QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Abstract Full Text (PDF) Online Supporting Material Purchase Article View Shopping Cart Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Isanaka, S. Articles by Villamor, E. Search for Related Content PubMed PubMed Citation Articles by Isanaka, S. Articles by Villamor, E.

---

Community and International Nutrition

Food Insecurity Is Highly Prevalent and Predicts Underweight but Not Overweight in Adults and School Children from Bogotá, Colombia^1–3,

⁴ Departments of Nutrition and Epidemiology, Harvard School of Public Health, Boston, MA 02115; ⁵ Department of Nutrition, National University of Colombia Medical School, Bogotá, Colombia; and ⁶ Department of Community Health, Warren Alpert Medical School of Brown University, Providence, RI 02903

^* To whom correspondence should be addressed. E-mail: evillamo{at}hsph.harvard.edu.

	ABSTRACT

TOP ABSTRACT Introduction Methods Results Discussion LITERATURE CITED

 
The aims of this study were to determine the sociodemographic and dietary correlates of household and child food insecurity in Bogotá, Colombia and to examine whether food insecurity is a risk factor for underweight or overweight in this population. We analyzed data from 2359 families with 2526 children 5–12 y of age who completed a cross-sectional survey conducted in 2006. The survey was representative of low- and middle-income families who had children enrolled in the public primary school system of Bogotá. We used a 16-item food insecurity scale, modified from the United States Household Food Security Survey Module, assessed children's dietary intake with a FFQ, and measured their height and weight. Mothers' anthropometry was obtained through self-report. We estimated adjusted prevalence ratios and 95% CI from binomial regression models. Household food insecurity with hunger and child food insecurity were each positively associated with maternal age, parity, and single parent status and inversely related to mean household income and number of home assets. Animal protein and snack food intake were inversely related to child food insecurity. In multivariate analyses, food-insecure children were 3 times more likely to be underweight than food-secure children (95% CI = 1.6, 5.4; P = 0.0007). Hunger in the household was significantly associated with maternal underweight. Food insecurity was not related to child stunting, child overweight, or maternal overweight. The prevalence of food insecurity in Bogotá is high and related to poverty. Food insecurity does not necessarily predict overweight in countries undergoing the nutrition transition.

	Introduction

TOP ABSTRACT Introduction Methods Results Discussion LITERATURE CITED

Despite the very high levels of food insecurity in Colombia and its potentially severe consequences, the population groups that are affected by food insecurity have not been characterized. This characterization is fundamental to adequately target new and available interventions, including Bogota's Secretary of Education School Feeding initiative (part of the "Bogota without Hunger" program), and the Food Pantry and Food Security projects from the Administrative Department of Social Welfare.

Also unknown are the actual consequences of food insecurity in this population. In particular, whereas some reports suggest that limited access to food could be related to overweight or obesity in children (10) and adults (11–13) from industrialized nations, it is not certain whether food insecurity could also be related to overweight and obesity in countries undergoing the nutrition transition, including Colombia.

We wanted to determine the correlates and nutritional consequences of food insecurity in low- and middle-income households in Bogotá, Colombia. We investigated the following: 1) which sociodemographic factors were related to household and child food insecurity; 2) whether child food insecurity was related to specific dietary patterns; 3) whether child food insecurity was associated with child nutritional status; and 4) whether household food insecurity was associated with maternal nutritional status.

	Methods

TOP ABSTRACT Introduction Methods Results Discussion LITERATURE CITED

 
    Study population. The study was conducted as part of a project on children's health and nutritional status in primary public schools of Bogotá, Colombia. In February 2006, we selected a representative sample of 4000 primary school children 5–12 y of age and their mothers using a cluster sampling strategy. We defined clusters as the classes (primary school grades 1–5) of all 361 public schools in the city by the end of 2005. We identified 8500 classes and randomly selected 166 of them to reach the target sample size. On wk 1 of class, we sought consent from the parents to participate in the study and obtained responses for 3207 children. The parents of 5 children declined participation; thus, the final sample size was 3202 children from 3032 households (after accounting for siblings).

Primary schooling coverage in Bogotá was 88% in 2005. The public school system enrolls 57% of all primary school children in the city, the vast majority of whom (89%) belong to low- and middle-socioeconomic strata (14). Our study is therefore generalizable to low- and middle-income families from Bogotá.

    Field procedures. During wk 1 of classes, we obtained information on the parents' sociodemographic characteristics, including age, marital status, education level, and the household socioeconomic status using a self-administered questionnaire that was completed and returned by 2466 (81%) households (the families of 2637 children). The questionnaire also included questions on weight and height of the mother (self-reported) and a food security survey (described below). During the following weeks, trained research assistants visited the schools to obtain anthropometric measurements in the children using standardized techniques (15). Height was measured to the nearest 1 mm using wall-mounted portable Seca 202 stadiometers and weight was measured to the nearest 0.1 kg on Tanita HS301 solar-powered electronic scales. Between May and June, we collected information on the children's usual dietary intake with FFQ that trained research dieticians applied to a random sample of 1027 mothers. The 38-item FFQ was developed by the study investigators based on the list of most frequently consumed foods according to the Colombia National Nutrition Survey 2005 (2) and from a questionnaire previously validated for use in a comparable setting from Costa Rica (16). There were 9 frequency response options: 4–5 times per day, 2–3 times per day, once per day, 5–6 times per week, 2–4 times per week, once per week, 1–3 times per month, less than once per month, or never. For each item, a reference portion size was described in natural units (e.g. 1 glass of milk or 1 egg) or standard weight and volume measures for commonly consumed servings in this population. The questionnaire inquired about the mean frequency of intake of standard portion sizes during the previous month. At the same visit when the FFQ was completed, we measured height and weight in a randomly selected subsample of 671 mothers. This group did not differ from mothers who were not measured in terms of age, socioeconomic status, food security level, or self-reported height or weight.

    Food security survey. We measured food security using a modified version of the Spanish-language USDA Household Food Security Survey Module (17) and the Community Childhood Hunger Identification Project (18). These scales draw on the idea that there are distinct levels of food insecurity and an orderly sequence of characteristic conditions and behaviors. This ordering results in response patterns where households that answer a particular question affirmatively tend to answer all less severe questions affirmatively as well and allows for characterization of household food insecurity as a sum of affirmative responses (19). The scales have been successfully adapted and validated for use among subgroups of the United States population (20–22) and internationally (23–25), including Latin American settings that are comparable to our study population (26,27). The resulting instrument included 16 questions referring to the 30 d prior to survey administration (Supplemental Table 1). Response options for all items were: never, sometimes, or often. We calculated Cronbach's coefficient to assess the internal consistency of the 16 questions. All questions were positively correlated with the scale and the was 0.91, suggesting acceptable internal consistency of the instrument (28). In addition, the proportion of affirmative responses decreased progressively from a maximum 78.3% for question 1 to a minimum 10.6% for question 16, in support of the ordering by severity.

    Data analyses. We excluded 107 households that had missing food security information. Thus, the final sample size was 2359 households corresponding to 2526 children. Although sociodemographic information was not available from households without food security data, their children did not differ significantly in terms of sex, age, or the prevalence of stunting, underweight, or overweight compared with children whose households provided food security information.

We created a food insecurity score by assigning a value of 1 to questions answered as "sometimes" or "often" and a value of 0 to questions answered as "never." Then we added the coded responses to calculate an ordinal score in which 0 corresponded to the most food-secure households and 16 to the households most severely affected by food insecurity.

Households were categorized into 4 groups using cut-off points aligned with the conditions and behaviors that are characteristic of each level of food insecurity as described by Bickel et al. (29) (Supplemental Table 1). Food secure households (0–2 affirmative responses) represent those households showing no or minimal evidence of food insecurity. Food insecure without hunger households (3–7 affirmative responses) are characterized by worrying about running out of food and making adjustments to food quality and variety but not reducing quantities of food intake below normal levels. Food insecure with moderate hunger households (8–12 affirmative responses) represent households with adults who skip or cut the size of their own meals and reduce their food intake below normal levels to provide for their children. Households that were food insecure with severe hunger (13–16 affirmative responses) include those where both adults and children reduce intake as a result of inadequate resources and experience hunger.

We used the 5 questions that specifically referred to the experiences of children to construct a measure of child food insecurity within households. Three or more affirmative responses in the 5 child-specific questions indicated a household with child food insecurity. This cut-off point was selected because it reflects a decrease in the quality and quantity of food directly available to children and identifies a more severe form of food insecurity than those based on the household (4,30).

Sociodemographic variables included the mother's age, education level, parity, and marital status, the father's age and education level, household size (number of people in the household), per capita daily income (the total household income divided by the household size), money spent on food per capita (the total amount of money spent on food divided by the number of people in the household), type of dwelling, home ownership, the household socioeconomic stratum according to the city's classification of the neighborhoods' public services fees, and the number of home assets owned (0–6) from a list that included refrigerator, bicycle, blender, television, stereo, and washing machine.

We conducted principal component analysis to identify dietary patterns, using as input the 38 items in the FFQ. The factors obtained were rotated by an orthogonal transformation to achieve a simpler structure that facilitates interpretability. We considered eigenvalues >1, the Scree test, and interpretability of the factors to determine the number of factors to retain (31). The standardized frequencies of intake for each food group were multiplied by the factor score coefficients and the sum of these products was the score for each derived factor. We identified 4 dietary patterns: traditional/starch (e.g. rice, potato, plantain), animal protein (e.g. beef/pork/veal/lamb, chicken/turkey, milk, cheese), cheaper protein (e.g. cow's tripe/liver/spleen, chicken giblet), and snacking (e.g. candy, ice cream, packed fried snacks, soda, fruit punch). The factor scores produced in the principal component analysis were categorized into quartiles to represent adherence to each pattern.

We assessed child nutritional status by calculating height-for-age, weight-for-age, and weight-for-height Z scores according to the WHO/National Center for Health Statistics reference (32). Stunting, underweight, and wasting were defined as height-for-age Z score < –2, weight-for-age Z score < –2, and weight-for-height Z score < –2, respectively. Child overweight and obesity were defined using the International Obesity Task Force recommendations (33). We calculated BMI as kg/m² from self-reported maternal weight and height. Maternal nutritional status was classified according to BMI categories as underweight (<18.5), adequate (18.5–24.9), overweight (25–29.9), or obese (30) (34).

For the analysis of sociodemographic characteristics as correlates of food insecurity, we chose 3 dichotomous outcomes: household food insecurity with hunger (moderate or severe), household food insecurity with severe hunger, and child food insecurity. We estimated the prevalence of the 3 outcomes by categories of sociodemographic predictors and tested the associations by use of the ² and Cochrane-Armitage tests. We obtained adjusted prevalence ratios and 95% CI for each outcome by fitting binomial regression models with generalized estimating equations (GEE) (35). In these models, each food insecurity endpoint was the outcome and predictors included the variables that were significant correlates in unadjusted analyses at P < 0.10. Only variables that remained significant at P < 0.05 or were considered relevant from the mechanistic viewpoint were retained in the final multivariate models. An exchangeable correlation matrix was used in the GEE models to adjust standard errors for potential correlation of the participants within sampling clusters (36). Adjusted P-values correspond to the Wald test for dichotomous predictors and to a test for trend for ordinal variables.

For the analysis of dietary patterns as predictors of child food insecurity, we tested for differences in the prevalence of child food insecurity by adherence to each of the 4 dietary patterns with the use of Cochrane-Armitage tests.

Finally, we examined whether child or household food insecurity were associated with child or maternal nutritional status, respectively. We estimated prevalence ratios and 95% CI for each anthropometric outcome according to food insecurity exposure categories using GEE with a binomial distribution specification and the log-link. Because socioeconomic and demographic variables are associated with both food insecurity and nutritional status, we adjusted the analyses for the potentially confounding effects of mother's age, maternal education, parity, and number of home assets. The child nutritional status models were additionally adjusted for child's age and sex. In the analysis of child nutritional status, unadjusted and adjusted P-values correspond to the ² test for proportions and Wald test, respectively. P-values in the analysis of maternal nutritional status were obtained using the test for trend (Cochrane-Armitage). Within-household correlations among children from the same families (n = 325) were accounted for using an exchangeable correlation matrix in the binomial GEE models. By comparing maternal self-reported vs. measured anthropometrics, we found that weight was underreported by a mean 2.1 kg, whereas height was overreported by 0.5 cm. Despite this limitation, the direction of associations between food insecurity and maternal nutritional status were similar in the subset of women with measured weight and height and in the whole population with reported data; hence, we present the results of the latter.

Values in the text are means ± SD. A P-value of 0.05 or less was considered significant. All analyses were conducted using the Statistical Analyses System software (SAS Institute).

The study protocol was approved by the Ethics Committee of the National University of Colombia Medical School and the Human Subjects Committee at the Harvard School of Public Health.

	Results

TOP ABSTRACT Introduction Methods Results Discussion LITERATURE CITED

 
Maternal age was 35.3 ± 6.8 y and literacy was high, at 99%. Sixty-eight percent of households had mean daily per capita income below 2 USD. Seventy-six percent of households reported some level of food insecurity. Forty-seven percent of households were food insecure without hunger, 18.2% were food insecure with moderate hunger, and 11.2% were food insecure with severe hunger. Child food insecurity was found in 15.3% of households.

    Sociodemographic correlates of household food insecurity. We examined the associations between sociodemographic characteristics and food insecurity with hunger (moderate or severe) or food insecurity with severe hunger (Table 1). In univariate analysis, mothers' age, parity, and single parent status were positively associated with food insecurity with hunger and with severe hunger, as were not living in a house or apartment and lack of home ownership. Mothers' and fathers' education were negatively associated with both outcomes, as were all measures of income and socioeconomic status.

View larger version (19K): [in this window] [in a new window]   FIGURE 1  Adherence to child dietary patterns in relation to child food insecurity in Bogotá, Colombia. Quartile 1 represents the lowest quartile of pattern adherence and quartile 4 the highest quartile of pattern adherence. In households with >1 child (n= 82), the factor scores produced in the principal component analysis for each child were averaged. The P-values for trend were derived from the Cochrane-Armitage test.

	Discussion

TOP ABSTRACT Introduction Methods Results Discussion LITERATURE CITED

Several of the associations we found between sociodemographic variables and food insecurity are consistent with those reported in other populations. They include the positive associations of household and child food insecurity with maternal age (39), parity (4,13,40,41), and single parent status (41,42); and the negative relations with maternal education (13,39,43–46) and indicators of socioeconomic status (13,24,39,40,43,44,46–48), such as income and home asset ownership. The consistency of our findings with those from several other populations suggests that the instrument we used provides valid indicators of food insecurity in this group.

We examined the relation between child food insecurity and dietary intake and found that children from insecure households had lower intake of animal protein and snack foods compared with children from secure households. Reduced meat consumption among food-insecure children had been found in poor, urban children in Seoul, South Korea (39), in urban households of Campinas, Brazil (24), in Mexican-American households with preschool-aged children (44), and as payday approached among Hispanic 5th graders in California (21). In Bolivia, households with greater food insecurity were similarly found to spend less money on meat (38). No difference in meat consumption was noted in a small population of resettled Liberian refugees in the United States, but this discordance may be due to different cultural preferences for meat (46). The reduction in meat consumption in food-insecure families is consistent with modeling efforts that have shown that the imposition of cost constraints results in lower meat intake (49).

The association of food insecurity with snack consumption has been less consistent than that with animal protein. A study in Latino households with young children in California found lower household inventory of snack foods at greater levels of food insecurity (20) and a study in urban households in Campinas, Brazil also found reduced intake of juice, candy, and soft drinks with increasing food insecurity (24). Food insecurity, however, was not associated with sweets or fast food consumption in adults in Trinidad (43) or with energy density in the United States (50). Inconsistency in the literature may be due to variation in the relative cost of snack foods and the coping strategies adopted in different populations. For instance, in the United States, households facing limited resources consume less expensive and more calorie-dense foods to maintain caloric intake at less cost, increasing their consumption of refined grains, added sugars, or fats (51). By contrast, in Bogotá we found that food-insecure children ate fewer snacks and commercial foods, such as fried snacks/chips, soda, or packed fruit punch. Resource scarcity in the Bogotá population may result in fewer purchases of commercial treats and high-fat foods, which may be relatively more expensive than homemade or traditional snacks. Food-insecure children may have less access to pocket money, which reduces their ability to purchase snacks and candy outside the home.

In this population, traditional food intake was not significantly related to food insecurity, suggesting that traditional foods, such as rice, plantain, and legumes may be more accessible than meats to most households in Bogotá. Mixed findings have been reported for the relationship of traditional foods with food insecurity in other settings. Kaiser et al. (20) found no difference in the inventory of traditional Mexican foods by food security among Latino households with young children in California, whereas Melgar-Quinonez et al. (38) found decreased spending on traditional staples, like roots and tubers, in Bolivia. These results emphasize that differences in food intake by levels of food insecurity will depend on the severity of food insecurity, the relative cost, availability, and desirability of alternative food items, and available coping strategies.

In our study, child food insecurity was a significant predictor of child underweight but not of stunting or overweight. The lack of association between food insecurity and child overweight is consistent with findings from several other populations (4,10,44,48,52,53). Child food insecurity is very severe by definition and likely consists of shortages of even the least-expensive, energy-dense foods that might lead to overweight. Food-insecure children, therefore, may be more likely to have very low total energy intake, which leads to under- rather than overweight. An association of food insecurity with low total energy intake in children has been reported previously (39).

In adults, mild to moderate food insecurity has been associated with overweight and obesity, particularly in women (11–13,40,54). Individuals experiencing these intermediate levels of food insecurity may resort to less expensive, energy-dense foods to maintain caloric intake at less cost, which can increase energy intake and contribute to overweight. However, as in the case of children and in several other adult populations (43,47), we found that severe food insecurity is more likely to be related to underweight rather than obesity. This finding may result from coping strategies by which mothers reduce their own energy intake to meet their children's needs.

Our study has some limitations. The survey was limited to households with school-aged children; this may have resulted in an underestimate of food insecurity, because especially vulnerable groups including households with preschool children and elderly people may have not been adequately represented. It is not possible to ascertain whether the associations between food insecurity, sociodemographic indicators, and nutritional status are causal due to the cross-sectional design of the survey. In addition, we were unable to control the associations between food insecurity, dietary intake, and nutritional status for the potential confounding effects of health status.

In conclusion, food insecurity in low- and middle-income families from Bogotá is highly prevalent and associated with poor living conditions. The high prevalence of food insecurity in this population supports the need for continuing programs aimed at improving food security and nutritional status of school children and their families. Given the inverse association found between child food insecurity and animal protein intake, an increase in the lean animal protein content of food supplementation programs for children should be considered. Severe insecurity in children and insecurity with hunger in adults appears to increase the risk of underweight but not overweight or obesity. Moderate food insecurity is not related to adult overweight in this population. As the nutrition transition moves forward in Colombia, it will be important to monitor the prevalence of food insecurity and the directions of its associations with health and nutritional outcomes over time, including changes in the prevalence of underweight, overweight, and chronic diseases, the prevalence of micronutrient deficiencies, the incidence of infectious morbidities, and school attendance and academic performance in children. Changes in dietary and physical activity patterns in countries undergoing the nutrition transition may alter the experiences of food insecurity, shifting its burden away from under-nutrition to greater levels of overweight and chronic conditions. Longitudinal studies that examine the impact of insecurity on these health and functional outcomes in adults and children are warranted. Further research is also needed on the coping strategies to which food-insecure Colombians resort to adapt the public health responses accordingly.

	ACKNOWLEDGMENTS

 
We thank Lilia Ramos and Angel Pérez from the City of Bogota's Secretary of Education for providing institutional and administrative support, Bibiana Trujillo for supervising data management, and Pedro Nel Pacheco from the National University of Colombia for assistance with the sample selection.

	FOOTNOTES

 
¹ Supported by the David Rockefeller Center for Latin American Studies at Harvard University, the City of Bogotá's Secretary of Education, and the National University of Colombia. Sheila Isanaka is supported in part by the National Cancer Institute (grant R25-CA098566).

² Author disclosures: S. Isanaka, M. Mora-Plazas, S. Lopez-Arana, A. Baylin, and E. Villamor, no conflicts of interest.

³ Supplemental Table 1 is available with the online posting of this paper at jn.nutrition.org.

Manuscript received 9 August 2007. Initial review completed 9 September 2007. Revision accepted 24 September 2007.

	LITERATURE CITED

TOP ABSTRACT Introduction Methods Results Discussion LITERATURE CITED

 

1. Life Sciences Research Organization. Core indicators of nutritional status for difficult-to-sample populations. J Nutr. 1990;120:1559–600.[Medline]

2. Instituto Colombiano de Bienestar Familiar. Encuesta Nacional de la Situacion Nutricional en Colombia (ENSIN). Bogota (Colombia): Instituto Colombia de Bienestar Familiar; 2005.

3. Cook JT, Frank DA, Berkowitz C, Black MM, Casey PH, Cutts DB, Meyers AF, Zaldivar N, Skalicky A, et al. Food insecurity is associated with adverse health outcomes among human infants and toddlers. J Nutr. 2004;134:1432–8.[Abstract/Free Full Text]

4. Skalicky A, Meyers AF, Adams WG, Yang Z, Cook JT, Frank DA. Child food insecurity and iron deficiency anemia in low-income infants and toddlers in the United States. Matern Child Health J. 2006;10:177–85.[Medline]

5. Stuff JE, Casey PH, Szeto KL, Gossett JM, Robbins JM, Simpson PM, Connell C, Bogle ML. Household food insecurity is associated with adult health status. J Nutr. 2004;134:2330–5.[Abstract/Free Full Text]

6. Kleinman RE, Murphy JM, Little M, Pagano M, Wehler CA, Regal K, Jellinek MS. Hunger in children in the United States: potential behavioral and emotional correlates. Pediatrics. 1998;101:E3.

7. Murphy JM, Wehler CA, Pagano ME, Little M, Kleinman RE, Jellinek MS. Relationship between hunger and psychosocial functioning in low-income American children. J Am Acad Child Adolesc Psychiatry. 1998;37:163–70.[Medline]

8. Alaimo K, Olson CM, Frongillo EA Jr. Food insufficiency and American school-aged children's cognitive, academic, and psychosocial development. Pediatrics. 2001;108:44–53.[Abstract/Free Full Text]

9. Jyoti DF, Frongillo EA, Jones SJ. Food insecurity affects school children's academic performance, weight gain, and social skills. J Nutr. 2005;135:2831–9.[Abstract/Free Full Text]

10. Casey PH, Szeto K, Lensing S, Bogle M, Weber J. Children in food-insufficient, low-income families: prevalence, health, and nutrition status. Arch Pediatr Adolesc Med. 2001;155:508–14.[Abstract/Free Full Text]

11. Wilde PE, Peterman JN. Individual weight change is associated with household food security status. J Nutr. 2006;136:1395–400.[Abstract/Free Full Text]

12. Adams EJ, Grummer-Strawn L, Chavez G. Food insecurity is associated with increased risk of obesity in California women. J Nutr. 2003;133:1070–4.[Abstract/Free Full Text]

13. Townsend MS, Peerson J, Love B, Achterberg C, Murphy SP. Food insecurity is positively related to overweight in women. J Nutr. 2001;131:1738–45.[Abstract/Free Full Text]

14. Alcaldía Mayor de Bogotá. Secretaría de Educación. Estadísticas del sector educativo de Bogotá 2005 y avances 2006. [Accessed 2007 Oct 9]. Available from: http://www.sedbogota.edu.co/secretaria/export/SED/seducativo/compendio_estadistico/M_STADISTICAS.PDF. 2006.

15. Lohman TG, Roche AF, Martorell R, editors. Anthropometric standardization reference manual Champaign (IL): Human Kinetics Books: 1988.

16. Kabagambe EK, Baylin A, Allan DA, Siles X, Spiegelman D, Campos H. Application of the method of triads to evaluate the performance of food frequency questionnaires and biomarkers as indicators of long-term dietary intake. Am J Epidemiol. 2001;154:1126–35.[Abstract/Free Full Text]

17. Harrison GG, Stormer A, Herman DR, Winham DM. Development of a Spanish-language version of the U.S. household food security survey module. J Nutr. 2003;133:1192–7.[Abstract/Free Full Text]

18. Wehler CA, Scott RI, Anderson JJ. The Community Childhood Hunger Identification Project: a model of domestic hunger: demonstration project in Seattle, Washington. J Nutr Educ. 1992;24:S29–35.

19. Hamilton W, Cook J, Thompson W, Buron L, Frongillo E, Olson C, Wehler C. Household Food Security in the United States in 1995: technical report of the Food Security Measurement Project. Alexandria (VA): USDA, Food and Consumer Services, Office of Analysis and Evaluation; 1997.

20. Kaiser LL, Melgar-Quinonez H, Townsend MS, Nicholson Y, Fujii ML, Martin AC, Lamp CL. Food insecurity and food supplies in Latino households with young children. J Nutr Educ Behav. 2003;35:148–53.[Medline]

21. Matheson DM, Varady J, Varady A, Killen JD. Household food security and nutritional status of Hispanic children in the fifth grade. Am J Clin Nutr. 2002;76:210–7.[Abstract/Free Full Text]

22. Derrickson JP, Fisher AG, Anderson JE. The core food security module scale measure is valid and reliable when used with Asians and Pacific Islanders. J Nutr. 2000;130:2666–74.[Abstract/Free Full Text]

23. Studdert LJ, Frongillo EA Jr, Valois P. Household food insecurity was prevalent in Java during Indonesia's economic crisis. J Nutr. 2001;131:2685–91.[Abstract/Free Full Text]

24. Perez-Escamilla R, Segall-Correa AM, Kurdian Maranha L, Sampaio Md Mde F, Marin-Leon L, Panigassi G. An adapted version of the U.S. Department of Agriculture Food Insecurity module is a valid tool for assessing household food insecurity in Campinas, Brazil. J Nutr. 2004;134:1923–8.[Abstract/Free Full Text]

25. Baig-Ansari N, Rahbar MH, Bhutta ZA, Badruddin SH. Child's gender and household food insecurity are associated with stunting among young Pakistani children residing in urban squatter settlements. Food Nutr Bull. 2006;27:114–27.[Medline]

26. Albert PL, Sanjur D. The adaptation and validation of the food security scale in a community of Caracas, Venezuela. Arch Latinoam Nutr. 2000;50:334–40.[Medline]

27. Alvarez MC, Estrada A, Montoya EC, Melgar-Quinonez H. Validation of a household food security scale in Antioquia, Colombia. Salud Publica Mex. 2006;48:474–81.[Medline]

28. Nunnally JC. Psychometric theory. 2nd ed. New York: McGraw-Hill; 1978.

29. Bickel G, Nord M, Price C, Hamilton W, Cook J. Guide to measuring household food security. Alexandria (VA): USDA, Food and Nutrition Service; 2000.

30. Nord M, Bickel G. Measuring children's food security in U.S. households, 1995–1999. USDA, Economic Research Service; 2002.

31. Hair JF Jr, Anderson RE, Tatham RL, Black WC. Multivariate data analysis. Englewood Cliffs (NJ): Prentice-Hall; 1998.

32. WHO. Measuring change in nutritional status. Geneva: WHO; 1983.

33. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ. 2000;320:1240–3.[Abstract/Free Full Text]

34. WHO Expert Committee on Physical Status. Physical status: the use and interpretation of anthropometry. Geneva: WHO; 1995.

35. Spiegelman D, Hertzmark E. Easy SAS calculations for risk or prevalence ratios and differences. Am J Epidemiol. 2005;162:199–200.[Free Full Text]

36. Fitzmaurice GM, Laird NM, Ware JH. Applied longitudinal analysis. Hoboken (NJ): Wiley; 2004.

37. Mercado C, Lorenzana P. Acceso y disponibilidad alimentaria familiar. Validación de instrumentos para su medición. Caracas: Fundacio Polar; 2000.

38. Melgar-Quinonez HR, Zubieta AC, MkNelly B, Nteziyaremye A, Gerardo MF, Dunford C. Household food insecurity and food expenditure in Bolivia, Burkina Faso, and the Philippines. J Nutr. 2006;136:S1431–7.[Abstract/Free Full Text]

39. Oh SY, Hong MJ. Food insecurity is associated with dietary intake and body size of Korean children from low-income families in urban areas. Eur J Clin Nutr. 2003;57:1598–604.[Medline]

40. Shariff ZM, Khor GL. Obesity and household food insecurity: evidence from a sample of rural households in Malaysia. Eur J Clin Nutr. 2005;59:1049–58.[Medline]

41. Rose D, Gundersen C, Oliveira V. Socio-economic determinants of food insecurity in the United States: evidence from the SIPP and CSFII datasets. Washington: Economic Research Service, USDA; 1998.

42. Alaimo K, Briefel RR, Frongillo EA Jr, Olson CM. Food insufficiency exists in the United States: results from the third National Health and Nutrition Examination Survey (NHANES III). Am J Public Health. 1998;88:419–26.[Abstract/Free Full Text]

43. Gulliford MC, Mahabir D, Rocke B. Food insecurity, food choices, and body mass index in adults: nutrition transition in Trinidad and Tobago. Int J Epidemiol. 2003;32:508–16.[Abstract/Free Full Text]

44. Kaiser LL, Melgar-Quinonez HR, Lamp CL, Johns MC, Sutherlin JM, Harwood JO. Food security and nutritional outcomes of preschool-age Mexican-American children. J Am Diet Assoc. 2002;102:924–9.[Medline]

45. Weigel MM, Armijos RX, Hall YP, Ramirez Y, Orozco R. The household food insecurity and health outcomes of U.S.-Mexico border migrant and seasonal farmworkers. J Immigr Minor Health. 2007;9:157–69.[Medline]

46. Hadley C, Sellen D. Food security and child hunger among recently resettled Liberian refugees and asylum seekers: a pilot study. J Immigr Minor Health. 2006;8:369–75.[Medline]

47. Sarlio-Lahteenkorva S, Lahelma E. Food insecurity is associated with past and present economic disadvantage and body mass index. J Nutr. 2001;131:2880–4.[Abstract/Free Full Text]

48. Rose D, Bodor JN. Household food insecurity and overweight status in young school children: results from the Early Childhood Longitudinal Study. Pediatrics. 2006;117:464–73.[Abstract/Free Full Text]

49. Darmon N, Ferguson EL, Briend A. Impact of a cost constraint on nutritionally adequate food choices for French women: an analysis by linear programming. J Nutr Educ Behav. 2006;38:82–90.[Medline]

50. Mendoza JA, Drewnowski A, Cheadle A, Christakis DA. Dietary energy density is associated with selected predictors of obesity in U.S. Children. J Nutr. 2006;136:1318–22.

51. Drewnowski A, Specter SE. Poverty and obesity: the role of energy density and energy costs. Am J Clin Nutr. 2004;79:6–16.[Abstract/Free Full Text]

52. Whitaker RC, Orzol SM. Obesity among US urban preschool children: relationships to race, ethnicity, and socioeconomic status. Arch Pediatr Adolesc Med. 2006;160:578–84.[Abstract/Free Full Text]

53. Casey PH, Simpson PM, Gossett JM, Bogle ML, Champagne CM, Connell C, Harsha D, McCabe-Sellers B, Robbins JM, et al. The association of child and household food insecurity with childhood overweight status. Pediatrics. 2006;118:e1406–13.[Abstract/Free Full Text]

54. Hanson KL, Sobal J, Frongillo EA. Gender and marital status clarify associations between food insecurity and body weight. J Nutr. 2007;137:1460–5.[Abstract/Free Full Text]

This article has been cited by other articles:

				C. M. McDonald, A. Baylin, J. E. Arsenault, M. Mora-Plazas, and E. Villamor Overweight Is More Prevalent Than Stunting and Is Associated with Socioeconomic Status, Maternal Obesity, and a Snacking Dietary Pattern in School Children from Bogota, Colombia J. Nutr., February 1, 2009; 139(2): 370 - 376. [Abstract] [Full Text] [PDF]

				E. Villamor, M. Mora-Plazas, Y. Forero, S. Lopez-Arana, and A. Baylin Vitamin B-12 Status Is Associated with Socioeconomic Level and Adherence to an Animal Food Dietary Pattern in Colombian School Children J. Nutr., July 1, 2008; 138(7): 1391 - 1398. [Abstract] [Full Text] [PDF]

This Article Abstract Full Text (PDF) Online Supporting Material Purchase Article View Shopping Cart Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Isanaka, S. Articles by Villamor, E. Search for Related Content PubMed PubMed Citation Articles by Isanaka, S. Articles by Villamor, E.