Mobility in Blue-Green Spaces Does Not Predict COVID-19 Transmission: A Global Analysis

Mobility restrictions during the COVID-19 pandemic ostensibly prevented the public from transmitting the disease in public places, but they also hampered outdoor recreation, despite the importance of blue-green spaces (e.g., parks and natural areas) for physical and mental health. We assess whether restrictions on human movement, particularly in blue-green spaces, affected the transmission of COVID-19. Our assessment uses a spatially resolved dataset of COVID-19 case numbers for 848 administrative units across 153 countries during the first year of the pandemic (February 2020 to February 2021). We measure mobility in blue-green spaces with planetary-scale aggregate and anonymized mobility flows derived from mobile phone tracking data. We then use machine learning forecast models and linear mixed-effects models to explore predictors of COVID-19 growth rates. After controlling for a number of environmental factors, we find no evidence that increased visits to blue-green space increase COVID-19 transmission. By contrast, increases in the total mobility and relaxation of other non-pharmaceutical interventions such as containment and closure policies predict greater transmission. Ultraviolet radiation stands out as the strongest environmental mitigant of COVID-19 spread, while temperature, humidity, wind speed, and ambient air pollution have little to no effect. Taken together, our analyses produce little evidence to support public health policies that restrict citizens from outdoor mobility in blue-green spaces, which corroborates experimental studies showing low risk of outdoor COVID-19 transmission. However, we acknowledge and discuss some of the challenges of big data approaches to ecological regression analyses such as this, and outline promising directions and opportunities for future research.

Alleviating confounding in spatio-temporal areal models with an application on crimes against women in India

Assessing associations between a response of interest and a set of covariates in spatial areal models is the leitmotiv of ecological regression. However, the presence of spatially correlated random effects can mask or even bias estimates of such associations due to confounding effects if they are not carefully handled. Though potentially harmful, confounding issues have often been ignored in practice leading to wrong conclusions about the underlying associations between the response and the covariates. In spatio-temporal areal models, the temporal dimension may emerge as a new source of confounding, and the problem may be even worse. In this work, we propose two approaches to deal with confounding of fixed effects by spatial and temporal random effects, while obtaining good model predictions. In particular, restricted regression and an apparently—though in fact not—equivalent procedure using constraints are proposed within both fully Bayes and empirical Bayes approaches. The methods are compared in terms of fixed-effect estimates and model selection criteria. The techniques are used to assess the association between dowry deaths and certain socio-demographic covariates in the districts of Uttar Pradesh, India.

Exposure to Perfluoroalkyl Substances and Mortality for COVID-19: A Spatial Ecological Analysis in the Veneto Region (Italy)

Background: In the context of the COVID-19 pandemic, there is interest in assessing if per- and polyfluoroalkyl substances (PFAS) exposures are associated with any increased risk of COVID-19 or its severity, given the evidence of immunosuppression by some PFAS. The objective of this paper is to evaluate at the ecological level if a large area (Red Zone) of the Veneto Region, where residents were exposed for decades to drinking water contaminated by PFAS, showed higher mortality for COVID-19 than the rest of the region. Methods: We fitted a Bayesian ecological regression model with spatially and not spatially structured random components on COVID-19 mortality at the municipality level (period between 21 February and 15 April 2020). The model included education score, background all-cause mortality (for the years 2015–2019), and an indicator for the Red Zone. The two random components are intended to adjust for potential hidden confounders. Results: The COVID-19 crude mortality rate ratio for the Red Zone was 1.55 (90% Confidence Interval 1.25; 1.92). From the Bayesian ecological regression model adjusted for education level and baseline all-cause mortality, the rate ratio for the Red Zone was 1.60 (90% Credibility Interval 0.94; 2.51). Conclusion: In conclusion, we observed a higher mortality risk for COVID-19 in a population heavily exposed to PFAS, which was possibly explained by PFAS immunosuppression, bioaccumulation in lung tissue, or pre-existing disease being related to PFAS.

Effects of chronic radiation exposure on phytoplankton communities in special industrial reservoirs of Mayak PA

A comparative study of phytoplankton communities inhabiting six reservoirs in the Southern Urals with different levels of radioactive contamination is presented. Along with increasing the level of radioactive contamination, the diversity of the phytoplankton species is decreases, however the abundance of surviving species and their biomass did not depend on the level of radiation contamination in the studied reservoirs. There were found no signs of ecological regression of phytoplankton communities caused by radioactive contamination up to 6.5 kBq l-1 in the reservoirs of the Techa river Reservoir Cascade R-11, R-10, R-4 and R-3. Ecological regression of the phytoplankton community was observed in reservoirs R-17 and R-9, with a total activity of β-emitting radionuclides 470 kBq l-1 and higher, a total activity of α-emitting radionuclides 220 kBq l-1 and higher. Ecological regression of communities was registered as a decrease in species diversity, fluctuations in the number of algae in the high ranges, and the predominant development of one species. Reduction of species diversity in adverse conditions and violations of interspecies relationships in the community often leads to sharp fluctuations in the biomass and abundance. Moreover, the highest biomass of the algal communities can result from either excessive species gain or species loss. In the most contaminated R-9 (Karachai Lake) species diversity is dramatically decreased to monoculture of a single cyanobacteria species, dominant species could vary in different years of the study.

Air pollution and COVID-19 mortality in the United States: Strengths and limitations of an ecological regression analysis

Assessing whether long-term exposure to air pollution increases the severity of COVID-19 health outcomes, including death, is an important public health objective. Limitations in COVID-19 data availability and quality remain obstacles to conducting conclusive studies on this topic. At present, publicly available COVID-19 outcome data for representative populations are available only as area-level counts. Therefore, studies of long-term exposure to air pollution and COVID-19 outcomes using these data must use an ecological regression analysis, which precludes controlling for individual-level COVID-19 risk factors. We describe these challenges in the context of one of the first preliminary investigations of this question in the United States, where we found that higher historical PM2.5 exposures are positively associated with higher county-level COVID-19 mortality rates after accounting for many area-level confounders. Motivated by this study, we lay the groundwork for future research on this important topic, describe the challenges, and outline promising directions and opportunities.

PM2.5 exposure as a risk factor for multiple sclerosis. An ecological study with a Bayesian mapping approach

Some environmental factors are associated with an increased risk of multiple sclerosis (MS). Air pollution could be a main one. This study was conducted to investigate the association of particulate matter 2.5 (PM2.5) concentrations with MS prevalence in the province of Pavia, Italy. The overall MS prevalence in the province of Pavia is 169.4 per 100,000 inhabitants. Spatial ground-level PM2.5 gridded data were analysed, by municipality, for the period 2010–2016. Municipalities were grouped by tertiles according to PM2.5 concentration. Ecological regression and Bayesian statistics were used to analyse the association between PM2.5 concentrations, degree of urbanization, deprivation index and MS risk. MS risk was higher among persons living in areas with an average winter PM2.5 concentration above the European annual limit value (25 μg/m3). The Bayesian map revealed sizeable MS high-risk clusters. The study found a relationship between low MS risk and lower PM2.5 levels, strengthening the suggestion that air pollution may be one of the environmental risk factors for MS.

Citizens’ initiatives for care and welfare in the Netherlands: an ecological analysis

Background In the Netherlands as well as in other countries citizens take initiatives to provide or maintain services in the area of care and welfare. Citizens’ initiatives (CI’s) are organisations some of which have a formal structure while others are informally connected groups of citizens, that are established by a group of citizens with the aim to increase the health and welfare within their local community and that are not focused on making a profit. Although CI’s have been around since at least the 1970’s little research has been done on the phenomenon, with most of it consisting of case studies or qualitative exploratory research. To fill part of this gap in knowledge, we have studied the geographical variation in the presence of CI’s in the Netherlands and tried to explain this variation. Methods Data on the presence of CI’s were obtained by combining two existing inventories. We did an ecological regression analysis to test hypotheses about the relationship between the presence of CI’s and the existence of a care vacuum, the capacity for self-organisation and models of action in local communities. Results We counted 452 CI’s in care and welfare in the Netherlands in January 2016. Our results show a spatial concentration of care initiatives in urban areas in the Randstad cities in the west of the country and in rural areas in the south-east. The presence of CI’s is only weakly associated with a care vacuum, but is related to indicators for the capacity of concerted action and models of action. Conclusion There are by now a considerable number of CI’s in the area of care and welfare in the Netherlands. Apparently, citizens take collective initiatives to provide services that are not, or no longer, available to the local community. The initiatives are concentrated in certain parts of the country. However, our theoretical model to explain this geographical pattern is only partially confirmed.

Ecological Regression with Partial Identification

Ecological inference (EI) is the process of learning about individual behavior from aggregate data. We relax assumptions by allowing for “linear contextual effects,” which previous works have regarded as plausible but avoided due to nonidentification, a problem we sidestep by deriving bounds instead of point estimates. In this way, we offer a conceptual framework to improve on the Duncan–Davis bound, derived more than 65 years ago. To study the effectiveness of our approach, we collect and analyze 8,430 $2\times 2$ EI datasets with known ground truth from several sources—thus bringing considerably more data to bear on the problem than the existing dozen or so datasets available in the literature for evaluating EI estimators. For the 88% of real data sets in our collection that fit a proposed rule, our approach reduces the width of the Duncan–Davis bound, on average, by about 44%, while still capturing the true district-level parameter about 99% of the time. The remaining 12% revert to the Duncan–Davis bound.