant — the quantitative regression found no significant socioeconomic associations, while qualitative participants clearly described financial hardship as a major barrier to adherence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Data triangulation matrix: integration of quantitative and qualitative findings\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuantitative finding\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eQualitative finding\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoint of integration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTriangulation outcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e78.8% of sedentary participants at high IDRS risk; OR 4.17 (95% CI 2.08–8.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDomestic roles, lack of safe spaces, and fatigue cited as drivers of physical inactivity (Themes 3, 4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQualitative strand contextualises why sedentary behaviour is the strongest quantitative predictor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eConfirmed and extended: both strands converge; qualitative findings add structural and gender-based depth\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65.9% of participants aged ≥ 40 years at high risk; OR 3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOlder participants expressed fatalism and lower motivation for lifestyle modification (Theme 4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQualitative data explains why older adults may resist preventive advice despite high quantitative risk scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eConfirmed and extended: fatalism identified as a psychosocial mediator of age-related risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFamily history: OR 2.81; 74.5% with one affected relative in high-risk category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFamily members serve dual roles as genetic risk carriers and active enablers of self-care (Theme 5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQualitative strand reveals the protective dimension of family absent from the quantitative OR estimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eComplementary: qualitative findings add nuance not captured in the quantitative estimate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo significant socioeconomic associations in logistic regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFinancial barriers (food vs. medicine trade-offs) cited as a major management obstacle (Theme 2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQuantitative survey failed to detect individual-level financial hardship captured in qualitative inquiry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiscordant: warrants stratified socioeconomic analysis in future studies\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemales at higher risk: 59.4% high risk; OR for male sex 0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale participants described stigma, dietary isolation, and sedentary domestic roles as compounding barriers (Themes 3, 4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQualitative strand maps social pathways underlying the sex differential in quantitative risk distribution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eConfirmed and extended: both strands confirm female vulnerability; qualitative findings explain the mechanisms\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eConfirmed and extended = both strands reach the same conclusion with qualitative findings adding explanatory depth. Complementary = strands address different dimensions of the same construct. Discordant = strands yield contradictory conclusions, warranting further investigation.\u003c/em\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis concurrent mixed-methods study provides important new evidence on the burden of diabetes risk factors and the lived experience of diabetes management in rural Ganjam district, Odisha. The finding that 65.9% of participants aged \u0026ge; 40 years were classified as high risk by the IDRS substantially exceeds rates reported in comparable Indian studies \u0026mdash; 33.4% in Meerut [18] and 31.5% in Andhra Pradesh [19] \u0026mdash; and likely reflects the older demographic profile of the study population, high rates of sedentary behaviour, and limited access to preventive healthcare in rural southern Odisha.\u003c/p\u003e\n\u003cp\u003eSedentary lifestyle emerged as the strongest independent predictor of high diabetes risk (OR: 4.17), consistent with Sri PKSSU et al. (2022), who identified physical inactivity in 71.7% of high-risk individuals across Andhra Pradesh [19]. The adjusted model\u0026apos;s Nagelkerke R\u0026sup2; of 0.31 and 74.8% classification accuracy confirm the IDRS as a valid and discriminating screening instrument in this setting \u0026mdash; a finding aligned with Dudeja et al. (2017), who demonstrated that 97% of confirmed undiagnosed diabetes cases in an urban slum had IDRS scores \u0026ge; 60 [11].\u003c/p\u003e\n\u003cp\u003eThe inverse association between male sex and high diabetes risk (OR: 0.32) likely reflects greater physical activity among males in agricultural labour, alongside sex differences in abdominal fat distribution. Female participants consistently showed higher waist circumference scores, a pattern reported by Prashant et al. (2022) [20]. The qualitative data further contextualises this disparity: female participants described sedentary domestic roles, stigma-related dietary isolation, and restricted access to physical activity as compounding barriers.\u003c/p\u003e\n\u003cp\u003eThe qualitative findings reveal that biological risk factors are substantially compounded by structural, psychosocial, and cultural determinants. Financial barriers creating trade-offs between medication and food \u0026mdash; directly captured in participant narratives \u0026mdash; represent a critical modifiable barrier that quantitative survey instruments failed to detect. This discordance between quantitative and qualitative strands is a methodologically important finding: it demonstrates that mixed-methods integration yields insights that neither approach produces independently. Fatalistic illness perceptions and stigma-driven diagnostic concealment identified in Themes 4 and 5 mirror patterns described by Bukhsh et al. (2020) in Pakistan [21] and Naithani et al. (2006) in the United Kingdom [22], suggesting cross-cultural transferability in South Asian contexts. The high-risk prevalence observed in this study is further contextualised by longitudinal data from urban south India documenting rising incidence of diabetes and pre-diabetes [23], as well as studies validating IDRS utility among medical students in northern India [24] and urban slum populations [25], collectively reinforcing the need for expanded community-based screening across all demographic groups in India.\u003c/p\u003e\n\u003ch3\u003e4.1. Strengths and limitations\u003c/h3\u003e\n\u003cp\u003eStrengths include the concurrent mixed-methods design with equal strand weighting; validated IDRS instrument; multistage PPS sampling; full two-step logistic regression reporting with model fit statistics; Braun and Clarke\u0026apos;s structured thematic framework; and formal data triangulation. Limitations include the cross-sectional quantitative design precluding causal inference; single-block restriction limiting generalisability; potential social desirability bias in physical activity self-reporting; absence of biochemical confirmation of diabetes status; and a qualitative sample of eight participants, which, while sufficient for thematic saturation, limits transferability.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis mixed-methods study demonstrates a high prevalence of diabetes risk factors in rural Ganjam district, Odisha. Sedentary lifestyle, advancing age, abdominal obesity, and family history of diabetes are the primary independent predictors of high IDRS risk. Qualitative inquiry reveals that effective diabetes prevention and management are constrained by structural barriers, limited health literacy, psychosocial burden, illness stigma, and inadequate social support \u0026mdash; determinants that quantitative data alone cannot fully capture. Data triangulation confirms convergence across key associations while revealing important discordances that highlight the added value of mixed-methods inquiry. Targeted IDRS-based community screening, family-centred diabetes education, stigma-reduction programmes, and policies addressing financial barriers to medication adherence are urgently needed to reduce the undiagnosed diabetes burden in rural Odisha.\u003c/p\u003e"},{"header":"6. Recommendations","content":"\u003col\u003e\n \u003cli\u003eScale up IDRS-based mass screening at primary healthcare centres in Ganjam district, targeting adults aged \u0026ge; 40 years, females, and individuals with sedentary lifestyles.\u003c/li\u003e\n \u003cli\u003eDeploy ASHA/ANM-led community awareness programmes addressing modifiable risk factors using culturally appropriate and gender-sensitive messaging.\u003c/li\u003e\n \u003cli\u003eIntegrate psychosocial support and stigma-reduction components into existing diabetes education sessions at Ayushman Bharat\u0026ndash;Health and Wellness Centres.\u003c/li\u003e\n \u003cli\u003eEstablish family-engagement modules and peer support networks to improve self-care adherence, with particular focus on socially isolated older women.\u003c/li\u003e\n \u003cli\u003eAddress financial barriers through subsidised medicine schemes and community-based nutritional support programmes.\u003c/li\u003e\n \u003cli\u003eConduct prospective cohort studies with biochemical confirmation of diabetes status and socioeconomic stratification to establish causal pathways and evaluate intervention effectiveness.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Declarations","content":"\u003cp\u003eFinancial Support and Sponsorship\u003c/p\u003e\n\u003cp\u003eThis research received no specific funding from any public, commercial, or not-for-profit agency.\u003c/p\u003e\n\u003cp\u003eFunding Declaration\u003c/p\u003e\n\u003cp\u003eThis study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. No funding was received for the conduct of this study or preparation of this article.\u003c/p\u003e\n\u003cp\u003eClinical Trial Number\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003eConflicts of Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003eEthical approval\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Institute Research and Ethics Committee, AIPH University, Bhubaneswar (Ref. No. AIPH/IREC/2023/04), and the Research and Ethics Committee, Department of Health and Family Welfare, Government of Odisha. The study was conducted in compliance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003eConsent to participate\u003c/p\u003e\n\u003cp\u003eWritten informed consent to participate was obtained from all participants prior to data collection. For qualitative in-depth interviews, additional written consent for audio recording was obtained from each participant.\u003c/p\u003e\n\u003cp\u003eConsent to publish\u003c/p\u003e\n\u003cp\u003eInformed consent to publish, including consent to publish anonymised quotations and data derived from their participation, was obtained from all participants as part of the written consent process conducted prior to data collection.\u003c/p\u003e\n\u003cp\u003eData Availability Statement\u003c/p\u003e\n\u003cp\u003eAll the data used in this study will be provided on request to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. Global status report on noncommunicable diseases 2022. Geneva: World Health Organization; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF Diabetes Atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022;183:109119.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClaypool KT, Chung MK, Deonarine A, Gregg EW, Patel CJ. Characteristics of undiagnosed diabetes in men and women under the age of 50 years in the Indian subcontinent: the National Family Health Survey (NFHS-4)/Demographic Health Survey 2015\u0026ndash;2016. BMJ Open Diabetes Res Care. 2020;8(1):e000965.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract. 2019;157:107843.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrabhakaran D, Anand S, Watkins D, Gaziano T, Wu Y, Mbanya JC et al. Cardiovascular, respiratory, and related disorders: key messages from Disease Control Priorities, 3rd edition. BMJ. 2018;361:k1512.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohan V, Unnikrishnan R, Shobana S, Malavika M, Anjana RM, Sudha V. Are excess carbohydrates the main link to diabetes and its complications in Asians? 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Front Endocrinol (Lausanne). 2020;11:534873.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaithani S, Gulliford M, Morgan M. Patients' perceptions and experiences of 'continuity of care' in diabetes. Health Expect. 2006;9(2):118\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohan V, Deepa M, Anjana RM, Lanthorn H, Deepa R. Incidence of diabetes and pre-diabetes in a selected urban south Indian population (CUPS-19). J Assoc Physicians India. 2008;56:152\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh MM, Mangla V, Pangtey R, Garg S. Risk assessment of diabetes using the Indian Diabetes Risk Score: a study on young medical students from northern India. Indian J Endocrinol Metab. 2019;23(1):86\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNittoori S, Wilson V. Risk of type 2 diabetes mellitus among urban slum population using Indian Diabetes Risk Score. Indian J Med Res. 2020;152(3):308\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Diabetes risk factors, Indian Diabetes Risk Score, mixed-methods, binary logistic regression, thematic analysis, data triangulation, rural India, non-communicable diseases","lastPublishedDoi":"10.21203/rs.3.rs-9184255/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9184255/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eNon-communicable diseases, particularly diabetes mellitus, represent a growing public health burden in rural India. Limited community-level data exist for Ganjam district, Odisha.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA concurrent mixed-methods, community-based cross-sectional study was conducted from May to June 2023 in Jagannath Prasad block, Ganjam district. A total of 341 adults aged\u0026thinsp;\u0026ge;\u0026thinsp;25 years were recruited via multistage Probability Proportional to Size (PPS) sampling. Diabetes risk was assessed using the validated Indian Diabetes Risk Score (IDRS). Binary logistic regression identified independent predictors of high risk. In-depth interviews (n\u0026thinsp;=\u0026thinsp;8) with participants living with diabetes were analysed using Braun and Clarke's thematic analysis. Findings were integrated through data triangulation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOverall, 52.5% of participants were classified as high risk (IDRS\u0026thinsp;\u0026ge;\u0026thinsp;60). Multivariable logistic regression identified sedentary lifestyle (OR: 4.17; 95% CI: 2.08\u0026ndash;8.35), age\u0026thinsp;\u0026ge;\u0026thinsp;40 years (OR: 3.22; 95% CI: 1.88\u0026ndash;5.05), family history (OR: 2.81; 95% CI: 1.56\u0026ndash;5.05), and elevated waist circumference (OR: 2.54; 95% CI: 1.49\u0026ndash;4.32) as significant independent predictors (Nagelkerke R\u0026sup2; = 0.31). Thematic analysis generated five themes: patient understanding, management challenges, self-care burden, illness perceptions, and social support.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eHigh diabetes risk in rural Ganjam is driven by both modifiable lifestyle factors and psychosocial determinants. IDRS-based community screening, family-centred self-care education, and targeted healthcare access interventions are urgently needed.\u003c/p\u003e","manuscriptTitle":"Diabetes risk profiling in rural Odisha using the Indian Diabetes Risk Score and thematic analysis of patient perspectives a concurrent mixed methods community based cross sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-28 13:47:23","doi":"10.21203/rs.3.rs-9184255/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-18T03:47:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-13T10:56:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278776596777355732772865079156837442640","date":"2026-05-04T07:18:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-27T13:14:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"57384699204826890361336446600487176601","date":"2026-04-21T08:34:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-20T05:45:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-09T11:13:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-06T15:27:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2026-04-06T14:01:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"af0410d2-4e05-43aa-a77b-c206c1521c9a","owner":[],"postedDate":"April 28th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-18T03:47:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-13T10:56:27+00:00","index":70,"fulltext":""},{"type":"reviewerAgreed","content":"278776596777355732772865079156837442640","date":"2026-05-04T07:18:01+00:00","index":69,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T03:55:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-28 13:47:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9184255","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9184255","identity":"rs-9184255","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}