Cause and context: place-based approaches to investigate how environments affect mental health

Objectives Our surroundings affect our mood, our recovery from stress, our behavior, and, ultimately, our mental health. Understanding how our surroundings influence mental health is central to creating healthy cities. However, the traditional observational methods now dominant in the psychiatric epidemiology literature are not sufficient to advance such an understanding. In this essay we consider potential alternative strategies, such as randomizing people to places, randomizing places to change, or harnessing natural experiments that mimic randomized experiments. Methods We discuss the strengths and weaknesses of these methodological approaches with respect to (1) defining the most relevant scale and characteristics of context, (2) disentangling the effects of context from the effects of individuals’ preferences and prior health, and (3) generalizing causal effects beyond the study setting. Results Promising alternative strategies include creating many small-scale randomized place-based trials, using the deployment of place-based changes over time as natural experiments, and using fluctuations in the changes in our surroundings in combination with emerging data collection technologies to better understand how surroundings influence mood, behavior, and mental health. Conclusions Improving existing research strategies will require interdisciplinary partnerships between those specialized in mental health, those advancing new methods for place effects on health, and those who seek to optimize the design of local environments

Residential Relocation by Older Adults in Response to Incident Cardiovascular Health Events: A Case-Crossover Analysis

We use a case-crossover analysis to explore the association between incident cardiovascular events and residential relocation to a new home address. Methods. We conducted an ambidirectional case-crossover analysis to explore the association between incident cardiovascular events and residential relocation to a new address using data from the Cardiovascular Health Study (CHS), a community-based prospective cohort study of 5,888 older adults from four U.S. sites beginning in 1989. Relocation was assessed twice a year during follow-up. Event occurrences were classified as present or absent for the period preceding the first reported move, as compared with an equal length of time immediately prior to and following this period. Results. Older adults (65+) that experience incident cardiovascular disease had an increased probability of reporting a change of residence during the following year (OR 1.6, 95% confidence interval (CI) = 1.2–2.1). Clinical conditions associated with relocation included stroke (OR: 2.0, 95% CI: 1.2–3.3), angina (OR: 1.6, 95% CI: 1.0–2.6), and congestive heart failure (OR: 1.5, 95% CI: 1.0–2.1). Conclusions. Major incident cardiovascular disease may increase the probability of residential relocation in older adults. Case-crossover analyses represent an opportunity to investigate triggering events, but finer temporal resolution would be crucial for future research on residential relocations

Building a Methodological Foundation for Impactful Urban Planetary Health Science

Anthropogenic environmental change will heavily impact cities, yet associated health risks will depend significantly on decisions made by urban leaders across a wide range of non-health sectors, including transport, energy, housing, basic urban services, and others. A subset of planetary health researchers focus on understanding the urban health impacts of global environmental change, and how these vary globally and within cities. Such researchers increasingly adopt collaborative transdisciplinary approaches to engage policy-makers, private citizens, and other actors in identifying and evaluating potential policy solutions that will reduce environmental impacts in ways that simultaneously promote health, equity, and/or local economies-in other words, maximising 'co-benefits'. This report presents observations from a participatory workshop focused on challenges and opportunities for urban planetary health research. The workshop, held at the 16th International Conference on Urban Health (ICUH) in Xiamen, China, in November 2019, brought together 49 participants and covered topics related to collaboration, data, and research impact. It featured research projects funded by the Wellcome Trust's Our Planet Our Health (OPOH) programme. This report aims to concisely summarise and disseminate participants' collective contributions to current methodological practice in urban planetary health research

Neighbourhood, Route and Workplace-Related Environmental Characteristics Predict Adults' Mode of Travel to Work

Commuting provides opportunities for regular physical activity which can reduce the risk of chronic disease. Commuters' mode of travel may be shaped by their environment, but understanding of which specific environmental characteristics are most important and might form targets for intervention is limited. This study investigated associations between mode choice and a range of objectively assessed environmental characteristics.Participants in the Commuting and Health in Cambridge study reported where they lived and worked, their usual mode of travel to work and a variety of socio-demographic characteristics. Using geographic information system (GIS) software, 30 exposure variables were produced capturing characteristics of areas around participants' homes and workplaces and their shortest modelled routes to work. Associations between usual mode of travel to work and personal and environmental characteristics were investigated using multinomial logistic regression.Of the 1124 respondents, 50% reported cycling or walking as their usual mode of travel to work. In adjusted analyses, home-work distance was strongly associated with mode choice, particularly for walking. Lower odds of walking or cycling rather than driving were associated with a less frequent bus service (highest versus lowest tertile: walking OR 0.61 [95% CI 0.20–1.85]; cycling OR 0.43 [95% CI 0.23–0.83]), low street connectivity (OR 0.22, [0.07–0.67]; OR 0.48 [0.26–0.90]) and free car parking at work (OR 0.24 [0.10–0.59]; OR 0.55 [0.32–0.95]). Participants were less likely to cycle if they had access to fewer destinations (leisure facilities, shops and schools) close to work (OR 0.36 [0.21–0.62]) and a railway station further from home (OR 0.53 [0.30–0.93]). Covariates strongly predicted travel mode (pseudo r-squared 0.74).Potentially modifiable environmental characteristics, including workplace car parking, street connectivity and access to public transport, are associated with travel mode choice, and could be addressed as part of transport policy and infrastructural interventions to promote active commuting

Independent Review Of Social And Population Variation In Mental Health Could Improve Diagnosis In DSM Revisions

At stake in the May 2013 publication of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), are billions of dollars in insurance payments and government resources, as well as the diagnoses and treatment of millions of patients. We argue that the most recent revision process has missed social determinants of mental health disorders and their diagnosis: environmental factors triggering biological responses that manifest themselves in behavior; differing cultural perceptions about what is normal and what is abnormal behavior; and institutional pressures related to such matters as insurance reimbursements, disability benefits, and pharmaceutical marketing. In addition, the experts charged with revising the DSM lack a systematic. way to take population-level variations in diagnoses into account. To address these problems, we propose the creation of an independent research review body that would monitor variations in diagnostic patterns, inform future DSM revisions, identify needed changes in mental health policy and practice, and recommend new avenues of research. Drawing on the best available knowledge, the review body would make possible more precise and equitable psychiatric diagnoses and interventions

Physical activity levels, ownership of goods promoting sedentary behaviour and risk of myocardial infarction: results of the INTERHEART study

Aims: To evaluate the association between occupational and leisure-time physical activity (PA), ownership of goods promoting sedentary behaviour, and the risk of myocardial infarction (MI) in different socio-economic populations of the world. Studies in developed countries have found low PA as a risk factor for cardiovascular disease, however, the protective effect of occupational PA is less certain. Moreover, ownership of goods promoting sedentary behaviour may be associated with an increased risk. Methods: In INTERHEART, a casecontrol study of 10 043 cases of first MI and 14 217 controls who did not report previous angina or physical disability completed a questionnaire on work and leisure-time PA. Results: Subjects whose occupation involved either light [multivariable-adjusted odds ratio (OR) 0.78, confidence interval (CI) 0.710.86] or moderate (OR 0.89, CI 0.800.99) PA were at a lower risk of MI, whereas those who did heavy physical labour were not (OR 1.02, CI 0.881.19), compared with sedentary subjects. Mild exercise (OR 0.87, CI 0.810.93) as well as moderate or strenuous exercise (OR 0.76, CI 0.690.82) was protective. The effect of PA was observed across countries with low, middle, and high income. Subjects who owned both a car and a television (TV) (multivariable-adjusted OR 1.27, CI 1.051.54) were at higher risk of MI compared with those who owned neither. Conclusion: Leisure-time PA and mild-to-moderate occupational PA, but not heavy physical labour, were associated with a reduced risk, while ownership of a car and TV was associated with an increased risk of MI across all economic regions

Disparities in trajectories of changes in the unhealthy food environment in New York City: A latent class growth analysis, 1990-2010.

Disparities in availability of food retailers in the residential environment may help explain racial/ethnic and socio-economic differences in obesity risk. Research is needed that describes whether food environment dynamics may contribute to equalizing conditions across neighborhoods or to amplifying existing inequalities over time. This study improves the understanding of how the BMI-unhealthy food environment has evolved over time in New York City. We use longitudinal census tract-level data from the National Establishment Time-Series (NETS) for New York City in the period 1990-2010 and implement latent class growth analysis (LCGA) to (1) examine trajectories of change in the number of unhealthy food outlets (characterized as selling calorie-dense foods such as pizza and pastries) at the census tract-level, and (2) examine how trajectories are related to socio-demographic characteristics of the census tract. Overall, the number of BMI-unhealthy food outlets increased between 1990 and 2010. We summarized trajectories of evolutions with a 5-class model that indicates a pattern of fanning out, such that census tracts with a higher initial number of BMI-unhealthy food outlets in 1990 experienced a more rapid increase over time. Finally, fully adjusted logistic regression models reveal a greater increase in BMI-unhealthy food outlets in census tracts with: higher baseline population size, lower baseline income, and lower proportion of Black residents. Greater BMI-unhealthy food outlet increases were also noted in the context of census tracts change suggestive of urbanization (increasing population density) or increasing purchasing power (increasing income)

Validation of Walk Score® for Estimating Neighborhood Walkability: An Analysis of Four US Metropolitan Areas

Neighborhood walkability can influence physical activity. We evaluated the validity of Walk Score® for assessing neighborhood walkability based on GIS (objective) indicators of neighborhood walkability with addresses from four US metropolitan areas with several street network buffer distances (i.e., 400-, 800-, and 1,600-meters). Address data come from the YMCA-Harvard After School Food and Fitness Project, an obesity prevention intervention involving children aged 5–11 years and their families participating in YMCA-administered, after-school programs located in four geographically diverse metropolitan areas in the US (n = 733). GIS data were used to measure multiple objective indicators of neighborhood walkability. Walk Scores were also obtained for the participant’s residential addresses. Spearman correlations between Walk Scores and the GIS neighborhood walkability indicators were calculated as well as Spearman correlations accounting for spatial autocorrelation. There were many significant moderate correlations between Walk Scores and the GIS neighborhood walkability indicators such as density of retail destinations and intersection density (p < 0.05). The magnitude varied by the GIS indicator of neighborhood walkability. Correlations generally became stronger with a larger spatial scale, and there were some geographic differences. Walk Score® is free and publicly available for public health researchers and practitioners. Results from our study suggest that Walk Score® is a valid measure of estimating certain aspects of neighborhood walkability, particularly at the 1600-meter buffer. As such, our study confirms and extends the generalizability of previous findings demonstrating that Walk Score is a valid measure of estimating neighborhood walkability in multiple geographic locations and at multiple spatial scales

Neighborhood built environment and physical activity of Japanese older adults: results from the Aichi Gerontological Evaluation Study (AGES)

<p>Abstract</p> <p>Background</p> <p>Although many studies have reported the association between neighborhood built environment (BE) and physical activity (PA), less is known about the associations for older populations or in countries besides the US and Australia. The aim of this paper is to examine the associations for older adult populations in Japan.</p> <p>Methods</p> <p>Our analyses were based on cross-sectional data from the Aichi Gerontological Evaluation Study (AGES), conducted in 2003. The respondents were older adults, aged 65 years or over (n = 9,414), from 8 municipalities across urban, suburban, and rural areas. The frequency of leisure time sports activity and total walking time were used as the outcome variables. Using geographic information systems (GIS), we measured residential density, street connectivity, number of local destinations, access to recreational spaces, and land slope of the respondents' neighborhoods, based on network distances with multiple radii (250 m, 500 m, 1,000 m). An ordinal logistic regression model was used to analyze the association between PA and BE measures.</p> <p>Results</p> <p>Population density and presence of parks or green spaces had positive associations with the frequency of sports activity, regardless of the selected buffer zone. The analysis of total walking time, however, showed only a few associations.</p> <p>Conclusions</p> <p>Our findings provide mixed support for the association between PA and the characteristics of BE measures, previously used in Western settings. Some characteristics of the neighborhood built environment may facilitate leisure time sports activity, but not increase the total walking time for Japanese older adults.</p

Identifying Unique Neighborhood Characteristics to Guide Health Planning for Stroke and Heart Attack: Fuzzy Cluster and Discriminant Analyses Approaches

Socioeconomic, demographic, and geographic factors are known determinants of stroke and myocardial infarction (MI) risk. Clustering of these factors in neighborhoods needs to be taken into consideration during planning, prioritization and implementation of health programs intended to reduce disparities. Given the complex and multidimensional nature of these factors, multivariate methods are needed to identify neighborhood clusters of these determinants so as to better understand the unique neighborhood profiles. This information is critical for evidence-based health planning and service provision. Therefore, this study used a robust multivariate approach to classify neighborhoods and identify their socio-demographic characteristics so as to provide information for evidence-based neighborhood health planning for stroke and MI.The study was performed in East Tennessee Appalachia, an area with one of the highest stroke and MI risks in USA. Robust principal component analysis was performed on neighborhood (census tract) socioeconomic and demographic characteristics, obtained from the US Census, to reduce the dimensionality and influence of outliers in the data. Fuzzy cluster analysis was used to classify neighborhoods into Peer Neighborhoods (PNs) based on their socioeconomic and demographic characteristics. Nearest neighbor discriminant analysis and decision trees were used to validate PNs and determine the characteristics important for discrimination. Stroke and MI mortality risks were compared across PNs. Four distinct PNs were identified and their unique characteristics and potential health needs described. The highest risk of stroke and MI mortality tended to occur in less affluent PNs located in urban areas, while the suburban most affluent PNs had the lowest risk.Implementation of this multivariate strategy provides health planners useful information to better understand and effectively plan for the unique neighborhood health needs and is important in guiding resource allocation, service provision, and policy decisions to address neighborhood health disparities and improve population health