The influence of diurnal temperature range on the incidence of respiratory syncytial virus in Japan

Impact of diurnal temperature variations on sputum bacterial detection in hospitalized patients with acute COPD exacerbation: a retrospective study from Fuzhou, China

OBJECTIVE

To investigate the association between meteorological data three days before admission and the status of sputum pathogens culture in hospitalized patients with Acute exacerbation of Chronic obstructive pulmonary disease (AECOPD) and respiratory infections.

METHODS

Data from 1,370 AECOPD patients (80.66% males, approximately 80% age > 70) with respiratory infections hospitalized in Fujian Provincial Hospital between December 2013 and December 2019 were collected. This cohort comprised, along with concurrent meteorological data from Fuzhou. Group differences were analyzed to compare the meteorological data three days prior to admission between patients with positive sputum pathogen cultures and those without. Logistic regression models were employed to investigate the association between meteorological parameters and the status of sputum pathogen cultures in patients with AECOPD and respiratory infections. Sensitivity analyses was conducted among the hospitalized patients from 2013 to 2016 and 2017-2019. Stratified analysis was performed to explore the factors affecting the effect of temperature differences and their interactions.

RESULTS

578(42.19%) cases had a positive sputum culture report indicating pathogen growth. 323 cases were found with Gram-negative bacteria, 160 with Gram-positive bacteria, and 114 with fungi. Uni-variate analysis revealed statistical differences in DTD three days prior to admission (DTD-3d) between the positive and negative sputum culture groups (p = 0.019). Multivariate analysis indicated that an increase in the risk of positive sputum pathogen cultures was associated with greater DTD three days before admission (DTD-3d), with OR1.657 (95%CI [ 1.328-1.981]). The risk of positive sputum pathogen cultures was higher in groups with greater DTD-3d. The findings were consistent across different admission periods. Stratified analysis showed that patients without respiratory failure were more affected by DTD-3d, and an interaction effect was observed (p < 0.001).

CONCLUSION

In coastal areas, the diurnal temperature difference three days prior to admission affects the sputum pathogen status in AECOPD patients with respiratory infections.

How climate change degrades child health: A systematic review and meta-analysis

BACKGROUND

Children are more vulnerable than adults to climate-related health threats, but reviews examining how climate change affects human health have been mainly descriptive and lack an assessment of the magnitude of health effects children face. This is the first systematic review and meta-analysis that identifies which climate-health relationships pose the greatest threats to children.

OBJECTIVES

We reviewed epidemiologic studies to analyse various child health outcomes due to climate change and identify the relationships with the largest effect size. We identify population-specific risks and provide recommendations for future research.

METHODS

We searched four large online databases for observational studies published up to 5 January 2023 following PRISMA (systematic review) guidelines. We evaluated each included study individually and aggregated relevant quantitative data. We used quantitative data in our meta-analysis, where we standardised effect sizes and compared them among different groupings of climate variables and health outcomes.

RESULTS

Of 1301 articles we identified, 163 studies were eligible for analysis. We identified many relationships between climate change and child health, the strongest of which was increasing risk (60 % on average) of preterm birth from exposure to temperature extremes. Respiratory disease, mortality, and morbidity, among others, were also influenced by climate changes. The effects of different air pollutants on health outcomes were considerably smaller compared to temperature effects, but with most (16/20 = 80 %) pollutant studies indicating at least a weak effect. Most studies occurred in high-income regions, but we found no geographical clustering according to health outcome, climate variable, or magnitude of risk. The following factors were protective of climate-related child-health threats: (i) economic stability and strength, (ii) access to quality healthcare, (iii) adequate infrastructure, and (iv) food security. Threats to these services vary by local geographical, climate, and socio-economic conditions. Children will have increased prevalence of disease due to anthropogenic climate change, and our quantification of the impact of various aspects of climate change on child health can contribute to the planning of mitigation that will improve the health of current and future generations.

Effects of meteorological factors on influenza transmissibility by virus type/subtype

BACKGROUND

Quantitative evidence on the impact of meteorological factors on influenza transmissibility across different virus types/subtypes is scarce, and no previous studies have reported the effect of hourly temperature variability (HTV) on influenza transmissibility. Herein, we explored the associations between meteorological factors and influenza transmissibility according to the influenza type and subtype in Guangzhou, a subtropical city in China.

METHODS

We collected influenza surveillance and meteorological data of Guangzhou between October 2010 and December 2019. Influenza transmissibility was measured using the instantaneous effective reproductive number (Rt). A gamma regression with a log link combined with a distributed lag non-linear model was used to assess the associations of daily meteorological factors with Rt by influenza types/subtypes.

RESULTS

The exposure-response relationship between ambient temperature and Rt was non-linear, with elevated transmissibility at low and high temperatures. Influenza transmissibility increased as HTV increased when HTV < around 4.5 °C. A non-linear association was observed between absolute humidity and Rt, with increased transmissibility at low absolute humidity and at around 19 g/m3. Relative humidity had a U-shaped association with influenza transmissibility. The associations between meteorological factors and influenza transmissibility varied according to the influenza type and subtype: elevated transmissibility was observed at high ambient temperatures for influenza A(H3N2), but not for influenza A(H1N1)pdm09; transmissibility of influenza A(H1N1)pdm09 increased as HTV increased when HTV < around 4.5 °C, but the transmissibility decreased with HTV when HTV < 2.5 °C and 3.0 °C for influenza A(H3N2) and B, respectively; positive association of Rt with absolute humidity was witnessed for influenza A(H3N2) even when absolute humidity was larger than 19 g/m3, which was different from that for influenza A(H1N1)pdm09 and influenza B.

CONCLUSIONS

Temperature variability has an impact on influenza transmissibility. Ambient temperature, temperature variability, and humidity influence the transmissibility of different influenza types/subtypes discrepantly. Our findings have important implications for improving preparedness for influenza epidemics, especially under climate change conditions.

Nonlinear and Multidelayed Effects of Meteorological Drivers on Human Respiratory Syncytial Virus Infection in Japan

In this study, we aimed to characterize the nonlinear and multidelayed effects of multiple meteorological drivers on human respiratory syncytial virus (HRSV) infection epidemics in Japan. The prefecture-specific weekly time-series of the number of newly confirmed HRSV infection cases and multiple meteorological variables were collected for 47 Japanese prefectures from 1 January 2014 to 31 December 2019. We combined standard time-series generalized linear models with distributed lag nonlinear models to determine the exposure-lag-response association between the incidence relative risks (IRRs) of HRSV infection and its meteorological drivers. Pooling the 2-week cumulative estimates showed that overall high ambient temperatures (22.7 °C at the 75th percentile compared to 16.3 °C) and high relative humidity (76.4% at the 75th percentile compared to 70.4%) were associated with higher HRSV infection incidence (IRR for ambient temperature 1.068, 95% confidence interval [CI], 1.056-1.079; IRR for relative humidity 1.045, 95% CI, 1.032-1.059). Precipitation revealed a positive association trend, and for wind speed, clear evidence of a negative association was found. Our findings provide a basic picture of the seasonality of HRSV transmission and its nonlinear association with multiple meteorological drivers in the pre-HRSV-vaccination and pre-coronavirus disease 2019 (COVID-19) era in Japan.

Health effect of temperature change on respiratory diseases in opposite phase in semi-arid region

The impact of temperature variation on health has attracted increasing attention under global climate change. A distributed lag non-linear model (DLNM) was performed to estimate the risk of two indicators of temperature change (diurnal temperature range (DTR) and temperature change between neighboring days (TCN)) on respiratory hospital visits in Lanzhou, a semi-arid climate city in western China from 2012 to 2018. The whole year is divided into two different temperature change periods according to the TCN of each solar term. The results showed that extreme high DTR can apparently enlarge respiratory risk, and it indicated strong cumulative relative risk (RR) in the temperature drop period. Extreme low TCN had strong adverse effects on respiratory diseases especially in temperature rise period, with the greatest RR of 1.068 (95% CI 1.004, 1.136). The effect of extreme high TCN was more obvious in temperature drop period, with a RR of 1.082 (95% CI 1.021, 1.148) at lag 7. Females were more affected by extreme temperature changes. Young people were more vulnerable to DTR, while TCN has a greater impact on the elderly.

The association between diurnal temperature range and clinic visits for upper respiratory tract infection among college students in Wuhan, China

The effects of daily mean temperature on health outcomes have been discussed in many previous studies, but few have considered the adverse impacts on upper respiratory tract infection (URTI) due to variance of temperature in one day. Diurnal temperature range (DTR) was a novel indicator calculated as maximum temperature minus minimum temperature on the same day. In this study, generalized additive model (GAM) with quasi-Poisson distribution was used to investigate the association between DTR and the number of daily outpatient visits for URTI among college students. Data about meteorological factors and air pollutants were provided by Hubei Meteorological Bureau and Wuhan Environmental Protection Bureau, respectively. Outpatient visits data were collected from the Hospital of Wuhan University from January 1, 2016, to December 31, 2018. Short-term exposure to DTR was associated with the increased risk of outpatient for URTI among all college students. Per 1 °C increased in DTR was associated with 0.73% (95%CI: 0.24, 1.21) increased in outpatient visits of all college students for URTI at lag 0 day. The greatest effect values were observed in males [1.35% (95%CI: 0.33,2.39)] at lag 0-6 days, and in females [0.86% (95%CI: 0.24, 1.49)] at lag 0-1 days. DTR had more adverse health impact in autumn and winter. Public health departments should consider the negative effect of DTR to formulate more effective prevention and control measures for protecting vulnerable people.

Disease burden of respiratory syncytial virus infection in the pediatric population in Japan

INTRODUCTION

Respiratory syncytial virus (RSV) is one of the most common causes of lower respiratory tract infections in children aged <5 years and is associated with long-term respiratory morbidities such as recurrent wheezing and asthma, decreased lung function, and allergic sensitization. The objective of this review was to evaluate the epidemiology and burden of RSV infection in the pediatric population in Japan.

METHODS

Studies indexed in PubMed and ICHUSHI databases during January 2010-December 2020 were manually reviewed. Data on proportion of RSV infections, seasonality, length of stay (LoS), mortality, medical expenses, and palivizumab use were extracted from the selected articles.

RESULTS

Ninety-three articles were included (PubMed, 64; ICHUSHI, 29). The proportion of patients/samples with an RSV infection was 5.5%-66.7%, and 6.0%-29.9% in the inpatient and outpatient departments, respectively. RSV infections generally occurred during autumn/winter; however, recently the peak has shifted to summer. The LoS was variable and depended on factors such as age, infection severity, wheezing, and RSV subgroups. Mortality rates varied from <1% to 19% depending on the infection severity. The average daily hospitalization and intensive care unit cost was JPY 34,548 while intensive care unit incurred an additional cost of JPY 541,293. Palivizumab was indicated for high-risk infants and 0%-3% of patients required hospitalization despite palivizumab use.

CONCLUSIONS

RSV imposes a significant burden on the Japanese healthcare system, suggesting a need to create awareness among caregivers of children, pregnant women and healthcare professionals to ensure early recognition of infection and adequate treatment or prophylaxis.

Significance between air pollutants, meteorological factors, and COVID-19 infections: probable evidences in India

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease represents the causative agent with a potentially fatal risk which is having great global human health concern. Earlier studies suggested that air pollutants and meteorological factors were considered as the risk factors for acute respiratory infection, which carries harmful pathogens and affects the immunity. The study intended to explore the correlation between air pollutants, meteorological factors, and the daily reported infected cases caused by novel coronavirus in India. The daily positive infected cases, concentrations of air pollutants, and meteorological factors in 288 districts were collected from January 30, 2020, to April 23, 2020, in India. Spearman's correlation and generalized additive model (GAM) were applied to investigate the correlations of four air pollutants (PM2.5, PM10, NO2, and SO2) and eight meteorological factors (Temp, DTR, RH, AH, AP, RF, WS, and WD) with COVID-19-infected cases. The study indicated that a 10 μg/m3 increase during (Lag0-14) in PM2.5, PM10, and NO2 resulted in 2.21% (95%CI: 1.13 to 3.29), 2.67% (95% CI: 0.33 to 5.01), and 4.56 (95% CI: 2.22 to 6.90) increase in daily counts of Coronavirus Disease 2019 (COVID 19)-infected cases respectively. However, only 1 unit increase in meteorological factor levels in case of daily mean temperature and DTR during (Lag0-14) associated with 3.78% (95%CI: 1.81 to 5.75) and 1.82% (95% CI: -1.74 to 5.38) rise of COVID-19-infected cases respectively. In addition, SO2 and relative humidity were negatively associated with COVID-19-infected cases at Lag0-14 with decrease of 7.23% (95% CI: -10.99 to -3.47) and 1.11% (95% CI: -3.45 to 1.23) for SO2 and for relative humidity respectively. The study recommended that there are significant correlations between air pollutants and meteorological factors with COVID-19-infected cases, which substantially explain the effect of national lockdown and suggested positive implications for control and prevention of the spread of SARS-CoV-2 disease.

Effect of diurnal temperature range on emergency room visits for acute upper respiratory tract infections

Background

An acute upper respiratory tract infection (URI) is the most common disease worldwide, irrespective of age or sex. This study aimed to evaluate the short-term effect of diurnal temperature range (DTR) on emergency room (ER) visits for URI in Seoul, Korea, between 2009 and 2013.

Methods

Daily ER visits for URI were selected from the National Emergency Department Information System, which is a nationwide daily reporting system for ER visits in Korea. URI cases were defined according to International Classification of Diseases, 10^thRevision codes J00–J06. The search for DTR effects associated with URI was performed using a semi-parametric generalized additive model approach with log link.

Results

There were 529,527 ER visits for URI during the study period, with a daily mean of 290 visits (range, 74–1942 visits). The mean daily DTR was 8.05 °C (range, 1.1–17.6 °C). The cumulative day (lag 02) effect of DTR above 6.57 °C per 1 °C increment was associated with a 1.42% (95% confidence interval [CI] 0.04–2.82) increase in total URI. Children (≤ 5 years of age) were affected by DTR above 6.57 °C per 1 °C, with 1.45% (95% CI 0.32–2.60) at lag 02, adults (19–64 years) with 2.77% (95% CI 0.39–5.20) at lag 07. When the DTR (lag02) was 6.57 °C to 11.03 °C, the relative risk was significant at 6.01% (95% CI 2.45–9.69) for every 1 °C increase in youth subjects aged for 6 to 18 years.

Conclusions

DTR was associated with a higher risk for ER visits for URI. In addition, the results suggested that the lag effects and relative risks of DTR on URI were quite different according to age.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12199-021-00974-w.

Meteorological drivers of respiratory syncytial virus infections in Singapore

Meteorological drivers are known to affect transmissibility of respiratory viruses including respiratory syncytial virus (RSV), but there are few studies quantifying the role of these drivers. We used daily RSV hospitalization data to estimate the daily effective reproduction number (Rt), a real-time measure of transmissibility, and examined its relationship with environmental drivers in Singapore from 2005 through 2015. We used multivariable regression models to quantify the proportion of the variance in Rt explained by each meteorological driver. After constructing a basic model for RSV seasonality, we found that by adding meteorological variables into this model we were able to explain a further 15% of the variance in RSV transmissibility. Lower and higher value of mean temperature, diurnal temperature range (DTR), precipitation and relative humidity were associated with increased RSV transmissibility, while higher value of maximum wind speed was correlated with decreased RSV transmissibility. We found that a number of meteorological drivers were associated with RSV transmissibility. While indoor conditions may differ from ambient outdoor conditions, our findings are indicative of a role of ambient temperature, humidity and wind speed in affecting RSV transmission that could be biological or could reflect indirect effects via the consequences on time spent indoors.

Association of diurnal temperature range with daily hospitalization for exacerbation of chronic respiratory diseases in 21 cities, China

Background

The association between diurnal temperature range (DTR) and hospitalization for exacerbation of chronic respiratory diseases (CRD) was rarely reported.

Objectives

To examine the association between DTR and daily hospital admissions for exacerbation of CRD and find out the potential effect of modifications on this association.

Method

Data on daily hospitalization for exacerbation of chronic obstructive pulmonary disease (COPD), asthma and bronchiectasis and meteorology measures from 2013 through 2017 were obtained from 21 cities in South China. After controlling the effects of daily mean temperature, relative humidity (RH), particulate matter < 2.5 μm diameter (PM_2.5) and other confounding factors, a standard generalized additive model (GAM) with a quasi-Poisson distribution was performed to evaluate the relationships between DTR and daily hospital admissions of CRD in a two-stage strategy. Subgroup analysis was performed to find potential modifications, including seasonality and population characteristics.

Result

Elevated risk of hospitalization for exacerbation of CRD (RR = 1.09 [95%CI: 1.08 to 1.11]) was associated with the increase in DTR (the 75th percentile versus the 25th percentile of DTR at lag0–6). The effects of DTR on hospital admissions for CRD were strong at low DTR in the hot season and high DTR in the cold season. The RR (the 75th percentile versus the 25th percentile of DTR at lag0–6) of hospitalization was 1.11 (95%CI: 1.08 to 1.12) for exacerbations of COPD and 1.09 (95%CI: 1.05 to 1.13) for asthma. The adverse effect of DTR on hospitalization for bronchiectasis was only observed in female patients (RR = 1.06 [95%CI: 1.03 to 1.10]).

Conclusion

Our study provided additional evidence for the association between DTR and daily hospitalization for exacerbation of CRD, and these associations are especially stronger in COPD patients and in the cold season than the hot season. Preventive measures to reduce the adverse impacts of DTR were needed for CRD patients.

Superensemble forecast of respiratory syncytial virus outbreaks at national, regional, and state levels in the United States

Respiratory syncytial virus (RSV) infections peak during the winter months in the United States, yet the timing, intensity, and onset of these outbreaks vary each year. An RSV vaccine is on the cusp of being released; precise models and accurate forecasts of RSV epidemics may prove vital for planning where and when the vaccine should be deployed. Accurate forecasts with sufficient spatial and temporal resolution could also be used to support the prevention or treatment of RSV infections. Previously, we developed and validated an RSV forecast system at the regional scale in the United States. This model-inference system had considerable forecast skill, relative to the historical expectance, for outbreak peak intensity, total outbreak size, and onset, but only marginal skill for predicting the timing of the outbreak peak. Here, we use a superensemble approach to combine three forecasting methods for RSV prediction in the US at three different spatial resolutions: national, regional, and state. At the regional and state levels, we find a substantial improvement of forecast skill, relative to historical expectance, for peak intensity, timing, and onset outbreak up to two months in advance of the predicted outbreak peak. Moreover, due to the greater variability of RSV outbreaks at finer spatial scales, we find that improvement of forecast skill at the state level exceeds that at the regional and national levels. Such finer scale superensemble forecasts may be more relevant for effecting local-scale interventions, particularly in communities with a high burden of RSV infection.

The association between diurnal temperature range and childhood hand, foot, and mouth disease: a distributed lag non-linear analysis

In recent years, hand, foot, and mouth disease (HFMD) has been increasingly recognized as a critical challenge to disease control and prevention in China. Previous studies have found that meteorological factors such as mean temperature and relative humidity were associated with HFMD. However, little is known about whether the diurnal temperature range (DTR) has any impact on HFMD. This study aimed to quantify the impact of DTR on childhood HFMD in 18 cities in Sichuan Province. A distributed lag non-linear model was adopted to explore the temporal lagged association of daily temperature with age-, gender- and pathogen-specific HFMD. A total of 290 123 HFMD cases aged 0–14 years were reported in the 18 cities in Sichuan Province. The DTR–HFMD relationships were non-linear in all subgroups. Children aged 6–14 years and male children were more vulnerable to the temperature changes. Large DTR had the higher risk estimates of HFMD incidence in cases of EV71 infection, while small DTR had the higher risk estimates of HFMD incidence in cases of CV-A16 infection. Our study suggested that DTR played an important role in the transmission of HFMD with non-linear and delayed effects.

Prediction of the respiratory syncitial virus epidemic using climate variables in Bogotá, D.C

lntroduction: The respiratory syncitial virus is one of the most common causes of mortality in children and older adults in the world. Objective: To predict the initial week of outbreaks and to establish the most relevant climate variables using naive Bayes classifiers and receiver operating characteristic curves (ROC). Materials and methods: The initial dates of the outbreaks in children less than five years old for the period 2005-2010 were obtained for Bogotá, Colombia. We selected the climatological variables using a correlation matrix and we constructed 1,020 models using different climatological variables and data from different weeks previous to the initial outbreak. In addition, we selected models using a six-year period (2005-2010), a four-year period (2005-2008), and a two-year period (2009-2010). We obtained the best predictive models and the most relevant climatological variables to predict the outbreak using naive Bayes classifiers and ROC curves. Results: The best models were those using a two-year period (2009-2010) and week 0, with 52% and 60% of effectiveness, respectively. Humidity was the most frequent variable in the best models (62%). Conclusions: We used naive Bayes classifiers to establish the best models to predict correctly the initial week of the outbreak. Our results suggest that the best models used humidity, wind speed and minimum temperature in outbreaks prediction.