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

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Abstract

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.

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Acknowledgements

The authors would like to acknowledge financial support from the National Institute of Child Health and Human Development (R01HD087460; K01HD067390; 5T32HD057822-07). The contents of the manuscript are the sole responsibility of the authors and do not necessarily reflect the official views of the funding agency.

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Author notes

    Authors and Affiliations

    1. Department of Epidemiology and Biostatistics, Dornsife School of Public Health, Urban Health Collaborative, Drexel University, 3600 Market St, Philadelphia, PA, 19104, USA

      Gina S. Lovasi

    2. Department of Epidemiology, School of Public Health, Harborview Injury Prevention and Research Center, University of Washington, Seattle, USA

      Stephen J. Mooney

    3. Department of Health Policy and Management, Columbia University Mailman School of Public Health, New York, USA

      Peter Muennig

    4. Division of Trauma, Emergency Surgery and Surgical Critical Care, New York University School of Medicine, New York, USA

      Charles DiMaggio

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    1. Gina S. Lovasi
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    2. Stephen J. Mooney
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    4. Charles DiMaggio
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    Correspondence to Gina S. Lovasi.

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    G. S. Lovasi, S. J. Mooney and P. Muennig are contributed equally to this work.

    Appendix: analytic issues regarding analyses of time and space

    Appendix: analytic issues regarding analyses of time and space

    Broadly, spatiotemporal data can be thought of as correlated observations of counts of events within fixed spatial and temporal units that evolve over time. The foundational Poisson model for spatial analyses assumes independent events over both place and time. Additional methodological considerations must be used to take possible correlations between place and time into account. There might, for example, be a clustering of events over both place and time. As an example, so-called Hawke’s processes can be used to predict events as disparate as the clustering of aftershocks following earthquakes [57] and terrorist attacks [58]. In the psychiatric literature, a similar approach was used to study possible clustering of psychiatric admissions to urban hospitals [59].

    As epidemiologists, we are most often interested in disease counts in small areas over some period of time marked by repeated observations. Methodological approaches to spatiotemporal data vary. Bayesian methods are frequently seen in this context and can be traced to work by Besag [60] which was extended by Bernardinelli [61] to include a linear term for space–time interaction, and by Knorr-Held [62] to include a non-parametric spatiotemporal time trend. These types of data have been used to used to characterize suicide risk by location and seasonal temperature [63] and the role of built environment on depressive symptoms [64]. Among the more intriguing applications of spatiotemporal modeling to psychiatric disorders is the characterization of resting-state neural activity in the brains of persons with schizophrenia [65]. An approach involving structural equation models was used to demonstrate a link between neighborhood and cognitive ability across generations [66].

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    Lovasi, G.S., Mooney, S.J., Muennig, P. et al. Cause and context: place-based approaches to investigate how environments affect mental health. Soc Psychiatry Psychiatr Epidemiol 51, 1571–1579 (2016). https://doi.org/10.1007/s00127-016-1300-x

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