The unequal face of hunger: How gender and race/colour have exacerbated food insecurity during the COVID-19 pandemic—a cross-sectional analysis

The unequal face of hunger: How gender and race/colour have exacerbated food insecurity during the COVID-19 pandemic—a cross-sectional analysis | 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 The unequal face of hunger: How gender and race/colour have exacerbated food insecurity during the COVID-19 pandemic—a cross-sectional analysis Gleiciane Bueno da Silva Luiz, Aline Alves Ferreira, Rosana Salles-Costa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4535555/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Sep, 2025 Read the published version in BMC Public Health → Version 1 posted 13 You are reading this latest preprint version Abstract Background The COVID-19 pandemic has aggravated Food Insecurity (FI) in a heterogeneous way across different household profiles. The aim of this study was to assess the influence of the first two years of the COVID-19 pandemic on FI in Brazil according to the intersections of race/colour and the gender of household heads. Methods Microdata from the 1st and 2nd Food Insecurity Surveys in the Context of the COVID-19 Pandemic (VIGISAN) were used (1st VIGISAN: n=2,180; 2nd VIGISAN: n=12,745). FI levels were compared according to the profiles created from the intersection of gender (male and female) and race/colour (white and black) for the household reference person. Odds ratio (OR) values were calculated using multinomial logistic regression models to assess the association between reference person profiles and FI levels. Results Although the proportion of households with moderate+severe FI increased in the first two years of the pandemic among households headed by black people, the prevalence of severe FI was significantly greater for households headed by black women (2.2 versus 3.2; 95% CI 2.52; 4.00). Conclusions The results of this study reinforce the need to plan equitable public policies that debate the intersectionality of gender and race/colour as a way of guaranteeing food and nutritional security in Brazil. Gender Inequality Racism Food Insecurity COVID-19 Figures Figure 1 Background Racial and gender inequality is a persistent problem in many countries, such as Brazil. The interaction between these inequalities promotes disparities in access to essential services for human development, such as food and nutrition. During the COVID-19 pandemic, these vulnerabilities increased. It is therefore necessary to understand how these structural inequalities affect FI in the face of a new viral pandemic. We searched the PubMed, Google Scholar, LILACS, and CAPES databases for information on the prevalence of FI in black women both before and during the COVID-19 pandemic. We used search terms in both English and Portuguese to find publications dated between 2018 and 2024. The search terms included “food insecurity”, “sexism”, “racism” and “COVID-19”. Studies that investigate FI considering the intersectionality of gender and race/skin colour in the context of the COVID-19 pandemic remain scarce. For data analysis, we used data from a nationally representative survey of the Brazilian population called the National Survey on Food Insecurity in the Context of the COVID-19 Pandemic (VIGISAN). During the pandemic, this was the only survey that provided data on food security (FS) and FI levels nationwide. Research focused on FI considering gender and race/skin colour during the pandemic remains scarce. This study provides important data and reflections for better understanding, based on intersectionality, how gender and race/colour have interacted dynamically during the COVID-19 pandemic, particularly reflecting the situation of FS/FI. In addition, the findings of the study support black movements that are fighting for anti-racist and anti-patriarchal public food and nutrition policies. During the COVID-19 pandemic, being part of the black and female population has been a major factor in the levels of FI in Brazilian homes, revealing the persistence of prejudice and the lack of government assistance aimed at this population group. The findings shed light on this group that is constantly made invisible despite its active participation in various spheres of society. Finally, we emphasize the importance of considering race/skin colour and gender intersectionally in the analysis of FI in the context of COVID-19 to provide support for the formulation of public policies that meet the demands of this population, focusing mainly on reducing the most serious levels of FI. Introduction In 2020, the world was surprised by a new global public health crisis caused by a highly transmissible and lethal virus that profoundly altered all spheres of humanity. 1 However, in the socioeconomic and demographic spheres, the consequences have been more pronounced. The measures necessary to contain the spread of the virus, such as lockdowns, have led to the loss of jobs or sources of income. 2 As a result, economic instability has made it difficult for many families to access resources to face the pandemic without jeopardizing their well-being and access to good food and nutrition. According to the United Nations (ONU), 892.7 million people experienced severe FI between 2020 and 2022. 3 In Latin America and the Caribbean alone, the number of people living with hunger reached 43.2 million. 3 FI occurs when people do not have secure access to sufficient quantities of safe and nutritious food for normal growth and development and an active and healthy life. 4 In addition, the concept of FI used in Brazil emphasizes that it can have significant impacts on health, both in terms of social exclusion, loss of self-esteem and emotional stress, as well as compromised nutritional status itself. 5 In other words, FI refers not only to food shortages but also to wider ramifications of such shortages, including social, psychological, and nutritional aspects, which negatively affect people's quality of life. Brazil is internationally recognized for its success in tackling hunger. The country was removed from the hunger map by the Food and Agriculture Organization (FAO) in 2014. 6 A significant part of this success was achieved through the development and implementation of the Brazilian Food Insecurity Scale (EBIA). This indicator is responsible for estimating FI at the household level. 5 However, since 2016, political and economic conditions have become unfavourable and incompatible with the agenda of promoting food and nutrition security (FNS). 7 The adoption of austerity policies throughout the country has once again exposed Brazil to high levels of household FI. Thus, with the arrival of the COVID-19 pandemic, concerns about future food supplies, shortages and even hunger became more severe. 8 , 9 However, these consequences have been heterogeneously observed in society. 2 Historically, the black population has suffered from constant violations of basic human and social rights. During the pandemic, it has become clear that the structural effects of racial and gender discrimination have placed this population in even more disadvantaged situations, thereby exacerbating existing social disparities. 2 , 10 Thus, analyses that take race/colour and gender into account show that these social indicators relate in different ways to the experience of families dealing with FI. 11 For black women, the situation is even more evident since the history of racism and sexism in Brazil means that these individuals suffer an overlap of these axes of oppression 12 and are saddled with a greater degree of vulnerability in guaranteeing FS, as evidenced by them suffering from the most severe levels of FI. 13 , 14 Thus, it has been observed that while the novel coronavirus disease could potentially affect everyone, the ability to obtain resources to overcome or prevent such infection, as well as manage the related consequences, was not equitable in Brazil. Given that many of black women serve as heads of household, all the paths led to an unequal increase in household FI in the country during the pandemic. 8 , 9 Given this scenario, this study aimed to assess the influence of the COVID-19 pandemic (2020–2022) on FS and FI levels in Brazil according to the race/colour and gender of the head of household. Methods This study was based on microdata from 2 nationally representative surveys assessing FI in the Brazilian population during the COVID-19 pandemic (1st and 2nd VIGISAN) carried out by the Brazilian Food and Nutrition Sovereignty and Security Research Network (Rede PENSSAN) in December 2020 (1st VIGISAN) and November 2021 to April 2022 (2nd VIGISAN). Both surveys have similar study designs, with a sample base that is representative of the national territory. The households were selected from the same census tracts used in the master sample of the Brazilian Institute of Geography and Statistics (IBGE) population surveys. For the selection of households, conglomerate sampling was used in three stages of selection (municipalities, census tracts, households). The 1st VIGISAN obtained a probabilistic sample of 2,180 households, with an estimated 95% confidence interval and a maximum margin of error of 2.1 percentage points for the estimates. In the 2nd VIGISAN, 12,745 households were sampled with a maximum margin of error for the total sample of 0.9 percentage points. More details on the sampling design can be found in the methodological section of the surveys. In both surveys, the household reference person was considered the individual responsible for decision-making in the household. 8 , 9 The outcome variables analysed were FS and FI levels, which were evaluated by the Brazilian Household Food Insecurity Measurement Scale (EBIA). The EBIA is a psychometric scale that assesses the interviewee's perception of access to food, in terms of quality and quantity, in the three months prior to the interview. 15 In the surveys, the validated short version of the EBIA was used to estimate FS and FI levels. 16 The choice to use the 8-item version of the EBIA was due to the need for a rapid population survey to reduce the risk of interviewer contamination. The 8 questions on the scale are dichotomous (yes/no), and each affirmative answer adds one point to the classification of the household's FI level. If all the questions are negative, the household is classified as FS. Based on the classifications, the households were classified into either FS or three levels of FI (mild, moderate, and severe). FS-classified households are considered to have regular and permanent access to high-quality food in sufficient quantity without compromising access to other essential needs. Mild FI represents concern or uncertainty about access to food in the future. Moderate FI refers to quantitative food reduction among adults and/or disruption in eating patterns resulting from food shortages among adults. Severe FI signifies a quantitative reduction in food among children. In this situation, hunger becomes a lived experience in households. In this study, the moderate FI and severe FI categories were grouped together as a way of analysing the most severe categories in terms of access to adequate food. 13 To understand the relationship between power systems (gender and race/skin colour) and FI during the pandemic, the intersectionality debate was adopted because its theoretical foundation seeks to capture the structural and dynamic consequences of the interaction between two or more axes of subordination. 17 To do this, the household references were stratified according to sex and race/skin colour, following methodologies previously used by other studies. 13 , 14 Gender was assessed using the biological variable sex, adopted from Brazilian population studies. 18 This variable takes the binary form “male” or “female”. However, for the intersectional debate, we used the gender debate to understand the complexities and nuances that extend beyond biological categories, considering social variables and how behaviours between men and women shape individual and collective experiences related to gender identity. Thus, the variable “gender” will be used to lead the discussions. Regarding race/skin colour, the self-declaration used in Brazilian population studies was applied, 18 which considers five options: white, black, yellow, brown or indigenous. Given that the 1st and 2nd VIGISAN address samples that do not account for ethnic and racial minorities, it was not possible to represent the indigenous and yellow categories; thus, both were excluded from the analysis (1st VIGISAN: 4.5%; 2nd VIGISAN: 3.4%). In Brazil, the black population is comprised of both black and brown individuals because both categories are of African descent and face similar challenges due to the racism rooted in Brazilian society. Therefore, in this study, the categories “black” and “brown” were consolidated into a single classification, resulting in the race/skin colour variable having two categories, namely, “white” and “black/brown”. Socio-demographic variables related to households and the person in charge of the household (head of the family), which are associated with FI, were assessed. The covariates were selected based on a theoretical review and a systematic review. The variables include: household information: region (North, Northeast, South/Southeast, or Central-West), area of household (urban or rural), number of residents (1–2, 3–5, or > 5), Presence of children under 5 (yes or no) and per capita family income (up to 1 MW or more than 1 MW) - considering the values in force during the reference period of each year: USD 287.79 (BRL 1,039.00) in 2020 and USD 327.00 (BRL 1,212.00) in 2022. Head of household information: age group, schooling (Iliterate, 1–7, 8–12, or > 12 years), and occupation (family farmer or rural producer, informal worker, formal worker, regular self-employed worker or individual entrepreneur, regular self-employed worker or individual entrepreneur or other (homemaker, pensioner, student, etc.) For the descriptive analyses, the proportion values and their respective 95% confidence intervals (95% CIs) were estimated. The prevalence of FS and levels of FI were calculated according to the reference person in the family. In the next stage, odds ratio (OR) values were calculated using multinominal logistic regression models to assess the association between the profiles of household reference persons who considered gender and race/skin colour (white man; white woman; black man; black woman), with the outcome levels of FI (using FS as a reference category). The models considered the adjustment for potential confounding variables (schooling, per capita family income, region, and area of the household) in the relationship with FI levels, which were determined based on a systematic review, tested separately (crude bivariate multinomial models) and included in the final adjusted model for those that obtained a p value < 0.05 (data not shown). The statistical analysis was carried out considering the complex sample design, 95% CI, using the 'svy' commands of Stata software, version 16.1 (StataCorp LLC, College Station, 2016), for this purpose. The utilized survey is part of a broad project aiming to monitor FS/FI in the context of COVID-19, coordinated by the PENSSAN Network and carried out by the Vox Populi Institute. This study was approved by the Research Ethics Committee of the Clementino Fraga Filho University Hospital of the Federal University of Rio de Janeiro - CAEE 30679914.0.0000.5257. This study uses databases whose information is aggregated without the possibility of individual identification; thus, it was not necessary to submit this information to the ethics committee of the National Research Ethics Commission (CONEP) according to Resolution N o . 510 of April 7, 2016. Results In Brazil, the proportion of households headed by black/brown individuals, regardless of gender, was greater than that of households headed by other categories (Table 1 ). In addition, comparing the two surveys, there was an increase in the proportion of heads of household with informal occupations and those who had regular jobs or who were individual entrepreneurs in the second year of the survey. The proportions of FS and mild FI decreased in 2022 (41.3% and 28.0%, respectively), while the more severe forms of FI (moderate and severe) increased (15.2% and 15.5%, respectively). The variations in FI levels were found to be significant when comparing the 95% CIs (Table 1 ). Table 1 Description of the sociodemographic characteristics of reference persons and households in Brazil, 2020–2022. Characteristics BRAZIL I VIGISAN (2021) II VIGISAN (2022) % 95% CI % 95% CI Characteristics of the head of household Race/color and gender profile** White man 20.42 18.1 – 22.9 19.1 18.0–20.2 White woman 17.9 15.7–20.3 17.4 16.4–18.5 Black/brown man 31.4 28.9–34.1 32.21 31.0–33.4 Black/brown woman 30.3 27.8–32.9 31.3 30.1–32.6 Age (years) Up to 24 5.0 4.0–6.3 5.5 4.91–6.03 25–39 22.8 20.5–25.1 26.5 25.4–27.7 40–59 43.0 40.3–45.9 42.3 41.0–43.7 60 or more 29.2 26.7–31.8 25.0 24.5–26.9 Schooling (years of study) 0–8 49.7 49.9–52.5 47.1 45.8–48.5 > 8 50.3 47.5–53.1 52.9 51.6–54.2 Occupation Family farmer or rural producer 2.6 1.91–3.60 1.2 1.0–1.5 Informal worker 15.6 13.4 – 18.0 17.1 16.1–18.2 Formal worker 22.0 19.5 – 24.7 26.8 25,5– 28.1 Regular self-employed worker or individual entrepreneur 15.7 13.5–18.1 17.8 16.3–18.6 Unemployed 8.5 6.9–10.4 8.8 7.5–9.2 Other (homemaker, pensioner, student, etc.) 35.2 32.3–38.3 29.2 27.8–30.5 Household Characteristics Area Urban 85.6 84.1–87.1 85.5 84.7–86.2 Rural 14.4 12.9–15.9 14.5 13.8–15.3 Region Central-West 7.7 7.0–8.5 7.6 7.2–10.0 North east 26.2 24.1–28.4 26.0 25.1–27.0 North 7.5 6.8–8.2 6.9 6.6–7.3 Southeast/ South 58.6 56.1–61.1 59.5 58.3–60.6 Number of residents 1–2 40.6 37.9–43.3 50.7 49.4–52.0 3–5 53.1 50.3–55.9 45.4 44.4–46.7 >5 6.3 5.2–7.7 4.0 3.5–4.5 Presence of children under 5 Yes 13.2 11.5–15.2 15.1 14.18–16.1 No 86.6 84.8–88.5 80.9 83.9 – 85.8 Per capita family income (Minimum wage***) Up to ½ MW 43.3 40.4–46.2 30.6 29.35–31.9 More than 1 MW 56.7 53.8–59.6 69.4 68.1–70.7 Food security and Levels of Food insecurity (FI) Security 44.8 42.0–47.6 41.3 40.0–42.7 Mild Insecurity 34.7 32.1–37.4 28.0 26.8–29.2 Moderate + Sereve Insecurity 20.5 18.4–22.7 30.7 29.4–32.0 Notes: *Proportions (%) and respective confidence intervals (95%CI); ** Black men and women were considered to be the combination of black and brown race/color; *** Considering the values in force during the reference period of each year: USD 287.79 (BRL 1,039.00) in 2020 and USD 327.00 (BRL 1,212.00) in 2022. Table 1 . Description of the sociodemographic characteristics of reference persons and households in Brazil, 2020–2022. When analysing the scenario of change in FS and FI levels, according to race/colour and gender profiles (Fig. 1 ), it was observed that households headed by white people showed an increase in FS, which was not observed in households headed by black/brown people. Figure 1 . Prevalence (%) of food insecurity according to race/colour and sex in the households in Brazil, 2020–2022. According to the ORs in the final adjusted model (Table 2 ), in 2020, the chance of moderate + severe FI was significantly greater among households headed by a black/brown woman (OR = 2.2; 95% CI 1.3; 3.7). After one year of the COVID-19 pandemic, the chance of mild FI was greater among households headed by both black/brown men and women (p value < 0.001). However, the severity of FI (moderate + severe FI) was significantly greater among households headed by black/brown women (OR = 3.2, 95% CI = 2.5-4.0) than among households headed by white men. Table 2 Odds Ratios and 95% Confidence Intervals of Sociodemographic Variables and Food Insecurity by Gender and Race/Colour in Brazil, 2020–2022. I VIGISAN (2020) II VIGISAN (2022) Gender and race/color profile Bivariate model Adjusted model ** Bivariate model Adjusted model ** OR 95% CI p-valor OR 95% CI p-valor OR 95% CI p-valor OR 95% CI p-valor Mild FI White man 1.0 * White woman 1.5 1,0–2.4 0.058 1.5 0.93–2.45 0.091 1.2 1.0–1.6 0.08 1.2 0.9–1.5 0.25 Black/brown man 1.2 0.8–1.7 0.395 1.0 0.64–1.54 0.995 1.7 1.4–2.1 < 0.001 1.4 1.1–1.7 < 0.001 Black/brown woman 1.9 1.3–2.9 < 0.001 1.4 0.88–2.13 0.156 2.1 1.7–2.6 < 0.001 1.5 1.2–1.9 < 0.001 Moderate + Severe FI White man 1.0 * White woman 2.2 1.3–3.9 < 0.001 1.8 1.0–3.3 0.05 2.0 1.6–2.6 < 0,001 1.9 1.5–2.6 < 0.001 Black/brown man 1.6 1.0–2.6 0.05 1.1 0.6–1.8 0.82 2.8 2.3–3.4 < 0,001 1.9 1.5–2.4 < 0.001 Black/brown woman 4.5 2.8–7.2 < 0.001 2.2 1.3–3.7 < 0.001 5.1 4.3–6.2 < 0,001 3.2 2.5–4.0 < 0.001 Notes: *Model reference category = Food Security; **Models adjusted for household location (urban/rural), household region (North, Northeast, South/Southeast, Midwest), schooling in years of study (Up to 8 years/9 years or more) and per capita household income (Up to 1MW/More than 1MW) - considering the values in force during the reference period of each year: USD 287.79 (BRL 1,039.00) in 2020 and USD 327.00 (BRL 1,212.00) in 2022. Table 2 . Odds Ratios and 95% Confidence Intervals of Sociodemographic Variables and Food Insecurity by Gender and Race/Colour in Brazil, 2020–2022. Discussion In the first years of the pandemic, families headed by black people had a worse FS status, to the detriment of an increase in all levels of FI. However, when a black woman served as the household's reference person, families were up to 4 times more likely to have moderate/severe FI. The significant increase in FI in families where the reference variable was female or black/brown in the first two years of the pandemic can be explained by the decrease in family income. Normally, the same social characteristics are described for black/brown women, i.e., working in undervalued professions, having a high probability of living in homes with a high number of residents, unfavourable conditions and precarious economic conditions, and performing multiple tasks (productive and reproductive life). 2 , 19 , 20 , 21 The paralysis of activities such as education, cultural events and nonessential businesses during the pandemic affected various economic activities that depended on the high level of movement of people in urban centres, as well as work in labour spaces. 22 As a result, the likelihood of layoffs and wage cuts, with a consequent increase in the number of unemployed and informal jobs, has tended to be greater among black and low-income women. 2 These results must be analysed from the point of view of the combination of racism and sexism, which in turn can produce a form of “social asphyxiation”, which is a term used by Sueli Carneiro that has negative repercussions on all dimensions of life. 12 This statement shows that the experience of being a black/brown woman in Brazil is accompanied by the possibility of experiencing the worst living conditions. As several possible factors explain this scenario, it is common for the lower-level Brazilian families to have female members working in informal or lower-paying jobs, with less schooling, and living in unhealthy housing and basic sanitation conditions. 20 Because these axes of oppression are structural conditions, negative consequences, such as hunger, were already observed before the pandemic began. In the study by Santos et al. (2023), 13 families headed by single black women with at least one child younger than 5 years old were three times more likely to live with moderate/severe FI in both 2004 and 2013. However, this likelihood became (four times greater in 2018, when FI began to increase again in Brazil. In another study carried out in the city of Salvador (northeast of the country), where 80% of the population is self-declared black and brown, the prevalence of FI was found to be greater in households headed by black women (mild FI: 25.6% and moderate or severe FI: 21.2%). 14 The findings of these previous studies help us understand that the scenario of experiencing FI was already being conditioned mainly among families headed by black/brown women. Although scientific evidence shows the vulnerabilities of this population group, there are few instances of public policies that consider the intersectionality of gender and race/colour in their formulations. Thus, it is clear that this situation is not just a result of the COVID-19 pandemic. In fact, the health crisis has exposed social weaknesses and increased vulnerabilities as a result of socioeconomic damage. To alleviate the socioeconomic crisis, in 2020, the federal government adopted a policy of income transfer by distributing so-called emergency aid as a social protection measure. 23 This was a financial benefit created with the aim of guaranteeing a minimum income for Brazilians during the COVID-19 pandemic. It was aimed at informal workers, individual microentrepreneurs, the self-employed and/or the unemployed. 23 However, recent studies have shown that this program has not been sufficient to curb the increase in severe FI in Brazil. 7 Delays in decision-making, bureaucratic procedures for accessing the benefits implemented and difficulties in using digital technology are some of the issues that may have contributed to this situation among vulnerable populations. 24 In addition, the decrease in the value of this emergency aid at the beginning of 2021 and the failure to grant it, especially during the period when the 2nd VIGISAN data collection began, may have contributed to the significant increase in the most severe forms of FI. Adding to the debate on these results is the fact that household chores and caring for children and adolescents overlap in families. According to Rodrigues et al. (2024), 21 the priority given to women in the reproductive sphere also contributes to their being less involved in productive work in Brazilian families, especially when this responsibility establishes a class relationship. The authors also point out that for women living in poverty, reconciling both productive and reproductive work intensifies the overload resulting from social responsibilities. The availability of public facilities to reduce the overload of tasks considered feminine, such as community kitchens, school meals, crèches and full-time schools, is important. In addition, in the first two years of the COVID-19 pandemic, in addition to being unpaid, female heads of household had their feminine duties overloaded in their homes due to the closure of nurseries and schools and the increase in the number of people infected and sick with the virus; this may also have resulted in an increase in FI in female-headed households. 8 , 9 The compulsory closure of school networks has forced children and adolescents to remain at home. Globally, the lives of millions of people in this group have been impacted by this measure, requiring emergency adaptation to the remote-home schooling model. 25 As a result, there has been an increase in the social responsibilities of female heads of household, coupled with the intense mental burden that the pandemic scenario has placed on them. Furthermore, in Brazil, according to the 2023 School Census, 26 the majority of children enrolled in early childhood education are black/brown. Many of these children benefit from the Brazilian National School Feeding Program (PNAE), which is a national program that covers students from the entire Brazilian public basic education network (early childhood education, elementary education, high school and youth and adult education) enrolled in public schools. 27 Among the PNAE's guidelines is the formation of healthy eating habits through the provision of meals and food and nutrition education. At the time of the school closures in Brazil, the federal government authorized the distribution of food purchased with PNAE funds to the parents or guardians of students in public basic education schools at the discretion of the local government during the period when classes were suspended. 27 However, importantly, these measures may not have been enough to contain the spread of FI, as the transfer of such aid did not guarantee that the person responsible had access to food in terms of quantity and quality, and the amount of food transferred to schools has not been adjusted since February 2017, despite the increase in food prices 28 compromising the quality of the meals served. 29 Women are still primarily responsible for caring for the home, children, elderly people and sick people. Although much of the unpaid care work in the world, especially in low-income countries, was already performed by women before the COVID-19 pandemic, there has been a significant increase in this burden. The resulting negative impact on women is likely to last for years. 10 Regarding the possible limitations of this study, even though the data analysed are representative of the Brazilian population, the debate on gender issues is limited since the “sex” variable adopted in the VIGISAN surveys was used; this variable is based on IBGE population surveys and was collected with only two response options (male and female), thereby excluding other representations of gender identities. The findings in this article highlight that the problems caused by structural inequalities and the COVID-19 pandemic still represent a challenge for reducing inequalities in access to healthy food for the Brazilian population. However, it is important to add to the debate on the increase in severe forms of FI in Brazil that public policies aimed either directly or indirectly at promoting food and nutritional security in Brazil began to be dismantled in 2016, which contributed to this setback in the fight against hunger. 7 Although there are policies aimed at the black population and those aimed at women, the two policy streams are not in dialogue. This situation poses a greater risk in times of crisis. These are important factors that may have contributed to the increase in inequalities marked by the debate on the intersectionality of gender and race discussed in this article. At the beginning of 2023, Brazil resumed its fight against hunger as one of the goals of the current federal government. The National Council for Food and Nutrition Security (CONSEA), which was dissolved at the beginning of 2019, was reestablished to include monitoring, planning and evaluating food and nutrition security policies. Recently, the 6th National Conference on Food and Nutritional Security recognized, in its first thematic axis, the need for state participation in overcoming inequalities, especially those related to racism and patriarchy, among other structural determinants. 30 Therefore, the results of this article corroborate the need to structure actions and strategies and reformulate public policies aimed at ensuring the eradication of hunger and guaranteeing the human right to adequate food through anti-racist and anti-patriarchal food systems. Abbreviations CAPES Coordination of Superior Level Staff Improvement CONEP Committee of the National Research Ethics Commission CONSEA Council for Food and Nutrition Security EBIA Brazilian Household Food Insecurity Measurement Scale FAO Food and Agriculture Organization FI Food Insecurity FNS Food and Nutrition Security FS Food Security IBGE Brazilian Institute of Geography and Statistics ONU United Nations OR Odds Ratio PENSSAN Network Food and Nutrition Sovereignty and Security Research Network PNAE Brazilian National School Feeding Program VIGISAN Insecurity Surveys in the Context of the COVID-19 Pandemic Declarations Ethics approval and consent to participate This study uses databases whose information is aggregated without the possibility of individual identification; thus, it was not necessary to submit this information to the ethics committee of the National Research Ethics Commission (CONEP) according to Resolution N o . 510 of April 7, 2016. Consent for publication Not applicable Availability of data and materials The data that support the findings of this study are available from PENSSAN Network but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. However, data may be available by contacting the PENSSAN Network (https://pesquisassan.net.br/) Competing interests The authors declare that they have no competing interests Funding R. S-C and GBS Luiz were partially supported by the Coordination of Superior Level Staff Improvement, Public Notice Emergency Strategic Program to Prevent and Combat Outbreaks, Endemics, Epidemics and Pandemics, grant number 88881.657811/2021-01. Authors' contributions GBSL contributed to the conception, literature review, data analysis and interpretation of the study; contributed to the writing and review of the manuscript. AAF contributed to the conception of the study, drafted and revised the manuscript. RSC contributed to the conception and data interpretation of the study, contributed to the writing and revision of the manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable References World Health Organisation. WHO Director-General’s Opening Remarks at the Media Briefing on COVID-19 - 11 March 2020 [Internet]. World Health Organization. 2020. Available in: https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020. Accessed on May 29, 2024. Alloatti MN, Matos de Oliveira, AL. Deepening and widening the gap: The impacts of the COVID‐19 pandemic on gender and racial inequalities in Brazil. Gender, work, and organization, 2023, 30 (1): 329-44. https://doi.org/10.1111/gwao.12909. FAO, IFAD, UNICEF, WFP and WHO. 2023. The State of Food Security and Nutrition in the World 2023.Urbanization, agrifood systems transformation and healthy diets across the rural–urban continuum. Rome, FAO. https://doi.org/10.4060/cc3017en. Food and Agriculture Organization of the United Nations. Trade and Food Security: Conceptualizing the Linkages. Rome: FAO, 2003. Available in: https://openknowledge.fao.org/server/api/core/bitstreams/310467a7-bc1d-416c-81c6-53b037ddb853/content. Accessed on May 27, 2024. Kepple AW, Segall-Corrêa AM. Conceituando e medindo segurança alimentar e nutricional. Ciência & Saúde Coletiva. 2011 Jan 1;16(1):187–99. https://doi.org/10.1590/S1413-81232011000100022. FAO, IFAD and WFP. 2014. The State of Food Insecurity in the World 2014. Strengthening the enabling environment for food security and nutrition. Rome, FAO. Available in: https://openknowledge.fao.org/server/api/core/bitstreams/ad888f5b-a590-43d9-b066-9a852b95777e/content. Salles-Costa R, Segall-Corrêa AM, Alexandre-Weiss VP, Pasquim EM, Paula NM de, Lignani J de B, et al. Rise and fall of household food security in Brazil, 2004 to 2022. Cadernos de Saúde Pública. 2023 Feb 6;39:e00191122. https://doi.org/10.1590/0102-311XEN191122. Brazilian Research Network on Food and Nutrition Sovereignty and Security (Rede Brasileira de Pesquisa em Soberania e Segurança Alimentar e Nutricional - Rede PENSSAN. I VIGISAN - National Survey of Food Insecurity in the Context of the Covid-19 in Brazil, 2021. Available in: https://olheparaafome.com.br/. Brazilian Research Network on Food and Nutrition Sovereignty and Security (Rede Brasileira de Pesquisa em Soberania e Segurança Alimentar e Nutricional - Rede PENSSAN. II VIGISAN - National Survey of Food Insecurity in the Context of the Covid-19 in Brazil, 2022. Available in: https://olheparaafome.com.br/. Power K. The COVID-19 pandemic has increased the care burden of women and families. Sustainability: Science Practice and Policy. 2020; 16 (1): 67-73. https://doi.org/10.1080/15487733.2020.1776561. Lignani J de B, Palmeira P de A, Antunes MML, Salles-Costa R. Relationship between social indicators and food insecurity: a systematic review. Revista Brasileira de Epidemiologia. 2020;23. https://doi.org/10.1590/1980-549720200068 . Carneiro, S. Racismo, sexismo e desigualdade no Brasil. São Paulo: Selo Negro Edições, 2011. Santos LA. Pérez-Escamilla R, Christine C, Aline Alves Ferreira, Salles-Costa R. Gender, skin color, and household composition explain inequities in household food insecurity in Brazil. PLOS global public health. 2023 Oct 3;3(10):e0002324–4. https://doi.org/10.1371/journal.pgph.0002324. Silva SO da, Santos SMC dos, Gama CM, Coutinho GR, Santos MEP dos, Silva N de J. A cor e o sexo da fome: análise da insegurança alimentar sob o olhar da interseccionalidade. Cadernos de Saúde Pública. 2022 Aug 5;38. https://doi.org/10.1590/0102-311XPT255621 . Segall-Corrêa AM, Marin-León L, Melgar-Quiñonez H, Pérez-Escamilla R. Refinement of the Brazilian household food insecurity measurement scale: recommendation for a 14-item EBIA. Rev Nutr. 2014;27(2):241–251. https://doi.org/10.1590/1415- 52732014000200010. Interlenghi GS, Reichenheim ME, Segall-Corrêa AM, Pérez-Escamilla R, Moraes CL, Salles-Costa R. Modeling Optimal Cutoffs for the Brazilian Household Food Insecurity Measurement Scale in a Nationwide Representative Sample. The Journal of Nutrition. 2017 May 31;147(7):1356–65. https://doi.org/10.3945/jn.117.249581. Crenshaw K. Documento para o encontro de especialistas em aspectos da discriminação racial relativos ao gênero. Revista Estudos Feministas. 2002; 10 (1): 171-88. https://doi.org/10.1590/S0104-026X2002000100011 . Instituto Brasileiro de Geografia e Estatística. Censo Demográfico 2022: Características dos domicílios. Resultados do universo. Rio de Janeiro: IBGE, 2024. Available in: https://biblioteca.ibge.gov.br/visualizacao/periodicos/3106/cd_2022_domicilios.pdf. Chandler R, Guillaume D, Parker AG, Mack A, Hamilton J, Dorsey J, et al. The impact of COVID-19 among Black women: evaluating perspectives and sources of information. Ethnicity & Health. 2020 Nov 5;26(1):80–93. https://doi.org/10.1080/13557858.2020.1841120. Instituto Brasileiro de Geografia e Estatística. Estatísticas de gênero: indicadores sociais das mulheres no Brasil. 3ª Edição. Estudos e Pesquisas: Informação Demográfica e Socioeconômica • n.38. Rio de Janeiro: IBGE, 2024. Available in: https://biblioteca.ibge.gov.br/visualizacao/livros/liv102066_notas_tecnicas.pdf. Rodrigues, A. V. de A., Costa, D. M., Costa, R. S. Desigualdades de gênero e renda das famílias brasileiras: implicações para a segurança alimentar e nutricional. Ciência & Saúde Coletiva. 2024. Available in: http://cienciaesaudecoletiva.com.br/artigos/desigualdades-de-genero-e-renda-das-familias-brasileiras-implicacoes-para-a-seguranca-alimentar-e-nutricional/19186?id=19186. Mattei L, Heinen VL. Impactos da crise da Covid-19 no mercado de trabalho brasileiro. Brazilian Journal of Political Economy. 2020 Dec; 40(4):647–68. https://doi.org/10.1590/0101-31572020-3200 . Brasil. Medida provisória nº 1.000, de 2 de setembro de 2020. Institui o auxílio emergencial residual para enfrentamento da emergência de saúde pública de importância internacional decorrente do coronavírus ( covid-19 ). Available in: https://www.planalto.gov.br/ccivil_03/_ato2019-2022/2020/mpv/mpv1000.htm . Melo A, Rodrigues M. Impactos da Implementação do Auxílio Emergencial para a População Negra. Cadernos de Campo: revista de ciências sociais. 2023 Jul 18;e023003–3. https://doi.org/10.47284/cdc.v23i00.18236 . Educação: do fechamento das escolas à recuperação. UNESCO. Available in: https://www.unesco.org/pt/covid-19/education-response. Brasil. Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira (INEP). Censo Escolar da Educação Básica 2022: Resumo Técnico. Brasília, 2023. Available in: https://download.inep.gov.br/publicacoes/institucionais/estatisticas_e_indicadores/resumo_tecnico_censo_escolar_2022.pdf. Brasil. Resolução nº 26, de 17 de junho de 2013. Dispõe sobre o atendimento da alimentação escolar aos alunos da educação básica no âmbito do Programa Nacional de Alimentação Escolar - PNAE. Brasília, 17 de junho de 2013. Available in: https://www.gov.br/fnde/pt-br/acesso-a-informacao/legislacao/resolucoes/2013/resolucao-cd-fnde-no-26-de-17-de-junho-de-2013. Zimmermann AS, Delgado NG. El desmonte del Sistema Nacional de Seguridad Alimentaria y Nutricional y la garantía del derecho­ humano a la alimentación en­ Brasil. Polis Revista Latinoamericana. 2022; 22:32-51.http://dx.doi.org/10.32735/s0718-6568/2022-n63-1771 . Observatório da Alimentação Escolar, Associação Nacional de Pesquisa em Financiamento da Educação. Em defesa de reajustes nos valores per capita do PNAE. 2021. Available in: https://alimentacaoescolar.org.br/media/acervo/documentos/Em_defesa_de_reajustes_nos_valores_per_capita_do_PNAE_-_FACTSHEET.pdf. Accessed on June 03, 2024. Conselho Nacional de Segurança Alimentar e Nutricional. Caderno de Orientações. 6ª Conferência Nacional de Segurança Alimentar e Nutricional. Brasília: CONSEA, 2023. Available in: . Acesso em: 30 dez. 2023 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Sep, 2025 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 10 Sep, 2024 Reviews received at journal 17 Aug, 2024 Reviewers agreed at journal 08 Aug, 2024 Reviewers agreed at journal 08 Aug, 2024 Reviewers agreed at journal 07 Aug, 2024 Reviews received at journal 05 Aug, 2024 Reviewers agreed at journal 30 Jul, 2024 Reviewers agreed at journal 30 Jul, 2024 Reviewers invited by journal 30 Jul, 2024 Editor invited by journal 06 Jun, 2024 Editor assigned by journal 06 Jun, 2024 Submission checks completed at journal 06 Jun, 2024 First submitted to journal 05 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4535555","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":312214454,"identity":"31bb5df4-43e7-461b-b3d5-8fc976af5e8e","order_by":0,"name":"Gleiciane Bueno da Silva Luiz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYDACdgaGwxBGA8MHBh6GBMJamGFaeA4wziBaCzOYIZEA1MJAhBZ5Z+aDhwvbbPL4Jd8YNlfIMOSZNxDQYniYLeHwzLa0YsnZOYaNZ3gYimUOENLSzGNwmLftcOKG2znmDxt4GBJnEHKYYTP/B6CW/4kbbp4xbCRKizwzDwNQy4HEDTd4iNRiwMxmcHjGueTEmT1phUAtEsUSBG1pb378uaDMLrGf/fDGxsYemzyCWgwOIPMYewhqANrSgML9QVjHKBgFo2AUjDwAAOzQQES9NNS1AAAAAElFTkSuQmCC","orcid":"","institution":"Postgraduate Program in Nutrition, Josué de Castro Nutrition Institute, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil.","correspondingAuthor":true,"prefix":"","firstName":"Gleiciane","middleName":"Bueno da Silva","lastName":"Luiz","suffix":""},{"id":312214455,"identity":"6e51884e-2897-497b-8ed2-b7216facb204","order_by":1,"name":"Aline Alves Ferreira","email":"","orcid":"","institution":"Department of Social and Applied Nutrition, Josué de Castro Nutrition Institute, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil.","correspondingAuthor":false,"prefix":"","firstName":"Aline","middleName":"Alves","lastName":"Ferreira","suffix":""},{"id":312214456,"identity":"2e5fa7f6-7f4a-4a79-898f-a570d56e17e5","order_by":2,"name":"Rosana Salles-Costa","email":"","orcid":"","institution":"Department of Social and Applied Nutrition, Josué de Castro Nutrition Institute, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil.","correspondingAuthor":false,"prefix":"","firstName":"Rosana","middleName":"","lastName":"Salles-Costa","suffix":""}],"badges":[],"createdAt":"2024-06-05 17:01:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4535555/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4535555/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-025-24427-z","type":"published","date":"2025-09-24T15:57:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":58680089,"identity":"33aef6e0-fde1-49a9-be05-4b605b767dc5","added_by":"auto","created_at":"2024-06-19 17:19:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":339843,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrevalence (%) of food insecurity according to race/colour and sex in the households in Brazil, 2020-2022.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: *Black men and women are the combination of black and brown race/colour. **(1a): Food Security; (2a): Mild Food Insecurity; (1c): Moderate + Sereve Food Insecurity.\u003c/p\u003e","description":"","filename":"Figure1page0001.png","url":"https://assets-eu.researchsquare.com/files/rs-4535555/v1/aaa79974c9fde6d61c9c18a2.png"},{"id":92430979,"identity":"701af812-37ae-4a47-a810-6835027499af","added_by":"auto","created_at":"2025-09-29 16:08:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1429750,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4535555/v1/607655f4-5050-4723-8d49-3d12da1aec12.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The unequal face of hunger: How gender and race/colour have exacerbated food insecurity during the COVID-19 pandemic—a cross-sectional analysis","fulltext":[{"header":"Background","content":"\u003cp\u003e\u0026nbsp; Racial and gender inequality is a persistent problem in many countries, such as Brazil. The interaction between these inequalities promotes disparities in access to essential services for human development, such as food and nutrition. During the COVID-19 pandemic, these vulnerabilities increased. It is therefore necessary to understand how these structural inequalities affect FI in the face of a new viral pandemic. We searched the PubMed, Google Scholar, LILACS, and CAPES databases for information on the prevalence of FI in black women both before and during the COVID-19 pandemic. We used search terms in both English and Portuguese to find publications dated between 2018 and 2024. The search terms included \u0026ldquo;food insecurity\u0026rdquo;, \u0026ldquo;sexism\u0026rdquo;, \u0026ldquo;racism\u0026rdquo; and \u0026ldquo;COVID-19\u0026rdquo;. Studies that investigate FI considering the intersectionality of gender and race/skin colour in the context of the COVID-19 pandemic remain scarce. For data analysis, we used data from a nationally representative survey of the Brazilian population called the National Survey on Food Insecurity in the Context of the COVID-19 Pandemic (VIGISAN). During the pandemic, this was the only survey that provided data on food security (FS) and FI levels nationwide.\u003c/p\u003e\n\u003cp\u003eResearch focused on FI considering gender and race/skin colour during the pandemic remains scarce. This study provides important data and reflections for better understanding, based on intersectionality, how gender and race/colour have interacted dynamically during the COVID-19 pandemic, particularly reflecting the situation of FS/FI. In addition, the findings of the study support black movements that are fighting for anti-racist and anti-patriarchal public food and nutrition policies.\u003c/p\u003e\n\u003cp\u003eDuring the COVID-19 pandemic, being part of the black and female population has been a major factor in the levels of FI in Brazilian homes, revealing the persistence of prejudice and the lack of government assistance aimed at this population group. The findings shed light on this group that is constantly made invisible despite its active participation in various spheres of society. Finally, we emphasize the importance of considering race/skin colour and gender intersectionally in the analysis of FI in the context of COVID-19 to provide support for the formulation of public policies that meet the demands of this population, focusing mainly on reducing the most serious levels of FI.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eIn 2020, the world was surprised by a new global public health crisis caused by a highly transmissible and lethal virus that profoundly altered all spheres of humanity.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e However, in the socioeconomic and demographic spheres, the consequences have been more pronounced. The measures necessary to contain the spread of the virus, such as lockdowns, have led to the loss of jobs or sources of income.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e As a result, economic instability has made it difficult for many families to access resources to face the pandemic without jeopardizing their well-being and access to good food and nutrition. According to the United Nations (ONU), 892.7\u0026nbsp;million people experienced severe FI between 2020 and 2022.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e In Latin America and the Caribbean alone, the number of people living with hunger reached 43.2\u0026nbsp;million.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eFI occurs when people do not have secure access to sufficient quantities of safe and nutritious food for normal growth and development and an active and healthy life.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e In addition, the concept of FI used in Brazil emphasizes that it can have significant impacts on health, both in terms of social exclusion, loss of self-esteem and emotional stress, as well as compromised nutritional status itself.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e In other words, FI refers not only to food shortages but also to wider ramifications of such shortages, including social, psychological, and nutritional aspects, which negatively affect people's quality of life.\u003c/p\u003e \u003cp\u003eBrazil is internationally recognized for its success in tackling hunger. The country was removed from the hunger map by the Food and Agriculture Organization (FAO) in 2014.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e A significant part of this success was achieved through the development and implementation of the Brazilian Food Insecurity Scale (EBIA). This indicator is responsible for estimating FI at the household level.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e However, since 2016, political and economic conditions have become unfavourable and incompatible with the agenda of promoting food and nutrition security (FNS).\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e The adoption of austerity policies throughout the country has once again exposed Brazil to high levels of household FI. Thus, with the arrival of the COVID-19 pandemic, concerns about future food supplies, shortages and even hunger became more severe. \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e However, these consequences have been heterogeneously observed in society.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eHistorically, the black population has suffered from constant violations of basic human and social rights. During the pandemic, it has become clear that the structural effects of racial and gender discrimination have placed this population in even more disadvantaged situations, thereby exacerbating existing social disparities.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Thus, analyses that take race/colour and gender into account show that these social indicators relate in different ways to the experience of families dealing with FI.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e For black women, the situation is even more evident since the history of racism and sexism in Brazil means that these individuals suffer an overlap of these axes of oppression\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e and are saddled with a greater degree of vulnerability in guaranteeing FS, as evidenced by them suffering from the most severe levels of FI.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThus, it has been observed that while the novel coronavirus disease could potentially affect everyone, the ability to obtain resources to overcome or prevent such infection, as well as manage the related consequences, was not equitable in Brazil. Given that many of black women serve as heads of household, all the paths led to an unequal increase in household FI in the country during the pandemic.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Given this scenario, this study aimed to assess the influence of the COVID-19 pandemic (2020\u0026ndash;2022) on FS and FI levels in Brazil according to the race/colour and gender of the head of household.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study was based on microdata from 2 nationally representative surveys assessing FI in the Brazilian population during the COVID-19 pandemic (1st and 2nd VIGISAN) carried out by the Brazilian Food and Nutrition Sovereignty and Security Research Network (Rede PENSSAN) in December 2020 (1st VIGISAN) and November 2021 to April 2022 (2nd VIGISAN).\u003c/p\u003e \u003cp\u003eBoth surveys have similar study designs, with a sample base that is representative of the national territory. The households were selected from the same census tracts used in the master sample of the Brazilian Institute of Geography and Statistics (IBGE) population surveys. For the selection of households, conglomerate sampling was used in three stages of selection (municipalities, census tracts, households). The 1st VIGISAN obtained a probabilistic sample of 2,180 households, with an estimated 95% confidence interval and a maximum margin of error of 2.1 percentage points for the estimates. In the 2nd VIGISAN, 12,745 households were sampled with a maximum margin of error for the total sample of 0.9 percentage points. More details on the sampling design can be found in the methodological section of the surveys. In both surveys, the household reference person was considered the individual responsible for decision-making in the household.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe outcome variables analysed were FS and FI levels, which were evaluated by the Brazilian Household Food Insecurity Measurement Scale (EBIA). The EBIA is a psychometric scale that assesses the interviewee's perception of access to food, in terms of quality and quantity, in the three months prior to the interview.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e In the surveys, the validated short version of the EBIA was used to estimate FS and FI levels.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e The choice to use the 8-item version of the EBIA was due to the need for a rapid population survey to reduce the risk of interviewer contamination. The 8 questions on the scale are dichotomous (yes/no), and each affirmative answer adds one point to the classification of the household's FI level. If all the questions are negative, the household is classified as FS.\u003c/p\u003e \u003cp\u003eBased on the classifications, the households were classified into either FS or three levels of FI (mild, moderate, and severe). FS-classified households are considered to have regular and permanent access to high-quality food in sufficient quantity without compromising access to other essential needs. Mild FI represents concern or uncertainty about access to food in the future. Moderate FI refers to quantitative food reduction among adults and/or disruption in eating patterns resulting from food shortages among adults. Severe FI signifies a quantitative reduction in food among children. In this situation, hunger becomes a lived experience in households. In this study, the moderate FI and severe FI categories were grouped together as a way of analysing the most severe categories in terms of access to adequate food.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTo understand the relationship between power systems (gender and race/skin colour) and FI during the pandemic, the intersectionality debate was adopted because its theoretical foundation seeks to capture the structural and dynamic consequences of the interaction between two or more axes of subordination.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e To do this, the household references were stratified according to sex and race/skin colour, following methodologies previously used by other studies.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eGender was assessed using the biological variable sex, adopted from Brazilian population studies.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e This variable takes the binary form \u0026ldquo;male\u0026rdquo; or \u0026ldquo;female\u0026rdquo;. However, for the intersectional debate, we used the gender debate to understand the complexities and nuances that extend beyond biological categories, considering social variables and how behaviours between men and women shape individual and collective experiences related to gender identity. Thus, the variable \u0026ldquo;gender\u0026rdquo; will be used to lead the discussions.\u003c/p\u003e \u003cp\u003eRegarding race/skin colour, the self-declaration used in Brazilian population studies was applied,\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e which considers five options: white, black, yellow, brown or indigenous. Given that the 1st and 2nd VIGISAN address samples that do not account for ethnic and racial minorities, it was not possible to represent the indigenous and yellow categories; thus, both were excluded from the analysis (1st VIGISAN: 4.5%; 2nd VIGISAN: 3.4%). In Brazil, the black population is comprised of both black and brown individuals because both categories are of African descent and face similar challenges due to the racism rooted in Brazilian society. Therefore, in this study, the categories \u0026ldquo;black\u0026rdquo; and \u0026ldquo;brown\u0026rdquo; were consolidated into a single classification, resulting in the race/skin colour variable having two categories, namely, \u0026ldquo;white\u0026rdquo; and \u0026ldquo;black/brown\u0026rdquo;.\u003c/p\u003e \u003cp\u003eSocio-demographic variables related to households and the person in charge of the household (head of the family), which are associated with FI, were assessed. The covariates were selected based on a theoretical review and a systematic review. The variables include: household information: region (North, Northeast, South/Southeast, or Central-West), area of household (urban or rural), number of residents (1\u0026ndash;2, 3\u0026ndash;5, or \u0026gt;\u0026thinsp;5), Presence of children under 5 (yes or no) and per capita family income (up to 1 MW or more than 1 MW) - considering the values in force during the reference period of each year: USD 287.79 (BRL 1,039.00) in 2020 and USD 327.00 (BRL 1,212.00) in 2022. Head of household information: age group, schooling (Iliterate, 1\u0026ndash;7, 8\u0026ndash;12, or \u0026gt;\u0026thinsp;12 years), and occupation (family farmer or rural producer, informal worker, formal worker, regular self-employed worker or individual entrepreneur, regular self-employed worker or individual entrepreneur or other (homemaker, pensioner, student, etc.)\u003c/p\u003e \u003cp\u003eFor the descriptive analyses, the proportion values and their respective 95% confidence intervals (95% CIs) were estimated. The prevalence of FS and levels of FI were calculated according to the reference person in the family. In the next stage, odds ratio (OR) values were calculated using multinominal logistic regression models to assess the association between the profiles of household reference persons who considered gender and race/skin colour (white man; white woman; black man; black woman), with the outcome levels of FI (using FS as a reference category). The models considered the adjustment for potential confounding variables (schooling, per capita family income, region, and area of the household) in the relationship with FI levels, which were determined based on a systematic review, tested separately (crude bivariate multinomial models) and included in the final adjusted model for those that obtained a p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (data not shown). The statistical analysis was carried out considering the complex sample design, 95% CI, using the 'svy' commands of Stata software, version 16.1 (StataCorp LLC, College Station, 2016), for this purpose.\u003c/p\u003e \u003cp\u003eThe utilized survey is part of a broad project aiming to monitor FS/FI in the context of COVID-19, coordinated by the PENSSAN Network and carried out by the Vox Populi Institute. This study was approved by the Research Ethics Committee of the Clementino Fraga Filho University Hospital of the Federal University of Rio de Janeiro - CAEE 30679914.0.0000.5257. This study uses databases whose information is aggregated without the possibility of individual identification; thus, it was not necessary to submit this information to the ethics committee of the National Research Ethics Commission (CONEP) according to Resolution N\u003csup\u003eo\u003c/sup\u003e. 510 of April 7, 2016.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn Brazil, the proportion of households headed by black/brown individuals, regardless of gender, was greater than that of households headed by other categories (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In addition, comparing the two surveys, there was an increase in the proportion of heads of household with informal occupations and those who had regular jobs or who were individual entrepreneurs in the second year of the survey. The proportions of FS and mild FI decreased in 2022 (41.3% and 28.0%, respectively), while the more severe forms of FI (moderate and severe) increased (15.2% and 15.5%, respectively). The variations in FI levels were found to be significant when comparing the 95% CIs (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of the sociodemographic characteristics of reference persons and households in Brazil, 2020\u0026ndash;2022.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eBRAZIL\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eI VIGISAN (2021)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eII VIGISAN (2022)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharacteristics of the head 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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace/color and gender profile**\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite man\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.1 \u0026ndash; 22.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.0\u0026ndash;20.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite woman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.7\u0026ndash;20.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.4\u0026ndash;18.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack/brown man\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.9\u0026ndash;34.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.0\u0026ndash;33.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack/brown woman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.8\u0026ndash;32.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.1\u0026ndash;32.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUp to 24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0\u0026ndash;6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.91\u0026ndash;6.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.5\u0026ndash;25.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.4\u0026ndash;27.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.3\u0026ndash;45.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.0\u0026ndash;43.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60 or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.7\u0026ndash;31.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.5\u0026ndash;26.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSchooling (years of study)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.9\u0026ndash;52.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.8\u0026ndash;48.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.5\u0026ndash;53.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.6\u0026ndash;54.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupation\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily farmer or rural producer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.91\u0026ndash;3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0\u0026ndash;1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInformal worker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.4 \u0026ndash; 18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.1\u0026ndash;18.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormal worker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.5 \u0026ndash; 24.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25,5\u0026ndash; 28.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegular self-employed worker or individual entrepreneur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.5\u0026ndash;18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.3\u0026ndash;18.6\u003c/p\u003e \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\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.9\u0026ndash;10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.5\u0026ndash;9.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther (homemaker, pensioner, student, etc.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.3\u0026ndash;38.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.8\u0026ndash;30.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold Characteristics\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eArea\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 \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\u003e85.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.1\u0026ndash;87.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.7\u0026ndash;86.2\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\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.9\u0026ndash;15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.8\u0026ndash;15.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral-West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.0\u0026ndash;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.2\u0026ndash;10.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth east\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.1\u0026ndash;28.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.1\u0026ndash;27.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.8\u0026ndash;8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.6\u0026ndash;7.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoutheast/ South\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.1\u0026ndash;61.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.3\u0026ndash;60.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of residents\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.9\u0026ndash;43.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.4\u0026ndash;52.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.3\u0026ndash;55.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.4\u0026ndash;46.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.2\u0026ndash;7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.5\u0026ndash;4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePresence of children under 5\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 \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\u003e13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.5\u0026ndash;15.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.18\u0026ndash;16.1\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\u003e86.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.8\u0026ndash;88.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.9 \u0026ndash; 85.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePer capita family income\u003c/b\u003e \u003cb\u003e(Minimum wage***)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUp to \u0026frac12; MW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.4\u0026ndash;46.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.35\u0026ndash;31.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than 1 MW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.8\u0026ndash;59.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.1\u0026ndash;70.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFood security and Levels of Food insecurity (FI)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecurity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.0\u0026ndash;47.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.0\u0026ndash;42.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild Insecurity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.1\u0026ndash;37.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.8\u0026ndash;29.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u0026thinsp;+\u0026thinsp;Sereve Insecurity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.4\u0026ndash;22.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.4\u0026ndash;32.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eNotes: *Proportions (%) and respective confidence intervals (95%CI); ** Black men and women were considered to be the combination of black and brown race/color; *** Considering the values in force during the reference period of each year: USD 287.79 (BRL 1,039.00) in 2020 and USD 327.00 (BRL 1,212.00) in 2022.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u0026lt;Insert Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here\u0026gt;\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cb\u003eDescription of the sociodemographic characteristics of reference persons and households in Brazil, 2020\u0026ndash;2022.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWhen analysing the scenario of change in FS and FI levels, according to race/colour and gender profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), it was observed that households headed by white people showed an increase in FS, which was not observed in households headed by black/brown people.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u0026lt;Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here\u0026gt;\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cb\u003ePrevalence (%) of food insecurity according to race/colour and sex in the households in Brazil, 2020\u0026ndash;2022.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAccording to the ORs in the final adjusted model (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), in 2020, the chance of moderate\u0026thinsp;+\u0026thinsp;severe FI was significantly greater among households headed by a black/brown woman (OR\u0026thinsp;=\u0026thinsp;2.2; 95% CI 1.3; 3.7). After one year of the COVID-19 pandemic, the chance of mild FI was greater among households headed by both black/brown men and women (p value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, the severity of FI (moderate\u0026thinsp;+\u0026thinsp;severe FI) was significantly greater among households headed by black/brown women (OR\u0026thinsp;=\u0026thinsp;3.2, 95% CI\u0026thinsp;=\u0026thinsp;2.5-4.0) than among households headed by white men.\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\u003eOdds Ratios and 95% Confidence Intervals of Sociodemographic Variables and Food Insecurity by Gender and Race/Colour in Brazil, 2020\u0026ndash;2022.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eI VIGISAN (2020)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e \u003cp\u003eII VIGISAN (2022)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender and race/color profile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eBivariate model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAdjusted model\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eBivariate model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eAdjusted model\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep-valor\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep-valor\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ep-valor\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003ep-valor\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMild FI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite man\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite woman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,0\u0026ndash;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.93\u0026ndash;2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u0026ndash;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.9\u0026ndash;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack/brown man\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8\u0026ndash;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.64\u0026ndash;1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.4\u0026ndash;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.1\u0026ndash;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack/brown woman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3\u0026ndash;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88\u0026ndash;2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.7\u0026ndash;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.2\u0026ndash;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModerate\u0026thinsp;+\u0026thinsp;Severe FI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite man\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite woman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3\u0026ndash;3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0\u0026ndash;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.6\u0026ndash;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.5\u0026ndash;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack/brown man\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0\u0026ndash;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6\u0026ndash;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.3\u0026ndash;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.5\u0026ndash;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack/brown woman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.8\u0026ndash;7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.3\u0026ndash;3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.3\u0026ndash;6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.5\u0026ndash;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eNotes: *Model reference category\u0026thinsp;=\u0026thinsp;Food Security; **Models adjusted for household location (urban/rural), household region (North, Northeast, South/Southeast, Midwest), schooling in years of study (Up to 8 years/9 years or more) and per capita household income (Up to 1MW/More than 1MW) - considering the values in force during the reference period of each year: USD 287.79 (BRL 1,039.00) in 2020 and USD 327.00 (BRL 1,212.00) in 2022.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u0026lt;Insert Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here\u0026gt;\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. \u003cb\u003eOdds Ratios and 95% Confidence Intervals of Sociodemographic Variables and Food Insecurity by Gender and Race/Colour in Brazil, 2020\u0026ndash;2022.\u003c/b\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the first years of the pandemic, families headed by black people had a worse FS status, to the detriment of an increase in all levels of FI. However, when a black woman served as the household's reference person, families were up to 4 times more likely to have moderate/severe FI.\u003c/p\u003e \u003cp\u003eThe significant increase in FI in families where the reference variable was female or black/brown in the first two years of the pandemic can be explained by the decrease in family income. Normally, the same social characteristics are described for black/brown women, i.e., working in undervalued professions, having a high probability of living in homes with a high number of residents, unfavourable conditions and precarious economic conditions, and performing multiple tasks (productive and reproductive life).\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e The paralysis of activities such as education, cultural events and nonessential businesses during the pandemic affected various economic activities that depended on the high level of movement of people in urban centres, as well as work in labour spaces. \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e As a result, the likelihood of layoffs and wage cuts, with a consequent increase in the number of unemployed and informal jobs, has tended to be greater among black and low-income women.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThese results must be analysed from the point of view of the combination of racism and sexism, which in turn can produce a form of \u0026ldquo;social asphyxiation\u0026rdquo;, which is a term used by Sueli Carneiro that has negative repercussions on all dimensions of life.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e This statement shows that the experience of being a black/brown woman in Brazil is accompanied by the possibility of experiencing the worst living conditions. As several possible factors explain this scenario, it is common for the lower-level Brazilian families to have female members working in informal or lower-paying jobs, with less schooling, and living in unhealthy housing and basic sanitation conditions.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eBecause these axes of oppression are structural conditions, negative consequences, such as hunger, were already observed before the pandemic began. In the study by Santos et al. (2023),\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e families headed by single black women with at least one child younger than 5 years old were three times more likely to live with moderate/severe FI in both 2004 and 2013. However, this likelihood became (four times greater in 2018, when FI began to increase again in Brazil. In another study carried out in the city of Salvador (northeast of the country), where 80% of the population is self-declared black and brown, the prevalence of FI was found to be greater in households headed by black women (mild FI: 25.6% and moderate or severe FI: 21.2%).\u003csup\u003e14\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe findings of these previous studies help us understand that the scenario of experiencing FI was already being conditioned mainly among families headed by black/brown women. Although scientific evidence shows the vulnerabilities of this population group, there are few instances of public policies that consider the intersectionality of gender and race/colour in their formulations. Thus, it is clear that this situation is not just a result of the COVID-19 pandemic. In fact, the health crisis has exposed social weaknesses and increased vulnerabilities as a result of socioeconomic damage.\u003c/p\u003e \u003cp\u003eTo alleviate the socioeconomic crisis, in 2020, the federal government adopted a policy of income transfer by distributing so-called emergency aid as a social protection measure.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e This was a financial benefit created with the aim of guaranteeing a minimum income for Brazilians during the COVID-19 pandemic. It was aimed at informal workers, individual microentrepreneurs, the self-employed and/or the unemployed.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e However, recent studies have shown that this program has not been sufficient to curb the increase in severe FI in Brazil.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Delays in decision-making, bureaucratic procedures for accessing the benefits implemented and difficulties in using digital technology are some of the issues that may have contributed to this situation among vulnerable populations.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e In addition, the decrease in the value of this emergency aid at the beginning of 2021 and the failure to grant it, especially during the period when the 2nd VIGISAN data collection began, may have contributed to the significant increase in the most severe forms of FI.\u003c/p\u003e \u003cp\u003eAdding to the debate on these results is the fact that household chores and caring for children and adolescents overlap in families. According to Rodrigues et al. (2024),\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e the priority given to women in the reproductive sphere also contributes to their being less involved in productive work in Brazilian families, especially when this responsibility establishes a class relationship. The authors also point out that for women living in poverty, reconciling both productive and reproductive work intensifies the overload resulting from social responsibilities. The availability of public facilities to reduce the overload of tasks considered feminine, such as community kitchens, school meals, cr\u0026egrave;ches and full-time schools, is important.\u003c/p\u003e \u003cp\u003eIn addition, in the first two years of the COVID-19 pandemic, in addition to being unpaid, female heads of household had their feminine duties overloaded in their homes due to the closure of nurseries and schools and the increase in the number of people infected and sick with the virus; this may also have resulted in an increase in FI in female-headed households.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe compulsory closure of school networks has forced children and adolescents to remain at home. Globally, the lives of millions of people in this group have been impacted by this measure, requiring emergency adaptation to the remote-home schooling model.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e As a result, there has been an increase in the social responsibilities of female heads of household, coupled with the intense mental burden that the pandemic scenario has placed on them.\u003c/p\u003e \u003cp\u003eFurthermore, in Brazil, according to the 2023 School Census,\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e the majority of children enrolled in early childhood education are black/brown. Many of these children benefit from the Brazilian National School Feeding Program (PNAE), which is a national program that covers students from the entire Brazilian public basic education network (early childhood education, elementary education, high school and youth and adult education) enrolled in public schools.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Among the PNAE's guidelines is the formation of healthy eating habits through the provision of meals and food and nutrition education. At the time of the school closures in Brazil, the federal government authorized the distribution of food purchased with PNAE funds to the parents or guardians of students in public basic education schools at the discretion of the local government during the period when classes were suspended.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e However, importantly, these measures may not have been enough to contain the spread of FI, as the transfer of such aid did not guarantee that the person responsible had access to food in terms of quantity and quality, and the amount of food transferred to schools has not been adjusted since February 2017, despite the increase in food prices\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e compromising the quality of the meals served.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWomen are still primarily responsible for caring for the home, children, elderly people and sick people. Although much of the unpaid care work in the world, especially in low-income countries, was already performed by women before the COVID-19 pandemic, there has been a significant increase in this burden. The resulting negative impact on women is likely to last for years.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eRegarding the possible limitations of this study, even though the data analysed are representative of the Brazilian population, the debate on gender issues is limited since the \u0026ldquo;sex\u0026rdquo; variable adopted in the VIGISAN surveys was used; this variable is based on IBGE population surveys and was collected with only two response options (male and female), thereby excluding other representations of gender identities.\u003c/p\u003e \u003cp\u003eThe findings in this article highlight that the problems caused by structural inequalities and the COVID-19 pandemic still represent a challenge for reducing inequalities in access to healthy food for the Brazilian population. However, it is important to add to the debate on the increase in severe forms of FI in Brazil that public policies aimed either directly or indirectly at promoting food and nutritional security in Brazil began to be dismantled in 2016, which contributed to this setback in the fight against hunger.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Although there are policies aimed at the black population and those aimed at women, the two policy streams are not in dialogue. This situation poses a greater risk in times of crisis. These are important factors that may have contributed to the increase in inequalities marked by the debate on the intersectionality of gender and race discussed in this article.\u003c/p\u003e \u003cp\u003eAt the beginning of 2023, Brazil resumed its fight against hunger as one of the goals of the current federal government. The National Council for Food and Nutrition Security (CONSEA), which was dissolved at the beginning of 2019, was reestablished to include monitoring, planning and evaluating food and nutrition security policies. Recently, the 6th National Conference on Food and Nutritional Security recognized, in its first thematic axis, the need for state participation in overcoming inequalities, especially those related to racism and patriarchy, among other structural determinants.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTherefore, the results of this article corroborate the need to structure actions and strategies and reformulate public policies aimed at ensuring the eradication of hunger and guaranteeing the human right to adequate food through anti-racist and anti-patriarchal food systems.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.328621908127207%\" valign=\"top\"\u003e\n \u003cp\u003eCAPES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.6713780918728%\" valign=\"top\"\u003e\n \u003cp\u003eCoordination of Superior Level Staff Improvement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.328621908127207%\" valign=\"top\"\u003e\n \u003cp\u003eCONEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.6713780918728%\" valign=\"top\"\u003e\n \u003cp\u003eCommittee of the National Research Ethics Commission\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.328621908127207%\" valign=\"top\"\u003e\n \u003cp\u003eCONSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.6713780918728%\" valign=\"top\"\u003e\n \u003cp\u003eCouncil for Food and Nutrition Security\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.328621908127207%\" valign=\"top\"\u003e\n \u003cp\u003eEBIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.6713780918728%\" valign=\"top\"\u003e\n \u003cp\u003eBrazilian Household Food Insecurity Measurement Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.328621908127207%\" valign=\"top\"\u003e\n \u003cp\u003eFAO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.6713780918728%\" valign=\"top\"\u003e\n \u003cp\u003eFood and Agriculture Organization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.328621908127207%\" valign=\"top\"\u003e\n \u003cp\u003eFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.6713780918728%\" valign=\"top\"\u003e\n \u003cp\u003eFood Insecurity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.328621908127207%\" valign=\"top\"\u003e\n \u003cp\u003eFNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.6713780918728%\" valign=\"top\"\u003e\n \u003cp\u003eFood and Nutrition Security\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.328621908127207%\" valign=\"top\"\u003e\n \u003cp\u003eFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.6713780918728%\" valign=\"top\"\u003e\n \u003cp\u003eFood Security\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.328621908127207%\" valign=\"top\"\u003e\n \u003cp\u003eIBGE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.6713780918728%\" valign=\"top\"\u003e\n \u003cp\u003eBrazilian Institute of Geography and Statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.328621908127207%\" valign=\"top\"\u003e\n \u003cp\u003eONU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.6713780918728%\" valign=\"top\"\u003e\n \u003cp\u003eUnited Nations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.328621908127207%\" valign=\"top\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.6713780918728%\" valign=\"top\"\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.328621908127207%\" valign=\"top\"\u003e\n \u003cp\u003ePENSSAN Network\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.6713780918728%\" valign=\"top\"\u003e\n \u003cp\u003eFood and Nutrition Sovereignty and Security Research Network\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.328621908127207%\" valign=\"top\"\u003e\n \u003cp\u003ePNAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.6713780918728%\" valign=\"top\"\u003e\n \u003cp\u003eBrazilian National School Feeding Program\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.328621908127207%\" valign=\"top\"\u003e\n \u003cp\u003eVIGISAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.6713780918728%\" valign=\"top\"\u003e\n \u003cp\u003eInsecurity Surveys in the Context of the COVID-19 Pandemic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study uses databases whose information is aggregated without the possibility of individual identification; thus, it was not necessary to submit this information to the ethics committee of the National Research Ethics Commission (CONEP) according to Resolution N\u003csup\u003eo\u003c/sup\u003e. 510 of April 7, 2016.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from PENSSAN Network but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. However, data may be available by contacting the PENSSAN Network (https://pesquisassan.net.br/)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR. S-C and GBS Luiz were partially supported by the Coordination of Superior Level Staff Improvement, Public Notice Emergency Strategic Program to Prevent and Combat Outbreaks, Endemics, Epidemics and Pandemics, grant number 88881.657811/2021-01.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGBSL contributed to the conception, literature review, data analysis and interpretation of the study; contributed to the writing and review of the manuscript. AAF contributed to the conception of the study, drafted and revised the manuscript. RSC contributed to the conception and data interpretation of the study, contributed to the writing and revision of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organisation. WHO Director-General\u0026rsquo;s Opening Remarks at the Media Briefing on COVID-19 - 11 March 2020 [Internet]. World Health Organization. 2020. Available in: https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020. Accessed on May 29, 2024.\u003c/li\u003e\n\u003cli\u003eAlloatti MN, Matos de Oliveira, AL. Deepening and widening the gap: The impacts of the COVID‐19 pandemic on gender and racial inequalities in Brazil. Gender, work, and organization, 2023, 30 (1): 329-44. https://doi.org/10.1111/gwao.12909.\u003c/li\u003e\n\u003cli\u003eFAO, IFAD, UNICEF, WFP and WHO. 2023. The State of Food Security and Nutrition in the World 2023.Urbanization, agrifood systems transformation and healthy diets across the rural\u0026ndash;urban continuum. Rome, FAO. https://doi.org/10.4060/cc3017en.\u003c/li\u003e\n\u003cli\u003eFood and Agriculture Organization of the United Nations. Trade and Food Security: Conceptualizing the Linkages. Rome: FAO, 2003. Available in: https://openknowledge.fao.org/server/api/core/bitstreams/310467a7-bc1d-416c-81c6-53b037ddb853/content. Accessed on May 27, 2024.\u003c/li\u003e\n\u003cli\u003eKepple AW, Segall-Corr\u0026ecirc;a AM. Conceituando e medindo seguran\u0026ccedil;a alimentar e nutricional. Ci\u0026ecirc;ncia \u0026amp; Sa\u0026uacute;de Coletiva. 2011 Jan 1;16(1):187\u0026ndash;99. https://doi.org/10.1590/S1413-81232011000100022. \u003c/li\u003e\n\u003cli\u003eFAO, IFAD and WFP. 2014. The State of Food Insecurity in the World 2014. Strengthening the enabling environment for food security and nutrition. Rome, FAO. Available in: https://openknowledge.fao.org/server/api/core/bitstreams/ad888f5b-a590-43d9-b066-9a852b95777e/content. \u003c/li\u003e\n\u003cli\u003eSalles-Costa R, Segall-Corr\u0026ecirc;a AM, Alexandre-Weiss VP, Pasquim EM, Paula NM de, Lignani J de B, et al. Rise and fall of household food security in Brazil, 2004 to 2022. Cadernos de Sa\u0026uacute;de P\u0026uacute;blica. 2023 Feb 6;39:e00191122. https://doi.org/10.1590/0102-311XEN191122.\u003c/li\u003e\n\u003cli\u003eBrazilian Research Network on Food and Nutrition Sovereignty and Security (Rede Brasileira de Pesquisa em Soberania e Seguran\u0026ccedil;a Alimentar e Nutricional - Rede PENSSAN. I VIGISAN - National Survey of Food Insecurity in the Context of the Covid-19 in Brazil, 2021. Available in: https://olheparaafome.com.br/. \u003c/li\u003e\n\u003cli\u003eBrazilian Research Network on Food and Nutrition Sovereignty and Security (Rede Brasileira de Pesquisa em Soberania e Seguran\u0026ccedil;a Alimentar e Nutricional - Rede PENSSAN. II VIGISAN - National Survey of Food Insecurity in the Context of the Covid-19 in Brazil, 2022. Available in: https://olheparaafome.com.br/.\u003c/li\u003e\n\u003cli\u003ePower K. The COVID-19 pandemic has increased the care burden of women and families. Sustainability: Science Practice and Policy. 2020; 16 (1): 67-73. https://doi.org/10.1080/15487733.2020.1776561.\u003c/li\u003e\n\u003cli\u003eLignani J de B, Palmeira P de A, Antunes MML, Salles-Costa R. Relationship between social indicators and food insecurity: a systematic review. Revista Brasileira de Epidemiologia. 2020;23. https://doi.org/10.1590/1980-549720200068 .\u003c/li\u003e\n\u003cli\u003eCarneiro, S. Racismo, sexismo e desigualdade no Brasil. S\u0026atilde;o Paulo: Selo Negro Edi\u0026ccedil;\u0026otilde;es, 2011.\u003c/li\u003e\n\u003cli\u003eSantos LA. P\u0026eacute;rez-Escamilla R, Christine C, Aline Alves Ferreira, Salles-Costa R. Gender, skin color, and household composition explain inequities in household food insecurity in Brazil. PLOS global public health. 2023 Oct 3;3(10):e0002324\u0026ndash;4. https://doi.org/10.1371/journal.pgph.0002324.\u003c/li\u003e\n\u003cli\u003eSilva SO da, Santos SMC dos, Gama CM, Coutinho GR, Santos MEP dos, Silva N de J. A cor e o sexo da fome: an\u0026aacute;lise da inseguran\u0026ccedil;a alimentar sob o olhar da interseccionalidade. Cadernos de Sa\u0026uacute;de P\u0026uacute;blica. 2022 Aug 5;38. https://doi.org/10.1590/0102-311XPT255621 .\u003c/li\u003e\n\u003cli\u003eSegall-Corr\u0026ecirc;a AM, Marin-Le\u0026oacute;n L, Melgar-Qui\u0026ntilde;onez H, P\u0026eacute;rez-Escamilla R. Refinement of the Brazilian household food insecurity measurement scale: recommendation for a 14-item EBIA. Rev Nutr. 2014;27(2):241\u0026ndash;251. https://doi.org/10.1590/1415- 52732014000200010.\u003c/li\u003e\n\u003cli\u003eInterlenghi GS, Reichenheim ME, Segall-Corr\u0026ecirc;a AM, P\u0026eacute;rez-Escamilla R, Moraes CL, Salles-Costa R. Modeling Optimal Cutoffs for the Brazilian Household Food Insecurity Measurement Scale in a Nationwide Representative Sample. The Journal of Nutrition. 2017 May 31;147(7):1356\u0026ndash;65. https://doi.org/10.3945/jn.117.249581.\u003c/li\u003e\n\u003cli\u003eCrenshaw K. Documento para o encontro de especialistas em aspectos da discrimina\u0026ccedil;\u0026atilde;o racial relativos ao g\u0026ecirc;nero. Revista Estudos Feministas. 2002; 10 (1): 171-88. https://doi.org/10.1590/S0104-026X2002000100011 .\u003c/li\u003e\n\u003cli\u003eInstituto Brasileiro de Geografia e Estat\u0026iacute;stica. Censo Demogr\u0026aacute;fico 2022: Caracter\u0026iacute;sticas dos domic\u0026iacute;lios. Resultados do universo. Rio de Janeiro: IBGE, 2024. Available in: https://biblioteca.ibge.gov.br/visualizacao/periodicos/3106/cd_2022_domicilios.pdf.\u003c/li\u003e\n\u003cli\u003eChandler R, Guillaume D, Parker AG, Mack A, Hamilton J, Dorsey J, et al. The impact of COVID-19 among Black women: evaluating perspectives and sources of information. Ethnicity \u0026amp; Health. 2020 Nov 5;26(1):80\u0026ndash;93. https://doi.org/10.1080/13557858.2020.1841120.\u003c/li\u003e\n\u003cli\u003eInstituto Brasileiro de Geografia e Estat\u0026iacute;stica. Estat\u0026iacute;sticas de g\u0026ecirc;nero: indicadores sociais das mulheres no Brasil. 3\u0026ordf; Edi\u0026ccedil;\u0026atilde;o. Estudos e Pesquisas: Informa\u0026ccedil;\u0026atilde;o Demogr\u0026aacute;fica e Socioecon\u0026ocirc;mica \u0026bull; n.38. Rio de Janeiro: IBGE, 2024. Available in: https://biblioteca.ibge.gov.br/visualizacao/livros/liv102066_notas_tecnicas.pdf.\u003c/li\u003e\n\u003cli\u003eRodrigues, A. V. de A., Costa, D. M., Costa, R. S. Desigualdades de g\u0026ecirc;nero e renda das fam\u0026iacute;lias brasileiras: implica\u0026ccedil;\u0026otilde;es para a seguran\u0026ccedil;a alimentar e nutricional. Ci\u0026ecirc;ncia \u0026amp; Sa\u0026uacute;de Coletiva. 2024. Available in: http://cienciaesaudecoletiva.com.br/artigos/desigualdades-de-genero-e-renda-das-familias-brasileiras-implicacoes-para-a-seguranca-alimentar-e-nutricional/19186?id=19186.\u003c/li\u003e\n\u003cli\u003eMattei L, Heinen VL. Impactos da crise da Covid-19 no mercado de trabalho brasileiro. Brazilian Journal of Political Economy. 2020 Dec; 40(4):647\u0026ndash;68. https://doi.org/10.1590/0101-31572020-3200 .\u003c/li\u003e\n\u003cli\u003eBrasil. Medida provis\u0026oacute;ria n\u0026ordm; 1.000, de 2 de setembro de 2020. Institui o aux\u0026iacute;lio emergencial residual para enfrentamento da emerg\u0026ecirc;ncia de sa\u0026uacute;de p\u0026uacute;blica de import\u0026acirc;ncia internacional decorrente do coronav\u0026iacute;rus (\u003cstrong\u003ecovid-19\u003c/strong\u003e). Available in: https://www.planalto.gov.br/ccivil_03/_ato2019-2022/2020/mpv/mpv1000.htm .\u003c/li\u003e\n\u003cli\u003eMelo A, Rodrigues M. Impactos da Implementa\u0026ccedil;\u0026atilde;o do Aux\u0026iacute;lio Emergencial para a Popula\u0026ccedil;\u0026atilde;o Negra. Cadernos de Campo: revista de ci\u0026ecirc;ncias sociais. 2023 Jul 18;e023003\u0026ndash;3. https://doi.org/10.47284/cdc.v23i00.18236 .\u003c/li\u003e\n\u003cli\u003eEduca\u0026ccedil;\u0026atilde;o: do fechamento das escolas \u0026agrave; recupera\u0026ccedil;\u0026atilde;o. UNESCO. Available in: https://www.unesco.org/pt/covid-19/education-response.\u003c/li\u003e\n\u003cli\u003eBrasil. Instituto Nacional de Estudos e Pesquisas Educacionais An\u0026iacute;sio Teixeira (INEP). Censo Escolar da Educa\u0026ccedil;\u0026atilde;o B\u0026aacute;sica 2022: Resumo T\u0026eacute;cnico. Bras\u0026iacute;lia, 2023. Available in: https://download.inep.gov.br/publicacoes/institucionais/estatisticas_e_indicadores/resumo_tecnico_censo_escolar_2022.pdf.\u003c/li\u003e\n\u003cli\u003eBrasil. Resolu\u0026ccedil;\u0026atilde;o n\u0026ordm; 26, de 17 de junho de 2013. Disp\u0026otilde;e sobre o atendimento da alimenta\u0026ccedil;\u0026atilde;o escolar aos alunos da educa\u0026ccedil;\u0026atilde;o b\u0026aacute;sica no \u0026acirc;mbito do Programa Nacional de Alimenta\u0026ccedil;\u0026atilde;o Escolar - PNAE. Bras\u0026iacute;lia, 17 de junho de 2013. Available in: https://www.gov.br/fnde/pt-br/acesso-a-informacao/legislacao/resolucoes/2013/resolucao-cd-fnde-no-26-de-17-de-junho-de-2013. \u003c/li\u003e\n\u003cli\u003eZimmermann AS, Delgado NG. El desmonte del Sistema Nacional de Seguridad Alimentaria y Nutricional y la garant\u0026iacute;a del derecho\u0026shy; humano a la alimentaci\u0026oacute;n en\u0026shy; Brasil. Polis Revista Latinoamericana. 2022; 22:32-51.http://dx.doi.org/10.32735/s0718-6568/2022-n63-1771 .\u003c/li\u003e\n\u003cli\u003eObservat\u0026oacute;rio da Alimenta\u0026ccedil;\u0026atilde;o Escolar, Associa\u0026ccedil;\u0026atilde;o Nacional de Pesquisa em Financiamento da Educa\u0026ccedil;\u0026atilde;o. Em defesa de reajustes nos valores per capita do PNAE. 2021. Available in: https://alimentacaoescolar.org.br/media/acervo/documentos/Em_defesa_de_reajustes_nos_valores_per_capita_do_PNAE_-_FACTSHEET.pdf. Accessed on June 03, 2024.\u003c/li\u003e\n\u003cli\u003eConselho Nacional de Seguran\u0026ccedil;a Alimentar e Nutricional. Caderno de Orienta\u0026ccedil;\u0026otilde;es. 6\u0026ordf; Confer\u0026ecirc;ncia Nacional de Seguran\u0026ccedil;a Alimentar e Nutricional. Bras\u0026iacute;lia: CONSEA, 2023. Available in: \u0026lt;https://www.gov.br/secretariageral/pt-br/consea/conferencia/CadernodeOrientacoes6aConferencia_final_16.06.23.pdf\u0026gt;. Acesso em: 30 dez. 2023\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-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":"Gender Inequality, Racism, Food Insecurity, COVID-19","lastPublishedDoi":"10.21203/rs.3.rs-4535555/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4535555/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe COVID-19 pandemic has aggravated Food Insecurity (FI) in a heterogeneous way across different household profiles. The aim of this study was to assess the influence of the first two years of the COVID-19 pandemic on FI in Brazil according to the intersections of race/colour and the gender of household heads.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMicrodata from the 1st and 2nd Food Insecurity Surveys in the Context of the COVID-19 Pandemic (VIGISAN) were used (1st VIGISAN: n=2,180; 2nd VIGISAN: n=12,745). FI levels were compared according to the profiles created from the intersection of gender (male and female) and race/colour (white and black) for the household reference person. Odds ratio (OR) values were calculated using multinomial logistic regression models to assess the association between reference person profiles and FI levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough the proportion of households with moderate+severe FI increased in the first two years of the pandemic among households headed by black people, the prevalence of severe FI was significantly greater for households headed by black women (2.2 versus 3.2; 95% CI 2.52; 4.00).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of this study reinforce the need to plan equitable public policies that debate the intersectionality of gender and race/colour as a way of guaranteeing food and nutritional security in Brazil.\u003c/p\u003e","manuscriptTitle":"The unequal face of hunger: How gender and race/colour have exacerbated food insecurity during the COVID-19 pandemic—a cross-sectional analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-19 17:19:43","doi":"10.21203/rs.3.rs-4535555/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-10T19:09:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-17T17:32:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"273672277118473719966923899552356806309","date":"2024-08-08T18:09:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238337045947910018444045029803361480747","date":"2024-08-08T17:23:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"302222600903878040707711707056270540285","date":"2024-08-07T11:25:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-05T16:46:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"44023960862386113969775611928080145190","date":"2024-07-30T15:37:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"210632774016478627561909947175154228038","date":"2024-07-30T13:23:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-30T12:47:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-06-06T09:44:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-06T05:00:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-06T04:59:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-06-05T17:00:04+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":"5805bd76-b8d6-462a-b943-53b7c772d876","owner":[],"postedDate":"June 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-29T16:07:15+00:00","versionOfRecord":{"articleIdentity":"rs-4535555","link":"https://doi.org/10.1186/s12889-025-24427-z","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-09-24 15:57:58","publishedOnDateReadable":"September 24th, 2025"},"versionCreatedAt":"2024-06-19 17:19:43","video":"","vorDoi":"10.1186/s12889-025-24427-z","vorDoiUrl":"https://doi.org/10.1186/s12889-025-24427-z","workflowStages":[]},"version":"v1","identity":"rs-4535555","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4535555","identity":"rs-4535555","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}