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Division of Nutritional Sciences, Cornell University, Ithaca, NY 14853-6301
1To whom correspondence should be addressed. E-mail: eaf1{at}cornell.edu.
See related article: J. Nutr. 133: 2225-2231.
Health officials, scientists and the public are increasingly concerned about the obesity epidemic and accompanying morbidity, mortality, health care costs, and loss of quality of life. This concern motivated Gibson’s article in this issue (1). Rapid shift in the distribution of relative weight to higher values has resulted in a substantially greater number of people being classified as obese. There is considerable interest in what policy and programmatic actions can and should be taken to reverse this shift. Besides constructing new policies and programs, we might also question whether existing policies and programs are inadvertently contributing to the problem.
Nutrition programs designed in the past were primarily intended to help avert nutritional inadequacies. Concern about possible unintended effects of nutrition programs is not new (2), but apparently little evidence of this has existed until the recent linking of program participation with obesity. In 2001 Townsend and colleagues, using nationally representative cross-sectional data, reported that Food Stamp Program (FSP) participation and overweight are associated for women, but not for men (3).
Gibson (1) studied FSP participation and obesity, attempting to overcome limitations in the Townsend study. Data used were from the 1985 to 1996 waves of the National Longitudinal Survey of Youth 1979; earlier data measured long-term family resources. Both concurrent and long-term FSP participation were associated with increased absolute risk of obesity of about 2 and 4.5 percentage points, respectively. The study used longitudinal data, but had no data on food insecurity. This commentary discusses Gibson’s use of data, conceptual frameworks, food insecurity and the role of program participation.
Longitudinal data have advantages in reducing effects of unmeasured factors, having a clear temporal sequence, directly measuring change, and estimating dynamic relations of change with other factors (4). They can provide the best means, absent a randomized design, to establish that observed associations are not due to confounding and are causal (5). Gibson’s analysis partially utilized these advantages. Indicator variables representing individuals accounted for unmeasured time-invariant factors that may have otherwise confounded the relation between FSP participation and obesity, but cannot account for unmeasured time-varying factors. The temporal sequence was partially used when obesity at one year was modeled as a function of resources and FSP participation in the previous five years. The analytic approach, however, did not directly measure change in weight or obesity and did not estimate fully dynamic relations.
Future research with these or other longitudinal data should directly examine how change is associated dynamically with other factors using multiple time points. For example, how does transition from not participating to participating in the FSP relate to subsequent weight gain or to transition from a nonobese to an obese state? Using multiple years of data and dynamic analytic approaches (4,6), these transitions can be examined in detail, using the temporal sequence to strengthen the plausibility that associations are causal.
Several studies investigated whether poverty and food insecurity of families is linked with obesity (3,7–14). The only consistent finding is that white women in families that are resource-constrained-as measured by poverty, food insecurity, or both-are more likely to be obese. Food insecurity in children is not associated with overweight except in older (8–16 y), non-Hispanic white girls (11–12). One study found that, although mild or moderate food insecurity is associated with a higher risk of obesity, severe food insecurity is associated with a lower risk (9–10); three other studies showed similar but nonsignificant trends (3,13–14). Since resource-constrained families are more likely to participate in nutrition programs, any association of program participation and obesity must be understood in the context of poverty and food insecurity.
Gibson’s conceptual framework depicted personal characteristics (including poverty and FSP participation) as causally related to obesity, either directly or by being mediated (i.e., indirectly) through food insecurity and health behaviors. This framework is generally consistent with others depicting the conceptual relations of poverty and other personal characteristics (but not program participation), food insecurity, and outcomes (11,15–16). However, Gibson’s framework and analysis lead to two questions. First, are placement of FSP participation among personal characteristics in the framework and assumed causal paths reasonable? Second, what are the possible consequences of not including a measure of food insecurity in analysis?
The framework assumed that FSP participation is a personal characteristic that differs over time, and causal flow is from FSP participation to food insecurity and health behaviors to obesity. An alternate view is that FSP participation is a behavior that responds to personal circumstances, including food insecurity. These are two fundamentally different perspectives. Does FSP participation represent something that a person is or that a person does?
Especially if FSP participation is a behavior, then the plausible causal relations are different than depicted in the framework. Gibson noted that interpretation of the effect of FSP participation is difficult if food insecurity influenced both FSP participation and obesity. That is, causality may operate, at least in part, reversed from that depicted by Gibson.
Furthermore, FSP participation is an optional behavior. Eligible people select themselves to participate or not. In general, social causation and social selection are two contrasting explanations for social relations (17–18). Determining the extent to which these two explanations operate requires designs that can differentiate them. Two such designs make use of structural relations in the data (17) and longitudinal data (18). Dynamic models could potentially differentiate these explanations if sufficient numbers of people changed over time in food insecurity, FSP participation and obesity (4–5).
Gibson wrote that not including food insecurity and health behaviors in the analytic models had both an advantage (gave an overall estimate) and a disadvantage (no insight into mechanisms). However, these would only be realized if the conceptual framework depicted the relations reasonably.
Gibson suggested that lack of association between FSP participation and obesity for low income men, as compared to that of women, be explained by lower exposure to the FSP. This explanation may make sense if we accept Gibson’s framework, but it does not fit with the more reasonable conceptualization that FSP participation is a behavior at least partially responding to poverty and food insecurity. Then, it is not surprising that FSP participation is not associated with obesity in men, because poverty and food insecurity are not associated with obesity in males (7,11).
An obvious possible explanation for the association observed between FSP participation and obesity is that both are caused in part by food insecurity. That is, FSP participation is an indicator of food insecurity. People select themselves to participate in a nutrition program because they have need (5). Need is not completely captured by eligibility based on income, and food insecurity may provide a better, but still not complete, way of capturing need. For example, food insecure elders participating in programs had the poorest outcomes, most likely because, among the food insecure, they had greater need and therefore participated (5). Gibson accounted for income but not assets in adjusting for FSP eligibility and did not have a measure of food insecurity, so FSP need was not adequately captured.
Selection might bring other explanations. For example, a possible scenario is that single, unemployed women are heavier. If they have a child (itself contributing to weight gain), they are more likely to become FSP participants. FSP participation is then spurious to the relation of resource constraints and obesity.
Gibson discussed two possible explanations, consumption of more or kinds of food that lead to obesity, and within-month variability resulting in periodic overconsumption. There is insufficient evidence about either to know about their importance.
A conceptualization of the role of program participation different from Gibson’s leads to different explanations. Food insecurity is a stressor, i.e., it leads to stress. Stress has broad effects (19–20), and is linked specifically to eating behaviors and obesity (21). A common model of stress depicts that conditioning factors modify whether stressors result in stress and whether stress causes poor outcomes. Important insights will be gained by investigating how program participation acts as a modifier between food insecurity and outcomes. Evidence for this idea is that food-insecure girls who participated in nutrition programs had a reduced risk for overweight when compared to food-insecure girls in nonparticipating households (22).
Despite Gibson’s thoughtful report, we cannot conclude whether FSP participation results in obesity because of important study limitations. Further research is needed to understand how poverty, food insecurity and program participation are related to obesity, and what are the mechanisms operating. This research will help clarify how policies and programs can best be designed to alleviate poverty and food insecurity, an important policy goal given that these forms of deprivation are associated with serious physical and mental health, academic and psychological problems, particularly in children (11).
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ACKNOWLEDGMENTS |
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Manuscript received 5 April 2003. Revision accepted 9 April 2003.
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LITERATURE CITED |
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 TOP LITERATURE CITED |
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1. Gibson, D. (2003) Food Stamp Program participation is related to obesity in low income women. J. Nutr. 133:2225-2231.
2. Rada, E. L. (1974) Medicating the Food Stamp Program. Am. J. Public Health 64:477-480.
3. Townsend, M., Peerson, J., Love, B., Achterberg, C. & Murphy, S. (2001) Food insecurity is positively related to overweight in women. J. Nutr. 131:1738-1745.
4. Frongillo, E. A. & Rowe, E. M. (1999) Challenges and solutions in using and analyzing longitudinal growth data. Chapter 5. Johnston, F. E. Eveleth, P. Zemel, B. eds. Human Growth in Context 1999:51-64 Smith-Gordon London .
5. Lee, J. S. & Frongillo, E. A. (2001) Understanding needs is important for assessing the impact of food assistance program participation on nutritional and health status in U. S. elderly persons. J. Nutr. 131:765-773.
6. Yamaguchi, K. (1991) Event History Analysis 1991:182 Sage Publications Newbury Park, CA .
7. Federation of American Societies for Experimental Biology, Life Sciences Research Office. (1995) Third Report on Nutrition Monitoring in the United States: Volume 1. Prepared for the Interagency Board for Nutrition Monitoring and Related Research. 1995:365 US Government Printing Office Washington, D. C.
8. Dietz, W. (1995) Does hunger cause obesity?. Pediatrics 95:766-767.
9. Frongillo, E. A., Olson, C. M., Rauschenbach, B. S. & Kendall, A. (1997) Nutritional consequences of food insecurity in a rural New York State county. Discussion Paper no. 1120–97 1997 Institute for Research on Poverty, University of Wisconsin Madison, WI .
10. Olson, C. M. (1999) Nutrition and health outcomes associated with food insecurity and hunger. J. Nutr. 129:521S-524S.
11. Alaimo, K., Olson, C. M. & Frongillo, E. A. (2001) Low family income and food insufficiency in relation to overweight in US children: is there a paradox?. Arch. Pediatr. Adolesc. Med. 155:1161-1167.
12. Casey, P. H., Szeto, K., Lensing, S., Bogle, M. & Weber, J (2001) Children in food-insufficient, low-income families—prevalence, health, and nutrition status. Arch. Pediatr. Adolesc. Med. 155:508-514.
13. Kaiser, L. L., Melgar-Quinonez, H. R., Lamp, C. L., Johns, M. C., Sutherlin, J. M. & Harwood, J. O. (2002) Food security and nutritional outcomes of preschool-age Mexican-American children. J. Amer. Diet. Assoc. 102:924-929.
14. Sarlio-Lähteenkorva, S. & Lahelma, E. (2001) Food insecurity is associated with past and present economic disadvantage and body mass index. J. Nutr. 131:2880-2884.
15. Life Sciences Research Organization (1990) Core indicators of nutritional status for difficult-to-sample populations. J. Nutr. 120(suppl):1559-1600.
16. Lee, J. S. & Frongillo, E. A. (2001) Nutritional and health consequences are associated with food insecurity among U. S. elderly persons. J. Nutr. 131:1503-1509.
17. Dohrenwend, B. P, Levav, I., Shrout, P. E., Schwartz, S., Naveh, G., Link, B. G., Skodol, A. E. & Stueve, A. (1992) Socioeconomic status and psychiatric disorders: the causation-selection issue. Science 255:946-952.
18. Sobal, J., Rauschenbach, B. S. & Frongillo, E. A. (1992) Marital status, fatness, and obesity. Soc. Sci. Med. 35:915-923.
19. Alaimo, K., Olson, C. M. & Frongillo, E. A. (2001) Food insufficiency and U. S. school-age children’s cognitive, academic, and psychosocial development. Pediatrics 108:44-53.
20. Alaimo, K., Olson, C. M. & Frongillo, E. A. (2002) Family food insufficiency, but not low family income, is positively associated with dysthymia and suicide symptoms in adolescents. J. Nut. 132:719-725.
21. Laitinen, J., Ek, E. & Sovio, U. (2002) Stress-related eating and drinking behavior and body mass index and predictors of this behavior. Prev. Med. 34:29-39.[Medline]
22. Jones, S. J., Jahns, L., Laraia, B. A. & Haughton, B. (2003) School-aged food insecure children who participate in food assistance are less likely to be at risk of overweight: Results from the Panel Study of Income Dynamics Child Development Supplement. Arch. Pediatr. Adolesc. Med. (in press).
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