Milken Institute School of Public Health Poster Presentations (Marvin Center & Video)

Title

Neighborhoods and Health: The Implications of These Relationships

Poster Number

86

Document Type

Poster

Status

Graduate Student - Masters

Abstract Category

Prevention and Community Health

Keywords

community health; diabetes; hypertension; neighborhoods; chronic disease

Publication Date

4-2017

Abstract

Background: Racial and ethnic minority groups have a higher prevalence of both diabetes and hypertension, which may be influenced by neighborhood-level food environment and sociodemographic factors. We evaluated whether the imbalance between available healthy and unhealthy food options was associated with cardiometabolic markers (A1C and Systolic Blood Pressure [SBP]) in an urban adult patient population.

Methods: We analyzed data from 4,729 patients from a hospital and outpatient system in Washington, DC with valid A1C, SBP, and home address data. We operationalized individuals' neighborhood food environment using the Centers for Disease Control and Prevention'modified retail food environment index (mRFEI) reported at the census tract level, which ranged from 0 in the least healthy food environments to 33in the healthiest in our study sample. We used Geographically Weighted Regression to predict A1C and SBP levels separately based on mRFEI controlling for age, neighborhood socioeconomic status, neighborhood racial composition, and distance to the primary care center.

Results: Overall, there was a small, but significant relationship between neighborhood food environments both clinical outcomes. For both A1C and SBP there was a slight negative relationship with improved food environment scores ( -0.00627, p-value: 0.00; -.39284, p-value: 0.00). However, after adjusting for additional covariates, the impact of food environments decrease for both A1C and SBP ( -0.00023, p-value: 0.21; -0.1169, p-value: 0.01). For A1C, the impact of food environment decreases as additional neighborhood variables are added into the model.

Conclusions: We found that neighborhood-level variables are correlated with particular clinical outcomes. Food environments may be important in managing chronic disease. However, this impact solely may be clinically minute, but important on a population level. Identifying the relationship between food environments, additional neighborhood variables, and cardiometabolic outcomes could be instrumental in improving the health conditions of urban residents.

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Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Open Access

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Poster to be presented at GW Annual Research Days 2017.

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Neighborhoods and Health: The Implications of These Relationships

Background: Racial and ethnic minority groups have a higher prevalence of both diabetes and hypertension, which may be influenced by neighborhood-level food environment and sociodemographic factors. We evaluated whether the imbalance between available healthy and unhealthy food options was associated with cardiometabolic markers (A1C and Systolic Blood Pressure [SBP]) in an urban adult patient population.

Methods: We analyzed data from 4,729 patients from a hospital and outpatient system in Washington, DC with valid A1C, SBP, and home address data. We operationalized individuals' neighborhood food environment using the Centers for Disease Control and Prevention'modified retail food environment index (mRFEI) reported at the census tract level, which ranged from 0 in the least healthy food environments to 33in the healthiest in our study sample. We used Geographically Weighted Regression to predict A1C and SBP levels separately based on mRFEI controlling for age, neighborhood socioeconomic status, neighborhood racial composition, and distance to the primary care center.

Results: Overall, there was a small, but significant relationship between neighborhood food environments both clinical outcomes. For both A1C and SBP there was a slight negative relationship with improved food environment scores ( -0.00627, p-value: 0.00; -.39284, p-value: 0.00). However, after adjusting for additional covariates, the impact of food environments decrease for both A1C and SBP ( -0.00023, p-value: 0.21; -0.1169, p-value: 0.01). For A1C, the impact of food environment decreases as additional neighborhood variables are added into the model.

Conclusions: We found that neighborhood-level variables are correlated with particular clinical outcomes. Food environments may be important in managing chronic disease. However, this impact solely may be clinically minute, but important on a population level. Identifying the relationship between food environments, additional neighborhood variables, and cardiometabolic outcomes could be instrumental in improving the health conditions of urban residents.