Social Determinants of COVID-19 Vaccination Rates: A Time-Constrained Multiple Mediation Analysis

Lee, Kyung Hee; Alemi, Farrokh; Yu, Jo-Vivian; Hong, Y. Alicia

doi:10.7759/cureus.35110

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…Errors in measuring the social, economic, environmental, and medical determinants of diabetes may lead to different conclusions. It is also possible that factors not included in our study may predict variations in diabetes prevalence (e.g., political circumstances) [ 44 ]. We encourage further studies to consider other social and medical variables not examined in this paper.…”

Section: Discussionmentioning

confidence: 99%

Social and Medical Determinants of Diabetes: A Time-Constrained Multiple Mediator Analysis

Alemi,

Lee,

Vang

et al. 2023

Cureus

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Background A number of studies have shown an association between social determinants of health and the emergence of obesity and diabetes, but whether the relationship is causal is not clear. Objective To test whether social, environmental, and medical determinants directly or indirectly affect population-level diabetes prevalence after controlling for mediator-mediator interactions. Methods Data were obtained from the CDC and supplemented with nine other data sources for 3,109 US counties. The dependent variable was the prevalence of diabetes in 2017. Independent variables were a given county's 30 social, environmental, and medical characteristics in 2015 and 2016. A network multiple mediation analysis was conducted. First, we used Least Absolute Shrinkage and Selection Operator (LASSO) regression to relate the 2017 diabetes rate in each county to 30 predictors measured in 2016, identifying statistically significant and robust predictors as the mediators within the network model and as direct determinants of 2017 diabetes. Second, each of the direct causes of diabetes was taken as a new response variable and LASSO-regressed on the same 30 independent variables measured in 2015, identifying the indirect (mediated) causes of diabetes. Subsequently, these direct and indirect predictors were used to construct a network model. The completed network was then employed to estimate the direct and mediated impact of variables on diabetes. Results For 2017 diabetes rates, 63% of the variation was explained by five variables measured in 2016: the percentage of residents who were (1) obese, (2) African American, (3) physically inactive, (4) in poor health condition, and (5) had a history of diabetes. These five direct predictors, measured in 2016, mediated the effect of indirect variables measured in 2015, including the percentage of residents who were (1) Hispanic, (2) physically distressed, (3) smokers, (4) living with children in poverty, (5) experiencing limited access to healthy foods, and (6) had low income. Conclusion All of the direct predictors of diabetes prevalence, except the percentage of residents who were African American, were medical conditions potentially influenced by lifestyles. Counties characterized by higher levels of obesity, inactivity, and poor health conditions exhibited increased diabetes rates in the following year. The impact of social determinants of illness, such as low income, children in poverty, and limited access to healthy foods, had an indirect effect on the health of residents and, consequently, increased the prevalence of diabetes.

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Section: Discussionmentioning

confidence: 99%

Social and Medical Determinants of Diabetes: A Time-Constrained Multiple Mediator Analysis

Alemi,

Lee,

Vang

et al. 2023

Cureus

Self Cite

View full text Add to dashboard Cite

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Impact of Political Leaning on COVID-19 Vaccine Hesitancy: A Network-Based Multiple Mediation Analysis

Alemi,

Lee

2023

Cureus

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Prior studies have shown that political affiliation affected COVID-19 vaccine hesitancy. This study reexamined the data to see if these findings hold after controlling for alternative explanations. The dependent variable in the study was COVID-19 vaccination rates in 3,109 counties in the United States as of April 2022. The study examined 36 possible alternative explanations for vaccine hesitancy, including demographic, social, economic, environmental, and medical variables known to affect vaccine hesitancy. County-level political affiliation was measured as a percent of voters in the county who were affiliated with Democratic or Republican political parties. Data were analyzed using a temporally constrained multiple mediation network, which allowed for the identification of both direct and indirect predictors of vaccination rates. Despite controlling for alternative explanations of hesitancy, there was a statistically significant relationship between the percentage of Republican supporters and rates of vaccine hesitancy. The higher the Republican affiliation, the lower the vaccination rates. It is possible that the Republican Party has played an organizing role in encouraging vaccine hesitancy and patient harm.

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Social Determinants of COVID-19 Vaccination Rates: A Time-Constrained Multiple Mediation Analysis

Cited by 2 publications

References 32 publications

Social and Medical Determinants of Diabetes: A Time-Constrained Multiple Mediator Analysis

Social and Medical Determinants of Diabetes: A Time-Constrained Multiple Mediator Analysis

Impact of Political Leaning on COVID-19 Vaccine Hesitancy: A Network-Based Multiple Mediation Analysis

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