Household Food Insecurity Among Dalit Ethnic Group in Bharatpur Metropolitan City, Chitwan, Nepal

Household Food Insecurity Among Dalit Ethnic Group in Bharatpur Metropolitan City, Chitwan, Nepal | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Household Food Insecurity Among Dalit Ethnic Group in Bharatpur Metropolitan City, Chitwan, Nepal Subash Koirala, Gayatri Khanal, Arun Koirala, Niki Shrestha, Eak Narayan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7000601/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Jan, 2026 Read the published version in BMC Nutrition → Version 1 posted 12 You are reading this latest preprint version Abstract Background The problem of food insecurity, from a public health perspective, is becoming increasingly pressing, as 9% of the global population is currently experiencing severe food insecurity most of whom reside in low- and middle-income countries. In Nepal, socially excluded ethnic groups are particularly vulnerable to acute food insecurity Objective The aim was to determine the proportion of households experiencing food insecurity and to identify the factors contributing to it. Methods A community-based cross-sectional study was conducted among 181 Dalit families in the Chitwan district of Nepal using a semi-structured questionnaire and were subsequently analyzed using both descriptive and inferential statistical methods. A multinomial logistic regression model was employed for this purpose, and its validity was assessed Results Around 44.2% of households were food secure, 20% were mild food insecure, 20.4% to moderate and the rest 12.2% were severe food insecure. The variables religion, sex of household head, education of household head, house type, own land and monthly income were found to be statistically significant. Compared to literate household head, households with illiterate household head are 7.28 (CI: 2.03-26. 10) times more likely to experience severe food insecurity. Conclusion The study highlights a high burden of food insecurity among Dalit households in Bharatpur. Sex of the household head, the literacy level of the household head, land ownership, religion and housing type were significant predictors of food insecurity in this study. These findings underscore the need for targeted policy interventions to address food insecurity within socially excluded and economically disadvantaged communities. Food insecurity Dalit Multinomial logistic regression Figures Figure 1 INTRODUCTION Food insecurity arises when individuals lack adequate physical, social, or economic means to access food that is sufficient, safe, and nutritious, aligning with their dietary requirements and personal preferences necessary for maintaining an active and healthy lifestyle [ 1 ]. Food insecurity represents a significant global public health challenge [ 2 , 3 ]. The United Nations’ Sustainable Development Goal 2 (SDG2), titled “Zero Hunger,” aims to address this issue by ensuring universal access to safe and nutritious food. This target emphasizes the eradication of hunger, the attainment of food security, the enhancement of nutrition, and the advancement of sustainable agricultural practices by the year 2030 [ 4 , 5 ]. However, obtaining this target is not easy due to social disparities existing in societies, especially in South Asian countries like Nepal [ 3 , 6 ]. According to FAO’s 2023 report, nearly 29–30% of the global population (approximately 2.3–2.4 billion people) experienced moderate or severe food insecurity, while about 9% (around 697 million people) faced severe food insecurity [ 3 ]. The highest burdens are observed in low- and middle-income countries (LMICs), especially in South Asia [ 3 ]. World Bank/FAO (2022) data indicate around 37% of the population lived in moderately or severely food-insecure households in Nepal [ 7 , 8 ]. Moreover, updated evidence from the Global Hunger Index 2023 shows Nepal ranked 69th out of 125 countries, indicating a moderate level of hunger and reflecting multidimensional threats from food insecurity [ 9 ]. Food insecurity and micronutrient deficiency are closely linked, especially in low- and middle-income countries including Nepal. Historically, the global estimate of people affected by one or more micronutrient deficiencies (“hidden hunger”) was about two billion [ 10 , 11 ], though recent pooled analyses suggest this is an underestimate: approximately 1 in 2 preschool-aged children and 2 in 3 women of reproductive Age have at least one core micronutrient deficiency worldwide [ 12 , 13 ]. Food insecurity can impact health both directly and indirectly through nutritional status (undernutrition or over nutrition). Poor macro- and micronutrient status, lack of fruit and vegetable consumption, and lack of dietary diversity contribute to higher prevalence of underweight, overweight/obesity, adverse blood lipid profiles, lower serum albumin, anemia, vitamin A deficiency, disordered eating behaviors, and impaired physical and mental health [ 2 , 3 , 10 ]. Around 29% of children under five and 19% of non-pregnant women aged 15–49 in Nepal suffer from iron deficiency, according to recent country-specific micronutrient assessments [ 13 , 14 ]. Dalit’s are considered a marginalized population in Nepal and continue to face structural inequalities in terms of education, income, land ownership, and access to services, despite affirmative policies [ 14 ]. Approximately 13.6% of Nepal’s total population are Dalit’s [ 15 ]. Food insecurity is common across ethnic groups in Nepal but disproportionately affects Dalit households: for example, among women of reproductive age, about 76% of Dalit women experienced household food insecurity in recent surveys, significantly higher than other groups [ 12 ]. It is common for Dalit families, often landless agricultural laborers in rural areas, to be severely malnourished, especially women and children [ 12 , 16 ]. Studies from India similarly indicate that food insecurity and malnutrition are particularly severe among Dalit women [17]. In Nepal, social policies have been designed to reduce disparities between Dalits and other groups: since 1997, public funds have been channeled into programs such as scholarships for Dalit children, income-generation activities, and awareness campaigns against caste discrimination; however, these programs are often poorly funded or implemented [ 14 , 18 ]. Nonetheless, such evidence is not available specifically for Chitwan district when Dalit (marginalized) community is this area is large, further underscoring the need for generating more evidence in this direction. The primary aim of this paper is to quantitatively assess the degree of food insecurity in Dalit households in Chitwan, Nepal. Also, this paper is dedicated to identifying socio-economic and demographic risk factors associated with food insecurity through appropriate statistical modeling. Methodology Study design, areas and participants A community-based cross-sectional study was conducted in Ward 6 of Bharatpur Metropolitan City, Chitwan, Nepal, between December 2021 and December 2022. Sample size calculation and techniques The required sample size was calculated using Cochran’s formula (z²pq/e²) [ 19 ]. The latest available data (2022) from the FAO/World Bank, reported via CEIC, shows that 37% of Nepal’s population experienced moderate or severe food insecurity [ 29 ]. With a 7.5% margin of error, the estimated sample size was at least 170. Four wards of Bharatpur Metropolitan City (i.e., Wards 5, 6, 7, and 8) were selected through a lottery method. A list of Dalit households in each selected ward was prepared one week prior to data collection using records obtained from the respective ward offices. These lists were acquired from the Tool Bikash Samittee present in each ward. All Dalit households were compiled into a single file, including names, addresses, and phone numbers. The listings were entered into Microsoft Excel 2010. The final sampling frame was created after removing households that were no longer residing in the area. Systematic random sampling was then employed to select the final respondents from this sampling frame. The sampling interval, ‘k,’ was determined by dividing the total number of households by the required sample size. A random number was generated in Microsoft Excel 2010 to identify the initial household, and subsequent respondents were selected at every k th interval. Data collection tools For this study, a self-designed, semi-structured questionnaire was employed. There are two sections on the questionnaire: A and B. Information pertaining to households is shown in Section A, while the Household Food Insecurity Access Scale (HFIAS) is represented in Section B. Household food insecurity status, the dependent variable, was assessed using the Household Food Insecurity Access Scale (HFIAS), a 9-item measure found within the "Household Food Security" section of the survey instrument. The HFIAS, a qualitative tool developed by the USAID Food and Nutrition Technical Assistance project, was also employed in the 2016 NDHS. It uses nine questions with a four-point Likert scale response format from 0 score to 3 score. Thus, the total HFIAS score ranges from 0 to 27. Based on established HFIAS guidelines, responses to these questions were used to categorize food insecurity into four levels: food secure (0–1), mildly food insecure (2–7), moderately food insecure (8–14), and severely food insecure (15–27). 14 This variable was measured using an ordinal scale. The internal consistency of the food insecurity scale was evaluated using Cronbach's alpha, yielding a value of 0.878, indicating strong inter-item reliability. 15 Data collection techniques Data collection was conducted in the local Nepali language by the researchers and Female Community Health Volunteers (FCHVs). Data were gathered through direct, in-person interviews over a three-month period. To maximize participation, a local female community health worker was enlisted. FCHVs received training prior to data collection to ensure consistency and accuracy. If information could not be obtained from a selected household after two attempts, the immediately preceding household was approached. This procedure ensured that the target sample size was achieved. Statistical analysis Prior to inputting the data into IBM SPSS version 20, manual validation and coding were performed in Epi-Data 3.1. Descriptive statistics were utilized to determine the prevalence of food insecurity among the Dalit households. The Shapiro-Wilk test, with a significance level of 0.05, was conducted to assess the normality of the data. Multinomial logistic regression analysis was used to examine the association between household food insecurity and its predictive factors. For both the outcome and explanatory categorical variables, the highest ordinal category served as the reference group. This allowed for the odds ratios to be interpreted as the likelihood of experiencing household food insecurity. Model fit was assessed using various statistical measures to determine how well the model aligned with the observed data. A primary evaluation involved the likelihood ratio test (LRT), which utilizes the 2×log-likelihood statistic. The LRT produces a chi-square statistic, allowing us to test the null hypothesis that there is no significant difference between a model without explanatory variables and the model incorporating them. Additionally, deviance and Pearson's chi-square statistics were employed to evaluate model fit. For these tests, a p-value exceeding 0.05 indicates that the null hypothesis is not rejected, signifying that the differences between observed and predicted values are not statistically significant. This suggests that the estimated parameters adequately represent the data. Furthermore, the model's predictive power was evaluated using pseudo R2. A higher pseudo R2 value implies that a greater proportion of the data's variability is explained by the model. Results Fig-1 Food insecurity status among Dalit household of Bharatpur metropolitan city chitwan, Nepal (n = 181) Figure depicts the distribution of Food insecurity among Dalit ethnic group, out of 80 (44.20%) were food secure, 42(23.20%) were mild food insecure, 37(20.40%) were moderately food insecure and 22(12.20%) were severe food insecure [Figure 1] . Table-1 Factors associated with food insecurity among Dalit household of Bharatpur Metropolitan city chitwan, Nepal (n = 181) Variables Category Severely food insecurity with referenced to moderately food insecurity Moderately food insecurity with referenced to mildly food insecurity Mildly food insecurity with referenced to food security OR 95% CI P-value OR 95% CI P-value OR 95% CI P-value Cast Damai 2.20 0.39–12.40 0.37 1.49 0.39–5.68 0.55 0.84 0.24–2.89 0.79 Kami 2.96 0.47–18.65 0.24 0.43 0.10–1.75 0.24 2.13 0.62–7.34 0.22 Sarki Reference category Religion Hindu 0.57 0.144–2.28 0.43 1.08 0.31–3.78 0.89 2.92 0.96–8.83 0.05 Non-hindu Reference category Age of HH 20–39 years 0.72 0.16–3.13 0.66 2.07 0.58–7.36 0.25 0.37 0.10–1.27 0.11 40–60 years 0.399 0.06–2.46 0.32 0.47 0.12–1.74 0.26 1.37 0.41–4.49 0.60 Above 60 Reference category Sex of HH Male 1.03 0.27–3.90 0.95 0.51 0.16–1.55 0.23 0.72 0.28–2.42 0.03 Female Reference category Education of HH Illiterate 7.28 2.03–26.10 0.002 0.38 0.12–1.14 0.08 3.52 1.24–10.01 0.01 Literate Reference category Highest education of house Just literate 0.53 0.11–2.78 0.45 1.32 0.36–4.77 0.67 2.16 0.64–7.29 0.21 Primary 0.618 0.12–3.05 0.55 1.21 0.34–4.25 0.75 1.50 0.49–4.54 0.47 Secondary and above Reference category Occupation of HH Unemployment 0.24 0.01–5.03 0.35 8.91 0.64-123.17 0.10 0.50 0.08–2.87 0.43 Agriculture 0.18 0.09–3.70 0.26 4.12 0.33–50.18 0.26 0.53 0.10–2.77 0.45 Labour 0.21 0.01–4.29 0.31 4.29 0.33–54.5 0.26 0.78 0.15–4.03 0.77 Service Reference category House type Kacha 1.32 0.37–4.62 0.66 1.91 0.67–5.49 0.22 3.82 1.26–11.56 0.017 others Reference category Own land Presence 0.24 0.06–0.89 0.03 1.33 0.49–3.56 0.56 0.49 0.20–1.18 0.11 Absence Reference category Rented land Presence 1.03 0.29–3.64 0.95 1.02 0.36–2.85 0.96 1.13 0.45–2.81 0.79 Absence Reference category Household status Rented 0.50 0.13–1.80 0.29 1.79 0.64–4.99 0.26 1.53 0.59–3.95 0.38 Own Reference category Monthly income ≤ 10000 0.341 0.08–1.39 0.133 1.49 0.48–4.59 0.48 4.86 1.88–12.55 0.001 > 10000 Reference category Foot note : Model adequacy test ,Log likelohhod ratio model fit test = χ 2 = 129.44; p-value 0.999, All VIF < 2, Nagelkerke R2 = 55.4% Table presents the odds ratios, confidence intervals, and p-values for the response variables across each category. The odds of a household with an illiterate head experiencing severe food insecurity relative to moderate food insecurity were 7.28 times higher (95% CI: 2.03–26.10) compared to households with a literate head. Conversely, households owning land had a 76% lower likelihood (odds ratio: 0.24, 95% CI: 0.06–0.89) of being severely food insecure relative to moderately food insecure, when compared to households without land ownership. Households identifying as Hindu had 2.92 times greater odds (95% CI: 0.96–8.83) of experiencing mild food insecurity compared to food security, relative to non-Hindu households. Similarly, literate household heads were associated with a 3.52 times higher odds of mild food insecurity relative to food security, compared to illiterate household heads. Regarding housing type, those residing in 'kaccha' houses had 3.82 times greater odds (95% CI: 1.26–11.56) of experiencing mild food insecurity relative to food security, when compared to those living in 'pakka' or mixed housing. The likelihood ratio test indicated a significant difference between the model with and without explanatory variables (χ2 = 129.44, p 0.999), indicating an adequate fit of the model to the data. Variance inflation factors (VIFs) for all covariates were below 2, confirming the absence of significant multicollinearity. The Nagelkerke R2 (pseudo R2) revealed that the model explained 55.4% of the variation in the dependent variable, indicating a moderate predictive power. Finally, the model's utility was assessed by comparing the overall classification percentage (47.2%) to the chance accuracy rate (28%). The fitted model improved classification accuracy by 65%, demonstrating its practical usefulness [Table 1] . Discussion Evidence on food insecurity has been more extensively studied in the general population; however, it has received comparatively less attention among one of Nepal’s most vulnerable groups the Dalit caste. This study documents the prevalence and correlates of food insecurity within the Dalit community of Bharatpur, Chitwan. With 55.8% of participants reporting some level of food insecurity, the findings highlight a significant burden of food insecurity among Dalit households in Bharatpur Metropolitan City. This result is consistent with findings from Pandey and Fusaro at the national level, who reported that approximately 56% of Nepali women of reproductive age experience food insecurity [ 19 ]. Although slightly lower than the 63.9% prevalence reported in the 2016 Nepal Demographic and Health Survey (NDHS), the findings reinforce that socially excluded populations in Nepal continue to face substantial barriers in accessing adequate food [ 20 ]. In both India and Nepal, Dalits are historically oppressed and socioeconomically marginalized experience higher levels of poverty and limited access to land, employment, and state resources [ 30 , 31 ]. These structural disadvantages translate directly into elevated rates of food insecurity. Household food insecurity was substantially correlated with a number of sociodemographic factors. Most significantly, the family head's educational attainment was a determining factor. Compared to their literate peers, heads of households who were illiterate were more than seven times as likely to face extreme food insecurity. This result is consistent with larger trends seen in LMICs, where dietary diversity and food access are strongly correlated with education [ 21 , 23 ]. A comparable pattern was noted in Andhra Pradesh, India, where Dalit women with little access to school and work possibilities experienced severe food insecurity [ 23 ]. Land ownership emerged as another significant determinant. Households that owned land were 76% less likely to be severely food insecure compared to landless households. This aligns with other evidence from rural South Asia, where land access is foundational for food production and livelihood security [ 22 ]. In Nepal, where caste-based landlessness persists among Dalit populations despite affirmative policies, this underscores systemic inequities in resource distribution [ 24 ]. The association between housing type and food insecurity further illustrates socio-economic disparities. Households living in kacha (temporary or substandard) housing were significantly more likely to be mildly food insecure. Poor housing often reflects deeper vulnerabilities, including unstable income and lack of access to basic services, which cumulatively contribute to food insecurity [ 25 ]. Religious affiliation was also significantly associated with mild food insecurity, where Hindu households had nearly three times higher odds than non-Hindus. While this association may not directly suggest causation, it could reflect intersectional disadvantages experienced by certain subgroups within the Dalit population. This warrants further qualitative exploration to understand cultural, social, or geographic factors that may mediate this association [ 26 ]. The study's multinomial logistic regression model demonstrated a strong fit (Nagelkerke R² = 55.4%), suggesting that the selected variables explain a substantial proportion of the variation in food insecurity. These findings are consistent with research from Canada and Korea, which found that logistic regression models can robustly capture the multifactorial nature of food insecurity, particularly when socio-economic indicators are included [ 25 , 26 ]. Ultimately, this study reinforces the need for targeted social protection programs and educational interventions among Dalit communities. Programs that combine income generation, education access, and land rights protection could serve as multi-pronged strategies to reduce food insecurity in these marginalized populations [ 27 , 28 ]. Moreover, food security programs must also adopt a caste sensitive approach, recognizing the deep-rooted social exclusion that Dalit’s face. Strength and limitation of the study This paper is the first paper identified the magnitude of food insecurity and its associated factors among the underprivileged group (Dalit) which has never done before in the study areas, is an important strength of this study. We employed the random sampling techniques which will ensure the representation of the study areas is another strength of the study. Present study analyses the exposure and outcome simultaneously, making it difficult to determine the temporal relationship. Thus, causal inferences cannot be drawn. Conclusion In conclusion, this research highlights the prevalence and predictors of food insecurity among households in the study area. The findings reveal that a significant proportion of households are food insecure, with a higher proportion being moderately and severely food insecure. The study also identifies several demographic and socioeconomic factors that are associated with food insecurity, including religion, sex of household head, education of household head, house type, ownership of land, and monthly income. Specifically, households with illiterate heads had a significantly higher likelihood of being severely food insecure. These results underscore the need for targeted interventions aimed at addressing the underlying social and economic factors contributing to food insecurity in the region. Declarations Ethics approval: This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki (2013 revision). Ethical approvals for the study were obtained from the Chitwan Medical College institutional review committee CMC-IRC (reference number: CMC-IRC 078079086). consent to participant: Each participant was clearly informed about their level of involvement and the types of questions they would be asked. The researcher also explained the purpose of data collection. During the informed consent process, participants were informed about the potential risks and benefits of the study, the measures taken to ensure confidentiality, the voluntary nature of their participation, their right to withdraw at any time without penalty, and how their data would be protected. Informed consent was obtained from all participants. All participant who select from systematic sampling were gives us consent so there was no non-response rate. Human Ethics and Consent to Participate: Not applicable Consent for publication: Not applicable Availability of data and materials: No websites or media outlets have published the data from this manuscript. The corresponding writers have information and will supply it if needed. Clinical trial number: Not applicable Competing interest: There is no competing interest for publication Funding: Chitwan Medical College and Teaching Hospital gives financial support for this research Authors contributions: Subash koirala: Design Topic, proposal writing, Data analysis and interpretation, Gayatri khanal: Proposal review questionnaire design, report writing, Niki Shrestha: Discussion writing Eak Narayan Poudel: Data collection, entry and logistic management Arun koirala: Data collection and entry Acknowledgement: We could not have carried out this research without the support of the Toll Sudar committee, the FCHV, and the participants from the corresponding wards. 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Available from: https://dalitcommission.gov.npdoaj.org Central Bureau of Statistics (CBS), Government of Nepal. National Population and Housing Census 2021: National Report. Kathmandu: CBS; 2021. Available from: https://censusnepal.cbs.gov.np/results/files/result-folder/National%20Report_English.pdfpublichealthupdate.com Paudel D, Thapa B, Gauchan E, et al. Food insecurity among women of reproductive age in Nepal: prevalence and correlates. BMC Public Health. 2020;20:1525. doi:10.1186/s12889-020-8298-4 bmcpublichealth.biomedcentral.compmc.ncbi.nlm.nih.gov [For India studies on Dalit women food insecurity/malnutrition, see e.g.:] Thorat S, Newman K. Caste and economic discrimination: scarred lives. India: Oxford University Press; 2010. (discusses structural inequalities affecting Dalits, including nutrition and food security). OR: Swayam Pragyan Parida et al. “Food insecurity among Dalit households in India” [specific article if available]. 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Available from: https://www.ceicdata.com/en/nepal/social-health-statistics/np-prevalence-of-moderate-or-severe-food-insecurity-in-the-population--of-population?utm_source=chatgpt.com Thapa, R., & Acharya, S. (2020). Caste, exclusion, and access to public goods in Nepal. Journal of Development Studies , 56(10), 1802–1818. https://doi.org/10.1080/00220388.2019.1697725 Thapa R, van Teijlingen E, Regmi PR, Heaslip V. Caste Exclusion and Health Discrimination in South Asia: A Systematic Review. Asia Pacific Journal of Public Health . 2021;33(8):828-838. doi:10.1177/10105395211014648 Additional Declarations No competing interests reported. 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Koirala","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIiWNgGAWjYBACCQbGBgaGAhCThwHItoEI8xDUYgDXkkaMFhBAaDlMWItke3Pbhx8GdgzyM3IPfvy547y8bvsBxgdv2xjyzHFokeY52DyzxyCZweBGXrI075nbhtvOJDAbzm1jKLZswK5FTiKxmYHHgJnBQCLHQJqx7XaC2Q0GNmneNobEDQdwa2H8Y1APdFiO8c+fbedAWth/49MiDdTCzGMA9PWNHDMJ3rYDYFuY8WmR7DnYzCxjcJzH4My7NGvetmSgXxKbJeeck0jcicMvEsfbHzO+qaiWk2/PPXzzZ5udvNnxwwc/vCmzSdyOI8RgADkiQJELjDADAlqwADK0jIJRMApGwfAEAMz1V5+vFmL5AAAAAElFTkSuQmCC","orcid":"","institution":"Chitwan Medical college and teaching Hospital","correspondingAuthor":true,"prefix":"","firstName":"Subash","middleName":"","lastName":"Koirala","suffix":""},{"id":498355897,"identity":"6f2e4a3e-01ff-4e27-b1ef-0c16f6101bf3","order_by":1,"name":"Gayatri Khanal","email":"","orcid":"","institution":"SRM institute of science and Technology, (SRMIST)","correspondingAuthor":false,"prefix":"","firstName":"Gayatri","middleName":"","lastName":"Khanal","suffix":""},{"id":498355898,"identity":"dda7a269-c952-4647-ac1c-e2cc476bd7fb","order_by":2,"name":"Arun Koirala","email":"","orcid":"","institution":"Chitwan Medical college and teaching Hospital","correspondingAuthor":false,"prefix":"","firstName":"Arun","middleName":"","lastName":"Koirala","suffix":""},{"id":498355899,"identity":"3141d440-8196-49d6-bb69-74ecef13a6ef","order_by":3,"name":"Niki Shrestha","email":"","orcid":"","institution":"Chitwan Medical college and teaching Hospital","correspondingAuthor":false,"prefix":"","firstName":"Niki","middleName":"","lastName":"Shrestha","suffix":""},{"id":498355900,"identity":"2b463af0-9160-4d16-8a12-dbfd0bab887b","order_by":4,"name":"Eak Narayan","email":"","orcid":"","institution":"Chitwan Medical college and teaching Hospital","correspondingAuthor":false,"prefix":"","firstName":"Eak","middleName":"","lastName":"Narayan","suffix":""}],"badges":[],"createdAt":"2025-06-29 04:23:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7000601/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7000601/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40795-025-01238-4","type":"published","date":"2026-01-06T15:58:55+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88973582,"identity":"e2f22ea3-62cd-4128-8914-a7998c7110ca","added_by":"auto","created_at":"2025-08-13 09:59:09","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":67447,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eFood insecurity status among Dalit household of Bharatpur metropolitan city chitwan, Nepal (n=181)\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7000601/v1/f7523b9ae30708d5675c53cf.jpg"},{"id":100069379,"identity":"d72abfb9-6cf8-4305-adbb-c21ade119867","added_by":"auto","created_at":"2026-01-12 16:13:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1345009,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7000601/v1/35378a4d-15da-4bed-994b-4180f51bf6b2.pdf"},{"id":88972184,"identity":"702c070d-360b-4091-984f-477db2ca2a63","added_by":"auto","created_at":"2025-08-13 09:51:09","extension":"sav","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":9612,"visible":true,"origin":"","legend":"","description":"","filename":"HFInew.sav","url":"https://assets-eu.researchsquare.com/files/rs-7000601/v1/4dd16afe9ba1c082c0c9c6b3.sav"},{"id":88972187,"identity":"caf5584d-9d74-42ed-a58e-5e10ee3f0631","added_by":"auto","created_at":"2025-08-13 09:51:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":582476,"visible":true,"origin":"","legend":"","description":"","filename":"questions.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7000601/v1/dc1ae90c918867529a5234c7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eHousehold Food Insecurity Among Dalit Ethnic Group in Bharatpur Metropolitan City, Chitwan, Nepal\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eFood insecurity arises when individuals lack adequate physical, social, or economic means to access food that is sufficient, safe, and nutritious, aligning with their dietary requirements and personal preferences necessary for maintaining an active and healthy lifestyle [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Food insecurity represents a significant global public health challenge [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The United Nations’ Sustainable Development Goal 2 (SDG2), titled “Zero Hunger,” aims to address this issue by ensuring universal access to safe and nutritious food. This target emphasizes the eradication of hunger, the attainment of food security, the enhancement of nutrition, and the advancement of sustainable agricultural practices by the year 2030 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, obtaining this target is not easy due to social disparities existing in societies, especially in South Asian countries like Nepal [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAccording to FAO’s 2023 report, nearly 29–30% of the global population (approximately 2.3–2.4\u0026nbsp;billion people) experienced moderate or severe food insecurity, while about 9% (around 697\u0026nbsp;million people) faced severe food insecurity [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The highest burdens are observed in low- and middle-income countries (LMICs), especially in South Asia [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. World Bank/FAO (2022) data indicate around 37% of the population lived in moderately or severely food-insecure households in Nepal [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Moreover, updated evidence from the Global Hunger Index 2023 shows Nepal ranked 69th out of 125 countries, indicating a moderate level of hunger and reflecting multidimensional threats from food insecurity [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Food insecurity and micronutrient deficiency are closely linked, especially in low- and middle-income countries including Nepal. Historically, the global estimate of people affected by one or more micronutrient deficiencies (“hidden hunger”) was about two billion [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], though recent pooled analyses suggest this is an underestimate: approximately 1 in 2 preschool-aged children and 2 in 3 women of reproductive Age have at least one core micronutrient deficiency worldwide [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Food insecurity can impact health both directly and indirectly through nutritional status (undernutrition or over nutrition). Poor macro- and micronutrient status, lack of fruit and vegetable consumption, and lack of dietary diversity contribute to higher prevalence of underweight, overweight/obesity, adverse blood lipid profiles, lower serum albumin, anemia, vitamin A deficiency, disordered eating behaviors, and impaired physical and mental health [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Around 29% of children under five and 19% of non-pregnant women aged 15–49 in Nepal suffer from iron deficiency, according to recent country-specific micronutrient assessments [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Dalit’s are considered a marginalized population in Nepal and continue to face structural inequalities in terms of education, income, land ownership, and access to services, despite affirmative policies [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Approximately 13.6% of Nepal’s total population are Dalit’s [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Food insecurity is common across ethnic groups in Nepal but disproportionately affects Dalit households: for example, among women of reproductive age, about 76% of Dalit women experienced household food insecurity in recent surveys, significantly higher than other groups [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. It is common for Dalit families, often landless agricultural laborers in rural areas, to be severely malnourished, especially women and children [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Studies from India similarly indicate that food insecurity and malnutrition are particularly severe among Dalit women [17]. In Nepal, social policies have been designed to reduce disparities between Dalits and other groups: since 1997, public funds have been channeled into programs such as scholarships for Dalit children, income-generation activities, and awareness campaigns against caste discrimination; however, these programs are often poorly funded or implemented [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Nonetheless, such evidence is not available specifically for Chitwan district when Dalit (marginalized) community is this area is large, further underscoring the need for generating more evidence in this direction.\u003c/p\u003e\u003cp\u003eThe primary aim of this paper is to quantitatively assess the degree of food insecurity in Dalit households in Chitwan, Nepal. Also, this paper is dedicated to identifying socio-economic and demographic risk factors associated with food insecurity through appropriate statistical modeling.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e\u003cstrong\u003eStudy design, areas and participants\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eA community-based cross-sectional study was conducted in Ward 6 of Bharatpur Metropolitan City, Chitwan, Nepal, between December 2021 and December 2022.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSample size calculation and techniques\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe required sample size was calculated using Cochran’s formula (z²pq/e²) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The latest available data (2022) from the FAO/World Bank, reported via CEIC, shows that 37% of Nepal’s population experienced moderate or severe food insecurity [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. With a 7.5% margin of error, the estimated sample size was at least 170. Four wards of Bharatpur Metropolitan City (i.e., Wards 5, 6, 7, and 8) were selected through a lottery method. A list of Dalit households in each selected ward was prepared one week prior to data collection using records obtained from the respective ward offices. These lists were acquired from the Tool Bikash Samittee present in each ward. All Dalit households were compiled into a single file, including names, addresses, and phone numbers. The listings were entered into Microsoft Excel 2010. The final sampling frame was created after removing households that were no longer residing in the area. Systematic random sampling was then employed to select the final respondents from this sampling frame. The sampling interval, ‘k,’ was determined by dividing the total number of households by the required sample size. A random number was generated in Microsoft Excel 2010 to identify the initial household, and subsequent respondents were selected at every \u003cem\u003ek\u003c/em\u003eth interval.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eData collection tools\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor this study, a self-designed, semi-structured questionnaire was employed. There are two sections on the questionnaire: A and B. Information pertaining to households is shown in Section A, while the Household Food Insecurity Access Scale (HFIAS) is represented in Section B. Household food insecurity status, the dependent variable, was assessed using the Household Food Insecurity Access Scale (HFIAS), a 9-item measure found within the \"Household Food Security\" section of the survey instrument. The HFIAS, a qualitative tool developed by the USAID Food and Nutrition Technical Assistance project, was also employed in the 2016 NDHS. It uses nine questions with a four-point Likert scale response format from 0 score to 3 score. Thus, the total HFIAS score ranges from 0 to 27. Based on established HFIAS guidelines, responses to these questions were used to categorize food insecurity into four levels: food secure (0–1), mildly food insecure (2–7), moderately food insecure (8–14), and severely food insecure (15–27).\u003csup\u003e14\u003c/sup\u003e This variable was measured using an ordinal scale. The internal consistency of the food insecurity scale was evaluated using Cronbach's alpha, yielding a value of 0.878, indicating strong inter-item reliability.\u003csup\u003e15\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eData collection techniques\u003c/b\u003e\u003c/p\u003e\u003cp\u003eData collection was conducted in the local Nepali language by the researchers and Female Community Health Volunteers (FCHVs). Data were gathered through direct, in-person interviews over a three-month period. To maximize participation, a local female community health worker was enlisted. FCHVs received training prior to data collection to ensure consistency and accuracy. If information could not be obtained from a selected household after two attempts, the immediately preceding household was approached. This procedure ensured that the target sample size was achieved.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\u003cp\u003ePrior to inputting the data into IBM SPSS version 20, manual validation and coding were performed in Epi-Data 3.1. Descriptive statistics were utilized to determine the prevalence of food insecurity among the Dalit households. The Shapiro-Wilk test, with a significance level of 0.05, was conducted to assess the normality of the data.\u003c/p\u003e\u003cp\u003eMultinomial logistic regression analysis was used to examine the association between household food insecurity and its predictive factors. For both the outcome and explanatory categorical variables, the highest ordinal category served as the reference group. This allowed for the odds ratios to be interpreted as the likelihood of experiencing household food insecurity.\u003c/p\u003e\u003cp\u003eModel fit was assessed using various statistical measures to determine how well the model aligned with the observed data. A primary evaluation involved the likelihood ratio test (LRT), which utilizes the 2×log-likelihood statistic. The LRT produces a chi-square statistic, allowing us to test the null hypothesis that there is no significant difference between a model without explanatory variables and the model incorporating them. Additionally, deviance and Pearson's chi-square statistics were employed to evaluate model fit. For these tests, a p-value exceeding 0.05 indicates that the null hypothesis is not rejected, signifying that the differences between observed and predicted values are not statistically significant. This suggests that the estimated parameters adequately represent the data. Furthermore, the model's predictive power was evaluated using pseudo R2. A higher pseudo R2 value implies that a greater proportion of the data's variability is explained by the model.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFig-1 Food insecurity status among Dalit household of Bharatpur metropolitan city chitwan, Nepal (n\u0026thinsp;=\u0026thinsp;181)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFigure depicts the distribution of Food insecurity among Dalit ethnic group, out of 80 (44.20%) were food secure, 42(23.20%) were mild food insecure, 37(20.40%) were moderately food insecure and 22(12.20%) were severe food insecure \u003cb\u003e[Figure 1]\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTable-1 Factors associated with food insecurity among Dalit household of Bharatpur Metropolitan city chitwan, Nepal (n\u0026thinsp;=\u0026thinsp;181)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e\u003cem\u003eSeverely food insecurity with referenced to moderately food insecurity\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e\u003cem\u003eModerately food insecurity with referenced to mildly food insecurity\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e\u003cem\u003eMildly food insecurity with referenced to food security\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" 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align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDamai\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.39\u0026ndash;12.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.39\u0026ndash;5.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.24\u0026ndash;2.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKami\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.47\u0026ndash;18.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.10\u0026ndash;1.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.62\u0026ndash;7.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSarki\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c11\" namest=\"c3\"\u003e\u003cp\u003e\u003cem\u003eReference category\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHindu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.144\u0026ndash;2.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.31\u0026ndash;3.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.96\u0026ndash;8.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-hindu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c11\" namest=\"c3\"\u003e\u003cp\u003e\u003cem\u003eReference category\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge of HH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u0026ndash;39 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.16\u0026ndash;3.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.58\u0026ndash;7.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.10\u0026ndash;1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40\u0026ndash;60 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u0026ndash;2.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.12\u0026ndash;1.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.41\u0026ndash;4.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbove 60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c11\" namest=\"c3\"\u003e\u003cp\u003e\u003cem\u003eReference category\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex of HH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.27\u0026ndash;3.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.16\u0026ndash;1.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.28\u0026ndash;2.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c11\" namest=\"c3\"\u003e\u003cp\u003e\u003cem\u003eReference category\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation of HH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIlliterate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.03\u0026ndash;26.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.12\u0026ndash;1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.24\u0026ndash;10.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLiterate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c11\" namest=\"c3\"\u003e\u003cp\u003e\u003cem\u003eReference category\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHighest education of house\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJust literate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.11\u0026ndash;2.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.36\u0026ndash;4.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.64\u0026ndash;7.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.12\u0026ndash;3.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.34\u0026ndash;4.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.49\u0026ndash;4.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c11\" namest=\"c3\"\u003e\u003cp\u003e\u003cem\u003eReference category\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOccupation of HH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnemployment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u0026ndash;5.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.64-123.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.08\u0026ndash;2.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgriculture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.09\u0026ndash;3.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.33\u0026ndash;50.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.10\u0026ndash;2.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLabour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u0026ndash;4.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.33\u0026ndash;54.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.15\u0026ndash;4.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eService\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c11\" namest=\"c3\"\u003e\u003cp\u003e\u003cem\u003eReference category\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHouse type\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKacha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.37\u0026ndash;4.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.67\u0026ndash;5.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.26\u0026ndash;11.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eothers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c11\" namest=\"c3\"\u003e\u003cp\u003e\u003cem\u003eReference category\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOwn land\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePresence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u0026ndash;0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.49\u0026ndash;3.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.20\u0026ndash;1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbsence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c11\" namest=\"c3\"\u003e\u003cp\u003e\u003cem\u003eReference category\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRented land\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePresence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.29\u0026ndash;3.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.36\u0026ndash;2.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.45\u0026ndash;2.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbsence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c11\" namest=\"c3\"\u003e\u003cp\u003e\u003cem\u003eReference category\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHousehold status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRented\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.13\u0026ndash;1.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.64\u0026ndash;4.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.59\u0026ndash;3.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOwn\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c11\" namest=\"c3\"\u003e\u003cp\u003e\u003cem\u003eReference category\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMonthly income\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;10000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08\u0026ndash;1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.48\u0026ndash;4.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.88\u0026ndash;12.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;10000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c11\" namest=\"c3\"\u003e\u003cp\u003e\u003cem\u003eReference category\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cem\u003eFoot note : Model adequacy test ,Log likelohhod ratio model fit test\u0026thinsp;=\u0026thinsp;χ 2\u0026thinsp;=\u0026thinsp;129.44; p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Pearson and deviance chi square test p value\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;0.999, All VIF\u0026thinsp;\u0026lt;\u0026thinsp;2, Nagelkerke R2\u0026thinsp;=\u0026thinsp;55.4%\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable presents the odds ratios, confidence intervals, and p-values for the response variables across each category. The odds of a household with an illiterate head experiencing severe food insecurity relative to moderate food insecurity were 7.28 times higher (95% CI: 2.03\u0026ndash;26.10) compared to households with a literate head. Conversely, households owning land had a 76% lower likelihood (odds ratio: 0.24, 95% CI: 0.06\u0026ndash;0.89) of being severely food insecure relative to moderately food insecure, when compared to households without land ownership. Households identifying as Hindu had 2.92 times greater odds (95% CI: 0.96\u0026ndash;8.83) of experiencing mild food insecurity compared to food security, relative to non-Hindu households. Similarly, literate household heads were associated with a 3.52 times higher odds of mild food insecurity relative to food security, compared to illiterate household heads. Regarding housing type, those residing in 'kaccha' houses had 3.82 times greater odds (95% CI: 1.26\u0026ndash;11.56) of experiencing mild food insecurity relative to food security, when compared to those living in 'pakka' or mixed housing. The likelihood ratio test indicated a significant difference between the model with and without explanatory variables (χ2\u0026thinsp;=\u0026thinsp;129.44, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that the explanatory variables significantly influenced the outcome. Furthermore, the Pearson and deviance chi-square tests showed non-significant results (p\u0026thinsp;\u0026gt;\u0026thinsp;0.999), indicating an adequate fit of the model to the data. Variance inflation factors (VIFs) for all covariates were below 2, confirming the absence of significant multicollinearity. The Nagelkerke R2 (pseudo R2) revealed that the model explained 55.4% of the variation in the dependent variable, indicating a moderate predictive power. Finally, the model's utility was assessed by comparing the overall classification percentage (47.2%) to the chance accuracy rate (28%). The fitted model improved classification accuracy by 65%, demonstrating its practical usefulness \u003cb\u003e[Table\u0026nbsp;1]\u003c/b\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eEvidence on food insecurity has been more extensively studied in the general population; however, it has received comparatively less attention among one of Nepal\u0026rsquo;s most vulnerable groups the Dalit caste. This study documents the prevalence and correlates of food insecurity within the Dalit community of Bharatpur, Chitwan. With 55.8% of participants reporting some level of food insecurity, the findings highlight a significant burden of food insecurity among Dalit households in Bharatpur Metropolitan City. This result is consistent with findings from Pandey and Fusaro at the national level, who reported that approximately 56% of Nepali women of reproductive age experience food insecurity [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Although slightly lower than the 63.9% prevalence reported in the 2016 Nepal Demographic and Health Survey (NDHS), the findings reinforce that socially excluded populations in Nepal continue to face substantial barriers in accessing adequate food [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In both India and Nepal, Dalits are historically oppressed and socioeconomically marginalized experience higher levels of poverty and limited access to land, employment, and state resources [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. These structural disadvantages translate directly into elevated rates of food insecurity.\u003c/p\u003e\u003cp\u003eHousehold food insecurity was substantially correlated with a number of sociodemographic factors. Most significantly, the family head's educational attainment was a determining factor. Compared to their literate peers, heads of households who were illiterate were more than seven times as likely to face extreme food insecurity. This result is consistent with larger trends seen in LMICs, where dietary diversity and food access are strongly correlated with education [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. A comparable pattern was noted in Andhra Pradesh, India, where Dalit women with little access to school and work possibilities experienced severe food insecurity [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLand ownership emerged as another significant determinant. Households that owned land were 76% less likely to be severely food insecure compared to landless households. This aligns with other evidence from rural South Asia, where land access is foundational for food production and livelihood security [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In Nepal, where caste-based landlessness persists among Dalit populations despite affirmative policies, this underscores systemic inequities in resource distribution [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe association between housing type and food insecurity further illustrates socio-economic disparities. Households living in kacha (temporary or substandard) housing were significantly more likely to be mildly food insecure. Poor housing often reflects deeper vulnerabilities, including unstable income and lack of access to basic services, which cumulatively contribute to food insecurity [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eReligious affiliation was also significantly associated with mild food insecurity, where Hindu households had nearly three times higher odds than non-Hindus. While this association may not directly suggest causation, it could reflect intersectional disadvantages experienced by certain subgroups within the Dalit population. This warrants further qualitative exploration to understand cultural, social, or geographic factors that may mediate this association [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe study's multinomial logistic regression model demonstrated a strong fit (Nagelkerke R\u0026sup2; = 55.4%), suggesting that the selected variables explain a substantial proportion of the variation in food insecurity. These findings are consistent with research from Canada and Korea, which found that logistic regression models can robustly capture the multifactorial nature of food insecurity, particularly when socio-economic indicators are included [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Ultimately, this study reinforces the need for targeted social protection programs and educational interventions among Dalit communities. Programs that combine income generation, education access, and land rights protection could serve as multi-pronged strategies to reduce food insecurity in these marginalized populations [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Moreover, food security programs must also adopt a caste sensitive approach, recognizing the deep-rooted social exclusion that Dalit\u0026rsquo;s face.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStrength and limitation of the study\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis paper is the first paper identified the magnitude of food insecurity and its associated factors among the underprivileged group (Dalit) which has never done before in the study areas, is an important strength of this study. We employed the random sampling techniques which will ensure the representation of the study areas is another strength of the study. Present study analyses the exposure and outcome simultaneously, making it difficult to determine the temporal relationship. Thus, causal inferences cannot be drawn.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this research highlights the prevalence and predictors of food insecurity among households in the study area. The findings reveal that a significant proportion of households are food insecure, with a higher proportion being moderately and severely food insecure. The study also identifies several demographic and socioeconomic factors that are associated with food insecurity, including religion, sex of household head, education of household head, house type, ownership of land, and monthly income. Specifically, households with illiterate heads had a significantly higher likelihood of being severely food insecure. These results underscore the need for targeted interventions aimed at addressing the underlying social and economic factors contributing to food insecurity in the region.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eThis study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki (2013 revision). Ethical approvals for the study were obtained from the Chitwan Medical College institutional review committee \u003cem\u003eCMC-IRC (reference number: CMC-IRC 078079086).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;consent to participant:\u0026nbsp;\u003c/strong\u003eEach participant was clearly informed about their level of involvement and the types of questions they would be asked. The researcher also explained the purpose of data collection. During the informed consent process, participants were informed about the potential risks and benefits of the study, the measures taken to ensure confidentiality, the voluntary nature of their participation, their right to withdraw at any time without penalty, and how their data would be protected. Informed consent was obtained from all participants. All participant who select from systematic sampling were gives us consent so there was no non-response rate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e No websites or media outlets have published the data from this manuscript. The corresponding writers have information and will supply it if needed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest:\u003c/strong\u003e There is no competing interest for publication\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e Chitwan Medical College and Teaching Hospital gives financial support for this research\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSubash koirala:\u003c/em\u003e\u0026nbsp; Design Topic, proposal writing, Data analysis and interpretation,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGayatri khanal:\u003c/em\u003e\u0026nbsp; Proposal review questionnaire design, report writing,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNiki Shrestha:\u003c/em\u003e Discussion writing\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEak Narayan Poudel:\u003c/em\u003e Data collection, entry and logistic management\u003c/p\u003e\n\u003cp\u003eArun koirala: Data collection and entry\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u003c/strong\u003e We could not have carried out this research without the support of the Toll Sudar committee, the FCHV, and the participants from the corresponding wards. We also acknowledge the funding provided for this study by the CMC research unit.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFood and Agriculture Organization of the United Nations. Food security exists when all people, at all times, have physical, social and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life. In: Committee on World Food Security Reform Document: Food security definitions. Rome: FAO; 2006. Available from: https://www.fao.org/cfs/policy-products/onlinegsf/1/en/fao.org\u003c/li\u003e\n\u003cli\u003eBaker KA. Food insecurity: a public health challenge. Gastroenterol Nurs. 2018 Mar-Apr;41(2):91\u0026ndash;92. doi:10.1097/SGA.0000000000000380 journals.lww.comnursingcenter.com\u003c/li\u003e\n\u003cli\u003eFAO, IFAD, UNICEF, WFP \u0026amp; WHO. The State of Food Security and Nutrition in the World 2023: Urbanization, agrifood systems transformation and healthy diets across the rural\u0026ndash;urban continuum. Rome: FAO; 2023. Available from: https://doi.org/10.4060/cc3017en openknowledge.fao.orgopenknowledge.fao.org\u003c/li\u003e\n\u003cli\u003eUnited Nations. Transforming our world: the 2030 Agenda for Sustainable Development. UN General Assembly Resolution A/RES/70/1. New York: United Nations; 2015. Available from: https://sdgs.un.org/2030agendasdgs.un.org\u003c/li\u003e\n\u003cli\u003eUnited Nations Department of Economic and Social Affairs. Goal 2: Zero Hunger. Available from: https://sdgs.un.org/goals/goal2 (Accessed 15 Jun 2025). un.orgun.org\u003c/li\u003e\n\u003cli\u003eFAO, IFAD, UNICEF, WFP \u0026amp; WHO. The State of Food Security and Nutrition in the World 2023: Regional overview for Asia and the Pacific. In: FAO SOFI 2023 report. Rome: FAO; 2023. Available from: https://doi.org/10.4060/cc3017en (see regional analysis on South Asia) openknowledge.fao.orgopenknowledge.fao.org\u003c/li\u003e\n\u003cli\u003eMinistry of Health and Population (MoHP) [Nepal], New ERA, and ICF. Nepal Demographic and Health Survey 2022. Kathmandu: MoHP, New ERA, and ICF; 2023. Available from: https://www.dhsprogram.com/publications/publication-fr370-dhs-final-reports.cfmpublichealthupdate.com\u003c/li\u003e\n\u003cli\u003eWorld Bank / FAO. Prevalence of moderate or severe food insecurity (% of population) \u0026ndash; Nepal, 2022. In: World Bank Data. Available from: https://data.worldbank.org/indicator/SN.ITK.FINS.ZS?locations=NP (indicates ~37% in 2022) ceicdata.com\u003c/li\u003e\n\u003cli\u003eConcern Worldwide \u0026amp; Welt Hunger Hilfe. Global Hunger Index 2023: Nepal ranked 69th out of 125 countries, moderate hunger. Available from: https://www.globalhungerindex.org/nepal.html (Accessed 15 Jun 2025). globalhungerindex.org\u003c/li\u003e\n\u003cli\u003eKennedy G, Nantel G, Shetty P. The scourge of \u0026ldquo;hidden hunger\u0026rdquo;: global dimensions of micronutrient deficiencies. In: FAO. Food, nutrition and agriculture. Rome: FAO; 2002. p. [pages]. (Estimate: \u0026ldquo;probably exceeds two billion\u0026rdquo;).\u003c/li\u003e\n\u003cli\u003eFAO / WHO / UNICEF. Micronutrient deficiencies estimates historically ~2 billion people. In: Various FAO/WHO nutrition documents. Example: FAO. The State of Food Security and Nutrition in the World 2023 (discussing \u0026ldquo;hidden hunger\u0026rdquo;). openknowledge.fao.orgopenknowledge.fao.org\u003c/li\u003e\n\u003cli\u003eStevens GA, Beal T, Mbuya MNM, Luo H, Neufeld LM, Maalouf-Manasseh Z, et al. Micronutrient deficiencies among preschool-aged children and women of reproductive age worldwide: a pooled analysis of individual-level data from population-representative surveys. Lancet Glob Health. 2022 Nov;10(11):e1590\u0026ndash;e1599. doi:10.1016/S2214-109X(22)00367-9 bmcpublichealth.biomedcentral.compmc.ncbi.nlm.nih.gov\u003c/li\u003e\n\u003cli\u003eMicronutrient Data Innovation Alliance (DInA). Micronutrient Forum Nepal Case Study: prevalence of iron deficiency among children under five and women 15\u0026ndash;49 in Nepal. Geneva: Micronutrient Forum; 2023. Available from: https://micronutrientforum.org/wp-content/uploads/2023/05/mnf_DInA-Nepal-Case-Study.pdflink.springer.com\u003c/li\u003e\n\u003cli\u003eNational Dalit Commission (NDC). Situation Analysis of Dalits in Nepal. Kathmandu: Government of Nepal; 2020. Available from: https://dalitcommission.gov.npdoaj.org\u003c/li\u003e\n\u003cli\u003eCentral Bureau of Statistics (CBS), Government of Nepal. National Population and Housing Census 2021: National Report. Kathmandu: CBS; 2021. Available from: https://censusnepal.cbs.gov.np/results/files/result-folder/National%20Report_English.pdfpublichealthupdate.com\u003c/li\u003e\n\u003cli\u003ePaudel D, Thapa B, Gauchan E, et al. Food insecurity among women of reproductive age in Nepal: prevalence and correlates. BMC Public Health. 2020;20:1525. doi:10.1186/s12889-020-8298-4 bmcpublichealth.biomedcentral.compmc.ncbi.nlm.nih.gov\u003c/li\u003e\n\u003cli\u003e[For India studies on Dalit women food insecurity/malnutrition, see e.g.:] Thorat S, Newman K. Caste and economic discrimination: scarred lives. India: Oxford University Press; 2010. (discusses structural inequalities affecting Dalits, including nutrition and food security).\u003c/li\u003e\n\u003cli\u003eOR: Swayam Pragyan Parida et al. \u0026ldquo;Food insecurity among Dalit households in India\u0026rdquo; [specific article if available].\u003c/li\u003e\n\u003cli\u003e(Adjust with exact reference when available.) bmcpublichealth.biomedcentral.com\u003c/li\u003e\n\u003cli\u003eGovernment of Nepal. Dalit empowerment programs: scholarships, income generation, awareness-raising initiatives since 1997. Kathmandu: Government of Nepal; various years (see NDC report)\u003c/li\u003e\n\u003cli\u003ePandey MR, Fusaro VA. Food insecurity among women of reproductive age in Nepal: Evidence from the Nepal Multiple Indicator Cluster Survey 2019. BMC Public Health. 2022; 22:456.\u003c/li\u003e\n\u003cli\u003eMinistry of Health, Nepal; New ERA; ICF. Nepal Demographic and Health Survey 2016. Kathmandu, Nepal: Ministry of Health; 2017.\u003c/li\u003e\n\u003cli\u003eSmith LC, Haddad L. Explaining child malnutrition in developing countries: A cross-country analysis. IFPRI Research Report 111. Washington, DC: International Food Policy Research Institute; 2000.\u003c/li\u003e\n\u003cli\u003ePant S, Thapa S, Nepal MK. Land ownership and food security among rural households in Nepal. Journal of Rural Development. 2018;37(3):453-72.\u003c/li\u003e\n\u003cli\u003eKshatriya GK, Acharya J. Food insecurity among Dalits in India: A caste-based analysis. Journal of Social Inclusion Studies. 2019;5(1):44-58.\u003c/li\u003e\n\u003cli\u003eMinistry of Land Management, Cooperatives and Poverty Alleviation, Nepal. National Land Policy and Caste-based Inequities. Kathmandu; 2019.\u003c/li\u003e\n\u003cli\u003eTarasuk V, Mitchell A, Dachner N. Household food insecurity in Canada, 2017-18. Toronto: Research to identify policy options to reduce food insecurity (PROOF); 2020.\u003c/li\u003e\n\u003cli\u003ePark JH, Kim JH. Socioeconomic determinants of food insecurity in South Korea: Analysis of the Korean National Health and Nutrition Examination Survey. Public Health Nutrition. 2021;24(3):514-22.\u003c/li\u003e\n\u003cli\u003eWorld Food Programme. Nepal Country Strategic Plan 2020-2024. Rome: WFP; 2019.\u003c/li\u003e\n\u003cli\u003eUnited Nations Development Programme. Promoting social inclusion and reducing poverty in Nepal: A multi-sectoral approach. Kathmandu: UNDP Nepal; 2021.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCEIC Data.\u003c/strong\u003e NP: Prevalence of moderate or severe food insecurity in the population (% of population). \u003cem\u003eSocial\u0026ndash;Health Statistics\u003c/em\u003e. CEIC; [cited 2025 Jun 22]. Available from: https://www.ceicdata.com/en/nepal/social-health-statistics/np-prevalence-of-moderate-or-severe-food-insecurity-in-the-population--of-population?utm_source=chatgpt.com\u003c/li\u003e\n\u003cli\u003eThapa, R., \u0026amp; Acharya, S. (2020). Caste, exclusion, and access to public goods in Nepal. \u003cem\u003eJournal of Development Studies\u003c/em\u003e, 56(10), 1802\u0026ndash;1818. https://doi.org/10.1080/00220388.2019.1697725\u003c/li\u003e\n\u003cli\u003eThapa R, van Teijlingen E, Regmi PR, Heaslip V. Caste Exclusion and Health Discrimination in South Asia: A Systematic Review. \u003cem\u003eAsia Pacific Journal of Public Health\u003c/em\u003e. 2021;33(8):828-838. doi:10.1177/10105395211014648\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutn","sideBox":"Learn more about [BMC Nutrition](http://bmcnutr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nutn/default.aspx","title":"BMC Nutrition","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Food insecurity, Dalit, Multinomial logistic regression","lastPublishedDoi":"10.21203/rs.3.rs-7000601/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7000601/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe problem of food insecurity, from a public health perspective, is becoming increasingly pressing, as 9% of the global population is currently experiencing severe food insecurity most of whom reside in low- and middle-income countries. In Nepal, socially excluded ethnic groups are particularly vulnerable to acute food insecurity\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eThe aim was to determine the proportion of households experiencing food insecurity and to identify the factors contributing to it.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA community-based cross-sectional study was conducted among 181 Dalit families in the Chitwan district of Nepal using a semi-structured questionnaire and were subsequently analyzed using both descriptive and inferential statistical methods. A multinomial logistic regression model was employed for this purpose, and its validity was assessed\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAround 44.2% of households were food secure, 20% were mild food insecure, 20.4% to moderate and the rest 12.2% were severe food insecure. The variables religion, sex of household head, education of household head, house type, own land and monthly income were found to be statistically significant. Compared to literate household head, households with illiterate household head are 7.28 (CI: 2.03-26. 10) times more likely to experience severe food insecurity.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe study highlights a high burden of food insecurity among Dalit households in Bharatpur. Sex of the household head, the literacy level of the household head, land ownership, religion and housing type were significant predictors of food insecurity in this study. These findings underscore the need for targeted policy interventions to address food insecurity within socially excluded and economically disadvantaged communities.\u003c/p\u003e","manuscriptTitle":"Household Food Insecurity Among Dalit Ethnic Group in Bharatpur Metropolitan City, Chitwan, Nepal","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-13 09:51:05","doi":"10.21203/rs.3.rs-7000601/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-16T06:39:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-13T15:51:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-10T15:35:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238532138651079633701831778530017665399","date":"2025-08-09T14:29:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"248682736275592445089798516204081425419","date":"2025-08-08T08:28:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"293170514211067281338240956340366234780","date":"2025-08-08T07:55:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-07T20:18:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180069991651020889315245255383369443210","date":"2025-08-07T19:16:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-07T14:24:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-16T08:15:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-08T08:38:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nutrition","date":"2025-07-08T08:34:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutn","sideBox":"Learn more about [BMC Nutrition](http://bmcnutr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nutn/default.aspx","title":"BMC Nutrition","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a6e209f4-d983-44ac-8fd0-536c7f689865","owner":[],"postedDate":"August 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T16:05:57+00:00","versionOfRecord":{"articleIdentity":"rs-7000601","link":"https://doi.org/10.1186/s40795-025-01238-4","journal":{"identity":"bmc-nutrition","isVorOnly":false,"title":"BMC Nutrition"},"publishedOn":"2026-01-06 15:58:55","publishedOnDateReadable":"January 6th, 2026"},"versionCreatedAt":"2025-08-13 09:51:05","video":"","vorDoi":"10.1186/s40795-025-01238-4","vorDoiUrl":"https://doi.org/10.1186/s40795-025-01238-4","workflowStages":[]},"version":"v1","identity":"rs-7000601","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7000601","identity":"rs-7000601","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}