Measuring the spatial distribution of health rankings in the United States

Davis, Will; Gordan, Alexander; Tchernis, Rusty

doi:10.1002/hec.4416

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…However, this model is not always accurate at the state level or lower-than-state level (eg, county, city, and census tract level) because it uses a predetermined set of weights for the SDOH factors. Two recent empirical studies of the CHR found that the influence of SDOH factors on health outcomes varies among different states in the United States [ 21 , 22 ]. In addition, existing CHR studies are limited to either the county level or the state level.…”

Section: Introductionmentioning

confidence: 99%

“…First, the weights proposed by experts in the CHR may not be applicable to the entire population because different locations may have various population characteristics and dissimilar social and policy environments. Two recent studies that empirically tested the CHR [ 21 , 22 ] revealed that the influence of health factors on health outcomes varies among the US states. In other words, the CHR model performed better in some states than in others.…”

Section: Introductionmentioning

confidence: 99%

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The Association Between Social Determinants of Health and Population Health Outcomes: Ecological Analysis

Vo¹,

Tao²,

Yan³

et al. 2023

JMIR Public Health Surveill

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Background With the increased availability of data, a growing number of studies have been conducted to address the impact of social determinants of health (SDOH) factors on population health outcomes. However, such an impact is either examined at the county level or the state level in the United States. The results of analysis at lower administrative levels would be useful for local policy makers to make informed health policy decisions. Objective This study aimed to investigate the ecological association between SDOH factors and population health outcomes at the census tract level and the city level. The findings of this study can be applied to support local policy makers in efforts to improve population health, enhance the quality of care, and reduce health inequity. Methods This ecological analysis was conducted based on 29,126 census tracts in 499 cities across all 50 states in the United States. These cities were grouped into 5 categories based on their population density and political affiliation. Feature selection was applied to reduce the number of SDOH variables from 148 to 9. A linear mixed-effects model was then applied to account for the fixed effect and random effects of SDOH variables at both the census tract level and the city level. Results The finding reveals that all 9 selected SDOH variables had a statistically significant impact on population health outcomes for ≥2 city groups classified by population density and political affiliation; however, the magnitude of the impact varied among the different groups. The results also show that 4 SDOH risk factors, namely, asthma, kidney disease, smoking, and food stamps, significantly affect population health outcomes in all groups (P<.01 or P<.001). The group differences in health outcomes for the 4 factors were further assessed using a predictive margin analysis. Conclusions The analysis reveals that population density and political affiliation are effective delineations for separating how the SDOH affects health outcomes. In addition, different SDOH risk factors have varied effects on health outcomes among different city groups but similar effects within city groups. Our study has 2 policy implications. First, cities in different groups should prioritize different resources for SDOH risk mitigation to maximize health outcomes. Second, cities in the same group can share knowledge and enable more effective SDOH-enabled policy transfers for population health.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Association Between Social Determinants of Health and Population Health Outcomes: Ecological Analysis

Vo¹,

Tao²,

Yan³

et al. 2023

JMIR Public Health Surveill

View full text Add to dashboard Cite

show abstract

Spatial Comprehensive Well-Being Composite Indicators Based on Bayesian Latent Factor Model: Evidence from Italian Provinces

Montorsi,

Gigliarano

2024

Soc Indic Res

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This paper proposes spatial comprehensive composite indicators to evaluate the well-being levels and ranking of Italian provinces with data from the Equitable and Sustainable Well-Being dashboard. We use a method based on Bayesian latent factor models, which allow us to include spatial dependence across Italian provinces, quantify uncertainty in the resulting estimates, and estimate data-driven weights for elementary indicators. The results reveal that our data-driven approach changes the resulting composite indicator rankings compared to those produced by traditional composite indicators’ approaches. Estimated social and economic well-being is unequally distributed among southern and northern Italian provinces. In contrast, the environmental dimension appears less spatially clustered, and its composite indicators also reach above-average levels in the southern provinces. The time series of well-being composite indicators of Italian macro-areas shows clustering and macro-areas discrimination on larger territorial units.

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A two–stage bayesian model for assessing the geography of racialized economic segregation and premature mortality across US counties

Xu,

McClure,

Quick

et al. 2024

Spatial and Spatio-temporal Epidemiology

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Measuring the spatial distribution of health rankings in the United States

Cited by 5 publications

References 28 publications

The Association Between Social Determinants of Health and Population Health Outcomes: Ecological Analysis

The Association Between Social Determinants of Health and Population Health Outcomes: Ecological Analysis

Spatial Comprehensive Well-Being Composite Indicators Based on Bayesian Latent Factor Model: Evidence from Italian Provinces

A two–stage bayesian model for assessing the geography of racialized economic segregation and premature mortality across US counties

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