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

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.

How did COVID-19 case distribution associate with the urban built environment? A community-level exploration in Shanghai focusing on non-linear relationship

Several associations between the built environment and COVID-19 case distribution have been identified in previous studies. However, few studies have explored the non-linear associations between the built environment and COVID-19 at the community level. This study employed the March 2022 Shanghai COVID-19 pandemic as a case study to examine the association between built-environment characteristics and the incidence of COVID-19. A non-linear modeling approach, namely the boosted regression tree model, was used to investigate this relationship. A multi-scale study was conducted at the community level based on buffers of 5-minute, 10-minute, and 15-minute walking distances. The main findings are as follows: (1) Relationships between built environment variables and COVID-19 case distribution vary across scales of analysis at the neighborhood level. (2) Significant non-linear associations exist between built-environment characteristics and COVID-19 case distribution at different scales. Population, housing price, normalized difference vegetation index, Shannon's diversity index, number of bus stops, floor-area ratio, and distance from the city center played important roles at different scales. These non-linear results provide a more refined reference for pandemic responses at different scales from an urban planning perspective and offer useful recommendations for a sustainable COVID-19 post-pandemic response.

Neighbourhood Walkability and Physical Activity during the COVID-19 Pandemic

This study investigated whether living in a walkable neighbourhood could mitigate the adverse effects of the lockdown and closure of public open spaces during the COVID-19 pandemic on physical activity among adults in Bangkok, Thailand. We conducted a telephone survey with 579 respondents and collected information on their physical activity, access to green open spaces, neighbourhood walkability, and socioeconomic characteristics during the pandemic. Our study indicates that living in a walkable neighbourhood is associated with a higher likelihood of engaging in sufficient physical activity during the pandemic. Furthermore, we confirm the influence of socioeconomic factors and health behaviours on physical activity levels, aligning with previous research. Notably, our study highlights the significant association between access to green open spaces during lockdown and increased physical activity. These results underscore the importance of promoting walkable neighbourhoods and ensuring accessible green spaces to enhance physical activity and improve health outcomes during and beyond the pandemic.

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.

Church Closings Were Associated with Higher COVID-19 Infection Rates: Implications for Community Health Equity

This study investigates the changes in physical church closings years 2013 to 2019 in New York City (NYC), Philadelphia, and Baltimore and the association with COVID-19 infection rates. We applied Bayesian spatial binomial models to analyze confirmed cases of COVID-19 as of February 28, 2022, in each city at the zip code-level. A one unit increase in the number of churches closed corresponded to a 5% higher COVID-19 infection rate, in NYC (rate ratio = 1.05, 95% credible interval = 1.02-1.08%), where the association was significant. Church closings appears to be an important indicator of neighborhood social vulnerability. Church closings should be routinely monitored as a structural determinant of community health and to advance health equity.

Identifying spatial heterogeneity of COVID-19 transmission clusters and their built-environment features at the neighbourhood scale

The identification of high-risk areas for infectious disease transmission and its built-environment features are crucial for targeted surveillance and early prevention efforts. While previous research has explored the association between infectious disease incidence and urban built environment, the investigation of spatial heterogeneity of built-environment features in high-risk areas has been insufficient. This paper aims to address this gap by analysing the spatial heterogeneity of COVID-19 clusters in Shanghai at the neighbourhood scale and examining associated built-environment features. Using a spatiotemporal clustering algorithm, the study analysed 1395 reported cases in Shanghai from March 6 to March 17, 2022. Both global Poisson regression (GPR) and geographically weighted Poisson regression (GWPR) models were applied to examine the association between built-environment variables and the size of COVID-19 clusters. Our findings suggest that larger COVID-19 clusters emerging in the suburbs compared with the downtown and multiple built-environment features are significantly associated with this pattern. Specifically, neighbourhoods with a higher proportion of commercial, public service and industrial land, higher centrality of metro stations, and proximity to hospitals are positively associated with larger COVID-19 clusters, while neighbourhoods with higher land use mix and green/open spaces density are associated with smaller COVID-19 clusters. Moreover, we identified that metro stations with high centrality present the highest risk in the downtown, while commercial and public service places exhibit the highest risk in the suburbs. By highlighting the overlooked spatial heterogeneity of built-environment features for high-risk areas, this study aims to provide valuable guidance for public health departments in implementing place-based interventions to effectively prevent the spread of potential epidemics.

Public health research on physical activity and COVID-19: Progress and updated priorities

•Research produced during the pandemic showed pre-diagnosis physical activity was associated with substantially lower risk of severe coronavirus disease 2019 (COVID-19) outcomes. •Pandemic restrictions on common places for physical activity were associated with decreased physical activity and increased sedentary behavior. •There were few studies of interventions to increase physical activity during the pandemic, the role of physical activity in COVID-19 inequities, and built environment contributions to COVID-19 outcomes. •Emerging research priorities include physical activity and long COVID and physical activity as a vaccine adjuvant. •Except for recommendations from the World Health Organization, physical activity was widely ignored in public health guidance.

Exploring the spatial pattern of community urban green spaces and COVID-19 risk in Wuhan based on a random forest model

Since 2019, COVID-19 has triggered a renewed investigation of the urban environment and disease outbreak. While the results have been inconsistent, it has been observed that the quantity of urban green spaces (UGS) is correlated with the risk of COVID-19. However, the spatial pattern has largely been ignored, especially on the community scale. In high-density communities where it is difficult to increase UGS quantity, UGS spatial pattern could be a crucial predictive variable. Thus, this study investigated the relative contribution of quantity and spatial patterns of UGS on COVID-19 risk at the community scale using a random forest (RF) regression model based on (n = 44) communities in Wuhan. Findings suggested that 8 UGS indicators can explain 35% of the risk of COVID-19, and the four spatial pattern metrics that contributed most were core, edge, loop, and branch whereas UGS quantity contributed least. The potential mechanisms between UGS and COVID-19 are discussed, including the influence of UGS on residents' social distance and environmental factors in the community. This study offers a new perspective on optimizing UGS for public health and sustainable city design to combat pandemics and inspire future research on the specific relationship between UGS spatial patterns and pandemics and therefore help establish mechanisms of UGS and pandemics.

Post-pandemic shared mobility and active travel in Alabama: A machine learning analysis of COVID-19 survey data

The COVID-19 pandemic has had unprecedented impacts on the way we get around, which has increased the need for physical and social distancing while traveling. Shared mobility, as an emerging travel mode that allows travelers to share vehicles or rides has been confronted with social distancing measures during the pandemic. On the contrary, the interest in active travel (e.g., walking and cycling) has been renewed in the context of pandemic-driven social distancing. Although extensive efforts have been made to show the changes in travel behavior during the pandemic, people's post-pandemic attitudes toward shared mobility and active travel are under-explored. This study examined Alabamians' post-pandemic travel preferences regarding shared mobility and active travel. An online survey was conducted among residents in the State of Alabama to collect Alabamians' perspectives on post-pandemic travel behavior changes, e.g., whether they will avoid ride-hailing services and walk or cycle more after the pandemic. Machine learning algorithms were used to model the survey data (N = 481) to identify the contributing factors of post-pandemic travel preferences. To reduce the bias of any single model, this study explored multiple machine learning methods, including Random Forest, Adaptive Boosting, Support Vector Machine, K-Nearest Neighbors, and Artificial Neural Network. Marginal effects of variables from multiple models were combined to show the quantified relationships between contributing factors and future travel intentions due to the pandemic. Modeling results showed that the interest in shared mobility would decrease among people whose one-way commuting time by driving is 30-45 min. The interest in shared mobility would increase for households with an annual income of $100,000 or more and people who reduced their commuting trips by over 50% during the pandemic. In terms of active travel, people who want to work from home more seemed to be interested in increasing active travel. This study provides an understanding of future travel preferences among Alabamians due to COVID-19. The information can be incorporated into local transportation plans that consider the impacts of the pandemic on future travel intentions.

Interactive impacts of walkability, social vulnerability, & travel behavior on COVID-19 mortality: A hierarchical Bayesian spatial random parameter approach

While existing research highlights the built and social environment impacts on COVID-19 mortality, no empirical evidence exists on how the built and social environments may interact to influence COVID-19 mortality. This study presents a rigorous empirical assessment of the interactive impacts of social vulnerability and walkability on neighborhood-level COVID-19 mortality rates. Based in King County, WA, a unique data infrastructure is created by spatially integrating diverse census tract-level data on COVID-19 mortalities, walkability characteristics, social vulnerability, and travel behavior measures. Advanced Markov Chain Monte Carlo (MCMC) based Full Bayes hierarchical spatial random parameter models are developed to simultaneously capture spatial and unobserved random heterogeneity. Around 46% of the neighborhoods had opposite levels of walkability and social vulnerability. Compared to low walkability and high social vulnerability, neighborhoods with high walkability and low social vulnerability (i.e., best case scenario) had on average 20.2% (95% Bayesian CI: -37.2% to -3.3%) lower COVID-19 mortality rates. Analysis of the interactive impacts when only one of the social and built environment metrics was in a healthful direction revealed significant offsetting effects - suggesting that the underlying structural social vulnerability issues faced by our communities should be addressed first for the infectious disease-related health impacts of walkable urban design to be observed. Concerning travel behavior, the findings indicate that COVID-19 mortality rates may be reduced by discouraging auto use and encouraging active transportation. The study methodologically contributes by simultaneously capturing spatial and unobserved heterogeneity in a holistic Full Bayesian framework.

Measuring the effects of Compactness/Sprawl on COVID 19 spread patterns at the neighborhood level

This study analyzes the compactness/sprawl index and its effects on the spread of COVID-19 in the neighborhoods of Ahvaz, Iran. Multiple Criteria Decision Making and GIS techniques were used to develop the index. Also, the effects of compactness/sprawl on COVID-19 were investigated using a regression model. It was found that when considering the number of COVID-19 cases per 1000 people, the compactness/sprawl index did not affect the spread of the disease. However, it had a low but significant effect if the raw number of cases was considered. Results also showed that the compactness index significantly affected the raw number of cases, with a coefficient of 0.291, indicating that more compact neighborhoods had more COVID-19 cases. This is unsurprising as more people live in compact areas and, therefore, the raw number of cases is also likely to be higher. In the absence of proper control measures, this could result in further contact between people, thereby, increasing the risk of virus spread. Overall, we found that compactness had a dual effect on the spread of COVID-19 in Ahvaz. We conclude that proper development and implementation of control measures in well-designed compact neighborhoods are essential for enhancing pandemic resilience.

Investigating the relationships between concentrated disadvantage, place connectivity, and COVID-19 fatality in the United States over time

BACKGROUND

Concentrated disadvantaged areas have been disproportionately affected by COVID-19 outbreak in the United States (US). Meanwhile, highly connected areas may contribute to higher human movement, leading to higher COVID-19 cases and deaths. This study examined the associations between concentrated disadvantage, place connectivity, and COVID-19 fatality in the US over time.

METHODS

Concentrated disadvantage was assessed based on the spatial concentration of residents with low socioeconomic status. Place connectivity was defined as the normalized number of shared Twitter users between the county and all other counties in the contiguous US in a year (Y = 2019). COVID-19 fatality was measured as the cumulative COVID-19 deaths divided by the cumulative COVID-19 cases. Using county-level (N = 3,091) COVID-19 fatality over four time periods (up to October 31, 2021), we performed mixed-effect negative binomial regressions to examine the association between concentrated disadvantage, place connectivity, and COVID-19 fatality, considering potential state-level variations. The moderation effects of county-level place connectivity and concentrated disadvantage were analyzed. Spatially lagged variables of COVID-19 fatality were added to the models to control for the effect of spatial autocorrelations in COVID-19 fatality.

RESULTS

Concentrated disadvantage was significantly associated with an increased COVID-19 fatality in four time periods (p < 0.01). More importantly, moderation analysis suggested that place connectivity significantly exacerbated the harmful effect of concentrated disadvantage on COVID-19 fatality in three periods (p < 0.01), and this significant moderation effect increased over time. The moderation effects were also significant when using place connectivity data from the previous year.

CONCLUSIONS

Populations living in counties with both high concentrated disadvantage and high place connectivity may be at risk of a higher COVID-19 fatality. Greater COVID-19 fatality that occurs in concentrated disadvantaged counties may be partially due to higher human movement through place connectivity. In response to COVID-19 and other future infectious disease outbreaks, policymakers are encouraged to take advantage of historical disadvantage and place connectivity data in epidemic monitoring and surveillance of the disadvantaged areas that are highly connected, as well as targeting vulnerable populations and communities for additional intervention.

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.

Data analytics during pandemics: a transportation and location planning perspective

The recent COVID-19 pandemic once again showed the value of harnessing reliable and timely data in fighting the disease. Obtained from multiple sources via different collection streams, an immense amount of data is processed to understand and predict the future state of the disease. Apart from predicting the spatio-temporal dynamics, it is used to foresee the changes in human mobility patterns and travel behaviors and understand the mobility and spread speed relationship. During this period, data-driven analytic approaches and Operations Research tools are widely used by scholars to prescribe emerging transportation and location planning problems to guide policy-makers in making effective decisions. In this study, we provide a review of studies which tackle transportation and location problems during the COVID-19 pandemic with a focus on data analytics. We discuss the major data collecting streams utilized during the pandemic era, highlight the importance of rapid and reliable data sharing, and give an overview of the challenges and limitations on the use of data.

Build Healthier: Post-COVID-19 Urban Requirements for Healthy and Sustainable Living

The COVID-19 pandemic has brought a renewed interest in urban environment and healthy living and the changes in urban environments which can make for a healthier living. Today, more than 50% of the global population lives in urban areas, and in Europe the number is 75%. We present a narrative review to explore considerations and necessary requirements to achieve health and well-being within strategies for healthy design and urban planning whilst rethinking urban spaces for a post-COVID-19 and carbon-neutral future. The achievement of health and well-being demands healthy design strategies, namely, (1) moving from the concept of infrastructure for processes to the infrastructure for healthy living—requirements for healthy places, cycling, walking, disintegrating the role of polluting traffic from the urban environments, social vulnerability and equality; (2) physical space that will achieve standards of ‘liveable communities’—open, green space requirements and standards for any built environment; (3) mainstreaming ‘in-the-walking distance’ cities and neighbourhoods for healthy physical activities for daily living; (4) exploring any of the new concepts that connect the nexus of urban spaces and public health and improving of the population’s well-being. Public health needs to be prioritised systematically in planning of built environments, energy generations, sustainable food production, and nutrition.

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.

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.

Causal evaluation of the health effects of light rail line: A Natural Experiment

BACKGROUND AND OBJECTIVE

No research to date has causally linked built environment data with health care costs derived from clinically assessed health outcomes within the framework of longitudinal intervention design. This study examined the impact of light rail transit (LRT) line intervention on health care costs after controlling for mode-specific objectively assessed moderateto-vigorous physical activity (MVPA), participant-level neighborhood environmental measures, demographics, attitudinal predispositions, and residential choices.

DATA AND METHODS

Based on a natural experiment related to a new LRT line in Portland - 282 individuals divided into treatment and control groups were prospectively followed during the pre- and post-intervention periods. For each individual, we harness high-resolution data on Electronic Medical Record (EMR) based health care costs, mode-specific MVPA, survey-based travel behavior, attitudinal/perception information, and objectively assessed built environment measures. Simulation-assisted longitudinal grouped random parameter models are developed to gain more accurate insights into the effects of LRT line intervention.

RESULTS

Regarding the "average effect" of the LRT line intervention, no statistically significant reductions in health care costs were observed for the treated individuals over time. However, substantial heterogeneity was observed not only in the magnitude of effects but its direction as well after controlling for the within- and between-individual variations. For a subgroup of treated individuals, the LRT line opening decreased health care costs over time relative to the control group. Further comparative analysis based on the findings of heterogeneity-based models revealed that the effect of LRT intervention for the treated individuals differed by individual characteristics, attitudes/perceptions, and neighborhood level environmental features.

CONCLUSIONS

The study revealed the presence of significant effect modifiers and distinct subgroup structures in the data related to the effects of LRT line intervention on health care costs. Severe implications of ignoring unobserved heterogeneity are highlighted. Limitations and potential avenues for future research are discussed.

The 2019 Conference on Health and Active Transportation: Research Needs and Opportunities

Active transportation (AT) is widely viewed as an important target for increasing participation in aerobic physical activity and improving health, while simultaneously addressing pollution and climate change through reductions in motor vehicular emissions. In recent years, progress in increasing AT has stalled in some countries and, furthermore, the coronavirus (COVID-19) pandemic has created new AT opportunities while also exposing the barriers and health inequities related to AT for some populations. This paper describes the results of the December 2019 Conference on Health and Active Transportation (CHAT) which brought together leaders from the transportation and health disciplines. Attendees charted a course for the future around three themes: Reflecting on Innovative Practices, Building Strategic Institutional Relationships, and Identifying Research Needs and Opportunities. This paper focuses on conclusions of the Research Needs and Opportunities theme. We present a conceptual model derived from the conference sessions that considers how economic and systems analysis, evaluation of emerging technologies and policies, efforts to address inclusivity, disparities and equity along with renewed attention to messaging and communication could contribute to overcoming barriers to development and use of AT infrastructure. Specific research gaps concerning these themes are presented. We further discuss the relevance of these themes considering the pandemic. Renewed efforts at research, dissemination and implementation are needed to achieve the potential health and environmental benefits of AT and to preserve positive changes associated with the pandemic while mitigating negative ones.