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Dynamic Urban Environmental Exposures on Depression and Suicide

Periodic Reporting for period 2 - NEEDS (Dynamic Urban Environmental Exposures on Depression and Suicide)

Reporting period: 2018-10-01 to 2020-03-31

Recent research found that mental health including depression and suicide mortality are affected by environmental exposures. The mechanisms underpinning these environment-mental health relations are poorly understood and not universally confirmed across studies.

State-of-the-art research assumed that the neighborhood within which people live are the sole health influencing environmental context. The NEEDS project calls this restricting assumption into question by arguing that our contemporary society is increasingly mobile and people are exposed to multiple kinds of environmental exposures during their daily lives (e.g. at their work place, during travel), and also over the course of their lives. Such dynamic environments can, for example, trigger, reduce, or amplify the risk of suffering from a mental disorder. With the aim to disentangle the complex relationships between dynamic environments, depression, and suicide, the NEEDS project will address this important knowledge gap.

The NEEDS project is the first to advance our conceptual and theoretical understanding of how dynamic exposures affect people’s depression and suicide mortality. This aim will be achieved by combining a number of methodological innovations such as tracking people’s mobility with GPS-enabled smartphones, population-wide register studies, and modelling from statistics and machine learning. Since depression and suicide are increasingly prevalent, knowledge about dynamic exposures will be key not only to revealing depression and suicide aetiologies, but also for informing the design of health-promoting interventions and the formulation of policy, resulting in a healthier urban living.
Within the first 30 months of the project, NEEDS took the following actions:
- We obtained all required legal allowances for carrying out the project including an ethical review, data management plan, privacy impact assessment etc. In parallel, the literature on environmental exposures, suicide and depression was reviewed which led to a systematic review paper and meta-analysis.
- In work package 1 we developed environmental indicators from multiple sources describing the natural environment (e.g. green space), the built environment (e.g. street design), and the social environment (e.g. social deprivation). The indicators were derived on an address level for different buffer sizes (i.e. 300, 600, 1000m). Smaller buffers aim to describe the immediate environment while larger ones reflect the wider spatial environmental context.
- We developed a questionnaire and a two-stage sampling design to collect primary data on people’s mental health, their environmental perceptions, demographics, socio-economics etc. (work package 2). The online survey was carried out together with Statistics Netherlands from September to November 2018. People were invited via a letter to fill in an online questionnaire through computer-assisted web interviewing. The survey was distributed to a representative sample of 45,000 people aged 18-64 years across the Netherlands. About 11,500 persons completed the survey, representing an overall response rate of approximately 26%. To reach such a response rate, two reminders were sent and iPads were raffled as incentives (i.e. one iPad per 2,000 respondents).
- We collected data on people’s daily mobility and social environment by means of a sensor-based smartphone app. Only people who did the survey were re-invited to participate in the smartphone-based data collection. The app was developed for Android and was made available via the Google Play store after extensive testing. The app data were transferred to a secured server following the highest technical standards at Utrecht University. To increase participation, we raffled vouchers as a gesture of our appreciation. In total, 821 participants downloaded the app, and 401 completed a full week of data collection with all sensors activated.
- After a series of plausibility checks and data quality assessments, the survey data was enriched with the environmental exposures to complement people’s environmental perceptions with objective environmental measures. Further, we applied trip detection algorithms to enrich the GPS data and to derived mode of transport used during travel.
- In work package 3, we prepared and linked Dutch register data. Based on the Tenth Edition of the International Classification of Diseases and Related Health Problems (ICD-10), suicide cases were identified from the cause of death register. We considered suicide cases aged 18–64 years from 1 January 2007 until 31 December 2016. Through incidence-density sampling, a random sample of 10 controls were selected from the population at risk by matching on year of birth, sex and calendar time. Suicide cases and controls were then linked to social deprivation etc. (work package 1).
- The longitudinal case-control data on suicide were used to develop statistical models assessing how the social environment represented through area-level social fragmentation and deprivation indices affect suicide risk.
The first project period of 30 months was dedicated to data collection, pre-processing, analytical tasks, and writing research manuscripts. In the remaining 30 months, we plan to continue to utilize the data collected thus far to produce high impact publications that contribute to our understanding of the complex relationships between dynamic environments, depression, and suicide.

Within work package 2 and work package 3 the central focus of the next months is the completion of manuscripts currently underway, plus analysis of the sensor data and linkage with objective, geographic data. The sensor data offers us numerous opportunities to explore the relationship between exposure to the built, natural and social environment in daily mobility and depression. At present, the GPS data has been cleaned, with each trip identified and classified by mode of transport. By applying buffers to these trips and examining the presence of surrounding built and natural features, we can gain an understanding of the multiple environments a person is exposed to and the link with depression. The processing and analysis of this data represents the next key priority of the work package. As with the publications associated with this project up until this point, we aim to use innovative techniques such as the application of weighting that accounts for temporal dimensions of exposure in this process (work package 2 and 3) Furthermore, we have a detailed overview of the associated social environment, comprised of social density data derived from Bluetooth, phone and message metadata, and social media use data. The analysis of this data runs concurrently with data derived from GPS tracking. Altogether, we plan to produce multiple outputs in the remaining months that examine the effects of these environments, individual and combined, on the risk and severity of depression. In WP3 we plan to develop new spatiotemporal exposure assessment methods which will be tested on suicide. In addition, there remains interest in exploring the survey data. In the next years, a selection of manuscripts concerning the relationship between noise and mental health are planned, as part of a PhD project. In line with the previous 30 months, we expect to disseminate our findings in the academic and public spheres through academic conferences and media engagement.