An Exploratory Study of Community Food Security Measurement to Inform Community-Based Intervention Strategies
Population increases, loss of arable land, and the dependence on non-renewable energy inputs threatens our global food system. Rising food prices impact vulnerable, low-income populations and strains community resources. Food security is access by all people at all times to enough food for a healthy lifestyle. Food insecurity is related to impaired development, decreased educational attainment, depression, anxiety, social isolation, and chronic diet-related diseases. Long-term health costs and impaired work forces impact community viability. This research is based on community food security models, which address health, social, economic, and environmental justice issues within the food system.
This study extends the knowledge of food security beyond socio-demographic predictors (e.g., unemployment). It includes food environment indicators (access to food stores, availability of various food stores, affordability, availability and use of public and private food assistance programs, and local food production). A method for measuring community food security and identifying protective and risk factors was developed.
Methods:
The purposive sample included 114 counties and one city in a Midwest state. The majority of the data came from the American Community Survey and the USDA Food Environment Atlas.
A validated model estimation technique was used to estimate county level food insecurity. Nine state level socio-demographic predictors were regressed onto USDA household food insecurity rates for every state. The coefficients were used as multipliers with county-level predictors, providing an estimation of the percentage of households who are food insecure in each county (9.18%-24.28%).
A PCA was used to reduce the food environment data into components. Extracted component scores were regressed onto the modeled county food insecurity estimates. Assumptions were checked, reducing the sample to 106. Component scores were used in a regression model to identify the most important food environment predictors of community food insecurity.
Component scores were multiplied by the coefficients in a regression equation, producing an estimate of the percentage of households that would be considered community food insecure (10.11%-22.88%). This model improved upon the socio-demographic model by including food environment predictors.
Results:
The PCA reduced the data from 46 to 22 indicators. Six components (food programs, access, agricultural production, direct farm sales, program usage, affordability and availability) were retained, accounting for 76.43% of the variance.
The regression model explained 58.7% of variance of community food security. Availability and affordability contributed the most, followed by program usage, access, and agricultural production.
Transportation limitations and distance to food stores were risk factors. Housing affordability protected communities. Low community food insecure counties distributed more emergency food and had more Community Supported Agriculture (CSA) programs.
Conclusions/Implications:
This method for measuring community food security allows a way to conceptualize systems components that can be manipulated through interventions. Examples include increasing affordable housing stock, piloting a CSA program, working to improve public transportation, or increasing emergency food supplies.
Since food insecurity exists in vulnerable populations, social workers can use this knowledge to cultivate a landscape for community food security.