Spatial association between green space and COPD mortality: a township-level ecological study in Chongqing, China

Abstract

Background There are regional differences in the effect of green space on mortality of Chronic obstructive pulmonary disease (COPD). We conduct an ecological study, using the administrative divisions of Chongqing townships in China as the basic unit, to investigate the association between COPD mortality and green space based on data of 313,013 COPD deaths in Chongqing from 2012 to 2020. Green space is defined by Fractional vegetation cover (FVC), which is further calculated based on the normalised vegetation index (NDVI) from satellite remote sensing imagery maps. Methods After processing the data, the non-linear relationship between green space and COPD mortality is revealed by generalised additive models; the spatial differences between green space and COPD mortality is described by geographically weighted regression models; and finally, the interpretive power and interaction of each factor on the spatial distribution of COPD mortality is examined by a geographic probe. Results The results show that the FVC local regression coefficients ranged from − 0.0397 to 0.0478, 63.0% of the regions in Chongqing have a positive correlation between green space and COPD mortality while 37.0% of the regions mainly in the northeast and west have a negative correlation. The interpretive power of the FVC factor on the spatial distribution of COPD mortality is 0.08. Conclusions Green space may be a potential risk factor for increased COPD mortality in some regions of Chongqing. This study is the first to reveal the relationship between COPD mortality and green space in Chongqing at the township scale, providing a basis for public health policy formulation in Chongqing.

Greenspace Exposure with Chronic Obstructive Pulmonary Disease: A Systematic Review

“…Tables 1 and S4 provide the characteristics of the studies included in this review, and Table 2 summarizes the studies' characteristics for study type, study location, study period, and publication year. Among the 16 studies included in the final review analysis, the most eligible studies (n = 14) were observational, with six cross-sectional [29,30,32,33,43,44], five cohort [27,28,31,46,47], one case-control [45], and two ecological studies [48,49]. The remaining two studies were experimental (one randomized controlled study [50] and one non-randomized controlled study [51]).…”

“…The years since the publication of these studies ranged from 2009 (n = 1) [29] to 2023 (n = 3) [44,47,48]. It is worth noting that after the study by J. Maas et al [29], there was a gap of nearly six years, with no relevant studies until after 2016 when researchers started to pay gradual attention and conduct studies in the direction of the correlation between greenspace exposure and COPD.…”

“…The number of relevant studies gradually increased after 2021, with five studies published in 2022 [27,43,45,46,49], the year with the highest number of publications. Among the included studies, seven were conducted in China [31,32,43,44,46,48,50], eight in Europe (the United Kingdom [27,30,47], Belgium [28], the Netherlands [29], Poland [45], Greece [49], and Germany [51]), and one in Atlanta [33].…”

“…Table 3 summarizes the characteristics of the greenspace indicators used in the 2 experimental and 14 observational studies, based on whether single or multiple greenspace indicators were used, the measurement methods for greenspace indicators, the sources of greenspace data, and the spatial resolution and buffer size. A higher percentage of studies (63%) [29,30,32,43,45,[47][48][49][50][51]] used a single greenspace indicator. For the experimental studies, the type of greenspace involved was forest, specifically using the forest bathing intervention for the experiments, and the control group relied on urban and indoor spaces as control environments.…”

“…Many indicators defined greenspace exposure, the most common being NDVI (n = 9) [28,[30][31][32]43,44,[47][48][49]. The NDVI is a measure of greenness at the regional level using GIS and is based on calculations from satellite imagery [52].…”