Association between long-term green space exposure and mortality in China: A difference-in-differences analysis of national data in 2000, 2010 and 2019.

BACKGROUND: Effects of green space on human health have been well-documented in western, high-income countries. Evidence for similar effects in China is limited. Moreover, the underlying mechanisms linking green space and mortality are yet to be established. We therefore conducted a nation-wide study to assess the association between green space and mortality in China using a difference-in-difference approach, which applied a causal framework and well controlled unmeasured confounding. In addition, we explored whether air pollution and air temperature could mediate the association. METHODS: In this analysis, we collected data on all-cause mortality and sociodemographic characteristics for each county in China from the 2000 and 2010 censuses and the 2020 Statistical Yearbook. Green space exposure was assessed using county-level normalized difference vegetation index (NDVI) and the percentage of green space (forest, grasslands, shrub land and wetland). We applied a difference-in-differences approach to evaluate the association between green space and mortality. We also performed mediation analysis (by air pollution and air temperature). RESULTS: Our sample consisted of 2726 counties in 2000 and 2010 as well as 1432 counties in 2019. In the 2000 versus 2019 comparison, a 0.1 unit increase in NDVI was associated with a 2.4 % reduction in mortality [95 % confidence interval (CI) 0.4-4.3 %], and a 10 % increase in percentage of green space was associated with a 4.7 % reduction (95 % CI 0-9.2 %) in mortality. PM2.5 and air temperature mediated 0.3 % to 12.3 % of the associations. CONCLUSIONS: Living in greener counties may be associated with lower risk of mortality in China. These findings could indicate the potential of a population-level intervention to reduce mortality in China, which has important public health implications at the county level.

Time to address the spatiotemporal uncertainties in COVID-19 research: Concerns and challenges.

In this correspondence, we emphasize methodological caveats of ecological studies assessing associations between COVID-19 and its physical and social environmental determinants. First, we stress that inference is error-prone due to the modifiable areal unit problem and the modifiable temporal unit problem. The possibility of confounding from using aggregated data is substantial due to the neglect of person-level factors. Second, studying the viral transmission of COVID-19 solely on people's residential neighborhoods is problematic because people are also exposed to nonhome locations and environments en-route along their daily mobility path. We caution against an uncritical application of aggregated data and reiterate the importance of stronger research designs (e.g., case-control studies) on an individual level. To address environmental contextual uncertainties due to people's day-to-day mobility, we call for people-centered studies with mobile phone data.