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Abstract Background Despite a number of respiratory syncytial virus (RSV) vaccine candidates being tested in clinical trials, disease-specific, self-reported instruments assessing symptom severity of RSV infection from the perspective of adult patients are still needed. The RSV Infection, Intensity and Impact Questionnaire (RSV-iiiQ) was adapted from the Influenza Intensity and Impact Questionnaire (FluiiQ™). This study evaluated some measurement properties of the RSV-iiiQ. Methods Data were collected in a web-based survey over two consecutive days. Participants completed the RSV-iiiQ, the Patient Global Impression of Severity, Sheehan Disability Scale, Patient Global Impression of Change, EQ-5D-5L, and a demographic questionnaire. Test-retest reliability, internal consistency, construct validity, and responsiveness of the RSV-iiiQ scales were assessed. Results 111 adults with RSV were enrolled and self-reported a variety of symptoms across the range of disease severity via a web-based platform. The RSV-iiiQ scales demonstrated satisfactory test-retest reliability, construct validity, and discriminating ability. One-factor confirmatory factor analyses confirmed that each of the four scales was sufficiently unidimensional, and internal consistencies indicated that the computation of RSV-iiiQ scale scores was plausible. Correlation-based analyses provided support for the construct validity of the RSV-iiiQ scores, and known groups analyses supported discriminating ability. Estimates of responsiveness of the scale scores were also satisfactory. Conclusions RSV infection is highly symptomatic and causes significant disease burden, and self-report instruments assessing symptom severity and impact are important for evaluation of new treatments. This study describes the preliminary psychometric properties of the RSV-iiiQ and indicates this tool may be useful for the assessment of the severity of symptoms and impact of acute RSV infection in adults. The findings also indicated two items, Runny nose and Ear pain, may be unnecessary and should be revisited using item response theory analysis with a larger sample size.

Abstract Background Antibiotic usage and antibiotic resistance (ABR) patterns changed during the COVID-19 pandemic. Inadequate empiric antibiotic therapy (IET) is a significant public health problem and contributes to ABR. We evaluated factors associated with IET before and during the COVID-19 pandemic to determine the impact of the pandemic on antibiotic management. Methods This multicenter, retrospective cohort analysis included hospitalized US adults who had a positive bacterial culture (specified gram-positive or gram-negative bacteria) from July 2019 to October 2021 in the BD Insights Research Database. IET was defined as antibacterial therapy within 48 h that was not active against the bacteria. ABR results were based on susceptibility testing and reports from local facilities. Multivariate analysis was used to identify risk factors associated with IET in patients with any positive bacterial culture and ABR-positive cultures, including multidrug-resistant (MDR) bacteria. Results Of 278,344 eligible patients in 269 hospitals, 56,733 (20.4%) received IET; rates were higher in patients with ABR-positive (n = 93,252) or MDR-positive (n = 39,000) cultures (34.9% and 45.0%, respectively). Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2)-positive patients had significantly higher rates of IET (25.9%) compared with SARS-CoV-2-negative (20.3%) or not tested (19.7%) patients overall and in the ABR and MDR subgroups. Patients with ABR- or MDR-positive cultures had more days of therapy and longer lengths of stay. In multivariate analyses, ABR, MDR, SARS-CoV-2-positive status, respiratory source, and prior admissions were identified as key IET risk factors. Conclusions IET remained a persistent problem during the COVID-19 pandemic and occurred at higher rates in patients with ABR/MDR bacteria or a co-SARS-CoV-2 infection.

Abstract Background Respiratory syncytial virus (RSV) causes significant morbidity and mortality in older adults. Despite a number of RSV vaccine candidates in clinical trials, there are no existing disease-specific, self-reported measures that assess the symptoms and severity of RSV infection from the perspective of adult patients with acute RSV. The objective of this study was to describe the initial conceptualization and development of the RSV Infection, Intensity and Impact Questionnaire (RSV-iiiQ), a new patient-reported outcome measure. Methods A targeted review of the literature identified relevant existing measures, symptoms, and impacts of RSV. A draft version of the RSV-iiiQ was developed based on the Influenza Intensity and Impact Questionnaire (Flu-iiQ) with expert input. Qualitative interviews (N = 20) were conducted with participants to optimize the RSV-iiiQ conceptual model and confirm the content validity of the RSV-iiiQ. Interviews included concept elicitation and a cognitive debriefing assessment. A draft conceptual framework was developed, and the electronic clinical outcome assessment was piloted. All steps of instrument development followed Food and Drug Administration guidance for patient-reported outcomes. Results In-depth concept elicitation interviews followed by cognitive debriefings demonstrated that the content of the items was comprehensive, covered the breadth of RSV symptoms and impacts, and was relevant to the experiences of individuals with RSV. Both the paper and electronic versions of the RSV-iiiQ were easily completed. Minor refinements were made to some items based on participant feedback, and the draft conceptual framework was refined. Conclusions The RSV-iiiQ was developed for use in clinical trials to measure the symptom intensity and impact of acute RSV infection from the perspective of adult patients. The tool was developed in accordance with current regulatory guidance and is useful to support patient-focused drug development.

Abstract Background Bloodstream infections (BSIs) are an important cause of morbidity and mortality in hospitalized patients. We evaluate incidence of community- and hospital-onset BSI rates and outcomes before and during the SARS-CoV-2 pandemic. Methods We conducted a retrospective cohort study evaluating patients who were hospitalized for ≥ 1 day with discharge or death between June 1, 2019, and September 4, 2021, across 271 US health care facilities. Community- and hospital-onset BSI and related outcomes before and during the SARS-CoV-2 pandemic, including intensive care admission rates, and overall and ICU-specific length of stay (LOS) was evaluated. Bivariate correlations were calculated between the pre-pandemic and pandemic periods overall and by SARS-CoV-2 testing status. Results Of 5,239,692 patient admissions, there were 20,113 community-onset BSIs before the pandemic (11.2/1000 admissions) and 39,740 (11.5/1000 admissions) during the pandemic (P ≤ 0.0062). Corresponding rates of hospital-onset BSI were 2,771 (1.6/1000 admissions) and 6,864 (2.0/1000 admissions; P

Abstract Background Past respiratory viral epidemics suggest that bacterial infections impact clinical outcomes. There is minimal information on potential co-pathogens in patients with coronavirus disease-2019 (COVID-19) in the US. We analyzed pathogens, antimicrobial use, and healthcare utilization in hospitalized US patients with and without severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). Methods This multicenter retrospective study included patients with > 1 day of inpatient admission and discharge/death between March 1 and May 31, 2020 at 241 US acute care hospitals in the BD Insights Research Database. We assessed microbiological testing data, antimicrobial utilization in admitted patients with ≥24 h of antimicrobial therapy, and length of stay (LOS). Results A total of 141,621 patients were tested for SARS-CoV-2 (17,003 [12.0%] positive) and 449,339 patients were not tested. Most (> 90%) patients tested for SARS-CoV-2 had additional microbiologic testing performed compared with 41.9% of SARS-CoV-2-untested patients. Non-SARS-CoV-2 pathogen rates were 20.9% for SARS-CoV-2-positive patients compared with 21.3 and 27.9% for SARS-CoV-2-negative and −untested patients, respectively. Gram-negative bacteria were the most common pathogens (45.5, 44.1, and 43.5% for SARS-CoV-2-positive, −negative, and −untested patients). SARS-CoV-2-positive patients had higher rates of hospital-onset (versus admission-onset) non-SARS-CoV-2 pathogens compared with SARS-CoV-2-negative or −untested patients (42.4, 22.2, and 19.5%, respectively), more antimicrobial usage (68.0, 45.2, and 25.1% of patients), and longer hospital LOS (mean [standard deviation (SD)] of 8.6 [11.4], 5.1 [8.9], and 4.2 [8.0] days) and intensive care unit (ICU) LOS (mean [SD] of 7.8 [8.5], 3.6 [6.2], and 3.6 [5.9] days). For all groups, the presence of a non-SARS-CoV-2 pathogen was associated with increased hospital LOS (mean [SD] days for patients with versus without a non-SARS-CoV-2 pathogen: 13.7 [15.7] vs 7.3 [9.6] days for SARS-CoV-2-positive patients, 8.2 [11.5] vs 4.3 [7.9] days for SARS-CoV-2-negative patients, and 7.1 [11.0] vs 3.9 [7.4] days for SARS-CoV-2-untested patients). Conclusions Despite similar rates of non-SARS-CoV-2 pathogens in SARS-CoV-2-positive, −negative, and −untested patients, SARS-CoV-2 was associated with higher rates of hospital-onset infections, greater antimicrobial usage, and extended hospital and ICU LOS. This finding highlights the heavy burden of the COVID-19 pandemic on healthcare systems and suggests possible opportunities for diagnostic and antimicrobial stewardship.

The COVID-19 pandemic has disrupted the seasonal patterns of several infectious diseases. Understanding when and where an outbreak may occur is vital for public health planning and response. We usually rely on well-functioning surveillance systems to monitor epidemic outbreaks. However, not all countries have a well-functioning surveillance system in place, or at least not for the pathogen in question. We utilized Google Trends search results for RSV-related keywords to identify outbreaks. We evaluated the strength of the Pearson correlation coefficient between clinical surveillance data and online search data and applied the Moving Epidemic Method (MEM) to identify country-specific epidemic thresholds. Additionally, we established pseudo-RSV surveillance systems, enabling internal stakeholders to obtain insights on the speed and risk of any emerging RSV outbreaks in countries with imprecise disease surveillance systems but with Google Trends data. Strong correlations between RSV clinical surveillance data and Google Trends search results from several countries were observed. In monitoring an upcoming RSV outbreak with MEM, data collected from both systems yielded similar estimates of country-specific epidemic thresholds, starting time, and duration. We demonstrate in this study the potential of monitoring disease outbreaks in real time and complement classical disease surveillance systems by leveraging online search data.

Background: Infectious diseases continue to cause significant impact on human health. Vaccines are instrumental in preventing infectious diseases and mitigating pandemics and epidemics. SARS-CoV-2 is the most recent example of an urgent pandemic that requires the development of vaccines. This study combined real-world data and geospatial visualization techniques to demonstrate methods to monitor and communicate the uptake and impact of existing and new vaccines. Methods: Observational data of existing pediatric rotavirus vaccines were used as an example. A large US national insurance claims database was accessed to build an analytic dataset for a 20-year period (1996–2017). For each week and multiple geographic scales, animated spatial and non-spatial visualization techniques were applied to demonstrate changes in seasonal rotavirus epidemic curves and population-based disease rates before, during, and after vaccine introduction in 2006. The geographic scales included national, state, county and zip code tabulation areas. An online web-based digital atlas was built to display either continuous or snapshot visualizations of disease patterns, vaccine uptake, and improved health outcomes after vaccination (http://www.mapvaccines.com). Results: Over 17 million zip code-weeks of data were available for analysis. The animations show geospatial patterns of rotavirus-related medical encounter rates peaking every year from November – February prior to vaccine availability in 2006. Visualizations showed increasing vaccination coverage rates at all geographic scales over time. Declines in medical encounter rates accelerated as vaccination coverage rapidly increased after 2010. The data maps also identified geographic hotspots with low vaccination rates and persistent disease rates. Conclusion: This project developed novel web-based methods to communicate location and time-based vaccine uptake and the related reduction in medical visits due to viral infection. Future applications of the visualization could be used by health agencies to monitor known or novel disease patterns over time in conjunction with close assessment of current and future vaccine utilization.

The identification of key “driver” groups in influenza epidemics is of much interest for the implementation of effective public health response strategies, including vaccination programs. However, the relative importance of different age groups in propagating epidemics is uncertain. During a communicable disease outbreak, some groups may be disproportionately represented during the outbreak's ascent due to increased susceptibility and/or contact rates. Such groups or subpopulations can be identified by considering the proportion of cases within the subpopulation occurring before (Bp) and after the epidemic peak (Ap) to calculate the subpopulation's relative risk, RR =Bp/Ap. We estimated RR for several subpopulations (age groups) using data on laboratory-confirmed US influenza hospitalizations during epidemics between 2009 and 2014. Additionally, we simulated various influenza outbreaks in an age-stratified population, relating the RR to the impact of vaccination in each subpopulation on the epidemic's initial effective reproductive number Re(0). We found that children aged 5–17 had the highest estimates of RR during the five largest influenza A outbreaks, though the relative magnitude of RR in this age group compared to other age groups varied, being highest for the 2009 A/H1N1 pandemic. For the 2010–2011 and 2012–2013 influenza B epidemics, adults aged 18–49, and 0–4 year-olds had the highest estimates of RR, respectively. For 83% of simulated epidemics, the group with the highest RR was also the group for which initial distribution of a given quantity of vaccine would result in the largest reduction of Re(0). In the largest 40% of simulated outbreaks, the group with the highest RR and the largest vaccination impact was children 5–17. While the relative importance of different age groups in propagating influenza outbreaks varies, children aged 5–17 play the leading role during the largest influenza A epidemics. Extra vaccination efforts for this group may contribute to reducing the epidemic's impact in the whole community.

Newly emerged highly pathogenic avian influenza (HPAI) A H5 viruses have caused outbreaks among birds in the United States. These viruses differ genetically from HPAI H5 viruses that previously caused human illness, most notably in Asia and Africa. To assess the risk for animal-to-human HPAI H5 virus transmission in the United States, we determined the number of persons with self-reported exposure to infected birds, the number with an acute respiratory infection (ARI) during a 10-day postexposure period, and the number with ARI who tested positive for influenza by real-time reverse transcription PCR or serologic testing for each outbreak during December 15, 2014–March 31, 2015. During 60 outbreaks in 13 states, a total of 164 persons were exposed to infected birds. ARI developed in 5 of these persons within 10 days of exposure. H5 influenza virus infection was not identified in any persons with ARI, suggesting a low risk for animal-to-human HPAI H5 virus transmission.

In 2003, surveillance for influenza in hospitalized persons was added to the Centers for Disease Control and Prevention Emerging Infections Program network. This surveillance enabled monitoring of the severity of influenza seasons and provided a platform for addressing priority questions associated with influenza. For enhanced surveillance capacity during the 2009 influenza pandemic, new sites were added to this platform. The combined surveillance platform is called the Influenza Hospitalization Surveillance Network (FluSurv-NET). FluSurv-NET has helped to determine the risk for influenza-associated illness in various segments of the US population, define the severity of influenza seasons and the 2009 pandemic, and guide recommendations for treatment and vaccination programs.

Diagnostic test sensitivity affects rate estimates for laboratory-confirmed influenza–associated hospitalizations. We used data from FluSurv-NET, a national population-based surveillance system for laboratory-confirmed influenza hospitalizations, to capture diagnostic test type by patient age and influenza season. We calculated observed rates by age group and adjusted rates by test sensitivity. Test sensitivity was lowest in adults >65 years of age. For all ages, reverse transcription PCR was the most sensitive test, and use increased from 65 years. After 2009, hospitalization rates adjusted by test sensitivity were ≈15% higher for children 65 years of age. Test sensitivity adjustments improve the accuracy of hospitalization rate estimates.

The effects of influenza on a population are attributable to the clinical severity of illness and the number of persons infected, which can vary greatly between seasons or pandemics. To create a systematic framework for assessing the public health effects of an emerging pandemic, we reviewed data from past influenza seasons and pandemics to characterize severity and transmissibility (based on ranges of these measures in the United States) and outlined a formal assessment of the potential effects of a novel virus. The assessment was divided into 2 periods. Because early in a pandemic, measurement of severity and transmissibility is uncertain, we used a broad dichotomous scale in the initial assessment to divide the range of historic values. In the refined assessment, as more data became available, we categorized those values more precisely. By organizing and prioritizing data collection, this approach may inform an evidence-based assessment of pandemic effects and guide decision making.

During August 2011, influenza A (H3N2) variant [A(H3N2)v] virus infection developed in a child who attended an agricultural fair in Pennsylvania, USA; the virus resulted from reassortment of a swine influenza virus with influenza A(H1N1)pdm09. We interviewed fair attendees and conducted a retrospective cohort study among members of an agricultural club who attended the fair. Probable and confirmed cases of A(H3N2)v virus infection were defined by serology and genomic sequencing results, respectively. We identified 82 suspected, 4 probable, and 3 confirmed case-patients who attended the fair. Among 127 cohort study members, the risk for suspected case status increased as swine exposure increased from none (4%; referent) to visiting swine exhibits (8%; relative risk 2.1; 95% CI 0.2–53.4) to touching swine (16%; relative risk 4.4; 95% CI 0.8–116.3). Fairs may be venues for zoonotic transmission of viruses with epidemic potential; thus, health officials should investigate respiratory illness outbreaks associated with agricultural events.

Through July 2009, a total of 43,677 laboratory-confirmed cases of influenza A pandemic (H1N1) 2009 were reported in the United States, which is likely a substantial underestimate of the true number. Correcting for under-ascertainment using a multiplier model, we estimate that 1.8 million–5.7 million cases occurred, including 9,000–21,000 hospitalizations.

School closure is a proposed strategy for reducing influenza transmission during a pandemic. Few studies have assessed how families respond to closures, or whether other interactions during closure could reduce this strategy’s effect. Questionnaires were administered to 220 households (438 adults and 355 children) with school-age children in a North Carolina county during an influenza B virus outbreak that resulted in school closure. Closure was considered appropriate by 201 (91%) households. No adults missed work to solely provide childcare, and only 22 (10%) households required special childcare arrangements; 2 households incurred additional costs. Eighty-nine percent of children visited at least 1 public location during the closure despite county recommendations to avoid large gatherings. Although behavior and attitudes might differ during a pandemic, these results suggest short-term closure did not cause substantial hardship for parents. Pandemic planning guidance should address the potential for transmission in public areas during school closure.

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.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.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.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.

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.

BACKGROUND:Prior to emergence in human populations, zoonoses such as SARS cause occasional infections in human populations exposed to reservoir species. The risk of widespread epidemics in humans can be assessed by monitoring the reproduction number R (average number of persons infected by a human case). However, until now, estimating R required detailed outbreak investigations of human clusters, for which resources and expertise are not always available. Additionally, existing methods do not correct for important selection and under-ascertainment biases. Here, we present simple estimation methods that overcome many of these limitations. METHODS AND FINDINGS:Our approach is based on a parsimonious mathematical model of disease transmission and only requires data collected through routine surveillance and standard case investigations. We apply it to assess the transmissibility of swine-origin influenza A H3N2v-M virus in the US, Nipah virus in Malaysia and Bangladesh, and also present a non-zoonotic example (cholera in the Dominican Republic). Estimation is based on two simple summary statistics, the proportion infected by the natural reservoir among detected cases (G) and among the subset of the first detected cases in each cluster (F). If detection of a case does not affect detection of other cases from the same cluster, we find that R can be estimated by 1-G; otherwise R can be estimated by 1-F when the case detection rate is low. In more general cases, bounds on R can still be derived. CONCLUSIONS:We have developed a simple approach with limited data requirements that enables robust assessment of the risks posed by emerging zoonoses. We illustrate this by deriving transmissibility estimates for the H3N2v-M virus, an important step in evaluating the possible pandemic threat posed by this virus. Please see later in the article for the Editors' Summary.

The goal of influenza vaccination programs is to reduce influenza-associated disease outcomes. Therefore, estimating the reduced burden of influenza as a result of vaccination over time and by age group would allow for a clear understanding of the value of influenza vaccines in the US, and of areas where improvements could lead to greatest benefits.To estimate the direct effect of influenza vaccination in the US in terms of averted number of cases, medically-attended cases, and hospitalizations over six recent influenza seasons.Using existing surveillance data, we present a method for assessing the impact of influenza vaccination where impact is defined as either the number of averted outcomes or as the prevented disease fraction (the number of cases estimated to have been averted relative to the number of cases that would have occurred in the absence of vaccination).We estimated that during our 6-year study period, the number of influenza illnesses averted by vaccination ranged from a low of approximately 1.1 million (95% confidence interval (CI) 0.6-1.7 million) during the 2006-2007 season to a high of 5 million (CI 2.9-8.6 million) during the 2010-2011 season while the number of averted hospitalizations ranged from a low of 7,700 (CI 3,700-14,100) in 2009-2010 to a high of 40,400 (CI 20,800-73,000) in 2010-2011. Prevented fractions varied across age groups and over time. The highest prevented fraction in the study period was observed in 2010-2011, reflecting the post-pandemic expansion of vaccination coverage.Influenza vaccination programs in the US produce a substantial health benefit in terms of averted cases, clinic visits and hospitalizations. Our results underscore the potential for additional disease prevention through increased vaccination coverage, particularly among nonelderly adults, and increased vaccine effectiveness, particularly among the elderly.

OBJECTIVE: Pneumonia is a leading cause of death in children worldwide. A simple clinical score predicting the probability of death in a young child with lower respiratory tract infection (LRTI) could aid clinicians in case management and provide a standardized severity measure during epidemiologic studies. METHODS: We analyzed 4,148 LRTI hospitalizations in children

BACKGROUND: Severe illness due to 2009 pandemic A(H1N1) infection has been reported among persons who are obese or morbidly obese. We assessed whether obesity is a risk factor for hospitalization and death due to 2009 pandemic influenza A(H1N1), independent of chronic medical conditions considered by the Advisory Committee on Immunization Practices (ACIP) to increase the risk of influenza-related complications. METHODOLOGY/PRINCIPAL FINDINGS: We used a case-cohort design to compare cases of hospitalizations and deaths from 2009 pandemic A(H1N1) influenza occurring between April-July, 2009, with a cohort of the U.S. population estimated from the 2003-2006 National Health and Nutrition Examination Survey (NHANES); pregnant women and children or=20 year olds, hospitalization was associated with being morbidly obese (BMI>or=40) for individuals with ACIP-recognized chronic conditions (OR = 4.9, 95% CI 2.4-9.9) and without ACIP-recognized chronic conditions (OR = 4.7, 95%CI 1.3-17.2). Among 2-19 year olds, hospitalization was associated with being underweight (BMIor=20 years without ACIP-recognized chronic medical conditions death was associated with obesity (OR = 3.1, 95%CI: 1.5-6.6) and morbid obesity (OR = 7.6, 95%CI 2.1-27.9). CONCLUSIONS/SIGNIFICANCE: Our findings support observations that morbid obesity may be associated with hospitalization and possibly death due to 2009 pandemic H1N1 infection. These complications could be prevented by early antiviral therapy and vaccination.

BackgroundAccurate measures of the severity of pandemic (H1N1) 2009 influenza (pH1N1) are needed to assess the likely impact of an anticipated resurgence in the autumn in the Northern Hemisphere. Severity has been difficult to measure because jurisdictions with large numbers of deaths and other severe outcomes have had too many cases to assess the total number with confidence. Also, detection of severe cases may be more likely, resulting in overestimation of the severity of an average case. We sought to estimate the probabilities that symptomatic infection would lead to hospitalization, ICU admission, and death by combining data from multiple sources.Methods and findingsWe used complementary data from two US cities: Milwaukee attempted to identify cases of medically attended infection whether or not they required hospitalization, while New York City focused on the identification of hospitalizations, intensive care admission or mechanical ventilation (hereafter, ICU), and deaths. New York data were used to estimate numerators for ICU and death, and two sources of data--medically attended cases in Milwaukee or self-reported influenza-like illness (ILI) in New York--were used to estimate ratios of symptomatic cases to hospitalizations. Combining these data with estimates of the fraction detected for each level of severity, we estimated the proportion of symptomatic patients who died (symptomatic case-fatality ratio, sCFR), required ICU (sCIR), and required hospitalization (sCHR), overall and by age category. Evidence, prior information, and associated uncertainty were analyzed in a Bayesian evidence synthesis framework. Using medically attended cases and estimates of the proportion of symptomatic cases medically attended, we estimated an sCFR of 0.048% (95% credible interval [CI] 0.026%-0.096%), sCIR of 0.239% (0.134%-0.458%), and sCHR of 1.44% (0.83%-2.64%). Using self-reported ILI, we obtained estimates approximately 7-9 x lower. sCFR and sCIR appear to be highest in persons aged 18 y and older, and lowest in children aged 5-17 y. sCHR appears to be lowest in persons aged 5-17; our data were too sparse to allow us to determine the group in which it was the highest.ConclusionsThese estimates suggest that an autumn-winter pandemic wave of pH1N1 with comparable severity per case could lead to a number of deaths in the range from considerably below that associated with seasonal influenza to slightly higher, but with the greatest impact in children aged 0-4 and adults 18-64. These estimates of impact depend on assumptions about total incidence of infection and would be larger if incidence of symptomatic infection were higher or shifted toward adults, if viral virulence increased, or if suboptimal treatment resulted from stress on the health care system; numbers would decrease if the total proportion of the population symptomatically infected were lower than assumed.