Global burden of pertussis in 204 countries and territories, from 1990 to 2019: results from the Global Burden of Disease Study 2019.

OBJECTIVES: This study aimed to examine the impact of pertussis on the global, regional, and national levels between 1990 and 2019. METHODS: Data on pertussis on a global scale from 1990 to 2019 were collected from the 2019 Global Burden of Disease Study. We performed a secondary analysis to report the global epidemiology and disease burden of pertussis. RESULTS: During the period spanning from 1990 to 2019, pertussis exhibited a steady global decline in the age-standardized incidence rate (ASIR), age-standardized disability-adjusted life years rate (ASYR), and age-standardized death rate (ASDR). Nevertheless, upon delving into an in-depth analysis of various regions, it was apparent that ASIR in southern sub-Saharan Africa, ASYR and ASDR in high-income North America, and ASDR in Western Europe and Australasia, were witnessing an upward trajectory. Moreover, a negative correlation was observed between the Socio­demographic Index (SDI) and burden inflicted by pertussis. Notably, the incidence of pertussis was comparatively lower in men than in women, with 0-4-year-olds emerging as the most profoundly affected demographic. CONCLUSION: The global pertussis burden decreased from 1990 to 2019. However, certain regions and countries faced an increasing disease burden. Therefore, urgent measures are required to alleviate the pertussis burden in these areas.

Interaction between air pollutants and meteorological factors on pulmonary tuberculosis in northwest China: A case study of eight districts in Urumqi.

Meteorological factors and air pollutants are associated with the spread of pulmonary tuberculosis (PTB), but few studies have examined the effects of their interactions on PTB. Therefore, this study investigated the impact of meteorological factors and air pollutants and their interactions on the risk of PTB in Urumqi, a city with a high prevalence of PTB and a high level of air pollution. The number of new PTB cases in eight districts of Urumqi from 2014 to 2019 was collected, along with data on meteorological factors and air pollutants for the same period. A generalized additive model was applied to explore the effects of meteorological factors and air pollutants and their interactions on the risk of PTB incidence. Segmented linear regression was used to estimate the nonlinear characteristics of the impact of meteorological factors on PTB. During 2014-2019, a total of 14,402 new cases of PTB were reported in eight districts, with March to May being the months of high PTB incidence. The exposure-response curves for temperature (Temp), relative humidity (RH), wind speed (WS), air pressure (AP), and diurnal temperature difference (DTR) were generally inverted "U" shaped, with the corresponding threshold values of - 5.411 °C, 52.118%, 3.513 m/s, 1021.625 hPa, and 8.161 °C, respectively. The effects of air pollutants on PTB were linear and lagged. All air pollutants were positively associated with PTB, except for O3, which was not associated with PTB, and the ER values for the effects on PTB were as follows: 0.931 (0.255, 1.612) for PM2.5, 1.028 (0.301, 1.760) for PM10, 5.061 (0.387, 9.952) for SO2, 2.830 (0.512, 5.200) for NO2, and 5.789 (1.508, 10.251) for CO. Meteorological factors and air pollutants have an interactive effect on PTB. The risk of PTB incidence was higher when in high Temp-high air pollutant, high RH-high air pollutant, high WS-high air pollutant, lowAP-high air pollutant, and high DTR-high air pollutant. In conclusion, both meteorological and pollutant factors had an influence on PTB, and the influence on PTB may have an interaction.

Effects and Interaction of Meteorological Factors on Pulmonary Tuberculosis in Urumqi, China, 2013-2019.

Background: Most existing studies have only investigated the delayed effect of meteorological factors on pulmonary tuberculosis (PTB). However, the effect of extreme climate and the interaction between meteorological factors on PTB has been rarely investigated. Methods: Newly diagonsed PTB cases and meteorological factors in Urumqi in each week between 2013 and 2019 were collected. The lag-exposure-response relationship between meteorological factors and PTB was analyzed using the distributed lag non-linear model (DLNM). The generalized additive model (GAM) was used to visualize the interaction between meteorological factors. Stratified analysis was used to explore the impact of meteorological factors on PTB in different stratification and RERI, AP and SI were used to quantitatively evaluate the interaction between meteorological factors. Results: A total of 16,793 newly diagnosed PTB cases were documented in Urumqi, China from 2013 to 2019. The median (interquartile range) temperature, relative humidity, wind speed, and PTB cases were measured as 11.3°C (-5.0-20.5), 57.7% (50.7-64.2), 4.1m/s (3.4-4.7), and 47 (37-56), respectively. The effects of temperature, relative humidity and wind speed on PTB were non-linear, which were found with the "N"-shaped, "L"-shaped, "N"-shaped distribution, respectively. With the median meteorological factor as a reference, extreme low temperature was found to have a protective effect on PTB. However, extreme high temperature, extreme high relative humidity, and extreme high wind speed were found to increase the risk of PTB and peaked at 31.8°C, 83.2%, and 7.6 m/s respectively. According to the existing monitoring data, no obvious interaction between meteorological factors was found, but low temperature and low humidity (RR = 1.149, 95%CI: 1.003-1.315), low temperature and low wind speed (RR = 1.273, 95%CI: 1.146-1.415) were more likely to cause the high incidence of PTB. Conclusion: Temperature, relative humidity and wind speed were found to play vital roles in PTB incidence with delayed and non-linear effects. Extreme high temperature, extreme high relative humidity, and extreme high wind speed could increase the risk of PTB. Moreover, low temperature and low humidity, low temperature and low wind speed may increase the incidence of PTB.

Assessing the Impact of Meteorological Conditions on Outpatient Visits for Childhood Respiratory Diseases in Urumqi, China.

OBJECTIVES: The aims of this study were to explore the impact of meteorological factors on respiratory diseases in children and to provide recommendations to local governments and health agencies to prevent respiratory diseases. METHODS: The exposure-lag effect between meteorological factors and the number of outpatients was investigated by constructing a distributed lag nonlinear model. RESULTS: Both high and low temperature will increase the risk of respiratory diseases in children, but low temperatures have a stronger effect compared with high temperatures (except for bronchopneumonia). High and low wind speeds can adversely affect respiratory diseases in children. CONCLUSIONS: Temperature and wind speed have an effect on children's respiratory diseases in Urumqi, and this effect has a time lag. Attention should be paid to the impact of adverse weather conditions on children's health.

Relationship Analysis of Inorganic Arsenic Exposure and Metabolic Syndrome Based on Propensity Score Matching in Xinjiang, China.

Purpose: The role of inorganic arsenic (iAs) in the risk of metabolic syndrome (MetS) remains unclear. This investigation focused on the effect of iAs exposure on MetS and whether the results are consistent in different subgroups. Patients and Methods: The present study was conducted on 629 men and 616 women aged 35-70 years and living in Xinjiang Uygur Autonomous Region, China. The 1:1 propensity score matching (PSM) was adopted to regulate the confounding factors, and the multivariate logistic regression was performed to assess the relationship between urinary iAs and MetS. Results: The median content of urinary iAs was examined as 2.20 µg/dL (interquartile range: 1.30-3.20 µg/dL), and the MetS prevalence reached 23.69% (295 cases/950 participants). After the confounding factors were adjusted, the ORs (95% CIs) for MetS from the minimal to the maximum urinary iAs quartiles reached 1.171 (0.736,1.863), 1.568 (1.008, 2.440) and 2.011 (1.296, 3.120), respectively (referencing 1.00) (P for trend=0.001). After the PSM, the urinary iAs content still plays a potential prediction role in MetS (P for trend=0.011). In addition, as revealed from the subgroup analysis, the urinary iAs content was a predictor of MetS in the female patients, whereas it did not serve as a significant predictor of MetS in the male patients (P for interaction<0.05). Conclusion: The increased urinary iAs content was associated with the increased prevalence of MetS in Chinese population. More attention should be paid to female urinary iAs content to avoid the high prevalence of MetS.

Investigation on spatial variability and influencing factors of drinking water iodine in Xinjiang, China.

BACKGROUND AND OBJECTIVES: Xinjiang is one of the areas in China with extremely severe iodine deficiency. The health of Xinjiang residents has been endangered for a long time. In order to provide reasonable suggestions for scientific iodine supplementation and improve the health and living standards of the people in Xinjiang, it is necessary to understand the spatial distribution of iodine content in drinking water and explore the influencing factors of spatial heterogeneity of water iodine content distribution. METHODS: The data of iodine in drinking water arrived from the annual water iodine survey in Xinjiang in 2017. The distribution of iodine content in drinking water in Xinjiang is described from three perspectives: sampling points, districts/counties, and townships/streets. ArcGIS was used for spatial auto-correlation analysis, mapping the distribution of iodine content in drinking water and visualizing the distribution of Geographically Weighted Regression (GWR) model parameter. Kriging method is used to predict the iodine content in water at non-sampling points. GWR software was used to build GWR model in order to find the factors affecting the distribution of iodine content in drinking water. RESULTS: There are 3293 sampling points in Xinjiang. The iodine content of drinking water ranges from 0 to 128 µg/L, the median is 4.15 µg/L. The iodine content in 78.6% of total sampling points are less than 10 µg/L, and only that in the 3.4% are more than 40 µg/L. Among 1054 towns' water samples in Xinjiang, 88.9% of the samples' water iodine content is less than 10 µg/L. Among the 94 studied areas, the median iodine content in drinking water in 87 areas was less than 10 µg/L, those values in 7 areas were between 10-40 µg/L, and the distribution of water iodine content in Xinjiang shows clustered. The GWR model established had found that the effects of soil type and precipitation on the distribution of iodine content in drinking water were statistically significant. CONCLUSIONS: The iodine content of drinking water in Xinjiang is generally low, but there are also some areas which their drinking water has high iodine content. Soil type and precipitation are the factors affecting the distribution of drinking water iodine content, and are statistically significant (P<0.05).