Impact of surface ultraviolet radiation intensity on hospital admissions for ischemic and hemorrhagic stroke: A large-scale database study using distributed lag nonlinear analysis, 2015-2022, in Harbin, China.

OBJECTIVES: Our aim is to evaluate the impact of surface ultraviolet radiation intensity on hospital admissions for stroke and to compare the correlation and differences among different subtypes of strokes. MATERIALS AND METHODS: We collected daily data on surface ultraviolet radiation intensity, temperature, air pollution, and hospital admissions for stroke in Harbin from 2015 to 2022. Using a distributed lag non-linear model, we determined the correlation between daily surface ultraviolet radiation intensity and the stroke admission rate. Relative risks (RR) with 95% confidence intervals (CI) and attributable fractions (AF) with 95% CI were calculated based on stroke subtypes, gender, and age groups. RESULTS: A total of 132,952 hospitalized stroke cases (including hemorrhagic and ischemic strokes) were included in the study. We assessed the non-linear effects of ultraviolet intensity on hospitalized patients with ischemic and hemorrhagic strokes. Compared to the maximum morbidity benchmark ultraviolet intensity (19.2 × 10^5 for ischemic stroke and 20.25 for hemorrhagic stroke), over the 0-10 day lag period, the RR for extreme low radiation (1st percentile) was 0.86 (95% CI: 0.77, 0.96), and the RR for extreme high radiation (99th percentile) was 0.86 (95% CI: 0.77, 0.96). In summary, -4.842% (95% CI: -7.721%, -2.167%) and -1.668% (95% CI: -3.061%, -0.33%) of ischemic strokes were attributed to extreme low radiation intensity with a lag of 0 to 10 days and extreme high radiation intensity with a lag of 0 to 5 days, respectively. The reduction in stroke hospitalization rates due to low or high ultraviolet intensity was more pronounced in females and younger individuals compared to males and older individuals. None of the mentioned ultraviolet intensity intensities and lag days had a statistically significant impact on hemorrhagic stroke. CONCLUSIONS: Our study fundamentally suggests that both lower and higher levels of surface ultraviolet radiation intensity in Harbin, China, contribute to a reduced incidence of ischemic stroke, with this effect lasting approximately 10 days. This finding holds significant potential for public health and clinical relevance.

Effects of apparent temperature on daily outpatient and inpatient visits for cause-specific respiratory diseases in Ganzhou, China: a time series study.

BACKGROUND: Non-optimum temperatures are associated with increased risk of respiratory diseases, but the effects of apparent temperature (AT) on respiratory diseases remain to be investigated. METHODS: Using daily data from 2016 to 2020 in Ganzhou, a large city in southern China, we analyzed the impact of AT on outpatient and inpatient visits for respiratory diseases. We considered total respiratory diseases and five subtypes (influenza and pneumonia, upper respiratory tract infection (URTI), lower respiratory tract infection (LRTI), asthma and chronic obstructive pulmonary disease [COPD]). Our analysis employed a distributed lag nonlinear model (DLNM) combined with a generalized additive model (GAM). RESULTS: We recorded 94,952 outpatients and 72,410 inpatients for respiratory diseases. We found AT significantly non-linearly associated with daily outpatient and inpatient visits for total respiratory diseases, influenza and pneumonia, and URTI, primarily during comfortable AT levels, while it was exclusively related with daily inpatient visits for LRTI and COPD. Moderate heat (32.1 °C, the 75.0th centile) was observed with a significant effect on both daily outpatient and inpatient visits for total respiratory diseases at a relative risk of 1.561 (1.161, 2.098) and 1.276 (1.027, 1.585), respectively (both P < 0.05), while the results of inpatients became insignificant with the adjustment for CO and O3. The attributable fractions in outpatients and inpatients were as follows: total respiratory diseases (24.43% and 18.69%), influenza and pneumonia (31.54% and 17.33%), URTI (23.03% and 32.91%), LRTI (37.49% and 30.00%), asthma (9.83% and 3.39%), and COPD (30.67% and 10.65%). Stratified analyses showed that children ≤5 years old were more susceptible to moderate heat than older participants. CONCLUSIONS: In conclusion, our results indicated moderate heat increase the risk of daily outpatient and inpatient visits for respiratory diseases, especially among children under the age of 5.

Effect of short-term ambient air pollution exposure on early miscarriage and pregnancy hormones with critical window identification.

BACKGROUND: Pregnancy hormones are particularly important in early miscarriage, and some evidence suggests that exposure to air pollution is associated with pregnancy hormones and miscarriage. However, the effects of air pollution on pregnancy hormone-mediated miscarriages have not yet been investigated. METHODS: We collected air pollution exposure measurements and pregnancy hormone tests from the participants. Logistic regression models were used to investigate the association between air pollution and early miscarriages. A distributed lag nonlinear model (DLNM) was used to investigate non-linear and delayed associations and identify the crucial window. We performed mediation analysis to estimate the potential association that may exist between pregnancy hormone levels and early miscarriage. RESULTS: Short-term exposure to CO and SO2 was associated with early miscarriage. Lag 22-28 days of exposure to both CO and SO2 and lag 15-21 days of exposure to CO were significantly positively associated with early miscarriage, with an obvious exposure dose response. Serum progesterone concentration explained 36.79 % of the association between lag 15-28 days of CO exposure and early miscarriage. CONCLUSION: This study provides evidence for the association between short-term exposure to air pollution and early miscarriage, and provides clues for further exploration of biological mechanisms.

Ambient Air Pollution and Risk of Admission Due to Asthma in the Three Largest Urban Agglomerations in Poland: A Time-Stratified, Case-Crossover Study.

Ambient air pollution in urban areas may trigger asthma exacerbations. We carried out a time-series analysis of the association between the concentrations of various air pollutants and the risk of hospital admission due to asthma over 7 days from exposure. We used distributed lag nonlinear models to analyze data gathered between 2010 and 2018 in the three largest urban agglomerations in Poland. Overall, there were 31,919 asthma hospitalizations. Over 7 days since exposure, the rate ratio (95%CI) for admission per 10 µg/m3 was 1.013 (1.002-1.024) for PM10; 1.014 (1.000-1.028) for PM2.5; 1.054 (1.031-1.078) for NO2; and 1.044 for SO2 (95%CI: 0.986-1.104). For all pollutants, the risk of admission was the greatest on the day of exposure (day 0), decreased below baseline on days 1 and 2, and then increased gradually up to day 6. The proportions (95%CI) of hospitalizations attributable to air pollution were 4.52% (0.80%-8.14%) for PM10; 3.74% (0.29%-7.11%) for PM2.5; 16.4% (10.0%-21.8%) for NO2; and 2.50% (-0.75%-5.36%) for SO2. In conclusion, PM2.5, PM10, NO2, and SO2 pollution was associated with an increased risk of hospital admission due to asthma in the three largest urban agglomerations in Poland over nine years.

Heat-related mortality prediction using low-frequency climate oscillation indices: Case studies of the cities of Montréal and Québec, Canada.

Heat-related mortality is an increasingly important public health burden that is expected to worsen with climate change. In addition to long-term trends, there are also interannual variations in heat-related mortality that are of interest for efficient planning of health services. Large-scale climate patterns have an important influence on summer weather and therefore constitute important tools to understand and predict the variations in heat-related mortality. Methods: In this article, we propose to model summer heat-related mortality using seven climate indices through a two-stage analysis using data covering the period 1981-2018 in two metropolitan areas of the province of Québec (Canada): Montréal and Québec. In the first stage, heat attributable fractions are estimated through a time series regression design and distributed lag nonlinear specification. We consider different definitions of heat. In the second stage, estimated attributable fractions are predicted using climate index curves through a functional linear regression model. Results: Results indicate that the Atlantic Multidecadal Oscillation is the best predictor of heat-related mortality in both Montréal and Québec and that it can predict up to 20% of the interannual variability. Conclusion: We found evidence that one climate index is predictive of summer heat-related mortality. More research is needed with longer time series and in different spatial contexts. The proposed analysis and the results may nonetheless help public health authorities plan for future mortality related to summer heat.

The correlation between temperature and the incidence of COVID-19 in four first-tier cities of China: a time series study.

The COVID-19 outbreak emerged in Wuhan, China, and was declared a global pandemic in March 2020. This study aimed to explore the association of daily mean temperature with the daily counts of COVID-19 cases in Beijing, Shanghai, Guangzhou, and Shenzhen, China. Data on daily confirmed cases of COVID-19 and daily mean temperatures were retrieved from the 4 first-tier cities in China. Distributed lag nonlinear models (DLNMs) were used to assess the association between daily mean temperature and the daily cases of COVID-19 during the study period. After controlling for the imported risk index and long-term trends, the distributed lag nonlinear model showed that there were nonlinear and lag relationships. The daily cumulative relative risk decreased for every 1.0 °C change in temperature in Shanghai, Guangzhou, and Shenzhen. However, the cumulative relative risk increased with a daily mean temperature below - 3 °C in Beijing and then decreased. Moreover, the delayed effects of lower temperatures mostly occurred within 6-7 days of exposure. There was a negative correlation between the cumulative relative risk of COVID-19 incidence and temperature, especially when the temperature was higher than - 3 °C. The conclusions from this paper will help government and health regulators in these cities take prevention and protection measures to address the COVID-19 crisis and the possible collapse of the health system in the future.

Delayed effects of climate on vital rates lead to demographic divergence in Amazonian forest fragments.

Deforestation often results in landscapes where remaining forest habitat is highly fragmented, with remnants of different sizes embedded in an often highly contrasting matrix. Local extinction of species from individual fragments is common, but the demographic mechanisms underlying these extinctions are poorly understood. It is often hypothesized that altered environmental conditions in fragments drive declines in reproduction, recruitment, or survivorship. The Amazon basin, in addition to experiencing continuing fragmentation, is experiencing climate change-related increases in the frequency and intensity of droughts and unusually wet periods. Whether plant populations in tropical forest fragments are particularly susceptible to extremes in precipitation remains unclear. Most studies of plants in fragments are relatively short (1-6 years), focus on a single life-history stage, and often do not compare to populations in continuous forest. Even fewer studies consider delayed effects of climate on demographic vital rates despite the importance of delayed effects in studies that consider them. Using a decade of demographic and climate data from an experimentally fragmented landscape in the Central Amazon, we assess the effects of climate on populations of an understory herb (Heliconia acuminata, Heliconiaceae). We used distributed lag nonlinear models to understand the delayed effects of climate (measured as standardized precipitation evapotranspiration index, SPEI) on survival, growth, and flowering. We detected delayed effects of climate up to 36 months. Extremes in SPEI in the previous year reduced survival, drought in the wet season 8-11 months prior to the February census increased growth, and drought two dry seasons prior increased flowering probability. Effects of extremes in precipitation on survival and growth were more pronounced in forest fragments compared to continuous forest. The complex delayed effects of climate and habitat fragmentation in our study point to the importance of long-term demography experiments in understanding the effects of anthropogenic change on plant populations.

Geographical Variations of the Minimum Mortality Temperature at a Global Scale: A Multicountry Study.

BACKGROUND: Minimum mortality temperature (MMT) is an important indicator to assess the temperature-mortality association, indicating long-term adaptation to local climate. Limited evidence about the geographical variability of the MMT is available at a global scale. METHODS: We collected data from 658 communities in 43 countries under different climates. We estimated temperature-mortality associations to derive the MMT for each community using Poisson regression with distributed lag nonlinear models. We investigated the variation in MMT by climatic zone using a mixed-effects meta-analysis and explored the association with climatic and socioeconomic indicators. RESULTS: The geographical distribution of MMTs varied considerably by country between 14.2 and 31.1 °C decreasing by latitude. For climatic zones, the MMTs increased from alpine (13.0 °C) to continental (19.3 °C), temperate (21.7 °C), arid (24.5 °C), and tropical (26.5 °C). The MMT percentiles (MMTPs) corresponding to the MMTs decreased from temperate (79.5th) to continental (75.4th), arid (68.0th), tropical (58.5th), and alpine (41.4th). The MMTs indreased by 0.8 °C for a 1 °C rise in a community's annual mean temperature, and by 1 °C for a 1 °C rise in its SD. While the MMTP decreased by 0.3 centile points for a 1 °C rise in a community's annual mean temperature and by 1.3 for a 1 °C rise in its SD. CONCLUSIONS: The geographical distribution of the MMTs and MMTPs is driven mainly by the mean annual temperature, which seems to be a valuable indicator of overall adaptation across populations. Our results suggest that populations have adapted to the average temperature, although there is still more room for adaptation.

Risk for preeclampsia following exposure to PM2.5 during pregnancy.

BACKGROUND: Previous findings concerning the risk for preeclampsia following exposure to particulate matter are inconclusive. METHODS: We used data from all singleton pregnancies of women insured by the "Clalit health services" (CHS) maintenance organization in southern Israel that resulted in delivery or perinatal mortality at Soroka Medical Center (SMC). Daily PM2.5 concentrations were estimated by a hybrid satellite-based model at one-squared kilometer spatial resolution. We used Cox proportional hazard models coupled with distributed lag models to examine the association between the mean exposure to PM2.5 in every gestational week and the diagnosis of preeclampsia, adjusting for maternal age, parity, year of birth, season of birth and socio-economic status. Hazard Ratios (HR) and 95% Confidence Intervals (CI) were calculated for individual gestational weeks and for cumulative exposure until the 25th gestational week. RESULTS: A total of 133,197 pregnancies ended at SMC during the study period, of which 68,126 (51.1%) were Jewish and 65,071 (48.9%) were Bedouin. For pregnancies of Jewish women, exposure to PM2.5 from the 7th until the 14st gestational week was significantly associated with preeclampsia (maximal HR = 1.06; 95%CI: 1.01 - 1.11 during the 10th gestational week per 10 µg/m3 increase in PM2.5). Cumulative exposure to PM2.5 during the first 25th gestational weeks was also significantly associated with preeclampsia (HR = 2.08; 95%CI: 1.10 - 3.94 per 10 µg/m3 increase in PM2.5). We observed no association for pregnancies of Bedouin women. CONCLUSIONS: Exposure to PM2.5 between the 7th and the 14st gestational weeks was associated with preeclampsia among Jewish women but not among Bedouin women.

Limited evidence for the role of environmental factors in the unusual peak of influenza in Brisbane during the 2018-2019 Australian summer.

OBJECTIVE: To explore the contribution of environmental factors in the unusual pattern of influenza activity observed in Brisbane, Australia during the summer of 2018-2019. METHODS: Distributed lag nonlinear models (DLNMs) were used to estimate the effect of environmental factors on weekly influenza incidence in Brisbane. Next generation sequencing was then employed to analyze minor and majority variants in influenza strains isolated from Brisbane children during this period. RESULTS: There were limited marked differences in the environmental factors observed in Brisbane between the 2018-2019 summer period and the same period of the proceeding years, with the exception of significant reduction in rainfall. DLNM showed that reduced rainfall in Brisbane (at levels consistent with the 2018-2019 period) correlated with a dramatic increase in the relative risk of influenza. Sulfur dioxide (SO2) levels were also increased in the 2018-2019 period, although these levels did not correlate with an increased risk of influenza. Sequencing of a limited number of pediatric influenza virus strains isolated during the 2018-2019 showed numerous mutations within the viral HA. CONCLUSIONS: Taken together, these data suggest a limited role for key environmental factors in the influenza activity observed in Brisbane, Australia during the summer of 2018-2019. One alternative explanation may that viral factors, in addition to other factors not studied herein, contributed to the unusual influenza season. Our findings provide fundamental information that may be beneficial to a better understanding of the seasonal trends of influenza virus.

Does climatic zone of birth modify the temperature-mortality association of London inhabitants during the warm season? A time-series analysis for 2004-2013.

BACKGROUND: It is known that on days with high temperatures higher mortality is observed and there is a minimum mortality temperature (MMT) point which is higher in places with warmer climate. This indicates some population adaptation to local climate but information on how quickly this adaptation will occur under climate change is lacking. METHODS: To investigate this, we associated daily mortality data with temperature during the warm period in 2004-2013 for London inhabitants born in five climatic zones (UK, Tropical, Sub-tropical, Boreal and Mixed). We fitted Poisson regression with distributed-lag non-linear models for each climatic zone group separately to estimate group-specific exposure-response associations and MMTs. We report relative risks of death comparing the 95th percentile (21 °C) and maximum (25 °C) of the temperature distribution in London with the zone-specific minimum mortality temperature. RESULTS: No heat-related mortality was observed for people born in countries with Sub-tropical and Mixed climates. We observed an increase of 26%, 35% and 39% in the risk of death at 25 °C compared to the MMT in people born in the UK (marine climate), Tropical and Boreal climate respectively. The temperatures with the lowest mortality in these groups ranged from 15.9 to 17.7 °C. DISCUSSION: Our findings imply that people born in different climatic zones do not adapt fully to their new environment within their lifetime. This implies that populations may not adapt readily to climate change and will suffer increased effects from heat. In the presence of climate change, policy makers should be aware of a delayed process of adaptation.

Association between climatic factors and varicella incidence in Guangzhou, Southern China, 2006-2018.

OBJECTIVE: To analyze the correlation between climatic factors and the incidence of varicella in Guangzhou, and improve the prevention measures about public health. METHODS: Data for daily climatic variables and varicella incidence from 2006 to 2018 in Guangzhou were collected from the Guangzhou Meteorological Bureau and the National Notifiable Disease Report System. Distributed lag nonlinear models were applied to evaluate the association between climatic factors and varicella incidence. RESULTS: The nonlinear effects of meteorological factors were observed. At lag day21，when the mean temperature was 31.8 °C, the relative risk was the highest as 1.11 (95% CI: 1.07-1.16). When the diurnal temperature range was 24.0 °C at lag day 20, the highest RR was 1.11 (95% CI: 1.05-1.17). For rainfall, the highest RR was 1.09 (95% CI: 1.01-1.19) at lag day 21，when the aggregate rainfall was 160 mm. When air pressure was 1028 hPa, the highest RR was 1.08 (95% CI: 1.04-1.13) at lag day 21. When wind speed was 0.7 m/s, the highest RR was 1.07 (95% CI: 1.04-1.11) at lag day 7. When the hours of sunshine were 9.0 h at lag day 21, the RR was highest as 1.04 (95% CI: 1.02-1.05). Aggregate rainfall, air pressure, and sunshine hours were positively correlated with the incidence of varicella, which was inconsistent with the wind velocity. Mean temperature showed a reverse U-shape curve relationship with varicella, while the diurnal temperature range showed a binomial distribution curve. The extreme effect of climatic factors on the varicella cases was statistically significant, apart from the extremely low effect of rainfall. CONCLUSION: Our preliminary results offered fundamental knowledge which might be benefit to give an insight into epidemic trends of varicella and develop an early warning system. We could use our findings about influential factors to strengthen the intervention and prevention of varicella.

The association between ambient particulate matters, nitrogen dioxide, and childhood scarlet fever in Hangzhou, Eastern China, 2014-2018.

BACKGROUND: The emerging cases of childhood scarlet fever (SF) and worsening air pollution problems in Chinese cities suggests a potential linkage between them. However, few studies had explored this association in a large childhood population. METHODS: We conducted a time-series analysis using the daily count of SF and the daily concentrations of particulate matters with an aerodynamic diameter of 2.5 (PM2.5) and 10 (PM10), as well as nitrogen dioxide (NO2) in Hangzhou, China from 2014 to 2018. Distributed lag nonlinear models were used to estimate the lag effects of PM2.5, PM10 and NO2 for a maximum lag of 10 days, which were quantified using relative risk (RR) comparing the adjusted risks at the 2.5th (extremely low effect) and 97.5th (extremely high effect) percentiles of concentration of the three air pollutants to that at the median. Stratified RRs by sex were also reported. RESULTS: Using the median concentration as reference, for extremely high effect, the RR was the highest on lag days 5, 6, and 3 for PM2.5, PM10, and NO2 respectively. While on lag day 0, the RR of PM2.5, PM10, and NO2 were 1.04 (95%CI: 0.90-1.20), 1.07 (95%CI: 0.92-1.24), and 1.08 (95%CI: 0.92-1.26) respectively, the RRs increased constantly and cumulatively to the maximum values of 1.88 (95%CI: 1.33-2.66), 1.82 (95%CI: 1.29-2.55), and 2.19 (95%CI: 1.47-3.27) for PM2.5, PM10, and NO2 respectively on lag day 10. Subgroup analyses showed that females appeared to be more vulnerable to the three pollutants than males. CONCLUSION: Our study provides evidence that PM2.5, PM10, and NO2 exert delayed effects on SF infection.

[Analysis on association between incidence of hand foot and mouth disease and meteorological factors in Xiamen, 2013-2017].

Objective: To study the influence of meteorological factors on the incidence of hand foot and mouth disease (HFMD) in Xiamen, Fujian province, and provide scientific evidence for the early warning, prediction, prevention and control of HFMD. Methods: Correlation analysis and distribution lag nonlinear models (DLNM) analysis of meteorological factors such as daily average pressure, daily average relative humidity, daily average temperature and sunshine hours and the incidence of HFMD in Xiamen during 2013 to 2017 were conducted by using R3.4.3 software. Results: A total of 36 464 cases of HFMD were reported in Xiamen during 2013-2017, and the incidence showed an upward trend (F=40.359, P=0.008). The daily average relative humidity, daily average temperature and sunshine hours were positively correlated with the incidence of HFMD (r>0), and the daily average site pressure was negatively correlated with the incidence of HFMD (r<0). In the case of a lag of 0-5 days, when the daily average pressure of the station was higher than 1 005 hPa, the risk of HFMD gradually increased with the increase of air pressure, and the risk of disease decreased with the increase of lag days. The risk was highest when air pressure was 1 017 hPa and at the lag of 0 day (RR=1.14, 95%CI: 0.67-1.94). When the relative humidity was higher than 95%, the risk of HFMD gradually increased with the increase of relative humidity, and the lag time ranged from 0 day to 10 days, which was most obvious on the 4(th) and 5(th) days. The risk was highest when relative humidity was 100% and at the lag of 5 days (RR=1.32, 95%CI: 1.02-1.71). When the air temperature was >28 ℃ and <8 ℃, the risk of HFMD existed, but the lag time was inconsistent. The relative risk was highest during 15-20 days at low air temperature, and the lag time at high air temperature was mainly during 5-15 days. The risk was highest when air temperature was 28 ℃ and at the lag of 4 days (RR=1.10, 95%CI: 0.94-1.29). The sunshine time was >12 h and lag of 0-3 days was a risk factor for the incidence of HFMD. The risk was highest when sunshine time was 13 h and the lag of 0 day (RR=1.20, 95%CI: 1.05-1.36). Conclusion: Meteorological factors such as daily average pressure, daily average relative humidity, daily average temperature and sunshine hours were associated with the incidence of HFMD with certain lag in Xiamen. So, it is suggested to use these data in the early warning system of HFMD.

Evaluation of wearable sensors for physiologic monitoring of individually experienced temperatures in outdoor workers in southeastern U.S.

Climate-related increases in global mean temperature and the intensification of heat waves present a significant threat to outdoor workers. Limited research has been completed to assess the potential differences in heat exposures that exist between individuals within similar microenvironments. Yet, there is a paucity of individual data characterizing patterns of individually experienced temperatures in workers and the associated physiologic heat strain response. The objective of this study was to apply a wearable sensor-based approach to examine the occupational, environmental, and behavioral factors that contribute to individual-level variations in heat strain in grounds maintenance workers. Outdoor workers from three diverse climatic locations in the southeastern United States - high temperature, high temperature + high humidity, and moderate temperature environments - participated in personal heat exposure monitoring during a 5-day work period in the summer. We performed Cox proportional hazards modeling to estimate associations between multiple heat strain events per worker and changes in individually experienced temperatures. Heat strain risk was higher among workers with a place to cool-off, higher education, and who worked in hotter temperatures. A mismatch was observed between workers' perceptions of heat strain and actual heat strain prevalence across exposure groups. We also used a quasi-Poisson regression with distributed lag non-linear function to estimate the non-linear and lag effects of individually experienced temperatures on risk of heat strain. The association between increasing temperature and heat strain was nonlinear and exhibited an U-shaped relationship. Heat strain was less common during issued heat warnings demonstrating behavioral adaptive actions taken by workers. This study is one of the first temperature monitoring studies to quantify the individual-level exposure-response function in this vulnerable population and highlights the elevated risk of heat strain both immediately and several days after worker exposure to high temperatures.

Effect of meteorological factors on scarlet fever incidence in Guangzhou City, Southern China, 2006-2017.

OBJECTIVE: To explore the relationship between meteorological factors and scarlet fever incidence from 2006 to 2017 in Guangzhou, the largest subtropical city of Southern China, and assist public health prevention and control measures. METHODS: Data for weekly scarlet fever incidence and meteorological variables from 2006 to 2017 in Guangzhou were collected from the National Notifiable Disease Report System (NNDRS) and the Guangzhou Meteorological Bureau (GZMB). Distributed lag nonlinear models (DLNMs) were conducted to estimate the effect of meteorological factors on weekly scarlet fever incidence in Guangzhou. RESULTS: We observed nonlinear effects of temperature, relative humidity, and wind velocity. The risk was the highest when the weekly mean temperature was 31 °C during lag week 14, yielding a relative risk (RR) of 1.48 (95% CI: 1.01-2.17). When relative humidity was 43.5% during lag week 0, the RR was 1.49 (95% CI: 1.04-2.12); the highest RR (1.55, 95% CI: 1.20-1.99) was reached when relative humidity was 93.5% during lag week 20. When wind velocity was 4.4 m/s during lag week 13, the RR was highest at 3.41 (95% CI: 1.57-7.44). Positive correlations were observed among weekly temperature ranges and atmospheric pressure with scarlet fever incidence, while a negative correlation was detected with aggregate rainfall. The cumulative extreme effect of meteorological variables on scarlet fever incidence was statistically significant, except for the high effect of wind velocity. CONCLUSION: Weekly mean temperature, relative humidity, and wind velocity had double-trough effects on scarlet fever incidence; high weekly temperature range, high atmospheric pressure, and low aggregate rainfall were risk factors for scarlet fever morbidity. Our findings provided preliminary, but fundamental, information that may be useful for a better understanding of epidemic trends of scarlet fever and for developing an early warning system. Laboratory surveillance for scarlet fever should be strengthened in the future.

The short-term effects of air pollutants on hospitalizations for respiratory disease in Hefei, China.

Previous studies have shown that ambient air pollution is associated with respiratory morbidity. However, the effects of air pollutants on health have rarely been studied in China. Our study aimed to estimate the short-term effects of particulate air pollutants on hospitalizations for three types of respiratory disease: pneumonia, chronic obstructive pulmonary disease (COPD), and asthma. We collected data on daily admissions for patients with each disease from the New Rural Cooperative Medical System (NRCMS) in Hefei, China. Daily records of air pollutants and meteorological data from January 2014 to March 2016 were also obtained. Distributed lag nonlinear models were employed in the analysis to evaluate the association between daily air pollutants and admissions. The highest effect of each pollutant on COPD hospital admission was observed with PM2.5 at lag 12 (RR = 1.068, 95% CI 1.017 to 1.121) and PM10 at lag 10 (RR = 1.031, 95% CI 1.002 to 1.060), for an increase of 10 µg/m3 in concentrations of the pollutants. The short-term effects of PM10 on asthma hospital admissions peaked at lag 12 (RR = 1.057, 95% CI 1.010 to 1.107). According to our stratified analysis, we found that the effects on COPD admission were more pronounced in the warm season than in the cold season, and the elderly (≥ 65 years) and females were more vulnerable to air pollution.

Analysis on association between incidence of hand foot and mouth disease and meteorological factors in Xiamen, 2013-2017 / 中华流行病学杂志

Objective@#To study the influence of meteorological factors on the incidence of hand foot and mouth disease (HFMD) in Xiamen, Fujian province, and provide scientific evidence for the early warning, prediction, prevention and control of HFMD.@*Methods@#Correlation analysis and distribution lag nonlinear models (DLNM) analysis of meteorological factors such as daily average pressure, daily average relative humidity, daily average temperature and sunshine hours and the incidence of HFMD in Xiamen during 2013 to 2017 were conducted by using R3.4.3 software.@*Results@#A total of 36 464 cases of HFMD were reported in Xiamen during 2013-2017, and the incidence showed an upward trend (F=40.359, P=0.008). The daily average relative humidity, daily average temperature and sunshine hours were positively correlated with the incidence of HFMD (r>0), and the daily average site pressure was negatively correlated with the incidence of HFMD (r<0). In the case of a lag of 0-5 days, when the daily average pressure of the station was higher than 1 005 hPa, the risk of HFMD gradually increased with the increase of air pressure, and the risk of disease decreased with the increase of lag days. The risk was highest when air pressure was 1 017 hPa and at the lag of 0 day (RR=1.14, 95%CI: 0.67-1.94). When the relative humidity was higher than 95%, the risk of HFMD gradually increased with the increase of relative humidity, and the lag time ranged from 0 day to 10 days, which was most obvious on the 4th and 5th days. The risk was highest when relative humidity was 100% and at the lag of 5 days (RR=1.32, 95%CI: 1.02-1.71). When the air temperature was >28 ℃ and <8 ℃, the risk of HFMD existed, but the lag time was inconsistent. The relative risk was highest during 15-20 days at low air temperature, and the lag time at high air temperature was mainly during 5-15 days. The risk was highest when air temperature was 28 ℃ and at the lag of 4 days (RR=1.10, 95%CI: 0.94-1.29). The sunshine time was >12 h and lag of 0-3 days was a risk factor for the incidence of HFMD. The risk was highest when sunshine time was 13 h and the lag of 0 day (RR=1.20, 95%CI: 1.05-1.36).@*Conclusion@#Meteorological factors such as daily average pressure, daily average relative humidity, daily average temperature and sunshine hours were associated with the incidence of HFMD with certain lag in Xiamen. So, it is suggested to use these data in the early warning system of HFMD.

Quantifying the effects of drought on abrupt growth decreases of major tree species in Switzerland.

Drought entails important effects on tree physiology, which may result in short- to long-term radial growth decreases. While the majority of studies have focused on annual drought-related variability of growth, relatively little is known about sustained growth decreases following drought years. We apply a statistical framework to identify climatic factors that induce abrupt growth decreases and may eventually result in tree mortality. We used tree-ring data from almost 500 standing dead trees and 200 living trees in eight sites of the Swiss network of strict forest reserves, including four of the most important Central European tree species (Abies alba, Picea abies, Fagus sylvatica and Quercus spp.). First, to assess short-term growth responses to drought under various climate and site conditions, we calculated correlations and linear mixed-effects models between ring-width indices (RWIs) and drought based on the Standardized Precipitation Evapotranspiration Index (SPEI). Second, to quantify drought effects on abrupt growth decreases, we applied distributed lag nonlinear models (DLNMs), which account for both delayed effects and the nonlinear relationship between the SPEI and the occurrence of abrupt growth decreases. Positive correlations between RWIs and the SPEI indicated short-term growth responses of all species, particularly at arid sites. Results of the DLNMs revealed species-specific growth responses to drought. For Quercus spp., abrupt growth decreases were more likely to occur several years following severe drought, whereas for P. abies, A. alba, and F. sylvatica abrupt growth decreases started frequently immediately in the drought year. We conclude that the statistical framework allows for quantifying the effects of drought intensity on the probability of abrupt growth decreases, which ultimately contributes to an improved understanding of climate impacts on forest community dynamics.