Exploring the Association of Household Location and Sociodemographic Profile on Decreasing Dietary Diversity in Occupied Palestine: A Serial Cross-Sectional Study

Exploring the Association of Household Location and Sociodemographic Profile on Decreasing Dietary Diversity in Occupied Palestine: A Serial Cross-Sectional Study | 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 Exploring the Association of Household Location and Sociodemographic Profile on Decreasing Dietary Diversity in Occupied Palestine: A Serial Cross-Sectional Study Chesa Cox, Weeam Hammoudeh, Tracy Kuo Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6474000/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract Background The prevalence of undernourishment is significantly higher in conflict-affected low- and middle-income countries (LMIC), compared to LMICs not experiencing conflict. Evidence suggests that in these settings households may adopt coping strategies such as consuming less nutritious food and thereby reducing food diversity to mitigate the impact of food insecurity. The long-term trend of food diversity in a protracted conflict setting has not been explored in detail due to challenges in collecting systematic and representative data in conflict-affected and fragile settings. Methods This study examines food diversity – measured using food consumption scores (FCS) – among Palestinians in the Gaza Strip and the West Bank, utilizing a serial cross-sectional design to analyze a systematically random sampled dataset that was collected by the Palestinian Central Bureau of Statistics – from 2014, 2016, 2018, and 2020. We analyzed the distribution of household location by survey year and used multivariate linear regression to evaluate factors associated with changes in food consumption score. Results The proportion of unacceptable food consumption scores increased from 0·8% in 2014 to 3·7% in 2020, indicating a growing food insecurity issue. For the West Bank, household location to the barrier, head of household gender (female), living in a refugee camp, and households with middle- or lower-income levels were associated with a reduction in FCS. For the Gaza Strip, households that reported minor mobility restrictions and middle- or lower-income levels were associated with a reduction in FCS. Conclusions The findings elucidate the long-term impact of conflict on household food diversity, highlight a significant and worsening issue of food insecurity amongst Palestinians residing in the occupied Palestinian territory, and underline urgent need to address this critical issue and further protect vulnerable populations in conflict-affected regions are needed. Food consumption score food insecurity Occupied Palestine Conflict-affected settings Undernourishment West Bank Gaza Strip Undernourishment Household food diversity Figures Figure 1 BACKGROUND The prevalence of undernourishment in conflict-affected low- and middle-income countries is significantly higher, ranging from 1·4 to 4·4% more on average, compared to their counterparts within the same economic classification that are not experiencing conflict [ 1 ]. In challenging environments affected by conflict, households may resort to coping strategies to withstand the negative externalities of conflict and ensure that household members receive sufficient food for consumption for as long as possible. These strategies may include consuming less nutritious food or limiting dietary variety so to focus on maintaining adequate caloric in-take – ultimately reducing the household's dietary diversity [ 2 – 5 ]. The sustained conflict and Israeli military occupation have severely hindered productivity, food production, and availability in both the West Bank and the Gaza Strip (hereafter Gaza) – regions in the occupied Palestinian territory (oPt) [ 6 ]. These challenges are largely attributed to restrictions on Palestinian mobility, destruction of infrastructure, and constraints imposed on water and food markets by Israeli regulations [ 7 ]. While studies have explored food insecurity in Gaza and among Palestinian refugees in Lebanon, data on the West Bank remains scarce [ 8 , 9 ]. Research has highlighted the associations between food insecurity (using the Radimer/ Cornell food security scale) and various sociodemographic factors in Gaza [ 8 ]. These studies do not sufficiently address the long-term trend and nuanced interactions of household location and sociodemographic factors across the oPt. The ongoing conflict in the oPt presents a unique opportunity to investigate factors associated with food insecurity. The protracted conflict and occupation of Palestine has fragmented communities in the region that is further complicated by military and administrative delineations such as Areas A, B, and C in the West Bank and buffer zones in Gaza (Fig. 1 ) [ 10 ]. Areas A, B, and C in the West Bank are administrative divisions established under the Oslo Accords, reflecting differing levels of Palestinian and Israeli control. Area A is under full Palestinian civil and security control, encompassing major Palestinian cities which generally have better access to basic services than areas B and C. Area B is under Palestinian civil control and Israeli security control, primarily including rural areas and towns which have basic services, but quality and access is inconsistent. Area C, which constitutes the majority of the West Bank, is under full Israeli civil and security control, covering Israeli settlements, military zones, and much of the region's agricultural land—facing the most significant challenges in accessing basic needs, such as food sources [ 11 ]. Buffer zones in Gaza are areas along the border with Israel where access is restricted or controlled. These zones are enforced by Israel and vary in size, with the most restrictive areas extending up to 300 meters from the border, where entry is prohibited. Beyond this, access restrictions extend up to between 1,000 and 1,500 meters, significantly limiting agricultural activity and construction. These divisions have created distinct geopolitical enclaves, each with varying levels of exposure to political and military strife. Our study represent an unique serial cross-sectional analysis that builds on previous work and explores the trend of food consumption and association between household characteristics and dietary diversity in the context of a protracted conflict [ 7 ]. Previous studies found that living in areas with higher political and agricultural hardships—such as Area C or living outside the barrier in West Bank or living near buffer zones in Gaza—is associated with increased food insecurity and reduced dietary diversity [ 7 , 12 ]. Furthermore, conflict reduced the overall resilience capacity of households to a certain extent, however, it induced aid response which may have mitigated food insecurity specifically in Gaza [ 12 ]. We add to previous work and hypothesize that 1) the median FCS score trends downwards throughout the years, due to long-term exposure to conflict. The findings provide insight on the long-term impact of conflict and occupation on dietary diversity. METHODS Study design We conducted a serial cross-sectional study using data from 2014, 2016, 2018, and 2020 on the association of household sociodemographic factors on FCS in a sample of Palestinians living in the West Bank and Gaza, collected by the Palestinian Central Bureau of Statistics using the Socio-Economic Conditions Survey. Subjects and sampling The target population for the survey includes all Palestinian households with regular residency (households with residents that are registered with the Palestinian Civil Registry) in Palestine during the period of the surveys in 2014, 2016, 2018, and 2020. Head of households completed the questionnaire on socio-economic conditions and food consumption on behalf of the household. The survey is a representative sample of the Palestinian population, using a sampling framework for that was based on enumeration areas, locality, and governorate levels. The sample design employed a three-stage stratified cluster systematic random sampling method where weights are calculated as the inverse of selection probabilities. Initially, weights are assigned to enumeration areas based on their selection probability, then to households within these areas, and adjusted for attrition and appropriate household estimates by strata. Finally, weights are assigned to individual household members and adjusted based on relevant population estimates by region, gender, and age group. This process corrects for selection biases and nonresponse, making the sample representative and ensuring the survey results are generalizable to the entire population. The Socio-Economic & Food Security Survey 2014: State of Palestine contains more information to the survey data, sampling strategy, and description [ 13 ]. As this is a serial cross-sectional study, the final dataset containing all 4 survey years may contain repeat households, as it is not possible to identify who has previously taken the survey. Measures One of the commonly used measures for food diversity is FCS, which is an index score calculated using self-reported household consumption of food groups over seven days. The frequency of each consumed food group is then weighted by nutritional value and summed to produce a score that is classified into poor, borderline, or acceptable [ 14 ]. The FCS is used to monitor changes in food consumption and aid prioritization and formulate policies that address food security in conflict zones, which has been seen in Yemen, Syria and South Sudan[ 15 – 17 ]. Previous work demonstrated that location (i.e., living inside/outside the barriers in West Bank and near buffer zones in Gaza) and limited mobility are drivers of food insecurity, our variables of interest included region (West Bank vs Gaza), locality type (urban, rural, refugee camp), proximity to barrier or buffer zones, self-reported household income, and extent that mobility has restricted the household. Because the head of household is the main decision maker and is responsible for financial support and welfare of the household, we are using the demographic variables of the head of household as a profile for the entire household. Demographic variables of interest include head of household education, refugee status, gender, hours worked, refugee status, and mobility were included in the analysis. Asset assessments were not consistent over survey years, making it difficult to quantitatively value household income, so household income used in this analysis was self-reported by head of household as very poor, poor, middle, or high income. Analysis We conducted analyses using R version 4·3.1. Linear regression model was separated by region using data from 2020—West Bank and Gaza—to compare FCS amongst households with different proximity to barrier or buffer zones and to account for regional difference in exposure to conflict-related hardships. Food consumption score was calculated following the methodology provided by the World Food Programme (WFP) where households sum consumption frequencies by food group per week, multiply the sum from each group by a weight designated by WFP, and then added together to obtain a final FCS [ 18 ]. The maximum FCS a household can obtain is 112. Households with a score under 35 is considered as having an ‘unacceptable’ food consumption status. The ‘unacceptable’ food consumption status is a combination of poor (FCS < 21) and borderline (FCS 21–35) food consumption status. Limitations The use of survey data may introduce biases inherent to self-reported surveys, and the inability to account for individual-level dietary intake data limits the granularity of the analysis. The observational nature of the serial cross-sectional study design prohibits causality, since data was collected at different points in time without tracking the same households. This design is susceptible to cohort effects, where changes may be due to the sampled population than true temporal trends. The potential for measurement error due to data collection changes between survey years. Despite these limitations, the data provide a robust, representative sample and a broad scope of variables for secondary analysis. Serial cross-sectional studies are a useful tool for identifying associations and trends within populations, though they should be supplemented with longitudinal data for more robust conclusions; future research could employ longitudinal cohort studies analyzed alongside ACLED data to elucidate causal relationships between socio-economic factors, violence caused by conflict, and dietary diversity in occupied Palestine. RESULTS Study Population A total of 26,934 households responded to the surveys from 2014, 2016, 2018, and 2020. Households were excluded if they did not have any food consumption data to calculate the FCS, resulting in the analysis of data from 23,129 households (Table 1 ). It is important to note that due to the data collection process, we are unable to determine whether the same households participated in multiple years; some households may have been included in the dataset more than once. Overall, the median household size was 5 members. Heads of households were typically male (90·5%) and married (90·3%), where approximately half completed a secondary level education (46·5%), are not refugees (57·1%), and work over 35 hours (50·5%). A higher proportion of households were in the West Bank (61·1%) and outside the barrier wall (56·8%), with 2020 having the highest proportion of households located in the West Bank (67·4%). The proportion of households within 1,000 meters of the buffer zone in Gaza averaged 2·6% across all years. Majority of households were in urban settings (72·5%) and self-reported as middle class (70·9%). The median FCS across all households was an 80·0 (range: 3 to 112), showing fluctuations over the survey years with median scores of 81·0 in 2014, 84·0 in 2016, 79·0 in 2018, and 78·0 in 2020—all within the acceptable threshold. Table 1 Descriptive characteristics of households (N = 23,129) amongst 4 waves (2014, 2016, 2018, 2020) of Socio-Economic Conditions Surveys done by the State of Palestine Overall 2014 2016 2018 2020 (N = 23129) (n = 7900) (n = 2195) (n = 9925) (n = 3109) Median FCS [Min, Max] 80 [3, 112] 81 [15, 112] 84 [16·5, 112] 79 [3, 112] 78 [10·5, 112] Household Locality Urban 16764 (72·5%) 5606 (71·0%) 1424 (64·9%) 7491 (75·5%) 2243 (72·1%) Rural 3868 (16·7%) 1468 (18·6%) 451 (20·5%) 1438 (14·5%) 511 (16·4%) Refugee Camp 2497 (10·8%) 826 (10·5%) 320 (14·6%) 996 (10·0%) 355 (11·4%) Households in West Bank 14138 (61·1%) 4892 (61·9%) 1255 (57·2%) 5897 (59·4%) 2094 (67·4%) Living in Area C 980 (4·2%) 283 (3·6%) 172 (7·8%) 420 (4·2%) 105 (3·4%) Inside barrier wall 993 (4·3%) 449 (5·7%) 121 (5·5%) 301 (3·0%) 122 (3·9%) Outside barrier wall 13138 (56·8%) 4435 (56·1%) 1134 (51·7%) 5597 (56·4%) 1972 (63·4%) Households in Gaza 8991 (38·9%) 3008 (38·1%) 940 (42·8%) 4028 (40·6%) 1015 (32·6%) 1000 m from buffer zone 8385 (36·3%) 2737 (34·6%) 915 (41·7%) 3755 (37·8%) 978 (31·5%) Extent that mobility restrictions impacted household Not at all 13270 (57·4%) 4005 (50·7%) 1595 (72·7%) 6185 (62·3%) 1485 (47·8%) Minor 5459 (23·6%) 1730 (21·9%) 354 (16·1%) 2403 (24·2%) 972 (31·3%) Very much 4173 (18·0%) 2100 (26·6%) 232 (10·6%) 1209 (12·2%) 632 (20·3%) Don't know 198 (0·9%) 59 (0·7%) 13 (0·6%) 126 (1·3%) 0 (0%) Head of Household Characteristics Median number of household members [Min, Max] 5·00 [1, 27] 6·00 [1, 25] 5·00 [1, 27] 5·00 [1, 27] 5·00 [ 1 , 17 ] Male 20931 (90·5%) 7148 (90·5%) 1990 (90·7%) 8966 (90·3%) 2827 (90·9%) Married 20876 (90·3%) 7111 (90·0%) 1973 (89·9%) 8975 (90·4%) 2817 (90·6%) Highest level of education received: secondary 10748 (46·5%) 3559 (45·1%) 1016 (46·3%) 4727 (47·6%) 1446 (46·5%) Works 35 + hours 11689 (50·5%) 3694 (46·8%) 1220 (55·6%) 5247 (52·9%) 1528 (49·1%) Not a refugee 13215 (57·1%) 4584 (58·0%) 1113 (50·7%) 5787 (58·3%) 1731 (55·7%) Received aid 14439 (62·4%) 4519 (57·2%) 1334 (60·8%) 6660 (67·1%) 1926 (61·9%) Received food assistance 6510 (28·1%) 2427 (30·7%) 623 (28·4%) 2567 (25·9%) 893 (28·7%) *Household income Rich 902 (4·3%) 271 (3·4%) ·· 461 (4·6%) 170 (5·5%) Middle 14841 (70·9%) 5826 (73·7%) ·· 6939 (69·9%) 2076 (66·8%) Poor 3743 (17·9%) 1368 (17·3%) ·· 1765 (17·8%) 610 (19·6%) Very poor 1441 (6·9%) 434 (5·5%) ·· 759 (7·7%) 248 (8·0%) *To calculate total proportion, did not include 2016 values in the denominator so N = 20934 Prevalence of Unacceptable Food Consumption Scores The prevalence of unacceptable or borderline food consumption scores (FCS) among 23,129 households over four survey years (2014, 2016, 2018, 2020) was 2·2%, with annual variations observed: 0·8% in 2014, 0·4% in 2016, 3·2% in 2018, and 3·7% in 2020, indicating an overall increase over time (Table 2 ). Geographically, participants from the West Bank had a higher prevalence of unacceptable FCS (1·3%) compared to Gaza (0·9%), with statistical significance demonstrated across regions (p < 0·001). Table 2 Proportion of households with unacceptable FCS ( ≤ 35) by location and sociodemographic factors Survey Year Total P-value 2014 2016 2018 2020 (N = 23129) (n = 7900) (n = 2195) (n = 9925) (n = 3109) Unacceptable FCS (%) 509 (2·2%) 62 (0·8%) 9 (0·4%) 322 (3·2%) 116 (3·7%) Location of Household Urban 408 (1·8%) ref 45 (0·6%) 7 (0·3%) 261 (2·6%) 95 (3·1%) Rural 56 (0·2%) *<0·001 7 (0·1%) 1 (0·0% 40 (0·4%) 8 (0·2%) Refugee camps 45 (0·2%) *<0·001 10 (0·1%) 1 (0·0%) 21 (0·2%) 13 (0·4%) Households in West Bank 302 (1·3%) ref 25 (0·3%) 6 (0·3%) 188 (1·9%) 83 (2·7%) Inside barrier wall 18 (0·1%) ref 0 (0·0%) 3 (0·1%) 12 (0·1%) 3 (0·1%) Outside barrier wall 284 (1·2%) 0·996 25 (0·3%) 3 (0·1%) 176 (1·8%) 80 (2·6%) Area C (West Bank only) 295 (1·3%) 25 (0·3%) 6 (0·3%) 184 (1·9%) 80 (2·6%) Yes 22 (0·1%) ref 1 (0·0%) 0 (0·0%) 19 (0·2%) 2 (0·1%) No 273 (1·2%) *0·012 24 (0·3%) 6 (0·3%) 165 (1·7%) 78 (2·5%) Households in Gaza 207 (0·9%) *<0·001 37 (0·5%) 3 (0·1%) 134 (1·4%) 33 (1·1%) < 1000m from buffer zone 9 (0·0%) 0·183 4 (0·1%) 0 (0·0%) 4 (0·0%) 1 (0·0%) ≥ 1000m and more 198 (0·9%) 0·200 33 (0·4%) 3 (0·1%) 130 (1·3%) 32 (1·0%) Extent that mobility restrictions impacted household Not at all 285 (1·2%) ref 25 (0·3%) 7 (0·3%) 195 (2·0%) 58 (1·9%) Minor 143 (0·6%) *<0·001 16 (0·2%) 2 (0·1%) 90 (0·9%) 35 (1·1%) Very much 71 (0·3%) *0·024 20 (0·3%) 0 (0·0%) 28 (0·3%) 23 (0·7%) Head of Household Characteristics Gender Male 436 (1·9%) ref 49 (0·6%) 8 (0·4%) 280 (2·8%) 99 (3·2%) Female 73 (0·3%) *<0·001 13 (0·2%) 1 (0·0%) 42 (0·4%) 17 (0·5%) Marital Status Single/Divorced/Separated /Widow 77 (0·3%) ref 11 (0·1%) 1 (0·0%) 47 (0·5%) 18 (0·6%) Married 430 (1·9%) *<0·001 49 (0·6%) 8 (0·4%) 275 (2·8%) 98 (3·2%) Highest level of education obtained Elementary 215 (0·9%) ref 32 (0·4%) 5 (0·1%) 128 (1·3%) 50 (1·6%) Secondary 227 (1·0%) *<0·001 22 (0·3%) 2 (0·1%) 151 (1·5%) 52 (1·7%) Intermediate and above 65 (0·3%) *<0·001 6 (0·1%) 2 (0·0%) 43 (0·4%) 14 (0·5%) Employment Unemployed 73 (0·3%) ref 10 (0·1%) 3 (0·1%) 50 (0·5%) 10 (0·3%) Working 1–24 hours 199 (0·9%) *<0·001 15 (0·2%) 3 (0·1%) 131 (1·3%) 50 (1·6%) Working 35 + hours 235 (1·0%) *<0·001 35 (0·4%) 3 (0·1%) 141 (1·4%) 56 (1·8%) Refugee Status Not a refugee 328 (1·4%) ref 30 (0·4%) 6 (0·3%) 221 (2·2%) 71 (2·3%) Refugee 179 (0·8%) *<0·001 30 (0·4%) 3 (0·1%) 101 (1·0%) 45 (1·4%) Received Aid Yes 202 (0·9%) ref 39 (0·5%) 4 (0·2%) 112 (1·1%) 47 (1·5%) No 307 (1·3%) *<0·001 23 (0·3%) 5 (0·2%) 210 (2·1%) 69 (2·2%) *Household income Rich 5 (0·0%) ref 0 (0·0%) ·· 5 (0·1%) 0 (0·0%) Middle 229 (1·1%) 0·058 16 (0·2%) ·· 160 (1·6%) 53 (1·7%) Poor 163 (0·8%) *< 0·001 26 (0·3%) ·· 99 (1·0%) 38 (1·2%) Very poor 103 (0·5%) *<0·001 20 (0·3%) ·· 58 (0·6%) 25 (0·8%) *To calculate total proportion, did not include 2016 values in the denominator so N = 20934 Among head of household characteristics, males reported higher unacceptable or borderline FCS (0·9%) compared to females (0·1%), with significant gender differences (p < 0·001). Regarding marital status, married participants showed a statistically significant higher prevalence (1·9%) compared to those who were single, divorced, separated, or widowed (0·3%) (p < 0·001). Intermediate level of education and above had a lower unacceptable household FCS proportion (0·3%), indicating significant educational disparities (p < 0·001). Employment status is also associated with unacceptable household FCS, with those working 35 + hours per week exhibiting higher prevalence (1·0%) compared to the unemployed (0·3%, p < 0·001). Refugee status and receipt of aid were significant predictors of unacceptable household FCS. Non-refugees and those not receiving aid demonstrated higher prevalence rates (0·6% and 0·6% respectively), compared to refugees (0·4%) and aid recipients (0·4%), both showing significant differences (p < 0·001). The extent of mobility restrictions also impacted FCS, with households reporting 'not at all' having a higher prevalence (1·2%) compared to those reporting 'very much' (0·3%, p < 0·024). Analysis of the 2020 survey data revealed regional differences in the factors associated with changes in food consumption scores (FCS) across 3,109 households in the West Bank and Gaza. Among the 2,094 households in West Bank, the unadjusted model indicated that living outside the barrier was associated with a significant decrease of FCS by 6·37 points (p < 0·001; 95% CI: -9·95 to -2·79) (Table 3 ). The final adjusted model for West Bank includes location of household to barriers, locality, mobility, household income level, and head of household sex, education, and employment, and refugee status. The adjusted model also suggests that living outside the barrier is associated with a decrease in FCS by 5·51 points (p = 0·002; 95% CI: -9·10 to -2·08). Households living in refugee camps are also associated with a decrease in FCS of 7·04 points (p < 0·001; 95% CI: -10.12 to -4·00). Household income level had a negative linear relationship with FCS—where middle income households were associated with a decrease in FCS of 2·61 points (p = 0·026; 95% CI: -5·81 to 0·59), poor households with a decrease of 13·01 points (p < 0·001; 95% CI: -16·89 to -9·12) and very poor households with a decrease 19·61 points (p < 0·001; 95% CI: -26·67 to -12·47). Education level of household heads had a positive linear relationship with FCS, where secondary and intermediate and higher education levels were associated with increases in FCS of 2·45 (p = 0·016; 95% CI: 0·45 to 4·42) and 5·27 (p < 0·001; 95% CI: 2·97 to 7·54), respectively. Those with a refugee status was also associated with an improvement of 5·28 points in FCS (p < 0·001; 95% CI 3·21 to 7·37). Households headed by females were associated with a decrease of 2·51 points that was marginally not statistically significant, but improved model fit (p = 0·093, 95% CI: -5·42 to 0·42). The extent of mobility restrictions impacting households as a predictor improved model fit but was barely statistically significant with “minor” impact having an associated decrease of 0·68 (p = 0·470; 95% CI: -2·56 to 1·18) and “very much” impact having an increase of 0·76 FCS points (p = 0·490; 95% CI: -1·39 to 2·92). Table 3 Crude and adjusted linear regression results for selected variable by region (West Bank and Gaza) with variables of interest associated with change in FCS using 2020 survey data (n = 3109). The unadjusted model for West Bank is glm(totalFCS ~ barrier), while the adjusted is glm(totalFCS ~ barrier + mobility + head of household sex + education + employment + locality + household income + refugee status). The unadjusted model for Gaza is glm(totalFCS ~ mobility), while the adjusted is glm(totalFCS ~ mobility + edu + marital status + household income). Crude Coefficient Crude p-value (95%CI) Adjusted Coefficient Adjusted p-value (95%CI) West Bank (n = 2094) Intercept 86·65 74·7 Living inside barrier ref ref Living outside barrier -6·37 *<0·001 (-9·95 – -2·79) -5·51 *0·002 (-9·10 – -2·08) Extent that mobility restrictions impacted household Not at all ref Minor -0·68 0·47 (-2·56–1·18) Very much 0·76 0·49 (-1·39–2·92) Head of household sex Male ref Female -2·51 0·093 (-5·42–0·42) Head of household education Elementary ref Secondary 2·45 *0·016 (0·45–4·42) Intermediate and above 5·27 *<0·001 (2·97–7·54) Locality Urban ref Rural 2·90 *0·004 (0·95–4·81) Refugee camps -7·04 *<0·001 (-10·12 – -4·00) Household income Rich ref Middle -2·61 *0·026 (-5·81–0·59) Poor -13·01 *<0·001 (-16·89 – -9·12) Very Poor -19·61 *<0·001 (-26·67– -12·47) Refugee status Not a refugee ref Refugee 5·28 *<0·001 (3·21–7·37) Gaza (n = 1015) Intercept 73·90 77·59 Extent that mobility restrictions impacted household Not at all ref ref Minor -3·40 *0·010 (-5·97 – -0·83) -3·50 *0·007 (68·98–86·19) Very much 2·85 0·059 (-0·11–5·80) 2·92 0·050 (-0·00–5·84) Head of household education Elementary ref Secondary 2·00 0·170 (-0·86–4·86) Intermediate and above 4·06 *0·014 (0·83–7·29) Marital status Single/Divorced/Separated/ Widow ref Married 4·0 0·049 (0·02–8·05) Household Income Rich ref Middle -7·57 0·054 (-15·27–0·13) Poor -10·47 0·009 (-18·2 – -2·67) Very Poor -13·04 0·001 (-21·01 – -5·07) ~In 2020, median of household FCS in West Bank is 79·5 (SD: 19·6), median of household FCS in Gaza is 76·5 (SD: 18·4) In Gaza, the proximity to the buffer zone was not significantly associated with changes in FCS. However, in the unadjusted, univariate analysis, the extent that mobility restrictions impacted household was statistically significant—where “minor” impact is associated with a decrease in 3·40 FCS points (p = 0·010; 95% CI: -5·97 to -0·83). For Gaza, the final adjusted model includes impact of restrictions on household mobility, household income level, and head of household education and marital status. Intermediate education and above was significantly associated with higher FCS; with an average increase of 4·06 FCS points (p = 0·014; 95% CI: 0·83 to 7·29). The adjusted model also suggests that being married was associated with an increase in FCS by 4·0 points (p = 0·049; 95% CI: 0·02 to 8·05), compared to those who were single, divorced, separated, or widowed. Household income level was negatively associated with food consumption score with poor and very poor being significantly associated with decreases of 10·47 (p = 0·009; 95% CI -18·2 to -2·67) and 13·04 (p = 0·001; 95% CI -21·01 to -5·07) FCS points, respectively. DISCUSSION The findings of this serial cross-sectional study shed light on the complex interplay between household location, head of household demographics, and changes in FCS in the oPt. Over the years, the median FCS remained within the acceptable threshold but had a declining trend from 2016 to 2020 (Table 1 ). This pattern suggests a gradual decrease in dietary diversity, likely due to conflict from the Israeli occupation impacting the economy, agriculture, and overall access to food. The increase in the proportion of households reporting unacceptable or borderline FCS from 0·4% in 2016 to 4·0% in 2020 demonstrates an increase in food insecurity over a relatively short period of four years (Table 2 ). This trend warrants attention from policymakers and stakeholders to mitigate further declines in food access. The data reveal regional differences in factors that are associated with changes in FCS using 2020 data. In the West Bank, location of household to barrier, restrictions on mobility, locality, household income, and head of household gender, education level, and refugee status are associated with changes in FCS. In Gaza, restrictions on mobility, household income, and head of household education and marital status are associated with FCS changes. This disparity can be attributed to several factors including geopolitical tensions, economic sanctions, and infrastructural challenges that are caused by conflict that uniquely affect each region [ 7 ]. In the West Bank, living outside the barrier, living in a refugee camp, minor mobility restrictions on household, household income, and households with female heads were associated with a decrease in FCS from a baseline value of 74·7. Households who had male heads were associated with an increase in FCS than their counterparts, which may reflect societal norms and structures that favor men in terms of employment opportunities and social stability [ 19 , 20 ]. The correlation between higher educational attainment and improved FCS underscores the importance of education in improving dietary diversity and food security, possibly through greater knowledge of nutrition or greater job opportunities that improve access to food through improved household income. Households who identify as middle income, poor, and very poor had an increasing negative association with FCS in our multivariate analysis, indicating that interventions directed towards those of lower socioeconomic status may have a bigger impact in improving FCS. Household locality had an impact on food diversity; using urban households as the reference, rural households were associated with an increase in FCS while refugee camps were associated with a decrease in FCS. Rural households may have greater access to farms and agricultural resources, which allows household members maintain their diet and FCS. While those in refugee camps have been recently displaced and have limited access to food supplies, which would decrease their household FCS. Households lead by refugees were associated with a statistically significant increase in FCS, but this may be due to refugees receiving food assistance from humanitarian programs, which could improve FCS; this finding is aligned with the evidence generated by Brück et al., suggesting the impact of aid in mitigating food insecurity. In Gaza, minor mobility restrictions on household and households with middle, poor, and very poor income levels were associated with a decrease in FCS from a baseline value of 77·59. The minor mobility restrictions in Gaza have a bigger negative impact on changes on FCS compared to West Bank as indicated by the larger coefficient—suggesting that mobility in Gaza may be a bigger issue than in the West Bank. Like West Bank, there was an increasing negative association with FCS from middle to poor and very poor income levels, which suggests a similar intervention providing food assistance to households of lower socioeconomic status would target households vulnerable to food insecurity. Households where the head is married and/or have higher than a secondary education were associated with positive changes in FCS. Being married can improve FCS as the combined income, shared responsibilities, social support, and economies of scale within a married household often lead to more consistent and nutritious diets. Similar to households in the West Bank, head of household education level is associated with a positive change in FCS, reinforcing the importance of education in preventing food insecurity even when facing the challenges that are experienced by households residing in conflict-affected and fragile settings. Given the increasing number of unacceptable FCS over the survey years, it is imperative for interventions to be targeted and tailored to the Palestinian population in the oPt. Strategies that enhance mobility, improve economic opportunities, provide educational resources, and promote ceasefire could be vital in improving food security for Palestinians. Additionally, continued support for refugees and effective distribution of humanitarian aid are essential to address the immediate needs while working towards long-term solutions to enhance food security in the region; this aspect is in line with findings from previous work that suggests aid may mitigate food insecurity in the region [ 12 ]. One of the potential explanations for the higher than expected FCS is that over the last decade, organizations such as the World Food Programme, United Nations Children Fund (UNICEF), and the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA) have provided food assistance or cash assistance to the most vulnerable. There is criticism that humanitarian aid hinders Palestinian efforts to localize food production—so solutions that not only provide aid and support but also allow the involvement of local communities are needed and should be prioritized to ensure sustainability of any improvements to food security and dietary diversity [ 21 ]. CONCLUSIONS The results of this study provide valuable insights into the factors affecting FCS in occupied Palestinian territory, highlighting the long-term trend and significant association of household location and head of household demographics in a protracted conflict setting. The data and analyses underscored the persistent and growing challenges faced by different demographic groups in the oPt. These findings provide a clear call to action for both local authorities and international stakeholders to prioritize humanitarian aid and reinforcing food security as well as advocate for cease fire, demilitarization, and peace. Abbreviations Food consumption score (FCS) occupied Palestinian territory (oPt) Gaza Strip (Gaza) World Food Programme (WFP) Armed Conflict Location and Event Data (ACLED) Confidence Interval (CI) United Nations Children Fund (UNICEF) United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA) University of California, San Francisco (UCSF) Institutional Review Board (IRB) Declarations Ethics approval and consent to participate The data used in this study is census data acquired from the Palestinian Central Bureau for Statistics and does not require informed consent to participate. In addition, the data was collected without personally identifiable information. This research is deemed exempt from UCSF Institutional Review Board (IRB) approval (23-40704). All procedures contributing to this work comply with the ethical standards of the Declaration of Helsinki. Consent for Publication Not applicable. Availability of data and materials Due to data ownership and confidentiality agreements the survey and data are not publicly available. As mentioned in the “Ethics approval and consent to participate” section, the data used in this study were obtained from a survey designed by the Palestinian Central Bureau of Statistics for census purposes that do not require formal informed consent to participate and was exempt from the UCSF IRB approval (24-40704). However, summary statistics and analysis methods are available upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding There was no funding source for this study. Authors’ contributions C.C. and T.K.L. conceptualized the study, designed the methodology, conducted the data analysis, and drafted the manuscript. T.K.L. and W.H. contributed to the data interpretation and provided critical revisions to the manuscript. The Palestinian Central Bureau of Statistics was responsible for data collection while T.K.L. was responsible for data acquisition. All authors contributed to the writing, reviewed the manuscript for intellectual content, and approved the final version for submission. Acknowledgments We sincerely thank Dr. Ali Mirzazadeh, Rachel Abbott, MSc, Amira Adam, MPH, and Ashley Mitchell, MPH, for their valuable input and advice on this manuscript. References Holleman C, Jackson J, Sánchez MV, Vos R, editors. Sowing the seeds of peace for food security: disentangling the nexus between conflict, food security and peace. Rome: Food and Agriculture Organization of the United Nations; 2017. Kendall A, Olson CM, Frongillo EA. Relationship of Hunger and Food Insecurity to Food Availability and Consumption. J Am Diet Assoc. 1996;96:1019–24. Tarasuk VS, Beaton GH. Women’s Dietary Intakes in the Context of Household Food Insecurity12. J Nutr. 1999;129:672–9. Dixon LB, Winkleby MA, Radimer KL. Dietary Intakes and Serum Nutrients Differ between Adults from Food-Insufficient and Food-Sufficient Families: Third National Health and Nutrition Examination Survey, 1988–1994. J Nutr. 2001;131:1232–46. Lee JS, Frongillo EA. Nutritional and Health Consequences Are Associated with Food Insecurity among U.S. Elderly Persons. J Nutr. 2001;131:1503–9. Trade UNC. on, Development. The Economic Costs of the Israeli Occupation for the Palestinian People [Internet]. United Nations; 2023. Available from: https://www.un-ilibrary.org/content/books/9789210023832 Lin TK, Kafri R, Hammoudeh W, Mitwalli S, Jamaluddine Z, Ghattas H, et al. Pathways to food insecurity in the context of conflict: the case of the occupied Palestinian territory. Confl Health. 2022;16:38. El Bilbeisi AH, Al-Jawaldeh A, Albelbeisi A, Abuzerr S, Elmadfa I, Nasreddine L. Households’ Food Insecurity and Its Association with Demographic and Socioeconomic Factors in Gaza Strip, Palestine: A Cross-Sectional Study. Ethiop J Health Sci. 2022;32:369–80. Salti N, Ghattas H. Food insufficiency and food insecurity as risk factors for physical disability among Palestinian refugees in Lebanon: Evidence from an observational study. Disabil Health J. 2016;9:655–62. Rickard J. The fragmentation of Palestine: identity and isolation in the twenty-first century. London: I.B. Tauris; 2022. Area C, of the West Bank. : Key humanitarian concerns - OCHA factsheet [Internet]. Question of Palestine. [cited 2024 Dec 17]. Available from: https://www.un.org/unispal/document/auto-insert-199136/ Brück T, d’Errico M, Pietrelli R. The effects of violent conflict on household resilience and food security: Evidence from the 2014 Gaza conflict. World Dev. 2019;119:203–23. Socio-Economic and Food Security Survey in the West Bank and Gaza Strip. (2012) - Palestine/FAO/UNRWA/WFP joint report [Internet]. Question of Palestine. [cited 2024 Jul 29]. Available from: https://www.un.org/unispal/document/auto-insert-201337/ Food Consumption Score (FCS) [Internet]. UNHCR Assessment and Monitoring Resource Centre. 2023 [cited 2024 Mar 31]. Available from: https://www.unhcr.org/handbooks/assessment/collect/food-consumption-score-fcs Marivoet W, Becquey E, Van Campenhout B. How well does the Food Consumption Score capture diet quantity, quality and adequacy across regions in the Democratic Republic of the Congo (DRC)? Food Sec. 2019;11:1029–49. An Evaluation of WFP’s Regional Response to the Syrian Crisis. 2011–2014 | World Food Programme [Internet]. 2015 [cited 2024 Jul 1]. Available from: https://www.wfp.org/publications/evaluation-wfps-regional-response-syrian-crisis-2011-2014 Annual Country Report. | World Food Programme [Internet]. [cited 2024 Jul 1]. Available from: https://www.wfp.org/operations/annual-country-report?operation_id=SD02&year=2023 Meta Data for the Food Consumption Score (FCS). Indicator | World Food Programme [Internet]. 2015 [cited 2024 Apr 1]. Available from: https://www.wfp.org/publications/meta-data-food-consumption-score-fcs-indicator Haj-Yahia MM. On the Characteristics of Patriarchal Societies, Gender Inequality, and Wife Abuse: The Case of Palestinian Society. 2005;20. Bargawi H, Alami R, Ziada H. Re-negotiating social reproduction, work and gender roles in occupied Palestine. Rev Int Polit Econ. 2022;29:1917–44. Asi YM. Achieving Food Security Through Localisation, Aid N. De-development and Food Sovereignty in the Palestinian Territories. Journal of Peacebuilding & Development. 2020;15:205–18. Additional Declarations No competing interests reported. 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Source: United Nations Office for the Coordination of Humanitarian Affairs (OCHA). Barriers and buffer zones are denoted by the green dotted line. The original figure is available at https://www.ochaopt.org/content/west-bank-including-east-jerusalem-and-gaza-strip-january-2019 [accessed Oct 17 2024].\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6474000/v1/7eb3392113634eb25f7bd394.png"},{"id":82886126,"identity":"d50beed6-57c5-4f0a-a13a-7ff65c1309fd","added_by":"auto","created_at":"2025-05-16 11:52:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4109870,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6474000/v1/9eb7ed32-150f-4d12-a43e-f63790c95aa3.pdf"},{"id":82883894,"identity":"70faa6a6-36c9-468b-88c7-ed3376f90551","added_by":"auto","created_at":"2025-05-16 11:28:14","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":67226,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptFinalDraftFCSinoPtTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6474000/v1/d0866af8e761709a31afc070.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the Association of Household Location and Sociodemographic Profile on Decreasing Dietary Diversity in Occupied Palestine: A Serial Cross-Sectional Study","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eThe prevalence of undernourishment in conflict-affected low- and middle-income countries is significantly higher, ranging from 1\u0026middot;4 to 4\u0026middot;4% more on average, compared to their counterparts within the same economic classification that are not experiencing conflict [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In challenging environments affected by conflict, households may resort to coping strategies to withstand the negative externalities of conflict and ensure that household members receive sufficient food for consumption for as long as possible. These strategies may include consuming less nutritious food or limiting dietary variety so to focus on maintaining adequate caloric in-take \u0026ndash; ultimately reducing the household's dietary diversity [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe sustained conflict and Israeli military occupation have severely hindered productivity, food production, and availability in both the West Bank and the Gaza Strip (hereafter Gaza) \u0026ndash; regions in the occupied Palestinian territory (oPt) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These challenges are largely attributed to restrictions on Palestinian mobility, destruction of infrastructure, and constraints imposed on water and food markets by Israeli regulations [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. While studies have explored food insecurity in Gaza and among Palestinian refugees in Lebanon, data on the West Bank remains scarce [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Research has highlighted the associations between food insecurity (using the Radimer/ Cornell food security scale) and various sociodemographic factors in Gaza [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These studies do not sufficiently address the long-term trend and nuanced interactions of household location and sociodemographic factors across the oPt.\u003c/p\u003e \u003cp\u003eThe ongoing conflict in the oPt presents a unique opportunity to investigate factors associated with food insecurity. The protracted conflict and occupation of Palestine has fragmented communities in the region that is further complicated by military and administrative delineations such as Areas A, B, and C in the West Bank and buffer zones in Gaza (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Areas A, B, and C in the West Bank are administrative divisions established under the Oslo Accords, reflecting differing levels of Palestinian and Israeli control. Area A is under full Palestinian civil and security control, encompassing major Palestinian cities which generally have better access to basic services than areas B and C. Area B is under Palestinian civil control and Israeli security control, primarily including rural areas and towns which have basic services, but quality and access is inconsistent. Area C, which constitutes the majority of the West Bank, is under full Israeli civil and security control, covering Israeli settlements, military zones, and much of the region's agricultural land\u0026mdash;facing the most significant challenges in accessing basic needs, such as food sources [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Buffer zones in Gaza are areas along the border with Israel where access is restricted or controlled. These zones are enforced by Israel and vary in size, with the most restrictive areas extending up to 300 meters from the border, where entry is prohibited. Beyond this, access restrictions extend up to between 1,000 and 1,500 meters, significantly limiting agricultural activity and construction. These divisions have created distinct geopolitical enclaves, each with varying levels of exposure to political and military strife.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOur study represent an unique serial cross-sectional analysis that builds on previous work and explores the trend of food consumption and association between household characteristics and dietary diversity in the context of a protracted conflict [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Previous studies found that living in areas with higher political and agricultural hardships\u0026mdash;such as Area C or living outside the barrier in West Bank or living near buffer zones in Gaza\u0026mdash;is associated with increased food insecurity and reduced dietary diversity [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Furthermore, conflict reduced the overall resilience capacity of households to a certain extent, however, it induced aid response which may have mitigated food insecurity specifically in Gaza [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. We add to previous work and hypothesize that 1) the median FCS score trends downwards throughout the years, due to long-term exposure to conflict. The findings provide insight on the long-term impact of conflict and occupation on dietary diversity.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eWe conducted a serial cross-sectional study using data from 2014, 2016, 2018, and 2020 on the association of household sociodemographic factors on FCS in a sample of Palestinians living in the West Bank and Gaza, collected by the Palestinian Central Bureau of Statistics using the Socio-Economic Conditions Survey.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSubjects and sampling\u003c/h3\u003e\n\u003cp\u003eThe target population for the survey includes all Palestinian households with regular residency (households with residents that are registered with the Palestinian Civil Registry) in Palestine during the period of the surveys in 2014, 2016, 2018, and 2020. Head of households completed the questionnaire on socio-economic conditions and food consumption on behalf of the household. The survey is a representative sample of the Palestinian population, using a sampling framework for that was based on enumeration areas, locality, and governorate levels. The sample design employed a three-stage stratified cluster systematic random sampling method where weights are calculated as the inverse of selection probabilities. Initially, weights are assigned to enumeration areas based on their selection probability, then to households within these areas, and adjusted for attrition and appropriate household estimates by strata. Finally, weights are assigned to individual household members and adjusted based on relevant population estimates by region, gender, and age group. This process corrects for selection biases and nonresponse, making the sample representative and ensuring the survey results are generalizable to the entire population. The Socio-Economic \u0026amp; Food Security Survey 2014: State of Palestine contains more information to the survey data, sampling strategy, and description [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. As this is a serial cross-sectional study, the final dataset containing all 4 survey years may contain repeat households, as it is not possible to identify who has previously taken the survey.\u003c/p\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003eOne of the commonly used measures for food diversity is FCS, which is an index score calculated using self-reported household consumption of food groups over seven days. The frequency of each consumed food group is then weighted by nutritional value and summed to produce a score that is classified into poor, borderline, or acceptable [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The FCS is used to monitor changes in food consumption and aid prioritization and formulate policies that address food security in conflict zones, which has been seen in Yemen, Syria and South Sudan[\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious work demonstrated that location (i.e., living inside/outside the barriers in West Bank and near buffer zones in Gaza) and limited mobility are drivers of food insecurity, our variables of interest included region (West Bank vs Gaza), locality type (urban, rural, refugee camp), proximity to barrier or buffer zones, self-reported household income, and extent that mobility has restricted the household. Because the head of household is the main decision maker and is responsible for financial support and welfare of the household, we are using the demographic variables of the head of household as a profile for the entire household. Demographic variables of interest include head of household education, refugee status, gender, hours worked, refugee status, and mobility were included in the analysis. Asset assessments were not consistent over survey years, making it difficult to quantitatively value household income, so household income used in this analysis was self-reported by head of household as very poor, poor, middle, or high income.\u003c/p\u003e\n\u003ch3\u003eAnalysis\u003c/h3\u003e\n\u003cp\u003eWe conducted analyses using R version 4\u0026middot;3.1. Linear regression model was separated by region using data from 2020\u0026mdash;West Bank and Gaza\u0026mdash;to compare FCS amongst households with different proximity to barrier or buffer zones and to account for regional difference in exposure to conflict-related hardships. Food consumption score was calculated following the methodology provided by the World Food Programme (WFP) where households sum consumption frequencies by food group per week, multiply the sum from each group by a weight designated by WFP, and then added together to obtain a final FCS [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The maximum FCS a household can obtain is 112. Households with a score under 35 is considered as having an \u0026lsquo;unacceptable\u0026rsquo; food consumption status. The \u0026lsquo;unacceptable\u0026rsquo; food consumption status is a combination of poor (FCS\u0026thinsp;\u0026lt;\u0026thinsp;21) and borderline (FCS 21\u0026ndash;35) food consumption status.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eThe use of survey data may introduce biases inherent to self-reported surveys, and the inability to account for individual-level dietary intake data limits the granularity of the analysis. The observational nature of the serial cross-sectional study design prohibits causality, since data was collected at different points in time without tracking the same households. This design is susceptible to cohort effects, where changes may be due to the sampled population than true temporal trends. The potential for measurement error due to data collection changes between survey years. Despite these limitations, the data provide a robust, representative sample and a broad scope of variables for secondary analysis. Serial cross-sectional studies are a useful tool for identifying associations and trends within populations, though they should be supplemented with longitudinal data for more robust conclusions; future research could employ longitudinal cohort studies analyzed alongside ACLED data to elucidate causal relationships between socio-economic factors, violence caused by conflict, and dietary diversity in occupied Palestine.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eA total of 26,934 households responded to the surveys from 2014, 2016, 2018, and 2020. Households were excluded if they did not have any food consumption data to calculate the FCS, resulting in the analysis of data from 23,129 households (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It is important to note that due to the data collection process, we are unable to determine whether the same households participated in multiple years; some households may have been included in the dataset more than once. Overall, the median household size was 5 members. Heads of households were typically male (90\u0026middot;5%) and married (90\u0026middot;3%), where approximately half completed a secondary level education (46\u0026middot;5%), are not refugees (57\u0026middot;1%), and work over 35 hours (50\u0026middot;5%). A higher proportion of households were in the West Bank (61\u0026middot;1%) and outside the barrier wall (56\u0026middot;8%), with 2020 having the highest proportion of households located in the West Bank (67\u0026middot;4%). The proportion of households within 1,000 meters of the buffer zone in Gaza averaged 2\u0026middot;6% across all years. Majority of households were in urban settings (72\u0026middot;5%) and self-reported as middle class (70\u0026middot;9%). The median FCS across all households was an 80\u0026middot;0 (range: 3 to 112), showing fluctuations over the survey years with median scores of 81\u0026middot;0 in 2014, 84\u0026middot;0 in 2016, 79\u0026middot;0 in 2018, and 78\u0026middot;0 in 2020\u0026mdash;all within the acceptable threshold.\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\u003eDescriptive characteristics of households (N\u0026thinsp;=\u0026thinsp;23,129) amongst 4 waves (2014, 2016, 2018, 2020) of Socio-Economic Conditions Surveys done by the State of Palestine\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;23129)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;7900)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2195)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;9925)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3109)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedian FCS [Min, Max]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 [3, 112]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e81 [15, 112]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e84 [16\u0026middot;5, 112]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e79 [3, 112]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e78 [10\u0026middot;5, 112]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold Locality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\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\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16764 (72\u0026middot;5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e5606 (71\u0026middot;0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1424 (64\u0026middot;9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7491 (75\u0026middot;5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2243 (72\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3868 (16\u0026middot;7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1468 (18\u0026middot;6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e451 (20\u0026middot;5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1438 (14\u0026middot;5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e511 (16\u0026middot;4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRefugee Camp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2497 (10\u0026middot;8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e826 (10\u0026middot;5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e320 (14\u0026middot;6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e996 (10\u0026middot;0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e355 (11\u0026middot;4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHouseholds in West Bank\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14138 (61\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e4892 (61\u0026middot;9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1255 (57\u0026middot;2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5897 (59\u0026middot;4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2094 (67\u0026middot;4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving in Area C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e980 (4\u0026middot;2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e283 (3\u0026middot;6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e172 (7\u0026middot;8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e420 (4\u0026middot;2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e105 (3\u0026middot;4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInside barrier wall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e993 (4\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e449 (5\u0026middot;7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e121 (5\u0026middot;5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e301 (3\u0026middot;0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e122 (3\u0026middot;9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutside barrier wall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13138 (56\u0026middot;8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e4435 (56\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1134 (51\u0026middot;7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5597 (56\u0026middot;4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1972 (63\u0026middot;4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHouseholds in Gaza\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8991 (38\u0026middot;9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e3008 (38\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e940 (42\u0026middot;8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4028 (40\u0026middot;6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1015 (32\u0026middot;6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;1000 m from buffer zone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e603 (2\u0026middot;6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e269 (3\u0026middot;4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e25 (1\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e272 (2\u0026middot;7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e37 (1\u0026middot;2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;1000 m from buffer zone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8385 (36\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2737 (34\u0026middot;6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e915 (41\u0026middot;7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3755 (37\u0026middot;8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e978 (31\u0026middot;5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExtent that mobility restrictions impacted household\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot at all\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13270 (57\u0026middot;4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e4005 (50\u0026middot;7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1595 (72\u0026middot;7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6185 (62\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1485 (47\u0026middot;8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5459 (23\u0026middot;6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1730 (21\u0026middot;9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e354 (16\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2403 (24\u0026middot;2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e972 (31\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery much\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4173 (18\u0026middot;0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2100 (26\u0026middot;6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e232 (10\u0026middot;6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1209 (12\u0026middot;2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e632 (20\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDon't know\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e198 (0\u0026middot;9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e59 (0\u0026middot;7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e13 (0\u0026middot;6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e126 (1\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHead of Household Characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian number of household members [Min, Max]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026middot;00 [1, 27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e6\u0026middot;00 [1, 25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e5\u0026middot;00 [1, 27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u0026middot;00 [1, 27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u0026middot;00 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e20931 (90\u0026middot;5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e7148 (90\u0026middot;5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1990 (90\u0026middot;7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8966 (90\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2827 (90\u0026middot;9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20876 (90\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e7111 (90\u0026middot;0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1973 (89\u0026middot;9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8975 (90\u0026middot;4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2817 (90\u0026middot;6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighest level of education received: secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10748 (46\u0026middot;5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e3559 (45\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1016 (46\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4727 (47\u0026middot;6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1446 (46\u0026middot;5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorks 35\u0026thinsp;+\u0026thinsp;hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11689 (50\u0026middot;5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e3694 (46\u0026middot;8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1220 (55\u0026middot;6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5247 (52\u0026middot;9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1528 (49\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot a refugee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13215 (57\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e4584 (58\u0026middot;0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1113 (50\u0026middot;7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5787 (58\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1731 (55\u0026middot;7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceived aid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14439 (62\u0026middot;4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e4519 (57\u0026middot;2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1334 (60\u0026middot;8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6660 (67\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1926 (61\u0026middot;9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceived food assistance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6510 (28\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2427 (30\u0026middot;7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e623 (28\u0026middot;4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2567 (25\u0026middot;9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e893 (28\u0026middot;7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e*Household income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e\u0026nbsp;\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 \u003cp\u003e902 (4\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e271 (3\u0026middot;4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026middot;\u0026middot;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e461 (4\u0026middot;6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e170 (5\u0026middot;5%)\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 \u003cp\u003e14841 (70\u0026middot;9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e5826 (73\u0026middot;7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026middot;\u0026middot;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6939 (69\u0026middot;9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2076 (66\u0026middot;8%)\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e3743 (17\u0026middot;9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e1368 (17\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026middot;\u0026middot;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1765 (17\u0026middot;8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e610 (19\u0026middot;6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1441 (6\u0026middot;9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e434 (5\u0026middot;5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026middot;\u0026middot;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e759 (7\u0026middot;7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e248 (8\u0026middot;0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e*To calculate total proportion, did not include 2016 values in the denominator so N\u0026thinsp;=\u0026thinsp;20934\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePrevalence of Unacceptable Food Consumption Scores\u003c/h3\u003e\n\u003cp\u003eThe prevalence of unacceptable or borderline food consumption scores (FCS) among 23,129 households over four survey years (2014, 2016, 2018, 2020) was 2\u0026middot;2%, with annual variations observed: 0\u0026middot;8% in 2014, 0\u0026middot;4% in 2016, 3\u0026middot;2% in 2018, and 3\u0026middot;7% in 2020, indicating an overall increase over time (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Geographically, participants from the West Bank had a higher prevalence of unacceptable FCS (1\u0026middot;3%) compared to Gaza (0\u0026middot;9%), with statistical significance demonstrated across regions (p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001).\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\u003eProportion of households with unacceptable FCS (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;35) by location and sociodemographic factors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003eSurvey Year\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;23129)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;7900)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2195)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;9925)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3109)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnacceptable FCS (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e509 (2\u0026middot;2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62 (0\u0026middot;8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (0\u0026middot;4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e322 (3\u0026middot;2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e116 (3\u0026middot;7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLocation of Household\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e408 (1\u0026middot;8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (0\u0026middot;6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (0\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e261 (2\u0026middot;6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95 (3\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 (0\u0026middot;2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u0026lt;0\u0026middot;001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (0\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0\u0026middot;0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40 (0\u0026middot;4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8 (0\u0026middot;2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRefugee camps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (0\u0026middot;2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u0026lt;0\u0026middot;001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (0\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0\u0026middot;0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21 (0\u0026middot;2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13 (0\u0026middot;4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHouseholds in West Bank\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e302 (1\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (0\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (0\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e188 (1\u0026middot;9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e83 (2\u0026middot;7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInside barrier wall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (0\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0\u0026middot;0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (0\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12 (0\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3 (0\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutside barrier wall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e284 (1\u0026middot;2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026middot;996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (0\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (0\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e176 (1\u0026middot;8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e80 (2\u0026middot;6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eArea C (West Bank only)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e295 (1\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (0\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (0\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e184 (1\u0026middot;9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e80 (2\u0026middot;6%)\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (0\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0\u0026middot;0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0\u0026middot;0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19 (0\u0026middot;2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 (0\u0026middot;1%)\u003c/p\u003e \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 \u003cp\u003e273 (1\u0026middot;2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*0\u0026middot;012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (0\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (0\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e165 (1\u0026middot;7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e78 (2\u0026middot;5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHouseholds in Gaza\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e207 (0\u0026middot;9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u0026lt;0\u0026middot;001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (0\u0026middot;5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (0\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e134 (1\u0026middot;4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33 (1\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1000m from buffer zone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (0\u0026middot;0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026middot;183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (0\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0\u0026middot;0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (0\u0026middot;0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (0\u0026middot;0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;1000m and more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e198 (0\u0026middot;9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026middot;200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (0\u0026middot;4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (0\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e130 (1\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32 (1\u0026middot;0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExtent that mobility restrictions impacted household\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot at all\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e285 (1\u0026middot;2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (0\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (0\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e195 (2\u0026middot;0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e58 (1\u0026middot;9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143 (0\u0026middot;6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u0026lt;0\u0026middot;001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (0\u0026middot;2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (0\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90 (0\u0026middot;9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35 (1\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery much\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 (0\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*0\u0026middot;024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (0\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0\u0026middot;0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28 (0\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23 (0\u0026middot;7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHead of Household Characteristics\u003c/b\u003e\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\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 \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 \u003cp\u003e436 (1\u0026middot;9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (0\u0026middot;6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (0\u0026middot;4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e280 (2\u0026middot;8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99 (3\u0026middot;2%)\u003c/p\u003e \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 \u003cp\u003e73 (0\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u0026lt;0\u0026middot;001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (0\u0026middot;2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0\u0026middot;0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42 (0\u0026middot;4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17 (0\u0026middot;5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle/Divorced/Separated\u003c/p\u003e \u003cp\u003e/Widow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77 (0\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (0\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0\u0026middot;0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47 (0\u0026middot;5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18 (0\u0026middot;6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e430 (1\u0026middot;9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u0026lt;0\u0026middot;001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (0\u0026middot;6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (0\u0026middot;4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e275 (2\u0026middot;8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e98 (3\u0026middot;2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHighest level of education obtained\u003c/b\u003e\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElementary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215 (0\u0026middot;9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (0\u0026middot;4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (0\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e128 (1\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50 (1\u0026middot;6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e227 (1\u0026middot;0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u0026lt;0\u0026middot;001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (0\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (0\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e151 (1\u0026middot;5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e52 (1\u0026middot;7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (0\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u0026lt;0\u0026middot;001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (0\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (0\u0026middot;0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43 (0\u0026middot;4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14 (0\u0026middot;5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmployment\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (0\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (0\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (0\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50 (0\u0026middot;5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10 (0\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking 1\u0026ndash;24 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e199 (0\u0026middot;9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u0026lt;0\u0026middot;001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (0\u0026middot;2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (0\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e131 (1\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50 (1\u0026middot;6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking 35\u0026thinsp;+\u0026thinsp;hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e235 (1\u0026middot;0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u0026lt;0\u0026middot;001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (0\u0026middot;4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (0\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e141 (1\u0026middot;4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e56 (1\u0026middot;8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRefugee Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot a refugee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e328 (1\u0026middot;4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (0\u0026middot;4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (0\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e221 (2\u0026middot;2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e71 (2\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRefugee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e179 (0\u0026middot;8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u0026lt;0\u0026middot;001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (0\u0026middot;4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (0\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e101 (1\u0026middot;0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45 (1\u0026middot;4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReceived Aid\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 \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 \u003cp\u003e202 (0\u0026middot;9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (0\u0026middot;5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (0\u0026middot;2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e112 (1\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47 (1\u0026middot;5%)\u003c/p\u003e \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 \u003cp\u003e307 (1\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u0026lt;0\u0026middot;001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (0\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (0\u0026middot;2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e210 (2\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e69 (2\u0026middot;2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e*Household income\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \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 \u003cp\u003e5 (0\u0026middot;0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0\u0026middot;0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026middot;\u0026middot;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (0\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0 (0\u0026middot;0%)\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 \u003cp\u003e229 (1\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026middot;058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (0\u0026middot;2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026middot;\u0026middot;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e160 (1\u0026middot;6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e53 (1\u0026middot;7%)\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e163 (0\u0026middot;8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u0026lt; 0\u0026middot;001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (0\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026middot;\u0026middot;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99 (1\u0026middot;0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38 (1\u0026middot;2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103 (0\u0026middot;5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u0026lt;0\u0026middot;001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (0\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026middot;\u0026middot;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58 (0\u0026middot;6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25 (0\u0026middot;8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e*To calculate total proportion, did not include 2016 values in the denominator so N\u0026thinsp;=\u0026thinsp;20934\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\u003eAmong head of household characteristics, males reported higher unacceptable or borderline FCS (0\u0026middot;9%) compared to females (0\u0026middot;1%), with significant gender differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001). Regarding marital status, married participants showed a statistically significant higher prevalence (1\u0026middot;9%) compared to those who were single, divorced, separated, or widowed (0\u0026middot;3%) (p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001). Intermediate level of education and above had a lower unacceptable household FCS proportion (0\u0026middot;3%), indicating significant educational disparities (p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001). Employment status is also associated with unacceptable household FCS, with those working 35\u0026thinsp;+\u0026thinsp;hours per week exhibiting higher prevalence (1\u0026middot;0%) compared to the unemployed (0\u0026middot;3%, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001). Refugee status and receipt of aid were significant predictors of unacceptable household FCS. Non-refugees and those not receiving aid demonstrated higher prevalence rates (0\u0026middot;6% and 0\u0026middot;6% respectively), compared to refugees (0\u0026middot;4%) and aid recipients (0\u0026middot;4%), both showing significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001). The extent of mobility restrictions also impacted FCS, with households reporting 'not at all' having a higher prevalence (1\u0026middot;2%) compared to those reporting 'very much' (0\u0026middot;3%, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;024).\u003c/p\u003e \u003cp\u003eAnalysis of the 2020 survey data revealed regional differences in the factors associated with changes in food consumption scores (FCS) across 3,109 households in the West Bank and Gaza. Among the 2,094 households in West Bank, the unadjusted model indicated that living outside the barrier was associated with a significant decrease of FCS by 6\u0026middot;37 points (p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001; 95% CI: -9\u0026middot;95 to -2\u0026middot;79) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The final adjusted model for West Bank includes location of household to barriers, locality, mobility, household income level, and head of household sex, education, and employment, and refugee status. The adjusted model also suggests that living outside the barrier is associated with a decrease in FCS by 5\u0026middot;51 points (p\u0026thinsp;=\u0026thinsp;0\u0026middot;002; 95% CI: -9\u0026middot;10 to -2\u0026middot;08). Households living in refugee camps are also associated with a decrease in FCS of 7\u0026middot;04 points (p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001; 95% CI: -10.12 to -4\u0026middot;00). Household income level had a negative linear relationship with FCS\u0026mdash;where middle income households were associated with a decrease in FCS of 2\u0026middot;61 points (p\u0026thinsp;=\u0026thinsp;0\u0026middot;026; 95% CI: -5\u0026middot;81 to 0\u0026middot;59), poor households with a decrease of 13\u0026middot;01 points (p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001; 95% CI: -16\u0026middot;89 to -9\u0026middot;12) and very poor households with a decrease 19\u0026middot;61 points (p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001; 95% CI: -26\u0026middot;67 to -12\u0026middot;47). Education level of household heads had a positive linear relationship with FCS, where secondary and intermediate and higher education levels were associated with increases in FCS of 2\u0026middot;45 (p\u0026thinsp;=\u0026thinsp;0\u0026middot;016; 95% CI: 0\u0026middot;45 to 4\u0026middot;42) and 5\u0026middot;27 (p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001; 95% CI: 2\u0026middot;97 to 7\u0026middot;54), respectively. Those with a refugee status was also associated with an improvement of 5\u0026middot;28 points in FCS (p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001; 95% CI 3\u0026middot;21 to 7\u0026middot;37). Households headed by females were associated with a decrease of 2\u0026middot;51 points that was marginally not statistically significant, but improved model fit (p\u0026thinsp;=\u0026thinsp;0\u0026middot;093, 95% CI: -5\u0026middot;42 to 0\u0026middot;42). The extent of mobility restrictions impacting households as a predictor improved model fit but was barely statistically significant with \u0026ldquo;minor\u0026rdquo; impact having an associated decrease of 0\u0026middot;68 (p\u0026thinsp;=\u0026thinsp;0\u0026middot;470; 95% CI: -2\u0026middot;56 to 1\u0026middot;18) and \u0026ldquo;very much\u0026rdquo; impact having an increase of 0\u0026middot;76 FCS points (p\u0026thinsp;=\u0026thinsp;0\u0026middot;490; 95% CI: -1\u0026middot;39 to 2\u0026middot;92).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCrude and adjusted linear regression results for selected variable by region (West Bank and Gaza) with variables of interest associated with change in FCS using 2020 survey data (n\u0026thinsp;=\u0026thinsp;3109). The unadjusted model for West Bank is glm(totalFCS\u0026thinsp;~\u0026thinsp;barrier), while the adjusted is glm(totalFCS\u0026thinsp;~\u0026thinsp;barrier\u0026thinsp;+\u0026thinsp;mobility\u0026thinsp;+\u0026thinsp;head of household sex\u0026thinsp;+\u0026thinsp;education\u0026thinsp;+\u0026thinsp;employment\u0026thinsp;+\u0026thinsp;locality\u0026thinsp;+\u0026thinsp;household income\u0026thinsp;+\u0026thinsp;refugee status). The unadjusted model for Gaza is glm(totalFCS\u0026thinsp;~\u0026thinsp;mobility), while the adjusted is glm(totalFCS\u0026thinsp;~\u0026thinsp;mobility\u0026thinsp;+\u0026thinsp;edu\u0026thinsp;+\u0026thinsp;marital status\u0026thinsp;+\u0026thinsp;household income).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude \u003c/p\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eCrude p-value (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdjusted p-value (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWest Bank (n\u0026thinsp;=\u0026thinsp;2094)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86\u0026middot;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74\u0026middot;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving inside barrier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving outside barrier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6\u0026middot;37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e*\u0026lt;0\u0026middot;001 (-9\u0026middot;95 \u0026ndash; -2\u0026middot;79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5\u0026middot;51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*0\u0026middot;002 (-9\u0026middot;10 \u0026ndash; -2\u0026middot;08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eExtent that mobility restrictions impacted household\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot at all\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0\u0026middot;68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026middot;47 (-2\u0026middot;56\u0026ndash;1\u0026middot;18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery much\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026middot;76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026middot;49 (-1\u0026middot;39\u0026ndash;2\u0026middot;92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead of household sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \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\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \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\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2\u0026middot;51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026middot;093 (-5\u0026middot;42\u0026ndash;0\u0026middot;42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead of household education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElementary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u0026middot;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*0\u0026middot;016 (0\u0026middot;45\u0026ndash;4\u0026middot;42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u0026middot;27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u0026lt;0\u0026middot;001 (2\u0026middot;97\u0026ndash;7\u0026middot;54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u0026middot;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*0\u0026middot;004 (0\u0026middot;95\u0026ndash;4\u0026middot;81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRefugee camps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7\u0026middot;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u0026lt;0\u0026middot;001 (-10\u0026middot;12 \u0026ndash; -4\u0026middot;00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \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\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \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\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2\u0026middot;61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*0\u0026middot;026 (-5\u0026middot;81\u0026ndash;0\u0026middot;59)\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=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-13\u0026middot;01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u0026lt;0\u0026middot;001 (-16\u0026middot;89 \u0026ndash; -9\u0026middot;12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery Poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-19\u0026middot;61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u0026lt;0\u0026middot;001 (-26\u0026middot;67\u0026ndash; -12\u0026middot;47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRefugee status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot a refugee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRefugee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u0026middot;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u0026lt;0\u0026middot;001 (3\u0026middot;21\u0026ndash;7\u0026middot;37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGaza (n\u0026thinsp;=\u0026thinsp;1015)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73\u0026middot;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77\u0026middot;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eExtent that mobility restrictions impacted household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot at all\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3\u0026middot;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e*0\u0026middot;010 (-5\u0026middot;97 \u0026ndash; -0\u0026middot;83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3\u0026middot;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*0\u0026middot;007 (68\u0026middot;98\u0026ndash;86\u0026middot;19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery much\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026middot;85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0\u0026middot;059 (-0\u0026middot;11\u0026ndash;5\u0026middot;80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u0026middot;92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026middot;050 (-0\u0026middot;00\u0026ndash;5\u0026middot;84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead of household education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElementary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u0026middot;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026middot;170 (-0\u0026middot;86\u0026ndash;4\u0026middot;86)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u0026middot;06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*0\u0026middot;014 (0\u0026middot;83\u0026ndash;7\u0026middot;29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle/Divorced/Separated/\u003c/p\u003e \u003cp\u003eWidow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u0026middot;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026middot;049 (0\u0026middot;02\u0026ndash;8\u0026middot;05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \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\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \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\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7\u0026middot;57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026middot;054 (-15\u0026middot;27\u0026ndash;0\u0026middot;13)\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=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-10\u0026middot;47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026middot;009 (-18\u0026middot;2 \u0026ndash; -2\u0026middot;67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery Poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-13\u0026middot;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026middot;001 (-21\u0026middot;01 \u0026ndash; -5\u0026middot;07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e~In 2020, median of household FCS in West Bank is 79\u0026middot;5 (SD: 19\u0026middot;6), median of household FCS in Gaza is 76\u0026middot;5 (SD: 18\u0026middot;4)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn Gaza, the proximity to the buffer zone was not significantly associated with changes in FCS. However, in the unadjusted, univariate analysis, the extent that mobility restrictions impacted household was statistically significant\u0026mdash;where \u0026ldquo;minor\u0026rdquo; impact is associated with a decrease in 3\u0026middot;40 FCS points (p\u0026thinsp;=\u0026thinsp;0\u0026middot;010; 95% CI: -5\u0026middot;97 to -0\u0026middot;83). For Gaza, the final adjusted model includes impact of restrictions on household mobility, household income level, and head of household education and marital status. Intermediate education and above was significantly associated with higher FCS; with an average increase of 4\u0026middot;06 FCS points (p\u0026thinsp;=\u0026thinsp;0\u0026middot;014; 95% CI: 0\u0026middot;83 to 7\u0026middot;29). The adjusted model also suggests that being married was associated with an increase in FCS by 4\u0026middot;0 points (p\u0026thinsp;=\u0026thinsp;0\u0026middot;049; 95% CI: 0\u0026middot;02 to 8\u0026middot;05), compared to those who were single, divorced, separated, or widowed. Household income level was negatively associated with food consumption score with poor and very poor being significantly associated with decreases of 10\u0026middot;47 (p\u0026thinsp;=\u0026thinsp;0\u0026middot;009; 95% CI -18\u0026middot;2 to -2\u0026middot;67) and 13\u0026middot;04 (p\u0026thinsp;=\u0026thinsp;0\u0026middot;001; 95% CI -21\u0026middot;01 to -5\u0026middot;07) FCS points, respectively.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe findings of this serial cross-sectional study shed light on the complex interplay between household location, head of household demographics, and changes in FCS in the oPt. Over the years, the median FCS remained within the acceptable threshold but had a declining trend from 2016 to 2020 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This pattern suggests a gradual decrease in dietary diversity, likely due to conflict from the Israeli occupation impacting the economy, agriculture, and overall access to food. The increase in the proportion of households reporting unacceptable or borderline FCS from 0\u0026middot;4% in 2016 to 4\u0026middot;0% in 2020 demonstrates an increase in food insecurity over a relatively short period of four years (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This trend warrants attention from policymakers and stakeholders to mitigate further declines in food access.\u003c/p\u003e \u003cp\u003eThe data reveal regional differences in factors that are associated with changes in FCS using 2020 data. In the West Bank, location of household to barrier, restrictions on mobility, locality, household income, and head of household gender, education level, and refugee status are associated with changes in FCS. In Gaza, restrictions on mobility, household income, and head of household education and marital status are associated with FCS changes. This disparity can be attributed to several factors including geopolitical tensions, economic sanctions, and infrastructural challenges that are caused by conflict that uniquely affect each region [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the West Bank, living outside the barrier, living in a refugee camp, minor mobility restrictions on household, household income, and households with female heads were associated with a decrease in FCS from a baseline value of 74\u0026middot;7. Households who had male heads were associated with an increase in FCS than their counterparts, which may reflect societal norms and structures that favor men in terms of employment opportunities and social stability [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The correlation between higher educational attainment and improved FCS underscores the importance of education in improving dietary diversity and food security, possibly through greater knowledge of nutrition or greater job opportunities that improve access to food through improved household income. Households who identify as middle income, poor, and very poor had an increasing negative association with FCS in our multivariate analysis, indicating that interventions directed towards those of lower socioeconomic status may have a bigger impact in improving FCS. Household locality had an impact on food diversity; using urban households as the reference, rural households were associated with an increase in FCS while refugee camps were associated with a decrease in FCS. Rural households may have greater access to farms and agricultural resources, which allows household members maintain their diet and FCS. While those in refugee camps have been recently displaced and have limited access to food supplies, which would decrease their household FCS. Households lead by refugees were associated with a statistically significant increase in FCS, but this may be due to refugees receiving food assistance from humanitarian programs, which could improve FCS; this finding is aligned with the evidence generated by Br\u0026uuml;ck et al., suggesting the impact of aid in mitigating food insecurity.\u003c/p\u003e \u003cp\u003eIn Gaza, minor mobility restrictions on household and households with middle, poor, and very poor income levels were associated with a decrease in FCS from a baseline value of 77\u0026middot;59. The minor mobility restrictions in Gaza have a bigger negative impact on changes on FCS compared to West Bank as indicated by the larger coefficient\u0026mdash;suggesting that mobility in Gaza may be a bigger issue than in the West Bank. Like West Bank, there was an increasing negative association with FCS from middle to poor and very poor income levels, which suggests a similar intervention providing food assistance to households of lower socioeconomic status would target households vulnerable to food insecurity. Households where the head is married and/or have higher than a secondary education were associated with positive changes in FCS. Being married can improve FCS as the combined income, shared responsibilities, social support, and economies of scale within a married household often lead to more consistent and nutritious diets. Similar to households in the West Bank, head of household education level is associated with a positive change in FCS, reinforcing the importance of education in preventing food insecurity even when facing the challenges that are experienced by households residing in conflict-affected and fragile settings.\u003c/p\u003e \u003cp\u003eGiven the increasing number of unacceptable FCS over the survey years, it is imperative for interventions to be targeted and tailored to the Palestinian population in the oPt. Strategies that enhance mobility, improve economic opportunities, provide educational resources, and promote ceasefire could be vital in improving food security for Palestinians. Additionally, continued support for refugees and effective distribution of humanitarian aid are essential to address the immediate needs while working towards long-term solutions to enhance food security in the region; this aspect is in line with findings from previous work that suggests aid may mitigate food insecurity in the region [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. One of the potential explanations for the higher than expected FCS is that over the last decade, organizations such as the World Food Programme, United Nations Children Fund (UNICEF), and the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA) have provided food assistance or cash assistance to the most vulnerable. There is criticism that humanitarian aid hinders Palestinian efforts to localize food production\u0026mdash;so solutions that not only provide aid and support but also allow the involvement of local communities are needed and should be prioritized to ensure sustainability of any improvements to food security and dietary diversity [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThe results of this study provide valuable insights into the factors affecting FCS in occupied Palestinian territory, highlighting the long-term trend and significant association of household location and head of household demographics in a protracted conflict setting. The data and analyses underscored the persistent and growing challenges faced by different demographic groups in the oPt. These findings provide a clear call to action for both local authorities and international stakeholders to prioritize humanitarian aid and reinforcing food security as well as advocate for cease fire, demilitarization, and peace.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eFood consumption score (FCS)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;occupied Palestinian territory (oPt)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Gaza Strip (Gaza)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;World Food Programme (WFP)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Armed Conflict Location and Event Data (ACLED)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Confidence Interval (CI)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;United Nations Children Fund (UNICEF)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;University of California, San Francisco (UCSF)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Institutional Review Board (IRB)\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003cbr\u003e\u0026nbsp;The data used in this study is census data acquired from the Palestinian Central Bureau for Statistics and does not require informed consent to participate. In addition, the data was collected without personally identifiable information. This research is deemed exempt from UCSF Institutional Review Board (IRB) approval (23-40704). All procedures contributing to this work comply with the ethical standards of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for Publication\u003cbr\u003e\u0026nbsp;\u003c/em\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003cbr\u003e\u0026nbsp;\u003c/em\u003eDue to data ownership and confidentiality agreements the survey and data are not publicly available. \u0026nbsp;As mentioned in the “Ethics approval and consent to participate” section, the data used in this study were obtained from a survey designed by the Palestinian Central Bureau of Statistics for census purposes that do not require formal informed consent to participate and was exempt from the UCSF IRB approval (24-40704). However, summary statistics and analysis methods are available upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003cbr\u003e\u0026nbsp;\u003c/em\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003cbr\u003e\u0026nbsp;There was no funding source for this study.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors’ contributions\u003cbr\u003e\u0026nbsp;\u003c/em\u003eC.C. and T.K.L. conceptualized the study, designed the methodology, conducted the data analysis, and drafted the manuscript. T.K.L. and W.H. contributed to the data interpretation and provided critical revisions to the manuscript. The Palestinian Central Bureau of Statistics was responsible for data collection while T.K.L. was responsible for data acquisition. All authors contributed to the writing, reviewed the manuscript for intellectual content, and approved the final version for submission.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgments\u003cbr\u003e\u0026nbsp;\u003c/em\u003eWe sincerely thank Dr. Ali Mirzazadeh, Rachel Abbott, MSc, Amira Adam, MPH, and Ashley Mitchell, MPH, for their valuable input and advice on this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHolleman C, Jackson J, S\u0026aacute;nchez MV, Vos R, editors. Sowing the seeds of peace for food security: disentangling the nexus between conflict, food security and peace. Rome: Food and Agriculture Organization of the United Nations; 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKendall A, Olson CM, Frongillo EA. Relationship of Hunger and Food Insecurity to Food Availability and Consumption. 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Ethiop J Health Sci. 2022;32:369\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalti N, Ghattas H. Food insufficiency and food insecurity as risk factors for physical disability among Palestinian refugees in Lebanon: Evidence from an observational study. Disabil Health J. 2016;9:655\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRickard J. The fragmentation of Palestine: identity and isolation in the twenty-first century. London: I.B. Tauris; 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArea C, of the West Bank. : Key humanitarian concerns - OCHA factsheet [Internet]. Question of Palestine. [cited 2024 Dec 17]. 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How well does the Food Consumption Score capture diet quantity, quality and adequacy across regions in the Democratic Republic of the Congo (DRC)? Food Sec. 2019;11:1029\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAn Evaluation of WFP\u0026rsquo;s Regional Response to the Syrian Crisis. 2011\u0026ndash;2014 | World Food Programme [Internet]. 2015 [cited 2024 Jul 1]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wfp.org/publications/evaluation-wfps-regional-response-syrian-crisis-2011-2014\u003c/span\u003e\u003cspan address=\"https://www.wfp.org/publications/evaluation-wfps-regional-response-syrian-crisis-2011-2014\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnnual Country Report. | World Food Programme [Internet]. [cited 2024 Jul 1]. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wfp.org/publications/meta-data-food-consumption-score-fcs-indicator\u003c/span\u003e\u003cspan address=\"https://www.wfp.org/publications/meta-data-food-consumption-score-fcs-indicator\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaj-Yahia MM. On the Characteristics of Patriarchal Societies, Gender Inequality, and Wife Abuse: The Case of Palestinian Society. 2005;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBargawi H, Alami R, Ziada H. Re-negotiating social reproduction, work and gender roles in occupied Palestine. Rev Int Polit Econ. 2022;29:1917\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsi YM. Achieving Food Security Through Localisation, Aid N. De-development and Food Sovereignty in the Palestinian Territories. Journal of Peacebuilding \u0026amp; Development. 2020;15:205\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Food consumption score, food insecurity, Occupied Palestine, Conflict-affected settings, Undernourishment, West Bank, Gaza Strip, Undernourishment, Household food diversity","lastPublishedDoi":"10.21203/rs.3.rs-6474000/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6474000/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eBackground\u003cbr\u003e\n\u003c/em\u003eThe prevalence of undernourishment is significantly higher in conflict-affected low- and middle-income countries (LMIC), compared to LMICs not experiencing conflict. Evidence suggests that in these settings households may adopt coping strategies such as consuming less nutritious food and thereby reducing food diversity to mitigate the impact of food insecurity. The long-term trend of food diversity in a protracted conflict setting has not been explored in detail due to challenges in collecting systematic and representative data in conflict-affected and fragile settings.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMethods\u003cbr\u003e\n\u003c/em\u003eThis study examines food diversity – measured using food consumption scores (FCS) – among Palestinians in the Gaza Strip and the West Bank, utilizing a serial cross-sectional design to analyze a systematically random sampled dataset that was collected by the Palestinian Central Bureau of Statistics – from 2014, 2016, 2018, and 2020. We analyzed the distribution of household location by survey year and used multivariate linear regression to evaluate factors associated with changes in food consumption score. \u003cbr\u003e\n\u003cem\u003eResults\u003cbr\u003e\n\u003c/em\u003eThe proportion of unacceptable food consumption scores increased from 0·8% in 2014 to 3·7% in 2020, indicating a growing food insecurity issue. For the West Bank, household location to the barrier, head of household gender (female), living in a refugee camp, and households with middle- or lower-income levels were associated with a reduction in FCS. For the Gaza Strip, households that reported minor mobility restrictions and middle- or lower-income levels were associated with a reduction in FCS. \u0026nbsp;\u003cbr\u003e\n\u003cem\u003eConclusions\u003cbr\u003e\n\u003c/em\u003eThe findings elucidate the long-term impact of conflict on household food diversity, highlight a significant and worsening issue of food insecurity amongst Palestinians residing in the occupied Palestinian territory, and underline urgent need to address this critical issue and further protect vulnerable populations in conflict-affected regions are needed.\u003c/p\u003e","manuscriptTitle":"Exploring the Association of Household Location and Sociodemographic Profile on Decreasing Dietary Diversity in Occupied Palestine: A Serial Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-16 11:28:09","doi":"10.21203/rs.3.rs-6474000/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-05T06:33:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-03T09:43:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-26T08:58:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-23T05:50:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176205209838482269154142949648141876473","date":"2025-05-19T04:59:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315738320742227344463960004261742755148","date":"2025-05-16T13:39:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278438180682624223942344140658083840450","date":"2025-05-16T08:29:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"316699742113785058485986260582528784457","date":"2025-05-16T04:17:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"312674921242704324826316196117476921906","date":"2025-05-14T08:55:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-13T20:54:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-08T07:06:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-28T10:32:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-25T04:34:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-04-25T04:33:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3d0af1d4-db43-4898-84b9-4a41e9abca43","owner":[],"postedDate":"May 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-11T10:38:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-16 11:28:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6474000","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6474000","identity":"rs-6474000","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}