Substandard housing and the risk of COVID-19 infection and disease severity: A retrospective cohort study

In this study we examine associations between substandard housing and the risk of COVID-19 infection and severity during the first year of the pandemic by linking individual-level housing and clinical datasets. Residents of Chelsea, Massachusetts who were tested for COVID-19 at any Mass General Brigham testing site and who lived at a property that had received a city housing inspection were included (N = 2873). Chelsea is a densely populated city with a high prevalence of substandard housing. Inspected properties with housing code violations were considered substandard; inspected properties without violations were considered adequate. COVID-19 infection was defined as any positive PCR test, and severe disease defined as hospitalization with COVID-19. We used a propensity score design to match individuals on variables including age, race, sex, and income. In the severity model, we also matched on ten comorbidities. We estimated the risk of COVID-19 infection and severity associated with substandard housing using Cox Proportional Hazards models for lockdown, the first phase of reopening, and the full study period. In our sample, 32% (919/2873) of individuals tested positive for COVID-19 and 5.9% (135/2297) had severe disease. During lockdown, substandard housing was associated with a 48% increased risk of COVID-19 infection (95%CI 1.1-2.0, p = 0.006). Through Phase 1 reopening, substandard housing was associated with a 39% increased infection risk (95%CI 1.1-1.8, p = 0.020). The difference in risk attenuated over the full study period. There was no difference in severe disease risk between the two groups. The increased risk, observed only during lockdown and early reopening - when residents were most exposed to their housing - strengthens claims that substandard housing conveys higher infection risk. The results demonstrate the value of combining cross-sector datasets. Existing city housing data can be leveraged 1) to identify and prioritize high-risk areas for future pandemic response, and 2) for longer-term housing solutions.

Use of smartphone mobility data to analyze city park visits during the COVID-19 pandemic

INTRODUCTION

The COVID-19 pandemic focused attention on city parks as important public resources. However, monitoring park use over time poses practical challenges. Thus, pandemic-related trends are unknown.

METHODS

We analyzed monthly mobility data from a large panel of smartphone devices, to assess park visits from January 2018 to November 2020 in the 50 largest U.S. cities.

RESULTS

In our sample of 11,890 city parks, visits declined by 36.0 % (95 % CI [27.3, 43.6], p < 0.001) from March through November 2020, compared to prior levels and trends. When we segmented the COVID-19 period into widespread closures (March-April) and reopenings (May-November), we estimated a small rebound in visits during reopenings. In park service areas where a greater proportion of residents were White and highincome, this rebound effect was larger.

CONCLUSIONS

Smartphone data can address an important gap for monitoring park visits. Park visits declined substantially in 2020 and disparities appeared to increase.

Entry Points: Gaining Momentum in Early-Stage Cross-Boundary Collaborations

To address complex social challenges, it is widely recognized that leaders from public, for-profit, and civic organizations should join forces. Yet, well-intended collaborators often struggle to achieve alignment and fail to gain traction in their joint efforts. This article proposes the concept of “entry points” as a key milestone in a collaboration's early stages. Using a unique set of rich, longitudinal data, we examine how ten cross-boundary teams with representation from ten city governments in North America and Europe searched for these entry points (i.e., opportunities for focused action to advance learning and progress towards their collective goals). Based on systematic coding, we propose factors that impeded or enabled the teams' abilities to find entry points in their collaborative work. The paper contributes to literatures on cross-boundary collaboration, problem-oriented governance, and paradoxes in organizational behavior, and it offers an analytic framework to help cross-boundary collaboration practitioners identify their entry points.

Effects of demolishing abandoned buildings on firearm violence: a moderation analysis using aerial imagery and deep learning

PURPOSE

Demolishing abandoned buildings has been found to reduce nearby firearm violence. However, these effects might vary within cities and across time scales. We aimed to identify potential moderators of the effects of demolitions on firearm violence using a novel approach that combined machine learning and aerial imagery.

METHODS

Outcomes were annual counts of fatal and non-fatal shootings in Rochester, New York, from 2000 to 2020. Treatment was demolitions conducted from 2009 to 2019. Units of analysis were 152×152 m grid squares. We used a difference-in-differences approach to test effects: (A) the year after each demolition and (B) as demolitions accumulated over time. As moderators, we used a built environment typology generated by extracting information from aerial imagery using convolutional neural networks, a deep learning approach, combined with k-means clustering. We stratified our main models by built environment cluster to test for moderation.

RESULTS

One demolition was associated with a 14% shootings reduction (incident rate ratio (IRR)=0.86, 95% CI 0.83 to 0.90, p<0.001) the following year. Demolitions were also associated with a long-term, 2% reduction in shootings per year for each cumulative demolition (IRR=0.98, 95% CI 0.95 to 1.00, p=0.02). In the stratified models, densely built areas with higher street connectivity displayed following-year effects, but not long-term effects. Areas with lower density and larger parcels displayed long-term effects but not following-year effects.

CONCLUSIONS

The built environment might influence the magnitude and duration of the effects of demolitions on firearm violence. Policymakers may consider complementary programmes to help sustain these effects in high-density areas.

Using Integrated City Data and Machine Learning to Identify and Intervene Early on Housing-Related Public Health Problems

CONTEXT

Housing is more than a physical structure-it has a profound impact on health. Enforcing housing codes is a primary strategy for breaking the link between poor housing and poor health.

OBJECTIVE

The objective of this study was to determine whether machine learning algorithms can identify properties with housing code violations at a higher rate than inspector-informed prioritization. We also show how city data can be used to describe the prevalence and location of housing-related health risks, which can inform public health policy and programs.

SETTING

This study took place in Chelsea, Massachusetts, a demographically diverse, densely populated, low-income city near Boston.

DESIGN

Using data from 1611 proactively inspected properties, representative of the city's housing stock, we developed machine learning models to predict the probability that a given property would have (1) any housing code violation, (2) a set of high-risk health violations, and (3) a specific violation with a high risk to health and safety (overcrowding). We generated predicted probabilities of each outcome for all residential properties in the city (N = 5989).

RESULTS

Housing code violations were present in 54% of inspected properties, 85% of which were classified as high-risk health violations. We predict that if the city were to use integrated city data and machine learning to identify at-risk properties, it could achieve a 1.8-fold increase in the number of inspections that identify code violations as compared with current practices.

CONCLUSION

Given the strong connection between housing and health, reducing public health risk at more properties-without the need for additional inspection resources-represents an opportunity for significant public health gains. Integrated city data and machine learning can be used to describe the prevalence and location of housing-related health problems and make housing code enforcement more efficient, effective, and equitable in responding to public health threats.

The impact of COVID-19 on trips to urban amenities: Examining travel behavior changes in Somerville, MA

While there has been much speculation on how the pandemic has affected work location patterns and home location choices, there is sparse evidence regarding the impacts that COVID-19 has had on amenity visits in American cities, which typically constitute over half of all urban trips. Using aggregate app-based GPS positioning data from smartphone users, this study traces the changes in amenity visits in Somerville, MA from January 2019 to December 2020, describing how visits to particular types of amenities have changed as a result of business closures during the public health emergency. Has the pandemic fundamentally shifted amenity-oriented travel behavior or is consumer behavior returning to pre-pandemic trends? To address this question, we calibrate discrete choice models that are suited to Census block-group level analysis for each of the 24 months in a two-year period, and use them to analyze how visitors' behavioral responses to various attributes of amenity clusters have shifted during different phases of the pandemic. Our findings suggest that in the first few months of the pandemic, amenity-visiting preferences significantly diverged from expected patterns. Even though overall trip volumes remained far below normal levels throughout the remainder of the year, preferences towards specific cluster attributes mostly returned to expected levels by September 2020. We also construct two scenarios to explore the implications of another shutdown and a full reopening, based on November 2020 consumer behavior. While government restrictions have played an important role in reducing visits to amenity clusters, our results imply that cautionary consumer behavior has played an important role as well, suggesting a likely long and slow path to economic recovery. By drawing on mobile phone location data and behavioral modeling, this paper offers timely insights to help decision-makers understand how this unprecedented health emergency is affecting amenity-related trips and where the greatest needs for intervention and support may exist.