The Role of Ozone as a Mediator of the Relationship Between Heat Waves and Mortality in 15 French Urban Areas.

Heat and tropospheric ozone have acute impacts on rates of premature death. Warm temperatures affect the photochemical processes in ozone formation, suggesting ozone as a mediator of the acute health effect of heat on mortality. We assembled a summertime daily time-series data set of 15 French urban areas during 2000-2015 to decompose the acute total effect of heat waves on mortality into natural direct and indirect effects using a regression-based product method under the potential outcomes framework. For each area, we estimated the effect of heat waves on mortality using a quasi-Poisson model with adjustment for covariates such as lagged nitrogen dioxide concentration, and we modeled ozone with a linear regression of heat waves and the same set of covariates. We pooled estimates across areas using random-effects models. We also provide R software code (R Foundation for Statistical Computing, Vienna, Austria) with which to reproduce or replicate our analysis. Most areas demonstrated evidence of mediation by ozone, with the pooled relative risks for natural indirect effects being 1.03 (95% confidence interval (CI): 1.02, 1.05), 1.03 (95% CI: 1.01, 1.04), and 1.04 (95% CI: 1.00, 1.07) for nonaccidental, cardiovascular, and respiratory mortality, respectively. We found evidence of a mediation effect by ozone in the association between heat waves and mortality in France which varied by geographic location and cause of mortality.

Neighborhood Built Environment and Sleep Health: A Longitudinal Study in Low-Income and Predominantly African-American Neighborhoods.

In the present study, we examined the associations between physical characteristics of neighborhoods and sleep health outcomes and assessed the mediating role of physical activity in these associations. A longitudinal study (the Pittsburgh Hill/Homewood Research on Eating, Shopping, and Health (PHRESH) Zzz Study; n = 1,051) was conducted in 2 low-income, predominately African-American neighborhoods in Pittsburgh, Pennsylvania, with repeated measures of neighborhood characteristics and sleep health outcomes from 2013 to 2018. Built environment measures of walkability, urban design, and neighborhood disorder were captured from systematic field observations. Sleep health outcomes included insufficient sleep, sleep duration, wakefulness after sleep onset, and sleep efficiency measured from 7-day actigraphy data. G-computations based on structural nested mean models were used to examine the total effects of each built environment feature, and causal mediation analyses were used to evaluate direct and indirect effects operating through physical activity. Urban design features were associated with decreased wakefulness after sleep onset (risk difference (RD) = -1.26, 95% confidence interval (CI): -4.31, -0.33). Neighborhood disorder (RD = -0.46, 95% CI: -0.86, -0.07) and crime rate (RD = -0.54, 95% CI: -0.93, -0.08) were negatively associated with sleep efficiency. Neighborhood walkability was not associated with sleep outcomes. We did not find a strong and consistent mediating role of physical activity. Interventions to improve sleep should target modifiable factors, including urban design and neighborhood disorder.

Associations of air pollution mixtures with ambulatory blood pressure: The MobiliSense sensor-based study.

Air pollution is acknowledged as a determinant of blood pressure (BP), supporting the hypothesis that air pollution, via hypertension and other mechanisms, has detrimental effects on human health. Previous studies evaluating the associations between air pollution exposure and BP did not consider the effect that air pollutant mixtures may have on BP. We investigated the effect of exposure to single species or their synergistic effects as air pollution mixture on ambulatory BP. Using portable sensors, we measured personal concentrations of black carbon (BC), nitrogen dioxide (NO2), nitrogen monoxide (NO), carbon monoxide (CO), ozone (O3), and particles with aerodynamic diameters below 2.5 µm (PM2.5). We simultaneously collected ambulatory BP measurements (30-min intervals, N = 3319) of 221 participants over one day of their lives. Air pollution concentrations were averaged over 5 min to 1 h before each BP measurement, and inhaled doses were estimated across the same exposure windows using estimated ventilation rates. Fixed-effect linear models as well as quantile G-computation techniques were applied to associate air pollutants' individual and combined effects with BP, adjusting for potential confounders. In mixture models, a quartile increase in air pollutant concentrations (BC, NO2, NO, CO, and O3) in the previous 5 min was associated with a 1.92 mmHg (95% CI: 0.63, 3.20) higher systolic BP (SBP), while 30-min and 1-h exposures were not associated with SBP. However, the effects on diastolic BP (DBP) were inconsistent across exposure windows. Unlike concentration mixtures, inhalation mixtures in the previous 5 min to 1 h were associated with increased SBP. Out-of-home BC and O3 concentrations were more strongly associated with ambulatory BP outcomes than in-home concentrations. In contrast, only the in-home concentration of CO reduced DBP in stratified analyses. This study shows that exposure to a mixture of air pollutants (concentration and inhalation) was associated with elevated SBP.

Neighborhood built environment typologies and adiposity in children and adolescents.

BACKGROUND/OBJECTIVES: Neighborhoods are complex, multidimensional systems. However, the interrelation between multiple neighborhood dimensions is seldom considered in relation to youth adiposity. We created a neighborhood typology using a range of built environment features and examined its association with adiposity in youth. SUBJECTS/METHODS: Analyses are based on data from the QUALITY cohort, an ongoing study on the natural history of obesity in Quebec youth with a history of parental obesity. Adiposity was measured at baseline (8-10 years) and follow up, ~8 years later. Neighborhood features were measured at baseline through in-person neighborhood assessments and geocoded administrative data and were summarized using principal components analysis. Neighborhood types were identified using cluster analysis. Associations between neighborhood types and adiposity were examined using multivariable linear regressions. RESULTS: Five distinct neighborhood types characterized by levels of walkability and traffic-related safety were identified. At ages 8-10 years, children in moderate walkability/low safety neighborhoods had higher BMI Z-scores [ß: 0.41 (0.12; 0.71), p = 0.007], fat mass index [ß: 1.22 (0.29; 2.16), p = 0.010], waist circumference [ß: 4.92 (1.63; 8.21), p = 0.003], and central fat mass percentage [ß: 1.60 (0.04; 3.16), p = 0.045] than those residing in moderate walkability/high safety neighborhoods. Attenuated associations were observed between neighborhood types and adiposity 8 years later. Specifically, residents of moderate walkability/low safety neighborhoods had a higher FMI [ß: 1.42 (-0.07; 2.90), p = 0.062], and waist circumference [ß: 5.04 (-0.26; 10.34), p = 0.062]. CONCLUSIONS: Neighborhoods characterized by lower traffic safety appear to be the most obesogenic to children, regardless of other walkability-related features. Policies targeting neighborhood walkability for children may need to prioritize vehicular traffic safety.

Operationalising the 20-minute neighbourhood.

BACKGROUND: Recent rapid growth in urban areas and the desire to create liveable neighbourhoods has brought about a renewed interest in planning for compact cities, with concepts like the 20-minute neighbourhood (20MN) becoming more popular. A 20MN broadly reflects a neighbourhood that allows residents to meet their daily (non-work) needs within a short, non-motorised, trip from home. The 20MN concept underpins the key planning strategy of Australia's second largest city, Melbourne, however the 20MN definition has not been operationalised. This study aimed to develop and operationalise a practical definition of the 20MN and apply this to two Australian state capital cities: Melbourne (Victoria) and Adelaide (South Australia). METHODS: Using the metropolitan boundaries for Melbourne and Adelaide, data were sourced for several layers related to five domains: 1) healthy food; 2) recreational resources; 3) community resources; 4) public open space; and 5) public transport. The number of layers and the access measures required for each domain differed. For example, the recreational resources domain only required a sport and fitness centre (gym) within a 1.5-km network path distance, whereas the public open space domain required a public open space within a 400-m distance along a pedestrian network and 8 ha of public open space area within a 1-km radius. Locations that met the access requirements for each of the five domains were defined as 20MNs. RESULTS: In Melbourne 5.5% and in Adelaide 7.6% of the population were considered to reside in a 20MN. Within areas classified as residential, the median number of people per square kilometre with a 20MN in Melbourne was 6429 and the median number of dwellings per square kilometre was 3211. In Adelaide's 20MNs, both population density (3062) and dwelling density (1440) were lower than in Melbourne. CONCLUSIONS: The challenge of operationalising a practical definition of the 20MN has been addressed by this study and applied to two Australian cities. The approach can be adapted to other contexts as a first step to assessing the presence of existing 20MNs and monitoring further implementation of this concept.

Objective and Subjective Neighborhood Crime Associated with Poor Sleep among Young Sexual Minority Men: a GPS Study.

Sleep disparities in sexual minority male (SMM) populations have received relatively little attention but they may be critical to explaining other health disparities seen among SMM, via neural or hormonal pathways. Recent research suggests that crime may be a psychosocial stressor that contributes to sleep disparities but that finding has been based on subjective measures of crime. We conducted the P18 Neighborhood Study of 250 SMM in New York City, including 211 with adequate GPS tracking data. We used the GPS tracking data to define daily path area activity spaces and tested the associations of violent crime in those activity spaces and in the subject's residential neighborhood, perceived neighborhood safety, and witnessing crime with a subjective measure of sleep. Using quasi-Poisson regression, adjusted for individual and neighborhood socio-demographics, we found that SMM who witnessed more types of crime experienced significantly more nights of poor sleep over the course of a month (RR: 1.16, 95%CI: 1.05-1.27, p-value: < 0.01). We did not find any associations between violent crime rates in either the activity area or residential area and sleep. Our findings support the conclusion that personal exposure to crime is associated with sleep problems and provide further evidence for the pathway between stress and sleep. The lack of association between neighborhood crime levels and sleep suggests that there must be personal experience with crime and ambient presence is insufficient to produce an effect.

Comparison of various heat waves definitions and the burden of heat-related mortality in France: Implications for existing early warning systems.

INTRODUCTION: In France, a heat warning system (HWS) has been implemented almost two decades ago and rely on some official heat wave (HW) definitions. However, no study has compared the burden associated with a large set of alternative HW definitions to the official definitions. Such comparison could be particularly helpful to identify HW conditions for which effective HWS would minimize the health burden across various geographical contexts and possibly update thresholds to trigger HWS. The aim of this study is to identify (and rank) definitions that drive the highest health burden in terms of mortality to inform future HWS across multiple cities in France. METHODS: Based on weather data for 16 French cities, we compared the two official definitions used in France to: i) the Excess Heat Factor (EHF) used in Australia, and ii) 18 alternative hypothetical HW definitions based on various combinations of temperature metrics, intensity, and duration. Propensity score matching and Poisson regressions were used to estimate the effect of each HW exposure on non-accidental mortality for the May-September period from 2000 to 2015. RESULTS: The associations between HW and mortality differed greatly depending on the definition. The greatest burden of heat was 1,055 (95% confidence interval "CI": [856; 1,302]) deaths per summer and was obtained with the EHF. The EHF identified HW with 2.46 (95% CI: [1.92; 3.58]) or 8.18 (95% CI: [6.63; 10.61]) times the global burden at the national level obtained with the climatological indicator of the French national weather service and the HW indicator of the French national HWS, respectively and was the most impactful definition pattern for both temperate oceanic and Mediterranean climate types. CONCLUSION: Identifying the set of extreme heat conditions that drive the highest health burden in a given geographical context is particularly helpful when designing or updating heat early warning systems.

Application of machine learning to predict transport modes from GPS, accelerometer, and heart rate data.

BACKGROUND: There has been an increased focus on active transport, but the measurement of active transport is still difficult and error-prone. Sensor data have been used to predict active transport. While heart rate data have very rarely been considered before, this study used random forests (RF) to predict transport modes using Global Positioning System (GPS), accelerometer, and heart rate data and paid attention to methodological issues related to the prediction strategy and post-processing. METHODS: The RECORD MultiSensor study collected GPS, accelerometer, and heart rate data over seven days from 126 participants living in the Ile-de-France region. RF models were built to predict transport modes for every minute (ground truth information on modes is from a GPS-based mobility survey), splitting observations between a Training dataset and a Test dataset at the participant level instead at the minute level. Moreover, several window sizes were tested for the post-processing moving average of the predicted transport mode. RESULTS: The minute-level prediction rate of being on trips vs. at a visited location was 90%. Final prediction rates of transport modes ranged from 65% for public transport to 95% for biking. Using minute-level observations from the same participants in the Training and Test sets (as RF spontaneously does) upwardly biases prediction rates. The inclusion of heart rate data improved prediction rates only for biking. A 3 to 5-min bandwidth moving average was optimum for a posteriori homogenization. CONCLUSION: Heart rate only very slightly contributed to better predictions for specific transport modes. Moreover, our study shows that Training and Test sets must be carefully defined in RF models and that post-processing with carefully chosen moving average windows can improve predictions.

Geographic Density and Uptake of Pre-exposure Prophylaxis (PrEP) Among Young Gay, Bisexual and Other Sexual Minority Men: A Global Positioning System (GPS) Study.

The geographic availability of pre-exposure prophylaxis (PrEP) providers is one important factor that significantly affects PrEP uptake. While most previous studies have employed spatial accessibility in static residential neighborhood definitions or self-reported healthcare accessibility, we examined the associations of the objectively measured geographic density of PrEP services with current PrEP use, using global positioning system (GPS) among sexual minority men (SMM) in New York City. 250 HIV-negative SMM participated in a 2-week GPS monitoring (January 2017-January 2018). Geographic PrEP density was measured as total numbers of PrEP providers in (1) individual activity space defined as daily path area of GPS points, (2) residential street network buffers and (3) census tract and ZIP code of residential locations. Geographic PrEP density within GPS-based activity space was positively associated with current PrEP use (prevalence ratio for 50-m activity space = 1.10, 95% confidence interval: [1.02, 1.18]). PrEP provider counts in residential buffer areas and administrative neighborhoods were not associated with PrEP use. Although it is not generalizable beyond New York City, our finding suggests the importance of daily mobility pattern in HIV prevention and PrEP implementation strategies.

A method to define the relevant ego-centred spatial scale for the assessment of neighbourhood effects: the example of cardiovascular risk factors.

INTRODUCTION: The neighbourhood in which one lives affects health through complex pathways not yet fully understood. A way to move forward in assessing these pathways direction is to explore the spatial structure of health phenomena to generate hypotheses and examine whether the neighbourhood characteristics are able to explain this spatial structure. We compare the spatial structure of two cardiovascular disease risk factors in three European urban areas, thus assessing if a non-measured neighbourhood effect or spatial processes is present by either modelling the correlation structure at individual level or by estimating the intra-class correlation within administrative units. METHODS: Data from three independent studies (RECORD, DHS and BaBi), covering each a European urban area, are used. The characteristics of the spatial correlation structure of cardiovascular risk factors (BMI and systolic blood pressure) adjusted for age, sex, educational attainment and income are estimated by fitting an exponential model to the semi-variogram based on the geo-coordinates of places of residence. For comparison purposes, a random effect model is also fitted to estimate the intra-class correlation within administrative units. We then discuss the benefits of modelling the correlation structure to evaluate the presence of unmeasured spatial effects on health. RESULTS: BMI and blood pressure are consistently found to be spatially structured across the studies, the spatial correlation structures being stronger for BMI. Eight to 22% of the variability in BMI were spatially structured with radii ranging from 100 to 240 m (range). Only a small part of the correlation of residuals was explained by adjusting for the correlation within administrative units (from 0 to 4 percentage points). DISCUSSION: The individual spatial correlation approach provides much stronger evidence of spatial effects than the multilevel approach even for small administrative units. Spatial correlation structure offers new possibilities to assess the relevant spatial scale for health. Stronger correlation structure seen for BMI may be due to neighbourhood socioeconomic conditions and processes like social norms at work in the immediate neighbourhood.

Psycho-social factors related to obesity and their associations with socioeconomic characteristics: the RECORD study.

OBJECTIVES: We aimed to describe the main psycho-social factors related to obesity in an adult population and to develop a unified construct (psycho-social profiles), to explore the associations between socioeconomic characteristics and these psycho-social profiles. METHODS: In its second wave, the RECORD Study assessed 6460 participants aged 30-79 years living in the Paris region between 2011 and 2014. Factor analyses followed by cluster analysis were applied to identify psycho-social profiles related to obesity. The two psycho-social profiles were adverse profile-negative body image, underestimation of the impact of weight in quality of life, low weight-related self-efficacy, and weight-related external locus of control; and favorable profile-positive body image, high self-efficacy, and internal locus of control. The relationship between three socioeconomic dimensions-current socioeconomic status, childhood socioeconomic status, and neighborhood education status-and psycho-social profiles was assessed through binomial logistic regression adjusted for age, gender, depression, living alone, and weight status. RESULTS: Contrary to hypotheses, there were no associations between socioeconomic characteristics and obesity-related psycho-social profiles after adjustment for body mass index. Depressive symptoms (OR 2.21, 95% CI 2.70, 4.04) and being female (3.31, 95% CI 2.70, 4.40) were associated with an adverse psycho-social profile. CONCLUSIONS: Psycho-social profiles could help to understand the multifactorial nature of the determinants of obesity. LEVEL OF EVIDENCE: Level V, cross-sectional descriptive study.

Combining sensor tracking with a GPS-based mobility survey to better measure physical activity in trips: public transport generates walking.

BACKGROUND: Policymakers need accurate data to develop efficient interventions to promote transport physical activity. Given the imprecise assessment of physical activity in trips, our aim was to illustrate novel advances in the measurement of walking in trips, including in trips incorporating non-walking modes. METHODS: We used data of 285 participants (RECORD MultiSensor Study, 2013-2015, Paris region) who carried GPS receivers and accelerometers over 7 days and underwent a phone-administered web mobility survey on the basis of algorithm-processed GPS data. With this mobility survey, we decomposed trips into unimodal trip stages with their start/end times, validated information on travel modes, and manually complemented and cleaned GPS tracks. This strategy enabled to quantify walking in trips with different modes with two alternative metrics: distance walked and accelerometry-derived number of steps taken. RESULTS: Compared with GPS-based mobility survey data, algorithm-only processed GPS data indicated that the median distance covered by participants per day was 25.3 km (rather than 23.4 km); correctly identified transport time vs. time at visited places in 72.7% of time; and correctly identified the transport mode in 67% of time (and only in 55% of time for public transport). The 285 participants provided data for 8983 trips (21,163 segments of observation). Participants spent a median of 7.0% of their total time in trips. The median distance walked per trip was 0.40 km for entirely walked trips and 0.85 km for public transport trips (the median number of accelerometer steps were 425 and 1352 in the corresponding trips). Overall, 33.8% of the total distance walked in trips and 37.3% of the accelerometer steps in trips were accumulated during public transport trips. Residents of the far suburbs cumulated a 1.7 times lower distance walked per day and a 1.6 times lower number of steps during trips per 8 h of wear time than residents of the Paris core city. CONCLUSIONS: Our approach complementing GPS and accelerometer tracking with a GPS-based mobility survey substantially improved transport mode detection. Our findings suggest that promoting public transport use should be one of the cornerstones of policies to promote physical activity.

Mobile Sensing in Environmental Health and Neighborhood Research.

Public health research has witnessed a rapid development in the use of location, environmental, behavioral, and biophysical sensors that provide high-resolution objective time-stamped data. This burgeoning field is stimulated by the development of novel multisensor devices that collect data for an increasing number of channels and algorithms that predict relevant dimensions from one or several data channels. Global positioning system (GPS) tracking, which enables geographic momentary assessment, permits researchers to assess multiplace personal exposure areas and the algorithm-based identification of trips and places visited, eventually validated and complemented using a GPS-based mobility survey. These methods open a new space-time perspective that considers the full dynamic of residential and nonresidential momentary exposures; spatially and temporally disaggregates the behavioral and health outcomes, thus replacing them in their immediate environmental context; investigates complex time sequences; explores the interplay among individual, environmental, and situational predictors; performs life-segment analyses considering infraindividual statistical units using case-crossover models; and derives recommendations for just-in-time interventions.

Attrition Bias Related to Missing Outcome Data: A Longitudinal Simulation Study.

BACKGROUND: Most longitudinal studies do not address potential selection biases due to selective attrition. Using empirical data and simulating additional attrition, we investigated the effectiveness of common approaches to handle missing outcome data from attrition in the association between individual education level and change in body mass index (BMI). METHODS: Using data from the two waves of the French RECORD Cohort Study (N = 7,172), we first examined how inverse probability weighting (IPW) and multiple imputation handled missing outcome data from attrition in the observed data (stage 1). Second, simulating additional missing data in BMI at follow-up under various missing-at-random scenarios, we quantified the impact of attrition and assessed how multiple imputation performed compared to complete case analysis and to a perfectly specified IPW model as a gold standard (stage 2). RESULTS: With the observed data in stage 1, we found an inverse association between individual education and change in BMI, with complete case analysis, as well as with IPW and multiple imputation. When we simulated additional attrition under a missing-at-random pattern (stage 2), the bias increased with the magnitude of selective attrition, and multiple imputation was useless to address it. CONCLUSIONS: Our simulations revealed that selective attrition in the outcome heavily biased the association of interest. The present article contributes to raising awareness that for missing outcome data, multiple imputation does not do better than complete case analysis. More effort is thus needed during the design phase to understand attrition mechanisms by collecting information on the reasons for dropout.

Collecting Mobility Data with GPS Methods to Understand the HIV Environmental Riskscape Among Young Black Men Who Have Sex with Men: A Multi-city Feasibility Study in the Deep South.

While research increasingly studies how neighborhood contexts influence HIV among gay, bisexual and other men who have sex with men (MSM) populations, to date, no research has used global positioning system (GPS) devices, an innovative method to study spatial mobility through neighborhood contexts, i.e., the environmental riskscape, among a sample of Black MSM. The purpose of this study was to examine the feasibility of collecting two-week GPS data (as measured by a pre- and post-surveys as well as objectively measured adherence to GPS protocol) among a geographically-diverse sample of Black MSM in the Deep South: Gulfport, MS, Jackson, MS, and New Orleans LA (n = 75). GPS feasibility was demonstrated including from survey items, e.g. Black MSM reported high ratings of pre-protocol acceptability, ease of use, and low levels of wear-related concerns. Findings from this study demonstrate that using GPS methods is acceptable and feasible among Black MSM in the Deep South.

Does the air pollution model influence the evidence of socio-economic disparities in exposure and susceptibility?

Studies assessing socio-economic disparities in air pollution exposure and susceptibility are usually based on a single air pollution model. A time stratified case-crossover study was designed to assess the impact of the type of model on differential exposure and on the differential susceptibility in the relationship between ozone exposure and daily mortality by socio-economic strata (SES) in Montreal. Non-accidental deaths along with deaths from cardiovascular and respiratory causes on the island of Montreal for the period 1991-2002 were included as cases. Daily ozone concentration estimates at partictaipants' residence were obtained from the five following air pollution models: Average value (AV), Nearest station model (NS), Inverse-distance weighting interpolation (IDW), Land-use regression model with back-extrapolation (LUR-BE) and Bayesian maximum entropy model combined with a land-use regression (BME-LUR). The prevalence of a low household income (< 20,000/year) was used as socio-economic variable, divided into two categories as a proxy for deprivation. Multivariable conditional logistic regressions were used considering 3-day average concentrations. Multiplicative and additive interactions (using Relative Excess Risk due to Interaction) as well as Cochran's tests were calculated and results were compared across the different air pollution models. Heterogeneity of susceptibility and exposure according to socio-economic status (SES) were found. Ratio of exposure across SES groups means ranged from 0.75 [0.74-0.76] to 1.01 [1.00-1.02], respectively for the LUR-BE and the BME-LUR models. Ratio of mortality odds ratios ranged from 1.01 [0.96-1.05] to 1.02 [0.97-1.08], respectively for the IDW and LUR-BE models. Cochran's test of heterogeneity between the air pollution models showed important heterogeneity regarding the differential exposure by SES, but the air pollution model was not found to influence heterogeneity regarding the differential susceptibility. The study showed air pollution models can influence the assessment of disparities in exposure according to SES in Montreal but not that of disparities in susceptibility.

MINDMAP: establishing an integrated database infrastructure for research in ageing, mental well-being, and the urban environment.

BACKGROUND: Urbanization and ageing have important implications for public mental health and well-being. Cities pose major challenges for older citizens, but also offer opportunities to develop, test, and implement policies, services, infrastructure, and interventions that promote mental well-being. The MINDMAP project aims to identify the opportunities and challenges posed by urban environmental characteristics for the promotion and management of mental well-being and cognitive function of older individuals. METHODS: MINDMAP aims to achieve its research objectives by bringing together longitudinal studies from 11 countries covering over 35 cities linked to databases of area-level environmental exposures and social and urban policy indicators. The infrastructure supporting integration of this data will allow multiple MINDMAP investigators to safely and remotely co-analyse individual-level and area-level data. Individual-level data is derived from baseline and follow-up measurements of ten participating cohort studies and provides information on mental well-being outcomes, sociodemographic variables, health behaviour characteristics, social factors, measures of frailty, physical function indicators, and chronic conditions, as well as blood derived clinical biochemistry-based biomarkers and genetic biomarkers. Area-level information on physical environment characteristics (e.g. green spaces, transportation), socioeconomic and sociodemographic characteristics (e.g. neighbourhood income, residential segregation, residential density), and social environment characteristics (e.g. social cohesion, criminality) and national and urban social policies is derived from publically available sources such as geoportals and administrative databases. The linkage, harmonization, and analysis of data from different sources are being carried out using piloted tools to optimize the validity of the research results and transparency of the methodology. DISCUSSION: MINDMAP is a novel research collaboration that is combining population-based cohort data with publicly available datasets not typically used for ageing and mental well-being research. Integration of various data sources and observational units into a single platform will help to explain the differences in ageing-related mental and cognitive disorders both within as well as between cities in Europe, the US, Canada, and Russia and to assess the causal pathways and interactions between the urban environment and the individual determinants of mental well-being and cognitive ageing in older adults.

Neighborhood Stigma and Sleep: Findings from a Pilot Study of Low-Income Housing Residents in New York City.

The primary objective of this study was to investigate the relationship between neighborhood stigma and sleep in a sample of low-income housing residents in New York City. Data were derived from the NYC Low-Income Housing, Neighborhoods, and Health Study (N = 120). Adults living in low-income housing completed a survey consisting of measures of neighborhood stigma, sleep quality, and sleep duration. Neighborhood stigma and sleep were self-reported. Associations between neighborhood stigma and sleep health were analyzed using generalized linear models with cluster variance estimation. Multivariable models adjusted for age, gender, race/ethnicity, income, education, employment status, obesity, the census block percentage of non-Hispanic black residents, and the census block percentage median household income. Results indicate that a reported negative media perception of the neighborhood was negatively associated with sleep quality and duration (p < 0.01). However, additional research is needed to explore neighborhood stigma as it relates to sleep.

The "Residential" Effect Fallacy in Neighborhood and Health Studies: Formal Definition, Empirical Identification, and Correction.

BACKGROUND: Because of confounding from the urban/rural and socioeconomic organizations of territories and resulting correlation between residential and nonresidential exposures, classically estimated residential neighborhood-outcome associations capture nonresidential environment effects, overestimating residential intervention effects. Our study diagnosed and corrected this "residential" effect fallacy bias applicable to a large fraction of neighborhood and health studies. METHODS: Our empirical application investigated the effect that hypothetical interventions raising the residential number of services would have on the probability that a trip is walked. Using global positioning systems tracking and mobility surveys over 7 days (227 participants and 7440 trips), we employed a multilevel linear probability model to estimate the trip-level association between residential number of services and walking to derive a naïve intervention effect estimate and a corrected model accounting for numbers of services at the residence, trip origin, and trip destination to determine a corrected intervention effect estimate (true effect conditional on assumptions). RESULTS: There was a strong correlation in service densities between the residential neighborhood and nonresidential places. From the naïve model, hypothetical interventions raising the residential number of services to 200, 500, and 1000 were associated with an increase by 0.020, 0.055, and 0.109 of the probability of walking in the intervention groups. Corrected estimates were of 0.007, 0.019, and 0.039. Thus, naïve estimates were overestimated by multiplicative factors of 3.0, 2.9, and 2.8. CONCLUSIONS: Commonly estimated residential intervention-outcome associations substantially overestimate true effects. Our somewhat paradoxical conclusion is that to estimate residential effects, investigators critically need information on nonresidential places visited.

An evaluation of transport mode shift policies on transport-related physical activity through simulations based on random forests.

BACKGROUND: Physical inactivity is widely recognized as one of the leading causes of mortality, and transport accounts for a large part of people's daily physical activity. This study develops a simulation approach to evaluate the impact of the Ile-de-France Urban Mobility Plan (2010-2020) on physical activity, under the hypothesis that the intended transport mode shifts are realized. METHODS: Based on the Global Transport Survey (2010, n = 21,332) and on the RECORD GPS Study (2012-2013, n = 229) from the French capital region of Paris (Ile-de-France), a simulation method was designed and tested. The simulation method used accelerometer data and random forest models to predict the impact of the transport mode shifts anticipated in the Mobility Plan on transport-related moderate-to-vigorous physical activity (T-MVPA). The transport mode shifts include less private motorized trips in favor of more public transport, walking, and biking trips. RESULTS: The simulation model indicated a mean predicted increase of 2 min per day of T-MVPA, in case the intended transport mode shifts in the Ile-de-France Urban Mobility Plan were realized. The positive effect of the transport mode shifts on T-MVPA would, however, be larger for people with a higher level of education. This heterogeneity in the positive effect would further increase the existing inequality in transport-related physical activity by educational level. CONCLUSIONS: The method presented in this paper showed a significant increase in transport-related physical activity in case the intended mode shifts in the Ile-de-France Urban Mobility Plan were realized. This simulation method could be applied on other important health outcomes, such as exposure to noise or air pollution, making it a useful tool to anticipate the health impact of transport interventions or policies.