Food Insecurity and the Behavioral and Intellectual Development of Children: A Review of the Evidence

Background: Given that an alarming 1 in 5 children in the USA are at risk of hunger (1 in 3 among black and Latino children), and that 3.9 million households with children are food insecure, it is crucial to understand how household food insecurity (HFI) affects the present and future well-being of our children. Purpose: The objectives of this review article are to: (i) examine the association between HFI and child intellectual, behavioral and psycho-emotional development, controlling for socio-economic indicators; (ii) review the hypothesis that HFI is indeed a mediator of the relationship between poverty and poor child development outcomes; (iii) examine if the potential impact of HFI on caregivers’ mental health well-being mediates the relationship between HFI and child development outcomes. Methods: Pubmed search using the key words “food insecurity children.” For articles to be included they had to: (i) be based on studies measuring HFI using an experience-based scale, (ii) be peer reviewed, and (iii) include child intellectual, behavioral and/or socio-emotional development outcomes. Studies were also selected based on backward and forward Pubmed searches, and from the authors’ files. After reviewing the abstracts based on inclusion criteria a total of 26 studies were selected. Results: HFI represents not only a biological but also a psycho-emotional and developmental challenge to children exposed to it. Children exposed to HFI are more likely to internalize or externalize problems, as compared to children not exposed to HFI. This in turn is likely to translate into poor academic/cognitive performance and intellectual achievement later on in life. A pathway through which HFI may affect child development is possibly mediated by caregivers’ mental health status, especially parental stress and depression. Thus, HFI is likely to foster dysfunctional family environments. Conclusion: Findings indicate that food insecure households may require continued food assistance and psycho-emotional support until they transition to a “stable” food secure situation. This approach will require a much better integration of social policies and access to programs offering food assistance and mental health services to those in need. Findings also fully justify increased access of vulnerable children to programs that promote early in life improved nutrition as well as early psycho-social and cognitive stimulation opportunities.

AN ANALYSIS OF THE RELATIVE INFLUENCE OF RACE, INCOME, EDUCATION, AND FOOD STAMP PROGRAM PARTICIPATION/NONPARTICIPATION ON THE FOOD AND NUTRIENT CONSUMPTION OF A NORTH FLORIDA URBAN CLINIC POPULATION IN CONJUNCTION WITH A SURVEY OF NUTRITION RELATED HABITS AND ATTITUDES

The relative influence of race, income, education, and Food Stamp Program participation/nonparticipation on the food and nutrient intake of 102 fecund women ages 18-45 years in a Florida urban clinic population was assessed using the technique of multiple regression analysis. Study subgroups were defined by race and Food Stamp Program participation status. Education was found to have the greatest influence on food and nutrient intake. Race was the next most influential factor followed in order by Food Stamp Program participation and income. The combined effect of the four independent variables explained no more than 19 percent of the variance for any of the food and nutrient intake variables. This would indicate that a more complex model of influences is needed if variations in food and nutrient intake are to be fully explained.^ A socioeconomic questionnaire was administered to investigate other factors of influence. The influence of the mother, frequency and type of restaurant dining, and perceptions of food intake and weight were found to be factors deserving further study.^ Dietary data were collected using the 24-hour recall and food frequency checklist. Descriptive dietary findings indicated that iron and calcium were nutrients where adequacy was of concern for all study subgroups. White Food Stamp Program participants had the greatest number of mean nutrient intake values falling below the 1980 Recommended Dietary Allowances (RDAs). When Food Stamp Program participants were contrasted to nonparticipants, mean intakes of six nutrients (kilocalories, calcium, iron, vitamin A, thiamin, and riboflavin) were below the 1980 RDA compared to five mean nutrient intakes (kilocalories, calcium, iron, thiamin and riboflavin) for the nonparticipants. Use of the Index of Nutritional Quality (INQ), however, revealed that the quality of the diet of Food Stamp Program participants per 1000 kilocalories was adequate with exception of calcium and iron. Intakes of these nutrients were also not adequate on a 1000 kilocalorie basis for the nonparticipant group. When mean nutrient intakes of the groups were compared using Student's t-test oleicacid intake was the only significant difference found. Being a nonparticipant in the Food Stamp Program was found to be associated with more frequent consumption of cookies, sweet rolls, doughnuts, and honey. The findings of this study contradict the negative image of the Food Stamp Program participant and emphasize the importance of education. ^

The characterization of residential fungal spores and the relationship with housing characteristics in the Houston area

A number of indoor environmental factors, including bioaerosol or aeroallergen concentrations have been identified as exacerbators for asthma and allergenic conditions of the respiratory system. People generally spend 90% to 95% of their time indoors. Therefore, understanding the environmental factors that affect the presence of aeroallergens indoors as well as outdoors is important in determining their health impact, and in identifying potential intervention methods. This study aimed to assess the relationship between indoor airborne fungal spore concentrations and indoor surface mold levels, indoor versus outdoor airborne fungal spore concentrations and the effect of previous as well as current water intrusion. Also, the association between airborne concentration of indoor fungal spores and surface mold levels and the age of the housing structure were examined. Further, the correlation between indoor concentrations of certain species was determined as well. ^ Air and surface fungal measurements and related information were obtained from a Houston-area data set compiled from visits to homes filing insurance claims. During the sampling visit these complaint homes exhibited either visible mold or a combination of visible mold and water intrusion problems. These data were examined to assess the relationships between the independent and dependent variables using simple linear regression analysis, and independent t-tests. To examine the correlation between indoor concentrations of certain species, Spearman correlation coefficients were used. ^ There were 126 houses sampled, with spring, n=43 (34.1%), and winter, n=42 (33.3%), representing the seasons with the most samples. The summer sample illustrated the highest geometric mean concentration of fungal spores, GM=5,816.5 relative to winter, fall and spring (GM=1,743.4, GM=3,683.5 and GM=2,507.4, respectively). In all seasons, greater concentrations of fungal spores were observed during the cloudy weather conditions. ^ The results indicated no statistically significant association between outdoor total airborne fungal spore concentration and total living room airborne fungal spore concentration (β = 0.095, p = 0.491). Second, living room surface mold levels were not associated with living room airborne fungal spore concentration, (β= 0.011, p = 0.669). Third, houses with and without previous water intrusion did not differ significantly with respect to either living room (t(111) = 0.710, p = 0.528) or bedroom (t(111) =1.673, p = 0.162) airborne fungal spore concentrations. Likewise houses with and without current water intrusion did not differ significantly with respect to living room (t(109)=0.716, p = 0.476) or bedroom (t(109) = 1.035, p = 0.304) airborne fungal spore concentration. Fourth, houses with and without current water intrusion did not differ significantly with respect to living room (χ 2 (5) = 5.61, p = 0.346), or bedroom (χ 2 (5) = 1.80, p = 0.875) surface mold levels. Fifth, the age of the house structure did not predict living room (β = 0.023, p = 0.102) and bedroom (β = 0.023, p = 0.065) surface mold levels nor living room (β = 0.002, p = 0.131) and bedroom (β = 0.001, p = 0.650) fungal spore airborne concentration. Sixth, in houses with visually observed mold growth there was statistically significant differences between the mean living room concentrations and mean outdoor concentrations for Cladosporium (t (107) = 11.73, p < 0.0001), Stachybotrys (t (106)=2.288, p = 0.024, and Nigrosporia (t (102) = 2.267, p = 0.025). Finally, there was a significant correlation between several living room fungal species pairs, namely, Cladosporium and Stachybotrys (r = 0.373, p <0.01, n=65), Curvularia and Aspergillus/Penicillium (r = 0.205, p < 0.05, n= 111)), Curvularia and Stachybotrys (r = 0.205, p < 0.05, n=111), Nigrospora and Chaetomium (r = 0.254, p < 0.01, n=105) and Stachybotrys and Nigrospora (r = 0.269, p < 0.01, n=105). ^ This study has demonstrated several positive findings, i.e., significant pairwise correlations of concentrations of several fungal species in living room air, and significant differences between indoor and outdoor concentrations of three fungal species in homes with visible mold. No association was observed between indoor and outdoor fungal spore concentrations. Neither living room nor bedroom airborne spore concentrations and surface mold levels were related to the age of the house or to water intrusion, either previous or current. Therefore, these findings suggest the need for evaluating additional parameters, as well as combinations of factors such as humidity, temperature, age of structure, ventilation, and room size to better understand the determinants of airborne fungal spore concentrations and surface mold levels in homes. ^