While many studies suggest evidence for the health benefits of nature, there is currently no standardized method to measure time spent in nature or nature contact, nor agreement on how best to define nature contact in research. The purpose of this review is to summarize how nature contact has been measured in recent health research and provide insight into current metrics of exposure to nature at individual and population scales. The most common methods include surrounding greenness, questionnaires, and global positioning systems (GPS) tracking. Several national-level surveys exist, though these are limited by their cross-sectional design, often measuring only a single component of time spent in nature, and poor links to measures of health. In future research, exposure assessment combining the quantifying (e.g., time spent in nature and frequency of visits to nature) and qualifying (e.g., greenness by the normalized difference of vegetation index (NDVI) and ratings on perception by individuals) aspects of current methods and leveraging innovative methods (e.g., experience sampling methods, ecological momentary assessment) will provide a more comprehensive understanding of the health effects of nature exposure and inform health policy and urban planning.

Background COVID-19 is an infectious disease that has killed more than 555,000 people in the US. During a time of social distancing measures and increasing social isolation, green spaces may be a crucial factor to maintain a physically and socially active lifestyle while not increasing risk of infection. Objectives We evaluated whether greenness was related to COVID-19 incidence and mortality in the US. Methods We downloaded data on COVID-19 cases and deaths for each US county up through June 7, 2020, from Johns Hopkins University, Center for Systems Science and Engineering Coronavirus Resource Center. We used April-May 2020 Normalized Difference Vegetation Index (NDVI) data, to represent the greenness exposure during the initial COVID-19 outbreak in the US. We fitted negative binomial mixed models to evaluate associations of NDVI with COVID-19 incidence and mortality, adjusting for potential confounders such as county-level demographics, epidemic stage, and other environmental factors. We evaluated whether the associations were modified by population density, proportion of Black residents, median home value, and issuance of stay-at-home orders. Results An increase of 0.1 in NDVI was associated with a 6% (95% Confidence Interval: 3%, 10%) decrease in COVID-19 incidence rate after adjustment for potential confounders. Associations with COVID-19 incidence were stronger in counties with high population density and in counties with stay-at-home orders. Greenness was not associated with COVID-19 mortality in all counties; however, it was protective in counties with higher population density. Discussion Exposures to NDVI were associated with reduced county-level incidence of COVID-19 in the US as well as reduced county-level COVID-19 mortality rates in densely populated counties.

Background: COVID-19 is an infectious disease that has killed more than 175,000 people in the US. During a time of social distancing measures and increasing social isolation, green spaces may be a crucial factor to maintain a physically and socially active lifestyle while not increasing risk of infection. Objectives: We evaluated whether greenness is related to COVID-19 incidence and mortality in the United States. Methods: We downloaded data on COVID-19 cases and deaths for each US county up through June 7, 2020, from Johns Hopkins University, Center for Systems Science and Engineering Coronavirus Resource Center. We used April-May 2020 Normalized Difference Vegetation Index (NDVI) data, to represent the greenness exposure during the initial COVID-19 outbreak in the US. We fitted negative binomial mixed models to evaluate associations of NDVI with COVID-19 incidence and mortality, adjusting for potential confounders such as county-level demographics, epidemic stage, and other environmental factors. We evaluated whether the associations were modified by population density, proportion of Black residents, median home value, and issuance of stay-at-home order. Results: An increase of 0.1 in NDVI was associated with a 6% (95% Confidence Interval: 3%, 10%) decrease in COVID-19 incidence rate after adjustment for potential confounders. Associations with COVID-19 incidence were stronger in counties with high population density and high median home values, and in counties with stay-at-home orders. Greenness was not associated with COVID-19 mortality in all counties; however, it was protective in counties with high percentages of Black residents, high median home value, and higher population density. Discussion: Exposures to NDVI had beneficial impacts on county-level incidence of COVID-19 in the US and may have reduced county-level COVID-19 mortality rates, especially in densely populated counties.

Background: Few studies have prospectively examined long-term associations between neighborhood socioeconomic status (nSES) and mortality risk, independent of demographic and lifestyle risk factors. Methods: We assessed associations between nSES and all-cause, nonaccidental mortality among women in the Nurses’ Health Study (NHS) 1986–2014 (N = 101,701) and Nurses’ Health Study II (NHSII) 1989–2015 (N = 101,230). Mortality was ascertained from the National Death Index (NHS: 19,228 deaths; NHSII: 1556 deaths). Time-varying nSES was determined for the Census tract of each residential address. We used principal component analysis (PCA) to identify nSES variable groups. Multivariable Cox proportional hazards models were conditioned on age and calendar period and included time-varying demographic, lifestyle, and individual SES factors. Results: For NHS, hazard ratios (HRs) comparing the fifth to first nSES quintiles ranged from 0.89 (95% confidence interval [CI] = 0.84, 0.94) for percent of households receiving interest/dividends, to 1.11 (95% CI = 1.06, 1.17) for percent of households receiving public assistance income. In NHSII, HRs ranged from 0.72 (95% CI: 0.58, 0.88) for the percent of households receiving interest/dividends, to 1.27 (95% CI: 1.07, 1.49) for the proportion of households headed by a single female. PCA revealed three constructs: education/income, poverty/wealth, and racial composition. The racial composition construct was associated with mortality (HRNHS: 1.03; 95% CI = 1.01, 1.04). Conclusion: In two cohorts with extensive follow-up, individual nSES variables and PCA component scores were associated with mortality. nSES is an important population-level predictor of mortality, even among a cohort of women with little individual-level variability in SES.