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

Can Comorbidity Data Explain Cross-State and Cross-National Difference in COVID-19 Death Rates?

Many efforts to predict the impact of COVID-19 on hospitalization, intensive care unit (ICU) utilization, and mortality rely on age and comorbidities. These predictions are foundational to learning, policymaking, and planning for the pandemic, and therefore understanding the relationship between age, comorbidities, and health outcomes is critical to assessing and managing public health risks. From a US government database of 1.4 million patient records collected in May 2020, we extracted the relationships between age and number of comorbidities at the individual level to predict the likelihood of hospitalization, admission to intensive care, and death. We then applied the relationships to each US state and a selection of different countries in order to see whether they predicted observed outcome rates. We found that age and comorbidity data within these geographical regions do not explain much of the international or within-country variation in hospitalization, ICU admission, or death. Identifying alternative explanations for the limited predictive power of comorbidities and age at the population level should be considered for future research.

A spatial interpretation of Australia's COVID-vulnerability

The school of social vulnerability in disaster sciences offers an alternative perspective on the current COVID-19 (coronavirus) pandemic crisis. Social vulnerability in general can be understood as a risk of exposure to hazard impacts, where vulnerability is embedded in the normal functioning of the society. The COVID-19 pandemic has exposed systemic (political and health care systems), demographic (aging, race) and,based on the results of our approach, spatial (spatial isolation and connectivity) yvulnerabilities as well. In this paper, we develop a risk prediction model based on two composite indicators of social vulnerability. These indicators reflect the two main contrasting risks associated with COVID-19, demographic vulnerability and, as consequences of the lockdowns, economic vulnerability. We conceptualise social vulnerability in the context of the extremely uneven spatial population distribution in Australia. Our approach helps extend understanding about the role of spatiality in the current pandemic disaster.

Écart de mobilité : estimation des seuils de mobilité requis pour maîtriser le SRAS-CoV-2 au Canada

CONTEXTE:

Les interventions non pharmacologiques demeurent le principal moyen de maîtriser le coronavirus du syndrome respiratoire aigu sévère 2 (SRAS-CoV-2) d’ici à ce que la couverture vaccinale soit suffisante pour donner lieu à une immunité collective. Nous avons utilisé des données de mobilité anonymisées de téléphones intelligents afin de quantifier le niveau de mobilité requis pour maîtriser le SRAS-CoV-2 (c.-à-d., seuil de mobilité), et la différence par rapport au niveau de mobilité observé (c.-à-d., écart de mobilité).

MÉTHODES:

Nous avons procédé à une analyse de séries chronologiques sur l’incidence hebdomadaire du SRAS-CoV-2 au Canada entre le 15 mars 2020 et le 6 mars 2021. Le paramètre mesuré était le taux de croissance hebdomadaire, défini comme le rapport entre les cas d’une semaine donnée et ceux de la semaine précédente. Nous avons mesuré les effets du temps moyen passé hors domicile au cours des 3 semaines précédentes à l’aide d’un modèle de régression log-normal, en tenant compte de la province, de la semaine et de la température moyenne. Nous avons calculé le seuil de mobilité et l’écart de mobilité pour le SRAS-CoV-2.

RÉSULTATS:

Au cours des 51 semaines de l’étude, en tout, 888 751 personnes ont contracté le SRAS-CoV-2. Chaque augmentation de 10 % de l’écart de mobilité a été associée à une augmentation de 25 % du taux de croissance des cas hebdomadaires de SRAS-CoV-2 (rapport 1,25, intervalle de confiance à 95 % 1,20–1,29). Comparativement à la mobilité prépandémique de référence de 100 %, le seuil de mobilité a été plus élevé au cours de l’été (69 %, écart interquartile [EI] 67 %–70 %), et a chuté à 54 % pendant l’hiver 2021 (EI 52 %–55 %); un écart de mobilité a été observé au Canada entre juillet 2020 et la dernière semaine de décembre 2020.

INTERPRÉTATION:

La mobilité permet de prédire avec fiabilité et constance la croissance des cas hebdomadaires et il faut maintenir des niveaux faibles de mobilité pour maîtriser le SRAS-CoV-2 jusqu’à la fin du printemps 2021. Les données de mobilité anonymisées des téléphones intelligents peuvent servir à guider le relâchement ou le resserrement des mesures de distanciation physique provinciales et régionales.

Perceived risk of infection while traveling during the COVID-19 pandemic: Insights from Columbus, OH

The COVID-19 outbreak caused major disruptions on individuals' out-of-home activities. Worldwide mandates to slow down the spread of the disease resulted in significant reductions in travel. This study analyzes the changes in individuals' travel outcomes and their risk perceptions related to exposure and specific travel modes during the COVID-19 pandemic. We use data collected through an online survey with residents of Columbus, OH from April 30 to May 7, 2020. Employing multiple generalized estimating equations (GEEs) with a logit link function, we analyze the perceived risk of infection while traveling with different modes controlling for socio-demographics. The findings show that on average individuals are more likely to find shared modes (i.e., transit, ride-hailing, carsharing) riskier as compared to individual ones (i.e., walking, autos) when it comes to COVID-19 exposure. This study also suggests that the associations between perceptions related to exposure and various travel modes vary across groups with (1) different primary mode preferences (auto users vs non-auto users (e.g., transit users, bicyclists, etc.)), and (2) different socio-demographics. For example, auto users are more likely to find shared modes such as ride-hailing or transit riskier as compared to personal car. The conclusions present recommendations for future transportation policies in the post-COVID era. These include building upon the emerging positive perceptions towards non-motorized modes as an opportunity to promote sustainable transportation as well as formulating viable solutions to address the high-risk perceptions associated with transit.

A Bayesian spatio-temporal analysis of mortality rates in Spain: application to the COVID-19 2020 outbreak

BACKGROUND

The number of deaths attributable to COVID-19 in Spain has been highly controversial since it is problematic to tell apart deaths having COVID as the main cause from those provoked by the aggravation by the viral infection of other underlying health problems. In addition, overburdening of health system led to an increase in mortality due to the scarcity of adequate medical care, at the same time confinement measures could have contributed to the decrease in mortality from certain causes. Our aim is to compare the number of deaths observed in 2020 with the projection for the same period obtained from a sequence of previous years. Thus, this computed mortality excess could be considered as the real impact of the COVID-19 on the mortality rates.

METHODS

The population was split into four age groups, namely: (< 50; 50-64; 65-74; 75 and over). For each one, a projection of the death numbers for the year 2020, based on the interval 2008-2020, was estimated using a Bayesian spatio-temporal model. In each one, spatial, sex, and year effects were included. In addition, a specific effect of the year 2020 was added ("outbreak"). Finally, the excess deaths in year 2020 were estimated as the count of observed deaths minus those projected.

RESULTS

The projected death number for 2020 was 426,970 people, the actual count being 499,104; thus, the total excess of deaths was 72,134. However, this increase was very unequally distributed over the Spanish regions.

CONCLUSION

Bayesian spatio-temporal models have proved to be a useful tool for estimating the impact of COVID-19 on mortality in Spain in 2020, making it possible to assess how the disease has affected different age groups accounting for effects of sex, spatial variation between regions and time trend over the last few years.

COVID-19 incidence and mortality in the Metropolitan Region, Chile: Time, space, and structural factors

Demographic, health, and socioeconomic factors significantly inform COVID-19 outcomes. This article analyzes the association of these factors and outcomes in Chile during the first five months of the pandemic. Using the municipalities Metropolitan Region's municipalities as the unit of analysis, the study looks at the role of time dynamics, space, and place in cases and deaths over a 100-day period between March and July 2020. As a result, common and idiosyncratic elements explain the prevalence and dynamics of infections and mortality. Social determinants of health, particularly multidimensional poverty index and use of public transportation play an important role in explaining differences in outcomes. The article contributes to the understanding of the determinants of COVID-19 highlighting the need to consider time-space dynamics and social determinants as key in the analysis. Structural factors are important to identify at-risk populations and to select policy strategies to prevent and mitigate the effects of COVID-19. The results are especially relevant for similar research in unequal settings.

Subway Ridership, Crowding, or Population Density: Determinants of COVID-19 Infection Rates in New York City

INTRODUCTION

This study aims to determine whether subway ridership and built environmental factors, such as population density and points of interests, are linked to the per capita COVID-19 infection rate in New York City ZIP codes, after controlling for racial and socioeconomic characteristics.

METHODS

Spatial lag models were employed to model the cumulative COVID-19 per capita infection rate in New York City ZIP codes (N=177) as of April 1 and May 25, 2020, accounting for the spatial relationships among observations. Both direct and total effects (through spatial relationships) were reported.

RESULTS

This study distinguished between density and crowding. Crowding (and not density) was associated with the higher infection rate on April 1. Average household size was another significant crowding-related variable in both models. There was no evidence that subway ridership was related to the COVID-19 infection rate. Racial and socioeconomic compositions were among the most significant predictors of spatial variation in COVID-19 per capita infection rates in New York City, even more so than variables such as point-of-interest rates, density, and nursing home bed rates.

CONCLUSIONS

Point-of-interest destinations not only could facilitate the spread of virus to other parts of the city (through indirect effects) but also were significantly associated with the higher infection rate in their immediate neighborhoods during the early stages of the pandemic. Policymakers should pay particularly close attention to neighborhoods with a high proportion of crowded households and these destinations during the early stages of pandemics.

Subway Ridership, Crowding, or Population Density: Determinants of COVID-19 Infection Rates in New York City

INTRODUCTION

This study aims to determine whether subway ridership and built environmental factors, such as population density and points of interests, are linked to the per capita COVID-19 infection rate in New York City ZIP codes, after controlling for racial and socioeconomic characteristics.

METHODS

Spatial lag models were employed to model the cumulative COVID-19 per capita infection rate in New York City ZIP codes (N=177) as of April 1 and May 25, 2020, accounting for the spatial relationships among observations. Both direct and total effects (through spatial relationships) were reported.

RESULTS

This study distinguished between density and crowding. Crowding (and not density) was associated with the higher infection rate on April 1. Average household size was another significant crowding-related variable in both models. There was no evidence that subway ridership was related to the COVID-19 infection rate. Racial and socioeconomic compositions were among the most significant predictors of spatial variation in COVID-19 per capita infection rates in New York City, even more so than variables such as point-of-interest rates, density, and nursing home bed rates.

CONCLUSIONS

Point-of-interest destinations not only could facilitate the spread of virus to other parts of the city (through indirect effects) but also were significantly associated with the higher infection rate in their immediate neighborhoods during the early stages of the pandemic. Policymakers should pay particularly close attention to neighborhoods with a high proportion of crowded households and these destinations during the early stages of pandemics.

The mobility gap: estimating mobility thresholds required to control SARS-CoV-2 in Canada

BACKGROUND

Nonpharmaceutical interventions remain the primary means of controlling severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) until vaccination coverage is sufficient to achieve herd immunity. We used anonymized smartphone mobility measures to quantify the mobility level needed to control SARS-CoV-2 (i.e., mobility threshold), and the difference relative to the observed mobility level (i.e., mobility gap).

METHODS

We conducted a time-series study of the weekly incidence of SARS-CoV-2 in Canada from Mar. 15, 2020, to Mar. 6, 2021. The outcome was weekly growth rate, defined as the ratio of cases in a given week versus the previous week. We evaluated the effects of average time spent outside the home in the previous 3 weeks using a log-normal regression model, accounting for province, week and mean temperature. We calculated the SARS-CoV-2 mobility threshold and gap.

RESULTS

Across the 51-week study period, a total of 888 751 people were infected with SARS-CoV-2. Each 10% increase in the mobility gap was associated with a 25% increase in the SARS-CoV-2 weekly case growth rate (ratio 1.25, 95% confidence interval 1.20-1.29). Compared to the prepandemic baseline mobility of 100%, the mobility threshold was highest in the summer (69%; interquartile range [IQR] 67%-70%), and dropped to 54% in winter 2021 (IQR 52%-55%); a mobility gap was present in Canada from July 2020 until the last week of December 2020.

INTERPRETATION

Mobility strongly and consistently predicts weekly case growth, and low levels of mobility are needed to control SARS-CoV-2 through spring 2021. Mobility measures from anonymized smartphone data can be used to guide provincial and regional loosening and tightening of physical distancing measures.

Population density and basic reproductive number of COVID-19 across United States counties

The basic reproductive number (R₀) is a function of contact rates among individuals, transmission probability, and duration of infectiousness. We sought to determine the association between population density and R₀ of SARS-CoV-2 across U.S. counties. We conducted a cross-sectional analysis using linear mixed models with random intercept and fixed slopes to assess the association of population density and R₀, and controlled for state-level effects using random intercepts. We also assessed whether the association was differential across county-level main mode of transportation percentage as a proxy for transportation accessibility, and adjusted for median household income. The median R₀ among the United States counties was 1.66 (IQR: 1.35–2.11). A population density threshold of 22 people/km² was needed to sustain an outbreak. Counties with greater population density have greater rates of transmission of SARS-CoV-2, likely due to increased contact rates in areas with greater density. An increase in one unit of log population density increased R₀ by 0.16 (95% CI: 0.13 to 0.19). This association remained when adjusted for main mode of transportation and household income. The effect of population density on R₀ was not modified by transportation mode. Our findings suggest that dense areas increase contact rates necessary for disease transmission. SARS-CoV-2 R₀ estimates need to consider this geographic variability for proper planning and resource allocation, particularly as epidemics newly emerge and old outbreaks resurge.

Association of built environment attributes with the spread of COVID-19 at its initial stage in China

Evidence of the association of built environment (BE) attributes with the spread of COVID-19 remains limited. As an additional effort, this study regresses a ratio of accumulative confirmed infection cases at the city level in China on both inter-city and intra-city BE attributes. A mixed geographically weighted regression model was estimated to accommodate both local and global effects of BE attributes. It is found that spatial clusters are mostly related to low infections in 28.63 % of the cities. The density of point of interests around railway stations, travel time by public transport to activity centers, and the number of flights from Hubei Province are associated with the spread. On average, the most influential BE attribute is the number of trains from Hubei Province. Higher infection ratios are associated with higher values of between-ness centrality in 70.98 % of the cities. In 79.22 % of the cities, the percentage of the aging population shows a negative association. A positive association of the population density in built-up areas is found in 68.75 % of county-level cities. It is concluded that the countermeasures in China could have well reflected spatial heterogeneities, and the BE could be further improved to mitigate the impacts of future pandemics.

Altitude and SARS-CoV-2 Infection in the First Pandemic Wave in Spain

After the first pandemic wave, a nationwide survey assessed the seroprevalence of SARS-CoV-2 antibodies in Spain and found notable differences among provinces whose causes remained unclear. This ecological study aimed to analyze the association between environmental and demographic factors and SARS-CoV-2 infection by province. The seroprevalence of SARS-CoV-2 antibodies by province was obtained from a nationwide representative survey performed in June 2020, after the first pandemic wave in Spain. Linear regression was used in the analysis. The seroprevalence of SARS-CoV-2 antibodies of the 50 provinces ranged from 0.2% to 13.6%. The altitude, which ranged from 5 to 1131 m, explained nearly half of differences in seroprevalence (R² = 0.47, p < 0.001). The seroprevalence in people residing in provinces above the median altitude (215 m) was three-fold higher (6.5% vs. 2.1%, p < 0.001). In the multivariate linear regression, the addition of population density significantly improved the predictive value of the altitude (R² = 0.55, p < 0.001). Every 100 m of altitude increase and 100 inhabitants/km² of increase in population density, the seroprevalence rose 0.84 and 0.63 percentage points, respectively. Environmental conditions related to higher altitude in winter–spring, such as lower temperatures and absolute humidity, may be relevant to SARS-CoV-2 transmission. Places with such adverse conditions may require additional efforts for pandemic control.

Who is staying home and who is not? Demographic, socioeconomic, and geographic differences in time spent outside the home during the COVID-19 outbreak in Japan

Studies have reported that many people changed their going-out behavior in response to the declaration of a state of emergency related to the coronavirus disease 2019 (COVID-19) in Japan. However, individual attributes of those who tended to stay home have not been examined. Therefore, this study examined the demographic, socioeconomic, and geographic characteristics of people who refrained from going out both before and after a state of emergency was declared. Using data from a nationwide online survey, this study retrospectively investigated the relative amount of time spent outside the home between mid-February and mid-May 2020. Multilevel linear regression analysis was performed to examine the association of time outside with demographic, socioeconomic, and geographic characteristics, and with the anxiety related to going out, in each period. Overall, respondents significantly reduced their time spent outside during the study period, especially after a state of emergency was declared. Those who were young, female, living with two or more people, had lower income, were not working, used public transportation, had chronic disease, and lived in large metropolitan areas were more likely to reduce time outside during a part of the study period. However, no significant differences were observed for occupational class, education, and neighborhood population density. Thus, the results showed a reduction in time outside during the COVID-19 outbreak and the existence of demographic, socioeconomic, and geographic differences in going-out behavior. Socioeconomic disparities and neighborhood differences in going-out behavior, and their influence on health should be continuously monitored.

Public Perception of the First Major SARS-Cov-2 Outbreak in the Suceava County, Romania

The first months of 2020 were marked by the rapid spread of the acute respiratory disease, which swiftly reached the proportions of a pandemic. The city and county of Suceava, Romania, faced an unprecedented crisis in March and April 2020, triggered not only by the highest number of infections nationwide but also by the highest number of infected health professionals (47.1% of the infected medical staff nationwide, in April 2020). Why did Suceava reach the peak number of COVID-19 cases in Romania? What were the vulnerability factors that led to the outbreak, the closure of the city of Suceava and neighboring localities, and the impossibility of managing the crisis with local resources? What is the relationship between the population's lack of confidence in the authorities' ability to solve the crisis, and their attitude towards the imposed measures? The present article aims to provide answers to the above questions by examining the attitudes of the public towards the causes that have led to the outbreak of an epidemiological crisis, systemic health problems, and the capacity of decision makers to intervene both at local and national level. The research is based on an online survey, conducted between April and May 2020, resulting in a sample of 1231 people from Suceava County. The results highlight that the development of the largest COVID-19 outbreak in Romania is, without a doubt, the result of a combination of factors, related to the medical field, decision makers, and the particularities of the population's behavior.

COVID-19 does not stop at open borders: Spatial contagion among local authority districts during England's first wave

Infectious diseases generate spatial dependence or contagion not only between individuals but also between geographical units. New infections in one local district do not just depend on properties of the district, but also on the strength of social ties of its population with populations in other districts and their own degree of infectiousness. We show that SARS-CoV-2 infections during the first wave of the pandemic spread across district borders in England as a function of pre-crisis commute to work streams between districts. Crucially, the strength of this spatial contagion depends on the phase of the epidemic. In the first pre-lockdown phase, the spread of the virus across district borders is high. During the lockdown period, the cross-border spread of new infections slows down significantly. Spatial contagion increases again after the lockdown is eased but not statistically significantly so.

Compact development and adherence to stay-at-home order during the COVID-19 pandemic: A longitudinal investigation in the United States

In the absence of a vaccine and medical treatments, social distancing remains the only option available to governments in order to slow the spread of global pandemics such as COVID-19 and save millions of lives. Despite the scientific evidence on the effectiveness of social distancing measures, they are not being practiced uniformly across the U.S. Accordingly, the role of compact development on the level of adherence to social distancing measures has not been empirically studied. This longitudinal study employs a natural experimental research design to investigative the impacts of compact development on reduction in travel to three types of destinations representing a range of essential and non-essential trips in 771 metropolitan counties in the U.S during the shelter-in-place order amid the COVID-19 pandemic. We employed Multilevel Linear Modeling (MLM) for the three longitudinal analyses in this study to model determinants of reduction in daily trips to grocery stores, parks, and transit stations; using travel data from Google and accounting for the hierarchical (two-level) structure of the data. We found that the challenges of practicing social distancing in compact areas are not related to minimizing essential trips. Quite the opposite, residents of compact areas have significantly higher reduction in trips to essential destinations such as grocery stores/pharmacies, and transit stations. However, residents of compact counties have significantly lower reduction in their trips to parks possibly due to the smaller homes, lack of private yards, and the higher level of anxiety amid the pandemic. This study offers a number of practical implications and directions for future research.

Early Phase Management of the SARS-CoV-2 Pandemic in the Geographic Area of the Veneto Region, in One of the World's Oldest Populations

The first cases of Coronavirus disease-2019 (COVID-19) were reported on 21 February in the small town of Vo’ near Padua in the Veneto region of Italy. This event led to 19,286 infected people in the region by 30 June 2020 (39.30 cases/10,000 inhabitants). Meanwhile, Rovigo Local Health Unit n. 5 (ULSS 5), bordering areas with high epidemic rates and having one of the world’s oldest populations, registered the lowest infection rates in the region (19.03 cases/10,000 inhabitants). The aim of this study was to describe timing and event management by ULSS 5 in preventing the propagation of infection within the timeframe spanning from 21 February to 30 June. Our analysis considered age, genetic clusters, sex, orography, the population density, pollution, and economic activities linked to the pandemic, according to the literature. The ULSS 5 Health Director General’s quick decision-making in the realm of public health, territorial assistance, and retirement homes were key to taking the right actions at the right time. Indeed, the number of isolated cases in the Veneto region was the highest among all the Italian regions at the beginning of the epidemic. Moreover, the implementation of molecular diagnostic tools, which were initially absent, enabled health care experts to make quick diagnoses. Quick decision-making, timely actions, and encouraging results were achieved thanks to a solid chain of command, despite a somewhat unclear legislative environment. In conclusion, we believe that the containment of the epidemic depends on the time factor, coupled with a strong sense of awareness and discretion in the Health Director General’s decision-making. Moreover, real-time communication with operating units and institutions goes hand in hand with the common goal of protecting public health.

Predicting spatial and temporal responses to non-pharmaceutical interventions on COVID-19 growth rates across 58 counties in New York State: A prospective event-based modeling study on county-level sociological predictors

BACKGROUND

Non-pharmaceutical interventions (NPIs) have been implemented in the New York State since the COVID-19 outbreak on March 1, 2020 to control the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Socioeconomic heterogeneity across counties closely manifests differences in the post-NPIs growth rate of incidence, which is a crucial indicator to guide future infectious control policy making. Few studies, however, examined the geospatial and sociological variations in the epidemic growth across different time points of NPIs.

OBJECTIVE

To guide a more effective reopening plan while controlling the transmission, the current study aims at 1) identifying hotspots of the growth rate of COVID-19 incidence among the 57 counties and New York City in NYS over time, and 2) examining the association of COVID-19 growth rates after eight critical NPIs time points and most relevant county-level sociological predictors.

METHODS

County-level COVID-19 incidence rates were retrieved from the Social Explorer Website between March 7, 2020 to June 22, 2020. 5-day moving average growth rates of COVID-19 incidence were calculated for 16 selected time points, including the dates of eight NPIs and their respective 14-day-lag-behind time points. A total of 36 county-level indicators were extracted from multiple public datasets. Geospatial mapping and heatmap were used to analyze spatial and temporal heterogeneity of county-level COVID-19 outbreak over selected NPIs-related dates. Generalized mixed effect least absolute shrinkage and selection operator (LASSO) regression, controlling for the 5-day moving average growth rates of COVID-19 testing rates, was employed to identify significant county-level predictors related to the changes of county-level COVID-19 growth rates over time.

RESULTS

COVID-19 infection increased and peaked by the end of March (η=22.50%). Growth rates of COVID-19 decreased by 50.48% after implementing NPIs such as closures of schools, non-essential businesses, parks, and subways. There was a geospatial shift in the region with the highest growth rates from New York metropolitan area towards Western and Northern regions over time. Proportions of population aged 45 years and above (β=3.25 [0.17-6.32]), living alone at residential houses (β=3.31 [0.39--6.22]), and proportion of crowd residential houses (β=6.15 [2.15-10.14]) were positively associated with the growth rate of COVID-19 infection. In contrast, living alone at rental houses (β=-2.47 [-4.83--0.12]) and rate of mental health providers (β=-1.11 [-1.95--0.28]) were negatively associated with COVID-19 growth rate across all 16 time points.

CONCLUSIONS

There are geospatial differences in COVID-19 incidence after implementing different NPIs. Socioeconomic, racial/ethnic, and healthcare resource disparities at the structural and historical levels across counties need to be considered in infection control policymaking to narrow the unequal health impact on vulnerable populations effectively.

CLINICALTRIAL