1. IDENTIFYING RISK FACTORS FOR FOOD INSECURITY AT THE ZIP CODE LEVEL
Brandon Ho1, Padma Swamy MD1,2, and Ana Monterrey MD, MPH1,2
1 Baylor College of Medicine , Houston, TX, USA
2 Department of Pediatrics, Texas Children’s Hospital, Houston, TX, USA
Introduction
Results
203 patients total screened
High Risk: 77506
(n=36; FI prevalence = 27%)
The United States Department of Agriculture defines food insecurity (FI) as a state of being without reliable access to a sufficient quantity of affordable, nutritious food. Nationally, the USDA reports 21% of children live in households experiencing food insecurity. In Texas, 15.4% of households (1 in 6) are affected by food insecurity. Numerous studies have established that these food insecure children are at an increased risk of obesity, illness, and poor school performance. As of October 2015, the American Academy of Pediatrics recognized food insecurity as a significant child health issue and recommended that all pediatricians routinely screen for food insecurity.
Previous studies have shown that food insecurity is closely related to poverty related factors such as income, education, and disability. Most of these studies were done at the county and state level to assess risk factors in order to predict food insecurity amongst large populations in the United States. However, no studies have assessed risk factors for food insecurity at the zip code level. The study’s objective is to assess food insecurity in a patient population to identify high risk zip codes and characterize their contributing risk factors.
Control: 77017
(n=12; FI prevalence = 0%)
9 dropped due to lack of FI status documentation
194 charts reviewed. 30 total zip codes identified
Using U.S. census data, various demographic factors such as poverty, unemployment, insurance status, SNAP enrollment, public assistance, household size, presence of food deserts and proximity to resources were assessed.
Zip codes with <10 total screened were excluded
8 zip codes remain for analysis
Comparison between and control showed no statistically significant relationship was found between FI status and age, sex, or BMI.
Table 1: Census Data Zip Code Characteristics
77017
77506
Total Population
6662
4186
Median age
31.7
27.9
Disability 9%
12.10%
College 7%
4.3%
Food Desert
Yes No
Unemployment
9.30%
13.6%
Below poverty level
20.3
31.2
Uninsured
13.25%
38.8%
SNAP enrollment
19.7%
28.2%
Public assistance income
1.86%
3.09%
Monthly housing costs (median)
$758
$688
>4-person household
40.1%
10.1%
1-person household
20.6%
47.5%
Household w/ children <18
48.2%
13.8%
No vehicles available
10.2% 5%
Abstract
Introduction: Food insecurity (FI) is a significant child health issue affecting over 15 million children in the United States. Studies show that these children are at an increased risk of obesity, illness, and school deficits. As of 2015, the AAP recommends that all pediatricians screen for FI. The study’s objective is to assess FI in a patient population to identify high risk zip codes and characterize their contributing risk factors.
Methods: A cross sectional retrospective study was conducted at a pediatric community clinic in Pasadena, TX which primarily serves low-income families. Screening for FI was performed using the Hunger Vital Sign validated 2-item questionnaire done at well child visits 0-18 years. Data on FI status, age, sex, BMI, and zip code were collected. Using census data, various other demographic risk factors were assessed to compare control and high risk zip codes.
Results: U.S. census data showed that high risk zip code (77506) compared to control (77017) had a higher rate of poverty (32.2% v 20.3%), unemployment (13.6% v 9.30%), uninsurance (38.8% v 13.25%), SNAP enrollment (28.2% v 19.7%), and public assistance income (3.09% v 1.86%) was not located within a food desert and has access to community programs such as the Houston Food Bank and Brighter Bites, whereas was located in a food desert and has no known community programs. No statistically significant relationship was found between FI status and age, sex, or BMI.
Conclusion: Census tract data shows that is in an area with higher rates of poverty, unemployment, uninsurance, SNAP enrollment, and public assistance income.
Figure 1: Distribution of Resources in Relation to Zip Codes of Interest
Yellow house = clinic, Blue marker= food pantry, Green marker = Brighter Bites, Orange = food desert
Conclusions
Methods
This study shows that risk factors related to poverty are associated with an increased likelihood of food insecurity. No statistical significance was seen between food insecurity status and age, sex, or BMI. Food desserts do not correlate as well to food insecurity levels at the zip code level. This could be due to the fact that food desert data does not reflect quality of food in an area, thus may be in an area with good availability but questionable quality of food.
Limitations of this study include the fact that assessment of risk factors was based on census data rather than direct survey of patients, which limits the ability to say was statistically significant from in regards to each risk factor. In addition, only had a sample size of 12 patients, so it may not fully reflect the amount of FI present in that zip code.
In conclusion, community clinics who routinely screen for food insecurity can play an important role in identifying and characterizing high risk areas in their community. Clinics can then use this data to partner with local community programs to develop initiatives to address community-specific barriers and encourage state and federal level policy change.
A cross sectional retrospective study was conducted from July 2016 – January 2017 at the Pediatric and Adolescent Health Center (PAHC). The PAHC is an outpatient clinic part of the Harris Health System that primarily serves low-income children living in the southeast Houston and Pasadena area.
Per AAP recommendations, all patients were screened for food insecurity at well child visits 0-18 years using the Hunger Vital Sign validated 2-item questionnaire: ”Within the past 12 months we worried whether our food would run out before we got money to buy more,” and "Within the past 12 months the food we bought just didn't last and we didn't have money to get more.“ In addition, data on food insecurity status, age, sex, BMI, and zip code were also collected.
Out of 30 total zip codes, 8 were analyzed after excluding zip codes with <10 total screened. High risk zip code with the highest prevalence of FI (n= 36; FI prevalence = 27%) was compared to the control which had no FI (n=12; FI prevalence = 0%). Using census data, various demographic factors such as poverty, unemployment, insurance status, SNAP enrollment, public assistance, household size, presence of food deserts and proximity to resources were assessed.
Bivariate statistical analysis was done comparing the two zip codes using T-tests for continuous variables and Chi-square analysis for categorical variables.
References
Barnidge, E., LaBarge, G., Krupsky, K. et al. “Screening for Food Insecurity in Pediatric Clinical Settings: Opportunities and Barriers.” J Community Health (2017) 42: 51.
Belsky, Daniel W. et al. “Context and Sequelae of Food Insecurity in Children’s Development.” American Journal of Epidemiology 172.7 (2010): 809–818. PMC.
Data Access and Dissemination Systems (DADS). "Your Geography Selections."American FactFinder. US Department of Commerce, 05 Oct Web. 18 Apr
"Map the Meal Gap." Food Insecurity in Texas. Feeding America, 2017.
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