Nature's Role in Supporting Health during the COVID-19 Pandemic: A Geospatial and Socioecological Study

Abstract

The COVID-19 pandemic has brought about unprecedented changes to human lifestyles across the world. The virus and associated social restriction measures have been linked to an increase in mental health conditions. A considerable body of evidence shows that spending time in and engaging with nature can improve human health and wellbeing. Our study explores nature's role in supporting health during the COVID-19 pandemic. We created web-based questionnaires with validated health instruments and conducted spatial analyses in a geographic information system (GIS). We collected data (n = 1184) on people's patterns of nature exposure, associated health and wellbeing responses, and potential socioecological drivers such as relative deprivation, access to greenspaces, and land-cover greenness. The majority of responses came from England, UK (n = 993). We applied a range of statistical analyses including bootstrap-resampled correlations and binomial regression models, adjusting for several potential confounding factors. We found that respondents significantly changed their patterns of visiting nature as a result of the COVID-19 pandemic. People spent more time in nature and visited nature more often during the pandemic. People generally visited nature for a health and wellbeing benefit and felt that nature helped them cope during the pandemic. Greater land-cover greenness within a 250 m radius around a respondent's postcode was important in predicting higher levels of mental wellbeing. There were significantly more food-growing allotments within 100 and 250 m around respondents with high mental wellbeing scores. The need for a mutually-advantageous relationship between humans and the wider biotic community has never been more important. We must conserve, restore and design nature-centric environments to maintain resilient societies and promote planetary health.

Keywords: COVID-19; coronavirus; green space; nature connectedness; nature-based interventions; planetary health; public health.

Figure 1

Buffer types and green space polygons used in this study. Green space shapefiles (green polygons) were imported into QGIS and buffer radii of 50, 100, 250, and 500 m were created. (A) shows an example where green space presence/abundance differs between buffer zones; (B) shows buffer zones with several green spaces within; (C) shows a buffer without any green spaces; and (D) provides an example of where green space polygons are touching the 500 m buffer but are not completely encapsulated—these would still be counted as being within this buffer zone.

Figure 2

Spatial outputs combined with England boundary datasets. (A) shows the distribution of OS Open Green Space polygons; (B) shows the distribution of georeferenced samples from the survey; (C) shows the Lower Super Output Areas with joined index of multiple deprivation quintile data, whereby 1 corresponds to relatively high deprivation (and lighter blue) and 5 corresponds to relatively low deprivation (and darker blue); and (D) shows distribution of forests > 0.5 ha.

Figure 3

Violin plots (A) typical duration spent in natural environments (e.g., parks, woodland) before (left) and during (right) the COVID-19 pandemic; and (B) typical frequency of visits to natural environments per week before (left) and during (right) the COVID-19 pandemic. The black diamond represents the mean value.

Figure 4

Boxplot for the GLM analysis (regarding environments of concern due to COVID-19), showing means and approximate 95% confidence intervals for the proportion of positive responses, where “yes” was recoded to “1”.

Figure 5

Proportions of participants who visited natural environments they would not usually visit (as a result of the COVID-19 pandemic). The top left (A) waffle plots show the most popular natural environments and (B) boxplot for the GLM analysis, shows means and approximate 95% confidence intervals for the proportion of positive responses, where “yes” was recoded to “1”.

Figure 6

Violin plots of different Likert scores (Y axis) denoting level of agreement (‘nature has helped me cope with COVID-19’) analysed by home/work situations (X axis). Plots display median values (red diamond), interquartile range (brown) and kernel density estimation (green). The strength of the kernel colour corresponds to the median value, and the strength of the boxplot colour corresponds to the sample size.