Urban Food Insecurity in Nigeria: A Multilevel Analysis of Nationally Representative Data

Urban Food Insecurity in Nigeria: A Multilevel Analysis of Nationally Representative Data | 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 Urban Food Insecurity in Nigeria: A Multilevel Analysis of Nationally Representative Data Kolawole D. Adeyemi, Biola Adeyemi, Temitope Ibiyemi, Oak-Hee Park, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8041739/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background With rapid urbanization, food insecurity has become an increasingly critical yet understudied issue in Sub-Saharan Africa’s urban population. This study addresses a significant research gap by examining the prevalence and determinants of food insecurity among urban households in Nigeria. Methodology: Food insecurity was assessed via the Food Insecurity Experience Scale (FIES), with item responses analyzed using the Rasch model to ensure valid measurement of the latent food insecurity construct. Multilevel logistic regression was then employed to identify key predictors of moderate or severe food insecurity. Results The results show that nearly 7 in 10 urban households experienced moderate or severe food insecurity. Higher risk for food insecurity was observed among households with heads aged 36–45, larger family size (> 5 members), lower education levels, and those living in the Southeast region. Protective factors for food insecurity included higher household wealth, homeownership, ownership of farmland or livestock, having children under the age of five, and living in the Northwest, Northeast, or Southwest regions. Conclusion The study provides robust empirical evidence underscoring the need for urban-focused, multisectoral policy responses. Interventions must extend beyond individual-level drivers to address structural inequities through regionally specific strategies. Scaling up social protection, advancing economic inclusion, and investing in urban agriculture are essential for ensuring equitable food security across Nigeria’s expanding urban landscape. Determinants Food Insecurity FIES Multilevel analysis Urban Food Security Urban Agriculture Urban Nigeria Urban Households Figures Figure 1 Figure 2 1.0 Background Migration from rural settings to urban areas in low- and middle-income countries is at an unprecedented rate, introducing complex challenges that disproportionately affect urban populations ( 1 – 3 ). According to recent United Nations reports, 55% of the world’s population currently resides in urban areas, with this proportion expected to rise to 68% by 2050 ( 1 ). Notably, 90% of this growth is projected to take place in cities within developing countries ( 4 ). This rapid urbanization has introduced new vulnerabilities, including increased pressure on infrastructure, housing, and social safety nets ( 5 – 8 ), all of which have exacerbated food insecurity. Once seen as a primarily rural issue, food insecurity has now emerged as a critical public health challenge in urban environments, particularly in Sub-Saharan Africa, where urbanization often outpaces the development of necessary services and systems ( 9 – 11 ). Urban food insecurity, characterized by limited or uncertain access to adequate and nutritious food, is becoming an increasingly common concern in many developing nations ( 9 , 12 – 14 ). In Nigeria, Africa's most populous nation, this urbanization trend is especially pronounced ( 1 , 15 ). The country’s urban population is projected to double from 113 million in 2020 to 215 million by 2050 ( 1 ), driven by population growth and rural-to-urban migration (Aliyu & Amadu, 2017; Auwalu & Bello, 2023; Tochukwu & Bilyaminu, 2024). However, this rapid expansion has brought significant challenges ( 19 ). Economic instability, including high inflation, unemployment, and underemployment, has left many urban households struggling to afford necessities, including food ( 18 , 20 ). Additionally, climate-related disruptions and insecurity, such as conflicts and armed banditry in agricultural regions, have further strained food supply chains, compounding the pressures on urban markets ( 10 , 21 ). While urban areas are often perceived as having better access to food due to the concentration of restaurants and food outlets, this availability does not ensure equitable access to nutritious, diverse, and affordable diets for all residents ( 22 ). Deep socioeconomic disparities, reliance on informal labor markets, high housing costs, and systemic inequalities have contributed to the proliferation of slums and informal settlements ( 13 , 23 ). Globally, urban slums are growing at an alarming rate, with the UN-Habitat estimating that over one billion people currently live in overcrowded urban slums, a figure expected to triple by 2050 ( 24 ). About 50% of this growth is concentrated in developing countries such as Nigeria, the Philippines, Ethiopia, Tanzania, India, the Democratic Republic of Congo, Egypt and Pakistan ( 24 ). These slums often lack basic amenities such as clean water, sanitation, and healthcare, creating environments where food insecurity is particularly prevalent. In Nigeria, approximately 55% of urban residents live in such conditions ( 25 , 26 ), underscoring the fragile living situations faced by this population. The current food insecurity situation is leading to significant consequences, including malnutrition and poor health outcomes at the individual level ( 27 , 28 ), increased financial stress and reduced educational attainment within households ( 29 ), and greater social instability, reduced economic productivity, and strained public resources at the community level ( 30 ). The broader global context further complicates these challenges. Recent crises, including economic shocks ( 31 ), the COVID-19 pandemic ( 32 ), the war in Ukraine ( 33 ), and escalating climate impacts, have disrupted food systems worldwide ( 34 , 35 ). These disruptions have stalled progress toward Sustainable Development Goal 2.1, which aims to end global hunger by 2030. According to the most recent State of Food Security and Nutrition in the World (SOFI) report, one in twelve people (approximately 673 million) faced hunger globally in 2024, while 2.3 billion experienced moderate or severe food insecurity ( 36 ). Africa bears a disproportionate share of this burden, with 20% of its population affected now over 307 million people ( 36 ), emphasizing the need for urgent and targeted interventions In Nigeria, food insecurity has reached concerning levels, with the number of food-insecure individuals projected to rise from 66.2 million (approximately 30% of the population) in 2023 to 100 million (around 45% of the population) by early 2024 ( 37 ). Historically, food insecurity in Nigeria has been framed as a rural issue, with limited attention paid to urban areas. However, urban food insecurity is a growing concern, driven by limited access to agricultural resources ( 38 ), high living costs, rapid population growth, rural-urban migration, and increase in urban slums ( 25 , 26 , 39 ). Nigeria’s urban landscape spans the country’s six geopolitical zones, North-Central, North-East, North-West, South-East, South-South, and South-West ( 40 ), each exhibiting distinct socio-political and economic conditions that mediate food access and vulnerability in complex ways ( 41 ). Yet, much of the existing literature remains geographically fragmented, with empirical focus concentrated on individual states ( 42 – 45 ) or specific region ( 23 ), rather than offering a comprehensive national analysis. This gap constrains our ability to understand the spatial heterogeneity of urban food insecurity and to design interventions that are appropriately tailored to the diverse urban realities across Nigeria. This study seeks to address this critical research gap. Thus, the aim of this study was to examine the prevalence and determinants of moderate or severe food insecurity among urban households in Nigeria using a nationally representative data. Through a socio-ecological framework, the research explores individual, household, and community-level factors contributing to food insecurity, shedding light on the structural and socioeconomic drivers at play. The findings from this study aims to inform policies and interventions tailored to the unique challenges of urban populations (and /or communities), with the goal of improving food security and resilience in Nigeria’s rapidly expanding cities. 1.2 Conceptual Framework 1.2.1 The Socio-ecological framework In this study, the Socio-Ecological Model (SEM) was adopted as the conceptual framework to examine the multi-level determinants of food insecurity in urban Nigeria. The SEM recognizes the dynamic interplay between individual, interpersonal, community, and societal-level factors, making it particularly suited to the complex and layered nature of food insecurity ( 46 , 47 ). The framework guided both the identification of explanatory variables and the multilevel analytical strategy, aligning well with the hierarchical structure of the dataset. This alignment between the framework and statistical models enhances the robustness of our findings. 2.0 Methodology 2.1 Ethical protocol This study utilized data from the 2021 Nigeria Multiple Indicator Cluster Survey (MICS6), whose protocol was approved by both the Steering and Technical Review Committees. These committees include representatives from the Nigeria’s National Bureau of Statistics (NBS), UNICEF, the Federal Ministry of Health, and other key stakeholders ( 48 ). According to the MICS6 documentation, verbal informed consent was obtained from all participants, with parental or guardian consent provided for minors ( 48 ). Participation was voluntary, and confidentiality and anonymity were assured. The de-identified dataset was accessed through a formal request to UNICEF, which granted permission conditional upon dissemination of findings and citing data source. As the study used publicly available, anonymized data, it was exempt from additional ethical review. 2.2 Data Source In the current study, we utilized quantitative data collected cross-sectionally from the 2021 Nigeria Multiple Indicator Cluster Survey (MICS6). The nationally representative survey gathers sociodemographic and health information from males and females aged 15 years and above. Detailed information on the data collection methods can be found in the survey report ( 48 ). The data are publicly available and can be accessed with permission from MICS Surveys ( https://mics.unicef.org/surveys ). 2.3 Study Design and Sampling Method This study used a cross-sectional design based on secondary data from the most recent Nigeria Multiple Indicator Cluster Survey (MICS6). A detailed description of the study design is available in the survey’s final report ( 48 ). The MICS6 survey utilized a multistage stratified cluster sampling approach that employed a probability proportional to size to select enumeration areas in the first stage based on the National Population and Housing Census (2006) of the Federal Republic of Nigeria (NPHC). In the second stage, 20 households of cluster size were selected using systematic random sampling approach within each enumeration area. Only one respondent was chosen randomly to respond on behalf of the household. Data were collected using Computer-Assisted Personal Interviewing (CAPI) technology through face-to-face interviews with respondents in their respective households. To ensure accurate reporting, we accounted for the complex survey design, including weighting, clustering, and stratification, by applying the appropriate sampling weights provided in the MICS6 dataset. 2.4 Study Population and Data Integration For this study, we extracted a subsample from the 2021 Multiple Indicator Cluster Survey (MICS6), focusing on households from urban Nigeria (Fig. 1 ). The original household dataset (HH) comprises 41,532 records, nested within 1,850 communities (clusters) ( 48 ). We sub-sampled by the following criteria: survey = MICS, area = URBAN and household who provided consent. To get individual household member characteristics, we merged the women (MN), men (MN), and children (CH) dataset with household (HH). Observations with "DON'T KNOW (DK)" and "NO RESPONSE" were considered missing and excluded from the analysis ( n = 147). After cleaning the data, our final sample included 10,680 households (unweighted), which represents 15,535 households in weighted terms. These households are from 575 different communities (clusters). This approach was appropriate because it allowed for the generation of a representative urban-specific sample while preserving the multi-level structure of the data. 2.4 Variable Measurement 2.4.1 Outcome Variable The primary outcome variable of this study was household food insecurity, assessed using the validated eight-item Food Insecurity Experience Scale (FIES) ( Supplementary Table A1 ) as outlined in the MICS6 methodology ( 48 ). The FIES was developed and validated by the Food and Agriculture Organization (FAO) of the United Nations to enable internationally comparable estimates aligned with Sustainable Development Goal (SDG) indicator 2.1 ( 49 , 50 ). Respondents reported their household's food-related experiences over the past 12 months, with each item scored as "Yes" ( 1 ), "No" (0), or "Don't know" (DK), following FAO guidelines and prior studies ( 50 – 52 ). A raw FIES score ranging from 0 to 8 was derived by summing affirmative responses. To generate globally comparable estimates of food insecurity among adults, we applied the Rasch model, in accordance with FAO's recommended methodology (Cafiero et al., 2018; FAO, 2016). The analysis was conducted using the FIES Shiny App, which applies the Rasch model to fit the data, evaluate model validity and reliability, perform equating to the global reference scale, and generate probabilistic estimates of moderate or severe food insecurity ( 55 ). Details of the FIES analytical procedures are provided in Supplementary Materials in an Appendix . The Rasch model is a specific form of Item Response Theory (IRT) that models the probability of a household affirming a given food insecurity item as a function of its latent level of food insecurity and the severity of the item ( 54 ). Using this model, we derived two standardized indicators: the prevalence of moderate or severe food insecurity (FImod + Sev) and the prevalence of severe food insecurity (FIsev), in accordance with FAO methodology. These indicators correspond to specific thresholds along the latent continuum of food insecurity, with FIsev reflecting more extreme deprivation than FImod + Sev. The psychometric performance of the Rasch model in our data was strong. All weighted infit statistics for the eight FIES items fell within the FAO-recommended range of 0.7 to 1.3 ( 54 ), thus confirming the internal consistency of the measure ( Supplementary Table A2 ). To assess local independence, we examined residual correlations among item pairs. As shown in Supplementary Table A3 , all residual correlations were below the commonly accepted threshold of |0.4| ( 55 ). This suggests that each item captures a distinct aspect of the food insecurity experience, and there are no redundant items that might compromise the scale’s ability to accurately measure the latent trait of food insecurity. Furthermore, item severity estimates were compared against the FAO global reference scale to assess cross-national equivalence. Most items demonstrated close alignment; however, “Few foods” and “Skipped meals” exceeded the FAO’s allowable deviation threshold of 0.35 logits and were therefore excluded from the equating process ( see Supplementary Table A4 ). Together, these results affirm the suitability of the FIES and Rasch model approach in this context, offering a valid, reliable, and cross-culturally comparable measure of latent food insecurity among study participants. 2.4.2 Independent variables The independent variables analyzed in this study were selected based on a thorough review of the literature, focusing on their relevance to the outcome variable (household food insecurity) and their availability within the MICS6 dataset. Guided by the Socio-ecological framework ( Supplementary Table A6 ), these variables were organized into three levels: individual, household, and community. At the individual level, the study included age of the household head (categorized as 16–25, 26–35, 36–45, and > 45 years) and educational attainment (no formal education, primary, junior secondary, senior secondary, and tertiary education). These variables were included based on prior literature ( 56 – 60 ). At the household level, covariates known to influence household food security outcomes were incorporated ( 61 – 66 ), including household size (≤ 5 vs. >5 members), gender of the household head (female vs. male), and household wealth index. The wealth index was constructed using housing characteristics captured in the household questionnaire and categorized into five quintiles: poorest, poorer, middle, richer, and richest ( 48 ). Additional household characteristics included type of housing tenure (own, rent, or other), agricultural land and livestock ownership (yes/no for each), and the presence of children under five years of age (yes/no). At the community level, geographic regions (North-Central, North-East, North-West, South-West, South-South, and South-West) and aggregated community level wealth quintile were included as community determinants of household food insecurity, in line with existing evidence ( 67 , 68 ). Although MICS6 does not directly measure community-level poverty, a proxy for community poverty was derived by calculating the mean household wealth index within each sampling cluster and categorizing communities into low-, middle-, and high community level poverty strata. 2.5 Statistical Analysis The 2021 Nigeria MICS6 dataset employed a complex survey design with non-self-weighting samples due to varying sampling fractions across states. To ensure national representativeness, the analysis incorporated sampling weights, clustering, and stratification, in line with established guidelines for complex survey data ( 48 , 69 ). Descriptive statistics, including weighted frequencies and population-level estimates of household characteristics, were computed using the survey package in R, developed by Lumley ( 70 ). Given the hierarchical structure of the data (10,680 households nested within 575 communities), a multilevel logistic regression approach was used, with individual and household covariates at Level 1 and community-level factors at Level 2. The dependent variable, moderate or severe food insecurity (MSFI), was operationalized as a binary outcome based on the FIES raw score. Individuals with a score of 4 or higher, reflecting a likelihood of experiencing MSFI, were coded as 1, while those with scores below four were coded as 0, following FAO guidelines ( 54 ). Analyses were conducted using R (v4.4.1) in RStudio (v2023.06.2 + 561) and Jupyter Notebook ( 71 ). 2.5.1 Multilevel Mixed-Effects Analysis To account for the hierarchical structure of the MICS6 dataset, households nested within communities, a two-level multilevel logistic regression model was employed to examine the determinants of moderate or severe food insecurity (MSFI). The intraclass correlation coefficient (ICC) was 13%, indicating that a substantial portion of the variance in food insecurity is attributable to differences across clusters, justifying the use of a multilevel approach. ICC was calculated as follows: ICC = \(\:\:\:\frac{{\delta\:}2}{\delta\:2\:+\:\pi\:2/3}\) Where \(\:{\delta\:}2\) indicates the estimated variance of cluster ( 72 – 74 ). The binary outcome variable (MSFI) was modeled using a generalized linear mixed-effects model with a LOGIT link and binomial distribution. Random intercept models were fitted to estimate the fixed effects of individual, household, and community level predictors, while accounting for between-cluster variation and survey weights ("hhweightmics"). Model estimation was performed using the glmer function from the lme4 package (v1.1-35.5) in R, employing maximum likelihood estimation with Laplace approximation (nAGQ = 1). To account for the binary nature of our outcome variable, the individual/household-level data is nested within higher-level categories (i.e. clusters), we fitted a series of two-level random intercept models to estimate the impact of individual/household and community-level factors on food insecurity. We employed a generalized linear mixed model with a binomial distribution and the LOGIT link function to compute the odds of moderate or severe food insecurity (MSFI). Four models were specified: Null model: no predictors, to estimate baseline variance. Model I: individual and household-level predictors (Level 1). Model II: community-level predictors (Level 2), including geopolitical zone and aggregated community wealth. Model III: Full model incorporating both Level 1 and Level 2 predictors. The general model took the form: $$\:\text{l}\text{o}\text{g}\text{i}\text{t}\left({\text{p}}_{\text{i}}\right)=\text{l}\text{o}\text{g}\left(\frac{{{\pi\:}}_{ij}}{1-{{\pi\:}}_{ij}}\right)={{\beta\:}}_{0}-{{\beta\:}}_{1}{X}_{1ij}+{{\beta\:}}_{2}{X}_{2ij}\:+\:\dots\:\:+\:{{\beta\:}}_{k}{X}_{kij}+{u}_{j}$$ where \(\:{\pi\:}_{ij}\) is the probability of MSFI for household \(\:i,\) in cluster (community) \(\:jth\) , are predictor variables at either level, \(\:{\beta\:}_{0}\) are fixed effects, \(\:{u}_{j\:}\) and represent the cluster-level random effect, assumed normally distributed with variance \(\:{\sigma\:}_{u}^{2}.\) A Likelihood Ratio Test (LRT) comparing the null two-level model to a single-level model strongly rejected the null hypothesis of zero between-cluster variance (LRT = 1697.59 (-2*(-848.79)) with 1 degree of freedom), confirming significant clustering. Additionally, we evaluated the model fit using the log-likelihood, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) ( 75 , 76 ), and the ICC criteria ( 77 ) (as shown in Supplementary Table A7 ). 3.0 Results 3.1 Characteristics of the sampled households Table 1 shows the demographic data, indicating that a large majority of household heads were male, making up 77.4% of the sample. Nearly half (49.03%) were over the age of 45, and the majority (85.86%) had received formal education. However, only about 30% of these heads had higher/tertiary education. Additionally, more than half of the households consist of fewer than five members (68.33%). Approximately 42% of the households in this study were situated in the South-West geopolitical zone. Regarding home ownership, just over half of the respondents (55.95%) resided in rented apartments (Table 1 ). With respect to agricultural assets, 28.33% of households reported owning agricultural land, while approximately 20% owned livestock. In terms of household wealth, the majority (60.61%) fell within the middle to richest wealth categories. However, at the community level, a substantial proportion (83.95%) was classified as high poverty level, based on the concentration of households in the lower wealth quintiles within those communities. (Table 1 ). Table 1 Socio-demographic and economic characteristics of urban household, MICS6 Variables Frequency (N) Weighted Frequency (N) Proportion (%) Individual level variables HH head Age (Years) 16–25 461 699 4.5 26–35 1896 2691 17.32 36–45 2948 4528 29.15 > 45 5375 7618 49.03 HH head Education * No Education 2021 2197 14.14 Primary 1770 2744 17.66 Junior secondary 415 673 4.33 Senior secondary 3266 5269 33.92 Higher/tertiary 3208 4654 29.95 Household level variables HH head Gender Female 2230 3511 22.6 Male 8450 12025 77.4 HH size ≤ 5 6896 10616 68.33 > 5 3784 4920 31.67 Type of household ownership Other 897 1099 7.07 Own 4793 5744 36.97 Rent 4990 8693 55.95 Household Wealth Quintile ** Poorest 2678 2821 18.16 Poor 2377 2987 19.23 Middle 2080 3263 21 Rich 1821 3319 21.36 Richest 1724 3146 20.25 Household Own Agricultural Land No 7142 11135 71.67 Yes 3538 4401 28.33 Household Own Livestock No 8004 12546 80.75 Yes 2676 2991 19.25 Number of children under 5 years old None 6054 9340 60.11 1 2728 3899 25.09 2+ 1898 2298 14.79 Community level variables Geographical Zones North-Central 2086 1650 10.62 North-East 1567 768 4.94 North-West 1469 1936 12.46 South-East 792 2615 16.83 South-South 1218 2087 13.44 South-West 3548 6480 41.71 Community poverty level/quintile Low 271 228 1.47 Medium 2412 2265 14.58 High 7997 13043 83.95 HH = Household * In the other context, these education levels roughly correspond to: less than high school (primary), high school diploma or equivalent (junior and senior secondary), and college degree or higher (tertiary education). ** Household wealth index was constructed using housing characteristics captured in the household questionnaire ( 48 ). 3.2 Prevalence of household FI in urban Nigeria The data presented in Fig. 2 and ( Supplementary Table A5) underscore the widespread prevalence of food insecurity among urban households in Nigeria. Nationally, an estimated 68.56% of urban households experienced moderate or severe food insecurity (FImod + Sev), while 27.19% were classified as severely food insecure (FIsev). Disaggregated responses to individual FIES items offer additional insight into the nature and severity of food insecurity in urban Nigeria ( see Supplementary Table A5 ). While these item-level responses are not intended to serve as standalone indicators, they help illuminate the lived experiences and specific challenges that households face in accessing sufficient and nutritious food. Approximately 76% of respondents reported having “worried about not having enough food,” while 71.7% indicated that they “were unable to eat healthy and nutritious food,” and a similar proportion stated that they “ate only a few kinds of foods.” Moreover, 64.8% reported that they “had to skip a meal,” and 68.9% said they “ate less than they thought they should” due to lack of resources. Additionally, 57.1% of respondents “ran out of food,” 49.7% “were hungry but did not eat,” and 28.7% reported that they “went without eating for a whole day.” These patterns reflect the depth and gradation of food insecurity, from anxiety about food access to more severe experiences of deprivation. Notably, substantial regional variation was observed: the South-East (75.71%) and North-Central (75.13%) zones recorded the highest prevalence of moderate or severe food insecurity, whereas the South-West reported the lowest (64.61%), indicating marked geographic disparities in food access. Table 2 Multivariate multilevel logistic regression analysis of determinants of moderate or Severe Food insecurity among urban households in Nigeria, MICS6. Variables Null Model Model I* Model II** Model III*** Fixed effects intercepts 0.99 (0.04) 4.55 (0.15) 4.54 (0.30) 5.45 (0.35) Individual-/household-level factors AOR [95% CI] p AOR [95% CI] P AOR [95% CI] p HH Gender Female Ref. Ref. Male 1.03 [0.92, 1.15] 0.6 1.05 [0.94, 1.17] 0.4 HH Age (Years) 16–25 Ref. Ref. 26–35 1.24 [1.00, 1.55] 0.055 1.26 [1.01, 1.57] 0.04 36–45 1.49 [1.20, 1.85] < 0.001 1.50 [1.20, 1.86] 45 1.24 [1.00, 1.54] 0.051 1.24 [1.00, 1.54] 0.051 Household size 5 1.37 [1.24, 1.52] < 0.001 1.38 [1.24, 1.53] < 0.001 HH Education No Education Ref. Ref. Primary 1.59 [1.36, 1.87] < 0.001 1.53 [1.31, 1.80] < 0.001 Junior secondary 1.52 [1.19, 1.92] < 0.001 1.47 [1.15, 1.86] 0.002 Senior secondary 1.40 [1.20, 1.64] < 0.001 1.36 [1.16, 1.59] < 0.001 Higher/tertiary 1.16 [0.99, 1.37] 0.072 1.13 [0.96, 1.33] 0.15 Household Own Dwelling Other Ref. Ref. Own 0.73 [0.60, 0.88] < 0.001 0.73 [0.61, 0.88] < 0.001 Rent 1.11 [0.93, 1.34] 0.2 1.09 [0.91, 1.30] 0.4 Household Wealth Quintile Poorest Ref. Ref. Poor 0.78 [0.66, 0.92] 0.003 0.08 [0.06, 0.10] < 0.001 Middle 0.53 [0.45, 0.62] < 0.001 0.49 [0.41, 0.58] < 0.001 Rich 0.28 [0.23, 0.33] < 0.001 0.74 [0.62, 0.87] < 0.001 Richest 0.09 [0.07, 0.11] < 0.001 0.26 [0.21, 0.31] < 0.001 Household Own Agricultural Land No Ref. Ref. Yes 0.89 [0.80, 0.99] 0.025 0.89 [0.80, 0.99] 0.026 Household Own Livestock No Ref. Ref. Yes 0.78 [0.69, 0.87] < 0.001 0.80 [0.71, 0.90] < 0.001 Having children under 5 years old No Ref. Ref. Yes 0.80 [0.71, 0.91] < 0.001 0.81 [0.72, 0.92] 0.001 Community-level factor Zones North-Central Ref. Ref. North-East 0.47 [0.33, 0.65] < 0.001 0.37 [0.26, 0.53] < 0.001 North-West 0.62 [0.46, 0.82] < 0.001 0.54 [0.39, 0.73] < 0.001 South-East 1.26 [0.91, 1.74] 0.2 1.49 [1.04, 2.14] 0.029 South-South 0.72 [0.54, 0.95] 0.02 0.79 [0.58, 1.08] 0.14 South-West 0.57 [0.45, 0.71] < 0.001 0.55 [0.43, 0.71] < 0.001 Community-level Poverty/Wealth Low Ref. Ref. Middle 1.31 [0.74, 2.30] 0.4 1.18 [0.64, 2.16] 0.6 High 0.80 [0.46, 1.38] 0.4 1.41 [0.77, 2.58] 0.3 HH: household head Notes : All estimates are weighted for the survey’s complex sampling design; Bolded text indicates statistical significance at < .05. Note : Results based on a generalized linear mixed model (GLMM) with a binomial outcome (moderate/severe food insecurity), fitted using maximum likelihood estimation (Laplace Approximation). The model was specified with a logit link function and included adaptive Gauss-Hermite quadrature (nAGQ = 1). All estimates were derived using the glmerMod function in R. *Model I: Individual and household level variables, **Model II: Community level variables, ***Model III: Individual, Household and Community level variables 3.3 Multilevel mixed effect logistic regression analysis for the multilevel factors of moderate to severe food insecurity Table 2 presents the results of the multilevel regression analysis, which highlighted several significant predictors of moderate or severe food insecurity (MSFI). After adjusting for both individual and household characteristics, as well as community-level factors ( Model III ), several key determinants emerged. Full model parameters are presented in Appendix . At the individual and household levels, the age of the household head emerged as a significant predictor of moderate or severe food insecurity. Compared to households headed by individuals aged 16–25, those led by individuals aged 26–35 exhibited a 26% higher likelihood of experiencing moderate or severe food insecurity (AOR = 1.26, p = 0.04). The association was even more pronounced among the 36–45 age group, who faced a 50% increase in the odds of food insecurity (AOR = 1.50, p < 0.001). Educational attainment of the household head was also significantly associated with food insecurity. Specifically, households in which the head had attained primary or secondary education exhibited higher odds of experiencing moderate or severe food insecurity compared to those with no formal education. Larger household size was another significant factor. Households with more than five members were more likely to experience moderate or severe food insecurity (AOR = 1.37, 95% CI: 1.24, 1.52, p < .001) compared to those with five or fewer members. In contrast, household wealth acted as a protective factor. Higher household wealth significantly reduced the odds of experiencing moderate or severe food insecurity ( p < .001). As the household wealth index increased, the likelihood of food insecurity decreased, with the wealthiest households showing significantly lower odds of food insecurity. Similarly, access to agricultural assets also offered significant protection against food insecurity. Households with agricultural land had nearly 1.2 times lower odds of experiencing moderate or severe food insecurity (AOR = 0.89, 95% CI: 0.80–0.99, p = 0.026) compared to households without agricultural land. Households with livestock had about 1.3 times lower odds (AOR = 0.80, 95% CI: 0.71–0.89, p < .001). Furthermore, households with children under five years of age were 1.2 times less likely to experience moderate or severe food insecurity (AOR = 0.81, 95% CI: 0.72–0.92, p < .001) compared to households without children in that age category, highlighting the potential influence of family dynamics on food security. Finally, homeownership was also significantly associated with reduced odds of food insecurity. Specifically, households that owned their homes had 1.4 times lower odds of experiencing food insecurity compared to those that did not own their homes (AOR = 0.71, 95% CI: 0.59–0.85, p < .001). At the community level, significant regional disparities emerged. Households in the North-East and North-West zones of urban Nigeria had lower odds of moderate or severe food insecurity compared to those in the North-Central zone (AOR = 0.38, 95% CI: 0.27, 0.53, p < .001 and AOR = 0.51, 95% CI: 0.37, 0.69, p < .001, respectively). The South-West region also had lower odds of moderate or severe food insecurity (AOR = 0.56, 95% CI: 0.44, 0.72, p < 0.001). Conversely, households in the South-East region had significantly higher odds of experiencing moderate or severe food insecurity (AOR = 1.49, 95% CI: 1.04, 2.14, p = .029). 4.0 Discussion Food insecurity remains a pressing public health concern in Nigeria, with vulnerable populations disproportionately affected. Although rural food insecurity has been widely examined, urban contexts, marked by rapid urbanization and deepening socioeconomic inequalities, have received limited empirical attention. This study addresses that gap, showing 7 out of 10 urban households experienced moderate to severe food insecurity. This study identified several key socioeconomic and demographic determinants: household head age, educational attainment, and household size were associated with increased risk of household food insecurity (HFI). Conversely, home ownership, household wealth, ownership of livestock or agricultural land, having children under five, and residing in certain regions were associated with reduced likelihood of HFI. The prevalence of moderate to severe food insecurity observed in our study (68.56%) aligns closely with the national estimate of 73.9% reported by the World Bank in 2022 ( 78 ). However, it is notably higher than the 18% reported by Jung (2023), who used data from the 2018/19 Nigeria General Household Survey, which used the same measurement tool (FIES). This discrepancy, while notable, likely reflects the significant economic and social shifts that have occurred since 2019, most notably the COVID-19 pandemic and subsequent economic disruptions ( 79 ), including sustained inflation, which have substantially impacted food access and affordability across Nigeria ( 80 ). While Studies have shown that food insecurity is a serious issue in many urban areas across sub-Saharan Africa ( 81 , 82 ), the rate observed in our study is notably among the highest. For example, a systematic review and meta-analysis of urban food insecurity in East Africa reported a pooled prevalence of 60.9%, with country-specific estimates ranging from 36.5% in Burundi to 91% in Sudan ( 81 ). Similarly, a multi-city study across 11 Southern African urban centers found that over 60% of urban households experienced severe food insecurity, with rates reaching 90% in informal settlements in Lusaka, Zambia ( 82 ). It is important to note that many of these studies employed the Household Food Insecurity Access Scale (HFIAS), which differs methodologically from the Food Insecurity Experience Scale (FIES) employed in our analysis. Despite these differences in measurement tools, the findings collectively underscore that while urban Nigeria experiences especially high levels of food insecurity, the prevalence observed in our study aligns with broader patterns reported across other African cities. This highlights the widespread and pressing nature of urban food insecurity across the continent. In this study, households headed by individuals aged 26–35 and 36–45 years are significantly more likely to experience MSFI compared to younger household heads aged 16–25 years. This aligns with previous studies from Nigeria and other similar regions ( 59 , 83 – 85 ), which has shown that food insecurity tends to increase with the age of the household head. For instance, a recent spatial analysis of household food insecurity by Jerumeh (2024) found that older household heads were more likely to experience food insecurity across Nigeria’s 109 senatorial districts, particularly in high-risk regions such as the North ( 83 ). This trend likely reflects the socioeconomic burdens commonly faced by individuals in this age group, including the dual pressures of supporting families and managing multiple financial obligations ( 86 ), pressures that are further intensified by the high cost of living in urban areas ( 87 ). Our study found that larger households, with more than five members, were more likely to experience MSFI, while households with children under five years were less likely to experience it. This highlights the complex relationship between household composition and food insecurity. The study finding that larger households (more than five members) were more likely to experience food insecurity, aligned with previous studies conducted in Nigeria ( 88 ), Ethiopia ( 89 , 90 ), and Sudan ( 91 ). However, our finding that households with children under five years were less vulnerable to food insecurity contrasts with several prior studies ( 65 , 92 – 94 ), which have reported a heightened risk among such households. For example, a study conducted in Ibadan, Nigeria, found that households with under-five children living in urban slums were seven times more likely to experience food insecurity compared to those in rural slums ( 65 ). Similarly, Yahaya et al. (2021) reported that 63% of households with under-five children in urban Ibadan were food insecure. In South Africa, research has shown that households with children, including those under five, had approximately 1.7 times the odds of experiencing food insecurity compared to those without children ( 93 ). A plausible explanation for our contrasting findings may lie in the protective behaviors exhibited by households with young children. Evidence suggests that adults in such households often prioritize children's nutritional needs, a phenomenon referred to as the "buffering" effect, where adults absorb the brunt of food insecurity to shield children from its most severe consequences ( 95 ). This dynamic may contribute to the appearance of lower food insecurity levels in households with young children, despite underlying adult hardship. Meanwhile, the impact of larger households on food insecurity likely reflects the increased competition for limited resources and higher consumption demands for larger households ( 96 ). The role of education in mitigating food insecurity is well-established, as it represents a critical component of human capital that enhances earning potential, decision-making abilities, and access to resources ( 97 ). However, our findings reveal a different relationship between the education level of household heads and food insecurity. Specifically, household heads with lower levels of education (primary or secondary) were significantly more likely to experience MSFI, likely reflecting limited access to stable employment, underemployment, and low-paying jobs associated with lower educational attainment ( 60 ). Surprisingly, this study did not find a statistically significant relationship between higher levels of education and odds of experiencing MSFI. Our findings diverge from earlier studies in urban areas in Nigeria, including those by (Adesoye & Adepoju, 2020; Omonona & Agoi 2007 & Roberts et al., 2019) which found that formal education (secondary and higher levels) reduced the likelihood of food insecurity. However, our findings were consistent with studies from Kenya and The Gambia, that reported that households with heads who had lower education levels (primary or secondary) were more likely to face severe food shortages ( 98 , 99 ). These mixed findings highlight the context-dependent nature of the education–food security relationship. While education can provide advantages in labor market participation and access to services, its protective effects may be undermined in urban settings characterized by high living costs, wage inequality, underemployment, and dependence on volatile market-based food systems. In such environments, even highly educated individuals may struggle to convert educational capital into food security, particularly in the face of systemic economic pressures and limited employment opportunities ( 100 ). Our results underscore the importance of moving beyond education as a standalone solution. While expanding access to quality education remains essential, it must be complemented by broader structural interventions that address employment quality, fair wages, social protection, and urban living conditions. Additionally, our study findings that households owning their homes were less likely to experience MSFI compared to others, aligned with findings from other studies from Nigeria ( 29 ), Ethiopia,( 101 ) and Iran ( 102 ). For example, a study by Roberts and colleagues among urban households in Lagos, Nigeria, found that respondents who owned their homes were more likely to be food secure compared to those living in rented homes ( 29 ). Homeownership can reduce household vulnerability by eliminating or lowering monthly rent obligations, thereby freeing up income for food and other essential needs. This is particularly relevant in a report by UN-Habitat, which highlights that homeownership in urban settings contributes to economic stability and reduces financial strain, which are critical factors in achieving food security ( 24 ). The reduced exposure to market-driven housing instability may also contribute to more consistent access to food. Taken together, these findings suggest that housing stability, as afforded by homeownership, plays a significant role in supporting household food security, particularly in economically pressured urban environments. Similarly, our findings further revealed a clear inverse relationship between household wealth and food insecurity, as household wealth increased, the likelihood of experiencing MSFI For example, a study by Roberts and colleagues among urban households in Lagos, Nigeria, found that respondents who owned their homes were more likely to be food secure compared to those living in rented accommodations significantly decreased. This reinforces the well-documented role of financial resources and economic stability as key protective factors against food insecurity. Our findings are in line with a growing body of evidence from across sub-Saharan Africa that highlights the strong association between wealth status and food security outcomes ( 8 , 81 , 103 ). For example, Gebremichael et al. (2022) reported that in East Africa, households in the poorest wealth quintile were nearly four times more likely to experience food insecurity compared to their wealthier counterparts. Similarly, a study in Togo found that those in the lowest wealth quintile had significantly higher relative risk ratios for both moderate and severe food insecurity 2.21 and 3.58 respectively, compared to wealthier households ( 103 ). Evidence from urban Ethiopia echoes this trend, showing that households with lower economic standing were 3.51 times more likely to be food insecure than those with relatively better financial means ( 8 ). These patterns underscore the critical influence of economic resources on household food security, particularly in urban Nigeria, where rising living costs and dependence on market-based food systems heighten vulnerability ( 104 ). In such settings, households with limited financial buffers are more susceptible to food price fluctuations, housing costs, and income shocks, all of which increase food insecurity. Our study found that ownership of agricultural land and livestock is associated with reduced odds of experiencing food insecurity highlighting the significant role that land-based assets play, even in urban settings. This result suggests that households with access to agricultural land are more likely to engage in subsistence or semi-subsistence farming, which serves as a direct buffer against food insecurity. Urban agriculture, facilitated by agricultural land ownership, emerges as a vital strategy to combat food insecurity ( 105 ). It allows households to grow their own produce, supplementing diets and reducing dependence on costly market-purchased foods. This practice not only provides a stable source of nutritious food but also helps mitigate the impact of food price fluctuations ( 106 ). The significance of these practices became even more apparent during COVID-19 pandemic when supply chain disruptions highlighted the vulnerability of urban food systems ( 107 ). It is noteworthy that data for this study was collected during the Covid-19 post-pandemic recovery period in Nigeria, reinforcing the timeliness and relevance of our findings. Our results align with those of Nwaka et al., (2020), who similarly found that urban households with access to land were significantly less likely to experience food insecurity. Livestock ownership emerged as another significant asset in mitigating food insecurity. Small-scale animal husbandry, such as raising poultry, goats, or rabbits, can serve as a sustainable source of protein and micronutrients, while also offering economic benefits through the sale of eggs, milk, or meat ( 108 , 109 ). Beyond its nutritional contributions, livestock ownership also functions as a form of savings or liquid assets, which can be mobilized during times of crisis ( 110 ). Evidence from rural and urban areas in Kenya and Ghana, have documented livestock farming as a viable strategy for poverty reduction and household economic empowerment ( 111 , 112 ). Our findings highlight the importance of land-based assets in urban food security strategies, suggesting that policies promoting urban agriculture and small-scale livestock keeping could significantly contribute to reducing food insecurity in urban areas. At the community level, our study highlights significant regional disparities in household food insecurity across urban Nigeria. Urban households in the North-East, North-West, and South-West were significantly less likely to be MSFI compared to those in the North-Central region, while the South-East showed notably higher risk. The lower odds of MSFI observed in the North-East and North-West were unexpected, given these regions well-documented challenges with conflict and environmental shocks such as droughts and floods which have impacted on the food insecurity situation ( 113 , 114 ). A plausible explanation for the observed results may lie in the concentration of food assistance programs through humanitarian organizations in these regions. Urban centers such as Maiduguri, Yola, and Damaturu have emerged as key hubs for aid distribution, potentially mediating improved food security outcomes relative to surrounding areas. Organizations such as the World Food Programme (WFP) and the United Nations have been actively involved in providing food aid, nutrition support, and other essential services to vulnerable populations, including internally displaced persons (IDPs) residing in these areas ( 115 ). These interventions may have mitigated food insecurity risk in these regions. However, it is important to note that our findings still report relatively high estimated prevalence rates of MSFI in both the North-East (66.11%) and North-West (68.76%), underscoring that while humanitarian aid may buffer some effects, food insecurity remains a substantial concern in these regions. In contrast, the reduced odds of food insecurity observed among urban households residing in the South-West region likely reflect the long-standing structural, economic, and geographic advantage that have historically positioned the region more favorably in terms of food access and livelihood opportunities. The South-West is home to major urban centers like Lagos and Ibadan, which benefit from relatively better infrastructure, market access, and diversified income-generating activities compared to other regions ( 116 – 118 ). These cities also attract more public and private investments and support more robust informal and formal employment sectors ( 116 , 118 , 119 ), all of which contribute to increased household purchasing power and food security. However, our findings also show that these advantages are not generally experienced. The prevalence of MSFI (66.91%) within the South-West underscores the persistent influence of income inequality ( 120 ), and high food prices ( 121 ), which continues to undermine food access for a segment of the population. This highlights the necessity of targeted and inclusive food security policies, even in seemingly advantaged urban settings. Of particular concern is the increased likelihood of food insecurity among urban households in Nigeria’s South-East region, which exhibited the highest estimated prevalence of moderate or severe food insecurity (MSFI) in our study, at 75.71%. Several structural and political factors may contribute to this pattern. This evidence may have stem from structural and economic disadvantages, including limited infrastructure ( 38 ), weak food distribution systems, and high urban food prices in this region ( 122 ). In recent years, the region has also experienced episodic insecurity and economic disruption linked to political instability and the sit-at-home orders enforced by the Indigenous People of Biafra (IPOB) ( 123 , 124 ). These disruptions have significantly impeded daily economic activity, especially for informal workers ( 123 , 124 ), hereby reducing household income and limiting food access. Combined with the lingering effects of the COVID-19 pandemic ( 80 ), these factors may have further exacerbated existing vulnerability. Together, these findings highlight the complex interplay between structural, political, and geographic factors that shape food insecurity in urban Nigeria. They highlight an urgent need for context-specific interventions that reflect the heterogeneity of urban experiences, moving beyond a one-size-fits-all approach toward more spatially and socioeconomically responsive policy design. Future research should also prioritize the collection of regionally disaggregated, urban-specific data to inform targeted responses and monitor shifting food insecurity dynamics in the aftermath of shocks. 4.1 Implications for research, policy and practice This study compels a fundamental reorientation of how food insecurity is conceptualized and addressed in rapidly urbanizing contexts such as Nigeria. While rural food insecurity continues to receive extensive scholarly and policy attention, our findings indicate that about 7 in 10 urban households face moderate to severe food insecurity, making a strong case for repositioning urban food insecurity as a central public health and development priority in Nigeria and similar areas across Sub-Saharan Africa. From a research perspective, there is a clear need for more regionally disaggregated, longitudinal, and urban-focused studies that can unpack the evolving dynamics of household food insecurity in urban settings. Future work should explore how structural factors such as housing, employment quality, inflation, and political instability within each geopolitical zone (e.g., “sit-at-home” orders in the South-East) interact with household characteristics to influence food access and HFI over time. Moreover, a mixed-methods approach could help explain counterintuitive findings, such as the protective behavior observed in households with young children or the non-significant associations between higher education and food security. On the policy front, the findings highlight the need for targeted, evidence-based strategies that reflect the specific socioeconomic and demographic realities of urban Nigeria, rather than relying solely on broad national approaches such as federal safety net programs. The protective role of homeownership, household assets, and land-based livelihoods points to the importance of affordable housing programs and urban livelihood support as foundational components of food security policy. Additionally, region-specific strategies informed by local data should guide intervention efforts, especially in high-risk urban areas. To address vulnerabilities tied to age, education, and household size, strengthened social protection mechanisms, including food vouchers, can provide critical support. Finally, investments in infrastructure for food storage, transportation, and distribution will improve food availability and affordability across urban centers. For practice, community-based intervention such as urban farming, backyard livestock rearing, and rooftop gardening present scalable opportunities to build household resilience. Programs that offer technical training, access to micro-credit, and market linkages can support the productive use of land-based assets among urban households. Additionally, housing stability, as indicated by the protective effect of homeownership, should be integrated into broader food security programming. In summary, this study reinforces the need for a multi-sectoral, region-based approach to urban food insecurity, one that combines evidence, responsive policy, and locally grounded practice to build more equitable and resilient urban food systems. 4.2 Strengths and Limitations Our study has several notable strengths that enhance its relevance in addressing food insecurity among urban households in Nigeria. A key strength lies in its contribution to filling the research gap on food insecurity within urban settings, where limited evidence exists compared to rural areas. By providing detailed insights into the prevalence and determinants of food insecurity, our findings hold significant potential to guide the development of evidence-based interventions tailored to urban contexts. A notable methodological strength is the use of the Food Insecurity Experience Scale (FIES) analyzed through the Rasch model, in line with FAO guidelines ( 54 ). This approach leverages the probabilistic framework of Item Response Theory to generate internally consistent and cross-culturally comparable estimates of food insecurity severity. The Rasch model not only ensures unidimensionality and item-level fit but also facilitates the calibration of individual household responses along a latent food insecurity continuum, enabling the derivation of prevalence indicators (moderate/severe and severe food insecurity) with psychometric rigor. Furthermore, the use of nationally representative survey data enhances the external validity of our results, allowing for greater generalization across diverse urban populations in Nigeria. The application of a multilevel logistic regression model represents an additional strength, as it appropriately accounts for the hierarchical nature of the data (households nested within communities). This modeling strategy adjusts for intra-cluster correlation and yields more precise estimates of standard errors and effect sizes, thereby mitigating the risk of Type I errors and strengthening the internal validity of our conclusions ( 125 ). Despite these strengths, our study has several limitations. One significant limitation is the cross-sectional nature of the data, which restricts our ability to establish causal relationships between the determinants and outcomes of food insecurity. Additionally, while the use of the Food Insecurity Experience Scale (FIES) enhances international comparability, it relies on self-reported data, which may introduce response bias. The timing of the survey, conducted as Nigeria and the rest of the world were recovering from the COVID-19 pandemic, presents another limitation. The pandemic caused significant economic and social disruptions, heightening food insecurity levels and potentially leading to findings that reflect a temporary crisis rather than longer-term trends. Moreover, the study does not fully capture seasonal variations, which could offer deeper insights into urban food insecurity dynamics. 5.0 Conclusions This study found that nearly 7 out of 10 urban households in Nigeria experience moderate or severe food insecurity, emphasizing the urgent public health challenge posed by urban food insecurity in the context of rapid urbanization and economic instability. The findings underscore the need for interventions and policies to address economic disparities and enhance access to affordable, nutritious food. Community-based strategies, such as urban agriculture and local food distribution networks, are recommended to provide immediate relief and foster long-term food security. Declarations 6.1. Ethics statement The data for this study were obtained from MIC through a formal request, which was approved with the condition to share the research findings. Since the study used publicly available, de-identified data, it was exempt from the human subject research approval process. 6.2. Consent This study utilized publicly available, de-identified secondary data from the 2021 Nigeria Multiple Indicator Cluster Survey (MICS6). According to the survey report, verbal informed consent was obtained from all respondents prior to participation (48). All participants were informed of the voluntary nature of their involvement, the confidentiality and anonymity of their responses, and their right to decline to answer any questions or to terminate the interview at any time. 6.3. Consent for publication Not applicable 6.4. Data availability statement The datasets utilized for this study are publicly available and can be accessed with permission from MICS Surveys (https://mics.unicef.org/surveys ). 6.5. Competing interests The authors declare that they have no competing interests. 6.6. Funding This research received no external funding. 6.7. Author contributions K.D. A.: Conceptualization, Methodology, Validation, Data Analysis, Writing – Original Draft, Writing – Review & Editing; B. A.: Methodology, Validation, Data Curation, Data Analysis; S.R. N.: Methodology, Validation, Data Analysis, Writing – Review & Editing, Supervision; O.-H. P.: Supervision, Validation, Writing – Review & Editing; T. I.: Validation, Writing – Review & Editing; J. K.: Writing – Review & Editing; G. E.: Writing – Review & Editing; T. M.: Writing – Review & Editing. All authors have read and agreed to the published version of the manuscript. 6.8. Acknowledgments We express our gratitude to the UNICEF MICS team for their assistance in accessing the data and addressing our questions regarding the survey's methodology and variables. References UN DESA. The 2018 Revision of the World Urbanization Prospects [Internet]. 2018 May [cited 2025 Jan 24]. Available from: https://www.un.org/en/desa/2018-revision-world-urbanization-prospects Soliman AM. Urbanization and Urban Informality in the Era of Globalization. Urban Informality. Cham: Springer International Publishing; 2021. pp. 85–120. Li X, Stringer LC, Dallimer M. The Impacts of Urbanisation and Climate Change on the Urban Thermal Environment in Africa. Climate. 2022;10(11):164. IPCC, Zhai P, Pirani A, Connors SL. In: Chen Y, et al. editors. Climate Change 2021 – The Physical Science Basis. Valérie Masson-Delmotte. Clotilde Péan: Cambridge University Press; 2021. Abu Hatab A, Cavinato MER, Lindemer A, Lagerkvist CJ. Urban sprawl, food security and agricultural systems in developing countries: A systematic review of the literature. Cities. 2019;94:129–42. Ngcamu BS. The effects of urbanisation on food security in Africa: An overview and synthesis of the literature. Environ Socio-economic Stud. 2022;10(2):40–8. Wudil AH, Usman M, Rosak-Szyrocka J, Pilař L, Boye M. Reversing Years for Global Food Security: A Review of the Food Security Situation in Sub-Saharan Africa (SSA). Volume 19. International Journal of Environmental Research and Public Health; 2022. Etana D, Tolossa D. Unemployment and Food Insecurity in Urban Ethiopia. Afr Dev Rev. 2017;29(1):56–68. Kiribou R, Bedadi B, Dimobe K, Ndemere J, Neya T, Ouedraogo V, et al. Urban farming system and food security in sub-Saharan Africa: Analysis of the current status and challenges. Volume 9. Urban Agriculture & Regional Food Systems; 2024. 1. Jung I. Food Insecurity in Nigeria: Food Supply Matters: Nigeria. Selected Issues Papers, 2023 (018), A001 [Internet]. 2023 [cited 2025 Jan 24]; Available from: https://www.elibrary.imf.org/view/journals/018/2023/018/article-A001-en.xml?ArticleTabs=fulltext Davies J, Hannah C, Guido Z, Zimmer A, McCann L, Battersby J, et al. Barriers to urban agriculture in Sub-Saharan Africa. Food Policy. 2021;103:101999. Macalou M, Keita SI, Coulibaly AB, Diamoutene AK. Urbanization and food security: evidence from Mali. Front Sustain Food Syst. 2023;7. Djan MA. Urban Food Security: Examining the Unique Challenges and Opportunities Associated with Ensuring Food Security in Urban Areas. Eur J Nutr Food Saf. 2023;15(9):42–52. Crush JS, Frayne GB. Urban food insecurity and the new international food security agenda. Dev South Afr. 2011;28(4):527–44. UNFPA. World Population Dashboard: Nigeria [Internet]. 2022 [cited 2024 Jun 25]. Available from: https://www.unfpa.org/data/world-population/NG Tochukwu SE, Bilyaminu T. Examining the Effects of High Poverty and Unemployment on Rural Urban Migration in Nigeria and its Consequences on Urban Resources and Rural Decline. J Child Adult Vaccines Immunol. 2024;8(1):001–13. Aliyu A, Amadu L. Urbanization, cities, and health: The challenges to Nigeria – A review. Ann Afr Med. 2017;16(4):149. Auwalu FK, Bello M. Exploring the Contemporary Challenges of Urbanization and the Role of Sustainable Urban Development: A Study of Lagos City, Nigeria. J Contemp Urban Affairs. 2023;7(1):175–88. Ayuba D, Akinga AY, Okeh DU, Ogaya JB, Precious FK, Lucero-Prisno DE. In. Migration and food security in urban Nigeria: The dilemma of the urban poor. 2023. pp. 235–65. NESG. NESG 2024Q2 UNEMPLOYMENT ALERT [Internet]. 2024 Nov [cited 2025 Jan 24]. Available from: file:///C:/Users/hp/Downloads/Unemployment%20Alert%202024Q2_1732787107.pdf Sadiq MS, Singh IP, Ahmad MM. The Influence of Armed Banditry on Food Supply Chains in North-West Nigeria: A Comprehensive Review. Kuwait Res J Agric Life Sci. 2024;1(1):1–11. Marie Ruel. Center for Strategic and International Studies. 2020 [cited 2025 Jan 24]. Growing Cities, Growing Food Insecurity: How to Protect the Poor during Rapid Urbanization. Available from: https://www.csis.org/analysis/growing-cities-growing-food-insecurity-how-protect-poor-during-rapid-urbanization Ikudayisi AA. Urban food security and socioeconomic sustainability: A multidimensional perspective. Green Technol Sustain. 2024;2(2). Un-Habitat. Un-Habitat. (2020). World Cities Report 2020: The value of sustainable urbanization. [Internet]. 2020 [cited 2025 Jan 24]. Available from: https://unhabitat.org/sites/default/files/2020/10/wcr_2020_report.pdf Olatunde O. The Aetiology of Ill Health Among Urban Slum Communities in Nigeria: A Systematic Review. 2025. Ogbonna SN, Ochie CN, Aniwada EC. Urban slum housing quality, and its public health implications in Nigeria: a case of urban slum residents in Enugu metropolis, South East, Nigeria. BMC Public Health. 2024;24(1):3231. Ilori T, Christofides N, Baldwin-Ragaven L. The relationship between food insecurity, purchasing patterns and perceptions of the food environment in urban slums in Ibadan, Nigeria. BMC Nutr. 2024;10(1):122. John C, Poh BK, Jalaludin MY, Michael G, Adedeji I, Oyenusi EE et al. Exploring disparities in malnutrition among under-five children in Nigeria and potential solutions: a scoping review. Front Nutr. 2024;10. Roberts AA, Osadare JO, Inem VA. Hunger in the midst of plenty: A survey of household food security among urban families in Lagos State, Nigeria. J Public Health Afr. 2019;10(1). Dauda Ayuba AY, Akinga DU, Okeh JB, Ogaya. Fadele Kehinde Precious, Don Eliseo Lucero-Prisno. Migration and food security in urban Nigeria: The dilemma of the urban poor. In: Marc J, Cohen, editors. Advances in Food Security and Sustainability. Elsevier; 2023. pp. 235–65. Drewnowski A. Food insecurity has economic root causes. Nat Food. 2022;3(8):555–6. Fang D, Thomsen MR, Nayga RM, Yang W. Food insecurity during the COVID-19 pandemic: evidence from a survey of low-income Americans. Food Secur. 2022;14(1). Yazbeck N, Mansour R, Salame H, Chahine NB, Hoteit M. The Ukraine–Russia War Is Deepening Food Insecurity, Unhealthy Dietary Patterns and the Lack of Dietary Diversity in Lebanon: Prevalence, Correlates and Findings from a National Cross-Sectional Study. Nutrients. 2022;14(17). Verschuur J, Li S, Wolski P, Otto FEL. Climate change as a driver of food insecurity in the 2007 Lesotho-South Africa drought. Sci Rep. 2021;11(1). Sibanda F, Gender. Climate Change and Food Insecurity: A Zimbabwean Rastafari Perspective. Pharos J Theol. 2023;104(2). FAO, IFAD, UNICEF, WFP, WHO., The State of Food Security and Nutrition in the World 2025 – Addressing high food price inflation for food security and nutrition. Rome; 2025 Jul. National Economic Summit Group. NESG Issues a Policy Brief on the Status of Food Security in Nigeria [Internet]. 2025 Mar [cited 2024 Nov 6]. Available from: https://nesgroup.org/blog/NESG-Issues-a-Policy-Brief-on-the-Status-of-Food-Security-in-Nigeria Olumba CC, Olumba CN, Alimba JO. Constraints to urban agriculture in southeast Nigeria. Humanit Soc Sci Commun. 2021;8(1). Bloch R, Monroy J, Fox S, Ojo A. Urbanisation and Urban Expansion in Nigeria. Urbanisation Research Nigeria; 2015. Fox S, Bloch R, Monroy J. Understanding the dynamics of Nigeria’s urban transition: A refutation of the ‘stalled urbanisation’ hypothesis. Urban Stud. 2018;55(5):947–64. Eze TC, Okpala CS, Ogbodo JC. Patterns of Inequality in Human Development Across Nigeria’s Six Geopolitical Zones. Developing Ctry Stud. 2014;4(8). Adepoju A, Oyegoke O. Correlates of food insecurity status of urban households in Ibadan metropolis, Oyo state, Nigeria. Int Food Res J. 2018;25(6). Nwaka ID, Akadiri S, Saint, Uma KE. Gender of the family head and food insecurity in urban and rural Nigeria. Afr J Economic Manage Stud. 2020;11(3). Olaniyi O. Assessment of households’ food access and food insecurity in Urban Nigeria: A case study of Lagos Metropolis. Global J Human-Social Science: E Econ. 2014;14(1). Enete AA, Achike AI. Urban agriculture and urban food insecurity/poverty in Nigeria: The case of Ohafia, south-east Nigeria. Outlook Agric. 2008;37(2). Bronfenbrenner U. Toward an experimental ecology of human development. Am Psychol. 1977;32(7). Schroeder K, Smaldone A. Food Insecurity: A Concept Analysis. Nurs Forum (Auckl). 2015;50(4). National Bureau of Statistics (NBS), and United Nations Children’s Fund (UNICEF). Multiple Indicator Cluster Survey 2021, Survey Findings Report [Internet]. Abuja, Nigeria; 2022 Aug [cited 2024 Aug 21]. Available from: https://www.unicef.org/nigeria/media/6316/file/2021%20MICS%20full%20report%20.pdf Gil JDB, Reidsma P, Giller K, Todman L, Whitmore A, van Ittersum M. Sustainable development goal 2: Improved targets and indicators for agriculture and food security. Ambio. 2019;48(7):685–98. FAO. Voices of the Hungry: Analyse the Data [Internet]. 2024 [cited 2024 Jun 5]. Available from: https://www.fao.org/in-action/voices-of-the-hungry/analyse-data/en/ Wambogo EA, Ghattas H, Leonard KL, Sahyoun NR. Validity of the Food Insecurity Experience Scale for Use in Sub-Saharan Africa and Characteristics of Food-Insecure Individuals. Curr Dev Nutr. 2018;2(9):nzy062. Jubayer A, Islam S, Nowar A, Nayan MM, Islam MdH. Validity of Food insecurity experience scale (FIES) for use in rural Bangladesh and prevalence and determinants of household food insecurity: An analysis of data from Bangladesh integrated household survey (BIHS) 2018–2019. Heliyon. 2023;9(6):e17378. Carlo Cafiero S, Viviani. Mark Nord. RM.weights: Weighted Rasch Modeling and Extensions using Conditional Maximum Likelihood. 2018. FAO. Methods for estimating comparable rates of food insecurity experienced by adults throughout the world [Internet]. Rome. 2016 [cited 2025 Aug 11]. Available from: https://openknowledge.fao.org/server/api/core/bitstreams/823b2fcd-bc52-4371-a31d-ea2aa74e63d5/content FAO, Using the FIES, App FAO. 2020 [cited 2025 Aug 11]. p. 22. Available from: https://openknowledge.fao.org/handle/20.500.14283/ca9318en Tabrizi JS, Nikniaz L, Sadeghi-Bazargani H, Farahbakhsh M, Nikniaz Z. Socio-demographic determinants of household food insecurity among Iranian: A population-based study from northwest of Iran. Iran J Public Health. 2018;47(6). Parekh N, Ali SH, O’Connor J, Tozan Y, Jones AM, Capasso A et al. Food insecurity among households with children during the COVID-19 pandemic: results from a study among social media users across the United States. Nutr J. 2021;20(1). Honarvar MR, Gholami M, Abdollahi Z, Ghotbabadi FS, Lashkarboluki F, Najafzadeh M et al. Household food insecurity and associated factors in the Northeast of Iran: a cross-sectional study: Household food security in Northern Iran. BMC Nutr. 2023;9(1). Drammeh W, Hamid NA, Rohana AJ. Determinants of Household Food Insecurity and Its Association with Child Malnutrition in Sub-Saharan Africa: A Review of the Literature. Curr Res Nutr Food Sci J. 2019;7(3):610–23. Mutisya M, Ngware MW, Kabiru CW, Kandala Nbakwin. The effect of education on household food security in two informal urban settlements in Kenya: a longitudinal analysis. Food Secur. 2016;8(4). Alpízar F, Saborío-Rodríguez M, Martínez-Rodríguez MR, Viguera B, Vignola R, Capitán T et al. Determinants of food insecurity among smallholder farmer households in Central America: recurrent versus extreme weather-driven events. Reg Environ Change. 2020;20(1). Herrera JP, Rabezara JY, Ravelomanantsoa NAF, Metz M, France C, Owens A et al. Food insecurity related to agricultural practices and household characteristics in rural communities of northeast Madagascar. Food Secur. 2021;13(6). Rashid FN, Sesabo JK, Lihawa RM, Mkuna E. Determinants of household food expenditure in Tanzania: implications on food security. Agric Food Secur. 2024;13(1). Samim SA, Hu Z, Stepien S, Amini SY, Rayee R, Niu K, et al. Food insecurity and related factors among farming families in takhar region, afghanistan. Sustain (Switzerland). 2021;13:18. Oderinde TM, Ilesanmi OS, Afolabi AA. Food insecurity and associated factors among households with under-5 children in slum communities in Ibadan, Nigeria. BMC Public Health. 2023;23(1). Khanam M, Ara G, Rahman AS, Islam Z, Farhad S, Khan SS et al. Factors affecting food security in women enrolled in a program for vulnerable group development. Curr Dev Nutr. 2020;4(4). Wight V, Kaushal N, Waldfogel J, Garfinkel I. Understanding the link between poverty and food insecurity among children: Does the definition of poverty matter? J Child Poverty. 2014;20(1). Pollard CM, Booth S, Correction. Food insecurity and hunger in rich countries-it is time for action against inequality (International Journal of Environmental Research and Public Health, 2019, 16, 1804). Vol. 17, International Journal of Environmental Research and Public Health. 2020. Bell BA, Onwuegbuzie AJ, Ferron JM, Jiao QG, Hibbard ST, Kromrey JD. Am J Public Health. 2012;102(7):1399–405. Use of Design Effects and Sample Weights in Complex Health Survey Data: A Review of Published Articles Using Data From 3 Commonly Used Adolescent Health Surveys. Lumley T. Complex Surveys. Wiley; 2010. R Core Team. R: A language and environment for statistical computing [Internet]. Viena, Austria: R Foundation for Statistical Computing; 2023 [cited 2024 Nov 17]. Available from: https://www.R-project.org/ Snijders TA, Bosker R. Multilevel analysis: An introduction to basic and advanced multilevel modeling. 2011. Merlo J. A brief conceptual tutorial on multilevel analysis in social epidemiology: investigating contextual phenomena in different groups of people. J Epidemiol Community Health (1978). 2005;59(9):729–36. Hedges LV, Rhoads CH. Correcting an analysis of variance for clustering. Br J Math Stat Psychol. 2011;64(1):20–37. Ailobhio DT, Ikughur JA. A Review of Some Goodness-of-Fit Tests for Logistic Regression Model. Asian J Probab Stat. 2024;26(7):75–85. Vrieze SI. Model selection and psychological theory: A discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Psychol Methods. 2012;17(2):228–43. Hox JJ, Moerbeek M, van de Schoot R. Multilevel Analysis. 3rd ed. Third edition. | New York, NY: Routledge, 2017. |: Routledge; 2017. 364. World bank. Prevalence of moderate or severe food insecurity in the population (%) [Data set]. [Internet]. 2023 [cited 2025 May 17]. Available from: https://data.worldbank.org/indicator/SN.ITK.MSFI.ZS?locations=NG Inegbedion H. Impact of COVID-19 on economic growth in Nigeria: opinions and attitudes. Heliyon. 2021;7(5):e06943. Omotayo AO, Omotoso AB, Daud SA, Omotayo OP, Adeniyi BA. Rising Food Prices and Farming Households Food Insecurity during the COVID-19 Pandemic: Policy Implications from SouthWest Nigeria. Agriculture. 2022;12(3):363. Gebremichael B, Beletew B, Bimerew M, Haile D, Biadgilign S, Baye K. Magnitude of urban household food insecurity in East Africa: a systematic review and meta-analysis. Public Health Nutr. 2022;25(4):994–1004. Nenguda R, Scholes MC. Appreciating the Resilience and Stability Found in Heterogeneity: A South African Perspective on Urban Household Food Security. Front Sustain Food Syst. 2022;6. Jerumeh TR. Mapping the spatial distribution of household food insecurity in Nigeria. GeoJournal. 2024;89(4):154. Omonona BT, Agoi GA, AN ANALYSIS OF FOOD SECURITY SITUATION AMONG NIGERIAN. URBAN HOUSEHOLDS: EVIDENCE FROM LAGOS STATE, NIGERIA. J Cent Eur. 2007;8(3):397406. Oyebanji A. Factors Influencing the Adoption of Urban Agriculture and Its Effects on Household Food Security in Southwestern Nigeria. Int J Agricultural Econ. 2024;9(5):259–67. Abanda JN, Dama U, Ndoungue VF, Akoa AO, Dzeuta MF, Asobochia AT, et al. Economic Stress, Precariousness and Risk of High Blood Pressure: A Descriptive Survey of Life Models within Households of the Central Region of Cameroon. Health N Hav. 2024;16(02):118–30. Xu X, Wang Q, Li C. The Impact of Dependency Burden on Urban Household Health Expenditure and Its Regional Heterogeneity in China: Based on Quantile Regression Method. Front Public Health. 2022;10. Obayelu OA, Akpan EI. Food insecurity transitions among rural households in Nigeria. Stud Appl Econ. 2021;39(1). Gebremichael B, Egata G, Assefa N. Dietary Diversity Practice and Associated Factors Among Infants and Young Children in Haramaya Town, Ethiopia. Int J Public Health Sci (IJPHS). 2017;6(3). Tadesse Tantu A, Demissie Gamebo T, Kuma Sheno B, Yohannis Kabalo M. Household food insecurity and associated factors among households in Wolaita Sodo town, 2015. Agric Food Secur. 2017;6(1). Bushara OA, Ibrahim HH. Food Security Status for the Household: A Case Study of Al-Qadarif State, Sudan. J Socialomics. 2017;6(4). Yahaya SP, Sanusi RA, Eyinla TE. F.O. Samuel. Household Food Insecurity and Nutrient Adequacy of Under-Five Children in Selected Urban Areas of Ibadan, Southwestern, Nigeria. Afr J Biomed Res [Internet]. 2021 Jan [cited 2025 May 23];24(1). Available from: file:///C:/Users/hp/Downloads/ajol-file-journals_264_articles_209090_submission_proof_209090-3145-519341-1-10-20210623%20(1).pdf Mkhize S, Libhaber E, Sewpaul R, Reddy P, Baldwin-Ragaven L. Child and adolescent food insecurity in South Africa: A household-level analysis of hunger. PLoS ONE. 2022;17(12):e0278191. Adeyanju OZ, Fadupin GT. Household food security status of rural mothers and nutritional status of their children under five years old in Ibadan, Nigeria. World Nutr. 2024;15(4):52–8. Balistreri KS. Family Structure and Child Food Insecurity: Evidence from the Current Population Survey. Soc Indic Res. 2018;138(3):1171–85. Akello MC, Mwesigwa D. Household Size and Household Food Security in Ngetta Ward, Lira City, Northern Uganda. Int J Developing Ctry Stud. 2023;5(1):88–109. Leoni S. A Historical Review of the Role of Education: From Human Capital to Human Capabilities. Rev Polit Econ. 2023;1–18. Augustine M, Kara, Lucy M, Kithu. Education Attainment of Head of Household and Household Food Security: A Case for Yatta Sub - County, Kenya. Am J Educ Res. 2020;8(8):558–66. Josephine Mendy GA, Asongwe, Raymond Ndip Nkongho. Vulnerability to food insecurity and coping strategies of agrarian households in the lower river region of the Gambia: Implication for policy. Int J Agricultural Sci Food Technol. 2020. Chen M, Huang X, Cheng J, Tang Z, Huang G. Urbanization and vulnerable employment: Empirical evidence from 163 countries in 1991–2019. Cities. 2023;135:104208. Borku AW, Utallo AU, Tora TT. Determinants of urban household vulnerability to food insecurity in southern Ethiopia. Discover Food. 2024;4(1):37. Pakravan-Charvadeh MR, Flora C, Khan HA. Simulating Potential Associated Socio-Economic Determinants With Sustainable Food Security (A Macro-Micro Spatial Quantitative Model). Front Public Health. 2022;10. Kota K, Chomienne MH, Yaya S. Examining the disparities: A cross-sectional study of socio-economic factors and food insecurity in Togo. PLoS ONE. 2023;18(11):e0294527. Idisi PO. Food security, economic growth and price stability nexus and conceptual issues. Economic Financial Review. 2021;59(4):9–31. Mead BR, Duncombe T, Gillespie R, Pugh N, Hardman CA. Does urban agriculture contribute to food security, and how might this be achieved? Proceedings of the Nutrition Society. 2024;83(3):195–203. Orsini F, Kahane R, Nono-Womdim R, Gianquinto G. Urban agriculture in the developing world: a review. Agron Sustain Dev. 2013;33(4):695–720. Samuel FO, Eyinla TE, Oluwaseun A, Leshi OO, Brai BIC, Afolabi WAO. Food Access and Experience of Food Insecurity in Nigerian Households during the COVID-19 Lockdown. Food Nutr Sci. 2021;12(11):1062–72. Baltenweck I, Enahoro D, Frija A, Tarawali S. Why Is Production of Animal Source Foods Important for Economic Development in Africa and Asia? Anim Front. 2020;10(4):22–9. Hossain ME, Hoque MA, Giorgi E, Fournié G, Das GB, Henning J. Impact of improved small-scale livestock farming on human nutrition. Sci Rep. 2021;11(1):191. Banda LJ, Tanganyika J. Livestock provide more than food in smallholder production systems of developing countries. Anim Front. 2021;11(2):7–14. Christian AK, Wilson ML, Aryeetey RNO, Jones AD. Livestock ownership, household food security and childhood anaemia in rural Ghana. PLoS ONE. 2019;14(7):e0219310. Smith J, Sones K, Grace D, MacMillan S, Tarawali S, Herrero M. Beyond milk, meat, and eggs: Role of livestock in food and nutrition security. Anim Front. 2013;3(1):6–13. Turk R, Thomas A. Food Insecurity in Nigeria: Food Supply Matters. Sel Issues Papers. 2023;2023(018):1. Adeyemi KD, Ibiyemi T, Taiwo O, Adeyemi B. Household Food Insecurity: Determinants and Coping Strategies in Northwest and Northeast Nigeria. J Hunger Environ Nutr. 2025;1–33. OCHA. HUMANITARIAN NEEDS OVERVIEW NIGERIA. 2023 Dec. Bloch R, Makarem N, Yunusa M, Papachristodoulou N, Crighton M. Economic Development in Urban Nigeria. Urbanisation Research Nigeria (URN) Research Report [Internet]. London; 2015 Jul [cited 2025 May 31]. Available from: https://assets.publishing.service.gov.uk/media/57a08971ed915d3cfd00024c/61250-URN_Theme_B_Economic_Report_FINAL.pdf Guven-Lisaniler F, Tuna G, Nwaka ID. Sectoral choices and wage differences among Nigerian public, private and self-employees. Int J Manpow. 2018;39(1):2–24. Nwaka ID, Emeagwali OL. Participation and returns from informal service-oriented non-farm enterprises: Evidence from a survey of Nigerian households. PLoS ONE. 2024;19(3):e0298794. Ikadeh MS, Cloete CE. The impact of shopping centre development on informal and small businesses in Lagos. Nigeria J Bus Retail Manage Res. 2020;14(03). Obayelu OA, Osho FR. How diverse are the diets of low-income urban households in Nigeria? J Agric Food Res. 2020;2:100018. Omotayo AO, Omotoso AB, Daud SA, Omotayo OP, Adeniyi BA. Rising Food Prices and Farming Households Food Insecurity during the COVID-19 Pandemic: Policy Implications from SouthWest Nigeria. Agriculture. 2022;12(3):363. Okonkwo UU, Ukaogo VO, Ejikeme JNU, Okagu G, Onu A. COVID-19 and the Food Deficit Economy in Southeastern Nigeria. Cogent Arts Humanit. 2021;8(1). Okpoko CC, Onyishi CN, Okeke SV. Public Perception of the Impact of IPOB sit-at-Home Order on the Economic and Educational Development of South-East, Nigeria. Ianna Journal of Interdisciplinary Studies [Internet]. 2025 Jan 1 [cited 2025 Jun 1];7(1):314–26. Available from: https://iannajournalofinterdisciplinarystudies.com/index.php/1/article/view/553 Owoeye DI, Ezeanya VE, Obiegbunam NG. Separatists’ Strategy: Appraising the Effects of IPOB Monday Sit-At-Home Order on political Economy (Socio-Political and Economic activities) of the South-East Region of Nigeria. International Journal of Advanced Academic Research [Internet]. 2022 [cited 2025 Jun 1];8(1):98–108. Available from: https://www.ijaar.org/articles/v8n1/ijaar-v8n1-Jan22-p8171.pdf Tom AB, Snijders, Roel J, Bosker. Multilevel Analysis An Introduction to Basic and Advanced Multilevel Modeling. 2nd ed. SAGE Publications Ltd; 2011. Additional Declarations No competing interests reported. Supplementary Files UrbanFoodInsecurityinNigeriaTablesKDA11052025.docx SupplementaryFile.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8041739","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":581372641,"identity":"11567a89-a95e-4851-9831-87c97ceb6f78","order_by":0,"name":"Kolawole D. Adeyemi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIiWNgGAWjYBACCQbmBiBlAWIbgAgefogEMx4tjA1gCq5Fso2ZRC0MBscIaJFsb2z8zFMhIcfAfnjjY56KbTLG9/uPSTBUWCc24NAizXOwWZrnjIQxA09asTHPmds8ZseY2SQYzqTj1CInkdggndsGJBlyzKR526BaGNsO49PS/Buopb6B/435b95/t3mM20Ba/uHWIi2R2AayJYFBIseMmbfhNo8BG0hLA24tkj0H26z/nJEwbJN4Viw559htHoljycYWCcfSjXFpkTjefPjmjAobeX7+5I0f3tTctudvPvjwxocaa1lcWuCADYiZeGC8BELKYYDxB7EqR8EoGAWjYEQBAMlyUETKPM1aAAAAAElFTkSuQmCC","orcid":"","institution":"Texas Tech University","correspondingAuthor":true,"prefix":"","firstName":"Kolawole","middleName":"D.","lastName":"Adeyemi","suffix":""},{"id":581372642,"identity":"16ebf967-d323-41c5-b91e-0a93afb5aeda","order_by":1,"name":"Biola Adeyemi","email":"","orcid":"","institution":"University of Minnesota","correspondingAuthor":false,"prefix":"","firstName":"Biola","middleName":"","lastName":"Adeyemi","suffix":""},{"id":581372645,"identity":"bd267ba9-f18f-49b4-b207-41cdd4eb9415","order_by":2,"name":"Temitope Ibiyemi","email":"","orcid":"","institution":"Texas State University","correspondingAuthor":false,"prefix":"","firstName":"Temitope","middleName":"","lastName":"Ibiyemi","suffix":""},{"id":581372648,"identity":"0e4bb8a4-0ae2-4b28-947e-5a54b94a6964","order_by":3,"name":"Oak-Hee Park","email":"","orcid":"","institution":"Texas Tech University","correspondingAuthor":false,"prefix":"","firstName":"Oak-Hee","middleName":"","lastName":"Park","suffix":""},{"id":581372650,"identity":"3163ca54-bca9-4e73-bd6d-140364db5b5a","order_by":4,"name":"Jacob Korir","email":"","orcid":"","institution":"Texas Tech University","correspondingAuthor":false,"prefix":"","firstName":"Jacob","middleName":"","lastName":"Korir","suffix":""},{"id":581372651,"identity":"1d26a60d-cb67-4904-9021-c1e05aa1a0d5","order_by":5,"name":"Gasana Elyvine","email":"","orcid":"","institution":"Texas Tech University","correspondingAuthor":false,"prefix":"","firstName":"Gasana","middleName":"","lastName":"Elyvine","suffix":""},{"id":581372652,"identity":"a057895b-76d3-41f8-9531-e270760c061b","order_by":6,"name":"Tomisin Mayaki","email":"","orcid":"","institution":"South Dakota State University","correspondingAuthor":false,"prefix":"","firstName":"Tomisin","middleName":"","lastName":"Mayaki","suffix":""},{"id":581372653,"identity":"db964b9c-0410-4cd6-a40d-cf2676af314c","order_by":7,"name":"Surya Raj Niraula","email":"","orcid":"","institution":"Texas Tech University","correspondingAuthor":false,"prefix":"","firstName":"Surya","middleName":"Raj","lastName":"Niraula","suffix":""}],"badges":[],"createdAt":"2025-11-05 20:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8041739/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8041739/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102209412,"identity":"a760e7fe-9b3d-4d2e-86ba-1db2c2fc2b77","added_by":"auto","created_at":"2026-02-09 12:13:12","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":60968,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic Representation of the Selection of Urban Households in the MICS6 Dataset.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8041739/v1/9008c396c212f28f19919a3d.jpg"},{"id":102209520,"identity":"b04a18e8-8690-4351-8dc5-479748a8c925","added_by":"auto","created_at":"2026-02-09 12:13:33","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":212705,"visible":true,"origin":"","legend":"\u003cp\u003eWeighted proportion of households who are moderately or severe food insecure and severely food insecure across the regions in urban Nigeria.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8041739/v1/5280c0c2b80965a2ae1049b4.jpg"},{"id":102209708,"identity":"90e1426a-c559-4365-8228-8d3430dd1ee2","added_by":"auto","created_at":"2026-02-09 12:13:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2423824,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8041739/v1/f70e7fdd-5018-4b3d-b4dd-88152e4f99a6.pdf"},{"id":102209614,"identity":"f19b82d6-e830-4d00-8bcc-cd8eb1a7edd2","added_by":"auto","created_at":"2026-02-09 12:13:38","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":39851,"visible":true,"origin":"","legend":"","description":"","filename":"UrbanFoodInsecurityinNigeriaTablesKDA11052025.docx","url":"https://assets-eu.researchsquare.com/files/rs-8041739/v1/2d08373b0445e412ea7d778c.docx"},{"id":102209376,"identity":"ad036a72-a4d8-496d-8011-ca025440d249","added_by":"auto","created_at":"2026-02-09 12:13:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":49573,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile.docx","url":"https://assets-eu.researchsquare.com/files/rs-8041739/v1/09bb84bbf844cb4618dd9dcb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Urban Food Insecurity in Nigeria: A Multilevel Analysis of Nationally Representative Data","fulltext":[{"header":"1.0 Background","content":"\u003cp\u003eMigration from rural settings to urban areas in low- and middle-income countries is at an unprecedented rate, introducing complex challenges that disproportionately affect urban populations (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). According to recent United Nations reports, 55% of the world\u0026rsquo;s population currently resides in urban areas, with this proportion expected to rise to 68% by 2050 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Notably, 90% of this growth is projected to take place in cities within developing countries (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). This rapid urbanization has introduced new vulnerabilities, including increased pressure on infrastructure, housing, and social safety nets (\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), all of which have exacerbated food insecurity. Once seen as a primarily rural issue, food insecurity has now emerged as a critical public health challenge in urban environments, particularly in Sub-Saharan Africa, where urbanization often outpaces the development of necessary services and systems (\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Urban food insecurity, characterized by limited or uncertain access to adequate and nutritious food, is becoming an increasingly common concern in many developing nations (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Nigeria, Africa's most populous nation, this urbanization trend is especially pronounced (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The country\u0026rsquo;s urban population is projected to double from 113\u0026nbsp;million in 2020 to 215\u0026nbsp;million by 2050 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), driven by population growth and rural-to-urban migration (Aliyu \u0026amp; Amadu, 2017; Auwalu \u0026amp; Bello, 2023; Tochukwu \u0026amp; Bilyaminu, 2024). However, this rapid expansion has brought significant challenges (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Economic instability, including high inflation, unemployment, and underemployment, has left many urban households struggling to afford necessities, including food (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Additionally, climate-related disruptions and insecurity, such as conflicts and armed banditry in agricultural regions, have further strained food supply chains, compounding the pressures on urban markets (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile urban areas are often perceived as having better access to food due to the concentration of restaurants and food outlets, this availability does not ensure equitable access to nutritious, diverse, and affordable diets for all residents (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Deep socioeconomic disparities, reliance on informal labor markets, high housing costs, and systemic inequalities have contributed to the proliferation of slums and informal settlements (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Globally, urban slums are growing at an alarming rate, with the UN-Habitat estimating that over one billion people currently live in overcrowded urban slums, a figure expected to triple by 2050 (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). About 50% of this growth is concentrated in developing countries such as Nigeria, the Philippines, Ethiopia, Tanzania, India, the Democratic Republic of Congo, Egypt and Pakistan (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). These slums often lack basic amenities such as clean water, sanitation, and healthcare, creating environments where food insecurity is particularly prevalent. In Nigeria, approximately 55% of urban residents live in such conditions (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), underscoring the fragile living situations faced by this population. The current food insecurity situation is leading to significant consequences, including malnutrition and poor health outcomes at the individual level (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), increased financial stress and reduced educational attainment within households (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), and greater social instability, reduced economic productivity, and strained public resources at the community level (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe broader global context further complicates these challenges. Recent crises, including economic shocks (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), the COVID-19 pandemic (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), the war in Ukraine (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), and escalating climate impacts, have disrupted food systems worldwide (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). These disruptions have stalled progress toward Sustainable Development Goal 2.1, which aims to end global hunger by 2030.\u003c/p\u003e \u003cp\u003eAccording to the most recent State of Food Security and Nutrition in the World (SOFI) report, one in twelve people (approximately 673\u0026nbsp;million) faced hunger globally in 2024, while 2.3\u0026nbsp;billion experienced moderate or severe food insecurity (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Africa bears a disproportionate share of this burden, with 20% of its population affected now over 307\u0026nbsp;million people (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), emphasizing the need for urgent and targeted interventions\u003c/p\u003e \u003cp\u003eIn Nigeria, food insecurity has reached concerning levels, with the number of food-insecure individuals projected to rise from 66.2\u0026nbsp;million (approximately 30% of the population) in 2023 to 100\u0026nbsp;million (around 45% of the population) by early 2024 (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Historically, food insecurity in Nigeria has been framed as a rural issue, with limited attention paid to urban areas. However, urban food insecurity is a growing concern, driven by limited access to agricultural resources (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), high living costs, rapid population growth, rural-urban migration, and increase in urban slums (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Nigeria\u0026rsquo;s urban landscape spans the country\u0026rsquo;s six geopolitical zones, North-Central, North-East, North-West, South-East, South-South, and South-West (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e), each exhibiting distinct socio-political and economic conditions that mediate food access and vulnerability in complex ways (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Yet, much of the existing literature remains geographically fragmented, with empirical focus concentrated on individual states (\u003cspan additionalcitationids=\"CR43 CR44\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e) or specific region (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), rather than offering a comprehensive national analysis. This gap constrains our ability to understand the spatial heterogeneity of urban food insecurity and to design interventions that are appropriately tailored to the diverse urban realities across Nigeria.\u003c/p\u003e \u003cp\u003eThis study seeks to address this critical research gap. Thus, the aim of this study was to examine the prevalence and determinants of moderate or severe food insecurity among urban households in Nigeria using a nationally representative data. Through a socio-ecological framework, the research explores individual, household, and community-level factors contributing to food insecurity, shedding light on the structural and socioeconomic drivers at play. The findings from this study aims to inform policies and interventions tailored to the unique challenges of urban populations (and /or communities), with the goal of improving food security and resilience in Nigeria\u0026rsquo;s rapidly expanding cities.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Conceptual Framework\u003c/h2\u003e \u003cdiv id=\"Sec3\" class=\"Section3\"\u003e \u003ch2\u003e1.2.1 The Socio-ecological framework\u003c/h2\u003e \u003cp\u003eIn this study, the Socio-Ecological Model (SEM) was adopted as the conceptual framework to examine the multi-level determinants of food insecurity in urban Nigeria. The SEM recognizes the dynamic interplay between individual, interpersonal, community, and societal-level factors, making it particularly suited to the complex and layered nature of food insecurity (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). The framework guided both the identification of explanatory variables and the multilevel analytical strategy, aligning well with the hierarchical structure of the dataset. This alignment between the framework and statistical models enhances the robustness of our findings.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"2.0 Methodology","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Ethical protocol\u003c/h2\u003e \u003cp\u003eThis study utilized data from the 2021 Nigeria Multiple Indicator Cluster Survey (MICS6), whose protocol was approved by both the Steering and Technical Review Committees. These committees include representatives from the Nigeria\u0026rsquo;s National Bureau of Statistics (NBS), UNICEF, the Federal Ministry of Health, and other key stakeholders (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). According to the MICS6 documentation, verbal informed consent was obtained from all participants, with parental or guardian consent provided for minors (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Participation was voluntary, and confidentiality and anonymity were assured. The de-identified dataset was accessed through a formal request to UNICEF, which granted permission conditional upon dissemination of findings and citing data source. As the study used publicly available, anonymized data, it was exempt from additional ethical review.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Source\u003c/h2\u003e \u003cp\u003eIn the current study, we utilized quantitative data collected cross-sectionally from the 2021 Nigeria Multiple Indicator Cluster Survey (MICS6). The nationally representative survey gathers sociodemographic and health information from males and females aged 15 years and above. Detailed information on the data collection methods can be found in the survey report (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). The data are publicly available and can be accessed with permission from MICS Surveys (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mics.unicef.org/surveys\u003c/span\u003e\u003cspan address=\"https://mics.unicef.org/surveys\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Study Design and Sampling Method\u003c/h2\u003e \u003cp\u003eThis study used a cross-sectional design based on secondary data from the most recent Nigeria Multiple Indicator Cluster Survey (MICS6). A detailed description of the study design is available in the survey\u0026rsquo;s final report (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). The MICS6 survey utilized a multistage stratified cluster sampling approach that employed a probability proportional to size to select enumeration areas in the first stage based on the National Population and Housing Census (2006) of the Federal Republic of Nigeria (NPHC). In the second stage, 20 households of cluster size were selected using systematic random sampling approach within each enumeration area. Only one respondent was chosen randomly to respond on behalf of the household. Data were collected using Computer-Assisted Personal Interviewing (CAPI) technology through face-to-face interviews with respondents in their respective households. To ensure accurate reporting, we accounted for the complex survey design, including weighting, clustering, and stratification, by applying the appropriate sampling weights provided in the MICS6 dataset.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Study Population and Data Integration\u003c/h2\u003e \u003cp\u003eFor this study, we extracted a subsample from the 2021 Multiple Indicator Cluster Survey (MICS6), focusing on households from urban Nigeria (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The original household dataset (HH) comprises 41,532 records, nested within 1,850 communities (clusters) (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). We sub-sampled by the following criteria: survey\u0026thinsp;=\u0026thinsp;MICS, area\u0026thinsp;=\u0026thinsp;URBAN and household who provided consent. To get individual household member characteristics, we merged the women (MN), men (MN), and children (CH) dataset with household (HH). Observations with \"DON'T KNOW (DK)\" and \"NO RESPONSE\" were considered missing and excluded from the analysis (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;147). After cleaning the data, our final sample included 10,680 households (unweighted), which represents 15,535 households in weighted terms. These households are from 575 different communities (clusters). This approach was appropriate because it allowed for the generation of a representative urban-specific sample while preserving the multi-level structure of the data.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Variable Measurement\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Outcome Variable\u003c/h2\u003e \u003cp\u003eThe primary outcome variable of this study was household food insecurity, assessed using the validated eight-item Food Insecurity Experience Scale (FIES) (\u003cb\u003eSupplementary Table A1\u003c/b\u003e) as outlined in the MICS6 methodology (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). The FIES was developed and validated by the Food and Agriculture Organization (FAO) of the United Nations to enable internationally comparable estimates aligned with Sustainable Development Goal (SDG) indicator 2.1 (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Respondents reported their household's food-related experiences over the past 12 months, with each item scored as \"Yes\" (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), \"No\" (0), or \"Don't know\" (DK), following FAO guidelines and prior studies (\u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). A raw FIES score ranging from 0 to 8 was derived by summing affirmative responses.\u003c/p\u003e \u003cp\u003eTo generate globally comparable estimates of food insecurity among adults, we applied the Rasch model, in accordance with FAO's recommended methodology (Cafiero et al., 2018; FAO, 2016). The analysis was conducted using the FIES Shiny App, which applies the Rasch model to fit the data, evaluate model validity and reliability, perform equating to the global reference scale, and generate probabilistic estimates of moderate or severe food insecurity (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Details of the FIES analytical procedures are provided in Supplementary Materials in an \u003cb\u003eAppendix\u003c/b\u003e. The Rasch model is a specific form of Item Response Theory (IRT) that models the probability of a household affirming a given food insecurity item as a function of its latent level of food insecurity and the severity of the item (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Using this model, we derived two standardized indicators: the prevalence of moderate or severe food insecurity (FImod\u0026thinsp;+\u0026thinsp;Sev) and the prevalence of severe food insecurity (FIsev), in accordance with FAO methodology. These indicators correspond to specific thresholds along the latent continuum of food insecurity, with FIsev reflecting more extreme deprivation than FImod\u0026thinsp;+\u0026thinsp;Sev.\u003c/p\u003e \u003cp\u003eThe psychometric performance of the Rasch model in our data was strong. All weighted infit statistics for the eight FIES items fell within the FAO-recommended range of 0.7 to 1.3 (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e), thus confirming the internal consistency of the measure (\u003cb\u003eSupplementary Table A2\u003c/b\u003e). To assess local independence, we examined residual correlations among item pairs. As shown in \u003cb\u003eSupplementary Table A3\u003c/b\u003e, all residual correlations were below the commonly accepted threshold of |0.4| (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). This suggests that each item captures a distinct aspect of the food insecurity experience, and there are no redundant items that might compromise the scale\u0026rsquo;s ability to accurately measure the latent trait of food insecurity. Furthermore, item severity estimates were compared against the FAO global reference scale to assess cross-national equivalence. Most items demonstrated close alignment; however, \u0026ldquo;Few foods\u0026rdquo; and \u0026ldquo;Skipped meals\u0026rdquo; exceeded the FAO\u0026rsquo;s allowable deviation threshold of 0.35 logits and were therefore excluded from the equating process (\u003cb\u003esee Supplementary Table A4\u003c/b\u003e). Together, these results affirm the suitability of the FIES and Rasch model approach in this context, offering a valid, reliable, and cross-culturally comparable measure of latent food insecurity among study participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Independent variables\u003c/h2\u003e \u003cp\u003eThe independent variables analyzed in this study were selected based on a thorough review of the literature, focusing on their relevance to the outcome variable (household food insecurity) and their availability within the MICS6 dataset. Guided by the Socio-ecological framework (\u003cb\u003eSupplementary Table A6\u003c/b\u003e), these variables were organized into three levels: individual, household, and community. At the individual level, the study included age of the household head (categorized as 16\u0026ndash;25, 26\u0026ndash;35, 36\u0026ndash;45, and \u0026gt;\u0026thinsp;45 years) and educational attainment (no formal education, primary, junior secondary, senior secondary, and tertiary education). These variables were included based on prior literature (\u003cspan additionalcitationids=\"CR57 CR58 CR59\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). At the household level, covariates known to influence household food security outcomes were incorporated (\u003cspan additionalcitationids=\"CR62 CR63 CR64 CR65\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e), including household size (\u0026le;\u0026thinsp;5 vs. \u0026gt;5 members), gender of the household head (female vs. male), and household wealth index. The wealth index was constructed using housing characteristics captured in the household questionnaire and categorized into five quintiles: poorest, poorer, middle, richer, and richest (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Additional household characteristics included type of housing tenure (own, rent, or other), agricultural land and livestock ownership (yes/no for each), and the presence of children under five years of age (yes/no). At the community level, geographic regions (North-Central, North-East, North-West, South-West, South-South, and South-West) and aggregated community level wealth quintile were included as community determinants of household food insecurity, in line with existing evidence (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e). Although MICS6 does not directly measure community-level poverty, a proxy for community poverty was derived by calculating the mean household wealth index within each sampling cluster and categorizing communities into low-, middle-, and high community level poverty strata.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe 2021 Nigeria MICS6 dataset employed a complex survey design with non-self-weighting samples due to varying sampling fractions across states. To ensure national representativeness, the analysis incorporated sampling weights, clustering, and stratification, in line with established guidelines for complex survey data (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e). Descriptive statistics, including weighted frequencies and population-level estimates of household characteristics, were computed using the \u003cem\u003esurvey\u003c/em\u003e package in R, developed by Lumley (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e). Given the hierarchical structure of the data (10,680 households nested within 575 communities), a multilevel logistic regression approach was used, with individual and household covariates at Level 1 and community-level factors at Level 2. The dependent variable, moderate or severe food insecurity (MSFI), was operationalized as a binary outcome based on the FIES raw score. Individuals with a score of 4 or higher, reflecting a likelihood of experiencing MSFI, were coded as 1, while those with scores below four were coded as 0, following FAO guidelines (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Analyses were conducted using R (v4.4.1) in RStudio (v2023.06.2\u0026thinsp;+\u0026thinsp;561) and Jupyter Notebook (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 Multilevel Mixed-Effects Analysis\u003c/h2\u003e \u003cp\u003eTo account for the hierarchical structure of the MICS6 dataset, households nested within communities, a two-level multilevel logistic regression model was employed to examine the determinants of moderate or severe food insecurity (MSFI). The intraclass correlation coefficient (ICC) was 13%, indicating that a substantial portion of the variance in food insecurity is attributable to differences across clusters, justifying the use of a multilevel approach. ICC was calculated as follows:\u003c/p\u003e \u003cp\u003eICC = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\:\\frac{{\\delta\\:}2}{\\delta\\:2\\:+\\:\\pi\\:2/3}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\delta\\:}2\\)\u003c/span\u003e\u003c/span\u003e indicates the estimated variance of cluster (\u003cspan additionalcitationids=\"CR73\" citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e). The binary outcome variable (MSFI) was modeled using a generalized linear mixed-effects model with a LOGIT link and binomial distribution. Random intercept models were fitted to estimate the fixed effects of individual, household, and community level predictors, while accounting for between-cluster variation and survey weights (\"hhweightmics\"). Model estimation was performed using the \u003cem\u003eglmer\u003c/em\u003e function from the \u003cem\u003elme4\u003c/em\u003e package (v1.1-35.5) in R, employing maximum likelihood estimation with Laplace approximation (nAGQ\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e \u003cp\u003eTo account for the binary nature of our outcome variable, the individual/household-level data is nested within higher-level categories (i.e. clusters), we fitted a series of two-level random intercept\u003c/p\u003e \u003cp\u003emodels to estimate the impact of individual/household and community-level factors on food insecurity.\u003c/p\u003e \u003cp\u003eWe employed a generalized linear mixed model with a binomial distribution and the LOGIT link function to compute the odds of moderate or severe food insecurity (MSFI).\u003c/p\u003e \u003cp\u003eFour models were specified:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eNull model: no predictors, to estimate baseline variance.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eModel I: individual and household-level predictors (Level 1).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eModel II: community-level predictors (Level 2), including geopolitical zone and aggregated community wealth.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eModel III: Full model incorporating both Level 1 and Level 2 predictors.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe general model took the form:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{l}\\text{o}\\text{g}\\text{i}\\text{t}\\left({\\text{p}}_{\\text{i}}\\right)=\\text{l}\\text{o}\\text{g}\\left(\\frac{{{\\pi\\:}}_{ij}}{1-{{\\pi\\:}}_{ij}}\\right)={{\\beta\\:}}_{0}-{{\\beta\\:}}_{1}{X}_{1ij}+{{\\beta\\:}}_{2}{X}_{2ij}\\:+\\:\\dots\\:\\:+\\:{{\\beta\\:}}_{k}{X}_{kij}+{u}_{j}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\pi\\:}_{ij}\\)\u003c/span\u003e\u003c/span\u003e is the probability of MSFI for household \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i,\\)\u003c/span\u003e\u003c/span\u003e in cluster (community) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:jth\\)\u003c/span\u003e\u003c/span\u003e, are predictor variables at either level, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{0}\\)\u003c/span\u003e\u003c/span\u003e are fixed effects, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{u}_{j\\:}\\)\u003c/span\u003e\u003c/span\u003eand represent the cluster-level random effect, assumed normally distributed with variance \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{u}^{2}.\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eA Likelihood Ratio Test (LRT) comparing the null two-level model to a single-level model strongly rejected the null hypothesis of zero between-cluster variance (LRT\u0026thinsp;=\u0026thinsp;1697.59 (-2*(-848.79)) with 1 degree of freedom), confirming significant clustering. Additionally, we evaluated the model fit using the log-likelihood, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e), and the ICC criteria (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e) (as shown in \u003cb\u003eSupplementary Table A7\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3.0 Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Characteristics of the sampled households\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the demographic data, indicating that a large majority of household heads were male, making up 77.4% of the sample. Nearly half (49.03%) were over the age of 45, and the majority (85.86%) had received formal education. However, only about 30% of these heads had higher/tertiary education. Additionally, more than half of the households consist of fewer than five members (68.33%). Approximately 42% of the households in this study were situated in the South-West geopolitical zone. Regarding home ownership, just over half of the respondents (55.95%) resided in rented apartments (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). With respect to agricultural assets, 28.33% of households reported owning agricultural land, while approximately 20% owned livestock. In terms of household wealth, the majority (60.61%) fell within the middle to richest wealth categories. However, at the community level, a substantial proportion (83.95%) was classified as high poverty level, based on the concentration of households in the lower wealth quintiles within those communities. (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eSocio-demographic and economic characteristics of urban household, MICS6\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency (N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeighted Frequency (N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProportion (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual level variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHH head Age (Years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u0026ndash;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHH head Education\u003c/b\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher/tertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold level variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHH head Gender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHH size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of household ownership\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOwn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold Wealth Quintile\u003c/b\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold Own Agricultural Land\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold Own Livestock\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of children under 5 years old\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCommunity level variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeographical Zones\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth-Central\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth-East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth-West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth-East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth-South\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth-West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCommunity poverty level/quintile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eHH\u003c/b\u003e\u0026thinsp;=\u0026thinsp;Household\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e*\u003c/b\u003eIn the other context, these education levels roughly correspond to: less than high school (primary), high school diploma or equivalent (junior and senior secondary), and college degree or higher (tertiary education).\u003c/p\u003e \u003cp\u003e \u003cb\u003e**\u003c/b\u003eHousehold wealth index was constructed using housing characteristics captured in the household questionnaire (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Prevalence of household FI in urban Nigeria\u003c/h2\u003e \u003cp\u003eThe data presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and (\u003cb\u003eSupplementary Table A5)\u003c/b\u003e underscore the widespread prevalence of food insecurity among urban households in Nigeria. Nationally, an estimated 68.56% of urban households experienced moderate or severe food insecurity (FImod\u0026thinsp;+\u0026thinsp;Sev), while 27.19% were classified as severely food insecure (FIsev). Disaggregated responses to individual FIES items offer additional insight into the nature and severity of food insecurity in urban Nigeria (\u003cb\u003esee Supplementary Table A5\u003c/b\u003e). While these item-level responses are not intended to serve as standalone indicators, they help illuminate the lived experiences and specific challenges that households face in accessing sufficient and nutritious food. Approximately 76% of respondents reported having \u0026ldquo;worried about not having enough food,\u0026rdquo; while 71.7% indicated that they \u0026ldquo;were unable to eat healthy and nutritious food,\u0026rdquo; and a similar proportion stated that they \u0026ldquo;ate only a few kinds of foods.\u0026rdquo; Moreover, 64.8% reported that they \u0026ldquo;had to skip a meal,\u0026rdquo; and 68.9% said they \u0026ldquo;ate less than they thought they should\u0026rdquo; due to lack of resources. Additionally, 57.1% of respondents \u0026ldquo;ran out of food,\u0026rdquo; 49.7% \u0026ldquo;were hungry but did not eat,\u0026rdquo; and 28.7% reported that they \u0026ldquo;went without eating for a whole day.\u0026rdquo; These patterns reflect the depth and gradation of food insecurity, from anxiety about food access to more severe experiences of deprivation. Notably, substantial regional variation was observed: the South-East (75.71%) and North-Central (75.13%) zones recorded the highest prevalence of moderate or severe food insecurity, whereas the South-West reported the lowest (64.61%), indicating marked geographic disparities in food access.\u003c/p\u003e \u003cp\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\u003eMultivariate multilevel logistic regression analysis of determinants of moderate or Severe Food insecurity among urban households in Nigeria, MICS6.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNull Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eModel I*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eModel II**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eModel III***\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects intercepts\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.55 (0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.54 (0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.45 (0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndividual-/household-level factors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \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\u003eAOR [95% CI]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eAOR [95% CI]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eAOR [95% CI]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHH Gender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03 [0.92, 1.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.05 [0.94, 1.17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHH Age (Years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.24 [1.00, 1.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.26 [1.01, 1.57]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u0026ndash;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.49 [1.20, 1.85]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.50 [1.20, 1.86]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.24 [1.00, 1.54]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.24 [1.00, 1.54]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.37 [1.24, 1.52]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.38 [1.24, 1.53]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHH Education\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.59 [1.36, 1.87]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.53 [1.31, 1.80]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.52 [1.19, 1.92]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.47 [1.15, 1.86]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.40 [1.20, 1.64]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.36 [1.16, 1.59]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher/tertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16 [0.99, 1.37]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.13 [0.96, 1.33]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold Own Dwelling\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOwn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.73 [0.60, 0.88]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.73 [0.61, 0.88]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11 [0.93, 1.34]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.09 [0.91, 1.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold Wealth Quintile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.78 [0.66, 0.92]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.08 [0.06, 0.10]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.53 [0.45, 0.62]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.49 [0.41, 0.58]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.28 [0.23, 0.33]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.74 [0.62, 0.87]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.09 [0.07, 0.11]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.26 [0.21, 0.31]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold Own Agricultural Land\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.89 [0.80, 0.99]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.89 [0.80, 0.99]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.026\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold Own Livestock\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.78 [0.69, 0.87]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.80 [0.71, 0.90]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHaving children under 5 years old\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.80 [0.71, 0.91]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.81 [0.72, 0.92]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCommunity-level factor\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eZones\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth-Central\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth-East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.47 [0.33, 0.65]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.37 [0.26, 0.53]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth-West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.62 [0.46, 0.82]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.54 [0.39, 0.73]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth-East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.26 [0.91, 1.74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.49 [1.04, 2.14]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth-South\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72 [0.54, 0.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.79 [0.58, 1.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth-West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.57 [0.45, 0.71]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.55 [0.43, 0.71]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCommunity-level Poverty/Wealth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.31 [0.74, 2.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.18 [0.64, 2.16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.80 [0.46, 1.38]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.41 [0.77, 2.58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.3\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\u003eHH: household head \u003cb\u003eNotes\u003c/b\u003e: All estimates are weighted for the survey\u0026rsquo;s complex sampling design; Bolded text indicates statistical significance at \u0026lt;\u0026thinsp;.05. \u003cb\u003eNote\u003c/b\u003e: Results based on a generalized linear mixed model (GLMM) with a binomial outcome (moderate/severe food insecurity), fitted using maximum likelihood estimation (Laplace Approximation). The model was specified with a logit link function and included adaptive Gauss-Hermite quadrature (nAGQ\u0026thinsp;=\u0026thinsp;1). All estimates were derived using the glmerMod function in R.\u003c/p\u003e \u003cp\u003e*Model I: Individual and household level variables, **Model II: Community level variables, ***Model III: Individual, Household and Community level variables\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.3 Multilevel mixed effect logistic regression analysis for the multilevel factors of moderate to severe food insecurity\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of the multilevel regression analysis, which highlighted several significant predictors of moderate or severe food insecurity (MSFI). After adjusting for both individual and household characteristics, as well as community-level factors (\u003cb\u003eModel III\u003c/b\u003e), several key determinants emerged. Full model parameters are presented in \u003cb\u003eAppendix\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eAt the individual and household levels, the age of the household head emerged as a significant predictor of moderate or severe food insecurity. Compared to households headed by individuals aged 16\u0026ndash;25, those led by individuals aged 26\u0026ndash;35 exhibited a 26% higher likelihood of experiencing moderate or severe food insecurity (AOR\u0026thinsp;=\u0026thinsp;1.26, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04). The association was even more pronounced among the 36\u0026ndash;45 age group, who faced a 50% increase in the odds of food insecurity (AOR\u0026thinsp;=\u0026thinsp;1.50, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Educational attainment of the household head was also significantly associated with food insecurity. Specifically, households in which the head had attained primary or secondary education exhibited higher odds of experiencing moderate or severe food insecurity compared to those with no formal education.\u003c/p\u003e \u003cp\u003eLarger household size was another significant factor. Households with more than five members were more likely to experience moderate or severe food insecurity (AOR\u0026thinsp;=\u0026thinsp;1.37, 95% CI: 1.24, 1.52, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) compared to those with five or fewer members. In contrast, household wealth acted as a protective factor. Higher household wealth significantly reduced the odds of experiencing moderate or severe food insecurity (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). As the household wealth index increased, the likelihood of food insecurity decreased, with the wealthiest households showing significantly lower odds of food insecurity. Similarly, access to agricultural assets also offered significant protection against food insecurity. Households with agricultural land had nearly 1.2 times lower odds of experiencing moderate or severe food insecurity (AOR\u0026thinsp;=\u0026thinsp;0.89, 95% CI: 0.80\u0026ndash;0.99, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026) compared to households without agricultural land. Households with livestock had about 1.3 times lower odds (AOR\u0026thinsp;=\u0026thinsp;0.80, 95% CI: 0.71\u0026ndash;0.89, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e \u003cp\u003eFurthermore, households with children under five years of age were 1.2 times less likely to experience moderate or severe food insecurity (AOR\u0026thinsp;=\u0026thinsp;0.81, 95% CI: 0.72\u0026ndash;0.92, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) compared to households without children in that age category, highlighting the potential influence of family dynamics on food security.\u003c/p\u003e \u003cp\u003eFinally, homeownership was also significantly associated with reduced odds of food insecurity. Specifically, households that owned their homes had 1.4 times lower odds of experiencing food insecurity compared to those that did not own their homes (AOR\u0026thinsp;=\u0026thinsp;0.71, 95% CI: 0.59\u0026ndash;0.85, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). At the community level, significant regional disparities emerged. Households in the North-East and North-West zones of urban Nigeria had lower odds of moderate or severe food insecurity compared to those in the North-Central zone (AOR\u0026thinsp;=\u0026thinsp;0.38, 95% CI: 0.27, 0.53, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001 and AOR\u0026thinsp;=\u0026thinsp;0.51, 95% CI: 0.37, 0.69, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, respectively). The South-West region also had lower odds of moderate or severe food insecurity (AOR\u0026thinsp;=\u0026thinsp;0.56, 95% CI: 0.44, 0.72, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, households in the South-East region had significantly higher odds of experiencing moderate or severe food insecurity (AOR\u0026thinsp;=\u0026thinsp;1.49, 95% CI: 1.04, 2.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.029).\u003c/p\u003e \u003c/div\u003e"},{"header":"4.0 Discussion","content":"\u003cp\u003eFood insecurity remains a pressing public health concern in Nigeria, with vulnerable populations disproportionately affected. Although rural food insecurity has been widely examined, urban contexts, marked by rapid urbanization and deepening socioeconomic inequalities, have received limited empirical attention. This study addresses that gap, showing 7 out of 10 urban households experienced moderate to severe food insecurity. This study identified several key socioeconomic and demographic determinants: household head age, educational attainment, and household size were associated with increased risk of household food insecurity (HFI). Conversely, home ownership, household wealth, ownership of livestock or agricultural land, having children under five, and residing in certain regions were associated with reduced likelihood of HFI.\u003c/p\u003e \u003cp\u003eThe prevalence of moderate to severe food insecurity observed in our study (68.56%) aligns closely with the national estimate of 73.9% reported by the World Bank in 2022 (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e). However, it is notably higher than the 18% reported by Jung (2023), who used data from the 2018/19 Nigeria General Household Survey, which used the same measurement tool (FIES). This discrepancy, while notable, likely reflects the significant economic and social shifts that have occurred since 2019, most notably the COVID-19 pandemic and subsequent economic disruptions (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e), including sustained inflation, which have substantially impacted food access and affordability across Nigeria (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e). While Studies have shown that food insecurity is a serious issue in many urban areas across sub-Saharan Africa (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e), the rate observed in our study is notably among the highest. For example, a systematic review and meta-analysis of urban food insecurity in East Africa reported a pooled prevalence of 60.9%, with country-specific estimates ranging from 36.5% in Burundi to 91% in Sudan (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e). Similarly, a multi-city study across 11 Southern African urban centers found that over 60% of urban households experienced severe food insecurity, with rates reaching 90% in informal settlements in Lusaka, Zambia (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e). It is important to note that many of these studies employed the Household Food Insecurity Access Scale (HFIAS), which differs methodologically from the Food Insecurity Experience Scale (FIES) employed in our analysis. Despite these differences in measurement tools, the findings collectively underscore that while urban Nigeria experiences especially high levels of food insecurity, the prevalence observed in our study aligns with broader patterns reported across other African cities. This highlights the widespread and pressing nature of urban food insecurity across the continent.\u003c/p\u003e \u003cp\u003eIn this study, households headed by individuals aged 26\u0026ndash;35 and 36\u0026ndash;45 years are significantly more likely to experience MSFI compared to younger household heads aged 16\u0026ndash;25 years. This aligns with previous studies from Nigeria and other similar regions (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan additionalcitationids=\"CR84\" citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e), which has shown that food insecurity tends to increase with the age of the household head. For instance, a recent spatial analysis of household food insecurity by Jerumeh (2024) found that older household heads were more likely to experience food insecurity across Nigeria\u0026rsquo;s 109 senatorial districts, particularly in high-risk regions such as the North (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e). This trend likely reflects the socioeconomic burdens commonly faced by individuals in this age group, including the dual pressures of supporting families and managing multiple financial obligations (\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e), pressures that are further intensified by the high cost of living in urban areas (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study found that larger households, with more than five members, were more likely to experience MSFI, while households with children under five years were less likely to experience it. This highlights the complex relationship between household composition and food insecurity. The study finding that larger households (more than five members) were more likely to experience food insecurity, aligned with previous studies conducted in Nigeria (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e), Ethiopia (\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e), and Sudan (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e). However, our finding that households with children under five years were less vulnerable to food insecurity contrasts with several prior studies (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan additionalcitationids=\"CR93\" citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e), which have reported a heightened risk among such households. For example, a study conducted in Ibadan, Nigeria, found that households with under-five children living in urban slums were seven times more likely to experience food insecurity compared to those in rural slums (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). Similarly, Yahaya et al. (2021) reported that 63% of households with under-five children in urban Ibadan were food insecure. In South Africa, research has shown that households with children, including those under five, had approximately 1.7 times the odds of experiencing food insecurity compared to those without children (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e). A plausible explanation for our contrasting findings may lie in the protective behaviors exhibited by households with young children. Evidence suggests that adults in such households often prioritize children's nutritional needs, a phenomenon referred to as the \"buffering\" effect, where adults absorb the brunt of food insecurity to shield children from its most severe consequences (\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e). This dynamic may contribute to the appearance of lower food insecurity levels in households with young children, despite underlying adult hardship. Meanwhile, the impact of larger households on food insecurity likely reflects the increased competition for limited resources and higher consumption demands for larger households (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe role of education in mitigating food insecurity is well-established, as it represents a critical component of human capital that enhances earning potential, decision-making abilities, and access to resources (\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e). However, our findings reveal a different relationship between the education level of household heads and food insecurity. Specifically, household heads with lower levels of education (primary or secondary) were significantly more likely to experience MSFI, likely reflecting limited access to stable employment, underemployment, and low-paying jobs associated with lower educational attainment (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). Surprisingly, this study did not find a statistically significant relationship between higher levels of education and odds of experiencing MSFI. Our findings diverge from earlier studies in urban areas in Nigeria, including those by (Adesoye \u0026amp; Adepoju, 2020; Omonona \u0026amp; Agoi 2007 \u0026amp; Roberts et al., 2019) which found that formal education (secondary and higher levels) reduced the likelihood of food insecurity. However, our findings were consistent with studies from Kenya and The Gambia, that reported that households with heads who had lower education levels (primary or secondary) were more likely to face severe food shortages (\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e). These mixed findings highlight the context-dependent nature of the education\u0026ndash;food security relationship. While education can provide advantages in labor market participation and access to services, its protective effects may be undermined in urban settings characterized by high living costs, wage inequality, underemployment, and dependence on volatile market-based food systems. In such environments, even highly educated individuals may struggle to convert educational capital into food security, particularly in the face of systemic economic pressures and limited employment opportunities (\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e). Our results underscore the importance of moving beyond education as a standalone solution. While expanding access to quality education remains essential, it must be complemented by broader structural interventions that address employment quality, fair wages, social protection, and urban living conditions.\u003c/p\u003e \u003cp\u003eAdditionally, our study findings that households owning their homes were less likely to experience MSFI compared to others, aligned with findings from other studies from Nigeria (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), Ethiopia,(\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e) and Iran (\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e). For example, a study by Roberts and colleagues among urban households in Lagos, Nigeria, found that respondents who owned their homes were more likely to be food secure compared to those living in rented homes (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Homeownership can reduce household vulnerability by eliminating or lowering monthly rent obligations, thereby freeing up income for food and other essential needs. This is particularly relevant in a report by UN-Habitat, which highlights that homeownership in urban settings contributes to economic stability and reduces financial strain, which are critical factors in achieving food security (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The reduced exposure to market-driven housing instability may also contribute to more consistent access to food. Taken together, these findings suggest that housing stability, as afforded by homeownership, plays a significant role in supporting household food security, particularly in economically pressured urban environments.\u003c/p\u003e \u003cp\u003eSimilarly, our findings further revealed a clear inverse relationship between household wealth and food insecurity, as household wealth increased, the likelihood of experiencing MSFI For example, a study by Roberts and colleagues among urban households in Lagos, Nigeria, found that respondents who owned their homes were more likely to be food secure compared to those living in rented accommodations significantly decreased. This reinforces the well-documented role of financial resources and economic stability as key protective factors against food insecurity. Our findings are in line with a growing body of evidence from across sub-Saharan Africa that highlights the strong association between wealth status and food security outcomes (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e, \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e). For example, Gebremichael et al. (2022) reported that in East Africa, households in the poorest wealth quintile were nearly four times more likely to experience food insecurity compared to their wealthier counterparts. Similarly, a study in Togo found that those in the lowest wealth quintile had significantly higher relative risk ratios for both moderate and severe food insecurity 2.21 and 3.58 respectively, compared to wealthier households (\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e). Evidence from urban Ethiopia echoes this trend, showing that households with lower economic standing were 3.51 times more likely to be food insecure than those with relatively better financial means (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). These patterns underscore the critical influence of economic resources on household food security, particularly in urban Nigeria, where rising living costs and dependence on market-based food systems heighten vulnerability (\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e). In such settings, households with limited financial buffers are more susceptible to food price fluctuations, housing costs, and income shocks, all of which increase food insecurity.\u003c/p\u003e \u003cp\u003eOur study found that ownership of agricultural land and livestock is associated with reduced odds of experiencing food insecurity highlighting the significant role that land-based assets play, even in urban settings. This result suggests that households with access to agricultural land are more likely to engage in subsistence or semi-subsistence farming, which serves as a direct buffer against food insecurity. Urban agriculture, facilitated by agricultural land ownership, emerges as a vital strategy to combat food insecurity (\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e). It allows households to grow their own produce, supplementing diets and reducing dependence on costly market-purchased foods. This practice not only provides a stable source of nutritious food but also helps mitigate the impact of food price fluctuations (\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e). The significance of these practices became even more apparent during COVID-19 pandemic when supply chain disruptions highlighted the vulnerability of urban food systems (\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e). It is noteworthy that data for this study was collected during the Covid-19 post-pandemic recovery period in Nigeria, reinforcing the timeliness and relevance of our findings. Our results align with those of Nwaka et al., (2020), who similarly found that urban households with access to land were significantly less likely to experience food insecurity. Livestock ownership emerged as another significant asset in mitigating food insecurity. Small-scale animal husbandry, such as raising poultry, goats, or rabbits, can serve as a sustainable source of protein and micronutrients, while also offering economic benefits through the sale of eggs, milk, or meat (\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e, \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e). Beyond its nutritional contributions, livestock ownership also functions as a form of savings or liquid assets, which can be mobilized during times of crisis (\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e). Evidence from rural and urban areas in Kenya and Ghana, have documented livestock farming as a viable strategy for poverty reduction and household economic empowerment (\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e). Our findings highlight the importance of land-based assets in urban food security strategies, suggesting that policies promoting urban agriculture and small-scale livestock keeping could significantly contribute to reducing food insecurity in urban areas.\u003c/p\u003e \u003cp\u003eAt the community level, our study highlights significant regional disparities in household food insecurity across urban Nigeria. Urban households in the North-East, North-West, and South-West were significantly less likely to be MSFI compared to those in the North-Central region, while the South-East showed notably higher risk.\u003c/p\u003e \u003cp\u003eThe lower odds of MSFI observed in the North-East and North-West were unexpected, given these regions well-documented challenges with conflict and environmental shocks such as droughts and floods which have impacted on the food insecurity situation (\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e, \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e). A plausible explanation for the observed results may lie in the concentration of food assistance programs through humanitarian organizations in these regions. Urban centers such as Maiduguri, Yola, and Damaturu have emerged as key hubs for aid distribution, potentially mediating improved food security outcomes relative to surrounding areas. Organizations such as the World Food Programme (WFP) and the United Nations have been actively involved in providing food aid, nutrition support, and other essential services to vulnerable populations, including internally displaced persons (IDPs) residing in these areas (\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e). These interventions may have mitigated food insecurity risk in these regions. However, it is important to note that our findings still report relatively high estimated prevalence rates of MSFI in both the North-East (66.11%) and North-West (68.76%), underscoring that while humanitarian aid may buffer some effects, food insecurity remains a substantial concern in these regions.\u003c/p\u003e \u003cp\u003eIn contrast, the reduced odds of food insecurity observed among urban households residing in the South-West region likely reflect the long-standing structural, economic, and geographic advantage that have historically positioned the region more favorably in terms of food access and livelihood opportunities. The South-West is home to major urban centers like Lagos and Ibadan, which benefit from relatively better infrastructure, market access, and diversified income-generating activities compared to other regions (\u003cspan additionalcitationids=\"CR117\" citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e). These cities also attract more public and private investments and support more robust informal and formal employment sectors (\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e, \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e, \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e), all of which contribute to increased household purchasing power and food security. However, our findings also show that these advantages are not generally experienced. The prevalence of MSFI (66.91%) within the South-West underscores the persistent influence of income inequality (\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e), and high food prices (\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e), which continues to undermine food access for a segment of the population. This highlights the necessity of targeted and inclusive food security policies, even in seemingly advantaged urban settings.\u003c/p\u003e \u003cp\u003eOf particular concern is the increased likelihood of food insecurity among urban households in Nigeria\u0026rsquo;s South-East region, which exhibited the highest estimated prevalence of moderate or severe food insecurity (MSFI) in our study, at 75.71%. Several structural and political factors may contribute to this pattern. This evidence may have stem from structural and economic disadvantages, including limited infrastructure (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), weak food distribution systems, and high urban food prices in this region (\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e). In recent years, the region has also experienced episodic insecurity and economic disruption linked to political instability and the \u003cem\u003esit-at-home\u003c/em\u003e orders enforced by the Indigenous People of Biafra (IPOB) (\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e, \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e). These disruptions have significantly impeded daily economic activity, especially for informal workers (\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e, \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e), hereby reducing household income and limiting food access. Combined with the lingering effects of the COVID-19 pandemic (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e), these factors may have further exacerbated existing vulnerability.\u003c/p\u003e \u003cp\u003eTogether, these findings highlight the complex interplay between structural, political, and geographic factors that shape food insecurity in urban Nigeria. They highlight an urgent need for context-specific interventions that reflect the heterogeneity of urban experiences, moving beyond a one-size-fits-all approach toward more spatially and socioeconomically responsive policy design. Future research should also prioritize the collection of regionally disaggregated, urban-specific data to inform targeted responses and monitor shifting food insecurity dynamics in the aftermath of shocks.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Implications for research, policy and practice\u003c/h2\u003e \u003cp\u003eThis study compels a fundamental reorientation of how food insecurity is conceptualized and addressed in rapidly urbanizing contexts such as Nigeria. While rural food insecurity continues to receive extensive scholarly and policy attention, our findings indicate that about 7 in 10 urban households face moderate to severe food insecurity, making a strong case for repositioning urban food insecurity as a central public health and development priority in Nigeria and similar areas across Sub-Saharan Africa.\u003c/p\u003e \u003cp\u003eFrom a research perspective, there is a clear need for more regionally disaggregated, longitudinal, and urban-focused studies that can unpack the evolving dynamics of household food insecurity in urban settings. Future work should explore how structural factors such as housing, employment quality, inflation, and political instability within each geopolitical zone (e.g., \u003cem\u003e\u0026ldquo;sit-at-home\u0026rdquo;\u003c/em\u003e orders in the South-East) interact with household characteristics to influence food access and HFI over time. Moreover, a mixed-methods approach could help explain counterintuitive findings, such as the protective behavior observed in households with young children or the non-significant associations between higher education and food security.\u003c/p\u003e \u003cp\u003eOn the policy front, the findings highlight the need for targeted, evidence-based strategies that reflect the specific socioeconomic and demographic realities of urban Nigeria, rather than relying solely on broad national approaches such as federal safety net programs. The protective role of homeownership, household assets, and land-based livelihoods points to the importance of affordable housing programs and urban livelihood support as foundational components of food security policy. Additionally, region-specific strategies informed by local data should guide intervention efforts, especially in high-risk urban areas. To address vulnerabilities tied to age, education, and household size, strengthened social protection mechanisms, including food vouchers, can provide critical support. Finally, investments in infrastructure for food storage, transportation, and distribution will improve food availability and affordability across urban centers.\u003c/p\u003e \u003cp\u003eFor practice, community-based intervention such as urban farming, backyard livestock rearing, and rooftop gardening present scalable opportunities to build household resilience. Programs that offer technical training, access to micro-credit, and market linkages can support the productive use of land-based assets among urban households. Additionally, housing stability, as indicated by the protective effect of homeownership, should be integrated into broader food security programming.\u003c/p\u003e \u003cp\u003e In summary, this study reinforces the need for a multi-sectoral, region-based approach to urban food insecurity, one that combines evidence, responsive policy, and locally grounded practice to build more equitable and resilient urban food systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Strengths and Limitations\u003c/h2\u003e \u003cp\u003eOur study has several notable strengths that enhance its relevance in addressing food insecurity among urban households in Nigeria. A key strength lies in its contribution to filling the research gap on food insecurity within urban settings, where limited evidence exists compared to rural areas. By providing detailed insights into the prevalence and determinants of food insecurity, our findings hold significant potential to guide the development of evidence-based interventions tailored to urban contexts. A notable methodological strength is the use of the Food Insecurity Experience Scale (FIES) analyzed through the Rasch model, in line with FAO guidelines (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). This approach leverages the probabilistic framework of Item Response Theory to generate internally consistent and cross-culturally comparable estimates of food insecurity severity. The Rasch model not only ensures unidimensionality and item-level fit but also facilitates the calibration of individual household responses along a latent food insecurity continuum, enabling the derivation of prevalence indicators (moderate/severe and severe food insecurity) with psychometric rigor. Furthermore, the use of nationally representative survey data enhances the external validity of our results, allowing for greater generalization across diverse urban populations in Nigeria. The application of a multilevel logistic regression model represents an additional strength, as it appropriately accounts for the hierarchical nature of the data (households nested within communities). This modeling strategy adjusts for intra-cluster correlation and yields more precise estimates of standard errors and effect sizes, thereby mitigating the risk of Type I errors and strengthening the internal validity of our conclusions (\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite these strengths, our study has several limitations. One significant limitation is the cross-sectional nature of the data, which restricts our ability to establish causal relationships between the determinants and outcomes of food insecurity. Additionally, while the use of the Food Insecurity Experience Scale (FIES) enhances international comparability, it relies on self-reported data, which may introduce response bias. The timing of the survey, conducted as Nigeria and the rest of the world were recovering from the COVID-19 pandemic, presents another limitation. The pandemic caused significant economic and social disruptions, heightening food insecurity levels and potentially leading to findings that reflect a temporary crisis rather than longer-term trends. Moreover, the study does not fully capture seasonal variations, which could offer deeper insights into urban food insecurity dynamics.\u003c/p\u003e \u003c/div\u003e"},{"header":"5.0 Conclusions","content":"\u003cp\u003eThis study found that nearly 7 out of 10 urban households in Nigeria experience moderate or severe food insecurity, emphasizing the urgent public health challenge posed by urban food insecurity in the context of rapid urbanization and economic instability. The findings underscore the need for interventions and policies to address economic disparities and enhance access to affordable, nutritious food. Community-based strategies, such as urban agriculture and local food distribution networks, are recommended to provide immediate relief and foster long-term food security.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e6.1.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Ethics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data for this study were obtained from MIC through a formal request, which was approved with the condition to share the research findings. Since the study used publicly available, de-identified data, it was exempt from the human subject research approval process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.2.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized publicly available, de-identified secondary data from the 2021 Nigeria Multiple Indicator Cluster Survey (MICS6). According to the survey report, verbal informed consent was obtained from all respondents prior to participation\u0026nbsp;(48). All participants were informed of the voluntary nature of their involvement, the confidentiality and anonymity of their responses, and their right to decline to answer any questions or to terminate the interview at any time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.3.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.4.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Data availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets utilized for this study are publicly available and can be accessed with permission from MICS Surveys (https://mics.unicef.org/surveys\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.5.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Competing 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\u003e6.6.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.7.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Author contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eK.D. A.: Conceptualization, Methodology, Validation, Data Analysis, Writing – Original Draft, Writing – Review \u0026amp; Editing; B. A.: Methodology, Validation, Data Curation, Data Analysis; S.R. N.: Methodology, Validation, Data Analysis, Writing – Review \u0026amp; Editing, Supervision; O.-H. P.: Supervision, Validation, Writing – Review \u0026amp; Editing; T. I.: Validation, Writing – Review \u0026amp; Editing; J. K.: Writing – Review \u0026amp; Editing; G. E.: Writing – Review \u0026amp; Editing; T. M.: Writing – Review \u0026amp; Editing. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.8.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Acknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe express our gratitude to the UNICEF MICS team for their assistance in accessing the data and addressing our questions regarding the survey's methodology and variables.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eUN DESA. The 2018 Revision of the World Urbanization Prospects [Internet]. 2018 May [cited 2025 Jan 24]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.un.org/en/desa/2018-revision-world-urbanization-prospects\u003c/span\u003e\u003cspan address=\"https://www.un.org/en/desa/2018-revision-world-urbanization-prospects\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoliman AM. Urbanization and Urban Informality in the Era of Globalization. Urban Informality. Cham: Springer International Publishing; 2021. pp. 85\u0026ndash;120.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi X, Stringer LC, Dallimer M. The Impacts of Urbanisation and Climate Change on the Urban Thermal Environment in Africa. Climate. 2022;10(11):164.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIPCC, Zhai P, Pirani A, Connors SL. In: Chen Y, et al. editors. Climate Change 2021 \u0026ndash; The Physical Science Basis. Val\u0026eacute;rie Masson-Delmotte. Clotilde P\u0026eacute;an: Cambridge University Press; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbu Hatab A, Cavinato MER, Lindemer A, Lagerkvist CJ. Urban sprawl, food security and agricultural systems in developing countries: A systematic review of the literature. Cities. 2019;94:129\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNgcamu BS. The effects of urbanisation on food security in Africa: An overview and synthesis of the literature. Environ Socio-economic Stud. 2022;10(2):40\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWudil AH, Usman M, Rosak-Szyrocka J, Pilař L, Boye M. Reversing Years for Global Food Security: A Review of the Food Security Situation in Sub-Saharan Africa (SSA). Volume 19. International Journal of Environmental Research and Public Health; 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEtana D, Tolossa D. Unemployment and Food Insecurity in Urban Ethiopia. Afr Dev Rev. 2017;29(1):56\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKiribou R, Bedadi B, Dimobe K, Ndemere J, Neya T, Ouedraogo V, et al. Urban farming system and food security in sub-Saharan Africa: Analysis of the current status and challenges. Volume 9. Urban Agriculture \u0026amp; Regional Food Systems; 2024. 1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJung I. Food Insecurity in Nigeria: Food Supply Matters: Nigeria. Selected Issues Papers, 2023 (018), A001 [Internet]. 2023 [cited 2025 Jan 24]; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.elibrary.imf.org/view/journals/018/2023/018/article-A001-en.xml?ArticleTabs=fulltext\u003c/span\u003e\u003cspan address=\"https://www.elibrary.imf.org/view/journals/018/2023/018/article-A001-en.xml?ArticleTabs=fulltext\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavies J, Hannah C, Guido Z, Zimmer A, McCann L, Battersby J, et al. Barriers to urban agriculture in Sub-Saharan Africa. Food Policy. 2021;103:101999.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacalou M, Keita SI, Coulibaly AB, Diamoutene AK. Urbanization and food security: evidence from Mali. Front Sustain Food Syst. 2023;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDjan MA. Urban Food Security: Examining the Unique Challenges and Opportunities Associated with Ensuring Food Security in Urban Areas. Eur J Nutr Food Saf. 2023;15(9):42\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrush JS, Frayne GB. Urban food insecurity and the new international food security agenda. Dev South Afr. 2011;28(4):527\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUNFPA. World Population Dashboard: Nigeria [Internet]. 2022 [cited 2024 Jun 25]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.unfpa.org/data/world-population/NG\u003c/span\u003e\u003cspan address=\"https://www.unfpa.org/data/world-population/NG\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTochukwu SE, Bilyaminu T. Examining the Effects of High Poverty and Unemployment on Rural Urban Migration in Nigeria and its Consequences on Urban Resources and Rural Decline. J Child Adult Vaccines Immunol. 2024;8(1):001\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAliyu A, Amadu L. Urbanization, cities, and health: The challenges to Nigeria \u0026ndash; A review. Ann Afr Med. 2017;16(4):149.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAuwalu FK, Bello M. Exploring the Contemporary Challenges of Urbanization and the Role of Sustainable Urban Development: A Study of Lagos City, Nigeria. J Contemp Urban Affairs. 2023;7(1):175\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAyuba D, Akinga AY, Okeh DU, Ogaya JB, Precious FK, Lucero-Prisno DE. In. Migration and food security in urban Nigeria: The dilemma of the urban poor. 2023. pp. 235\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNESG. NESG 2024Q2 UNEMPLOYMENT ALERT [Internet]. 2024 Nov [cited 2025 Jan 24]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003efile:///C:/Users/hp/Downloads/Unemployment%20Alert%202024Q2_1732787107.pdf\u003c/span\u003e\u003cspan address=\"http://file:///C:/Users/hp/Downloads/Unemployment%20Alert%202024Q2_1732787107.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSadiq MS, Singh IP, Ahmad MM. The Influence of Armed Banditry on Food Supply Chains in North-West Nigeria: A Comprehensive Review. Kuwait Res J Agric Life Sci. 2024;1(1):1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarie Ruel. Center for Strategic and International Studies. 2020 [cited 2025 Jan 24]. Growing Cities, Growing Food Insecurity: How to Protect the Poor during Rapid Urbanization. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.csis.org/analysis/growing-cities-growing-food-insecurity-how-protect-poor-during-rapid-urbanization\u003c/span\u003e\u003cspan address=\"https://www.csis.org/analysis/growing-cities-growing-food-insecurity-how-protect-poor-during-rapid-urbanization\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIkudayisi AA. Urban food security and socioeconomic sustainability: A multidimensional perspective. Green Technol Sustain. 2024;2(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUn-Habitat. Un-Habitat. (2020). World Cities Report 2020: The value of sustainable urbanization. [Internet]. 2020 [cited 2025 Jan 24]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://unhabitat.org/sites/default/files/2020/10/wcr_2020_report.pdf\u003c/span\u003e\u003cspan address=\"https://unhabitat.org/sites/default/files/2020/10/wcr_2020_report.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlatunde O. The Aetiology of Ill Health Among Urban Slum Communities in Nigeria: A Systematic Review. 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOgbonna SN, Ochie CN, Aniwada EC. Urban slum housing quality, and its public health implications in Nigeria: a case of urban slum residents in Enugu metropolis, South East, Nigeria. BMC Public Health. 2024;24(1):3231.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIlori T, Christofides N, Baldwin-Ragaven L. The relationship between food insecurity, purchasing patterns and perceptions of the food environment in urban slums in Ibadan, Nigeria. BMC Nutr. 2024;10(1):122.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohn C, Poh BK, Jalaludin MY, Michael G, Adedeji I, Oyenusi EE et al. Exploring disparities in malnutrition among under-five children in Nigeria and potential solutions: a scoping review. Front Nutr. 2024;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoberts AA, Osadare JO, Inem VA. Hunger in the midst of plenty: A survey of household food security among urban families in Lagos State, Nigeria. J Public Health Afr. 2019;10(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDauda Ayuba AY, Akinga DU, Okeh JB, Ogaya. Fadele Kehinde Precious, Don Eliseo Lucero-Prisno. Migration and food security in urban Nigeria: The dilemma of the urban poor. In: Marc J, Cohen, editors. Advances in Food Security and Sustainability. Elsevier; 2023. pp. 235\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDrewnowski A. Food insecurity has economic root causes. Nat Food. 2022;3(8):555\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFang D, Thomsen MR, Nayga RM, Yang W. Food insecurity during the COVID-19 pandemic: evidence from a survey of low-income Americans. Food Secur. 2022;14(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYazbeck N, Mansour R, Salame H, Chahine NB, Hoteit M. The Ukraine\u0026ndash;Russia War Is Deepening Food Insecurity, Unhealthy Dietary Patterns and the Lack of Dietary Diversity in Lebanon: Prevalence, Correlates and Findings from a National Cross-Sectional Study. Nutrients. 2022;14(17).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerschuur J, Li S, Wolski P, Otto FEL. Climate change as a driver of food insecurity in the 2007 Lesotho-South Africa drought. Sci Rep. 2021;11(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSibanda F, Gender. Climate Change and Food Insecurity: A Zimbabwean Rastafari Perspective. Pharos J Theol. 2023;104(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFAO, IFAD, UNICEF, WFP, WHO., The State of Food Security and Nutrition in the World 2025 \u0026ndash; Addressing high food price inflation for food security and nutrition. Rome; 2025 Jul.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Economic Summit Group. NESG Issues a Policy Brief on the Status of Food Security in Nigeria [Internet]. 2025 Mar [cited 2024 Nov 6]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nesgroup.org/blog/NESG-Issues-a-Policy-Brief-on-the-Status-of-Food-Security-in-Nigeria\u003c/span\u003e\u003cspan address=\"https://nesgroup.org/blog/NESG-Issues-a-Policy-Brief-on-the-Status-of-Food-Security-in-Nigeria\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlumba CC, Olumba CN, Alimba JO. Constraints to urban agriculture in southeast Nigeria. Humanit Soc Sci Commun. 2021;8(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBloch R, Monroy J, Fox S, Ojo A. Urbanisation and Urban Expansion in Nigeria. Urbanisation Research Nigeria; 2015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFox S, Bloch R, Monroy J. Understanding the dynamics of Nigeria\u0026rsquo;s urban transition: A refutation of the \u0026lsquo;stalled urbanisation\u0026rsquo; hypothesis. Urban Stud. 2018;55(5):947\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEze TC, Okpala CS, Ogbodo JC. Patterns of Inequality in Human Development Across Nigeria\u0026rsquo;s Six Geopolitical Zones. Developing Ctry Stud. 2014;4(8).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdepoju A, Oyegoke O. Correlates of food insecurity status of urban households in Ibadan metropolis, Oyo state, Nigeria. Int Food Res J. 2018;25(6).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNwaka ID, Akadiri S, Saint, Uma KE. Gender of the family head and food insecurity in urban and rural Nigeria. Afr J Economic Manage Stud. 2020;11(3).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlaniyi O. Assessment of households\u0026rsquo; food access and food insecurity in Urban Nigeria: A case study of Lagos Metropolis. Global J Human-Social Science: E Econ. 2014;14(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEnete AA, Achike AI. Urban agriculture and urban food insecurity/poverty in Nigeria: The case of Ohafia, south-east Nigeria. Outlook Agric. 2008;37(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBronfenbrenner U. Toward an experimental ecology of human development. Am Psychol. 1977;32(7).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchroeder K, Smaldone A. Food Insecurity: A Concept Analysis. Nurs Forum (Auckl). 2015;50(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Bureau of Statistics (NBS), and United Nations Children\u0026rsquo;s Fund (UNICEF). Multiple Indicator Cluster Survey 2021, Survey Findings Report [Internet]. Abuja, Nigeria; 2022 Aug [cited 2024 Aug 21]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.unicef.org/nigeria/media/6316/file/2021%20MICS%20full%20report%20.pdf\u003c/span\u003e\u003cspan address=\"https://www.unicef.org/nigeria/media/6316/file/2021%20MICS%20full%20report%20.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGil JDB, Reidsma P, Giller K, Todman L, Whitmore A, van Ittersum M. Sustainable development goal 2: Improved targets and indicators for agriculture and food security. Ambio. 2019;48(7):685\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFAO. Voices of the Hungry: Analyse the Data [Internet]. 2024 [cited 2024 Jun 5]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fao.org/in-action/voices-of-the-hungry/analyse-data/en/\u003c/span\u003e\u003cspan address=\"https://www.fao.org/in-action/voices-of-the-hungry/analyse-data/en/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWambogo EA, Ghattas H, Leonard KL, Sahyoun NR. Validity of the Food Insecurity Experience Scale for Use in Sub-Saharan Africa and Characteristics of Food-Insecure Individuals. Curr Dev Nutr. 2018;2(9):nzy062.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJubayer A, Islam S, Nowar A, Nayan MM, Islam MdH. Validity of Food insecurity experience scale (FIES) for use in rural Bangladesh and prevalence and determinants of household food insecurity: An analysis of data from Bangladesh integrated household survey (BIHS) 2018\u0026ndash;2019. Heliyon. 2023;9(6):e17378.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarlo Cafiero S, Viviani. Mark Nord. RM.weights: Weighted Rasch Modeling and Extensions using Conditional Maximum Likelihood. 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFAO. Methods for estimating comparable rates of food insecurity experienced by adults throughout the world [Internet]. Rome. 2016 [cited 2025 Aug 11]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://openknowledge.fao.org/server/api/core/bitstreams/823b2fcd-bc52-4371-a31d-ea2aa74e63d5/content\u003c/span\u003e\u003cspan address=\"https://openknowledge.fao.org/server/api/core/bitstreams/823b2fcd-bc52-4371-a31d-ea2aa74e63d5/content\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFAO, Using the FIES, App FAO. 2020 [cited 2025 Aug 11]. p. 22. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://openknowledge.fao.org/handle/20.500.14283/ca9318en\u003c/span\u003e\u003cspan address=\"https://openknowledge.fao.org/handle/20.500.14283/ca9318en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTabrizi JS, Nikniaz L, Sadeghi-Bazargani H, Farahbakhsh M, Nikniaz Z. Socio-demographic determinants of household food insecurity among Iranian: A population-based study from northwest of Iran. Iran J Public Health. 2018;47(6).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParekh N, Ali SH, O\u0026rsquo;Connor J, Tozan Y, Jones AM, Capasso A et al. Food insecurity among households with children during the COVID-19 pandemic: results from a study among social media users across the United States. Nutr J. 2021;20(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHonarvar MR, Gholami M, Abdollahi Z, Ghotbabadi FS, Lashkarboluki F, Najafzadeh M et al. Household food insecurity and associated factors in the Northeast of Iran: a cross-sectional study: Household food security in Northern Iran. BMC Nutr. 2023;9(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDrammeh W, Hamid NA, Rohana AJ. Determinants of Household Food Insecurity and Its Association with Child Malnutrition in Sub-Saharan Africa: A Review of the Literature. Curr Res Nutr Food Sci J. 2019;7(3):610\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMutisya M, Ngware MW, Kabiru CW, Kandala Nbakwin. The effect of education on household food security in two informal urban settlements in Kenya: a longitudinal analysis. Food Secur. 2016;8(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlp\u0026iacute;zar F, Sabor\u0026iacute;o-Rodr\u0026iacute;guez M, Mart\u0026iacute;nez-Rodr\u0026iacute;guez MR, Viguera B, Vignola R, Capit\u0026aacute;n T et al. Determinants of food insecurity among smallholder farmer households in Central America: recurrent versus extreme weather-driven events. Reg Environ Change. 2020;20(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerrera JP, Rabezara JY, Ravelomanantsoa NAF, Metz M, France C, Owens A et al. Food insecurity related to agricultural practices and household characteristics in rural communities of northeast Madagascar. Food Secur. 2021;13(6).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRashid FN, Sesabo JK, Lihawa RM, Mkuna E. Determinants of household food expenditure in Tanzania: implications on food security. Agric Food Secur. 2024;13(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamim SA, Hu Z, Stepien S, Amini SY, Rayee R, Niu K, et al. Food insecurity and related factors among farming families in takhar region, afghanistan. Sustain (Switzerland). 2021;13:18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOderinde TM, Ilesanmi OS, Afolabi AA. Food insecurity and associated factors among households with under-5 children in slum communities in Ibadan, Nigeria. BMC Public Health. 2023;23(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhanam M, Ara G, Rahman AS, Islam Z, Farhad S, Khan SS et al. Factors affecting food security in women enrolled in a program for vulnerable group development. Curr Dev Nutr. 2020;4(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWight V, Kaushal N, Waldfogel J, Garfinkel I. Understanding the link between poverty and food insecurity among children: Does the definition of poverty matter? J Child Poverty. 2014;20(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePollard CM, Booth S, Correction. Food insecurity and hunger in rich countries-it is time for action against inequality (International Journal of Environmental Research and Public Health, 2019, 16, 1804). Vol. 17, International Journal of Environmental Research and Public Health. 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBell BA, Onwuegbuzie AJ, Ferron JM, Jiao QG, Hibbard ST, Kromrey JD. Am J Public Health. 2012;102(7):1399\u0026ndash;405. Use of Design Effects and Sample Weights in Complex Health Survey Data: A Review of Published Articles Using Data From 3 Commonly Used Adolescent Health Surveys.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLumley T. Complex Surveys. Wiley; 2010.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team. R: A language and environment for statistical computing [Internet]. Viena, Austria: R Foundation for Statistical Computing; 2023 [cited 2024 Nov 17]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org/\u003c/span\u003e\u003cspan address=\"https://www.R-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSnijders TA, Bosker R. Multilevel analysis: An introduction to basic and advanced multilevel modeling. 2011.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMerlo J. A brief conceptual tutorial on multilevel analysis in social epidemiology: investigating contextual phenomena in different groups of people. J Epidemiol Community Health (1978). 2005;59(9):729\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHedges LV, Rhoads CH. Correcting an analysis of variance for clustering. Br J Math Stat Psychol. 2011;64(1):20\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAilobhio DT, Ikughur JA. A Review of Some Goodness-of-Fit Tests for Logistic Regression Model. Asian J Probab Stat. 2024;26(7):75\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVrieze SI. Model selection and psychological theory: A discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Psychol Methods. 2012;17(2):228\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHox JJ, Moerbeek M, van de Schoot R. Multilevel Analysis. 3rd ed. Third edition. | New York, NY: Routledge, 2017. |: Routledge; 2017. 364.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld bank. Prevalence of moderate or severe food insecurity in the population (%) [Data set]. [Internet]. 2023 [cited 2025 May 17]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.worldbank.org/indicator/SN.ITK.MSFI.ZS?locations=NG\u003c/span\u003e\u003cspan address=\"https://data.worldbank.org/indicator/SN.ITK.MSFI.ZS?locations=NG\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInegbedion H. Impact of COVID-19 on economic growth in Nigeria: opinions and attitudes. Heliyon. 2021;7(5):e06943.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOmotayo AO, Omotoso AB, Daud SA, Omotayo OP, Adeniyi BA. Rising Food Prices and Farming Households Food Insecurity during the COVID-19 Pandemic: Policy Implications from SouthWest Nigeria. Agriculture. 2022;12(3):363.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGebremichael B, Beletew B, Bimerew M, Haile D, Biadgilign S, Baye K. Magnitude of urban household food insecurity in East Africa: a systematic review and meta-analysis. Public Health Nutr. 2022;25(4):994\u0026ndash;1004.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNenguda R, Scholes MC. Appreciating the Resilience and Stability Found in Heterogeneity: A South African Perspective on Urban Household Food Security. Front Sustain Food Syst. 2022;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJerumeh TR. Mapping the spatial distribution of household food insecurity in Nigeria. GeoJournal. 2024;89(4):154.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOmonona BT, Agoi GA, AN ANALYSIS OF FOOD SECURITY SITUATION AMONG NIGERIAN. URBAN HOUSEHOLDS: EVIDENCE FROM LAGOS STATE, NIGERIA. J Cent Eur. 2007;8(3):397406.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOyebanji A. Factors Influencing the Adoption of Urban Agriculture and Its Effects on Household Food Security in Southwestern Nigeria. Int J Agricultural Econ. 2024;9(5):259\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbanda JN, Dama U, Ndoungue VF, Akoa AO, Dzeuta MF, Asobochia AT, et al. Economic Stress, Precariousness and Risk of High Blood Pressure: A Descriptive Survey of Life Models within Households of the Central Region of Cameroon. Health N Hav. 2024;16(02):118\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu X, Wang Q, Li C. The Impact of Dependency Burden on Urban Household Health Expenditure and Its Regional Heterogeneity in China: Based on Quantile Regression Method. Front Public Health. 2022;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eObayelu OA, Akpan EI. Food insecurity transitions among rural households in Nigeria. Stud Appl Econ. 2021;39(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGebremichael B, Egata G, Assefa N. Dietary Diversity Practice and Associated Factors Among Infants and Young Children in Haramaya Town, Ethiopia. Int J Public Health Sci (IJPHS). 2017;6(3).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTadesse Tantu A, Demissie Gamebo T, Kuma Sheno B, Yohannis Kabalo M. Household food insecurity and associated factors among households in Wolaita Sodo town, 2015. Agric Food Secur. 2017;6(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBushara OA, Ibrahim HH. Food Security Status for the Household: A Case Study of Al-Qadarif State, Sudan. J Socialomics. 2017;6(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYahaya SP, Sanusi RA, Eyinla TE. F.O. Samuel. Household Food Insecurity and Nutrient Adequacy of Under-Five Children in Selected Urban Areas of Ibadan, Southwestern, Nigeria. Afr J Biomed Res [Internet]. 2021 Jan [cited 2025 May 23];24(1). Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003efile:///C:/Users/hp/Downloads/ajol-file-journals_264_articles_209090_submission_proof_209090-3145-519341-1-10-20210623%20(1).pdf\u003c/span\u003e\u003cspan address=\"http://file:///C:/Users/hp/Downloads/ajol-file-journals_264_articles_209090_submission_proof_209090-3145-519341-1-10-20210623%20(1).pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMkhize S, Libhaber E, Sewpaul R, Reddy P, Baldwin-Ragaven L. Child and adolescent food insecurity in South Africa: A household-level analysis of hunger. PLoS ONE. 2022;17(12):e0278191.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdeyanju OZ, Fadupin GT. Household food security status of rural mothers and nutritional status of their children under five years old in Ibadan, Nigeria. World Nutr. 2024;15(4):52\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalistreri KS. Family Structure and Child Food Insecurity: Evidence from the Current Population Survey. Soc Indic Res. 2018;138(3):1171\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkello MC, Mwesigwa D. Household Size and Household Food Security in Ngetta Ward, Lira City, Northern Uganda. Int J Developing Ctry Stud. 2023;5(1):88\u0026ndash;109.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeoni S. A Historical Review of the Role of Education: From Human Capital to Human Capabilities. Rev Polit Econ. 2023;1\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAugustine M, Kara, Lucy M, Kithu. Education Attainment of Head of Household and Household Food Security: A Case for Yatta Sub - County, Kenya. Am J Educ Res. 2020;8(8):558\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJosephine Mendy GA, Asongwe, Raymond Ndip Nkongho. Vulnerability to food insecurity and coping strategies of agrarian households in the lower river region of the Gambia: Implication for policy. Int J Agricultural Sci Food Technol. 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen M, Huang X, Cheng J, Tang Z, Huang G. Urbanization and vulnerable employment: Empirical evidence from 163 countries in 1991\u0026ndash;2019. Cities. 2023;135:104208.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorku AW, Utallo AU, Tora TT. Determinants of urban household vulnerability to food insecurity in southern Ethiopia. Discover Food. 2024;4(1):37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePakravan-Charvadeh MR, Flora C, Khan HA. Simulating Potential Associated Socio-Economic Determinants With Sustainable Food Security (A Macro-Micro Spatial Quantitative Model). Front Public Health. 2022;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKota K, Chomienne MH, Yaya S. Examining the disparities: A cross-sectional study of socio-economic factors and food insecurity in Togo. PLoS ONE. 2023;18(11):e0294527.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIdisi PO. Food security, economic growth and price stability nexus and conceptual issues. Economic Financial Review. 2021;59(4):9\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMead BR, Duncombe T, Gillespie R, Pugh N, Hardman CA. Does urban agriculture contribute to food security, and how might this be achieved? Proceedings of the Nutrition Society. 2024;83(3):195\u0026ndash;203.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrsini F, Kahane R, Nono-Womdim R, Gianquinto G. Urban agriculture in the developing world: a review. Agron Sustain Dev. 2013;33(4):695\u0026ndash;720.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamuel FO, Eyinla TE, Oluwaseun A, Leshi OO, Brai BIC, Afolabi WAO. Food Access and Experience of Food Insecurity in Nigerian Households during the COVID-19 Lockdown. Food Nutr Sci. 2021;12(11):1062\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaltenweck I, Enahoro D, Frija A, Tarawali S. Why Is Production of Animal Source Foods Important for Economic Development in Africa and Asia? Anim Front. 2020;10(4):22\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHossain ME, Hoque MA, Giorgi E, Fourni\u0026eacute; G, Das GB, Henning J. Impact of improved small-scale livestock farming on human nutrition. Sci Rep. 2021;11(1):191.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBanda LJ, Tanganyika J. Livestock provide more than food in smallholder production systems of developing countries. Anim Front. 2021;11(2):7\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChristian AK, Wilson ML, Aryeetey RNO, Jones AD. Livestock ownership, household food security and childhood anaemia in rural Ghana. PLoS ONE. 2019;14(7):e0219310.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith J, Sones K, Grace D, MacMillan S, Tarawali S, Herrero M. Beyond milk, meat, and eggs: Role of livestock in food and nutrition security. Anim Front. 2013;3(1):6\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTurk R, Thomas A. Food Insecurity in Nigeria: Food Supply Matters. Sel Issues Papers. 2023;2023(018):1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdeyemi KD, Ibiyemi T, Taiwo O, Adeyemi B. Household Food Insecurity: Determinants and Coping Strategies in Northwest and Northeast Nigeria. J Hunger Environ Nutr. 2025;1\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOCHA. HUMANITARIAN NEEDS OVERVIEW NIGERIA. 2023 Dec.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBloch R, Makarem N, Yunusa M, Papachristodoulou N, Crighton M. Economic Development in Urban Nigeria. Urbanisation Research Nigeria (URN) Research Report [Internet]. London; 2015 Jul [cited 2025 May 31]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://assets.publishing.service.gov.uk/media/57a08971ed915d3cfd00024c/61250-URN_Theme_B_Economic_Report_FINAL.pdf\u003c/span\u003e\u003cspan address=\"https://assets.publishing.service.gov.uk/media/57a08971ed915d3cfd00024c/61250-URN_Theme_B_Economic_Report_FINAL.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuven-Lisaniler F, Tuna G, Nwaka ID. Sectoral choices and wage differences among Nigerian public, private and self-employees. Int J Manpow. 2018;39(1):2\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNwaka ID, Emeagwali OL. Participation and returns from informal service-oriented non-farm enterprises: Evidence from a survey of Nigerian households. PLoS ONE. 2024;19(3):e0298794.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIkadeh MS, Cloete CE. The impact of shopping centre development on informal and small businesses in Lagos. Nigeria J Bus Retail Manage Res. 2020;14(03).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eObayelu OA, Osho FR. How diverse are the diets of low-income urban households in Nigeria? J Agric Food Res. 2020;2:100018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOmotayo AO, Omotoso AB, Daud SA, Omotayo OP, Adeniyi BA. Rising Food Prices and Farming Households Food Insecurity during the COVID-19 Pandemic: Policy Implications from SouthWest Nigeria. Agriculture. 2022;12(3):363.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOkonkwo UU, Ukaogo VO, Ejikeme JNU, Okagu G, Onu A. COVID-19 and the Food Deficit Economy in Southeastern Nigeria. Cogent Arts Humanit. 2021;8(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOkpoko CC, Onyishi CN, Okeke SV. Public Perception of the Impact of IPOB sit-at-Home Order on the Economic and Educational Development of South-East, Nigeria. Ianna Journal of Interdisciplinary Studies [Internet]. 2025 Jan 1 [cited 2025 Jun 1];7(1):314\u0026ndash;26. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iannajournalofinterdisciplinarystudies.com/index.php/1/article/view/553\u003c/span\u003e\u003cspan address=\"https://iannajournalofinterdisciplinarystudies.com/index.php/1/article/view/553\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOwoeye DI, Ezeanya VE, Obiegbunam NG. Separatists\u0026rsquo; Strategy: Appraising the Effects of IPOB Monday Sit-At-Home Order on political Economy (Socio-Political and Economic activities) of the South-East Region of Nigeria. International Journal of Advanced Academic Research [Internet]. 2022 [cited 2025 Jun 1];8(1):98\u0026ndash;108. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ijaar.org/articles/v8n1/ijaar-v8n1-Jan22-p8171.pdf\u003c/span\u003e\u003cspan address=\"https://www.ijaar.org/articles/v8n1/ijaar-v8n1-Jan22-p8171.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTom AB, Snijders, Roel J, Bosker. Multilevel Analysis An Introduction to Basic and Advanced Multilevel Modeling. 2nd ed. SAGE Publications Ltd; 2011.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Determinants, Food Insecurity, FIES, Multilevel analysis, Urban Food Security, Urban Agriculture, Urban Nigeria, Urban Households","lastPublishedDoi":"10.21203/rs.3.rs-8041739/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8041739/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eWith rapid urbanization, food insecurity has become an increasingly critical yet understudied issue in Sub-Saharan Africa\u0026rsquo;s urban population. This study addresses a significant research gap by examining the prevalence and determinants of food insecurity among urban households in Nigeria.\u003c/p\u003e\u003ch2\u003eMethodology:\u003c/h2\u003e \u003cp\u003eFood insecurity was assessed via the Food Insecurity Experience Scale (FIES), with item responses analyzed using the Rasch model to ensure valid measurement of the latent food insecurity construct. Multilevel logistic regression was then employed to identify key predictors of moderate or severe food insecurity.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results show that nearly 7 in 10 urban households experienced moderate or severe food insecurity. Higher risk for food insecurity was observed among households with heads aged 36\u0026ndash;45, larger family size (\u0026gt;\u0026thinsp;5 members), lower education levels, and those living in the Southeast region. Protective factors for food insecurity included higher household wealth, homeownership, ownership of farmland or livestock, having children under the age of five, and living in the Northwest, Northeast, or Southwest regions.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe study provides robust empirical evidence underscoring the need for urban-focused, multisectoral policy responses. Interventions must extend beyond individual-level drivers to address structural inequities through regionally specific strategies. Scaling up social protection, advancing economic inclusion, and investing in urban agriculture are essential for ensuring equitable food security across Nigeria\u0026rsquo;s expanding urban landscape.\u003c/p\u003e","manuscriptTitle":"Urban Food Insecurity in Nigeria: A Multilevel Analysis of Nationally Representative Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-09 12:11:17","doi":"10.21203/rs.3.rs-8041739/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":"a614216f-9901-4105-91bc-72d686ea7ba1","owner":[],"postedDate":"February 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T08:26:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-09 12:11:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8041739","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8041739","identity":"rs-8041739","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}