Geospatial evaluation of COVID-19 mortality: Influence of socio-economic status and underlying health conditions in contiguous USA

A Bayesian spatio-temporal model of COVID-19 spread in England

Exploring the spatio-temporal variations of COVID-19 transmission and its potential determinants could provide a deeper understanding of the dynamics of disease spread. This study aimed to investigate the spatio-temporal spread of COVID-19 infections in England, and examine its associations with socioeconomic, demographic and environmental risk factors. We obtained weekly reported COVID-19 cases from 7 March 2020 to 26 March 2022 at Middle Layer Super Output Area (MSOA) level in mainland England from publicly available datasets. With these data, we conducted an ecological study to predict the COVID-19 infection risk and identify its associations with socioeconomic, demographic and environmental risk factors using a Bayesian hierarchical spatio-temporal model. The Bayesian model outperformed the ordinary least squares model and geographically weighted regression model in terms of prediction accuracy. The spread of COVID-19 infections over space and time was heterogeneous. Hotspots of infection risk exhibited inconsistent clustering patterns over time. Risk factors found to be positively associated with COVID-19 infection risk were: annual household income [relative risk (RR) = 1.0008, 95% Credible Interval (CI) 1.0005-1.0012], unemployment rate [RR = 1.0027, 95% CI 1.0024-1.0030], population density on the log scale [RR = 1.0146, 95% CI 1.0129-1.0164], percentage of Caribbean population [RR = 1.0022, 95% CI 1.0009-1.0036], percentage of adults aged 45-64 years old [RR = 1.0031, 95% CI 1.0024-1.0039], and particulate matter ( PM 2.5 ) concentrations [RR = 1.0126, 95% CI 1.0083-1.0167]. The study highlights the importance of considering socioeconomic, demographic, and environmental factors in analysing the spatio-temporal variations of COVID-19 infections in England. The findings could assist policymakers in developing tailored public health interventions at a localised level.

Identifying determinants for the seropositive rate of schistosomiasis in Hunan province, China: A multi-scale geographically weighted regression model

BACKGROUND

Schistosomiasis is of great public health concern with a wide distribution and multiple determinants. Due to the advances in schistosomiasis elimination and the need for precision prevention and control, identifying determinants at a fine scale is urgent and necessary, especially for resource deployment in practice. Our study aimed to identify the determinants for the seropositive rate of schistosomiasis at the village level and to explore their spatial variations in local space.

METHODOLOGY

The seropositive rates of schistosomiasis were collected from 1714 villages or communities in Human Province, and six spatial regression models including ordinary least squares (OLS), spatial lag model (SLM), spatial error model (SEM), geographically weighted regression (GWR), robust GWR (RGWR) and multiscale GWR (MGWR) were used to fit the data.

PRINCIPAL/FINDINGS

MGWR was the best-fitting model (R2: 0.821, AICc:2727.092). Overall, the nearest distance from the river had the highest mean negative correlation, followed by proportion of households using well water and the annual average daytime surface temperature. The proportions of unmodified toilets showed the highest mean positive correlation, followed by the snail infested area, and the number of cattle. In spatial variability, the regression coefficients for the nearest distance from the river, annual average daytime surface temperature and the proportion of unmodified toilets were significant in all villages or communities and varied little in local space. The other significant determinants differed substantially in local space and had significance ratios ranging from 41% to 70%, including the number of cattle, the snail infested area and the proportion of households using well water.

CONCLUSIONS/SIGNIFICANCE

Our study shows that MGWR was well performed for the spatial variability of schistosomiasis in Hunan province. The spatial variability was different for different determinants. The findings for the determinants for the seropositive rate and mapped variability for some key determinants at the village level can be used for developing precision intervention measure for schistosomiasis control.

Spatial machine learning for predicting physical inactivity prevalence from socioecological determinants in Chicago, Illinois, USA

The increase in physical inactivity prevalence in the USA has been associated with neighborhood characteristics. While several studies have found an association between neighborhood and health, the relative importance of each component related to physical inactivity or how this value varies geographically (i.e., across different neighborhoods) remains unexplored. This study ranks the contribution of seven socioecological neighborhood factors to physical inactivity prevalence in Chicago, Illinois, using machine learning models at the census tract level, and evaluates their predictive capabilities. First, we use geographical random forest (GRF), a recently proposed nonlinear machine learning regression method that assesses each predictive factor's spatial variation and contribution to physical inactivity prevalence. Then, we compare the predictive performance of GRF to geographically weighted artificial neural networks, another recently proposed spatial machine learning algorithm. Our results suggest that poverty is the most important determinant in the Chicago tracts, while on the other hand, green space is the least important determinant in the rise of physical inactivity prevalence. As a result, interventions can be designed and implemented based on specific local circumstances rather than broad concepts that apply to Chicago and other large cities.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10109-023-00415-y.

Daily changes in spatial accessibility to ICU beds and their relationship with the case-fatality ratio of COVID-19 in the state of Texas, USA

During the COVID-19 pandemic, many patients could not receive timely healthcare services due to limited availability and access to healthcare resources and services. Previous studies found that access to intensive care unit (ICU) beds saves lives, but they overlooked the temporal dynamics in the availability of healthcare resources and COVID-19 cases. To fill this gap, our study investigated daily changes in ICU bed accessibility with an enhanced two-step floating catchment area (E2SFCA) method in the state of Texas. Along with the increased temporal granularity of measurements, we uncovered two phenomena: 1) aggravated spatial inequality of access during the pandemic, and 2) the retrospective relationship between insufficient ICU bed accessibility and the high case-fatality ratio of COVID-19 in rural areas. Our findings suggest that those locations should be supplemented with additional healthcare resources to save lives in future pandemic scenarios.

Interactive impacts of walkability, social vulnerability, & travel behavior on COVID-19 mortality: A hierarchical Bayesian spatial random parameter approach

While existing research highlights the built and social environment impacts on COVID-19 mortality, no empirical evidence exists on how the built and social environments may interact to influence COVID-19 mortality. This study presents a rigorous empirical assessment of the interactive impacts of social vulnerability and walkability on neighborhood-level COVID-19 mortality rates. Based in King County, WA, a unique data infrastructure is created by spatially integrating diverse census tract-level data on COVID-19 mortalities, walkability characteristics, social vulnerability, and travel behavior measures. Advanced Markov Chain Monte Carlo (MCMC) based Full Bayes hierarchical spatial random parameter models are developed to simultaneously capture spatial and unobserved random heterogeneity. Around 46% of the neighborhoods had opposite levels of walkability and social vulnerability. Compared to low walkability and high social vulnerability, neighborhoods with high walkability and low social vulnerability (i.e., best case scenario) had on average 20.2% (95% Bayesian CI: -37.2% to -3.3%) lower COVID-19 mortality rates. Analysis of the interactive impacts when only one of the social and built environment metrics was in a healthful direction revealed significant offsetting effects - suggesting that the underlying structural social vulnerability issues faced by our communities should be addressed first for the infectious disease-related health impacts of walkable urban design to be observed. Concerning travel behavior, the findings indicate that COVID-19 mortality rates may be reduced by discouraging auto use and encouraging active transportation. The study methodologically contributes by simultaneously capturing spatial and unobserved heterogeneity in a holistic Full Bayesian framework.

COVID-19 mortality and deprivation: pandemic, syndemic, and endemic health inequalities

COVID-19 has exacerbated endemic health inequalities resulting in a syndemic pandemic of higher mortality and morbidity rates among the most socially disadvantaged. We did a scoping review to identify and synthesise published evidence on geographical inequalities in COVID-19 mortality rates globally. We included peer-reviewed studies, from any country, written in English that showed any area-level (eg, neighbourhood, town, city, municipality, or region) inequalities in mortality by socioeconomic deprivation (ie, measured via indices of multiple deprivation: the percentage of people living in poverty or proxy factors including the Gini coefficient, employment rates, or housing tenure). 95 papers from five WHO global regions were included in the final synthesis. A large majority of the studies (n=86) found that COVID-19 mortality rates were higher in areas of socioeconomic disadvantage than in affluent areas. The subsequent discussion reflects on how the unequal nature of the pandemic has resulted from a syndemic of COVID-19 and endemic inequalities in chronic disease burden.

Application of Data Science for Cluster Analysis of COVID-19 Mortality According to Sociodemographic Factors at Municipal Level in Mexico

Mexico is among the five countries with the largest number of reported deaths from COVID-19 disease, and the mortality rates associated to infections are heterogeneous in the country due to structural factors concerning population. This study aims at the analysis of clusters related to mortality rate from COVID-19 at the municipal level in Mexico from the perspective of Data Science. In this sense, a new application is presented that uses a machine learning hybrid algorithm for generating clusters of municipalities with similar values of sociodemographic indicators and mortality rates. To provide a systematic framework, we applied an extension of the International Business Machines Corporation (IBM) methodology called Batch Foundation Methodology for Data Science (FMDS). For the study, 1,086,743 death certificates corresponding to the year 2020 were used, among other official data. As a result of the analysis, two key indicators related to mortality from COVID-19 at the municipal level were identified: one is population density and the other is percentage of population in poverty. Based on these indicators, 16 municipality clusters were determined. Among the main results of this research, it was found that clusters with high values of mortality rate had high values of population density and low poverty levels. In contrast, clusters with low density values and high poverty levels had low mortality rates. Finally, we think that the patterns found, expressed as municipality clusters with similar characteristics, can be useful for decision making by health authorities regarding disease prevention and control for reinforcing public health measures and optimizing resource distribution for reducing hospitalizations and mortality.