The impact of weather on influenza and pneumonia mortality in New York City, 1975-2002: a retrospective study

Beyond the sine wave: unveiling climate-driven baseline mortality cycles

The relationship between temperature and mortality is well-established, with higher mortality rates occurring in moderate climates during winter. Studies on COVID-19 and influenza-related excess deaths often assume a sine-like wave pattern for baseline mortality. This study aims to assess the accuracy of this approximation in capturing the observed mortality pattern and explore its linkage with climate. Weekly mortality data from European regions (2000-2019) were modeled using the seasonal-trend decomposition procedure based on Loess. Cycles were grouped into clusters, and underlying trends were extracted using principal component analysis. Generalized linear models assuming a sine-like pattern were used to test predictive value. Cluster analysis divided the regional cycles approximately into continental and temperate climate regions, further subdivided into oceanic and Mediterranean. While the continental region exhibited a sine-like mortality pattern, it displayed modest deviations that compounded further south. The period of elevated winter mortality became shorter but more intense, while decreased summer mortality became more pronounced yet delayed. This study improves weekly estimations of excess mortality models by providing enhanced baselines. The deviation from the sine-like approximation mirrors the idealized outbreak pattern from epidemiological models with sharper surges and more gradual declines. The results point to winter infections, impacted by acquired immunity and weather conditions, as the primary drivers of fluctuations in mortality. In warmer regions, there is an apparent shift toward a lower number of overall infections within a compressed time span.

The Dynamics of COVID-19 in Hiroshima Prefecture Compared to Japan and Its Association With Meteorological Factors: A Comparative Analysis

Introduction Despite the implementation of countermeasures and mass vaccination programs, the COVID-19 pandemic incidence was a vital public health concern. This study aimed to explore the dynamics of COVID-19 cases and assess the association of COVID-19 pandemic epidemiological data with meteorological factors in Hiroshima Prefecture compared to Japan. Methods We analyzed COVID-19 pandemic data in Japan's Hiroshima Prefecture from January 16, 2020, to May 9, 2023. Meteorological factors were examined at different time frames, and Spearman correlation coefficients were calculated for COVID-19 variables and variants based on GISAID whole genome analysis. Results Hiroshima Prefecture reported 816,788 COVID-19 cases and 1,371 fatalities, with a city-to-rural case ratio of 0.97:1. Infection rates were 17.42% for Japan and 15.83% for Hiroshima. Gender-wise, the ratio was 99:1, and the 30-39 age group in Hiroshima had the highest cases (15.5%). Among all meteorological factors, daily and 14-day average wind speed showed a weak correlation with incidence (-0.1954, P < 0.01; 0.3669 P < 0.01), fatalities (-0.1148, P < 0.01; -0.2232 P < 0.01), and incidence rate (-0.2042, P < 0.01; -0.3751, P < 0.01), respectively. Clade GRA was most frequent (39.7%), and among 61 variants, B.1.1.7, AY.29, and BA.1.1.2 were predominant. Precipitation was associated significantly with the Alpha variant (0.3373, P<0.01), while the Delta variant (0.2934, <0.05) weakly correlated with humidity. Conclusion COVID-19 pandemic trends in Hiroshima Prefecture paralleled Japan's, yet with lower incidence and fatalities compared to most prefectures. Significant associations were found between meteorological factors and COVID-19 metrics, including incidence, fatalities, incidence rate, and mutations in Hiroshima.

Extreme temperatures increase the risk of pediatric pneumonia: a systematic review and meta-analysis

Introduction

The impact of climate change on ambient temperatures threatens to worsen pediatric pneumonia-related outcomes considerably. This study examined the associations of temperature variation and extreme temperature with pediatric pneumonia-related events using a meta-analysis.

Methods

We systematically searched PubMed, Medline, Embase, and Web of Science databases for relevant literature, and the quality of evidence was assessed. Fixed and random-effects meta-analyses were performed to calculate the pooled relative risks (RRs) of the associations with pneumonia-related events.

Results

We observed that a 1°C temperature variation increased the RR of pneumonia events by 1.06-fold (95% confidence interval (CI): 1.03-1.10). A 1°C temperature variation increased the RR by 1.10-fold of the pediatric pneumonia hospital admissions (95% CI: 1.00-1.21) and 1.06-fold of the pediatric pneumonia emergency department visits (95% CI: 1.01-1.10). Extreme cold increased the RR by 1.25-fold of the pediatric pneumonia events (95% CI: 1.07-1.45). A 1°C temperature variation increased the RR of pneumonia events in children by 1.19-fold (95% CI: 1.08-1.32), girls by 1.03-fold (95% CI: 1.02-1.05), and in temperate climate zones by 1.07-fold (95% CI: 1.03-1.11). Moreover, an increase in extreme cold increased the RR of pneumonia events in children by 2.43-fold (95% CI: 1.72-3.43), girls by 1.96-fold (95% CI: 1.29-2.98) and in temperate climate zones by 2.76-fold (95% CI: 1.71-4.47).

Conclusion

Our study demonstrated that pediatric pneumonia events are more prevalent among children, particularly girls, and individuals residing in temperate climate zones. Climate change represents an emergent public health threat, affecting pediatric pneumonia treatment and prevention..

Systematic Review Registration

PROSPERO (CRD42022378610).

Spatiotemporal effects of meteorological conditions on global influenza peaks

BACKGROUND

Numerous studies have suggested that meteorological conditions such as temperature and absolute humidity are highly indicative of influenza outbreaks. However, the explanatory power of meteorological factors on the seasonal influenza peaks varied widely between countries at different latitudes.

OBJECTIVES

We aimed to explore the modification effects of meteorological factors on the seasonal influenza peaks in multi-countries.

METHODS

Data on influenza positive rate (IPR) were collected across 57 countries and data on meteorological factors were collected from ECMWF Reanalysis v5 (ERA5). We used linear regression and generalized additive models to investigate the spatiotemporal associations between meteorological conditions and influenza peaks in cold and warm seasons.

RESULTS

Influenza peaks were significantly correlated with months with both lower and higher temperatures. In temperate countries, the average intensity of cold season peaks was stronger than that of warm season peaks. However, the average intensity of warm season peaks was stronfger than of cold season peaks in tropical countries. Temperature and specific humidity had synergistic effects on influenza peaks at different latitudes, stronger in temperate countries (cold season: R2=0.90; warm season: R2=0.84) and weaker in tropical countries (cold season: R2=0.64; warm season: R2=0.03). Furthermore, the effects could be divided into cold-dry and warm-humid modes. The temperature transition threshold between the two modes was 16.5-19.5 °C. During the transition from cold-dry mode to warm-humid mode, the average 2 m specific humidity increased by 2.15 times, illustrating that transporting a large amount of water vapor may compensate for the negative effect of rising temperatures on the spread of the influenza virus.

CONCLUSION

Differences in the global influenza peaks were related to the synergistic influence of temperature and specific humidity. The global influenza peaks could be divided into cold-dry and warm-humid modes, and specific thresholds of meteorological conditions were needed for the transition of the two modes.

Influence of meteorological factors on the severity of COVID-19 in Spain: Observational ecological study

Objectives

Evaluating whether meteorological and geographical variables could be associated with the severity of COVID-19 in Spain.

Methods

An ecological study was performed to analyze the influence of meteorological and geographical factors in hospital admissions and deaths due to COVID-19 in the 52 provinces of Spain (24 coastal and 28 inland regions), during the first three pandemic waves. Medical and mortality data were collected from the Carlos III Health Institute (ISCIII) and meteorological variables were requested to the Spanish State Meteorological Agency (AEMET).

Results

Regarding the diagnosed cases it is remarkable that the percentage of patients hospitalized for COVID-19 was lower in the coastal provinces than in the inland ones (8.7 ± 2.6% vs. 11.5 ± 2.6%; p = 9.9 × 10^-5). Furthermore, coastal regions registered a lower percentage of mortality than inland regions (2.0 ± 0.6% vs. 3.1 ± 0.8%; p = 1.7 × 10^-5). Mean air temperature was inversely correlated both with COVID-19 hospitalizations (Rho: -0.59; p = 3.0 × 10^-6) and mortality (Rho: -0.70; p = 5.3 × 10^-9). In those provinces with a mean air temperature <10 °C mortality by COVID-19 was twice that of those with >16 °C. Finally, we found an association between mortality and the location of the province (coastal/inland), altitude, patient age and the average air temperature; the latter was inversely and independently correlated with mortality (non standardised B coeff.: -0.24; IC 95%: -0.31 to -0.16; p = 2.38 × 10^-8).

Conclusions

The average air temperature was inversely associated with COVID-19 mortality in our country during the first three waves of the pandemic.

How do El Niño Southern Oscillation (ENSO) and local meteorological factors affect the incidence of seasonal influenza in New York state

Background

Research is lacking in examining how multiple climate factors affect the incidence of seasonal influenza. We investigated the associations between El Niño Southern Oscillation (ENSO), meteorological factors, and influenza incidence in New York State, United States.

Method

We collected emergency department visit data for influenza from the New York State Department of Health. ENSO index was obtained from the National Oceanic and Atmospheric Administration. Meteorological factors, Google Flu Search Index (GFI), and Influenza-like illness (ILI) data in New York State were also collected. Wavelet analysis was used to quantitatively estimate the coherence and phase difference of ENSO, temperature, precipitation, relative humidity, and absolute humidity with emergency department visits of influenza in New York State. Generalized additive models (GAM) were employed to examine the exposure-response relationships between ENSO, weather, and influenza. GFI and ILI data were used to simulate synchronous influenza visits.

Results

The influenza epidemic in New York State had multiple periodic and was primarily on the 1-year scale. The incidence of influenza closely followed the low ENSO index by an average of two months, and the lag period of ENSO on influenza was shorter during 2015-2018. Low temperature in the previous 2 weeks and low absolute humidity in the prior week were positively associated with influenza incidence in New York State. We found an l-shaped association between ENSO index and influenza, a parabolic relationship between temperature in the previous two weeks and influenza, and a linear negative association between absolute humidity in the previous week and influenza. The simulation models including GFI and ILI had higher accuracy for influenza visit estimation.

Conclusions

Low ENSO index, low temperature, and low absolute humidity may drive the influenza epidemics in New York State. The findings can help us deepen the understanding of the climate-influenza association, and help to develop an influenza forecasting model.

Roles for Pathogen Interference in Influenza Vaccination, with Implications to Vaccine Effectiveness (VE) and Attribution of Influenza Deaths

Pathogen interference is the ability of one pathogen to alter the course and clinical outcomes of infection by another. With up to 3000 species of human pathogens the potential combinations are vast. These combinations operate within further immune complexity induced by infection with multiple persistent pathogens, and by the role which the human microbiome plays in maintaining health, immune function, and resistance to infection. All the above are further complicated by malnutrition in children and the elderly. Influenza vaccination offers a measure of protection for elderly individuals subsequently infected with influenza. However, all vaccines induce both specific and non-specific effects. The specific effects involve stimulation of humoral and cellular immunity, while the nonspecific effects are far more nuanced including changes in gene expression patterns and production of small RNAs which contribute to pathogen interference. Little is known about the outcomes of vaccinated elderly not subsequently infected with influenza but infected with multiple other non-influenza winter pathogens. In this review we propose that in certain years the specific antigen mix in the seasonal influenza vaccine inadvertently increases the risk of infection from other non-influenza pathogens. The possibility that vaccination could upset the pathogen balance, and that the timing of vaccination relative to the pathogen balance was critical to success, was proposed in 2010 but was seemingly ignored. Persons vaccinated early in the winter are more likely to experience higher pathogen interference. Implications to the estimation of vaccine effectiveness and influenza deaths are discussed.

No Impact of Seasonality of Diagnoses on Baseline Tumor Immune Infiltration, Response to Treatment, and Prognosis in BC Patients Treated with NAC

Simple Summary

High tumor-infiltrating lymphocyte (TIL) levels are associated with an increased response to neoadjuvant chemotherapy (NAC) in breast cancer (BC). The seasonal fluctuation of TILs in breast cancer is poorly documented. In this study, we compared pre- and post-treatment immune infiltration, the treatment response as assessed by means of pathological complete response (pCR) rates, and survival according to the seasonality of BC diagnoses in a clinical cohort of patients treated with NAC. We found no association between seasonality and baseline TIL levels or pCR rates. We found that post-NAC stromal lymphocyte infiltration was lower when cancer was diagnosed in the summer, especially in the subgroup of patients with TNBC. Our data do not support the hypothesis that the seasonality of diagnoses has a major impact on the natural history of BC treated with NAC.

Abstract

Breast cancer (BC) is the most common cancer in women worldwide. Neoadjuvant chemotherapy (NAC) makes it possible to monitor in vivo response to treatment. Several studies have investigated the impact of the seasons on the incidence and detection of BC, on tumor composition, and on the prognosis of BC. However, no evidence is available on their association with immune infiltration and the response to treatment. The objective of this study was to analyze pre- and post-NAC immune infiltration as assessed by TIL levels, the response to treatment as assessed by pathological complete response (pCR) rates, and oncological outcomes as assessed by relapse-free survival (RFS) or overall survival (OS) according to the seasonality of BC diagnoses in a clinical cohort of patients treated with neoadjuvant chemotherapy. Out of 1199 patients, the repartition of the season at BC diagnosis showed that 27.2% were diagnosed in fall, 25.4% in winter, 24% in spring, and 23.4% in summer. Baseline patient and tumor characteristics, including notable pre-NAC TIL levels, were not significantly different in terms of the season of BC diagnosis. Similarly, the pCR rates were not different. No association for oncological outcome was identified. Our data do not support the idea that the seasonality of diagnoses has a major impact on the natural history of BC treated with NAC.

Profile and dynamics of infectious diseases: a population-based observational study using multi-source big data

Background

The current surveillance system only focuses on notifiable infectious diseases in China. The arrival of the big-data era provides us a chance to elaborate on the full spectrum of infectious diseases.

Methods

In this population-based observational study, we used multiple health-related data extracted from the Shandong Multi-Center Healthcare Big Data Platform from January 2013 to June 2017 to estimate the incidence density and describe the epidemiological characteristics and dynamics of various infectious diseases in a population of 3,987,573 individuals in Shandong province, China.

Results

In total, 106,289 cases of 130 infectious diseases were diagnosed among the population, with an incidence density (ID) of 694.86 per 100,000 person-years. Besides 73,801 cases of 35 notifiable infectious diseases, 32,488 cases of 95 non-notifiable infectious diseases were identified. The overall ID continuously increased from 364.81 per 100,000 person-years in 2013 to 1071.80 per 100,000 person-years in 2017 (χ² test for trend, P < 0.0001). Urban areas had a significantly higher ID than rural areas, with a relative risk of 1.25 (95% CI 1.23–1.27). Adolescents aged 10–19 years had the highest ID of varicella, women aged 20–39 years had significantly higher IDs of syphilis and trichomoniasis, and people aged ≥ 60 years had significantly higher IDs of zoster and viral conjunctivitis (all P < 0.05).

Conclusions

Infectious diseases remain a substantial public health problem, and non-notifiable diseases should not be neglected. Multi-source-based big data are beneficial to better understand the profile and dynamics of infectious diseases.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07313-6.

The Indoor Environment and Otitis Media among Australian Children: A National Cross-Sectional Study

The association between the indoor environment and lifetime prevalence of otitis media (OM) in Australian children was assessed. We analysed data from a cross-sectional study of children, aged 7–11 years, performed in twelve Australian cities during 2007–2008. The main outcome was a parental report of their child’s diagnosis with OM by a doctor. Information on the indoor environment (energy sources used for heating, cooling, and cooking, pets, and second-hand smoke exposure), in the first year of life and at present, was collected from parents by a questionnaire. Multi-level logistic regression models were used to adjust for individual- and area-level confounders. Our analysis comprised 2872 children (51% female, mean age: 10.0 (SD 1.2)). Of those, 1097 (39%) were reported to have OM. Exposure to gas heating in the first year of life was significantly associated with higher odds of OM in adjusted models (OR:1.22; 95% CI: 1.00,1.47), as was current exposure to reverse-cycle air conditioning (OR: 1.52, 95% CI: 1.27,1.82). Ownership of a cat or dog at any time was also associated with high odds of OM (OR: 1.50; 95% CI: 1.17,1.92). No other significant associations were observed. In this national study of Australian children, indoor environmental exposures associated with the lifetime prevalence of OM were gas heating, reverse-cycle air conditioning and pet ownership. Exposures in both early life and later childhood may both play a role in OM.

Correlating indoor and outdoor temperature and humidity in a sample of buildings in tropical climates

The incidence of several respiratory viral infections has been shown to be related to climate. Because humans spend most of their time indoors, measures of indoor climate, rather than outdoor climate, may be better predictors of disease incidence and transmission. Therefore, understanding the relationship between indoor and outdoor climate will help illuminate their influence on the seasonality of diseases caused by respiratory viruses. Indoor-outdoor relationships between temperature and humidity have been documented in temperate regions, but little information is available for tropical regions, where seasonal patterns of respiratory viral diseases differ. We have examined indoor-outdoor correlations of temperature, relative humidity (RH), and absolute humidity (AH) over a 1-year period in each of seven tropical cities. Across all cities, the average monthly indoor temperature was 25 ± 3°C (mean ± standard deviation) with a range of 20-30°C. The average monthly indoor RH was 66 ± 9% with a range of 50-78%, and the average monthly indoor AH was 15 ± 3 g/m³ with a range of 10-23 g/m³ . Indoor AH and RH were linearly correlated with outdoor AH when the air conditioning (AC) was off, suggesting that outdoor AH may be a good proxy of indoor humidity in the absence of AC. All indoor measurements were more strongly correlated with outdoor measurements as distance from the equator increased. Such correlations were weaker during the wet season, especially when AC was in operation. These correlations will provide insight for assessing the seasonality of respiratory viral infections using outdoor climate data, which is more widely available than indoor data, even though transmission of these diseases mainly occurs indoors.

The Impact of Weather and Air Pollution on Viral Infection and Disease Outcome Among Pediatric Pneumonia Patients in Chongqing, China, from 2009 to 2018: A Prospective Observational Study

BACKGROUND

For pediatric pneumonia, the meteorological and air pollution indicators have been frequently investigated for their association with viral circulation but not for their impact on disease severity.

METHODS

We performed a 10-year prospective, observational study in 1 hospital in Chongqing, China, to recruit children with pneumonia. Eight commonly seen respiratory viruses were tested. Autoregressive distributed lag (ADL) and random forest (RF) models were used to fit monthly detection rates of each virus at the population level and to predict the possibility of severe pneumonia at the individual level, respectively.

RESULTS

Between 2009 and 2018, 6611 pediatric pneumonia patients were included, and 4846 (73.3%) tested positive for at least 1 respiratory virus. The patient median age was 9 months (interquartile range, 4‒20). ADL models demonstrated a decent fitting of detection rates of R2 > 0.7 for respiratory syncytial virus, human rhinovirus, parainfluenza virus, and human metapneumovirus. Based on the RF models, the area under the curve for host-related factors alone was 0.88 (95% confidence interval [CI], .87‒.89) and 0.86 (95% CI, .85‒.88) for meteorological and air pollution indicators alone and 0.62 (95% CI, .60‒.63) for viral infections alone. The final model indicated that 9 weather and air pollution indicators were important determinants of severe pneumonia, with a relative contribution of 62.53%, which is significantly higher than respiratory viral infections (7.36%).

CONCLUSIONS

Meteorological and air pollution predictors contributed more to severe pneumonia in children than did respiratory viruses. These meteorological data could help predict times when children would be at increased risk for severe pneumonia and when interventions, such as reducing outdoor activities, may be warranted.

Systematic review of the effects of environmental factors on virus inactivation: implications for coronavirus disease 2019

Environmental factors such as temperature and relative humidity can affect the inactivation and transmission of coronaviruses. By reviewing medical experiments on virus survival and virus transmission between infected and susceptible species in different temperature and humidity conditions, this study explores the influence of temperature and relative humidity on the survival and transmission of viruses, and provides suggestions, with experimental evidence, for the environmental control measures of Coronavirus Disease 2019. The results indicated that (1) virus viability and infectivity is increased at a low temperature of 5 ℃ and reduced at higher temperatures. (2) Virus survival and transmission is highly efficient in a dry environment with low relative humidity, and also in a wet environment with high relative humidity, and it is minimal at intermediate relative humidity. Therefore, in indoor environments, the lack of heating in winter or overventilation, leading to low indoor temperature, can help virus survival and help susceptible people being infected. On the contrary, modulating the indoor relative humidity at an intermediate level is conducive to curb epidemic outbreaks.

Excess Winter Mortality (EWM) as a Dynamic Forensic Tool: Where, When, Which Conditions, Gender, Ethnicity and Age

To investigate the dynamic issues behind intra- and international variation in EWM (Excess Winter Mortality) using a rolling monthly EWM calculation. This is used to reveal seasonal changes in the EWM calculation and is especially relevant nearer to the equator where EWM does not reach a peak at the same time each year. In addition to latitude country specific factors determine EWM. Females generally show higher EWM. Differences between the genders are highly significant and seem to vary according to the mix of variables active each winter. The EWM for respiratory conditions in England and Wales ranges from 44% to 83%, which is about double the all-cause mortality equivalent. A similar magnitude of respiratory EWM is observed in other temperate countries. Even higher EWM can be seen for specific respiratory conditions. Age has a profound effect on EWM with a peak at puberty and then increases EWM at older ages. The gap between male and female EWM seems to act as a diagnostic tool reflecting the infectious/metrological mix in each winter. Difference due to ethnicity are also observed. An EWM equivalent calculation for sickness absence demonstrates how other health-related variables can be linked to EWM. Midway between the equator and the poles show the highest EWM since such areas tend to neglect the importance of keeping dwellings warm in the winter. Pandemic influenza does not elevate EWM, although seasonal influenza plays a part each winter. Pandemic influenza and changes in influenza strain/variant mix do, however, create structural breaks in the time series and this implies that comparing EWM between studies conducted over different times can be problematic. Cancer is an excellent example of the usefulness of rolling method since cancer EWM drifts each year, in some years increasing winter EWM and in other years diminishing it. In addition, analysis of sub-national EWM in the UK reveals high spatiotemporal granularity indicating roles for infectious outbreaks. The rolling method gives greater insight into the dynamic nature of EWM, which otherwise lies concealed in the current static method.

Effect of extreme hot and cold weather on cause-specific hospitalizations in Sweden: A time series analysis

Considering that several meteorological variables can contribute to weather vulnerability, the estimation of their synergetic effects on health is particularly useful. The spatial synoptic classification (SSC) has been used in biometeorological applications to estimate the effect of the entire suite of weather conditions on human morbidity and mortality. In this study, we assessed the relationships between extremely hot and dry (dry tropical plus, DT+) and hot and moist (moist tropical plus, MT+) weather types in summer and extremely cold and dry (dry polar plus, DP+) and cold and moist (moist polar, MP+) weather types in winter and cardiovascular and respiratory hospitalizations by age and sex. Time-series quasi-Poisson regression with distributed lags was used to assess the relationship between oppressive weather types and daily hospitalizations over 14 subsequent days in the extended summer (May to August) and 28 subsequent days during the extended winter (November to March) over 24 years in 4 Swedish locations from 1991 to 2014. In summer, exposure to hot weather types appeared to reduce cardiovascular hospitalizations while increased the risk of hospitalizations for respiratory diseases, mainly related to MT+. In winter, the effect of cold weather on both cause-specific hospitalizations was small; however, MP+ was related to a delayed increase in cardiovascular hospitalizations, whilst MP+ and DP + increased the risk of hospitalizations due to respiratory diseases. This study provides useful information for the staff of hospitals and elderly care centers who can help to implement protective measures for patients and residents. Also, our results could be helpful for vulnerable people who can adopt protective measures to reduce health risks.

A comparison of the effect of weather and climate on emergency department visitation in Roanoke and Charlottesville, Virginia

Compared with mortality, the impact of weather and climate on human morbidity is less well understood, especially in the cold season. We examined the relationships between weather and emergency department (ED) visitation at hospitals in Roanoke and Charlottesville, Virginia, two locations with similar climates and population demographic profiles. Using patient-level data obtained from electronic medical records, each patient who visited the ED was linked to that day's weather from one of 8 weather stations in the region based on each patient's ZIP code of residence. The resulting 2010-2017 daily ED visit time series were examined using a distributed lag non-linear model to account for the concurrent and lagged effects of weather. Total ED visits were modeled separately for each location along with subsets based on gender, race, and age. The relationship between the relative risk of ED visitation and temperature or apparent temperature over lags of one week was positive and approximately linear at both locations. The relative risk increased about 5% on warm, humid days in both cities (lag 0 or lag 1). Cold conditions had a protective effect, with up to a 15% decline on cold days, but ED visits increased by 4% from 2 to 5 days after the cold event. The effect of thermal extremes tended to be larger for non-whites and the elderly, and there was some evidence of a greater lagged response for non-whites in Roanoke. Females in Roanoke were more impacted by winter cold conditions than males, who were more likely to show a lagged response at high temperatures. In Charlottesville, males sought ED attention at lower temperatures than did females. The similarities in the ED response patterns between these two hospitals suggest that certain aspects of the response may be generalizable to other locations that have similar climates and demographic profiles.

Weather impact on airborne coronavirus survival

The contribution of this paper toward understanding of airborne coronavirus survival is twofold: We develop new theoretical correlations for the unsteady evaporation of coronavirus (CoV) contaminated saliva droplets. Furthermore, we implement the new correlations in a three-dimensional multiphase Eulerian-Lagrangian computational fluid dynamics solver to study the effects of weather conditions on airborne virus transmission. The new theory introduces a thermal history kernel and provides transient Nusselt (Nu) and Sherwood (Sh) numbers as a function of the Reynolds (Re), Prandtl (Pr), and Schmidt numbers (Sc). For the first time, these new correlations take into account the mixture properties due to the concentration of CoV particles in a saliva droplet. We show that the steady-state relationships induce significant errors and must not be applied in unsteady saliva droplet evaporation. The classical theory introduces substantial deviations in Nu and Sh values when increasing the Reynolds number defined at the droplet scale. The effects of relative humidity, temperature, and wind speed on the transport and viability of CoV in a cloud of airborne saliva droplets are also examined. The results reveal that a significant reduction of virus viability occurs when both high temperature and low relative humidity occur. The droplet cloud's traveled distance and concentration remain significant at any temperature if the relative humidity is high, which is in contradiction with what was previously believed by many epidemiologists. The above could explain the increase in CoV cases in many crowded cities around the middle of July (e.g., Delhi), where both high temperature and high relative humidity values were recorded one month earlier (during June). Moreover, it creates a crucial alert for the possibility of a second wave of the pandemic in the coming autumn and winter seasons when low temperatures and high wind speeds will increase airborne virus survival and transmission.

The impact of climate and antigenic evolution on seasonal influenza virus epidemics in Australia

Although seasonal influenza viruses circulate globally, prevention and treatment occur at the level of regions, cities, and communities. At these scales, the timing, duration and magnitude of epidemics vary substantially, but the underlying causes of this variation are poorly understood. Here, based on analyses of a 15-year city-level dataset of 18,250 laboratory-confirmed and antigenically-characterised influenza virus infections from Australia, we investigate the effects of previously hypothesised environmental and virological drivers of influenza epidemics. We find that anomalous fluctuations in temperature and humidity do not predict local epidemic onset timings. We also find that virus antigenic change has no consistent effect on epidemic size. In contrast, epidemic onset time and heterosubtypic competition have substantial effects on epidemic size and composition. Our findings suggest that the relationship between influenza population immunity and epidemiology is more complex than previously supposed and that the strong influence of short-term processes may hinder long-term epidemiological forecasts.

The burden of influenza and pneumonia mortality attributable to absolute humidity among elderly people in Chongqing, China, 2012-2018

OBJECTIVE

To examine the association between absolute humidity (AH) and influenza and pneumonia (P&I) mortality, and to quantify P&I mortality burden attributable to non-optimum AHs among elderly people aged ≥65 years in Chongqing, the largest municipality of China.

METHODS

Daily data of P&I mortality from 2012 to 2018, and the contemporaneous meteorological data in the study area were collected. Distributed lag non-linear model (DLNM) was applied to estimate the non-linear and delayed effects of absolute humidity (AH) on P&I mortality. Then, attributable deaths were calculated for the dry and moist AH, defined as AH below and above the minimum mortality AH (MMAH), respectively. Moderate and extreme AHs were defined using cutoffs at the 2.5th and 97.5th percentiles of AH.

RESULTS

The relationship between AH and P&I mortality was a U-shaped curve. The MMAH was 11.5 g/m³ (46.4th percentile). In total, 25.7% (95% confidence interval: 10.0-38.2) of P&I mortality (4673 deaths) was attributed to non-optimum AHs. Low AHs were responsible for 12.7% of the P&I death burden (95%CI: 0.2-20.1), while high AHs for 13.0% (95%CI: -9.4-25.7). Extreme low and high AHs accounted for 3.7% (95%CI: 0.1-6.8) and 3.0% (95%CI: 0-5.4) of P&I mortality.

CONCLUSIONS

Our study showed that both low AHs and high AHs are responsible for considerable AH-related P&I mortality burden among elderly people. Our results may have important public health implications for the development of relevant intervention policies to reduce P&I deaths among the elderly.

Epidemic features of seasonal influenza transmission among eight different climate zones in Gansu, China

BACKGROUNDS

Seasonal influenza remains epidemic globally with a substantial health burden. Understanding the transmission patterns and epidemic features of influenza may facilitate the improvement of preventive and control measures. This study aims to assess the epidemic features of influenza among different climate zones and identify high-risk zones across Gansu province, China.

METHODS

We collected weekly influenza cases at county-level between 1st January 2012 and 31st December 2016, as well as climate zones classification shapefile data from Köppen-Geiger climate map. We compared the epidemic features (Frequency index (α), Duration index (β) and Intensity index (γ)) of influenza among different climate zones. Spatial cluster analysis was used to examine the high-risk areas of transmission of influenza.

RESULTS

The distribution of cases existed significant differences among eight climate zones (F-test: 267.02, p < 0.05). The highest mean weekly incidence rate (per 100,000 population) was 0.59 in snow climate with dry winter and warm summer (Dwb). The primary (relative risk (RR): 3.61, p < 0.001) and secondary (RR: 2.45, p < 0.001) clusters were located in Dwb. The highest values of α, β and γ were 1.00, 261 and 154.38 in Dwb. The hot spots (high-high clusters) of the epidemic indices were detected in Dwb.

CONCLUSIONS

This study found the variability of epidemic features of influenza among eight climate zones. We highlight that Dwb was the high-risk zone where influenza clustered with the highest incidence rate and epidemic temporal indices. This provide further insight into potential improvement of preventive measures by climate zones to minimize the impact of epidemics.

Association of meteorological factors and atmospheric particulate matter with the incidence of pneumonia: an ecological study

OBJECTIVES

Inconsistent results have been found between pneumonia and meteorological factors. We aimed to identify principal meteorological factors associated with pneumonia, and to estimate the effect size and lag time.

METHODS

This was nationwide population-based study used a healthcare claims database merged with a weather database in eight metropolitan cities in Korea. We applied a stepwise approach using the Granger causality test and generalized additive model to elucidate the association between weekly pneumonia incidence (WPI) and meteorological factors/air pollutants (MFAP). Impulse response function was used to examine the time lag.

RESULTS

In total, 2 011 424 cases of pneumonia were identified from 2007 to 2017. Among MFAP, diurnal temperature range (DTR), humidity and particulate matter ≤2.5 μm in diameter (PM_2.5) showed statistically significant associations with WPI (p < 0.001 for all 3 MFAPs). The association of DTR and WPI showed an inverted U pattern for bacterial and unspecified pneumonia, whereas for viral pneumonia, WPI increased gradually in a more linear manner with DTR and no substantial decline. Humidity showed a consistent pattern in all three pneumonia categories. WPI steeply increased up to 10 to 20 μg/m³ of PM_2.5 but did not show a further increase in higher concentrations. On the basis of the result, we examined the effect of MFAP in different lag times up to 3 weeks.

CONCLUSIONS

DTR, humidity and PM_2.5 were identified as MFAP most closely associated with WPI. With the model, we were able to visualize the effect-time association of MFAP and WPI.

Examining the diurnal temperature range enigma: why is human health related to the daily change in temperature?

An increasing number of epidemiological studies are finding statistical evidence that diurnal temperature range (DTR) is positively correlated to human morbidity and mortality despite the lack of clear clinical understanding. We examine a 14-year daily time series of emergency department (ED) admissions to the University of Virginia Medical Center in Charlottesville, Virginia, relative to long-term climate records from the Charlottesville/Albemarle County Airport weather station and the Spatial Synoptic Classification. DTR has a consistent strong positive correlation (r ~ 0.5) with maximum temperature in all months but only a weak, negative correlation (r ~- 0.1) with minimum temperature except in late summer (r ~- 0.4). Warm season DTR is highest on dry air mass days with low dew point temperatures. Cool season DTR is unrelated to morning temperature. Using a distributed lag non-linear model with an emphasis on DTR and its seasonal variation, after stratifying the models by season, we find that ED visits are linked to extreme cold events (cold days and nights) and high DTR in the cold season. In the warm season, ED visits are also linked to high DTR, but these are cool, dry, and pleasant days. The existing confusion regarding interpretation of DTR impacts on health might be rectified through a more careful analysis of the underlying physical factors that drive variations in DTR over the course of a year.

Humidity-Dependent Decay of Viruses, but Not Bacteria, in Aerosols and Droplets Follows Disinfection Kinetics

The transmission of some infectious diseases requires that pathogens can survive (i.e., remain infectious) in the environment, outside the host. Relative humidity (RH) is known to affect the survival of some microorganisms in the environment; however, the mechanism underlying the relationship has not been explained, particularly for viruses. We investigated the effects of RH on the viability of bacteria and viruses in both suspended aerosols and stationary droplets using traditional culture-based approaches. Results showed that viability of bacteria generally decreased with decreasing RH. Viruses survived well at RHs lower than 33% and at 100%, whereas their viability was reduced at intermediate RHs. We then explored the evaporation rate of droplets consisting of culture media and the resulting changes in solute concentrations over time; as water evaporates from the droplets, solutes such as sodium chloride in the media become more concentrated. Based on the results, we suggest that inactivation of bacteria is influenced by osmotic pressure resulting from elevated concentrations of salts as droplets evaporate. We propose that the inactivation of viruses is governed by the cumulative dose of solutes or the product of concentration and time, as in disinfection kinetics. These findings emphasize that evaporation kinetics play a role in modulating the survival of microorganisms in droplets.

The complex associations of climate variability with seasonal influenza A and B virus transmission in subtropical Shanghai, China

Most previous studies focused on the association between climate variables and seasonal influenza activity in tropical or temperate zones, little is known about the associations in different influenza types in subtropical China. The study aimed to explore the associations of multiple climate variables with influenza A (Flu-A) and B virus (Flu-B) transmissions in Shanghai, China. Weekly influenza virus and climate data (mean temperature (MeanT), diurnal temperature range (DTR), relative humidity (RH) and wind velocity (Wv)) were collected between June 2012 and December 2018. Generalized linear models (GLMs), distributed lag non-linear models (DLNMs) and regression tree models were developed to assess such associations. MeanT exerted the peaking risk of Flu-A at 1.4 °C (2-weeks' cumulative relative risk (RR): 14.88, 95% confidence interval (CI): 8.67-23.31) and 25.8 °C (RR: 12.21, 95%CI: 6.64-19.83), Flu-B had the peak at 1.4 °C (RR: 26.44, 95%CI: 11.52-51.86). The highest RR of Flu-A was 23.05 (95%CI: 5.12-88.45) at DTR of 15.8 °C, that of Flu-B was 38.25 (95%CI: 15.82-87.61) at 3.2 °C. RH of 51.5% had the highest RR of Flu-A (9.98, 95%CI: 4.03-26.28) and Flu-B (4.63, 95%CI: 1.95-11.27). Wv of 3.5 m/s exerted the peaking RR of Flu-A (7.48, 95%CI: 2.73-30.04) and Flu-B (7.87, 95%CI: 5.53-11.91). DTR ≥ 12 °C and MeanT <22 °C were the key drivers for Flu-A and Flu-B, separately. The study found complex non-linear relationships between climate variability and different influenza types in Shanghai. We suggest the careful use of meteorological variables in influenza prediction in subtropical regions, considering such complex associations, which may facilitate government and health authorities to better minimize the impacts of seasonal influenza.

Incorporating calendar effects to predict influenza seasonality in Milwaukee, Wisconsin

Social outings can trigger influenza transmission, especially in children and elderly. In contrast, school closures are associated with reduced influenza incidence in school-aged children. While influenza surveillance modelling studies typically account for holidays and mass gatherings, age-specific effects of school breaks, sporting events and commonly celebrated observances are not fully explored. We examined the impact of school holidays, social events and religious observances for six age groups (all ages, ⩽4, 5–24, 25–44, 45–64, ⩾65 years) on four influenza outcomes (tests, positives, influenza A and influenza B) as reported by the City of Milwaukee Health Department Laboratory, Milwaukee, Wisconsin from 2004 to 2009. We characterised holiday effects by analysing average weekly counts in negative binomial regression models controlling for weather and seasonal incidence fluctuations. We estimated age-specific annual peak timing and compared influenza outcomes before, during and after school breaks. During the 118 university holiday weeks, average weekly tests were lower than in 140 school term weeks (5.93 vs. 11.99 cases/week, P < 0.005). The dampening of tests during Winter Break was evident in all ages and in those 5–24 years (RR = 0.31; 95% CI 0.22–0.41 vs. RR = 0.14; 95% CI 0.09–0.22, respectively). A significant increase in tests was observed during Spring Break in 45–64 years old adults (RR = 2.12; 95% CI 1.14–3.96). Milwaukee Public Schools holiday breaks showed similar amplification and dampening effects. Overall, calendar effects depend on the proximity and alignment of an individual holiday to age-specific and influenza outcome-specific peak timing. Better quantification of individual holiday effects, tailored to specific age groups, should improve influenza prevention measures.

Forecasting influenza activity using self-adaptive AI model and multi-source data in Chongqing, China

Background

Early detection of influenza activity followed by timely response is a critical component of preparedness for seasonal influenza epidemic and influenza pandemic. However, most relevant studies were conducted at the regional or national level with regular seasonal influenza trends. There are few feasible strategies to forecast influenza activity at the local level with irregular trends.

Methods

Multi-source electronic data, including historical percentage of influenza-like illness (ILI%), weather data, Baidu search index and Sina Weibo data of Chongqing, China, were collected and integrated into an innovative Self-adaptive AI Model (SAAIM), which was constructed by integrating Seasonal Autoregressive Integrated Moving Average model and XGBoost model using a self-adaptive weight adjustment mechanism. SAAIM was applied to ILI% forecast in Chongqing from 2017 to 2018, of which the performance was compared with three previously available models on forecasting.

Findings

ILI% showed an irregular seasonal trend from 2012 to 2018 in Chongqing. Compared with three reference models, SAAIM achieved the best performance on forecasting ILI% of Chongqing with the mean absolute percentage error (MAPE) of 11·9%, 7·5%, and 11·9% during the periods of the year 2014–2016, 2017, and 2018 respectively. Among the three categories of source data, historical influenza activity contributed the most to the forecast accuracy by decreasing the MAPE by 19·6%, 43·1%, and 11·1%, followed by weather information (MAPE reduced by 3·3%, 17·1%, and 2·2%), and Internet-related public sentiment data (MAPE reduced by 1·1%, 0·9%, and 1·3%).

Interpretation

Accurate influenza forecast in areas with irregular seasonal influenza trends can be made by SAAIM with multi-source electronic data.

Latitude and daily-weather effects on gobbling activity of wild turkeys in Mississippi

Weather has been recognized as a density independent factor influencing the abundance, distribution, and behavior of vertebrates. Male wild turkeys' (Meleagris gallopavo) breeding behavior includes vocalizations and courtship displays to attract females, the phenology of which can vary with latitude. State biologists design spring turkey-hunting season frameworks centered on annual vocalization patterns to maximize hunter engagement. The Mississippi Department of Wildlife, Fisheries, and Parks has traditionally instituted a statewide, 7-week, spring harvest season. However, hunters routinely argue that different peaks in gobbling activity across the state exist. The objective of this study was to determine whether differences in peak gobbling activity existed across a latitudinal gradient of Mississippi and assess the effect of weather on gobbling. During 2008 and 2009, we conducted a statewide gobbling survey. We used generalized additive mixed models to describe the probability and frequency of gobbling activity within northern and southern regions of the state. We also investigated the effect of daily weather conditions on gobbling activity. Our results revealed an approximate 10-14-day difference in peak gobbling activity between southern and northern Mississippi. The majority of all gobbling activity occurred within the current spring harvest framework. Perhaps more importantly, gobbling activity was more prevalent on days of regionally dry conditions (i.e., less humid) according to the Spatial Synoptic Classification. Our results provide information on gobbling activity phenology relative to hunting-season dates and weather-response information. Our approach may be particularly applicable in states with relatively shorter seasons or highly variable daily weather conditions that moderate gobbling frequency.

Association between Weather Types based on the Spatial Synoptic Classification and All-Cause Mortality in Sweden, 1991⁻2014

Much is known about the adverse health impact of high and low temperatures. The Spatial Synoptic Classification is a useful tool for assessing weather effects on health because it considers the combined effect of meteorological factors rather than temperature only. The aim of this study was to assess the association between oppressive weather types and daily total mortality in Sweden. Time-series Poisson regression with distributed lags was used to assess the relationship between oppressive weather (Dry Polar, Dry Tropical, Moist Polar, and Moist Tropical) and daily deaths over 14 days in the extended summer (May to September), and 28 days during the extended winter (November to March), from 1991 to 2014. Days not classified as oppressive weather served as the reference category. We computed relative risks with 95% confidence intervals, adjusting for trends and seasonality. Results of the southern (Skåne and Stockholm) and northern (Jämtland and Västerbotten) locations were pooled using meta-analysis for regional-level estimates. Analyses were performed using the dlnm and mvmeta packages in R. During summer, in the South, the Moist Tropical and Dry Tropical weather types increased the mortality at lag 0 through lag 3 and lag 6, respectively. Moist Polar weather was associated with mortality at longer lags. In the North, Dry Tropical weather increased the mortality at shorter lags. During winter, in the South, Dry Polar and Moist Polar weather increased mortality from lag 6 to lag 10 and from lag 19 to lag 26, respectively. No effect of oppressive weather was found in the North. The effect of oppressive weather types in Sweden varies across seasons and regions. In the North, a small study sample reduces precision of estimates, while in the South, the effect of oppressive weather types is more evident in both seasons.

Effect of meteorological factors on influenza-like illness from 2012 to 2015 in Huludao, a northeastern city in China

Background

This study aims to describe the epidemiological patterns of influenza-like illness (ILI) in Huludao, China and seek scientific evidence on the link of ILI activity with weather factors.

Methods

Surveillance data of ILI cases between January 2012 and December 2015 was collected in Huludao Central Hospital, meteorological data was obtained from the China Meteorological Data Service Center. Generalized additive model (GAM) was used to seek the relationship between the number of ILI cases and the meteorological factors. Multiple Smoothing parameter estimation was made on the basis of Poisson distribution, where the number of weekly ILI cases was treated as response, and the smoothness of weather was treated as covariates. Lag time was determined by the smallest Akaike information criterion (AIC). Smoothing coefficients were estimated for the prediction of the number of ILI cases.

Results

A total of 29, 622 ILI cases were observed during the study period, with children ILI cases constituted 86.77%. The association between ILI activity and meteorological factors varied across different lag periods. The lag time for average air temperature, maximum air temperature, minimum air temperature, vapor pressure and relative humidity were 2, 2, 1, 1 and 0 weeks, respectively. Average air temperature, maximum air temperature, minimum air temperature, vapor pressure and relative humidity could explain 16.5%, 9.5%, 18.0%, 15.9% and 7.7% of the deviance, respectively. Among the temperature indexes, the minimum temperature played the most important role. The number of ILI cases peaked when minimum temperature was around -13 °C in winter and 18 °C in summer. The number of cases peaked when the relative humidity was equal to 43% and then began to decrease with the increase of relative humidity. When the humidity exceeded 76%, the number of ILI cases began to rise.

Conclusions

The present study first analyzed the relationship between meteorological factors and ILI cases with special consideration of the length of lag period in Huludao, China. Low air temperature and low relative humidity (cold and dry weather condition) played a considerable role in the epidemic pattern of ILI cases. The trend of ILI activity could be possibly predicted by the variation of meteorological factors.

Mechanistic insights into the effect of humidity on airborne influenza virus survival, transmission and incidence

Influenza incidence and seasonality, along with virus survival and transmission, appear to depend at least partly on humidity, and recent studies have suggested that absolute humidity (AH) is more important than relative humidity (RH) in modulating observed patterns. In this perspective article, we re-evaluate studies of influenza virus survival in aerosols, transmission in animal models and influenza incidence to show that the combination of temperature and RH is equally valid as AH as a predictor. Collinearity must be considered, as higher levels of AH are only possible at higher temperatures, where it is well established that virus decay is more rapid. In studies of incidence that employ meteorological data, outdoor AH may be serving as a proxy for indoor RH in temperate regions during the wintertime heating season. Finally, we present a mechanistic explanation based on droplet evaporation and its impact on droplet physics and chemistry for why RH is more likely than AH to modulate virus survival and transmission.

Mechanistic insights into the effect of humidity on airborne influenza virus survival, transmission and incidence

Influenza incidence and seasonality, along with virus survival and transmission, appear to depend at least partly on humidity, and recent studies have suggested that absolute humidity (AH) is more important than relative humidity (RH) in modulating observed patterns. In this perspective article, we re-evaluate studies of influenza virus survival in aerosols, transmission in animal models and influenza incidence to show that the combination of temperature and RH is equally valid as AH as a predictor. Collinearity must be considered, as higher levels of AH are only possible at higher temperatures, where it is well established that virus decay is more rapid. In studies of incidence that employ meteorological data, outdoor AH may be serving as a proxy for indoor RH in temperate regions during the wintertime heating season. Finally, we present a mechanistic explanation based on droplet evaporation and its impact on droplet physics and chemistry for why RH is more likely than AH to modulate virus survival and transmission.

Climate Change and Healthcare Sustainability in the Agincourt Sub-District, Kruger to Canyons Biosphere Region, South Africa

As low-income communities are most vulnerable to climate-associated health concerns, access to healthcare will increase in importance as a key priority in South Africa. This study explores healthcare sustainability in the Agincourt sub-district, Kruger to Canyons Biosphere Region in Mpumalanga, South Africa. A rapid assessment and response methodology (RAR) was implemented, which includes the examination of previous studies conducted in the sub-district, the mapping of healthcare facilities in the area, and the implementation of a facility infrastructure and workforce capacity investigation by means of key informant (KI) interviews at eight healthcare facilities. Findings indicate that the greatest need across the facilities relate to access to medical doctors and pharmacists. None of the facilities factored climate associations with health into their clinical care strategies. The necessity to train healthcare facility staff on aspects related to climate change, health, and sustainability is highlighted. Environmental health practitioners should also be incorporated in grassroots community climate adaptation strategies. Outcomes further indicate the need for the advancement of integrated healthcare and climate adaptation strategies that focus on strengthening healthcare systems, which may include novel technological approaches such as telemedicine. Policy makers need to be proactive and pre-emptive in finding and improving processes and models to render healthcare services prepared for climate change.

The effects of meteorological factors on influenza among children in Guangzhou, China

Background

Influenza seriously affects the health of children, yet little evidence is available on the association between meteorological factors and the occurrence of influenza among children in subtropical regions. The current study aimed to explore the effects of meteorological factors on influenza among children in Guangzhou, a subtropical city in China.

Methods

The distributed lag nonlinear model (DLNM) was used to assess the effects of meteorological factors on children influenza occurrence in Guangzhou, China. Daily number of influenza cases among children aged 0‐17 years from 2013 to 2017 were obtained from the National Information System for Disease Control and Prevention.

Results

Mean temperature, relative humidity, and atmospheric pressure were associated with influenza cases. The relative risks (RRs) increased as temperature fell below 20°C. The relationship between relative humidity and influenza cases could be described with a U‐shaped curve, and the RRs increased if relative humidity was lower than 50% or higher than 80%. The risk of influenza increased with rising atmospheric pressure with 1005 hPa as the break point. The cold effect, humid effect, dry effect, high‐pressure effect, and low‐pressure effect showed statistical significance both in female and male. The cold effect increased with age. The humid‐effect affects all age ranges of children, but dry effect mainly affected 4‐14 years old. High‐pressure effect mainly affected the 0‐3 years old, whereas low‐pressure effect protected preschool children aged 0‐6 years old.

Conclusion

Mean temperature, relative humidity, and atmospheric pressure might be important predictors of the influenza occurrence among children in Guangzhou.

Respiratory hospital admissions and weather changes: a retrospective study in Charlottesville, Virginia, USA

In most midlatitude locations, human morbidity and mortality are highly seasonal, with winter peaks driven by respiratory disease and associated comorbidities. But the transition between high and low mortality/morbidity months varies spatially. We use a measure of the thermal biophysical strain imposed on the respiratory system-the Acclimatization Thermal Strain Index (ATSI)-to examine respiratory hospital admissions in Charlottesville, VA. Daily respiratory admissions to the University of Virginia over a 19-year period are compared to ATSI values derived from hourly surface weather data acquired from the Charlottesville airport. Negative ATSI values (associated with transitions from warm (and humid) to cold (and dry) conditions) are related to admission peaks at seasonal and weekly timescales, whereas positive ATSI values (cold to warm) exhibit weaker relationships. This research marks the first application of the ATSI to human morbidity, and results suggest that respiratory strain may account for how people who are acclimated to different climates respond to short-term weather changes.

Heat/mortality sensitivities in Los Angeles during winter: a unique phenomenon in the United States

BACKGROUND

Extreme heat is often associated with elevated levels of human mortality, particularly across the mid-latitudes. Los Angeles, CA exhibits a unique, highly variable winter climate, with brief periods of intense heat caused by downsloping winds commonly known as Santa Ana winds. The goal is to determine if Los Angeles County is susceptible to heat-related mortality during the winter season. This is the first study to specifically evaluate heat-related mortality during the winter for a U.S. city.

METHODS

Utilizing the Spatial Synoptic Classification system in Los Angeles County from 1979 through 2010, we first relate daily human mortality to synoptic air mass type during the winter season (December, January, February) using Welch's t-tests. However, this methodology is only somewhat effective at controlling for important inter- and intra-annual trends in human mortality unrelated to heat such as influenza outbreaks. As a result, we use distributed lag nonlinear modeling (DLNM) to evaluate if the relative risk of human mortality increases during higher temperatures in Los Angeles, as the DLNM is more effective at controlling for variability at multiple temporal scales within the human mortality dataset.

RESULTS

Significantly higher human mortality is uncovered in winter when dry tropical air is present in Los Angeles, particularly among those 65 years and older (p < 0.001). The DLNM reveals the relative risk of human mortality increases when above average temperatures are present. Results are especially pronounced for maximum and mean temperatures, along with total mortality and those 65 + .

CONCLUSIONS

The discovery of heat-related mortality in winter is a unique finding in the United States, and we recommend stakeholders consider warning and intervention techniques to mitigate the role of winter heat on human health in the County.

Physico-chemical characteristics of evaporating respiratory fluid droplets

The detailed physico-chemical characteristics of respiratory droplets in ambient air, where they are subject to evaporation, are poorly understood. Changes in the concentration and phase of major components in a droplet-salt (NaCl), protein (mucin) and surfactant (dipalmitoylphosphatidylcholine)-may affect the viability of any pathogens contained within it and thus may affect the efficiency of transmission of infectious disease by droplets and aerosols. The objective of this study is to investigate the effect of relative humidity (RH) on the physico-chemical characteristics of evaporating droplets of model respiratory fluids. We labelled these components in model respiratory fluids and observed evaporating droplets suspended on a superhydrophobic surface using optical and fluorescence microscopy. When exposed to continuously decreasing RH, droplets of different model respiratory fluids assumed different morphologies. Loss of water induced phase separation as well as indication of a decrease in pH. The presence of surfactant inhibited the rapid rehydration of the non-volatile components. An enveloped virus, ϕ6, that has been proposed as a surrogate for influenza virus appeared to be homogeneously distributed throughout the dried droplet. We hypothesize that the increasing acidity and salinity in evaporating respiratory droplets may affect the structure of the virus, although at low enough RH, crystallization of the droplet components may eliminate their harmful effects.

The effects of synoptic weather on influenza infection incidences: a retrospective study utilizing digital disease surveillance

The environmental drivers and mechanisms of influenza dynamics remain unclear. The recent development of influenza surveillance--particularly the emergence of digital epidemiology--provides an opportunity to further understand this puzzle as an area within applied human biometeorology. This paper investigates the short-term weather effects on human influenza activity at a synoptic scale during cold seasons. Using 10 years (2005-2014) of municipal level influenza surveillance data (an adjustment of the Google Flu Trends estimation from the Centers for Disease Control's virologic surveillance data) and daily spatial synoptic classification weather types, we explore and compare the effects of weather exposure on the influenza infection incidences in 79 cities across the USA. We find that during the cold seasons the presence of the polar [i.e., dry polar (DP) and moist polar (MP)] weather types is significantly associated with increasing influenza likelihood in 62 and 68% of the studied cities, respectively, while the presence of tropical [i.e., dry tropical (DT) and moist tropical (MT)] weather types is associated with a significantly decreasing occurrence of influenza in 56 and 43% of the cities, respectively. The MP and the DP weather types exhibit similar close positive correlations with influenza infection incidences, indicating that both cold-dry and cold-moist air provide favorable conditions for the occurrence of influenza in the cold seasons. Additionally, when tropical weather types are present, the humid (MT) and the dry (DT) weather types have similar strong impacts to inhibit the occurrence of influenza. These findings suggest that temperature is a more dominating atmospheric factor than moisture that impacts the occurrences of influenza in cold seasons.

Climate Classification is an Important Factor in Assessing Quality-of-Care Across Hospitals

Climate is a known modulator of disease, but its impact on hospital performance metrics remains unstudied. We assess the relationship between Köppen-Geiger climate classification and hospital performance metrics, specifically 30-day mortality, as reported in Hospital Compare, and collected for the period July 2013 through June 2014 (7/1/2013-06/30/2014). A hospital-level multivariate linear regression analysis was performed while controlling for known socioeconomic factors to explore the relationship between all-cause mortality and climate. Hospital performance scores were obtained from 4,524 hospitals belonging to 15 distinct Köppen-Geiger climates and 2,373 unique counties. Model results revealed that hospital performance metrics for mortality showed significant climate dependence (p < 0.001) after adjusting for socioeconomic factors. Climate is a significant factor in evaluating hospital 30-day mortality rates. These results demonstrate that climate classification is an important factor when comparing hospital performance across the United States.

Impacts of cold weather on all-cause and cause-specific mortality in Texas, 1990-2011

Cold weather was estimated to account for more than half of weather-related deaths in the U.S. during 2006-2010. Studies have shown that cold-related excessive mortality is especially relevant with decreasing latitude or in regions with mild winter. However, only limited studies have been conducted in the southern U.S. The purpose of our study is to examine impacts of cold weather on mortality in 12 major Texas Metropolitan Areas (MSAs) for the 22-year period, 1990-2011. Our study used a two-stage approach to examine the cold-mortality association. We first applied distributed lag non-linear models (DLNM) to 12 major MSAs to estimate cold effects for each area. A random effects meta-analysis was then used to estimate pooled effects. Age-stratified and cause-specific mortalities were modeled separately for each MSA. Most of the MSAs were associated with an increased risk in mortality ranging from 0.1% to 5.0% with a 1 °C decrease in temperature below the cold thresholds. Higher increased mortality risks were generally observed in MSAs with higher average daily mean temperatures and lower latitudes. Pooled effect estimate was 1.58% (95% Confidence Interval (CI) [0.81, 2.37]) increase in all-cause mortality risk with a 1 °C decrease in temperature. Cold wave effects in Texas were also examined, and several MSAs along the Texas Gulf Coast showed statistically significant cold wave-mortality associations. Effects of cold on all-cause mortality were highest among people over 75 years old (1.86%, 95% CI [1.09, 2.63]). Pooled estimates for cause-specific mortality were strongest in myocardial infarction (4.30%, 95% CI [1.18, 7.51]), followed by respiratory diseases (3.17%, 95% CI [0.26, 6.17]) and ischemic heart diseases (2.54%, 95% CI [1.08, 4.02]). In conclusion, cold weather generally increases mortality risk significantly in Texas, and the cold effects vary with MSAs, age groups, and cause-specific deaths.

Occupational Respiratory Diseases of Miners from Two Gold Mines in Ghana

Objective: This study investigated respiratory disorders among gold miners in Ghana, a sub-Saharan African country. Material and Methods: A cross-sectional exploratory design that employed quantitative methods was conducted among 1001 male workers from the Obuasi and Tarkwa mines from December 2015 to April 2016. A total of 1001 workers, consisting of 505 and 496 underground and surface miners, respectively, were involved. The cross-sectional descriptive design was used because data was collected from participants of different experiences by selected participants at a time. Results: The study found significant association between age, educational background, marital status and drinking alcohol on respiratory disorders. The prevalence of asthma, pneumonia, bronchitis and emphysema were respectively 47.55%, 14.29%, 9.69% and 5.10%. Coughing was the most cited respiratory symptom (35.4%). Conclusions: The study documents important evidence on the level of respiratory disorders among miners in Ghana. Instituting appropriate health education interventions and improving the working environment is critical to improving the overall health and preventing respiratory disorders among miners.

A four year seasonal survey of the relationship between outdoor climate and epidemiology of viral respiratory tract infections in a temperate climate

BACKGROUND

The relation between weather conditions, viral transmission and seasonal activity of respiratory viruses is not fully understood.

OBJECTIVES

To investigate the impact of outdoor weather in a temperate climate setting on the seasonal epidemiology of viruses causing respiratory tract infections, particularly influenza A (IFA).

STUDY DESIGN

In total, 20,062 clinical nasopharyngeal swab samples referred for detection of respiratory pathogens using a multiplex PCR panel, between October 2010 and July 2013, were included. Results of PCR detection were compared with local meteorological data for the same period.

RESULTS

Low temperature and vapor pressure (VP) were associated with weekly incidence of IFA, respiratory syncytial virus, metapneumovirus, bocavirus and adenovirus but no association with relative humidity was found. The incidence of human rhinovirus and enterovirus was independent of temperature. During seasonal IFA outbreaks, the weekly drop of average temperature (compared with the week before) was strongly associated with the IFA incidence recorded the following week.

CONCLUSION

A sudden drop in outdoor temperature might activate the annual influenza epidemic in a temperate climate by facilitating aerosol spread in dry air. These conditions also seem to affect the incidence of other respiratory pathogens but not human rhino- or enterovirus, suggesting that routes of infection other than aerosol may be relevant for these agents.

Cold, dry air is associated with influenza and pneumonia mortality in Auckland, New Zealand

The relationship between weather and influenza and pneumonia mortality was examined retrospectively using daily data from 1980 to 2009 in Auckland, New Zealand, a humid, subtropical location. Mortality events, defined when mortality exceeded 0·95 standard deviation above the mean, followed periods of anomalously cold air (t_a.m. = −4·1, P < 0·01; t_p.m. = −4·2, P < 0·01) and/or anomalously dry air (t_a.m. = −4·1, P < 0·01; t_p.m. = −3·8, P < 0·01) by up to 19 days. These results suggest that respiratory infection is enhanced during unusually cold conditions and during conditions with unusually low humidity, even in a subtropical location where humidity is typically high.

Abiotic stress of ambient cold temperature regulates the host receptivity to pathogens by cell surfaced sialic acids

Ambient cold temperature, as an abiotic stress, regulates the survival, stability, transmission, and infection of pathogens. However, the effect of cold temperature on the host receptivity to the pathogens has not been fully studied. In this study, the expression of terminal α-2,3- and α-2,6-sialic acids were increased in murine lung tissues, especially bronchial epithelium, by exposure to cold condition. The expression of several sialyltransferases were also increased by exposure to cold temperature. Furthermore, in human bronchial epithelial BEAS-2B cells, the expressions of α-2,3- and α-2,6-sialic acids, and mRNA levels of sialyltransferases were increased in the low temperature condition at 33 °C. On the other hand, the treatment of Lith-Gly, a sialyltransferase inhibitor, blocked the cold-induced expression of sialic acids on surface of BEAS-2B cells. The binding of influenza H1N1 hemagglutinin (HA) toward BEAS-2B cells cultured at low temperature condition was increased, compared to 37 °C. In contrast, the cold-increased HA binding was blocked by treatment of lithocholicglycine and sialyl-N-acetyl-D-lactosamines harboring α-2,3- and α-2,6-sialyl motive. These results suggest that the host receptivity to virus at cold temperature results from the expressions of α-2,3- and α-2,6-sialic acids through the regulation of sialyltransferase expression.

Humidity: A review and primer on atmospheric moisture and human health

Research examining associations between weather and human health frequently includes the effects of atmospheric humidity. A large number of humidity variables have been developed for numerous purposes, but little guidance is available to health researchers regarding appropriate variable selection. We examine a suite of commonly used humidity variables and summarize both the medical and biometeorological literature on associations between humidity and human health. As an example of the importance of humidity variable selection, we correlate numerous hourly humidity variables to daily respiratory syncytial virus isolates in Singapore from 1992 to 1994. Most water-vapor mass based variables (specific humidity, absolute humidity, mixing ratio, dewpoint temperature, vapor pressure) exhibit comparable correlations. Variables that include a thermal component (relative humidity, dewpoint depression, saturation vapor pressure) exhibit strong diurnality and seasonality. Humidity variable selection must be dictated by the underlying research question. Despite being the most commonly used humidity variable, relative humidity should be used sparingly and avoided in cases when the proximity to saturation is not medically relevant. Care must be taken in averaging certain humidity variables daily or seasonally to avoid statistical biasing associated with variables that are inherently diurnal through their relationship to temperature.

A systematic evaluation of the lagged effects of spatiotemporally relative surface weather types on wintertime cardiovascular-related mortality across 19 US cities

Previous research using varying methods has shown that the day-to-day variability in cardiovascular (CV)-related mortality is correlated with a number of different meteorological variables, though these relationships can vary geographically. This research systematically examines the relationship between anomalous winter CV-related mortality and geographically and seasonally relative multivariate surface weather types derived from a recently developed gridded weather typing classification (GWTC) for cities in varying climate regions of the United States of America (USA). Results indicate that for all locations examined, during winter, a dry and cool (DC) weather type is significantly related to increased CV-related mortality, especially in the 2 weeks immediately after it occurs, with no apparent mortality displacement. Across the USA as a whole, the peak of this relationship is a 4.1% increase in CV-related mortality at a lag of 3 days. Spike days in CV-related mortality show similar trends, being over 50% more likely 2 to 4 days after the DC type occurs. A humid and warm (HW) weather type exhibited a significant and opposite relationship to that of DC. While these results for DC and HW were statistically significant at every location examined, the magnitudes were larger in the warmer locations. Among other weather types, Warm Front Passages (WFP) were also related to significant increases in CV-related mortality, especially 1 day after they occurred. Though this link was much more varied geographically than results found with DC or HW, it suggests that sequences of multiple DC days followed by WFP may result in increased CV-related mortality.

Relation of Total and Cardiovascular Death Rates to Climate System, Temperature, Barometric Pressure, and Respiratory Infection

A distinct seasonal pattern in total and cardiovascular death rates has been reported. The factors contributing to this pattern have not been fully explored. Seven locations (average total population 71,354,000) were selected where data were available including relatively warm, cold, and moderate temperatures. Over the period 2004 to 2009, there were 2,526,123 all-cause deaths, 838,264 circulatory deaths, 255,273 coronary heart disease deaths, and 135,801 ST-elevation myocardial infarction (STEMI) deaths. We used time series and multivariate regression modeling to explore the association between death rates and climatic factors (temperature, dew point, precipitation, barometric pressure), influenza levels, air pollution levels, hours of daylight, and day of week. Average seasonal patterns for all-cause and cardiovascular deaths were very similar across the 7 locations despite differences in climate. After adjusting for multiple covariates and potential confounders, there was a 0.49% increase in all-cause death rate for every 1°C decrease. In general, all-cause, circulatory, coronary heart disease and STEMI death rates increased linearly with decreasing temperatures. The temperature effect varied by location, including temperature's linear slope, cubic fit, positional shift on the temperature axis, and the presence of circulatory death increases in locally hot temperatures. The variable effect of temperature by location suggests that people acclimatize to local temperature cycles. All-cause and circulatory death rates also demonstrated sizable associations with influenza levels, dew point temperature, and barometric pressure. A greater understanding of how climate, temperature, and barometric pressure influence cardiovascular responses would enhance our understanding of circulatory and STEMI deaths.

[Excess mortality associated with influenza in Spain in winter 2012]

OBJECTIVE

An excess of mortality was detected in Spain in February and March 2012 by the Spanish daily mortality surveillance system and the «European monitoring of excess mortality for public health action» program. The objective of this article was to determine whether this excess could be attributed to influenza in this period.

METHODS

Excess mortality from all causes from 2006 to 2012 were studied using time series in the Spanish daily mortality surveillance system, and Poisson regression in the European mortality surveillance system, as well as the FluMOMO model, which estimates the mortality attributable to influenza. Excess mortality due to influenza and pneumonia attributable to influenza were studied by a modification of the Serfling model. To detect the periods of excess, we compared observed and expected mortality.

RESULTS

In February and March 2012, both the Spanish daily mortality surveillance system and the European mortality surveillance system detected a mortality excess of 8,110 and 10,872 deaths (mortality ratio (MR): 1.22 (95% CI:1.21-1.23) and 1.32 (95% CI: 1.29-1.31), respectively). In the 2011-12 season, the FluMOMO model identified the maximum percentage (97%) of deaths attributable to influenza in people older than 64 years with respect to the mortality total associated with influenza (13,822 deaths). The rate of excess mortality due to influenza and pneumonia and respiratory causes in people older than 64 years, obtained by the Serfling model, also reached a peak in the 2011-2012 season: 18.07 and 77.20, deaths per 100,000 inhabitants, respectively.

CONCLUSION

A significant increase in mortality in elderly people in Spain was detected by the Spanish daily mortality surveillance system and by the European mortality surveillance system in the winter of 2012, coinciding with a late influenza season, with a predominance of the A(H3N2) virus, and a cold wave in Spain. This study suggests that influenza could have been one of the main factors contributing to the mortality excess observed in the winter of 2012 in Spain.

Unusually cold and dry winters increase mortality in Australia

Seasonal patterns in mortality have been recognised for decades, with a marked excess of deaths in winter, yet our understanding of the causes of this phenomenon is not yet complete. Research has shown that low and high temperatures are associated with increased mortality independently of season; however, the impact of unseasonal weather on mortality has been less studied. In this study, we aimed to determine if unseasonal patterns in weather were associated with unseasonal patterns in mortality. We obtained daily temperature, humidity and mortality data from 1988 to 2009 for five major Australian cities with a range of climates. We split the seasonal patterns in temperature, humidity and mortality into their stationary and non-stationary parts. A stationary seasonal pattern is consistent from year-to-year, and a non-stationary pattern varies from year-to-year. We used Poisson regression to investigate associations between unseasonal weather and an unusual number of deaths. We found that deaths rates in Australia were 20-30% higher in winter than summer. The seasonal pattern of mortality was non-stationary, with much larger peaks in some winters. Winters that were colder or drier than a typical winter had significantly increased death risks in most cities. Conversely summers that were warmer or more humid than average showed no increase in death risks. Better understanding the occurrence and cause of seasonal variations in mortality will help with disease prevention and save lives.