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Subjective Health and Happiness in the United States: Gender Differences in the Effects of Socioeconomic Status Indicators.

ABSTRACT: Background:Education, employment, and marital status are among the main socioeconomic status (SES) indicators that are associated with subjective health and happiness. The effects of these SES indicators may, however, be different for various demographic groups. Aims:To understand if SES indicators differently impact men and women, we tested gender differences in the effects of education, employment, and marital status on the subjective health and happiness of American adults. Methods:This cross-sectional study used data of the General Social Survey (GSS), a series of nationally representative surveys between 1972 and 2018 in the US. Our analytical sample included 65,814 adults. The main independent variables were education attainment, marital status, and employment. Outcomes were self-rated health (SRH) and happiness measured using single items. Age and year of the study were covariates. Gender was the moderator. Results:Overall, high education, being employed, and being married were associated with better SRH and happiness. We, however, found significant interactions between gender and educational attainment, marital status, and employment on the outcomes, which suggested that the effect of high education and marital status were stronger for women. In comparison, the effect of employment was stronger for men. Some inconsistencies in the results were observed for SRH compared to happiness. Conclusions:In the United States, while education, employment, and marital status are critical social determinants of subjective health and happiness, these effects vary between women and men. Men's outcomes seem to be more strongly shaped by employment, while women's outcomes are more strongly shaped by education and marital status.

SUBMITTER: Maharlouei N

PROVIDER: S-EPMC7304555 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Subjective Health and Happiness in the United States: Gender Differences in the Effects of Socioeconomic Status Indicators.

Maharlouei Najmeh N Cobb Sharon S Bazargan Mohsen M Assari Shervin S

Journal of mental health & clinical psychology 20200514 2

<h4>Background</h4>Education, employment, and marital status are among the main socioeconomic status (SES) indicators that are associated with subjective health and happiness. The effects of these SES indicators may, however, be different for various demographic groups.<h4>Aims</h4>To understand if SES indicators differently impact men and women, we tested gender differences in the effects of education, employment, and marital status on the subjective health and happiness of American adults.<h4> ...[more]

PMID: 32568256

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Project description:BackgroundWith the availability of multiple Coronavirus Disease 2019 (COVID-19) vaccines and the predicted shortages in supply for the near future, it is necessary to allocate vaccines in a manner that minimizes severe outcomes, particularly deaths. To date, vaccination strategies in the United States have focused on individual characteristics such as age and occupation. Here, we assess the utility of population-level health and socioeconomic indicators as additional criteria for geographical allocation of vaccines.Methods and findingsCounty-level estimates of 14 indicators associated with COVID-19 mortality were extracted from public data sources. Effect estimates of the individual indicators were calculated with univariate models. Presence of spatial autocorrelation was established using Moran's I statistic. Spatial simultaneous autoregressive (SAR) models that account for spatial autocorrelation in response and predictors were used to assess (i) the proportion of variance in county-level COVID-19 mortality that can explained by identified health/socioeconomic indicators (R2); and (ii) effect estimates of each predictor. Adjusting for case rates, the selected indicators individually explain 24%-29% of the variability in mortality. Prevalence of chronic kidney disease and proportion of population residing in nursing homes have the highest R2. Mortality is estimated to increase by 43 per thousand residents (95% CI: 37-49; p < 0.001) with a 1% increase in the prevalence of chronic kidney disease and by 39 deaths per thousand (95% CI: 34-44; p < 0.001) with 1% increase in population living in nursing homes. SAR models using multiple health/socioeconomic indicators explain 43% of the variability in COVID-19 mortality in US counties, adjusting for case rates. R2 was found to be not sensitive to the choice of SAR model form. Study limitations include the use of mortality rates that are not age standardized, a spatial adjacency matrix that does not capture human flows among counties, and insufficient accounting for interaction among predictors.ConclusionsSignificant spatial autocorrelation exists in COVID-19 mortality in the US, and population health/socioeconomic indicators account for a considerable variability in county-level mortality. In the context of vaccine rollout in the US and globally, national and subnational estimates of burden of disease could inform optimal geographical allocation of vaccines.

Dataset Information

Subjective Health and Happiness in the United States: Gender Differences in the Effects of Socioeconomic Status Indicators.

Publications

Subjective Health and Happiness in the United States: Gender Differences in the Effects of Socioeconomic Status Indicators.

Similar Datasets

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