The 2016 election of United States (U.S.) President Donald Trump was a political event that may have affected population-level mental health. A prominent theme in the Trump election was anti-immigrant policy that contributed to a racist and xenophobic sociopolitical climate. Applying a symbolic dis/empowerment framework, this study examines whether there was an effect of the Trump election on the mental health of the U.S. population that differed by race/ethnicity, language of interview, and state-level support for Trump or Clinton. We used data from the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System 2011-2018 to examine trends in poor mental health days in the five months after the U.S Presidential election (November 2016 to March 2017) compared to all other survey months. We conducted difference-in-differences analyses using negative binomial regression models to examine the effect of the five post-election months on the rate of poor mental health days, comparing six population categories: 1) non-Latinx white populations in Trump states, 2) non-Latinx white populations in Clinton states, 3) English-speaking Latinx populations in Trump states, 4) English-speaking Latinx populations in Clinton states, 5) Spanish-speaking Latinx populations in Trump states, and 6) Spanish-speaking Latinx populations in Clinton states. White populations in Clinton states reported more poor mental health days in response to the five months post-election period compared to white populations in Trump states. English-speaking Latinx people living in Trump states experienced higher than expected poor mental health days in November 2016 and February 2017. Spanish-speaking Latinx people, by contrast, reported fewer poor mental health days in the post-election period. The 2016 U.S. presidential election preceded population-level changes in mental health that support a symbolic dis/empowerment framework. We discuss possible explanations and the mental health implications for future major political events.
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http://dx.doi.org/10.1016/j.socscimed.2021.114417 | DOI Listing |
Hypertension
January 2025
Clinical Research Institute, Institute of Advanced Clinical Medicine, Peking University, Beijing, China (X.Z., W.X., Y.W.).
Background: Although the information on the validation status of electronic sphygmomanometer (ES) devices in use in health care institutions and households is much more clinically relevant than that of ES models available on the market, it remains insufficient.
Methods: A national survey was conducted across all administrative regions of mainland China to assess the validation status of ESs. Fifty-eight cities were selected with stratification by municipality, provincial capital, and other cities, and health care institutions and households in each city were chosen by convenience to identify ES devices in use according to the study protocol.
Internet Interv
March 2025
Department of Public Health, University Of Copenhagen, Øster Farimagsgade 5, 1353 København K, Denmark.
Parental relationship dissolution is among the most prevalent life crises for youths and is associated with both short- and long-term intra- and interpersonal struggles. Extant support programs tend to be in-person and in a group format. However, the structure and personnel needed for these programs make them costly to implement, less accessible, and difficult to scale.
View Article and Find Full Text PDFDigit Health
January 2025
Department of Communication, Institute of Collaborative Innovation, Center for Research in Greater Bay Area, University of Macau, Macau, China.
Background: China is experiencing a rapid increase in its aging population, leading to the emergence of significant challenges to improve the quality of life (QoL) of older adults. The study aims to explore the potential benefits of using mobile health technology in improving the QoL for older Chinese adults.
Method: This study utilized a subsample of adults aged 60 and above from a cross-sectional, population-based national survey conducted among Chinese adults (N = 852).
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