Case Fatality Rates Based on Population Estimates of Influenza-Like Illness Due to Novel H1N1 Influenza: New York City, May–June 2009

BACKGROUND: The public health response to pandemic influenza is contingent on the pandemic strain's severity. In late April 2009, a potentially pandemic novel H1N1 influenza strain (nH1N1) was recognized. New York City (NYC) experienced an intensive initial outbreak that peaked in late May, providing the need and opportunity to rapidly quantify the severity of nH1N1. METHODS AND FINDINGS: Telephone surveys using rapid polling methods of approximately 1,000 households each were conducted May 20-27 and June 15-19, 2009. Respondents were asked about the occurrence of influenza-like illness (ILI, fever with either cough or sore throat) for each household member from May 1-27 (survey 1) or the preceding 30 days (survey 2). For the overlap period, prevalence data were combined by weighting the survey-specific contribution based on a Serfling model using data from the NYC syndromic surveillance system. Total and age-specific prevalence of ILI attributed to nH1N1 were estimated using two approaches to adjust for background ILI: discounting by ILI prevalence in less affected NYC boroughs and by ILI measured in syndromic surveillance data from 2004-2008. Deaths, hospitalizations and intensive care unit (ICU) admissions were determined from enhanced surveillance including nH1N1-specific testing. Combined ILI prevalence for the 50-day period was 15.8% (95% CI:13.2%-19.0%). The two methods of adjustment yielded point estimates of nH1N1-associated ILI of 7.8% and 12.2%. Overall case-fatality (CFR) estimates ranged from 0.054-0.086 per 1000 persons with nH1N1-associated ILI and were highest for persons>or=65 years (0.094-0.147 per 1000) and lowest for those 0-17 (0.008-0.012). Hospitalization rates ranged from 0.84-1.34 and ICU admission rates from 0.21-0.34 per 1000, with little variation in either by age-group. CONCLUSIONS: ILI prevalence can be quickly estimated using rapid telephone surveys, using syndromic surveillance data to determine expected "background" ILI proportion. Risk of severe illness due to nH1N1 was similar to seasonal influenza, enabling NYC to emphasize preventing severe morbidity rather than employing aggressive community mitigation measures

Disease surveillance using a hidden Markov model

<p>Abstract</p> <p>Background</p> <p>Routine surveillance of disease notification data can enable the early detection of localised disease outbreaks. Although hidden Markov models (HMMs) have been recognised as an appropriate method to model disease surveillance data, they have been rarely applied in public health practice. We aimed to develop and evaluate a simple flexible HMM for disease surveillance which is suitable for use with sparse small area count data and requires little baseline data.</p> <p>Methods</p> <p>A Bayesian HMM was designed to monitor routinely collected notifiable disease data that are aggregated by residential postcode. Semi-synthetic data were used to evaluate the algorithm and compare outbreak detection performance with the established Early Aberration Reporting System (EARS) algorithms and a negative binomial cusum.</p> <p>Results</p> <p>Algorithm performance varied according to the desired false alarm rate for surveillance. At false alarm rates around 0.05, the cusum-based algorithms provided the best overall outbreak detection performance, having similar sensitivity to the HMMs and a shorter average time to detection. At false alarm rates around 0.01, the HMM algorithms provided the best overall outbreak detection performance, having higher sensitivity than the cusum-based Methods and a generally shorter time to detection for larger outbreaks. Overall, the 14-day HMM had a significantly greater area under the receiver operator characteristic curve than the EARS C3 and 7-day negative binomial cusum algorithms.</p> <p>Conclusion</p> <p>Our findings suggest that the HMM provides an effective method for the surveillance of sparse small area notifiable disease data at low false alarm rates. Further investigations are required to evaluation algorithm performance across other diseases and surveillance contexts.</p

Excess healthcare burden during 1918-1920 influenza pandemic in Taiwan: implications for post-pandemic preparedness

<p>Abstract</p> <p>Background</p> <p>It is speculated that the 2009 pandemic H1N1 influenza virus might fall into a seasonal pattern during the current post-pandemic period with more severe clinical presentation for high-risk groups identified during the 2009 pandemic. Hence the extent of likely excess healthcare needs during this period must be fully considered. We will make use of the historical healthcare record in Taiwan during and after the 1918 influenza pandemic to ascertain the scope of potential excess healthcare burden during the post-pandemic period.</p> <p>Methods</p> <p>To establish the healthcare needs after the initial wave in 1918, the yearly healthcare records (hospitalizations, outpatients, etc.) in Taiwan during 1918-1920 are compared with the corresponding data from the adjacent "baseline" years of 1916, 1917, 1921, and 1922 to estimate the excess healthcare burden during the initial outbreak in 1918 and in the years immediately after.</p> <p>Results</p> <p>In 1918 the number of public hospital outpatients exceeded the yearly average of the baseline years by 20.11% (95% CI: 16.43, 25.90), and the number of hospitalizations exceeded the corresponding yearly average of the baseline years by 12.20% (10.59, 14.38), while the excess number of patients treated by the public medics was statistically significant at 32.21% (28.48, 39.82) more than the yearly average of the baseline years. For 1920, only the excess number of hospitalizations was statistically significant at 19.83% (95% CI: 17.21, 23.38) more than the yearly average of the baseline years.</p> <p>Conclusions</p> <p>Considerable extra burden with significant loss of lives was reported in 1918 by both the public medics system and the public hospitals. In comparison, only a substantial number of excess hospitalizations in the public hospitals was reported in 1920, indicating that the population was relatively unprepared for the first wave in 1918 and did not fully utilize the public hospitals. Moreover, comparatively low mortality was reported by the public hospitals and the public medics during the second wave in 1920 even though significantly more patients were hospitalized, suggesting that there had been substantially less fatal illnesses among the hospitalized patients during the second wave. Our results provide viable parameters for assessing healthcare needs for post-pandemic preparedness.</p

Predicting Pneumonia and Influenza Mortality from Morbidity Data

BACKGROUND: Few European countries conduct reactive surveillance of influenza mortality, whereas most monitor morbidity. METHODOLOGY/PRINCIPAL FINDINGS: We developed a simple model based on Poisson seasonal regression to predict excess cases of pneumonia and influenza mortality during influenza epidemics, based on influenza morbidity data and the dominant types/subtypes of circulating viruses. Epidemics were classified in three levels of mortality burden (“high”, “moderate” and “low”). The model was fitted on 14 influenza seasons and was validated on six subsequent influenza seasons. Five out of the six seasons in the validation set were correctly classified. The average absolute difference between observed and predicted mortality was 2.8 per 100,000 (18% of the average excess mortality) and Spearman's rank correlation coefficient was 0.89 (P = 0.05). CONCLUSIONS/SIGNIFICANCE: The method described here can be used to estimate the influenza mortality burden in countries where specific pneumonia and influenza mortality surveillance data are not available

Digital NFATc2 Activation per Cell Transforms Graded T Cell Receptor Activation into an All-or-None IL-2 Expression

The expression of interleukin-2 (IL-2) is a key event in T helper (Th) lymphocyte activation, controlling both, the expansion and differentiation of effector Th cells as well as the activation of regulatory T cells. We demonstrate that the strength of TCR stimulation is translated into the frequency of memory Th cells expressing IL-2 but not into the amount of IL-2 per cell. This molecular switch decision for IL-2 expression per cell is located downstream of the cytosolic Ca2+ level. Here we show that in a single activated Th cell, NFATc2 activation is digital but NF-κB activation is graded after graded T cell receptor (TCR) signaling. Subsequently, NFATc2 translocates into the nucleus in an all-or-none fashion per cell, transforming the strength of TCR-stimulation into the number of nuclei positive for NFATc2 and IL-2 transcription. Thus, the described NFATc2 switch regulates the number of Th cells actively participating in an immune response

Detecting the start of an influenza outbreak using exponentially weighted moving average charts

Background. Influenza viruses cause seasonal outbreaks in temperate climates, usually during winter and early spring, and are endemic in tropical climates. The severity and length of influenza outbreaks vary from year to year. Quick and reliable detection of the start of an outbreak is needed to promote public health measures. Methods. We propose the use of an exponentially weighted moving average (EWMA) control chart of laboratory confirmed influenza counts to detect the start and end of influenza outbreaks. Results. The chart is shown to provide timely signals in an example application with seven years of data from Victoria, Australia. Conclusions. The EWMA control chart could be applied in other applications to quickly detect influenza outbreaks

Revised estimates of influenza-associated excess mortality, United States, 1995 through 2005

<p>Abstract</p> <p>Background</p> <p>Excess mortality due to seasonal influenza is thought to be substantial. However, influenza may often not be recognized as cause of death. Imputation methods are therefore required to assess the public health impact of influenza. The purpose of this study was to obtain estimates of monthly excess mortality due to influenza that are based on an epidemiologically meaningful model.</p> <p>Methods and Results</p> <p>U.S. monthly all-cause mortality, 1995 through 2005, was hierarchically modeled as Poisson variable with a mean that linearly depends both on seasonal covariates and on influenza-certified mortality. It also allowed for overdispersion to account for extra variation that is not captured by the Poisson error. The coefficient associated with influenza-certified mortality was interpreted as ratio of total influenza mortality to influenza-certified mortality. Separate models were fitted for four age categories (<18, 18–49, 50–64, 65+). Bayesian parameter estimation was performed using Markov Chain Monte Carlo methods. For the eleven year study period, a total of 260,814 (95% CI: 201,011–290,556) deaths was attributed to influenza, corresponding to an annual average of 23,710, or 0.91% of all deaths.</p> <p>Conclusion</p> <p>Annual estimates for influenza mortality were highly variable from year to year, but they were systematically lower than previously published estimates. The excellent fit of our model with the data suggest validity of our estimates.</p

Validation of Statistical Models for Estimating Hospitalization Associated with Influenza and Other Respiratory Viruses

BACKGROUND: Reliable estimates of disease burden associated with respiratory viruses are keys to deployment of preventive strategies such as vaccination and resource allocation. Such estimates are particularly needed in tropical and subtropical regions where some methods commonly used in temperate regions are not applicable. While a number of alternative approaches to assess the influenza associated disease burden have been recently reported, none of these models have been validated with virologically confirmed data. Even fewer methods have been developed for other common respiratory viruses such as respiratory syncytial virus (RSV), parainfluenza and adenovirus. METHODS AND FINDINGS: We had recently conducted a prospective population-based study of virologically confirmed hospitalization for acute respiratory illnesses in persons <18 years residing in Hong Kong Island. Here we used this dataset to validate two commonly used models for estimation of influenza disease burden, namely the rate difference model and Poisson regression model, and also explored the applicability of these models to estimate the disease burden of other respiratory viruses. The Poisson regression models with different link functions all yielded estimates well correlated with the virologically confirmed influenza associated hospitalization, especially in children older than two years. The disease burden estimates for RSV, parainfluenza and adenovirus were less reliable with wide confidence intervals. The rate difference model was not applicable to RSV, parainfluenza and adenovirus and grossly underestimated the true burden of influenza associated hospitalization. CONCLUSION: The Poisson regression model generally produced satisfactory estimates in calculating the disease burden of respiratory viruses in a subtropical region such as Hong Kong

Modeling emergency department visit patterns for infectious disease complaints: results and application to disease surveillance

BACKGROUND: Concern over bio-terrorism has led to recognition that traditional public health surveillance for specific conditions is unlikely to provide timely indication of some disease outbreaks, either naturally occurring or induced by a bioweapon. In non-traditional surveillance, the use of health care resources are monitored in "near real" time for the first signs of an outbreak, such as increases in emergency department (ED) visits for respiratory, gastrointestinal or neurological chief complaints (CC). METHODS: We collected ED CCs from 2/1/94 – 5/31/02 as a training set. A first-order model was developed for each of seven CC categories by accounting for long-term, day-of-week, and seasonal effects. We assessed predictive performance on subsequent data from 6/1/02 – 5/31/03, compared CC counts to predictions and confidence limits, and identified anomalies (simulated and real). RESULTS: Each CC category exhibited significant day-of-week differences. For most categories, counts peaked on Monday. There were seasonal cycles in both respiratory and undifferentiated infection complaints and the season-to-season variability in peak date was summarized using a hierarchical model. For example, the average peak date for respiratory complaints was January 22, with a season-to-season standard deviation of 12 days. This season-to-season variation makes it challenging to predict respiratory CCs so we focused our effort and discussion on prediction performance for this difficult category. Total ED visits increased over the study period by 4%, but respiratory complaints decreased by roughly 20%, illustrating that long-term averages in the data set need not reflect future behavior in data subsets. CONCLUSION: We found that ED CCs provided timely indicators for outbreaks. Our approach led to successful identification of a respiratory outbreak one-to-two weeks in advance of reports from the state-wide sentinel flu surveillance and of a reported increase in positive laboratory test results

Online detection and quantification of epidemics

<p>Abstract</p> <p>Background</p> <p>Time series data are increasingly available in health care, especially for the purpose of disease surveillance. The analysis of such data has long used periodic regression models to detect outbreaks and estimate epidemic burdens. However, implementation of the method may be difficult due to lack of statistical expertise. No dedicated tool is available to perform and guide analyses.</p> <p>Results</p> <p>We developed an online computer application allowing analysis of epidemiologic time series. The system is available online at <url>http://www.u707.jussieu.fr/periodic_regression/</url>. The data is assumed to consist of a periodic baseline level and irregularly occurring epidemics. The program allows estimating the periodic baseline level and associated upper forecast limit. The latter defines a threshold for epidemic detection. The burden of an epidemic is defined as the cumulated signal in excess of the baseline estimate. The user is guided through the necessary choices for analysis. We illustrate the usage of the online epidemic analysis tool with two examples: the retrospective detection and quantification of excess pneumonia and influenza (P&I) mortality, and the prospective surveillance of gastrointestinal disease (diarrhoea).</p> <p>Conclusion</p> <p>The online application allows easy detection of special events in an epidemiologic time series and quantification of excess mortality/morbidity as a change from baseline. It should be a valuable tool for field and public health practitioners.</p