The Relationship between Perceptions and Objective Measures of Greenness.

Exposure to greenness has been studied through objective measures of remote visualization of greenspace; however, the link to how individuals interpret spaces as green is missing. We examined the associations between three objective greenspace measures with perceptions of greenness. We used a subsample (n = 175; 2018-2019) from an environmental cardiovascular risk cohort to investigate perceptions of residential greenness. Participants completed a 17-item survey electronically. Objective measurements of greenness within 300 m buffer around participants home included normalized difference vegetation index (NDVI), tree canopy and leaf area index. Principal component analysis reduced the perceived greenspaces to three dimensions reflecting natural vegetation, tree cover and built greenspace such as parks. Our results suggest significant positive associations between NDVI, tree canopy and leaf area and perceived greenness reflecting playgrounds; also, associations between tree canopy and perceived greenness reflecting tree cover. These findings indicate that the most used objective greenness measure, NDVI, as well as tree canopy and leaf area may most align with perceptions of parks, whereas tree canopy alone captures individuals' perceptions of tree cover. This highlights the need for research to understand the complexity of green metrics and careful interpretation of data based on the use of subjective or objective measures of greenness.

The Detection of Periodic Reemergence Events of SARS-CoV-2 Delta Strain in Communities Dominated by Omicron.

Despite entering an endemic phase, SARS-CoV-2 remains a significant burden to public health across the global community. Wastewater sampling has consistently proven utility to understanding SARS-CoV-2 prevalence trends and genetic variation as it represents a less biased assessment of the corresponding communities. Here, we report that ongoing monitoring of SARS-CoV-2 genetic variation in samples obtained from the wastewatersheds of the city of Louisville in Jefferson county Kentucky has revealed the periodic reemergence of the Delta strain in the presence of the presumed dominant Omicron strain. Unlike previous SARS-CoV-2 waves/emergence events, the Delta reemergence events were geographically restricted in the community and failed to spread into other areas as determined by wastewater analyses. Moreover, the reemergence of the Delta strain did not correlate with vaccination rates as communities with lower relative vaccination have been, to date, not affected. Importantly, Delta reemergence events correlate with increased public health burdens, as indicated by increased daily case rates and mortality relative to non-Delta wastewatershed communities. While the underlying reasons for the reemergence of the Delta variant remain unclear, these data reaffirm the ongoing importance of wastewater genomic analyses towards understanding SARS-CoV-2 as it enters the endemic phase.

Mortality risk associated with greenness, air pollution, and physical activity in a representative U.S. cohort.

Several cohort studies suggest greenness is associated with decreased mortality risk. Potential confounding by or interactions between physical activity and air pollution remains unclear. This study evaluates associations of greenness, air pollution, and physical activity with mortality risk and investigates confounding and effect modification across these key risk factors. National Health Interview Survey (NHIS) data covering 1997-2014 were linked to the National Death Index to generate a cohort of 403,748 individuals with 39,528 deaths. Greenness, represented by census-tract Normalized Difference Vegetation Index (NDVI) for the seasonal period of May-October, was averaged over the years 2003-2016. Air pollution was estimated by census-tract level PM2.5 concentrations from 1999 to 2015. Cox Proportional Hazard Models were used to estimate hazard ratios (HR) for differences in greenness, air pollution, and physical activity. Alternative models that evaluated potential confounding and stratified models that evaluated effect modification were examined. Mortality risks were associated with PM2.5 (HR = 1.14, 95% CI: 1.09-1.19 per 10 µg/m3) and physical inactivity (1.49, 1.44-1.54 relative to sufficiently active), but not with greenness (1.01, 0.99-1.03 per IQR). The PM2.5-mortality association was mitigated at high levels of greenness (1.05, 0.91-1.22). There was no strong evidence of confounding between air pollution, physical activity, and greenness. However, stratified analysis suggested effect modification for PM2.5 and NDVI by physical activity. A significant protective greenness-mortality association was observed for only highly active individuals (0.91, 0.86-0.96). Also, relatively high PM2.5-mortality HRs were observed for more physically active individuals (1.25, 1.12-1.40). PM2.5 air pollution and physical inactivity are robustly associated with mortality risk. Greenness may be most beneficial and air pollution relatively harmful to highly active individuals. This analysis provides evidence that, in addition to not smoking, being physically active and living in a clean, green environment contributes to improved health and reduced risk of mortality.

The Rapid Assessment of Aggregated Wastewater Samples for Genomic Surveillance of SARS-CoV-2 on a City-Wide Scale.

Throughout the course of the ongoing SARS-CoV-2 pandemic there has been a need for approaches that enable rapid monitoring of public health using an unbiased and minimally invasive means. A major way this has been accomplished is through the regular assessment of wastewater samples by qRT-PCR to detect the prevalence of viral nucleic acid with respect to time and location. Further expansion of SARS-CoV-2 wastewater monitoring efforts to include the detection of variants of interest/concern through next-generation sequencing has enhanced the understanding of the SARS-CoV-2 outbreak. In this report, we detail the results of a collaborative effort between public health and metropolitan wastewater management authorities and the University of Louisville to monitor the SARS-CoV-2 pandemic through the monitoring of aggregate wastewater samples over a period of 28 weeks. Through the use of next-generation sequencing approaches the polymorphism signatures of Variants of Concern/Interest were evaluated to determine the likelihood of their prevalence within the community on the basis of their relative dominance within sequence datasets. Our data indicate that wastewater monitoring of water quality treatment centers and smaller neighborhood-scale catchment areas is a viable means by which the prevalence and genetic variation of SARS-CoV-2 within a metropolitan community of approximately one million individuals may be monitored, as our efforts detected the introduction and emergence of variants of concern in the city of Louisville. Importantly, these efforts confirm that regional emergence and spread of variants of interest/concern may be detected as readily in aggregate wastewater samples as compared to the individual wastewater sheds. Furthermore, the information gained from these efforts enabled targeted public health efforts including increased outreach to at-risk communities and the deployment of mobile or community-focused vaccination campaigns.

High-throughput sequencing of SARS-CoV-2 in wastewater provides insights into circulating variants.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) likely emerged from a zoonotic spill-over event and has led to a global pandemic. The public health response has been predominantly informed by surveillance of symptomatic individuals and contact tracing, with quarantine, and other preventive measures have then been applied to mitigate further spread. Non-traditional methods of surveillance such as genomic epidemiology and wastewater-based epidemiology (WBE) have also been leveraged during this pandemic. Genomic epidemiology uses high-throughput sequencing of SARS-CoV-2 genomes to inform local and international transmission events, as well as the diversity of circulating variants. WBE uses wastewater to analyse community spread, as it is known that SARS-CoV-2 is shed through bodily excretions. Since both symptomatic and asymptomatic individuals contribute to wastewater inputs, we hypothesized that the resultant pooled sample of population-wide excreta can provide a more comprehensive picture of SARS-CoV-2 genomic diversity circulating in a community than clinical testing and sequencing alone. In this study, we analysed 91 wastewater samples from 11 states in the USA, where the majority of samples represent Maricopa County, Arizona (USA). With the objective of assessing the viral diversity at a population scale, we undertook a single-nucleotide variant (SNV) analysis on data from 52 samples with >90% SARS-CoV-2 genome coverage of sequence reads, and compared these SNVs with those detected in genomes sequenced from clinical patients. We identified 7973 SNVs, of which 548 were "novel" SNVs that had not yet been identified in the global clinical-derived data as of 17th June 2020 (the day after our last wastewater sampling date). However, between 17th of June 2020 and 20th November 2020, almost half of the novel SNVs have since been detected in clinical-derived data. Using the combination of SNVs present in each sample, we identified the more probable lineages present in that sample and compared them to lineages observed in North America prior to our sampling dates. The wastewater-derived SARS-CoV-2 sequence data indicates there were more lineages circulating across the sampled communities than represented in the clinical-derived data. Principal coordinate analyses identified patterns in population structure based on genetic variation within the sequenced samples, with clear trends associated with increased diversity likely due to a higher number of infected individuals relative to the sampling dates. We demonstrate that genetic correlation analysis combined with SNVs analysis using wastewater sampling can provide a comprehensive snapshot of the SARS-CoV-2 genetic population structure circulating within a community, which might not be observed if relying solely on clinical cases.

Time spent outdoors and sleep normality: A preliminary investigation.

INTRODUCTION: Sleep deficiency is associated with health risks, and time outdoors is related to health benefits. This study assessed time outdoors and its association with sleep normality. METHODS: As part of a health study in Louisville, Kentucky, 735 participants completed questionnaires on their health status, behaviors, neighborhoods, and demographics in 2018-2019. The measures included information on sleep, time outdoors, and mental and physical health. Participant characteristics were assessed by dichotomized sleep normality (N=728), and logistic regression (N=709) examined potential associations between time outdoors and sleep. RESULTS: As time spent outdoors increased from ≤4 hours to >4 - ≤8 hours (OR=1.04; 95% CI: 0.65-1.64) and >8 - ≤12 hours (OR=1.17; 95% CI: 0.63-2.17), odds of normal sleep increased; however, those who spent >12 - ≤16 hours (OR=0.63; 95% CI: 0.31-1.27) or >16 hours (OR=0.83; 95% CI: 0.45-1.53) outdoors had a lower likelihood of normal sleep. No associations between time outdoors and sleep were significant. There was a significant trend of less bodily pain associated with normal sleep (p<0.001) and in the association of depression and sleep, where odds of normal sleep decreased as depression severity increased (p<0.001). CONCLUSIONS: Consistent with extant literature, findings indicate associations between less pain and increased odds of normal sleep and between higher severity of depression and lower odds of normal sleep. Findings for an overall association between time outdoors and sleep normality were not significant. Future work should seek to better explicate the predictor variables to assess how greenness and activity type shape associations with sleep.

The importance of urban planning: Views of greenness and open space is reversely associated with self-reported views and depressive symptoms.

INTRODUCTION: Exposure to green spaces is beneficial to mental health in a variety of ways, ranging from stress reduction to increased attentiveness and elevated self-esteem. The impact of views of greenness, as opposed to direct exposure, has been examined, but the association between self-reported views and depressive symptoms is not known. The purpose of this study is to examine the relationship between views of greenness and Patient Health Questionnaire-9 (PHQ-9) score. METHODS: Questionnaire responses from 191 participants in the Health, Environment, and Action in Louisville (HEAL) study were examined. Univariate statistical analyses included Mann-Whitney U, Kruskal-Wallis, and Spearman rank tests. Inferential statistical analysis was linear regression. RESULTS: Participant satisfaction with residential greenness was significantly associated with reduced PHQ-9 score (partially adjusted: linear coefficient = -0.42; 95% CI: -0.70 - -0.14; fully adjusted: linear coefficient = -0.21; 95% CI: -0.44 - 0.02). Additionally, being satisfied with local greenness was significantly associated with having views of greenness from home (linear coefficient = 1.97; 95% CI: 1.23-2.68). CONCLUSIONS: Though views of greenness were not directly associated with depression, satisfaction with local greenness was associated with reduced PHQ-9 score, and having views of greenness from home was crudely associated with increased greenness satisfaction. The findings suggest urban greening interventions that focus on greenness satisfaction may be a strategy to reduce depression. Further research is necessary to better understand these relationships.

Greenness, air pollution, and mortality risk: A U.S. cohort study of cancer patients and survivors.

BACKGROUND: Several studies suggest that living in areas of high surrounding greenness may be associated with a lower cardiopulmonary mortality risk. However, associations of greenness with specific causes of death in cancer patients and survivors has not been examined and it is unknown whether this relationship is affected by area levels of fine particulate matter air pollution (PM2.5). This study evaluated associations between greenness and PM2.5 on causes of death in a large, U.S.-based cohort of cancer patients and survivors. METHODS: Surveillance, Epidemiology and End Results (SEER) data were used to generate a cohort of 5,529,005 cancer patients and survivors from 2000 to 2016. Census-tract Normalized Difference Vegetation Index (NDVI) during May-October from 2003 to 2016 was population-weighted to act as a county-level greenness measure. County-level PM2.5 exposure was estimated from annual concentrations averaged from 1999 to 2015. Cox Proportional Hazards models were used to estimate the association between greenness, PM2.5, and cause-specific mortality while controlling for age, sex, race, and other individual and county level variables. FINDINGS: An IQR increase in greenness was associated with a decrease in cancer mortality for cancer patients (Hazard ratio of 0.94, 95% CI: 0.93-0.95), but not for cardiopulmonary mortality (0.98, 95% CI: 0.96-1.00). Inversely, an increase in 10 µg/m3 PM2.5 was associated with increased cardiopulmonary mortality (1.24, 95% CI: 1.19-1.29), but not cancer mortality (0.99, 95% CI: 0.97-1.00). Hazard ratios were robust to inclusion of PM2.5 in models with greenness and vice versa. Although exposure estimates were constant over most stratifications, greenness seemed to benefit individuals diagnosed with high survivability cancers (0.92, 95% CI: 0.90-0.95) more than those with low survivability cancers (0.98. 95% CI: 0.96-0.99). INTERPRETATION: Higher levels of greenness are associated with lower cancer mortality in cancer patients. The evidence suggests minimal confounding between greenness and PM2.5 exposures and risk of mortality.

High-throughput sequencing of SARS-CoV-2 in wastewater provides insights into circulating variants.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged from a zoonotic spill-over event and has led to a global pandemic. The public health response has been predominantly informed by surveillance of symptomatic individuals and contact tracing, with quarantine, and other preventive measures have then been applied to mitigate further spread. Non-traditional methods of surveillance such as genomic epidemiology and wastewater-based epidemiology (WBE) have also been leveraged during this pandemic. Genomic epidemiology uses high-throughput sequencing of SARS-CoV-2 genomes to inform local and international transmission events, as well as the diversity of circulating variants. WBE uses wastewater to analyse community spread, as it is known that SARS-CoV-2 is shed through bodily excretions. Since both symptomatic and asymptomatic individuals contribute to wastewater inputs, we hypothesized that the resultant pooled sample of population-wide excreta can provide a more comprehensive picture of SARS-CoV-2 genomic diversity circulating in a community than clinical testing and sequencing alone. In this study, we analysed 91 wastewater samples from 11 states in the USA, where the majority of samples represent Maricopa County, Arizona (USA). With the objective of assessing the viral diversity at a population scale, we undertook a single-nucleotide variant (SNV) analysis on data from 52 samples with >90% SARS-CoV-2 genome coverage of sequence reads, and compared these SNVs with those detected in genomes sequenced from clinical patients. We identified 7973 SNVs, of which 5680 were novel SNVs that had not yet been identified in the global clinical-derived data as of 17th June 2020 (the day after our last wastewater sampling date). However, between 17th of June 2020 and 20th November 2020, almost half of the SNVs have since been detected in clinical-derived data. Using the combination of SNVs present in each sample, we identified the more probable lineages present in that sample and compared them to lineages observed in North America prior to our sampling dates. The wastewater-derived SARS-CoV-2 sequence data indicates there were more lineages circulating across the sampled communities than represented in the clinical-derived data. Principal coordinate analyses identified patterns in population structure based on genetic variation within the sequenced samples, with clear trends associated with increased diversity likely due to a higher number of infected individuals relative to the sampling dates. We demonstrate that genetic correlation analysis combined with SNVs analysis using wastewater sampling can provide a comprehensive snapshot of the SARS-CoV-2 genetic population structure circulating within a community, which might not be observed if relying solely on clinical cases.

Green environments and cardiovascular health.

Several large epidemiological studies have found robust associations between greenness and the risk of cardiovascular disease (CVD). These studies report that close residential proximity to greenness is associated with a decrease in cardiovascular mortality as well as major adverse cardiovascular events. Although mechanisms underlying this link are not well understood, the beneficial health effects of greenness have been linked to its ability to relieve stress, decrease air pollution, and encourage physical activity. Greenness in residential neighborhoods could also increase access to healthy goods and services, as well as social interactions. Research into the health effects of greenness could provide new insights into the environmental determinants of CVD risk and could inform the development of actionable greenness-based strategies to prevent CVD and its clinical manifestations.

Defining the Human Envirome: An Omics Approach for Assessing the Environmental Risk of Cardiovascular Disease.

Both genetic and environmental factors contribute to the development of cardiovascular disease, but in comparison with genetics, environmental factors have received less attention. Evaluation of environmental determinants of cardiovascular disease is limited by the lack of comprehensive omics approaches for integrating multiple environmental exposures. Hence, to understand the effects of the environment as a whole (envirome), it is important to delineate specific domains of the environment and to assess how, individually and collectively; these domains affect cardiovascular health. In this review, we present a hierarchical model of the envirome; defined by 3 consecutively nested domains, consisting of natural, social, and personal environments. Extensive evidence suggests that features of the natural environment such as sunlight, altitude, diurnal rhythms, vegetation, and biodiversity affect cardiovascular health. However, the effects of the natural environment are moderated by the social environment comprised of built environments, agricultural and industrial activities, pollutants and contaminants, as well as culture, economic activities, and social networks that affect health by influencing access to healthcare, social cohesion, and socioeconomic status. From resources available within society, individuals create personal environments, characterized by private income, wealth and education, and populated by behavioral and lifestyle choices relating to nutrition, physical activity, sleep, the use of recreational drugs, and smoking. An understanding of the interactions between different domains of the envirome and their integrated effects on cardiovascular health could lead to the development of new prevention strategies and deeper insights into etiologic processes that contribute to cardiovascular disease risk and susceptibility.