Health implications of urban tree canopy policy scenarios in Denver and Phoenix: A quantitative health impact assessment.

BACKGROUND: Urban tree canopy (UTC) goals are a popular policy to increase urban vegetation, support climate strategies, and encourage a healthy environment. Health studies related to UTC are needed across cities to support evidence-based decision-making. METHODS: We used a quantitative Health Impact Assessment (HIA) to model the annual number of premature deaths prevented, and the number of stroke and dementia cases, under UTC goals in Denver, Colorado, and Phoenix, Arizona, USA, using standing policy goals (20% and 25% UTC, respectively) and 50% ("half-way") attainment scenarios from current levels (16.5% and 13% UTC, respectively), using publicly accessible national datasets, and a proportional representation of UTC change to standardize across methodologies. We estimated UTC health impacts by relating UTC with scenario-based changes in the Normalized Difference Vegetation Index (NDVI) and considered health equity in UTC distributions and benefits. RESULTS: We projected that at 2020 populations, uniform 20% UTC attainment across Denver block groups would avert 200 (95% uncertainty interval: (UI) 100, 306) annual premature deaths among adults 18 and older, along with 4.1 (95% UI: 2.2, 6.7) annual cases of stroke (adults ≥35), and 2.6 (95% UI: 1.5, 4.1) cases of dementia (adults ≥65), with "halfway" attainment from current levels (16.5% UTC) capturing ∼64% of these benefits. In Phoenix, uniform 25% UTC would annually prevent 368 (95% UI: 181, 558) premature deaths, 8.7 (95% UI: 4.7, 13.9) cases of stroke, and 5,1 (95% UI: 2.9, 8.0) of dementia, with the "halfway" scenario (17% UTC) achieving ∼44% of these results. Both cities saw significantly different greenspace exposures and health outcomes by socioeconomic vulnerability. Denver had more spatially and socioeconomically heterogeneous projected health benefits than Phoenix. CONCLUSIONS: Implementing UTC goals can prevent excess mortality and chronic diseases among urban residents. UTC goals can be used as a health promotion and prevention tool.

Impact of native-plants policy scenarios on premature mortality in Denver: A quantitative health impact assessment.

BACKGROUND: Cities often use non-native plants such as turf grass to expand green space. Native plants, however, may require less water and maintenance and have co-benefits for local biodiversity, including pollinators. Previous studies estimating mortality averted by adding green space have not considered the provision of native plants as part of the greening policies. AIM: We aim to estimate premature deaths that would be prevented by the implementation of native-plants policy scenarios in the City of Denver, Colorado, USA. METHODS: After conducting interviews with local expert stakeholders, we designed four native-plants policy scenarios: (1) greening 30% of all city census-block groups to the greenness level of native plants, (2) adding 200-foot native-plants buffers around riparian areas, (3) constructing large water retention ponds landscaped with native plants, and (4) greening parking lots. We defined the normalized difference vegetation index (NDVI) corresponding to native plants by measuring the NDVI at locations with known native or highly diverse vegetation. Using a quantitative health-impact assessment approach, we estimated premature mortality averted under each scenario, comparing alternative NDVI with the baseline value. RESULTS: In the most ambitious scenario, we estimated that 88 (95% uncertainty interval (UI): 20, 128) annual premature deaths would be prevented by greening 30% of the area of census block groups with native plants. We estimated that greening 30% of parking-lot surface with native plants would prevent 14 annual deaths (95% UI: 7, 18), adding the native buffers around riparian areas would prevent 13 annual deaths (95% UI: 2, 20), and adding the proposed stormwater retention ponds would prevent no annual deaths (95% UI: 0, 1). CONCLUSION: Using native plants to increase green spaces has the potential to prevent premature deaths in the City of Denver, but results were sensitive to the definition of native plants and the policy scenario.

Association between passively collected walking and bicycling data and purposefully collected active commuting survey data-United States, 2019.

Commercially-available location-based services (LBS) data derived primarily from mobile devices may provide an alternative to surveys for monitoring physically-active transportation. Using Spearman correlation, we compared county-level metrics of walking and bicycling from StreetLight with metrics of physically-active commuting among U.S. workers from the American Community Survey. Our strongest pair of metrics ranked counties (n = 298) similarly for walking (rho = 0.53 [95% CI: 0.44-0.61]) and bicycling (rho = 0.61 [0.53-0.67]). Correlations were higher for denser and more urban counties. LBS data may offer public health and transportation professionals timely information on walking and bicycling behavior at finer geographic scales than some existing surveys.

Bicycle infrastructure and the incidence rate of crashes with cars: A case-control study with Strava data in Atlanta.

Introduction: Bicycling has individual and collective health benefits. Safety concerns are a deterrent to bicycling. Incomplete data on bicycling volumes has limited epidemiologic research investigating safety impacts of bicycle infrastructure, such as protected bike lanes. Methods: In this case-control study, set in Atlanta, Georgia, USA between 2016-10-01 and 2018-08-31, we estimated the incidence rate of police-reported crashes between bicyclists and motor vehicles (n = 124) on several types of infrastructure (off-street paved trails, protected bike lanes, buffered bike lanes, conventional bike lanes, and sharrows) per distance ridden and per intersection entered. To estimate underlying bicycling (the control series), we used a sample of high-resolution bicycling data from Strava, an app, combined with data from 15 on-the-ground bicycle counters to adjust for possible selection bias in the Strava data. We used model-based standardization to estimate effects of treatment on the treated. Results: After adjustment for selection bias and confounding, estimated ratio effects on segments (excluding intersections) with protected bike lanes (incidence rate ratio [IRR] = 0.5 [95% confidence interval: 0.0, 2.5]) and buffered bike lanes (IRR = 0 [0,0]) were below 1, but were above 1 on conventional bike lanes (IRR = 2.8 [1.2, 6.0]) and near null on sharrows (IRR = 1.1 [0.2, 2.9]). Per intersection entry, estimated ratio effects were above 1 for entries originating from protected bike lanes (incidence proportion ratio [IPR] = 3.0 [0.0, 10.8]), buffered bike lanes (IPR = 16.2 [0.0, 53.1]), and conventional bike lanes (IPR = 3.2 [1.8, 6.0]), and were near 1 and below 1, respectively, for those originating from sharrows (IPR = 0.9 [0.2, 2.1]) and off-street paved trails (IPR = 0.7 [0.0, 2.9]). Conclusions: Protected bike lanes and buffered bike lanes had estimated protective effects on segments between intersections but estimated harmful effects at intersections. Conventional bike lanes had estimated harmful effects along segments and at intersections.

Have Paved Trails and Protected Bike Lanes Led to More Bicycling in Atlanta?: A Generalized Synthetic-Control Analysis.

BACKGROUND: Bicycling is an important form of physical activity in populations. Research assessing the effect of infrastructure on bicycling with high-resolution smartphone data is emerging in several places, but it remains limited in low-bicycling US settings, including the Southeastern US. The Atlanta area has been expanding its bicycle infrastructure, including off-street paved trails such as the Atlanta BeltLine and some protected bike lanes. METHODS: Using the generalized synthetic-control method, we estimated effects of five groups of off-street paved trails and protected bike lanes on bicycle ridership in their corresponding areas. To measure bicycling, we used 2 years (October 1, 2016 to September 30, 2018) of monthly Strava data in Atlanta's urban core along with data from 15 on-the-ground counters to adjust for spatiotemporal variation in app use. RESULTS: Considering all infrastructure as one joint intervention, an estimated 1.10 (95% confidence interval [CI]: 0.99, 1.18) times more bicycle-distance was ridden than would have been expected in the same areas had the infrastructure not been built, when defining treatment areas by the narrower of two definitions (defined in text). The Atlanta BeltLine Westside Trail and Proctor Creek Greenway had especially strong effect estimates, e.g., ratios of 1.45 (95% CI: 1.12, 1.86) and 1.55 (1.10, 2.14) under each treatment-area definition, respectively. We estimated that other infrastructure had weaker positive or no effects on bicycle-distance ridden. CONCLUSIONS: This study advances research on the topic because of its setting in the US Southeast, simultaneous assessment of several infrastructure groups, and data-driven approach to estimating effects. See video abstract at, http://links.lww.com/EDE/B936.

On selection bias in comparison measures of smartphone-generated population mobility: an illustration of no-bias conditions with a commercial data source.

PURPOSE: Passively generated cell-phone location ("mobility") data originally intended for commercial use has become frequently used in epidemiologic research, notably during the COVID-19 pandemic to study the impact of physical-distancing recommendations on aggregate population behavior (e.g., average daily mobility). Given the opaque nature of how individuals are selected into these datasets, researchers have cautioned that their use may give rise to selection bias, yet little guidance exists for assessing this potential threat to validity in mobility-data research. Through an example analysis of cell-phone-derived mobility data, we present a set of conditions to guide the assessment of selection bias in measures comparing aggregate mobility patterns over time and between groups. METHODS: We specifically consider bias in measures comparing group-level mobility in the same group (difference, ratio, percent difference) and between groups (difference in differences, ratio of ratios, ratio of percent differences). We illustrate no-bias conditions in these measures through an example comparing block-group-level mobility between income groups in United States metro areas before (January 1st-March 10, 2020) and after (March 11th-April 19th, 2020) the day COVID-19 was declared a pandemic. RESULTS: Within-group contrasts describing mobility over time, especially for the higher-income decile, were expected to be most resistant to bias during the example study period. CONCLUSIONS: The presented conditions can be used to assess the susceptibility to selection bias of group-level measures comparing mobility. Importantly, they can be used even without knowledge of the degree of bias in each group at each time point. We further highlight links between no-bias principles originating in epidemiology and economics, showing that certain assumptions (e.g., parallel trends) can apply to biases beyond their original application.

At-risk-measure Sampling in Case-Control Studies with Aggregated Data.

Transient exposures are difficult to measure in epidemiologic studies, especially when both the status of being at risk for an outcome and the exposure change over time and space, as when measuring built-environment risk on transportation injury. Contemporary "big data" generated by mobile sensors can improve measurement of transient exposures. Exposure information generated by these devices typically only samples the experience of the target cohort, so a case-control framework may be useful. However, for anonymity, the data may not be available by individual, precluding a case-crossover approach. We present a method called at-risk-measure sampling. Its goal is to estimate the denominator of an incidence rate ratio (exposed to unexposed measure of the at-risk experience) given an aggregated summary of the at-risk measure from a cohort. Rather than sampling individuals or locations, the method samples the measure of the at-risk experience. Specifically, the method as presented samples person-distance and person-events summarized by location. It is illustrated with data from a mobile app used to record bicycling. The method extends an established case-control sampling principle: sample the at-risk experience of a cohort study such that the sampled exposure distribution approximates that of the cohort. It is distinct from density sampling in that the sample remains in the form of the at-risk measure, which may be continuous, such as person-time or person-distance. This aspect may be both logistically and statistically efficient if such a sample is already available, for example from big-data sources like aggregated mobile-sensor data.

Effect of the ASCENT Intervention to Increase Knowledge of Kidney Allocation Policy Changes Among Dialysis Providers.

INTRODUCTION: The Allocation System Changes for Equity in Kidney Transplantation (ASCENT) trial was a cluster-randomized pragmatic, effectiveness-implementation study designed to test whether a multicomponent educational intervention targeting leadership, clinic staff, and patients in dialysis facilities improved knowledge and awareness of the 2014 Kidney Allocation System (KAS) change. METHODS: Participants included 690 dialysis facility medical directors, nephrologists, social workers, and other staff within 655 US dialysis facilities, with 51% (n = 334) in the intervention group and 49% (n = 321) in the control group. Intervention activities included a webinar targeting medical directors and facility staff, an approximately 10-minute educational video targeting dialysis staff, an approximately 10-minute educational video targeting patients, and a facility-specific audit and feedback report of transplant performance. The control group received a standard United Network for Organ Sharing brochure. Provider knowledge was a secondary outcome of the ASCENT trial and the primary outcome of this study; knowledge was assessed as a cumulative score on a 5-point Likert scale (higher score = greater knowledge). Intention-to-treat analysis was used. RESULTS: At baseline, nonintervention providers had a higher mean knowledge score (mean ± SD, 2.45 ± 1.43) than intervention providers (mean ± SD, 2.31 ± 1.46). After 3 months, the average knowledge score was slightly higher in the intervention (mean ± SD, 3.14 ± 1.28) versus nonintervention providers (mean ± SD, 3.07 ± 1.24), and the estimated mean difference in knowledge scores between the groups at follow-up minus the mean difference at baseline was 0.25 (95% confidence interval [CI], 0.11-0.48; P = 0.039). The effect size (0.41) was low to moderate. CONCLUSION: Dialysis facility provider education could help extend the impact of a national policy change in organ allocation.

A definition of the causal effect of a political party's nominee on the U.S. general presidential election using counterfactual response types.

The electability of the candidates for the 2020 Democratic U.S. presidential nomination was frequently debated. Arguments regarding a candidate's electability often claimed that they would affect the general election by changing the behavior of a certain subset of eligible voters. For example, is it more important electorally that a candidate drive turnout or swing voting? As lay consumers of political opinion, we were having difficulty weighing these questions from a strategic standpoint. Although candidate electability is a nebulous term that might be interpreted in various ways, one interpretation of the term is a population-based causal question: What would the effect of the Democratic nominee be on the presidential election result? Population-based causal questions are commonly studied in epidemiology. To aid interpretation of electability arguments, we frame the question through a counterfactual model used in epidemiology. Specifically, we define the causal effect by characterizing the population of eligible voters into nine counterfactual response types. The definition clarifies our ability to interpret arguments regarding the electability of the candidates. For example, the causal effect can be subdivided into three parts: the effect of the nominee on (1) Democratic turnout, (2) Republican turnout, and (3) swing voting. We show using notation that the third part has twice the weight as the other two. The definition follows intuition. However, we hope its formalization using counterfactual response types may foster interdisciplinary communication.

Sampling and Sampling Frames in Big Data Epidemiology.

PURPOSE OF REVIEW: The 'big data' revolution affords the opportunity to reuse administrative datasets for public health research. While such datasets offer dramatically increased statistical power compared with conventional primary data collection, typically at much lower cost, their use also raises substantial inferential challenges. In particular, it can be difficult to make population inferences because the sampling frames for many administrative datasets are undefined. We reviewed options for accounting for sampling in big data epidemiology. RECENT FINDINGS: We identified three common strategies for accounting for sampling when the data available were not collected from a deliberately constructed sample: 1) explicitly reconstruct the sampling frame, 2) test the potential impacts of sampling using sensitivity analyses, and 3) limit inference to sample. SUMMARY: Inference from big data can be challenging because the impacts of sampling are unclear. Attention to sampling frames can minimize risks of bias.

Comparing bicyclists who use smartphone apps to record rides with those who do not: implications for representativeness and selection bias.

Increasing population levels of cycling has the potential to improve public health by increasing physical activity. As cyclists have begun using smartphone apps to record trips, researchers have begun using data generated from these apps to monitor cycling levels and evaluate cycling-related interventions. The goal of this research is to assess the extent to which app-using cyclists represent the broader cycling population to inform whether use of app-generated data in bike-infrastructure intervention research may bias effect estimates. Using an intercept survey, we asked 95 cyclists throughout Atlanta, Georgia, USA about their use of GPS-based smartphone apps to record bike rides. We asked respondents to draw their common bike routes, from which we assessed the proportion of ridership captured by app-generated data sources overall and on types of bicycle infrastructure. We measured socio-demographics and bike-riding habits, including cyclist type, ride frequency, and most common ride purpose. Cyclists who used smartphone apps to record their bike rides differed from those who did not across some but not all socio-demographic characteristics and differed in several bike-riding attributes. App users rode more frequently, self-classified as stronger riders, and rode proportionately more for leisure. Although groups had similar infrastructure preferences at the person level, differences appeared at the level of the estimated ride, where, for example, the proportion of ridership captured by an app on protected bike lanes was lower than the overall proportion of ridership captured. A sample calculation illustrated how such differences may induce selection bias in smartphone-data-based research on infrastructure and motor-vehicle-cyclist crash risk. We illustrate in the sample scenario how the bias can be corrected, assuming inverse-probability-of-selection weights can be accurately specified. The presented bias-adjustment method may be useful for future bike-infrastructure research using smartphone-generated data.

Effect of Cardiorespiratory Fitness on Academic Achievement is Stronger in High-SES Elementary Schools Compared to Low.

BACKGROUND: Academic achievement is influenced by factors at the student, school, and community levels. We estimated the effect of cardiorespiratory fitness performance on academic performance at the school level in Georgia elementary schools and examined effect modification by sociodemographic factors. METHODS: This study is a repeat cross-sectional analysis of Georgia elementary schools between 2011 and 2014 (approximately 1138 schools per year). Multivariable beta regression estimated the effect of the proportion of 4th and 5th graders meeting cardiorespiratory fitness standards on the proportion of 5th graders passing standardized tests for Reading, English and Language Arts, Mathematics, Science, and Social Studies and considered potential interaction by school-level socioeconomic status (SES), racial composition, and urbanity. RESULTS: There was a 0.15 higher estimated odds (OR: 1.15 (1.09, 1.22)) of passing the mathematics standardized test for every 10-percentage-point increase in school-level cardiorespiratory fitness among high-SES schools and 0.04 higher odds (OR: 1.04 (1.02, 1.05)) for low-SES schools. This pattern was similar for other academic subjects. No effect modification by racial composition or urbanity was observed for any academic subject. CONCLUSIONS: Promoting physical fitness may be effective in improving academic performance among high-SES schools, but additional strategies may be needed among lower-SES schools.

Routine Assessment and Promotion of Physical Activity in Healthcare Settings: A Scientific Statement From the American Heart Association.

Physical inactivity is one of the most prevalent major health risk factors, with 8 in 10 US adults not meeting aerobic and muscle-strengthening guidelines, and is associated with a high burden of cardiovascular disease. Improving and maintaining recommended levels of physical activity leads to reductions in metabolic, hemodynamic, functional, body composition, and epigenetic risk factors for noncommunicable chronic diseases. Physical activity also has a significant role, in many cases comparable or superior to drug interventions, in the prevention and management of >40 conditions such as diabetes mellitus, cancer, cardiovascular disease, obesity, depression, Alzheimer disease, and arthritis. Whereas most of the modifiable cardiovascular disease risk factors included in the American Heart Association's My Life Check - Life's Simple 7 are evaluated routinely in clinical practice (glucose and lipid profiles, blood pressure, obesity, and smoking), physical activity is typically not assessed. The purpose of this statement is to provide a comprehensive review of the evidence on the feasibility, validity, and effectiveness of assessing and promoting physical activity in healthcare settings for adult patients. It also adds concrete recommendations for healthcare systems, clinical and community care providers, fitness professionals, the technology industry, and other stakeholders in order to catalyze increased adoption of physical activity assessment and promotion in healthcare settings and to contribute to meeting the American Heart Association's 2020 Impact Goals.

Racial/ethnic disparities in waitlisting for deceased donor kidney transplantation 1 year after implementation of the new national kidney allocation system.

The impact of a new national kidney allocation system (KAS) on access to the national deceased-donor waiting list (waitlisting) and racial/ethnic disparities in waitlisting among US end-stage renal disease (ESRD) patients is unknown. We examined waitlisting pre- and post-KAS among incident (N = 1 253 100) and prevalent (N = 1 556 954) ESRD patients from the United States Renal Data System database (2005-2015) using multivariable time-dependent Cox and interrupted time-series models. The adjusted waitlisting rate among incident patients was 9% lower post-KAS (hazard ratio [HR]: 0.91; 95% confidence interval [CI], 0.90-0.93), although preemptive waitlisting increased from 30.2% to 35.1% (P < .0001). The waitlisting decrease is largely due to a decline in inactively waitlisted patients. Pre-KAS, blacks had a 19% lower waitlisting rate vs whites (HR: 0.81; 95% CI, 0.80-0.82); following KAS, disparity declined to 12% (HR: 0.88; 95% CI, 0.85-0.90). In adjusted time-series analyses of prevalent patients, waitlisting rates declined by 3.45/10 000 per month post-KAS (P < .001), resulting in ≈146 fewer waitlisting events/month. Shorter dialysis vintage was associated with greater decreases in waitlisting post-KAS (P < .001). Racial disparity reduction was due in part to a steeper decline in inactive waitlisting among minorities and a greater proportion of actively waitlisted minority patients. Waitlisting and racial disparity in waitlisting declined post-KAS; however, disparity remains.

Geographical variation in health-related physical fitness and body composition among Chilean 8th graders: a nationally representative cross-sectional study.

PURPOSE: In addition to excess adiposity, low cardiorespiratory fitness (CRF) and low musculoskeletal fitness (MSF) are important independent risk factors for future cardio-metabolic disease in adolescents, yet global fitness surveillance in adolescents is poor. The objective of this study was to describe and investigate geographical variation in levels of health-related physical fitness, including CRF, MSF, body mass index (BMI), and waist circumference (WC) in Chilean 8th graders. METHODS: This cross-sectional study was based on a population-based, representative sample of 19,929 8th graders (median age = 14 years) in the 2011 National Physical Education Survey from Chile. CRF was assessed with the 20-meter shuttle run test, MSF with standing broad jump, and body composition with BMI and WC. Data were classified according to health-related standards. Prevalence of levels of health-related physical fitness was mapped for each of the four variables, and geographical variation was explored at the country level by region and in the Santiago Metropolitan Area by municipality. RESULTS: Girls had significantly higher prevalence of unhealthy CRF, MSF, and BMI than boys (p<0.05). Overall, 26% of boys and 55% of girls had unhealthy CRF, 29% of boys and 35% of girls had unhealthy MSF, 29% of boys and 44% of girls had unhealthy BMI, and 31% of adolescents had unhealthy WC. High prevalence of unhealthy fitness levels concentrates in the northern and middle regions of the country and in the North and Southwest sectors for the Santiago Metropolitan Area. CONCLUSION: Prevalence of unhealthy CRF, MSF, and BMI is relatively high among Chilean 8th graders, especially in girls, when compared with global estimates. Identification of geographical regions and municipalities with high prevalence of unhealthy physical fitness presents opportunity for targeted intervention.