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Family and Community Medicine

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Abstract

Food insecurity (FI) is a pervasive problem. It is linked to education and parental SES. The burden is not distributed equally. Younger and older groups are at greater risk along with those who identify with marginalized groups. The purpose of this study was examine how self-reported social determinants are associated with FI in the lower Rio Grande Valley.

The study was conducted at safety net clinics (14) in the RGV from April 1, 2021, to Dec 1, 2021. A common instrument was developed used across the clinics. Goals were to identify what social determinants of health are clients of each safety net clinic facing; what needs are identified; what programs need to be developed. The primary endpoint for this study was the presence of FI using a two-question FI assessment. FI was deemed present if it was affirmed on either question. A total of 5643 records were used in the study. Overall descriptive statistics were created for all covariates and stratified by FI. Chi-Square analysis was used to measure association of FI with social determinants and binary logistic regression predicting FI to get unadjusted/adjusted Odds ratios (ORs).

The most common home issues reported are no working smoke detectors (n=357, 6.5%) and bug infestation (n=273, 4.9%). Of the 5643 people, 21.8% (n=1232) faced some issue with their home. In the past 12 months 31.9% (n=1785) faced FI, with 8.9% (n=500) facing it fairly often/frequently. Additionally, 1598 (28.6%) said they bought food, it did not last and did not have money to purchase more with 464 (8.3%) saying this occurred fairly often/frequently. Those who preferred Spanish had 130% higher odds of FI (OR = 2.3 95% CI 1.60-2.38, p

The results quantify the concerns about social determinants of those who use the safety net clinics. The findings suggest the problems are multi-factorial. However, some factors such as language preference, indications of financial hardship and concerns about housing can be used to categorize those at higher risk for FI.

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Economic Factors Affecting Patients Who Use Safety Net Clinics and Food Insecurity in Rio Grande Valley

Food insecurity (FI) is a pervasive problem. It is linked to education and parental SES. The burden is not distributed equally. Younger and older groups are at greater risk along with those who identify with marginalized groups. The purpose of this study was examine how self-reported social determinants are associated with FI in the lower Rio Grande Valley.

The study was conducted at safety net clinics (14) in the RGV from April 1, 2021, to Dec 1, 2021. A common instrument was developed used across the clinics. Goals were to identify what social determinants of health are clients of each safety net clinic facing; what needs are identified; what programs need to be developed. The primary endpoint for this study was the presence of FI using a two-question FI assessment. FI was deemed present if it was affirmed on either question. A total of 5643 records were used in the study. Overall descriptive statistics were created for all covariates and stratified by FI. Chi-Square analysis was used to measure association of FI with social determinants and binary logistic regression predicting FI to get unadjusted/adjusted Odds ratios (ORs).

The most common home issues reported are no working smoke detectors (n=357, 6.5%) and bug infestation (n=273, 4.9%). Of the 5643 people, 21.8% (n=1232) faced some issue with their home. In the past 12 months 31.9% (n=1785) faced FI, with 8.9% (n=500) facing it fairly often/frequently. Additionally, 1598 (28.6%) said they bought food, it did not last and did not have money to purchase more with 464 (8.3%) saying this occurred fairly often/frequently. Those who preferred Spanish had 130% higher odds of FI (OR = 2.3 95% CI 1.60-2.38, p

The results quantify the concerns about social determinants of those who use the safety net clinics. The findings suggest the problems are multi-factorial. However, some factors such as language preference, indications of financial hardship and concerns about housing can be used to categorize those at higher risk for FI.

 

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