Semantic Scholar Open Access 2021 28 sitasi

Demographics, politics, and health factors predict mask wearing during the COVID-19 pandemic: a cross-sectional study

George B. Cunningham Calvin Nite

Abstrak

Wearing a protective face covering can reduce the spread of COVID-19, but Americans’ compliance with wearing a mask is uneven. The purpose of this study is to examine the association between health determinants (Health Behaviors, Clinical Care, Social and Economic Conditions, and the Physical Environment) and mask wearing at the county level. Data were collected from publicly available sources, including the County Health Rankings and the New York Times. The dependent variable was the percent of county residents who reported frequently or always wearing a mask when in public. County demographics and voting patterns served as controls. Two-levels random effects regression models were used to examine the study hypotheses. Results indicate that, after considering the effects of the controls, Health Behaviors were positively associated with mask wearing, the Physical Environment held a negative association, and Clinical Care and Social and Behavioral Factors were unrelated. Results indicate that patterns of healthy behaviors can help predict compliance with public health mandates that can help reduce the spread of COVID-19. From an instutitional theory perspective, the data suggest counties develop collective values and norms around health. Thus, public health officials can seek to alter governance structures and normative behaviors to improve healthy behaviors.

Topik & Kata Kunci

Penulis (2)

G

George B. Cunningham

C

Calvin Nite

Format Sitasi

Cunningham, G.B., Nite, C. (2021). Demographics, politics, and health factors predict mask wearing during the COVID-19 pandemic: a cross-sectional study. https://doi.org/10.1186/s12889-021-11424-1

Akses Cepat

Lihat di Sumber doi.org/10.1186/s12889-021-11424-1
Informasi Jurnal
Tahun Terbit
2021
Bahasa
en
Total Sitasi
28×
Sumber Database
Semantic Scholar
DOI
10.1186/s12889-021-11424-1
Akses
Open Access ✓