Longitudinal analyses of the relationship between development density and the COVID-19 morbidity and mortality rates: Early evidence from 1,165 metropolitan counties in the United States

Potential physical distance in the open urban grey space of city counties in Poland and COVID-19 cases and deaths throughout the pandemic

BACKGROUND

This paper reports the structure of grey space and the number of cases and deaths throughout the COVID-19 pandemic (from March 2020 to June 2023) in 66 city counties in Poland.

METHODS

Three main components of urban grey space (built-up areas, transport areas, and industrial areas) and the potential physical distance between residents in the open grey space - was determined. The total number of COVID-19 cases and deaths covered the entire period of the pandemic (totalling 1,214 days) was identified. The incidence and mortality density rates and the case fatality ratio were calculated. Simple and multiple linear regression models were developed to predict the quantitative characteristics of COVID-19 independent of city size.

RESULTS

Within the open spaces of cities, the average distance between residents was 17.7 m and was several times greater than that in closed spaces (1.5-2.0 m), which significantly reduced the risk of COVID-19 infection. Strong relationships were observed between the potential physical distance in the grey space structure and the total number of COVID-19 cases and deaths. The coefficient of determination (R2) for these relationships in the eight city groups by population was 0.90 for cases and 0.88 for deaths (significance level p = 0.001).

CONCLUSION

The study contributes to understanding how potential physical distance based on population density in grey space, might have influenced the course of COVID-19 during the pandemic. These findings can be applied to planning antiviral protection and to implementing future multilevel restrictions aimed at reducing the reproduction of SARS-CoV-2 in cities of various sizes.

Pathogenic built environment? Reflections on modeling spatial determinants of health in urban settings considering the example of COVID-19 studies

The triad of host, agent, and environment has become a widely accepted framework for understanding infectious diseases and human health. While modern medicine has traditionally focused on the individual, there is a renewed interest in the role of the environment. Recent studies have shifted from an early-twentieth-century emphasis on individual factors to a broader consideration of contextual factors, including environmental, climatic, and social settings as spatial determinants of health. This shifted focus has been particularly relevant in the context of the COVID-19 pandemic, where the built environment in urban settings is increasingly recognized as a crucial factor influencing disease transmission. However, operationalizing the complexity of associations between the built environment and health for empirical analyses presents significant challenges. This study aims to identify key caveats in the operationalization of spatial determinants of health for empirical analysis and proposes guiding principles for future research. We focus on how the built environment in urban settings was studied in recent literature on COVID-19. Based on a set of criteria, we analyze 23 studies and identify explicit and implicit assumptions regarding the health-related dimensions of the built environment. Our findings highlight the complexities and potential pitfalls, referred to as the 'spatial trap,' in the current approaches to spatial epidemiology concerning COVID-19. We conclude with recommendations and guiding questions for future studies to avoid falsely attributing a built environment impact on health outcomes and to clarify explicit and implicit assumptions regarding the health-related dimensions.

Spatial Analysis of Health System Factors in Infectious Disease Management: Lessons Learned from the COVID-19 Pandemic in Korea

Infectious disease outbreaks present ongoing and substantial challenges to health systems at local, national, and global levels, testing their preparedness, response capabilities, and resilience. This study aimed to identify and analyze critical health system-level factors that influence infection outbreaks, focusing on the experience of the COVID-19 pandemic in Korea. Conducted as a secondary data analysis, this study utilized national datasets from Korea. Given the inherent spatial dependencies in the spread of infectious diseases, we employed a spatial lag model to analyze data. While city-specific characteristics did not emerge as significant factors, health system variables, particularly the number of community health centers and health budgets, showed significant influence on the course of the COVID-19 outbreak, along with spatial autocorrelation coefficients. Our findings underscore the importance of enhancing public healthcare infrastructure, considering regional specificities, and promoting collaboration among local governments to bolster preparedness for future outbreaks. These insights are crucial for policymakers and healthcare professionals in formulating effective strategies to prevent, manage, and mitigate the impact of infectious disease outbreaks.

Spatiotemporal dynamics of epidemiology diseases: mobility based risk and short-term prediction modeling of COVID-19

Nowadays, epidemiological modeling is applied to a wide range of diseases, communicable and non-communicable, namely AIDS, Ebola, influenza, Dengue, Malaria, Zika. More recently, in the context of the last pandemic declared by the World Health Organization (WHO), several studies applied these models to SARS-CoV-2. Despite the increasing number of researches using spatial analysis, some constraints persist that prevent more complex modeling such as capturing local epidemiological dynamics or capturing the real patterns and dynamics. For example, the unavailability of: (i) epidemiological information such as the frequency with which it is made available; (ii) sociodemographic and environmental factors (e.g., population density and population mobility) at a finer scale which influence the evolution patterns of infectious diseases; or (iii) the number of cases information that is also very dependent on the degree of testing performed, often with severe territorial disparities and influenced by context factors. Moreover, the delay in case reporting and the lack of quality control in epidemiological information is responsible for biases in the data that lead to many results obtained being subject to the ecological fallacy, making it difficult to identify causal relationships. Other important methodological limitations are the control of spatiotemporal dependence, management of non-linearity, ergodicy, among others, which can impute inconsistencies to the results. In addition to these issues, social contact, is still difficult to quantify in order to be incorporated into modeling processes. This study aims to explore a modeling framework that can overcome some of these modeling methodological limitations to allow more accurate modeling of epidemiological diseases. Based on Geographic Information Systems (GIS) and spatial analysis, our model is developed to identify group of municipalities where population density (vulnerability) has a stronger relationship with incidence (hazard) and commuting movements (exposure). Specifically, our framework shows how to operate a model over data with no clear trend or seasonal pattern which is suitable for a short-term predicting (i.e., forecasting) of cases based on few determinants. Our tested models provide a good alternative for when explanatory data is few and the time component is not available, once they have shown a good fit and good short-term forecast ability.

The effect of the urban exposome on COVID-19 health outcomes: A systematic review and meta-analysis

BACKGROUND

The global severity of SARS-CoV-2 illness has been associated with various urban characteristics, including exposure to ambient air pollutants. This systematic review and meta-analysis aims to synthesize findings from ecological and non-ecological studies to investigate the impact of multiple urban-related features on a variety of COVID-19 health outcomes.

METHODS

On December 5, 2022, PubMed was searched to identify all types of observational studies that examined one or more urban exposome characteristics in relation to various COVID-19 health outcomes such as infection severity, the need for hospitalization, ICU admission, COVID pneumonia, and mortality.

RESULTS

A total of 38 non-ecological and 241 ecological studies were included in this review. Non-ecological studies highlighted the significant effects of population density, urbanization, and exposure to ambient air pollutants, particularly PM2.5. The meta-analyses revealed that a 1 μg/m3 increase in PM2.5 was associated with a higher likelihood of COVID-19 hospitalization (pooled OR 1.08 (95% CI:1.02-1.14)) and death (pooled OR 1.06 (95% CI:1.03-1.09)). Ecological studies, in addition to confirming the findings of non-ecological studies, also indicated that higher exposure to nitrogen dioxide (NO2), ozone (O3), sulphur dioxide (SO2), and carbon monoxide (CO), as well as lower ambient temperature, humidity, ultraviolet (UV) radiation, and less green and blue space exposure, were associated with increased COVID-19 morbidity and mortality.

CONCLUSION

This systematic review has identified several key vulnerability features related to urban areas in the context of the recent COVID-19 pandemic. The findings underscore the importance of improving policies related to urban exposures and implementing measures to protect individuals from these harmful environmental stressors.

Advancing application of satellite remote sensing technologies for linking atmospheric and built environment to health

Background

The intricate interplay between human well-being and the surrounding environment underscores contemporary discourse. Within this paradigm, comprehensive environmental monitoring holds the key to unraveling the intricate connections linking population health to environmental exposures. The advent of satellite remote sensing monitoring (SRSM) has revolutionized traditional monitoring constraints, particularly limited spatial coverage and resolution. This innovation finds profound utility in quantifying land covers and air pollution data, casting new light on epidemiological and geographical investigations. This dynamic application reveals the intricate web connecting public health, environmental pollution, and the built environment.

Objective

This comprehensive review navigates the evolving trajectory of SRSM technology, casting light on its role in addressing environmental and geographic health issues. The discussion hones in on how SRSM has recently magnified our understanding of the relationship between air pollutant exposure and population health. Additionally, this discourse delves into public health challenges stemming from shifts in urban morphology.

Methods

Utilizing the strategic keywords "SRSM," "air pollutant health risk," and "built environment," an exhaustive search unfolded across prestigious databases including the China National Knowledge Network (CNKI), PubMed and Web of Science. The Citespace tool further unveiled interconnections among resultant articles and research trends.

Results

Synthesizing insights from a myriad of articles spanning 1988 to 2023, our findings unveil how SRMS bridges gaps in ground-based monitoring through continuous spatial observations, empowering global air quality surveillance. High-resolution SRSM advances data precision, capturing multiple built environment impact factors. Its application to epidemiological health exposure holds promise as a pioneering tool for contemporary health research.

Conclusion

This review underscores SRSM's pivotal role in enriching geographic health studies, particularly in atmospheric pollution domains. The study illuminates how SRSM overcomes spatial resolution and data loss hurdles, enriching environmental monitoring tools and datasets. The path forward envisions the integration of cutting-edge remote sensing technologies, novel explorations of urban-public health associations, and an enriched assessment of built environment characteristics on public well-being.

How do density, employment and transit affect the prevalence of COVID-19 pandemic? A study of 3,141 counties across the United States

Previous research has explored the effect of the built environment on the spread of the coronavirus disease (COVID-19) pandemic. This study extends the existing literature by examining the relationship between pandemic prevalence and density, employment, and transit factors at the county level. Using multilinear spatial-lag regressions and time series clustering analyses on the Smart Location Database encompassing 3141 counties in the United States, our findings reveal the following: (1) Density, employment, and transit variables yield heterogeneous effects to infection rate, death rate, and mortality rate. (2) Pedestrian-oriented road density is positively correlated to the prevalence of COVID-19, every 0.011 miles/acre increase is associated with 1% increase in the infection rate. (3) A consistent negative correlation is observed between jobs per household and infection rate, while a decrease in unemployment rate leads to an increase in the death rate. (4) The results from time series analysis suggest that areas characterized by low auto-oriented intersection density but high pedestrian-oriented road density are more susceptible to the impacts of pandemics. This highlights the need to prioritize pandemic prevention efforts in the suburban and rural areas with low population density, as emphasized in existing literature emphasized.

The emergence of a suburban penalty during the 1918/19 influenza pandemic in Malta: The role of a marketplace, railway, and measles

The Malta 1918/19 influenza experience adds to our understanding of the pandemic by illustrating the importance of suburban populations, their vulnerabilities, and elevated mortality rates. Studies on the socio-geographical variation in the 1918/19 influenza mortality has largely overlooked the suburban experience, and thus the often-hidden heterogeneity of the disease experience is missing. A comparison of mortality rates across the three settlement types (urban, suburban, and rural) for the second wave of the pandemic revealed that there were significant differences across the settlement types (x2 = 22.67, 2df, p <0.0001). There was a statistically significant divide between suburban settlement type versus urban and rural communities. Further, the geographical division of the central suburban region had the highest mortality rate at 4.28 per 1000 living of all suburban regions. A closer examination of the central suburban communities revealed that the town of Birchicara was the driving force behind the elevated influenza mortality, with a rate of 5.28 per 1000 living. The exceedingly high rate of influenza mortality in Birchicara was significantly different from the other suburban communities (Z = 2.915, p = 0.004). Birchicara was notable as both a transmission and burden hotspot for influenza infection because of a unique conflation of factors not observed elsewhere on the island. Foremost, was the pitkali market, which was a produce wholesale distributing centre; second, was the fact that the train station was a central hub especially for Maltese labourers; third, was that the measles epidemic in 1916/17 contributed to elevated childhood influenza deaths because the presence of military personnel and their families. We argue that the interaction of the three factors, and in particular, the measles epidemic with childhood influenza, amounted to a syndemic. Factors associated with urbanization and high rates of infectious diseases, such as overcrowding and infant mortality, did not play a primary role in the syndemic.

What is the optimal country for minimum COVID-19 morbidity and mortality rates?

The SARS-CoV-2 is a deceptive virus. Despite the remarkable progress in genetic sequencing and subsequent vaccine development, the world continues to grapple with the ominous threats of rapidly appearing SARS-CoV-2 variants. The objective of this manuscript is to rank world countries based on the anticipated scope of COVID-19 morbidity and mortality, measured in terms of prevalence per 1 million persons, from the lowest to the highest. The ranking of 162 countries is based on predictions of empirical models, which include three explanatory variables: hospital beds per thousand persons, population density, and the median age of the country's population. Referring to the COVID-19 scope of morbidity, the lowest likelihood of infection is obtained in Niger and Mali, where the dominant characteristic is the young median age (15.1-16.4 years). Referring to the COVID-19 scope of mortality, the lowest likelihood is obtained in Singapore. For Singapore, the dominant feature is the high population density. The optimal solution is intensive vaccination campaigns in the initial phase of the pandemic, particularly among countries with low GDP per capita. Yet, vaccinations may work only where the personal immune system is healthy and thus respond by creating antibodies to the SARS-CoV2 virus. Referring to populations that lack the natural protection of the healthy immune system and thus cannot be vaccinated (e.g., old people, cancer patients undergoing chemotherapy treatments), a complementary solution might be coordination between countries and the establishment of field hospitals, testing laboratories, isolation of areas, humanitarian aid-in the same manner of treatment in other disasters like earthquakes.

Analysis of Country-Level Risk Factors of COVID-19 Mortality Across Countries of Asia: A Generalised Estimating Equation Approach

This research aimed to investigate the death counts from coronavirus disease (COVID-19) across Asian countries by selecting 42 countries with a nonzero death count. Several studies have assessed personal-level factors that affect mortality rates in patients with COVID-19. However, the influence of country-level factors is still debatable. The results of a generalised estimating equation confirmed that the expected death counts across the countries in the middle quartile group of gross domestic product, upper quartile group of population density and lower quartile group of hospital bed count were higher than those in the countries in other quartile groups. The results further confirmed the positive association of the percentage of the population aged ≥65 years and time (day) with the death count. Visualisations and descriptive statistics showed that the death count increased over time across the Asian countries, with maximum death count occurring in the third quarter of the year 2020. These findings support the recommendation that countries with lower number of hospital beds per 1,000 persons, higher percentage of the population aged 65 years and older and middle and lower economies should take more precautions to reduce the death count from COVID-19.

Designing pandemic resilient cities: Exploring the impacts of the built environment on infection risk perception and subjective well-being

Since the beginning of the COVID-19 pandemic, authorities around the world explored ways to slowdown the spread of the disease while maintaining the physical and mental health of individuals. They redistributed the street space to promote physical activity and non-motorized travel while meeting the social distancing requirements. Although the statistics showed significant increases in walking and bicycling trips during the pandemic, we have limited knowledge about the associations between built environment characteristics, COVID-19 infection risk perception while traveling, and subjective well-being. This study assesses the impacts of the built environment on subjective well-being and infection risk perception while traveling during the pandemic. It uses data collected from the residents of Columbus, Ohio, through a multi-wave survey conducted at different time points during the COVID-19 outbreak. By employing a structural equation modeling approach, it explores the associations between residential neighborhood characteristics, individuals' subjective well-being, and perceived infection risk while using non-motorized modes and shared micromobility. The findings show that those living in more compact, accessible, and walkable neighborhoods are less likely to perceive active travel and shared micromobility as risky in terms of COVID-19 infection. Our results also show that built environment characteristics have an indirect positive effect on the subjective well-being of individuals. The findings of our study demonstrate that built environment interventions can help promote physical activity and support mental health of individuals at this critical time. Our study also indicates that designing compact neighborhoods will be a crucial element of pandemic resilient cities in the post-COVID-19 era.

Mechanisms influencing the factors of urban built environments and coronavirus disease 2019 at macroscopic and microscopic scales: The role of cities

In late 2019, the coronavirus disease 2019 (COVID-19) pandemic soundlessly slinked in and swept the world, exerting a tremendous impact on lifestyles. This study investigated changes in the infection rates of COVID-19 and the urban built environment in 45 areas in Manhattan, New York, and the relationship between the factors of the urban built environment and COVID-19. COVID-19 was used as the outcome variable, which represents the situation under normal conditions vs. non-pharmacological intervention (NPI), to analyze the macroscopic (macro) and microscopic (micro) factors of the urban built environment. Computer vision was introduced to quantify the material space of urban places from street-level panoramic images of the urban streetscape. The study then extracted the microscopic factors of the urban built environment. The micro factors were composed of two parts. The first was the urban level, which was composed of urban buildings, Panoramic View Green View Index, roads, the sky, and buildings (walls). The second was the streets' green structure, which consisted of macrophanerophyte, bush, and grass. The macro factors comprised population density, traffic, and points of interest. This study analyzed correlations from multiple levels using linear regression models. It also effectively explored the relationship between the urban built environment and COVID-19 transmission and the mechanism of its influence from multiple perspectives.

Survival analysis and mortality predictors of COVID-19 in a pediatric cohort in Mexico

Background

The new coronavirus SARS-CoV-2 pandemic has been relatively less lethal in children; however, poor prognosis and mortality has been associated with factors such as access to health services. Mexico remained on the list of the ten countries with the highest case fatality rate (CFR) in adults. It is of interest to know the behavior of COVID-19 in the pediatric population. The aim of this study was to identify clinical and sociodemographic variables associated with mortality due to COVID-19 in pediatric patients.

Objective

Using National open data and information from the Ministry of Health, Mexico, this cohort study aimed to identify clinical and sociodemographic variables associated with COVID-19 mortality in pediatric patients.

Method

A cohort study was designed based on National open data from the Ministry of Health, Mexico, for the period April 2020 to January 2022, and included patients under 18 years of age with confirmed SARS-CoV-2 infection. Variables analyzed were age, health services used, and comorbidities (obesity, diabetes, asthma, cardiovascular disease, immunosuppression, high blood pressure, and chronic kidney disease). Follow-up duration was 60 days, and primary outcomes were death, hospitalization, and requirement of intensive care. Statistical analysis included survival analysis, prediction models created using the Cox proportional hazards model, and Kaplan-Meier estimation curves.

Results

The cohort included 261,099 cases with a mean age of 11.2 ± 4 years, and of these, 11,569 (4.43%) were hospitalized and 1,028 (0.39%) died. Variables associated with risk of mortality were age under 12 months, the presence of comorbidities, health sector where they were treated, and first wave of infection.

Conclusion

Based on data in the National database, we show that the pediatric fatality rate due to SARS-CoV-2 is similar to that seen in other countries. Access to health services and distribution of mortality were heterogeneous. Vulnerable groups were patients younger than 12 months and those with comorbidities.

Effect of COVID-19 lockdowns on city-center and suburban housing markets: Evidence from Hangzhou, China

In 2020, governments worldwide enforced lockdowns to contain the spread of COVID-19, severely impeding aspects of daily life such as work, school, and tourism. Consequently, numerous economic activities were affected. Before the COVID-19 outbreak, city-center housing markets in areas surrounding popular tourist attractions performed better than did suburban housing markets because of the output of the tourism industry. This study examines the changes in the performance of city-center and suburban housing markets in regions with popular tourist attractions after the lockdown. Specifically, the dynamics of city-center and suburban housing markets in Hangzhou, where West Lake is located, and the changes in the information transfer between these housing markets after the lockdown are explored. Transaction data from January 1, 2019 to September 30, 2020 are used to perform analysis, in which adjusted housing prices and asking prices are employed to measure market performance and sellers' pricing strategies, and transaction volume and time on the market are used to measure market liquidity and transaction frequency. The results reveal that the effects of lockdowns differ between city-center and suburban housing markets. After the lockdown, a substantial structural change is observed in the suburban housing market; the volatility risk of housing prices decreases substantially, causing an increase in transaction premiums. Housing prices and transaction volume increase in the city-center housing market after the lockdown; this is possibly because of the influence from the overall housing market booms. In addition, because sellers raise their asking prices and the transaction time is extended, the sellers in the city-center housing market are particularly influenced by the disposition effect. This leads to a reversal in the lead-lag relationship between the city center and suburban housing markets in terms of informativeness. Specifically, before the lockdown, the city-center market transfers information to the suburban market, but after the lockdown, the suburban market transfers information to the city-center market. The COVID-19 pandemic has changed the world in many aspects; this paper finds that it will also change the development pattern of the real estate market in different locations.

Do the determinants of COVID-19 transmission differ by epidemic wave? Evidence from U.S. counties

This paper uses data from the United States to examine determinants of the spread of COVID-19 during three different epidemic waves. We address how sociodemographic and economic attributes, industry composition, density, crowding in housing, and COVID-19-related variables are associated with the transmission of COVID-19. After controlling for spatial autocorrelation, our findings indicate that the percentage of people in poverty, number of restaurants, and percentage of workers teleworking were associated with the COVID-19 incidence rate during all three waves. Our results also show that dense areas were more vulnerable to the transmission of COVID-19 after the first epidemic wave. Regarding the density of supermarkets, our study elaborates the negative aspects of wholesale retail stores, which likely provide a vulnerable place for virus transmission. Our results suggest that sociodemographic and economic attributes were the determinants of the early phase of the pandemic, while density showed positive association with the transmission during subsequent waves. We provide implications for regions serving as gateway cities with high density and number of population. To add, we further provide evidence that non-pharmaceutical interventions in the early stage may mitigate the virus transmission.

Effects of the built environment and human factors on the spread of COVID-19: A systematic literature review

Soon after its emergence, COVID-19 became a global problem. While different types of vaccines and treatments are now available, still non-pharmacological policies play a critical role in managing the pandemic. The literature is enriched enough to provide comprehensive, practical, and scientific insights to better deal with the pandemic. This research aims to find out how the built environment and human factors have affected the transmission of COVID-19 on different scales, including country, state, county, city, and urban district. This is done through a systematic literature review of papers indexed on the Web of Science and Scopus. Initially, these databases returned 4264 papers, and after different stages of screening, we found 166 relevant papers and reviewed them. The empirical papers that had at least one case study and analyzed the effects of at least one built environment factor on the spread of COVID-19 were selected. Results showed that the driving forces can be divided into seven main categories: density, land use, transportation and mobility, housing conditions, demographic factors, socio-economic factors, and health-related factors. We found that among other things, overcrowding, public transport use, proximity to public spaces, the share of health and services workers, levels of poverty, and the share of minorities and vulnerable populations are major predictors of the spread of the pandemic. As the most studied factor, density was associated with mixed results on different scales, but about 58 % of the papers reported that it is linked with a higher number of cases. This study provides insights for policymakers and academics to better understand the dynamic roles of the non-pharmacological driving forces of COVID-19 at different levels.

Correlations between the urban built environmental factors and the spatial distribution at the community level in the reported COVID-19 samples: A case study of Wuhan

COVID-19 has dramatically changed the lifestyle of people, especially in urban environments. This paper investigated the variations of built environments that were measurably associated with the spread of COVID-19 in 150 Wuhan communities. The incidence rate in each community before and after the lockdown (January 23, 2020), as respective dependent variables, represented the situation under normal circumstances and non-pharmaceutical interventions (NPI). After controlling the population density, floor area ratio (FAR), property age and sociodemographic factors, the built environmental factors in two spatial dimensions, the 15-minute walking life circle and the 10-minute cycling life circle, were brought into the Hierarchical Linear Regression Model and the Ridge Regression Model. The results indicated that before lockdown, the number of markets and schools were positively associated with the incidence rate, while community population density and FAR were negatively associated with COVID-19 transmission. After lockdown, FAR, GDP, the number of hospitals (in the 15-minute walking life circle) and the bus stations (in the 10-minute cycling life circle) became negatively correlated with the incidence rate, while markets remained positive. This study effectively extends the discussions on the association between the urban built environment and the spread of COVID-19. Meanwhile, given the limitations of sociodemographic data sources, the conclusions of this study should be interpreted and applied with caution.

COVID19 in Latin America: informal settlements and the politics of urbicide

Informal settlements in Latin America express pandemic idiosyncrasies, calling global attention to historical problems related to a specific urbanization pattern. This article stresses COVID19 implications in the main urban planning canon: the notion of densification as an urban solution. Traditionally invisible social groups and territories acquire relevance, but now as a source of biological risk. Urban density appears as a contradictory trigger point, outlining new debates about informal settlements and their metrics. Evidence shows that trends in health discourse are striving to legitimize and enhance "urbicides" in this scenario, already underway through State action or inaction.

Vulnerable or resilient? The response of informal settlements to COVID-19: The case of urban village communities in Beijing

The COVID-19 pandemic has brought topics of the impact, response and adaptation of cities in emergencies to the forefront. When compared with formal settlements, the problems faced by informal settlements are more prominent. We propose the framework of an actor-network theory, substantiated by an empirical study of three typical informal settlements in Haidian District, Beijing, in which the process, characteristics and internal mechanism of the spatial reconstruction of the informal settlements in response to COVID-19 are closely scrutinised. Human actors such as local governments, community volunteers, landlords, tenants and non-human actors all participated in the response to COVID-19 according to their goal vision and political logic, with the local government as the core driving force, forming an integrated actor network. Rooted in the special locality of informal settlements, the actor network was both hierarchical and flexible, and its inherent dynamism has proven to be efficient during COVID-19, resulting in social adaptation and spatial reconstruction. This study contributes to the cautiously optimistic estimate of similar urban community resilience in terms of global epidemics and enriches the understanding of their interlacing dynamics from the perspective of spatial reconstruction.

The median age of a city's residents and population density influence COVID 19 mortality growth rates: policy implications

Background

SARS-CoV-2 is an infectious virus, which has generated a global pandemic. Israel was one of the first countries to vaccinate its population, inaugurating the program on December 20, 2020. The objective of the current study is to investigate the projected daily COVID19 mortality growth rate with higher median age and population size of cities under two scenarios: with and without the BNT162b2 Pfizer vaccination against the SAR-COV2 virus.

Methods

This study employs a panel data-set. We follow the COVID19 mortality growth rate in each of the 173 Israeli cities and towns starting from March 21, 2020 (10 days after the first documentation of COVID19 cases in Israel) until September 21, 2021, where the BNT162b2 Pfizer vaccinations were available starting from December 20, 2020.

Results

Referring to the median age of municipal residents, findings suggest that the BNT162b2 Pfizer vaccinations attenuate the rise in anticipated daily mortality growth rate for cities and towns in which the median population age is 30 years old (the range in median age among the residents in the municipalities surveyed is 11–41 years). Moreover, referring to population size of cities, findings demonstrate that while under the scenario without vaccination, the daily mortality growth rate is anticipated to rise, under a comparable scenario with vaccination, daily mortality growth rate is anticipated to drop.

Conclusions

In crowded cities, where the median age is high, two perspectives of early and intensive public policy interventions are clearly required. The first perspective is extensive medical treatment, namely, extension of availability of medical physical and online services; dispensing designated medications; expansion of hospitalization facilities and information services particularly to susceptible populations. All measures will be taken with attention to age accessibility of these means. The second perspective is prevention via establishment of testing and vaccination complexes; elevation of dedicated health services, generating selective lockdowns; education for increasing awareness to social distancing, wearing masks and other preventive means.

The relationship between the built environment and respiratory health: Evidence from a longitudinal study in Indonesia

Multiple studies have discussed the relationship between the built environment and non-infectious diseases, but research involving infectious diseases and the built environment is scarce. How the built environment is associated with infectious diseases varies across areas, and previous literature produces mixed results. This study investigated the relationship between the built environment and infectious diseases in Indonesia, which has different settings compared to developed countries. We combined the longitudinal panel data, Indonesian Family Life Survey (IFLS), and land cover data to examine the relationship between the built environment and the likelihood of contracting respiratory infectious diseases. We focused on the sprawl index to measure the built environment. The study confirmed that a sprawling neighbourhood is linked to lower respiratory infection symptoms by employing a fixed effect method. The association is more evident in urban areas and for females. The results also suggested that the linkage works through housing quality, such as housing crowdedness and ventilation, and neighbourhood conditions like neighbourhood transportation modes and air pollution levels. Thus, our results underlined the need to consider the health consequences of the densification policy and determine the direction of landscape planning and policy.

Urban-Rural Differences in Health Care Utilization and COVID-19 Outcomes in Patients With Type 2 Diabetes

Introduction

Two studies in Pennsylvania aimed to determine whether community type and community socioeconomic deprivation (CSD) 1) modified associations between type 2 diabetes (hereinafter, diabetes) and COVID-19 hospitalization outcomes, and 2) influenced health care utilization among individuals with diabetes during the COVID-19 pandemic.

Methods

The hospitalization study evaluated a retrospective cohort of patients hospitalized with COVID-19 through 2020 for COVID-19 outcomes: death, intensive care unit (ICU) admission, mechanical ventilation, elevated D-dimer, and elevated troponin level. We used adjusted logistic regression models, adding interaction terms to evaluate effect modification by community type (township, borough, or city census tract) and CSD. The utilization study included patients with diabetes and a clinical encounter between 2017 and 2020. Autoregressive integrated moving average time-series models evaluated changes in weekly rates of emergency department and outpatient visits, hemoglobin A_1c (HbA_1c) laboratory tests, and antihyperglycemic medication orders from 2018 to 2020.

Results

In the hospitalization study, of 2,751 patients hospitalized for COVID-19, 1,020 had diabetes, which was associated with ICU admission and elevated troponin. Associations did not differ by community type or CSD. In the utilization study, among 93,401 patients with diabetes, utilization measures decreased in March 2020. Utilization increased in July, and then began to stabilize or decline through the end of 2020. Changes in HbA_1c tests and medication order trends during the pandemic differed by community type and CSD.

Conclusion

Diabetes was associated with selected outcomes among individuals hospitalized for COVID-19, but these did not differ by community features. Utilization trajectories among individuals with diabetes during the pandemic were influenced by community type and CSD and could be used to identify individuals at risk of gaps in diabetes care.

Validation of a neighborhood-level COVID Local Risk Index in 47 large U.S. cities

OBJECTIVES

To present the COVID Local Risk Index (CLRI), a measure of city- and neighborhood-level risk for SARS COV-2 infection and poor outcomes, and validate it using sub-city SARS COV-2 outcome data from 47 large U.S. cities.

METHODS

Cross-sectional validation analysis of CLRI against SARS COV-2 incidence, percent positivity, hospitalization, and mortality. CLRI scores were validated against ZCTA-level SARS COV-2 outcome data gathered in 2020-2021 from public databases or through data use agreements using a negative binomial model.

RESULTS

CLRI was associated with each SARS COV-2 outcome in pooled analysis. In city-level models, CLRI was positively associated with positivity in 11/14 cities for which data were available, hospitalization in 6/6 cities, mortality in 13/14 cities, and incidence in 33/47 cities.

CONCLUSIONS

CLRI is a valid tool for assessing sub-city risk of SARS COV-2 infection and illness severity. Stronger associations with positivity, hospitalization and mortality may reflect differential testing access, greater weight on components associated with poor outcomes than transmission, omitted variable bias, or other reasons. City stakeholders can use the CLRI, publicly available on the City Health Dashboard (www.cityhealthdashboard.com), to guide SARS COV-2 resource allocation.

Associations between COVID-19 Pandemic, Lockdown Measures and Human Mobility: Longitudinal Evidence from 86 Countries

Recognizing an urgent need to understand the dynamics of the pandemic's severity, this longitudinal study is conducted to explore the evolution of complex relationships between the COVID-19 pandemic, lockdown measures, and social distancing patterns in a diverse set of 86 countries. Collecting data from multiple sources, a structural equation modeling (SEM) technique is applied to understand the interdependencies between independent variables, mediators, and dependent variables. Results show that lockdown and confinement measures are very effective to reduce human mobility at retail and recreation facilities, transit stations, and workplaces and encourage people to stay home and thereby control COVID-19 transmission at critical times. The study also found that national contexts rooted in socioeconomic and institutional factors influence social distancing patterns and severity of the pandemic, particularly with regard to the vulnerability of people, treatment costs, level of globalization, employment distribution, and degree of independence in society. Additionally, this study portrayed a mutual relationship between the COVID-19 pandemic and human mobility. A higher number of COVID-19 confirmed cases and deaths reduces human mobility and the countries with reduced personal mobility have experienced a deepening of the severity of the pandemic. However, the effect of mobility on pandemic severity is stronger than the effect of pandemic situations on mobility. Overall, the study displays considerable temporal changes in the relationships between independent variables, mediators, and dependent variables considering pandemic situations and lockdown regimes, which provides a critical knowledge base for future handling of pandemics. It has also accommodated some policy guidelines for the authority to control the transmission of COVID-19.

Measurement of contagion spatial spread probability in public places: A case study on COVID-19

The scale and scope of the COVID-19 epidemic have highlighted the need for timely control of viral transmission. This paper proposed a new spatial probability model of epidemic infection using an improved Wasserstein distance algorithm and Monte Carlo simulation. This method identifies the public places in which COVID-19 spreads and grows easily. The Wasserstein Distance algorithm is used to calculate the distribution similarity between COVID-19 cases and the public places. Further, we used hypothesis tests and Monte Carlo simulation to estimate the spatial spread probability of COVID-19 in different public places. We used Snow's data to test the stability and accuracy of this measurement. This verification proved that our method is reliable and robust. We applied our method to the detailed geographic data of COVID-19 cases and public places in Wuhan. We found that, rather than financial service institutions and markets, public buildings such as restaurants and hospitals in Wuhan are 95 percent more likely to be the public places of COVID-19 spread.

Geographies of infections: built environment and COVID-19 pandemic in metropolitan Melbourne

This paper uses spatial statistical techniques to reflect on geographies of COVID-19 infections in metropolitan Melbourne. We argue that the evolution of the COVID-19 pandemic, which has become widespread since early 2020 in Melbourne, typically proceeds through multiple built environment attributes - diversity, destination accessibility, distance to transit, design, and density. The spread of the contagion is institutionalised within local communities and postcodes, and reshapes movement practices, discourses, and structures of administrative politics. We demonstrate how a focus on spatial patterns of the built environment can inform scholarship on the spread of infections associated with COVID-19 pandemic and geographies of infections more broadly, by highlighting the consistency of built environment influences on COVID-19 infections across three waves of outbreaks. A focus on the built environment influence seeks to enact visions of the future as new variants emerge, illustrating the importance of understanding geographies of infections as global cities adapt to 'COVID-normal' living. We argue that understanding geographies of infections within cities could be a springboard for pursuing sustainable urban development via inclusive compact, mixed-use development and safe public transport.

The Spatial Association of Demographic and Population Health Characteristics with COVID-19 Prevalence Across Districts in India

In less-developed countries, the lack of granular data limits the researcher's ability to study the spatial interaction of different factors on the COVID-19 pandemic. This study designs a novel database to examine the spatial effects of demographic and population health factors on COVID-19 prevalence across 640 districts in India. The goal is to provide a robust understanding of how spatial associations and the interconnections between places influence disease spread. In addition to the linear Ordinary Least Square regression model, three spatial regression models-Spatial Lag Model, Spatial Error Model, and Geographically Weighted Regression are employed to study and compare the variables explanatory power in shaping geographic variations in the COVID-19 prevalence. We found that the local GWR model is more robust and effective at predicting spatial relationships. The findings indicate that among the demographic factors, a high share of the population living in slums is positively associated with a higher incidence of COVID-19 across districts. The spatial variations in COVID-19 deaths were explained by obesity and high blood sugar, indicating a strong association between pre-existing health conditions and COVID-19 fatalities. The study brings forth the critical factors that expose the poor and vulnerable populations to severe public health risks and highlight the application of geographical analysis vis-a-vis spatial regression models to help explain those associations.

Impact of working from home on activity-travel behavior during the COVID-19 Pandemic: An aggregate structural analysis

The ongoing COVID-19 pandemic has created significant public health concerns that led the public and private sectors to impose stay-at-home and work-from-home policies. Although working from home has been a conventional albeit infrequent behavior, the prevalence of this option was significantly and rapidly accelerated during the pandemic. This study explored the impacts of working from home on activity-travel behavior during the pandemic. Both work and non-work activity participation declined during the pandemic but to what extent was this due to working from home? How did working from home affect other measures of travel such as person-miles traveled? We approached these questions by developing a Structural Regression model and using cross-sectional data for the early phase of the pandemic when the infection curve was flattened and activity-travel behavior became relatively stable following the drastic changes observed during the pandemic's initial shock. Combining U.S. county-level data from the Maryland Transportation Institute and Google Mobility Reports, we concluded that the proportion of people working from home directly depended on pandemic severity and associated public health policies as well as on a range of socio-economic characteristics. Working from home contributed to a reduction in workplace visits. It also reduced non-work activities but only via a reduction in non-work activities linked to work. Finally, a higher working from home proportion in a county corresponded to a reduction in average person-miles traveled. A higher degree of state government responses to containment and closure policies contributed to an increase in working from home, and decreases in workplace and non-workplace visits and person-miles traveled in a county. The results of this study provide important insights into changes in activity-travel behavior associated with working from home as a response strategy to major disruptions such as those imposed by a pandemic.

The intricate association of COVID-19 pandemic with ecological issues

This critical narrative review is intended to emphasize the comprehensive ecological issues related to the evolution of the novel coronavirus, the environmental factors associated with the disease progress, and the impact the pandemic is having on the environment. Approximately 60% of the emerging infectious disease of the last century (including deadly viruses like HIV, Ebola, Influenza, coronavirus strains like SARS, MERS) are linked to zoonotic spillover. Therefore, to escape the emergence of newer cross-species infections, proper precautionary measures should be taken. Every country has specific rules to deal with the biomedical waste produced in hospitals. But the COVID-19 pandemic has posed a unique global challenge due to the overwhelming amount of biomedical waste generated from dedicated COVID hospitals, diagnostic facilities, quarantine centers, and home quarantine facilities. Moreover, inappropriate disposal of masks by the general public may contaminate the environment turning it into a potential health hazard. Therefore, strict adherence to Biomedical Waste Management Guidelines for proper disposal of masks and other medical waste by all concerned is a must. Lockdown has brought about tremendous improvement in conditions of the world's atmosphere, hydrosphere, and biosphere. Dramatic improvement in air quality index, decrease in water, and noise pollution are some of the positive aspects of lockdown. However, these effects are temporary. But these teach an important lesson to the world to take some permanent measures to bring down greenhouse gases and other toxic emissions. Some harmful effects of lockdown are illegal deforestation, wildlife trafficking, encroachment of reserved areas etc.

Impact of environmental and individual factors on COVID-19 mortality in children and adolescents in Mexico: An observational study

Background

During the Covid-19 pandemic, children and adolescents faced poverty, potentially dying from preventable causes, or missing out essential vaccines. The aim of this study was to assess potential environmental and individual factors associated to COVID-19 mortality in children and adolescents in Mexico.

Methods

This cross-sectional study analysed the official data of 131,001 children under 10 years of age and adolescents between 10 and 19 years of age with COVID-19 disease, in Mexico. Participants were diagnosticated between March 2020 and June 13, 2021. The environmental variables such as malnutrition, vaccination coverage and social lag index were evaluated at the state level. Multilevel models were fitted to evaluate the association between environmental and individual factors and COVID-19 mortality.

Findings

A total of 773 (0.6%) children and adolescents died due to COVID-19. Younger age (OR = 0.878, 95%CI: 0.869-0.888), diabetes (OR = 3.898, 95%CI: 2.596-5.851), immunosuppression (OR = 5.410, 95%CI: 4.088-7.158), obesity (OR = 1.876, 95%CI: 1.397-2.521), hypertension (OR = 1.906, 95%CI: 1.239-2.932), cardiovascular disease (OR = 2.288, 95%CI: 1.482-3.531), and chronic kidney disease (OR = 13.250, 95%CI: 9.066-19.350) were associated with mortality. COVID-19 mortality was directly associated with social lag index and malnutrition (OR_{very high} = 2.939, 95%CI: 1.111-7.775, and OR = 1.390, 95%CI: 1.073-1.802, respectively), and inversely associated with population density (OR = 0.374, 95%CI: 0.204-0.688). Finally, children and adolescents living in areas with a higher percentage of people with incomplete education (OR = 1.045, 95%CI: 1.011-1.081), of children of school age of 6–14 years who do not attend school (OR = 1.266, 95%CI: 1.032-1.554), and of illiterate population aged 15 and over (OR = 1.086, 95%CI: 0.999-1.179) were associated with a higher risk of COVID-19 mortality.

Interpretation

Malnutrition, social lag index and population density are key factors to understand COVID-19 mortality in children and adolescents. Also, age and pre-existing comorbidities were also associated with worse COVID-19 prognosis.

Funding

No funding was secured for this study.

The Effect of the Built Environment on the COVID-19 Pandemic at the Initial Stage: A County-Level Study of the USA

The COVID-19 pandemic affected how people interact with the built environment and ways of human habitation are facing significant challenges. However, the existing literature has not adequately addressed how the built environment affected the early prevalence of the pandemic. This research aims to extend the existing literature by relating the initial stage pandemic conditions with more comprehensive measures of the built environment including density, diversity, road network, and accessibility at the county level across the United States and conducting bi-weekly comparisons. We collected infection, death, and mortality data in 3141 counties between 1 March to 8 June 2020 and collected seventeen built environment attributes. Our results show that: (1) Road density and street intersection density were significantly associated with the infection rate; (2) Population density only maintained a positive correlation to the prevalence of COVID-19 during the first two weeks, after which the relationship became negative; and (3) Transit accessibility also contributed significantly to the pandemic and the accessibility of transit-oriented jobs was highly correlated to the infection rate in the first two weeks. The study provides valuable insights for policymakers and stakeholders to adopt resource allocation strategies for context-specific conditions.

Assessing Trade-Offs and Optimal Ranges of Density for Life Expectancy and 12 Causes of Mortality in Metro Vancouver, Canada, 1990-2016

Background: Understanding and managing the impacts of population growth and densification are important steps for sustainable development. This study sought to evaluate the health trade-offs associated with increasing densification and to identify the optimal balance of neighbourhood densification for health. Methods: We linked population density with a 27-year mortality dataset in Metro Vancouver that includes census-tract levels of life expectancy (LE), cause-specific mortalities, and area-level deprivation. We applied two methods: (1) difference-in-differences (DID) models to study the impacts of densification changes from the early 1990s on changes in mortality over a 27-year period; and (2) smoothed cubic splines to identify thresholds of densification at which mortality rates accelerated. Results: At densities above ~9400 persons per km², LE began to decrease more rapidly. By cause, densification was linked to decreased mortality for major causes of mortality in the region, such as cardiovascular diseases, neoplasms, and diabetes. Greater inequality with increasing density was observed for causes such as human immunodeficiency virus and acquired immunodeficiency syndrome (HIV/AIDS), sexually transmitted infections, and self-harm and interpersonal violence. Conclusions: Areas with higher population densities generally have lower rates of mortality from the major causes, but these environments are also associated with higher relative inequality from largely preventable causes of death.

Structural modeling of COVID-19 spread in relation to human mobility

Human mobility is considered as one of the prominent non-pharmaceutical interventions to control the spread of the pandemic (positive effect from mobility to infection). Conversely, the spread of the pandemic triggered massive changes to people's daily schedules by limiting their movement (negative effect from infection to mobility). The purpose of this study is to investigate this bi-directional relationship between human mobility and COVID-19 spread across U.S. counties during the early phase of the pandemic when infection rates were stabilizing and activity-travel behavior reflected a fairly steady return to normal following the drastic changes observed during the pandemic's initial shock. In particular, we applied Structural Regression (SR) model to investigate a bi-directional relationship between COVID-19 infection rate and the degree of human mobility in a county in association with socio-demographic and location characteristics of that county, and state-wide COVID-19 policies. Combining U.S. county-level cross-sectional data from multiple sources, our model results suggested that during the study period, human mobility and infection rate in a county both influenced each other, but in an opposite direction. Metropolitan counties experienced higher infection and lower mobility than non-metropolitan counties in the early stage of the pandemic. Counties with highly infected neighboring counties and more external trips had a higher infection rate. During the study period, community mitigation strategies, such as stay at home order, emergency declaration, and non-essential business closure significantly reduced mobility whereas public mask mandate significantly reduced infection rates. The findings of this study will provide important insights to policy makers in understanding the two-way relationship between human mobility and COVID-19 spread and to derive mobility-driven policy actions accordingly.

Human mobility impacts on the surging incidence of COVID‐19 in India

Human mobility triggers how fast and where infectious diseases spread and modelling community flows helps assess the impact of social distancing policies and advance our understanding of community behaviour in such circumstances. This study investigated the relationship between human mobility and the surging incidence of COVID‐19 in India. We performed a generalised estimating equation with a Poisson log‐linear model to analyse the daily mobility rate and new cases of COVID‐19 between 14 March and 11 September 2020. We found that mobility to grocery and retail locations was significantly associated (p < 0.01) with the incidence of COVID‐19, these being crowded and unorganised in most parts of India. In contrast, visits to parks, workplaces, and transit stations did not considerably affect the changing COVID‐19 cases over time. In particular, workplaces equipped with social distancing protocols or low‐density open spaces are much less susceptible to the spread of the virus. These findings suggest that human mobility data, geographic information, and health geography modelling have significant potential to inform strategic decision‐making during pandemics because they provide actionable knowledge of when and where communities might be exposed to the disease.

A stochastic contact network model for assessing outbreak risk of COVID-19 in workplaces

The COVID-19 pandemic has drastically shifted the way people work. While many businesses can operate remotely, a large number of jobs can only be performed on-site. Moreover as businesses create plans for bringing workers back on-site, they are in need of tools to assess the risk of COVID-19 for their employees in the workplaces. This study aims to fill the gap in risk modeling of COVID-19 outbreaks in facilities like offices and warehouses. We propose a simulation-based stochastic contact network model to assess the cumulative incidence in workplaces. First-generation cases are introduced as a Bernoulli random variable using the local daily new case rate as the success rate. Contact networks are established through randomly sampled daily contacts for each of the first-generation cases and successful transmissions are established based on a randomized secondary attack rate (SAR). Modification factors are provided for SAR based on changes in airflow, speaking volume, and speaking activity within a facility. Control measures such as mask wearing are incorporated through modifications in SAR. We validated the model by comparing the distribution of cumulative incidence in model simulations against real-world outbreaks in workplaces and nursing homes. The comparisons support the model's validity for estimating cumulative incidences for short forecasting periods of up to 15 days. We believe that the current study presents an effective tool for providing short-term forecasts of COVID-19 cases for workplaces and for quantifying the effectiveness of various control measures. The open source model code is made available at github.com/abhineetgupta/covid-workplace-risk.

Do COVID19 infection rates change over time and space? Population density and socio-economic measures as regressors

The COVID19 pandemic motivated an interesting debate, which is related directly to core issues in urban economics, namely, the advantages and disadvantages of dense cities. On the one hand, compact areas facilitate more intensive human interaction and could lead to higher exposure to the infection, which make them the potential epicenter of the pandemic crisis. On the other hand, dense areas tend to provide superior health and educational systems, which are better prepared to handle pandemics, leading to higher recovery rates and lower mortality rates. The objective of the current study is to test the relationship between COVID19 infection rates (cases÷population) as the dependent variable, and two explanatory variables, population density and socio-economic measures, within two timeframes: May 11, 2020 and January 19, 2021. We use a different methodology to address the relationship between COVID19 spread and population density by fitting a parabolic, instead of a linear, model, while controlling socio-economic indices. We thus apply a better examination of the factors that shape the COVID19 spread across time and space by permitting a non-monotonic relationship. Israel provides an interesting case study based on a highly non-uniform distribution of urban population, and diversified populations. Results of the analyses demonstrate two patterns of change: 1) a significant rise in the median and average infection-population ratio for each level of population density; and 2) a moderate (a steep) rise in infection rates with increased population density on May 11, 2020 (January 19, 2021) for population densities of 4000 to 20,000 persons per square kilometer. The significant rise in the average and median infection-population ratios might be as attributed to the outcome of new COVID19 variants (i.e., the British and the South African mutants), which, in turn, intensify the virus spread. The steeper slope of infection rates and the rise in the standard deviation of the infection-population ratio may be explained by non-uniform spatial distribution of: dissemination of information in a variety of language; different levels of medical infrastructure in different parts of the country; varying levels of compliance to social distancing rules; and strict (limited) compliance to social distancing rules. The last factor of limited compliance might be the outcome of premature optimism due to extensive scope of the vaccination campaign in Israel, which is located in first place globally.

For Better or Worse: Self-reported Changes in Kratom and Other Substance Use as a Result of the COVID-19 Pandemic

Background:

Kratom is taken to self-treat pain and symptoms of psychiatric disorders, including substance-use disorders (SUDs) and opioid withdrawal. Before COVID-19, kratom use was increasing in the US, however, there are few published data on whether that trend continued during the COVID-19 pandemic, which could have affected kratom use in multiple ways.

Aim:

To examine COVID-19-related changes in kratom use and how these changes were experienced, relative to changes in other commonly used substances.

Methods:

Using Amazon Mechanical Turk, 2615 evaluable surveys were completed between September 2020 and March 2021. Responses from past-month and past-year kratom-using adults (N = 174) indicating changes for the better or worse were examined using generalized linear mixed effects models, and relevant open-text responses (n = 85) were thematically coded.

Results:

For kratom 33% (n = 58) reported a Covid-related increase and 24% (n = 42) reported a Covid-related decrease. Controlling for changes in amount used, alcohol (OR = 5.02), tobacco (OR = 4.72), and nonmedical opioid use (OR = 3.42) were all more likely to have changed for the worse, compared with kratom use. Relative to decreases in kratom use, decreases in alcohol (OR = 3.21) and tobacco (OR = 6.18) use were more likely to be changes for the better. Cannabis use was the only substance to display a probability lower than 50% of being a decrease for the better, and of the increases, cannabis use displayed the highest probability of being for the better.

Conclusions:

Increases in kratom and cannabis use were less likely than alcohol and tobacco to be reported as changes for the worse, and decreases in kratom and cannabis use were more likely than alcohol and tobacco to be reported as changes for the better. These findings indicate that people differently conceptualize their relationships with kratom and cannabis, compared to their relationships with alcohol and tobacco.

COVID-19 crisis monitor: assessing the effectiveness of exit strategies in the State of São Paulo, Brazil

As COVID-19-related health indicators improve after restrictive measures were set in place in different parts of the world, governments are expected to guide how to ease interventions while minimizing the risk of resurgent outbreaks. Whereas epidemiologists track the progress of the disease using daily indicators to understand the pandemic better, economic activity indicators are usually available at a lower frequency and with considerable time lags. We propose and implement a timely trade-based regional economic activity indicator (EAI) that uses high-frequency traffic data to monitor daily sectoral economic activity in different sectors for the Brazilian State of São Paulo, a highly impacted region, overcoming the challenge of real-time assessment of the economy amid the COVID-19 outbreak. We then use this novel set of information combined with hospitalization rates to provide a first assessment of the São Paulo Plan, the COVID-19 exit strategy designed to gradually lifting interventions introduced to control the outbreak in the State. Available data show that, in its first 60 days, the phased strategy pursued in São Paulo has been effective in gradually reactivating economic activity while maintaining the adequate responsiveness of the healthcare system.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00168-021-01085-8.

Reproducibility of Research During COVID-19: Examining the Case of Population Density and the Basic Reproductive Rate from the Perspective of Spatial Analysis

The emergence of the novel SARS-CoV-2 coronavirus and the global COVID-19 pandemic in 2019 led to explosive growth in scientific research. Alas, much of the research in the literature lacks conditions to be reproducible, and recent publications on the association between population density and the basic reproductive number of SARS-CoV-2 are no exception. Relatively few papers share code and data sufficiently, which hinders not only verification but additional experimentation. In this article, an example of reproducible research shows the potential of spatial analysis for epidemiology research during COVID-19. Transparency and openness means that independent researchers can, with only modest efforts, verify findings and use different approaches as appropriate. Given the high stakes of the situation, it is essential that scientific findings, on which good policy depends, are as robust as possible; as the empirical example shows, reproducibility is one of the keys to ensure this.

Reproducible Science Is Vital for a Stronger Evidence Base During the COVID-19 Pandemic

Reproducible research becomes even more imperative as we build the evidence base on SARS-CoV-2 epidemiology, diagnosis, prevention, and treatment. In his study, Paez assessed the reproducibility of COVID-19 research during the pandemic, using a case study of population density. He found that most articles that assess the relationship of population density and COVID-19 outcomes do not publicly share data and code, except for a few, including our paper, which he stated "illustrates the importance of good reproducibility practices". Paez recreated our analysis using our code and data from the perspective of spatial analysis, and his new model came to a different conclusion. The disparity between our and Paez's findings, as well as other existing literature on the topic, give greater impetus to the need for further research. As there has been near exponential growth of COVID-19 research across a wide range of scientific disciplines, reproducible science is a vital component to produce reliable, rigorous, and robust evidence on COVID-19, which will be essential to inform clinical practice and policy in order to effectively eliminate the pandemic.

The impacts of the built environment on the incidence rate of COVID-19: A case study of King County, Washington

With COVID-19 prevalent worldwide, current studies have focused on the factors influencing the epidemic. In particular, the built environment deserves immediate attention to produce place-specific strategies to prevent the further spread of coronavirus. This research assessed the impact of the built environment on the incidence rate in King County, US and explored methods of researching infectious diseases in urban areas. Using principal component analysis and the Pearson correlation coefficient to process the data, we built multiple linear regression and geographically weighted regression models at the ZIP code scale. Results indicated that although socioeconomic indicators were the primary factors influencing COVID-19, the built environment affected COVID-19 cases from different aspects. Built environment density was positively associated with incidence rates. Specifically, increased open space was conducive to reducing incidence rates. Within each community, overcrowded households led to an increase in incidence rates. This study confirmed previous research into the importance of socioeconomic variables and extended the discussion on spatial and temporal variation in the impacts of urban density on the spread of COVID, effectively guiding sustainable urban development.

Mandatory Public Health Measures for Coronavirus-19 Are Associated With Improved Mortality, Equity and Economic Outcomes

The impact of public health measures on the coronavirus-2019 pandemic was analyzed by comparing mandatory versus voluntary nonpharmaceutical interventions between 2 comparable European countries and among 3 U.S. states. Using an ecological retrospective cohort study design, we examine differences in mortality, economic impact, and equity. Compared to voluntary policies, mandatory shelter-in-place policies were associated with a 3- to 5-fold lower population-adjusted mortality in the U.S. model and between 11- to 12-fold lower in the European one. Voluntary shelter-in-place measures were associated with overall increased mortality cost, as measured by value of a statistical life; somewhat greater decreases in gross domestic product; and substantial negative impacts on minority communities, who experienced markedly increased mortality rates (the percentage of minority deaths was 2.3 and 4 times greater in the U.S. model and 14.5 times higher in the European one) and mortality cost (2.7- and 4.5-fold higher in the U.S. model and 11.1-fold higher in the European one). We conclude that voluntary policies are less effective than mandatory ones, based on historical precedent and the current analysis. Negative effects on health equity mirrored the increased mortality outcomes of voluntary policies, and there was no apparent economic benefit associated with voluntary measures.

Treating two pandemics for the price of one: Chronic and infectious disease impacts of the built and natural environment

Compact walkable environments with greenspace support physical activity and reduce the risk for depression and several obesity-related chronic diseases, including diabetes and heart disease. Recent evidence confirms that these chronic diseases increase the severity of COVID-19 infection and mortality risk. Conversely, denser transit supportive environments may increase risk of exposure to COVID-19 suggesting the potential for contrasting chronic versus infectious disease impacts of community design. A handful of recent studies have examined links between density and COVID-19 mortality rates reporting conflicting results. Population density has been used as a surrogate of urban form to capture the degree of walkability and public transit versus private vehicle travel demand. The current study employs a broader range of built environment features (density, design, and destination accessibility) and assesses how chronic disease mediates the relationship between built and natural environment and COVID-19 mortality. Negative and significant relationships are observed between built and natural environment features and COVID-19 mortality when accounting for the mediating effect of chronic disease. Findings underscore the importance of chronic disease when assessing relationships between COVID-19 mortality and community design. Based on a rigorous simulation-assisted random parameter path analysis framework, we further find that the relationships between COVID-19 mortality, obesity, and key correlates exhibit significant heterogeneity. Ignoring this heterogeneity in highly aggregate spatial data can lead to incorrect conclusions with regards to the relationship between built environment and COVID-19 transmission. Results presented here suggest that creating walkable environments with greenspace is associated with reduced risk of chronic disease and/or COVID-19 infection and mortality.

Magnitude, change over time, demographic characteristics and geographic distribution of excess deaths among nursing home residents during the first wave of COVID-19 in France: a nationwide cohort study

BACKGROUND

The objectives were to assess the excess deaths among Nursing Home (NH) residents during the first wave of the COVID-19 pandemic, to determine their part in the total excess deaths and whether there was a mortality displacement.

METHODS

We studied a cohort of 494,753 adults in 6,515 NHs in France exposed to COVID-19 pandemic (from 1 March to 31 May 2020) and compared with the 2014-2019 cohorts using data from the French National Health Data System. The main outcome was death. Excess deaths and standardized mortality ratios (SMRs) were estimated.

RESULT

There were 13,505 excess deaths. Mortality increased by 43% (SMR: 1.43). The mortality excess was higher among males than females (SMR: 1.51 and 1.38) and decreased with increasing age (SMRs in females: 1.61 in the 60-74 age group, 1.58 for 75-84, 1.41 for 85-94 and 1.31 for 95 or over; males: SMRs: 1.59 for 60-74, 1.69 for 75-84, 1.47 for 85-94 and 1.41 for 95 or over). No mortality displacement effect was observed up until 30 August 2020. By extrapolating to all NH residents nationally (N = 570,003), we estimated that they accounted for 51% of the general population excess deaths (N = 15,114 out of 29,563).

CONCLUSION

NH residents accounted for half of the total excess deaths in France during the first wave of the COVID-19 pandemic. The excess death rate was higher among males than females and among younger than older residents.

Neighborhood-level COVID-19 hospitalizations and mortality relationships with built environment, active and sedentary travel

Most of the existing literature concerning the links between built environment and COVID-19 outcomes is based on aggregate spatial data averaged across entire cities or counties. We present neighborhood level results linking census tract-level built environment and active/sedentary travel measures with COVID-19 hospitalization and mortality rates in King County Washington. Substantial variations in COVID-19 outcomes and built environment features existed across neighborhoods. Using rigorous simulation-assisted discrete outcome random parameter models, the results shed new lights on the direct and indirect connections between built environment, travel behavior, positivity, hospitalization, and mortality rates. More mixed land use and greater pedestrian-oriented street connectivity is correlated with lower COVID-19 hospitalization/fatality rates. Greater participation in sedentary travel correlates with higher COVID-19 hospitalization and mortality whereas the reverse is true for greater participation in active travel. COVID-19 hospitalizations strongly mediate the relationships between built environment, active travel, and COVID-19 survival. Ignoring unobserved heterogeneity even when higher resolution smaller area spatial data are harnessed leads to inaccurate conclusions.

Do population density, socio-economic ranking and Gini Index of cities influence infection rates from coronavirus? Israel as a case study

A prominent characteristic of the COVID-19 pandemic is the marked geographic variation in COVID-19 prevalence. The objective of the current study is to assess the influence of population density and socio-economic measures (socio-economic ranking and the Gini Index) across cities on coronavirus infection rates. Israel provides an interesting case study based on the highly non-uniform distribution of urban populations, the existence of one of the most densely populated cities in the world and diversified populations. Moreover, COVID19 challenges the consensus regarding compact planning design. Consequently, it is important to analyze the relationship between COVID19 spread and population density. The outcomes of our study show that ceteris paribus projected probabilities to be infected from coronavirus rise with population density from 1.6 to 2.72% up to a maximum of 5.17-5.238% for a population density of 20,282-20,542 persons per square kilometer (sq. km.). Above this benchmark, the anticipated infection rate drops up to 4.06-4.50%. Projected infection rates of 4.06-4.50% are equal in cities, towns and regional councils (Local Authorities) with the maximal population density of 26,510 and 11,979-13,343 persons per sq. km. A possible interpretation is that while denser cities facilitate human interactions, they also enable and promote improved health infrastructure. This, in turn, contributes to medical literacy, namely, elevated awareness to the benefits associated with compliance with hygienic practices (washing hands), social distancing rules and wearing masks. Findings may support compact planning design principles, namely, development of dense, mixed use, walkable and transit accessible community design in compact and polycentric regions. Indeed, city planners should weigh the costs and benefits of many risk factors, including the COVID19 pandemic.

Community design and hypertension: Walkability and park access relationships with cardiovascular health

BACKGROUND

There is an increased literature focusing on the role of the built and natural environments in preventing hypertension. However, very few studies have quantitively analyzed specific pathways through which urban form affects blood pressure levels.

OBJECTIVES

To examine how features of the built and natural environments relate to hypertension and the mediating role of transportation and leisure walking and body mass index in this relationship.

METHODS

We examined the association between neighbourhood walkability and park availability with hypertension through generalized linear models in two independent population cohorts. One Cohort was 22,418 adults (My Health My Community) and the other cohort was 11,972 adults (BC Generations Project). We employed a path analysis modelling approach to explore the presence and significance of mediating factors that may contribute to any association between walkability or park availability and hypertension. This study intentionally employed walkability measures enforced through municipal zoning and subdivision regulations legally underpinned by health, safety, and welfare. All models were adjusted for socioeconomic and other characteristics where data were available.

RESULTS

Our analysis of two population-based Canadian cohorts consistently found that higher levels of walkability and park accessibility were both associated with significantly lower odds of self-reported hypertension, especially for lower income individuals. Mediation analysis showed that obesity accounted for 50% and 52.9% of the total effect of walkability and park accessibility on hypertension, respectively.

DISCUSSION

We suggest an integrated population health approach that considers multimorbidity as a result of exposure to car-dependent areas and the lack of green spaces. Longitudinal research is needed to document causal effects of built and natural environments on hypertension.

Did Covid-19 hit harder in peripheral areas? The case of Italian municipalities

The first wave of Covid-19 pandemic had a geographically heterogeneous impact even within the most severely hit regions. Exploiting a triple-differences methodology, we find that in Italy Covid-19 hit relatively harder in peripheral areas: the excess mortality in peripheral areas was almost double that of central ones in March 2020 (1.2 additional deaths every 1000 inhabitants). We leverage a rich dataset on Italian municipalities to explore mechanisms behind this gradient. We first show that socio-demographic and economic features at municipal level are highly collinear, making it hard to identify single-variable causal relationships. Using Principal Components Analysis we model excess mortality and show that areas with higher excess mortality have lower income, lower education, larger households, lower trade and higher industrial employments, and older population. Our findings highlight a strong centre-periphery gradient in the harshness of Covid-19, which we believe is also highly relevant from a policy-making standpoint.

Review of Associations between Built Environment Characteristics and Severe Acute Respiratory Syndrome Coronavirus 2 Infection Risk

The coronavirus disease 2019 pandemic has stimulated intensive research interest in its transmission pathways and infection factors, e.g., socioeconomic and demographic characteristics, climatology, baseline health conditions or pre-existing diseases, and government policies. Meanwhile, some empirical studies suggested that built environment attributes may be associated with the transmission mechanism and infection risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, no review has been conducted to explore the effect of built environment characteristics on the infection risk. This research gap prevents government officials and urban planners from creating effective urban design guidelines to contain SARS-CoV-2 infections and face future pandemic challenges. This review summarizes evidence from 25 empirical studies and provides an overview of the effect of built environment on SARS-CoV-2 infection risk. Virus infection risk was positively associated with the density of commercial facilities, roads, and schools and with public transit accessibility, whereas it was negatively associated with the availability of green spaces. This review recommends several directions for future studies, namely using longitudinal research design and individual-level data, considering multilevel factors and extending to diversified geographic areas.