Context Influenza and respiratory syncytial virus (RSV) cause substantial morbidity and mortality. Statistical methods used to estimate deaths in the United States attributable to influenza have not accounted for RSV circulation. Objective To develop a statistical model using national mortality and viral surveillance data to estimate annual influenza- and RSV-associated deaths in the United States, by age group, virus, and influenza type and subtype. Design, Setting, and Population Age-specific Poisson regression models using national viral surveillance data for the 1976-1977 through 1998-1999 seasons were used to estimate influenza-associated deaths. Influenza- and RSV-associated deaths were simultaneously estimated for the 1990-1991 through 1998-1999 seasons. Main Outcome Measures Attributable deaths for 3 categories: underlying pneumonia and influenza, underlying respiratory and circulatory, and all causes. Results Annual estimates of influenza-associated deaths increased significantly between the 1976-1977 and 1998-1999 seasons for all 3 death categories (P Conclusions Mortality associated with both influenza and RSV circulation disproportionately affects elderly persons. Influenza deaths have increased substantially in the last 2 decades, in part because of aging of the population, underscoring the need for better prevention measures, including more effective vaccines and vaccination programs for elderly persons.

/pdf/mortality-associated-with-influenza-and-respiratory-3ilr9rktwq.pdf

Mortality Associated With Influenza and Respiratory Syncytial Virus in the United States

Systematic Review of Emergency Department Crowding: Causes, Effects, and Solutions

Objective: To examine the relationship between hospital and emergency department (ED) occupancy, as indicators of hospital overcrowding, and mortality after emergency admission. Design: Retrospective analysis of 62495 probabilistically linked emergency hospital admissions and death records. Setting: Three tertiary metropolitan hospitals between July 2000 and June 2003. Participants: All patients 18 years or older whose first ED attendance resulted in hospital admission during the study period. Main outcome measures: Deaths on days 2, 7 and 30 were evaluated against an Overcrowding Hazard Scale based on hospital and ED occupancy, after adjusting for age, diagnosis, referral source, urgency and mode of transport to hospital. Results: There was a linear relationship between the Overcrowding Hazard Scale and deaths on Day 7 (r= 0.98; 95% Cl, 0.79-1.00). An Overcrowding Hazard Scale > 2 was associated with an increased Day 2, Day 7 and Day 30 hazard ratio for death of 1.3 (95% Cl, 1.1-1.6), 1.3 (95% Cl, 1.2-1.5) and 1.2 (95% Cl, 1.1-1.3), respectively. Deaths at 30 days associated with an Overcrowding Hazard Scale > 2 compared with one of < 3 were undifferentiated with respect to age, diagnosis, urgency, transport mode, referral source or hospital length of stay, but had longer ED durations of stay (risk ratio per hour of ED stay, 1.1; 95% Cl, 1.1-1.1; P< 0.001) and longer physician waiting times (risk ratio per hour of ED wait, 1.2; 95% Cl, 1.1-1.3; P = 0.01). Conclusions: Hospital and ED overcrowding is associated with increased mortality. The Overcrowding Hazard Scale may be used to assess the hazard associated with hospital and ED overcrowding. Reducing overcrowding may improve outcomes for patients requiring emergency hospital admission.

/pdf/the-association-between-hospital-overcrowding-and-mortality-25f8gvxxc1.pdf

The association between hospital overcrowding and mortality among patients admitted via Western Australian emergency departments

For centuries, novel strains of influenza have emerged to produce human pandemics, causing widespread illness, death, and disruption. There have been four influenza pandemics in the past hundred years. During this time, globalization processes, alongside advances in medicine and epidemiology, have altered the way these pandemics are experienced. Drawing on international case studies, this paper provides a review of the impact of past influenza pandemics, while examining the evolution of our understanding of, and response to, these viruses. This review argues that pandemic influenza is in part a consequence of human development, and highlights the importance of considering outbreaks within the context of shifting global landscapes. While progress in infectious disease prevention, control, and treatment has improved our ability to respond to such outbreaks, globalization processes relating to human behaviour, demographics, and mobility have increased the threat of pandemic emergence and accelerated global disease transmission. Preparedness planning must continue to evolve to keep pace with this heightened risk. Herein, we look to the past for insights on the pandemic experience, underlining both progress and persisting challenges. However, given the uncertain timing and severity of future pandemics, we emphasize the need for flexible policies capable of responding to change as such emergencies develop.

https://www.mdpi.com/2076-0817/5/4/66/pdf

Reviewing the History of Pandemic Influenza: Understanding Patterns of Emergence and Transmission.

Background We developed a practical influenza forecast model based on real-time, geographically focused, and easy to access data, designed to provide individual medical centers with advanced warning of the expected number of influenza cases, thus allowing for sufficient time to implement interventions. Secondly, we evaluated the effects of incorporating a real-time influenza surveillance system, Google Flu Trends, and meteorological and temporal information on forecast accuracy. Methods Forecast models designed to predict one week in advance were developed from weekly counts of confirmed influenza cases over seven seasons (2004–2011) divided into seven training and out-of-sample verification sets. Forecasting procedures using classical Box-Jenkins, generalized linear models (GLM), and generalized linear autoregressive moving average (GARMA) methods were employed to develop the final model and assess the relative contribution of external variables such as, Google Flu Trends, meteorological data, and temporal information. Results A GARMA(3,0) forecast model with Negative Binomial distribution integrating Google Flu Trends information provided the most accurate influenza case predictions. The model, on the average, predicts weekly influenza cases during 7 out-of-sample outbreaks within 7 cases for 83% of estimates. Google Flu Trend data was the only source of external information to provide statistically significant forecast improvements over the base model in four of the seven out-of-sample verification sets. Overall, the p-value of adding this external information to the model is 0.0005. The other exogenous variables did not yield a statistically significant improvement in any of the verification sets. Conclusions Integer-valued autoregression of influenza cases provides a strong base forecast model, which is enhanced by the addition of Google Flu Trends confirming the predictive capabilities of search query based syndromic surveillance. This accessible and flexible forecast model can be used by individual medical centers to provide advanced warning of future influenza cases.

/pdf/influenza-forecasting-with-google-flu-trends-jhqzj5i6hf.pdf

Influenza Forecasting with Google Flu Trends

In December 1997, media reported hospital overcrowding and “the worst [flu epidemic] in the past two decades” in Los Angeles County (LAC). We found that rates of pneumonia and influenza deaths, hospitalizations, and claims were substantially higher for the 1997–98 influenza season than the previous six seasons. Hours of emergency medical services (EMS) diversion (when emergency departments could not receive incoming patients) peaked during the influenza seasons studied; the number of EMS diversion hours per season also increased during the seasons 1993–94 to 1997–98, suggesting a decrease in medical care capacity during influenza seasons. Over the seven influenza seasons studied, the number of licensed beds decreased 12%, while the LAC population increased 5%. Our findings suggest that the capacity of health-care systems to handle patient visits during influenza seasons is diminishing.

/pdf/medical-care-capacity-for-influenza-outbreaks-los-angeles-1b0ihodryc.pdf

Medical care capacity for influenza outbreaks, Los Angeles.

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Medical care capacity for influenza outbreaks, Los Angeles.