Dataset Information

Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach.

ABSTRACT: The socio-demographic factors have a substantial impact on the overall casualties caused by the Coronavirus (COVID-19). In this study, the global and local spatial association between the key socio-demographic variables and COVID-19 cases and deaths in the European regions were analyzed using the spatial regression models. A total of 31 European countries were selected for modelling and subsequent analysis. From the initial 28 socio-demographic variables, a total of 2 (for COVID-19 cases) and 3 (for COVID-19 deaths) key variables were filtered out for the regression modelling. The spatially explicit regression modelling and mapping were done using four spatial regression models such as Geographically Weighted Regression (GWR), Spatial Error Model (SEM), Spatial Lag Model (SLM), and Ordinary Least Square (OLS). Additionally, Partial Least Square (PLS) and Principal Component Regression (PCR) was performed to estimate the overall explanatory power of the regression models. For the COVID cases, the local R2 values, which suggesting the influences of the selected socio-demographic variables on COVID cases and death, were found highest in Germany, Austria, Slovenia, Switzerland, Italy. The moderate local R2 was observed for Luxembourg, Poland, Denmark, Croatia, Belgium, Slovakia. The lowest local R2 value for COVID-19 cases was accounted for Ireland, Portugal, United Kingdom, Spain, Cyprus, Romania. Among the 2 variables, the highest local R2 was calculated for income (R2?=?0.71), followed by poverty (R2?=?0.45). For the COVID deaths, the highest association was found in Italy, Croatia, Slovenia, Austria. The moderate association was documented for Hungary, Greece, Switzerland, Slovakia, and the lower association was found in the United Kingdom, Ireland, Netherlands, Cyprus. This suggests that the selected demographic and socio-economic components, including total population, poverty, income, are the key factors in regulating overall casualties of COVID-19 in the European region. In this study, the influence of the other controlling factors, such as environmental conditions, socio-ecological status, climatic extremity, etc. have not been considered. This could be the scope for future research.

SUBMITTER: Sannigrahi S

PROVIDER: S-EPMC7395296 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach.

Sannigrahi Srikanta S Pilla Francesco F Basu Bidroha B Basu Arunima Sarkar AS Molter Anna A

Sustainable cities and society 20200801

The socio-demographic factors have a substantial impact on the overall casualties caused by the Coronavirus (COVID-19). In this study, the global and local spatial association between the key socio-demographic variables and COVID-19 cases and deaths in the European regions were analyzed using the spatial regression models. A total of 31 European countries were selected for modelling and subsequent analysis. From the initial 28 socio-demographic variables, a total of 2 (for COVID-19 cases) and 3 ...[more]

PMID: 32834939

Similar Datasets

Project description:To provide data that can guide community-targeted practices, policies, and interventions in urban metropolitan areas, we used geospatial analysis to examine the community-level opioid overdose death determinants and their spatial variation across a study area. We obtained spatial datasets containing multiple, high-quality measures of socioeconomic conditions, public health status, and demographics for analysis and visualization in geographic information systems. We employed a multiscale modeling approach (multiscale geographically weighted regression; MGWR) to provide a comprehensive and robust analysis of opioid overdose death determinants, explain how geospatial patterns vary across scales across Milwaukee County in 2019, and examine the differential influence of factors locally, regionally, and globally. We subsequently examined how associations varied with the racial/ethnic composition of communities by dividing Milwaukee County into White-majority, Black-majority, and Hispanic-majority regions according to census data and conducting separate, independent modeling processes. Overall, the multiscale model explained 83% of opioid overdose death variability across neighborhoods in Milwaukee County using 12 selected variables. Statistical analysis and geovisualization of patterns, trends, and clusters using MGWR unveiled dramatic racialized health disparities in Milwaukee, showing how factors that influenced opioid overdose deaths varied across diverse communities in Milwaukee. The observed geographic variation in relationships included the impact of naloxone availability and incarceration rates on overdose deaths with pronounced differences between White communities and communities of color. Understanding, community-level factors that contribute to overdose risk should guide targeted community-level solutions. Overall, our findings demonstrate the value of precision epidemiology using MGWR analysis for defining and guiding responses to public health challenges.

Project description:Background:Studies on aggregation of taste preferences among children and their siblings as well as their parents are scarce. We investigated the familial aggregation of taste preferences as well as the effect of sex, age, country of residence and education on variation in taste preferences in the pan- European I.Family cohort. Method:Thirteen thousand one hundred sixty-five participants from 7 European countries, comprising 2,230 boys <12 years, 2,110 girls <12 years, 1,682 boys ?12 years, 1,744 girls ?12 years and 5,388 parents, completed a Food and Beverage Preference Questionnaire containing 63 food items representing the taste modalities sweet, bitter, salty and fatty. We identified food items that represent the different taste qualities using factor analysis. On the basis of preference ratings for these food and drink items, a preference score for each taste was calculated for children and parents individually. Sibling and parent-child correlations for taste preference scores were calculated. The proportion of variance in children's preference scores that could be explained by their parents' preference scores and potential correlates including sex, age and parental educational was explored. Results:Mean taste preferences for sweet, salty and fatty decreased and for bitter increased with age. Taste preference scores correlated stronger between siblings than between children and parents. Children's salty preference scores could be better explained by country than by family members. Children's fatty preference scores could be better explained by family members than by country. Age explained 17% of the variance in sweet and 16% of the variance in fatty taste preference. Sex and education were not associated with taste preference scores. Conclusion:Taste preferences are correlated between siblings. Country could explain part of the variance of salty preference scores in children which points to a cultural influence on salt preference. Further, age also explained a relevant proportion of variance in sweet and fatty preference scores.

Project description:ObjectivesXinjiang is one of the high TB burden provinces of China. A spatial analysis was conducted using geographical information system (GIS) technology to improve the understanding of geographic variation of the pulmonary TB occurrence in Xinjiang, its predictors, and to search for targeted interventions.MethodsNumbers of reported pulmonary TB cases were collected at county/district level from TB surveillance system database. Population data were extracted from Xinjiang Statistical Yearbook (2006~2014). Spatial autocorrelation (or dependency) was assessed using global Moran's I statistic. Anselin's local Moran's I and local Getis-Ord statistics were used to detect local spatial clusters. Ordinary least squares (OLS) regression, spatial lag model (SLM) and geographically-weighted regression (GWR) models were used to explore the socio-demographic predictors of pulmonary TB incidence from global and local perspectives. SPSS17.0, ArcGIS10.2.2, and GeoDA software were used for data analysis.ResultsIncidence of sputum smear positive (SS+) TB and new SS+TB showed a declining trend from 2005 to 2013. Pulmonary TB incidence showed a declining trend from 2005 to 2010 and a rising trend since 2011 mainly caused by the rising trend of sputum smear negative (SS-) TB incidence (p<0.0001). Spatial autocorrelation analysis showed the presence of positive spatial autocorrelation for pulmonary TB incidence, SS+TB incidence and SS-TB incidence from 2005 to 2013 (P <0.0001). The Anselin's Local Moran's I identified the "hotspots" which were consistently located in the southwest regions composed of 20 to 28 districts, and the "coldspots" which were consistently located in the north central regions consisting of 21 to 27 districts. Analysis with the Getis-Ord Gi* statistic expanded the scope of "hotspots" and "coldspots" with different intensity; 30 county/districts clustered as "hotspots", while 47 county/districts clustered as "coldspots". OLS regression model included the "proportion of minorities" and the "per capita GDP" as explanatory variables that explained 64% the variation in pulmonary TB incidence (adjR2 = 0.64). The SLM model improved the fit of the OLS model with a decrease in AIC value from 883 to 864, suggesting "proportion of minorities" to be the only statistically significant predictor. GWR model also improved the fitness of regression (adj R2 = 0.68, AIC = 871), which revealed that "proportion of minorities" was a strong predictor in the south central regions while "per capita GDP" was a strong predictor for the southwest regions.ConclusionThe SS+TB incidence of Xinjiang had a decreasing trend during 2005-2013, but it still remained higher than the national average in China. Spatial analysis showed significant spatial autocorrelation in pulmonary TB incidence. Cluster analysis detected two clusters-the "hotspots", which were consistently located in the southwest regions, and the "coldspots", which were consistently located in the north central regions. The exploration of socio-demographic predictors identified the "proportion of minorities" and the "per capita GDP" as predictors and may help to guide TB control programs and targeting intervention.

Project description:Purpose:Globally and in the European region, the road traffic injuries (RTI) have emerged as a major public health and development problem, killing the most productive adult members of a population, including women. This study aimed to identify the key socio-demographic determinants of premature and avoidable RTI mortality in reproductive-aged women (15-49 years) in Georgia. Materials and Methods:The study employed verbal autopsy data from the second national reproductive age mortality survey (RAMOS 2014). Univariate and multivariate logistic regression models were fitted using the Firth method to assess the crude and adjusted effects of each individual level socio-demographic factor on the odds of RTI-attributed death, with corresponding 95% confidence intervals (COR and AOR, 95% CI). Results:Of 843 women aged 15-49 years, 78 (9.3%) were the victims of fatal traffic crashes. After multivariate adjustment, the odds of dying from RTI were significantly higher in women aged 15-29 years (AOR=7.73, 95% CI= 4.20 to 14.20), those being employed (AOR=2.11, 95% CI= 1.22 to 3.64) and the wealthiest (AOR=2.88, 95% CI= 1.44 to 5.77) compared, respectively, to their oldest (40-49 years), unemployed and poorest counterparts. Conversely, there were no statistically significant ethnic, marital, rural/urban, and educational disparities in women's RTI fatalities. Overall, motorized four-wheeler occupants (78.2%), particularly passengers (71.8%), appeared to be the most common victims of fatal road injuries than pedestrians (20.5%). Alarmingly, the vast majority (85.9%) of any type of road users died instantly at the scene of collision, as compared to deaths en route to hospital (1.3%) or in hospital (11.5%). Conclusion:Age, employment, and wealth status appeared to be the strong independent predictors of young women's RTI mortality in Georgia. Future comprehensive research would be advantageous for further deciphering the differential impact of social determinants on traffic-induced fatalities, as a vital platform for evidence-based remedial actions on this predictable and preventable safety hazard.

Dataset Information

Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach.

Publications

Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets