Next Article in Journal
Adherence of Healthcare Workers to Saudi Management Guidelines of Heat-Related Illnesses during Hajj Pilgrimage
Next Article in Special Issue
Food Acquisition and Daily Life for U.S. Families with 4- to 8-Year-Old Children during COVID-19: Findings from a Nationally Representative Survey
Previous Article in Journal
Degradation of Bisphenol A in an Aqueous Solution by a Photo-Fenton-Like Process Using a UV KrCl Excilamp
Previous Article in Special Issue
Crepis vesicaria L. subsp. taraxacifolia Leaves: Nutritional Profile, Phenolic Composition and Biological Properties
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 18
Issue 3
10.3390/ijerph18031160
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Food Insecurity among Low-Income Food Handlers: A Nationwide Study in Brazilian Community Restaurants

by
Ingrid C. Fideles
1,
Rita de Cassia Coelho de Almeida Akutsu
2,*,
Rosemary da Rocha Fonseca Barroso
1,
Jamacy Costa-Souza
1,
Renata Puppin Zandonadi
2,
António Raposo
3,* and
Raquel Braz Assunção Botelho
2
1
Department of Food Science, School of Nutrition, Federal University of Bahia, Salvador 40110-150, Brazil
2
Department of Nutrition, Faculty of Health Sciences, University of Brasilia, Brasilia 70910-900, Brazil
3
CBIOS (Research Center for Biosciences and Health Technologies), Universidade Lusófona de Humanidades e Tecnologias, Campo Grande 376, 1749-024 Lisboa, Portugal
*
Authors to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2021, 18(3), 1160; https://doi.org/10.3390/ijerph18031160
Submission received: 24 November 2020 / Revised: 21 January 2021 / Accepted: 26 January 2021 / Published: 28 January 2021
(This article belongs to the Special Issue Food Safety, Security, Sustainability and Nutrition as Priority Objectives of the Food Sector)
Versions Notes

Abstract

:
This study aims to evaluate food insecurity (FI) among Brazilian Community restaurant food handlers and its associated factors. This cross-sectional study was performed with a representative sample of 471 food handlers working in community restaurants (CR) from all Brazilian regions. Participants are mostly female (62.2%), ≤40 years old (67.7%), with a partner (52.0%), and with up to eight years of education (54.1%). Predictors of participants’ socioeconomic status and CR geographic location are associated with the household food insecurity categories (p < 0.05). The predictors of socioeconomic conditions are associated with mild and moderate/severe FI category. Workers with less education are twice as likely to belong to the category with the highest FI severity. Lower per capita household income increased the chances of belonging to the mild insecurity category by 86%. It more than doubled the chance to be in the category of moderate/severe insecurity. Predictors of health status, lifestyle, and work are not associated with any multinomial outcome categories. However, working in the South, Southeast, or Midwest regions of Brazilian decreased the chances of belonging to one of the FI categories, with significance only for the mild category. Variables that show an association for this population are per capita household income for the different levels of FI and the CR region for mild FI. A high prevalence of FI in this population points to the need for more studies with low-income workers to prevent FI and its health consequences.

1. Introduction

Food insecurity is a global public health problem, affecting more than 2 billion people worldwide [1]. Food security is a basic need covering access to safe, sufficient, and proper nutritional food [2,3]. Among adults, food insecurity is related to the high prevalence of chronic health problems that compromise this population’s quality of life and longevity. It is strongly linked to economic vulnerability [1,4]. In Brazil, the estimation is that more than 14 million households have some food insecurity level [5]; additionally, people eat more outside their homes because they have less time to prepare meals. In Brazil, to the low-income population, eating out tends to represent an intake of cheaper and fast snacks, usually representing inadequate nutrient intake that influences their health outcomes increasing the risk of chronic diseases and nutrient deficits [2,6,7,8].
In Brazil, people with no or limited access to adequate, safe, and nutritional food are characterized in food insecurity situations (FIS) [5]. FIS can generate fear of the inability to obtain food (influencing quantity or quality of food choice) or generate hunger in the most severe cases due to the scarcity of food at home [5]. The FIS determinants can present social, economic, and political nature, affecting populations’ health. The living and working conditions of individuals and population groups are related to their health situation [9]. Among the factors associated with FIS, mainly in the moderate and severe categories of FI, income, and weight excess have been highlighted [10]. Women in developed countries have higher chances of being overweight and obese when in FIS [11].
Based on this information, the Brazilian Government developed the community restaurant (CR) program as a strategy to face the FIS. CRs were created to offer safe, cheap, and healthy meals to the low-income population [12,13]. The CR Program is one of the programs integrated into the “Fome Zero” network of actions, a social inclusion policy established in 2003 [13]. From the beginning, the Government planned to increase the number of CR distributed throughout the Brazilian territory located in regions with more significant numbers of low-income people, reaching 135 CRs in 2020 [13,14]. The production and distribution of CR involve professionals with different education and income levels [15], highlighting the food handlers that directly produce meals [13]. Among food handlers, it is common to have weight excess [15,16,17,18], low education levels, and low-income [15,19], making them more susceptible to FIS [10] even though they work in places that produce a great amount of food. Godoy et al. [20] studied food insecurity among CR customers. Despite a high percentage of FI (40.6% for males and 43.8% for females), there were no significant associations between FI and Body Mass Index or body fat percentage. There were significant associations between FI and household income and educational levels. There was also an association with Brazilian’s regions among females, FI being worse in the North and Northeast regions.
Few studies evaluated FIS among workers in Brazil [21,22,23] and in the world [24,25,26,27]. However, only three of these studies were performed with professionals working in food service. A study showed a high prevalence of FIS perception among these professionals in Canada compared to other professionals [24]. The two studies conducted in Brazil only evaluated food handlers in a specific region without a nationwide Brazilian representative sample of food handlers [21,22]. Studies with the population of food handlers demonstrate their susceptibility to food insecurity [21,22], to excess weight [22,28], to management in foodservice with a greater focus on the costs involved with the production of meals than with the health of workers [29]. Even though the studies found in Brazil were both developed in the format of case studies [30,31,32], geographically delimited to institutional spaces, such as universities, Brazilian states or municipalities [21,22,23,28,31,32,33], food insecurity is multifactorial and, in CR, we hypothesize that socioeconomic and demographic factors of food handlers are associated with food insecurity. Since the program tends to increase the number of CR in Brazilian territory, more CRs open over the country, possibly resulting in more opportunities to decrease FI among customers and more work opportunities for food handlers. However, these food handlers need to be closely investigated because of their well-being, quality of life, and food security to guarantee motivation to work and meals safely produced. Therefore, the objective was to evaluate nationwide the food insecurity among Brazilian CR food handlers and its associated factors. As it has an exploratory character, the study sought to answer the following question: What are the factors associated with food insecurity among food handlers in Brazilian CR? We expect to provide data allowing to promote interventions to reduce food insecurity among this vulnerable group that works inside foodservices but does not overcome FI inside their families.

2. Materials and Methods

2.1. Design, Settings, and Participants

This cross-sectional study was performed in Brazilian CR (focused on the low-income Brazilian population offering meals from Monday to Friday). It was conducted according to the Declaration of Helsinki guidelines and approved by the University of Brasília Research Ethics Committee (#037210). Written informed consent was obtained from all participants.
The basis of the sample calculation was the official list of all existing CR throughout the Brazilian territory at the moment of data collection [13]. With CR nationwide selection, food handlers in different Brazilian regions and similar work conditions were evaluated. Researchers were allowed to enter all of the CR with nationwide permission. The restaurant inclusion criteria were to be part of the Brazilian CR program, sign the Institutional Acknowledgement Agreement by the CR responsible, and offer daily more than 500 meals. With more than 500 meals, CR allowed researchers to evaluate many workers, helping to achieve a representative sample. Restaurants that provide less than 500 daily meals are considered small and present fewer food handlers than medium or large restaurants that offer more than 500 daily meals [34]. There were 65 existing CR in Brazil, and all of them met the inclusion criteria.
From the 65 CR, a sampling plan was calculated considering a level of significance (α) of 5% [35] using the “survey select” of the SAS 9.1.3 program (SAS Institute Inc., Cary, NC, USA). Therefore, a minimum of 31 CR existing in each of the five Brazilian regions (North, Northeast, Midwest, South, and Southeast) was randomly selected to be part of the study. The final sample of 36 restaurants was used, respecting the stratification criteria by the Brazilian region. The distribution of randomly drawn CRs was proportional to the number of CR in each region. The researchers visited 36 CR and included them in the sample (4 in North, 10 in Northeast, 1 in Midwest, 15 in Southeast, and 6 in South). All of the working food handlers in the 36 drawn CR were invited to participate in this study (n = 1062). Data collection was carried out over four consecutive days to cover the various work schedules to guarantee access to the largest possible number of handlers in each CR. Therefore, from the total of 1062 handlers working on the selected CRs, 970 met the inclusion criteria (e.g., not being pregnant, and workers on vacation or not working due to medical issues). From them, 471 (48.6%) agreed to participate and completed the study.
Some handlers refused to participate in the research because they did not want to stop working or worried about employability. Even though researchers explained that participation would not influence their work, some decided not to participate in the study. A total of 383 individuals was necessary to be a representative sample stratified in the five Brazilian regions according to the number of food handlers in each region. Participants were not compensated for the participation. They were just informed about the importance of the study for their category. The study used a 95% confidence interval and an error of 4%, respecting the minimum handlers’ sample per Brazilian region [35]. Therefore, it was necessary to achieve a minimum of 11% of the handlers from the North, 28% from the Northeast, 3% from the Midwest, 41% from the Southeast, and 17% from the South.

2.2. Data Collection

Trained researchers performed data collection using standardized instruments to identify socio-demographic characteristics [36] and the Brazilian Food Insecurity Scale (EBIA/BFIS) [37]. The socio-demographic variables were gender, age, educational level, per capita household income, marital status, smoking status, participation in a governmental program, and how many years or months the individual has worked in the CR. The presence of diagnosed non-communicable diseases (NCDs) (by a physician) was self-reported. The information was recorded in a specific form showing the presence or absence of one or more than one of the following NCDs: Systemic arterial hypertension (SAH), type 2 diabetes mellitus (DM), and others (cancer, dyslipidemia, cardiovascular diseases, respiratory diseases, depression). The self-reported NCD data was used because population studies widely use this method for its convenience and economy [36,38].
The individuals who agreed to participate signed the consent form after receiving information about the research. Before participants’ lunch in a reserved room, weight and height were measured with a Plenna® (São Paulo, Brazil) weighing scale (150 kg) and a stadiometer (220 cm). Participants had to take off their shoes and coats. After that, the body mass index (BMI) was calculated [39]. The anthropometric status based on body mass index classification [39] was dichotomized on weight excess (0. No; 1. Yes). The cut-off point used to indicate excess weight was a BMI value ≥ 25 kg/m2, which covers both the category of overweight and obesity.

2.3. Dependent Variable

The study’s outcome, household food security situation (HFSS), was obtained through EBIA/BFIS adapted and validated for the Brazilian population [40,41]. The EBIA/BFIS seeks to assess the perception and experience of household residents’ hunger in the three months before the instrument application. The positive answers to the 14 questions of the instrument categorize the level of food security/insecurity (considering the age of the residents) in food security (0 points); mild food insecurity (1–5 points in the presence of residents <18 years old, or 1–3 points in the absence of residents <18 years old); moderate food insecurity (6–9 points in the presence of residents <18 years old, or 4–5 in the absence of residents <18 years old) and, severe food insecurity (10–14 points in the presence of residents <18 years old, or 6–8 points in the absence of residents <18 years). Based on the methodology adopted by Panigassi et al. [42], for this study, the HFSS categories were grouped into food security, mild food insecurity, and moderate/severe food insecurity.

2.4. Statistical Analysis

The data were analyzed with the STATA 15.0® (StataCorp LP, College Station, TX, USA), using frequencies to describe the categorical variables and Pearson’s chi-square test (χ2) to identify associations (p-value < 0.05).
The outcome with three categories of HFSS, multinomial (polytomous) logistic regression models were used. They are applicable for outcomes with three or more levels [43]. For this study, the category “food security” was chosen as a reference. In this type of analysis, the logistic model will have two logit functions: the ratio between Y = 1 and Y = 0 and the ratio between Y = 2 and Y = 0, with Y = 0 as the referent. The numbers 0, 1, and 2 represent the food security status classification for statistical analysis—(0) Food Security—the household has regular and permanent access to food in sufficient quantity and quality without compromising access to other needs; (1) Mild food insecurity—at this level there is uncertainty regarding access to food in the future, with a change in the quality of food, but without compromising the amount of food; (2) includes the levels of moderate food insecurity and severe, in which there is already a quantitative reduction in access to food, causing changes in the dietary pattern of residents at home. These categories were grouped to enable comparison with population data given that national food insecurity assessment studies [44] and international [45] present their analyses considering the grouped on these two levels.
Bivariate multinomial regression models, with HFSS as the dependent variable, were applied to all predictor variables. This stage helped to understand the initial associations of the determinant factors for the HFSS of this population and the magnitude of each predictor’s effect by calculating the gross Odds Ratio (OR) and their respective 95% Confidence Intervals (CI). The predictor variables were included in the multivariate model based on two assumptions. The first assumption was the theoretical basis underlying the HFSS. The second one was the statistical decision based on the bivariate multinomial model result with a value of p < 0.20 [16]. Thus, the multivariate multinomial model was composed of the predictors that met the assumptions and were adjusted together, without considering hierarchical order or determination level.
The backward stepwise procedure was used for the selection in the final model. The Likelihood Ratio Test (LRT) was used to test hypotheses about the significance of the predictor variables, that is, to evaluate the effect at all levels of the outcome simultaneously, which affects the number of parameters tested and the degrees of freedom associated with the test [46]. Thus, the comparison of the observed and the expected values using the likelihood function was based on the expression:
D =   − 2 l n L r e d u c e d − − 2 l n L f u l l ) ~ X 2
The full model corresponds to the complete multivariate model and the reduced model to the model without a corresponding predictor, following a chi-square distribution with degrees of freedom equal to the number of set parameters (defined as zero under the null hypothesis—H0). Statistically significant results (p < 0.05) reject H0 and indicate that the predictor is significant for the model, being maintained in the final model.

3. Results

The socioeconomic and demographic characteristics, health status, HFSS, and aspects related to food handlers’ work in CR are in Table 1. The sample was composed mainly of women (62.2%), aged ≤40 years old (67.7%), with a partner (52.0%), and with up to eight years of education (54.1%). In most households, there were up to two people employed (78.8%), less than six rooms (64.4%), per capita household income below half a minimum wage (40%), and 19.3% of the workers in government programs. Among the health risk behaviors or conditions, overweight and alcohol use was more prevalent (60.8% and 46%, respectively) when compared to the smoking habit (17.2%) and the presence of NCDs (19.1%). More than half of these professionals were working in the CR for ≥12 months (57.3%) (Table 1). For most of the factors in Table 1, the median value was used to split the sample and perform the analysis.
Predictors of socioeconomic status (education, per capita household income, and participation in governmental programs) and geographic location of the CR were associated with the HFSS categories by the chi-square test (p < 0.05) (Table 1).
There was no significant association between mild FI and the demographic variables in the bivariate multinomial analysis. In contrast, the predictors of socioeconomic conditions were associated with both the mild and the moderate/severe FI category. Workers with less education were twice as likely to belong to the category with the highest severity of food insecurity (OR: 2.17; 95% CI: 1.25–3.77). Having lower per capita household income increased the chances of belonging to the category of mild insecurity by 86% (OR: 1.86; 95% CI: 1.20–2.88) and more than doubled the chance to be in the category of moderate/severe Insecurity (OR: 3.8; 95% CI: 2.18–6.60) in addition to participating in governmental programs (OR: 3.17; 95% CI: 1.75–5.74). Living in households with fewer rooms was also associated with the most severe food insecurity category (OR: 1.87; 95% CI: 1.05–3.34) (Table 2).
Predictors of health status, lifestyle, and work were not associated with any of the multinomial outcomes’ categories at a 5% significance level. However, working in the South, Southeast, or Midwest regions of Brazilian decreased the chances of belonging to one of the Food Insecurity categories between 21% to 42%, with significance only for the mild category (OR: 0.58; 95% CI: 0.38–0.88) (Table 2).
The bivariate multinomial regression models confirmed the associations of education, per capita income, participation in a governmental program, and the Brazilian region with the categories of HFSS, with an increase in the number of rooms among the predictors. Besides, variables that showed association with any of the categories of HFSS (p-value < 0.20) were also included in the multivariate multinomial model (gender, number of people working at home, and the presence of NCDs) (Table 2).
Table 3 shows the results of the multivariate model containing eight predictors selected in the previous steps. They were associated with at least one of the categories of HFSS with a value of p < 0.20. In the complete crude model, per capita income (Mild Insecurity—OR: 1.64; 95% CI: 1.01–2.66/Moderate/Severe Insecurity—OR: 2.7; 95% CI: 1.48–4.94); participation in a governmental program (Moderate/Severe Insecurity—OR: 2.02; 95% CI: 1.04–3.92) and CR region (Mild insecurity—OR: 0.5; 95% CI: 0.31–0.80) maintained an association with HFSS, increasing the chances of a CR worker belonging to one of the risk categories for food insecurity when in unfavorable socioeconomic situations, and compared to those with better socioeconomic conditions.
A stepwise backward method was applied to the multinomial model to adjust the final HFSS model. Calculating the odds ratios and standard errors and the model’s goodness adjustment indexes guided the model’s choice that best-suited data. Reduced models with smaller Akaike information criterion (AIC) than the previous model indicated the removal of the tested variable, as well as the p-value (>0.05) of the LRT (Table 4). The variables per capita household income, number of rooms, and number of people working (at the same home), in addition to the CR Brazilian region, remained in the model (Table 5).
Despite being maintained in the final model, considering the LRT results, the number of rooms and the number of people working did not maintain the association with food insecurity in the adjusted model. When tested, they were not confirmed (range of estimates < 10%). The multivariate model results confirmed the statistical significance of per capita household income as a predictor of HFSS. Food handlers with per capita household income equal to or less than half the minimum wage were 1.77 times more likely to be in a situation of mild food insecurity (OR: 1.77; CI95: 1.12–2.80) and around three times more likely to be in a situation of moderate/severe food insecurity (OR: 3.52; CI95: 2.00–6.19) when compared to individuals belonging to the food security category. In the adjusted model, the association with the CR Brazilian region was maintained in mild food insecurity. Therefore, the food handlers working in CR located in the Midwest/Southeast/South regions were 47% less likely to experience mild food insecurity (OR: 0.53; CI95: 0.34–0.83) compared to individuals in food security (Table 5).

4. Discussion

Food insecurity is a public health problem, affecting individuals in all parts of the world. The latest survey conducted in 2018 by the Food and Agriculture Organization of the United Nations (FAO) showed that about two billion people suffered from moderate or severe food insecurity, representing 26.4% of the world population [1]. In Latin America, including Brazil, 188 million people had the perception of moderate to severe food insecurity, representing 30.9% of the population [1]. In Brazil, the last populational study that assessed food insecurity in households was the National Household Sample Survey (PNAD/NHSS), carried out in 2013 and showed a prevalence of 22.6% of food insecurity in the population [36].
In Brazil, the foodservice market employs about 250 thousand food handlers [47], who work in several foodservice segments, such as this study’s locus (the CR). The findings can be extended to others in the foodservice segment, such as the food handlers working in hospitals, industrial or commercial food services. The activities of workers in the Brazilian foodservice sector revolve around a common goal, the production of meals for groups, whether healthy or sick [47], and, in the case of CR handlers, they provide meals for groups in social vulnerability [13]. The results of this study can be extended to the population of food handlers, given the peculiarities of this sector that include: (a) the composition of teams with professionals from different levels of educational background and with different and complementary activities [31]; (b) by workers from the different foodservice segments with a high prevalence of excess weight [30,32,48,49,50]; (c) by workers without professional training to carry out the activities, the in-service learning process takes place, which also leads to high turnover in the sector [31]; (d) inadequate work conditions, characterized by the requirement of long hours, rhythm and intense efforts with work overload, tasks such as repetitive movements, leading to absenteeism and high turnover of workers and the prevalence of work-related diseases [31]. This scenario reinforces the importance of the study and its scope.
Our results showed a total prevalence of 44.4% of food insecurity, much higher than the general population [51]. Moderate/severe food insecurity was almost twice the prevalence of the Brazilian population. In this study, socio-demographic characteristics showed that most food handlers were females in the age group ≤40 years old, similar to previous studies carried out with food handlers [15,16,18,22].
Two studies on food handlers from CR, the first in the city of Rio de Janeiro (n = 273 individuals from 7 CR) [21] and the second in the city of Belo Horizonte (n = 180 individuals from 4 CR) [22] showed a prevalence of total food insecurity of 53.7% and 24%, respectively. This prevalence, as in our study, is higher than food insecurity for the Brazilian population. It is worth mentioning that in these studies [21,22], most food handlers perceived themselves in mild food insecurity, according to the EBIA/BFIS classification [36]. There is a concern with access to food in the future, and it may impact the quality of the diet, choosing to buy cheaper foods [52].
In our study, an association was found between income and food insecurity, maintained in the regression model. Of the study participants, 39.9% had a per capita income at home equal to or less than half the minimum wage, classified as low-income families, according to the Brazilian Government [53]. Moreover, income showed a more significant impact on households with moderate to severe insecurity. Low-income families were about three times more likely to have food insecurity than workers with income above half the minimum wage. Other variables related to household income were associated with food insecurity as the number of rooms in the house and the number of individuals working, also maintained in the final regression model. Another variable related to household income that showed an association with food insecurity was the participation in a governmental program. Almost 40% of participants presented the requirements to receive governmental benefits [53], but only 19.3% of the participants received any benefit. Godoy et al. [20] also showed differences in food insecurity prevalence and income and education for CR customers. Among males, food insecurity prevalence was 64.2% when per capita income was below ¼ minimum wage and 54.8% from ¼ to half of the minimum wage. Females showed similar results, with 61.5% in food insecurity when per capita income was lower than ¼ minimum wage, and 54.8% when income was between ¼ to half of the minimum wage. CR customers with less than eight years of education also presented higher food insecurity for males and females (51% and 56.3%, respectively) [20].
In a previous study with the Brazilian population, 54.8% of households with moderate to severe food insecurity received per capita income up to half a minimum wage [51]. In the study carried out with food handlers in the CR in Rio de Janeiro, 59.4% of workers reported having another job to increase their income [21]. Per capita household income showed an inverse association with food insecurity in a study developed with food handlers in CR in Belo Horizonte/Brazil [22].
In a study that assessed food insecurity among workers in Canada, multivariate regression revealed that increased income independently decreases the chances of food insecurity in Canadian families, as well as having more workers in the family or at home. Income was considered a significant factor for the chances of food insecurity at home [24]. Some studies show an association between FI lower per capita income at home [53,54,55].
Schooling was associated with food insecurity at home in the bivariate analysis (p = 0.02), not remaining in the final regression model. In our study, 54.1% of the participants had an education level equal to or less than eight years, in agreement with other studies conducted with this professional category, identifying the highest percentage of individuals with educational level up to eight years of formal study (equivalent to elementary school) [19,21,34]. In general, education is associated with food insecurity among the general Brazilian population [51], in which a higher level of education relates to the lower prevalence of moderate or severe insecurity. Since food handlers in Brazil receive low salaries and present fewer years of formal education, they are more related to food insecurity even though working with food production.
In the study by Falcão et al. [21], an association was found between education and food insecurity in food handlers’ homes. In the studied sample, the risk of those who had up to nine years of formal study was almost three times more likely to find themselves in a food insecurity situation than those with higher education.
Another variable that showed association with food insecurity was the region of the CR. Food handlers from the Midwest-South regions perceived themselves in household food security. The National Household Sample Survey (PNAD/NHSS) [47] identified a higher prevalence of food insecurity in the North and Northeast regions (30%) than the Midwest/Southeast/South regions (less than 20%). Bezerra et al. [54] also demonstrated higher FI in the North (40.3%) and the Northeast (46.1%) with positive and moderate correlation between FI and the percentage of the extremely poor population. Gubert et al. [55] demonstrated a reduction in FI prevalence in all Brazilian states from 2004 and 2013, higher in the Northeast region. Santos et al. [56] evaluated the tendency and associated factors of food insecurity in Brazil, analyzing the NHSS from 2004, 2009, and 2013. These researchers verified that even though there was a reduction in food insecurity levels with time, there was an increase in the association force between food insecurity and regions North and Northeast of Brazil. Residents from these regions had three to four more chances of having moderate to severe food insecurity. With CR customers, only for females, there was an association between region and food insecurity, with higher prevalence in the north and northeast regions [20].
This study did not show an association between food security and excess weight, contrary to other studies [57,58,59,60] that showed an association between food insecurity and excess weight. Shamah-Levy et al. [60] associated food insecurity with undernutrition and obesity, discussing that both situations come from poor eating habits. In children, Kac et al. [58] discussed a less consistent association with overweight, but Franklin et al. [57] studying women presented a higher prevalence of excess weight with food insecurity. Godoy et al. [61] did not find a significant association between food insecurity and BMI or body fat percentage for CR’s male or female customers. Even though obesity is included in the NCDs, excess weight was not included as NCD for this study’s associations. There was an association between food insecurity and NCDs; however, it was not maintained in the model.
One of this study’s contributions is the confirmation of the dichotomy between FI and the performance in the food field. In the case of this study, places designed to promote access to food (CR) to prevent the occurrence of food insecurity also constitute a workspace for people in FI. Besides, the geographic location of the CRs act as protectors, confirming that regional inequalities in Brazil affect the same population group differently and confirming their claim that income is associated with FI. It is important to emphasize that the mean income from the North and Northeast regions is almost half of the income from workers from the South and Southeast regions in Brazil. This inequality is also observed in this food handler group and needs to be discussed inside a governmental program.

5. Conclusions

In Brazil, few studies assess workers’ food insecurity. Food handlers play an essential role in promoting food security as responsible for producing and supplying meals in hygienic and nutritional conditions suitable for the population using this state equipment. These workers have characteristics, confirmed in the study, as low education and low income, which places them both as actors and target population of public policies aimed at food insecurity. This study is the first to assess food handlers nationwide, and a high prevalence of food insecurity was observed, higher than that of the general population. For moderate/severe food insecurity, it was almost twice the Brazilian population. Based on the study design, it is not possible to establish a causal link between the variables, but the study shows paths for further studies. The variables that showed an association for this population were the household income per capita for the different levels of food insecurity and the CR region for mild food insecurity. The high prevalence of food insecurity in this population and the return of the increase in the prevalence of food insecurity in the Brazilian and worldwide population point to the need for further studies on the theme with categories of workers, mainly low-income, which allow identifying factors related to the work environment and developing policies and interventions aimed at preventing food insecurity and its consequences for health.

Author Contributions

Conceptualization, I.C.F.; R.d.C.C.d.A.A.; R.d.R.F.B.; J.C.-S.; methodology, I.C.F.; R.d.C.C.d.A.A.; R.d.R.F.B.; J.C.-S.; R.P.Z.; R.B.A.B.; validation, I.C.F.; R.d.C.C.d.A.A.; R.d.R.F.B.; J.C.-S.; formal analysis, I.C.F.; R.d.C.C.d.A.A.; R.d.R.F.B.; J.C.-S.; R.P.Z.; R.B.A.B.; investigation, I.C.F.; R.d.C.C.d.A.A.; R.P.Z.; R.B.A.B.; data curation, I.C.F.; R.d.C.C.d.A.A.; writing—original draft preparation, I.C.F.; R.d.C.C.d.A.A.; R.P.Z.; R.B.A.B.; writing—review and editing, I.C.F.; R.d.C.C.d.A.A.; R.P.Z.; A.R.; R.B.A.B.; visualization, A.R.; supervision, R.d.C.C.d.A.A.; project administration, R.d.C.C.d.A.A.; R.B.A.B.; funding acquisition, A.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of University of Brasília (protocol code #037210).

Informed Consent Statement

Written informed consent was obtained from all participants.

Data Availability Statement

Data sharing is not applicable to this article.

Acknowledgments

The authors acknowledge the support of “Ministério do Desenvolvimento Social”.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. FAO; IFAD; UNICEF; WFP; WHO. El Estado de la Seguridad Alimentaria y la Nutrición en el Mundo 2019; FAO: Roma, Italy, 2019; Available online: http://www.fao.org/publications/es (accessed on 25 January 2020).
  2. Araújo, F.R.; Santos, D.F.; Araújo, M.A.D. O direito humano à alimentação adequada promovido por políticas de acesso a alimentos: O caso da unidade Natal-RN do Projeto Café do Trabalhador. Rev. Políticas Públicas 2011, 15, 267–276. Available online: http://www.periodicoseletronicos.ufma.br/index.php/rppublica/article/view/842 (accessed on 15 April 2019).
  3. General Assembly of the United Nations. Universal Declaration of Human Rights; United Nations Organization: New York, NY, USA, 1948; Available online: http://www.un.org/en/universal-declaration-human-rights/ (accessed on 9 July 2018).
  4. Brown, E.M.; Tarasuk, V. Money speaks: Reductions in severe food insecurity follow the Canada Child Benefit. Prev. Med. 2019, 129, 105876. [Google Scholar] [CrossRef] [PubMed]
  5. IBGE. Segurança Alimentar; Instituto Brasileiro de Geografia e Estatística: Rio de Janeiro, Brazil, 2013. Available online: https://biblioteca.ibge.gov.br/visualizacao/livros/liv91984.pdf (accessed on 25 June 2020).
  6. Godoy, K.C.; Sávio, K.E.O.; Akutsu, R.d.C.; Gubert, M.B.; Botelho, R.B.A. Perfil e situação de insegurança alimentar dos usuários dos Restaurantes Populares no Brasil. Cad. Saude. Publica 2014, 30, 1239–1249. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. Bezerra, I.N.; Sichieri, R. Características e gastos com alimentação fora do domicílio no Brasil. Rev. Saude. Publica 2010, 44, 221–229. [Google Scholar] [CrossRef]
  8. Lachat, C.; Nago, E.; Verstraeten, R.; Roberfroid, D.; Van Camp, J.; Kolsteren, P. Eating out of home and its association with dietary intake: A systematic review of the evidence. Obes. Rev. 2012, 13, 329–346. [Google Scholar] [CrossRef]
  9. Buss, P.M.; Pellegrini Filho, A. A saúde e seus determinantes sociais. Physis Rev. Saúde Coletiva 2007, 17, 77–93. [Google Scholar] [CrossRef]
  10. Food and Agriculture Organization of the United Nations. The State of Food Security and Nutrition in the World 2019. Safeguarding against Economic Slowdowns and Downturns; FAO: Rome, Italy, 2019. [Google Scholar]
  11. Laraia, B.A. Food Insecurity and Chronic Disease. Adv. Nutr. 2013, 4, 203–212. [Google Scholar] [CrossRef] [Green Version]
  12. Carrijo, A.P.; Botelho, R.B.A.; Akutsu, R.C.C.A.; Zandonadi, R.P. What Low-Income Brazilians Are Eating in Popular Restaurants Contributing to Promote Their Health? Nutrients 2018, 10, 414. [Google Scholar] [CrossRef] [Green Version]
  13. Brasil. Manual-Programa Restaurante Popular; Ministério do Desenvolvimento Social e Combate à Fome: Brasília, Brazil, 2004. Available online: http://bvsms.saude.gov.br/bvs/publicacoes/projeto_logico_restaurante_popular.pdf (accessed on 22 October 2018).
  14. Brazil. Mais de 130 Restaurantes Populares Garantem Alimentação Saudável Para População Vulnerável. 2020; pp. 1–5. Available online: https://www.gov.br/cidadania/pt-br/noticias-e-conteudos/desenvolvimento-social/noticias-desenvolvimento-social/mais-de-130-restaurantes-populares-garantem-alimentacao-saudavel-para-populacao-vulneravel (accessed on 27 December 2020).
  15. Fideles, I.C.; Akutsu, R.d.C.C.d.A.; Costa, P.R.F.; Costa-Souza, J.; Botelho, R.B.A.; Zandonadi, R.P. Brazilian community restaurants’ low-income food handlers: Association between the nutritional status and the presence of non-communicable chronic diseases. Sustainability 2020, 12, 3467. [Google Scholar] [CrossRef] [Green Version]
  16. Boclin, K.d.L.S.; Blank, N. Prevalência de sobrepeso e obesidade em trabalhadores de cozinhas dos hospitais públicos estaduais da Grande Florianópolis, Santa Catarina. Rev. Bras. Saúde. Ocup. 2010, 35, 124–130. [Google Scholar] [CrossRef]
  17. Scarparo, A.L.S.; Amaro, F.S.; Oliveira, A.B. Caracterização e Avaliação Antropométrica dos Trabalhadores dos Restaurantes Universitários da Universidade Federal do Rio Grande do Sul|Scarparo|Clinical & Biomedical Research. 2010, p. 30. Available online: https://seer.ufrgs.br/hcpa/article/view/15382 (accessed on 10 December 2019).
  18. Simon, M.I.S.d.S.; Garcia, C.A.; Lino, N.D.; Forte, G.C.; Fontoura, I.d.D.; Oliveira, A.B.A.d. Avaliação nutricional dos profissionais do serviço de nutrição e dietética de um hospital terciário de Porto Alegre. Cad. Saúde Coletiva 2014, 22, 69–74. [Google Scholar] [CrossRef]
  19. Cavalli, S.B.; Salay, E. Gestão de pessoas em unidades produtoras de refeições comerciais e a segurança alimentar. Rev. Nutr. 2007, 20, 657–667. [Google Scholar] [CrossRef]
  20. Godoy, K.; Sávio, K.E.d.O.; Akutsu, R.d.C.; Gubert, M.B.; Botelho, R.B.A. Insegurança alimentar e estado nutricional entre indivíduos em situação de vulnerabilidade social no Brasil. Cienc. Saude Coletiva 2017, 22, 607–616. [Google Scholar] [CrossRef] [Green Version]
  21. Falcão, A.C.M.L.; de Aguiar, O.B.; da Fonseca, M.d.J.M. Association of socioeconomic, labor and health variables related to food insecurity in workers of the popular restaurants in the city of Rio de Janeiro. Rev. Nutr. 2015, 28, 77–87. [Google Scholar] [CrossRef]
  22. Costa, B.V.d.L.; Horta, P.M.; Ramos, S.A. Food insecurity and overweight among government-backed economy restaurant workers. Rev. Nutr. 2019, 32. [Google Scholar] [CrossRef]
  23. Sobrinho, F.M.; Silva, Y.C.; Abreu, M.N.S.; Pereira, S.C.L.; Júnior, C.S.D. Fatores determinantes da insegurança alimentar e nutricional: Estudo realizado em Restaurantes Populares de Belo Horizonte, Minas Gerais, Brasil. Cienc. Saude Coletiva 2014, 19, 1601–1611. [Google Scholar] [CrossRef] [Green Version]
  24. McIntyre, L.; Bartoo, A.C.; Emery, J.C.H. When working is not enough: Food insecurity in the Canadian labour force. Public Health Nutr. 2014, 17, 49–57. [Google Scholar] [CrossRef] [Green Version]
  25. Sharkey, J.R.; Johnson, C.M.; Dean, W.R. Less-healthy eating behaviors have a greater association with a high level of sugar-sweetened beverage consumption among rural adults than among urban adults. Food Nutr. Res. 2011, 55. [Google Scholar] [CrossRef] [Green Version]
  26. Oyefara, J.L. Food insecurity, HIV/AIDS pandemic and sexual behaviour of female commercial sex workers in Lagos metropolis, Nigeria. SAHARA J. 2007, 4, 626–635. [Google Scholar] [CrossRef]
  27. Hill, B.G.; Moloney, A.G.; Mize, T.; Himelick, T.; Guest, J.L. Prevalence and predictors of food insecurity in migrant farmworkers in Georgia. Am. J. Public Health 2011, 101, 831–833. [Google Scholar] [CrossRef]
  28. Colares, L.G.T.; De Freitas, C.M. Work process and workers’ health in a food and nutrition unit: Prescribed versus actual work. Cad. Saude Publica 2007, 23, 3011–3020. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  29. Matos, C.H.d.; Proença, R.P.d.C. Condições de trabalho e estado nutricional de operadores do setor de alimentação coletiva: Um estudo de caso. Rev. Nutr. 2003, 16, 493–502. [Google Scholar] [CrossRef] [Green Version]
  30. Isosaki, M.; Cardoso, E.; Glina, D.M.R.; Pustiglione, M.; Rocha, L.E. Intervention in a hospital foodservice and its effects on musculoskeletal symptoms. Rev. Nutr. 2011, 24, 449–462. [Google Scholar] [CrossRef] [Green Version]
  31. Dos Santos, J.; Ferreira, A.A.; Meira, K.C.; Pierin, A.M.G. Excess weight in employees of food and nutrition units at a university in São Paulo State. Einstein 2013, 11, 486–491. [Google Scholar] [CrossRef] [Green Version]
  32. De Aguiar, O.B.; Valente, J.G.; da Fonseca, M.d.J.M. Descrição sócio-demogŕfica, laboral e de saúde dos trabalhadores do setor de serviços de alimentaçõ dos restaurantes populares do estado do Rio de Janeiro. Rev. Nutr. 2010, 23, 969–982. [Google Scholar] [CrossRef] [Green Version]
  33. Boclin, K.d.L.S.; Blank, N. Excesso de peso: Característica dos trabalhadores de cozinhas coletivas? Rev. Bras. Saúde. Ocup. 2006, 31, 41–47. [Google Scholar] [CrossRef]
  34. Antunes, M.T.; Dal Bosco, S.M. Gestão em Unidades de Alimentação e Nutrição da Teoria à Prática. Editora Appris. 2020. Available online: https://books.google.com.br/books?id=gbfRDwAAQBAJ&pg=PT195&lpg=PT195&dq=uan+500+refeições&source=bl&ots=b59PT4BwP3&sig=ACfU3U3Lo0T7ytQ1uAKAwZbIDrzvFx3Sfg&hl=pt-BR&sa=X&ved=2ahUKEwjCx8bE78boAhUMGLkGHTzlDuIQ6AEwBXoECAwQLA#v=onepage&q=uan500refeições&f=fal (accessed on 1 April 2020).
  35. Cochran, W.G. Sampling Techniques; John Wiley & Sons: Hoboken, NZ, USA, 2007. [Google Scholar]
  36. Brasil; de Insegurança, E.B. Alimentar-EBIA: Análise Psicométrica de Uma Dimensão da Segurança Alimentar e Nutricional Ministério do Desenvolvimento Sociale Combate à Fome; Secretaria de Avaliação e Gestão da Informação. 2014. Available online: www.mds.gov.br/sagi (accessed on 25 June 2020).
  37. Vigitel: O Que é, Como Funciona, Quando Utilizar e Resultados. 2019. Available online: http://portalms.saude.gov.br/saude-de-a-z/vigitel (accessed on 18 March 2019).
  38. Malta, D.C.; Bernal, R.T.I.; Andrade, S.S.C.d.A.; da Silva, M.M.A.; Velasquez-Melendez, G. Prevalence of and factors associated with self-reported high blood pressure in Brazilian adults. Rev. Saude Publica 2017, 51. [Google Scholar] [CrossRef]
  39. WHO. Mean Body Mass Index (BMI); WHO: Geneva, Switzerland, 2017. [Google Scholar]
  40. Segall-Corrêa, A.M.; Marin-Leon, L. A segurança alimentar no Brasil: Proposição e usos da escala brasileira de medida da insegurança alimentar (EBIA) de 2003 a 2009. Segurança Aliment Nutr. 2015, 16, 1–19. [Google Scholar] [CrossRef]
  41. Pérez-Escamilla, R.; Segall-Corrêa, A.M. Food Insecurity Measurement and Indicators. Rev. Nutr. 2008, 21, 15s–26s. Available online: https://www.scielo.br/scielo.php?pid=S1415-52732008000700003&script=sci_arttext&tlng=en (accessed on 25 June 2020). [CrossRef]
  42. Panigassi, G.; Segall-Corrêa, A.M.; Marin-León, L.; Pérez-Escamilla, R.; Sampaio, M.D.F.A.; Maranha, L.K. Insegurança alimentar como indicador de iniqüidade: Análise de inquérito populacional. Cad. Saude Publica 2008, 24, 2376–2384. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Kleinbaum, D.G.; Klein, M. Introduction to Logistic Regression; Springer: Berlin, Heidelberg, Germany, 2010; Chapter 12. [Google Scholar] [CrossRef]
  44. IBGE. Pesquisa de Orçamentos Familiares 2017–2018: Análise do Consumo Alimentar no Brasil; Instituto Brasileiro de Geografia e Estatística: Rio de Janeiro, Brazil, 2020. [Google Scholar]
  45. FAO. Food Security and Nutrition in the World; FAO: Rome, Italy, 2020. [Google Scholar]
  46. Hosmer, D.; Lemeshow, S. Applied Logistic Regression, 2nd Ed. ed; John Wiley: New York, NY, USA, 2000; Available online: http://resource.heartonline.cn/20150528/1_3kOQSTg.pdf (accessed on 26 September 2019).
  47. IBGE. Pesquisa Nacional por Amostra de Domicílio Contínua (PNAD Contínua); IBGE: Rio de Janeiro, Brazil, 2020. [Google Scholar]
  48. ABERC. Mercado Real. Assoc. Bras. das Empres. Refeições Coletivas. 2020. Available online: https://www.aberc.com.br/mercadoreal.asp?IDMenu=21 (accessed on 27 December 2020).
  49. Brazil. Art. 4 do Decreto 6135/07. Casa civil. 2007. Available online: https://www.jusbrasil.com.br/topicos/10827947/artigo-4-do-decreto-n-6135-de-26-de-junho-de-2007 (accessed on 25 June 2020).
  50. Lopes, T.S.; Sichieri, R.; Salles-Costa, R.; Veiga, G.V.; Pereira, R.A. Family food insecurity and nutritional risk in adolescents from a low-income area of RIO de Janeiro, Brazil. J. Biosoc. Sci. 2013, 45, 661–674. [Google Scholar] [CrossRef] [PubMed]
  51. Souza, N.N.D.; Dias, M.D.M.; Sperandio, N.; Franceschini, S.D.C.C.; Priore, S.E. Perfil socioeconômico e insegurança alimentar e nutricional de famílias beneficiárias do Programa Bolsa Família no município de Viçosa, Estado de Minas Gerais, Brasil, em 2011: Um estudo epidemiológico transversal. Epidemiol. Serviços. Saúde 2012, 21, 655–662. [Google Scholar] [CrossRef]
  52. Lawrence, M.A.; Friel, S.; Wingrove, K.; James, S.W.; Candy, S. Formulating policy activities to promote healthy and sustainable diets. Public Health Nutr. 2015, 18, 2333–2340. [Google Scholar] [CrossRef] [Green Version]
  53. IBGE-Instituto Brasileiro de Geografia e estatística. Características Gerais dos Domicílios e dos Moradores: 2018; IBGE: Rio de Janeiro, Brazil, 2019. [Google Scholar]
  54. Bezerra, M.S.; Jacob, M.C.M.; Ferreira, M.A.F.; Vale, D.; Mirabal, I.R.B.; Lyra, C.D.O. Food and nutritional insecurity in Brazil and its correlation with vulnerability markers. Cienc. Saude. Coletiva 2020, 25, 3833–3846. [Google Scholar] [CrossRef]
  55. Gubert, M.B.; dos Santos, S.M.C.; Santos, L.M.P.; Perez-Escamilla, R. A Municipal-level analysis of secular trends in severe food insecurity in Brazil between 2004 and 2013. Glob. Food Sec. 2017, 14, 61–67. [Google Scholar] [CrossRef]
  56. Santos, T.G.; Cardoso Da Silveira, J.A.; Longo-Silva, G.; Ramires, E.K.N.M.; Menezes, R.C.E.D. Tendência e fatores associados à insegurança alimentar no Brasil: Pesquisa Nacional por. Cad. Saúde Pública 2018, 34, 66917. [Google Scholar] [CrossRef] [Green Version]
  57. Franklin, B.; Jones, A.; Love, D.; Puckett, S.; Macklin, J.; White-Means, S. Exploring mediators of food insecurity and obesity: A review of recent literature. J. Community Health 2012, 37, 253–264. [Google Scholar] [CrossRef] [Green Version]
  58. Kac, G.; Schlüssel, M.M.; Pérez-Escamilla, R.; Velásquez-Melendez, G.; da Silva, A.A.M. Household Food Insecurity Is Not Associated with BMI for Age or Weight for Height among Brazilian Children Aged 0–60 Months. PLoS ONE 2012, 7, e45747. [Google Scholar] [CrossRef]
  59. Kac, G.; Velasquez-Melendez, G.; Schlüssel, M.M.; Segall-Côrrea, A.M.; Silva, A.A.; Pérez-Escamilla, R. Severe food insecurity is associated with obesity among Brazilian adolescent females. Public Health Nutr. 2012, 15, 1854–1860. [Google Scholar] [CrossRef] [Green Version]
  60. Shamah-Levy, T.; Mundo-Rosas, V.; Rivera-Dommarco, J.A. La magnitud de la inseguridad alimentaria en México: Su relación con el estado de nutrición y con factores socioeconómicos. Salud. Pública Méx 2014, s79–s85. Available online: http://www.scielosp.org/scielo.php?script=sci_arttext&pid=S0036-36342014000700012 (accessed on 2 July 2020). [CrossRef]
  61. Godoy, K.C.; Sávio, K.E.; Gubert, M.B.; Botelho, R.B. Socio-demographic and food insecurity characteristics of soup-kitchen users in Brazil. Cad. Saude Publica 2014, 30, 1239–1249. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Table
Table 1. Descriptive analysis of the study population, considering the distribution in Household Food Security Situation of demographic, socioeconomic characteristics, health status and lifestyle, and work-related aspects.
Table 1. Descriptive analysis of the study population, considering the distribution in Household Food Security Situation of demographic, socioeconomic characteristics, health status and lifestyle, and work-related aspects.
Variables% (n)Food Security % (n)Food Insecurityp-Value *
Mild % (n)Moderate/Severe % (n)
Gender (n = 471)0.44
Female62.2 (293)53.9 (158)29.01 (85)17.06 (50)
Male37.8 (178)58.4 (104)28.65 (51)12.92 (23)
Age group (n = 471)0.56
≤40 years old67.7 (319)54.23 (173)30.41 (97)15.36 (49)
>40 years old32.3 (152)58.55 (89)25.66 (39)15.79 (24)
Education level (n = 471)0.02
≤08 years of study54.1 (255)51.37 (131)29.02 (74)19.61 (50)
>08 years of study45.9 (216)60.65 (131)28.70 (62)10.65 (23)
Marital status (n = 471)0.43
With partner52.0 (245)55.1 (135)31.02 (76)13.88 (34)
Without a partner48.0 (226)56.2 (127)26.55 (60)17.26 (39)
Number of workers in the family (n = 471)0.15
1 or 2 work78.8 (364)53.8 (196)28.85 (105)17.31 (63)
Above 2 workers 21.2 (98)63.3(62)26.53 (26)10.2 (10)
Income (per capita minimum wage) 1 (n = 451)0.00
>1/2 MW 60.1 (271)64.6 (175)25.8 (70)9.6 (26)
≤1/2 MW39.9 (180)43.3 (78)32.22 (58)24.44 (44)
Governmental program participation (n = 471)0.00
No80.7 (380)59.2 (225)28.16 (107)12.63 (48)
Yes19.3 (91)40.7 (37)31.87 (29)27.47 (25)
Number of rooms at home (n = 463)0.07
<6 rooms35.6 (165)61.8 (102)26.67 (44)11.52 (19)
≥6 rooms64.4 (298)52.0 (155)29.87 (89)18.12 (54)
Health status and lifestyle
NCD (n = 471)0.19
Yes19.1 (90)62.2 (56)20.0 (18)17.78 (16)
No80.9 (381)54.1 (206)30.97 (118)14.96 (57)
Weight excess (n = 469)0.52
Yes 60.8 (285)53.7 (153)29.82 (85)16.49 (47)
No39.2 (184)58.7 (108)27.72 (51)13.59 (25)
The habit of drinking alcohol (n = 470)0.99
No54.0 (254)55.5 (141)29.13 (74)15.35 (39)
Yes46.0 (216)55.6 (120)28.70 (62)15.74 (34)
Smoking habit (n = 470)0.25
No82.8 (389)55.0 (214)30.33 (118)14.65 (57)
Yes17.2 (81)58.0 (47)22.22 (18)19.75 (16)
Work-related aspects
Time working at the CR (n = 471)0.58
<12 months42.7 (201)58.2 (117)27.86 (56)13.93 (28)
≥12 months57.3 (270)53.7 (145)29.63 (80)16.67 (45)
Management model (n = 471)0.79
Direct management38.0 (179)53.6 (96)30.17 (54)16.2 (29)
Outsourced62.0 (292)56.8 (166)28.1 (82)15.07 (44)
Brazilian region of CR (n = 471)0.03
North/Northeast45.7 (215)49.8 (107)34.42 (74)15.81 (34)
Midwest/South/Southeast54.4 (256)60.6 (155)24.22 (62)15.23 (39)
* Pearson’s chi-square p-value. 1 Minimum wage (MW): 175.90 USD per month.
Table
Table 2. Bivariate multinomial logit model with odds ratio (OR) estimation between household food security situation (reference group: food security) and demographic, socioeconomic, health, lifestyle predictors, and those related to work in a Community Restaurant in all the Brazilian regions.
Table 2. Bivariate multinomial logit model with odds ratio (OR) estimation between household food security situation (reference group: food security) and demographic, socioeconomic, health, lifestyle predictors, and those related to work in a Community Restaurant in all the Brazilian regions.
Predictor VariablesMild InsecurityModerate/Severe Insecurity
ORSEp-ValueIC95%ORSEp-ValueIC95%
Gender (n = 471)
Female1.10.240.670.72–1.681.430.40.200.82–2.49
Marital status (n = 471)
Without partnet 0.840.180.410.55–1.271.220.320.450.73–2.05
Age group (n = 471)
>40 years old 0.780.180.280.50–1.230.950.270.860.55–1.65
Education level (n = 471)
Elementary Education (complete/incomplete)1.190.250.400.79–1.812.170.610.001.25–3.77
Income (per capita minimum wage) (n = 451)
≤1/2 MW 1.860.420.001.20–2.883.81.070.002.18–6.60
Number of rooms
<6 rooms1.330.300.200.86–2.071.870.550.031.05–3.34
Number of workers in the family
1 or 2 work1.280.340.350.76–2.141.990.740.060.96–4.12
Governmental program participation (n = 471)
Yes1.650.450.070.96–2.823.170.960.001.75–5.74
Health status and lifestyle
Weight excess (n = 469)
Yes1.180.260.450.77–1.801.330.370.310.77–2.29
NCD (n = 471)
No1.780.520.051.00–3.170.970.310.920.52–1.82
Smoking habit (n = 470)
Yes0.690.210.220.39–1.251.280.420.450.68–2.42
The habit of drinking alcohol(n = 470)
Yes0.980.210.940.65–1.491.020.270.930.61–1.72
Work aspects
Time working at CR (n = 471)
≥12 months1.150.250.510.76–1.751.30.350.340.76–2.21
Management model (n = 471)
Outsourced 0.880.190.550.57–1.340.880.240.630.52–1.49
Brazilian region (n = 471)
Midwest/South/Southeast0.580.120.010.38–0.880.790.210.3810.47–1.33
OR: Odds ratio; SE: standard error. * Chi-Square test statistic p-value. 1 Minimum wage (MW): 175.90 USD per month.
Table
Table 3. Multivariate multinomial logit model for predictors of Household Food Security Situation (reference group: food security) in food handlers of community restaurants.
Table 3. Multivariate multinomial logit model for predictors of Household Food Security Situation (reference group: food security) in food handlers of community restaurants.
VariablesMild InsecurityModerate/Severe Insecurity
ORSEp-ValueIC95%ORSEp-ValueIC95%
Incomplete/complete elementary education1.270.30.320.79–2.021.760.540.070.96–3.22
Gender: Female1.270.310.320.79–2.051.230.380.500.67–2.26
Income: ≤1/2 MW 1.640.40.041.01–2.662.70.830.0011.48–4.94
Number of rooms ≥ 60.830.200,450.52–1.340.650.210,180.35–1.22
1 or 2 work in the family 1.020.290.930.59–1.791.220.480.610.56–2.64
Government program participation: Yes1.290.390.410.71–2.342.020.680.041.04–3.92
NCD: No1.620.510.120.88–2.981.070.380.850.53–2.15
Brazilian region: Midwest/South/Southeast0.50.120.0040.31–0.800.680.20.200.38–1.22
OR: Odds ratio; SE: standard error.
Table
Table 4. Comparison of the complete and incomplete model performed using the Likelihood Ratio Test (LRT)—stepwise backward method.
Table 4. Comparison of the complete and incomplete model performed using the Likelihood Ratio Test (LRT)—stepwise backward method.
LRT (Complete and Incomplete Model)Obs.ll(Nulo)ll (Model)glAICBICX2
(LR X2 (2))		p > X2
Complete Model backward 439−427.5−402.818841.70915.2049.310.00
Model without education439−427.49−404.6516841.296906.6493.630.16
Model without education and gender439−427.49−405.1114838.211895.3944.540.34
Model without education, gender, and income458−447.10−431.2712886.54936.06656.880,00
Model without education, gender, and number of workers443−431.19−409.6812843.359892.4813.690.03
Model without education, gender, and government program439−427.49−407.5112839.028888.0429.360.15
Model without education, Gender, government program, and number of rooms443−430.49−412.3310844.652885.58818.980.01
Model without education, Gender, government program, and Region439−427.49−411.2010842.407883.25216.740.03
Model without education, Gender, government program, and NCD439−427.49−408.6410837.273878.11811.610.17
LRT: Likelihood Ratio Test; ll = Log likelihood; gl = degrees of freedom; AIC = Akaike information criterion; BIC = Bayesian information criterion.
Table
Table 5. An adjusted multivariate model of the Household Food Security Situation (HFSS) (reference group: food security) determinants among food handlers in Brazilian community restaurants.
Table 5. An adjusted multivariate model of the Household Food Security Situation (HFSS) (reference group: food security) determinants among food handlers in Brazilian community restaurants.
HFSS Model AdjustedMild InsecurityModerate/Severe Insecurity
ORSEp-ValueIC95%ORSEp-ValueIC95%
Income ≤ 1/2 MW 1.770.410.02 *1.12–2.803.521.020.00 *2.00–6.19
Number of rooms < 60.770.180.270.48–1.230.580.180.080.31–1.07
Number of workers: 1 or 2 1.080.300.790.62–1.871.370.530.420.64–2.92
Brazilian region: Midwest/South/Southeast0.530.120.01 *0.34–0.830.760.210.320.43–1.32
Model adjusted for income, number of rooms, and workers in the household and region of the CR. 95% CI: 95% confidence interval; OR: Odds Ratio; SE: standard error. * significance level p < 0.05.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Fideles, I.C.; Akutsu, R.d.C.C.d.A.; Barroso, R.d.R.F.; Costa-Souza, J.; Zandonadi, R.P.; Raposo, A.; Botelho, R.B.A. Food Insecurity among Low-Income Food Handlers: A Nationwide Study in Brazilian Community Restaurants. Int. J. Environ. Res. Public Health 2021, 18, 1160. https://doi.org/10.3390/ijerph18031160

AMA Style

Fideles IC, Akutsu RdCCdA, Barroso RdRF, Costa-Souza J, Zandonadi RP, Raposo A, Botelho RBA. Food Insecurity among Low-Income Food Handlers: A Nationwide Study in Brazilian Community Restaurants. International Journal of Environmental Research and Public Health. 2021; 18(3):1160. https://doi.org/10.3390/ijerph18031160

Chicago/Turabian Style

Fideles, Ingrid C., Rita de Cassia Coelho de Almeida Akutsu, Rosemary da Rocha Fonseca Barroso, Jamacy Costa-Souza, Renata Puppin Zandonadi, António Raposo, and Raquel Braz Assunção Botelho. 2021. "Food Insecurity among Low-Income Food Handlers: A Nationwide Study in Brazilian Community Restaurants" International Journal of Environmental Research and Public Health 18, no. 3: 1160. https://doi.org/10.3390/ijerph18031160

APA Style

Fideles, I. C., Akutsu, R. d. C. C. d. A., Barroso, R. d. R. F., Costa-Souza, J., Zandonadi, R. P., Raposo, A., & Botelho, R. B. A. (2021). Food Insecurity among Low-Income Food Handlers: A Nationwide Study in Brazilian Community Restaurants. International Journal of Environmental Research and Public Health, 18(3), 1160. https://doi.org/10.3390/ijerph18031160

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop