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Previous studies have demonstrated that socioeconomic factors are associated with COVID-19 incidence. In this study, we analysed a broad range of socioeconomic indicators in relation to hospitalised cases in the Paris area.<br /><br /> We extracted 303 socioeconomic indicators from French census data for 855 residential units in Paris and assessed their association with COVID-19 hospitalisation risk.<br /><br /> The indicators most associated with hospitalisation risk were the third decile of population income (OR=9.10, 95% CI 4.98 to 18.39), followed by the primary residence rate (OR=5.87, 95% CI 3.46 to 10.61), rate of active workers in unskilled occupations (OR=5.04, 95% CI 3.03 to 8.85) and rate of women over 15 years old with no diploma (OR=5.04, 95% CI 3.03 to 8.85). Of note, population demographics were considerably less associated with hospitalisation risk. Among these indicators, the rate of women aged between 45 and 59 years (OR=2.17, 95% CI 1.40 to 3.44) exhibited the greatest level of association, whereas population density was not associated. Overall, 86% of COVID-19 hospitalised cases occurred within the 45% most deprived areas.<br /><br /> Studying a broad range of socioeconomic indicators using census data and hospitalisation data as a readily available and large resource can provide real-time indirect information on populations with a high incidence of COVID-19.<br /><br />Studying a broad range of socioeconomic indicators using census data and hospitalisation data as a readily available and large resource can provide real-time indirect information on populations with a high incidence of COVID-19.<br /><br /> In 1948, Japan started a short-term publicity and enforcement campaign for traffic safety nationwide, and since 1952, the campaign has been conducted twice a year for 10 days. We aimed to quantify the short-term effect of the spring sessions of the campaign, which were conducted in different months in different years, on road fatalities in Japan using data from 1949 to 2019.<br /><br /> We obtained national police data on the monthly number of road deaths and conducted a time series regression analysis with three steps smoothing the long-term patterns with the natural cubic spline function, calculating the ratio of the monthly number of deaths to the corresponding smoothed value, and regressing the ratio on the number of months from January 1949 and the binary variable for the conduct of spring sessions. We repeated the analysis for four subperiods (1949-1964, 1965-1989, 1990-2004 and 2005-2019).<br /><br /> During the study period, there were 632577 road deaths. Our analysis revealed that the spring sessions changed the number of deaths per day by -2.5% (95% CI -4.1% to -0.9%) in the months when they were conducted. In the four subperiods, the estimated changes were -4.5% (95% CI -8.9% to -0.1%), -2.6% (95% CI -5.0% to -0.1%), -0.1% (95% CI -2.9 to 2.7) and -3.5% (95% CI -7.9 to 0.9).<br /><br /> Road fatalities were reduced in the months when the spring sessions of the campaign were conducted, but the reduction was modest. The effect might have been somewhat larger until 1964, when Japan was a middle-income country.<br /><br />Road fatalities were reduced in the months when the spring sessions of the campaign were conducted, but the reduction was modest. [https://www.selleckchem.com/products/asciminib-abl001.html ABL001] The effect might have been somewhat larger until 1964, when Japan was a middle-income country.<br /><br /> The public health response to the SARS-CoV-2 (COVID-19) pandemic has had a detrimental impact on employment and there are concerns the impact may be greatest among the most vulnerable. We examined the characteristics of those who experienced changes in employment status during the early months of the pandemic.<br /><br /> Data were collected from a cross-sectional, nationally representative household survey of the working age population (18-64 years) in Wales in May/June 2020 (n=1379). We looked at changes in employment and being placed on furlough since February 2020 across demographics, contract type, job skill level, health status and household factors. χ<br /><br /> or Fisher's exact test and multinomial logistic regression models examined associations between demographics, subgroups and employment outcomes.<br /><br /> Of our respondents, 91.0% remained in the same job in May/June 2020 as they were in February 2020, 5.7% were now in a new job and 3.3% experienced unemployment. In addition, 24% of our respondents reported being placed on furlough. Non-permanent contract types, individuals who reported low mental well-being and household financial difficulties were all significant factors in experiencing unemployment. Being placed on 'furlough' was more likely in younger (18-29 years) and older (60-64 years) workers, those in lower skilled jobs and from households with less financial security.<br /><br /> A number of vulnerable population groups were observed to experience detrimental employment outcomes during the initial stage of the COVID-19 pandemic. Targeted support is needed to mitigate against both the direct impacts on employment, and indirect impacts on financial insecurity and health.<br /><br />A number of vulnerable population groups were observed to experience detrimental employment outcomes during the initial stage of the COVID-19 pandemic. Targeted support is needed to mitigate against both the direct impacts on employment, and indirect impacts on financial insecurity and health.<br /><br /> The current SARS-CoV-2 pandemic has especially affected individuals living in conglomerate settings having poverty as common characteristic. However, evidence of the association between COVID-19 severity and social determinants is still scarce, particularly, for Latin American countries. The objective was to assess the effect of socioeconomic deprivation in the clinical severity of COVID-19 infection among different localities of Bogotá, Colombia.<br /><br /> Secondary analyses using data of SARS-CoV-2 infected cases in Bogotá from 6 March 2020 to 19 April 2020 were carried out. Direct and indirect indicators of deprivation at area level and individual demographic characteristics (age, sex and type of case) were included in the analyses.<br /><br /> 1684 COVID-19 cases were included in the study. There were 217 (12.9%; 95% CI 11.3 to 14.5) serious cases, of which 32.6% (95% CI 26.4 to 38.8) cases were deceased. In the multilevel logistic regression, age, sex (female), type of case (different of imported case), number of serious cases recorded the previous day and multidimensional poverty were associated with serious cases (median OR 1.