Estimates of the prevalence of pandemic (H1N1) 2009, United States, April-July 2009

The healthcare seeking rate of individuals with influenza like illness: a meta-analysis

BACKGROUND

Not all individuals with Influenza like illness (ILI) seek healthcare. Knowing the proportion that do is important to evaluate the actual burden and fatality rate of ILI-relevant diseases, such as seasonal influenza and human infection with avian influenza. A number of studies have investigated the healthcare seeking rate, but the results varied from 0.16 to 0.85. We conducted this analysis for better understanding the healthcare seeking rate for ILI, and providing fundamental data for researchers in relevant fields.

METHODS

In this meta-analysis, a total of 799 articles, published as of 13 December 2016, were retrieved from Pubmed, Embase, Web of Science and Cochrane, and 11 of them were included after screening. The pooled estimates and factors which influence healthcare seeking rates were analysed.

RESULTS

The overall pooled healthcare seeking rate was 0.52 (95% CI: 0.46-0.59). The rate was significantly higher during the H1N1 pandemic in 2009 (0.61, 95% CI: 0.51-0.74), in children (0.56, 95% CI: 0.55-0.57) and in patients with documented fever (0.62, 95% CI: 0.53-0.72) than during non-pandemic periods (0.39, 95% CI: 0.33-0.45), in adults (0.45, 95% CI: 0.42-0.48) and in patients without documented fever (0.44, 95% CI: 0.38-0.50). Meta-regression indicated that these three factors could jointly explain 70.1% of the total heterogeneity among published studies.

CONCLUSION

The healthcare seeking rate of ILI patients is needed for estimation of the burden of ILI in the general population based on data from routine ILI sentinel surveillance systems.

Estimated rates of influenza-associated outpatient visits during 2001-2010 in 6 US integrated healthcare delivery organizations

Background

Population‐based estimates of influenza‐associated outpatient visits including both pandemic and interpandemic seasons are uncommon. Comparisons of such estimates with laboratory‐confirmed rates of outpatient influenza are rare.

Objective

To estimate influenza‐associated outpatient visits in 6 US integrated healthcare delivery organizations enrolling ~7.7 million persons.

Methods

Using negative binomial regression methods, we modeled rates of influenza‐associated visits with ICD‐9‐CM‐coded pneumonia or acute respiratory outpatient visits during 2001‐10. These estimated counts were added to visits coded specifically for influenza to derive estimated rates. We compared these rates with those observed in 2 contemporaneous studies recording RT‐PCR‐confirmed influenza outpatient visits.

Results

Outpatient rates estimated with pneumonia visits were 39 (95% confidence interval [CI], 30‐70) and 203 (95% CI, 180‐240) per 10 000 person‐years, respectively, for interpandemic and pandemic seasons. Corresponding rates estimated with respiratory visits were 185 (95% CI, 161‐255) and 542 (95% CI, 441‐823) per 10 000 person‐years. During the pandemic, children aged 2‐17 years had the largest increase in rates (when estimated with pneumonia visits, from 64 [95% CI, 50‐121] to 381 [95% CI, 366‐481]). Rates estimated with pneumonia visits were consistent with rates of RT‐PCR‐confirmed influenza visits during 4 of 5 seasons in 1 comparison study. In another, rates estimated with pneumonia visits during the pandemic for children and adults were consistent in timing, peak, and magnitude.

Conclusions

Estimated rates of influenza‐associated outpatient visits were higher in children than adults during pre‐pandemic and pandemic seasons. Rates estimated with pneumonia visits plus influenza‐coded visits were similar to rates from studies using RT‐PCR‐confirmed influenza.

Annual estimates of the burden of seasonal influenza in the United States: A tool for strengthening influenza surveillance and preparedness

Background

Estimates of influenza disease burden are broadly useful for public health, helping national and local authorities monitor epidemiologic trends, plan and allocate resources, and promote influenza vaccination. Historically, estimates of the burden of seasonal influenza in the United States, focused mainly on influenza‐related mortality and hospitalization, were generated every few years. Since the 2010‐2011 influenza season, annual US influenza burden estimates have been generated and expanded to include estimates of influenza‐related outpatient medical visits and symptomatic illness in the community.

Methods

We used routinely collected surveillance data, outbreak field investigations, and proportions of people seeking health care from survey results to estimate the number of illnesses, medical visits, hospitalizations, and deaths due to influenza during six influenza seasons (2010‐2011 through 2015‐2016).

Results

We estimate that the number of influenza‐related illnesses that have occurred during influenza season has ranged from 9.2 million to 35.6 million, including 140 000 to 710 000 influenza‐related hospitalizations.

Discussion

These annual efforts have strengthened public health communications products and supported timely assessment of the impact of vaccination through estimates of illness and hospitalizations averted. Additionally, annual estimates of influenza burden have highlighted areas where disease surveillance needs improvement to better support public health decision making for seasonal influenza epidemics as well as future pandemics.

The parameter identification problem for SIR epidemic models: identifying unreported cases

A SIR epidemic model is analyzed with respect to identification of its parameters, based upon reported case data from public health sources. The objective of the analysis is to understand the relation of unreported cases to reported cases. In many epidemic diseases the ratio of unreported to reported cases is very high, and of major importance in implementing measures for controlling the epidemic. This ratio can be estimated by the identification of parameters for the model from reported case data. The analysis is applied to three examples: (1) the Hong Kong seasonal influenza epidemic in New York City in 1968-1969, (2) the bubonic plague epidemic in Bombay, India in 1906, and (3) the seasonal influenza epidemic in Puerto Rico in 2016-2017.

National burden of hospitalized and non-hospitalized influenza-associated severe acute respiratory illness in Kenya, 2012-2014

BACKGROUND

Influenza-associated respiratory illness was substantial during the emergence of the 2009 influenza pandemic. Estimates of influenza burden in the post-pandemic period are unavailable to guide Kenyan vaccine policy.

OBJECTIVES

To update estimates of hospitalized and non-hospitalized influenza-associated severe acute respiratory illness (SARI) during a post-pandemic period (2012-2014) and describe the incidence of disease by narrow age categories.

METHODS

We used data from Siaya County Referral Hospital to estimate age-specific base rates of SARI. We extrapolated these base rates to other regions within the country by adjusting for regional risk factors for acute respiratory illness (ARI), regional healthcare utilization for acute respiratory illness, and the proportion of influenza-positive SARI cases in each region, so as to obtain region-specific rates.

RESULTS

The mean annual rate of hospitalized influenza-associated SARI among all ages was 21 (95% CI 19-23) per 100 000 persons. Rates of non-hospitalized influenza-associated SARI were approximately 4 times higher at 82 (95% CI 74-90) per 100 000 persons. Mean annual rates of influenza-associated SARI were highest in children <2 years of age with annual hospitalization rates of 147 (95% CI of 134-160) per 100 000 persons and non-hospitalization rates of 469 (95% CI 426-517) per 100 000 persons. For the period 2012-2014, there were between 8153 and 9751 cases of hospitalized influenza-associated SARI and 31 785-38 546 cases of non-hospitalized influenza-associated SARI per year.

CONCLUSIONS

The highest burden of disease was observed among children <2 years of age. This highlights the need for strategies to prevent influenza infections in this age group.

Do animal exhibitors support and follow recommendations to prevent transmission of variant influenza at agricultural fairs? A survey of animal exhibitor households after a variant influenza virus outbreak in Michigan

Influenza A viruses circulate in swine and can spread rapidly among swine when housed in close proximity, such as at agricultural fairs. Youth who have close and prolonged contact with influenza-infected swine at agricultural fairs may be at increased risk of acquiring influenza virus infection from swine. Animal and human health officials have issued written measures to minimize influenza transmission at agricultural exhibitions; however, there is little information on the knowledge, attitudes, and practice (KAP) of these measures among animal exhibitors. After an August 2016 outbreak of influenza A(H3N2) variant (“H3N2v”) virus infections (i.e., humans infected with swine influenza viruses) in Michigan, we surveyed households of animal exhibitors at eight fairs (including one with known H3N2v infections) to assess their KAP related to variant virus infections and their support for prevention measures. Among 170 households interviewed, most (90%, 151/167) perceived their risk of acquiring influenza from swine to be low or very low. Animal exhibitor households reported high levels of behaviours that put them at increased risk of variant influenza virus infections, including eating or drinking in swine barns (43%, 66/154) and hugging, kissing or snuggling with swine at agricultural fairs (31%, 48/157). Among several recommendations, including limiting the duration of swine exhibits and restricting eating and drinking in the animal barns, the only recommendation supported by a majority of households was the presence of prominent hand-washing stations with a person to monitor hand-washing behaviour (76%, 129/170). This is a unique study of KAP among animal exhibitors and highlights that animal exhibitor households engage in behaviours that could increase their risk of variant virus infections and have low support for currently recommended measures to minimize infection transmission. Further efforts are needed to understand the lack of support for recommended measures and to encourage healthy behaviours at fairs.

Estimating Direct and Indirect Protective Effect of Influenza Vaccination in the United States

With influenza vaccination rates in the United States recently exceeding 45% of the population, it is important to understand the impact that vaccination is having on influenza transmission. In this study, we used a Bayesian modeling approach, combined with a simple dynamical model of influenza transmission, to estimate this impact. The combined framework synthesized evidence from a range of data sources relating to influenza transmission and vaccination in the United States. We found that, for seasonal epidemics, the number of infections averted ranged from 9.6 million in the 2006-2007 season (95% credible interval (CI): 8.7, 10.9) to 37.2 million (95% CI: 34.1, 39.6) in the 2012-2013 season. Expressed in relative terms, the proportion averted ranged from 20.8% (95% CI: 16.8, 24.3) of potential infections in the 2011-2012 season to 47.5% (95% CI: 43.7, 50.8) in the 2008-2009 season. The percentage averted was only 1.04% (95% CI: 0.15, 3.2) for the 2009 H1N1 pandemic, owing to the late timing of the vaccination program in relation to the pandemic in the Northern hemisphere. In the future, further vaccination coverage, as well as improved influenza vaccines (especially those offering better protection in the elderly), could have an even stronger effect on annual influenza epidemics.

Using a community based survey of healthcare seeking behavior to estimate the actual magnitude of influenza among adults in Beijing during 2013-2014 season

Background

Due to a lack of survey of health care seeking behavior for influenza, the actual magnitude of influenza in Beijing of China has not been well described.

Methods

During 2013–2014 influenza season, two cross-sectional household surveys were carried out respectively during the epidemic and non-epidemic season of influenza. A structured survey was undertaken with individuals who were ≥18 years selected by a multistage random sampling method in the study. Health care seeking behaviors were then examined to estimate the actual case number of influenza, using a multiplier model.

Results

A total of 14,665 adults were interviewed. 61.9% of ILI cases consulted a physician. The consultation rate during epidemic period is higher than that during non-epidemic period (67.9% vs. 52.3%). Similarly, the proportion of healthcare usage of general hospital during epidemic period is higher than that was during non-epidemic period (27.1% vs. 19.0%, p = 0.008). Lack of insurance and education reduced healthcare seeking significantly in this study. It was estimated that there were 379,767 (90% CI = [281,934, 526,565]) confirmed cases of influenza amongst adults in Beijing, during 2013–2014 influenza season, with an incidence rate of 2.0%.

Conclusions

The surveillance system for ILI and virological data has the potential to provide baseline case number to estimate the actual annual magnitude of influenza. Given the changes in healthcare seeking behavior over time, sentinel surveillance on healthcare seeking behavior are required to be established for better estimate of the true case number of influenza.

Impact of vaccination on influenza mortality in children <5years old in Mexico

BACKGROUND

Influenza is a leading cause of respiratory tract infections among children. In Mexico, influenza vaccination was included in the National Immunization Program since 2004. However, the population health effects of the vaccine on children have not been fully described. Thus, we estimated the impact of influenza immunization in terms of mortality associated with this virus among children younger than 5years of age in Mexico.

METHODS

Mortality rates and years of life lost associated with influenza were estimated using national mortality register data for the period 1998-2012. Age-stratified and cause-specific mortality rates were estimated for all-cause, respiratory and cardiovascular events. Influenza-associated mortality was compared between the period prior to introduction of the influenza vaccine as part of the National Immunization Program (1998-2004) and the period thereafter (2004-2012).

RESULTS

During the 1998-2012 winter seasons, the average number of all-cause, respiratory and cardiovascular deaths attributable to influenza were 1186, 794 and 21, respectively. Influenza-associated mortality was higher prior to the vaccination period than after influenza was included in the immunization program for all-cause (mean 1660 vs. 780) and respiratory (mean 1063 vs. 563) mortality, but no reduction was seen for cardiovascular mortality. The proportion of all-cause and respiratory deaths attributable to influenza was significantly lower in the post-vaccine period compared with the pre-vaccine period (P<0.001), but no reduction was seen in the proportion of cardiovascular deaths. There was an average annual reduction of 66,558years of life lost in the post-vaccine compared with the pre-vaccine period.

CONCLUSION

The introduction of influenza vaccination within the Mexican Immunization Program was associated with a reduction in mortality rates attributable to this virus among children younger than 5years of age.

Total Economic Consequences of an Influenza Outbreak in the United States

Pandemic influenza represents a serious threat not only to the population of the United States, but also to its economy. In this study, we analyze the total economic consequences of potential influenza outbreaks in the United States for four cases based on the distinctions between disease severity and the presence/absence of vaccinations. The analysis is based on data and parameters on influenza obtained from the Centers for Disease Control and the general literature. A state-of-the-art economic impact modeling approach, computable general equilibrium, is applied to analyze a wide range of potential impacts stemming from the outbreaks. This study examines the economic impacts from changes in medical expenditures and workforce participation, and also takes into consideration different types of avoidance behavior and resilience actions not previously fully studied. Our results indicate that, in the absence of avoidance and resilience effects, a pandemic influenza outbreak could result in a loss in U.S. GDP of $25.4 billion, but that vaccination could reduce the losses to $19.9 billion. When behavioral and resilience factors are taken into account, a pandemic influenza outbreak could result in GDP losses of $45.3 billion without vaccination and $34.4 billion with vaccination. These results indicate the importance of including a broader set of causal factors to achieve more accurate estimates of the total economic impacts of not just pandemic influenza but biothreats in general. The results also highlight a number of actionable items that government policymakers and public health officials can use to help reduce potential economic losses from the outbreaks.

Identifying cost-effective dynamic policies to control epidemics

We describe a mathematical decision model for identifying dynamic health policies for controlling epidemics. These dynamic policies aim to select the best current intervention based on accumulating epidemic data and the availability of resources at each decision point. We propose an algorithm to approximate dynamic policies that optimize the population's net health benefit, a performance measure which accounts for both health and monetary outcomes. We further illustrate how dynamic policies can be defined and optimized for the control of a novel viral pathogen, where a policy maker must decide (i) when to employ or lift a transmission-reducing intervention (e.g. school closure) and (ii) how to prioritize population members for vaccination when a limited quantity of vaccines first become available. Within the context of this application, we demonstrate that dynamic policies can produce higher net health benefit than more commonly described static policies that specify a pre-determined sequence of interventions to employ throughout epidemics. Copyright © 2016 John Wiley & Sons, Ltd.

CDC Grand Rounds: Modeling and Public Health Decision-Making

Mathematical models incorporate various data sources and advanced computational techniques to portray real-world disease transmission and translate the basic science of infectious diseases into decision-support tools for public health. Unlike standard epidemiologic methods that rely on complete data, modeling is needed when there are gaps in data. By combining diverse data sources, models can fill gaps when critical decisions must be made using incomplete or limited information. They can be used to assess the effect and feasibility of different scenarios and provide insight into the emergence, spread, and control of disease. During the past decade, models have been used to predict the likelihood and magnitude of infectious disease outbreaks, inform emergency response activities in real time (1), and develop plans and preparedness strategies for future events, the latter of which proved invaluable during outbreaks such as severe acute respiratory syndrome and pandemic influenza (2-6). Ideally, modeling is a multistep process that involves communication between modelers and decision-makers, allowing them to gain a mutual understanding of the problem to be addressed, the type of estimates that can be reliably generated, and the limitations of the data. As models become more detailed and relevant to real-time threats, the importance of modeling in public health decision-making continues to grow.

Estimated burden of group a streptococcal pharyngitis among children in Beijing, China

BACKGROUND

Burden of Group A streptococcus (GAS) pharyngitis is scarce in developing countries, still unknown in China. The objective of this study was to determine the incidence of clinical cases of pharyngitis and GAS culture-positive pharyngitis, and their outpatient visits among children aged 0-14 years in Beijing, the capital of China.

METHODS

Multiplier model was used to estimate the numbers of pharyngitis cases, based on reported numbers of clinical cases and GAS culture-positive rates from GAS surveillances in Beijing, consultation rate, population coverage of GAS surveillances, sampling success rate, and test sensitivity of GAS culture from previous studies, surveys and surveillances.

RESULTS

An average of 29804.6 (95 % CI: 28333.2-31276.0) clinical cases of pharyngitis per 100,000 person-years occurred among children aged 0-14 years, resulting in correspondingly 19519.0 (95 % CI: 18516.7-20521.2) outpatient visits per 100,000 person-years from 2012 to 2014 in Beijing. On average, there were 2685.1 (95 % CI: 2039.6-3330.6) GAS culture-positive cases of pharyngitis and 1652.7 (95 % CI: 1256.5-2049.0) outpatient visits per 100,000 person-years during the same period. The estimated burden of GAS pharyngitis was significantly higher than that of scarlet fever. Children aged 5-14 years had a higher burden of GAS pharyngitis than those aged 0-4 years.

CONCLUSIONS

The present data suggests that GAS pharyngitis is very common in children in China. Further studies and surveillances are needed to monitor trends and the effectiveness of control measures.

Modeling in Real Time During the Ebola Response

To aid decision-making during CDC's response to the 2014-2016 Ebola virus disease (Ebola) epidemic in West Africa, CDC activated a Modeling Task Force to generate estimates on various topics related to the response in West Africa and the risk for importation of cases into the United States. Analysis of eight Ebola response modeling projects conducted during August 2014-July 2015 provided insight into the types of questions addressed by modeling, the impact of the estimates generated, and the difficulties encountered during the modeling. This time frame was selected to cover the three phases of the West African epidemic curve. Questions posed to the Modeling Task Force changed as the epidemic progressed. Initially, the task force was asked to estimate the number of cases that might occur if no interventions were implemented compared with cases that might occur if interventions were implemented; however, at the peak of the epidemic, the focus shifted to estimating resource needs for Ebola treatment units. Then, as the epidemic decelerated, requests for modeling changed to generating estimates of the potential number of sexually transmitted Ebola cases. Modeling to provide information for decision-making during the CDC Ebola response involved limited data, a short turnaround time, and difficulty communicating the modeling process, including assumptions and interpretation of results. Despite these challenges, modeling yielded estimates and projections that public health officials used to make key decisions regarding response strategy and resources required. The impact of modeling during the Ebola response demonstrates the usefulness of modeling in future responses, particularly in the early stages and when data are scarce. Future modeling can be enhanced by planning ahead for data needs and data sharing, and by open communication among modelers, scientists, and others to ensure that modeling and its limitations are more clearly understood. The activities summarized in this report would not have been possible without collaboration with many U.S. and international partners (http://www.cdc.gov/vhf/ebola/outbreaks/2014-west-africa/partners.html).

Cost-effectiveness of seasonal inactivated influenza vaccination among pregnant women

OBJECTIVE

To evaluate the cost-effectiveness of seasonal inactivated influenza vaccination among pregnant women using data from three recent influenza seasons in the United States.

DESIGN, SETTING, AND PARTICIPANTS

We developed a decision-analytic model following a cohort of 5.2 million pregnant women and their infants aged <6 months to evaluate the cost-effectiveness of vaccinating women against seasonal influenza during pregnancy from a societal perspective. The main outcome measures were quality-adjusted life-year (QALY) gained and cost-effectiveness ratios. Data sources included surveillance data, epidemiological studies, and published vaccine cost data. Sensitivity analyses were also performed. All costs and outcomes were discounted at 3% annually.

MAIN OUTCOME MEASURES

Total costs (direct and indirect), effects (QALY gains, averted case numbers), and incremental cost-effectiveness of seasonal inactivated influenza vaccination among pregnant women (cost per QALY gained).

RESULTS

Using a recent benchmark of 52.2% vaccination coverage among pregnant women, we studied a hypothetical cohort of 2,753,015 vaccinated pregnant women. With an estimated vaccine effectiveness of 73% among pregnant women and 63% among infants <6 months, QALY gains for each season were 305 (2010-2011), 123 (2011-2012), and 610 (2012-2013). Compared with no vaccination, seasonal influenza vaccination during pregnancy was cost-saving when using data from the 2010-2011 and 2012-2013 influenza seasons. The cost-effectiveness ratio was greater than $100,000/QALY with the 2011-2012 influenza season data, when CDC reported a low attack rate compared to other recent seasons.

CONCLUSIONS

Influenza vaccination for pregnant women can reduce morbidity from influenza in both pregnant women and their infants aged <6 months. Seasonal influenza vaccination during pregnancy is cost-saving during moderate to severe influenza seasons.

Bayesian estimation of the dynamics of pandemic (H1N1) 2009 influenza transmission in Queensland: A space-time SIR-based model

BACKGROUND

A pandemic strain of influenza A spread rapidly around the world in 2009, now referred to as pandemic (H1N1) 2009. This study aimed to examine the spatiotemporal variation in the transmission rate of pandemic (H1N1) 2009 associated with changes in local socio-environmental conditions from May 7-December 31, 2009, at a postal area level in Queensland, Australia.

METHOD

We used the data on laboratory-confirmed H1N1 cases to examine the spatiotemporal dynamics of transmission using a flexible Bayesian, space-time, Susceptible-Infected-Recovered (SIR) modelling approach. The model incorporated parameters describing spatiotemporal variation in H1N1 infection and local socio-environmental factors.

RESULTS

The weekly transmission rate of pandemic (H1N1) 2009 was negatively associated with the weekly area-mean maximum temperature at a lag of 1 week (LMXT) (posterior mean: -0.341; 95% credible interval (CI): -0.370--0.311) and the socio-economic index for area (SEIFA) (posterior mean: -0.003; 95% CI: -0.004--0.001), and was positively associated with the product of LMXT and the weekly area-mean vapour pressure at a lag of 1 week (LVAP) (posterior mean: 0.008; 95% CI: 0.007-0.009). There was substantial spatiotemporal variation in transmission rate of pandemic (H1N1) 2009 across Queensland over the epidemic period. High random effects of estimated transmission rates were apparent in remote areas and some postal areas with higher proportion of indigenous populations and smaller overall populations.

CONCLUSIONS

Local SEIFA and local atmospheric conditions were associated with the transmission rate of pandemic (H1N1) 2009. The more populated regions displayed consistent and synchronized epidemics with low average transmission rates. The less populated regions had high average transmission rates with more variations during the H1N1 epidemic period.

Revealing the True Incidence of Pandemic A(H1N1)pdm09 Influenza in Finland during the First Two Seasons - An Analysis Based on a Dynamic Transmission Model

The threat of the new pandemic influenza A(H1N1)pdm09 imposed a heavy burden on the public health system in Finland in 2009-2010. An extensive vaccination campaign was set up in the middle of the first pandemic season. However, the true number of infected individuals remains uncertain as the surveillance missed a large portion of mild infections. We constructed a transmission model to simulate the spread of influenza in the Finnish population. We used the model to analyse the two first years (2009-2011) of A(H1N1)pdm09 in Finland. Using data from the national surveillance of influenza and data on close person-to-person (social) contacts in the population, we estimated that 6% (90% credible interval 5.1 – 6.7%) of the population was infected with A(H1N1)pdm09 in the first pandemic season (2009/2010) and an additional 3% (2.5 – 3.5%) in the second season (2010/2011). Vaccination had a substantial impact in mitigating the second season. The dynamic approach allowed us to discover how the proportion of detected cases changed over the course of the epidemic. The role of time-varying reproduction number, capturing the effects of weather and changes in behaviour, was important in shaping the epidemic.

In 2009, the threat of the new pandemic influenza A(H1N1)pdm09 (referenced in media as ‘swine flu’) created a heavy burden to the public health systems wordwide. In Finland, an extensive vaccination campaign was set up in the middle of the first pandemic season 2009/2010. However, the true number of infected individuals remains uncertain as the surveillance missed a large portion of mild infections. We built a probabilistic model of influenza transmission that accounts for observation bias and the possible impact of the changing weather and population behaviour. We used the model to simulate the spread of influenza in Finland during the two first years (2009-2011) of A(H1N1)pdm09 in Finland. Using data from the national surveillance of influenza and data on social contacts in the population, we estimated that 9% of the population was infected with A(H1N1)pdm09 during the studied period. Vaccination had a substantial impact in mitigating the second season.

Estimated Number of Patients with Influenza A(H1)pdm09, or Other Viral Types, from 2010 to 2014 in Japan

Infectious disease surveillance systems provide information crucial for protecting populations from influenza epidemics. However, few have reported the nationwide number of patients with influenza-like illness (ILI), detailing virological type. Using data from the infectious disease surveillance system in Japan, we estimated the weekly number of ILI cases by virological type, including pandemic influenza (A(H1)pdm09) and seasonal-type influenza (A(H3) and B) over a four-year period (week 36 of 2010 to week 18 of 2014). We used the reported number of influenza cases from nationwide sentinel surveillance and the proportions of virological types from infectious agents surveillance and estimated the number of cases and their 95% confidence intervals. For the 2010/11 season, influenza type A(H1)pdm09 was dominant: 6.48 million (6.33-6.63), followed by types A(H3): 4.05 million (3.90-4.21) and B: 2.84 million (2.71-2.97). In the 2011/12 season, seasonal influenza type A(H3) was dominant: 10.89 million (10.64-11.14), followed by type B: 5.54 million (5.32-5.75). In conclusion, close monitoring of the estimated number of ILI cases by virological type not only highlights the huge impact of previous influenza epidemics in Japan, it may also aid the prediction of future outbreaks, allowing for implementation of control and prevention measures.

Occupational exposures to influenza among healthcare workers in the United States

The objective of this study is to estimate the annual number of occupational exposures to influenza among healthcare workers that result from providing direct and supportive care to influenza patients in acute care, home care and long-term care settings. Literature review was used to identify healthcare utilization for influenza, and worker activity patterns. This information was used, with Monte Carlo simulation, to tabulate the mean annual number of occupational exposures. Given a medium-sized epidemic with a 6% annual symptomatic influenza incidence proportion, the mean number of occupational exposures was estimated to be 81.8 million annually. Among the approximately 14 million healthcare workers, this corresponds to 5.8 exposures per worker annually, on average. Exposures, however, are likely concentrated among subsets of healthcare workers. Occupational exposures were most numerous in ambulatory care settings (38%), followed by long-term care facilities (30%) and home care settings (21%). The annual number of occupational exposures to influenza is high, but not every occupational exposure will result in infection. Some infection control activities, like patient isolation, can reduce the number of occupational exposures.

Comparison of machine learning classifiers for influenza detection from emergency department free-text reports

Influenza is a yearly recurrent disease that has the potential to become a pandemic. An effective biosurveillance system is required for early detection of the disease. In our previous studies, we have shown that electronic Emergency Department (ED) free-text reports can be of value to improve influenza detection in real time. This paper studies seven machine learning (ML) classifiers for influenza detection, compares their diagnostic capabilities against an expert-built influenza Bayesian classifier, and evaluates different ways of handling missing clinical information from the free-text reports. We identified 31,268 ED reports from 4 hospitals between 2008 and 2011 to form two different datasets: training (468 cases, 29,004 controls), and test (176 cases and 1620 controls). We employed Topaz, a natural language processing (NLP) tool, to extract influenza-related findings and to encode them into one of three values: Acute, Non-acute, and Missing. Results show that all ML classifiers had areas under ROCs (AUC) ranging from 0.88 to 0.93, and performed significantly better than the expert-built Bayesian model. Missing clinical information marked as a value of missing (not missing at random) had a consistently improved performance among 3 (out of 4) ML classifiers when it was compared with the configuration of not assigning a value of missing (missing completely at random). The case/control ratios did not affect the classification performance given the large number of training cases. Our study demonstrates ED reports in conjunction with the use of ML and NLP with the handling of missing value information have a great potential for the detection of infectious diseases.

Is population structure sufficient to generate area-level inequalities in influenza rates? An examination using agent-based models

Background

In New Haven County, CT (NHC), influenza hospitalization rates have been shown to increase with census tract poverty in multiple influenza seasons. Though multiple factors have been hypothesized to cause these inequalities, including population structure, differential vaccine uptake, and differential access to healthcare, the impact of each in generating observed inequalities remains unknown. We can design interventions targeting factors with the greatest explanatory power if we quantify the proportion of observed inequalities that hypothesized factors are able to generate. Here, we ask if population structure is sufficient to generate the observed area-level inequalities in NHC. To our knowledge, this is the first use of simulation models to examine the causes of differential poverty-related influenza rates.

Methods

Using agent-based models with a census-informed, realistic representation of household size, age-structure, population density in NHC census tracts, and contact rates in workplaces, schools, households, and neighborhoods, we measured poverty-related differential influenza attack rates over the course of an epidemic with a 23 % overall clinical attack rate. We examined the role of asthma prevalence rates as well as individual contact rates and infection susceptibility in generating observed area-level influenza inequalities.

Results

Simulated attack rates (AR) among adults increased with census tract poverty level (F = 30.5; P < 0.001) in an epidemic caused by a virus similar to A (H1N1) pdm09. We detected a steeper, earlier influenza rate increase in high-poverty census tracts—a finding that we corroborate with a temporal analysis of NHC surveillance data during the 2009 H1N1 pandemic. The ratio of the simulated adult AR in the highest- to lowest-poverty tracts was 33 % of the ratio observed in surveillance data. Increasing individual contact rates in the neighborhood did not increase simulated area-level inequalities. When we modified individual susceptibility such that it was inversely proportional to household income, inequalities in AR between high- and low-poverty census tracts were comparable to those observed in reality.

Discussion

To our knowledge, this is the first study to use simulations to probe the causes of observed inequalities in influenza disease patterns. Knowledge of the causes and their relative explanatory power will allow us to design interventions that have the greatest impact on reducing inequalities.

Conclusion

Differential exposure due to population structure in our realistic simulation model explains a third of the observed inequality. Differential susceptibility to disease due to prevailing chronic conditions, vaccine uptake, and smoking should be considered in future models in order to quantify the role of additional factors in generating influenza inequalities.

Electronic supplementary material

The online version of this article (doi:10.1186/s12889-015-2284-2) contains supplementary material, which is available to authorized users.

Early Characterization of the Severity and Transmissibility of Pandemic Influenza Using Clinical Episode Data from Multiple Populations

The potential rapid availability of large-scale clinical episode data during the next influenza pandemic suggests an opportunity for increasing the speed with which novel respiratory pathogens can be characterized. Key intervention decisions will be determined by both the transmissibility of the novel strain (measured by the basic reproductive number R0) and its individual-level severity. The 2009 pandemic illustrated that estimating individual-level severity, as described by the proportion pC of infections that result in clinical cases, can remain uncertain for a prolonged period of time. Here, we use 50 distinct US military populations during 2009 as a retrospective cohort to test the hypothesis that real-time encounter data combined with disease dynamic models can be used to bridge this uncertainty gap. Effectively, we estimated the total number of infections in multiple early-affected communities using the model and divided that number by the known number of clinical cases. Joint estimates of severity and transmissibility clustered within a relatively small region of parameter space, with 40 of the 50 populations bounded by: pC, 0.0133-0.150 and R0, 1.09-2.16. These fits were obtained despite widely varying incidence profiles: some with spring waves, some with fall waves and some with both. To illustrate the benefit of specific pairing of rapidly available data and infectious disease models, we simulated a future moderate pandemic strain with pC approximately ×10 that of 2009; the results demonstrating that even before the peak had passed in the first affected population, R0 and pC could be well estimated. This study provides a clear reference in this two-dimensional space against which future novel respiratory pathogens can be rapidly assessed and compared with previous pandemics.

Incidence of medically attended influenza infection and cases averted by vaccination, 2011/2012 and 2012/2013 influenza seasons

BACKGROUND

We estimated the burden of outpatient influenza and cases prevented by vaccination during the 2011/2012 and 2012/2013 influenza seasons using data from the United States Influenza Vaccine Effectiveness (US Flu VE) Network.

METHODS

We defined source populations of persons who could seek care for acute respiratory illness (ARI) at each of the five US Flu VE Network sites. We identified all members of the source population who were tested for influenza during US Flu VE influenza surveillance. Each influenza-positive subject received a sampling weight based on the proportion of source population members who were tested for influenza, stratified by site, age, and other factors. We used the sampling weights to estimate the cumulative incidence of medically attended influenza in the source populations. We estimated cases averted by vaccination using estimates of cumulative incidence, vaccine coverage, and vaccine effectiveness.

RESULTS

Cumulative incidence of medically attended influenza ranged from 0.8% to 2.8% across sites during 2011/2012 and from 2.6% to 6.5% during the 2012/2013 season. Stratified by age, incidence ranged from 1.2% among adults 50 years of age and older in 2011/2012 to 10.9% among children 6 months to 8 years of age in 2012/2013. Cases averted by vaccination ranged from 4 to 41 per 1000 vaccinees, depending on the study site and year.

CONCLUSIONS

The incidence of medically attended influenza varies greatly by year and even by geographic region within the same year. The number of cases averted by vaccination varies greatly based on overall incidence and on vaccine coverage.

Net Costs Due to Seasonal Influenza Vaccination--United States, 2005-2009

Background

Seasonal influenza causes considerable morbidity and mortality across all age groups, and influenza vaccination was recommended in 2010 for all persons aged 6 months and above. We estimated the averted costs due to influenza vaccination, taking into account the seasonal economic burden of the disease.

Methods

We used recently published values for averted outcomes due to influenza vaccination for influenza seasons 2005-06, 2006-07, 2007-08, and 2008-09, and age cohorts 6 months-4 years, 5-19 years, 20-64 years, and 65 years and above. Costs were calculated according to a payer and societal perspective (in 2009 US$), and took into account medical costs and productivity losses.

Results

When taking into account direct medical costs (payer perspective), influenza vaccination was cost saving only for the older age group (65≥) in seasons 2005-06 and 2007-08. Using the same perspective, influenza vaccination resulted in total costs of $US 1.7 billion (95%CI: $US 0.3–4.0 billion) in 2006-07 and $US 1.8 billion (95%CI: $US 0.1–4.1 billion) in 2008-09. When taking into account a societal perspective (and including the averted lost earnings due to premature death) averted deaths in the older age group influenced the results, resulting in cost savings for all ages combined in season 07-08.

Discussion

Influenza vaccination was cost saving in the older age group (65≥) when taking into account productivity losses and, in some seasons, when taking into account medical costs only. Averted costs vary significantly per season; however, in seasons where the averted burden of deaths is high in the older age group, averted productivity losses due to premature death tilt overall seasonal results towards savings. Indirect vaccination effects and the possibility of diminished case severity due to influenza vaccination were not considered, thus the averted burden due to influenza vaccine may be even greater than reported.

Using Twitter to Measure Public Discussion of Diseases: A Case Study

Background

Twitter is increasingly used to estimate disease prevalence, but such measurements can be biased, due to both biased sampling and inherent ambiguity of natural language.

Objective

We characterized the extent of these biases and how they vary with disease.

Methods

We correlated self-reported prevalence rates for 22 diseases from Experian’s Simmons National Consumer Study (n=12,305) with the number of times these diseases were mentioned on Twitter during the same period (2012). We also identified and corrected for two types of bias present in Twitter data: (1) demographic variance between US Twitter users and the general US population; and (2) natural language ambiguity, which creates the possibility that mention of a disease name may not actually refer to the disease (eg, “heart attack” on Twitter often does not refer to myocardial infarction). We measured the correlation between disease prevalence and Twitter disease mentions both with and without bias correction. This allowed us to quantify each disease’s overrepresentation or underrepresentation on Twitter, relative to its prevalence.

Results

Our sample included 80,680,449 tweets. Adjusting disease prevalence to correct for Twitter demographics more than doubles the correlation between Twitter disease mentions and disease prevalence in the general population (from .113 to .258, P <.001). In addition, diseases varied widely in how often mentions of their names on Twitter actually referred to the diseases, from 14.89% (3827/25,704) of instances (for stroke) to 99.92% (5044/5048) of instances (for arthritis). Applying ambiguity correction to our Twitter corpus achieves a correlation between disease mentions and prevalence of .208 ( P <.001). Simultaneously applying correction for both demographics and ambiguity more than triples the baseline correlation to .366 ( P <.001). Compared with prevalence rates, cancer appeared most overrepresented in Twitter, whereas high cholesterol appeared most underrepresented.

Conclusions

Twitter is a potentially useful tool to measure public interest in and concerns about different diseases, but when comparing diseases, improvements can be made by adjusting for population demographics and word ambiguity.

Investigation of an Outbreak of Variant Influenza A(H3N2) Virus Infection Associated With an Agricultural Fair-Ohio, August 2012

BACKGROUND

In 2012, one third of cases in a multistate outbreak of variant influenza A(H3N2) virus ([H3N2]v) infection occurred in Ohio. We conducted an investigation of (H3N2)v cases associated with agricultural Fair A in Ohio.

METHODS

We surveyed Fair A swine exhibitors and their household members. Confirmed cases had influenza-like illness (ILI) and a positive laboratory test for (H3N2)v, and probable cases had ILI. We calculated attack rates. We determined risk factors for infection, using multivariable log-binomial regression.

RESULTS

We identified 20 confirmed and 94 probable cases associated with Fair A. Among 114 cases, the median age was 10 years, there were no hospitalizations or deaths, and 82% had swine exposure. In the exhibitor household cohort of 359 persons (83 households), we identified 6 confirmed cases (2%) and 40 probable cases (11%). An age of <10 years was a significant risk factor (P < .01) for illness. One instance of likely human-to-human transmission was identified.

CONCLUSIONS

In this (H3N2)v outbreak, no evidence of sustained human-to-human (H3N2)v transmission was found. Our risk factor analysis contributed to the development of the recommendation that people at increased risk of influenza-associated complications, including children aged <5 years, avoid swine barns at fairs during the 2012 fair season.

[Importance of health CRM in pandemics and health alerts]

OBJECTIVE

The aim of this article is to demonstrate the importance of the role a health CRM can play in a pandemic or health alert. During the influenza-A pandemic, Salud Responde played a very important role. Its main objective was to establish protocols and citizens advice lines that would avoid patients with mild influenza-A symptoms going to health centre.

DESIGN

A triage system was developed around the Siebel CRM (software tool) to achieve this objective. This allowed the Salud Responde staff to establish the severity of the patient depending on the symptoms and the risk factors of the patient, as well as being able to inform, give health advice or refer the patient to medical centres if necessary.

SETTING

All patients (a total of 56,497) who were attended by Salud Responde within its influenza-A service portfolio have been included.

PARTICIPANTS

Patients who were attended by Salud Responde.

MAIN MEASUREMENTS

The data have been extracted from the Salud Responde data base.

RESULTS

Salud Responde attended to 56,497 patients during the influenza-A pandemic, of whom 48,287 patients did not require health care.

CONCLUSIONS

Salud Responde attended to 56,497 patients, of whom 48,287 patients did not require health care. Apart from any financial savings that this could entail, it contributed to minimising the pandemic, avoiding the patient having to go to a health centre to receive medical care or information, and prevented, to a great extent, the flooding of casualty departments.

Estimating influenza disease burden from population-based surveillance data in the United States

Annual estimates of the influenza disease burden provide information to evaluate programs and allocate resources. We used a multiplier method with routine population-based surveillance data on influenza hospitalization in the United States to correct for under-reporting and estimate the burden of influenza for seasons after the 2009 pandemic. Five sites of the Influenza Hospitalization Surveillance Network (FluSurv-NET) collected data on the frequency and sensitivity of influenza testing during two seasons to estimate under-detection. Population-based rates of influenza-associated hospitalization and Intensive Care Unit admission from 2010-2013 were extrapolated to the U.S. population from FluSurv-NET and corrected for under-detection. Influenza deaths were calculated using a ratio of deaths to hospitalizations. We estimated that influenza-related hospitalizations were under-detected during 2010-11 by a factor of 2.1 (95%CI 1.7-2.9) for age < 18 years, 3.1 (2.4-4.5) for ages 18-64 years, and 5.2 (95%CI 3.8-8.3) for age 65+. Results were similar in 2011-12. Extrapolated estimates for 3 seasons from 2010-2013 included: 114,192-624,435 hospitalizations, 18,491-95,390 ICU admissions, and 4,915-27,174 deaths per year; 54-70% of hospitalizations and 71-85% of deaths occurred among adults aged 65+. Influenza causes a substantial disease burden in the U.S. that varies by age and season. Periodic estimation of multipliers across multiple sites and age groups improves our understanding of influenza detection in sentinel surveillance systems. Adjusting surveillance data using a multiplier method is a relatively simple means to estimate the impact of influenza and the subsequent value of interventions to prevent influenza.

Accounting for behavioral responses during a flu epidemic using home television viewing

BACKGROUND

Theory suggests that individual behavioral responses impact the spread of flu-like illnesses, but this has been difficult to empirically characterize. Social distancing is an important component of behavioral response, though analyses have been limited by a lack of behavioral data. Our objective is to use media data to characterize social distancing behavior in order to empirically inform explanatory and predictive epidemiological models.

METHODS

We use data on variation in home television viewing as a proxy for variation in time spent in the home and, by extension, contact. This behavioral proxy is imperfect but appealing since information on a rich and representative sample is collected using consistent techniques across time and most major cities. We study the April-May 2009 outbreak of A/H1N1 in Central Mexico and examine the dynamic behavioral response in aggregate and contrast the observed patterns of various demographic subgroups. We develop and calibrate a dynamic behavioral model of disease transmission informed by the proxy data on daily variation in contact rates and compare it to a standard (non-adaptive) model and a fixed effects model that crudely captures behavior.

RESULTS

We find that after a demonstrable initial behavioral response (consistent with social distancing) at the onset of the outbreak, there was attenuation in the response before the conclusion of the public health intervention. We find substantial differences in the behavioral response across age subgroups and socioeconomic levels. We also find that the dynamic behavioral and fixed effects transmission models better account for variation in new confirmed cases, generate more stable estimates of the baseline rate of transmission over time and predict the number of new cases over a short horizon with substantially less error.

CONCLUSIONS

Results suggest that A/H1N1 had an innate transmission potential greater than previously thought but this was masked by behavioral responses. Observed differences in behavioral response across demographic groups indicate a potential benefit from targeting social distancing outreach efforts.

The burden of influenza-associated critical illness hospitalizations

OBJECTIVE

Influenza is the most common vaccine-preventable disease in the United States; however, little is known about the burden of critical illness due to influenza virus infection. Our primary objective was to estimate the proportion of all critical illness hospitalizations that are attributable to seasonal influenza.

DESIGN

Retrospective cohort study.

SETTING

Arizona, California, and Washington from January 2003 to March 2009.

PATIENTS

All adults hospitalized with critical illness, defined by International Classification of Diseases, 9th Edition, Clinical Modification diagnosis and procedure codes for acute respiratory failure, severe sepsis, or in-hospital death.

MEASUREMENTS AND MAIN RESULTS

We combined the complete hospitalization discharge databases for three U.S. states, regional influenza virus surveillance, and state census data. Using negative binomial regression models, we estimated the incidence rates of adult influenza-associated critical illness hospitalizations and compared them with all-cause event rates. We also compared modeled outcomes to International Classification of Diseases, 9th Edition, Clinical Modification-coded influenza hospitalizations to assess potential underrecognition of severe influenza disease. During the study period, we estimated that 26,760 influenza-associated critical illness hospitalizations (95% CI, 14,541, 47,464) occurred. The population-based incidence estimate for influenza-associated critical illness was 12.0 per 100,000 person-years (95% CI, 6.6, 21.6) or 1.3% of all critical illness hospitalizations (95% CI, 0.7%, 2.3%). During the influenza season, 3.4% of all critical illness hospitalizations (95% CI, 1.9%, 5.8%) were attributable to influenza. There were only 2,612 critical illness hospitalizations with International Classification of Diseases, 9th Edition, Clinical Modification-coded influenza diagnoses, suggesting influenza is either undiagnosed or undercoded in a substantial proportion of critical illness.

CONCLUSIONS

Extrapolating our data to the 2010 U.S. population, we estimate that about 28,000 adults are hospitalized for influenza-associated critical illness annually. Influenza in many of these critically ill patients may be undiagnosed. Critical care physicians should have a high index of suspicion for influenza in the ICU, particularly when influenza is known to be circulating in their communities.

On the uniqueness of epidemic models fitting a normalized curve of removed individuals

The susceptible-infected-removed (SIR) and the susceptible-exposed-infected-removed (SEIR) epidemic models with constant parameters are adequate for describing the time evolution of seasonal diseases for which available data usually consist of fatality reports. The problems associated with the determination of system parameters starts with the inference of the number of removed individuals from fatality data, because the infection to death period may depend on health care factors. Then, one encounters numerical sensitivity problems for the determination of the system parameters from a correct but noisy representative of the number of removed individuals. Finally as the available data is necessarily a normalized one, the models fitting this data may not be unique. We prove that the parameters of the (SEIR) model cannot be determined from the knowledge of a normalized curve of "Removed" individuals and we show that the proportion of removed individuals, [Formula: see text], is invariant under the interchange of the incubation and infection periods and corresponding scalings of the contact rate. On the other hand we prove that the SIR model fitting a normalized curve of removed individuals is unique and we give an implicit relation for the system parameters in terms of the values of [Formula: see text] and [Formula: see text], where [Formula: see text] is the steady state value of [Formula: see text] and [Formula: see text] and [Formula: see text] are the values of [Formula: see text] and its derivative at the inflection point [Formula: see text] of [Formula: see text]. We use these implicit relations to provide a robust method for the estimation of the system parameters and we apply this procedure to the fatality data for the H1N1 epidemic in the Czech Republic during 2009. We finally discuss the inference of the number of removed individuals from observational data, using a clinical survey conducted at major hospitals in Istanbul, Turkey, during 2009 H1N1 epidemic.

Estimation of force of infection based on different epidemiological proxies: 2009/2010 Influenza epidemic in Malta

Information about infectious disease outbreaks is often gathered indirectly, from doctor's reports and health board records. It also typically underestimates the actual number of cases, but the relationship between the observed proxies and the numbers that drive the diseases is complicated, nonlinear and potentially time- and state-dependent. We use a combination of data collection from the 2009-2010 H1N1 outbreak in Malta, compartmental modelling and Bayesian inference to explore the effect of using various sources of information (consultations, doctor's diagnose, swabbing and molecular testing) on estimation of the effective basic reproduction ratio, R(t). Different proxies and different sampling rates (daily and weekly) lead to similar behaviour of R(t) as the epidemic unfolds, although individual parameters (force of infection, length of latent and infectious period) vary. We also demonstrate that the relationship between different proxies varies as epidemic progresses, with the first period characterised by high ratio of consultations and influenza diagnoses to actual confirmed cases of H1N1. This has important consequences for modelling that is based on reconstructing influenza cases from doctor's reports.

Pneumonia and influenza, and respiratory and circulatory hospital admissions in Belgium: a retrospective database study

Background

Influenza infections can lead to viral pneumonia, upper respiratory tract infection or facilitate co-infection by other pathogens. Influenza is associated with the exacerbation of chronic conditions like diabetes and cardiovascular disease and consequently, these result in acute hospitalizations. This study estimated the number, proportions and costs from a payer perspective of hospital admissions related to severe acute respiratory infections.

Methods

We analyzed retrospectively, a database of all acute inpatient stays from a non-random sample of eleven hospitals using the Belgian Minimal Hospital Summary Data. Codes from the International Classification of Diseases, Ninth Revision, Clinical Modification was used to identify and diagnose cases of pneumonia and influenza (PI), respiratory and circulatory (RC), and the related complications.

Results

During 2002–2007, we estimated relative hospital admission rates of 1.69% (20960/1237517) and 21.79% (269634/1237517) due to primary PI and RC, respectively. The highest numbers of hospital admissions with primary diagnosis as PI were reported for the elderly patient group (n = 10184) followed by for children below five years of age (n = 3451).

Of the total primary PI and RC hospital admissions, 56.14% (11768/20960) and 63.48% (171172/269634) of cases had at least one possible influenza-related complication with the highest incidence of complications reported for the elderly patient group. Overall mortality rate in patients with PI and RC were 9.25% (1938/20960) and 5.51% (14859/269634), respectively. Average lengths of hospital stay for PI was 11.6 ± 12.3 days whereas for RC it was 9.1 ± 12.7 days. Annual average costs were 20.2 and 274.6 million Euros for PI and RC hospitalizations. Average cost per hospitalization for PI and RC were 5779 and 6111 Euros (2007), respectively. These costs increased with the presence of complications (PI: 7159, RC: 7549 Euros).

Conclusion

The clinical and economic burden of primary influenza hospitalizations in Belgium is substantial. The elderly patient group together with children aged <18 years were attributed with the majority of all primary PI and RC hospitalizations.

Trial registration

Not applicable.

Estimating the number of hand, foot and mouth disease amongst children aged under-five in Beijing during 2012, based on a telephone survey of healthcare seeking behavior

Background

Over the last decade, increases in the number of outbreaks of hand, foot and mouth disease (HFMD) have shifted the disease into the public health spotlight in China. Children under the age of five years are particularly susceptible, with fatalities recorded. However, estimating the burden of HFMD has been difficult to conduct to date.

Methods

In 2012, a cross-sectional survey of healthcare-seeking behaviour for HFMD was undertaken, using computer assisted telephone interviewing (CATI) technology. Sample of telephone numbers was obtained from the Beijing Immunization Information System. Respondents were parents or guardians of children under the age of five. Multiplier model was used to estimate the number of HFMD case, following the telephone survey of healthcare-seeking behavior. The number of laboratory-confirmed cases was also estimated based on the monthly positive rate of each subtype of virus causing HFMD. The age-specific case fatality rate (CFR) was calculated based on the ratio of reported deaths to the estimated number of cases.

Results

For children under five, the consultation rate of parent-defined cases was estimated at 77.8% ((95% CI = [75.2, 80.4]). Parents or legal guardians of children aged between two and four years were more likely to seek healthcare than those of children aged less than two years. For children under the age of five, we estimated that there were 40,165 (95% CI = [38,471, 41,974]) HFMD cases, with an incidence rate of 5.6%, and 22,166 (95% CI = [21,150, 23,295]) laboratory-confirmed cases in Beijing during 2012. The overall CFR was estimated at 10 deaths per 100,000 cases, while for children aged less than two years it was 15.6 deaths per 100,000 cases.

Conclusions

Given the public health impact of HFMD in China, control measures need to be prioritized for children < 2 years, due to the higher CFR in this age group. Sentinel surveillance approaches could be used to monitor trends and the impact of control measures.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2334-14-437) contains supplementary material, which is available to authorized users.

Estimated paediatric mortality associated with influenza virus infections, United States, 2003–2010

Death certificate reports and laboratory-confirmed influenza deaths probably underestimate paediatric deaths attributable to influenza. Using US mortality data for persons aged <18 years who died during 28 September 2003 to 2 October 2010, we estimated influenza-attributable deaths using a generalized linear regression model based on seasonal covariates, influenza-certified deaths (deaths for which influenza was a reported cause of death), and occurrence during the 2009 pandemic period. Of 32 783 paediatric deaths in the death categories examined, 853 (3%) were influenza-certified. The estimated number of influenza-attributable deaths over the study period was 1·8 [95% confidence interval (CI) 1·3–2·8] times higher than the number of influenza-certified deaths. Influenza-attributable deaths were 2·1 (95% CI 1·5–3·4) times higher than influenza-certified deaths during the non-pandemic period and 1·1 (95% CI 1·0–1·8) times higher during the pandemic. Overall, US paediatric deaths attributable to influenza were almost twice the number reported by death certificate codes in the seasons prior to the 2009 pandemic.

Disparities among 2009 pandemic influenza A (H1N1) hospital admissions: a mixed methods analysis--Illinois, April-December 2009

During late April 2009, the first cases of 2009 pandemic influenza A (H1N1) (pH1N1) in Illinois were reported. On-going, sustained local transmission resulted in an estimated 500,000 infected persons. We conducted a mixed method analysis using both quantitative (surveillance) and qualitative (interview) data; surveillance data was used to analyze demographic distribution of hospitalized cases and follow-up interview data was used to assess health seeking behavior. Invitations to participate in a telephone interview were sent to 120 randomly selected Illinois residents that were hospitalized during April-December 2009. During April-December 2009, 2,824 pH1N1 hospitalizations occurred in Illinois hospitals; median age (interquartile range) at admission was 24 (range: 6-49) years. Hospitalization rates/100,000 persons for blacks and Hispanics, regardless of age or sex were 2-3 times greater than for whites (blacks, 36/100,000 (95% Confidence Interval ([95% CI], 33-39)); Hispanics, 35/100,000 [95%CI,32-37] (; whites, 13/100,000[95%CI, 12-14); p<0.001). Mortality rates were higher for blacks (0.9/100,000; p<0.09) and Hispanics (1/100,000; p<0.04) when compared with the mortality rates for whites (0.6/100,000). Of 33 interview respondents, 31 (94%) stated that they had heard of pH1N1 before being hospitalized, and 24 (73%) did not believed they were at risk for pH1N1. On average, respondents reported experiencing symptoms for 2 days (range: 1-7) before seeking medical care. When asked how to prevent pH1N1 infection in the future, the most common responses were getting vaccinated and practicing hand hygiene. Blacks and Hispanics in Illinois experienced disproportionate pH1N1 hospitalization and mortality rates. Public health education and outreach efforts in preparation for future influenza pandemics should include prevention messaging focused on perception of risk, and ensure community wide access to prevention messages and practices.

Clinical care for severe influenza and other severe illness in resource-limited settings: the need for evidence and guidelines

The 2009 influenza A (H1N1) pandemic highlighted the importance of quality hospital care of the severely ill, yet there is evidence that the impact of the 2009 pandemic was highest in low‐ and middle‐income countries with fewer resources. Recent data indicate that death and suffering from seasonal influenza and severe illness in general are increased in resource‐limited settings. However, there are limited clinical data and guidelines for the management of influenza and other severe illness in these settings. Life‐saving supportive care through syndromic case management is used successfully in high‐resource intensive care units and in global programs such as the Integrated Management of Childhood Illness (IMCI). While there are a variety of challenges to the management of the severely ill in resource‐limited settings, several new international initiatives have begun to develop syndromic management strategies for these environments, including the World Health Organization's Integrated Management of Adult and Adolescent Illness Program. These standardized clinical guidelines emphasize syndromic case management and do not require high‐resource intensive care units. These efforts must be enhanced by quality clinical research to provide missing evidence and to refine recommendations, which must be carefully integrated into existing healthcare systems. Realizing a sustainable, global impact on death and suffering due to severe influenza and other severe illness necessitates an ongoing and concerted international effort to iteratively generate, implement, and evaluate best‐practice management guidelines for use in resource‐limited settings.

Impact of H1N1 on socially disadvantaged populations: summary of a systematic review

BACKGROUND

Previous reviews found that the H1N1 pandemic was associated with a large proportion of hospitalizations, severe illness, workplace absenteeism, and high costs. However, the burden among socially disadvantaged groups of the population is unclear. This is a summary of a previously published systematic review commissioned by the World Health Organization on the burden of H1N1 pandemic (influenza A/Mexico/2009 (H1N1)) among socially disadvantaged populations.

METHODS

MEDLINE and EMBASE were searched to identify studies reporting hospitalization, severe illness, and mortality attributable to the 2009 H1N1 pandemic among socially disadvantaged populations, including ethnic minorities and low-income or lower-middle-income economy countries (LIC/LMIC). SAS and Review Manager were used to conduct random effects meta-analysis.

RESULTS

Forty-eight cohort studies and 14 companion reports including 44 777 patients were included after screening 787 citations and 164 full-text articles. Twelve of the included studies provided data on LIC/LMIC, including one study from Guatemala, two from Morocco, one from Pakistan, and eight from India, plus four companion reports. The rest provided data on ethnic minorities living in high-income economy countries (HIC). Significantly more hospitalizations were observed among ethnic minorities versus nonethnic minorities in two North American studies [1313 patients, odds ratio (OR) 2·26 (95% confidence interval: 1·53-3·32)]. Among hospitalized patients in HIC, statistically significant differences in intensive care unit admissions (n = 8 studies, 15 352 patients, OR 0·84 [0·69-1·02]) and deaths (n = 6 studies, 14 757 patients, OR 0·85 [95% CI: 0·73-1·01]) were not observed.

CONCLUSION

We found significantly more hospitalizations among ethnic minorities versus nonethnic minorities in North America, yet no differences in intensive care unit admissions or deaths among H1N1-infected hospitalized patients were observed in North America and Australia. Our results suggest a similar burden of H1N1 between ethnic minorities and nonethnic minorities living in HIC.

Working-age adults with diabetes experience greater susceptibility to seasonal influenza: a population-based cohort study

AIMS/HYPOTHESIS

The aim of this work was to compare the incidence of illness attributable to influenza in working-age adults (age <65 years) with and without diabetes.

METHODS

We performed a cohort study using administrative data from Manitoba, Canada, between 2000 and 2008. All working-age adults with diabetes were identified and matched with up to two non-diabetic controls. We analysed the rates of influenza-like illness physician visits and hospitalisations, pneumonia and influenza hospitalisations, and all-cause hospitalisations. Multivariable regressions were used to estimate the influenza-attributable rate of each outcome.

RESULTS

We included 745,777 person-years of follow-up among 166,715 subjects. The median age was 50-51 years and 48-49% were women; adults with diabetes had more comorbidities and were more likely to be vaccinated for influenza than those without diabetes. Compared with similar adults without diabetes, those with diabetes had a 6% greater (RR 1.06, 95% CI 1.02, 1.10; absolute risk difference 6 per 1,000 adults per year) increase in all-cause hospitalisations associated with influenza, representing a total of 54 additional hospitalisations. There were no differences in the influenza-attributable rates of influenza-like illness (p = 0.06) or pneumonia and influenza (p = 0.11).

CONCLUSIONS/INTERPRETATION

Guidelines calling for influenza vaccinations in diabetic, in addition to elderly, adults implicitly single out working-age adults with diabetes. The evidence supporting such guidelines has hitherto been scant. We found that working-age adults with diabetes appear more susceptible to serious influenza-attributable illness. These findings represent the strongest available evidence for targeting diabetes as an indication for influenza vaccination, irrespective of age.

Determining the dynamics of influenza transmission by age

Background

It is widely accepted that influenza transmission dynamics vary by age; however methods to quantify the reproductive number by age group are limited. We introduce a simple method to estimate the reproductive number by modifying the method originally proposed by Wallinga and Teunis and using existing information on contact patterns between age groups. We additionally perform a sensitivity analysis to determine the potential impact of differential healthcare seeking patterns by age. We illustrate this method using data from the 2009 H1N1 Influenza pandemic in Gauteng Province, South Africa.

Results

Our results are consistent with others in showing decreased transmission with age. We show that results can change markedly when we make the account for differential healthcare seeking behaviors by age.

Conclusions

We show substantial heterogeneity in transmission by age group during the Influenza A H1N1 pandemic in South Africa. This information can greatly assist in targeting interventions and implementing social distancing measures.

Preexisting CD4+ T-cell immunity in human population to avian influenza H7N9 virus: whole proteome-wide immunoinformatics analyses

In 2013, a novel avian influenza H7N9 virus was identified in human in China. The antigenically distinct H7N9 surface glycoproteins raised concerns about lack of cross-protective neutralizing antibodies. Epitope-specific preexisting T-cell immunity was one of the protective mechanisms in pandemic 2009 H1N1 even in the absence of cross-protective antibodies. Hence, the assessment of preexisting CD4+ T-cell immunity to conserved epitopes shared between H7N9 and human influenza A viruses (IAV) is critical. A comparative whole proteome-wide immunoinformatics analysis was performed to predict the CD4+ T-cell epitopes that are commonly conserved within the proteome of H7N9 in reference to IAV subtypes (H1N1, H2N2, and H3N2). The CD4+ T-cell epitopes that are commonly conserved (∼556) were further screened against the Immune Epitope Database (IEDB) to validate their immunogenic potential. This analysis revealed that 45.5% (253 of 556) epitopes are experimentally proven to induce CD4+ T-cell memory responses. In addition, we also found that 23.3% of CD4+ T-cell epitopes have ≥90% of sequence homology with experimentally defined CD8+ T-cell epitopes. We also conducted the population coverage analysis across different ethnicities using commonly conserved CD4+ T-cell epitopes and corresponding HLA-DRB1 alleles. Interestingly, the indigenous populations from Canada, United States, Mexico and Australia exhibited low coverage (28.65% to 45.62%) when compared with other ethnicities (57.77% to 94.84%). In summary, the present analysis demonstrate an evidence on the likely presence of preexisting T-cell immunity in human population and also shed light to understand the potential risk of H7N9 virus among indigenous populations, given their high susceptibility during previous pandemic influenza events. This information is crucial for public health policy, in targeting priority groups for immunization programs.

Estimating acute viral hepatitis infections from nationally reported cases

OBJECTIVES

Because only a fraction of patients with acute viral hepatitis A, B, and C are reported through national surveillance to the Centers for Disease Control and Prevention, we estimated the true numbers.

METHODS

We applied a simple probabilistic model to estimate the fraction of patients with acute hepatitis A, hepatitis B, and hepatitis C who would have been symptomatic, would have sought health care tests, and would have been reported to health officials in 2011.

RESULTS

For hepatitis A, the frequencies of symptoms (85%), care seeking (88%), and reporting (69%) yielded an estimate of 2730 infections (2.0 infections per reported case). For hepatitis B, the frequencies of symptoms (39%), care seeking (88%), and reporting (45%) indicated 18 730 infections (6.5 infections per reported case). For hepatitis C, the frequency of symptoms among injection drug users (13%) and those infected otherwise (48%), proportion seeking care (88%), and percentage reported (53%) indicated 17 100 infections (12.3 infections per reported case).

CONCLUSIONS

These adjustment factors will allow state and local health authorities to estimate acute hepatitis infections locally and plan prevention activities accordingly.

Influenza hospitalizations among american indian/alaska native people and in the United States general population

BACKGROUND

Historically, American Indian/Alaska Native (AI/AN) people have experienced a disproportionate burden of infectious disease morbidity compared with the general US population. We evaluated whether a disparity in influenza hospitalizations exists between AI/AN people and the general US population.

METHODS

We used Indian Health Service hospital discharge data (2001-2011) for AI/AN people and 13 State Inpatient Databases (2001-2008) to provide a comparison to the US population. Hospitalization rates were calculated by respiratory year (July-June). Influenza-specific hospitalizations were defined as discharges with any influenza diagnoses. Influenza-associated hospitalizations were calculated using negative binomial regression models that incorporated hospitalization and influenza laboratory surveillance data.

RESULTS

The mean influenza-specific hospitalization rate/100 000 persons/year during the 2001-2002 to 2007-2008 respiratory years was 18.6 for AI/AN people and 15.6 for the comparison US population. The age-adjusted influenza-associated hospitalization rate for AI/AN people (98.2; 95% confidence interval [CI], 51.6-317.8) was similar to the comparison US population (58.2; CI, 34.7-172.2). By age, influenza-associated hospitalization rates were significantly higher among AI/AN infants (<1 year) (1070.7; CI, 640.7-2969.5) than the comparison US infant population (210.2; CI, 153.5-478.5).

CONCLUSIONS

American Indian/Alaska Native people had higher influenza-specific hospitalization rates than the comparison US population; a significant influenza-associated hospitalization rate disparity was detected only among AI/AN infants because of the wide CIs inherent to the model. Taken together, the influenza-specific and influenza-associated hospitalization rates suggest that AI/AN people might suffer disproportionately from influenza illness compared with the general US population.

Measuring underreporting and under-ascertainment in infectious disease datasets: a comparison of methods

BACKGROUND

Efficient and reliable surveillance and notification systems are vital for monitoring public health and disease outbreaks. However, most surveillance and notification systems are affected by a degree of underestimation (UE) and therefore uncertainty surrounds the 'true' incidence of disease affecting morbidity and mortality rates. Surveillance systems fail to capture cases at two distinct levels of the surveillance pyramid: from the community since not all cases seek healthcare (under-ascertainment), and at the healthcare-level, representing a failure to adequately report symptomatic cases that have sought medical advice (underreporting). There are several methods to estimate the extent of under-ascertainment and underreporting.

METHODS

Within the context of the ECDC-funded Burden of Communicable Diseases in Europe (BCoDE)-project, an extensive literature review was conducted to identify studies that estimate ascertainment or reporting rates for salmonellosis and campylobacteriosis in European Union Member States (MS) plus European Free Trade Area (EFTA) countries Iceland, Norway and Switzerland and four other OECD countries (USA, Canada, Australia and Japan). Multiplication factors (MFs), a measure of the magnitude of underestimation, were taken directly from the literature or derived (where the proportion of underestimated, under-ascertained, or underreported cases was known) and compared for the two pathogens.

RESULTS

MFs varied between and within diseases and countries, representing a need to carefully select the most appropriate MFs and methods for calculating them. The most appropriate MFs are often disease-, country-, age-, and sex-specific.

CONCLUSIONS

When routine data are used to make decisions on resource allocation or to estimate epidemiological parameters in populations, it becomes important to understand when, where and to what extent these data represent the true picture of disease, and in some instances (such as priority setting) it is necessary to adjust for underestimation. MFs can be used to adjust notification and surveillance data to provide more realistic estimates of incidence.

Impact of the 2009 influenza (H1N1) pandemic on the United States military health care system

BACKGROUND

During public health emergencies, the Military Health System experiences challenges similar to those across the U.S. public and private health systems. This study explored how 1 such event, the 2009/2010 influenza (H1N1) pandemic, impacted health care utilization and associated costs in the Military Health System.

METHODS

Data from the Military Data Repository were used to examine diagnoses, claims data, and dates of services with respect to military or civilian care during 2004-2009/2010 influenza seasons. Comparison analysis was conducted through two-tailed t-tests and regression models.

RESULTS

There was a significant increase in inpatient and outpatient health care utilization during the 2009/2010 H1N1 pandemic year, most markedly for emergency department visits. The 2009/2010 H1N1 pandemic cost the Department of Defense $100 million compared to influenza-related health care costs incurred in previous influenza seasons. Highest health care utilization costs were found in children less than age 5. The greatest cost burden was attributed to immunizations for active duty personnel delivered at military facilities.

CONCLUSION

Annual trend analysis of costs and health care utilization would be helpful to plan and resource emerging influenza pandemics and to identify subgroups at greatest risk for contracting influenza.