Time to address the spatiotemporal uncertainties in COVID-19 research: Concerns and challenges

Geographic uncertainties in external exposome studies: A multi-scale approach to reduce exposure misclassification

BACKGROUND

Many studies on environment-health associations have emphasized that the selected buffer size (i.e., the scale of the geographic context when exposures are assigned at people's address location) may affect estimated effect sizes. However, there is limited methodological progress in addressing these buffer size-related uncertainties.

AIM

We aimed to 1) develop a statistical multi-scale approach to address buffer-related scale effects in cohort studies, and 2) investigate how environment-health associations differ between our multi-scale approach and ad hoc selected buffer sizes.

METHODS

We used lacunarity analyses to determine the largest meaningful buffer size for multiple high-resolution exposure surfaces (i.e., fine particulate matter [PM2.5], noise, and the normalized difference vegetation index [NDVI]). Exposures were linked to 7.7 million Dutch adults at their home addresses. We assigned exposure estimates based on buffers with fine-grained distance increments until the lacunarity-based upper limit was reached. Bayesian Cox model averaging addressed geographic uncertainties in the estimated exposure effect sizes within the exposure-specific upper buffer limits on mortality. Z-tests assessed statistical differences between averaged effect sizes and those obtained through pre-selected 100, 300, 1200, and 1500 m buffers.

RESULTS

The estimated lacunarity curves suggested exposure-specific upper buffer size limits; the largest was for NDVI (960 m), followed by noise (910 m) and PM2.5 (450 m). We recorded 845,229 deaths over eight years of follow-up. Our multi-scale approach indicated that higher values of NDVI were health-protectively associated with mortality risk (hazard ratio [HR]: 0.917, 95 % confidence interval [CI]: 0.886-0.948). Increased noise exposure was associated with an increased risk of mortality (HR: 1.003, 95 % CI: 1.002-1.003), while PM2.5 showed null associations (HR:0.998, 95 % CI: 0.997-1.000). Effect sizes of NDVI and noise differed significantly across the averaged and prespecified buffers (p < 0.05).

CONCLUSIONS

Geographic uncertainties in residential-based exposure assessments may obscure environment-health associations or risk spurious ones. Our multi-scale approach produced more consistent effect estimates and mitigated contextual uncertainties.

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.

Exploring the Spatial Relative Risk of COVID-19 in Berlin-Neukölln

Identifying areas with high and low infection rates can provide important etiological clues. Usually, areas with high and low infection rates are identified by aggregating epidemiological data into geographical units, such as administrative areas. This assumes that the distribution of population numbers, infection rates, and resulting risks is constant across space. This assumption is, however, often false and is commonly known as the modifiable area unit problem. This article develops a spatial relative risk surface by using kernel density estimation to identify statistically significant areas of high risk by comparing the spatial distribution of address-level COVID-19 cases and the underlying population at risk in Berlin-Neukölln. Our findings show that there are varying areas of statistically significant high and low risk that straddle administrative boundaries. The findings of this exploratory analysis further highlight topics such as, e.g., Why were mostly affluent areas affected during the first wave? What lessons can be learned from areas with low infection rates? How important are built structures as drivers of COVID-19? How large is the effect of the socio-economic situation on COVID-19 infections? We conclude that it is of great importance to provide access to and analyse fine-resolution data to be able to understand the spread of the disease and address tailored health measures in urban settings.

Towards building resilient cities to pandemics: A review of COVID-19 literature

With the global prevalence of COVID-19 disease, the concept of urban resilience against pandemics has drawn the attention of a wide range of researchers, urban planners, and policymakers. This study aims to identify the major dimensions and principles of urban resilience to pandemics through a systematic review focused on lessons learned from the COVID-19 pandemic and comparing different perspectives regarding resilient urban environments to such diseases. Based on the findings, the study proposes a conceptual framework and a series of principles of urban resilience to pandemics, consisting of four spatial levels: housing, neighborhoods, city, and the regional and national scales, and three dimensions of pandemic resilience: pandemic-related health requirements, environmental psychological principles, and general resilience principles. The findings show that resilient cities should be able to implement the pandemic-related health requirements, the psychological principles of the environment to reduce the stresses caused by the pandemic, and the general principles of resilience in the smart city context. This framework provides scholars and policymakers with a comprehensive understanding of resilience on different scales and assists them in making better-informed decisions.

Nature's contributions in coping with a pandemic in the 21st century: A narrative review of evidence during COVID-19

While COVID-19 lockdowns have slowed coronavirus transmission, such structural measures also have unintended consequences on mental and physical health. Growing evidence shows that exposure to the natural environment (e.g., blue-green spaces) can improve human health and wellbeing. In this narrative review, we synthesized the evidence about nature's contributions to health and wellbeing during the first two years of the COVID-19 pandemic. We found that during the pandemic, people experienced multiple types of nature, including both outdoors and indoors. Frequency of visits to outdoor natural areas (i.e., public parks) depended on lockdown severity and socio-cultural contexts. Other forms of nature exposure, such as spending time in private gardens and viewing outdoor greenery from windows, may have increased. The majority of the evidence suggests nature exposure during COVID-19 pandemic was associated with less depression, anxiety, stress, and more happiness and life satisfaction. Additionally, nature exposure was correlated with less physical inactivity and fewer sleep disturbances. Evidence was mixed regarding associations between nature exposure and COVID-related health outcomes, while nature visits might be associated with greater rates of COVID-19 transmission and mortality when proper social distancing measures were not maintained. Findings on whether nature exposure during lockdowns helped ameliorate health inequities by impacting the health of lower-socioeconomic populations more than their higher-socioeconomic counterparts for example were mixed. Based on these findings, we argue that nature exposure may have buffered the negative mental and behavioral impacts of lockdowns during the COVID-19 pandemic. Recovery and resilience during the current crises and future public health crises might be improved with nature-based infrastructure, interventions, designs, and governance.

Nature's contributions in coping with a pandemic in the 21st century: A narrative review of evidence during COVID-19

While COVID-19 lockdowns have slowed coronavirus transmission, such structural measures also have unintended consequences on mental and physical health. Growing evidence shows that exposure to the natural environment (e.g., blue-green spaces) can improve human health and wellbeing. In this narrative review, we synthesized the evidence about nature's contributions to health and wellbeing during the first two years of the COVID-19 pandemic. We found that during the pandemic, people experienced multiple types of nature, including both outdoors and indoors. Frequency of visits to outdoor natural areas (i.e., public parks) depended on lockdown severity and socio-cultural contexts. Other forms of nature exposure, such as spending time in private gardens and viewing outdoor greenery from windows, may have increased. The majority of the evidence suggests nature exposure during COVID-19 pandemic was associated with less depression, anxiety, stress, and more happiness and life satisfaction. Additionally, nature exposure was correlated with less physical inactivity and fewer sleep disturbances. Evidence was mixed regarding associations between nature exposure and COVID-related health outcomes, while nature visits might be associated with greater rates of COVID-19 transmission and mortality when proper social distancing measures were not maintained. Findings on whether nature exposure during lockdowns helped ameliorate health inequities by impacting the health of lower-socioeconomic populations more than their higher-socioeconomic counterparts for example were mixed. Based on these findings, we argue that nature exposure may have buffered the negative mental and behavioral impacts of lockdowns during the COVID-19 pandemic. Recovery and resilience during the current crises and future public health crises might be improved with nature-based infrastructure, interventions, designs, and governance.

Urban parks as a potential mitigator of suicide rates resulting from global pandemics: Empirical evidence from past experiences in Seoul, Korea

Globally, the increased suicide rate of the general population has become a concern not only because of the COVID-19 pandemic, but also because of its associated socioeconomic insecurity, loss of jobs, and economic shocks. This study employed robust fixed-effects panel models to empirically identify the mitigating effects of infectious diseases, via urban parks, on the suicide rate, and to examine gender differences in this regard, based on previous experiences in Seoul, Korea. We found that the differentiating mitigating effect did not significantly affect suicide rates during the 2015 MERS epidemic. However, during the 2009 H1N1 pandemic, wherein the number of confirmed cases was very high and diffused nationwide, urban parks significantly reduced the suicide rates for both men and women. The role of parks as a mitigator was more enhanced in cities with a high number of confirmed cases if it was associated with economic shocks. However, this effect was significant only in the suicide rates of men, not women. During a pandemic, urban parks can help maintain social interaction and sustain physical activities (i.e., walking and exercise) while maintaining physical distance. National and local governments should develop urban parks to actively control the suicide rate influenced by movement restriction measures inevitably occurring during the spread of infectious diseases.

The private rental housing market before and during the COVID-19 pandemic: A submarket analysis in Cracow, Poland

How the COVID-19 pandemic has altered the segmentation of residential rental markets is largely unknown. We therefore assessed rental housing submarkets before and during the pandemic in Cracow, Poland. We used geographically and temporally weighted regression to investigate the marginal prices of housing attributes over space-time. The marginal prices were further reduced to a few principal components per time period and spatially clustered to identify housing submarkets. Finally, we applied the adjusted Rand index to evaluate the spatiotemporal stability of the housing submarkets. The results revealed that the pandemic outbreak significantly lowered rents and modified the relevance of some housing characteristics for rental prices. Proximity to the university was no longer among the residential amenities during the pandemic. Similarly, the virus outbreak diminished the effect of a housing unit's proximity to the city center. The market partitioning showed that the number of Cracow's residential rental submarkets increased significantly as a result of the COVID-19 pandemic, as it enhanced the spatial variation in the marginal prices of covariates. Our findings suggest that the emergence of the coronavirus reshaped the residential rental market in three ways: Rents were decreased, the underlying rental price-determining factors changed, and the spatiotemporal submarket structure was altered.

Green space and cardiovascular disease: A systematic review with meta-analysis

Exposure to green space has been proposed to be beneficially associated with cardiovascular morbidity and mortality. Many studies have explored this topic, but the results remain conflicting. We aimed to evaluate the epidemiological evidence on this topic by performing a systematic review with meta-analysis. We searched PubMed, Web of Science and Embase for studies on the association between green space and cardiovascular disease (CVD) that were published till January 2022. Two authors independently performed study selection, data extraction, quality assessment, and risk of bias assessment. For studies providing detailed numeric data, we also conducted quantitative meta-analyses and calculated the pooled odd ratios (ORs) for associations between the most commonly used exposure estimate (normalized difference vegetative index [NDVI]) and five CVD events: CVD mortality, ischemic heart disease (IHD) mortality, cerebrovascular disease (CBVD) mortality, and stroke incidence/prevalence. Additional analyses were conducted to explore the geographical scale effects of NDVI. Publication bias tests were also conducted. Of the 6787 records identified, 53 studies were eligible for inclusion. These studies covered 18 countries and included data from more than 100 million persons. Meta-analyses showed that a 0.1 increase in NDVI was significantly associated with 2-3% lower odds of CVD mortality (OR: 0.97, 95% CI: 0.96-0.99), IHD mortality (OR: 0.98, 95% CI: 0.96-1.00), CBVD mortality (OR: 0.98, 95% CI: 0.97-1.00), and stroke incidence/prevalence (OR: 0.98, 95% CI: 0.96-0.99). There was no significant difference between the pooled estimates for different buffer sizes. No evidence of publication bias was detected. We provide strong and robust evidence for the beneficial effects of green space exposure on cardiovascular health. More prospective studies and mechanistic studies, especially that conducted in low- and middle-income countries, are merited to strengthen our conclusions.

Greenspace and park use associated with less emotional distress among college students in the United States during the COVID-19 pandemic

The COVID-19 pandemic has negatively affected many people's psychological health. Impacts may be particularly severe among socially vulnerable populations such as college students, a group predisposed to mental health problems. Outdoor recreation and visits to greenspaces such as parks offer promising pathways for addressing the mental health challenges associated with COVID-19. During the early stages of the pandemic (March-May 2020), we surveyed 1280 college students at four large public universities across the United States (U.S.) to assess how, and why, outdoor recreation and park use changed since the emergence of COVID-19. We also measured students' self-reported levels of emotional distress (a proxy for psychological health) and assessed potential demographic and contextual correlates of distress, including county-level per capita park area and greenness, using generalized linear models. We found that 67% of students reported limiting outdoor activities and 54% reported reducing park use during the pandemic. Students who reduced their use of outdoor spaces cited structural reasons (e.g., lockdowns), concerns about viral transmission, and negative emotions that obstructed active lifestyles. Students who maintained pre-pandemic park use levels expressed a desire to be outdoors in nature, often with the explicit goal of improving mental and physical health. Emotional distress among students was widespread. Models showed higher levels of emotional distress were associated with reducing park use during the pandemic and residing in counties with a smaller area of parks per capita. This study of U.S. college students supports the value of park-based recreation as a health promotion strategy for diverse populations of young adults during a time of crisis.

The impact of the COVID-19 pandemic on people's mobility: A longitudinal study of the U.S. from March to September of 2020

This paper examines changes in people's mobility over a 7-month period (from March 1st to September 30th, 2020) during the COVID-19 pandemic in the U.S. using longitudinal models and county-level mobility data obtained from people's anonymized mobile phone signals. It differentiates two distinct waves of the study period: Wave 1 (March-June) and Wave 2 (June-September). It also analyzes the relationships of these mobility changes with various social, spatial, policy, and political factors. The results indicate that mobility changes in Wave 1 have a V-shaped trend: people's mobility first declined at the early stage of the COVID-19 pandemic (March-April) but quickly recovered to the pre-pandemic mobility levels from April to June. The rates of mobility changes during this period are significantly associated with most of our key variables, including political partisanship, poverty level, and the strictness of mobility restriction policies. For Wave 2, there was very little mobility decline despite the existence of mobility restriction policies and the COVID-19 pandemic becoming more severe. Our findings suggest that restricting people's mobility to control the pandemic may be effective only for a short period, especially in liberal democratic societies. Further, since poor people (who are mostly essential workers) kept traveling during the pandemic, health authorities should pay special attention to these people by implementing policies to mitigate their high COVID-19 exposure risk.

Methodological limitations in studies assessing the effects of environmental and socioeconomic variables on the spread of COVID-19: a systematic review

BACKGROUND

While numerous studies have assessed the effects of environmental (meteorological variables and air pollutants) and socioeconomic variables on the spread of the COVID-19 pandemic, many of them, however, have significant methodological limitations and errors that could call their results into question. Our main objective in this paper is to assess the methodological limitations in studies that evaluated the effects of environmental and socioeconomic variables on the spread of COVID-19.

MAIN BODY

We carried out a systematic review by conducting searches in the online databases PubMed, Web of Science and Scopus up to December 31, 2020. We first excluded those studies that did not deal with SAR-CoV-2 or COVID-19, preprints, comments, opinion or purely narrative papers, reviews and systematic literature reviews. Among the eligible full-text articles, we then excluded articles that were purely descriptive and those that did not include any type of regression model. We evaluated the risk of bias in six domains: confounding bias, control for population, control of spatial and/or temporal dependence, control of non-linearities, measurement errors and statistical model. Of the 5631 abstracts initially identified, we were left with 132 studies on which to carry out the qualitative synthesis. Of the 132 eligible studies, we evaluated 63.64% of the studies as high risk of bias, 19.70% as moderate risk of bias and 16.67% as low risk of bias.

CONCLUSIONS

All the studies we have reviewed, to a greater or lesser extent, have methodological limitations. These limitations prevent conclusions being drawn concerning the effects environmental (meteorological and air pollutants) and socioeconomic variables have had on COVID-19 outcomes. However, we dare to argue that the effects of these variables, if they exist, would be indirect, based on their relationship with social contact.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s12302-021-00550-7.