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A stochastic volatility estimation of VIX index's latent volatility is used for the United States of America, as a proxy for the adjustments in the levels of investors' uncertainty related to current and future economic policies. The impact of monetary policy stance on such measure is examined in the framework of the distributed lag non-linear models (DLNM). We place this analysis in the literature stream emphasizing the various sources of heterogeneity concerning investors' expectations. The main finding is that the monetary policy does impact non-linearly the adjustments in investors' predictions. While a tighter monetary policy does generally contribute to an increase in VIX's latent volatility, the shape of such effect varies across different GLM and GAM specifications of DLNM. This outcome remains robust, even if: (1) we control for the global price of Brent crude and consumers' confidence; (2) we use, instead of the stochastic framework, a Markov-switching GARCH-based estimator; or (3) we replace the monetary policy instrument with monetary policy uncertainty. We argue that accounting for its nonlinear effects on financial markets is of critical importance for the design of a monetary policy pursuing global financial stability. [ABSTRACT FROM AUTHOR]

Rapid changes in thermal environments are threatening many species worldwide. Thermal acclimatisation may partially buffer species from the impacts of these changes, but currently, the knowledge about the temporal dynamics of acclimatisation remains limited. Moreover, acclimatisation phenotypes are typically determined in laboratory conditions that lack the variability and stochasticity that characterise the natural environment. Through a distributed lag non‐linear model (DLNM), we use field data to assess how the timing and magnitude of past thermal exposures influence thermal tolerance. We apply the model to two Scottish freshwater Ephemeroptera species living in natural thermal conditions. Model results provide evidence that rapid heat hardening effects are dramatic and reflect high rates of change in temperatures experienced over recent hours to days. In contrast, temperature change magnitude impacted acclimatisation over the course of weeks but had no impact on short‐term responses. Our results also indicate that individuals may de‐acclimatise their heat tolerance in response to cooler environments. Based on the novel insights provided by this powerful modelling approach, we recommend its wider uptake among thermal physiologists to facilitate more nuanced insights in natural contexts, with the additional benefit of providing evidence needed to improve the design of laboratory experiments. [ABSTRACT FROM AUTHOR]

Background A growing body of evidence has reported positive associations between long-term exposure to air pollution and poor COVID-19 outcomes. Inconsistent findings have been reported for short-term air pollution, mostly from ecological study designs. Using individual-level data, we studied the association between short-term variation in air pollutants [nitrogen dioxide (NO2), particulate matter with a diameter of <2.5 µm (PM2.5) and a diameter of <10 µm (PM10) and ozone (O3)] and hospital admission among individuals diagnosed with COVID-19. Methods The COVAIR-CAT (Air pollution in relation to COVID-19 morbidity and mortality: a large population-based cohort study in Catalonia, Spain) cohort is a large population-based cohort in Catalonia, Spain including 240 902 individuals diagnosed with COVID-19 in the primary care system from 1 March until 31 December 2020. Our outcome was hospitalization within 30 days of COVID-19 diagnosis. We used individual residential address to assign daily air-pollution exposure, estimated using machine-learning methods for spatiotemporal prediction. For each pandemic wave, we fitted Cox proportional-hazards models accounting for non-linear-distributed lagged exposure over the previous 7 days. Results Results differed considerably by pandemic wave. During the second wave, an interquartile-range increase in cumulative weekly exposure to air pollution (lag0_7) was associated with a 12% increase (95% CI: 4% to 20%) in COVID-19 hospitalizations for NO2, 8% (95% CI: 1% to 16%) for PM2.5 and 9% (95% CI: 3% to 15%) for PM10. We observed consistent positive associations for same-day (lag0) exposure, whereas lag-specific associations beyond lag0 were generally not statistically significant. Conclusions Our study suggests positive associations between NO2, PM2.5 and PM10 and hospitalization risk among individuals diagnosed with COVID-19 during the second wave. Cumulative hazard ratios were largely driven by exposure on the same day as hospitalization. [ABSTRACT FROM AUTHOR]

Abstract Rapid changes in thermal environments are threatening many species worldwide. Thermal acclimatisation may partially buffer species from the impacts of these changes, but currently, the knowledge about the temporal dynamics of acclimatisation remains limited. Moreover, acclimatisation phenotypes are typically determined in laboratory conditions that lack the variability and stochasticity that characterise the natural environment. Through a distributed lag non‐linear model (DLNM), we use field data to assess how the timing and magnitude of past thermal exposures influence thermal tolerance. We apply the model to two Scottish freshwater Ephemeroptera species living in natural thermal conditions. Model results provide evidence that rapid heat hardening effects are dramatic and reflect high rates of change in temperatures experienced over recent hours to days. In contrast, temperature change magnitude impacted acclimatisation over the course of weeks but had no impact on short‐term responses. Our results also indicate that individuals may de‐acclimatise their heat tolerance in response to cooler environments. Based on the novel insights provided by this powerful modelling approach, we recommend its wider uptake among thermal physiologists to facilitate more nuanced insights in natural contexts, with the additional benefit of providing evidence needed to improve the design of laboratory experiments.

IntroductionThe precise associations between temperature-related indices and mental and behavioral disorders (MBDs) have yet to be fully elucidated. Our study aims to ascertain the most effective temperature-related index and assess its immediate impact on emergency ambulance dispatches (EADs) due to MBDs in Shenzhen, China.MethodsEADs data and meteorological data from January 1, 2013, to December 31, 2020, in Shenzhen were collected. Distributed lag non-linear models (DLNMs) were utilized to examine the non-linear and lagged effects of temperature-related indices on EADs due to MBDs. The Quasi Akaike Information criterion (QAIC) was used to determine the optimal index after standardizing temperature-related indices. After adjusting for confounding factors in the model, we estimated the immediate and cumulative effects of temperature on EADs due to MBDs.ResultsThe analysis of short-term temperature effects on EADs due to MBDs revealed Humidex as the most suitable index. Referring to the optimal Humidex (3.2th percentile, 12.00°C), we observed a significant effect of Humidex over the threshold (34.6th percentile, 26.80°C) on EADs due to MBDs at lag 0–5. The cumulative relative risks for high temperature (90th percentile, 41.90°C) and extreme high temperature (99th percentile, 44.20°C) at lag 0–5 were 1.318 (95% CI: 1.159–1.499) and 1.338 (95% CI: 1.153–1.553), respectively. No significant cold effect was observed on EADs due to MBDs.ConclusionHigh Humidex was associated with more EADs due to MBDs in subtropical regions. Health authorities should implement effective measures to raise public awareness of risks related to high temperature and protect vulnerable populations.

BackgroundMost existing studies have only investigated the direct effects of the built environment on respiratory diseases. However, there is mounting evidence that the built environment of cities has an indirect influence on public health via influencing air pollution. Exploring the “urban built environment-air pollution-respiratory diseases” cascade mechanism is important for creating a healthy respiratory environment, which is the aim of this study.MethodsThe study gathered clinical data from 2015 to 2017 on patients with respiratory diseases from Tongji Hospital in Wuhan. Additionally, daily air pollution levels (sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter (PM2.5, PM10), and ozone (O3)), meteorological data (average temperature and relative humidity), and data on urban built environment were gathered. We used Spearman correlation to investigate the connection between air pollution and meteorological variables; distributed lag non-linear model (DLNM) was used to investigate the short-term relationships between respiratory diseases, air pollutants, and meteorological factors; the impacts of spatial heterogeneity in the built environment on air pollution were examined using the multiscale geographically weighted regression model (MGWR).ResultsDuring the study period, the mean level of respiratory diseases (average age 54) was 15.97 persons per day, of which 9.519 for males (average age 57) and 6.451 for females (average age 48); the 24 h mean levels of PM10, PM2.5, NO2, SO2 and O3 were 78.056 μg/m3, 71.962 μg/m3, 54.468 μg/m3, 12.898 μg/m3, and 46.904 μg/m3, respectively; highest association was investigated between PM10 and SO2 (r = 0.762, p

ObjectiveAnalyzing the epidemiological characteristics of influenza cases among children aged 0–17 years in Guangzhou from 2019 to 2022. Assessing the relationships between multiple meteorological factors and influenza, improving the early warning systems for influenza, and providing a scientific basis for influenza prevention and control measures.MethodsThe influenza data were obtained from the Chinese Center for Disease Control and Prevention. Meteorological data were provided by Guangdong Meteorological Service. Spearman correlation analysis was conducted to examine the relevance between meteorological factors and the number of influenza cases. Distributed lag non-linear models (DLNM) were used to explore the effects of meteorological factors on influenza incidence.ResultsThe relationship between mean temperature, rainfall, sunshine hours, and influenza cases presented a wavy pattern. The correlation between relative humidity and influenza cases was illustrated by a U-shaped curve. When the temperature dropped below 13°C, Relative risk (RR) increased sharply with decreasing temperature, peaking at 5.7°C with an RR of 83.78 (95% CI: 25.52, 275.09). The RR was increased when the relative humidity was below 66% or above 79%, and the highest RR was 7.50 (95% CI: 22.92, 19.25) at 99%. The RR was increased exponentially when the rainfall exceeded 1,625 mm, reaching a maximum value of 2566.29 (95% CI: 21.85, 3558574.07) at the highest rainfall levels. Both low and high sunshine hours were associated with reduced incidence of influenza, and the lowest RR was 0.20 (95% CI: 20.08, 0.49) at 9.4 h. No significant difference of the meteorological factors on influenza was observed between males and females. The impacts of cumulative extreme low temperature and low relative humidity on influenza among children aged 0–3 presented protective effects and the 0–3 years group had the lowest RRs of cumulative extreme high relative humidity and rainfall. The highest RRs of cumulative extreme effect of all meteorological factors (expect sunshine hours) were observed in the 7–12 years group.ConclusionTemperature, relative humidity, rainfall, and sunshine hours can be used as important predictors of influenza in children to improve the early warning system of influenza. Extreme weather reduces the risk of influenza in the age group of 0–3 years, but significantly increases the risk for those aged 7–12 years.

Background: Dengue fever is a growing concern for public health under future climate variability. This study aims to investigate the dengue fever from 35 cities/counties linked with historical observation and anomaly of weather variables from 4 weather stations in Indonesia. Method: We collected monthly surveillance data of dengue fever in central java, temperature and precipitation from Tegal, Semarang, Tunggul wulung and Sleman weather stations, and flood event from 2009 to 2019. The distributed non-linear model was adopted to evaluate the effect of extremes weather variables and anomalies on the dengue risks. The extreme thresholds were defined at 5th and 99th percentile. Random-effects meta-analysis was applied to estimate weather station-specific pooled relative risk (RR) and 95% confidence intervals (CI) for the studied areas. Result: Dengue prevalence rates were higher in the rainy season (Nov–March) compared to dry season (Apr–Oct). Extreme high temperature was positively associated with dengue fever in Semarang with RR of 4.92 (95 % CI: 1.01, 24.0). Extreme low precipitation was positively associated with dengue fever in Tegal with RR of 9.60 (95 % CI: 2.65, 34.6). The risk of dengue fever in western part of Central Java, especially in the Tunggul wulung, was positively associated with extreme high anomaly of precipitation [RR = 4.05 (95 % CI: 1.86, 13.7). Meanwhile, extreme low anomaly of precipitation was positively associated with the risk of dengue fever with RR of 2.75 (95 % CI: 1.75, 4.32) in Semarang. Conclusion: These findings highlight the importance of considering weather variability in addressing the risks associated with dengue fever in Central Java, Indonesia.

Nearly a quarter of the world's land has already been polluted by artificial light. And numerous human and animal studies have corroborated that light at night can disrupt metabolism. Therefore, we aimed to estimate the association between outdoor artificial light at night (ALAN) and the presence of metabolic disease. Daily hospital admission cases from Ningxia, China, between 2014 and 2020 were included. Cumulative associations between outdoor ALAN and metabolic disease were estimated using logistic regression and distributed lagged non-linear models (DLNM) with lags of 0–30 days and stratified analysis by age groups and gender. The results suggest that 26.80% of metabolic disease cases in Ningxia can be attributed to outdoor ALAN and that men, especially in men aged 46–59 years, are more susceptible to lighting. Policymakers need to develop measures and facilities in corresponding areas, such as universal access to indoor blackout curtains. In particular, men should be urged to minimize going outside at night and to develop protective measures specifically for men. [ABSTRACT FROM AUTHOR]

Abstract Background Although adaptation to continuously rising ambient temperatures is an emerging topic and has been widely studied at a global scale, detailed analysis of the joint indicators for long-term adaptation in Spain are scarce. This study aims to explore temporal variations of the minimum mortality temperature and mortality burden from heat and cold between 1979 and 2018. Methods We collected individual all-cause mortality and climate reanalysis data for 4 decades at a daily time step. To estimate the temperature-mortality association for each decade, we fitted a quasi-Poisson time-series regression model using a distributed lag non-linear model with 21 days of lag, controlling for trends and day of the week. We also calculated attributable mortality fractions by age and sex for heat and cold, defined as temperatures above and below the optimum temperature, which corresponds to the minimum mortality in each period. Results We analysed over 14 million deaths registered in Spain between 1979 and 2018. The optimum temperature estimated at a nationwide scale declined from 21 °C in 1979–1988 to 16 °C in 1999–2008, and raised to 18 °C in 2009–2018. The mortality burden from moderate cold showed a 3-fold reduction down to 2.4% in 2009–2018. Since 1988–1999, the mortality risk attributable to moderate (extreme) heat reduced from 0.9% (0.8%) to 0.6% (0.5%). The mortality risk due to heat in women was almost 2 times larger than in men, and did not decrease over time. Conclusion Despite the progressively warmer temperatures in Spain, we observed a persistent flattening of the exposure-response curves, which marked an expansion of the uncertainty range of the optimal temperatures. Adaptation has been produced to some extent in a non-uniform manner with a substantial decrease in cold-related mortality, while for heat it became more apparent in the most recent decade only.

Accumulating evidence suggested that the risk of preterm births (PTBs) following prenatal exposure to air pollution was inconclusive. The aim of this study is to investigate the relationship between air pollution exposure in the days before delivery and PTB and assess the threshold effect of short-term prenatal exposure to air pollution on PTB. This study collected data including meteorological factors, air pollutants, and information in Birth Certificate System from 9 districts during 2015–2020 in Chongqing, China. Generalized additive models (GAMs) with the distributed lag non-linear models were conducted to assess the acute impact of air pollutants on the daily counts of PTB, after controlling for potential confounding factors. We observed that PM2.5 was related to increased occurrence of PTB on lag 0–3 and lag 10–21 days, with the strongest on the first day (RR = 1.017, 95%CI: 1.000–1.034) and then decreasing. The thresholds of PM2.5 for lag 1–7 and 1–30 days were 100 μg/m3 and 50 μg/m3, respectively. The lag effect of PM10 on PTB was very similar to that of PM2.5. In addition, the lagged and cumulative exposure of SO2 and NO2 was also associated with the increased risk of PTB. The lag relative risk and cumulative relative risk of CO exposure were the strongest, with a maximum RR at lag 0 (RR = 1.044, 95%CI: 1.018, 1.069). Importantly, the exposure–response curve of CO showed that RR increased rapidly when the concentration exceeded 1000 μg/m3. This study indicated significant associations between air pollution and PTB. The relative risk decreases with day lag, while the cumulative effect increases. Thus, pregnant women should understand the risk of air pollution and try to avoid high concentration exposure. [ABSTRACT FROM AUTHOR]

Background: Although adaptation to continuously rising ambient temperatures is an emerging topic and has been widely studied at a global scale, detailed analysis of the joint indicators for long-term adaptation in Spain are scarce. This study aims to explore temporal variations of the minimum mortality temperature and mortality burden from heat and cold between 1979 and 2018. Methods: We collected individual all-cause mortality and climate reanalysis data for 4 decades at a daily time step. To estimate the temperature-mortality association for each decade, we fitted a quasi-Poisson time-series regression model using a distributed lag non-linear model with 21 days of lag, controlling for trends and day of the week. We also calculated attributable mortality fractions by age and sex for heat and cold, defined as temperatures above and below the optimum temperature, which corresponds to the minimum mortality in each period. Results: We analysed over 14 million deaths registered in Spain between 1979 and 2018. The optimum temperature estimated at a nationwide scale declined from 21 °C in 1979–1988 to 16 °C in 1999–2008, and raised to 18 °C in 2009–2018. The mortality burden from moderate cold showed a 3-fold reduction down to 2.4% in 2009–2018. Since 1988–1999, the mortality risk attributable to moderate (extreme) heat reduced from 0.9% (0.8%) to 0.6% (0.5%). The mortality risk due to heat in women was almost 2 times larger than in men, and did not decrease over time. Conclusion: Despite the progressively warmer temperatures in Spain, we observed a persistent flattening of the exposure-response curves, which marked an expansion of the uncertainty range of the optimal temperatures. Adaptation has been produced to some extent in a non-uniform manner with a substantial decrease in cold-related mortality, while for heat it became more apparent in the most recent decade only. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases are recognised as a significant burden on healthcare systems globally. In this study, the acute impacts of air pollution, especially from shipping emissions, on cardiovascular hospital admissions in Klang, Malaysia, a notable port district, are explored. Time-series analysis and Quasi-Poisson regressions, combined with distributed lag non-linear models, were employed to examine the relationships between daily levels of pollutants and hospital admissions for cardiovascular issues. Focus was placed on the pollutants NO 2 (nitrogen dioxide), SO 2 (sulphur dioxide), O 3 (ozone), particulate matters with diameters ≤10 μm (PM 10), particulate matters with diameters ≤2.5 μm (PM 2.5), and CO (carbon monoxide), with consideration of lag effects up to 7 days. An increment of 10 μg/m3 in NO 2 concentrations was associated with a 7.32% increase in cardiovascular admissions on the exposure day, with statistically significant elevations in relative risks at lag 0 were observed for admissions among patients with ischaemic heart disease, those aged less than 65 years, and male patients. At lag 4, an association was identified between O 3 levels and an increase in relative risk of 1.0513 for hypertensive diseases. Two-pollutant models further highlighted significant interactions between NO 2 and PM 10 , which influenced overall cardiovascular admissions, admissions due to ischaemic heart disease, and particularly affected patients below 65 years old and males, especially at earlier lags. Additionally, interactions involving NO 2 and O 3 were noted, impacting overall cardiovascular admissions, as well as admissions among patients aged less than 65 years and male patients. The findings underscore the severe health risks posed by air pollution in port areas, highlighting the urgent need for targeted interventions to mitigate these effects. Stricter air quality regulations in port cities are supported by the results to protect public health. • The relationship between air pollution and cardiovascular hospitalisations in a port district was studied. • Nitrogen dioxide was associated with cardiovascular hospitalisations, with less pronounced associations with other pollutants. • Younger males (below 65 years old) are highly susceptible to the effects of NO 2. • A collaborative approach with input from health, environment, and shipping experts is recommended. [ABSTRACT FROM AUTHOR]

Scrub typhus, caused by Orientia tsutsugamushi, is a serious public health problem in the Asia-Pacific region, threatening the health of more than one billion people. China is one of the countries with the most serious disease burden of scrub typhus. Previous epidemiological evidence indicated that meteorological factors may affect the incidence of scrub typhus, but there was limited evidence for the correlation between local natural environment factors dominated by meteorological factors and scrub typhus. This study aimed to evaluate the correlation between monthly scrub typhus incidence and meteorological factors in areas with high scrub typhus prevalence using a distributed lag non-linear model (DLNM). The monthly data on scrub typhus cases in ten provinces from 2006 to 2018 and meteorological parameters were obtained from the Public Health Science Data Center and the National Meteorological Data Sharing Center. The results of the single-variable and multiple-variable models showed a non-linear relationship between incidence and meteorological factors of mean temperature (Tmean), rainfall (RF), sunshine hours (SH), and relative humidity (RH). Taking the median of meteorological factors as the reference value, the relative risks (RRs) of monthly Tmean at 0°C, RH at 46%, and RF at 800 mm were most significant, with RRs of 2.28 (95% CI: 0.95–5.43), 1.71 (95% CI: 1.39–2.09), and 3.33 (95% CI: 1.89–5.86). In conclusion, relatively high temperature, high humidity, and favorable rainfall were associated with an increased risk of scrub typhus.

Background: We previously identified associations between trimester-specific NO2 exposures and reduced fetal growth in the Spanish INfancia y Medio Ambiente (INMA) project. Here, we use temporally refined exposure estimates to explore the impact of narrow (weekly) windows of exposure on fetal growth. Methods: We included 1,685 women from INMA with serial ultrasounds at 12, 20, and 34 gestational weeks. We measured biparietal diameter (BPD), femur length, and abdominal circumference (AC) and from them calculated estimated fetal weight (EFW). We calculated z-scores describing trajectories of each parameter during early (0-12 weeks), mid (12-20 weeks), and late (20-34 weeks) pregnancy, based on longitudinal growth curves from mixed-effects models. We estimated weekly NO2 exposures at each woman's residence using land-use regression models. We applied distributed lag nonlinear models to identify sensitive windows of exposure. We present effect estimates as the percentage change in fetal growth per 10 [mu]g/m3 increase in NO2 exposure, and we calculated cumulative effect estimates by aggregating estimates across adjacent lags. Results: We identified weeks 5-12 as a sensitive window for NO2 exposure on late EFW (cumulative [beta] = -3.0%; 95% CI = -4.1%, -1.9%). We identified weeks 6-19 as a sensitive window for late growth in BPD (cumulative [beta] = -2.0%; 95% CI = -2.7%, -1.4%) and weeks 8-13 for AC (cumulative [beta] = -0.68%; 95% CI = -0.97%, -0.40%). We found suggestive evidence that third trimester NO2 exposure is associated with increased AC, BPD, and EFW growth in late pregnancy. Conclusions: Our findings are consistent with the hypothesis that NO2 exposure is associated with alterations in growth of EFW, BPD, and AC dependent on the specific timing of exposure during gestation. [ABSTRACT FROM AUTHOR]

Abstract Background Brucellosis is a serious public health problem primarily affecting livestock workers. The strong seasonality of the disease indicates that climatic factors may play important roles in the transmission of the disease. However, the associations between climatic variability and human brucellosis are still poorly understood. Methods Data for a 14-year series of human brucellosis cases and seven climatic factors were collected in Yulin City from 2005 to 2018, one of the most endemic areas in northern China. Using cross-correlation analysis, the Granger causality test, and a distributed lag non-linear model (DLNM), we assessed the quantitative relationships and exposure-lag-response effects between monthly climatic factors and human brucellosis. Results A total of 7103 cases of human brucellosis were reported from 2005 to 2018 in Yulin City with a distinct peak between April and July each year. Seasonal fluctuations in the transmission of human brucellosis were significantly affected by temperature, sunshine duration, and evaporation. The effects of climatic factors were non-linear over the 6-month period, and higher values of these factors usually increased disease incidence. The maximum separate relative risk (RR) was 1.36 (95% confidence interval [CI], 1.03–1.81) at a temperature of 17.4 °C, 1.12 (95% CI, 1.03–1.22) with 311 h of sunshine, and 1.18 (95% CI, 0.94–1.48) with 314 mm of evaporation. In addition, the effects of these three climatic factors were cumulative, with the highest RRs of 2.27 (95% CI, 1.09–4.57), 1.54 (95% CI, 1.10–2.18), and 1.27 (95% CI, 0.73–2.14), respectively. Conclusions In Yulin, northern China, variations in climatic factors, especially temperature, sunshine duration, and evaporation, contributed significantly to seasonal fluctuations of human brucellosis within 6 months. The key determinants of brucellosis transmission and the identified complex associations are useful references for developing strategies to reduce the disease burden.

Abstract Background Typhus group rickettsiosis (TGR), which is a neglected vector-borne infectious disease, including epidemic typhus and endemic typhus. We explored the lag effects and nonlinear association between meteorological factors and TGR incidence in Xishuangbanna Dai autonomous prefecture from 2005 to 2017, China. Methods A Poisson regression with a distributed lag nonlinear model (DLNM) was utilized to analyze TGR cases data and the contemporaneous meteorological data. Results A J-shaped nonlinear association between weekly mean temperature and TGR incidence was found. The cumulative exposure to weekly mean temperature indicated that the RR increased with the increment of temperature. Taking the median value as the reference, lower temperatures could decrease the risk of TGR incidence, while higher temperatures could increase the risk of TGR incidence and last for 21 weeks. We also found a reversed U-shaped nonlinear association between weekly mean precipitation and TGR incidence. Precipitation between 5 mm and 13 mm could increase the risk of TGR incidence. Taking the median value as the reference, no precipitation and lower precipitation could decrease the risk of TGR incidence, while higher precipitation could increase the risk of TGR incidence and last for 18 weeks. Conclusions The prevention and control measures of TGR should be implemented according to climatic conditions by the local government and health departments in order to improve the efficiency.

Introduction: The precise associations between temperature-related indices and mental and behavioral disorders (MBDs) have yet to be fully elucidated. Our study aims to ascertain the most effective temperature-related index and assess its immediate impact on emergency ambulance dispatches (EADs) due to MBDs in Shenzhen, China., Methods: EADs data and meteorological data from January 1, 2013, to December 31, 2020, in Shenzhen were collected. Distributed lag non-linear models (DLNMs) were utilized to examine the non-linear and lagged effects of temperature-related indices on EADs due to MBDs. The Quasi Akaike Information criterion (QAIC) was used to determine the optimal index after standardizing temperature-related indices. After adjusting for confounding factors in the model, we estimated the immediate and cumulative effects of temperature on EADs due to MBDs., Results: The analysis of short-term temperature effects on EADs due to MBDs revealed Humidex as the most suitable index. Referring to the optimal Humidex (3.2 th percentile, 12.00°C), we observed a significant effect of Humidex over the threshold (34.6 th percentile, 26.80°C) on EADs due to MBDs at lag 0-5. The cumulative relative risks for high temperature (90 th percentile, 41.90°C) and extreme high temperature (99 th percentile, 44.20°C) at lag 0-5 were 1.318 (95% CI: 1.159-1.499) and 1.338 (95% CI: 1.153-1.553), respectively. No significant cold effect was observed on EADs due to MBDs., Conclusion: High Humidex was associated with more EADs due to MBDs in subtropical regions. Health authorities should implement effective measures to raise public awareness of risks related to high temperature and protect vulnerable populations., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Yin, Jingesi, Yin, Chen, Huang, Cheng, Li, Liu, Wang, Yin and Jiang.)

Background: Scrub typhus was epidemic in the western Pacific Ocean area and East Asia, scrub typhus epidemic in densely populated areas in southern China. To better understand the association between meteorological variables, Southern Oscillation Index (SOI), and scrub typhus incidence in Guangzhou was benefit to the control and prevention.Methodology/Principal Findings: We collected weekly data for scrub typhus cases and meteorological variables in Guangzhou, and Southern Oscillation Index from 2006 to 2018, and used the distributed lag non-linear models to evaluate the relationships between meteorological variables, SOI and scrub typhus. The median value of each variable was set as the reference. The high-risk occupations were farmer (51.10%), house worker (17.51%), and retiree (6.29%). The non-linear relationships were observed with different lag weeks. For example, when the mean temperature was 27.7°C with1-week lag, the relative risk (RR) was highest as 1.08 (95% CI: 1.01–1.17). The risk was the highest when the relative humidity was 92.0% with 9-week lag, with the RR of 1.10 (95% CI: 1.02–1.19). For aggregate rainfall, the highest RR was 1.06 (95% CI: 1.03–1.11), when it was 83.0 mm with 4-week lag. When the SOI was 19 with 11-week lag, the highest RR was 1.06 (95% CI: 1.01–1.12). Most of the extreme effects of SOI and meteorological factors on scrub typical cases were statistically significant.Conclusion/Significance: The high-risk occupations of scrub typhus in Guangzhou were farmer, house worker, and retiree. Meteorological factors and SOI played an important role in scrub typhus occurrence in Guangzhou. Non-linear relationships were observed in almost all the variables in our study. Approximately, mean temperature, and relative humidity positively correlated to the incidence of scrub typhus, on the contrary to atmospheric pressure and weekly temperature range (WTR). Aggregate rainfall and wind velocity showed an inverse-U curve, whereas the SOI appeared the bimodal distribution. These findings can be helpful to facilitate the development of the early warning system to prevent the scrub typhus.

Objective: In this study, it was aimed to compare the performance results of the methods modeled by using generalized additive models (GAM) and distributed lag non-linear models (DLNM) methods from real data of three different outcome variables of three separate diseases related to air pollution. Material and Methods: The data were retrospectively obtained from three hospitals under the General Secretariat of Gaziantep province public hospitals for a total of 1,916 days between 01 January 2009 and 31 March 2014. Response variables were number of the emergency unit admission, hospitalization and mortality due to asthma, chronic obstructive pulmonary disease (COPD) and pneumonia. The response variables were estimated by GAM and DLNM methods by building four different models and the performances of the models were compared. Results: When the estimation performances of GAM and DLNM methods are compared for each of the dependent variables in the prediction of hospitalizations due to asthma, GAM model IV [Akaike Information Criteria (AIC) (4,280.63)] values were found to perform the best. It was observed that DLNM method performed better than GAM in models established for the prediction of almost all other dependent variables. For when compare the odds ratio (OR) plot estimated on particulate matter (PM10); it was seen that GAM method made predictions with lower standard error compared to DLNM methods. Conclusion: When the models created with each dependent variable were compared; it was generally observed that superior performance was obtained from the DLNM method. However, the lowest standard error in the OR charts were observed in the models using the GAM method. [ABSTRACT FROM AUTHOR]

Objective: Analyzing the epidemiological characteristics of influenza cases among children aged 0-17 years in Guangzhou from 2019 to 2022. Assessing the relationships between multiple meteorological factors and influenza, improving the early warning systems for influenza, and providing a scientific basis for influenza prevention and control measures., Methods: The influenza data were obtained from the Chinese Center for Disease Control and Prevention. Meteorological data were provided by Guangdong Meteorological Service. Spearman correlation analysis was conducted to examine the relevance between meteorological factors and the number of influenza cases. Distributed lag non-linear models (DLNM) were used to explore the effects of meteorological factors on influenza incidence., Results: The relationship between mean temperature, rainfall, sunshine hours, and influenza cases presented a wavy pattern. The correlation between relative humidity and influenza cases was illustrated by a U-shaped curve. When the temperature dropped below 13°C, Relative risk (RR) increased sharply with decreasing temperature, peaking at 5.7°C with an RR of 83.78 (95% CI: 25.52, 275.09). The RR was increased when the relative humidity was below 66% or above 79%, and the highest RR was 7.50 (95% CI: 22.92, 19.25) at 99%. The RR was increased exponentially when the rainfall exceeded 1,625 mm, reaching a maximum value of 2566.29 (95% CI: 21.85, 3558574.07) at the highest rainfall levels. Both low and high sunshine hours were associated with reduced incidence of influenza, and the lowest RR was 0.20 (95% CI: 20.08, 0.49) at 9.4 h. No significant difference of the meteorological factors on influenza was observed between males and females. The impacts of cumulative extreme low temperature and low relative humidity on influenza among children aged 0-3 presented protective effects and the 0-3 years group had the lowest RRs of cumulative extreme high relative humidity and rainfall. The highest RRs of cumulative extreme effect of all meteorological factors (expect sunshine hours) were observed in the 7-12 years group., Conclusion: Temperature, relative humidity, rainfall, and sunshine hours can be used as important predictors of influenza in children to improve the early warning system of influenza. Extreme weather reduces the risk of influenza in the age group of 0-3 years, but significantly increases the risk for those aged 7-12 years., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Chen, Liu, Yue, Chen, Hu, Zhou, Liang, Lin, Qin, Feng, Wang and Wu.)

Background: Brucellosis is a serious public health problem primarily affecting livestock workers. The strong seasonality of the disease indicates that climatic factors may play important roles in the transmission of the disease. However, the associations between climatic variability and human brucellosis are still poorly understood.Methods: Data for a 14-year series of human brucellosis cases and seven climatic factors were collected in Yulin City from 2005 to 2018, one of the most endemic areas in northern China. Using cross-correlation analysis, the Granger causality test, and a distributed lag non-linear model (DLNM), we assessed the quantitative relationships and exposure-lag-response effects between monthly climatic factors and human brucellosis.Results: A total of 7103 cases of human brucellosis were reported from 2005 to 2018 in Yulin City with a distinct peak between April and July each year. Seasonal fluctuations in the transmission of human brucellosis were significantly affected by temperature, sunshine duration, and evaporation. The effects of climatic factors were non-linear over the 6-month period, and higher values of these factors usually increased disease incidence. The maximum separate relative risk (RR) was 1.36 (95% confidence interval [CI], 1.03-1.81) at a temperature of 17.4 °C, 1.12 (95% CI, 1.03-1.22) with 311 h of sunshine, and 1.18 (95% CI, 0.94-1.48) with 314 mm of evaporation. In addition, the effects of these three climatic factors were cumulative, with the highest RRs of 2.27 (95% CI, 1.09-4.57), 1.54 (95% CI, 1.10-2.18), and 1.27 (95% CI, 0.73-2.14), respectively.Conclusions: In Yulin, northern China, variations in climatic factors, especially temperature, sunshine duration, and evaporation, contributed significantly to seasonal fluctuations of human brucellosis within 6 months. The key determinants of brucellosis transmission and the identified complex associations are useful references for developing strategies to reduce the disease burden. [ABSTRACT FROM AUTHOR]

Background: Typhus group rickettsiosis (TGR), which is a neglected vector-borne infectious disease, including epidemic typhus and endemic typhus. We explored the lag effects and nonlinear association between meteorological factors and TGR incidence in Xishuangbanna Dai autonomous prefecture from 2005 to 2017, China.Methods: A Poisson regression with a distributed lag nonlinear model (DLNM) was utilized to analyze TGR cases data and the contemporaneous meteorological data.Results: A J-shaped nonlinear association between weekly mean temperature and TGR incidence was found. The cumulative exposure to weekly mean temperature indicated that the RR increased with the increment of temperature. Taking the median value as the reference, lower temperatures could decrease the risk of TGR incidence, while higher temperatures could increase the risk of TGR incidence and last for 21 weeks. We also found a reversed U-shaped nonlinear association between weekly mean precipitation and TGR incidence. Precipitation between 5 mm and 13 mm could increase the risk of TGR incidence. Taking the median value as the reference, no precipitation and lower precipitation could decrease the risk of TGR incidence, while higher precipitation could increase the risk of TGR incidence and last for 18 weeks.Conclusions: The prevention and control measures of TGR should be implemented according to climatic conditions by the local government and health departments in order to improve the efficiency. [ABSTRACT FROM AUTHOR]

Exposure–lag–response associations shed light on the duration of pathogenesis for radiation-induced diseases. To investigate such relations for lung cancer mortality in the German uranium miners of the Wismut company, we apply distributed lag non-linear models (DLNMs) which offer a flexible description of the lagged risk response to protracted radon exposure. Exposure–lag functions are implemented with B-Splines in Cox models of proportional hazards. The DLNM approach yielded good agreement of exposure–lag–response surfaces for the German cohort and for the previously studied cohort of American Colorado miners. For both cohorts, a minimum lag of about 2 year for the onset of risk after first exposure explained the data well, but possibly with large uncertainty. Risk estimates from DLNMs were directly compared with estimates from both standard radio-epidemiological models and biologically based mechanistic models. For age > 45 year, all models predict decreasing estimates of the Excess Relative Risk (ERR). However, at younger age, marked differences appear as DLNMs exhibit ERR peaks, which are not detected by the other models. After comparing exposure–responses for biological processes in mechanistic risk models with exposure–responses for hazard ratios in DLNMs, we propose a typical period of 15 year for radon-related lung carcinogenesis. The period covers the onset of radiation-induced inflammation of lung tissue until cancer death. The DLNM framework provides a view on age-risk patterns supplemental to the standard radio-epidemiological approach and to biologically based modeling. [ABSTRACT FROM AUTHOR]

PM 2.5 exposure has been associated with intima-media thickness (cIMT) increase. However, very few studies distinguished between left and right cIMT in relation to PM 2.5 exposure. To evaluate associations between chronic exposure to PM 2.5 and cIMT at bilateral, left, and right in adults from Mexico City. This study comprised 913 participants from the control group, participants without personal or family history of cardiovascular disease, of the Genetics of Atherosclerosis Disease Mexican study (GEA acronym in Spanish), recruited at the Instituto Nacional de Cardiología Ignacio Chávez from June 2008 to January 2013. To assess the associations between chronic exposure to PM 2.5 (per 5 μg/m3 increase) at different lag years (1–4 years) and cIMT (bilateral, left, and right) we applied distributed lag non-linear models (DLNMs). The median and interquartile range for cIMT at bilateral, left, and right, were 630 (555, 735), 640 (550, 750), and 620 (530, 720) μm, respectively. Annual average PM 2.5 exposure was 26.64 μg/m3, with median and IQR, of 24.46 (23.5–25.46) μg/m3. Results from DLNMs adjusted for age, sex, body mass index, low-density lipoproteins, and glucose, showed that PM 2.5 exposure for year 1 and 2, were positively and significantly associated with right-cIMT [6.99% (95% CI: 3.67; 10.42) and 2.98% (0.03; 6.01), respectively]. Negative associations were observed for PM 2.5 at year 3 and 4 and right-cIMT; however only year 3 was statistically significant [-2.83% (95% CI: 5.12; −0.50)]. Left-cIMT was not associated with PM 2.5 exposure at any lag year. The increase in bilateral cIMT followed a similar pattern as that observed for right-cIMT, but with lower estimates. Our results suggest different susceptibility between left and right cIMT associated with PM 2.5 exposure highlighting the need of measuring both, left and right cIMT, regarding ambient air pollution in epidemiological studies. [Display omitted] • Our study is the first in Latin America to assess PM 2.5 and cIMT associations. • PM 2.5 levels are at least 4 times higher than the WHO's limit value of 5 μg/m3. • PM 2.5 is associated with bilateral cIMT and this association is driven by right cIMT. • We found different susceptibility to PM 2.5 depending on carotid side (left vs. right). [ABSTRACT FROM AUTHOR]

Background and objectives: Despite global warming, the climate in Northern Europe is generally cold, and the large number of deaths due to non-optimal temperatures is likely due to cold temperatures. The aim of the current study is to investigate the association between cold temperatures and all-cause mortality, as well as cause-specific mortality, in Tallinn and Riga in North-Eastern Europe. Materials and Methods: We used daily information on deaths from state death registries and minimum temperatures from November to March over the period 1997−2015 in Tallinn and 2009−2015 in Riga. The relationship between the daily minimum temperature and mortality was investigated using the Poisson regression, combined with a distributed lag non-linear model considering lag times of up to 21 days. Results: We found significantly higher all-cause mortality owing to cold temperatures both in Tallinn (Relative Risk (RR) = 1.28, 95% Confidence Interval (CI) 1.01−1.62) and in Riga (RR = 1.41, 95% CI 1.11−1.79). In addition, significantly increased mortality due to cold temperatures was observed in the 75+ age group (RR = 1.64, 95% CI 1.17−2.31) and in cardiovascular mortality (RR = 1.83, 95% CI 1.31−2.55) in Tallinn and in the under 75 age group in Riga (RR = 1.58, 95% CI 1.12−2.22). In this study, we found no statistically significant relationship between mortality due to respiratory or external causes and cold days. The cold-related attributable fraction (AF) was 7.4% (95% CI -3.7−17.5) in Tallinn and 8.3% (95% CI -0.5−16.3) in Riga. This indicates that a relatively large proportion of deaths in cold periods can be related to cold in North-Eastern Europe, where winters are relatively harsh.

The relationship between temperature and mortality is well established but has seldom been investigated in terms of external causes. In some Eastern European countries, external cause mortality is substantial. Deaths owing to external causes are the third largest cause of mortality in Estonia, after cardiovascular disease and cancer. Death rates owing to external causes may reflect behavioural changes among a population. The aim for the current study was to investigate if there is any association between temperature and external cause mortality, in Estonia. We collected daily information on deaths from external causes (ICD-10 diagnosis codes V00-Y99) and maximum temperatures over the period 1997-2013. The relationship between daily maximum temperature and mortality was investigated using Poisson regression, combined with a distributed lag non-linear model considering lag times of up to 10 days. We found significantly higher mortality owing to external causes on hot (the same and previous day) and cold days (with a lag of 1-3 days). The cumulative relative risks for heat (an increase in temperature from the 75th to 99th percentile) were 1.24 (95% confidence interval, 1.14-1.34) and for cold (a decrease from the 25th to 1st percentile) 1.19 (1.03-1.38). Deaths due to external causes might reflect changes in behaviour among a population during periods of extreme hot and cold temperatures and should therefore be investigated further, because such deaths have a severe impact on public health, especially in Eastern Europe where external mortality rates are high. [ABSTRACT FROM AUTHOR]

It is well known that meteorological conditions influence the comfort and human health. Southern European countries, including Portugal, show the highest mortality rates during winter, but the effects of extreme cold temperatures in Portugal have never been estimated. The objective of this study was the estimation of the effect of extreme cold temperatures on the risk of death in Lisbon and Oporto, aiming the production of scientific evidence for the development of a real-time health warning system. Poisson regression models combined with distributed lag non-linear models were applied to assess the exposure-response relation and lag patterns of the association between minimum temperature and all-causes mortality and between minimum temperature and circulatory and respiratory system diseases mortality from 1992 to 2012, stratified by age, for the period from November to March. The analysis was adjusted for over dispersion and population size, for the confounding effect of influenza epidemics and controlled for long-term trend, seasonality and day of the week. Results showed that the effect of cold temperatures in mortality was not immediate, presenting a 1-2-day delay, reaching maximum increased risk of death after 6-7 days and lasting up to 20-28 days. The overall effect was generally higher and more persistent in Lisbon than in Oporto, particularly for circulatory and respiratory mortality and for the elderly. Exposure to cold temperatures is an important public health problem for a relevant part of the Portuguese population, in particular in Lisbon. [ABSTRACT FROM AUTHOR]

Tree mortality as a crucial element of natural forest dynamics is still a poorly understood process. Abrupt growth decreases are known to occur several years or decades before complete cessation of growth. Hence, identifying and linking these growth decreases to potential inciting factors such as drought and frost will improve our understanding of mortality processes. We analyzed nine Central European tree species including six coniferous species ( Abies alba , Picea abies , Larix decidua , Pinus sylvestris , Pinus cembra , Pinus montana ) and three broadleaved species ( Fagus sylvatica , Quercus spp., Acer pseudoplatanus ). Tree-ring data from 848 standing dead trees from 14 forest reserves all over Switzerland were sampled. We applied distributed lag non-linear models to relate abrupt growth decreases to drought and frost. The results indicate for many species that both drought and frost have a moderate to major impact on abrupt growth decreases prior to tree death. While late frost in spring may instantaneously result in sustained abrupt growth decreases in most species except Scots pine and mountain pine, severe drought over several months in spring may either show an immediate negative impact on growth, such as in beech, or feature negative reactions that are lagged by several years, such as in oak and Scots pine. Thus, extreme climatic conditions have an essential influence on abrupt growth decreases that finally result in tree death, although variability of the reactions within and among species is high. [ABSTRACT FROM AUTHOR]

Objective We used log-linear and log-log exposure-response (E-R) functions to model the association between PM 2.5 exposure and non-elective hospitalizations for pneumonia, and estimated the attributable hospital costs by using the effect estimates obtained from both functions. Methods We used hospital discharge data on 3519 non-elective pneumonia admissions from UZ Brussels between 2007 and 2012 and we combined a case-crossover design with distributed lag models. The annual averted pneumonia hospitalization costs for a reduction in PM 2.5 exposure from the mean (21.4 μg/m 3 ) to the WHO guideline for annual mean PM 2.5 (10 μg/m 3 ) were estimated and extrapolated for Belgium. Results Non-elective hospitalizations for pneumonia were significantly associated with PM 2.5 exposure in both models. Using a log-linear E-R function, the estimated risk reduction for pneumonia hospitalization associated with a decrease in mean PM 2.5 exposure to 10 μg/m 3 was 4.9%. The corresponding estimate for the log-log model was 10.7%. These estimates translate to an annual pneumonia hospital cost saving in Belgium of €15.5 million and almost €34 million for the log-linear and log-log E-R function, respectively. Discussion Although further research is required to assess the shape of the association between PM 2.5 exposure and pneumonia hospitalizations, we demonstrated that estimates for health effects and associated costs heavily depend on the assumed E-R function. These results are important for policy making, as supra-linear E-R associations imply that significant health benefits may still be obtained from additional pollution control measures in areas where PM levels have already been reduced. [ABSTRACT FROM AUTHOR]