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. 2023 Jan 6;23(1):37.
doi: 10.1186/s12889-022-14868-1.

The impact of social determinants of health on obesity and diabetes disparities among Latino communities in Southern California

Affiliations

The impact of social determinants of health on obesity and diabetes disparities among Latino communities in Southern California

Joseph C Cleveland 3rd et al. BMC Public Health. .

Abstract

Background: Social determinants of health (SDoH) describe the complex network of circumstances that impact an individual before birth and across the lifespan. SDoH contextualize factors in a community that are associated with chronic disease risk and certain health disparities. The main objective of this study was to explore the impact of SDoH on the prevalence of obesity and diabetes, and whether these factors explain disparities in these health outcomes among Latinos in Southern California.

Methods: We utilized three composite indices that encompass different SDoH: the Healthy Places Index (HPI), Social Vulnerability Index (SVI), and CalEnviroScreen (CES). Univariate linear regression models explored the associations between index scores with adult obesity, adult diabetes, and childhood obesity.

Results: Communities with lower HPI scores were associated with higher prevalence of metabolic disease and a greater proportion of Latino residents. Cities in the lowest decile of HPI scores had 71% of the population identifying as Latino compared to 12% in the highest decile. HPI scores explained 61% of the variability in adult obesity (p < 0.001), 41% of the variability in childhood obesity (p < 0.001), and 47% of the variability in adult diabetes (p < 0.001). Similar results were observed when examining SVI and CES with these health outcomes.

Conclusions: These results suggest that Latinos in Southern California live in communities with adverse SDoH and face a greater burden of adult obesity, diabetes, and childhood obesity.

Keywords: CalEnviroScreen; Childhood Obesity; Healthy Places Index; Latino; Obesity; Social Determinants of Health; Social Vulnerability Index; Type 2 Diabetes.

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Conflict of interest statement

Michael I. Goran receives book royalties. Michael I. Goran is a scientific advisor for Yumi. Juan Espinoza is a paid consultant for AI Health. AI Health played no role in the design, execution, analysis, or write up of this work. AI Health did not play a role in the decision to publish this manuscript and had no editorial input. The authors declare no other competing interests.

Figures

Fig. 1
Fig. 1
Comparison of SDoH Represented in HPI, CES, and SVI. This figure highlights each of the SDoH indicators captured by the HPI, CES, and SVI. From top to bottom, the figure highlights the indicators that are specific to each index, the indicators shared by two indices, and the indicators shared by all three indices
Fig. 2
Fig. 2
Distribution of SDoH Represented in HPI, CES, and SVI. At the top of the figure, the distribution of the SDoH domains in each index is presented. At the bottom of the figure, the domains in which SDoH are categorized are indicated
Fig. 3
Fig. 3
Southern California Communities with a Higher Proportion of Latino Residents had a Lower HPI Score. Each point on the scatterplot represents one of the 367 Southern California cities included in the analysis. Cities were grouped into counties and are displayed using distinct colors. The x-axis represents the percent Latino within each of the Southern California cities. The y-axis represents the HPI Score Percentile, with 0 representing the community with the least healthy community conditions and 100 representing the community with the healthiest community conditions
Fig. 4
Fig. 4
Cities with Lower HPI Scores Have Higher Percent Latino Populations than Cities with Higher Scores. Each bar on this graph represents a decile of city-level HPI scores. Each number label corresponds to the percent race/ethnicity in each of the deciles. The top decile (10) indicates the decile with the highest HPI scores and the decile with the bottom decile (1) indicates the decile with the lowest HPI scores. As demonstrated in the figure above, increasing deciles of disadvantage (i.e., going down the graph) have a notably higher percent Latino population
Fig. 5
Fig. 5
Less Healthy Environments are Associated with a Greater Prevalence of Obesity and Diabetes. Each dot on the scatterplot represents a city within the ten counties investigated in this analysis. The x-axis shows the percentile HPI score, and a higher percentile score indicates a healthier community. The y-axis shows the percentile score of the prevalence of each health outcome relative to each other (i.e., a percentile score of 100 translates to the city with the highest prevalence of adult diabetes or obesity). Each city on the scatterplot shows a gradation of the percent Latino in each city. A red shading indicates a higher percent Latino compared to a purple shading. Panel A shows the relationship between adults with diabetes (excluding gestational diabetes) and HPI score. Panel B shows the relationship between adults with obesity and HPI score
Fig. 6
Fig. 6
Less Healthy Environments are Associated with a Greater Prevalence of Childhood Obesity. Each point on the scatterplot represents a city within the ten counties investigated in this analysis. The x-axis shows the percentile HPI score, and a higher percentile score indicates a healthier community. The y-axis shows the percentile score of the prevalence of each health outcome relative to each other (i.e., a percentile score of 100 translates to the city with the highest prevalence of adult or childhood obesity). Each point (i.e., city) on the scatterplot is colored based on the percent Latino in each city. A red shading indicates a higher percent Latino compared to a purple shading. Panel A shows the relationship between adults with obesity and HPI score. Panel B shows the relationship between children with obesity and HPI score. 162 cities had available adult obesity data compared to 341 cities with available childhood obesity data

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