Decomposing racial differences in adolescent smoking in the U.S

Along party Lines: Examining the gubernatorial party difference in COVID-19 mortality rates in U.S. Counties

Drawing upon the literatures on risk factors for COVID-19 and the roles of political party and political partisanship in COVID-19 policies and outcomes, this study quantifies the extent to which differences in Republican- and Democrat-governed counties' observable characteristics explain the Republican - Democrat gap in COVID-19 mortality rate in the United States. We analyze the county COVID-19 mortality rate between February 1 and December 31, 2020 and employ the Blinder-Oaxaca decomposition method. We estimate the extent to which differences in county characteristics - demographic, socioeconomic, employment, health status, healthcare access, area geography, and Republican vote share, explain the difference in COVID-19 mortality rates in counties governed by Republican vs Democrat governors. Among 3,114 counties, Republican-governed counties had significantly higher COVID-19 mortality than did Democrat-governed counties (127 ± 86 vs 97 ± 80 per 100,000 population, p < 0.001). Results are sensitive to which weights are used: of the total gap of 30.3 deaths per 100,000 population, 12.8 to 20.5 deaths, or 42.2-67.7 %, are explained by differences in observable characteristics of Republican- and Democratic-governed counties. Difference in support for President Trump between Republican- and Democrat-governed counties explains 25 % of the additional deaths in Republican counties. Policies aimed at improving population health and lowering racial disparity in COVID-19 outcomes may also be correlated with reducing the partisan gap in COVID-19 mortality.

A detailed explanation and graphical representation of the Blinder-Oaxaca decomposition method with its application in health inequalities

This paper introduces the Blinder-Oaxaca decomposition method to be applied in explaining inequality in health outcome across any two groups. In order to understand every aspect of the inequality, multiple regression model can be used in a way to decompose the inequality into contributing factors. The method can therefore be indicated to what extent of the difference in mean predicted outcome between two groups is due to differences in the levels of observable characteristics (acceptable and fair). Assuming the identical characteristics in the two groups, the remaining inequality can be due to differential effects of the characteristics, maybe discrimination, and unobserved factors that not included in the model. Thus, using the decomposition methods can identify the contribution of each particular factor in moderating the current inequality. Accordingly, more detailed information can be provided for policy-makers, especially concerning modifiable factors. The method is theoretically described in detail and schematically presented. In the following, some criticisms of the model are reviewed, and several statistical commands are represented for performing the method, as well. Furthermore, the application of it in the health inequality with an applied example is presented.

Disparities in COVID-19 mortality by county racial composition and the role of spring social distancing measures

Available COVID-19 data shows higher shares of cases and deaths occur among Black Americans, but reporting of data by race is poor. This paper investigates disparities in county-level mortality rates across counties with higher and lower than national average Black population shares using nonlinear regression decomposition and estimates potential differential impact of social distancing measures. I find counties with Black population shares above the national share have mortality rates 2 to 3 times higher than in other counties. Observable differences in living conditions, health, and work characteristics reduce the disparity to approximately 1.25 to 1.65 overall, and explain 100% of the disparity at 21 days after the first case. Though higher rates of comorbidities in counties with higher Black population shares are an important predictor, living situation factors like single parenthood and population density are just as important. Higher rates of co-residence with grandchildren explain 11% of the 21 day disparity but do not appear important by 42 days, suggesting families may have been better able to protect vulnerable family members later in the epidemic. To analyze differential effects of social distancing measures use two approaches. First, I exploit the timing of interventions relative to the first case among counties that began their epidemic at the same time. Second, I use event study analysis to analyze within-county changes in mortality. Findings for social distancing measures are not always consistent across approaches. Overall, I find no evidence that school closures were less effective in counties with larger Black population shares, and some estimates suggest closures may have disproportionately helped more diverse counties and counties with high rates of grandparent and grandchild co-residence. Conversely, stay at home orders are less clearly associated with mortality in any counties, reaching peak unemployment did not reduce mortality in any models, and some estimates indicate reaching peak unemployment before the first case was associated with higher mortality rates, especially in more diverse counties.

Is it Time to Quit? Smoking Persistence and Self-Rated Health

This study evaluates the impact of smoking on self-rated health using a British cohort born in 1970 that was followed through adult life. Records were taken for this dataset many times; individual self-rated health was first recorded in 1996 at age 26, and afterward at ages 30, 34, and 42. The smoking rate over time determined membership in the groups of current-smokers, never-smokers, and former-smokers. Estimates showed that the current-smokers group produced an increase in the probability of being in poor health with respect to never-smokers of about 10 percentage points in the long term. This result was also consistent when we used former-smokers as the control group, or other model specifications. The baseline estimates were not contradicted by robustness checks and policy implications of these results were discussed.

Decomposing adult obesity trends in China (1991-2011)

Using data from the China Health and Nutrition Survey, this study analyses two decades (1991-2011) of change in adult obesity in China with a focus on whether rising obesity rates result from all population cohorts becoming more obese across time (intra-cohort change) or recent cohorts being more obese than their earlier counterparts (inter-cohort change or cohort replacement). To do so, we decompose changes in the body mass index (BMI), waist circumference (WC), general obesity and abdominal obesity of adults aged 20+ using both Firebaugh's linear decomposition and Das Gupta's non-linear technique. Our analysis not only reveals significant increases in both general and abdominal obesity (8.1 and 32 percentage points, respectively) in the two decades studied but shows that the rising means in all four measures are mostly attributable to intra-cohort change. In fact, contrary to findings for the U.S., inter-cohort change for the Chinese sample is actually negative, implying that cohort replacement attenuates intra-cohort change over time. Given that intra-cohort change is the central force for the increase in BMI, WC and obesity with individual increases in obesity widely distributed across all cohorts and age groups over time, policy interventions should focus more broadly on all age groups and birth cohorts.

Decomposing Gender Disparity in Total Physical Activity among Iranian Adults

OBJECTIVES

While gender differences in physical activity (PA) have been reported, their origin is not well understood. The present study aimed to identify factors contributing to this disparity.

METHODS

This was a population-based cross-sectional study based on the 2011 surveillance of risk factors of non-communicable diseases that was conducted among Iranian adults. Multi-staged sampling was performed to obtain the required study sample. The primary outcome was gender differences in the prevalence of sufficient physical activity (SPA). Total physical activity (TPA) was calculated as metabolic equivalents (MET) per minute during a typical week, as recommended by the World Health Organization. On this basis, achieving 600 MET-min/wk or more was defined as SPA. The nonlinear Blinder-Oaxaca decomposition technique was used to explain the disparity.

RESULTS

The predicted gap was 19.50%. About one-third of the gap was due to differences in the level of observable covariates. Among them, work status contributed the most (29.61%). A substantial portion of the gap remained unexplained by such differences, of which about 40.41% was related to unobservable variables. The differential effects of standard of living, ethnicity, and smoking status made the largest contribution, accounting for 37.36, 35.47, and 28.50%, respectively.

CONCLUSIONS

Interventions to reduce the gender gap in PA should focus on increasing TPA among housewives and women with chronic diseases, as well as those with a higher standard of living. In addition, it is essential to explore the impact of ethnicity and smoking status on women’s TPA in order to promote health.