ORIGINAL PAPER

Household food insecurity in small municipalitiesin Northeastern Brazil: a validation study

Rodrigo Pinheiro de Toledo Vianna &

Amber J. Hromi-Fiedler & Ana Maria Segall-Correa &

Rafael Pérez-Escamilla

Received: 21 July 2011 /Accepted: 15 March 2012 /Published online: 19 April 2012# Springer Science+Business Media B.V. & International Society for Plant Pathology 2012

Abstract The State of Paraiba in Northeastern Brazil ranksas the fourth poorest state in the country. The objectives ofthis study are to conduct the psychometric validation of theBrazilian Household Food Insecurity Scale (EBIA), to assessthe household food insecurity (HFI) prevalence, and to iden-tify the association between HFI, poverty and dietary intake ina representative sample of Paraiba’s 14 poorest municipalities(N04533). All municipalities included had fewer than50,000 inhabitants. EBIA had strong internal consistency(Cronbach’s alpha00.93 and 0.90 in households with andwithout children, respectively). The percentage of affirmativeresponses for each item was inversely associated with house-hold income and the item curves were parallel across socio-economic strata. Rasch modeling indicated that: a) scale itemsseverities followed theoretical expectations, b) all items hadan adequate fit to the scale confirming its unidimensionality,

and c) items ‘functioned’ similarly across key subpopulationcharacteristics including: urban/rural; men/women; younger/older; poor/less poor; Bolsa Familia enrollment (yes/no). HFIprevalence was higher in rural than in urban areas (55.5 % vs.49.9 %, p<0.0005) and severe food insecurity was substan-tially higher in rural areas (14.0 % vs. 9.0 %, p<0.0005). HFIseverity was inversely associated with household income,positively associated with daily sugar consumption and in-versely associated with daily consumption of bread and nutri-ent dense foods (fruits, vegetables, and dairy). In conclusion,EBIA had strong internal and external validity at the munic-ipal level. Findings are particularly relevant for Brazil where89.1 % of municipalities (4,957 out of 5,565 municipalities)have less than 50,000 inhabitants.

Keywords Brazil . Food intake . Food security .

Psychometric analysis . Rasch modeling

Introduction

According to the United Nations Development Program(2003) the State of Paraiba in Northeastern Brazil ranks asthe 24th poorest state out of the Country’s 27 states. In 2005,Paraiba had 3.6 million inhabitants with one-third living inurban areas in the largest municipalities. The rest live insmaller municipalities equally distributed across urban andrural areas (IBGE 2007). Climatic and other environmentalconditions strongly influence lifestyles and livelihood strat-egies in Paraiba. Water scarcity and drought represent seri-ous challenges for the development of the State. In 2007 theinfant mortality rate was 38 per 1,000 live births, and lifeexpectancy at birth was 69 years (65.6 y among men).About one-quarter (23.5 %) of the state’s population liveson a monthly per capita income under 25 % of the minimum

R. P. de Toledo Vianna :A. J. Hromi-FiedlerYale School of Public Health,135 College Street, Suite 200,New Haven, CT 06510, USA

R. P. de Toledo ViannaNutrition Department,Federal University of Paraíba—CNPq Fellowship,135 College Street, Suite 200,New Haven, CT 06510, USA

A. M. Segall-CorreaSocial and Preventive Medicine Department,State University of Campinas,135 College Street, Suite 200,New Haven, CT 06510, USA

R. Pérez-Escamilla (*)Office of Community Health, Yale School of Public Health,135 College Street, Suite 200,New Haven, CT 06510, USAe-mail: rafael.perez-escamilla@yale.edu

Food Sec. (2012) 4:295–303DOI 10.1007/s12571-012-0181-4

wage and 29.6 % live on an income between 25 % and 50 %of the minimum wage, with poverty being worst in ruralareas (IBGE 2008).

A national survey conducted in 2004 documented thatwhereas 34 % of Brazilian households were food insecure thiswas true for 46% of households in the Northeast, with a similarprevalence found in Paraiba. Overall the Northeast Region hadthe fourth worst severe food insecurity prevalence (15.7 %) inthe country (IBGE 2006). These household food insecurityestimates were measured with the Brazilian Food InsecurityScale (EBIA) applied to a nationally representative sample.EBIA is an experience-based scale that asks the respondent toreport on the household’s food access situation over a 3-monthperiod (Pérez-Escamilla and Segall-Corrêa 2008).

National surveys provide important information about thefood insecurity situation at the national and regional levels.However, they do not provide reliable representative informa-tion at the municipal level. It is particularly important to gatherthis information in small municipalities as their circumstancesand vulnerabilities are substantially different from those oflarger geographical areas. Municipal level surveys usuallydon’t receive the same level of advanced logistical and tech-nical support as national surveys. This calls for the need tovalidate EBIA at the municipal level when applied by locallytrained community interviewers. Thus, the objectives of thisstudy are to: 1) conduct the psychometric validation of EBIA;2) assess the food insecurity situation; and 3) test the externalvalidity of EBIA by examining the association between HFI,poverty and dietary intake in 14 of Paraiba’s poorest munic-ipalities, all of which have <50,000 inhabitants. This work hassignificant policy implications because if EBIA is valid inParaiba’s countryside it is also likely to be valid in the restof the small municipalities in Brazil, a country where 89.1 %of municipalities (4,957 out of 5,565 municipalities) havefewer than 50,000 inhabitants, and could benefit from localHFI surveillance systems.

Study design and methods

Field work

We conducted a population survey representative of each of the14municipalities that were identified in 2000 by the ministry ofSocial Development as being the poorest in the State (Fig. 1).These municipalities were given the highest priority when theFome Zero (i.e. Zero Hunger) program was launched in 2003.

A stratified sampling design was used. Proportional sam-pling was stratified by area of residence (urban vs. rural) ineachmunicipality and the households were selected randomly.A geographic information system was used to select thehouseholds within each stratum per municipality. Based on

randomly selected coordinates the nearest group of houses inrural areas or a block of residences in urban areas was selected.Sample size estimations per municipality were based on thefollowing assumptions: a) expected household food insecurityprevalence of 50 %, b) maximum estimate error of 5 %, c)confidence interval of 95 % for the proportion estimate. Theinitial sample size estimate indicated the need for 4,645 house-holds. However, power analysis based on the actual level offood insecurity measured in each municipality indicated theneed for sampling 4,495 households confirming that our actualsample size was sufficient (N04533). Twelve local communityinterviewers were selected in each municipality based on thefollowing criteria: a) at least a high school education; b)unrestricted time availability during duration of study; and c)not employed in health or public administration services. Allinterviewers received a 16 hours training where the objectivesof the study were explained and the appropriate way to admin-ister the questionnaire to study participants demonstrated.

Data were collected between May and September 2005 viaface-to-face home interviews with the household respondents.The pre-tested questionnaire collected demographic (i.e. gen-der, age), socio-economic (per capita household income), foodintake (17-item food group frequency questionnaire) andhousehold food insecurity (measured via the EBIA question-naire) (data Table 1) (Segall-Corrêa et al. 2004). EBIA’ssummative score was used to classify each household intofour mutually exclusive categories: food secure (FS), mildlyfood insecure (FI mild), moderately food insecure (FI moder-ate), or severely food insecure (FI severe). In households withchildren (i.e. responding to all 15 EBIA items) the followingclassification algorithm was used: food secure (EBIA score00), mildly food insecure (score01–5), moderately food inse-cure (score06–10), severely food insecure (score011–15). Inhouseholds without children under 18 y (i.e. responding to8 EBIA items) the corresponding cut-offs were; food secure(EBIA score00), mildly food insecure (score01–3), moder-ately food insecure (score04–6), severely food insecure(score07–8). As previously reported (Kac et al. 2012), thesecut-off points, initially proposed and validated by Pérez-Esca-milla et al. (2004), were subsequently confirmed based on theequivalence of the thresholds for households with and withoutchildren, both of which are based on scales derived from theinterval-level Rasch model (Melgar-Quinonez et al. 2008).

In accordance with resolution 196/96 of the BrazilianMinistry of Health National Health Council, the study wasapproved by the Research Ethics Committee of the FederalUniversity of Paraiba Health Sciences Center.

Statistical analyses

Data were entered from paper files using theMicrosoft Access®computer program. The data entry program included features to

296 R.P. Toledo Vianna et al.

minimize data entry errors. Once the data were entered, variabledistributions were examined to detect implausible values. In thevery few instances when this occurred, these errors were cor-rected by going back to the original paper files. Once allcorrections were made an exploratory analysis was conductedfor final verification and recoding of continuous variables intocategorical values using category ranges defined a priori.

The Access data set was then exported into SPSS forWindows®, version 19. As a first approximation, the internalconsistency of the EBIA scale (based on the summative score)was assessed with the Cronbach’s alpha statistic using a value>0.85 as indicative of adequacy.

This first approximation was followed with an in-depthnon-linear Rasch modeling psychometric analysis. TheRasch model is one type of Item Response Theory (IRT)model that has been used by researchers to evaluate theinternal validity of the original and adapted versions of theU.S. Household Food Security Survey Module (Derricksonet al. 2000; Hromi-Fiedler et al. 2009; Melgar-Quinonez etal. 2008). The Rasch model is a one-parameter logistic IRTmodel that is used to calculate a household-specific foodinsecurity level and an item-specific severity calibration(Derrickson et al. 2000; Wilde 2004). More specifically,the Rasch model expects that food secure respondents areless likely to affirm mild to severe food insecurity questions

while food insecure respondents are more likely to affirmthem (Smith et al. 2002). Two sets of statistics are generatedby the Rasch model to assess the scale’s internal validity:severity values and FIT statistics (Bond and Fox 2001).

Severity estimates were generated at both a householdand item level separately based on observed values from anadministered survey (Smith et al. 2002). The severity of anitem is expressed as an item calibration value, which deter-mines the placement of the item along a uniform scale on alogistic (i.e. log-odds) metric (Derrickson et al. 2000). Themore severe an item is, the fewer affirmative responses itwill receive, and the lower the item calibration value will be.

Populations can be compared based on the individualitem calibration value to assess differences in item ‘func-tioning’. This study compared the item severity betweenseveral subgroups and the whole sample to examine if therewere differences based on sub-population characteristics.The subgroups examined were: urban/rural area, male/femalerespondent, enrolled in federal support program (yes/no), low/high income, and presence/absence of children in the house-hold. The item severities for each of the groups were adjustedby the item severity for the same item for the whole sampleusing the formula below. For example, to compare the itemseverity for those who lived in urban areas to the wholesample, the follow calculation was used:

½ð urban item severity � urban items severity meanð Þ=urban items severity standard deviationÞ� whole samples severity standard deviationþ whole sample severity mean�

Fig. 1 South America, Brazil and Paraiba State and its 14 poorestmunicipalities (Araruna, Areial, Aroeiras, Bananeiras, BernardinoBatista, Boqueirão, Cacimba de Dentro, Esperança, Itabaiana, Nova

Floresta, Picuí, Queimadas, São José dos Ramos e Umbuzeiro).Source: Google maps http://maps.google.com/ and Tabwin3.5www.datasus.gov.br/tabwin/tabwin.htm

Household food insecurity in small municipalities in Northeastern Brazil 297

The differential item functioning (DIF) between urbanand rural areas was then calculated by comparing the ad-justed item severities between the two groups. As recom-mended by Hackett et al. (2008), we used a strict criterion ofitem comparability of an absolute DIF of 0.5 logit units or less.

FIT statistics were generated to examine the assumptionsthat the scale is unidimensional and measures only oneconstruct, i.e., household food insecurity (Bond and Fox2001). FIT statistics are the ratio of the square of the differ-ence between the expected vs. the observed estimates andare identified as mean square residuals (Derrickson et al.

2000). How well an item fits the unidimensional food inse-curity construct is mainly assessed by the INFIT value.Items with an infit mean square value between 0.7 and 1.3are considered to meet the underlying unidimensional foodinsecurity construct.

Rasch model analyses were conducted with WINSTEPSversion 3.72. The specific analyses involved: a) estimatingRasch item severities, b) estimating Rasch item INFIT values,c) conducting DIF analyses comparing item ‘functioning’ forvarious subgroups (i.e. urban and rural residence; children andno children in households; lower and higher income; enrolled

Table 2 Household size and food insecure prevalence in small municipalities. Paraíba, Brazil, 2005

Total (n04,484) Urban area (n02,404) Rural area (n02,080)

Household size—persons per household (mean±SE) 3.98±0.06 3.77±0.07 4.23±0.08*

Household food secure 47.5 % 50.1 % 44.5 %*

Household mildly food insecure 23.6 % 25.2 % 21.6 %

Household moderately food insecure 17.6 % 15.7 % 19.9 %

Household severely food insecure 11.3 % 9.0 % 14.0 %

*p<0.0005 (urban vs. rural)

Table 1 Brazilian Household Food insecurity Scale (Escala Brasiliera de Insegurança Alimentar—EBIA)

N Code Items

1 Aa—worried were you worried that you would run out of food before being able to buy or receive more food?

2 A—food finished did you run out of food before having money to buy more?

3 A—no healthy and varied food did you run out of money to have a healthy and varied diet?

4 Cb—just few foods to eat did you have to offer your children/adolescents just a few foods because you ran out of money?

5 C—no healthy and varied food were you unable to offer your children/adolescents a healthy and varied diet because you didn’thave enough money?

6 C—not enough food did any of the children/adolescents not eat enough because there wasn’t enough money tobuy food?

7 A—skipped meals did you or any adult in your household ever reduce the size of meals or skip meals because therewasn’t enough money to buy food?

8 A—ate less than would like did you ever eat less than what you thought you should because there wasn’t enough money tobuy food?

9 A—hungry but didn’t have food to eat did you ever feel hungry but didn’t eat because there wasn’t enough money to buy food?

10 A—lost weight did you lose weight because you didn’t have enough money to buy food?

11 A—didn’t eat all day did you or any other adult in your household ever go without eating for a whole day or havejust 1 meal in a whole day because there wasn’t enough money to buy food?

12 C—ate less did you ever reduce the size of meals of your children/adolescents because there wasn’t enoughmoney to buy food?

13 C—skipped meals did your children/adolescents ever have to skip a meal because there wasn’t enough moneyto buy food?

14 C—hungry but didn’t have food to eat were your children/adolescents ever hungry but you just couldn’t buy more food?

15 C—didn’t eat all day did your children ever go without food for a whole day because there wasn’t enough moneyto buy food?

a Adult itemsb Children items, only asked in households with children under 18 years of age

Each item referred to conditions experienced during a 3-month period prior to the survey

298 R.P. Toledo Vianna et al.

and not enrolled in conditional cash transfer “Bolsa Familia”Program) compared to the whole sample.

To test for item curve parallelism we plotted the percentof affirmative responses for each item as a function ofhousehold income (four categories expressed as incomeper capita).

Lastly, in order to test the external validity of EBIA inthis sample chi-square cross-tab analyses were conducted.We measured the associations of the four-level householdfood insecurity variable with socio-economic, demographic,and food intake indicators. A 2-sided p value <0.05 wasused as criterion of statistical significance. Results were notadjusted for clustering.

Results

Of the 4,533 household respondents that were interviewed,4,484 answered the EBIA questions. The sample householdsize was larger and household food insecurity was moreprevalent in rural than in urban areas (Table 2).

A total of 3,009 (67.1 %) interviews were conducted inhouseholds with children under 18 years old and 1,475(32.9 %) were conducted in households where only adultslived. Households without children were significantlysmaller than those with children (2.6 vs 4.6 persons, respec-tively) and also had higher incomes (0.7 vs. 0.4 monthly percapita minimum wage). Consistent with these differences

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all day

% p

ositi

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up to 0.25 MW

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1.01 or more MW

Fig. 2 Percent of affirmative responses for each EBIA item as afunction of household income in the 14 poorest municipalities in theState of Paraiba, Brazil (n04484). A: Adult items; C: Children items,only asked in households with children under 18 years of age. Each

item referred to conditions experienced during a 3-months periodprevious to the survey application. Key at the top right of the figurerefers to the proportion of minimum wage per capita per householdcategory

0

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finished

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foods toeat

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C – ateless thanwouldneed

A – lostweight

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but didn'thave

food toeat

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but didn'thave

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seve

rity

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ues

Fig. 3 EBIA Rasch item severity values* in the 14 poorest munici-palities in the State of Paraiba, Brazil (n04484). A: Adult items; C:Children items, only asked in households with children under 18 yearsof age. Each item referred to conditions experienced during a 3-months

period previous to the survey application. * Note that the severityvalues represent log units, thus each unit represents a ten-fold differencein severity magnitude

Household food insecurity in small municipalities in Northeastern Brazil 299

households without children were more likely to be foodsecure (66.6 % vs. 38.1 %) and substantially less likely to beseverely food insecure (3.1 % vs. 15.4 %) compared withhouseholds where children lived.

Cronbach’s alpha for the 15-item EBIA (answered byhouseholds with children) was 0.93 (n02830) and 0.90 forthe 8-item EBIA (answered by households without children).Thus, EBIA had a strong internal consistency in the munici-palities included in the sample.

With regards to the external validation of EBIA, asexpected, the percentage of affirmative responses for each itemwas inversely associated with household income (Fig. 2). Thefinding that the curves were parallel across socio-economicstrata indicates that the severity captured by the scale items wassimilarly interpreted across groups with different incomelevels.

Rasch modeling results clearly indicated that EBIAfollowed theoretical expectations. The lowest severity

was found for those items reflecting the mildest levelsof food insecurity (worried, adult dietary quality). The nextlevel of severity encompasses items representing dietary qual-ity issues among children and lowering food intake amongadults followed by a similar coping behavior among children.Lastly the most severe items capture hunger first among adultsfollowed by children (Fig. 3). Thus, the psychometric behav-ior of EBIA strongly met a key Rasch model assumption (i.e.,that more ‘difficult’ items should load with higher severitiesthan less ‘difficult’ items). The fact that the items capturedseverities dispersed throughout the continuum of values fol-lowing a quasi-linear pattern is also a positive psychometricattribute of the scale.

Rasch model results also showed that all the EBIAitems fitted well within the expected estimates confirm-ing the unidimensionality assumption of the Rasch mod-el (Fig. 4). In this instance this finding means that theEBIA summative score based on all 15 items accurately

0

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A –no healthy and varied

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C –ate less than would

need

A –was hungry but didn't have food to eat

C –skipped meals

C –didn't eat all day

INFIT min max

Fig. 4 EBIA items INFITvalues in the 14 poorestmunicipalities in the State ofParaiba, Brazil (n04484). A:Adult items; C: Children items,only asked in households withchildren under 18 years of age.Each item referred to conditionsexperienced during a 3-monthsperiod prior to the survey

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q14

Fig. 5 Comparison of relative item severities from EBIA by household area: urban (n02404) and rural (n02080) vs. total sample (n04484). Itemnumbers correspond to question numbers indicated in Table 1

300 R.P. Toledo Vianna et al.

captures the severity of the household food insecurityconstruct.

DIF analyses showed that the items ‘functioned’ similarlyacross subgroups. DIF values for all contrasts were less thanthe 0.5 cutoff. Thus, EBIA was psychometrically valid inurban and rural areas, as well as among lower and higherincome groups (Fig. 5). DIF analyses also confirmed thepractically identical ‘functioning’ of the EBIA items as afunction of children in the household, respondent’s gender,and enrollment in the ‘Bolsa Familia’ conditional cashtransfer program (data not shown).

Household food insecurity severity was inversely associatedwith household per capita income following a dose–responsepattern (Fig. 6).

The association between the severity of food insecurityand the intake of different foods is displayed in Fig. 7.Confirming the external validity of the scale, the probabilityof consuming sugar on a daily basis increased as a functionof the degree of food insecurity. By contrast the likelihoodof daily consumption of staples such as bread and nutrientdense foods (fruits, vegetables, and dairy) decreased as afunction of the degree of food insecurity.

Discussion

Our study demonstrates the utility of an experience-based scalefor assessing the household food insecurity situation at themunicipal level in a highly vulnerable area in Brazil. WhereasEBIA has been previously validated in national samples(Segall-Corrêa et al. 2004) and in urban areas (Pérez-Escamillaet al. 2004), for the first time our study validates its use in smallmunicipalities in Brazil. Findings are highly relevant as theydemonstrate the validity of EBIA for directly estimating accu-rate HFI prevalences at the municipal level and its potential forfood security diagnosis, local targeting of programs and mon-itoring of goals at the local level.

All the analyses performed on EBIA in our study con-firmed the scale’s validity. EBIA had strong internal consis-tency (Cronbach’s alpha >0.90), the scale was unidimensionalwith all items fitting well to the scale, and the severity of theitems was equally interpreted across socio-economic strata(parallelism of item curves). Furthermore the severity of theitems loaded in the expected theoretical sequence and ‘func-tioned’ similarly across key subgroup characteristics. Specif-ically, the scale performed equally well in both urban and ruralareas, households with and without children, and men andwomen. The fact that the items also ‘functioned’ equally wellamong those enrolled and not enrolled in the ‘Bolsa Familia’program provides reassurances that participation in this majorgovernment assistance program did not bias the participants’responses, even in very disadvantaged municipalities such asthe ones included in our sample.

Households in the small and most disadvantaged munic-ipalities of Paraiba were more vulnerable to food insecurityif they had children living in them. As expected there was astrong inverse dose–response relationship between house-hold income and severity of household food insecurity. Themore food insecure the households were, the more likely therespondent also were to consume sugar on a daily basis andthe less likely they were to consume fruits, vegetables, dairyand bread on a daily basis. Thus, household food insecurity

FS FI mild FI moderate FI severe0%

10%

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30%

40%

50%

60%

70%

80%

90%

up to 0.25 MW

0.26 to 0.5 MW

0.51 to 1.0 MW

1.01 or more MW

Fig. 6 Household foodinsecurity severity byhousehold per capita incomein the 14 poorest municipalitiesin the State of Paraiba, Brazil(n03906). P<0.05 for allcomparisons across food (in)security categories. Key at theright of the figure refers to theproportion of minimum wageper capita per householdcategory

0%

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Fig. 7 Percent of respondents consuming specific foods on a dailybasis as a function of severity of household food insecurity in 14municipalities in the State of Paraiba, Brazil (n04477). P<0.05 forall comparisons across food groups

Household food insecurity in small municipalities in Northeastern Brazil 301

severity is associated with dietary quality in small munici-palities in Paraiba.

This study demonstrates that EBIA is a low-cost highlyvalid scale with strong internal and external validity that canbe reliably applied in a relatively short period of time by welltrained paraprofessionals living in the target areas. In thisinstance EBIA provided very valuable and timely informationin spite of having severe budget limitations and having had toface major climatic challenges that made it very difficult toconduct surveys in the municipalities in Paraiba.

The food insecurity prevalence observed in our sample wasclose to the one observed for Paraiba in the 2004 nationalsample survey (IBGE 2006). However, the small municipali-ties’ characteristics are different from the capitals or metro-politan areas. Indeed, food insecurity prevalence estimatesbased on the national data are not likely to represent the foodinsecurity situation well in small municipalities (Gubert et al.2010) and may be of limited value for guiding local initiatives.Thus, confirming the validity of EBIA in small municipalities,and not just in national surveys as previously demonstrated, isessential for supporting policy makers to better understand thelocal specificity of the determinants and consequences of foodinsecurity, to accurately identify the populations at highest riskfor food insecurity, as well as to evaluate the impact of socialand food assistance programs.

Our findings are in agreement with and strongly comple-ment previous studies conducted in Brazil showing that pov-erty is a serious risk factor for household food insecurity inurban areas and at the national level (Panigassi et al. 2008;Santos et al. 2010). Indeed, the excellent validity demonstrat-ed in this study provides assurance that EBIA has a highpotential for serving as a valid tool to target and evaluate foodsecurity programs in small and disadvantaged municipalitiestypical of Brazil and many other countries in Latin Americaand the Caribbean. Moreover this is confirmed by smallresearch studies conducted all over Brazil as well as the factthat the validity of EBIA at the national level stands acrossmacro-regions (IBGE 2006). Thus, we recommend that EBIAis applied in Brazil at the municipal level through locallytrained community interviewers. Because of the high costinvolved with collecting data that are municipally representa-tive through nationally representative surveys, we recommendthat EBIA is included as part of the municipal level food andnutrition monitoring systems currently in place. We also rec-ommend that EBIA is applied at the municipal level on an adhoc basis to assist with the local decision making process inrelation to food, nutrition and social assistance programs.

Acknowledgments We are grateful to the Brazilian Research andScience Council (CNPq) for supporting this study through grant no.503359/2003-3 and 201796/2010-4. Presented at the International Scien-tific Symposium on Food & Nutrition Security Information: From validmeasurement to effective decision-making. UN Food and AgriculturalOrganization (FAO) Headquarters, Rome, 17–19 January 2012.

Conflict of interest The authors have no conflict of interest to declare.

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Dr. Rodrigo Pinheiro De ToledoVianna is Professor at the De-partment of Nutrition, FederalUniversity of Paraiba, Brazil.His work focuses on householdfood insecurity measurement atthe local level using experience-based scales, mental healthamong health care providers,and infant feeding patterns anddeterminants. His pioneer workin Paraiba was instrumental forthe development of the BrazilianHousehold Food InsecurityScale (EBIA). He is a visiting

professor at the Yale School of Public Health. He obtained his PhD inepidemiology from the University of Campinas.

Dr. Amber Hromi-Fiedler is anAssociate Research Scientist atthe Yale School of Public Health.Her work focuses on maternal nu-trition during pregnancy, house-ho ld food in secu r i t y andmaternal health, and psychomet-ric analyses of household foodinsecurity experience-basedscales. She has been a collabora-tor of household security mea-surement projects conducted inAlbania, Brazil, Ghana, Mexicoand the United States. She re-ceived an MPh and a PhD in Nu-

trition from the University of Connecticut.

Dr. Ana Maria-Segall Correais Professor at the Departmentof Social and Preventive Medi-cine, The University of Campi-nas, Brazil. Her work focuses oninfant feeding, household foodinsecurity measurement, and therelationship of food insecuritywith mental health and chronicdiseases. She is the Director ofthe Brazilian Household FoodSecurity Measurement Projectthat led to the development andnational validation of the BrazilianHousehold Food Insecurity Scale

(EBIA). She received her MD degree from the University of Brasilia andher PhD in epidemiology from the University of Campinas.

Dr. Rafael Pérez-Escamilla isProfessor of Epidemiology andDirector, Office of CommunityHealth, Yale School of PublicHealth. His work focuses on in-fant feeding, maternal-child nutri-tion, food insecurity measurementand outcomes, and health inequi-ties. He has been a senior advisorto community nutrition programsas well as household food securitymeasurement projects funded byFAO, PAHO, UNDP, WHO,UNICEF, UNESCO, USDA,USAID, The World Bank, the

Gates Foundation, and the Governments of Mexico and Brazil. His workhas been central for the adaptation, validation, and national utilization ofexperience–based scales including the Brazilian Household Food Secu-rity Scale (EBIA), the Mexican Food Insecurity Scale (EMSA) and theLatin American and Caribbean Food Security Scale (ELCSA). He re-ceived anMS in Food Science and a PhD inNutrition from the Universityof California at Davis.

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