“Changing Landscape of Undernutrition in Reproductive-Age Indian Women: Analysis of the nationally representative data, 1998-2021”

“Changing Landscape of Undernutrition in Reproductive-Age Indian Women: Analysis of the nationally representative data, 1998-2021” | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article “Changing Landscape of Undernutrition in Reproductive-Age Indian Women: Analysis of the nationally representative data, 1998-2021” Sirshendu Chaudhuri, Varun Agiwal, Nirupama AY, Yashaswini Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4753444/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 Aim: This study investigates the national and regional distribution and trend of undernutrition among non-pregnant reproductive age-group (15-44 years) women in India based on the National Family Health Survey (NFHS) data round 2 (1998-99) to round 5 (2019-21). Subject and Methods: Undernutrition was defined as a body mass index (BMI) <18.5 kg/m 2 . Association between undernutrition and socio-demographic variables including caste, place of residence (Rural and urban as reported in NFHS), marital status, educational level, and wealth index were examined using multiple logistic regression with multi-level modelling (MLM) and reported adjusted odds ratio (aOR). Results: A total of 461,093 women’s record was analyzed. The prevalence of undernutrition among women in the country reduced from 32.8% (n=22,890) in NFHS-2 to 27.1% (n=42,401) in NFHS-5 (average decadal reduction 2.7%). High intrastate variations (difference between highest and lowest prevalence district >20%) were noted across the country. NFHS-5 data shows that women belong to the poorest wealth index (aOR 1.68; 1.6, 1.76), up to preschool education (aOR 1.29; 1.23, 1.36), and unmarried women (aOR 1.54; 1.49, 1.59) are at risk of having undernutrition. Conclusion: Undernutrition among non-pregnant women has remained high in India. Inter-state and intra-state disparities and inequalities among the various social groups visibly exist for this ignored health issue. Body Mass Index Malnutrition Reproductive age group Women health Health policy Figures Figure 1 Figure 2 Figure 3 Introduction Undernutrition among reproductive age-group women is one of the major public health problems in the developing world. An estimated 120 million women in low-income countries are regarded as underweight, with a body mass index (BMI) less than 18.5 kg/m2.[1] Adequate nutrition in this age group is a key determinant for maternal health and favourable pregnancy outcomes. Women with poor health and nutrition are more likely to give birth to low-birthweight infants, paving way for the inevitable vicious cycle.[2] Children born to undernourished women are in turn, highly prone to be undernourished, which can lead to poor cognitive development, stunting, and a higher risk of morbidity and mortality. [3] Maternal and child undernutrition are the underlying causes of at least 3.5 million deaths each year and constitutes 11% of the total global disease burden.[4] Besides, malnutrition is often associated with multiple micronutrient deficiencies that can lead to severe neonatal complications among the offspring.[5] The pre-pregnancy nutritional status of women is a good predictor of maternal and child health outcomes.[6] The United Nations is committed to achieve several health-related goals including the reduction of maternal mortality ratio (<70 per 100,000 live births) and childhood mortality like neonatal mortality (<12 per 100,000 live births) and under-five mortality (<25 per 100,000 live births) by the end of 2030. Unfortunately, there are major challenges remaining in the Asian and African countries towards meeting these targets.[7] In India, the state-wise variations in the performance indicators for SDG-2 & 3 goals are remarkably high according to the 2020-21 SDG report.[8] Evidence suggests that the nutritional status of the Indian population has improved over time. The prevalence of chronic energy deficiency in India has declined from 52 % during 1975-79 to 34 % during 2011-12. Median intakes of most of the nutrients have increased over the same period, although they were below recommended levels.[9] Despite this improvement, the prevalence of thinness (body mass index or BMI <16 for adult women) remained high among pregnant women and nulliparous married women.[10,11] Multiple stakeholders like governments, donors and private sector entities have previously committed to work on nutrition. Unfortunately, the country lacks a direct policy to improve the nutritional status of the non-pregnant, non-lactating reproductive women age group. Several health programs in India, including- ICDS, Anemia Mukt Bharat, and POSHAN Abhiyaan, are bridging the nutritional gap among pregnant and lactating women.[12,13] To assess the impact of these programs or to develop a new policy to address undernutrition among the women in the reproductive age group, the availability of the epidemiological distribution of the condition is a critical element which is lacking in India at the country level. In this background, the state-level distribution and trend of undernutrition among women of reproductive age group in India based on the National Family Health Survey (NFHS) data were examined. In addition, the district-level hot spots of high levels of undernutrition based on the recent NFHS round 5 (2019-20) data have been identified. Lastly, the relationship of under-nutrition of women in reproductive age group (non-pregnant, non-lactating), with socio-demographic determinants at the state level were also examined. Methods A secondary data analysis based on the National Family Health Survey (NFHS) data, round 2 (1998-99) to round 5 (2019-21) was conducted. NFHS surveys are large, country-wide surveys conducted in the representative sampled households in the Indian states and Union Territories (UT). The survey is conducted by the Ministry of Health and Family Welfare, Government of India. To date, five surveys have been conducted NFHS 1 (1991-92), NFHS 2 (1998-99), NFHS 3 (2005-06), NFHS 4 (2015-16) and NFHS 5 (2019-20), the fifth version being the largest in terms of the number of population recruited. The NFHS surveys collect information on various household-related indicators, health and nutritional indicators, with a special focus on maternal and child health at the national level. In the recent editions of the survey, additional biomarkers have also been included. The detailed methodology of the NFHS surveys is provided in the NFHS reports.[14–17] In brief, multi-stage sampling was used to identify the representative primary sampling units (PSU) from the rural and urban areas. Households were selected from the PSU for interviews and biomarker sampling from the individual. The PSU were selected in such a way that it represents the districts, state and eventually the country.[18] Data source and management: For the present analysis, permission from the Demographic and Health Survey (DHS) by the United States Agency for International Development (USAID) for using the datasets of the last four NFHS surveys (NFHS 2 to 5) was taken. The data sets for the entire country were imported to STATA software and examined for missing and inconsistent values for the targeted variables among the reproductive age-group women. We excluded all the pregnant women from the analyses. All the missing values were removed. The outcome variable undernutrition was defined as body mass index (BMI) below 18.5. BMI was calculated by weight (Kg) of the target population divided by the square of the height (meter) of the individual. Individuals with BMI below 10 and more than 50, considering these to be outliers, were excluded. Statistical consideration: Datasets were analyzed in STATA software, version 14.0. The distribution of undernutrition was presented by descriptive statistics. Prevalence was estimated with a 95% confidence interval (CI). The trend of proportional distribution of undernutrition in the five NFHS surveys, and the state-wise distribution of NFHS were showed. Geographical distributions were shown for NFHS 5 with the hot spots in the country. The hotspots as districts with a high proportion of undernutrition (>90 th centile) were defined. The maps were created using QGIS version 3.2. Undernutrition distribution was examined in relation to socio-demographic variables like- caste, place of residence (Rural and urban as reported in NFHS), marital status, educational level, and wealth index. Univariate analysis, followed by multivariate regression analysis after adjusting for state-level clustering effect by multi-level modelling (MLM) were used to examine the effect of selected socio-demographic variables on undernutrition. While odds ratio (OR) was used to determine the risk in univariate analysis, adjusted odds ratio (aOR) was calculated to estimate the risk in the final regression model. A p-value <0.05 was considered statistically significant. Compliance with ethical standard: The study was based on open source anonymized secondary data. The present work was exempted from the full ethics committee review. Informed consent was not required for the study as no participants were recruited in it. Results The data for non-pregnant women aged between 15 and 44 years for NFHS-2 to NFHS-5 was defined. The number of women analyzed in the present paper were- 69,748 in NFHS-2; 103,709 in NFHS-3; 138,209 in NFHS-4; and 149,427 in NFHS-5. The majority of the women belonged to the non-tribal-, rural residence-, married-, and secondary education group. (Table 1) The overall prevalence of undernutrition among unmarried women in the country was 32.8% (n=22,890) in NFHS-2. The prevalence reduced to 30.5% (n=31,666) in NFHS-3 but increased to 32.1% (44,297) in NFHS-4. However, a major decline to 27.1% (n=42,401) in NFHS-5 (2019) was noted. Considering the commencement year of NFHS-2 and NFHS-5, an average decadal reduction of 2.7% in undernutrition in this population in the country were defined. Table 1: Socio-demographic distribution of the reproductive age-group women (15-44 years), NFHS 2-5, India Variables NFHS-2 (N=69,748) Frequency (%) NFHS-3 (N=103,709) Frequency (%) NFHS-4 (138,209) Frequency (%) NFHS-5 (N=149,427) Frequency (%) Caste Non-tribal 60,889 (87.8) 88,776 (86.8) 106,369 (80.5) 117,307 (78.5) Tribal 8,473 (12.2) 13,452 (13.2) 25,811 (19.5) 32,120 (21.5) Data unavailable - - Place of residence Urban 21,753 (31.2) 46,752 (45.1) 41,308 (29.9) 37,564 (24.0) Rural 47,995 (68.8) 56,957 (54.9) 96,901 (70.1) 118,761 (76.0) Marital status Ever married - 74,890 (72.2) 98,705 (76.3) 116,690 (74.7) Never married - 28,819 (27.8) 30,682 (23.7) 39,631 (25.3) Don’t know - - - 4 (0.0) Education Higher (>10) 6,533 (9.4) 10,755 (10.4) 13,740 (9.9) 19,849 (12.7) Secondary (6-10) 17,333 (24.9) 46,847 (45.2) 56,496 (40.9) 71,466 (45.7) Primary (Up to 5) 12,108 (17.4) 14,750 (14.2) 20,483 (14.8) 22,261 (14.2) No education/ preschool 33,756 (48.4) 31,346 (30.2) 47,210 (34.2) 42,713 (27.3) Don’t know - 11 (0.01) 280 (0.2) 36 (0.02) Income (Wealth index) Richest - 31,614 (30.5) 25,233 (18.3) 22,916 (14.7) Richer - 25,467 (24.6) 28,978 (21.0) 30,702 (19.6) Middle - 20,101 (19.4) 29,656 (21.5) 33,670 (21.5) Poorer - 14,797 (14.3) 28,405 (20.6) 35,122 (22.5) Poorest - 11,730 (11.3) 25,937 (18.8) 33,915 (21.7) The state-wise trend of proportion showed a substantial variation. While states like Andhra Pradesh, Jammu & Kashmir, Karnataka, Meghalaya, Mizoram, Odisha, and Uttarakhand showed a substantial reduction (>10%) in undernutrition from the baseline till the year 2021, the proportion increased in states including- Delhi (NCT), Gujarat, Haryana, Maharashtra, Nagaland, and Punjab. (Figure 1) A steady decline was noticed in states like- Jammu & Kashmir, Jharkhand, Karnataka, Mizoram, Odisha, Uttarakhand and West Bengal. Figure 1: State-wise distribution of undernutrition, NFHS 2-5 Analysis of the recent NFHS-5 data suggests that presently, the major burden of the condition is distributed in the western and central parts of the country. On the other hand, the North-East states bear the lowest burden of undernutrition. (Figure 1) States including Gujarat, Maharashtra and Madhya Pradesh together account for 71.4% of the districts (hotspots) with a high burden (>90 th percentile) of undernutrition. (Figure 2, Supplementary table 1) Figure 2: Distribution of hotspots (Districts with undernutrition >90th percentile), NFHS-5, India The intrastate variations of undernutrition proportion at the district level are substantially high (difference between highest and lowest prevalence district >20%) for states including- West Bengal (32.4%), Maharashtra (31.2%), Odisha (30.6%), Gujarat (28.2%), Madhya Pradesh (27.8%), Chattishgarh (26.8%), Uttar Pradesh (26.6%), Nagaland (24.9%), Tamil Nadu (24.6%), Karnataka (22.7%), and Punjab (21.2%). (Figure 3). Figure 3: Within state variations of undernutrition distribution, NFHS-5, India Footnote: Blue bar indicates difference between highest and lowest proportion districts in a state =20% Women belong to the poorest wealth index, up to preschool education, and unmarried women are at risk of developing undernutrition. However, the risk has reduced for all the variables in the last two decades. (Table 2) Table 2: Distribution of undernutrition among reproductive age group (15-44 years) women, NFHS2-5, India Variables NFHS-2 NFHS-3 NFHS-4 NFHS-5 # (%) OR (aOR; 95% CI) # (%) OR (aOR; 95% CI) # (%) OR (aOR; 95% CI) # (%) OR (aOR; 95% CI) Overall prevalence 22,890 (32.8) N= 31,666 (30.5) N= 44,297 (32.1) 42,401 (27.1) Caste Non-tribal 19,811 (32.5) Ref 27,536 (31.0) Ref 33,458 (31.5) Ref 31,126 (26.5) Ref Tribal 2,901 (34.2) 1.08 (1.04; 0.98, 1.1) 3,641 (27.1) 0.83 (0.9; 0.85, 0.95) 9,210 (35.7) 1.2 (1.08; 1.05, 1.12) 9,655 (30.1) 1.18 (1.06; 1.04, 1.1) Place of residence Urban 4,750 (21.8) 11,039 (23.6) Ref 11,793 (28.6) Ref 8,766 (23.3) Ref Rural 18,140 (37.8) 2.17 (1.76; 1.69, 1.83) 20,627 (36.2) 1.84 (1.17; 1.13, 1.21) 32,504 (33.5) 1.26 (0.99; 0.96, 1.03) 33,635 (27.1) 1.3 (1.06; 1.02, 1.09) Marital status Ever married - 20,818 (27.8) Ref 31,422 (31.8) Ref 30,037 (25.7) Ref Never married - 10,848 (37.6) 1.57 (2.20; 2.13, 2.28) 10,970 (35.8) 1.19 (1.4; 1.36, 1.44) 12,364 (31.2) 1.3 (1.54; 1.49, 1.59) Education Higher (>10) 937 (14.3) Ref 1,878 (17.5) Ref 3,757 (27.3) Ref 4,453 (22.4) Ref Secondary (6-10) 4,271 (24.6) 1.95 (1.68; 1.55, 1.82) 13,381 (28.6) 1.89 (1.34; 1.26, 1.42) 17,234 (30.5) 1.17 (1.14; 1.09, 1.19) 19,086 (26.7) 1.26 (1.21; 1.16, 1.26) Primary (Up to 5) 4,046 (33.4) 3.0 (2.31; 2.13, 2.51) 4,616 (31.3) 2.2 (1.27; 1.18, 1.36) 6,720 (32.8) 1.3 (1.24; 1.17, 1.31) 6,259 (28.12) 1.35 (1.31; 1.25, 1.38) No formal education/ preschool 13,629 (40.4) 4.04 (2.83; 2.62, 3.06) 11,787 (37.6) 2.85 (1.45; 1.36, 1.55) 16,493 (34.9) 1.43 (1.22; 1.16, 1.28) 12,599 (29.5) 1.44 (1.29; 1.23, 1.36) Income (Wealth index) Richest - 5,675 (17.8) Ref. 6,376 (25.3) Ref. 4,614 (20.1) Ref. Richer - 6,977 (27.4) 1.72 (1.71; 1.63, 1.79) 8,509 (29.4) 1.23 (1.23; 1.18, 1.28) 7,466 (24.3) 1.27 (1.2; 1.15, 1.25) Middle - 6,931 (34.5) 2.41 (2.33; 2.22, 2.45) 9,697 (32.7) 1.44 (1.44; 1.37, 1.5) 9,091 (27.0) 1.47 (1.34; 1.28, 1.4) Poorer - 6,287 (42.5) 3.38 (3.1; 2.93, 3.29) 9,958 (35.1) 1.6 (1.57; 1.5, 1.64) 10,387 (29.6) 1.67 (1.52; 1.45, 1.59) Poorest - 5,796 (49.4) 4.46 (3.82; 3.58, 4.08) 9,757 (37.6) 1.78 (1.75; 1.67, 1.84) 10,843 (32.0) 1.86 (1.68; 1.6, 1.76) NFHS-2 suggested that rural women had a 1.76 times higher risk of developing undernutrition. However, rural and urban populations bore a similar risk in subsequent times. When sub-grouped into different age categories (NFHS-5), it was found that teenagers (15-19 population) have the highest proportion of undernutrition compared to the other age groups in all the variables. (Table 3) Table 3: Distribution of undernutrition in different age category, NFHS-5 Variables Age category in years 15-19, n (%) 20-30, n (%) >30, n (%) Overall 7,344 (37.2) 10,768 (24.8) 24,289 (26.1) Place of residence Tribal 1,773 (40.6) 2,463 (27.9) 5,419 (28.6) Non-tribal 5,346 (36.4) 7,901 (24.1) 17,879 (25.6) Place of residence Urban 1,384 (34.6) 2,352 (21.8) 5,030 (22.1) Rural 5,960 (37.8) 8,416 (25.7) 19,259 (27.4) Marital status Ever married 489 (34.2) 5,563 (22.8) 23,985 (26.4) Never married 6,855 (37.4) 5,205 (27.2) 304 (14.0) Education no education/ preschool 272 (35.4) 1,620 (27.8) 10,707 (29.6) Primary (Up to 5) 452 (40.5) 1,128 (26.6) 4,679 (27.7) Secondary (6-10) 5,952 (36.9) 4,982 (24.4) 8,152 (23.3) Higher (>10) 668 (38.1) 3,037 (23.3) 748 (14.8) Income (Wealth index) Richest 625 (28.8) 1,337 (19.3) 2,652 (19.2) Richer 1,277 (35.7) 1,908 (21.3) 4,281 (23.6) Middle 1,567 (36.3) 2,402 (25.3) 5,122 (25.8) Poorer 1,910 (39.4) 2,534 (27.0) 5,943 (28.5) Poorest 1,965 (40.6) 2,587 (29.6) 6,291 (30.9) Discussion The present study finds that one out of every four women in the reproductive age group is currently suffering from undernutrition in India. The burden has changed only marginally at the national level in the last two decades. Though a significant decline was noticed in the last four years, the changes vary substantially at the state level. Rather, the proportion of undernutrition has increased in a few states during the period of analyses. Besides, the majority of the hotspots are located within a specific geographic location. High variations of intra-state disparities indicate the need for a strong commitment to combating the condition. Earlier evidence in the country on the distribution of undernutrition among adult populations was largely restricted to the specific sub-population like tribal groups, pregnant women, or lactating women.[19-21] One multi-state large survey in 2011, with more than 23,000 non-pregnant, non-lactating women estimated that 32.5% of this population had chronic energy deficiency (BMI <18.5). Analyses of underweight among the reproductive age-group women based in LMIC countries indicated that the reduction of undernutrition prevalence in India between 1990 and 2018 has remained on the lower side, even lower than many other LMICs including the neighboring countries like Bangladesh and Pakistan.[22] The same study also reported that this prevalence is going to reduce only marginally over the next decade.[22] Nevertheless, both these large studies were limited to the country-level or sub-regional-level estimate of the burden. In the present analysis, the regional disparities in the trend and the districts with a high burden were identified. The intra-state disparities and clustering of hotspots largely indicate that implementation of the current strategies that indirectly address the challenge of undernutrition needs rigorous evaluation. The root cause of undernutrition across the globe is poverty.[23,24] Other major factors include low dietary intake, reduced absorption of micro- and macro-nutrients, infections, and other systemic illnesses.[25] A large survey in India indicated the burden of undernutrition among the non-pregnant, non-lactating women was higher among women who belonged to tribal communities (47.5%), low-socio-economic groups (42.7%), and those who were illiterate (38.8%).[9] The current study found that wealth index and education consistently played a critical role in deciding the chance of malnutrition in this age group. In addition, women who never married have a strong predilection to have malnutrition when compared to those who ever got married. Contrary to the finding in the earlier study, it was found that tribal populations and rural populations did not show any significant difference in undernutrition burden compared to the non-tribal and the urban counterparts. The difference could be due to the existence of inequalities among the various groups and state-level performance in the efficiency of implementing the policies to reduce the gap among these groups.[26,27] Interestingly, it was found that the risk of undernutrition has reduced significantly over time. For example, while the poorest wealth index section had a 4.5 times higher risk of undernutrition compared to the richest during 2005-06 (NFHS 3), it has come down to 1.9 during 2019-21 (NFHS 5). The same was noted for education, caste and place of residence. The risk reduction could be due to an indication of the reduction in inequalities among the various social groups through effective implementation of various social protection schemes.[28-30] Limitation: Only a few factors that might affect undernutrition were considered. However, there could have been several other determinant risks which were not taken into consideration in the present analysis. However, the selected risk factors gave an important clue on the changing pattern of the risk. Conclusion Despite its multifaceted implications, undernutrition among the reproductive age group women is a common and neglected public health issue in India. Although the country runs the world’s largest government-funded nutritional program - the Integrated Child Development Services (ICDS) scheme - to alleviate undernutrition, the beneficiaries are often limited to specific groups like children and pregnant women. (14) Other health programs like the anaemia reduction and hookworm infestation programs can improve the overall health of a woman, however, these programs cannot improve the nutritional status as a whole in the community. Considering the inter-state and intra-state disparities, the high burden of undernutrition is a reflection of the system's ignorance and inefficiency. Inequalities among the various social groups visibly exist in relation to undernutrition. As the health condition in question is a complex entity, the effect of indirect programs as well as the social factors must be looked at to strengthen the evidence around undernutrition in this age group. Evidence generation in this area could be a major focus area as the condition has both a direct and indirect link with the Sustainable Development Goals. Declarations Funding : Not available Conflicts of interest/Competing interests : None declared. Data availability : Data can be made available on reasonable request to the corresponding author. Code availability : Code for data analysis can be made available on reasonable request to the corresponding author. References Kobati GY, Lartey A, Marquis GS, Colecraft EK, Butler LM. Dietary intakes and body mass indices of non-pregnant, non-lactating (npnl) women from the Coastal and Guinea savannah zones of Ghana. Afr J Food Agric Nutr Dev. 2012;12(1):5843–61. Rao KM, Balakrishna N, Arlappa N, Laxmaiah A, Brahmam GNV. Diet and Nutritional Status of Women in India. J Hum Ecol. 2010;29(3):165–70. Victora CG, Adair L, Fall C, Hallal PC, Martorell R, Richter L, et al. Maternal and child undernutrition: consequences for adult health and human capital. Lancet. 2008;371(9609):340–57. Wubie A, Seid O, Eshetie S, Dagne S, Menber Y, Wasihun Y, et al. Determinants of chronic energy deficiency among non-pregnant and non-lactating women of reproductive age in rural Kebeles of Dera District, North West Ethiopia, 2019: Unmatched case control study. PLoS ONE. 2020;15(10):e0241341. Antony AC, Vora RM, Karmarkar SJ. The silent tragic reality of Hidden Hunger, anaemia, and neural-tube defects (NTDs) in India. The Lancet Regional Health - Southeast Asia [Internet]. 2022 Nov 1 [cited 2022 Oct 23];6. https://www.thelancet.com/journals/lansea/article/PIIS2772-3682(22)00087-7/fulltext . Li L, Chen Y, Lin Z, Lin W, Liu Y, Ou W, et al. Association of pre-pregnancy body mass index with adverse pregnancy outcome among first-time mothers. PeerJ. 2020;8:e10123. Sustainable Development Report. 2022 [Internet]. [cited 2022 Oct 23]. https://dashboards.sdgindex.org/ . Niti Aayog, SDG INDIA INDEX & DASHBOARD 2020-21 PARTNERSHIPS IN THE DECADE OF ACTION [Internet]. Samsad Marg, New Delhi; 2021. https://india.un.org/en/173167-sdg-india-index-and-dashboard-2020-21 . Meshram II, Balakrishna N, Sreeramakrishna K, Rao KM, Kumar RH, Arlappa N, et al. Trends in nutritional status and nutrient intakes and correlates of overweight/obesity among rural adult women (≥ 18–60 years) in India: National Nutrition Monitoring Bureau (NNMB) national surveys. Public Health Nutr. 2016;19(5):767–76. Sethi V, Dinachandra K, Murira Z, Gausman J, Bhanot A, de Wagt A, et al. Nutrition status of nulliparous married Indian women 15–24 years: Decadal trends, predictors and program implications. PLoS ONE. 2019;14(8):e0221125. Choedon T, Sethi V, Chowdhury R, Bhatia N, Dinachandra K, Murira Z, et al. Population estimates and determinants of severe maternal thinness in India. Int J Gynaecol Obstet. 2021;155(3):380–97. Ministry of Women and Child Development. Integrated Child Development Services (ICDS) Sceme [Internet]. [cited 2023 Mar 21]. http://icds-wcd.nic.in/icds.aspx . International Food Policy Research Institute. POSHAN [Internet]. [cited 2023 Mar 21]. https://www.ifpri.org/project/poshan . Indian Institute of Population Science, ICF. National Family Health Survey (NFHS-5). 2019-21: India: Volume 1 [Internet]. Mumbai: IIPS; 2021 [cited 2023 Mar 21]. https://dhsprogram.com/pubs/pdf/FR375/FR375.pdf . International Institute for Population Sciences. National Family Health Survey (NFHS-4), 2015-16 [Internet]. Mumbai, India; 2017 Dec [cited 2023 Mar 21]. http://rchiips.org/nfhs/NFHS-4Reports/India.pdf . Indian Institute of Population Science, Macro International. National Family Health Survey (NFHS-3), 2005-06: India: Volume 1 [Internet]. Mumbai: IIPS. 2007 [cited 2023 Mar 21]. https://dhsprogram.com/pubs/pdf/frind3/frind3-vol1andvol2.pdf . International Institute for Population Sciences, ORC Macro. National Family Health Survey (NFHS-2), 1998–99 [Internet]. India, Mumbai: IIPS. 2000 [cited 2023 Mar 21]. https://www.dhsprogram.com/pubs/pdf/FRIND2/FRIND2.pdf . Dandona R, Pandey A, Dandona L. A review of national health surveys in India. Bull World Health Organ. 2016;94(4):286–A296. Agarwal S, Sethi V. Nutritional Disparities among Women in Urban India. J Health Popul Nutr. 2013;31(4):531–7. Biswas S, Hansda CK, Singh N, Malla VR, Pal AK. Determinants of nutritional status among scheduled tribe women in India. Clin Epidemiol Global Health. 2022;17:101119. Singh KD, Choedon T, Bhanot A, Kaur N, Chopra M, Sabharwal M, et al. Tipping the Scales: Population Estimates and Risk Factors for Severe Thinness, Thinness, Overweight and Obesity Among Pregnant Women and Mothers in India. Curr Developments Nutr. 2020;4(Supplement2):975. Hasan MM, Ahmed S, Soares Magalhaes RJ, Fatima Y, Biswas T, Mamun AA. Double burden of malnutrition among women of reproductive age in 55 low- and middle-income countries: progress achieved and opportunities for meeting the global target. Eur J Clin Nutr. 2022;76(2):277–87. Ma ZF, Wang CW, Lee YY, Editorial. Malnutrition: A Cause or a Consequence of Poverty? Front Public Health. 2022;9:796435. Siddiqui F, Salam RA, Lassi ZS, Das JK. The Intertwined Relationship Between Malnutrition and Poverty. Front Public Health. 2020;8:453. Saunders J, Smith T. Malnutrition: causes and consequences. Clin Med (Lond). 2010;10(6):624–7. Oxfam. India’s Unequal Healthcare Story [Internet]. [cited 2023 Feb 21]. https://www.oxfamindia.org/press-release/india-inequality-report-2021-indias-unequal-healthcare-story . Subramanian. Health Inequalities in India: The Axes of Stratification – The Brown Journal of World Affairs [Internet]. [cited 2023 Feb 21]. https://bjwa.brown.edu/14-2/health-inequalities-in-india-the-axes-of-stratification/ . George NA, McKay FH. The Public Distribution System and Food Security in India. Int J Environ Res Public Health. 2019;16(17):3221. Dilip T, Dandona R, Dandona L. The national employment guarantee scheme and inequities in household spending on food and non-food determinants of health in rural India. Int J Equity Health. 2013;12(1):84. World Bank. World Bank. [cited 2023 Feb 21]. Schemes to Systems: Lessons from Social Protection in India. https://www.worldbank.org/en/news/feature/2019/11/21/lessons-from-social-protection-india-schemes-to-systems . Additional Declarations No competing interests reported. Supplementary Files Supplementarytable1.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 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-4753444","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":335776198,"identity":"6a6767c5-5242-47ba-8e3c-46c09bef5414","order_by":0,"name":"Sirshendu Chaudhuri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYDACZjBiYGwAIYYKkAhzAylazoBEGAloYYBrAZFtDHA2TiDfzvv4c0HFHdkN5w83f+adVxvN3w7U8qNiG04tBofZzaRnnHlmvOFGYps077bjuTMOMzYw9py5jVsLMxsbM2/b4cQNNxjbmHm3HcttAGphZmzDrUW+mY35M+8/oJbzB4EOm3Msdz4hLQyH2RikeRuAWg4kNgAZNbkbCGkxOMzGJs1z7LDxTKBfJOccO5C7EajlID6/yPcfY/7MU3NYtu/88ccf3tTU5c47f/jggx8VeByG7k4weYBo9UBQR4riUTAKRsEoGCEAAKZnXctvPwpjAAAAAElFTkSuQmCC","orcid":"","institution":"Indian Institute of Public Health Hyderabad","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Sirshendu","middleName":"","lastName":"Chaudhuri","suffix":""},{"id":335776199,"identity":"c7d76051-50c1-4084-bebc-61c93429f8c6","order_by":1,"name":"Varun Agiwal","email":"","orcid":"","institution":"Indian Institute of Public Health Hyderabad","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Varun","middleName":"","lastName":"Agiwal","suffix":""},{"id":335776201,"identity":"7741b324-56b7-49e7-9156-723986c8e462","order_by":2,"name":"Nirupama AY","email":"","orcid":"","institution":"Indian Institute of Public Health Hyderabad","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nirupama","middleName":"","lastName":"AY","suffix":""},{"id":335776203,"identity":"8199f509-b1ef-418c-ac6b-0a3888793d21","order_by":3,"name":"Yashaswini Kumar","email":"","orcid":"","institution":"Indian Institute of Public Health Hyderabad","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yashaswini","middleName":"","lastName":"Kumar","suffix":""}],"badges":[],"createdAt":"2024-07-17 04:21:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4753444/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4753444/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62895917,"identity":"6d0668d1-7f13-4df5-b81e-5e2a5ff20c8e","added_by":"auto","created_at":"2024-08-20 19:17:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":108118,"visible":true,"origin":"","legend":"\u003cp\u003eState-wise distribution of undernutrition, NFHS 2-5\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4753444/v1/a341ea26700739c4e57b1865.png"},{"id":62895915,"identity":"08b82c76-576c-487a-859a-2ec6878855de","added_by":"auto","created_at":"2024-08-20 19:17:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":186660,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of hotspots (Districts with undernutrition \u0026gt;90th percentile), NFHS-5, India\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4753444/v1/5fc895f70dcd2fbc3247446c.png"},{"id":62895918,"identity":"0bbf7ea1-5acc-4b41-bc8a-d746d523c358","added_by":"auto","created_at":"2024-08-20 19:17:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":115978,"visible":true,"origin":"","legend":"\u003cp\u003eWithin state variations of undernutrition distribution, NFHS-5, India\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4753444/v1/b6a58b610014ad7ea0b2b5e5.png"},{"id":66675955,"identity":"ec0b85dd-2863-4502-a270-256c759ec408","added_by":"auto","created_at":"2024-10-15 11:09:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1127596,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4753444/v1/0565116a-394f-4061-ae03-c5abea1bb979.pdf"},{"id":62896391,"identity":"84784a3a-3864-4d7f-9e4a-450e8b7351f4","added_by":"auto","created_at":"2024-08-20 19:25:21","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18528,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4753444/v1/fffc55a48a1cd0e7714073fd.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"“Changing Landscape of Undernutrition in Reproductive-Age Indian Women: Analysis of the nationally representative data, 1998-2021”","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUndernutrition among reproductive age-group women is one of the major public health problems in the developing world. An estimated 120 million women in low-income countries are regarded as underweight, with a body mass index (BMI) less than 18.5 kg/m2.[1]\u0026nbsp;Adequate nutrition in this age group is a key determinant for maternal health and favourable pregnancy outcomes. Women with poor health and nutrition are more likely to give birth to low-birthweight infants, paving way for the inevitable vicious cycle.[2]\u0026nbsp;Children born to undernourished women are in turn, highly prone to be undernourished, which can lead to poor cognitive development, stunting, and a higher risk of morbidity and mortality. [3]\u0026nbsp; Maternal and child undernutrition are the underlying causes of at least 3.5 million deaths each year and constitutes 11% of the total global disease burden.[4]\u0026nbsp;Besides, malnutrition is often associated with multiple micronutrient deficiencies that can lead to severe neonatal complications among the offspring.[5]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe pre-pregnancy nutritional status of women is a good predictor of maternal and child health outcomes.[6] The United Nations is committed to achieve several health-related goals including the reduction of maternal mortality ratio (\u0026lt;70 per 100,000 live births) and childhood mortality like neonatal mortality (\u0026lt;12 per 100,000 live births) and under-five mortality (\u0026lt;25 per 100,000 live births) by the end of 2030. Unfortunately, there are major challenges remaining in the Asian and African countries towards meeting these targets.[7]\u0026nbsp;In India, the state-wise variations in the performance indicators for SDG-2 \u0026amp; 3 goals are remarkably high according to the 2020-21 SDG report.[8]\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEvidence suggests that the nutritional status of the Indian population has improved over time. The prevalence of chronic energy deficiency in India has declined from 52 % during 1975-79 to 34 % during 2011-12. Median intakes of most of the nutrients have increased over the same period, although they were below recommended levels.[9] Despite this improvement, the prevalence of thinness (body mass index or BMI \u0026lt;16 for adult women) remained high among pregnant women and nulliparous married women.[10,11]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMultiple stakeholders like governments, donors and private sector entities have previously committed to work on nutrition. \u0026nbsp;Unfortunately, the country lacks a direct policy to improve the nutritional status of the non-pregnant, non-lactating reproductive women age group. Several health programs in India, including- ICDS, Anemia Mukt Bharat, and POSHAN Abhiyaan, are bridging the nutritional gap among pregnant and lactating women.[12,13] To assess the impact of these programs or to develop a new policy to address undernutrition among the women in the reproductive age group, the availability of the epidemiological distribution of the condition is a critical element which is lacking in India at the country level. In this background, the state-level distribution and trend of undernutrition among women of reproductive age group in India based on the National Family Health Survey (NFHS) data were examined. In addition, the district-level hot spots of high levels of undernutrition based on the recent NFHS round 5 (2019-20) data have been identified. Lastly, the relationship of under-nutrition of women in reproductive age group (non-pregnant, non-lactating), with socio-demographic determinants at the state level were also examined.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eA secondary data analysis based on the National Family Health Survey (NFHS) data, round 2 (1998-99) to round 5 (2019-21) was conducted. NFHS surveys are large, country-wide surveys conducted in the representative sampled households in the Indian states and Union Territories (UT).\u003csup\u003e\u0026nbsp;\u003c/sup\u003eThe survey is conducted by the Ministry of Health and Family Welfare, Government of India. To date, five surveys have been conducted NFHS 1 (1991-92), NFHS 2 (1998-99), NFHS 3 (2005-06), NFHS 4 (2015-16) and NFHS 5 (2019-20), the fifth version being the largest in terms of the number of population recruited. The NFHS surveys collect information on various household-related indicators, health and nutritional indicators, with a special focus on maternal and child health at the national level. In the recent editions of the survey, additional biomarkers have also been included. The detailed methodology of the NFHS surveys is provided in the NFHS reports.[14\u0026ndash;17]\u003csup\u003e\u0026nbsp;\u003c/sup\u003eIn brief, multi-stage sampling was used to identify the representative primary sampling units (PSU) from the rural and urban areas. Households were selected from the PSU for interviews and biomarker sampling from the individual. The PSU were selected in such a way that it represents the districts, state and eventually the country.[18]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData source and management: For the present analysis, permission from the Demographic and Health Survey (DHS) by the United States Agency for International Development (USAID) for using the datasets of the last four NFHS surveys (NFHS 2 to 5) was taken. The data sets for the entire country were imported to STATA software and examined for missing and inconsistent values for the targeted variables among the reproductive age-group women. We excluded all the pregnant women from the analyses. All the missing values were removed. The outcome variable undernutrition was defined as body mass index (BMI) below 18.5. \u0026nbsp;BMI was calculated by weight (Kg) of the target population divided by the square of the height (meter) of the individual. Individuals with BMI below 10 and more than 50, considering these to be outliers, were excluded.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatistical consideration: Datasets were analyzed in STATA software, version 14.0. The distribution of undernutrition was presented by descriptive statistics. Prevalence was estimated with a 95% confidence interval (CI). The trend of proportional distribution of undernutrition in the five NFHS surveys, and the state-wise distribution of NFHS were showed. Geographical distributions were shown for NFHS 5 with the hot spots in the country. The hotspots as districts with a high proportion of undernutrition (\u0026gt;90\u003csup\u003eth\u003c/sup\u003e centile) were defined. The maps were created using QGIS version 3.2. Undernutrition distribution was examined in relation to socio-demographic variables like- caste, place of residence (Rural and urban as reported in NFHS), marital status, educational level, and wealth index. Univariate analysis, followed by multivariate regression analysis after adjusting for state-level clustering effect by multi-level modelling (MLM) were used to examine the effect of selected socio-demographic variables on undernutrition. While odds ratio (OR) was used to determine the risk in univariate analysis, adjusted odds ratio (aOR) was calculated to estimate the risk in the final regression model. \u0026nbsp;A p-value \u0026lt;0.05 was considered statistically significant. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompliance with ethical standard: The study was based on open source anonymized secondary data. The present work was exempted from the full ethics committee review. Informed consent was not required for the study as no participants were recruited in it.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe data for non-pregnant women aged between 15 and 44 years for NFHS-2 to NFHS-5 was defined. The number of women analyzed in the present paper were- 69,748 in NFHS-2; 103,709 in NFHS-3; 138,209 in NFHS-4; and 149,427 in NFHS-5. The majority of the women belonged to the non-tribal-, rural residence-, married-, and secondary education group. (Table 1) The overall prevalence of undernutrition among unmarried women in the country was 32.8% (n=22,890) in NFHS-2. The prevalence reduced to 30.5% (n=31,666) in NFHS-3 but increased to 32.1% (44,297) in NFHS-4. However, a major decline to 27.1% (n=42,401) in NFHS-5 (2019) was noted. Considering the commencement year of NFHS-2 and NFHS-5, an average decadal reduction of 2.7% in undernutrition in this population in the country were defined.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1: Socio-demographic distribution of the reproductive age-group women (15-44 years), NFHS 2-5, India\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.384401114206128%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNFHS-2 (N=69,748)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNFHS-3 (N=103,709)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNFHS-4 (138,209)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNFHS-5 (N=149,427)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCaste\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.384401114206128%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003eNon-tribal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.384401114206128%\"\u003e\n \u003cp\u003e60,889 (87.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e88,776 (86.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e106,369 (80.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e117,307 (78.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003eTribal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.384401114206128%\"\u003e\n \u003cp\u003e8,473 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e13,452 (13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e25,811 (19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e32,120 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003eData unavailable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.384401114206128%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlace of residence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.384401114206128%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.384401114206128%\"\u003e\n \u003cp\u003e21,753 (31.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e46,752 (45.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e41,308 (29.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e37,564 (24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.384401114206128%\"\u003e\n \u003cp\u003e47,995 (68.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e56,957 (54.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e96,901 (70.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e118,761 (76.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.384401114206128%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003eEver married\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.384401114206128%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e74,890 (72.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e98,705 (76.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e116,690 (74.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003eNever married\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.384401114206128%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e28,819 (27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e30,682 (23.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e39,631 (25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003eDon\u0026rsquo;t know\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.384401114206128%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e4 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.384401114206128%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003eHigher (\u0026gt;10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.384401114206128%\"\u003e\n \u003cp\u003e6,533 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e10,755 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e13,740 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e19,849 (12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003eSecondary (6-10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.384401114206128%\"\u003e\n \u003cp\u003e17,333 (24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e46,847 (45.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e56,496 (40.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e71,466 (45.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003ePrimary (Up to 5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.384401114206128%\"\u003e\n \u003cp\u003e12,108 (17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e14,750 (14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e20,483 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e22,261 (14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003eNo education/ preschool\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.384401114206128%\"\u003e\n \u003cp\u003e33,756 (48.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e31,346 (30.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e47,210 (34.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e42,713 (27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003eDon\u0026rsquo;t know\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.384401114206128%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e11 (0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e280 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e36 (0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome (Wealth index)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.384401114206128%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003eRichest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.384401114206128%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e31,614 (30.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e25,233 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e22,916 (14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003eRicher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.384401114206128%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e25,467 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e28,978 (21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e30,702 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.384401114206128%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e20,101 (19.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e29,656 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e33,670 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003ePoorer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.384401114206128%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e14,797 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e28,405 (20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e35,122 (22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003ePoorest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.384401114206128%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e11,730 (11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e25,937 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77715877437326%\"\u003e\n \u003cp\u003e33,915 (21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe state-wise trend of proportion showed a substantial variation. While states like Andhra Pradesh, Jammu \u0026amp; Kashmir, Karnataka, Meghalaya, Mizoram, Odisha, and Uttarakhand showed a substantial reduction (\u0026gt;10%) in undernutrition from the baseline till the year 2021, the proportion increased in states including- Delhi (NCT), Gujarat, Haryana, Maharashtra, Nagaland, and Punjab. (Figure 1) A steady decline was noticed in states like- Jammu \u0026amp; Kashmir, Jharkhand, Karnataka, Mizoram, Odisha, Uttarakhand and West Bengal.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 1: State-wise distribution of undernutrition, NFHS 2-5\u003c/p\u003e\n\u003cp\u003eAnalysis of the recent NFHS-5 data suggests that presently, the major burden of the condition is distributed in the western and central parts of the country. On the other hand, the North-East states bear the lowest burden of undernutrition. (Figure 1) States including Gujarat, Maharashtra and Madhya Pradesh together account for 71.4% of the districts (hotspots) with a high burden (\u0026gt;90\u003csup\u003eth\u003c/sup\u003e percentile) of undernutrition. (Figure 2, Supplementary table 1)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 2: Distribution of hotspots (Districts with undernutrition \u0026gt;90th percentile), NFHS-5, India\u003c/p\u003e\n\u003cp\u003eThe intrastate variations of undernutrition proportion at the district level are substantially high (difference between highest and lowest prevalence district \u0026gt;20%) for states including- West Bengal (32.4%), Maharashtra (31.2%), Odisha (30.6%), Gujarat (28.2%), Madhya Pradesh (27.8%), Chattishgarh (26.8%), Uttar Pradesh (26.6%), Nagaland (24.9%), Tamil Nadu (24.6%), Karnataka (22.7%), and Punjab (21.2%). (Figure 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 3: Within state variations of undernutrition distribution, NFHS-5, India\u003c/p\u003e\n\u003cp\u003eFootnote: \u003cem\u003eBlue bar indicates difference between highest and lowest proportion districts in a state \u0026lt;20% and red bar indicates a difference \u0026gt;=20%\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWomen belong to the poorest wealth index, up to preschool education, and unmarried women are at risk of developing undernutrition.\u0026nbsp;However, the risk has reduced for all the variables in the last two decades. (Table 2)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2: Distribution of undernutrition among reproductive age group (15-44 years) women, NFHS2-5, India\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNFHS-2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNFHS-3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNFHS-4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNFHS-5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e# (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eOR\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(aOR; 95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e# (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eOR\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(aOR; 95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e# (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eOR\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(aOR; 95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e# (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eOR\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(aOR; 95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall prevalence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e22,890 (32.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eN= 31,666 (30.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eN=\u0026nbsp;44,297 (32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e42,401 (27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCaste\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003eNon-tribal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e19,811 (32.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e27,536 (31.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e33,458 (31.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e31,126 (26.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eTribal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e2,901 (34.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003cp\u003e(1.04; 0.98, 1.1)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e3,641 (27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003cp\u003e(0.9; 0.85, 0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e9,210 (35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003cp\u003e(1.08; 1.05, 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e9,655 (30.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003cp\u003e(1.06; 1.04, 1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlace of residence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e4,750 (21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e11,039 (23.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e11,793 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e8,766 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e18,140 (37.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e2.17\u003c/p\u003e\n \u003cp\u003e(1.76; 1.69, 1.83)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e20,627 (36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e1.84\u003c/p\u003e\n \u003cp\u003e(1.17; 1.13, 1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e32,504 (33.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003cp\u003e(0.99; 0.96, 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e33,635 \u0026nbsp;(27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003cp\u003e(1.06; 1.02, 1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003eEver married\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e20,818 (27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e31,422 (31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e30,037 (25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003eNever married\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e10,848 (37.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003cp\u003e(2.20; 2.13, 2.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e10,970 (35.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003cp\u003e(1.4; 1.36, 1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e12,364 (31.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003cp\u003e(1.54; 1.49, 1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003eHigher (\u0026gt;10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e937 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e1,878 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e3,757 (27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e4,453 (22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003eSecondary (6-10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e4,271 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003cp\u003e(1.68; 1.55, 1.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e13,381 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003cp\u003e(1.34; 1.26, 1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e17,234 (30.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003cp\u003e(1.14; 1.09, 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e19,086 (26.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003cp\u003e(1.21; 1.16, 1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003ePrimary (Up to 5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e4,046 (33.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003cp\u003e(2.31; 2.13, 2.51)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e4,616 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003cp\u003e(1.27; 1.18, 1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e6,720 (32.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003cp\u003e(1.24; 1.17, 1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e6,259 (28.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003cp\u003e(1.31; 1.25, 1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003eNo formal education/ preschool\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e13,629 (40.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e4.04\u003c/p\u003e\n \u003cp\u003e(2.83; 2.62, 3.06)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e11,787 (37.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e2.85\u003c/p\u003e\n \u003cp\u003e(1.45; 1.36, 1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e16,493 (34.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003cp\u003e(1.22; 1.16, 1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e12,599 (29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003cp\u003e(1.29; 1.23, 1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome (Wealth index)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003eRichest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e5,675 (17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e6,376 (25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e4,614 (20.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003eRicher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e6,977 (27.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003cp\u003e(1.71; 1.63, 1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e8,509 (29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003cp\u003e(1.23; 1.18, 1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e7,466 (24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003cp\u003e(1.2; 1.15, 1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e6,931 (34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e2.41\u003c/p\u003e\n \u003cp\u003e(2.33; 2.22, 2.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e9,697 (32.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003cp\u003e(1.44; 1.37, 1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e9,091 (27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003cp\u003e(1.34; 1.28, 1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003ePoorer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e6,287 (42.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e3.38\u003c/p\u003e\n \u003cp\u003e(3.1; 2.93, 3.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e9,958 (35.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003cp\u003e(1.57; 1.5, 1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e10,387 (29.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003cp\u003e(1.52; 1.45, 1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003ePoorest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e5,796 (49.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e4.46\u003c/p\u003e\n \u003cp\u003e(3.82; 3.58, 4.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e9,757 (37.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003cp\u003e(1.75; 1.67, 1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e10,843 (32.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e1.86\u003c/p\u003e\n \u003cp\u003e(1.68; 1.6, 1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;NFHS-2 suggested that rural women had a 1.76 times higher risk of developing undernutrition. However, rural and urban populations bore a similar risk in subsequent times. When sub-grouped into different age categories (NFHS-5), it was found that teenagers (15-19 population) have the highest proportion of undernutrition compared to the other age groups in all the variables. (Table 3)\u003c/p\u003e\n\u003cp\u003eTable 3: Distribution of undernutrition in different age category, NFHS-5\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003e\u003cbr\u003e\u003c/th\u003e\n \u003cth\u003e\u003cbr\u003e\u003c/th\u003e\n \u003cth\u003e\u003cbr\u003e\u003c/th\u003e\n \u003cth\u003e\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.6089850249584%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"65.3910149750416%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge category in years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.1323155216285%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e15-19, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.587786259541986%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e20-30, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.279898218829516%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;30, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.6089850249584%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.62728785357737%\" valign=\"top\"\u003e\n \u003cp\u003e7,344 (37.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.96339434276206%\" valign=\"top\"\u003e\n \u003cp\u003e10,768 (24.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\" valign=\"top\"\u003e\n \u003cp\u003e24,289 (26.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.6089850249584%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlace of residence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.62728785357737%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.96339434276206%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.6089850249584%\" valign=\"top\"\u003e\n \u003cp\u003eTribal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.62728785357737%\" valign=\"top\"\u003e\n \u003cp\u003e1,773 (40.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.96339434276206%\" valign=\"top\"\u003e\n \u003cp\u003e2,463 (27.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\" valign=\"top\"\u003e\n \u003cp\u003e5,419 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.6089850249584%\" valign=\"top\"\u003e\n \u003cp\u003eNon-tribal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.62728785357737%\" valign=\"top\"\u003e\n \u003cp\u003e5,346 (36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.96339434276206%\" valign=\"top\"\u003e\n \u003cp\u003e7,901 (24.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\" valign=\"top\"\u003e\n \u003cp\u003e17,879 (25.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.6089850249584%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlace of residence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.62728785357737%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.96339434276206%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.6089850249584%\" valign=\"top\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.62728785357737%\" valign=\"top\"\u003e\n \u003cp\u003e1,384 (34.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.96339434276206%\" valign=\"top\"\u003e\n \u003cp\u003e2,352 (21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\" valign=\"top\"\u003e\n \u003cp\u003e5,030 (22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.6089850249584%\" valign=\"top\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.62728785357737%\" valign=\"top\"\u003e\n \u003cp\u003e5,960 (37.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.96339434276206%\" valign=\"top\"\u003e\n \u003cp\u003e8,416 (25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\" valign=\"top\"\u003e\n \u003cp\u003e19,259 (27.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.6089850249584%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.62728785357737%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.96339434276206%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.6089850249584%\" valign=\"top\"\u003e\n \u003cp\u003eEver married\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.62728785357737%\" valign=\"top\"\u003e\n \u003cp\u003e489 (34.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.96339434276206%\" valign=\"top\"\u003e\n \u003cp\u003e5,563 (22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\" valign=\"top\"\u003e\n \u003cp\u003e23,985 (26.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.6089850249584%\" valign=\"top\"\u003e\n \u003cp\u003eNever married\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.62728785357737%\" valign=\"top\"\u003e\n \u003cp\u003e6,855 (37.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.96339434276206%\" valign=\"top\"\u003e\n \u003cp\u003e5,205 (27.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\" valign=\"top\"\u003e\n \u003cp\u003e304 (14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.6089850249584%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.62728785357737%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.96339434276206%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.6089850249584%\" valign=\"top\"\u003e\n \u003cp\u003eno education/ preschool\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.62728785357737%\" valign=\"top\"\u003e\n \u003cp\u003e272 (35.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.96339434276206%\" valign=\"top\"\u003e\n \u003cp\u003e1,620 (27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\" valign=\"top\"\u003e\n \u003cp\u003e10,707 (29.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.6089850249584%\" valign=\"top\"\u003e\n \u003cp\u003ePrimary (Up to 5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.62728785357737%\" valign=\"top\"\u003e\n \u003cp\u003e452 (40.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.96339434276206%\" valign=\"top\"\u003e\n \u003cp\u003e1,128 (26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\" valign=\"top\"\u003e\n \u003cp\u003e4,679 (27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.6089850249584%\" valign=\"top\"\u003e\n \u003cp\u003eSecondary (6-10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.62728785357737%\" valign=\"top\"\u003e\n \u003cp\u003e5,952 (36.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.96339434276206%\" valign=\"top\"\u003e\n \u003cp\u003e4,982 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\" valign=\"top\"\u003e\n \u003cp\u003e8,152 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.6089850249584%\" valign=\"top\"\u003e\n \u003cp\u003eHigher (\u0026gt;10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.62728785357737%\" valign=\"top\"\u003e\n \u003cp\u003e668 (38.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.96339434276206%\" valign=\"top\"\u003e\n \u003cp\u003e3,037 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\" valign=\"top\"\u003e\n \u003cp\u003e748 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.6089850249584%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome (Wealth index)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.62728785357737%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.96339434276206%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.6089850249584%\" valign=\"top\"\u003e\n \u003cp\u003eRichest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.62728785357737%\" valign=\"top\"\u003e\n \u003cp\u003e625 (28.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.96339434276206%\" valign=\"top\"\u003e\n \u003cp\u003e1,337 (19.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\" valign=\"top\"\u003e\n \u003cp\u003e2,652 (19.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.6089850249584%\" valign=\"top\"\u003e\n \u003cp\u003eRicher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.62728785357737%\" valign=\"top\"\u003e\n \u003cp\u003e1,277 (35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.96339434276206%\" valign=\"top\"\u003e\n \u003cp\u003e1,908 (21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\" valign=\"top\"\u003e\n \u003cp\u003e4,281 (23.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.6089850249584%\" valign=\"top\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.62728785357737%\" valign=\"top\"\u003e\n \u003cp\u003e1,567 (36.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.96339434276206%\" valign=\"top\"\u003e\n \u003cp\u003e2,402 (25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\" valign=\"top\"\u003e\n \u003cp\u003e5,122 (25.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.6089850249584%\" valign=\"top\"\u003e\n \u003cp\u003ePoorer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.62728785357737%\" valign=\"top\"\u003e\n \u003cp\u003e1,910 (39.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.96339434276206%\" valign=\"top\"\u003e\n \u003cp\u003e2,534 (27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\" valign=\"top\"\u003e\n \u003cp\u003e5,943 (28.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.6089850249584%\" valign=\"top\"\u003e\n \u003cp\u003ePoorest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.62728785357737%\" valign=\"top\"\u003e\n \u003cp\u003e1,965 (40.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.96339434276206%\" valign=\"top\"\u003e\n \u003cp\u003e2,587 (29.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\" valign=\"top\"\u003e\n \u003cp\u003e6,291 (30.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study finds that one out of every four women in the reproductive age group is currently suffering from undernutrition in India. The burden has changed only marginally at the national level in the last two decades. Though a significant decline was noticed in the last four years, the changes vary substantially at the state level. Rather, the proportion of undernutrition has increased in a few states during the period of analyses. Besides, the majority of the hotspots are located within a specific geographic location. High variations of intra-state disparities indicate the need for a strong commitment to combating the condition.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEarlier evidence in the country on the distribution of undernutrition among adult populations was largely restricted to the specific sub-population like tribal groups, pregnant women, or lactating women.[19-21]\u003csup\u003e\u0026nbsp;\u003c/sup\u003eOne multi-state large survey in 2011, with more than 23,000 non-pregnant, non-lactating women estimated that 32.5% of this population had chronic energy deficiency (BMI \u0026lt;18.5). Analyses of underweight among the reproductive age-group women based in LMIC countries indicated that the reduction of undernutrition prevalence in India between 1990 and 2018 has remained on the lower side, even lower than many other LMICs including the neighboring countries like Bangladesh and Pakistan.[22]\u003csup\u003e\u0026nbsp;\u003c/sup\u003eThe same study also reported that this prevalence is going to reduce only marginally over the next decade.[22]\u0026nbsp;Nevertheless, both these large studies were limited to the country-level or sub-regional-level estimate of the burden. In the present analysis, the regional disparities in the trend and the districts with a high burden were identified. The intra-state disparities and clustering of hotspots largely indicate that implementation of the current strategies that indirectly address the challenge of undernutrition needs rigorous evaluation.\u003c/p\u003e\n\u003cp\u003eThe root cause of undernutrition across the globe is poverty.[23,24]\u0026nbsp;Other major factors include low dietary intake, reduced absorption of micro- and macro-nutrients, infections, and other systemic illnesses.[25]\u0026nbsp;A large survey in India indicated the burden of undernutrition among the non-pregnant, non-lactating women was higher among women who belonged to tribal communities (47.5%), low-socio-economic groups (42.7%), and those who were illiterate (38.8%).[9]\u0026nbsp;The current study found that wealth index and education consistently played a critical role in deciding the chance of malnutrition in this age group. In addition, women who never married have a strong predilection to have malnutrition when compared to those who ever got married. Contrary to the finding in the earlier study, it was found that tribal populations and rural populations did not show any significant difference in undernutrition burden compared to the non-tribal and the urban counterparts. \u0026nbsp;The difference could be due to the existence of inequalities among the various groups and state-level performance in the efficiency of implementing the policies to reduce the gap among these groups.[26,27] Interestingly, it was found that the risk of undernutrition has reduced significantly over time. For example, while the poorest wealth index section had a 4.5 times higher risk of undernutrition compared to the richest during 2005-06 (NFHS 3), it has come down to 1.9 during 2019-21 (NFHS 5). The same was noted for education, caste and place of residence. The risk reduction could be due to an indication of the reduction in inequalities among the various social groups through effective implementation of various social protection schemes.[28-30]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLimitation: Only a few factors that might affect undernutrition were considered. However, there could have been several other determinant risks which were not taken into consideration in the present analysis. However, the selected risk factors gave an important clue on the changing pattern of the risk.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eDespite its multifaceted implications, undernutrition among the reproductive age group women is a common and neglected public health issue in India. Although the country runs the world\u0026rsquo;s largest government-funded nutritional program - the Integrated Child Development Services (ICDS) scheme - to alleviate undernutrition, the beneficiaries are often limited to specific groups like children and pregnant women. (14) Other health programs like the anaemia reduction and hookworm infestation programs can improve the overall health of a woman, however, these programs cannot improve the nutritional status as a whole in the community. Considering the inter-state and intra-state disparities, the high burden of undernutrition is a reflection of the system\u0026apos;s ignorance and inefficiency. Inequalities among the various social groups visibly exist in relation to undernutrition. As the health condition in question is a complex entity, the effect of indirect programs as well as the social factors must be looked at to strengthen the evidence around undernutrition in this age group. Evidence generation in this area could be a major focus area as the condition has both a direct and indirect link with the Sustainable Development Goals.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: Not available\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest/Competing interests\u003c/strong\u003e: None declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e: Data can be made available on reasonable request to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e: Code for data analysis can be made available on reasonable request to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKobati GY, Lartey A, Marquis GS, Colecraft EK, Butler LM. Dietary intakes and body mass indices of non-pregnant, non-lactating (npnl) women from the Coastal and Guinea savannah zones of Ghana. Afr J Food Agric Nutr Dev. 2012;12(1):5843\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRao KM, Balakrishna N, Arlappa N, Laxmaiah A, Brahmam GNV. Diet and Nutritional Status of Women in India. J Hum Ecol. 2010;29(3):165\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVictora CG, Adair L, Fall C, Hallal PC, Martorell R, Richter L, et al. Maternal and child undernutrition: consequences for adult health and human capital. Lancet. 2008;371(9609):340\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWubie A, Seid O, Eshetie S, Dagne S, Menber Y, Wasihun Y, et al. Determinants of chronic energy deficiency among non-pregnant and non-lactating women of reproductive age in rural Kebeles of Dera District, North West Ethiopia, 2019: Unmatched case control study. PLoS ONE. 2020;15(10):e0241341.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAntony AC, Vora RM, Karmarkar SJ. The silent tragic reality of Hidden Hunger, anaemia, and neural-tube defects (NTDs) in India. The Lancet Regional Health - Southeast Asia [Internet]. 2022 Nov 1 [cited 2022 Oct 23];6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.thelancet.com/journals/lansea/article/PIIS2772-3682(22)00087-7/fulltext\u003c/span\u003e\u003cspan address=\"https://www.thelancet.com/journals/lansea/article/PIIS2772-3682(22)00087-7/fulltext\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi L, Chen Y, Lin Z, Lin W, Liu Y, Ou W, et al. Association of pre-pregnancy body mass index with adverse pregnancy outcome among first-time mothers. PeerJ. 2020;8:e10123.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSustainable Development Report. 2022 [Internet]. [cited 2022 Oct 23]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dashboards.sdgindex.org/\u003c/span\u003e\u003cspan address=\"https://dashboards.sdgindex.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiti Aayog, SDG INDIA INDEX \u0026amp; DASHBOARD 2020-21 PARTNERSHIPS IN THE DECADE OF ACTION [Internet]. Samsad Marg, New Delhi; 2021. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://india.un.org/en/173167-sdg-india-index-and-dashboard-2020-21\u003c/span\u003e\u003cspan address=\"https://india.un.org/en/173167-sdg-india-index-and-dashboard-2020-21\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeshram II, Balakrishna N, Sreeramakrishna K, Rao KM, Kumar RH, Arlappa N, et al. Trends in nutritional status and nutrient intakes and correlates of overweight/obesity among rural adult women (\u0026ge;\u0026thinsp;18\u0026ndash;60 years) in India: National Nutrition Monitoring Bureau (NNMB) national surveys. Public Health Nutr. 2016;19(5):767\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSethi V, Dinachandra K, Murira Z, Gausman J, Bhanot A, de Wagt A, et al. Nutrition status of nulliparous married Indian women 15\u0026ndash;24 years: Decadal trends, predictors and program implications. PLoS ONE. 2019;14(8):e0221125.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoedon T, Sethi V, Chowdhury R, Bhatia N, Dinachandra K, Murira Z, et al. Population estimates and determinants of severe maternal thinness in India. Int J Gynaecol Obstet. 2021;155(3):380\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinistry of Women and Child Development. Integrated Child Development Services (ICDS) Sceme [Internet]. [cited 2023 Mar 21]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://icds-wcd.nic.in/icds.aspx\u003c/span\u003e\u003cspan address=\"http://icds-wcd.nic.in/icds.aspx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInternational Food Policy Research Institute. POSHAN [Internet]. [cited 2023 Mar 21]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ifpri.org/project/poshan\u003c/span\u003e\u003cspan address=\"https://www.ifpri.org/project/poshan\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIndian Institute of Population Science, ICF. National Family Health Survey (NFHS-5). 2019-21: India: Volume 1 [Internet]. Mumbai: IIPS; 2021 [cited 2023 Mar 21]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com/pubs/pdf/FR375/FR375.pdf\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com/pubs/pdf/FR375/FR375.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInternational Institute for Population Sciences. National Family Health Survey (NFHS-4), 2015-16 [Internet]. Mumbai, India; 2017 Dec [cited 2023 Mar 21]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rchiips.org/nfhs/NFHS-4Reports/India.pdf\u003c/span\u003e\u003cspan address=\"http://rchiips.org/nfhs/NFHS-4Reports/India.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIndian Institute of Population Science, Macro International. National Family Health Survey (NFHS-3), 2005-06: India: Volume 1 [Internet]. Mumbai: IIPS. 2007 [cited 2023 Mar 21]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com/pubs/pdf/frind3/frind3-vol1andvol2.pdf\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com/pubs/pdf/frind3/frind3-vol1andvol2.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInternational Institute for Population Sciences, ORC Macro. National Family Health Survey (NFHS-2), 1998\u0026ndash;99 [Internet]. India, Mumbai: IIPS. 2000 [cited 2023 Mar 21]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.dhsprogram.com/pubs/pdf/FRIND2/FRIND2.pdf\u003c/span\u003e\u003cspan address=\"https://www.dhsprogram.com/pubs/pdf/FRIND2/FRIND2.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDandona R, Pandey A, Dandona L. A review of national health surveys in India. Bull World Health Organ. 2016;94(4):286\u0026ndash;A296.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgarwal S, Sethi V. Nutritional Disparities among Women in Urban India. J Health Popul Nutr. 2013;31(4):531\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBiswas S, Hansda CK, Singh N, Malla VR, Pal AK. Determinants of nutritional status among scheduled tribe women in India. Clin Epidemiol Global Health. 2022;17:101119.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh KD, Choedon T, Bhanot A, Kaur N, Chopra M, Sabharwal M, et al. Tipping the Scales: Population Estimates and Risk Factors for Severe Thinness, Thinness, Overweight and Obesity Among Pregnant Women and Mothers in India. Curr Developments Nutr. 2020;4(Supplement2):975.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHasan MM, Ahmed S, Soares Magalhaes RJ, Fatima Y, Biswas T, Mamun AA. Double burden of malnutrition among women of reproductive age in 55 low- and middle-income countries: progress achieved and opportunities for meeting the global target. Eur J Clin Nutr. 2022;76(2):277\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa ZF, Wang CW, Lee YY, Editorial. Malnutrition: A Cause or a Consequence of Poverty? Front Public Health. 2022;9:796435.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiddiqui F, Salam RA, Lassi ZS, Das JK. The Intertwined Relationship Between Malnutrition and Poverty. Front Public Health. 2020;8:453.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaunders J, Smith T. Malnutrition: causes and consequences. Clin Med (Lond). 2010;10(6):624\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOxfam. India\u0026rsquo;s Unequal Healthcare Story [Internet]. [cited 2023 Feb 21]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.oxfamindia.org/press-release/india-inequality-report-2021-indias-unequal-healthcare-story\u003c/span\u003e\u003cspan address=\"https://www.oxfamindia.org/press-release/india-inequality-report-2021-indias-unequal-healthcare-story\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSubramanian. Health Inequalities in India: The Axes of Stratification \u0026ndash; The Brown Journal of World Affairs [Internet]. [cited 2023 Feb 21]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bjwa.brown.edu/14-2/health-inequalities-in-india-the-axes-of-stratification/\u003c/span\u003e\u003cspan address=\"https://bjwa.brown.edu/14-2/health-inequalities-in-india-the-axes-of-stratification/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeorge NA, McKay FH. The Public Distribution System and Food Security in India. Int J Environ Res Public Health. 2019;16(17):3221.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDilip T, Dandona R, Dandona L. The national employment guarantee scheme and inequities in household spending on food and non-food determinants of health in rural India. Int J Equity Health. 2013;12(1):84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Bank. World Bank. [cited 2023 Feb 21]. Schemes to Systems: Lessons from Social Protection in India. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.worldbank.org/en/news/feature/2019/11/21/lessons-from-social-protection-india-schemes-to-systems\u003c/span\u003e\u003cspan address=\"https://www.worldbank.org/en/news/feature/2019/11/21/lessons-from-social-protection-india-schemes-to-systems\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":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":"Body Mass Index, Malnutrition, Reproductive age group, Women health, Health policy","lastPublishedDoi":"10.21203/rs.3.rs-4753444/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4753444/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAim: This study investigates the national and regional distribution and trend of undernutrition among non-pregnant reproductive age-group (15-44 years) women in India based on the National Family Health Survey (NFHS) data round 2 (1998-99) to round 5 (2019-21).\u003c/p\u003e\n\u003cp\u003eSubject and Methods: Undernutrition was defined as a body mass index (BMI) \u0026lt;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e. Association between undernutrition and socio-demographic variables including caste, place of residence (Rural and urban as reported in NFHS), marital status, educational level, and wealth index were examined using multiple logistic regression with multi-level modelling (MLM) and reported adjusted odds ratio (aOR).\u003c/p\u003e\n\u003cp\u003eResults: A total of 461,093 women’s record was analyzed. The prevalence of undernutrition among women in the country reduced from 32.8% (n=22,890) in NFHS-2 to 27.1% (n=42,401) in NFHS-5 (average decadal reduction 2.7%). High intrastate variations (difference between highest and lowest prevalence district \u0026gt;20%) were noted across the country. NFHS-5 data shows that women belong to the poorest wealth index (aOR 1.68; 1.6, 1.76), up to preschool education (aOR 1.29; 1.23, 1.36), and unmarried women (aOR 1.54; 1.49, 1.59) are at risk of having undernutrition.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion: Undernutrition among non-pregnant women has remained high in India. Inter-state and intra-state disparities and inequalities among the various social groups visibly exist for this ignored health issue.\u003c/p\u003e","manuscriptTitle":"“Changing Landscape of Undernutrition in Reproductive-Age Indian Women: Analysis of the nationally representative data, 1998-2021”","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-20 19:17:16","doi":"10.21203/rs.3.rs-4753444/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":"316d4130-d78e-482e-bf2d-96c473cdde92","owner":[],"postedDate":"August 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-28T10:53:28+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-20 19:17:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4753444","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4753444","identity":"rs-4753444","version":["v1"]},"buildId":"CiT4i_kKBbxQbnFL0ufpk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}