Towards development of functional climate-driven early warning systems for climate-sensitive infectious diseases: Statistical models and recommendations.

Climate, weather and environmental change have significantly influenced patterns of infectious disease transmission, necessitating the development of early warning systems to anticipate potential impacts and respond in a timely and effective way. Statistical modelling plays a pivotal role in understanding the intricate relationships between climatic factors and infectious disease transmission. For example, time series regression modelling and spatial cluster analysis have been employed to identify risk factors and predict spatial and temporal patterns of infectious diseases. Recently advanced spatio-temporal models and machine learning offer an increasingly robust framework for modelling uncertainty, which is essential in climate-driven disease surveillance due to the dynamic and multifaceted nature of the data. Moreover, Artificial Intelligence (AI) techniques, including deep learning and neural networks, excel in capturing intricate patterns and hidden relationships within climate and environmental data sets. Web-based data has emerged as a powerful complement to other datasets encompassing climate variables and disease occurrences. However, given the complexity and non-linearity of climate-disease interactions, advanced techniques are required to integrate and analyse these diverse data to obtain more accurate predictions of impending outbreaks, epidemics or pandemics. This article presents an overview of an approach to creating climate-driven early warning systems with a focus on statistical model suitability and selection, along with recommendations for utilizing spatio-temporal and machine learning techniques. By addressing the limitations and embracing the recommendations for future research, we could enhance preparedness and response strategies, ultimately contributing to the safeguarding of public health in the face of evolving climate challenges.

Risk assessment and prediction of nosocomial infections based on surveillance data using machine learning methods.

BACKGROUND: Nosocomial infections with heavy disease burden are becoming a major threat to the health care system around the world. Through long-term, systematic, continuous data collection and analysis, Nosocomial infection surveillance (NIS) systems are constructed in each hospital; while these data are only used as real-time surveillance but fail to realize the prediction and early warning function. Study is to screen effective predictors from the routine NIS data, through integrating the multiple risk factors and Machine learning (ML) methods, and eventually realize the trend prediction and risk threshold of Incidence of Nosocomial infection (INI). METHODS: We selected two representative hospitals in southern and northern China, and collected NIS data from 2014 to 2021. Thirty-nine factors including hospital operation volume, nosocomial infection, antibacterial drug use and outdoor temperature data, etc. Five ML methods were used to fit the INI prediction model respectively, and to evaluate and compare their performance. RESULTS: Compared with other models, Random Forest showed the best performance (5-fold AUC = 0.983) in both hospitals, followed by Support Vector Machine. Among all the factors, 12 indicators were significantly different between high-risk and low-risk groups for INI (P < 0.05). After screening the effective predictors through importance analysis, prediction model of the time trend was successfully constructed (R2 = 0.473 and 0.780, BIC = -1.537 and -0.731). CONCLUSIONS: The number of surgeries, antibiotics use density, critical disease rate and unreasonable prescription rate and other key indicators could be fitted to be the threshold predictions of INI and quantitative early warning.

Impacts of heatwaves on type 2 diabetes mortality in China: a comparative analysis between coastal and inland cities.

The impacts of extreme temperatures on diabetes have been explored in previous studies. However, it is unknown whether the impacts of heatwaves appear variations between inland and coastal regions. This study aims to quantify the associations between heat exposure and type 2 diabetes mellitus (T2DM) deaths in two cities with different climate features in Shandong Province, China. We used a case-crossover design by quasi-Poisson generalized additive regression with a distributed lag model with lag 2 weeks, controlling for relative humidity, the concentration of air pollution particles with a diameter of 2.5 µm or less (PM2.5), and seasonality. The wet- bulb temperature (Tw) was used to measure the heat stress of the heatwaves. A significant association between heatwaves and T2DM deaths was only found in the coastal city (Qingdao) at the lag of 2 weeks at the lowest Tw = 14℃ (relative risk (RR) = 1.49, 95% confidence interval (CI): 1.11-2.02; women: RR = 1.51, 95% CI: 1.02-2.24; elderly: RR = 1.50, 95% CI: 1.08-2.09). The lag-specific effects were significant associated with Tw at lag of 1 week at the lowest Tw = 14℃ (RR = 1.14, 95% CI: 1.03-1.26; women: RR = 1.15, 95% CI: 1.01-1.31; elderly: RR = 1.15, 95% CI: 1.03-1.28). However, no significant association was found in Jian city. The research suggested that Tw was significantly associated with T2DM mortality in the coastal city during heatwaves on T2DM mortality. Future strategies should be implemented with considering socio-environmental contexts in regions.

Japanese Encephalitis Emergence in Australia: The Potential Population at Risk.

In Australia, Japanese encephalitis virus circulated in tropical north Queensland between 1995 and 2005. In 2022, a dramatic range expansion across the southern states has resulted in 30 confirmed human cases and 6 deaths. We discuss the outbreak drivers and estimate the potential size of the human population at risk.

Built environment and schizophrenia re-hospitalization risk in China: A cohort study.

BACKGROUND: Built environment exposure, characterized by ubiquity and changeability, has the potential to be the prospective target of public health policy. However, little research has been conducted to explore its impact on schizophrenia. This study aimed to investigate the association between built environmentand and schizophrenia rehospitalization by simultaneously considering substantial built environmental exposures. METHODS: We recruited eligible schizophrenia patients from Hefei, Anhui Province, China between 2017 and 2019. The main outcome for this study was the time interval until the first recurrent hospital admission occurred within one year after discharge. For each included subject, we estimated the built environment exposures, including population density, walkability, land use mix, green and blue space, public transportation accessibility and road traffic indicator. Lasso (Least Absolute Shrinkage and Selection Operator) analysis was used to select the key variables. Multivariable Cox regression model was applied to obtain hazard ratio (HR) and its corresponding 95% confidence intervals (CI). Further, we also evaluated the joint effects of built environment characteristics on rehospitalization for schizophrenia by Quantile g-computation model. RESULTS: A total of 1564 hospitalized schizophrenia patients were enrolled, with 347 patients (22.2%) had a rehospitalization within one-year after discharge. Multivariable Cox regression analysis indicated that the re-hospitalization rate for schizophrenia would be higher in areas with a high population density (HR: 1.10, 95%CI: 1.04-1.16). Nonetheless, compared to the reference (Q1), participants who lived in a neighborhood with the highest walkability and NDVI (Normalized Difference Vegetation Index) (Q4) had a 76% and 47% lower risk of re-hospitalization within one year (HR:0.24, 95%CI: 0.13-0.45; and 0.53, 95%CI:0.32-0.85), respectively. Moreover, quantile-based g-computation analyses revealed that increased walkability and green space significantly eliminated the adverse effects of population density increases on schizophrenia patients, with a HR ratio of 0.61 (95%CI:0.48,0.79) per one quartile change at the same time. CONCLUSION: Our study provides scientific evidence for the significant role of built environment in schizophrenia rehospitalization, suggesting that optimizing the built environment is required in designing and building a healthy city.

Impact of climate factors and climate-gene interaction on systemic lupus erythematosus patients' response to glucocorticoids therapy.

BACKGROUND: Glucocorticoids (GCs) were the essential drugs for systemic lupus erythematosus (SLE). However, different patients differ substantially in their response to GCs treatment. Our current study aims at investigating whether climate variability and climate-gene interaction influence SLE patients' response to the therapy of GCs. METHODS: In total, 778 SLE patients received therapy of GCs for a study of 12-week follow-up. The efficacy of GCs treatment was evaluated using the Systemic Lupus Erythematosus Disease Activity Index. The climatic data were provided by China Meteorological Data Service Center. Additive and multiplicative interactions were examined. RESULTS: Compared with patients with autumn onset, the efficacy of GCs in patients with winter onset is relatively poor (ORadj = 1.805, 95%CIadj : 1.181-3.014, padj = 0.020). High mean relative humidity during treatment decreased the efficacy of GCs (ORadj = 1.033, 95%CIadj : 1.008-1.058, padj = 0.011), especially in female (ORadj = 1.039, 95%CIadj : 1.012-1.067, padj = 0.004). There was a significant interaction between sunshine during treatment and TRAP1 gene rs12597773 on GCs efficacy (Recessive model: AP = 0.770). No evidence of significant interaction was found between climate factors and the GR gene polymorphism on the improved GCs efficacy in the additive model. Multiplicative interaction was found between humidity in the month prior to treatment and GR gene rs4912905 on GCs efficacy (Dominant model: OR = 0.470, 95%CI: 0.244-0.905, p = 0.024). CONCLUSIONS: Our findings suggest that climate variability influences SLE patients' response to the therapy of GCs. Interactions between climate and TRAP1/GR gene polymorphisms were related to GCs efficacy. The results guide the individualized treatment of SLE patients.

Air pollution and liver cancer: A systematic review.

Air pollution has previously been linked to several adverse health outcomes, but the potential association between air pollution and liver cancer remains unclear. We searched PubMed, EMBASE, and Web of Science from inception to 10 October 2021, and manually reviewed the references of relevant papers to further identify any related literature investigating possible associations between air pollution and liver cancer. Risk estimates values were represented by statistical associations based on quantitative analyses. A total of 13 cohort studies obtained from 11 articles were included, with 10,961,717 participants. PM2.5 was the most frequently examined pollutant (included in 11 studies), followed by NO2 and NOx (included in 6 studies), and fewer studies focused on other pollutants (PM2.5 absorbance, PM10, PM2.5-10, O3, and BC). In all the 16 associations for liver cancer mortality, 14 associations reported the effect of PM2.5 on liver cancer mortality. Eight associations on PM2.5 were significant, showing a suggestive association between PM2.5 and liver cancer mortality. Among 24 associations shown by risk estimates for liver cancer incidence, most associations were not statistically significant. For other air pollutants, no positive associations were presented in these studies. PM2.5 was the most frequently examined pollutant, followed by NO2 and NOx, and fewer studies focused on other pollutants. PM2.5 was associated with liver cancer mortality, but there was no association for other air pollutants. Future research should use advanced statistical methods to further assess the impact of multiple air pollutants on liver cancer in the changing socio-environmental context.

Prototypes virus of hand, foot and mouth disease infections and severe cases in Gansu, China: a spatial and temporal analysis.

BACKGROUND: Little research has been conducted on the spatio-temporal relationship between the severe cases and the enteroviruses infections of hand, foot and mouth disease (HFMD). This study aimed to investigate epidemic features and spatial clusters of HFMD incidence rates and assess the relationship between Enterovirus 71 (EV71) and Coxsackievirus A16 (CoxA16) and severe cases of HMFD in Gansu province, China. METHODS: Weekly county-specific data on HFMD between 1st January and 31st December 2018 were collected from the China Infectious Disease Information System (CIDIS), including enterovirus type (EV71 and CoxA16), severe and non-severe cases in Gansu province, China. Temporal risk [frequency index (α), duration index (ß) and intensity index (γ)] and spatial cluster analysis were used to assess epidemic features and identify high-risk areas for HFMD. Time-series cross-correlation function and regression model were used to explore the relationship between the ratios of two types of viruses (i.e. EV71/Cox16) (EC) and severe cases index (i.e. severe cases/non-severe cases) (SI) of HFMD. RESULTS: Some counties in Dingxi City, Gansu were identified as a hot spot for the temporal risk indices. Time-series cross-correlation analysis showed that SI was significantly associated with EC (r = 0.417, P < 0.05) over a 4-week time lag. The regression analysis showed that SI was positively associated with EC (ß = 0.04, 95% confidence interval (CI) 0.02-0.06). CONCLUSION: The spatial patterns of HFMD incidence were associated with enteroviruses in Gansu. The research suggested that the EC could be considered a potential early warning sign for predicting severe cases of HFMD in Gansu province.

Climate variability, socio-ecological factors and dengue transmission in tropical Queensland, Australia: A Bayesian spatial analysis.

BACKGROUND: Dengue is a wide-spread mosquito-borne disease globally with a likelihood of becoming endemic in tropical Queensland, Australia. The aim of this study was to analyse the spatial variation of dengue notifications in relation to climate variability and socio-ecological factors in the tropical climate zone of Queensland, Australia. METHODS: Data on the number of dengue cases and climate variables including minimum temperature, maximum temperature and rainfall for the period of January 1st, 2010 to December 31st, 2015 were obtained for each Statistical Local Area (SLA) from Queensland Health and Australian Bureau of Meteorology, respectively. Socio-ecological data including estimated resident population, percentage of Indigenous population, housing structure (specifically terrace house), socio-economic index and land use types for each SLA were obtained from Australian Bureau of Statistics, and Australian Bureau of Agricultural and Resource Economics and Sciences, respectively. To quantify the relationship between dengue, climate and socio-ecological factors, multivariate Poisson regression models in a Bayesian framework were developed with a conditional autoregressive prior structure. Posterior parameters were estimated using Bayesian Markov Chain Monte Carlo simulation with Gibbs sampling. RESULTS: In the tropical climate zone of Queensland, the estimated number of dengue cases was predicted to increase by 85% [95% Credible Interval (CrI): 25%, 186%] and 7% (95% CrI: 0.1%, 14%) for a 1-mm increase in average annual rainfall and 1% increase in the proportion of terrace houses, respectively. The estimated spatial variation (structured random effects) was small compared to the remaining unstructured variation, suggesting that the inclusion of covariates resulted in no residual spatial autocorrelation in dengue data. CONCLUSIONS: Climate and socio-ecological factors explained much of the heterogeneity of dengue transmission dynamics in the tropical climate zone of Queensland. Results will help to further understand the risk factors of dengue transmission and will provide scientific evidence in designing effective local dengue control programs in the most needed areas.

Extreme weather conditions and dengue outbreak in Guangdong, China: Spatial heterogeneity based on climate variability.

BACKGROUND: Previous studies have shown associations between local weather factors and dengue incidence in tropical and subtropical regions. However, spatial variability in those associations remains unclear and evidence is scarce regarding the effects of weather extremes. OBJECTIVES: We examined spatial variability in the effects of various weather conditions on the unprecedented dengue outbreak in Guangdong province of China in 2014 and explored how city characteristics modify weather-related risk. METHODS: A Bayesian spatial conditional autoregressive model was used to examine the overall and city-specific associations of dengue incidence with weather conditions including (1) average temperature, temperature variation, and average rainfall; and (2) weather extremes including numbers of days of extremely high temperature and high rainfall (both used 95th percentile as the cut-off). This model was run for cumulative dengue cases during five months from July to November (accounting for 99.8% of all dengue cases). A further analysis based on spatial variability was used to validate the modification effects by economic, demographic and environmental factors. RESULTS: We found a positive association of dengue incidence with average temperature in seven cities (relative risk (RR) range: 1.032 to 1.153), a positive association with average rainfall in seven cities (RR range: 1.237 to 1.974), and a negative association with temperature variation in four cities (RR range: 0.315 to 0.593). There was an overall positive association of dengue incidence with extremely high temperature (RR:1.054, 95% credible interval (CI): 1.016 to 1.094), without evidence of variation across cities, and an overall positive association of dengue with extremely high rainfall (RR:1.505, 95% CI: 1.096 to 2.080), with seven regions having stronger associations (RR range: 1.237 to 1.418). Greater effects of weather conditions appeared to occur in cities with higher economic level, lower green space coverage and lower elevation. CONCLUSIONS: Spatially varied effects of weather conditions on dengue outbreaks necessitate area-specific dengue prevention and control measures. Extremes of temperature and rainfall have strong and positive associations with dengue outbreaks.

The associations of air pollution and socioeconomic factors with esophageal cancer in China based on a spatiotemporal analysis.

Rapid urbanization and industrialization in China have incurred serious air pollution and consequent health concerns. In this study, we examined the modifying effects of urbanization and socioeconomic factors on the association between PM2.5 and incidence of esophageal cancer (EC) in 2000-2015 using spatiotemporal techniques and a quasi-Poisson generalized linear model. The results showed a downward trend of EC and high-risk areas aggregated in North China and Huai River Basin. In addition, a stronger association between PM2.5 and incidence was observed in low urbanization group, and the association was stronger for females than males. When exposure time-windows were adjusted as 0, 5, 10, 15 years, the incidence risk increased by 2.48% (95% CI: 2.23%, 2.73%), 2.20% (95% CI: 1.91%, 2.49%), 2.18% (95% CI%: 1.92%, 2.43%), 1.87% (95% CI%:1.64, 2.10%) for males, respectively and 4.03% (95% CI: 3.63%, 4.43%), 2.20% (95% CI: 1.91%, 2.49%), 3.97% (95% CI: 3.54%, 4.41%), 3.06% (95% CI: 2.71%, 3.41%) for females, respectively. The findings indicated people in low urbanization group faced with a stronger EC risk caused by PM2.5, which contributes to a more comprehensive understanding of combating EC challenges related to PM2.5 pollution.

A regional suitable conditions index to forecast the impact of climate change on dengue vectorial capacity.

BACKGROUND: The mosquitoes Aedes aegypti and Ae. albopictus are the primary vectors of dengue virus, and their geographic distributions are predicted to expand further with economic development, and in response to climate change. We aimed to estimate the impact of future climate change on dengue transmission through the development of a Suitable Conditions Index (SCI), based on climatic variables known to support vectorial capacity. We calculated the SCI based on various climate change scenarios for six countries in the Asia-Pacific region (Australia, China, Indonesia, The Philippines, Thailand and Vietnam). METHODS: Monthly raster climate data (temperature and precipitation) were collected for the period January 2005 to December 2018 along with projected climate estimates for the years 2030, 2050 and 2070 using Representative Concentration Pathway (RCP) 4·5, 6·0 and 8·5 emissions scenarios. We defined suitable temperature ranges for dengue transmission of between 17·05-34·61 °C for Ae. aegypti and 15·84-31·51 °C for Ae. albopictus and then developed a historical and predicted SCI based on weather variability to measure the expected geographic limits of dengue vectorial capacity. Historical and projected SCI values were compared through difference maps for the six countries. FINDINGS: Comparing different emission scenarios across all countries, we found that most South East Asian countries showed either a stable pattern of high suitability, or a potential decline in suitability for both vectors from 2030 to 2070, with a declining pattern particularly evident for Ae. albopictus. Temperate areas of both China and Australia showed a less stable pattern, with both moderate increases and decreases in suitability for each vector in different regions between 2030 and 2070. INTERPRETATION: The SCI will be a useful index for forecasting potential dengue risk distributions in response to climate change, and independently of the effects of human activity. When considered alongside additional correlates of infection such as human population density and socioeconomic development indicators, the SCI could be used to develop an early warning system for dengue transmission.

A call for better understanding of social media in surveillance and management of noncommunicable diseases.

Using social media for health purposes has attracted much attention over the past decade. Given the challenges of population ageing and changes in national health profile and disease patterns following the epidemiologic transition, researchers and policy-makers should pay attention to the potential of social media in chronic disease surveillance, management and support. This commentary overviews the evidence base for this inquiry and outlines the key challenges to research laying ahead. The authors provide concrete suggestions and recommendations for developing a research agenda to guide future investigation and action on this topic.

Using internet-based query and climate data to predict climate-sensitive infectious disease risks: a systematic review of epidemiological evidence.

The use of internet-based query data offers a novel approach to improve disease surveillance and provides timely disease information. This paper systematically reviewed the literature on infectious disease predictions using internet-based query data and climate factors, discussed the current research progress and challenges, and provided some recommendations for future studies. We searched the relevant articles in the PubMed, Scopus, and Web of Science databases between January 2000 and December 2019. We initially included studies that used internet-based query data to predict infectious disease epidemics, then we further filtered and appraised the studies that used both internet-based query data and climate factors. In total, 129 relevant papers were included in the review. The results showed that most studies used a simple descriptive approach (n=80; 62%) to detect epidemics of influenza (including influenza-like illness (ILI)) (n=88; 68%) and dengue (n=9; 7%). Most studies (n=61; 47%) purely used internet search metrics to predict the epidemics of infectious diseases, while only 3 out of the 129 papers included both climate variables and internet-based query data. Our research shows that including internet-based query data and climate variables could better predict climate-sensitive infectious disease epidemics; however, this method has not been widely used to date. Moreover, previous studies did not sufficiently consider the spatiotemporal uncertainty of infectious diseases. Our review suggests that further research should use both internet-based query and climate data to develop predictive models for climate-sensitive infectious diseases based on spatiotemporal models.

Temperature-sensitive morbidity indicator: consequence from the increased ambulance dispatches associated with heat and cold exposure.

Current development of temperature-related health early warning systems mainly arises from knowledge of temperature-related mortality or hospital-based morbidity. However, due to the delay in data reporting and limits in hospital capacity, these indicators cannot be used in health risk assessments timely. In this study, we examine temperature impacts on emergency ambulance and discuss the benefits of using this near real-time indicator for risk assessment and early warning. We collected ambulance dispatch data recording individual characteristics and preliminary diagnoses between 2015 and 2016 in Shenzhen, China. Distributed lag nonlinear model was used to examine the effects of high and low temperatures on ambulance dispatches during warm and cold seasons. Lag effects were also assessed to evaluate the sensitivity of ambulance dispatches in reflecting immediate health reactions. Stratified analyses by gender, age, and a wide range of diagnoses were performed to identify vulnerable subgroups. Disease-specific numbers of ambulance dispatches attributable to non-optimal temperature were calculated to determine the related medical burdens. Effects of temperature on ambulance dispatches appeared to be acute on the current day. Males, people aged 18-44 years, were more susceptible to non-optimal temperatures. Highest RR during heat exposure by far was for urinary disease, alcohol intoxication, and traumatic injury, while alcohol intoxication and cardiovascular disease were especially sensitive to cold exposure, causing the main part of health burden. The development of local health surveillance systems by utilizing ambulance dispatch data are important for temperature impact assessments and medical resource reallocation.

Extreme weather events and dengue outbreaks in Guangzhou, China: a time-series quasi-binomial distributed lag non-linear model.

Dengue transmission is climate-sensitive and permissive conditions regularly cause large outbreaks in Asia-Pacific area. As climate change progresses, extreme weather events such as heatwaves and unusually high rainfall are predicted more intense and frequent, but their impacts on dengue outbreaks remain unclear so far. This paper aimed to investigate the relationship between extreme weather events (i.e., heatwaves, extremely high rainfall and extremely high humidity) and dengue outbreaks in China. We obtained daily number of locally acquired dengue cases and weather factors for Guangzhou, China, for the period 2006-2015. The definition of dengue outbreaks was based on daily number of locally acquired cases above the threshold (i.e., mean + 2SD of daily distribution of dengue cases during peaking period). Heatwave was defined as ≥2 days with temperature ≥ 95th percentile, and extreme rainfall and humidity defined as daily values ≥95th percentile during 2006-2015. A generalized additive model was used to examine the associations between extreme weather events and dengue outbreaks. Results showed that all three extreme weather events were associated with increased risk of dengue outbreaks, with a risk increase of 115-251% around 6 weeks after heatwaves, 173-258% around 6-13 weeks after extremely high rainfall, and 572-587% around 6-13 weeks after extremely high humidity. Each extreme weather event also had good capacity in predicting dengue outbreaks, with the model's sensitivity, specificity, accuracy, and area under the receiver operating characteristics curve all exceeding 86%. This study found that heatwaves, extremely high rainfall, and extremely high humidity could act as potential drivers of dengue outbreaks.

Global, regional, and national burden of lung cancer and its attributable risk factors, 1990 to 2017.

BACKGROUND: China's lung cancer (LC) burden plays a pivotal role in the global cancer epidemic. Comparing LC burden and population attributable fractions (PAFs) of risk factors between China and other countries/regions is essential to inform effective intervention. The Global Burden of Disease (GBD) study provides a unique opportunity for such comparisons. METHODS: We extracted the number of LC deaths, age-standardized death rates (ASDRs), age-standardized disability-adjusted life-year (DALY) rates, and PAFs of risk factors for LC deaths between 1990 and 2017 from GBD 2017. The annual percentage change (APC) was used to quantify the trends of LC ASDRs and age-standardized DALY rates. The relationship between the APC of LC ASDR and Socio-demographic Index was assessed among China and other countries. RESULTS: Globally, the ASDR for LC decreased in men (APC, -0.66% [95% CI, -0.69 to -0.62]) but increased in women (APC, 0.31% [95% CI, 0.26 to 0.36]) from 1990 to 2017. The ASDRs in China increased both for men (APC, 1.12% [95% CI, 1.03 to 1.20]) and women (APC, 0.80% [95% CI, 0.70 to 0.89]). The increased LC death numbers among men (312,798) and women (139,115) in China accounted for 59.39% and 43.01% of global increases. LC years of life lost accounted for the majority of LC DALYs globally and in China. The risk factors with the highest PAFs of LC death in China were smoking and ambient particulate matter. The ASDRs for LC associated with ambient particulate matter in China ranked second globally. CONCLUSIONS: The trends of LC ASDRs and age-standardized DALY rates and the PAFs of risk factors vary markedly by region, indicating a need for tailored measures to reduce LC burden and improve health equality. China's LC ASDRs are among the highest in the world, and the primary intervention priorities in China should be control of ambient particulate matter and tobacco usage.

Sporadic occurrence of H9N2 avian influenza infections in human in Anhui province, eastern China: A notable problem.

H9N2 viruses can cause great economic losses to the domestic poultry industry when co-infected with other influenza viruses or pathogens. . To better understand the molecular characteristics of H9N2 avian influenza viruses (AIVs) and analyze the genetic evolutionary relationship, we isolated three H9N2 subtypes AIVs from nasopharyngeal swab specimens from the three cases reported in Anhui province since 2015, and systematically reviewed the genome-wide data of 21 poultry--isolated H9N2 viruses during 1998-2017. The six internal genes of three human-isolated viruses and recent poultry-isolated viruses (since 2014) in Anhui province presented high gene homologies with HPAI H7N9, even including H10N8 and H5N6. The three human-isolated H9N2 AIVs and poultry-isolated viruses (since 2008) in Anhui province were highly similar, and classified into genotype S. Seven N-linked potential glycosylation sites in the HA protein were detected in the three human-isolated viruses, which also appeared in poultry-isolated H9N2 AIVs. None of the human-isolated H9N2 AIVs had the I368V mutation in PB1 protein, but all the poultry-isolated H9N2 viruses in 2017 carried this mutation. Multidisciplinary, cross-regional and cross-sectoral approaches are warranted to address complex public health challenges and achieve the goal of 'one health'.

The Efficacy and Safety of Nicorandil for Periprocedural Myocardial Injury in Patients Undergoing PCI: A Meta-Analysis.

PURPOSE: To evaluate the efficacy and safety of nicorandil for periprocedural myocardial injury in patients undergoing PCI through meta-analysis of randomized controlled trials. METHODS: We analyzed the clinical data of patients including the incidence of periprocedural myocardial injury (PMI) and major adverse cardiovascular events (MACE) from selected articles. RCTs were retrieved from medical literature databases. RR and 95% confidence intervals (CI) were calculated to compare the endpoints. RESULTS: In total, 15 articles (16 trial comparisons) were retrieved which contained 2221 patients. In general, 1130 patients (50.9%) were randomized to the experimental group, whereas 1091 patients (49.1%) were randomized to the control group. The result showed that nicorandil significantly reduced the incidence of PMI and MACE after PCI compared to the control group. CONCLUSIONS: Overall, early use of nicorandil in patients undergoing percutaneous coronary intervention (PCI) was associated with a significant reduction of PMI and MACE.

Live poultry market closure and avian influenza A (H7N9) infection in cities of China, 2013-2017: an ecological study.

BACKGROUND: Previous studies have proven that the closure of live poultry markets (LPMs) was an effective intervention to reduce human risk of avian influenza A (H7N9) infection, but evidence is limited on the impact of scale and duration of LPMs closure on the transmission of H7N9. METHOD: Five cities (i.e., Shanghai, Suzhou, Shenzhen, Guangzhou and Hangzhou) with the largest number of H7N9 cases in mainland China from 2013 to 2017 were selected in this study. Data on laboratory-confirmed H7N9 human cases in those five cities were obtained from the Chinese National Influenza Centre. The detailed information of LPMs closure (i.e., area and duration) was obtained from the Ministry of Agriculture. We used a generalized linear model with a Poisson link to estimate the effect of LPMs closure, reported as relative risk reduction (RRR). We used classification and regression trees (CARTs) model to select and quantify the dominant factor of H7N9 infection. RESULTS: All five cities implemented the LPMs closure, and the risk of H7N9 infection decreased significantly after LPMs closure with RRR ranging from 0.80 to 0.93. Respectively, a long-term LPMs closure for 10-13 weeks elicited a sustained and highly significant risk reduction of H7N9 infection (RRR = 0.98). Short-time LPMs closure with 2 weeks in every epidemic did not reduce the risk of H7N9 infection (p > 0.05). Partially closed LPMs in some suburbs contributed only 35% for reduction rate (RRR = 0.35). Shenzhen implemented partial closure for first 3 epidemics (p > 0.05) and all closure in the latest 2 epidemic waves (RRR = 0.64). CONCLUSION: Our findings suggest that LPMs all closure in whole city can be a highly effective measure comparing with partial closure (i.e. only urban closure, suburb and rural remain open). Extend the duration of closure and consider permanently closing the LPMs will help improve the control effect. The effect of LPMs closure seems greater than that of meteorology on H7N9 transmission.