Determination and risk assessment of perfluorinated alkylated substances (PFASs) in pork liver from mainland, China

Abstract. To enhance the estimation accuracy of economic loss and casualty in seismic risk assessment, a high-resolution building exposure model is necessary. Previous studies in developing global and regional building exposure models usually use coarse administrative-level (e.g. country or sub-country level) census data as model inputs, which cannot fully reflect the spatial heterogeneity of buildings in large countries like China. To develop a high-resolution residential building stock model for mainland China, this paper uses finer urbanity-level population and building-related statistics extracted from the records in the tabulation of the 2010 population census of the People's Republic of China (hereafter abbreviated as the “2010 census”). In the 2010 census records, for each province, the building-related statistics are categorized into three urbanity levels (urban, township, and rural). To disaggregate these statistics into high-resolution grid level, we need to determine the urbanity attributes of grids within each province. For this purpose, the geo-coded population density profile (with 1 km × 1 km resolution) developed in the 2015 Global Human Settlement Layer (GSHL) project is selected. Then for each province, the grids are assigned with urban, township, or rural attributes according to the population density in the 2015 GHSL profile. Next, the urbanity-level building-related statistics can be disaggregated into grids, and the 2015 GHSL population in each grid is used as the disaggregation weight. Based on the four structure types (steel and reinforced concrete, mixed, brick and wood, other) and five storey classes (1, 2–3, 4–6, 7–9, ≥10) of residential buildings classified in the 2010 census records, we reclassify the residential buildings into 17 building subtypes attached with both structure type and storey class and estimate their unit construction prices. Finally, we develop a geo-coded 1 km × 1 km resolution residential building exposure model for 31 provinces of mainland China. In each 1 km × 1 km grid, the floor areas of the 17 residential building subtypes and their replacement values are estimated. The model performance is evaluated to be satisfactory, and its practicability in seismic risk assessment is also confirmed. Limitations of the proposed model and directions for future improvement are discussed. The whole modelling process presented in this paper is fully reproducible, and all the modelled results are publicly accessible.

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Missed opportunities for venous thromboembolism prophylaxis during pregnancy and the postpartum period: evidence from mainland China in 2019

BMC Pregnancy and Childbirth ◽

10.1186/s12884-021-03863-w ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Zhekun Zhao ◽

Qiongjie Zhou ◽

Xiaotian Li

Keyword(s):

Risk Assessment ◽

Deep Vein Thrombosis ◽

Pulmonary Thromboembolism ◽

Mainland China ◽

Anticoagulant Treatment ◽

Maternal Healthcare ◽

Missed Opportunities ◽

Vein Thrombosis ◽

Mechanical Methods ◽

Deep Vein

Abstract Background Venous thromboembolism (VTE) has become one of the leading causes of maternal mortality. Thromboprophylaxis is recommended for the prevention of this condition; however, its use appears to be insufficient. Therefore, in this study, we aimed to identify the missed opportunities for VTE prophylaxis in hospitals that provide maternal healthcare in mainland China. Methods In this cross-sectional survey-based study, we collected case data on pregnant and puerperal women with deep vein thrombosis and pulmonary thromboembolism from January 1st to December 31st, 2019. Demographics, obstetric information, VTE risk assessment scores, and prophylaxis-related information were recorded. Thromboprophylaxis included mobilization, mechanical methods, and treatment with anticoagulants. Results Data corresponding to a total of 106 cases from 26 hospitals across China were collected, and 100 (94.3%) cases, 75 cases involving deep vein thrombosis and 25 cases involving pulmonary thromboembolism, were included in the final analysis. VTE occurred in 80% of the patients at the postpartum stage, while 20 patients developed the disease during the antenatal stage. Cesarean section, advanced maternal age, and obesity were the most common risk factors related to VTE during the postpartum stage, while a previous VTE-related history was a prominent risk factor among antenatal cases. Up to 75% of the patients had one or more missed opportunities for prophylaxis. The lack of the implementation of mechanical methods (60.8% vs. 24.5%, P < 0.001) and anticoagulant treatment (61.1% vs. 48.7%, P < 0.001) were more common in general hospitals compared to those of specialized hospitals. In women assessed as high-risk, anticoagulant treatment was lacking in 41 (54.7%) cases. More importantly, the lack of the implementation of mechanical methods was more common among women assessed as low-risk (56.0% vs. 38.7%, P < 0.001). Among the antenatal cases, the lack of treatment with anticoagulants (100.0% vs. 48.5%, P < 0.001) and implementation of mechanical methods (70.0% vs. 36.7%, P < 0.001) was highlighted. In addition, the lack of early mobilization was much more prominent among the PTE cases (10.5% vs. 37.5%, P < 0.001). Conclusions At least one prophylactic opportunity was missed in most of the enrolled Chinese women with VTE during the course of their maternal healthcare. Missed prophylactic opportunities varied with the type of hospitals, risk assessment, onset timing and disease type. Further efforts from patients, healthcare systems, and healthcare providers are needed for improving the implementation of preventative strategies.

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Residential building stock modeling for mainland China targeted for seismic risk assessment

10.5194/nhess-2021-26 ◽

2021 ◽

Author(s):

Danhua Xin ◽

James Edward Daniell ◽

Hing-Ho Tsang ◽

Friedemann Wenzel

Keyword(s):

Risk Assessment ◽

High Resolution ◽

Seismic Risk ◽

Residential Buildings ◽

Mainland China ◽

Residential Building ◽

Exposure Model ◽

Building Stock ◽

Seismic Risk Assessment ◽

Residential Building Stock

Abstract. Previous seismic damage reports have shown that the damage and collapse of buildings is the leading cause of fatality and property loss. To enhance the estimation accuracy of economic loss and fatality in seismic risk assessment, a high-resolution building exposure model is important. Previous studies in developing global and regional building exposure models usually use coarse administrative level (e.g., county, or sub-country level) census data as model inputs, which cannot fully reflect the spatial heterogeneity of buildings in large countries like China. To develop a high-resolution residential building stock model for mainland China, this paper uses finer urbanity level population and building-related statistics extracted from the records in Tabulation of the 2010 Population Census of the People’s Republic of China (hereafter abbreviated as the “2010-census”). In the 2010-census records, for each province, the building-related statistics are categorized into three urbanity levels (urban, township, and rural). Statistics of each urbanity level are from areas with a similar development background but belong to different administrative prefectures and counties. Due to privacy protection-related issues, these urbanity level statistics are not geo-coded. Therefore, before disaggregating these statistics into high-resolution grid level, we need to determine the urbanity attributes of grids within each province. For this purpose, the geo-coded population density profile (with 1 km × 1 km resolution) developed in the 2015 Global Human Settlement Layer (GSHL) project is selected to divide the 31 provinces of mainland China into 1 km × 1 km grids. Then for each province, the grids are assigned with urban/township/rural attributes according to the population density in the 2015 GHSL profile. Next for each urbanity of each province, the urbanity level building-related statistics extracted from the 2010-census records can be disaggregated into the 2015 GHSL geo-coded grids, and the 2015 GHSL population in each grid is used as the disaggregation weight. Based on the four structure types (steel/reinforced-concrete, mixed, brick/wood, other) and five storey classes (1, 2–3, 4–6, 7–9, ≥ 10) of residential buildings classified in the 2010-census records, we reclassify the residential buildings into 17 building subtypes attached with both structure type and storey class and estimate their unit construction prices. Finally, we develop a geo-coded 1 km × 1 km resolution residential building exposure model for 31 provinces of mainland China. In each 1 km × 1 km grid, the floor areas of the 17 residential building subtypes and their replacement values are estimated. To evaluate the model performance, comparisons with the wealth capital stock values estimated in previous studies at the administrative prefecture-level and with the residential floor area statistics in the 2010-census at the administrative county/prefecture-level are conducted. The practicability of the modeled results in seismic risk assessment is also checked by estimating the seismic loss of residential buildings in Sichuan Province combined with the intensity map of the 2008 Wenchuan Ms8.0 earthquake and an empirical loss function developed from historical seismic damage information in China. Our estimated seismic loss range is close to that derived from field investigation reports. Limitations of this paper and future improvement directions are discussed. More importantly, the whole modeling process of this paper is fully reproducible, and all the modeled results are publicly accessible. Given that the building stock in China is changing rapidly, the results can be conveniently updated when new datasets are available.

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