Background Air quality health index (AQHI) is derived from exposure-response coefficients calculated from air pollution and morbidity/mortality time series, which helps to understand the overall short-term health impacts of air pollution. Objective To study the effects of common air pollutants on respiratory diseases in Urumqi and to develop an AQHI for the risk of respiratory diseases in the city. Methods The daily outpatient volume data of respiratory diseases from The First Affiliated Hospital of Xinjiang Medical University, meteorological data (daily mean temperature and daily mean relative humidity), and air pollutants [fine particulate matter (PM_2.5), inhalable particulate matter (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon dioxide (CO), and ozone (O₃)] in Urumqi City, Xinjiang, China were collected from January 1, 2017 to December 31, 2021. A distributed lag nonlinear model based on quasi-Poisson distribution was constructed by time-stratified case crossover design. Adopting zero concentration of air pollutants as reference, the exposure-response coefficient (β value) was used to quantify the impact of included air pollutants on the risk of seeking medical treatment for respiratory diseases, and the AQHI was established. The association of between AQHI and the incidence of respiratory diseases and between air quality index (AQI) and the incidence of respiratory diseases was compared to evaluate the prediction effect of AQHI. Results Each 10 µg·m⁻³ increase in PM₁₀, SO₂, NO₂, and O₃ concentrations presented the highest excess risk of seeking outpatient services at 3 d cumulative lag (Lag03) and 2d cumulative lag (Lag02), with increased risks of morbidity of 0.687% (95%CI: 0.101%, 1.276%), 17.609% (95%CI: 3.253%, 33.961%), 13.344% (95%CI: 8.619%, 18.275%), and 4.921% (95%CI: 1.401%, 8.502%), respectively. There was no statistically significant PM_2.5 or CO lag effect. An AQHI was constructed based on a model containing PM₁₀, SO₂, NO₂, and O₃, and the results showed that the excess risk of respiratory disease consultation for the whole population, different genders, ages, or seasons for each inter-quartile range increase in the AQHI was higher than the corresponding value of AQI. Conclusion PM₁₀, SO₂, NO₂, and O₃ impact the number of outpatient visits for respiratory diseases in Urumqi, and the constructed AQHI for the risk of respiratory diseases in Urumqi outperforms the AQI in predicting the effect of air pollution on respiratory health.

Background The effects of meteorological factors and air pollutants on ischemic heart disease (IHD) hospitalizations in Urumqi have not been fully understood. Objective To investigate the effects of meteorological conditions (temperature, relative humidity) and common air pollutants [fine particulate matter (PM_2.5), inhalable particulate matter (PM₁₀), ozone (O₃), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and carbon monoxide (CO)] on the daily hospitalization volume of IHD, and to provide a scientific basis for the development of targeted prevention and management strategies. Methods Basic information of 90622 IHD hospitalized patients in Urumqi City was collected from January 1, 2014 to December 31, 2022, and daily air pollutant concentration data and meteorological data were collected during the same period. The meteorological data included daily mean temperature, daily maximum temperature, daily minimum temperature, and daily mean relative humidity, and the common air pollutants included PM_2.5, PM₁₀, daily maximum 8-h average concentration of O₃ (O₃_8 h), SO₂, NO₂, and CO. Descriptive statistical analyses (mean, standard deviation, and quartiles) were performed on the collected data. A distribution lag nonlinear model (DLNM) based on time-stratified case-crossover design was established to estimate the lagged effects (RR) as well as the cumulative lagged effects (RR_cum) of daily mean temperature, relative humidity, and different air pollutants on the number of IHD admissions, stratified by gender and age (<60 and ≥60 years). Subsequently, a generalized additive model (GAM) was used to explore the effects of cold waves (≤ P₅ of the daily minimum temperature and lasting ≥3 d) versus heat waves (≥ P₉₅ of the daily maximum temperature and lasting ≥3 d) on IHD hospitalizations and the potential interactions between temperature and the more influential air pollutants [particulate matter (PM) and O₃_8 h] on IHD hospitalizations. Results The maximum number of IHD hospitalizations in a single day in Urumqi from January 1, 2014 to December 31, 2022 was 100, and the average daily number of hospitalized IHD patients was 27. Daily mean temperature, relative humidity, and air pollutants had different nonlinear relationships and hysteresis with IHD hospitalizations. Extremely low temperatures (<P₁: −18 °C) showed the greatest risk to admission for the total IHD population at a cumulative lag of 31 d (lag0-31) (RR_cum=2.092, 95%CI: 1.261,3.471). Female [lag0-31, RR_cum and 95%CI: 3.416 (2.296,5.082)] and people ≥60 years old [lag0-31, RR_cum and 95%CI: 2.356 (1.681,3.303)] were more sensitive to extremely low temperatures. The risk of IHD hospitalization was greatest on lag20 for cold waves (RR=1.242, 95%CI: 1.053, 1.465), and no lagged effect of heat waves on the number of IHD hospitalizations was found. There was a positive interaction (SI >1, P<0.001) between temperature, PM, and O₃_8 h on the risk of IHD hospitalization, especially for low temperature-high PM and O₃_8 h. Conclusion Both low temperature and high pollutant concentrations associate with an increase in IHD hospitalizations with lagged effects in Urumqi from 2014 to 2022, while cold waves associate with a higher risk of IHD admissions. Females and people aged 60 years and older are at high risk.

Objective : To analyze the incidence characteristics and trends in pulmonary tuberculosis in the Hotan prefecture , before and after the epidemic , and to provide a reference basis for the formulation and evaluation of tuberculosis prevention and control measures in the Hotan prefecture . Methods : The Hotan prefecture ’s pulmonary tuberculosis incidence data was collected between 2015 and 2021 . Joinpoint regression (JPR) model and Interrupted Time Series (ITS) model were established to explore the incidence trend of pulmonary tuberculosis , as well as the impact of COVID-19 prevention and control measures in Xinjiang on the incidence trend in Hotan , respectively. Furthermore , an analysis of variations in incidence among different age and gender subgroups was carried out. Results: The results of the JPR model showed that from 2015 to 2021 , the reported incidence rate of pulmonary tuberculosis in the Hotan prefecture initially increased and then decreased , with a turning point appearing in December 2018 . The incidence rate in males was slightly higher than that in females , and the turning point and incidence trend were consistent with the overall trend . Among all age subgroups , those ≥60 age group had the highest incidence rate , with the trend also showing an initial increase followed by a decrease . A turning point in the incidence rate for the under 18 age group appeared in June 2021 , yet the trend was not statistically significant (P > 0. 05) .The turning points in the 19 - 59 age group and in those aged ≥60 were consistent with the overall trend . The results of the ITS model showed that the incidence rate of pulmonary tuberculosis in the Hotan prefecture significantly decreased since January 2020 , dropping from 319. 28 per 100 000 in 2019 to 155 . 88 per 100 000 in 2021 , a decrease of 51 . 16% year-on-year , with a monthly average reduction of 0. 049 per 100 000 . Conclusion In 2018 ,Xinjiang province integrated tuberculosis screening into the universal health checkup for the entire population ,which led to the identification of numerous cases of tuberculosis . In the Hotan prefecture , the reported incidence of pulmonary tuberculosis peaked in December 2018 and then started to decline . Under the impact of COVID-19 isolation measures in Xinjiang , the reported incidence rate showed a notable decrease starting in January 2020 . Reiterating preventive measures and remaining watchful for the possible appearance of latent tuberculosis patients is crucial as the pandemic fades .