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Introduction: The effects of exposure to particulate matter and frailty, as well as its exposure-response relationship, have not been effectively explored. This study aimed to explore the association between long-term exposure to particulate matter and frailty state and each dimension in Chinese middle-aged and older adults, in addition to the exposure-response relationship. Methods: The data were obtained from the National Urban Air Quality Real-Time Dissemination Platform and China Health and Retirement Longitudinal Study (CHARLS). Frailty was measured by a frailty index containing 39 indicators. Annual averages of seven pollutants were calculated from hourly monitoring data. We used multilevel regression modeling to explore the association between long-term exposure to particulate matter and frailty. Meanwhile, we explored the exposure-response relationship based on a multilevel generalized summation model. We performed a sensitivity analysis using a multi-pollution model and a quantile-based g-computation (QGC) model. Results: A total of 15,611 participants were included in the analysis. We find that long-term exposure to PM2.5 was associated with an increased risk of pre-frailty and frailty (all p < 0.05). PMc and PM10 exhibited similar associations. The exposure-response relationship between PM2.5 showed a linear relationship, whereas the exposure-response relationship between PM10, PMc showed a nonlinear relationship. Elevated PM2.5 concentrations showed significant positive associations with the number of chronic disease score, IADL score, and functional limitation status score (all p < 0.05). PM10 and PMc showed similar positive correlations. These results remained robust after sensitivity analyses using a multi-pollution model and QGC model. Conclusion: Chronic exposure to particulate matter was significantly associated with increased risk of frailty. The exposure-response relationship between PM2.5 concentration and frailty showed a linear relationship, and the exposure-response relationship between PM10 and PMc showed a nonlinear relationship. Exposure to a mixture of pollutants carried a higher risk of frailty than exposure to a single pollutant. [ABSTRACT FROM AUTHOR]

Syndromic surveillance is a type of public health surveillance that utilizes nonspecific indicators or symptoms associated with a particular disease or condition to detect and track disease outbreaks early. However, data completeness has been a significant challenge for syndromic surveillance systems in many countries. Incomplete data may make it difficult to accurately identify anomalies or trends in surveillance data. In this study, a new disease mapping method based on a high‐accuracy, low‐rank tensor completion (HaLRTC) algorithm is proposed to estimate the quarterly positivity rate of the human influenza virus (IFV) based on highly insufficient 2010–2015 respiratory syndromic surveillance data from the subtropical monsoon region of China. The HaLRTC algorithm is a spatiotemporal interpolation method applied to fill in missing or incomplete data using a low‐rank tensor structure. The results show that the accuracy (R2 = 0.880, RMSE = 0.037) of the proposed method is much higher than that of three traditional disease mapping methods: Cokriging, hierarchical Bayesian, and sandwich estimation methods. This study provides a new disease mapping approach to improve the quality and completeness of data in syndrome surveillance or other familiar systems with a large proportion of missing data. [ABSTRACT FROM AUTHOR]

Background This study aimed to assess the long-term effects of size-specific particulate matter (PM) on frailty transitions in middle-aged and older Chinese adults. Methods We included 13 910 participants ≥45 y of age from the China Health and Retirement Longitudinal Study (CHARLS) for 2015 and 2018 who were classified into three categories in 2015 according to their frailty states: robust, prefrail and frail. Air quality data were obtained from the National Urban Air Quality Real-time Publishing Platform. A two-level logistic regression model was used to examine the association between concentrations of PM and frailty transitions. Results At baseline, the total number of robust, prefrail and frail participants were 7516 (54.0%), 4324 (31.1%) and 2070 (14.9%), respectively. Significant associations were found between PM concentrations and frailty transitions. For each 10 μg/m3 increase in the 3-y averaged 2.5-μm PM (PM2.5) concentrations, the risk of worsening in frailty increased in robust (odds ratio [OR] 1.06 [95% confidence interval {CI} 1.01 to 1.12]) and prefrail (OR 1.07 [95% CI 1.01 to 1.13]) participants, while the probability of improvement in frailty in prefrail (OR 0.91 [95% CI 0.84 to 0.98]) participants decreased. In addition, the associations of PM10 and coarse fraction of PM with frailty transitions showed similar patterns. Conclusions Long-term exposure to PM was associated with higher risks of worsening and lower risks of improvement in frailty among middle-aged and older adults in China. [ABSTRACT FROM AUTHOR]

Arsenic is a highly dangerous metal that has been linked to a number of adverse health effects in both adults and children, including birth defects. Yet few epidemiologic studies have examined the relationship between arsenic levels in the soil and the risk of birth defects. The purpose of the authors' study was to examine this association among people exposed to environmental pollution in a developed area of China. The authors used global positioning system to locate the coordinates of 80 villages in 40 towns for soil sampling. Soil samples were analyzed for arsenic content. Logistic regression was used to investigate the relationship between exposure to arsenic and birth defects, controlling for potentially confounding factors. The authors found that exposure to arsenic in any amount increased the risk of birth defects. The positive association found between arsenic exposure and birth defects warrants further study, and future large-scale population-based studies are needed with an emphasis on individual-level exposure and confounding variables., Introduction Arsenic is a widely distributed metalloid occurring in rock, soil, water, and air. It is usually considered along with lead, cadmium, and mercury as a threat to human health [...]

Abstract Background Birth defects are a major cause of infant mortality and disability in many parts of the world. Neural tube defects (NTDs) are one of the most common types of birth defects. In 2001, the Chinese population and family planning commission initiated a national intervention program for the prevention of birth defects. A key step in the program was the introduction of folic acid supplementation. Of interest in the present study was to determine whether folic acid supplementation has the same protective effect on NTDs under various geographical and socioeconomic conditions within the Chinese population and the nature in which the influence of environmental factors varied after folic acid supplementation. Methods In this study, Heshun was selected as the region of interest as a surrogate for helping to answer some of the questions raised in this study on the impact of the intervention program. Spatial filtering in combination with GIS software was used to detect annual potential clusters from 1998 to 2005 in Heshun, and Kruskal-wallis test and multivariate regression were applied to identify the environmental risk factors for NTDs among various regions. Results In 1998, a significant (p < 0.100) NTDs cluster was detected in the west of Heshun. After folic acid supplementation, the significant clusters gradually moved from west to east. However, during the study period, most of the clusters appeared in the middle region of Heshun where more than 95 percent of the coal mines of Heshun are located. For the analysis, buffer regions of the coal mine zone were built in a GIS environment. It was found that the correlations between environmental risk factors and NTDs vary among the buffer regions. Conclusion This suggests that the government needs to adapt the intervention measures according to local conditions. More attention needs to be paid to the poor and to people living in areas near coal mines.

Background Neural tube defects (NTDs) are congenital birth defects of the central nervous system that affect 0.5–2 per 1000 pregnancies worldwide. Therefore effective interventions for birth defects, especially NTDs, are very important. Methods Yuanping City is a coal mining city in Shanxi Province, China, with a high incidence of NTDs. This study evaluates the effects of NTD interventions in this city after adjusting for covariates that characterize the native environment. The number of NTD cases and births for the 18 towns in Yuanping City from 2007 to 2014 were included in the study. A shared-component zero-inflated Poisson regression was applied to analyse the temporal–spatial variance among the incidence rates of NTDs in Yuanping City before and after the interventions. Results The results showed that existing interventions to mitigate birth defects, such as folic acid supplementation, reduced the incidence of NTDs by 53.5% in coal mining areas in Yuanping City. However, the NTD risk in areas near coal mines, especially unrestored coal mines, was still high, even after the intervention. Conclusions The government should focus on health hazards related to mining and agricultural production and should provide education and resources to reduce environmental exposure. Reducing environmental risks should be regarded as an early intervention strategy to mitigate birth defects. [ABSTRACT FROM AUTHOR]

Brucellosis is a natural epidemic zoonotic disease. Liaoning province, north‐east of China, has been among the top 10 provinces with highest brucellosis incidence. In this study, the spatial and temporal distribution of brucellosis in Liaoning Province from 2006 through 2017 was analysed using the Bayesian theory of space–time modelling. The study found that in Liaoning Province, (a) all regions of the entire study area were stable counties; (b) the risk of brucellosis declined slowly with time without an obvious trend; (c) the declining trend of disease risk in three sub‐hot‐spot counties was faster than the overall trend, whereas in other counties, the trend was similar to the overall trend. Furthermore, the time and spatial trends of brucellosis incidence in Liaoning Province were calculated and analysed. These results may provide a theoretical and scientific basis for the public health department to develop targeted effective prevention and control measures for the disease. [ABSTRACT FROM AUTHOR]

Background Esophageal cancer (EC) is one of the most common cancers, causing many people to die every year worldwide. Accurate estimations of the spatial distribution of EC are essential for effective cancer prevention. Methods EC mortality surveillance data covering 964 surveyed counties in China in 2014 and three classes of auxiliary data, including physical condition, living habits and living environment data, were collected. Genetic programming (GP), a hierarchical Bayesian model and sandwich estimation were used to estimate the spatial distribution of female EC mortality. Finally, we evaluated the accuracy of the three mapping methods. Results The results show that compared with the root square mean error (RMSE) of the hierarchical Bayesian model at 6.546 and the sandwich estimation at 7.611, the RMSE of GP is the lowest at 5.894. According to the distribution estimated by GP, the mortality of female EC was low in some regions of Northeast China, Northwest China and southern China; in some regions downstream of the Yellow River Basin, north of the Yangtze River in the Yangtze River Basin and in Southwest China, the mortality rate was relatively high. Conclusions This paper provides an accurate map of female EC mortality in China. A series of targeted preventive measures can be proposed based on the spatial disparities displayed on the map. [ABSTRACT FROM AUTHOR]

High-accuracy spatial distribution estimation is crucial for cancer prevention and control. Due to their complicated pathogenic factors, the distributions of many cancers' mortalities appear blocky, and spatial heterogeneity is common. However, most of the commonly used cancer mapping methods are based on spatial autocorrelation theory. Sandwich estimation is a new method based on spatial heterogeneity theory. A modified sandwich estimation method suitable for the estimation of cancer mortality distribution is proposed in this study. The variances of cancer mortality data are used to fuse sandwich estimation results from various auxiliary variables, the feasibility of which in estimating cancer mortality distributions is explained theoretically. The breast cancer (BC) mortality of the Chinese mainland in 2005 was taken as a case, and the accuracy of the modified sandwich estimation method was compared with that of the Hierarchical Bayesian (HB), the Co-Kriging (CK) and the Ordinary Kriging (OK) methods. The accuracy of the modified sandwich estimation method was better than the HB, the CK and the OK methods, and the estimation result from the modified sandwich estimation method was more likely to be acceptable. Therefore, this study represents an attempt to apply the sandwich estimation method to the estimation of cancer mortality distributions with strong spatial heterogeneity, which holds great potential for further application. [ABSTRACT FROM AUTHOR]