Personal and Indoor PM_2.5 Exposure from Burning Solid Fuels in Vented and Unvented Stoves in a Rural Region of China with a High Incidence of Lung Cancer

The combustion of biomass and coal is the dominant source of household air pollution (HAP) in China, and contributes significantly to the total burden of disease in the Chinese population. To characterize HAP exposure related to solid fuel use and ventilation patterns, an exposure assessment study of 163 nonsmoking female heads of households enrolled from 30 villages was conducted in Xuanwei and Fuyuan, two neighboring rural counties with high incidence of lung cancer due to the burning of smoky coal (a bituminous coal, which in health evaluations is usually compared to smokeless coalan anthracite coal available in some parts of the area). Personal and indoor 24-h PM_2.5 samples were collected over two consecutive days in each household, with approximately one-third of measurements retaken in a second season. The overall geometric means (GM) of personal PM_2.5 concentrations in Xuanwei and Fuyuan were 166 [Geometric Standard Deviation (GSD):2.0] and 146 (GSD:1.9) μg/m³, respectively, which were similar to the indoor PM_2.5 air concentrations [GM­(GSD):162 (2.1) and 136 (2.0) μg/m³, respectively]. Personal PM_2.5 was moderately highly correlated with indoor PM_2.5 (Spearman <i>r </i>= 0.70, <i>p</i> < 0.0001). Burning wood or plant materials (tobacco stems, corncobs etc.) resulted in the highest personal PM_2.5 concentrations (GM:289 and 225 μg/m³, respectively), followed by smoky coal, and smokeless coal (GM:148 and 115 μg/m³, respectively). PM_2.5 levels of vented stoves were 34–80% lower than unvented stoves and firepits across fuel types. Mixed effect models indicated that fuel type, ventilation, number of windows, season, and burning time per stove were the main factors related to personal PM_2.5 exposure. Lower PM_2.5 among vented stoves compared with unvented stoves and firepits is of interest as it parallels the observation of reduced risks of malignant and nonmalignant lung diseases in the region

Performance metrics of the machine learning algorithm for female and male participants.

Performance metrics of the machine learning algorithm for female and male participants.</p

Sex differences in baseline characteristics.

Considering sex as a biological variable in modern digital health solutions, we investigated sex-specific differences in the trajectory of four physiological parameters across a COVID-19 infection. A wearable medical device measured breathing rate, heart rate, heart rate variability, and wrist skin temperature in 1163 participants (mean age = 44.1 years, standard deviation [SD] = 5.6; 667 [57%] females). Participants reported daily symptoms and confounders in a complementary app. A machine learning algorithm retrospectively ingested daily biophysical parameters to detect COVID-19 infections. COVID-19 serology samples were collected from all participants at baseline and follow-up. We analysed potential sex-specific differences in physiology and antibody titres using multilevel modelling and t-tests. Over 1.5 million hours of physiological data were recorded. During the symptomatic period of infection, men demonstrated larger increases in skin temperature, breathing rate, and heart rate as well as larger decreases in heart rate variability than women. The COVID-19 infection detection algorithm performed similarly well for men and women. Our study belongs to the first research to provide evidence for differential physiological responses to COVID-19 between females and males, highlighting the potential of wearable technology to inform future precision medicine approaches.</div

SARS-CoV-2 Nucleocapsid (N) antibody (AB) values stratified according to sex.

SARS-CoV-2 Nucleocapsid (N) antibody (AB) values stratified according to sex.</p

Trajectory of the four analysed physiological parameters across the course of a confirmed COVID-19 infection centred around participant-reported symptom onset.

The values of each physiological parameter (with 95% CIs) were normalized according to each individual’s baseline measurements and collapsed across females (n = 56) and males (n = 26).</p

Results from multilevel linear mixed models showing the main effects of infection phase, sex, age, medication, drug and alcohol intake, BMI, and hypertension, as well as interactions between sex and infection phase with regards to changes in physiological signals.

Results from multilevel linear mixed models showing the main effects of infection phase, sex, age, medication, drug and alcohol intake, BMI, and hypertension, as well as interactions between sex and infection phase with regards to changes in physiological signals.</p

Study flow chart of the 1,163 participants that are enrolled in the COVI-GAPP study.

The cut-off levels used for positive and negative values were ≥ 1.0 and ≤ 0.1, respectively. Values between 0.2–0.9 were considered as gray zone * Successful bracelet synchronization on more than 50% of days around symptom onset.</p

Results from multilevel linear mixed models showing the main effects of infection phase and sex as well as the interactions between the two with regards to changes in physiological signals.

Results from multilevel linear mixed models showing the main effects of infection phase and sex as well as the interactions between the two with regards to changes in physiological signals.</p