Sociodemographic and environmental health risk factor of COVID-19 in Jakarta, Indonesia: An ecological study

Abstract

Since December 2019, the COVID-19 pandemic has rapidly emerged on a global scale. Many factors have influenced the spread of COVID-19. This research studies the sociodemographic and environmental health risk factors associated with COVID-19. The study used an ecological study design with subdistricts as its unit of analysis. The total population was 44 subdistricts. Data analysis used correlation and linear regression tests. The study results showed that the average COVID-19 incident rate in Jakarta is 99.8 per 10,000 population. Risk factors for the spread of COVID-19 were associated with population's high level of education (B = 3.094, p value<0.001), population density (B = 0.275, p value = 0.029), and slum area (B = 0.404, p value<0.001). The main risk factor for the spread of COVID-19 in Jakarta is high level of education, which can reflect a higher economic status to the population and a tendency to be more mobile. The government needs to enforce a mobility restriction to lessen the spread of COVID-19.

Willingness of healthcare workers to treat COVID-19 patients during the pandemic: Extended theory of planned behavior

“…Hypothesis 3 shows that perceived behavioral control positively influences willingness to treat COVID-19 patients (β = 0.423, t-value = 6.585, p-value = 0.000). Empirical studies also found that the positive behavioral control of healthcare workers leads to treating COVID-19 patients, even those at high risk of the virus spreading from one person to another (Zakianis et al, 2021). In addition, positive behavior encourages the individual to show a stronger intention to perform a job.…”

Impact of local mask mandates upon COVID-19 case rates in Oklahoma

“…The most apparent of these differences would seem to be degree of urbanization. While relatively little research is available on COVID-19 transmission in rural vs. urban areas, increased population density has been shown to favor disease propagation [ 19 ]. The municipalities adopting mask mandates were generally larger than communities not adopting mandates, and include not only the two largest major metropolitan areas (Oklahoma City and Tulsa), but eight of the ten largest population centers of the state.…”

COVID-19 severity determinants inferred through ecological and epidemiological modeling

“…Estimating the saturation (end of the peak) is straightforward in most cases, through both case counts and m/r reaching a saturation (nearly constant) value. We set the following aims for the selected significant predictors of m/r : i ) test if we can recover clinically observed dependencies, ii ) uncover additional risk factors for COVID-19 clinical severity, suitable to extract from ecological study design [ [28] , [29] , [30] ], iii ) compare with significant predictors of COVID-19 transmissibility ( R 0 ) that we previously obtained [ 25 , 27 ]. We here indeed obtained different predictors for m/r compared to R 0 [ 25 , 27 ].…”