Background: While greenness has been associated with lower depression, the generalizability of this association in arid landscapes remains undetermined. We assessed the association between depression and residential greenness, but also brownness and grayness among nursing students living in El Paso, Texas (the Chihuahuan desert).
Methods: Depression was measured with the Patient Health Questionnaire-9 scale and greenness with the normalized difference vegetation index across three buffer sizes (i.e., 250, 500, and 1000 m). Using data from the National Land Cover Database, two additional measures of land patterns were analyzed: grayness and brownness. Structural equation models were used to assess the relationships of these land patterns to depression and quantify the indirect effects of peer alienation.
Results: After adjusting for individual characteristics, at buffers 250 m, greenness was not associated with a decrease in the Incidence Rate Ratios (IRR) of depression (IRR, 0.51; 95% CI, 0.12-2.10); however, grayness and brownness were respectively associated with increases by 64% (IRR, 1.64; 95% CI, 1.07-2.52) and decreases by 35% (IRR, 0.65; 95% CI, 0.42-0.99). At buffer 250 m, peer alienation explained 17.43% (95% CI, -1.79-36.66) of the association between depression and brownness, suggesting a pathway to depression.
Conclusions: We did not observe an association between depression and residential greenness in El Paso, Texas. However, we did observe a protective association between brownness and depression and an adverse association with grayness. These results have theoretical implications as they were based on commonly used frameworks in this literature, and adverse association of brownness (and the lack of greenness) and depression was expected.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7662514 | PMC |
| http://dx.doi.org/10.3390/ijerph17218146 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Insights into Landscape Structure Change in Urbanising Rainforest and Guinea Savanna Ecological Regions of Nigeria.
Environ Manage
January 2025
Department of Geoecology, Institute of Geosciences and Geography, Martin Luther University, Halle-Wittenberg, Halle (Saale), Germany.
In the face of unabated urban expansion, understanding the intrinsic characteristics of landscape structure is pertinent to preserving ecological diversity and managing the supply of ecosystem services. This study integrates machine-learning-based geospatial and landscape ecological techniques to assess the dynamics of landscape structure in cities of the rainforest (Akure and Owerri) and Guinea savanna (Makurdi and Minna) ecological regions of Nigeria between 1986 and 2022. Supervised classification using the random forest (RF) machine-learning classifier was performed on Landsat images on the Google Earth Engine (GEE) platform, and landscape metrics were calculated with FRAGSTATS to assess landscape composition, configuration, and connectivity.
View Article and Find Full Text PDFDo Land Use Plans Affirmatively Further Fair Housing?: Measuring Progress.
J Am Plann Assoc
July 2023
Berkeley, California.
Problem Research Strategy And Findings: The 1968 Fair Housing Act required local government recipients of federal money to take meaningful actions to affirmatively further fair housing (AFFH). Current fair housing analysis requirements are copious but do not request an assessment of how land use policies affect the potential for neighborhood integration. A recent California law requires local governments to include AFFH analysis in existing planning processes, and state guidelines encourage the measurement of the spatial distribution of planned sites for low-income housing with respect to opportunity.
View Article and Find Full Text PDFFrom anecdotes to evidence: Environmental DNA detection of Arctic charr (Salvelinus alpinus L.) at the southern limit of its circumpolar range.
J Fish Biol
January 2025
School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK.
The urgency of rapid species monitoring is at an all-time high due to the increasing threat of climate change to global ecosystems, in particular freshwater habitats. Fish such as Arctic charr, Salvelinus alpinus, are particularly vulnerable to increasing water temperatures and changes in land use due to their dependence on cold waters and confinement to lacustrine environments. Nonetheless, current monitoring practices, relying on physical capture of organisms, are hindered by resource constraints, desire to manage habitats for recreational fishing, and restricted access to sites.
View Article and Find Full Text PDF[Prediction of potential geographic distribution of in Yunnan Province using random forest and maximum entropy models].
Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi
December 2024
Yunnan Institute of Endemic Diseases Control and Prevention, Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Dali, Yunnan 671000, China.
Objective: To predict the potential geographic distribution of in Yunnan Province using random forest (RF) and maximum entropy (MaxEnt) models, so as to provide insights into surveillance and control in Yunnan Province.
Methods: The snail survey data in Yunnan Province from 2015 to 2016 were collected and converted into snail distribution site data. Data of 22 environmental variables in Yunnan Province were collected, including twelve climate variables (annual potential evapotranspiration, annual mean ground surface temperature, annual precipitation, annual mean air pressure, annual mean relative humidity, annual sunshine duration, annual mean air temperature, annual mean wind speed, ≥ 0 ℃ annual accumulated temperature, ≥ 10 ℃ annual accumulated temperature, aridity and index of moisture), eight geographical variables (normalized difference vegetation index, landform type, land use type, altitude, soil type, soil textureclay content, soil texture-sand content and soil texture-silt content) and two population and economic variables (gross domestic product and population).
Interactive and unimodal relationships between plant biomass, abiotic factors, and plant diversity in global grasslands.
Commun Biol
January 2025
Dept of Ecology, Evolution, and Behavior, University of Minnesota, St Paul, USA.
Grasslands cover approximately a third of the Earth's land surface and account for about a third of terrestrial carbon storage. Yet, we lack strong predictive models of grassland plant biomass, the primary source of carbon in grasslands. This lack of predictive ability may arise from the assumption of linear relationships between plant biomass and the environment and an underestimation of interactions of environmental variables.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!