Community and International Nutrition

Community and International Nutrition An Adapted Version of the U.S. Department of Agriculture Food Insecurity Module Is a Valid Tool for Assessing Household Food Insecurity in Campinas, Brazil1 Rafael Pe´rez-Escamilla,2 Ana Maria Segall-Correâ,* Lucia Kurdian Maranha,* Maria de Fa´tima Archanjo Sampaio,* Leticia Marıń-Leoń,* and Giseli Panigassi* Department of Nutritional Sciences, University of Connecticut, Storrs, CT 06269-4017 and *Department of Social and Preventive Medicine, University of Campinas, Saõ Paulo, Brazil ABSTRACT Until recently, Brazil did not have a national instrument with which to assess household food insecurity (FI). The objectives of this study were as follows: 1) to describe the process of adaptation and validation of the 15-item USDA FI module, and 2) to assess its validity in the city of Campinas. The USDA scale was translated into Portuguese and subsequently tested for content and face validity through content expert and focus groups made up of community members. This was followed by a quantitative validation based on a convenience (n ⫽ 125) and a representative (n ⫽ 847) sample. Key adaptations involved replacing the term “balanced meal” with “healthy and varied diet,” to construct items as questions rather than statements, and to ensure that respondents understood that information would not be used to determine program eligibility. Chronbach’s ␣ was 0.91 and the scale item response curves were parallel across the 4 household income strata. FI severity level was strongly associated in a dose-response manner (P ⬍ 0.001) with income strata and the probability of daily intake of fruits, vegetables, meat/fish, and dairy. These findings were replicated in the 2 independent survey samples. Results indicate that the adapted version of the USDA food insecurity module is valid for the population of Campinas. This validation methodology has now been replicated in urban and/or rural areas of 4 additional states with similar results. Thus, Brazil now has a household food insecurity instrument that can be used to set national goals, to follow progress, and to evaluate its national hunger and poverty eradication programs. J. Nutr. 134: 1923–1928, 2004. KEY WORDS: ● Brazil ● household food insecurity USDA food insecurity module

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Fome Zero Program

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hunger

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According to both the USDA and the WHO, household food security can be defined as access to a diet of enough quantity and quality for all household members at all times and through socially acceptable ways to maximize the chances for a healthy and active life. Food insecurity (FI)3 has been assessed indirectly through energy balance sheets and child anthropometry. However, these 2 approaches have not always been useful for guiding food security polices at the national, regional, or local level (1,2). Thus, researchers recognized the need to also measure this phenomenon through more direct experiential approaches at the household level. During the 1990s, the USDA led the effort to develop a valid scale that was capable of measuring household food insecurity in the United States (3,4). This work built heavily on the Radimer/ Cornell hunger scale (5,6) and the Childhood Hunger Identification Project scale (7). The USDA-led effort eventually resulted in the adoption of a standard FI module in the U.S. Current Population Survey as well as in the National Health

and Nutrition Examination Survey. The availability of this tool fostered an exponential growth in U.S. FI prevalence surveys and research seeking to understand both the FI determinants and its consequences (3,5,8 –11). Because of the simplicity of the USDA scale, several countries expressed an interest in adopting it for assessing household FI in their populations. The case in point is Brazil, a country with a hunger eradication program in place known as “Fome Zero” (“Zero Hunger”). In spite of this national agenda, until recently, Brazil had not adopted a valid and standard FI instrument that would allow the government to set FI national goals, improve the targeting of their food and social assistance programs, and measure progress across time. Thus, the objectives of this study were as follows: 1) to describe the process of adaptation and validation of the USDA FI scale, and 2) to assess the validity of this scale in an urban area in Brazil. SUBJECTS AND PROCEDURES This study was approved by the Human Subjects Review Committee of The University of Campinas and The University of Connecticut. Setting. Campinas has ⬃1 million inhabitants and is located 100 km northwest of the City of Saõ Paulo. The city, originally founded by coffee barons, developed as one of the major industrial corridors in Brazil, housing major transnational companies in the chemical, computer, and biotechnology sectors. Although some of the neighbor-

1

Supported by the Office of the Executive Director of the Brazilian Ministry of Health and the Pan American Health Organization office in Brazil. R.P.-E. was supported by the Saõ Paulo’s State Research Foundation (FAPESP). 2 To whom correspondence should be addressed. E-mail: [email protected]. 3 Abbreviations used: FI, food insecurity; ISA-SP, Health Survey of the State of Saõ Paulo; PSU, primary sampling unit.

0022-3166/04 $8.00 © 2004 American Society for Nutritional Sciences. Manuscript received 23 April 2004. Initial review completed 11 May 2004. Revision accepted 25 May 2004. 1923

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PE´ REZ-ESCAMILLA ET AL.

TABLE 1 Food insecurity questionnaire items. English back-translation from Portuguese1 Item

During the last 3 months

1) 2) 3) 4) 5)2 6)2 7) 8) 9) 10) 11)

were you worried that you would run out of food before being able to buy or receive more food? did you run out of food before having money to buy more? did you run out of money to have a healthy and varied diet? did you have to consume just a few foods because you ran out of money? were you unable to offer your children/adolescents a healthy and varied diet because you didn’t have enough money? did any of the children/adolescents not eat enough because there wasn’t enough money to buy food? did you or any adult in your household ever reduce the size of meals or skip meals because there wasn’t enough money to buy food? did you ever eat less than what you thought you should because there wasn’t enough money to buy food? did you ever feel hungry but didn’t eat because there wasn’t enough money to buy food? did you lose weight because you didn’t have enough money to buy food? did you or any other adult in your household ever go without eating for a whole day or have just 1 meal in a whole day because there wasn’t enough money to buy food? did you ever reduce the size of meals of your children/adolescents because there wasn’t enough money to buy food? did your children/adolescents ever have to skip a meal because there wasn’t enough money to buy food? were your children/adolescents ever hungry but you just couldn’t buy more food? did your children ever go without food for a whole day because there wasn’t enough money to buy food?

12)2 13)2 14)2 15)2

1 For all items, except item #10, an affirmative response was followed by asking “How often did this happen?” Response options were: (a) almost every day, (b) on just a few days, (c) on only 1 or 2 days, (d) doesn’t know or refuses to answer. An affirmative response for item 10 was followed by asking “How much weight did you lose?” Response options were: (a) little, (b) some, (c) a lot, (d) doesn’t know or refuses to answer. 2 Items asked only in households with members ⱕ18 y old.

hoods in this city are very wealthy, others are extremely impoverished shanty towns or “favelas.” Thus, Campinas is an ideal setting for testing the psychometric behavior of the food insecurity scale. Instrument adaptation and qualitative validation. The 15-item USDA FI scale and its 3 subitems were translated into Portuguese by one of the authors (A.M.S.-C.). The translated instrument was then subjected to a question-by-question review by a panel of 13 experts on FI and/or public health nutrition from the University of Campinas, the Health and Welfare Secretary of the district of Campinas, the Ministry of Health, the “Fome Zero” coordinator in Campinas, and the University of Connecticut. The revised instrument that resulted from this meeting was then presented and thoroughly discussed at a focus group meeting with community members who had experienced household food insecurity; the meeting took place in a local church in the City’s southwest health district. The focus groups participants’ selection process also ensured that a range of ages and both genders were represented. This meeting was attended by 3 adult women and 3 adult men ranging from young to elderly adults and was moderated by a research staff member with expertise in anthropological research. The 2 study directors, as well as a local nutritionist, were also present and took notes throughout the session. Participants were not offered any economic incentive for their participation and were clearly told that the information provided would not be used in any decision concerning food assistance or social benefits. The focus group meeting lasted 3 h and was divided into 2 segments. First, participants were asked to provide their definitions of the following terms each of which was prominently displayed, one at a time, in a paper sheet placed in the center of the circle: “varied diet,” “healthy diet,” “healthy and varied diet,” “dietary quality,” “enough food,” “enough money,” “nutritious food,” “hunger,” “food security,” and “food insecurity.” The 2nd segment of the focus group involved a question-byquestion discussion of the modified instrument resulting from the expert panel meeting. Soon after the convenience sample survey described below, a 3rd focus group was conducted with the interviewers participating in the quantitative phase of the scale validity assessment to confirm the understanding of the questionnaire items by respondents and the ease of application of the instrument. Instrument quantitative validation. The adapted instrument resulting from the focus groups (Table 1) was then used for the quantitative validation phase, which included both a convenience and a representative sample from the City of Campinas.4

4 The Portuguese version of Table 1, the food insecurity questionnaire items, is available with the on-line posting of this paper at www.nutrition.org.

Convenience sample. The revised instrument resulting from the focus group was subjected to a quantitative validation by applying it to a convenience sample of 125 households purposely selected from 4 neighborhoods located in the southwest health district of Campinas and representing different socioeconomic strata ranging from extremely poor to middle-class households. Because it was essential to ensure the presence of very poor households, very marginalized communities or “favelas” were included, as well as a subgroup of 25 mothers recruited from child nutrition recovery centers. This survey was conducted between May 14 and 16, 2003. Interviews were conducted by trained and supervised nutrition undergraduate and graduate students. Representative survey. Once the quantitative validity of the survey was established, the FI scale was applied to a representative sample of the city. A sample of the noninstitutionalized civilian population living in urban areas in the municipality of Campinas was selected using stratified cluster sampling in 2 stages. The census tracks correspond to the primary sampling units (PSUs) and the households to the secondary units. The sampling framework used was similar to the one used for the 2000 Health Survey of the State of Sa˜ o Paulo (ISA-SP). The PSUs were 30 census tracks selected with probability proportional to the number of households and drawn from 3 strata (percentage of head of household with college level education: 25 vs. 26 –50 vs. ⬎ 50%) in equal numbers (i.e., 10 tracks per strata). This sampling framework was based on the 1996 census conducted by the Brasilian Institute of Geography and Statistics and an updated listing and mapping of all households in the 30 census tracks that took place in 2000 and was carried out by ISA-SP personnel. Of the 1000 households randomly chosen, 847 responded to the interview, yielding a nonresponse rate of 15.3%. The overwhelming reason explaining the nonresponse rate was the inability to find these households. Interviews were conducted during weekend days to maximize the chances of finding the target respondent (i.e., person in charge of food preparation). When necessary, households were revisited until the target respondent was found. Interviews were conducted by trained and supervised college students in the fields of nutrition, nursing, and food and agricultural engineering. Interviewers were randomly assigned to the different census tracks and were provided with accurate maps and addresses of the randomly chosen households. Household income. For identifying social strata in the convenience sample, the survey included a monthly household income question from which respondents had to choose one of the following monthly income ranges, expressed as multiples of the official minimum wage in Brazil: ⬍1 minimum wage, 1–2 minimum wages, 3– 4 minimum wages, and ⱖ5

FOOD INSECURITY SCALE VALIDATION IN CAMPINAS, BRAZIL

minimum wages. The representative sample survey itemized all income sources available to the family and then classified the total monthly income into the same minimum wage strata. Food intake. Food group intake was measured with a short food group frequency questionnaire, designed specifically for this study; the questionnaire asked the respondent whether s/he consumed every day (at least once per day) the following: cereals, tubers and roots, milk, dairy products, eggs, fruits and natural juices, vegetables, legumes, meat/poultry/fish, candy, soft drinks. Validity criteria. As recommended by Frongillo (12), there were 4 validity criteria established a priori: 1) an expected Chronbach ␣ ⱖ 0.85; 2) parallelism on item response curves across socioeconomic strata; 3) a clear-cut dose-response relationship between socioeconomic strata and level of FI; 4) a clear-cut dose-response between “nutritious foods” (fruits, vegetables, animal protein, dairy) consumption and level of FI. In addition, we added a 5th validity criterion requiring that the replicability of findings in 2 independent samples drawn from the same population be demonstrated. Data analyses. SPSS for Windows (version 11.5) was used to enter the data and conduct all statistical analyses for the convenience sample survey. Analyses and estimates from the representative survey were conducted taking into account the complex survey sampling design using the Complex Samples module from SPSS for Windows (version 12.0). Data were entered and cleaned by trained and supervised graduate students at the Department of Social and Preventive Medicine at UNICAMP. All of the FI scale questions were recoded into 2 categories [(often/sometimes vs. never) or (yes vs. no)]. Each item was given a score of 1 if the answer pointed toward FI, or 0 if it was in the food security direction. Among convenience sample respondents who gave a negative answer to the first 4 items, the likelihood for giving a positive response to any of the subsequent items was negligible. Thus, for the representative sample survey, it was decided not to ask the rest of the items among respondents answering negatively to the first 4 scale items and to assume in the analyses that their responses to these items was also negative. The subquestions related to frequency of occurrence were not included in the analyses. Thus, the theoretical food insecurity score range was from 0 to 15 in households with children/teenagers and from 0 to 9 in households in which only adults lived. An additive total score was created and households were classified into 4 mutually exclusive levels of food (in)security using the following “equidistant” algorithms: 1) food secure (score ⫽ 0); 2) mild FI [score: 1–5 (households with children/teenagers; 1–3 (households with only adults)]; 3) moderate FI (6 –10; 4 – 6); and 4) severe FI (11–15; 7–9). The Chronbach ␣ internal consistency test was run with households with children/ adolescents as a basis because these were the ones with responses available for all 15 items. To test the parallelism of the item response curves, we plotted the percentage of affirmative responses to each of the FI scale items across 4 household income strata. The ␹2-square test was used to examine the association between household income and items’ response (yes/ no), and the associations of FI severity category with food intake and socioeconomic strata. Statistical significance was based on a 2-sided probability value ⱕ 0.05.

RESULTS Samples’ characteristics. The great majority of respondents in the convenience and representative surveys were women who were in their early 40s; ⬃50% of them were Caucasian. Although there was ⬃1 more room per household in the representative than in the convenience sample, the number of bedrooms was similar. Given the sampling approach used with the convenience sample, it is not surprising that households participating in that survey were more likely than households in the representative survey to have member(s) ⱕ 18 y old. Similarly, it is not surprising that households from the convenience sample were poorer (Table 2). USDA survey adaptation. The focus group with community members and survey interviewers clearly showed that these disadvantaged individuals of diverse socioeconomic

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TABLE 2 Sample descriptive characteristics in convenience and representative household food insecurity surveys in Campinas, Brazil1,2 Sample

Respondent’s gender, % F Respondent’s age, % 18–30 y 31–50 y 51–76 y Race, % Caucasian Households with member(s) ⱕ 18 y, % Rooms in household, n Bedrooms, n Monthly household income,3 % ⬍1 MW 1–2 MW 3–4 MW ⱖ5 MW

Convenience (n ⫽ 125)

Representative (n ⫽ 847)

85.6

80.4

28.8 44.0 27.2 44.8

25.2 39.4 35.4 59.9

78.4 4.9 ⫾ 0.16 2.0 ⫾ 0.07

47.0 6.1 ⫾ 0.2 2.3 ⫾ 0.07

12.1 36.2 33.6 18.1

4.0 16.8 24.4 54.7

1 Values are means ⫾ SEM or %. 2 Representative sample results based on weighted estimates. 3 MW, minimum wage.

backgrounds and ages had a conceptual understanding of the phenomena of food insecurity and hunger similar to the one documented in the U.S. Indeed, and as illustrated by the following quotes from different participants, the discussion on the topic of hunger evoked deeply emotional responses, reflecting not only the physical but also the psychoemotional pain associated with hunger: “Hunger is the worst pain . . . the worst form of violence that can exist,” “Hunger is difficult to tolerate, and triggers the instinct of men to commit crimes to survive,” “Hunger is when you get home and you don’t have anything to feed your children and they are crying. And when you have to slap them so that they can sleep and forget why their tummies are making noises.” Furthermore, the USDA food (in)security questionnaire was rated as highly appropriate by both the content experts and the community members participating in the focus groups. However, the following modifications were recommended and implemented (Table 1): 1) questions were asked in relation to the 3 mo, rather than the 12 mo preceding the survey; 2) questions targeting households with member(s) ⱕ 18 y used the term “children/adolescents” instead of just children; 3) the term “healthy and varied” diet replaced the term “balanced meal” used in the original USDA FI module; 4) all questionnaire items were constructed as questions rather than statements; 5) each question receiving an affirmative response was followed by a subquestion assessing to what extent the FI problem was experienced; and 6) at the beginning of the survey, it was made clear that this information was not going to be used to either include or exclude anybody from food and/or social assistance programs. The clarity of the food insecurity module was further confirmed at the focus group with the interviewers who applied the convenience sample survey. Thus, minimal changes had to be implemented to the FI module subsequently used in the representative survey. Internal consistency and parallelism. Chronbach’s ␣ was 0.91 in both the convenience (n ⫽ 119, 15 items) and the

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FIGURE 1 Food insecurity scale item response curves across household income strata. Panel A refers to the convenience sample (n ⫽ 125) and panel B to the representative sample (n ⫽ 847) drawn from the City of Campinas, Brazil. Chronbach’s ␣ was 0.91 in both the convenience (n ⫽ 119, 15 items) and the representative (n ⫽ 456, 15 items) sample. Except for item 15 in the representative sample survey and item 13 in the convenience sample, the trend across socioeconomic groups was significant for all items (P ⬍ 0.05). MW: minimum wage.

FIGURE 2 Food (in)security level as a function of household income. Panels A and C refer to the convenience sample (n ⫽ 125) and panels B and D to the representative sample (n ⫽ 847) drawn from the City of Campinas, Brazil. All trends were significant (P ⬍ 0.001).

representative (n ⫽ 456, 15 items) sample. Except for the last item in the representative sample survey, the scale item response curves in both surveys were parallel across income strata indicating that the likelihood of an affirmative response to all items increased as monthly household income decreased (Fig. 1). Food insecurity severity level and household income. The likelihood for a household to be food secure was significantly (P ⬍ 0.001) and positively associated in a dose-response manner with household monthly income strata, and this was true for both survey samples. None of the households earning ⬍1 minimum wage in the convenience sample survey were food secure. Consistent with this, only 8.1% of households earning this level of income were food secure in the representative survey (Fig. 2). In both survey samples, practically none of the households earning ⬎4 minimum wages were severely food insecure. Similarly, in both surveys, severe household food insecurity was inversely associated (P ⬍ 0.001) in a dose-response manner with monthly household income (Fig. 2). Food insecurity severity level and food intake. The analyses detected foods that were highly sensitive (Fig. 3) and foods that were insensitive or only moderately sensitive (Fig. 4) to the level of food insecurity. Figure 3 shows that in both survey samples, the level of food insecurity was significantly (P ⬍ 0.001) and strongly associated with the likelihood of daily consumption of fruit, non-root/tuber vegetables, and meat. Among convenience sample households that were severely food insecure, the likelihood of daily consumption of fruits, non-root/tuber vegetables, and meat was zero. Among representative sample participants, this likelihood ranged from 13.5 to 31.9%. By contrast, the likelihood of daily consumption of these foods was very high among food-secure households in both surveys, ranging from 70.4 to 91%. Similar patterns of association were found for milk, dairy products, juices, candy, and soft drinks (data not shown). The likelihood of daily egg consumption was insensitive to the level of food insecurity, with a P-value ⬎ 0.7 in both surveys’ samples. In the representative sample, there was a significant (P ⬍ 0.001) relation between severe food insecurity and a lower likelihood of daily bean consumption. In the convenience sample, this relation was not significant (P ⫽ 0.28) (Fig. 4). In both samples, the relative reduction in the

FOOD INSECURITY SCALE VALIDATION IN CAMPINAS, BRAZIL

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FIGURE 3 Likelihood of daily consumption of foods highly sensitive to food insecurity level as a function of household income. Panels A, C and E refer to the convenience sample (n ⫽ 125) and panels B, D, and F to the representative sample (n ⫽ 847) drawn from the City of Campinas, Brazil. All trends were significant (P ⬍ 0.001). FI-I, mild food insecurity; FI-II, moderate food insecurity; FI-III, severe food insecurity.

FIGURE 4 Likelihood of daily consumption of foods not sensitive to food insecurity level as a function of household income. Panels A, C and E refer to the convenience sample (n ⫽ 125) and panels B, D, and F to the representative sample (n ⫽ 847) drawn from the City of Campinas, Brazil. Trend P-values: panel A (P ⫽ 0.871), panel B (P ⫽ 0.752), panel C (P ⫽ 0.280), panel D (P ⬍ 0.001), panel E (P ⫽ 0.015), panel F (P ⫽ 0.015). FI-I: mild food insecurity, FI-II: moderate food insecurity, FI-III: severe food insecurity.


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likelihood of daily bean consumption among severely food insecure households (vs. food secure households) was modest and very similar, ranging from 17 to 18%. Similarly, the likelihood of cereal group consumption was associated with a moderate significant reduction (range: 13⫺20%) in the presence of severe, but not milder levels, of food insecurity (Fig. 4). DISCUSSION Findings from this study indicate that an adapted version of the USDA food insecurity module is a valid tool for assessing food insecurity in Campinas, Brazil. First, the adapted version has content and face validity because it involved heavy input and approval from both local content-experts and target community members. Second, the parallelism in the likelihood of affirmative responses for each scale item across income strata strongly suggests that the interpretation of the different levels of FI severity probed by the different items was similar across the socioeconomic spectrum. Third, the internal consistency of the scale was excellent. Fourth, FI severity level was strongly associated in a dose-response manner with income strata and the likelihood of daily intake of nutritious foods such as fruits and vegetables. These results are highly consistent with Frongillo’s proposed criteria for assessing the validity of food insecurity scales (12). In addition, these psychometric validity findings were replicated in 2 independent samples drawn from the same population. The USDA food insecurity module or primary scales from which it was developed were used to assess food insecurity determinants or consequences in countries as diverse as Indonesia (13) and Venezuela (14), and among different ethnic groups in the U.S. and Canada (12), such as Latinos (9) and Asian-Pacific islanders (15) not specifically included in the original development and validation. In the few instances in which rigorous psychometric testing was conducted (12,14,15), results are fully consistent with our conclusion that the USDA FI module has a strong external validity. Key adaptations deemed necessary for the application of the USDA FI module in Campinas are highly consistent with qualitative results reported by Harrison et al. (16) when they tested a translated version of the instrument with Latinos living in the U.S. In both instances, the term “balanced meal” was found to be difficult to interpret by most community members. Similarly, they also concluded that the grammatical structure had to be simplified. An important qualitative finding in our study was the strong association that focus group participants made between dietary quality and the microbial and overall safety of foods (i.e., foods that don’t make you sick). Thus, it is important that future research efforts identify valid food safety questions that can be incorporated into household FI modules. It is important to recognize that researchers who applied a qualitative methodology similar to the one used for the development of the Radimer/Cornell hunger scale in South Asia, ended up proposing food insecurity scales that differed substantially from the USDA food insecurity module (17). Provided that these efforts are followed by rigorous psychometric validations, these approaches are invaluable for specific local research and/or programmatic efforts and complement enormously the approaches needed to meet the need for “national” food insecurity scales that can be used to set national goals and follow trends. A unique contribution from this study is its multi-institutional and community participatory nature, and the fact that it used both qualitative and quantitative approaches to test the validity of the scale in a developing country. As a result, the

government of Brazil welcomed the results from this rapid validation assessment. Thus, the effort initiated in Campinas was extended nationally and has now involved the replication of the validation methodology in 3 additional urban areas in the Sates of Amazonas, Paraı´ba, and Brası´lia and in 5 rural areas including the 4 original states and the State of Mato Grosso. Results from these replications were totally consistent with the Campinas findings (18); as a result, the Brazilian government decided to incorporate the adapted FI module into national surveys and to make it available to researchers all over the country. This was an essential step that Brazil had to take to set national goals and be able to monitor the effect of its “Fome Zero” policies, which represent a top national priority for its government. ACKNOWLEDGMENTS We thank the Brazilian Ministry of Health and the Pan American Health Organization office in Brazil for their encouragement and support. Rafael Pe´ rez-Escamilla initiated this project while on sabbatical leave as a visiting professor of epidemiology, Department of Social and Preventive Medicine, University of Campinas. The research team is also grateful to Cecilia Goi Porto for her statistical advice with the sampling procedures for the representative sample survey.

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