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.

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.

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.

Further Inspection: Integrating Housing Code Enforcement and Social Services to Improve Community Health.

As a result of working inside homes, city housing inspectors witness hidden and serious threats to public health. However, systems to respond to the range of problems they encounter are lacking. In this study, we describe the impact and enabling environment for integrating a novel Social Service Referral Program within the Inspectional Services Department in Chelsea, MA. To evaluate the first eight months of the program, we used a mixed-methods approach combining quantitative data from 15 referrals and qualitative interviews with six key informants (inspectors, a case manager, and city leadership). The most common services provided to residents referred by inspectors were for fuel, food, and rent assistance; healthcare; hoarding; and homelessness prevention. Half of referred residents were not receiving other social services. Inspectors reported increased work efficiency and reduced psychological burden because of the program. Interviewees described how quality of life improved not only for referred residents but also for the surrounding neighborhood. A simple referral process that made inspectors' jobs easier and a trusted, well-connected service provider funded to carry out the work facilitated the program's uptake and impact. Housing inspectors' encounters with residents present a unique opportunity to expand the public health impact of housing code enforcement.

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.

Strong, light, multifunctional fibers of carbon nanotubes with ultrahigh conductivity.

Broader applications of carbon nanotubes to real-world problems have largely gone unfulfilled because of difficult material synthesis and laborious processing. We report high-performance multifunctional carbon nanotube (CNT) fibers that combine the specific strength, stiffness, and thermal conductivity of carbon fibers with the specific electrical conductivity of metals. These fibers consist of bulk-grown CNTs and are produced by high-throughput wet spinning, the same process used to produce high-performance industrial fibers. These scalable CNT fibers are positioned for high-value applications, such as aerospace electronics and field emission, and can evolve into engineered materials with broad long-term impact, from consumer electronics to long-range power transmission.

Generation of local concentration gradients by gas-liquid contacting.

We present a generic concept to create local concentration gradients, based on the absorption of gases or vapors in a liquid. A multilayer microfluidic device with crossing gas and liquid channels is fabricated by micromilling and used to generate multiple gas-liquid contacting regions, separated by a hydrophobic membrane. Each crossing can acts as both a microdosing and microstripping region. Furthermore, the liquid and gas flow rate can be controlled independently of each other. The focus of this conceptual article is on the generation of pH gradients, by locally supplying acidic or basic gases/vapors, such as carbon dioxide, hydrochloric acid, and ammonia, visualized by pH-sensitive dyes. Stationary and moving gradients are presented in devices with 500-microm channel width, depths of 200-400 microm, and lengths of multiple centimeters. It is shown that the method allows for multiple consecutive switching gradients in a single microchannel. Absorption measurements in a microcontactor with the model system CO2/water are presented to indicate the dependence of gas absorption rate on channel depth and residence time. Achievable concentration ranges are ultimately limited by the solubility of used components. The reported devices are easy to fabricate, and their application is not limited to pH gradients. Two proof of principles are demonstrated to indicate new opportunities: (i) local crystallization of NaCl using HCl vapor and (ii) consecutive reactions of ammonia with copper(II) ions in solution.