Background The prevalence of landscape fires has increased, particularly in low-income and middle-income countries (LMICs). We aimed to assess the impact of exposure to landscape fire smoke (LFS) on the health of children. MethodsWe conducted a sibling-matched case-control study and selected 552 155 children (aged <18 years) from Demographic and Health Surveys in 55 LMICs from 2000 to 2014. Each deceased child was matched with their sibling(s). The exposure indicators were fire-sourced PM 2•5 and dry-matter emissions. We associated these exposure indicators with child mortality using conditional regressions, and derived an exposure-response function using a non-linear model. Based on the association, we quantified the global burden of fire-attributable child deaths in LMICs from 2000 to 2014. Findings Each 1 µg/m³ increment of fire-sourced PM 2•5 was associated with a 2•31% (95% CI 1•50-3•13) increased risk of child mortality. The association was robust to different models. The exposure-response function was superlinear and suggested per-unit exposure to larger fires was more toxic. Based on our non-linear exposureresponse function, we estimated that between 2000 and 2014, the five countries with the largest number of child deaths associated with fire-sourced PM 2•5 were Nigeria (164 000 [126 000 to 209 000] annual deaths), Democratic Republic of the Congo (126 000 [95% CI 114 000 to 139 000] annual deaths), India (65 900 [−22 200 to 147 000] annual deaths), Uganda (30 200 [24 500 to 36 300] annual deaths), and Indonesia (28 900 [19 100 to 38 400]).Interpretation Exposure to landscape fire smoke contributes substantially to the global burden of child mortality.

The household energy mix has significant impacts on human health and climate, as it contributes greatly to many health- and climate-relevant air pollutants. Compared to the well-established urban energy statistical system, the rural household energy statistical system is incomplete and is often associated with high biases. Via a nationwide investigation, this study revealed high contributions to energy supply from coal and biomass fuels in the rural household energy sector, while electricity comprised ∼20%. Stacking (the use of multiple sources of energy) is significant, and the average number of energy types was 2.8 per household. Compared to 2012, the consumption of biomass and coals in 2017 decreased by 45% and 12%, respectively, while the gas consumption amount increased by 204%. Increased gas and decreased coal consumptions were mainly in cooking, while decreased biomass was in both cooking (41%) and heating (59%). The time-sharing fraction of electricity and gases (E&G) for daily cooking grew, reaching 69% in 2017, but for space heating, traditional solid fuels were still dominant, with the national average shared fraction of E&G being only 20%. The non-uniform spatial distribution and the non-linear increase in the fraction of E&G indicated challenges to achieving universal access to modern cooking energy by 2030, particularly in less-developed rural and mountainous areas. In some non-typical heating zones, the increased share of E&G for heating was significant and largely driven by income growth, but in typical heating zones, the time-sharing fraction was &lt;5% and was not significantly increased, except in areas with policy intervention. The intervention policy not only led to dramatic increases in the clean energy fraction for heating but also accelerated the clean cooking transition. Higher income, higher education, younger age, less energy/stove stacking and smaller family size positively impacted the clean energy transition.

Recently, considerable efforts have been focused on intensifying the screening process for asymptomatic COVID-19 cases in the Chinese Mainland, especially for up to 10 million citizens living in Wuhan City by nucleic acid testing. However, a high percentage of domestic asymptomatic cases did not develop into symptomatic ones, which is abnormal and has drawn considerable public attention. Here, we aimed to investigate the prevalence of COVID-19 infections in the Chinese Mainland from a statistical perspective, as it is of referential significance for other regions. By conservatively assuming a development time lag from pre-symptomatic (i.e., referring to the infected cases that were screened before the COVID-19 symptom onset) to symptomatic as an incubation time of 5.2 days, our results indicated that 92.5% of those tested in Wuhan City, China, and 95.1% of those tested in the Chinese Mainland should have COVID-19 syndrome onset, which was extremely higher than their corresponding practical percentages of 0.8% and 3.3%, respectively. We propose that a certain false positive rate may exist if large-scale nucleic acid screening tests for asymptomatic cases are conducted in common communities with a low incidence rate. Despite adopting relatively high-sensitivity, high-specificity detection kits, we estimated a very low prevalence of COVID-19 infections, ranging from 10 −6 to 10 −4 in both Wuhan City and the Chinese Mainland. Thus, the prevalence rate of asymptomatic infections in China had been at a very low level. Furthermore, given the lower prevalence of the infection, close examination of the data for false positive results is necessary to minimize social and economic impacts.

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