Ambient fine particulate matter pollution (PM_2.5) is one leading cause of disease burden, but no study has quantified the association between daily PM_2.5 exposure and life expectancy. We aimed to assess the potential benefits in life expectancy by attaining the daily PM_2.5 standards in 72 cities of China during 2013–2016. We applied a two-stage approach for the analysis. At the first stage, we used a generalized additive model (GAM) with a Gaussian link to examine the city-specific short-term association between daily PM_2.5 and years of life lost (YLL); at the second stage, a random-effects meta-analysis was used to generate the regional and national estimations. We further estimated the potential gains in life expectancy (PGLE) by assuming that ambient PM_2.5 has met the Chinese National Ambient Air Quality Standard (NAAQS, 75 μg/m³) or the ambient air quality guideline (AQG) of the World Health Organization (WHO) (25 μg/m³). We also calculated the attributable fraction (AF), which denoted the proportion of YLL attributable to a higher-than-standards daily mean PM_2.5 concentration. During the period from January 18, 2013 to December 31, 2016, we recorded 1,226,849 nonaccidental deaths in the study area. We observed significant associations between daily PM_2.5 and YLL: each 10 μg/m³ increase in three-day–averaged (lag₀₂) PM_2.5 concentrations corresponded to an increment of 0.43 years of life lost (95% CI: 0.29–0.57). We estimated that 168,065.18 (95% CI: 114,144.91–221,985.45) and 68,684.95 (95% CI: 46,648.79–90,721.11) years of life lost can be avoided by achieving WHO’s AQG and Chinese NAAQS in the study area, which corresponded to 0.14 (95% CI: 0.09–0.18) and 0.06 (95% CI: 0.04–0.07) years of gain in life expectancy for each death in these cities. We observed differential regional estimates across the 7 regions, with the highest gains in the Northwest region (0.28 years of gain [95% CI: 0.06–0.49]) and the lowest in the North region (0.08 [95% CI: 0.02–0.15]). Furthermore_, using WHO’s AQG and Chinese NAAQS as the references, we estimated that 1.00% (95% CI: 0.68%–1.32%) and 0.41% (95% CI: 0.28%–0.54%) of YLL could be attributable to the PM_2.5 exposure at the national level. Findings from this study were mainly limited by the unavailability of data on individual PM_2.5 exposure. This study indicates that significantly longer life expectancy could be achieved by a reduction in the ambient PM_2.5 concentrations. It also highlights the need to formulate a stricter ambient PM_2.5 standard at both national and regional levels of China to protect the population’s health.

Differential findings have been reported on the association between neighborhood greenness and chronic obstructive pulmonary disease (COPD). The underlying reasons might be the different types of vegetation and the diagnosis methods used in different studies. In this nationwide cross-sectional study in China, we examined the linkage between neighborhood greenness and COPD prevalence among 66,752 adults aged 40 years and above. Neighborhood greenness was estimated using the normalized difference vegetation index (NDVI) based on satellite imagery within buffers of 100, 300, 500, 1000, 2000, 3000 and 5000 m of residential community of the participants. COPD was defined according to the 2017 Global Initiative for Chronic Obstructive Lung Disease lung function criteria. A two-level logistic regression model was applied to estimate the associations. Finally, 9134 adults were classified as COPD. We observed significant positive associations between neighborhood greenness and COPD prevalence. The odds ratio for each interquartile range increase in NDVI within 100 m buffer was 1.08 (95% CI: 1.01, 1.15) after adjustment for potential confounders. Consistent associations were observed across all other NDVI buffer sizes. Stratified analyses revealed that younger adults (40-65 years) and urban residents might be the vulnerable subpopulations. Further regional analyses found that residents from the Northeastern and Northern China were more likely to have this association. Our results indicated that neighborhood greenness might be one risk factor of COPD prevalence. Our study have important public health implications for allocating the surrounding green spaces among living areas, especially for those with respiratory illness; however, the findings and the underlying mechanisms warrant further examinations in longitudinal settings.

Although exposure to air pollution increases the risk of premature mortality and years of life lost (YLL), the effects of daily air quality improvement to the life expectancy of respiratory diseases remained unclear. We applied a generalized additive model (GAM) to assess the associations between daily PM_2.5 exposure and YLL from respiratory diseases in 96 Chinese cities during 2013–2016. We further estimated the avoidable YLL, potential gains in life expectancy, and the attributable fraction by assuming daily PM_2.5 concentration decrease to the air quality standards of China and World Health Organization. Regional and national results were generated by random-effects meta-analysis. A total of 861,494 total respiratory diseases and 586,962 chronic obstructive pulmonary disease (COPD) caused death from 96 Chinese cities were recorded during study period. Each 10 μg/m³ increase of PM_2.5 in 3-day moving average (lag02) was associated with 0.16 (95% CI: 0.08, 0.24) years increment in life expectancy from total respiratory diseases. The highest effect was observed in Southwest region with 0.42 (95% CI: 0.22, 0.62) years increase in life expectancy. By attaining the WHO's Air Quality Guidelines, we estimated that an average of 782.09 (95% CI: 438.29, 1125.89) YLLs caused by total respiratory death in each city could be avoided, which corresponded to 1.15% (95% CI: 0.67%, 1.64%) of the overall YLLs, and 0.12 (95% CI: 0.07, 0.17) years increment in life expectancy. The results of COPD were generally consistent with total respiratory diseases. Our findings indicate that reduction in daily PM_2.5 concentrations might lead to longer life expectancy from respiratory death.

Being a globally emerging mite-borne zoonotic disease, scrub typhus is a serious public health concern in Nepal. Mapping environmental suitability and quantifying the human population under risk of the disease is important for prevention and control efforts. In this study, we model and map the environmental suitability of scrub typhus using the ecological niche approach, machine learning modeling techniques, and report locations of scrub typhus along with several climatic, topographic, Normalized Difference Vegetation Index (NDVI), and proximity explanatory variables and estimated population under the risk of disease at a national level. Both MaxEnt and RF technique results reveal robust predictive power with test The area under curve (AUC) and true skill statistics (TSS) of above 0.8 and 0.6, respectively. Spatial prediction reveals that environmentally suitable areas of scrub typhus are widely distributed across the country particularly in the low-land Tarai and less elevated river valleys. We found that areas close to agricultural land with gentle slopes have higher suitability of scrub typhus occurrence. Despite several speculations on the association between scrub typhus and proximity to earthquake epicenters, we did not find a significant role of proximity to earthquake epicenters in the distribution of scrub typhus in Nepal. About 43% of the population living in highly suitable areas for scrub typhus are at higher risk of infection, followed by 29% living in suitable areas of moderate-risk, and about 22% living in moderately suitable areas of lower risk. These findings could be useful in selecting priority areas for surveillance and control strategies effectively.