Nighttime lights, urban features, household poverty, depression, and obesity

Yi-An Liao; Liliana Garcia-Mondragon; Deniz Konac; Xiaoxuan Liu; Alex Ing; Ran Goldblatt; Le Yu; Edward D Barker

doi:10.1007/s12144-022-02754-3

Nighttime lights, urban features, household poverty, depression, and obesity

Curr Psychol. 2022 Feb 16:1-12. doi: 10.1007/s12144-022-02754-3. Online ahead of print.

Authors

Yi-An Liao^{1

2

3}, Liliana Garcia-Mondragon^{1

2

3}, Deniz Konac^{4

5}, Xiaoxuan Liu^{6

7}, Alex Ing⁸, Ran Goldblatt⁹, Le Yu^{6

10}, Edward D Barker⁴

Affiliations

¹ Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF UK.
² International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, 80804 Germany.
³ Max Planck Institute of Psychiatry, Munich, 80804 Germany.
⁴ Department of Psychology, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AF UK.
⁵ Department of Psychology, Adana Alparslan Turkes Science and Technology University, Adana, Turkey.
⁶ Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, 100084 People's Republic of China.
⁷ Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190 People's Republic of China.
⁸ European Molecular Biology Laboratory, Meyerhofstraße 1, Heidelberg, 69117 Germany.
⁹ New Light Technologies Inc., Washington, DC 20005 USA.
¹⁰ Ministry of Education Ecological Field Station for East Asian Migratory Birds, Beijing, 100084 People's Republic of China.

Abstract

Nighttime Light Emission (NLE) is associated with diminished mental and physical health. The present study examines how NLE and associated urban features (e.g., air pollution, low green space) impact mental and physical wellbeing. We included 200,393 UK Biobank Cohort participants with complete data. The study was carried out in two steps. In Step1, we assessed the relationship between NLE, deprivation, pollution, green space, household poverty and mental and physical symptoms. In Step2, we examined the role of NLE on environment-symptom networks. We stratified participants into high and low NLE and used gaussian graphical model to identify nodes which bridged urban features and mental and physical health problems. We then compared the global strength of these networks in high vs low NLE. We found that higher NLE associated with higher air pollution, less green space, higher economic and neighborhood deprivation, higher household poverty and higher depressed mood, higher tiredness/lethargy and obesity (R_{training_mean} = 0.2624, P _{training_mean} < .001; R_{test_mean} = 0.2619, P _{test_mean} < .001). We also found that the interaction between environmental risk factors and mental, physical problems (overall network connectivity) was higher in the high NLE network than in the low NLE network (t = 0.7896, P < .001). In areas with high NLE, economic deprivation, household poverty and waist circumference acted as bridge factors between the key urban features and mental health symptoms. In conclusion, NLE, urban features, household poverty and mental and physical symptoms are all interrelated. In areas with high NLE, urban features associate with mental and physical health problems at a greater magnitude than in areas with low NLE.

Supplementary information: The online version contains supplementary material available at 10.1007/s12144-022-02754-3.

Keywords: Depression; Network analysis; Nighttime Light Emission; Obesity; Urban features.