Association between urban green space and postpartum depression, and the role of physical activity: a retrospective cohort study in Southern California

Green spaces exposure and the risk of common psychiatric disorders: A meta-analysis

Objective

To explore the effects of green spaces exposure on common psychiatric disorders.

Methods

PubMed, Embase, Web of Science and MEDLINE were screened and articles published prior to November 15, 2023 were included. Analyses were performed on common psychiatric disorders, categorized into depression, anxiety, dementia, schizophrenia, and attention deficit hyperactivity disorder (ADHD). And the subgroup analyses were conducted for depression, anxiety, dementia, and schizophrenia.

Results

In total, 2,0064 studies were retrieved, 59 of which were included in our study; 37 for depression, 14 for anxiety, 8 for dementia, 7 for schizophrenia and 5 for ADHD. Green spaces were found to benefit the moderation of psychiatric disorders (OR = 0.91, 95% CI: 0.89 to 0.92). Green spaces positively influence depression (OR = 0.89, 95% CI: 0.86 to 0.93), regardless of the cross-sectional or cohort studies. Green spaces can also help mitigate the risk of anxiety (OR = 0.94, 95%CI:0.92 to 0.96). As an important index for measuring green spaces, a higher normalized difference vegetation index (NDVI) level related to a lower level of depression (OR = 0.95, 95%CI:0.91 to 0.98) and anxiety (OR = 0.95, 95%:0.92 to 0.98). The protection was also found in dementia (OR = 0.95, 95% CI: 0.93 to 0.96), schizophrenia (OR = 0.74, 95% CI: 0.67 to 0.82), and ADHD (OR = 0.89, 95% CI: 0.86 to 0.92) results.

Conclusion

Green spaces decrease the risk of psychiatric disorders, including depression, anxiety, dementia, schizophrenia, and ADHD. Further studies on green spaces and psychiatric disorders are needed, and more green spaces should be considered in city planning.

"Urban-Satellite" estimates in the ABCD Study: Linking Neuroimaging and Mental Health to Satellite Imagery Measurements of Macro Environmental Factors

While numerous studies over the last decade have highlighted the important influence of environmental factors on mental health, globally applicable data on physical surroundings are still limited. Access to such data and the possibility to link them to epidemiological studies is critical to unlocking the relationship of environment, brain and behaviour and promoting positive future mental health outcomes. The Adolescent Brain Cognitive Development (ABCD) Study is the largest ongoing longitudinal and observational study exploring brain development and child health among children from 21 sites across the United States. Here we describe the linking of the ABCD study data with satellite-based "Urban-Satellite" (UrbanSat) variables consisting of 11 satellite-data derived environmental indicators associated with each subject's residential address at their baseline visit, including land cover and land use, nighttime lights, and population characteristics. We present these UrbanSat variables and provide a review of the current literature that links environmental indicators with mental health, as well as key aspects that must be considered when using satellite data for mental health research. We also highlight and discuss significant links of the satellite data variables to the default mode network clustering coefficient and cognition. This comprehensive dataset provides the foundation for large-scale environmental epidemiology research.

Mapping high-risk clusters and identifying place-based risk factors of mental health burden in pregnancy

Purpose

Despite affecting up to 20% of women and being the leading cause of preventable deaths during the perinatal and postpartum period, maternal mental health conditions are chronically understudied. This study is the first to identify spatial patterns in perinatal mental health conditions, and relate these patterns to place-based social and environmental factors that drive cluster development.

Methods

We performed spatial clustering analysis of emergency department (ED) visits for perinatal mood and anxiety disorders (PMAD), severe mental illness (SMI), and maternal mental disorders of pregnancy (MDP) using the Poisson model in SatScan from 2016 to 2019 in North Carolina. Logistic regression was used to examine the association between patient and community-level factors and high-risk clusters.

Results

The most significant spatial clustering for all three outcomes was concentrated in smaller urban areas in the western, central piedmont, and coastal plains regions of the state, with odds ratios greater than 3 for some cluster locations. Individual factors (e.g., age, race, ethnicity) and contextual factors (e.g., racial and socioeconomic segregation, urbanity) were associated with high risk clusters.

Conclusions

Results provide important contextual and spatial information concerning at-risk populations with a high burden of maternal mental health disorders and can better inform targeted locations for the expansion of maternal mental health services.

Advancing application of satellite remote sensing technologies for linking atmospheric and built environment to health

Background

The intricate interplay between human well-being and the surrounding environment underscores contemporary discourse. Within this paradigm, comprehensive environmental monitoring holds the key to unraveling the intricate connections linking population health to environmental exposures. The advent of satellite remote sensing monitoring (SRSM) has revolutionized traditional monitoring constraints, particularly limited spatial coverage and resolution. This innovation finds profound utility in quantifying land covers and air pollution data, casting new light on epidemiological and geographical investigations. This dynamic application reveals the intricate web connecting public health, environmental pollution, and the built environment.

Objective

This comprehensive review navigates the evolving trajectory of SRSM technology, casting light on its role in addressing environmental and geographic health issues. The discussion hones in on how SRSM has recently magnified our understanding of the relationship between air pollutant exposure and population health. Additionally, this discourse delves into public health challenges stemming from shifts in urban morphology.

Methods

Utilizing the strategic keywords "SRSM," "air pollutant health risk," and "built environment," an exhaustive search unfolded across prestigious databases including the China National Knowledge Network (CNKI), PubMed and Web of Science. The Citespace tool further unveiled interconnections among resultant articles and research trends.

Results

Synthesizing insights from a myriad of articles spanning 1988 to 2023, our findings unveil how SRMS bridges gaps in ground-based monitoring through continuous spatial observations, empowering global air quality surveillance. High-resolution SRSM advances data precision, capturing multiple built environment impact factors. Its application to epidemiological health exposure holds promise as a pioneering tool for contemporary health research.

Conclusion

This review underscores SRSM's pivotal role in enriching geographic health studies, particularly in atmospheric pollution domains. The study illuminates how SRSM overcomes spatial resolution and data loss hurdles, enriching environmental monitoring tools and datasets. The path forward envisions the integration of cutting-edge remote sensing technologies, novel explorations of urban-public health associations, and an enriched assessment of built environment characteristics on public well-being.

Analysis of Heat Exposure During Pregnancy and Severe Maternal Morbidity

Importance

The rate of severe maternal morbidity (SMM) is continuously increasing in the US. Evidence regarding the associations of climate-related exposure, such as environmental heat, with SMM is lacking.

Objective

To examine associations between long- and short-term maternal heat exposure and SMM.

Design, Setting, and Participants

This retrospective population-based epidemiological cohort study took place at a large integrated health care organization, Kaiser Permanente Southern California, between January 1, 2008, and December 31, 2018. Data were analyzed from February to April 2023. Singleton pregnancies with data on SMM diagnosis status were included.

Exposures

Moderate, high, and extreme heat days, defined as daily maximum temperatures exceeding the 75th, 90th, and 95th percentiles of the time series data from May through September 2007 to 2018 in Southern California, respectively. Long-term exposures were measured by the proportions of different heat days during pregnancy and by trimester. Short-term exposures were represented by binary variables of heatwaves with 9 different definitions (combining percentile thresholds with 3 durations; ie, ≥2, ≥3, and ≥4 consecutive days) during the last gestational week.

Main Outcomes and Measures

The primary outcome was SMM during delivery hospitalization, measured by 20 subconditions excluding blood transfusion. Discrete-time logistic regression was used to estimate associations with long- and short-term heat exposure. Effect modification by maternal characteristics and green space exposure was examined using interaction terms.

Results

There were 3446 SMM cases (0.9%) among 403 602 pregnancies (mean [SD] age, 30.3 [5.7] years). Significant associations were observed with long-term heat exposure during pregnancy and during the third trimester. High exposure (≥80th percentile of the proportions) to extreme heat days during pregnancy and during the third trimester were associated with a 27% (95% CI, 17%-37%; P < .001) and 28% (95% CI, 17%-41%; P < .001) increase in risk of SMM, respectively. Elevated SMM risks were significantly associated with short-term heatwave exposure under all heatwave definitions. The magnitude of associations generally increased from the least severe (HWD1: daily maximum temperature >75th percentile lasting for ≥2 days; odds ratio [OR], 1.32; 95% CI, 1.17-1.48; P < .001) to the most severe heatwave exposure (HWD9: daily maximum temperature >95th percentile lasting for ≥4 days; OR, 2.39; 95% CI, 1.62-3.54; P < .001). Greater associations were observed among mothers with lower educational attainment (OR for high exposure to extreme heat days during pregnancy, 1.43; 95% CI, 1.26-1.63; P < .001) or whose pregnancies started in the cold season (November through April; OR, 1.37; 95% CI, 1.24-1.53; P < .001).

Conclusions and Relevance

In this retrospective cohort study, long- and short-term heat exposure during pregnancy was associated with higher risk of SMM. These results might have important implications for SMM prevention, particularly in a changing climate.