The Influence of Personality and Demographic Characteristics on Aggressive Driving Behaviors in Eastern Chinese Drivers.

PURPOSE: Road safety research is important due to the large number of road traffic fatalities globally. This study investigated the influences of age, driving experience and other covariates on aggressive driving behavior. METHODS: A cross-sectional survey was conducted in Yixing City, Wuxi City, Jiangsu Province, China. Regression analysis was applied to explore the influences of age and driving experience and their interactions with other covariates on aggressive driving behavior. Two analyses methodologies were used to assess the simple effect of the interactions. Firstly, the Jamovi automatic analysis classification program was used to calculate the simple slope test. Second, the SPSS macro program was also used to calculate the simple slope test also. RESULTS: A total of 570 drivers (247 males, 282 females) participated in the survey. A negative correlation was found between age and aggressive driving behaviors, and a positive correlation was found between neuroticism and aggressive driving behaviors in the multiple regression analysis. Significant associations were also found between age, driving experience, and depression, as well as age, driving experience, and neuroticism. Simple slope tests showed that depressive symptoms could increase aggressive behaviors in the elderly and experienced drivers. When experiencing neuroticism, individuals with higher driving experience were more aggressive in driving than shorter experienced drivers. CONCLUSION: Age and neuroticism influenced aggressive driving behaviors. Veteran drivers could be aggressive drivers when experiencing depressive symptoms or neuroticism. Mobile intervention could be sent to the potentially risky drivers, which would be safe and broadly feasible to prevent aggressive driving behavior in the background of COVID-19.

Accessing the syndemic of COVID-19 and malaria intervention in Africa (preprint)

Background: The pandemic of the coronavirus disease 2019 (COVID-19) has caused substantial disruptions to health services in the low and middle-income countries with a high burden of other diseases, such as malaria in sub-Saharan Africa. The aim of this study is to assess the impact of COVID-19 pandemic on malaria transmission potential in malaria-endemic countries in Africa. Methods: : We present a data-driven method to quantify the extent to which the COVID-19 pandemic, as well as various non-pharmaceutical interventions (NPIs), could lead to the change of malaria transmission potential in 2020. First, we adopt a particle Markov Chain Monte Carlo method to estimate epidemiological parameters in each country by fitting the time series of the cumulative number of reported COVID-19 cases. Then, we simulate the epidemic dynamics of COVID-19 under two groups of NPIs: (i) contact restriction and social distancing, and (ii) early identification and isolation of cases. Based on the simulated epidemic curves, we quantify the impact of COVID-19 epidemic and NPIs on the distribution of insecticide-treated nets (ITNs). Finally, by treating the total number of ITNs available in each country in 2020, we evaluate the negative effects of COVID-19 pandemic on malaria transmission potential based on the notion of vectorial capacity. Results: : In this paper, we conduct case studies in four malaria-endemic countries, Ethiopia, Nigeria, Tanzania, and Zambia, in Africa. The epidemiological parameters (i.e., the basic reproduction number R_0 and the duration of infection D_I) of COVID-19 in each country are estimated as follows: Ethiopia (R_0=1.57, D_I=5.32), Nigeria (R_0=2.18, D_I=6.58), Tanzania (R_0=2.47, D_I=6.01), and Zambia (R_0=2.12, D_I=6.96). Based on the estimated epidemiological parameters, the epidemic curves simulated under various NPIs indicated that the earlier the interventions are implemented, the better the epidemic is controlled. Moreover, the effect of combined NPIs is better than contact restriction and social distancing only. By treating the total number of ITNs available in each country in 2020 as a baseline, our results show that even with stringent NPIs, malaria transmission potential will remain higher than expected in the second half of 2020. Conclusions: : By quantifying the impact of various NPI response to the COVID-19 pandemic on malaria transmission potential, this study provides a way to jointly address the syndemic between COVID-19 and malaria in malaria-endemic countries in Africa. The results suggest that the early intervention of COVID-19 can effectively reduce the scale of the epidemic and mitigate its impact on malaria transmission potential. Keywords : COVID-19 pandemic; Non-pharmaceutical interventions; Particle Markov chain Monte Carlo; Insecticide-treated nets; Vectorial capacity; Malaria transmission potential

Prevalence and risks of severe events for cancer patients with COVID-19 infection: a systematic review and meta-analysis (preprint)

Background The corona virus disease 2019 (COVID-19) pandemic poses a severe challenge to public health, especially to those patients with underlying diseases. In this meta-analysis, we studied the prevalence of cancer among patients with COVID-19 infection and their risks of severe events. Methods We searched the Pubmed, Embase and MedRxiv databases for studies between December 2019 and May 3, 2020 using the following key words and terms: sars-cov-2, covid-19, 2019-ncov, 2019 novel coronavirus, corona virus disease-2019, clinical, clinical characteristics, clinical course, epidemiologic features, epidemiology, and epidemiological characteristics. We extracted data following PICO (patient, intervention, comparison and outcome) chart. Statistical analyses were performed with R Studio (version 3.5.1) on the group-level data. We assessed the studies risk of bias in accordance to the adjusted Joanna Briggs Institute. We estimated the prevalence or risks for severe events including admission into intensive care unit or death using meta-analysis with random effects. Findings Out of the 2,551 studies identified, 32 studies comprising 21,248 participants have confirmed COVID-19. The total prevalence of cancer in COVID-19 patients was 3.97% (95% CI, 3.08% to 5.12%), higher than that of the total cancer rate (0.29%) in China. Stratification analysis showed that the overall cancer prevalence of COVID-19 patients in China was 2.59% (95% CI, 1.72% to 3.90%), and the prevalence reached 3.79% in Wuhan (95% CI, 2.51% to 5.70%) and 2.31% (95% CI, 1.16% to 4.57%) in other areas outside Wuhan in China. The incidence of ICU admission in cancer patients with COVID-19 was 26.80% (95% CI, 21.65% to 32.67%) and the mortality was 24.32% (95% CI, 13.95% to 38.91%), much higher than the overall rates of COVID-19 patients in China. The fatality in COVID patients with cancer was lower than those with cardiovascular disease (OR 0.49; 95% CI, 0.34 to 0.71; p=0.39), but comparable with other comorbidities such as diabetes (OR 1.32; 95% CI, 0.42 to 4.11; p=0.19), hypertension (OR 1.27; 95% CI, 0.35 to 4.62; p=0.13), and respiratory diseases (OR 0.79; 95% CI, 0.47 to 1.33; p=0.45). Interpretation This comprehensive meta-analysis on the largest number of patients to date provides solid evidence that COVID-19 infection significantly and negatively affected the disease course and prognosis of cancer patients. Awareness of this could help guide clinicians and health policy makers in combating cancer in the context of COVID-19 pandemic.