Cognitive impairment contributes to functional impairment in schizophrenia. Yet, little is known about how environmental characteristics are related to cognition in schizophrenia. By examining how cognition and the environment are intertwined, it may be possible to identify modifiable risk and protective factors that can improve cognitive outcomes in schizophrenia. We aimed to identify multivariate associations between cognition and three geospatial characteristics (built-space density, habitable green spaces, and public spaces for social interaction) within one's immediate neighborhood among individuals with schizophrenia. We recruited participants with schizophrenia from three sites - an urban metropolitan and two towns in southern India. We administered standard cognitive assessments and performed a principal axis factoring to identify episodic memory, cognitive control, and social inference-making factors for use in further analyses. We estimated geospatial characteristics of an individual's neighborhood, i.e., up to 1 km around the residence, by sourcing data from Google Earth. We performed unconditional and conditional (to examine the effect of clinical covariates) canonical correlation analyses to understand the multivariate relationship between cognition and geospatial characteristics. We analyzed data from 208 participants; the first canonical cognitive variate (higher social inference-making and poorer cognitive control) shared 24% of the variance (r = 0.49; P < 0.001) with the first geospatial variate (lower built density and poorer access to public spaces). Years of education, age at onset, and place of residence significantly modulated this relationship. We observe differential associations of the built environment with social and non-social cognition in schizophrenia, and highlight the clinical and demographic characteristics that shape these associations.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.ajp.2023.103593 | DOI Listing |
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 PDFConserv Physiol
January 2025
Geospatial Ecology of Marine Megafauna Lab, Marine Mammal Institute, Department of Fisheries, Wildlife and Conservation Sciences, Oregon State University, 2030 SE Marine Science Dr, Newport, OR 97365, USA.
Understanding wildlife reproductive seasonality is crucial for effective management and long-term monitoring of species. This study investigates the seasonal variability of testosterone in male Pacific Coast Feeding Group (PCFG) gray whales, using an eight-year dataset (2016-2023) of individual sightings, drone-based photogrammetry and endocrine analysis of faecal samples. We analyzed the relationship between faecal testosterone levels and total body length (TL), body condition (body area index, BAI), sexual maturity and day of the year using generalized additive mixed models.
View Article and Find Full Text PDFBMJ Open
December 2024
Division of Research, Kaiser Permanente, Pleasanton, California, USA.
Objectives: The US Preventive Services Task Force recommends screening of adults aged 35-70 with a body mass index ≥25 kg/m for type 2 diabetes and referral of individuals who screen positive for pre-diabetes to evidence-based prevention strategies. The diabetes burden in the USA is predicted to triple by 2060 necessitating strategic diabetes prevention efforts, particularly in areas of highest need. This study aimed to identify pre-diabetes hotspots using geospatial mapping to inform targeted diabetes prevention strategies.
View Article and Find Full Text PDFRisk Manag Healthc Policy
January 2025
Achutha Menon Centre for Health Science Studies, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala, India.
Purpose: Waste mismanagement is a growing concern in developing countries where unsustainable practices such as open dumping and open burning are rampant. This study examined the risk perceptions of the residents living in proximity to the Brahmapuram dump yard, situated in Ernakulam district of Kerala State, India- A site marked by persistent local protests, public outrage, and legal disputes arising from issues related to waste mismanagement. The study focused on the geospatial and sociodemographic factors that might influence these perceptions.
View Article and Find Full Text PDFBMC Cancer
January 2025
Department of Community & Family Medicine, All India Institute of Medical Sciences, 151001, Bathinda, Punjab, India.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!