Short-term effects of low-level PM_(2.5),PM_(10),O_(3),and tropical meteorological conditions on emergency department visits for respiratory diseases in Haikou,China

Objective:To assess the correlation between atmospheric pollutants,meteorological factors,and emergency department visits for respiratory diseases in Haikou City.Methods:Daily data on atmospheric pollutants,meteorological factors,and emergency department visits for respiratory diseases in Haikou City from 2018 to 2021 were collected.The Spearman rank correlation test was used to analyze the correlation,and a distributed lag non-linear model was employed to analyze the health effects and lag impacts of environmental factors.Subgroup analyses were conducted based on sex and age.Results:According to the criteria of International Classification of Diseases(ICD-10:J00-J99),a total of 221913 cases were included,accounting for 21.3%of the total emergency department visits in Haikou City.For every 1℃increase in temperature,the risk of emergency department visits increased by 1.029%(95%CI 1.016%-1.042%).Relative humidity greater than 80%reduced the risk of visits,while higher atmospheric pressure(>1010 hpa)also decreased the likelihood of daily emergency department visits.Higher concentrations of PM_(2.5)(30-50μg/m^(3)),PM10(>60μg/m^(3)),and O_(3)(75-125μg/m^(3))were associated with increased visits.Higher temperatures(>25℃)have a greater impact on females and children aged 0-14 years,while males are more sensitive to low atmospheric pressure.Individuals aged 65 and above exhibited increased sensitivity to O_(3)concentration,and the effects of PM2.5,PM10,and O_(3)are more pronounced in individuals over 14 years old.Conclusions:Short-term exposure to high temperatures,particulate matter pollutants(PM_(2.5)and PM_(10)),and ozone(O_(3))is associated with increased emergency department visits for respiratory diseases.

Spatiotemporal Distribution and Epidemiological Characteristics of Hospital Admissions for Carbon Monoxide Poisoning in Guangdong,China,2013-2020

Objective This study aimed to determine the spatiotemporal distribution and epidemiological characteristics of hospital admissions for carbon monoxide poisoning(COP)in Guangdong,China,from 2013 to 2020.Methods Data on age-and sex-specific numbers of hospital admissions due to COP in Guangdong(2013-2020)were collected.Daily temperatures were downloaded through the China Meteorological Data Sharing Service System.We analyzed temporal trends through time series decomposition and used spatial autocorrelation analysis to detect spatial clustering.The distributed lag nonlinear model was used to quantify the effects of temperature.Results There were 48,854 COP admissions over the study period.The sex ratio(male to female)was1:1.74.The concentration ratios(M)ranged from 0.73-0.82.The highest risk occurred in January(season index=3.59).Most cases were concentrated in the northern mountainous areas of Guangdong with high-high clustering.COP in the study region showed significant spatial autocorrelation,and the global Moran’s I value of average annual hospital admission rates for COP was 0.447(P<0.05).Low temperatures were associated with high hospital admission rates for COP,with a lag lasting 7 days.With a lag of 0 days,the effects of low temperatures[5th(12℃)]on COP were 2.24-3.81,as compared with the reference temperature[median(24℃)].Conclusion COP in Guangdong province showed significant temporal and spatial heterogeneity.Low temperature was associated with a high risk of COP,and the influence had a lag lasting 7 days.

Epidemiological characteristics of tuberculosis incidence and its macro-influence factors in Chinese mainland during 2014-2021

Background Tuberculosis(TB)remains a pressing public health issue,posing a significant threat to individuals'well-being and lives.This study delves into the TB incidence in Chinese mainland during 2014-2021,aiming to gain deeper insights into their epidemiological characteristics and explore macro-level factors to enhance control and prevention.Methods TB incidence data in Chinese mainland from 2014 to 2021 were sourced from the National Notifiable Disease Reporting System(NNDRS).A two-stage distributed lag nonlinear model(DLNM)was constructed to evaluate the lag and non-linearity of daily average temperature(℃,Atemp),average relative humidity(%,ARH),average wind speed(m/s,AWS),sunshine duration(h,SD)and precipitation(mm,PRE)on the TB incidence.A spatial panel data model was used to assess the impact of demographic,medical and health resource,and economic factors on TB incidence.Results A total of 6,587,439 TB cases were reported in Chinese mainland during 2014-2021,with an average annual incidence rate of 59.17/100,000.The TB incidence decreased from 67.05/100,000 in 2014 to 46.40/100,000 in 2021,notably declining from 2018 to 2021(APC=-8.87%,95%CI:-11.97,-6.85%).TB incidence rates were higher among males,farmers,and individuals aged 65 years and older.Spatiotemporal analysis revealed a significant cluster in Xinjiang,Qinghai,and Xizang from March 2017 to June 2019(RR=3.94,P<0.001).From 2014 to 2021,the proportion of etiologically confirmed cases increased from 31.31%to 56.98%,and the time interval from TB onset to diagnosis shortened from 26 days(IQR:10-56 days)to 19 days(IQR:7-44 days).Specific meteorological conditions,including low temperature(<16.69℃),high relative humidity(>71.73%),low sunshine duration(<6.18 h)increased the risk of TB incidence,while extreme low wind speed(<2.79 m/s)decreased the risk.The spatial Durbin model showed positive associations between TB incidence rates and sex ratio(β=1.98),number of beds in medical and health institutions per 10,000 population(β=0.90),and total health expenses(β=0.55).There were negative associations between TB incidence rates and population(β=-1.14),population density(β=-0.19),urbanization rate(β=-0.62),number of medical and health institutions(β=-0.23),and number of health technicians per 10,000 population(β=-0.70).Conclusions Significant progress has been made in TB control and prevention in China,but challenges persist among some populations and areas.Varied relationships were observed between TB incidence and factors from meteorological,demographic,medical and health resource,and economic aspects.These findings underscore the importance of ongoing efforts to strengthen TB control and implement digital/intelligent surveillance for early risk detection and comprehensive interventions.

Light at night and lung cancer risk:A worldwide interdisciplinary and time-series study

Background:Light at night(LAN)has become a concern in interdisciplinary research in recent years.This global interdisciplinary study aimed to explore the exposure-lag-response association between LAN exposure and lung cancer incidence.Methods:LAN data were obtained from the Defense Meteorological Satellite Program’s Operational Linescan System.Data of lung cancer incidence,socio-demographic index,and smoking prevalence of populations in 201 countries/territories from 1992 to 2018 were collected from the Global Burden of Disease Study.Spearman correlation tests and population-weighted linear regression analysis were used to evaluate the correlation between LAN exposure and lung cancer incidence.A distributed lag nonlinear model(DLNM)was used to assess the exposure-lag effects of LAN exposure on lung cancer incidence.Results:The Spearman correlation coefficients were 0.286-0.355 and the population-weighted linear regression correlation coefficients were 0.361-0.527.After adjustment for socio-demographic index and smoking preva-lence,the Spearman correlation coefficients were 0.264-0.357 and the population-weighted linear regression correlation coefficients were 0.346-0.497.In the DLNM,the maximum relative risk was 1.04(1.02-1.06)at LAN exposure of 8.6 with a 2.6-year lag time.After adjustment for socio-demographic index and smoking prevalence,the maximum relative risk was 1.05(1.02-1.07)at LAN exposure of 8.6 with a 2.4-year lag time.Conclusion:High LAN exposure was associated with increased lung cancer incidence,and this effect had a specific lag period.Compared with traditional individual-level studies,this group-level study provides a novel paradigm of effective,efficient,and scalable screening for risk factors.

Total and cause-specific mortality attributable to cold spells in China:A multicity and multicounty study

Climate change has resulted in an increase in the frequency and intensity of extreme cold weather events,but few multicity or multicounty researches have explored the association between cold spells and mortality risk and burden.We collected daily data on climate,sociodemo-graphic factors and mortality in 18 cities/counties across 11 geographical regions for the period of November to March 2014-2018.A distributed lag nonlinear model was used to examine the association between cold spells and mortality after adjustment for confounding factors.Twelve definitions of cold spells were used.Multi-meta regression analysis was applied to pool the impacts over different regions.Cold spells were significantly associated with all-cause mortality at lag 0-21(CRR:1.38,95%CI:1.21,1.57).In addition to respiratory diseases and cir-culatory system diseases,digestive,endocrine and nervous system diseases and injury were also affected by cold spells.The magnitude of the impacts of cold spells on mortality varied among the diseases investigated,with the highest risk estimate found for influenza and pneumonia(CRR:2.00,95%CI:1.45,2.76)and the lowest estimate found for injury(CRR:1.26,95%CI:1.09,1.46).The fraction of all-cause mortality attributable to cold spells was 2.31%(95%CI:0.90%,3.46%).Among the regional differences,the attributable burden of all-cause mortality was higher in rural areas and subtropical monsoon climate zone,with attributable fractions of 2.85%(95%CI:1.23%,4.11%)and 3.36%(95%CI:0.55%,5.35%),respectively.Cold spells increased mortality from a range of diseases.Women,older adults and residents of rural areas and subtropical monsoon climate zone were more vulnerable to cold spells impacts.The findings may help to formulate preventive strategies and early warning response plans to reduce mortality burden of extreme cold events.

An exploration of the relationship between wastewater viral signals and COVID-19 hospitalizations in Ottawa,Canada

Monitoring of viral signal in wastewater is considered a useful tool for monitoring the burden of COVID-19,especially during times of limited availability in testing.Studies have shown that COVID-19 hospitalizations are highly correlated with wastewater viral signals and the increases in wastewater viral signals can provide an early warning for increasing hospital admissions.The association is likely nonlinear and time-varying.This project employs a distributed lag nonlinear model(DLNM)(Gasparrini et al.,2010)to study the nonlinear exposure-response delayed association of the COVID-19 hospitalizations and SARS-CoV-2 wastewater viral signals using relevant data from Ottawa,Canada.We consider up to a 15-day time lag from the average of SARS-CoV N1 and N2 gene concen-trations to COVID-19 hospitalizations.The expected reduction in hospitalization is adjusted for vaccination efforts.A correlation analysis of the data verifies that COVID-19 hospital-izations are highly correlated with wastewater viral signals with a time-varying rela-tionship.Our DLNM based analysis yields a reasonable estimate of COVID-19 hospitalizations and enhances our understanding of the association of COVID-19 hospi-talizations with wastewater viral signals.

Ambient Temperature and Years of Life Lost:A National Study in China

Although numerous studies have investigated premature deaths attributable to temperature,effects of temperature on years of life lost(YLL)remain unclear.We estimated the relationship between temperatures and YLL,and quantified the YLL per death caused by temperature in China.We collected daily meteorological and mortality data,and calculated the daily YLL values for 364 locations(2013–2017 in Yunnan,Guangdong,Hunan,Zhejiang,and Jilin provinces,and 2006–2011 in other locations)in China.A time-series design with a distributed lag nonlinear model was first employed to estimate the location-specific associations between temperature and YLL rates(YLL/100,000 population),and a multivariate meta-analysis model was used to pool location-specific associations.Then,YLL per death caused by temperatures was calculated.The temperature and YLL rates consistently showed U-shaped associations.A mean of 1.02(95%confidence interval:0.67,1.37)YLL per death was attributable to temperature.Cold temperature caused 0.98 YLL per death with most from moderate cold(0.84).The mean YLL per death was higher in those with cardiovascular diseases(1.14),males(1.15),younger age categories(1.31 in people aged 65–74 years),and in central China(1.34)than in those with respiratory diseases(0.47),females(0.87),older people(0.85 in people R75 years old),and northern China(0.64)or southern China(1.19).The mortality burden was modified by annual temperature and temperature variability,relative humidity,latitude,longitude,altitude,education attainment,and central heating use.Temperatures caused substantial YLL per death in China,which was modified by demographic and regional characteristics.

The Relationship between Interest Rates and Inflation:Examining the Fisher Effect in China

This study revisits the Fisher effect using a different empirical method that considers a potential nonlinear relationship between interest rates(treasury bond rates)and inflation in China.The rising uncertainty and asymmetric information in financial markets between bond holders and bond issuers suggest such a potential nonlinear relationship.To this aim,we apply Shin et al.'s(2014)nonlinear autoregressive distributed lag(NARDL)model with asymmetric dynamic multipliers for the sample period 2002M7-2018M4.The empirical findings reveal symmetric and asymmetric partial Fisher effects for all sample bond rates in China.Furthermore,we find that 20-year bond rates experience the lowest partial Fisher effect.