The deviation of farmers' willingness and behavior of domestic waste separation: a study on neighborhood effects and policy interventions.

Based on the perspective of combining informal and formal systems, this paper empirically explores the impact of neighborhood effects and policy interventions on the deviation of farmers' willingness and behavior of domestic waste separation (DWS) by using data from the China Land Economy Survey (CLES) and constructing a probit model. It should be explained that the neighborhood effect in this paper refers to the fact that the behavior of farmers is highly susceptible to the behavior of their neighbors in the process of production and living. The results of the study show that neighborhood effects and policy interventions have a significant negative impact on the deviation of farmers' willingness and behavior of DWS, respectively. Comparison of marginal effects shows that neighborhood effects > environmental advocacy > reward and punishment policies. From the interaction effects as a whole, neighborhood effects and policy interventions have complementary effects on the deviation of farmers' willingness and behavior of DWS, with the complementary effects of neighborhood effects and environmental advocacy being more significant. Heterogeneity analysis reveals that neighborhood effects completely replace the inhibitory effect of policy interventions on the deviation of high-income farmers' willingness and behavior of DWS, but have no effect on political elite farmers. The interaction between neighborhood effects and policy interventions has complementary effects on low-income farmers and ordinary farmers, with the complementary effects of neighborhood effects and environmental advocacy being more significant.

Inequality characteristics and influencing factors of CO2 emissions per capita in Jiangsu Province, China.

Analyzing the inequality characteristics and influencing factors of CO2 emissions per capita (CEPC) is conducive to balancing regional development and CO2 emissions reduction. This study applied the Gini coefficient and Theil index to investigate the CEPC inequalities during 2005-2017 at the county level in Jiangsu Province, China. Considering the spatial spillover and interaction effects, the factors influencing CEPC were analyzed by a hierarchical spatial autoregressive model. The results showed that the inequalities in CEPC first increased and then decreased at the inter-regional, and inter-county levels. The spatial pattern of CEPC was stable, and there was a significantly positive spatial autocorrelation of CEPC at the county level. The High-High type counties were mainly located in Sunan (southern Jiangsu). The spatial interaction effects of the CEPC between the prefecture and county levels indicated that governments at the prefecture level should integrate their county governments to reduce the CEPC. Moreover, carbon intensity, GDP per capita, land urbanization, and industrial structure play an important role in reducing CEPC. Our findings provide a scientific basis for formulating reasonable and effective carbon emission reduction policies.

Surveillance of noise exposure level in industrial enterprises-Jiangsu Province, China, 2022.

Occupational noise exposure is the most prominent problem in industrial enterprises in Jiangsu Province. Since 2019, China has established the National Surveillance System for Occupational Hazards in the workplace to grasp the current occupational hazards in critical industries, including occupational noise. According to the Work Plan for Surveillance of Occupational Hazards in the Workplace (2022) issued by the National Health Commission of the People's Republic of China, the noise exposure level of 3,142 enterprises in our province was monitored, the median and interquartile range (IQR) were calculated, and the distribution of noise exposure level was described by industry classification, enterprise-scale and ownership type of the enterprise. The railway, shipping, aerospace, and other transportation equipment manufacturing industries exhibited the highest proportion (42.9%) of individual noise exposure levels exceeding 85 dB(A), followed by the motor vehicles manufacturing industry (36.4%). The proportion of individual noise exposure levels exceeding 85 dB(A) was higher in medium and small enterprises, with rates of 28.1 and 28.6%, respectively. The highest proportion of personal noise exposure levels exceeding 85 dB(A) was observed in Hong Kong, Macao and Taiwan investment enterprises (37.5%), followed by incorporated companies (34.6%) and limited liability companies (28.1%), the lowest was state-owned enterprises(15.5%). The areas with excessive noise are primarily concentrated in grinding, welding, machining, cutting, and other related operations, accounting for 61.2% of the total. Among these operations, grinding accounts for 29.8%. The highest environmental noise and individual noise intensity were found in sandblasting and grinding positions, with individual noise intensities of 115.5 dB(A) and 108.4 dB(A), respectively. The noise exposure risk is so high that cannot be ignored in the manufacturing industry, especially in Hong Kong, Macao, and Taiwan investment enterprises, incorporated companies and medium and small enterprises.

Occurrence, distribution, and risk assessment of pesticides in surface water and sediment in Jiangsu Province, China.

The occurrence and distribution of 157 pesticides were investigated in surface water and sediment in Jiangsu Province, China. Gas chromatography-mass spectrometry was used to analyze and quantify these pesticides, and the risk quotient method was used to evaluate their respective environmental risk. The results showed that 91 pesticides were detected in surface water. The organophosphates (OPPs), fungicides, and amide herbicides were predominant. The total concentration in surface water ranged from 63.7 to 22,463 ng/L, 3.90 to 7262 ng/L, and ND to 34,120 ng/L, respectively. The mean concentration was 3479 ng/L, 1644 ng/L, and 1878 ng/L, respectively. The concentration range of detected pesticides in the Yangtze River Basin was generally lower than that in the Huai River Basin. In sediment samples, a total of 63 pesticides were detected. OPPs and amide herbicides were also ranked highest; the total concentration in sediment samples ranged from 2951 to 47,739 ng/g and 106 to 12,996 ng/g, respectively. And the mean concentrations was 6971 ng/g and 5130 ng/g, respectively. Suqian City had the highest concentration for OPPs and amide herbicides in the Huai River Basin, followed by Huai'an City, while Nanjing City and Yangzhou City ranked highest in the Yangtze River Basin. The spatial distribution of pesticides in Jiangsu Province indicated a concentration significantly higher in the western and northern regions than in the eastern and southern regions, and a concentration generally higher in lakes than in rivers. The risk assessment results showed that OPPs, fungicides, amide herbicides, organochlorines, and triazine herbicides in most surface water samples posed a high risk and had regional pollution characteristics. In sediment samples, organochlorines, carbamates, other herbicides, and other insecticides posed a high risk in northern Jiangsu Province, whereas OPPs, amide herbicides, and triazine herbicides posed high risks everywhere in Jiangsu Province.

Carbon budget and evaluation of marine industry in Jiangsu Province, China.

The measurement and evaluation of carbon budget of marine industry is the basis for promoting green and efficient development of marine economy under the goal of carbon neutrality. We constructed a carbon accounting system for the marine industry in Jiangsu Province, and assessed carbon efficiency and neutrality. The results showed that from 2016 to 2020, the total amount of marine carbon sinks in Jiangsu Province were 894.8 to 2773.2 thousand tons, while carbon emissions of major marine industries were 3538.4 to 4350.6 thousand tons. The net emissions of marine industries ranged from 1478.7 to 2906.1 thousand tons. Both of carbon sinks and emissions were significantly increased in this period. In terms of carbon sinks, the offshore wind power accounted for the largest contribution, followed by ecosystem carbon sequestration, and mariculture carbon sequestration was the smallest. In terms of carbon emissions, the marine transportation industry played a dominant role, followed by coastal tourism and marine fisheries, while the marine engineering and construction industry and marine shipping industry accounted for a small proportion. In general, the carbon neutral status showed that marine industry in Jiangsu Province was in carbon deficit from 2016-2020, but the net emissions were decreasing year by year. The net carbon sink efficiency of mariculture in Jiangsu Province was lower than the national level, and carbon efficiency of offshore wind power was stable.

[Change trends and related risk factors of disease burden on mesothelioma in Jiangsu Province from 1990 to 2019].

Objective: To analyze the change trends and risk factors of mesothelioma disease burden in Jiangsu Province from 1990 to 2019. Methods: In January 2022, using the 2019 Global Burden of Disease Study Data, the Joinpoint regression model was used to analyze the change trends of incidence, mortality, disable-adjusted life years (DALY) and premature mortality of mesothelioma residents in Jiangsu Province from 1990 to 2019, and the attribution level of mesothelioma risk factors was estimated by population attributing fraction. Results: The standardized incidence rates of mesothelioma in Jiangsu Province from 1990 to 2019 ranged from 0.07/10(5) to 0.09/10(5), with an average annual percentage change (AAPC) of -1.1% (t=-13.56, P<0.001). AAPCs in males and females were -0.3% (t=-2.18, P=0.029) and -1.6% (t=-11.39, P<0.001), respectively. The standardized mortality rates of mesothelioma ranged from 0.07/10(5) to 0.09/10(5), the AAPC was -1.1% (t=-12.23, P<0.001), AAPC was -1.6% (t=-14.09, P<0.001) for females, and there was no significant change in males (t=-1.83, P=0.068). The premature mortality was 0.004%-0.006%, the AAPC was -1.0% (t=-4.40, P<0.001), AAPC was -1.7% (t=-13.72, P<0.001) for females, and there was no significant change in males (t=-0.68, P=0.495). The standardized DALY rates ranged from 1.86/10(5) to 2.32/10(5), the AAPC was -0.9% (t=-11.08, P<0.001), AAPC was -1.6% (t=-11.05, P<0.001) for females, and there was no significant change in males (t=-0.95, P=0.343). Both the standardized years of life lost (YLL) rate and the standardized years lived with disability (YLD) rate showed a decreasing trend, and the AAPCs were -0.9% (t=-7.66, P<0.001) and -1.0% (t=-12.88, P<0.001), respectively. The proportion of YLL in DALY was more than 98.5%. Among the risk factors for mesothelioma burden attribution, the AAPC attributed to occupational asbestos exposure of DALY was 1.4% (t=3.43, P=0.001). The AAPC of DALY rate of standardized attribution was -1.7% (t=-12.11, P<0.001) . Conclusion: The overall burden of mesothelioma in Jiangsu Province is decreasing, occupational asbestos exposure is still the main risk factor of mesothelioma in Jiangsu Province, and early diagnosis and treatment should be strengthened.

Compensation mechanisms for fishermen quit fishing: A case of Jiangsu province, China.

The 'Ten-year Ban on Fishing' policy was designed by the Chinese government to protect the biodiversity of the Yangtze River basin. Fishermen are the ultimate implementers of the fishing ban policy. Therefore, a scientific compensation mechanism for fishers to stop fishing is the basis for ensuring the continuous implementation of the policy. First, we conducted a survey with 309 fishermen in eight cities along the Yangtze River in Jiangsu province. We also analyzed living conditions of fishermen before and after quitting fishing based on descriptive statistical analysis. Based on the theory of sustainable livelihood, a binary logistic regression model was used to analyze the relationship between fishermen's willingness to quit fishing and five types of livelihood capital (natural, material, human, financial, and social capital). The results showed that fishermen face severe livelihood sustainability issues after ceasing to fish and that their willingness to quit is closely related to the five types of livelihood capital. Based on this, and according to different age groups, this study constructed a compensation mechanism for retired fishermen from two aspects: monetary and social security compensation. The research results can provide a theoretical framework for other provinces in the Yangtze River basin to formulate a compensation system for fishermen.

Assessing the mediating role of motivation in the relationship between perceived management support and perceived job satisfaction among family doctors in Jiangsu province, China.

BACKGROUND: The study aimed to examine the influence of perceived management support on job satisfaction through the mediating role of motivation among family doctors in the Jiangsu province of China. METHODS: Six dimensions of motivation were employed as mediators in the association between perceived management support and job satisfaction in collecting data to analyze the hypothesized relationships in the present study. A total of 600 questionnaires were distributed to family doctors in Jiangsu province. Structural equation model (SEM) in the analysis of a moment structure (AMOS) version 26 software was used to estimate the path coefficients. RESULTS: Perceived management support has a significant positive relationship with job satisfaction. Motivation had a fully mediated relationship with the association between perceived management support and job satisfaction. CONCLUSIONS: The study's findings suggest motivation is important in enhancing family doctors' satisfaction and must not be underestimated. It, therefore, offers diverse recommendations for enhancing motivation among healthcare professionals.

[Risk predictive models of healthcare-seeking delay among imported malaria patients in Jiangsu Province based on the machine learning].

OBJECTIVE: To create risk predictive models of healthcare-seeking delay among imported malaria patients in Jiangsu Province based on machine learning algorithms, so as to provide insights into early identification of imported malaria cases in Jiangsu Province. METHODS: Case investigation, first symptoms and time of initial diagnosis of imported malaria patients in Jiangsu Province in 2019 were captured from Infectious Disease Report Information Management System and Parasitic Disease Prevention and Control Information Management System of Chinese Center for Disease Control and Prevention. The risk predictive models of healthcare-seeking delay among imported malaria patients were created with the back propagation (BP) neural network model, logistic regression model, random forest model and Bayesian model using thirteen factors as independent variables, including occupation, species of malaria parasite, main clinical manifestations, presence of complications, severity of disease, age, duration of residing abroad, frequency of malaria parasite infections abroad, incubation period, level of institution at initial diagnosis, country of origin, number of individuals travelling with patients and way to go abroad, and time of healthcare-seeking delay as a dependent variable. Logistic regression model was visualized using a nomogram, and the nomogram was evaluated using calibration curves. In addition, the efficiency of the four models for prediction of risk of healthcare-seeking delay among imported malaria patients was evaluated using the area under curve (AUC) of receiver operating characteristic curve (ROC). The importance of each characteristic was quantified and attributed by using SHAP to examine the positive and negative effects of the value of each characteristic on the predictive efficiency. RESULTS: A total of 244 imported malaria patients were enrolled, including 100 cases (40.98%) with the duration from onset of first symptoms to time of initial diagnosis that exceeded 24 hours. Logistic regression analysis identified a history of malaria parasite infection [odds ratio (OR) = 3.075, 95% confidential interval (CI): (1.597, 5.923)], long incubation period [OR = 1.010, 95% CI: (1.001, 1.018)] and seeking healthcare in provincial or municipal medical facilities [OR = 12.550, 95% CI: (1.158, 135.963)] as risk factors for delay in seeking healthcare among imported malaria cases. BP neural network modeling showed that duration of residing abroad, incubation period and age posed great impacts on delay in healthcare-seek among imported malaria patients. Random forest modeling showed that the top five factors with the greatest impact on healthcare-seeking delay included main clinical manifestations, the way to go abroad, incubation period, duration of residing abroad and age among imported malaria patients, and Bayesian modeling revealed that the top five factors affecting healthcare-seeking delay among imported malaria patients included level of institutions at initial diagnosis, age, country of origin, history of malaria parasite infection and individuals travelling with imported malaria patients. ROC curve analysis showed higher overall performance of the BP neural network model and the logistic regression model for prediction of the risk of healthcare-seeking delay among imported malaria patients (Z = 2.700 to 4.641, all P values < 0.01), with no statistically significant difference in the AUC among four models (Z = 1.209, P > 0.05). The sensitivity (71.00%) and Youden index (43.92%) of the logistic regression model was higher than those of the BP neural network (63.00% and 36.61%, respectively), and the specificity of the BP neural network model (73.61%) was higher than that of the logistic regression model (72.92%). CONCLUSIONS: Imported malaria cases with long duration of residing abroad, a history of malaria parasite infection, long incubation period, advanced age and seeking healthcare in provincial or municipal medical institutions have a high likelihood of delay in healthcare-seeking in Jiangsu Province. The models created based on the logistic regression and BP neural network show a high efficiency for prediction of the risk of healthcare-seeking among imported malaria patients in Jiangsu Province, which may provide insights into health management of imported malaria patients.

Estimation of surface ozone concentration over Jiangsu province using a high-performance deep learning model.

Recently, the global background concentration of ozone (O3) has demonstrated a rising trend. Among various methods, groun-based monitoring of O3 concentrations is highly reliable for research analysis. To obtain information on the spatial characteristics of O3 concentrations, it is necessary that the ground monitoring sites be constructed in sufficient density. In recent years, many researchers have used machine learning models to estimate surface O3 concentrations, which cannot fully provide the spatial and temporal information contained in a sample dataset. To solve this problem, the current study utilized a deep learning model called the Residual connection Convolutional Long Short-Term Memory network (R-ConvLSTM) to estimate daily maximum 8-hr average (MDA8) O3 over Jiangsu province, China during 2020. In this research, the R-ConvLSTM model not only provides the spatiotemporal information of MDA8 O3, but also involves residual connection to avoid the problem of gradient explosion and gradient disappearance with the deepening of network layers. We utilized the TROPOMI total O3 column retrieved from Sentinel-5 Precursor, ERA5 reanalysis meteorological data, and other supplementary data to build a pre-trained dataset. The R-ConvLSTM model achieved an overall sample-base cross-validation (CV) R2 of 0.955 with root mean square error (RMSE) of 9.372 µg/m3. Model estimation also showed a city-based CV R2 of 0.896 with RMSE of 14.029 µg/m3, the highest MDA8 O3 in spring being 122.60 ± 31.60 µg/m3 and the lowest in winter being 69.93 ± 18.48 µg/m3.

Changes in environmental performance with firm relocation and its influencing mechanism: An evidence of chemical industry in jiangsu, China.

A micro firm relocation perspective plays important roles in deepening the understanding and knowledge on the environmental effects of industrial transfer and related mechanisms, however, such research and cases are currently scarce. Based on the building of both the database of firm relocation information and a conceptual framework of changes in environmental performance with firm relocation (ΔEP), involving factors of firm heterogeneity and changes in both locational conditions and whole-process pollution treatment paths, chemical firms in Jiangsu Province were selected to explore their ΔEP and related influencing mechanisms by the Wilcoxon signed-rank test of two paired samples and a binary logistic regression model in this study, respectively. Results showed that during 1998-2014, chemical firm relocation experienced a fluctuating growth trend with a continuous surge in inter-city relocation, accompanied by a deterioration in the environmental performance (EP) with a significant reduction of pollution removal intensity (p < 0.01) after firm relocation. Most firms relocated from Southern Jiangsu (72.5%) to places adjoining Jiangsu Province (58.5%), along the river and the coast (63.4%), and in the third- and fourth-tier cities (73.5%), respectively. As for these factors, the low development level of transfer-out (DTOR) and transfer-in region (DTIR) both resulted in the downgrading EP with firm relocation, but the inter-city relocation style (RS) and strict environmental regulation (ER) were the opposite. The benefits for upgrading EP after relocation conferred by the promotion of source-process treatment were limited by RS, DTOR, and DTIR. Among firms relocated to low DTIRs, the higher the firm competitiveness in terms of capital, technology, and environmental awareness, the greater the upgrading EP probability. When firms transferred to regions with stricter ER, the likelihood of improving EP increased more for those firms with weak competency. In a word, in order to prevent the pollution haven effect, superior governments should shrink regional differences in ER policies, whereas local governments in transfer-in regions should provide targeted and essential support in funding and technology according to the firm heterogeneity and fully consider actual conditions to make environmental measures in the future.

[Establishment and Spatial Distribution of S/IVOCs Emission Inventory in Jiangsu Province in 2019].

The semi-/intermediate volatile organic compound (S/IVOCs) emissions inventory of Jiangsu province was established in 2019 using the activity data of various S/IVOCs emission sources, emission factors, and an estimation method. S/IVOCs emissions for each source and city in Jiangsu province were analyzed. The total amount of S/IVOCs emissions in Jiangsu province in 2019 was 637.31 Gg. Industrial sources were the major source of total S/IVOCs emissions accounting for 63.42% (404.20 Gg), followed by residential on-road mobile sources (22.23%), and off-road mobile sources accounted for the least (0.06%). Suzhou had the highest S/IVOCs emissions in 2019, accounting for 25.40% (161.86 Gg) of the total S/IVOCs emissions in Jiangsu province. The S/IVOCs emission intensity per unit area in Suzhou was the highest, reaching 18.70 t·km-2, and the emission intensity per unit GDP was the highest in Lianyungang (22.45 t·100 million yuan-1). The spatial distribution map revealed that S/IVOCs emissions in southern Jiangsu were relatively higher. The difference in the total emission of S/IVOCs, emission intensity per unit area, and emission intensity per unit of GDP were quite different among cities. The uncertainty range of S/IVOCs emissions was -88.46%-224.38% in Jiangsu province in 2019. The uncertainty range of biomass burning sources was the largest (-96.40%-277.17%).

[Prevalence and molecular characterization of Cryptosporidium in captive-bred Mustela putorius furo in Jiangsu Province].

OBJECTIVE: To investigate the prevalence and molecular features of Cryptosporidium in captive-bred Mustela putorius furo in Jiangsu Province. METHODS: A total of 290 fresh stool samples were collected from a ferret farm in Jiangsu Province on May 2017, and the small subunit rRNA (SSU rRNA) gene of Cryptosporidium was amplified in stool samples using nested PCR assay. The actin, cowp and gp60 genes were amplified in positive samples and sequenced to characterize Cryptosporidium species/genotypes. RESULTS: A total of 18 stool samples were tested positive for Cryptosporidium SSU rRNA gene, with a detection rate of 6.2%. Sequence and phylogenetic analyses of SSU rRNA, actin and cowp genes characterized Cryptosporidium isolated from captive-bred ferrets as Cryptosporidium sp. ferret genotype. In addition, gp60 gene was amplified in 10 out of 18 stool samples tested positive for Cryptosporidium. CONCLUSIONS: Cryptosporidium is widely prevalent in captive-bred ferrets in Jiangsu Province, and Cryptosporidium sp. ferret genotype is the only Cryptosporidium genotype in ferrets.

Identifying spatial agglomeration and driving forces of land use by green industries: a case study of Jiangsu Province, China.

In response to China's aims of becoming "carbon-neutral," the development of green industries such as renewable energy and recycling has flourished. Based on 2015 and 2019 data, this study uses spatial autocorrelation to analyze the evolution of land use by the green industries in Jiangsu Province. The Geodetector model was also applied to identify the driving factors underlying these spatial patterns. The spatial variability of green industrial land use in Jiangsu Province is significant, with the land-use area gradually decreasing from Southern to Northern Jiangsu. In terms of spatial-temporal changes, there is an increase in land use and a trend of expansion in the central and northern regions of Jiangsu. Land use by green industries in the province exhibits a more significant spatial clustering pattern but with a weakened clustering effect. The clustering types are mainly H-H and L-L, with the H-H type distributed mainly in the Su-Xi-Chang region and the L-L type distributed mainly in the Northern Jiangsu region. The levels of technology, economic development, industrialization, and industrial diversification are important individual driving factors, and the interaction among the different factors enhances their driving forces. This study suggests that spatial spillover effects should be focused to promote the coordinated development of regional energy-saving and environmental protection industries. At the same time, joint efforts should be made from the aspects of resources, government, economy, and related industries to promote the agglomeration of land for energy-saving and environmental protection industries.

Survey of radiotherapy resources in Jiangsu province in 2020 / 中华放射医学与防护杂志

Objective:To investigate the current situation of radiotherapy resources in medical institutions at all levels in Jiangsu province.Methods:The survey was initiated with the subproject " Medical Radiation Protection Monitoring" of the National Key Occupational Disease Monitoring Project. Unified electronic questionnaires were designed based on the survey content, distributed and recycled through the network, to investigate all medical institutions (excluding military hospitals) conducting radiotherapy in Jiangsu province in 2020.Results:By the end of 2020, there were 113 medical institutions (excluding military hospitals) carrying out radiotherapy in Jiangsu Province, including 86 hospitals Level Ⅲ, 23 hospitals Level Ⅱ, 2 hospitals Level I, and 2 unrated hospitals, with the largest number of hospitals in northern Jiangsu. A total of 2 033 workers were engaged in radiotherapy, with inclusion of 319 medical physicists, accounting for 15.69% of the total, slightly higher than the national average. There were 195 sets of radiotherapy equipment, including 153 medical linear accelerators and 31 afterloading units, 9 Gamma Knife and 2 60Co therapeutic machines, respectively. The number of accelerators per million population was 1.80, more than nationwide average, 1.45, in 2018. The annual number of patients treated with radiotherapy was 92 441, with an annual treatment frequency of 1.090 per 1 000 population, lower than international health-care level I. Conclusions:Radiotherapy resources in Jiangsu province exceed the national average, but may not fully meet the medical requirements of existing patients, and there is still a significant gap from international health-care level I.

Risk predictive models of healthcare-seeking delay among imported malaria patients in Jiangsu Province based on the machine learning / 中国血吸虫病防治杂志

Objective To create risk predictive models of healthcare-seeking delay among imported malaria patients in Jiangsu Province based on machine learning algorithms, so as to provide insights into early identification of imported malaria cases in Jiangsu Province. Methods Case investigation, first symptoms and time of initial diagnosis of imported malaria patients in Jiangsu Province in 2019 were captured from Infectious Disease Report Information Management System and Parasitic Disease Prevention and Control Information Management System of Chinese Center for Disease Control and Prevention. The risk predictive models of healthcare-seeking delay among imported malaria patients were created with the back propagation (BP) neural network model, logistic regression model, random forest model and Bayesian model using thirteen factors as independent variables, including occupation, species of malaria parasite, main clinical manifestations, presence of complications, severity of disease, age, duration of residing abroad, frequency of malaria parasite infections abroad, incubation period, level of institution at initial diagnosis, country of origin, number of individuals travelling with patients and way to go abroad, and time of healthcare-seeking delay as a dependent variable. Logistic regression model was visualized using a nomogram, and the nomogram was evaluated using calibration curves. In addition, the efficiency of the four models for prediction of risk of healthcare-seeking delay among imported malaria patients was evaluated using the area under curve (AUC) of receiver operating characteristic curve (ROC). The importance of each characteristic was quantified and attributed by using SHAP to examine the positive and negative effects of the value of each characteristic on the predictive efficiency. Results A total of 244 imported malaria patients were enrolled, including 100 cases (40.98%) with the duration from onset of first symptoms to time of initial diagnosis that exceeded 24 hours. Logistic regression analysis identified a history of malaria parasite infection [odds ratio (OR) = 3.075, 95% confidential interval (CI): (1.597, 5.923)], long incubation period [OR = 1.010, 95% CI: (1.001, 1.018)] and seeking healthcare in provincial or municipal medical facilities [OR = 12.550, 95% CI: (1.158, 135.963)] as risk factors for delay in seeking healthcare among imported malaria cases. BP neural network modeling showed that duration of residing abroad, incubation period and age posed great impacts on delay in healthcare-seek among imported malaria patients. Random forest modeling showed that the top five factors with the greatest impact on healthcare-seeking delay included main clinical manifestations, the way to go abroad, incubation period, duration of residing abroad and age among imported malaria patients, and Bayesian modeling revealed that the top five factors affecting healthcare-seeking delay among imported malaria patients included level of institutions at initial diagnosis, age, country of origin, history of malaria parasite infection and individuals travelling with imported malaria patients. ROC curve analysis showed higher overall performance of the BP neural network model and the logistic regression model for prediction of the risk of healthcare-seeking delay among imported malaria patients (Z = 2.700 to 4.641, all P values < 0.01), with no statistically significant difference in the AUC among four models (Z = 1.209, P > 0.05). The sensitivity (71.00%) and Youden index (43.92%) of the logistic regression model was higher than those of the BP neural network (63.00% and 36.61%, respectively), and the specificity of the BP neural network model (73.61%) was higher than that of the logistic regression model (72.92%). Conclusions Imported malaria cases with long duration of residing abroad, a history of malaria parasite infection, long incubation period, advanced age and seeking healthcare in provincial or municipal medical institutions have a high likelihood of delay in healthcare-seeking in Jiangsu Province. The models created based on the logistic regression and BP neural network show a high efficiency for prediction of the risk of healthcare-seeking among imported malaria patients in Jiangsu Province, which may provide insights into health management of imported malaria patients.

Prevalence and molecular characterization of Cryptosporidium in captive-bred Mustela putorius furo in Jiangsu Province / 中国血吸虫病防治杂志

Objective To investigate the prevalence and molecular features of Cryptosporidium in captive-bred Mustela putorius furo in Jiangsu Province.. Methods A total of 290 fresh stool samples were collected from a ferret farm in Jiangsu Province on May 2017, and the small subunit rRNA (SSU rRNA) gene of Cryptosporidium was amplified in stool samples using nested PCR assay. The actin, cowp and gp60 genes were amplified in positive samples and sequenced to characterize Cryptosporidium species/genotypes. Results A total of 18 stool samples were tested positive for Cryptosporidium SSU rRNA gene, with a detection rate of 6.2%. Sequence and phylogenetic analyses of SSU rRNA, actin and cowp genes characterized Cryptosporidium isolated from captive-bred ferrets as Cryptosporidium sp. ferret genotype. In addition, gp60 gene was amplified in 10 out of 18 stool samples tested positive for Cryptosporidium. Conclusions Cryptosporidium is widely prevalent in captive-bred ferrets in Jiangsu Province, and Cryptosporidium sp. ferret genotype is the only Cryptosporidium genotype in ferrets.

Surveillance of Aedes populations in Jiangsu Province in 2020 / 中国血吸虫病防治杂志

Objective To investigate the seasonal Aedes population fluctuation and the resistance of Aedes populations to common insecticides in Jiangsu Province in 2020, so as to provide insights into vector-borne infectious diseases control.. Methods One village was randomly sampled from each of Xinbei District of Changzhou City and Zhangjiagang County of Suzhou City in southern Jiangsu Province, Hai’an County of Nantong City and Yandu District of Yancheng City in Central Jiangsu Province, and Suining County of Xuzhou City and Sihong County of Suqian City in northern Jiangsu Province during the period between May and October, 2020. A small ponding container was sampled, and larval Aedes mosquitoes were collected using straws once each in early and late stages of each month. All larvae were bred in laboratory to adults for population identification. In addition, larval breeding were observed in all small ponding containers in and out of 30 households that were randomly sampled from six surveillance sites, and the larval mosquito density was estimated using Breteau index. Larval A. albopictus mosquitoes were sampled around Cuiyuan New Village in Jintan District of Changzhou City, and bred in laboratory to the first offspring generation, and the susceptibility of adult female mosquitoes to deltamethrin, lambda-cyhalothrin, malathion, and propoxur was tested using the filter-paper bioassay recommended by WHO. Results A total of 1 165 larval Aedes mosquitoes were captured from small ponding containers in six surveillance sites of Jiangsu Province in 2020, and all were identified as A. albopictus following eclosion. The largest number of Aedes larvae captured was found in July. A total of 1 152 households were investigated in six surveillance sites, and the mean Breteau indexes were 9.58, 13.20, 13.71, 13.20, 12.18 and 5.58 from May to October, respectively, while a high Aedes transmission risk was seen in Xinbei District of Changzhou City, with a higher Breteau index than in Suining (H = 23.667, Padjusted = 0.001) and Sihong (H = 22.500, Padjusted = 0.003) counties. The field-captured A. albopictus from Cuiyuan New Village in Jintan District of Changzhou City remained sensitive to malathion, but was resistant to propoxur, and developed high-level resistance to deltamethrin and lambda-cyhalothrin. Conclusions A. albopictus was present in southern, central and northern Jiangsu Province in 2020, and the larval density peaked in July. A. albopictus captured from Cuiyuan New Village in Jintan District of Changzhou City has developed high-level resistance to pyrethroid pesticides.

[Analysis of active surveillance results of pneumoconiosis in Jiangsu Province in 2019].

Objective: To conduct active surveillance of pneumoconiosis in Jiangsu Province in 2019, to understand the status quo of occupational health of dust-exposed workers, timely detect occupational health damage and occupational hazard factors, and provide scientific basis for the prevention and control of pneumoconiosis in the future. Methods: From January to December 2019, counties (cities and districts) with relatively concentrated dust hazard enterprises in Jiangsu Province were selected as monitoring points to carry out active surveillance of pneumoconiosis, and occupational health examination of dust workers (3540 people) in 255 enterprises were carried out, including chest radiographs and lung function examinations. SPSS 26.0 statistical software was used for statistical analysis of the data, and Chi-square test was used to analyze the difference level of the rate between different groups such as gender, age and service. Results: Among the 255 employers, small enterprises accounted for the highest proportion (186 enterprises, 72.94%), private economy accounted for the highest proportion (225 enterprises, 88.24%), and machinery manufacturing industry accounted for the highest proportion (44 enterprises, 17.25%). Among the 3540 workers exposed to dust, 6 suspected occupational diseases (0.17%), 50 occupational contraindications (1.41%), and 1469 other diseases or abnormalities (41.50%) were detected. Among the 3540 workers exposed to dust, 399 (11.27%) had abnormal chest radiography results, and 848 (23.95%) had abnormal lung function indicators. The abnormal detection rate of chest radiographs in males (12.25%, 359/2931) was higher than that in females (6.57%, 40/609), and the abnormal detection rate of lung function in females (32.18%, 196/609) was higher than that in males (22.24%, 652/2931), with statistical significance (P<0.05). The age of the workers exposed to dust ranged from 17 to 75 (41.78±10.39) years old, and the years of service in contact with dust of the workers ranged from 0.08 to 42 (6.72±7.47) years. There were statistically significant differences in the detection rate of abnormal chest radiography and abnormal lung function among different age and seniority workers (P<0.05). The abnormal rates of forced vital capacity (FVC), forced expiratory volume in the first second (FEV(1)) and FEV(1)/FVC were 13.79% (488/3540), 15.28% (541/3540) and 1.86% (66/3540), respectively. There were significant differences in the detection rate of abnormal chest radiography and abnormal lung function among workers exposed to different dust types (P<0.05) . Conclusion: There are few suspected occupational diseases detected by active surveillance of pneumoconiosis in Jiangsu Province in 2019, and there are differences in occupational health examination results among workers with different dust types, ages, genders and dust exposure years.

Spatial and Temporal Characteristics and Drivers of Agricultural Carbon Emissions in Jiangsu Province, China.

Scientific measurement and analysis of the spatial and temporal distribution characteristics of agricultural carbon emissions (ACEs) and the influencing factors are important prerequisites for the formulation of reasonable ACEs reduction policies. Compared with previous studies, this paper fully considers the heterogeneity of rice carbon emission coefficients, measures and analyzes the spatial and temporal characteristics of ACEs in Jiangsu Province from three carbon sources, including agricultural land use, rice cultivation, and livestock and poultry breeding, and explores spatial clustering patterns and driving factors, which can provide a reference for agricultural low-carbon production. The results indicate that from 2005 to 2020, Jiangsu's ACEs showed a decreasing trend, with an average annual decrease of 0.32%, while agricultural carbon emission density (ACED) showed an increasing trend, with an average annual increase of 0.16%. The area with the highest values for ACEs is concentrated in the northern region of Jiangsu, while the areas with the highest values for ACED are distributed in the southern region. The spatial clustering characteristics of ACEs have been strengthening. The "H-H" agglomeration is mainly concentrated in Lianyungang and Suqian, while the "L-L" agglomeration is concentrated in Zhenjiang, Changzhou, and Wuxi. Each 1% change in rural population, economic development level, agricultural technology factors, agricultural industry structure, urbanization level, rural investment, and per capita disposable income of farmers causes changes of 0.112%, -0.127%, -0.116%, 0.192%, -0.110%, -0.114%, and -0.123% in Jiangsu's ACEs, respectively. To promote carbon emission reduction in agriculture in Jiangsu Province, we should actively promote the development of regional synergistic carbon reduction, accelerate the construction of new urbanization, and guide the coordinated development of agriculture, forestry, animal husbandry, and fisheries industries.