Educational Disparities in COVID-19 Prevention in China: The Role of Contextual Danger, Perceived Risk, and Interventional Context
Abstract
:1. Introduction
Theory and Hypotheses
2. Materials and Methods
2.1. Data
2.2. Measurements
2.3. Statistical Analyses
3. Results
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
Appendix A
Variable Name | Survey Question | Coding |
---|---|---|
Outcomes | ||
Mask wearing in public | In order to prevent the coronavirus infection, are you currently practicing the following things: wear a mask when you go out | 5-point scale, 1 = not at all, 5 = all the time |
Handwashing after a public outing | In order to prevent the coronavirus infection, are you currently practicing the following things: wash your hand when you return from outside | 5-point scale, 1 = not at all, 5 = all the time |
Limited public outing | In order to prevent the coronavirus infection, are you currently practicing the following things: avoid unnecessary outing | 5-point scale, 1 = not at all, 5 = all the time |
Independent Variable | ||
Education | What is your highest degree in education? | 1 = high school or lower, 2 = associate degree, 3 = bachelor’s degree or above |
Moderator | ||
Contextual cues of danger | During the coronavirus outbreak, whether any of your family members were confirmed to have COVID-19? | 0 = no, 1 = yes |
During the coronavirus outbreak, whether any of your family members were quarantined/hospitalized due to suspected coronavirus positivity or close contact with someone with confirmed positivity? | 0 = no, 1 = yes | |
During the coronavirus outbreak, whether there are confirmed COVID-19 cases in the neighborhood/village that you are living in? | 0 = no, 1 = yes | |
Perceived risk of an outbreak | In your opinion, what is the coronavirus outbreak risk in the place you live? Is it high, medium, low, or none? | 0 = none or low, 1 = medium or high |
Interventional context | Which city are you currently living in? | 0 =all other places, 1 = Wuhan |
Covariates | ||
Sex | What is your sex? | 0 = female, 1 = male |
Age | Which year were you born? | in years |
Urbanicity | Are you currently living in the urban area or countryside? | 0 = rural, 1 = urban |
Income | In 2019, what is the average income for each person within your household (that is, the total income divided by the number of family members)? | in logged ten-thousand Ұ |
Occupation | What is your occupation? | 1 = executives or professionals, 2 = unskilled or low-skilled labor, 3 = currently not in the labor force, 4 = other |
Self-rated social ranking | In comparison to other people in the area you live, do you consider your family as the upper class, upper-middle class, middle class, lower-middle class, or lower class. | 5-point scale, 1 = lower class, 5 = upper class |
Individual coronavirus exposure | During the coronavirus outbreak, were you confirmed to have COVID-19? | 0 = no, 1 = yes |
During the coronavirus outbreak, were you quarantined/hospitalized due to suspected coronavirus positivity or close contact with someone with confirmed positivity? | 0 = no, 1 = yes |
References
- Lieberman-Cribbin, W.; Tuminello, S.; Flores, R.M.; Taioli, E. Disparities in COVID-19 Testing and Positivity in New York City. Am. J. Prev. Med. 2020, 59, 326–332. [Google Scholar] [CrossRef] [PubMed]
- Karaye, I.M.; Horney, J.A. The Impact of Social Vulnerability on COVID-19 in the U.S.: An Analysis of Spatially Varying Relationships. Am. J. Prev. Med. 2020, 59, 317–325. [Google Scholar] [CrossRef] [PubMed]
- Webb Hooper, M.; Nápoles, A.M.; Pérez-Stable, E.J. COVID-19 and Racial/Ethnic Disparities. JAMA 2020, 323, 2466–2467. [Google Scholar] [CrossRef] [PubMed]
- Lewnard, J.A.; Lo, N.C. Scientific and ethical basis for social-distancing interventions against COVID-19. Lancet Infect. Dis. 2020, 20, 631–633. [Google Scholar] [CrossRef]
- Sunjaya, A.P.; Jenkins, C. Rationale for universal face masks in public against COVID-19. Respirology 2020, 25, 678–679. [Google Scholar] [CrossRef] [PubMed]
- Clouston, S.A.P.; Richards, M.; Cadar, D.; Hofer, S.M. Educational Inequalities in Health Behaviors at Midlife: Is There a Role for Early-life Cognition? J. Health Soc. Behav. 2015, 56, 323–340. [Google Scholar] [CrossRef]
- Brunello, G.; Fort, M.; Schneeweis, N.; Winter-Ebmer, R. The Causal Effect of Education on Health: What is the Role of Health Behaviors? Health Econ. 2016, 25, 314–336. [Google Scholar] [CrossRef]
- Hecht, E.M.; Layton, M.R.; Abrams, G.A.; Rabil, A.M.; Landy, D.C. Healthy Behavior Adherence: The National Health and Nutrition Examination Survey, 2005–2016. Am. J. Prev. Med. 2020, 59, 270–273. [Google Scholar] [CrossRef]
- Shankar, A.; McMunn, A.; Steptoe, A. Health-Related Behaviors in Older Adults: Relationships with Socioeconomic Status. Am. J. Prev. Med. 2010, 38, 39–46. [Google Scholar] [CrossRef]
- Link, B.G.; Phelan, J. Social Conditions as Fundamental Causes of Disease. J. Health Soc. Behav. 1995, 35, 80–94. [Google Scholar] [CrossRef]
- Ross, C.E.; Mirowsky, J. Sex differences in the effect of education on depression: Resource multiplication or resource substitution? Soc. Sci. Med. 2006, 63, 1400–1413. [Google Scholar] [CrossRef]
- Ross, C.E.; Mirowsky, J. The interaction of personal and parental education on health. Soc. Sci. Med. 2011, 72, 591–599. [Google Scholar] [CrossRef]
- Sen, A. Editorial: Human capital and human capability. World Dev. 1997, 25, 1959–1961. [Google Scholar] [CrossRef]
- Mirowsky, J.; Ross, C.E. Education, Health, and the Default American Lifestyle. J. Health Soc. Behav. 2015, 56, 297–306. [Google Scholar] [CrossRef]
- Lawrence, E.M. Why Do College Graduates Behave More Healthfully Than Those Who Are Less Educated? J. Health Soc. Behav. 2017, 58, 291–306. [Google Scholar] [CrossRef]
- Baker, D.P.; Smith, W.C.; Muñoz, I.G.; Jeon, H.; Fu, T.; Leon, J.; Salinas, D.; Horvatek, R. The Population Education Transition Curve: Education Gradients across Population Exposure to New Health Risks. Demography 2017, 54, 1873–1895. [Google Scholar] [CrossRef]
- Baker, D.P.; Leon, J.; Collins, J.M. Facts, Attitudes, and Health Reasoning About HIV and AIDS: Explaining the Education Effect on Condom Use Among Adults in Sub-Saharan Africa. Aids Behav. 2011, 15, 1319–1327. [Google Scholar] [CrossRef]
- Zajacova, A.; Lawrence, E.M. The Relationship between Education and Health: Reducing Disparities through a Contextual Approach. Annu. Rev. Public Health 2018, 39, 273–289. [Google Scholar] [CrossRef]
- Cutler, D.M.; Lleras-Muney, A. Understanding differences in health behaviors by education. J. Health Econ. 2010, 29, 1–28. [Google Scholar] [CrossRef]
- Pampel, F.C.; Krueger, P.M.; Denney, J.T. Socioeconomic Disparities in Health Behaviors. Annu. Rev. Sociol. 2010, 36, 349–370. [Google Scholar] [CrossRef]
- Phelan, J.C.; Link, B.G.; Tehranifar, P. Social Conditions as Fundamental Causes of Health Inequalities: Theory, Evidence, and Policy Implications. J. Health Soc. Behav. 2010, 51, S28–S40. [Google Scholar] [CrossRef] [PubMed]
- Link, B.G. Epidemiological Sociology and the Social Shaping of Population Health. J. Health Soc. Behav. 2008, 49, 367–384. [Google Scholar] [CrossRef] [PubMed]
- Pampel, F.C. Diffusion, cohort change, and social patterns of smoking. Soc. Sci. Res. 2005, 34, 117–139. [Google Scholar] [CrossRef]
- de Walque, D. Education, Information, and Smoking Decisions: Evidence from Smoking Histories in the United States, 1940–2000. J. Hum. Resour. 2010, 45, 682–717. [Google Scholar] [CrossRef]
- Miech, R. The Formation of a Socioeconomic Health Disparity: The Case of Cocaine Use during the 1980s and 1990s. J. Health Soc. Behav. 2008, 49, 352–366. [Google Scholar] [CrossRef] [PubMed]
- Miech, R.A.; Chilcoat, H.; Harder, V.S. The increase in the association of education and cocaine use over the 1980s and 1990s: Evidence for a ’historical period’ effect. Drug Alcohol Depend. 2005, 79, 311–320. [Google Scholar] [CrossRef] [PubMed]
- Margolis, R. Educational Differences in Healthy Behavior Changes and Adherence among Middle-aged Americans. J. Health Soc. Behav. 2013, 54, 353–368. [Google Scholar] [CrossRef]
- Duberstein, P.R.; Chen, M.; Chapman, B.P.; Hoerger, M.; Saeed, F.; Guancial, E.; Mack, J.W. Fatalism and educational disparities in beliefs about the curability of advanced cancer. Patient Educ. Couns. 2018, 101, 113–118. [Google Scholar] [CrossRef]
- Lawlor, D.A.; Frankel, S.; Shaw, M.; Ebrahim, S.; Smith, G.D. Smoking and ill health: Does lay epidemiology explain the failure of smoking cessation programs among deprived populations? Am. J. Public Health 2003, 93, 266–270. [Google Scholar] [CrossRef]
- Ajzen, I. The Theory of Planned Behavior. Organ. Behav. Hum. Decis. Process. 1991, 50, 179–211. [Google Scholar] [CrossRef]
- Mackenbach, J.P.; Kulhánová, I.; Bopp, M.; Deboosere, P.; Eikemo, T.A.; Hoffmann, R.; Kulik, M.C.; Leinsalu, M.; Martikainen, P.; Menvielle, G.; et al. Variations in the relation between education and cause-specific mortality in 19 European populations: A test of the “fundamental causes” theory of social inequalities in health. Soc. Sci. Med. 2015, 127, 51–62. [Google Scholar] [CrossRef] [PubMed]
- Masters, R.K.; Link, B.G.; Phelan, J.C. Trends in education gradients of ‘preventable’ mortality: A test of fundamental cause theory. Soc. Sci. Med. 2015, 127, 19–28. [Google Scholar] [CrossRef] [PubMed]
- Rydland, H.T.; Solheim, E.F.; Eikemo, T.A. Educational inequalities in high- vs. low-preventable health conditions: Exploring the fundamental cause theory. Soc. Sci. Med. 2020, 113145. [Google Scholar] [CrossRef] [PubMed]
- Hoynes, H.; Miller, D.; Simon, D. Income, the Earned Income Tax Credit, and Infant Health. Am. Econ. J. Econ. Policy 2015, 7, 172–211. [Google Scholar] [CrossRef]
- Wendel-Vos, G.C.W.; Dutman, A.E.; Verschuren, W.M.M.; Ronckers, E.T.; Ament, A.; van Assema, P.; van Ree, J.; Ruland, E.C.; Schuit, A.J. Lifestyle Factors of a Five-Year Community-Intervention Program: The Hartslag Limburg Intervention. Am. J. Prev. Med. 2009, 37, 50–56. [Google Scholar] [CrossRef]
- Pan, A.; Liu, L.; Wang, C.; Guo, H.; Hao, X.; Wang, Q.; Huang, J.; He, N.; Yu, H.; Lin, X.; et al. Association of Public Health Interventions With the Epidemiology of the COVID-19 Outbreak in Wuhan, China. JAMA 2020, 323, 1915–1923. [Google Scholar] [CrossRef]
- World Health Organization. Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19); WHO: Geneva, Switzerland, 2020. [Google Scholar]
- Ministry of Industry and Information Technology of China. Briefing on the Major Development Indicators in the Communications Industry: Janurary–June 2020. Available online: http://www.miit.gov.cn/n1146312/n1146904/n1648372/c8021317/content.html (accessed on 16 August 2020).
- Cai, J.; Coyte, P.C.; Zhao, H. Determinants of and socio-economic disparities in self-rated health in China. Int. J. Equity Health 2017, 16, 7. [Google Scholar] [CrossRef]
- Liu, J.; Zhang, Y. Health status and health disparity in China: A demographic and socioeconomic perspective. China Popul. Dev. Stud. 2019, 2, 301–322. [Google Scholar] [CrossRef]
- Zhou, X. Increasing Returns to Education, Changing Labor Force Structure, and the Rise of Earnings Inequality in Urban China, 1996–2010. Soc. Forces 2014, 93, 429–455. [Google Scholar] [CrossRef]
- Lüdecke, D.; von dem Knesebeck, O. Protective Behavior in Course of the COVID-19 Outbreak—Survey Results from Germany. Front. Public Health 2020, 8. [Google Scholar] [CrossRef]
- Al-Hanawi, M.K.; Angawi, K.; Alshareef, N.; Qattan, A.M.N.; Helmy, H.Z.; Abudawood, Y.; Alqurashi, M.; Kattan, W.M.; Kadasah, N.A.; Chirwa, G.C.; et al. Knowledge, Attitude and Practice Toward COVID-19 among the Public in the Kingdom of Saudi Arabia: A Cross-Sectional Study. Front. Public Health 2020, 8. [Google Scholar] [CrossRef] [PubMed]
- Groves, R.M.; Peytcheva, E. The Impact of Nonresponse Rates on Nonresponse Bias: A Meta-Analysis. Public Opin. Q. 2008, 72, 167–189. [Google Scholar] [CrossRef]
Wuhan (N = 1038) | Other Areas (N = 2289) | p-Value b | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Total | Level of Education | p-Value a | Total | Level of Education | p-Value a | ||||||
High School or Lower (79.9) | Associate Degree (7.2) | Bachelor’s Degree or Above (12.9) | High School or Lower (85.96) | Associate Degree (5.0) | Bachelor’s Degree or Above (9.1) | ||||||
Outcomes | |||||||||||
Mask Wearing | 3.8 ± 0.4 | 3.8 ± 0.4 | 3.9 ± 0.3 | 3.9 ± 0.4 | 0.019 | 3.6 ± 0.7 | 3.6 ± 0.7 | 3.6 ± 0.6 | 3.6 ± 0.6 | 0.000 | 0.000 |
Handwashing | 3.7 ± 0.5 | 3.7 ± 0.5 | 3.8 ± 0.5 | 3.8 ± 0.5 | 0.058 | 3.6 ± 0.7 | 3.5 ± 0.7 | 3.6 ± 0.7 | 3.6 ± 0.6 | 0.002 | 0.000 |
Limited Public Outing | 3.5 ± 0.7 | 3.5 ± 0.7 | 3.5 ± 0.7 | 3.4 ± 0.8 | 0.818 | 3.4 ± 0.8 | 3.3 ± 0.8 | 3.2 ± 0.8 | 3.3 ± 0.8 | 0.083 | 0.000 |
Covariates | |||||||||||
Male | 42.9 | 39.9 | 55.4 | 54.5 | 0.000 | 51.5 | 50.9 | 58.4 | 53.3 | 0.181 | 0.000 |
Age | 48.1 ± 2.6 | 50.9 ± 16.2 | 39.7 ± 14.0 | 35.7 ± 12.3 | 0.000 | 43.9 ± 15.8 | 45.7 ± 15.7 | 35.4 ± 12.7 | 31.4 ± 11.5 | 0.000 | 0.000 |
Urban | 87 | 85.2 | 93.8 | 94.2 | 0.000 | 53.7 | 48.6 | 82.9 | 85.3 | 0.000 | 0.000 |
Occupation | |||||||||||
Executives/professionals | 10.5 | 4.3 | 20.8 | 42.7 | 0.000 | 7.8 | 3.2 | 24.7 | 41.9 | 0.000 | 0.003 |
Unskilled or low-skilled labor | 46.4 | 48.5 | 47.6 | 32.4 | 60.0 | 64.2 | 48.6 | 25.9 | |||
Not in the labor force | 36.7 | 40.9 | 23.0 | 18.3 | 26.2 | 26.8 | 17.9 | 25.5 | |||
Other | 6.5 | 6.3 | 8.6 | 6.6 | 6.0 | 5.8 | 8.8 | 6.7 | |||
Average annual individual income within household (in ten thousand) | 3.3 ± 4.2 | 2.5 ± 3.0 | 4.2 ± 3.8 | 7.5 ± 7.2 | 0.000 | 2.8 ± 4.4 | 2.3 ± 3.6 | 4.6 ± 6.9 | 6. ± 7.2 | 0.000 | 0.001 |
Subjective Social Ranking | 2.4 ± 0.8 | 2.3 ± 0.8 | 2.7 ± 0.6 | 3.0 ± 0.6 | 0.000 | 2.4 ± 0.85 | 2.3 ± 0.9 | 2.7 ± 0.7 | 2.8 ± 0.7 | 0.000 | 0.029 |
Individual Coronavirus Positive or Suspected Positive | 2.8 | 2.6 | 2.1 | 4.3 | 0.369 | 0.8 | 0.6 | 1.4 | 1.8 | 0.046 | 0.001 |
Context Cue of Danger: Yes | 56.4 | 51.7 | 75.7 | 75.2 | 0 | 6.0 | 4.9 | 11.9 | 13.2 | 0 | 0.000 |
Perceived Local Risk of COVID-19 Outbreak: Medium/High | 18.5 | 17.3 | 18.7 | 25.4 | 0.086 | 9.0 | 9.3 | 8.1 | 6.7 | 0.295 | 0.000 |
Context Cue of Danger | Mask Wearing a | Handwashing a | Limited Public Outing a | |||
---|---|---|---|---|---|---|
Present (N = 2171) | Absent (N = 1156) | Present (N = 2171) | Absent (N = 1156) | Present (N = 2171) | Absent (N = 1156) | |
Intercept | 3.54 *** | 3.63 *** | 3.36 *** | 3.13 *** | 3.27 *** | 3.08 *** |
(3.41, 3.67) | (3.48, 3.77) | (3.22, 3.50) | (2.94, 3.31) | (3.09, 3.44) | (2.81, 3.34) | |
Education b | ||||||
≤High School | ||||||
Associate Degree | 0 | 0.10 ** | 0.04 | 0.10 * | −0.04 | 0.18 ** |
(−0.08, 0.07) | (0.03, 0.17) | (−0.04, 0.12) | (0.01, 0.19) | (−0.14, 0.06) | (0.06, 0.31) | |
≥Bachelor’s Degree | 0.02 | 0.12 *** | 0.10 * | 0.12 ** | 0.05 | 0.15 * |
(−0.06, 0.09) | (0.05, 0.19) | (0.02, 0.18) | (0.04, 0.21) | (−0.05, 0.15) | (0.02, 0.27) | |
Perceived Risk of Local Outbreak | Zero/Low (N = 2870) | Medium/High (N = 457) | Zero/Low (N = 2870) | Medium/High (N = 457) | Zero/Low (N = 2870) | Medium/High (N = 457) |
Intercept | 3.55 *** | 3.43 *** | 3.31 *** | 3.14 *** | 3.18 *** | 3.16 *** |
(3.44, 3.66) | (3.20, 3.65) | (3.19, 3.42) | (2.84, 3.44) | (3.03, 3.33) | (2.74, 3.59) | |
Education c | ||||||
≤High School | ||||||
Associate Degree | 0.01 | 0.17** | 0.04 | 0.24 ** | −0.01 | 0.42 *** |
(−0.05, 0.07) | (0.05, 0.30) | (−0.03, 0.10) | (0.07, 0.41) | (−0.09, 0.07) | (0.18, 0.67) | |
≥Bachelor’s Degree | 0.03 | 0.20 ** | 0.09 ** | 0.22 ** | 0.04 | 0.36 ** |
(−0.03, 0.09) | (0.08, 0.33) | (0.03, 0.16) | (0.06, 0.38) | (−0.04, 0.13) | (0.12, 0.59) | |
Interventional Context | Wuhan (N = 1038) | Other Areas (N = 2289) | Wuhan (N = 1038) | Other Areas (N = 2289) | Wuhan (N = 1038) | Other Areas (N = 2289) |
Intercept | 3.71 *** | 3.51 *** | 3.24 *** | 3.30 *** | 3.12 *** | 3.22 *** |
(3.57, 3.85) | (3.38, 3.64) | (3.04, 3.43) | (3.16, 3.44) | (2.84, 3.39) | (3.05, 3.39) | |
Education d | ||||||
≤High School | ||||||
Associate Degree | 0.02 | 0.04 | 0.08 | 0.05 | 0.04 | 0.04 |
(−0.05, 0.08) | (−0.03, 0.11) | (−0.01, 0.18) | (−0.03, 0.13) | (−0.09, 0.17) | (−0.05, 0.14) | |
≥Bachelor’s Degree | −0.02 | 0.08 * | 0.04 | 0.14 *** | −0.01 | 0.12 * |
(−0.09, 0.04) | (0.01, 0.16) | (−0.06, 0.13) | (0.07, 0.22) | (−0.14, 0.12) | (0.03, 0.22) |
Context Cue of Danger | Mask Wearing a | Handwashing a | Limited Public Outing a | |||
---|---|---|---|---|---|---|
Present (N = 1514) | Absent (N = 1051) | Present (N = 1514) | Absent (N = 1051) | Present (N = 1514) | Absent (N = 1051) | |
Intercept | 3.75 *** | 3.69 *** | 3.38 *** | 3.28 *** | 3.04 *** | 2.86 *** |
(3.61, 3.90) | (3.55, 3.83) | (3.21, 3.55) | (3.11, 3.46) | (2.82, 3.25) | (2.59, 3.12) | |
Education b | ||||||
≤High School | ||||||
Associate Degree | −0.04 | 0.09 * | 0.02 | 0.12 ** | −0.05 | 0.18 ** |
(−0.12, 0.03) | (0.02, 0.17) | (−0.07, 0.11) | (0.03, 0.21) | (−0.17, 0.06) | (0.04, 0.32) | |
≥Bachelor’s Degree | 0.01 | 0.10 ** | 0.11 * | 0.14 ** | 0.06 | 0.14 * |
(−0.07, 0.08) | (0.03, 0.17) | (0.02, 0.20) | (0.06, 0.23) | (−0.06, 0.17) | (0.01, 0.28) | |
Perceived Risk of Local Outbreak | Zero/Low (N = 2183) | Medium/High (N = 382) | Zero/Low (N = 2183) | Medium/High (N = 382) | Zero/Low (N = 2183) | Medium/High (N = 382) |
Intercept | 3.72 *** | 3.62 *** | 3.32 *** | 3.30 *** | 2.93 *** | 3.09 *** |
(3.61, 3.83) | (3.39, 3.85) | (3.19, 3.45) | (3.00, 3.61) | (2.75, 3.10) | (2.61, 3.56) | |
Education c | ||||||
≤High School | ||||||
Associate Degree | −0.01 | 0.15 * | 0.04 | 0.23 ** | −0.02 | 0.43 ** |
(−0.07, 0.05) | (0.02, 0.28) | (−0.03, 0.11) | (0.06, 0.41) | (−0.11, 0.07) | (0.16, 0.70) | |
≥Bachelor’s Degree | 0.01 | 0.22 *** | 0.10 ** | 0.26 ** | 0.04 | 0.37 ** |
(−0.04, 0.07) | (0.10, 0.34) | (0.03, 0.17) | (0.09, 0.43) | (−0.05, 0.13) | (0.11, 0.63) | |
Interventional Context | Wuhan (N = 935) | Other Areas (N = 1630) | Wuhan (N = 935) | Other Areas (N = 1630) | Wuhan (N = 935) | Other Areas (N = 1630) |
Intercept | 3.91 *** | 3.67 *** | 3.39 *** | 3.30 *** | 3.07 *** | 2.96 *** |
(3.78, 4.04) | (3.53, 3.82) | (3.20, 3.57) | (3.14, 3.46) | (2.79, 3.34) | (2.74, 3.17) | |
Education d | ||||||
≤High School | ||||||
Associate Degree | 0 | 0.02 | 0.07 | 0.06 | 0.05 | 0.04 |
(−0.06, 0.07) | (−0.06, 0.09) | (−0.02, 0.16) | (−0.03, 0.14) | (−0.09, 0.19) | (−0.07, 0.16) | |
≥Bachelor’s Degree | −0.04 | 0.09 * | 0.03 | 0.17 *** | −0.01 | 0.14 * |
(−0.10, 0.03) | (0.01, 0.16) | (−0.06, 0.12) | (0.08, 0.26) | (−0.15, 0.13) | (0.02, 0.25) |
Context Cue of Danger | Mask Wearing a | Handwashing a | Limited Public Outing a | |||
---|---|---|---|---|---|---|
Present (N = 1846) | Absent (N = 443) | Present (N = 1846) | Absent (N = 443) | Present (N = 1846) | Absent (N = 443) | |
Intercept | 3.51 *** | 3.65 *** | 3.37 *** | 3.25 *** | 3.32 *** | 3.22 *** |
(3.36, 3.66) | (3.38, 3.93) | (3.22, 3.53) | (2.92, 3.59) | (3.13, 3.51) | (2.79, 3.65) | |
Education b | ||||||
≤High School | ||||||
Associate Degree | −0.01 | 0.23 ** | 0.05 | 0.07 | −0.02 | 0.28 * |
(−0.09, 0.08) | (0.09, 0.37) | (−0.04, 0.14) | (−0.10, 0.24) | (−0.13, 0.09) | (0.06, 0.51) | |
≥Bachelor’s Degree | 0.04 | 0.31 *** | 0.13 ** | 0.22 * | 0.09 | 0.28 * |
(−0.05, 0.12) | (0.17, 0.44) | (0.04, 0.22) | (0.05, 0.39) | (−0.02, 0.19) | (0.06, 0.49) | |
Perceived Risk of Local Outbreak | Zero/Low (N = 2051) | Medium/High (N = 238) | Zero/Low (N = 2051) | Medium/High (N = 238) | Zero/Low (N = 2051) | Medium/High (N = 238) |
Intercept | 3.52 *** | 3.48 *** | 3.36 *** | 3.12 *** | 3.24 *** | 3.55 *** |
(3.37, 3.66) | (3.13, 3.84) | (3.22, 3.51) | (2.63, 3.60) | (3.05, 3.42) | (2.95, 4.15) | |
Education c | ||||||
≤High School | ||||||
Associate Degree | 0.01 | 0.33 ** | 0.02 | 0.40 ** | 0 | 0.50 ** |
(−0.07, 0.09) | (0.12, 0.54) | (−0.06, 0.10) | (0.12, 0.68) | (−0.10, 0.10) | (0.16, 0.85) | |
≥Bachelor’s Degree | 0.05 | 0.35 *** | 0.11 ** | 0.45 ** | 0.09 | 0.46 ** |
(−0.02, 0.13) | (0.14, 0.55) | (0.03, 0.19) | (0.18, 0.72) | (−0.01, 0.19) | (0.12, 0.79) |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Li, M.; Wang, W. Educational Disparities in COVID-19 Prevention in China: The Role of Contextual Danger, Perceived Risk, and Interventional Context. Int. J. Environ. Res. Public Health 2021, 18, 3383. https://doi.org/10.3390/ijerph18073383
Li M, Wang W. Educational Disparities in COVID-19 Prevention in China: The Role of Contextual Danger, Perceived Risk, and Interventional Context. International Journal of Environmental Research and Public Health. 2021; 18(7):3383. https://doi.org/10.3390/ijerph18073383
Chicago/Turabian StyleLi, Miao, and Weidong Wang. 2021. "Educational Disparities in COVID-19 Prevention in China: The Role of Contextual Danger, Perceived Risk, and Interventional Context" International Journal of Environmental Research and Public Health 18, no. 7: 3383. https://doi.org/10.3390/ijerph18073383