Determinants of Household Food Insecurity in Rural Zahedan: A Food and Nutrition System Perspective

Determinants of Household Food Insecurity in Rural Zahedan: A Food and Nutrition System Perspective | 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 Determinants of Household Food Insecurity in Rural Zahedan: A Food and Nutrition System Perspective Mahdieh Sheikhi, Hassan Eini-Zinab, Seyed Mehdi Tabatabaei, Nasrin Omidvar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7292945/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Apr, 2026 Read the published version in Food Production, Processing and Nutrition → Version 1 posted You are reading this latest preprint version Abstract Household food insecurity (FI) in rural areas is influenced by multiple determinants within the food and nutrition system (FNS), encompassing production, distribution, consumption, and nutritional status. This cross-sectional study evaluated 321 randomly selected households across six rural villages in Zahedan, Iran, to identify key determinants of FI from a FNS perspective. FI affected 53.3% of households, which were characterized by larger family sizes, lower income and educational levels among adult women, and greater reliance on government subsidies. Villages with greater agricultural diversity and higher nutritional yield showed significantly reduced odds of FI, whereas villages exporting over half their produce experienced a 7.9-fold increased risk. Households with homestead food production yielding less than 5% protein faced significantly higher FI risk (OR = 2.82; 95% CI: 1.50–5.28). Lower nutrient adequacy scores were also strongly associated with FI. Among adult women in FI households, prevalence of both wasting and overweight increased, while abdominal obesity was less common (OR = 0.461; 95% CI: 0.243–0.878). These findings highlight critical determinants within the rural FNS contributing to household FI and related nutritional challenges, underscoring the need for integrated policies to address socioeconomic disparities and enhance agricultural and nutritional resilience. Food and nutrition system Food insecurity Agricultural diversity Nutritional adequacy Figures Figure 1 Introduction Food insecurity (FI) remains a pressing global public health concern. As of 2022, an estimated 29.6% of the world’s population experienced moderate to severe FI, with rural populations disproportionately affected (33.3%) compared to their urban counterparts (26.0%) (FAO et al., 2023). This challenge has been exacerbated by the compounded effects of conflict, climate change, and the economic fallout of the COVID-19 pandemic, particularly in fragile economies (Dubowitz et al., 2021 ). FI is closely linked to poor diet quality, which contributes to various forms of malnutrition and elevates of chronic diseases (WHO, 2020 ). Addressing this complex issue requires a deeper understanding of the underlying ‎determinants of food insecurity within local food and nutrition systems (FNS)‎ (Dave et al., 2023 ). The FNS framework offers a comprehensive, systems-based lens for analyzing the multiple ‎interconnected processes that influence food security including production, distribution, ‎consumption, and nutrition (Bogard et al., 2018 ). However, policy efforts have often focused narrowly on individual components, such as ‎agricultural yield or food prices, neglecting the dynamic interactions across the system (Burchi et al., 2011 ). This fragmented approach has contributed to the widespread availability of calorie-dense, ‎nutrient-poor diets, undermining nutritional outcomes despite improvements in food supply‎ (Bogard et al., 2018 ). A holistic evaluation of the FNS is therefore critical to identify systemic gaps and inform ‎more integrated and effective interventions‎ (Van Berkum et al., 2018 ). This systems-based approach is particularly vital in rural Zahedan, situated in Sistan and Baluchestan Province, one of Iran’s most food-insecure regions. More than 70% of the region's population lives in rural areas, and over half experience moderate to severe FI (Kolahdooz et al., 2012 ; Melesse et al., 2020 ). The region faces significant environmental constraints—prolonged drought, chronic water scarcity, and extreme temperatures—which further hinder agricultural productivity and food availability (Zare Abianeh et al., 2015 ). Despite these challenges, research examining the systemic determinants of food insecurity through an integrated FNS lens in this region remains scarce. To address this gap, the present study adopts an adapted version of the FNS framework proposed by Bogard et al. (Bogard et al., 2018 ), which categorizes the food system into four interlinked stages: production, distribution, consumption, and nutrition. Each stage is assessed using relevant indicators. Production is evaluated based on nutritional yield, production diversity, protein supply, and the share of dietary energy from non-staple crops. Distribution is examined through the proportion of local agricultural output sold externally, the cost of a healthy diet, and sources of food purchases (e.g., local vs. urban markets). Consumption is analyzed by considering the household food budget share as an indicator of economic prioritization and dietary quality; and nutrition is measured using anthropometric indicators—body mass index (BMI) and waist circumference—of adult women, reflecting the long-term health implications of the food system (Fig. 1). By employing this integrated and context-specific framework, the study aims to identify key determinants of household food insecurity in rural Zahedan and provide actionable evidence for the design of coordinated interventions that strengthen all stages of the FNS, ultimately improving food and nutrition outcomes in vulnerable rural settings. Materials and methods Study setting and design This cross-sectional study was conducted in Zahedan rural communities in Sistan and Baluchistan Province (Southeast of Iran), from April to July 2019. The sampling unit were the village, household, and/or individuals. Zahedan consists of three districts and randomly selected two villages from each district. According to Cochran's formula and considering the population of rural households, a sample size of 320 households was determined. The inclusion criteria for households comprised Iranian nationality, residency in the village for more than 6 months, and consent to participate in the study. This section presents the methods used to evaluate nutritional quality across the FNS subsystems (Agricultural production, distribution, consumption, and nutrition). Agriculture production subsystem Data on crop and horticulture, livestock, poultry, and aquaculture production numbers possessed over the past year were collected through interviews with experts in the agriculture sector and farmers. Based on the collected data, key indicators such as protein supply, production diversity, share of energy from non-staple foods, and nutritional yield were calculated. Protein supply was estimated through a multi-step approach based solely on annual agricultural products, as there was no livestock, poultry, or aquaculture units in the studied villages. First, the percentage of agricultural products loss at different stages of the food system—namely production, transportation and storage, distribution, processing, and consumption—was calculated based on values reported by the Food and Agriculture Organization (FAO) for South and Southeast Asia (Gustafsson et al., 2013 ). Second, the percentage of non-edible parts of foods and the conversion ratio from raw to cooked forms were calculated (Ghafarpor et al., 1999 ). Third, after determining the per capita agricultural production (in grams) for each agricultural product, protein content was estimated using the Iranian food composition table (Esmaeili & Hoshshyarrad, 2018 ) and the USDA Food Data Central (USDA, 2021 ). Fourth, to calculate per capita agricultural production adjusted for energy needs, Adult Male Equivalent (AME) (Weisell & Dop, 2012 ) units for each household member were computed. AME, defined by FAO, reflects an individual's energy requirement relative to a standard adult male aged 18–30 years with a moderate level of physical activity. Accordingly, the village population, categorized by age and gender was extracted from the Ministry of Health’s Integrated Health System (Jafari et al., 2020 ) to compute the total AME for each village over one year (Weisell & Dop, 2012 ). Finally, protein supply was calculated by dividing the total protein derived from village-level agricultural production by the total protein requirement of the village's AME population, based on Dietary Reference Intakes (DRI) (FAO/WHO, 2001 ). Production diversity was assessed by calculating a production diversity score, which was determined by counting the number of different food groups produced by the farms over the past year. Share of energy from non-staples was calculated as the percentage of the energy content (kcal per capita) derived from the edible portion of non staple foods produced for human consumption on the farm (Ghafarpor et al., 1999 ). In this classification, cereals, roots, and tubers were considered staples, while all other food items were categorized as non-staples. A higher share of energy from non-staples is generally associated with better dietary quality (Alioma et al., 2022 ). To compute this indicator, agricultural product losses and non-edible portions of foods were estimated using the same method applied for the protein supply indicator. The energy content of the edible foods was then calculated using the Iranian food composition table (Esmaeili & Hoshshyarrad, 2018 ). Ultimately, this indicator reflects the percentage of total available food energy (kilocalories) sourced from non-staple foods (Gustafson et al., 2016 ). Nutritional yield is a relatively new concept that goes beyond just the weight or volume of food produced. It considers not only the quantity of a crop but also its quality in terms of nutrients (Bogard et al., 2018 ). It is defined as the number of adults, on average both male and female (not pregnant or lactating) between 19 and 50 years old, who would be able to obtain 100% of the dietary reference intakes (DRI) of energy from one hectare of food produced annually (Bogard et al., 2018 ). The calorie content of agricultural products in rural areas was calculated based on the Iranian food composition table (Esmaeili & Hoshshyarrad, 2018 ). Following the recommendation of the FAO and the World Health Organization (WHO), an energy requirement of 2800 kilocalories per day was assumed for an adult male with moderate physical activity. Therefore, the nutritional yield of all village-produced items was separately calculated and then aggregated. To depict the extent to which agricultural production in each village meets the needs of its resident population, the village's population was disaggregated by age and gender from the SIB system, and converted into AME. Subsequently, after determining the AMEs for each village, the nutritional yield of production was divided by the village's AMEs for adult males. This facilitated the calculation of the percentage of calorie provision from agricultural production for each village. Homestead production was estimated using a 12-item questionnaire administered through interviews with mothers. The questionnaire collected detailed information on the percentage of households with home gardens, including the types and quantities of products produced annually. It also provided data relevant to key indicators such as protein supply and production diversity. The questionnaire was developed by a multidisciplinary team of researchers, nutritionists, and agricultural experts and validated through pilot testing with a representative sample to ensure clarity, relevance, and accuracy. The protein supply at the household level was estimated using homestead food production data, which were first converted to grams. Deductions were then made for the percentage of food loss at the consumption stage (Gustafsson et al., 2013 ) and the proportion of non-edible parts. Subsequently, the raw-to-cooked conversion coefficient was applied (Ghafarpor et al., 1999 ), and for each food product, protein content was calculated using the Iranian food composition table (Esmaeili & Hoshshyarrad, 2018 ) and the USDA food data (USDA, 2021 ). The household's AME was estimated based on the number of household members present over the year, categorized by age and sex. Protein requirement were calculated for the household's AME using the DRI for adult males. Finally, the total protein content of homestead produced foods was divided by the protein requirement of the household's AME to obtain the household level protein supply. The production diversity of each household was measured by calculating a diversity score, which was based on the number of different types of foods produced by the household over the past year. Each unique food item contributed one unit to the score, regardless of quantity (Bogard et al., 2018 ). Distribution subsystem This subsystem was assessed using three key indicators: local agriculture distribution, cost of a healthy diet, and source of food purchasing. These indicators collectively evaluate the availability, accessibility, and affordability of food within the local food system. Local agriculture distribution was evaluated through an index designed to quantify the efficiency of food flow ‎within a defined geographic area (Rossi et al., 2018 ; Sobal et al., 1998 ). This index considered the proportion of agricultural products reaching local village markets versus external ones, ‎based on interviews with experts from the Agriculture Jihad Organization and local farmer. Cost of a healthy diet was estimated using a method previously validated in Iran. In our study, we estimated the cost of a healthy, low-cost, and environmentally sustainable diet using a method previously employed in Iran (Eini-Zinab et al., 2021 ), which employed linear programming to design a diet that met four simultaneous goals: maximizing the nutrient-rich food index, minimizing cost, reducing water footprint, and lowering carbon footprint. Goal Programming techniques were used to balance these objectives, and data from 24-hour diet recalls (collected on two non-consecutive days) were used to compare current dietary costs with the modeled optimal diet. Source of food purchasing was determined through the same dietary recall data. For each food item consumed, the source of purchase was recorded. The number and percentage of food items obtained from each source were then calculated, providing insights into household dependence on different food supply channels. Consumption subsystem The consumption subsystem was evaluated using four complementary indicators: household characteristics, household food insecurity access scale (HFIAS), mean adequacy ratio (MAR), and food budget share. Together, these indicators provide a comprehensive picture of household dietary quality, nutritional adequacy, food access, and economic capacity. Household‎ characteristics, including demographic and socioeconomic profiles, were collected using a validated questionnaire from the Statistical Center of Iran (StatisticalCenter, 2015 ). Variables such as age, gender, education, employment, income, household size, asset ownership, and participant in subsidy plans were recorded. A welfare index was constructed by assigning weighted scores (0 to 10) to household assets based on price and necessity, and households were categorized into tertiles of low, medium, and high welfare using visual binning. Household FI was measured using the HFIAS, a validated tool in the Iranian context (Mohammadi et al., 2012 ). This scale includes nine questions addressing food access over the past four weeks, with responses categorized into frequency levels (rarely, sometimes, or often). Based on total scores, households were classified into food secure, mildly, moderately, or severely FI groups (Coates et al., 2007 ). MAR was calculated based on dietary intake data from two non-consecutive 24-hour recalls collected by trained community health workers. All consumed foods were weighted, and nutrient composition was analyzed using Iranian and USDA food composition tables. Energy and nutrient intake adequacy was assessed by calculating Nutrient Adequacy Ratios (NARs) for energy, protein, and 10 micronutrients (e.g., iron, zinc, calcium, vitamins A, B, C, and folate), and the MAR was derived by averaging the NARs (M’Kaibi et al., 2015 ). AME units were used to adjust for household composition and energy needs based on age and sex (FAO/WHO, 2001 ; WHO/UNU, 2007 ). Food budget share was determined by estimating the proportion of daily household income spent on food. After recording the monetary value of food consumed (from dietary recalls), food expenditure was divided by total daily income, calculated from detailed reports of income sources and amounts gathered through the household socioeconomic questionnaire (Lele et al., 2016 ). This indicator reflects the economic pressure of food costs on households and provides insights into food affordability. Nutrition subsystem Body weight and height of adult women in each household were measured using a digital scale (Seca, Germany) and a non-elastic measuring tape. Body Mass Index (BMI) was calculated as weight (kg) divided by height squared (m²). Weight status was classified into the following categories: underweight (BMI < 18.5), normal weight (18.5–24.9), overweight (25–29.9), and obese (BMI ≥ 30) (WHO, 2021 ). Waist circumference of adult women (young and middle-aged groups) was also measured to the nearest millimeter using a non-elastic measuring tape, positioned midway between the lowest rib and the iliac crest. Abdominal obesity was defined as a waist circumference greater than 80 cm (Brown, 2016 ). Statistical analysis Socioeconomic characteristics were described using means and standard deviations for continuous variables, or percentages for categorical variables. The normality of quantitative variables was assessed using the Kolmogorov-Smirnov test. As none of the continuous variables followed a normal distribution, non-parametric Mann-Whitney U tests were employed to compare differences between food-secure and food-insecure households. Chi-square tests were used to compare categorical variables. To examine the associations between FNS indicators and household FI, logistic regression models were applied. For unadjusted models, multilevel logistic regression was used for village-level indicators, and binary logistic regression for household-level indicators (implemented in R software). Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated to quantify associations. The adjusted models controlled for potential confounders including family size, sex, education level, employment status of the household head, income, and welfare index. For the analysis of nutritional indicators, household FI was treated as th independent variable, given its established relationship with women's anthropometric outcomes (Townsend et al., 2001 ). In the unadjusted models, multinomial logistic regression was used to assess the association between BMI categories and household FI, and linear regression was used for abdominal obesity. All adjusted models included the same set of confounders. All data analysed using SPSS version 16.0 (SPSS Inc., Chicago, IL, USA) and R software. A p-value of ≤ 0.05 was considered statistically significant. Results Household‎’s food security status A total of 321 households from six villages in the Zahedan district participated in the study. A high prevalence of moderate to severe food insecurity (53.3%) was observed among the households. Significant sociodemographic differences were identified between food-secure and food-insecure households. Food-insecure households had larger family sizes and lower incomes. Food-secure households reported higher incomes—both with and without cash aid—and were more likely to receive income from paid employment, whereas food-insecure households relied more heavily on national subsidies. The welfare index was higher among food-secure households, and employment rates of household heads, as well as education levels among adult women, were also higher in these households (Table 1 ). Notably, no significant differences were found in gender, marital status, or education level of the household head, nor in household housing status, between the two groups. Table 1 Comparison of household characteristics by food security status in Zahedan rural areas Variables Household‎’s food security status (n = 321) FS 1 FI 2 150 (46.7%) 171 (53.3%) Mean (SE) Mean (SE) p-value 3 Family size (persons) 4.28 (0.15) 4.98 (0.16) 0.004 Age of household‎’s head (years) 43.72 (1.39) 43.45 (0.95) 0.235 Age of adult women (years) 38.12 (1.30) 39.26 (1.01) 0.139 Household‎’s income (rials 4 ) 13361259.0 (994321.5) 7644593.8 (565466.2) ‎<0.001‎ Household‎’s income without cash aid (rial 4 ) 10957500.0 (982080.1) 5008812.5 (540812.1) ‎<0.001 Source of household income (percentage) Paid job 57.97 (3.37) 39.10 (3.04) ‎<0.0001 National subsidy 33.79 (2.83) 50.53 (2.72) ‎<0.0001 Imam Khomeini Relief Committee subsidy 7.13 (1.59) 9.71 (1.59) 0.126 n (%) n (%) p-value 5 Household‎’s Welfare index Low 42 (40) 63 (60) 0.002 Medium 41 (39) 64 (61) High 66 (60) 44 (40) Type of home occupancy Ownership 117 (47) 132 (53) 0.775 Rent/other 32 (45.1) 39 (54.9) Household‎’s under an additional subsidy plan (excluding the national subsidy ) Yes 32 (42.7) 43 (57.3) 0.421 No 118 (48) 128 (52) Household‎’s head characteristics Sex Male 128 (48.5) 136 (51.5) 0.175 Female 22 (38.6) 35 (61.4) Education level Illiterate and primary 94 (43.7) 121 (56.3) 0.124 High school and higher education 56 (52.8) 50 (47.2) Employment Employed 104 (55) 85 (45) 0.001 Unemployed/ housewife 46 (35.4) 84 (64.6) Marital status Married 123 (47.7) 135 (52.3) 0.492 Other (Single / Divorced / Widowed) 27 (42.9) 36 (57.1) Household‎’s ‎adult women characteristics Education level Illiterate and primary 112 (42.9) 149 (57.1) 0.009 High school and higher education 31 (63.3) 18 (36.7) Employed 12 (57.1) 9 (42.9) 0.264 Employment Unemployed/ housewife 123 (44.6) 153 (55.4) ¹ Food Secure, ²Food Insecure, 3 P-value for Mann–Whitney U tests , 4 Rial is the currency of Iran, 5 P-value for Chi-squared tests. Agriculture production subsystem Overall, 12 agricultural products were cultivated in the villages, including crops (wheat), fodder (alfalfa, barley, maize), vegetables (tomatoes, cucumbers, eggplants, onions), and fruits (pomegranates, pistachios, grapes, berries). Local production provided, on average, 31% of the rural population’s annual dietary protein requirements. Notably, greater diversity in agricultural products and higher nutritional yield were associated with a decreased likelihood of household food insecurity (Table 2 ). This association was statistically significant in both unadjusted and adjusted models, with a stronger effect observed in the adjusted model. Households experiencing food insecurity (FI) exhibited higher rates of homestead activities compared to food-secure (FS) households. Notably, 27% of the households practiced home gardening, 42.9% raised livestock or poultry, and 16.4% engaged in both. In contrast, 27.8% of all households reported no agricultural production, and 54.6% obtained less than 5% of their dietary protein from this source. A low protein supply (< 5%) significantly increased the odds of FI (unadjusted OR = 3.19, adjusted OR = 2.82) compared to households with no production (Table 2 ). Interestingly, homestead diversity—defined as engagement in both gardening and livestock/poultry activities—was associated with a twofold increase in the likelihood of experiencing FI (OR = 2.00, 95% CI: 1.05–3.79) in both models (Table 2 ). Table 2 Association between agriculture production sub‎system with household food insecurity status in rural Zahedan, Iran Level Predictors Household‎ food insecurity 1 Unadjusted Adjusted 2 OR CI 95% OR CI 95% Village 3 Protein Supply (%) Less than 10 1 1 10–50 0.39 0.11–1.32 0.33 0.05–1.94 More than 50 1.25 0.37–4.23 1.53 0.26–8.72 Production diversity (0–10) 0.96 ** 0.94–0.98 0.95 ** 0.92–0.98 Share of energy from non-staples‎ (0-100) 1.00 0.99–1.01 1.00 0.98–1.02 Nutritional yield (%) Less than 10 1 1 10–50 0.13 ** 0.04–0.48 0.07 ** 0.01–0.37 More than 50 0.28 * 0.08–0.92 0.17 * 0.03–0.85 Household 4 Production diversity None or One product 1 1 2–10 Diversity 2.02 ** 1.22–3.35 2.00 * 1.05–3.79 Protein Supply No production 1 1 Less than 4.9 3.19 *** 1.87–5.43 ‌2.82 *** 1.50–5.28 More than 5 1.54 0.78–3.04 2.29 0.96–5.43 ¹Food Secure is reference. 2 Adjusted for family size, sex, education, and employment of household‎’s head, household‎’s income and welfare index. 3 Using multilevel regression. 4 Using binary logistic regression. *p < 0.05, **p < 0.01, ***p 50% of produce) were significantly associated ‎with household FI compared to those that did not export agricultural products. This association was statistically significant in both unadjusted and adjusted models, with the adjusted model showing an association strength nearly 7.9 times greater (95% CI: 2.27–27.51) (Table 3 ). Households sourced approximately 58% of their food from the city market, compared to 23% ‎from the village market. Notably, FI households purchased a lower proportion of their food items ‎from the city market. While city market purchases were associated with a trend toward lower odds of FI, ‎this association was weak and lost statistical significance after adjusting for confounders ‎(Table 3 ). The cost of a healthy diet showed no significant association with FI in either model. Table 3 Association between distribution stage with household‎ food insecurity in rural Zahedan, Iran Level Predictors Household‎’s food insecurity 1 Unadjusted Adjusted 2 OR CI 95% OR CI 95% Village 3 Local agriculture distribution to outside rural (%) No distribution 1 1 1–50% 1.39 0.79–2.45 1.79 0.54–5.88 More than 50% 5.68 *** 3.02–10.70 7.90 *** 2.27–27.51 Household 4 Cost of a healthy diet 0.99 0.97-1.00 1.00 0.98–1.01 Food purchased source (%) city 0.99 * 0.98–0.99 0.99 0.98-1.00 Village 1.01 0.99–1.01 1.00 0.99–1.01 ¹ Food Secure is reference. 2 Adjusted for family size, sex, education, and employment of household‎’s head, household‎’s income and welfare index. 3 Using multilevel regression. 4 Using binary logistic regression. *p < 0.05, **p < 0.01, ***p < 0.001. Consumption subsystem Households with FS exhibited higher MAR scores, indicating a significant inverse association be‎tween MAR and FI. However, in the adjusted model, this association remained statistically significant only for households in the second tertile of MAR compared to the reference group (OR = 0.53; ‎‎95% CI: 0.31–0.92) (Table 4 ). Food expenditure showed a positive association with FI in the un‎adjusted model, but this association became non-significant after adjusting for confounding factors. Table 4 Association between consumption stage and ‎household‎ food insecurity in rural Zahedan, Iran Predictors Household‎’s food insecurity 1,2 Unadjusted Adjusted 3 OR CI 95% OR CI 95% Mean adequacy ratio ‎(%) Tertile1 (36-60.5) 1 1 Tertile2 (60.6–75.5) 0.87 0.50–1.51 0.53 * 0.31–0.92 Tertile3 (75.6–98.5) 0.880.47–1.66 0.72 0.37–1.41 ‎Food budget share Tertile1 (5–49) 1 1 Tertile2 (49.1–81.4) 2.85 *** 1.49–5.42 1.85 0.84–4.10 Tertile3 (> 81.4) 4.76 *** 2.65–8.57 1.95 0.79–4.82 ¹ Food Secure is reference. 2 Using binary logistic regression. 3 Adjusted for family size, sex, education, and employment of household‎’s head, household‎’s income and welfare index. *p < 0.05, **p < 0.01, ***p < 0.001 Nutrition subsystem ‎ The rates of wasting, overweight, obesity, and abdominal obesity among adult women in house‎holds were recorded at 20.1%, 20.4%, 12.3%, and 35.5%, respectively. Notably, higher incidences ‎of wasting and overweight were observed in FI households. In these households, FI was associated with a 70% increase in the likelihood of women experiencing wasting (OR = 1.7; 95% CI: 0.98–2.91) in ‎the unadjusted model; however, this association lost statistical significance after adjusting for ‎confounding factors (Table 5 ). FI also showed mixed effects on overweight and obesity: it increased the odds of being overweight by 50% in the unadjusted model (OR = 1.5; 95% CI: ‎0.82–2.75), while decreasing the likelihood of obesity. However, these associations did not reach ‎statistical significance in either model. Interestingly, the likelihood of abdominal obesity was significantly lower among women in FI households, with a consistent reduction observed in both the unadjusted (45%) and adjusted (53%) models (Table 5 ). Table 5 Association between household‎ food insecurity and nutrition stage in rural Zahedan, Iran BMI 1 of household‎’s adult women 2 Unadjusted Adjusted 3 Predictors Wasting Overweight Obesity Wasting Overweight Obesity OR (CI95%) OR (CI95%) OR (CI95%) OR (CI95%) OR (CI95%) Household food security ‎ Food secure 1 1 1 1 1 1 Food insecure 1.690 (0.925–3.089) 1.582 (0.868–2.882) 0.830 (0.410–1.677) 1.515 (0.767–2.993) 1.577 (0.786–3.165) 0.665 (0.290–1.526) 1 In dependent variable, Body Mass Index, the normal weight category is reference group. 2 Using multinominal logistic regression, 3 Adjusted for family size, household‎’s income and welfare index. *p < 0.05, **p < 0.01, ***p < 0.001. Table 5 (continued) Abdominal obesity 1 of household's adult women 2 Predictors Unadjusted Adjusted 3 OR (CI95%) OR (CI95%) Household‎ food security Food secure 1 1 Food insecure 0.546 (0.322–0.925)* 0.461 (0.243–0.878)* 1 Normal waist circumference is reference. *p < 0.05, **p < 0.01, ***p < 0.001. 2 Using linear regression. 3 Adjusted for sex, ageand education and marital status of household‎’s head, residence status, household‎’s income. Discussion This study highlights a concerningly high prevalence of FI among rural households in Zahedan, with over half experiencing moderate to severe levels of FI. Our findings reveal that FI is significantly associated with several interrelated factors: lower household income, larger family size, and lower education levels among adult women. Moreover, food-insecure households tend to exhibit limited agricultural diversity, reduced availability of home-produced protein sources, inadequate access to city markets, and lower dietary nutrient adequacy. Notably, households in villages that export a substantial share of their agricultural products are more likely to experience FI, suggesting that high external outflows of local food may undermine household-level food security. These results underscore the complex and interconnected social, economic, and agricultural dimensions of food insecurity in this region. Key socioeconomic factors such as lower income, larger family size, and unemployment of the household head were the main contributors associated with household FI. Similarly, other studies conducted in Iran have reported comparable findings. For instance, a study in northern Iran found that approximately half of the rural households experienced varying degrees of FI, strongly influenced by socioeconomic status and depressive conditions of the household head (Shakiba et al., 2021 ). This comparison highlights the pervasive nature of FI across different rural regions in Iran, driven by similar socioeconomic challenges. The high prevalence of FI in the studied community is particularly alarming given the predominant reliance on government subsidies as the primary income source for purchasing food. Despite the national unconditional cash transfers (UCTs) program targeted at the lower income brackets, the persistent high rates of FI suggest that these measures are insufficient by themselves to mitigate the risks associated with FI. Several studies have shown that even with the implementation of UCTs programs, high levels of FI can persist due to insufficient cash transfers, rising food prices, unemployment and underemployment, dependency on subsidies, health and socioeconomic burdens, limited education and skills, and regional disparities (Houngbe et al., 2017 ). In addition to the socioeconomic factors, it is crucial to consider how variables within the rural FNS affects household FI. The significant association between the diversity of agricultural products and nutritional yield with decreased likelihood of household FI supports the notion that agricultural diversification can enhance FS. This aligns with the concept of nutrition-sensitive agriculture, which focuses on improving the nutritional quality of food systems and ensuring that agricultural practices contribute to better health outcomes (Sibhatu et al., 2015 ). Moreover, the low percentage of protein supply and share of energy from non-staple foods suggests that mere presence of agricultural production is insufficient. Rather, the quality, utilization, and integration of these products into a balanced diet are crucial. Increasing agricultural output must be coupled with efforts to raise nutritional awareness and improve food utilization practices among rural households (Thompson & Amoroso, 2014 ). The study findings reveal an intriguing paradox: FI was higher in households with greater engagement in homestead activities such as gardening and livestock or poultry raising. This may reflect deeper socioeconomic and structural issues, where reliance on subsistence farming indicates limited access to profitable markets or the inability to produce beyond immediate family needs. Similar observations were noted in other studies where subsistence farmers often face greater FI due to the low economic returns of their labor-intensive activities (Maxwell & Slater, 2003 ). The significant impact of agricultural distribution on household FI observed in Zahedan's villages resonates with findings from similar contexts globally. Studies in sub-Saharan Africa have show that while exporting agricultural products can drive economic growth, it often does so at the expense of local food availability (Bjornlund et al., 2022 ). Therefore, regions engaging heavily in exports may inadvertently undermine their own FS (Bjornlund et al., 2022 ). Another research demonstrated that regions exporting substantial portions of their agricultural yield faced increased local food prices and heightened FI among low-income households due to reduced local food supply (Naylor & Falcon, 2010 ). In the FI households, the trend of less purchasing from city markets mirrors findings from Latin America. It was revealed that logistical and financial barriers to accessing city markets, where food diversity and quality are typically higher, disproportionately affect poorer households, thereby exacerbating their FI (Fenton, 2013 ). At the consumption stage, the association between the MAR and FI observed in Zahedan aligns with global trends showing that dietary quality significantly impacts FI status and vice versa. Actually, improving dietary diversity, which is closely related to MAR, significantly mitigates the risk of FI, particularly in lower-income settings (WHO, 2022 ). The findings of the present study indicate that the consumption stage is plagued by imbalances and exhibits fluctuations in both quantity and quality. These imbalances refer to the inconsistent availability and intake of food, where households may experience periods of both surplus and deficit. Fluctuations in quantity can result from various factors such as seasonal variations in agricultural production, economic instability, or disruptions in supply chains (Fanzo, 2023 ). Consequently, households may rely heavily on staple foods with lower nutritional value during times of scarcity, leading to potential deficiencies in essential vitamins and minerals. Addressing these issues requires a multifaceted approach, including supporting agricultural diversification to ensure a stable and balanced food supply enhancing market access and affordability, educating households on nutritional practices, and improving food storage and preservation methods (Fanzo, 2023 ). The consequences of an imbalance between FNS stages and the socioeconomic status of households manifest notably in nutrition and health outcomes. For instance, a dual spectrum of malnutrition was observed within FI households, characterized by both wasting and overweight among adult women. However, However, while rates of obesity and abdominal obesity tend to be lower in adult women from these households, a mild relationship exists between FI and weight abnormalities. Findings from a study on household food consumption patterns and nutritional status underscored that FI is associated with overweight in Iranian women living in FI households (Mohammadi et al., 2008 ). This association is complex and contingent upon the country's stage of nutrition transition. FI can impact body weight through various mechanisms, leading to either weight gain or loss depending on coping strategies. Mild and severe FI can, in turn, promote weight gain and weight loss respectively, often by prompting inappropriate eating patterns (Mohammadi et al., 2013 ). The primary strength of the present study lies in its comprehensive FNS approach to elucidate the principal causes contributing to FI within rural households. Furthermore, it represents a pioneering effort in Iran by investigating the relationship between household FI and the anthropometric status of women specifically in rural regions. However, certain limitations warrant consideration. First, data necessary for computing the spatial and economic dimensions of food purchasing were collected only during two non-consecutive 24-hour dietary recalls, rather than annually. Additionally, the cross-sectional design precludes establishing causal relationships. Future investigations would greatly benefit from longitudinal designs to explore the interplay between FNS stages, particularly homestead production, and household FI. Conclusions This study sheds light on the multifaceted and deeply rooted nature of food insecurity among rural households in Zahedan, emphasizing how socioeconomic disadvantages and disruptions across FNS stages contribute to the problem. Despite national assistance programs, persistent food insecurity reflects the insufficiency of income-based interventions alone. Agricultural diversity, market access, dietary quality, and intra-household dynamics—especially women’s education and employment—emerge as critical determinants of food security. The paradox of higher food insecurity among households engaged in subsistence farming underscores the limitations of self-reliance strategies without structural and market support. Furthermore, the dual burden of malnutrition, including undernutrition and overweight among adult women, highlights the complex health implications of FI in transitional nutrition environments. To effectively address rural food insecurity, policies must extend beyond short-term financial aid and embrace integrated approaches targeting agricultural sustainability, dietary quality, and social equity. This includes promoting nutrition-sensitive agriculture, improving rural market infrastructure, strengthening women’s empowerment, and enhancing community-level nutritional awareness. Given the limitations of the cross-sectional design, future longitudinal research is needed to capture the dynamic interactions between socioeconomic status, agricultural practices, and nutritional outcomes over time. Such evidence will be crucial for shaping more responsive, equitable, and sustainable food security strategies in vulnerable rural settings. Abbreviations AME Adult Male Equivalent BMI Body Mass Index CI Confidence Intervals‎ DRI Dietary Reference Intake FAO Food and Agriculture Organization FI Food Insecurity FNS Food and Nutrition System FS Food Security HFIAS Household Food Insecurity Access Scale MAR Mean Adequacy Ratio WHO World Health Organization OR Odds Ratios UCTs Unconditional Cash Transfers Declarations Ethics approval and consent to participate This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving research study participants were approved by the National Nutrition and Food Technolo‎gy Research Institute, Shahid Beheshti University of Medical ‎Sciences, Tehran, Iran (ID: ‎‎“IR.SBMU.NNFTRI.1398.032”)‎. Written informed consent was obtained from all participants. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding None. Authors' contributions MS: conceptualization, methodology, data curation, formal analysis, visualization, writing original draft, writing- review and editing. HEZ: conceptualization, methodology, formal analysis, writing- review and editing. SMT: conceptualization, methodology, writing- review and editing. NO: conceptualization, methodology, supervision, validation, writing- review and editing. All authors read and approved the final manuscript. Acknowledgements The authors would like to thank all participants for their time and cooperation. References Alioma, R., Zeller, M., & Ling, Y. K. (2022). Analysis of long-term prices of micronutrient-dense and starchy staple foods in developing countries. 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Rome: Food Security Information Network Measuring Food and Nutrition Security Technical Working Group , 177 . http://www.fsincop.net/topics/fns-measurement M’Kaibi, F. K., Steyn, N. P., Ochola, S., & Du Plessis, L. (2015). Effects of agricultural biodiversity and seasonal rain on dietary adequacy and household food security in rural areas of Kenya. Bmc Public Health , 15 (1), 422. https://doi.org/https://doi.org/10.3945/an.112.003343 Maxwell, S., & Slater, R. (2003). Food policy old and new. Development policy review , 21 (5-6), 531–553. https://doi.org/https://doi.org/10.1111/j.1467-8659.2003.00222.x Melesse, M. B., van den Berg, M., Béné, C., de Brauw, A., & Brouwer, I. D. (2020). Metrics to analyze and improve diets through food Systems in low and Middle Income Countries. Food Security , 12 (5), 1085–1105. https://doi.org/https://doi.org/10.1007/s12571-020-01091-2 Mohammadi, F., Omidvar, N., Harrison, G. G., Ghazi-Tabatabaei, M., Abdollahi, M., Houshiar-Rad, A., & Dorosty, A. R. (2013). Is household food insecurity associated with overweight/obesity in women? Iranian journal of public health , 42 (4), 380. Mohammadi, F., Omidvar, N., Houshiar-Rad, A., Khoshfetrat, M. R., Abdollahi, M., & Mehrabi, Y. (2012). Validity of an adapted Household Food Insecurity Access Scale in urban households in Iran. Public health nutrition , 15 (1), 149–157. https://doi.org/10.1017/S1368980011001376 Mohammadi, F., Omidvar, N., Rad, H., Mehrabi, A., Y., & Abdollahi, M. (2008). Association of food security and body weight status of adult members of Iranian households. Iranian journal of nutrition sciences & food technology , 3 (2), 41–53. http://nsft.sbmu.ac.ir/article-1-80-en.html Naylor, R. L., & Falcon, W. P. (2010). Food security in an era of economic volatility. Population and development review , 36 (4), 693–723. https://doi.org/https://doi.org/10.1111/j.1728-4457.2010.00354.x Rossi, J., Woods, T. A., & Davis, A. F. (2018). The Local Food System Vitality Index: A pilot analysis to demonstrate a process for measuring system performance and development. Journal of Agriculture Food Systems and Community Development , 8 (3), 137–158. https://doi.org/https://doi.org/10.5304/jafscd.2018.083.014 Shakiba, M., Salari, A., & Mahdavi-Roshan, M. (2021). Food insecurity status and associated factors among rural households in the north of Iran. Nutrition and Health , 27 (3), 301–307. https://doi.org/https://doi.org/10.1177/0260106021996840 Sibhatu, K. T., Krishna, V. V., & Qaim, M. (2015). Production diversity and dietary diversity in smallholder farm households. Proceedings of the National Academy of Sciences , 112 (34), 10657–10662. https://doi.org/https://doi.org/10.1073/pnas.1510982112 Sobal, J., Khan, L. K., & Bisogni, C. (1998). A conceptual model of the food and nutrition system. Social science & medicine , 47 (7), 853–863. https://doi.org/https://doi.org/10.1016/S0277-9536(98)00104-X StatisticalCenter (2015). Questionnaire of cost and income statistics of urban and rural households. https://www.amar.org.ir/Portals/0/info-unit/Files/94.pdf . Accessed 13 May 2021‎. Thompson, B., & Amoroso, L. (2014). Improving diets and nutrition: food-based approaches . CABI. Townsend, M. S., Peerson, J., Love, B., Achterberg, C., & Murphy, S. P. (2001). Food insecurity is positively related to overweight in women. The Journal of nutrition , 131 (6), 1738–1745. https://doi.org/https://doi.org/10.1093/jn/131.6.1738 USDA (2021). Food Data Central. https://fdc.nal.usda.gov/ Van Berkum, S., Dengerink, J., & Ruben, R. (2018). The food systems approach: sustainable solutions for a sufficient supply of healthy food . Weisell, R., & Dop, M. C. (2012). The adult male equivalent concept and its application to Household Consumption and Expenditures Surveys (HCES). Food and nutrition bulletin , 33 (3_suppl2). https://doi.org/https://doi.org/10.1177%2F15648265120333S203. 157-S162. WHO (2020). The state of food security and nutrition in the world 2020: transforming food systems for affordable healthy diets (Vol. 2020). Food & Agriculture Org. WHO (2021). Body mass index. https://doi. org/https://www.euro.who.int/en/health-topics/disease-prevention/nutrition/a-healthy-lifestyle/body-mass-index-bmi WHO (2022). The state of food security and nutrition in the world 2022: Repurposing food and agricultural policies to make healthy diets more affordable (Vol. 2022). Food & Agriculture Org. WHO/UNU. (2007). Protein and amino acid requirements in human nutrition (Vol. 935). World Health Organization. Zare Abianeh, H., Sabziparvar, A., Marofi, S., Ghiyami, F., Mirmasoud, S. S., & Kazemi, A. (2015). Analyzing and monitoring the meteorological droughts in the region of Sistan and Balouchestan. Journal of Environmental Science and Technology , 17 (1), 49–61. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7292945","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":500109097,"identity":"b72ebe6c-6bd3-4353-9605-7e624f7781fb","order_by":0,"name":"Mahdieh Sheikhi","email":"","orcid":"","institution":"Zahedan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mahdieh","middleName":"","lastName":"Sheikhi","suffix":""},{"id":500109098,"identity":"2a5a2ede-523f-4183-a22a-1e12bf8d309e","order_by":1,"name":"Hassan Eini-Zinab","email":"","orcid":"","institution":"National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medi-cal Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hassan","middleName":"","lastName":"Eini-Zinab","suffix":""},{"id":500109099,"identity":"5034dc17-fdff-4154-b131-55fc1510c909","order_by":2,"name":"Seyed Mehdi Tabatabaei","email":"","orcid":"","institution":"Zahedan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Seyed","middleName":"Mehdi","lastName":"Tabatabaei","suffix":""},{"id":500109100,"identity":"ab8ff5cc-3a4c-45f9-b222-6fb23872a8dc","order_by":3,"name":"Nasrin Omidvar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYBACfgbGBgk4L4HBBkgyNh7Ap0WyAVVLGkhLA14tBkBZCST+YTCJX8u1w423eWruMZizH3/24eGO83Zr2w8Dbamxicap5XZiszXPsWIGy54c4xmJZ24nbzuTCNRyLC23AbeWNskZbAlAF+YwMyS23U42OwDUwthwmICWf0At558/Bmo5l2x2/iFhLRIf24BabiQYA7UcsDO7QcAWydmJzRYf+xJ4DG68AWlJTjC7AbQlAY9f+KXTH95I+JYgZ3A+/THjzzY7e7Pz6Q8ffKixwakFBnhgjESwygQCylGAPSmKR8EoGAWjYGQAAA+WZmhO/cSVAAAAAElFTkSuQmCC","orcid":"","institution":"National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medi-cal Sciences","correspondingAuthor":true,"prefix":"","firstName":"Nasrin","middleName":"","lastName":"Omidvar","suffix":""}],"badges":[],"createdAt":"2025-08-04 15:53:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7292945/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7292945/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s43014-026-00364-1","type":"published","date":"2026-04-14T15:59:35+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89366292,"identity":"e1342944-a446-4e4d-9bd4-a78d0bc9ad47","added_by":"auto","created_at":"2025-08-19 09:20:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":91561,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7292945/v1/26f7dca79f5998ba99407b9c.png"},{"id":107352322,"identity":"2cce1259-127c-4bcf-84fd-8c57c92d5f57","added_by":"auto","created_at":"2026-04-20 16:13:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":810232,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7292945/v1/464ecc0b-752d-434a-b8ef-c7c5a99274aa.pdf"},{"id":89367513,"identity":"2190efed-7f8d-4210-b3eb-d4797fca535e","added_by":"auto","created_at":"2025-08-19 09:28:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":541098,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7292945/v1/e6a6778e51a3273a19201f4f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Determinants of Household Food Insecurity in Rural Zahedan: A Food and Nutrition System Perspective","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFood insecurity (FI) remains a pressing global public health concern. As of 2022, an estimated 29.6% of the world\u0026rsquo;s population experienced moderate to severe FI, with rural populations disproportionately affected (33.3%) compared to their urban counterparts (26.0%) (FAO et al., 2023). This challenge has been exacerbated by the compounded effects of conflict, climate change, and the economic fallout of the COVID-19 pandemic, particularly in fragile economies (Dubowitz et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). FI is closely linked to poor diet quality, which contributes to various forms of malnutrition and elevates of chronic diseases (WHO, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Addressing this complex issue requires a deeper understanding of the underlying \u0026lrm;determinants of food insecurity within local food and nutrition systems (FNS)\u0026lrm; (Dave et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe FNS framework offers a comprehensive, systems-based lens for analyzing the multiple \u0026lrm;interconnected processes that influence food security including production, distribution, \u0026lrm;consumption, and nutrition (Bogard et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, policy efforts have often focused narrowly on individual components, such as \u0026lrm;agricultural yield or food prices, neglecting the dynamic interactions across the system (Burchi et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This fragmented approach has contributed to the widespread availability of calorie-dense, \u0026lrm;nutrient-poor diets, undermining nutritional outcomes despite improvements in food supply\u0026lrm; (Bogard et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). A holistic evaluation of the FNS is therefore critical to identify systemic gaps and inform \u0026lrm;more integrated and effective interventions\u0026lrm; (Van Berkum et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis systems-based approach is particularly vital in rural Zahedan, situated in Sistan and Baluchestan Province, one of Iran\u0026rsquo;s most food-insecure regions. More than 70% of the region's population lives in rural areas, and over half experience moderate to severe FI (Kolahdooz et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Melesse et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The region faces significant environmental constraints\u0026mdash;prolonged drought, chronic water scarcity, and extreme temperatures\u0026mdash;which further hinder agricultural productivity and food availability (Zare Abianeh et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Despite these challenges, research examining the systemic determinants of food insecurity through an integrated FNS lens in this region remains scarce. To address this gap, the present study adopts an adapted version of the FNS framework proposed by Bogard et al. (Bogard et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), which categorizes the food system into four interlinked stages: production, distribution, consumption, and nutrition. Each stage is assessed using relevant indicators. Production is evaluated based on nutritional yield, production diversity, protein supply, and the share of dietary energy from non-staple crops. Distribution is examined through the proportion of local agricultural output sold externally, the cost of a healthy diet, and sources of food purchases (e.g., local vs. urban markets). Consumption is analyzed by considering the household food budget share as an indicator of economic prioritization and dietary quality; and nutrition is measured using anthropometric indicators\u0026mdash;body mass index (BMI) and waist circumference\u0026mdash;of adult women, reflecting the long-term health implications of the food system (Fig.\u0026nbsp;1).\u003c/p\u003e\u003cp\u003eBy employing this integrated and context-specific framework, the study aims to identify key determinants of household food insecurity in rural Zahedan and provide actionable evidence for the design of coordinated interventions that strengthen all stages of the FNS, ultimately improving food and nutrition outcomes in vulnerable rural settings.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy setting and design\u003c/h2\u003e\u003cp\u003eThis cross-sectional study was conducted in Zahedan rural communities in Sistan and Baluchistan Province (Southeast of Iran), from April to July 2019. The sampling unit were the village, household, and/or individuals. Zahedan consists of three districts and randomly selected two villages from each district. According to Cochran's formula and considering the population of rural households, a sample size of 320 households was determined. The inclusion criteria for households comprised Iranian nationality, residency in the village for more than 6 months, and consent to participate in the study. This section presents the methods used to evaluate nutritional quality across the FNS subsystems (Agricultural production, distribution, consumption, and nutrition).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAgriculture production subsystem\u003c/h3\u003e\n\u003cp\u003eData on crop and horticulture, livestock, poultry, and aquaculture production numbers possessed over the past year were collected through interviews with experts in the agriculture sector and farmers. Based on the collected data, key indicators such as protein supply, production diversity, share of energy from non-staple foods, and nutritional yield were calculated.\u003c/p\u003e\u003cp\u003eProtein supply was estimated through a multi-step approach based solely on annual agricultural products, as there was no livestock, poultry, or aquaculture units in the studied villages. First, the percentage of agricultural products loss at different stages of the food system\u0026mdash;namely production, transportation and storage, distribution, processing, and consumption\u0026mdash;was calculated based on values reported by the Food and Agriculture Organization (FAO) for South and Southeast Asia (Gustafsson et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Second, the percentage of non-edible parts of foods and the conversion ratio from raw to cooked forms were calculated (Ghafarpor et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Third, after determining the per capita agricultural production (in grams) for each agricultural product, protein content was estimated using the Iranian food composition table (Esmaeili \u0026amp; Hoshshyarrad, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and the USDA Food Data Central (USDA, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Fourth, to calculate per capita agricultural production adjusted for energy needs, Adult Male Equivalent (AME) (Weisell \u0026amp; Dop, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) units for each household member were computed. AME, defined by FAO, reflects an individual's energy requirement relative to a standard adult male aged 18\u0026ndash;30 years with a moderate level of physical activity. Accordingly, the village population, categorized by age and gender was extracted from the Ministry of Health\u0026rsquo;s Integrated Health System (Jafari et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) to compute the total AME for each village over one year (Weisell \u0026amp; Dop, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Finally, protein supply was calculated by dividing the total protein derived from village-level agricultural production by the total protein requirement of the village's AME population, based on Dietary Reference Intakes (DRI) (FAO/WHO, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eProduction diversity was assessed by calculating a production diversity score, which was determined by counting the number of different food groups produced by the farms over the past year.\u003c/p\u003e\u003cp\u003eShare of energy from non-staples was calculated as the percentage of the energy content (kcal per capita) derived from the edible portion of non staple foods produced for human consumption on the farm (Ghafarpor et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). In this classification, cereals, roots, and tubers were considered staples, while all other food items were categorized as non-staples. A higher share of energy from non-staples is generally associated with better dietary quality (Alioma et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To compute this indicator, agricultural product losses and non-edible portions of foods were estimated using the same method applied for the protein supply indicator. The energy content of the edible foods was then calculated using the Iranian food composition table (Esmaeili \u0026amp; Hoshshyarrad, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Ultimately, this indicator reflects the percentage of total available food energy (kilocalories) sourced from non-staple foods (Gustafson et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e Nutritional yield is a relatively new concept that goes beyond just the weight or volume of food produced. It considers not only the quantity of a crop but also its quality in terms of nutrients (Bogard et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It is defined as the number of adults, on average both male and female (not pregnant or lactating) between 19 and 50 years old, who would be able to obtain 100% of the dietary reference intakes (DRI) of energy from one hectare of food produced annually (Bogard et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The calorie content of agricultural products in rural areas was calculated based on the Iranian food composition table (Esmaeili \u0026amp; Hoshshyarrad, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Following the recommendation of the FAO and the World Health Organization (WHO), an energy requirement of 2800 kilocalories per day was assumed for an adult male with moderate physical activity. Therefore, the nutritional yield of all village-produced items was separately calculated and then aggregated. To depict the extent to which agricultural production in each village meets the needs of its resident population, the village's population was disaggregated by age and gender from the SIB system, and converted into AME. Subsequently, after determining the AMEs for each village, the nutritional yield of production was divided by the village's AMEs for adult males. This facilitated the calculation of the percentage of calorie provision from agricultural production for each village.\u003c/p\u003e\u003cp\u003eHomestead production was estimated using a 12-item questionnaire administered through interviews with mothers. The questionnaire collected detailed information on the percentage of households with home gardens, including the types and quantities of products produced annually. It also provided data relevant to key indicators such as protein supply and production diversity. The questionnaire was developed by a multidisciplinary team of researchers, nutritionists, and agricultural experts and validated through pilot testing with a representative sample to ensure clarity, relevance, and accuracy.\u003c/p\u003e\u003cp\u003eThe protein supply at the household level was estimated using homestead food production data, which were first converted to grams. Deductions were then made for the percentage of food loss at the consumption stage (Gustafsson et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and the proportion of non-edible parts. Subsequently, the raw-to-cooked conversion coefficient was applied (Ghafarpor et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), and for each food product, protein content was calculated using the Iranian food composition table (Esmaeili \u0026amp; Hoshshyarrad, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and the USDA food data (USDA, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The household's AME was estimated based on the number of household members present over the year, categorized by age and sex. Protein requirement were calculated for the household's AME using the DRI for adult males. Finally, the total protein content of homestead produced foods was divided by the protein requirement of the household's AME to obtain the household level protein supply.\u003c/p\u003e\u003cp\u003eThe production diversity of each household was measured by calculating a diversity score, which was based on the number of different types of foods produced by the household over the past year. Each unique food item contributed one unit to the score, regardless of quantity (Bogard et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eDistribution subsystem\u003c/h3\u003e\n\u003cp\u003eThis subsystem was assessed using three key indicators: local agriculture distribution, cost of a healthy diet, and source of food purchasing. These indicators collectively evaluate the availability, accessibility, and affordability of food within the local food system.\u003c/p\u003e\u003cp\u003eLocal agriculture distribution was evaluated through an index designed to quantify the efficiency of food flow \u0026lrm;within a defined geographic area (Rossi et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sobal et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). This index considered the proportion of agricultural products reaching local village markets versus external ones, \u0026lrm;based on interviews with experts from the Agriculture Jihad Organization and local farmer.\u003c/p\u003e\u003cp\u003eCost of a healthy diet was estimated using a method previously validated in Iran. In our study, we estimated the cost of a healthy, low-cost, and environmentally sustainable diet using a method previously employed in Iran (Eini-Zinab et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which employed linear programming to design a diet that met four simultaneous goals: maximizing the nutrient-rich food index, minimizing cost, reducing water footprint, and lowering carbon footprint. Goal Programming techniques were used to balance these objectives, and data from 24-hour diet recalls (collected on two non-consecutive days) were used to compare current dietary costs with the modeled optimal diet.\u003c/p\u003e\u003cp\u003eSource of food purchasing was determined through the same dietary recall data. For each food item consumed, the source of purchase was recorded. The number and percentage of food items obtained from each source were then calculated, providing insights into household dependence on different food supply channels.\u003c/p\u003e\n\u003ch3\u003eConsumption subsystem\u003c/h3\u003e\n\u003cp\u003eThe consumption subsystem was evaluated using four complementary indicators: household characteristics, household food insecurity access scale (HFIAS), mean adequacy ratio (MAR), and food budget share. Together, these indicators provide a comprehensive picture of household dietary quality, nutritional adequacy, food access, and economic capacity.\u003c/p\u003e\u003cp\u003eHousehold\u0026lrm; characteristics, including demographic and socioeconomic profiles, were collected using a validated questionnaire from the Statistical Center of Iran (StatisticalCenter, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Variables such as age, gender, education, employment, income, household size, asset ownership, and participant in subsidy plans were recorded. A welfare index was constructed by assigning weighted scores (0 to 10) to household assets based on price and necessity, and households were categorized into tertiles of low, medium, and high welfare using visual binning.\u003c/p\u003e\u003cp\u003eHousehold FI was measured using the HFIAS, a validated tool in the Iranian context (Mohammadi et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This scale includes nine questions addressing food access over the past four weeks, with responses categorized into frequency levels (rarely, sometimes, or often). Based on total scores, households were classified into food secure, mildly, moderately, or severely FI groups (Coates et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMAR was calculated based on dietary intake data from two non-consecutive 24-hour recalls collected by trained community health workers. All consumed foods were weighted, and nutrient composition was analyzed using Iranian and USDA food composition tables. Energy and nutrient intake adequacy was assessed by calculating Nutrient Adequacy Ratios (NARs) for energy, protein, and 10 micronutrients (e.g., iron, zinc, calcium, vitamins A, B, C, and folate), and the MAR was derived by averaging the NARs (M\u0026rsquo;Kaibi et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). AME units were used to adjust for household composition and energy needs based on age and sex (FAO/WHO, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; WHO/UNU, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFood budget share was determined by estimating the proportion of daily household income spent on food. After recording the monetary value of food consumed (from dietary recalls), food expenditure was divided by total daily income, calculated from detailed reports of income sources and amounts gathered through the household socioeconomic questionnaire (Lele et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This indicator reflects the economic pressure of food costs on households and provides insights into food affordability.\u003c/p\u003e\n\u003ch3\u003eNutrition subsystem\u003c/h3\u003e\n\u003cp\u003eBody weight and height of adult women in each household were measured using a digital scale (Seca, Germany) and a non-elastic measuring tape. Body Mass Index (BMI) was calculated as weight (kg) divided by height squared (m\u0026sup2;). Weight status was classified into the following categories: underweight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5), normal weight (18.5\u0026ndash;24.9), overweight (25\u0026ndash;29.9), and obese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30) (WHO, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWaist circumference of adult women (young and middle-aged groups) was also measured to the nearest millimeter using a non-elastic measuring tape, positioned midway between the lowest rib and the iliac crest. Abdominal obesity was defined as a waist circumference greater than 80 cm (Brown, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eSocioeconomic characteristics were described using means and standard deviations for continuous variables, or percentages for categorical variables. The normality of quantitative variables was assessed using the Kolmogorov-Smirnov test. As none of the continuous variables followed a normal distribution, non-parametric Mann-Whitney U tests were employed to compare differences between food-secure and food-insecure households. Chi-square tests were used to compare categorical variables.\u003c/p\u003e\u003cp\u003eTo examine the associations between FNS indicators and household FI, logistic regression models were applied. For unadjusted models, multilevel logistic regression was used for village-level indicators, and binary logistic regression for household-level indicators (implemented in R software). Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated to quantify associations. The adjusted models controlled for potential confounders including family size, sex, education level, employment status of the household head, income, and welfare index.\u003c/p\u003e\u003cp\u003eFor the analysis of nutritional indicators, household FI was treated as th independent variable, given its established relationship with women's anthropometric outcomes (Townsend et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). In the unadjusted models, multinomial logistic regression was used to assess the association between BMI categories and household FI, and linear regression was used for abdominal obesity. All adjusted models included the same set of confounders.\u003c/p\u003e\u003cp\u003eAll data analysed using SPSS version 16.0 (SPSS Inc., Chicago, IL, USA) and R software. A p-value of \u0026le;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eHousehold\u0026lrm;\u0026rsquo;s food security status\u003c/h2\u003e\u003cp\u003eA total of 321 households from six villages in the Zahedan district participated in the study. A high prevalence of moderate to severe food insecurity (53.3%) was observed among the households. Significant sociodemographic differences were identified between food-secure and food-insecure households. Food-insecure households had larger family sizes and lower incomes. Food-secure households reported higher incomes\u0026mdash;both with and without cash aid\u0026mdash;and were more likely to receive income from paid employment, whereas food-insecure households relied more heavily on national subsidies. The welfare index was higher among food-secure households, and employment rates of household heads, as well as education levels among adult women, were also higher in these households (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Notably, no significant differences were found in gender, marital status, or education level of the household head, nor in household housing status, between the two groups.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of household characteristics by food security status in Zahedan rural areas\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"3\" nameend=\"c2\" namest=\"c1\" rowspan=\"4\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003eHousehold\u0026lrm;\u0026rsquo;s food security status (n\u0026thinsp;=\u0026thinsp;321)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFS\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFI\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e150 (46.7%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e171 (53.3%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean (SE)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean (SE)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFamily size (persons)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.28 (0.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.98 (0.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAge of household\u0026lrm;\u0026rsquo;s head (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43.72 (1.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.45 (0.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.235\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAge of adult women (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.12 (1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.26 (1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.139\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHousehold\u0026lrm;\u0026rsquo;s income (rials\u003csup\u003e4\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13361259.0\u003c/p\u003e\u003cp\u003e(994321.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7644593.8 (565466.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u0026lrm;\u0026lt;0.001\u0026lrm;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHousehold\u0026lrm;\u0026rsquo;s income without cash aid (rial\u003csup\u003e4\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10957500.0\u003c/p\u003e\u003cp\u003e(982080.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5008812.5 (540812.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u0026lrm;\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eSource of household income (percentage)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePaid job\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57.97 (3.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.10 (3.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u0026lrm;\u0026lt;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNational subsidy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.79 (2.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50.53 (2.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lrm;\u0026lt;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eImam Khomeini Relief Committee subsidy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.13 (1.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.71 (1.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003csup\u003e\u003cb\u003e5\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eHousehold\u0026lrm;\u0026rsquo;s Welfare index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42 (40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63 (60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41 (39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64 (61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66 (60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44 (40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eType of home occupancy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOwnership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e117 (47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e132 (53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.775\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRent/other\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32 (45.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39 (54.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHousehold\u0026lrm;\u0026rsquo;s under an additional subsidy plan (excluding the national subsidy )\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32 (42.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43 (57.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.421\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e118 (48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e128 (52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHousehold\u0026lrm;\u0026rsquo;s head characteristics\u003c/p\u003e\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\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e128 (48.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e136 (51.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (38.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35 (61.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eEducation level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIlliterate and primary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94 (43.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e121 (56.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.124\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh school and higher education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56 (52.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50 (47.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eEmployment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEmployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e104 (55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e85 (45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnemployed/ housewife\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46 (35.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84 (64.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e123 (47.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e135 (52.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.492\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther (Single / Divorced / Widowed)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (42.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36 (57.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHousehold\u0026lrm;\u0026rsquo;s \u0026lrm;adult women characteristics\u003c/p\u003e\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\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIlliterate and primary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e112 (42.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e149 (57.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.009\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\u003eHigh school and higher education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31 (63.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 (36.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\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\u003eEmployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (57.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (42.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.264\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnemployed/ housewife\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e123 (44.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e153 (55.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u0026sup1; Food Secure, \u0026sup2;Food Insecure, \u003csup\u003e3\u003c/sup\u003e P-value for Mann\u0026ndash;Whitney U tests\u003csup\u003e, 4\u003c/sup\u003eRial is the currency of Iran, \u003csup\u003e5\u003c/sup\u003eP-value for Chi-squared tests.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eAgriculture production subsystem\u003c/h2\u003e\u003cp\u003eOverall, 12 agricultural products were cultivated in the villages, including crops (wheat), fodder (alfalfa, barley, maize), vegetables (tomatoes, cucumbers, eggplants, onions), and fruits (pomegranates, pistachios, grapes, berries). Local production provided, on average, 31% of the rural population\u0026rsquo;s annual dietary protein requirements. Notably, greater diversity in agricultural products and higher nutritional yield were associated with a decreased likelihood of household food insecurity (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This association was statistically significant in both unadjusted and adjusted models, with a stronger effect observed in the adjusted model.\u003c/p\u003e\u003cp\u003eHouseholds experiencing food insecurity (FI) exhibited higher rates of homestead activities compared to food-secure (FS) households. Notably, 27% of the households practiced home gardening, 42.9% raised livestock or poultry, and 16.4% engaged in both. In contrast, 27.8% of all households reported no agricultural production, and 54.6% obtained less than 5% of their dietary protein from this source. A low protein supply (\u0026lt;\u0026thinsp;5%) significantly increased the odds of FI (unadjusted OR\u0026thinsp;=\u0026thinsp;3.19, adjusted OR\u0026thinsp;=\u0026thinsp;2.82) compared to households with no production (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Interestingly, homestead diversity\u0026mdash;defined as engagement in both gardening and livestock/poultry activities\u0026mdash;was associated with a twofold increase in the likelihood of experiencing FI (OR\u0026thinsp;=\u0026thinsp;2.00, 95% CI: 1.05\u0026ndash;3.79) in both models (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between agriculture production sub\u0026lrm;system with household food insecurity status in rural Zahedan, Iran\u003c/p\u003e\u003c/div\u003e\u003c/caption\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\" colspan=\"2\" morerows=\"2\" nameend=\"c2\" namest=\"c1\" rowspan=\"3\"\u003e\u003cp\u003eLevel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" morerows=\"2\" nameend=\"c6\" namest=\"c3\" rowspan=\"3\"\u003e\u003cp\u003ePredictors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e\u003cp\u003eHousehold\u0026lrm; food insecurity\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eUnadjusted\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003eAdjusted\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCI 95%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eCI 95%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003eVillage\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c3\" namest=\"c2\" rowspan=\"3\"\u003e\u003cp\u003eProtein Supply (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eLess than 10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u0026ndash;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.11\u0026ndash;1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.05\u0026ndash;1.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMore than 50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.37\u0026ndash;4.23\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\u003e0.26\u0026ndash;8.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eProduction diversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0\u0026ndash;10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e0.96\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.94\u0026ndash;0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.95\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.92\u0026ndash;0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eShare of energy from non-staples\u0026lrm;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0-100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.99\u0026ndash;1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.98\u0026ndash;1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c3\" namest=\"c2\" rowspan=\"3\"\u003e\u003cp\u003eNutritional yield (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLess than 10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u0026ndash;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e0.13\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.04\u0026ndash;0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.07\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.01\u0026ndash;0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMore than 50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e0.28\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.08\u0026ndash;0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.17\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.03\u0026ndash;0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eHousehold\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c3\" namest=\"c2\" rowspan=\"2\"\u003e\u003cp\u003eProduction diversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eNone or One product\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u0026ndash;10 Diversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e2.02\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.22\u0026ndash;3.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.00\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.05\u0026ndash;3.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c3\" namest=\"c2\" rowspan=\"3\"\u003e\u003cp\u003eProtein Supply\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo production\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLess than 4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e3.19\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.87\u0026ndash;5.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026zwnj;2.82\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.50\u0026ndash;5.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMore than 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e1.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.78\u0026ndash;3.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.96\u0026ndash;5.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003e\u0026sup1;Food Secure is reference. \u003csup\u003e2\u003c/sup\u003eAdjusted for family size, sex, education, and employment of household\u0026lrm;\u0026rsquo;s head, household\u0026lrm;\u0026rsquo;s income and welfare index. \u003csup\u003e3\u003c/sup\u003eUsing multilevel regression. \u003csup\u003e4\u003c/sup\u003eUsing binary logistic regression. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eDistribution subsystem\u003c/h2\u003e\u003cp\u003eApproximately half of the agricultural products from the studied villages were distributed outside \u0026lrm;the local markets. Villages with higher export rates (\u0026gt;\u0026thinsp;50% of produce) were significantly associated \u0026lrm;with household FI compared to those that did not export agricultural products. This association was statistically significant in both unadjusted and adjusted models, with the adjusted model showing an association strength nearly 7.9 times greater (95% CI: 2.27\u0026ndash;27.51) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHouseholds sourced approximately 58% of their food from the city market, compared to 23% \u0026lrm;from the village market. Notably, FI households purchased a lower proportion of their food items \u0026lrm;from the city market. While city market purchases were associated with a trend toward lower odds of FI, \u0026lrm;this association was weak and lost statistical significance after adjusting for confounders \u0026lrm;(Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The cost of a healthy diet showed no significant association with FI in either model.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between distribution stage with household\u0026lrm; food insecurity in rural Zahedan, Iran\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eLevel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c3\" namest=\"c2\" rowspan=\"3\"\u003e\u003cp\u003ePredictors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\u003cp\u003eHousehold\u0026lrm;\u0026rsquo;s food insecurity\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eUnadjusted\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eAdjusted\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCI 95%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCI 95%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eVillage\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eLocal agriculture distribution to outside rural (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo distribution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026ndash;50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.79\u0026ndash;2.45\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\u003e0.54\u0026ndash;5.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMore than 50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.68\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.02\u0026ndash;10.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.90\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.27\u0026ndash;27.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eHousehold\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eCost of a healthy diet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.97-1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.98\u0026ndash;1.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFood purchased source (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ecity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.99\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.98\u0026ndash;0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.98-1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVillage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.99\u0026ndash;1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.99\u0026ndash;1.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u0026sup1; Food Secure is reference. \u003csup\u003e2\u003c/sup\u003eAdjusted for family size, sex, education, and employment of household\u0026lrm;\u0026rsquo;s head, household\u0026lrm;\u0026rsquo;s income and welfare index. \u003csup\u003e3\u003c/sup\u003eUsing multilevel regression. \u003csup\u003e4\u003c/sup\u003eUsing binary logistic regression. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eConsumption subsystem\u003c/h2\u003e\u003cp\u003eHouseholds with FS exhibited higher MAR scores, indicating a significant inverse association be\u0026lrm;tween MAR and FI. However, in the adjusted model, this association remained statistically significant only for households in the second tertile of MAR compared to the reference group (OR\u0026thinsp;=\u0026thinsp;0.53; \u0026lrm;\u0026lrm;95% CI: 0.31\u0026ndash;0.92) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Food expenditure showed a positive association with FI in the un\u0026lrm;adjusted model, but this association became non-significant after adjusting for confounding factors.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between consumption stage and \u0026lrm;household\u0026lrm; food insecurity in rural Zahedan, Iran\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c2\" namest=\"c1\" rowspan=\"3\"\u003e\u003cp\u003ePredictors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003eHousehold\u0026lrm;\u0026rsquo;s food insecurity\u003csup\u003e1,2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eUnadjusted\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eAdjusted\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCI 95%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCI 95%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eMean adequacy ratio \u0026lrm;(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTertile1 (36-60.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTertile2 (60.6\u0026ndash;75.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.87 0.50\u0026ndash;1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.53\u003csup\u003e*\u003c/sup\u003e 0.31\u0026ndash;0.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTertile3 (75.6\u0026ndash;98.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.880.47\u0026ndash;1.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.72 0.37\u0026ndash;1.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u0026lrm;Food budget share\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTertile1 (5\u0026ndash;49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTertile2 (49.1\u0026ndash;81.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.85\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.49\u0026ndash;5.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.84\u0026ndash;4.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTertile3 (\u0026gt;\u0026thinsp;81.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.76\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.65\u0026ndash;8.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.79\u0026ndash;4.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u0026sup1; Food Secure is reference. \u003csup\u003e2\u003c/sup\u003e Using binary logistic regression. \u003csup\u003e3\u003c/sup\u003eAdjusted for family size, sex, education, and employment of household\u0026lrm;\u0026rsquo;s head, household\u0026lrm;\u0026rsquo;s income and welfare index. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eNutrition subsystem\u003c/h2\u003e\u003cp\u003e\u003cb\u003e\u0026lrm;\u003c/b\u003e The rates of wasting, overweight, obesity, and abdominal obesity among adult women in house\u0026lrm;holds were recorded at 20.1%, 20.4%, 12.3%, and 35.5%, respectively. Notably, higher incidences \u0026lrm;of wasting and overweight were observed in FI households. In these households, FI was associated with a 70% increase in the likelihood of women experiencing wasting (OR\u0026thinsp;=\u0026thinsp;1.7; 95% CI: 0.98\u0026ndash;2.91) in \u0026lrm;the unadjusted model; however, this association lost statistical significance after adjusting for \u0026lrm;confounding factors (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e5\u003c/span\u003e). FI also showed mixed effects on overweight and obesity: it increased the odds of being overweight by 50% in the unadjusted model (OR\u0026thinsp;=\u0026thinsp;1.5; 95% CI: \u0026lrm;0.82\u0026ndash;2.75), while decreasing the likelihood of obesity. However, these associations did not reach \u0026lrm;statistical significance in either model. Interestingly, the likelihood of abdominal obesity was significantly lower among women in FI households, with a consistent reduction observed in both the unadjusted (45%) and adjusted (53%) models (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between household\u0026lrm; food insecurity and nutrition stage in rural Zahedan, Iran\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\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\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c12\" namest=\"c7\"\u003e\u003cp\u003eBMI\u003csup\u003e1\u003c/sup\u003e of household\u0026lrm;\u0026rsquo;s adult women\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\u003cp\u003eUnadjusted\u003c/p\u003e\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\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003eAdjusted\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePredictors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eWasting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eOverweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eObesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eWasting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eOverweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eObesity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eOR (CI95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eOR (CI95%)\u003c/p\u003e\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\u003cp\u003eOR (CI95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eOR (CI95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eOR (CI95%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHousehold food security\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u0026lrm; Food secure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eFood insecure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.690 (0.925\u0026ndash;3.089)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1.582 (0.868\u0026ndash;2.882)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.830 (0.410\u0026ndash;1.677)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.515 (0.767\u0026ndash;2.993)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.577 (0.786\u0026ndash;3.165)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.665 (0.290\u0026ndash;1.526)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eIn dependent variable, Body Mass Index, the normal weight category is reference group. \u003csup\u003e2\u003c/sup\u003eUsing multinominal logistic regression, \u003csup\u003e3\u003c/sup\u003eAdjusted for family size, household\u0026lrm;\u0026rsquo;s income and welfare index. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e(continued)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eAbdominal obesity\u003csup\u003e1\u003c/sup\u003e of household's adult women\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003ePredictors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnadjusted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAdjusted\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOR (CI95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR (CI95%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHousehold\u0026lrm; food security\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFood secure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFood insecure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.546 (0.322\u0026ndash;0.925)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.461 (0.243\u0026ndash;0.878)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eNormal waist circumference is reference. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. \u003csup\u003e2\u003c/sup\u003e Using linear regression. \u003csup\u003e3\u003c/sup\u003eAdjusted for sex, ageand education and marital status of household\u0026lrm;\u0026rsquo;s head, residence status, household\u0026lrm;\u0026rsquo;s income.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study highlights a concerningly high prevalence of FI among rural households in Zahedan, with over half experiencing moderate to severe levels of FI. Our findings reveal that FI is significantly associated with several interrelated factors: lower household income, larger family size, and lower education levels among adult women. Moreover, food-insecure households tend to exhibit limited agricultural diversity, reduced availability of home-produced protein sources, inadequate access to city markets, and lower dietary nutrient adequacy. Notably, households in villages that export a substantial share of their agricultural products are more likely to experience FI, suggesting that high external outflows of local food may undermine household-level food security. These results underscore the complex and interconnected social, economic, and agricultural dimensions of food insecurity in this region.\u003c/p\u003e\u003cp\u003eKey socioeconomic factors such as lower income, larger family size, and unemployment of the household head were the main contributors associated with household FI. Similarly, other studies conducted in Iran have reported comparable findings. For instance, a study in northern Iran found that approximately half of the rural households experienced varying degrees of FI, strongly influenced by socioeconomic status and depressive conditions of the household head (Shakiba et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This comparison highlights the pervasive nature of FI across different rural regions in Iran, driven by similar socioeconomic challenges.\u003c/p\u003e\u003cp\u003eThe high prevalence of FI in the studied community is particularly alarming given the predominant reliance on government subsidies as the primary income source for purchasing food. Despite the national unconditional cash transfers (UCTs) program targeted at the lower income brackets, the persistent high rates of FI suggest that these measures are insufficient by themselves to mitigate the risks associated with FI. Several studies have shown that even with the implementation of UCTs programs, high levels of FI can persist due to insufficient cash transfers, rising food prices, unemployment and underemployment, dependency on subsidies, health and socioeconomic burdens, limited education and skills, and regional disparities (Houngbe et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn addition to the socioeconomic factors, it is crucial to consider how variables within the rural FNS affects household FI. The significant association between the diversity of agricultural products and nutritional yield with decreased likelihood of household FI supports the notion that agricultural diversification can enhance FS. This aligns with the concept of nutrition-sensitive agriculture, which focuses on improving the nutritional quality of food systems and ensuring that agricultural practices contribute to better health outcomes (Sibhatu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMoreover, the low percentage of protein supply and share of energy from non-staple foods suggests that mere presence of agricultural production is insufficient. Rather, the quality, utilization, and integration of these products into a balanced diet are crucial. Increasing agricultural output must be coupled with efforts to raise nutritional awareness and improve food utilization practices among rural households (Thompson \u0026amp; Amoroso, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe study findings reveal an intriguing paradox: FI was higher in households with greater engagement in homestead activities such as gardening and livestock or poultry raising. This may reflect deeper socioeconomic and structural issues, where reliance on subsistence farming indicates limited access to profitable markets or the inability to produce beyond immediate family needs. Similar observations were noted in other studies where subsistence farmers often face greater FI due to the low economic returns of their labor-intensive activities (Maxwell \u0026amp; Slater, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe significant impact of agricultural distribution on household FI observed in Zahedan's villages resonates with findings from similar contexts globally. Studies in sub-Saharan Africa have show that while exporting agricultural products can drive economic growth, it often does so at the expense of local food availability (Bjornlund et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, regions engaging heavily in exports may inadvertently undermine their own FS (Bjornlund et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Another research demonstrated that regions exporting substantial portions of their agricultural yield faced increased local food prices and heightened FI among low-income households due to reduced local food supply (Naylor \u0026amp; Falcon, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the FI households, the trend of less purchasing from city markets mirrors findings from Latin America. It was revealed that logistical and financial barriers to accessing city markets, where food diversity and quality are typically higher, disproportionately affect poorer households, thereby exacerbating their FI (Fenton, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAt the consumption stage, the association between the MAR and FI observed in Zahedan aligns with global trends showing that dietary quality significantly impacts FI status and vice versa. Actually, improving dietary diversity, which is closely related to MAR, significantly mitigates the risk of FI, particularly in lower-income settings (WHO, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The findings of the present study indicate that the consumption stage is plagued by imbalances and exhibits fluctuations in both quantity and quality. These imbalances refer to the inconsistent availability and intake of food, where households may experience periods of both surplus and deficit. Fluctuations in quantity can result from various factors such as seasonal variations in agricultural production, economic instability, or disruptions in supply chains (Fanzo, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consequently, households may rely heavily on staple foods with lower nutritional value during times of scarcity, leading to potential deficiencies in essential vitamins and minerals.\u003c/p\u003e\u003cp\u003eAddressing these issues requires a multifaceted approach, including supporting agricultural diversification to ensure a stable and balanced food supply enhancing market access and affordability, educating households on nutritional practices, and improving food storage and preservation methods (Fanzo, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe consequences of an imbalance between FNS stages and the socioeconomic status of households manifest notably in nutrition and health outcomes. For instance, a dual spectrum of malnutrition was observed within FI households, characterized by both wasting and overweight among adult women. However, However, while rates of obesity and abdominal obesity tend to be lower in adult women from these households, a mild relationship exists between FI and weight abnormalities. Findings from a study on household food consumption patterns and nutritional status underscored that FI is associated with overweight in Iranian women living in FI households (Mohammadi et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This association is complex and contingent upon the country's stage of nutrition transition. FI can impact body weight through various mechanisms, leading to either weight gain or loss depending on coping strategies. Mild and severe FI can, in turn, promote weight gain and weight loss respectively, often by prompting inappropriate eating patterns (Mohammadi et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe primary strength of the present study lies in its comprehensive FNS approach to elucidate the principal causes contributing to FI within rural households. Furthermore, it represents a pioneering effort in Iran by investigating the relationship between household FI and the anthropometric status of women specifically in rural regions. However, certain limitations warrant consideration. First, data necessary for computing the spatial and economic dimensions of food purchasing were collected only during two non-consecutive 24-hour dietary recalls, rather than annually. Additionally, the cross-sectional design precludes establishing causal relationships. Future investigations would greatly benefit from longitudinal designs to explore the interplay between FNS stages, particularly homestead production, and household FI.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study sheds light on the multifaceted and deeply rooted nature of food insecurity among rural households in Zahedan, emphasizing how socioeconomic disadvantages and disruptions across FNS stages contribute to the problem. Despite national assistance programs, persistent food insecurity reflects the insufficiency of income-based interventions alone. Agricultural diversity, market access, dietary quality, and intra-household dynamics\u0026mdash;especially women\u0026rsquo;s education and employment\u0026mdash;emerge as critical determinants of food security. The paradox of higher food insecurity among households engaged in subsistence farming underscores the limitations of self-reliance strategies without structural and market support. Furthermore, the dual burden of malnutrition, including undernutrition and overweight among adult women, highlights the complex health implications of FI in transitional nutrition environments.\u003c/p\u003e\u003cp\u003eTo effectively address rural food insecurity, policies must extend beyond short-term financial aid and embrace integrated approaches targeting agricultural sustainability, dietary quality, and social equity. This includes promoting nutrition-sensitive agriculture, improving rural market infrastructure, strengthening women\u0026rsquo;s empowerment, and enhancing community-level nutritional awareness. Given the limitations of the cross-sectional design, future longitudinal research is needed to capture the dynamic interactions between socioeconomic status, agricultural practices, and nutritional outcomes over time. Such evidence will be crucial for shaping more responsive, equitable, and sustainable food security strategies in vulnerable rural settings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAME Adult Male Equivalent\u003c/p\u003e\u003cp\u003eBMI Body Mass Index\u003c/p\u003e\u003cp\u003eCI Confidence Intervals\u0026lrm;\u003c/p\u003e\u003cp\u003eDRI Dietary Reference Intake\u003c/p\u003e\u003cp\u003eFAO Food and Agriculture Organization\u003c/p\u003e\u003cp\u003eFI Food Insecurity\u003c/p\u003e\u003cp\u003eFNS Food and Nutrition System\u003c/p\u003e\u003cp\u003eFS Food Security\u003c/p\u003e\u003cp\u003eHFIAS Household Food Insecurity Access Scale\u003c/p\u003e\u003cp\u003eMAR Mean Adequacy Ratio\u003c/p\u003e\u003cp\u003eWHO World Health Organization\u003c/p\u003e\u003cp\u003eOR Odds Ratios\u003c/p\u003e\u003cp\u003eUCTs Unconditional Cash Transfers\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving research study participants were approved by the National Nutrition and Food Technolo\u0026lrm;gy Research Institute, Shahid Beheshti University of Medical \u0026lrm;Sciences, Tehran, Iran (ID: \u0026lrm;\u0026lrm;\u0026ldquo;IR.SBMU.NNFTRI.1398.032\u0026rdquo;)\u0026lrm;. Written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMS: conceptualization, methodology, data curation, formal analysis, visualization, writing original draft, writing- review and editing. HEZ: conceptualization, methodology, formal analysis, writing- review and editing. SMT: conceptualization, methodology, writing- review and editing. NO: conceptualization, methodology, supervision, validation, writing- review and editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all participants for their time and cooperation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlioma, R., Zeller, M., \u0026amp; Ling, Y. K. (2022). 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World Health Organization.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZare Abianeh, H., Sabziparvar, A., Marofi, S., Ghiyami, F., Mirmasoud, S. S., \u0026amp; Kazemi, A. (2015). Analyzing and monitoring the meteorological droughts in the region of Sistan and Balouchestan. \u003cem\u003eJournal of Environmental Science and Technology\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(1), 49\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Food and nutrition system, Food insecurity, Agricultural diversity, Nutritional adequacy","lastPublishedDoi":"10.21203/rs.3.rs-7292945/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7292945/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHousehold food insecurity (FI) in rural areas is influenced by multiple determinants within the food and nutrition system (FNS), encompassing production, distribution, consumption, and nutritional status. This cross-sectional study evaluated 321 randomly selected households across six rural villages in Zahedan, Iran, to identify key determinants of FI from a FNS perspective. FI affected 53.3% of households, which were characterized by larger family sizes, lower income and educational levels among adult women, and greater reliance on government subsidies. Villages with greater agricultural diversity and higher nutritional yield showed significantly reduced odds of FI, whereas villages exporting over half their produce experienced a 7.9-fold increased risk. Households with homestead food production yielding less than 5% protein faced significantly higher FI risk (OR\u0026thinsp;=\u0026thinsp;2.82; 95% CI: 1.50\u0026ndash;5.28). Lower nutrient adequacy scores were also strongly associated with FI. Among adult women in FI households, prevalence of both wasting and overweight increased, while abdominal obesity was less common (OR\u0026thinsp;=\u0026thinsp;0.461; 95% CI: 0.243\u0026ndash;0.878). These findings highlight critical determinants within the rural FNS contributing to household FI and related nutritional challenges, underscoring the need for integrated policies to address socioeconomic disparities and enhance agricultural and nutritional resilience.\u003c/p\u003e","manuscriptTitle":"Determinants of Household Food Insecurity in Rural Zahedan: A Food and Nutrition System Perspective","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-19 09:20:44","doi":"10.21203/rs.3.rs-7292945/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f55e4165-4405-494f-aa51-3cfa0a00550b","owner":[],"postedDate":"August 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T16:13:15+00:00","versionOfRecord":{"articleIdentity":"rs-7292945","link":"https://doi.org/10.1186/s43014-026-00364-1","journal":{"identity":"food-production-processing-and-nutrition","isVorOnly":false,"title":"Food Production, Processing and Nutrition"},"publishedOn":"2026-04-14 15:59:35","publishedOnDateReadable":"April 14th, 2026"},"versionCreatedAt":"2025-08-19 09:20:44","video":"","vorDoi":"10.1186/s43014-026-00364-1","vorDoiUrl":"https://doi.org/10.1186/s43014-026-00364-1","workflowStages":[]},"version":"v1","identity":"rs-7292945","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7292945","identity":"rs-7292945","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}