Decomposing socioeconomic inequality in lean diabetes among middle-aged adults and elderly in India

Decomposing socioeconomic inequality in lean diabetes among middle-aged adults and elderly in India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Decomposing socioeconomic inequality in lean diabetes among middle-aged adults and elderly in India Abhishek Kumar, Suraj Maiti, Priyamadhaba Behera, Sanjay K Mohanty This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5435500/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Lean diabetes is a subtype of diabetes (BMI < 18.5 Kg/m 2) with severe microvascular complications. Unlike diabetes, the prevalence of lean diabetes is higher among poor and marginalised populations. We decompose the socioeconomic inequalities in lean diabetes among middle-aged adults and elderly (45+ years) using nationally representative data from India. Methods: The Longitudinal Ageing Study in India (LASI) wave-1 microdata with a complete case analytic sample size of 58,824 individuals (45+) were utilised. We combined self-reported medically diagnosed diabetic conditions with BMI and identified the lean diabetic cases. Descriptive statistics and multivariable logistic regression were used to examine the prevalence and predictors of lean diabetes. Erreygers’ concentration index (ECI) and decomposition analyses were used to examine the contribution of socioeconomic factors in lean diabetes. Results: The prevalence of lean diabetes among older adults 45+ was 0.8% (95% CI: 0.7%, 0.9%); ranging from 1.1% (95% CI: 0.7%, 1.6%) among the poorest MPCE quintile to 0.5% (95% CI: 0.3%,0.6%) among richest MPCE quintile. A negative ECI (-.006) suggests pro-poor inequality in lean diabetes. The decomposition shows that the economic condition of households measured by monthly per capita consumption expenditure explains the largest variation in socio-economic inequality of lean diabetes (72%) followed by the place of residence (24%) and education (20%). Conclusion: The health care system in India needs to pay attention to the high burden of lean diabetes among the socially and economically disadvantaged populations in the diabetes care cascade. Health sciences/Health care Health sciences/Diseases Health sciences/Diseases/Cardiovascular diseases Health sciences/Diseases/Nutrition disorders Diabetes lean diabetes socio-economic inequality concentration index India Figures Figure 1 Figure 2 Key Points Socioeconomic inequality in lean diabetes : Socioeconomic inequalities are reflected in the prevalence of lean diabetes, which is significantly distributed among the poorest quintile (1.1 percent) than the richest quintile of 0.5 percent. Lean diabetes predominates among socioeconomically disadvantaged groups: The ECI of lean diabetes is -0.006, which denotes a pro-poor inequality, thus constituting a more prevalent disease among poor older adults. Decomposition results show that the largest contributors to inequality are economic status (measured by monthly per capita consumption expenditure) (71.3%), place of residence (23.9%), and education (19.5%). Why Does This Paper Matter? This paper shows that lean diabetes is a serious health issue among poor older populations, which is often neglected in the formulation of health policies. Diabetes is more dangerous for leaner and poorer individuals than previously evidenced. Introduction Diabetes is the most prevalent and growing disease worldwide. 1 , 2 Type 2 diabetes raises the blood sugar (glucose) and increases the likelihood of co-morbidity, complications and mortality. 3 According to the International Diabetes Federation (IDF), diabetes caused 6.7 million deaths in adults aged 20 to 79 years in 2021, accounting for 12.2% of global deaths, with 32.6% of these deaths occurring in people under 60. 4 Disability-adjusted life years (DALY) attributed to diabetes increased from 52·7 million years (95% CI: 45·3, 62·2) in 2010 to 78·9 million years (95% CI: 66·8, 94·5) in 2021. 5 Diabetes-related health expenditure was estimated at USD 966 billion in 2021 and is projected to be USD 1054 billion by 2045. 6 According to evidence from developing countries, people with lean diabetes frequently have a history of childhood malnutrition, low socioeconomic status, and onset at a young age. 7 – 10 Furthermore, a sedentary lifestyle, poor eating habits, stress, and financial difficulties can exacerbate diabetic conditions. 9 , 11 – 13 A follow-up study in Germany shows that individuals with lean diabetes have 2.52 times higher odds for mortality and 2.50 times higher odds for hypoglycaemia than those with diabetes and obesity (BMI > 30 Kg/m2). 14 A small-scale study conducted by Oommen et al. (2019) in Tamil Nadu found that lean diabetes is more prevalent among the elderly, illiterate and those who worked in manual labour than non-lean diabetics. 15 It appears more severe than its counterparts and has a higher risk of micro-vascular consequences. 9 India is presently home to the world's second-largest number of diabetic patients. An estimated 77 million adults in India have diabetes, making it a severe public health concern. 4 , 16 In 2016, diabetes was ranked as the 13th leading cause of disability-adjusted life years. 17 Type 2 diabetes is more common in urban areas and among those belonging to high socioeconomic groups. 13 , 18 – 20 Several studies have highlighted that a higher BMI generally increases the risk of type 2 diabetes, although the relationship is not always direct. 21 , 22 A few recent studies estimated the prevalence and variations of lean diabetes among women in the childbearing age group and men aged 15–54 years in India using data from the National Family and Health Survey-5. They found a higher prevalence of lean diabetes among poor, less educated and rural residents. 23 This paper aims to estimate the prevalence and variations of lean diabetes among older adults in India and identify the factors that lead to socioeconomic inequality in lean diabetes. The paper has been conceptualised with the following rationale. First, the prevalence and factors contributing to socioeconomic inequality of lean type 2 diabetes in India are not well established. We came across only one nationally representative study in India that estimated the prevalence of lean diabetes among a sub-sample of the population: women aged 15–49 and men aged 15–54. We have extended the analyses for the 45 + population, among whom the diabetes prevalence is higher at 19.7% (95%CI: 19.3%,20.1%) compared to those aged 15–29 years 5.1% (95%CI:5.0%, 5.2%] and 30–44 years (95%CI: 13.0%, 13.4%) . 20 , 24 Second, for the first time, we decompose the socioeconomic inequality of the most prevalent disease among a vulnerable population group (45+), including the elderly. Methods Data The microdata from the Longitudinal Aging Study in India (LASI Wave 1) data have been used in the study. The LASI (Wave 1) dataset was conducted during 2017-18 from a representative sample of 73,796 individuals aged 45 years and above and their spouses across 42,949 households. 25 In this study, our sample is limited to 58,824 participants aged 45 years and above. The LASI surveys collected comprehensive information on health, socio-economic and demographic conditions, and household conditions in sample households. The household information includes amenities, housing, consumer durables, and modules on consumption, income, and loans and debts. The individual module was comprehensive and collected self-reported diagnosed health conditions, measured health conditions (biomarker blood pressure, blood glucose levels), and the activity of daily living along with work, employment, and family and social networks. 25 Measures Lean Diabetes We used medically diagnosed, self-reported diabetes in our study to define lean diabetes. Individuals who responded "yes" to the question “Has any health professional ever diagnosed you with the following chronic conditions or diseases (Diabetes or high blood sugar)?” were considered diabetic. LASI also collected blood samples to estimate Hb1Ac in the population, but the data has not yet been publicly available. The Body Mass Index (BMI) was computed by dividing an individual's weight (in kilograms) by the square of their height (in meters). Height was measured in centimetres with a stadiometer, while weight was obtained in kilograms using a Seca 803 digital weighing scale. Further, the BMI was categorized into four categories (< 18.5 Kg/m2, 18.5 Kg/m2 to less than 23.0 Kg/m2, 23 Kg/m2 to less than 25 Kg/m2 and greater than or equal to 25 Kg/m2). We defined lean diabetes as a specific subtype of diabetes among those with lower body mass index (BMI < 18.5 Kg/m2), which is the primary outcome variable of this study. 9 , 26 – 28 Socioeconomic and demographic factors Based on the extensive literature review, 18 , 20 , 21 , 23 we have used a set of socio-economic and demographic variables in this study: Sex of the respondents (male and female), age (45–54, 55–64,65–69 and 75 + years), place of residence (Rural and Urban), religion (Hindu, Muslim, Christian, and others), Caste (scheduled caste[SC], scheduled tribe[ST], other backward class[OBC], others), marital status (currently married, widowed, and others (divorced/separated), education (no schooling, up to 5 years, 5–9 years completed, ten years or above), work (currently working previously worked but not currently working and never worked). We used the monthly per-capita quintile (MPCE), which excludes health expenditures, to reflect household economic conditions. The MPCE was divided into five quintiles: poorest, poor, middle, richer, and richest; health insurance (yes/no); and risk factors such as alcohol and smoking (yes/no). Finally, we used the region as a geographic variation indicator (North, Central, East, Northeast, West, and South). Statistical Analyses Descriptive statistics were used to summarise the background characteristics and measure the prevalence and variations of lean diabetes across socioeconomic characteristics. We have used a multivariable binary logistic regression model to identify determinants of lean diabetes. $$\:\text{log}\left(p\left(y=1\right)\right)={\beta\:}_{0}+{\beta\:}_{1}{x}_{1}+---+{\beta\:}_{k}{x}_{k}$$ Erreygers’ Concentration index (ECI) and concentration curve were used to determine the socioeconomic inequalities in the prevalence of Lean diabetes. 29 The decomposition of ECI was done to identify the contributing factors to socioeconomic inequality. Concentration index The concentration curve (CC) visually represents the cumulative distribution of diabetes based on MPCE rank, indicating disparities. It is a scaled covariance of a health factor (lean diabetes) with the ranking variable (MPCE score) to understand how much a section of individuals is better off (worse off) in terms of a health condition. This is twice the area between the concentration curve and the line of equality (45-degree line). The formula for CI is, $$\:CI=2/n\mu\:{\sum\:}_{i}^{n}hiRi-1$$ Where n represents the sample size, µ denotes the mean of the health outcome variable hi, and Ri indicates the individual's rank in the wealth distribution. Acknowledging the binary nature of the outcome variable, the study applied Erreygers' correction to the traditional CI, transforming it into a quasi-absolute measure suitable for binary health outcomes. 30 – 32 The magnitude of the ECI indicates the intensity and variability of the association between the health variable and the position in the distribution of living standards. The sign of the ECI reveals the direction of the relationship. 33 The ECI is defined as: ECI = (4µ) / (b − a) C Where C is the usual concentration index defined above, and a and b are the lower and upper bounds of the range of health outcome variables, respectively. A negative C value suggests concentration of the health outcome variable among lower socioeconomic status levels, while a positive value indicates concentration among more affluent individuals. The decomposition of Erreygers’ concentration index has been done to identify the potential contributing factors of the socioeconomic inequality in the prevalence of diabetes and Lean diabetes among middle-aged adults and the elderly in India. All analyses were carried out using STATA 17.0. Results Table 1 represents the key summary characteristics of three distinct groups: lean diabetic, diabetic but not lean diabetic and non-diabetic. The lean diabetic sample was older (median age 65 years [IQR: 58,73]) compared to the non-lean diabetic sample. The lean diabetic sample had lower educational attainment (last years of schooling, 3 years) and was predominately rural residents compared to the non-lean diabetic sample. Socioeconomic disparities are pronounced; there is a higher proportion of SC and ST for those who are lean diabetic compared to counterparts; also, the mean monthly per capita expenditure (MPCE) is lowest for lean diabetics (INR 2327) compared to the non-lean diabetic group (INR 4265) showing a stark contrast in the proportion of poor among lean diabetic (50.8%) and non-lean diabetic samples (25.9%). The percentage distribution of the sample characteristics is given in Supplementary Material (SM) Table S1 . As shown in Fig. 1 , the marginal probability of diabetes, along with different BMI cutoffs, highlights a significant portion of underweight individuals having type 2 diabetes. Also, the distribution of different cutoffs of BMI across the different socioeconomic characteristics is given in SM Table S2 . Table 1 Summary Profile (Weighted %) and 95% CI Summary Characteristics Overall Sample Lean Diabetic Non-lean diabetic Non-diabetic N 58824 419 7031 51374 Median Age (IQR)(years) 58 (50, 66) 65 (58, 72) 62 (55, 69) 59 (50, 67) % Male 46.2 (45.3, 47.0) 60.4 (52.9, 67.3) 46 (43.0, 49.1) 46.1 (45.2, 46.9) % Residing in Urban Area 29.3 (28.3, 30.2) 19.4 (14.8, 25.0) 53.0 (49.9, 56.0) 26.4 (25.5, 27.4) Mean years of schooling (SD) 4 (4.8) 3 (4.2) 6 (5.2) 4 (4.7) % Living along with spouse children 56.7 (55.8, 57.6) 46.3 (39.4, 53.6) 54.5 (51.2, 57.8) 57.1 (56.2, 57.9) % Health insurance 20.5 (19.9, 21.0) 19.9 (15.41, 25.51) 22.2 (20.2, 24.3) 20.3 (19.7, 20.8) % Currently working 47.5 (46.6, 48.3) 28.4 (22.5, 35.2) 33 (30.4, 35.8) 49.4 (48.5, 50.3) % Poor 40 (39.2, 40.8) 50.8 (43.5, 58.1) 25.9 (23.7, 28.2) 41.6 (40.8, 42.5) % SC & ST 28.4 (27.8, 29.1) 34.1 (27.2, 41.7) 16.6 (14.9, 18.5) 29.8 (29.1, 30.5) Mean MPCE 3002 (2972, 30323) 2327 (2037, 2615) 4265 (4130, 4399) 2853 (2823, 2882) Mean Household Size (SD) 5 (2.7) 5 (2.8) 5 (2.7) 5 (2.7) Table 2 shows the age-sex-adjusted prevalence of lean diabetes among overall and diabetic samples across various socioeconomic groups. Results show that lean diabetes prevalence decreases monotonically from the poorest fifth (1.1%, 95%CI: 0.7%,1.6%) to the richest fifth (0.5%, 95%CI: 0.3%,0.6%) in the overall sample and from 15.5% (95%CI: 10.4%,20.6%) to 2.4%, (95%CI: 1.5%,3.2%) among those who are diabetic. A similar pattern was observed across the education gradient, with the highest prevalence of lean diabetes observed among those with no educational attainment in the overall sample (0.9%, 95%CI: 0.7%,1.1%) and 14.3% (95%CI: 11.4%,17.2%) among the total diabetic sample. Also, lean diabetes was higher in the rural part in both overall sample (0.9%, 95%CI: 0.7%,1.1%) and (11.3%, 95%CI: 8.9%,13.3%) among diabetics, compared to urban (0.5%, 95%CI: 0.4%,0.7%) and (2.6%, 95%CI: 1.7%,3.4%) among diabetics respectively. In addition, the prevalence of lean diabetes was higher among SC & STs, widowed or separated and those who smoke and consume alcohol compared to their respective counterparts. The unadjusted prevalence of lean diabetes is given in SM Table S3 . Adjusted odds ratios (AOR) of lean diabetes are given in SM Table S4 . Figure S1 in SM shows the AORs across the socioeconomic gradient of lean diabetes and diabetes in India. Table 2 Age-sex-adjusted prevalence of lean diabetes in overall and diabetic samples across socioeconomic and demographic characteristics among older adults in India, 2017-18 Characteristics Lean diabetes (overall sample 58324) 95% CI Lean diabetes (among Diabetic 7450) 95% CI Overall 0.8 (0.7, 0.9) 6.8 (5.6, 8.1) Age 45–54 0.3 (0.2, 0.4) 3.7 (2.5, 4.9) 55–64 0.9 (0.6, 1.1) 6.6 (4.8, 8.4) 65–74 1.0 (0.8, 1.3) 6.8 (5.1, 8.6) 75+ 1.6 (1.0, 2.3) 13.8 (8.5, 19.1) Sex Male 1.0 (0.8, 1.2) 8.5 (6.8, 10.1) Female 0.6 (0.4, 0.7) 5.2 (3.8, 6.6) MPCE Quintile Poorest 1.1 (0.7, 1.6) 15.5 (10.4, 20.6) Poorer 0.8 (0.6, 1.1) 9.6 (6.7, 12.4) Middle 0.9 (0.6, 1.1) 8.6 (6.3, 11.0) Richer 0.6 (0.4, 0.8) 4.8 (3.2, 6.3) Richest 0.5 (0.3, 0.6) 2.4 (1.5, 3.2) Education No Schooling 0.9 (0.7, 1.1) 14.3 (11.4, 17.2) Less Than 5 Years 0.9 (0.6, 1.3) 8.2 (5.3, 11.2) 5–9 Years Completed 0.6 (0.4, 0.8) 4.3 (2.9, 5.8) 10 Years or More 0.5 (0.3, 0.7) 2.2 (1.3, 3.0) Place of Residence Rural 0.9 (0.7, 1.1) 11.1 (8.9, 13.3) Urban 0.5 (0.4, 0.7) 2.6 (1.7, 3.4) Caste SC 1.1 (0.7, 1.5) 13.0 (8.8, 17.2) ST 0.7 (0.4, 1.0) 14.8 (9.2, 20.5) OBC 0.8 (0.6, 0.9) 5.9 (4.5, 7.4) Others 0.7 (0.5, 0.8) 4.6 (3.3, 5.8) Religion Hindu 0.7 (0.6, 0.9) 6.6 (5.2, 8.0) Muslim 1.0 (0.6, 1.3) 7.8 (5.1, 10.6) Christian 1.0 (0.5, 1.5) 6.0 (2.6, 9.4) Others 1.0 (0.2, 1.8) 7.9 (2.2, 13.6) Marital Status Married 0.7 (0.6, 0.9) 6.1 (4.9, 7.3) Widowed 0.9 (0.6, 1.3) 9.0 (5.8, 12.2) Others 0.9 (0.1, 1.6) 8.6 (1.7, 15.4) Living Arrangement Living Alone 1.1 (0.5, 1.7) 10.8 (4.7, 16.8) Living with Spouse & others 0.9 (0.6, 1.1) 6.6 (4.5, 8.7) Living with spouse & Children 0.7 (0.5, 0.9) 5.9 (4.5, 7.3) Living With Children & Others 0.9 (0.6, 1.2) 8.4 (5.7, 11.2) Work Status Currently Working 0.5 (0.3, 0.6) 5.9 (4.0, 7.9) Previously Worked 1.1 (0.8, 1.5) 8.0 (5.4, 10.7) Never Worked 1.0 (0.7, 1.3) 5.9 (3.7, 8.0) Health Insurance No 0.8 (0.6, 1.0) 6.9 (5.4, 8.4) Yes 0.8 (0.6, 1.0) 6.2 (4.5, 7.9) Smoking No 0.7 (0.5, 0.8) 4.5 (3.6, 5.5) Yes 0.9 (0.7, 1.2) 12.4 (9.0, 15.8) Alcohol Use No 0.8 (0.7, 1.0) 6.6 (5.2, 7.9) Yes 0.7 (0.4, 0.9) 7.8 (5.0, 10.5) Table 3. Erreygers Concentration Index of diabetes and lean diabetes among middle-aged adults and elderly in India, 2017–2018 Prevalence Index value 95% CI P value Diabetes 0.094 (0.078, 0.109) p < 0.001 Lean diabetes -0.006 (-0.009, -0.002) p < 0.001 Non-lean diabetes 0.099 (0.084, 0.114) P < 0.001 ECIs, as shown in Table 3 , for overall type 2 diabetes is 0.094 (95% CI: 0.078, 0.109), and the non-lean diabetes sample is 0.099 (95% CI: 0.084, 0.114), suggesting a pro-rich inequality means that the prevalence of diabetes is more concentrated among those belonging to the high socioeconomic groups. On the contrary, lean diabetes with an ECI of -0.006 (95% CI: -0.009, -0.002) indicates a pro-poor inequality, which means it is more prevalent among the low socioeconomic or vulnerable population of older adults and elderly. The concentration curve, as shown in Fig. 2 , shows the contrasting nature of socioeconomic inequalities in lean diabetes with overall type 2 diabetes and non-lean diabetes among middle-aged adults and elderly in India. ECIs by socioeconomic characteristics are given in SM Table S5 . The results of the decomposition analysis of the socioeconomic inequality are shown in Table 4 and in Figure S2 (SM) . The largest contribution to the inequality is attributable to MPCE (71.29%), followed by place of residence (23.90%), which substantially contributes to the overall inequality. Education (19.52%) and social cast (18.77%) also contribute significantly. Regional differences contributed positively to the inequality in the West (9.35%) and the Northern regions of India. Also, smoking behaviour (7.68%) contributed to the overall inequality in the prevalence of lean diabetes among middle-aged and elderly in India. Working status has shown − 14.25% contribution. The eastern (-3.89%) and central regions (-1.54%) also negatively contributed to the inequality, suggesting that concentration was in favour of economically advantaged groups. Table 4 Decomposition of socioeconomic inequality Lean diabetes prevalence among middle-aged adults and elderly Characteristic Elasticity Concentration Index Contribution Percent Contribution MPCE Quintile -0.004 0.960 -0.003938 71.29 Education -0.004 0.293 -0.001078 19.52 Social Caste -0.005 0.188 -0.001037 18.77 Sex -0.018 -0.008 0.000155 -2.80 Place of residence -0.004 0.314 -0.001320 23.90 Work status -0.016 -0.485 0.000787 -14.25 Religion 0.001 0.028 0.000039 -0.71 Marital status 0.002 -0.023 -0.000050 0.90 Health insurance 0.000 0.019 0.000006 -0.11 Smoking 0.004 -0.117 -0.000424 7.68 Alcohol use -0.002 -0.020 0.000031 -0.56 North -0.002 0.086 -0.000177 3.21 Central -0.001 -0.156 0.000085 -1.54 East -0.002 -0.124 0.000215 -3.89 North-east 0.000 0.008 -0.000002 0.04 West -0.004 0.135 -0.000516 9.35 South 0.000 0.052 0.000000 0.00 Total CI -0.007 Actual CI -0.006 Residual -0.001 Discussion This is the first-ever study that estimated the prevalence and socioeconomic variation and decomposed the socioeconomic inequality of lean diabetes among older adults in India. This paper adds to the literature by extending the earlier work of Behera et al 2024. 23 While the paper by Behera et al. (2024) examined the prevalence and variations in lean diabetes in India among women aged 15–49 years and men aged 15–54 years, 23 our study examined the socioeconomic inequality in the prevalence of lean type 2 diabetes among middle-aged adults and the elderly in India and the major contributing factors to this socioeconomic inequality. We found that lean type 2 diabetes shows a higher prevalence among those with no educational attainment, low MPCE quintile and marginalized groups (SCs and STs), highlighting the need for interventions to address these disparities. Within the middle-aged adult and elderly population, lean diabetes exhibited a more pronounced pro-poor distribution, indicating a higher prevalence among socio-economically disadvantaged individuals. This contrasts with the general prevalence of diabetes, which tends to be higher among those with a more affluent socio-economic status. 13 , 18 – 20 Furthermore, the increased prevalence of lean type 2 diabetes among people who use substances like alcohol and tobacco emphasizes the importance of targeted interventions aimed at reducing substance use and improving overall health outcomes. Our findings are similar to those of previous studies. 20 , 34 , 35 Lean type 2 diabetes increases with age and is more prevalent among males in rural areas. In addition, there is significant regional variation in lean diabetes prevalence across geographical locations, with the highest rates observed in central and eastern regions of India, primarily including Uttar Pradesh, Odisha, Bihar, Jharkhand, Madhya Pradesh, and Chhattisgarh. The results are similar to those of a recent study by Behera et al. (2024). 23 ECI value of -0.006 for lean diabetes, showcasing its pro-poor inequality; findings align with the study by Behera et al. (2024). Decomposition results revealed that the inequality in the prevalence of lean diabetes is mainly because of MPCE quintile, education, and social caste, while other factors show minor contributions to the inequality and the findings supported by previous studies. 36 , 37 Bridging the existing literature gap in lean diabetes in India, our research calls for targeted health strategies, particularly in the Indian context. These strategies should not only address the prevalent obesity-related diabetes but also go beyond conventional BMI-centric approaches. Acknowledging the influence of socioeconomic factors and navigating the distinct challenges posed by lean diabetes in India, with a particular emphasis on socio-economically disadvantaged and vulnerable communities, is an upcoming necessity. Dissecting the socio-economic disparities associated with lean diabetes, this study provides a novel perspective on the distribution of diabetes among middle-aged adults and the elderly. Public health initiatives are needed to manage blood sugar levels in lean type 2 diabetes patients and to prevent lean pre-diabetes from progressing to diabetes, given their higher complication rates and prevalence among lower wealth quintiles who might struggle to afford treatment. As the global population ages, addressing these specific patterns becomes imperative for ensuring effective and equitable healthcare delivery to the elderly, particularly those with lean diabetes and existing socioeconomic vulnerabilities. Universal health care can mitigate health disparities related to chronic conditions, including diabetes, by providing equitable health care and resources for all individuals, regardless of socio-economic status. Such systems promote better health outcomes and quality of life through essential health services, preventive care, chronic disease management, and addressing health disparities linked to lean pre-diabetes and lean diabetes. Our study also has some limitations. First, the diabetes data are medically diagnosed but self-reported, which may result in inaccuracies or underreporting. Second, the current study utilized cross-sectional data and could not provide causality inferences. Further research is required to understand the longitudinal aspects. Finally, there is a chance of reporting bias, as participants may not accurately recall or fully disclose their health conditions. Declarations Funding None Author Contribution Conception and design of study: SKM, AK, SM Data analysis: AK, SM Writing, initial draft of paper: AK, SKM, SM, PB All authors read and approved the final version of the manuscript. 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A compendium of perspectives on diabetes: a challenge for sustainable health in the modern era. Diabetes, metabolic syndrome and obesity. 2021 Jun 17 :2775–2787. https://doi.org/10.2147/DMSO.S304751 Maiti, S., Akhtar, S., Upadhyay, A. K. & Mohanty, S. K. Socioeconomic inequality in awareness, treatment and control of diabetes among adults in India: evidence from National Family Health Survey of India (NFHS), 2019–2021. Sci. Rep. 13 (1), 2971. https://doi.org/10.1038/s41598-023-29978-y (2023). Bai, K., Chen, X., Song, R., Shi, W. & Shi, S. Association of body mass index and waist circumference with type 2 diabetes mellitus in older adults: a cross-sectional study. BMC Geriatr. 22 (1), 489. https://doi.org/10.1186/s12877-022-03145-w (2022). Kuang, M. et al. Association of LDL: HDL ratio with prediabetes risk: a longitudinal observational study based on Chinese adults. Lipids Health Dis. 21 (1), 44. https://doi.org/10.1186/s12944-022-01655-5 (2022). Behera, S. M. et al. Socioeconomic gradient of lean diabetes in India: Evidence from National Family Health Survey, 2019–21. PLOS Global Public. Health . 4 (5), e0003172. https://doi.org/10.1371/journal.pgph.0003172 (2024). Green, H. et al. Awareness, Treatment, and Control of Diabetes in India: A Nationally Representative Survey of Adults Aged 45 Years and Older. http://dx.doi.org/10.2139/ssrn.4065607 International Institute for Population Sciences (IIPS). National Programme for Health Care of Elderly (NPHCE), Ministry of Health and Family Welfare (MOHFW), Harvard T.H. Chan School of Public Health, University of Southern California. Longitudinal Ageing Study in India Wave 1, 2017-18, Report. Mumbai: International Institute for Population Sciences; (2020). Barma, P. D., Ranabir, S., Prasad, L. & Singh, T. P. Clinical and biochemical profile of lean type 2 diabetes mellitus. Indian J. Endocrinol. Metabol. 15 (Suppl1), S40–S43. https://doi.org/10.4103/2230-8210.83061 (2011). Lontchi-Yimagou, E. et al. An Atypical Form of Diabetes Among Individuals with Low BMI. Diabetes Care . 45 (6), 1428–1437. 10.2337/dc21-1957 (2022). Nishida, C. et al. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 363 (9403), 157–163. 10.1016/S0140-6736(03)15268-3 (2004). O'Donnel, O., Van Doorslaer, E., Wagstaff, A. & Lindelow, M. Analyzing health equity using household survey data: a guide to techniques and their implementation (World Bank, 2008). Erreygers, G. Correcting the concentration index. J. Health Econ. 28 (2), 504–515. https://doi.org/10.1016/j.jhealeco.2008.02.003 (2009). Kjellsson, G. & Gerdtham, U. G. On correcting the concentration index for binary variables. J. Health. Econ. 32 (3), 659–670. https://doi.org/10.1016/j.jhealeco.2012.10.012 (2013). Wagstaff, A. Correcting the concentration index: a comment. J. Health. Econ. 28 (2), 516–. https://doi.org/10.1016/j.jhealeco.2008.12.003 (2009). 20. Erreygers, G. & Van Ourti, T. Measuring socioeconomic inequality in health, health care and health financing by means of rank-dependent indices: a recipe for good practice. J. Health. Econ. 30 (4), 685–694. https://doi.org/10.1016/j.jhealeco.2011.04.004 (2011). Anjana, R. M. et al. Prevalence of diabetes and prediabetes in 15 states of India: results from the ICMR–INDIAB population-based cross-sectional study. lancet Diabetes Endocrinol. 5 (8), 585–596. https://doi.org/10.1016/S2213-8587(17)30174-2 (2017). Narayan, K. V. & Kanaya, A. M. Why are South Asians prone to type 2 diabetes? A hypothesis based on underexplored pathways. Diabetologia 63 (6), 1103–1109. https://doi.org/10.1007/s00125-020-05132-5 (2020). Rodríguez-Sánchez, B. & Cantarero-Prieto, D. Socioeconomic differences in the associations between diabetes and hospital admission and mortality among older adults in Europe. Econ. Hum. Biol. 33 , 89–100. 10.1016/j.ehb.2018.12.007 (2019). Mutyambizi, C., Booysen, F., Stokes, A., Pavlova, M. & Groot, W. Lifestyle and socio-economic inequalities in diabetes prevalence in South Africa: A decomposition analysis. PLoS One . 14 (1). 10.1371/journal.pone.0211208 (2019). Additional Declarations No competing interests reported. Supplementary Files supplementfileforleandiabetespaperJAGS.docx Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5435500","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":406453608,"identity":"ba6e5497-3a17-4e71-a067-2b1d9e4ea32e","order_by":0,"name":"Abhishek Kumar","email":"","orcid":"","institution":"International Institute for Population Sciences","correspondingAuthor":false,"prefix":"","firstName":"Abhishek","middleName":"","lastName":"Kumar","suffix":""},{"id":406453609,"identity":"853a2bc0-e3e3-4210-b839-fbc4028d158f","order_by":1,"name":"Suraj Maiti","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYLACCRDBDCIqQAzmBlK0nAExGInQAgeMbWASvxb5GbkPP1gwHE7czs68TeLjvNpo/naglh8V23BqMbiRbiwhAdSys5mtTHLmtuO5Mw4zNjD2nLmNW4tEGgNYy4bDPGbSvNuO5TYAtTAztuHWIj8jjfkHXMvfOcdy5xPSwnAjjQ1hC2NDTe4GQloMzjxjs5AwSDfecJit2LLn2IHcjUAtB/H5Rb49jfm2RIW17Ibzhzfe+FFTlzvv/OGDD35U4HEYEDBLGDSDbQTiw2CRA3jVAwHjB4Y6mJY6QopHwSgYBaNgBAIAEeRYBBCV5c0AAAAASUVORK5CYII=","orcid":"","institution":"Virginia Tech","correspondingAuthor":true,"prefix":"","firstName":"Suraj","middleName":"","lastName":"Maiti","suffix":""},{"id":406453610,"identity":"5fd130e8-9842-4f67-8de7-cdee5c7d30e1","order_by":2,"name":"Priyamadhaba Behera","email":"","orcid":"","institution":"All India Institute of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Priyamadhaba","middleName":"","lastName":"Behera","suffix":""},{"id":406453611,"identity":"826f7fff-0996-49d5-ad73-a97237df62c6","order_by":3,"name":"Sanjay K Mohanty","email":"","orcid":"","institution":"International Institute for Population Sciences","correspondingAuthor":false,"prefix":"","firstName":"Sanjay","middleName":"K","lastName":"Mohanty","suffix":""}],"badges":[],"createdAt":"2024-11-12 02:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5435500/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5435500/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76748257,"identity":"a9fd5788-9121-4fab-85aa-21ab56343c2a","added_by":"auto","created_at":"2025-02-20 09:27:48","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":24789,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVariation of Marginal probability of diabetes prevalence with different BMI cutoffs.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5435500/v1/620775ede78a1b9900d7ad16.jpeg"},{"id":76748276,"identity":"150c2e90-fc88-4e83-93fa-4fd0288f1480","added_by":"auto","created_at":"2025-02-20 09:27:49","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":35554,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConcentration curve for diabetes and lean diabetes among older adults in India (2017-18)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5435500/v1/ff88771b2145182f2c4b5f94.jpeg"},{"id":92982727,"identity":"ef64cd6d-0974-44b6-b495-ef9fb4444696","added_by":"auto","created_at":"2025-10-07 20:16:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1405497,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5435500/v1/1de306c4-628a-4320-802b-97d698def5af.pdf"},{"id":76748262,"identity":"506c4e82-beb6-4622-b11c-038c8d82781c","added_by":"auto","created_at":"2025-02-20 09:27:48","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":151047,"visible":true,"origin":"","legend":"","description":"","filename":"supplementfileforleandiabetespaperJAGS.docx","url":"https://assets-eu.researchsquare.com/files/rs-5435500/v1/ad7b6785e2e8b859920a71fd.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Decomposing socioeconomic inequality in lean diabetes among middle-aged adults and elderly in India","fulltext":[{"header":"Key Points","content":"\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003e\u003cstrong\u003eSocioeconomic inequality in lean diabetes\u003c/strong\u003e: Socioeconomic inequalities are reflected in the prevalence of lean diabetes, which is significantly distributed among the poorest quintile (1.1 percent) than the richest quintile of 0.5 percent.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eLean diabetes predominates among socioeconomically disadvantaged groups:\u003c/strong\u003e The ECI of lean diabetes is -0.006, which denotes a pro-poor inequality, thus constituting a more prevalent disease among poor older adults.\u003c/li\u003e\n \u003cli\u003eDecomposition results show that the largest contributors to inequality are economic status (measured by monthly per capita consumption expenditure) (71.3%), place of residence (23.9%), and education (19.5%).\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Why Does This Paper Matter? ","content":"\u003cp\u003eThis paper shows that lean diabetes is a serious health issue among poor older populations, which is often neglected in the formulation of health policies. Diabetes is more dangerous for leaner and poorer individuals than previously evidenced. \u0026nbsp;\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eDiabetes is the most prevalent and growing disease worldwide.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Type 2 diabetes raises the blood sugar (glucose) and increases the likelihood of co-morbidity, complications and mortality.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e According to the International Diabetes Federation (IDF), diabetes caused 6.7\u0026nbsp;million deaths in adults aged 20 to 79 years in 2021, accounting for 12.2% of global deaths, with 32.6% of these deaths occurring in people under 60.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Disability-adjusted life years (DALY) attributed to diabetes increased from 52\u0026middot;7\u0026nbsp;million years (95% CI: 45\u0026middot;3, 62\u0026middot;2) in 2010 to 78\u0026middot;9\u0026nbsp;million years (95% CI: 66\u0026middot;8, 94\u0026middot;5) in 2021.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Diabetes-related health expenditure was estimated at USD 966\u0026nbsp;billion in 2021 and is projected to be USD 1054\u0026nbsp;billion by 2045.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAccording to evidence from developing countries, people with lean diabetes frequently have a history of childhood malnutrition, low socioeconomic status, and onset at a young age.\u003csup\u003e\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Furthermore, a sedentary lifestyle, poor eating habits, stress, and financial difficulties can exacerbate diabetic conditions.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e A follow-up study in Germany shows that individuals with lean diabetes have 2.52 times higher odds for mortality and 2.50 times higher odds for hypoglycaemia than those with diabetes and obesity (BMI\u0026thinsp;\u0026gt;\u0026thinsp;30 Kg/m2).\u003csup\u003e14\u003c/sup\u003e A small-scale study conducted by Oommen et al. (2019) in Tamil Nadu found that lean diabetes is more prevalent among the elderly, illiterate and those who worked in manual labour than non-lean diabetics.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e It appears more severe than its counterparts and has a higher risk of micro-vascular consequences.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIndia is presently home to the world's second-largest number of diabetic patients. An estimated 77\u0026nbsp;million adults in India have diabetes, making it a severe public health concern.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e In 2016, diabetes was ranked as the 13th leading cause of disability-adjusted life years.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Type 2 diabetes is more common in urban areas and among those belonging to high socioeconomic groups.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Several studies have highlighted that a higher BMI generally increases the risk of type 2 diabetes, although the relationship is not always direct.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e A few recent studies estimated the prevalence and variations of lean diabetes among women in the childbearing age group and men aged 15\u0026ndash;54 years in India using data from the National Family and Health Survey-5. They found a higher prevalence of lean diabetes among poor, less educated and rural residents.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThis paper aims to estimate the prevalence and variations of lean diabetes among older adults in India and identify the factors that lead to socioeconomic inequality in lean diabetes. The paper has been conceptualised with the following rationale. First, the prevalence and factors contributing to socioeconomic inequality of lean type 2 diabetes in India are not well established. We came across only one nationally representative study in India that estimated the prevalence of lean diabetes among a sub-sample of the population: women aged 15\u0026ndash;49 and men aged 15\u0026ndash;54. We have extended the analyses for the 45\u0026thinsp;+\u0026thinsp;population, among whom the diabetes prevalence is higher at 19.7% (95%CI: 19.3%,20.1%) compared to those aged 15\u0026ndash;29 years 5.1% (95%CI:5.0%, 5.2%] and 30\u0026ndash;44 years (95%CI: 13.0%, 13.4%) .\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Second, for the first time, we decompose the socioeconomic inequality of the most prevalent disease among a vulnerable population group (45+), including the elderly.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData\u003c/h2\u003e \u003cp\u003eThe microdata from the Longitudinal Aging Study in India (LASI Wave 1) data have been used in the study. The LASI (Wave 1) dataset was conducted during 2017-18 from a representative sample of 73,796 individuals aged 45 years and above and their spouses across 42,949 households.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e In this study, our sample is limited to 58,824 participants aged 45 years and above. The LASI surveys collected comprehensive information on health, socio-economic and demographic conditions, and household conditions in sample households. The household information includes amenities, housing, consumer durables, and modules on consumption, income, and loans and debts. The individual module was comprehensive and collected self-reported diagnosed health conditions, measured health conditions (biomarker blood pressure, blood glucose levels), and the activity of daily living along with work, employment, and family and social networks.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eLean Diabetes\u003c/h2\u003e \u003cp\u003eWe used medically diagnosed, self-reported diabetes in our study to define lean diabetes. Individuals who responded \"yes\" to the question \u003cem\u003e\u0026ldquo;Has any health professional ever diagnosed you with the following chronic conditions or diseases (Diabetes or high blood sugar)?\u0026rdquo;\u003c/em\u003e were considered diabetic. LASI also collected blood samples to estimate Hb1Ac in the population, but the data has not yet been publicly available.\u003c/p\u003e \u003cp\u003eThe Body Mass Index (BMI) was computed by dividing an individual's weight (in kilograms) by the square of their height (in meters). Height was measured in centimetres with a stadiometer, while weight was obtained in kilograms using a Seca 803 digital weighing scale. Further, the BMI was categorized into four categories (\u0026lt;\u0026thinsp;18.5 Kg/m2, 18.5 Kg/m2 to less than 23.0 Kg/m2, 23 Kg/m2 to less than 25 Kg/m2 and greater than or equal to 25 Kg/m2).\u003c/p\u003e \u003cp\u003eWe defined \u003cem\u003elean diabetes\u003c/em\u003e as a specific subtype of diabetes among those with lower body mass index (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 Kg/m2), which is the primary outcome variable of this study.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSocioeconomic and demographic factors\u003c/h3\u003e\n\u003cp\u003eBased on the extensive literature review,\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e we have used a set of socio-economic and demographic variables in this study: Sex of the respondents (male and female), age (45\u0026ndash;54, 55\u0026ndash;64,65\u0026ndash;69 and 75\u0026thinsp;+\u0026thinsp;years), place of residence (Rural and Urban), religion (Hindu, Muslim, Christian, and others), Caste (scheduled caste[SC], scheduled tribe[ST], other backward class[OBC], others), marital status (currently married, widowed, and others (divorced/separated), education (no schooling, up to 5 years, 5\u0026ndash;9 years completed, ten years or above), work (currently working previously worked but not currently working and never worked). We used the monthly per-capita quintile (MPCE), which excludes health expenditures, to reflect household economic conditions. The MPCE was divided into five quintiles: poorest, poor, middle, richer, and richest; health insurance (yes/no); and risk factors such as alcohol and smoking (yes/no). Finally, we used the region as a geographic variation indicator (North, Central, East, Northeast, West, and South).\u003c/p\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003eDescriptive statistics were used to summarise the background characteristics and measure the prevalence and variations of lean diabetes across socioeconomic characteristics. We have used a multivariable binary logistic regression model to identify determinants of lean diabetes.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{log}\\left(p\\left(y=1\\right)\\right)={\\beta\\:}_{0}+{\\beta\\:}_{1}{x}_{1}+---+{\\beta\\:}_{k}{x}_{k}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eErreygers\u0026rsquo; Concentration index (ECI) and concentration curve were used to determine the socioeconomic inequalities in the prevalence of Lean diabetes.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e The decomposition of ECI was done to identify the contributing factors to socioeconomic inequality.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eConcentration index\u003c/h2\u003e \u003cp\u003eThe concentration curve (CC) visually represents the cumulative distribution of diabetes based on MPCE rank, indicating disparities. It is a scaled covariance of a health factor (lean diabetes) with the ranking variable (MPCE score) to understand how much a section of individuals is better off (worse off) in terms of a health condition. This is twice the area between the concentration curve and the line of equality (45-degree line).\u003c/p\u003e \u003cp\u003eThe formula for CI is,\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:CI=2/n\\mu\\:{\\sum\\:}_{i}^{n}hiRi-1$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere n represents the sample size, \u0026micro; denotes the mean of the health outcome variable hi, and Ri indicates the individual's rank in the wealth distribution.\u003c/p\u003e \u003cp\u003eAcknowledging the binary nature of the outcome variable, the study applied Erreygers' correction to the traditional CI, transforming it into a quasi-absolute measure suitable for binary health outcomes.\u003csup\u003e\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e The magnitude of the ECI indicates the intensity and variability of the association between the health variable and the position in the distribution of living standards. The sign of the ECI reveals the direction of the relationship.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe ECI is defined as:\u003c/p\u003e \u003cp\u003eECI = (4\u0026micro;) / (b\u0026thinsp;\u0026minus;\u0026thinsp;a) C\u003c/p\u003e \u003cp\u003eWhere C is the usual concentration index defined above, and a and b are the lower and upper bounds of the range of health outcome variables, respectively. A negative C value suggests concentration of the health outcome variable among lower socioeconomic status levels, while a positive value indicates concentration among more affluent individuals. The decomposition of Erreygers\u0026rsquo; concentration index has been done to identify the potential contributing factors of the socioeconomic inequality in the prevalence of diabetes and Lean diabetes among middle-aged adults and the elderly in India. All analyses were carried out using STATA 17.0.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e represents the key summary characteristics of three distinct groups: lean diabetic, diabetic but not lean diabetic and non-diabetic. The lean diabetic sample was older (median age 65 years [IQR: 58,73]) compared to the non-lean diabetic sample. The lean diabetic sample had lower educational attainment (last years of schooling, 3 years) and was predominately rural residents compared to the non-lean diabetic sample. Socioeconomic disparities are pronounced; there is a higher proportion of SC and ST for those who are lean diabetic compared to counterparts; also, the mean monthly per capita expenditure (MPCE) is lowest for lean diabetics (INR 2327) compared to the non-lean diabetic group (INR 4265) showing a stark contrast in the proportion of poor among lean diabetic (50.8%) and non-lean diabetic samples (25.9%). The percentage distribution of the sample characteristics is given in \u003cstrong\u003eSupplementary Material (SM) Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e. As shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, the marginal probability of diabetes, along with different BMI cutoffs, highlights a significant portion of underweight individuals having type 2 diabetes. Also, the distribution of different cutoffs of BMI across the different socioeconomic characteristics is given in \u003cstrong\u003eSM Table S2\u003c/strong\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary Profile (Weighted %) and 95% CI\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSummary Characteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOverall Sample\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLean Diabetic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-lean diabetic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-diabetic\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e58824\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e419\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e7031\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e51374\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedian Age (IQR)(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58 (50, 66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65 (58, 72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62 (55, 69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59 (50, 67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.2 (45.3, 47.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.4 (52.9, 67.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (43.0, 49.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.1 (45.2, 46.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% Residing in Urban Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.3 (28.3, 30.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.4 (14.8, 25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.0 (49.9, 56.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.4 (25.5, 27.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean years of schooling (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% Living along with spouse children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.7 (55.8, 57.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.3 (39.4, 53.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.5 (51.2, 57.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.1 (56.2, 57.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% Health insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.5 (19.9, 21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.9 (15.41, 25.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.2 (20.2, 24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.3 (19.7, 20.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% Currently working\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.5 (46.6, 48.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.4 (22.5, 35.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (30.4, 35.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.4 (48.5, 50.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% Poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (39.2, 40.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.8 (43.5, 58.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.9 (23.7, 28.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.6 (40.8, 42.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% SC \u0026amp; ST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.4 (27.8, 29.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.1 (27.2, 41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.6 (14.9, 18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.8 (29.1, 30.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean MPCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3002 (2972, 30323)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2327 (2037, 2615)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4265 (4130, 4399)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2853 (2823, 2882)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean Household Size (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the age-sex-adjusted prevalence of lean diabetes among overall and diabetic samples across various socioeconomic groups. Results show that lean diabetes prevalence decreases monotonically from the poorest fifth (1.1%, 95%CI: 0.7%,1.6%) to the richest fifth (0.5%, 95%CI: 0.3%,0.6%) in the overall sample and from 15.5% (95%CI: 10.4%,20.6%) to 2.4%, (95%CI: 1.5%,3.2%) among those who are diabetic. A similar pattern was observed across the education gradient, with the highest prevalence of lean diabetes observed among those with no educational attainment in the overall sample (0.9%, 95%CI: 0.7%,1.1%) and 14.3% (95%CI: 11.4%,17.2%) among the total diabetic sample. Also, lean diabetes was higher in the rural part in both overall sample (0.9%, 95%CI: 0.7%,1.1%) and (11.3%, 95%CI: 8.9%,13.3%) among diabetics, compared to urban (0.5%, 95%CI: 0.4%,0.7%) and (2.6%, 95%CI: 1.7%,3.4%) among diabetics respectively. In addition, the prevalence of lean diabetes was higher among SC \u0026amp; STs, widowed or separated and those who smoke and consume alcohol compared to their respective counterparts. The unadjusted prevalence of lean diabetes is given in \u003cstrong\u003eSM Table S3\u003c/strong\u003e. Adjusted odds ratios (AOR) of lean diabetes are given in \u003cstrong\u003eSM Table S4\u003c/strong\u003e. \u003cstrong\u003eFigure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e in SM\u003c/strong\u003e shows the AORs across the socioeconomic gradient of lean diabetes and diabetes in India.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAge-sex-adjusted prevalence of lean diabetes in overall and diabetic samples across socioeconomic and demographic characteristics among older adults in India, 2017-18\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLean diabetes (overall sample 58324)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLean diabetes (among Diabetic 7450)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.7, 0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(5.6, 8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e45\u0026ndash;54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.2, 0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(2.5, 4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e55\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.6, 1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(4.8, 8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e65\u0026ndash;74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.8, 1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(5.1, 8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.0, 2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(8.5, 19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.8, 1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(6.8, 10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.4, 0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(3.8, 6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMPCE Quintile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePoorest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.7, 1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(10.4, 20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePoorer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.6, 1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(6.7, 12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.6, 1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(6.3, 11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRicher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.4, 0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(3.2, 6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRichest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.3, 0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(1.5, 3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo Schooling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.7, 1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e14.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(11.4, 17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLess Than 5 Years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.6, 1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(5.3, 11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5\u0026ndash;9 Years Completed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.4, 0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(2.9, 5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e10 Years or More\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.3, 0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(1.3, 3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlace of Residence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.7, 1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(8.9, 13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.4, 0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(1.7, 3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCaste\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.7, 1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e13.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(8.8, 17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.4, 1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e14.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(9.2, 20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.6, 0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(4.5, 7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.5, 0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(3.3, 5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eReligion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHindu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.6, 0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(5.2, 8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMuslim\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.6, 1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(5.1, 10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eChristian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.5, 1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(2.6, 9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.2, 1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(2.2, 13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.6, 0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(4.9, 7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.6, 1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(5.8, 12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.1, 1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(1.7, 15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiving Arrangement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLiving Alone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.5, 1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e10.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(4.7, 16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLiving with Spouse \u0026amp; others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.6, 1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(4.5, 8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLiving with spouse \u0026amp; Children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.5, 0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(4.5, 7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLiving With Children \u0026amp; Others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.6, 1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(5.7, 11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eWork Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCurrently Working\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.3, 0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(4.0, 7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePreviously Worked\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.8, 1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(5.4, 10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNever Worked\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.7, 1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(3.7, 8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth Insurance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.6, 1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(5.4, 8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.6, 1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(4.5, 7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.5, 0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(3.6, 5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.7, 1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e12.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(9.0, 15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol Use\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.7, 1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(5.2, 7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.4, 0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(5.0, 10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;3. Erreygers Concentration Index of diabetes and lean diabetes among middle-aged adults and elderly in India, 2017\u0026ndash;2018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrevalence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndex value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e(0.078, 0.109)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLean diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e(-0.009, -0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-lean diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e(0.084, 0.114)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eECIs, as shown in \u003cstrong\u003eTable\u0026nbsp;3\u003c/strong\u003e, for overall type 2 diabetes is 0.094 (95% CI: 0.078, 0.109), and the non-lean diabetes sample is 0.099 (95% CI: 0.084, 0.114), suggesting a pro-rich inequality means that the prevalence of diabetes is more concentrated among those belonging to the high socioeconomic groups. On the contrary, lean diabetes with an ECI of -0.006 (95% CI: -0.009, -0.002) indicates a pro-poor inequality, which means it is more prevalent among the low socioeconomic or vulnerable population of older adults and elderly. The concentration curve, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, shows the contrasting nature of socioeconomic inequalities in lean diabetes with overall type 2 diabetes and non-lean diabetes among middle-aged adults and elderly in India. ECIs by socioeconomic characteristics are given in \u003cstrong\u003eSM Table S5\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eThe results of the decomposition analysis of the socioeconomic inequality are shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cstrong\u003eand in Figure S2 (SM)\u003c/strong\u003e. The largest contribution to the inequality is attributable to MPCE (71.29%), followed by place of residence (23.90%), which substantially contributes to the overall inequality. Education (19.52%) and social cast (18.77%) also contribute significantly. Regional differences contributed positively to the inequality in the West (9.35%) and the Northern regions of India. Also, smoking behaviour (7.68%) contributed to the overall inequality in the prevalence of lean diabetes among middle-aged and elderly in India. Working status has shown \u0026minus;\u0026thinsp;14.25% contribution. The eastern (-3.89%) and central regions (-1.54%) also negatively contributed to the inequality, suggesting that concentration was in favour of economically advantaged groups.\u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDecomposition of socioeconomic inequality Lean diabetes prevalence among middle-aged adults and elderly\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eElasticity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConcentration Index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eContribution\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercent Contribution\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMPCE Quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.003938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial Caste\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlace of residence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWork status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-14.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReligion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealth insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlcohol use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorth-east\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eActual CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidual\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first-ever study that estimated the prevalence and socioeconomic variation and decomposed the socioeconomic inequality of lean diabetes among older adults in India. This paper adds to the literature by extending the earlier work of Behera et al 2024.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e While the paper by Behera et al. (2024) examined the prevalence and variations in lean diabetes in India among women aged 15\u0026ndash;49 years and men aged 15\u0026ndash;54 years,\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e our study examined the socioeconomic inequality in the prevalence of lean type 2 diabetes among middle-aged adults and the elderly in India and the major contributing factors to this socioeconomic inequality.\u003c/p\u003e \u003cp\u003eWe found that lean type 2 diabetes shows a higher prevalence among those with no educational attainment, low MPCE quintile and marginalized groups (SCs and STs), highlighting the need for interventions to address these disparities. Within the middle-aged adult and elderly population, lean diabetes exhibited a more pronounced pro-poor distribution, indicating a higher prevalence among socio-economically disadvantaged individuals. This contrasts with the general prevalence of diabetes, which tends to be higher among those with a more affluent socio-economic status.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Furthermore, the increased prevalence of lean type 2 diabetes among people who use substances like alcohol and tobacco emphasizes the importance of targeted interventions aimed at reducing substance use and improving overall health outcomes. Our findings are similar to those of previous studies.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eLean type 2 diabetes increases with age and is more prevalent among males in rural areas. In addition, there is significant regional variation in lean diabetes prevalence across geographical locations, with the highest rates observed in central and eastern regions of India, primarily including Uttar Pradesh, Odisha, Bihar, Jharkhand, Madhya Pradesh, and Chhattisgarh. The results are similar to those of a recent study by Behera et al. (2024).\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eECI value of -0.006 for lean diabetes, showcasing its pro-poor inequality; findings align with the study by Behera et al. (2024). Decomposition results revealed that the inequality in the prevalence of lean diabetes is mainly because of MPCE quintile, education, and social caste, while other factors show minor contributions to the inequality and the findings supported by previous studies.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eBridging the existing literature gap in lean diabetes in India, our research calls for targeted health strategies, particularly in the Indian context. These strategies should not only address the prevalent obesity-related diabetes but also go beyond conventional BMI-centric approaches. Acknowledging the influence of socioeconomic factors and navigating the distinct challenges posed by lean diabetes in India, with a particular emphasis on socio-economically disadvantaged and vulnerable communities, is an upcoming necessity. Dissecting the socio-economic disparities associated with lean diabetes, this study provides a novel perspective on the distribution of diabetes among middle-aged adults and the elderly. Public health initiatives are needed to manage blood sugar levels in lean type 2 diabetes patients and to prevent lean pre-diabetes from progressing to diabetes, given their higher complication rates and prevalence among lower wealth quintiles who might struggle to afford treatment. As the global population ages, addressing these specific patterns becomes imperative for ensuring effective and equitable healthcare delivery to the elderly, particularly those with lean diabetes and existing socioeconomic vulnerabilities. Universal health care can mitigate health disparities related to chronic conditions, including diabetes, by providing equitable health care and resources for all individuals, regardless of socio-economic status. Such systems promote better health outcomes and quality of life through essential health services, preventive care, chronic disease management, and addressing health disparities linked to lean pre-diabetes and lean diabetes. Our study also has some limitations. First, the diabetes data are medically diagnosed but self-reported, which may result in inaccuracies or underreporting. Second, the current study utilized cross-sectional data and could not provide causality inferences. Further research is required to understand the longitudinal aspects. Finally, there is a chance of reporting bias, as participants may not accurately recall or fully disclose their health conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNone\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConception and design of study: SKM, AK, SM Data analysis: AK, SM Writing, initial draft of paper: AK, SKM, SM, PB All authors read and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll the data are available in the public domain at https://www.iipsdata.ac.in/datacatalog_detail/5\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Global status report on noncommunicable diseases 2014. 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Biol.\u003c/em\u003e \u003cb\u003e33\u003c/b\u003e, 89\u0026ndash;100. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ehb.2018.12.007\u003c/span\u003e\u003cspan address=\"10.1016/j.ehb.2018.12.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMutyambizi, C., Booysen, F., Stokes, A., Pavlova, M. \u0026amp; Groot, W. Lifestyle and socio-economic inequalities in diabetes prevalence in South Africa: A decomposition analysis. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e14\u003c/b\u003e (1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0211208\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0211208\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Diabetes, lean diabetes, socio-economic inequality, concentration index, India","lastPublishedDoi":"10.21203/rs.3.rs-5435500/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5435500/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003eLean diabetes is a subtype of diabetes (BMI \u0026lt; 18.5 Kg/m 2) with severe microvascular complications. Unlike diabetes, the prevalence of lean diabetes is higher among poor and marginalised populations. We decompose the socioeconomic inequalities in lean diabetes among middle-aged adults and elderly (45+ years) using nationally representative data from India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The Longitudinal Ageing Study in India (LASI) wave-1 microdata with a complete case analytic sample size of 58,824 individuals (45+) were utilised. We combined self-reported medically diagnosed diabetic conditions with BMI and identified the lean diabetic cases. Descriptive statistics and multivariable logistic regression were used to examine the prevalence and predictors of lean diabetes. Erreygers’ concentration index (ECI) and decomposition analyses were used to examine the contribution of socioeconomic factors in lean diabetes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The prevalence of lean diabetes among older adults 45+ was 0.8% (95% CI: 0.7%, 0.9%); ranging from 1.1% (95% CI: 0.7%, 1.6%) among the poorest MPCE quintile to 0.5% (95% CI: 0.3%,0.6%) among richest MPCE quintile. A negative ECI (-.006) suggests pro-poor inequality in lean diabetes. The decomposition shows that the economic condition of households measured by monthly per capita consumption expenditure explains the largest variation in socio-economic inequality of lean diabetes (72%) followed by the place of residence (24%) and education (20%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The health care system in India needs to pay attention to the high burden of lean diabetes among the socially and economically disadvantaged populations in the diabetes care cascade.\u003c/p\u003e","manuscriptTitle":"Decomposing socioeconomic inequality in lean diabetes among middle-aged adults and elderly in India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-20 09:27:44","doi":"10.21203/rs.3.rs-5435500/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cfea774a-30ca-4b1c-91ce-47203ba0a817","owner":[],"postedDate":"February 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":43344397,"name":"Health sciences/Health care"},{"id":43344398,"name":"Health sciences/Diseases"},{"id":43344399,"name":"Health sciences/Diseases/Cardiovascular diseases"},{"id":43344400,"name":"Health sciences/Diseases/Nutrition disorders"}],"tags":[],"updatedAt":"2025-10-07T20:08:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-20 09:27:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5435500","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5435500","identity":"rs-5435500","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}