Political Polarization During 2017-2021

Padala, Akshay; Lee, Conrad

doi:10.47611/jsrhs.v10i3.1817

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…Specifically, we observed a major divide in public health and the different impacts of SDOH factors on population health outcomes between different political support in the United States, which contains only 2 main political parties. The results reinforce the current political climate in the United States, namely, the polarization between the 2 parties and how it affects health care outcome [ 69 - 71 ]. These findings add complexity for city officials who are monitoring and maintaining the quality of public health while upholding the political ideals and expectations through policy enforcement.…”

Section: Discussionsupporting

confidence: 62%

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: Discussionsupporting

confidence: 62%

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

“…Specifically, we observed a major divide in public health and the different impacts of SDOH factors on population health outcomes between different political support in the United States, which contains only 2 main political parties. The results reinforce the current political climate in the United States, namely, the polarization between the 2 parties and how it affects health care outcome [69][70][71]. These findings add complexity for city officials who are monitoring and maintaining the quality of public health while upholding the political ideals and expectations through policy enforcement.…”

Section: Principal Findingssupporting

confidence: 62%

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

Vo¹,

Tao²,

Yan³

et al. 2022

Preprint

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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 (<i>P</i><.01 or <i>P</i><.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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“…On her campaign platform, Flowers advocated raising the legal age for gun purchases from 18 to 21, and improving background checks for gun buyers [46]. According to Padala and Conrad [47] gun control when used within a political debate exhibits a unique ability to be divisive compared to other issues, making it central to understanding how polarized political opinions operate in the Trump era. For section (a), the graph provides visual data on subjectivity across states, with Alabama and Illinois emerging with darker color intensities, indicating the highest subjectivity.…”

Section: Sentiment Analysis For the Gubernatorial Electionsmentioning

confidence: 99%

Polarizing Topics on Twitter in the 2022 United States Elections

Katalinić,

Dunđer,

Seljan

2023

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Politically polarizing issues are a growing concern around the world, creating divisions along ideological lines, which was also confirmed during the 2022 United States midterm elections. The purpose of this study was to explore the relationship between the results of the 2022 U.S. midterm elections and the topics that were covered during the campaign. A dataset consisting of 52,688 tweets in total was created by collecting tweets of senators, representatives and governors who participated in the elections one month before the start of the elections. Using unsupervised machine learning, topic modeling is built on the collected data and visualized to represent topics. Furthermore, supervised machine learning is used to classify tweets to the corresponding political party, whereas sentiment analysis is carried out in order to detect polarity and subjectivity. Tweets from participating politicians, U.S. states and involved parties were found to correlate with polarizing topics. This study hereby explored the relationship between the topics that were creating a divide between Democrats and Republicans during their campaign and the 2022 U.S. midterm election outcomes. This research found that polarizing topics permeated the Twitter (today known as X) campaign, and that all elections were classified as highly subjective. In the Senate and House elections, this classification analysis showed significant misclassification rates of 21.37% and 24.15%, respectively, indicating that Republican tweets often aligned with traditional Democratic narratives.

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Political Polarization During 2017-2021

Cited by 4 publications

References 4 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

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

Polarizing Topics on Twitter in the 2022 United States Elections

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