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

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.

Decline in temperature and humidity increases the occurrence of influenza in cold climate

BACKGROUND

Both temperature and humidity may independently or jointly contribute to the risk of influenza infections. We examined the relations between the level and decrease of temperature, humidity and the risk of influenza A and B virus infections in a subarctic climate.

METHODS

We conducted a case-crossover study among military conscripts (n = 892) seeking medical attention due to respiratory symptoms during their military training period and identified 66 influenza A and B cases by PCR or serology. Meteorological data such as measures of average and decline in ambient temperature and absolute humidity (AH) during the three preceding days of the onset (hazard period) and two reference periods, prior and after the onset were obtained.

RESULTS

The average temperature preceding the influenza onset was -6.8 ± 5.6°C and AH 3.1 ± 1.3 g/m3. A decrease in both temperature and AH during the hazard period increased the occurrence of influenza so that a 1°C decrease in temperature and 0.5 g decrease per m3 in AH increased the estimated risk by 11% [OR 1.11 (1.03 to 1.20)] and 58% [OR 1.58 (1.28 to 1.96)], respectively. The occurrence of influenza infections was positively associated with both the average temperature [OR 1.10 per 1°C (95% confidence interval 1.02 to 1.19)] and AH [OR 1.25 per g/m3 (1.05 to 1.49)] during the hazard period prior to onset.

CONCLUSION

Our results demonstrate that a decrease rather than low temperature and humidity per se during the preceding three days increase the risk of influenza episodes in a cold climate.

The SSC: a decade of climate-health research and future directions

This year marks the tenth anniversary of the development of the revised Spatial Synoptic Classification, the "SSC", by Scott Sheridan. This daily weather-type classification scheme has become one of the key analytical tools implemented in a diverse range of climatological investigations, including analysis of air quality variability, human health, vegetation growth, precipitation and snowfall trends, and broader analyses of historical and future climatic variability and trends. The continued and expanding use of the SSC motivates a review and comparison of the system's research and geographic foci to date, with the goal of identifying promising areas for future efforts, particularly within the context of human health and climate change. This review also assesses how the SSC has complemented and compares with other current environmental epidemiological studies in weather and health.

Undercorrection of hypernatremia is frequent and associated with mortality

Background

About 1% of patients admitted to the Emergency Department (ED) have hypernatremia, a condition associated with a mortality rate of 20 to 60%. Management recommendations originate from intensive care unit studies, in which patients and medical diseases differ from those in ED.

Methods

We retrospectively studied clinical characteristics, treatments, and outcomes of severely hypernatremic patients in the ED and risk factors associated with death occurrence during hospitalization.

Results

During 2010, 85 cases of severe hypernatremia ≥150 mmol/l were admitted to ED. Hypernatremia occurred in frail patients: mean age 79.7 years, 55% institutionalized, 28% with dementia.

Twenty four percent of patients died during hospitalization. Male gender and low mean blood pressure (MBP) were independently associated with death, as well as slow natremia correction speed, but not the severity of hyperosmolarity at admission. Infusion solute was inappropriate for 45% of patients with MBP <70 mmHg who received hypotonic solutes and 22% of patients with MBP ≥70 mmHg who received isotonic solutes or were not perfused.

Conclusions

This is the first study assessing outcome of hypernatremic patients in the ED according to the treatment provided. It appears that not only a too quick, but also a too slow correction speed is associated with an increased risk of death regardless of initial natremia. Medical management of hypernatremic patients must be improved regarding evaluation and treatment.

Functional polymorphisms of interferon-gamma affect pneumonia-induced sepsis

OBJECTIVE

Sepsis is an inflammatory syndrome caused by infection, and both its incidence and mortality are high. Because interferon-gamma (IFN-γ) plays an important role in inflammation, this work assessed IFN-γ single nucleotide polymorphism (SNPs) that may be associated with sepsis.

METHODS

A total of 196 patients with pneumonia-induced sepsis and 213 age- and sex-matched healthy volunteers participated in our study from July 2012 to July 2013 in Guangzhou, China. Patient clinical information was collected. Clinical pathology was assessed in subgroups defined based on clinical criteria, APACHE II (acute physiology and chronic health evaluation) and SOFA (sepsis-related organ failure assessment) scores and discharge rate. Four functional SNPs, -1616T/C (rs2069705), -764G/C (rs2069707), +874A/T (rs2430561) and +3234C/T (rs2069718), were genotyped by Snapshot in both sepsis patients and healthy controls. Pearson's chi-square test or Fisher's exact test were used to analyze the distribution of the SNPs, and the probability values (P values), odds ratios (OR) and 95% confidence intervals (CIs) were calculated.

RESULTS

No mutations in the IFN-γ -764G/C SNP were detected among the participants in our study. The +874A/T and +3234C/T SNPs were in strong linkage disequilibrium (LD) (r(2) = 0.894). The -1616 TC+TT, +874 AT+AA genotype and the TAC haplotype were significantly associated with sepsis susceptibility, while the CTT haplotype was associated with protection against sepsis incidence. Genotype of -1616 TT wasn't only protective against severity of sepsis, but also against higher APACHE II and SOFA scores as +874 AA and +3234 CC. The TAC haplotype was was protective against progression to severe sepsis either.

CONCLUSION

Our results suggest that functional IFN-γ SNPs and their haplotypes are associated with pneumonia-induced sepsis.

Air pollution, temperature and pediatric influenza in Brisbane, Australia

Previous studies have demonstrated the importance of weather variables in influencing the incidence of influenza. However, the role of air pollution is often ignored in identifying the environmental drivers of influenza. This research aims to examine the impacts of air pollutants and temperature on the incidence of pediatric influenza in Brisbane, Australia. Lab-confirmed daily data on influenza counts among children aged 0-14years in Brisbane from 2001 January 1st to 2008 December 31st were retrieved from Queensland Health. Daily data on maximum and minimum temperatures for the same period were supplied by the Australian Bureau of Meteorology. Winter was chosen as the main study season due to it having the highest pediatric influenza incidence. Four Poisson log-linear regression models, with daily pediatric seasonal influenza counts as the outcome, were used to examine the impacts of air pollutants (i.e., ozone (O3), particulate matter≤10μm (PM10) and nitrogen dioxide (NO2)) and temperature (using a moving average of ten days for these variables) on pediatric influenza. The results show that mean temperature (Relative risk (RR): 0.86; 95% Confidence Interval (CI): 0.82-0.89) was negatively associated with pediatric seasonal influenza in Brisbane, and high concentrations of O3 (RR: 1.28; 95% CI: 1.25-1.31) and PM10 (RR: 1.11; 95% CI: 1.10-1.13) were associated with more pediatric influenza cases. There was a significant interaction effect (RR: 0.94; 95% CI: 0.93-0.95) between PM10 and mean temperature on pediatric influenza. Adding the interaction term between mean temperature and PM10 substantially improved the model fit. This study provides evidence that PM10 needs to be taken into account when evaluating the temperature-influenza relationship. O3 was also an important predictor, independent of temperature.

A respiratory alert model for the Shenandoah Valley, Virginia, USA

Respiratory morbidity (particularly COPD and asthma) can be influenced by short-term weather fluctuations that affect air quality and lung function. We developed a model to evaluate meteorological conditions associated with respiratory hospital admissions in the Shenandoah Valley of Virginia, USA. We generated ensembles of classification trees based on six years of respiratory-related hospital admissions (64,620 cases) and a suite of 83 potential environmental predictor variables. As our goal was to identify short-term weather linkages to high admission periods, the dependent variable was formulated as a binary classification of five-day moving average respiratory admission departures from the seasonal mean value. Accounting for seasonality removed the long-term apparent inverse relationship between temperature and admissions. We generated eight total models specific to the northern and southern portions of the valley for each season. All eight models demonstrate predictive skill (mean odds ratio = 3.635) when evaluated using a randomization procedure. The predictor variables selected by the ensembling algorithm vary across models, and both meteorological and air quality variables are included. In general, the models indicate complex linkages between respiratory health and environmental conditions that may be difficult to identify using more traditional approaches.

Relating weather types to asthma-related hospital admissions in New York State

Many previous studies have looked into the relationship between asthma and individual weather variables, but comparatively few have looked at this relationship using holistic weather types (WTs). Utilizing the Spatial Synoptic Classification, this research considers up to 6 days of lag time while investigating the asthma-to-WT relationship in two age groups (under 18 and 18 and over) throughout New York State. Results indicate that a cold and dry WT in autumn corresponds to increased asthma admissions and spike days in admissions in New York City (NYC) for the school-aged population, while hot and dry WTs in summer correspond to spike days in asthma admissions in both age groups. However, results vary considerably for other regions, seasons and WTs, and spike day analysis yields clearer results than the analysis of total anomalous admissions. When stratified by multiple regions and age groups, the sample size of daily asthma admissions is a limiting factor outside of NYC.

A nice day for an infection? Weather conditions and social contact patterns relevant to influenza transmission

Although there is no doubt that significant morbidity and mortality occur during annual influenza epidemics, the role of contextual circumstances, which catalyze seasonal influenza transmission, remains unclear. Weather conditions are believed to affect virus survival, efficiency of transmission and host immunity, but seasonality may also be driven by a tendency of people to congregate indoors during periods of bad weather. To test this hypothesis, we combined data from a social contact survey in Belgium with local weather data. In the absence of a previous in-depth weather impact analysis of social contact patterns, we explored the possibilities and identified pitfalls. We found general dominance of day-type (weekend, holiday, working day) over weather conditions, but nonetheless observed an increase in long duration contacts ([Formula: see text]1 hour) on regular workdays with low temperatures, almost no precipitation and low absolute humidity of the air. Interestingly, these conditions are often assumed to be beneficial for virus survival and transmission. Further research is needed to establish the impact of the weather on social contacts. We recommend that future studies sample over a broad spectrum of weather conditions and day types and include a sufficiently large proportion of holiday periods and weekends.

Spatial diffusion of influenza outbreak-related climate factors in Chiang Mai Province, Thailand

Influenza is one of the most important leading causes of respiratory illness in the countries located in the tropical areas of South East Asia and Thailand. In this study the climate factors associated with influenza incidence in Chiang Mai Province, Northern Thailand, were investigated. Identification of factors responsible for influenza outbreaks and the mapping of potential risk areas in Chiang Mai are long overdue. This work examines the association between yearly climate patterns between 2001 and 2008 and influenza outbreaks in the Chiang Mai Province. The climatic factors included the amount of rainfall, percent of rainy days, relative humidity, maximum, minimum temperatures and temperature difference. The study develops a statistical analysis to quantitatively assess the relationship between climate and influenza outbreaks and then evaluate its suitability for predicting influenza outbreaks. A multiple linear regression technique was used to fit the statistical model. The Inverse Distance Weighted (IDW) interpolation and Geographic Information System (GIS) techniques were used in mapping the spatial diffusion of influenza risk zones. The results show that there is a significance correlation between influenza outbreaks and climate factors for the majority of the studied area. A statistical analysis was conducted to assess the validity of the model comparing model outputs and actual outbreaks.