Mortality burden of the 2009-10 influenza pandemic in the United States: improving the timeliness of influenza severity estimates using inpatient mortality records

Disease burden and high-risk populations for complications in patients with acute respiratory infections: a scoping review

Background

Acute respiratory infections (ARIs) represent a significant public health concern in the U.S. This study aimed to describe the disease burden of ARIs and identify U.S. populations at high risk of developing complications.

Methods

This scoping review searched PubMed and EBSCO databases to analyze U.S. studies from 2013 to 2022, focusing on disease burden, complications, and high-risk populations associated with ARIs.

Results

The study included 60 studies and showed that ARI is associated with a significant disease burden and healthcare resource utilization (HRU). In 2019, respiratory infection and tuberculosis caused 339,703 cases per 100,000 people, with most cases being upper respiratory infections and most deaths being lower respiratory infections. ARI is responsible for millions of outpatient visits, especially for influenza and pneumococcal pneumonia, and indirect costs of billions of dollars. ARI is caused by multiple pathogens and poses a significant burden on hospitalizations and outpatient visits. Risk factors for HRU associated with ARI include age, chronic conditions, and socioeconomic factors.

Conclusion

The review underscores the substantial disease burden of ARIs and the influence of age, chronic conditions, and socioeconomic status on developing complications. It highlights the necessity for targeted strategies for high-risk populations and effective pathogen detection to prevent severe complications and reduce HRU.

What can we learn from historical pandemics? A systematic review of the literature

What are the insights from historical pandemics for policymaking today? We carry out a systematic review of the literature on the impact of pandemics that occurred since the Industrial Revolution and prior to Covid-19. Our literature searches were conducted between June 2020 and September 2023, with the final review encompassing 169 research papers selected for their relevance to understanding either the demographic or economic impact of pandemics. We include literature from across disciplines to maximise our knowledge base, finding many relevant articles in journals which would not normally be on the radar of social scientists. Our review identifies two gaps in the literature: (1) the need to study pandemics and their effects more collectively rather than looking at them in isolation; and (2) the need for more study of pandemics besides 1918 Spanish Influenza, especially milder pandemic episodes. These gaps are a consequence of academics working in silos, failing to draw on the skills and knowledge offered by other disciplines. Synthesising existing knowledge on pandemics in one place provides a basis upon which to identify the lessons in preparing for future catastrophic disease events.

Inference of age-dependent case-fatality ratios for seasonal influenza virus subtypes A(H3N2) and A(H1N1)pdm09 and B lineages using data from the Netherlands

Background

Despite the known relatively high disease burden of influenza, data are lacking regarding a critical epidemiological indicator, the case-fatality ratio. Our objective was to infer age-group and influenza (sub)type specific values by combining modelled estimates of symptomatic incidence and influenza-attributable mortality.

Methods

The setting was the Netherlands, 2011/2012 through 2019/2020 seasons. Sentinel surveillance data from general practitioners and laboratory testing were synthesised to supply age-group specific estimates of incidence of symptomatic infection, and ecological additive modelling was used to estimate influenza-attributable deaths. These were combined in an Bayesian inferential framework to estimate case-fatality ratios for influenza A(H3N2), A(H1N1)pdm09 and influenza B, per 5-year age-group.

Results

Case-fatality estimates were highest for influenza A(H3N2) followed by influenza B and then A(H1N1)pdm09 and were highest for the 85+ years age-group, at 4.76% (95% credible interval [CrI]: 4.52-5.01%) for A(H3N2), followed by influenza B at 4.08% (95% CrI: 3.77-4.39%) and A(H1N1)pdm09 at 2.51% (95% CrI: 2.09-2.94%). For 55-59 through 85+ years, the case-fatality risk was estimated to double with every 3.7 years of age.

Conclusions

These estimated case-fatality ratios, per influenza sub(type) and per age-group, constitute valuable information for public health decision-making, for assessing the retrospective and prospective value of preventative interventions such as vaccination and for health economic evaluations.

Excess Mortality during the COVID-19 Pandemic in Cities of Chile: Magnitude, Inequalities, and Urban Determinants

We estimated excess mortality in Chilean cities during the COVID-19 pandemic and its association with city-level factors. We used mortality, and social and built environment data from the SALURBAL study for 21 Chilean cities, composed of 81 municipalities or "comunas", grouped in 4 macroregions. We estimated excess mortality by comparing deaths from January 2020 up to June 2021 vs 2016-2019, using a generalized additive model. We estimated a total of 21,699 (95%CI 21,693 to 21,704) excess deaths across the 21 cities. Overall relative excess mortality was highest in the Metropolitan (Santiago) and the North regions (28.9% and 22.2%, respectively), followed by the South and Center regions (17.6% and 14.1%). At the city-level, the highest relative excess mortality was found in the Northern cities of Calama and Iquique (around 40%). Cities with higher residential overcrowding had higher excess mortality. In Santiago, capital of Chile, municipalities with higher educational attainment had lower relative excess mortality. These results provide insight into the heterogeneous impact of COVID-19 in Chile, which has served as a magnifier of preexisting urban health inequalities, exhibiting different impacts between and within cities. Delving into these findings could help prioritize strategies addressed to prevent deaths in more vulnerable communities.

Trends and Outcomes of Hospitalized Influenza Patients With End-Stage Kidney Disease: Insights From the National Inpatient Sample 2010-2019

Introduction Influenza causes significant morbidity and mortality annually in the United States (US) and people with chronic medical conditions are thought to be at higher risk for severe disease and death. Infection is a leading cause of death for patients with end-stage kidney disease (ESKD). We used a national-level inpatient database to study the trend of influenza hospitalizations and in-hospital mortality for patients without and with ESKD. Methods The National Inpatient Sample (NIS) 2010-2019 was used. A primary diagnosis of influenza was identified using ICD-9-CM (487.X, 488.X) and ICD-10-CM codes (J09.X, J10.X, J11.X). ESKD was identified using a validated algorithm identifying patients with a diagnosis of ESKD or procedure code for dialysis and excluding patients with a diagnosis of acute kidney injury. Other diagnoses and procedures were identified using validated algorithms based on ICD-9-CM, ICD-10-CM, and ICD-10-PCS codes. Discharge-level weights were used to estimate the total number of admissions in the NIS universe. Weighted multivariable logistic regression was performed to study the association between ESKD and in-hospital death. Results 131,942 admissions with a primary diagnosis of influenza with 4,647 admissions for ESKD patients among them were included in our analysis. Admissions varied by influenza season and ESKD patients accounted for 2.91% to 3.65% of all influenza admissions each season. 2,081 influenza patients (1.58%) died in the hospital and 115 patients with influenza and ESKD (2.47%) died in the hospital. Age-adjusted in-hospital mortality varied from season to season but was consistently higher in ESKD patients (2.25% vs 1.38%). ESKD was a risk factor for in-hospital death (OR 1.26, 95% CI 1.15-1.38) after adjusting for age, gender, primary payer, heart failure, chronic lung disease, obesity, drug abuse, immunocompromised status, bacterial pneumonia, the Charlson Comorbidity Index, and the influenza season. Conclusion ESKD patients accounted for a significant proportion of influenza hospitalizations in the US from 2010-11 to the 2018-19 influenza season. Among people hospitalized primarily for influenza, age-adjusted in-hospital mortality varied from season to season and was consistently higher in ESKD patients. For people hospitalized primarily for influenza, ESKD was an independent risk factor for in-hospital death.

The Negative Impact of COVID-19 on Life Insurers

Understanding COVID-19 induced mortality risk is significant for life insurers to better analyze their financial sustainability after the outbreak of COVID-19. To capture the mortality effect caused by COVID-19 among all ages, this study proposes a temporary adverse mortality jump model to describe the dynamics of mortality in a post-COVID-19 pandemic world based on the weekly death numbers from 2015 to 2021 in the United States. As a comparative study, the Lee-Carter model is used as the base case to represent the dynamics of mortality without COVID-19. Then we compare the force of mortality, the survival probability and the liability of a life insurer by considering COVID-19 and those without COVID-19. We show that a life insurer's financial sustainability will deteriorate because of the higher mortality rates than expected in the wake of COVID-19. Our results remain unchanged when we also consider the effect of interest rate risk by adopting the Vasicek and CIR models.

On the effect of COVID-19 pandemic in the excess of human mortality. The case of Brazil and Spain

Excess of deaths is a technique used in epidemiology to assess the deaths caused by an unexpected event. For the present COVID–19 pandemic, we discuss the performance of some linear and nonlinear time series forecasting techniques widely used for modeling the actual pandemic and provide estimates for this metric from January 2020 to April 2021. We apply the results obtained to evaluate the evolution of the present pandemic in Brazil and Spain, which allows in particular to compare how well (or bad) these countries have managed the pandemic. For Brazil, our calculations refute the claim made by some officials that the present pandemic is “a little flu”. Some studies suggest that the virus could be lying dormant across the world before been detected for the first time. In that regard, our results show that there is no evidence of deaths by the virus in 2019.

Estimating the cumulative incidence of COVID-19 in the United States using influenza surveillance, virologic testing, and mortality data: Four complementary approaches

Effectively designing and evaluating public health responses to the ongoing COVID-19 pandemic requires accurate estimation of the prevalence of COVID-19 across the United States (US). Equipment shortages and varying testing capabilities have however hindered the usefulness of the official reported positive COVID-19 case counts. We introduce four complementary approaches to estimate the cumulative incidence of symptomatic COVID-19 in each state in the US as well as Puerto Rico and the District of Columbia, using a combination of excess influenza-like illness reports, COVID-19 test statistics, COVID-19 mortality reports, and a spatially structured epidemic model. Instead of relying on the estimate from a single data source or method that may be biased, we provide multiple estimates, each relying on different assumptions and data sources. Across our four approaches emerges the consistent conclusion that on April 4, 2020, the estimated case count was 5 to 50 times higher than the official positive test counts across the different states. Nationally, our estimates of COVID-19 symptomatic cases as of April 4 have a likely range of 2.3 to 4.8 million, with possibly as many as 7.6 million cases, up to 25 times greater than the cumulative confirmed cases of about 311,000. Extending our methods to May 16, 2020, we estimate that cumulative symptomatic incidence ranges from 4.9 to 10.1 million, as opposed to 1.5 million positive test counts. The proposed combination of approaches may prove useful in assessing the burden of COVID-19 during resurgences in the US and other countries with comparable surveillance systems.

Excess All-Cause Deaths during Coronavirus Disease Pandemic, Japan, January-May 20201

To provide insight into the mortality burden of coronavirus disease (COVID-19) in Japan, we estimated the excess all-cause deaths for each week during the pandemic, January–May 2020, by prefecture and age group. We applied quasi-Poisson regression models to vital statistics data. Excess deaths were expressed as the range of differences between the observed and expected number of all-cause deaths and the 95% upper bound of the 1-sided prediction interval. A total of 208–4,322 all-cause excess deaths at the national level indicated a 0.03%–0.72% excess in the observed number of deaths. Prefecture and age structure consistency between the reported COVID-19 deaths and our estimates was weak, suggesting the need to use cause-specific analyses to distinguish between direct and indirect consequences of COVID-19.

Trends in suicide in Japan by gender during the COVID-19 pandemic, up to September 2020

Suicide is an extreme consequence of the psychological burden associated with the coronavirus disease 2019 (COVID-19) counter-measures. A quasi-Poisson regression was applied to monthly suicide mortality data obtained from the National Police Agency to estimate the gender-specific excess/exiguous suicide deaths during the COVID-19 pandemic in Japan up to September 2020. We found excess suicide deaths among women in July, August and September, but not among men. Our results indicate the importance of COVID-19 related suicide prevention, especially for women. Timely access to mental health care and financial and social support is urgently needed, as is optimal treatment for mental illness.

Public health emergencies of international concern: a historic overview

RATIONALE

The International Health Regulations (IHR) have been the governing framework for global health security since 2007. Declaring public health emergencies of international concern (PHEIC) is a cornerstone of the IHR. Here we review how PHEIC are formally declared, the diseases for which such declarations have been made from 2007 to 2020 and justifications for such declarations.

KEY FINDINGS

Six events were declared PHEIC between 2007 and 2020: the 2009 H1N1 influenza pandemic, Ebola (West African outbreak 2013-2015, outbreak in Democratic Republic of Congo 2018-2020), poliomyelitis (2014 to present), Zika (2016) and COVID-19 (2020 to present). Poliomyelitis is the longest PHEIC. Zika was the first PHEIC for an arboviral disease. For several other emerging diseases a PHEIC was not declared despite the fact that the public health impact of the event was considered serious and associated with potential for international spread.

RECOMMENDATIONS

The binary nature of a PHEIC declaration is often not helpful for events where a tiered or graded approach is needed. The strength of PHEIC declarations is the ability to rapidly mobilize international coordination, streamline funding and accelerate the advancement of the development of vaccines, therapeutics and diagnostics under emergency use authorization. The ultimate purpose of such declaration is to catalyse timely evidence-based action, to limit the public health and societal impacts of emerging and re-emerging disease risks while preventing unwarranted travel and trade restrictions.

Estimation of Excess Deaths Associated With the COVID-19 Pandemic in the United States, March to May 2020

IMPORTANCE

Efforts to track the severity and public health impact of coronavirus disease 2019 (COVID-19) in the United States have been hampered by state-level differences in diagnostic test availability, differing strategies for prioritization of individuals for testing, and delays between testing and reporting. Evaluating unexplained increases in deaths due to all causes or attributed to nonspecific outcomes, such as pneumonia and influenza, can provide a more complete picture of the burden of COVID-19.

OBJECTIVE

To estimate the burden of all deaths related to COVID-19 in the United States from March to May 2020.

DESIGN, SETTING, AND POPULATION

This observational study evaluated the numbers of US deaths from any cause and deaths from pneumonia, influenza, and/or COVID-19 from March 1 through May 30, 2020, using public data of the entire US population from the National Center for Health Statistics (NCHS). These numbers were compared with those from the same period of previous years. All data analyzed were accessed on June 12, 2020.

MAIN OUTCOMES AND MEASURES

Increases in weekly deaths due to any cause or deaths due to pneumonia/influenza/COVID-19 above a baseline, which was adjusted for time of year, influenza activity, and reporting delays. These estimates were compared with reported deaths attributed to COVID-19 and with testing data.

RESULTS

There were approximately 781 000 total deaths in the United States from March 1 to May 30, 2020, representing 122 300 (95% prediction interval, 116 800-127 000) more deaths than would typically be expected at that time of year. There were 95 235 reported deaths officially attributed to COVID-19 from March 1 to May 30, 2020. The number of excess all-cause deaths was 28% higher than the official tally of COVID-19-reported deaths during that period. In several states, these deaths occurred before increases in the availability of COVID-19 diagnostic tests and were not counted in official COVID-19 death records. There was substantial variability between states in the difference between official COVID-19 deaths and the estimated burden of excess deaths.

CONCLUSIONS AND RELEVANCE

Excess deaths provide an estimate of the full COVID-19 burden and indicate that official tallies likely undercount deaths due to the virus. The mortality burden and the completeness of the tallies vary markedly between states.

Estimating the Cumulative Incidence of COVID-19 in the United States Using Four Complementary Approaches

Effectively designing and evaluating public health responses to the ongoing COVID-19 pandemic requires accurate estimation of the prevalence of COVID-19 across the United States (US). Equipment shortages and varying testing capabilities have however hindered the useful-ness of the official reported positive COVID-19 case counts. We introduce four complementary approaches to estimate the cumulative incidence of symptomatic COVID-19 in each state in the US as well as Puerto Rico and the District of Columbia, using a combination of excess influenza-like illness reports, COVID-19 test statistics, COVID-19 mortality reports, and a spatially structured epidemic model. Instead of relying on the estimate from a single data source or method that may be biased, we provide multiple estimates, each relying on different assumptions and data sources. Across our four approaches emerges the consistent conclusion that on April 4, 2020, the estimated case count was 5 to 50 times higher than the official positive test counts across the different states. Nationally, our estimates of COVID-19 symptomatic cases as of April 4 have a likely range of 2.2 to 4.9 million, with possibly as many as 8.1 million cases, up to 26 times greater than the cumulative confirmed cases of about 311,000. Extending our method to May 16, 2020, we estimate that cumulative symptomatic incidence ranges from 6.0 to 10.3 million, as opposed to 1.5 million positive test counts. The proposed combination of approaches may prove useful in assessing the burden of COVID-19 during resurgences in the US and other countries with comparable surveillance systems.

Association of Influenza Activity and Environmental Conditions With the Risk of Invasive Pneumococcal Disease

IMPORTANCE

Streptococcus pneumoniae is the most commonly identified cause of bacterial pneumonia, and invasive pneumococcal disease (IPD) has a high case fatality rate. The wintertime coseasonality of influenza and IPD in temperate countries has suggested that pathogen-pathogen interaction or environmental conditions may contribute to IPD risk.

OBJECTIVES

To evaluate the short-term associations of influenza activity and environmental exposures with IPD risk in temperate countries and to examine the generalizability of such associations across multiple jurisdictions.

DESIGN, SETTING, AND PARTICIPANTS

This case-crossover analysis of 19 566 individuals with IPD from 1998 to 2011 combined individual-level outcomes of IPD and population-level exposures. Participants lived in 12 jurisdictions in Canada (the province of Alberta and cities of Toronto, Vancouver, and Halifax), Australia (Perth, Sydney, Adelaide, Brisbane, and Melbourne), and the United States (Baltimore, Providence, and Philadelphia). Data were analyzed in 2019.

EXPOSURES

Influenza activity, mean temperature, absolute humidity, and UV radiation at delays of 1 to 3 weeks before case occurrence in each jurisdiction.

MAIN OUTCOMES AND MEASURES

Matched odds ratios (ORs) for IPD associated with changes in exposure variables, estimated using multivariable conditional logistic regression models. Heterogeneity in effects across jurisdictions were evaluated using random-effects meta-analytic models.

RESULTS

This study included 19 566 patients: 9629 from Australia (mean [SD] age, 42.8 [30.8] years; 5280 [54.8%] men), 8522 from Canada (only case date reported), and 1415 from the United States (only case date reported). In adjusted models, increased influenza activity was associated with increases in IPD risk 2 weeks later (adjusted OR [aOR] per SD increase, 1.07; 95% CI, 1.01-1.13). Increased humidity was associated with decreased IPD risk 1 week later (aOR per 1 g/m3, 0.98; 95% CI, 0.96-1.00). Other associations were heterogeneous; metaregression suggested that combinations of environmental factors might represent unique local risk signatures. For example, the heterogeneity in effects of UV radiation and humidity at a 2-week lag was partially explained by variation in temperature (UV index: coefficient, 0.0261; 95% CI, 0.0078 to 0.0444; absolute humidity: coefficient, -0.0077; 95% CI, -0.0125 to -0.0030).

CONCLUSIONS AND RELEVANCE

In this study, influenza was associated with increased IPD risk in temperate countries. This association was not explained by coseasonality or case characteristics and appears generalizable. Absolute humidity was associated with decreased IPD risk in the same jurisdictions. The generalizable nature of these associations has important implications for influenza control and advances the understanding of the seasonality of this important disease.

Influenza-attributable years of life lost in older adults in a subtropical city in China, 2012-2017: A modeling study based on a competing risks approach

OBJECTIVE

The aim of this study was to estimate influenza-attributable years of life lost (YLL) in older adults in subtropical Hefei, China during the years 2012-2017, based on a competing risks approach.

METHODS

The quasi-Poisson model was fitted to weekly numbers of all-cause deaths by 5-year age groups for older adults ≥60 years of age. The product of the weekly influenza-like illness consultation rate and the proportion of specimens that tested positive for influenza was taken as the measurement of influenza activity, which was incorporated into the model as an exploratory variable. Excess deaths associated with influenza were calculated by subtracting baseline deaths (setting influenza activity to zero) from fitted deaths. Influenza-attributable YLL accounting for competing risks was estimated using restricted mean lifetime survival analysis.

RESULTS

The annual influenza-attributable YLL was highest in the 75-79 years age group (565 per 100,000 persons, 95% confidence interval 550-580), followed by the 80-84, 70-74, 85-89, 65-69, and 60-64 years age groups. Influenza A(H3N2) virus was associated with higher YLL than A(H1N1) and B viruses. Influenza-attributable YLL accounted for 1.03-1.53% of total YLL, and the proportion would be overestimated to 2.91-7.34% if the traditional Kaplan-Meier method ignoring competing risks was used.

CONCLUSIONS

Although influenza-associated mortality increased with age, influenza-attributable YLL was found to be highest in the 75-79 years age group.

Estimating the early death toll of COVID-19 in the United States

Background

Efforts to track the severity and public health impact of the novel coronavirus, COVID-19, in the US have been hampered by testing issues, reporting lags, and inconsistency between states. Evaluating unexplained increases in deaths attributed to broad outcomes, such as pneumonia and influenza (P&I) or all causes, can provide a more complete and consistent picture of the burden caused by COVID-19.

Methods

We evaluated increases in the occurrence of deaths due to P&I above a seasonal baseline (adjusted for influenza activity) or due to any cause across the United States in February and March 2020. These estimates are compared with reported deaths due to COVID-19 and with testing data.

Results

There were notable increases in the rate of death due to P&I in February and March 2020. In a number of states, these deaths pre-dated increases in COVID-19 testing rates and were not counted in official records as related to COVID-19. There was substantial variability between states in the discrepancy between reported rates of death due to COVID-19 and the estimated burden of excess deaths due to P&I. The increase in all-cause deaths in New York and New Jersey is 1.5-3 times higher than the official tally of COVID-19 confirmed deaths or the estimated excess death due to P&I.

Conclusions

Excess P&I deaths provide a conservative estimate of COVID-19 burden and indicate that COVID-19-related deaths are missed in locations with inadequate testing or intense pandemic activity.

State-level estimates of excess hospitalizations and deaths associated with influenza

Background

National estimates of influenza burden may not reflect state‐level influenza activity, and local surveillance may not capture the full burden of influenza.

Methods

To provide state‐level information about influenza burden, we estimated excess pneumonia and influenza (P&I) and respiratory and circulatory (R&C) hospitalizations and deaths in Colorado from local hospital discharge records, death certificates, and influenza virus surveillance using negative binomial models.

Results

From July 2007 to June 2016, influenza was associated with an excess of 17 911 P&I hospitalizations (95%CI: 15 227, 20 354), 30 811 R&C hospitalizations (95%CI: 24 344, 37 176), 1,064 P&I deaths (95%CI: 757, 1298), and 3828 R&C deaths (95%CI: 2060, 5433). There was a large burden of influenza A(H1N1) among persons aged 0‐64 years, with high median seasonal rates of excess hospitalization among persons aged 0‐4 years. Persons aged ≥65 years experienced the largest numbers and highest median seasonal rates of excess hospitalization and death associated with influenza A (H3N2). The burden of influenza B was generally lower, with elevated median seasonal rates of excess hospitalization among persons aged 0‐4 years and ≥65 years.

Conclusions

These findings complement existing influenza surveillance. Periodic state‐level estimates of influenza disease burden may be useful for setting state public health priorities and planning prevention and control initiatives.

Years of Life Lost Due to Influenza-Attributable Mortality in Older Adults in the Netherlands: A Competing-Risks Approach

We estimated the influenza mortality burden in adults aged 60 years or older in the Netherlands in terms of years of life lost, taking into account competing mortality risks. Weekly laboratory surveillance data for influenza and other respiratory pathogens and weekly extreme temperature served as covariates in Poisson regression models fitted to weekly mortality data, specific to age group, for the period 1999-2000 through 2012-2013. Burden for age groups 60-64 years through 85-89 years was computed as years of life lost before age 90 (YLL90), using restricted mean lifetime survival analysis and accounting for competing risks. Influenza-attributable mortality burden was greatest for persons aged 80-84 years, at 914 YLL90 per 100,000 persons (95% uncertainty interval: 867, 963), followed by persons aged 85-89 years (787 YLL90/100,000; 95% uncertainty interval: 741, 834). Ignoring competing mortality risks in the computation of influenza-attributable YLL90 would lead to substantial overestimation of burden, from 3.5% for persons aged 60-64 years to 82% for those aged 80-89 years at death. Failure to account for competing mortality risks has implications for the accuracy of disease-burden estimates, especially among persons aged 80 years or older. Because the mortality burden borne by the elderly is notably high, prevention initiatives may benefit from being redesigned to more effectively prevent infection in the oldest age groups.

Heterogeneity in Estimates of the Impact of Influenza on Population Mortality: A Systematic Review

Influenza viruses are associated with a substantial global burden of morbidity and mortality every year. Estimates of influenza-associated mortality often vary between studies due to differences in study settings, methods, and measurement of outcomes. We reviewed 103 published articles assessing population-based influenza-associated mortality through searches of PubMed and Embase, and we identified considerable variation in the statistical methods used across studies. Studies using regression models with an influenza activity proxy applied 4 approaches to estimate influenza-associated mortality. The estimates increased with age and ranged widely, from -0.3-1.3 and 0.6-8.3 respiratory deaths per 100,000 population for children and adults, respectively, to 4-119 respiratory deaths per 100,000 population for older adults. Meta-regression analysis identified that study design features were associated with the observed variation in estimates. The estimates increased with broader cause-of-death classification and were higher for older adults than for children. The multiplier methods tended to produce lower estimates, while Serfling-type models were associated with higher estimates than other methods. No "average" estimate of excess mortality could reliably be made due to the substantial variability of the estimates, partially attributable to methodological differences in the studies. Standardization of methodology in estimation of influenza-associated mortality would permit improved comparisons in the future.

Predictive Modeling of Influenza Shows the Promise of Applied Evolutionary Biology

Seasonal influenza is controlled through vaccination campaigns. Evolution of influenza virus antigens means that vaccines must be updated to match novel strains, and vaccine effectiveness depends on the ability of scientists to predict nearly a year in advance which influenza variants will dominate in upcoming seasons. In this review, we highlight a promising new surveillance tool: predictive models. Based on data-sharing and close collaboration between the World Health Organization and academic scientists, these models use surveillance data to make quantitative predictions regarding influenza evolution. Predictive models demonstrate the potential of applied evolutionary biology to improve public health and disease control. We review the state of influenza predictive modeling and discuss next steps and recommendations to ensure that these models deliver upon their considerable biomedical promise.

Mortality burden from seasonal influenza and 2009 H1N1 pandemic influenza in Beijing, China, 2007-2013

BACKGROUND

Data about influenza mortality burden in northern China are limited. This study estimated mortality burden in Beijing associated with seasonal influenza from 2007 to 2013 and the 2009 H1N1 pandemic.

METHODS

We estimated influenza-associated excess mortality by fitting a negative binomial model using weekly mortality data as the outcome of interest with the percent of influenza-positive samples by type/subtype as predictor variables.

RESULTS

From 2007 to 2013, an average of 2375 (CI 1002-8688) deaths was attributed to influenza per season, accounting for 3% of all deaths. Overall, 81% of the deaths attributed to influenza occurred in adults aged ≥65 years, and the influenza-associated mortality rate in this age group was higher than the rate among those aged <65 years (113.6 [CI 49.5-397.4] versus 4.4 [CI 1.7-18.6] per 100 000, P < .05). The mortality rate associated with the 2009 H1N1 pandemic in 2009/2010 was comparable to that of seasonal influenza during the seasonal years (19.9 [CI 10.4-33.1] vs 17.2 [CI 7.2-67.5] per 100 000). People aged <65 years represented a greater proportion of all deaths during the influenza A(H1N1)pdm09 pandemic period than during the seasonal epidemics (27.0% vs 17.7%, P < .05).

CONCLUSIONS

Influenza is an important contributor to mortality in Beijing, especially among those aged ≥65 years. These results support current policies to give priority to older adults for seasonal influenza vaccination and help to define the populations at highest risk for death that could be targeted for pandemic influenza vaccination.

Enhanced Surveillance for Fatal Dengue-Like Acute Febrile Illness in Puerto Rico, 2010-2012

BACKGROUND

Dengue is a leading cause of morbidity throughout the tropics; however, accurate population-based estimates of mortality rates are not available.

METHODS/PRINCIPAL FINDINGS

We established the Enhanced Fatal Acute Febrile Illness Surveillance System (EFASS) to estimate dengue mortality rates in Puerto Rico. Healthcare professionals submitted serum and tissue specimens from patients who died from a dengue-like acute febrile illness, and death certificates were reviewed to identify additional cases. Specimens were tested for markers of dengue virus (DENV) infection by molecular, immunologic, and immunohistochemical methods, and were also tested for West Nile virus, Leptospira spp., and other pathogens based on histopathologic findings. Medical records were reviewed and clinical data abstracted. A total of 311 deaths were identified, of which 58 (19%) were DENV laboratory-positive. Dengue mortality rates were 1.05 per 100,000 population in 2010, 0.16 in 2011 and 0.36 in 2012. Dengue mortality was highest among adults 19-64 years and seniors ≥65 years (1.17 and 1.66 deaths per 100,000, respectively). Other pathogens identified included 34 Leptospira spp. cases and one case of Burkholderia pseudomallei and Neisseria meningitidis.

CONCLUSIONS/SIGNIFICANCE

EFASS showed that dengue mortality rates among adults were higher than reported for influenza, and identified a leptospirosis outbreak and index cases of melioidosis and meningitis.

Deaths associated with respiratory syncytial and influenza viruses among persons ≥5 years of age in HIV-prevalent area, South Africa, 1998-2009(1)

We estimated deaths attributable to influenza and respiratory syncytial virus (RSV) among persons >5 years of age in South Africa during 1998-2009 by applying regression models to monthly deaths and laboratory surveillance data. Rates were expressed per 100,000 person-years. The mean annual number of seasonal influenza-associated deaths was 9,093 (rate 21.6). Persons >65 years of age and HIV-positive persons accounted for 50% (n = 4,552) and 28% (n = 2,564) of overall seasonal influenza-associated deaths, respectively. In 2009, we estimated 4,113 (rate 9.2) influenza A(H1N1)pdm09-associated deaths. The mean of annual RSV-associated deaths during the study period was 511 (rate 1.2); no RSV-associated deaths were estimated in persons >45 years of age. Our findings support the recommendation for influenza vaccination of older persons and HIV-positive persons. Surveillance for RSV should be strengthened to clarify the public health implications and severity of illness associated with RSV infection in South Africa.

Influenza Burden and Transmission in the Tropics

Each year, influenza causes substantial mortality and morbidity worldwide. It is important to understand influenza in the tropics because of the significant burden in the region and its relevance to global influenza circulation. In this review, influenza burden, transmission dynamics, and their determinants in the tropics are discussed. Environmental, cultural, and social conditions in the tropics are very diverse and often differ from those of temperate regions. Theories that account for and predict influenza dynamics in temperate regions do not fully explain influenza epidemic patterns observed in the tropics. Routine surveillance and household studies have been useful in understanding influenza dynamics in the tropics, but these studies have been limited to only some regions; there is still a lack of information regarding influenza burden and transmission dynamics in many tropical countries. Further studies in the tropics will provide useful insight on many questions that remain.

TGF-β-induced IL-6 prevents development of acute lung injury in influenza A virus-infected F508del CFTR-heterozygous mice

As the eighth leading cause of annual mortality in the USA, influenza A viruses are a major public health concern. In 20% of patients, severe influenza progresses to acute lung injury (ALI). However, pathophysiological mechanisms underlying ALI development are poorly defined. We reported that, unlike wild-type (WT) C57BL/6 controls, influenza A virus-infected mice that are heterozygous for the F508del mutation in the cystic fibrosis transmembrane conductance regulator (HETs) did not develop ALI. This effect was associated with higher IL-6 and alveolar macrophages (AMs) at 6 days postinfection (d.p.i.) in HET bronchoalveolar lavage fluid (BALF). In the present study, we found that HET AMs were an important source of IL-6 at 6 d.p.i. Infection also induced TGF-β production by HET but not WT mice at 2 d.p.i. TGF-β neutralization at 2 d.p.i. (TGF-N) significantly reduced BALF IL-6 in HETs at 6 d.p.i. Neither TGF-N nor IL-6 neutralization at 4 d.p.i. (IL-6-N) altered postinfection weight loss or viral replication in either mouse strain. However, both treatments increased influenza A virus-induced hypoxemia, pulmonary edema, and lung dysfunction in HETs to WT levels at 6 d.p.i. TGF-N and IL-6-N did not affect BALF AM and neutrophil numbers but attenuated the CXCL-1/keratinocyte chemokine response in both strains and reduced IFN-γ production in WT mice. Finally, bone marrow transfer experiments showed that HET stromal and myeloid cells are both required for protection from ALI in HETs. These findings indicate that TGF-β-dependent production of IL-6 by AMs later in infection prevents ALI development in influenza A virus-infected HET mice.

Infection of mice with influenza A/WSN/33 (H1N1) virus alters alveolar type II cell phenotype

Influenza viruses cause acute respiratory disease of great importance to public health. Alveolar type II (ATII) respiratory epithelial cells are central to normal lung function and are a site of influenza A virus replication in the distal lung. However, the consequences of infection for ATII cell function are poorly understood. To determine the impact of influenza infection on ATII cells we used C57BL/6-congenic SP-C(GFP) mice that express green fluorescent protein (GFP) under the control of the surfactant protein-C (SP-C) promoter, which is only active in ATII cells. Most cells isolated from the lungs of uninfected SP-C(GFP) mice were GFP(+) but did not express the alveolar type I (ATI) antigen podoplanin (PODO). ATII cells were also EpCAM(+) and α2,3-linked sialosaccharide(+). Infection with influenza A/WSN/33 virus caused severe hypoxemia and pulmonary edema. This was accompanied by loss of whole lung GFP fluorescence, reduced ATII cell yields, increased ATII cell apoptosis, reduced SP-C gene and protein expression in ATII cell lysates, and increased PODO gene and protein levels. Flow cytometry indicated that infection decreased GFP(+)/PODO(-) cells and increased GFP(-)/PODO(+) and GFP(-)/PODO(-) cells. Very few GFP(+)/PODO(+) cells were detectable. Finally, infection resulted in a significant decline in EpCAM expression by PODO(+) cells, but had limited effects on α2,3-linked sialosaccharides. Our findings indicate that influenza infection results in a progressive differentiation of ATII cells into ATI-like cells, possibly via an SP-C(-)/PODO(-) intermediate, to replace dying or dead ATI cells. However, impaired SP-C synthesis is likely to contribute significantly to reduced lung compliance in infected mice.

Are influenza-associated morbidity and mortality estimates for those ≥ 65 in statistical databases accurate, and an appropriate test of influenza vaccine effectiveness?

PURPOSES

To assess the accuracy of estimates using statistical databases of influenza-associated morbidity and mortality, and precisely measure influenza vaccine effectiveness.

PRINCIPAL RESULTS

Laboratory testing of influenza is incomplete. Death certificates under-report influenza. Statistical database models are used as an alternative to randomised controlled trials (RCTs) to assess influenza vaccine effectiveness. Evidence of the accuracy of influenza morbidity and mortality estimates was sought from: (1) Studies comparing statistical models. For four studies Poisson and ARIMA models produced higher estimates than Serfling, and Serfling higher than GLM. Which model is more accurate is unknown. (2) Studies controlling confounders. Fourteen studies mostly controlled one confounder (one controlled comorbidities), and limited control of confounders limits accuracy.

EVIDENCE FOR VACCINE EFFECTIVENESS WAS SOUGHT FROM

(1) Studies of regions with increasing vaccination rates. Of five studies two controlled for confounders and one found a positive vaccination effect. Three studies did not control confounders and two found no effect of vaccination. (2) Studies controlling multiple confounders. Of thirteen studies only two found a positive vaccine effect and no mortality differences between vaccinees and non-vaccinees in non-influenza seasons, showing confounders were controlled. Key problems are insufficient testing for influenza, using influenza-like illness, heterogeneity of seasonal and pandemic influenza, population aging, and incomplete confounder control (co-morbidities, frailty, vaccination history) and failure to demonstrate control of confounders by proving no mortality differences between vaccinees and non-vaccinees in non-influenza seasons.

MAJOR CONCLUSIONS

Improving model accuracy requires proof of no mortality differences in pre-influenza periods between the vaccinated and non-vaccinated groups, and reduction in influenza morbidity and mortality in seasons with a good vaccine match, more virulent strains, in the younger elderly with less immune senescence, and specific outcomes (laboratory-confirmed outcomes, pneumonia deaths). Proving influenza vaccine effectiveness requires appropriately powered RCTs, testing participants with RT-PCR tests, and comprehensively monitoring morbidity and mortality.

A robust parameter estimation method for estimating disease burden of respiratory viruses

BACKGROUND

Poisson model has been widely applied to estimate the disease burden of influenza, but there has been little success in providing reliable estimates for other respiratory viruses.

METHODS

We compared the estimates of excess hospitalization rates derived from the Poisson models with different combinations of inference methods and virus proxies respectively, with the aim to determine the optimal modeling approach. These models were validated by comparing the estimates of excess hospitalization attributable to respiratory viruses with the observed rates of laboratory confirmed paediatric hospitalization for acute respiratory infections obtained from a population based study.

RESULTS

The Bayesian inference method generally outperformed the classical likelihood estimation, particularly for RSV and parainfluenza, in terms of providing estimates closer to the observed hospitalization rates. Compared to the other proxy variables, age-specific positive counts provided better estimates for influenza, RSV and parainfluenza, regardless of inference methods. The Bayesian inference combined with age-specific positive counts also provided valid and reliable estimates for excess hospitalization associated with multiple respiratory viruses in both the 2009 H1N1 pandemic and interpandemic period.

CONCLUSIONS

Poisson models using the Bayesian inference method and virus proxies of age-specific positive counts should be considered in disease burden studies on multiple respiratory viruses.

Mortality associated with seasonal and pandemic influenza and respiratory syncytial virus among children <5 years of age in a high HIV prevalence setting--South Africa, 1998-2009

BACKGROUND

There are few published data describing the mortality burden associated with influenza and respiratory syncytial virus (RSV) infection in children in low- and middle-income countries and particularly from Africa and settings with high prevalence of human immunodeficiency virus (HIV).

METHODS

We modeled the excess mortality attributable to influenza (seasonal and pandemic) and RSV infection by applying Poisson regression models to monthly all-respiratory and pneumonia and influenza deaths, using national influenza and RSV laboratory surveillance data as covariates. In addition, we estimated the seasonal influenza- and RSV-associated deaths among HIV-infected and -uninfected children using Poisson regression models that incorporated HIV prevalence and highly active antiretroviral therapy coverage as covariates.

RESULTS

In children <5 years of age, the mean annual numbers of seasonal influenza- and RSV-associated all-respiratory deaths were 452 (8 per 100 000 person-years [PY]) and 546 (10 per 100 000 PY), respectively. Infants <1 year of age experienced higher mortality rates compared with children 1-4 years of age for both influenza (22 vs 5 per 100 000 PY) and RSV (35 vs 4 per 100 000 PY). HIV-infected compared with HIV-uninfected children <5 years of age were at increased risk of death associated with influenza (age-adjusted relative risk [aRR], 11.5; 95% confidence interval [CI], 9.6-12.6) and RSV (aRR, 8.1; 95% CI, 6.9-9.3) infection. In 2009, we estimated 549 (11 per 100 000 PY) all-respiratory influenza A(H1N1)pdm09-associated deaths among children aged <5 years.

CONCLUSIONS

Our findings support increased research efforts to guide and prioritize interventions such as influenza vaccination and HIV prevention in low- and middle-income countries with high HIV prevalence such as South Africa.

Mortality attributable to influenza in England and Wales prior to, during and after the 2009 pandemic

Very different influenza seasons have been observed from 2008/09-2011/12 in England and Wales, with the reported burden varying overall and by age group. The objective of this study was to estimate the impact of influenza on all-cause and cause-specific mortality during this period. Age-specific generalised linear regression models fitted with an identity link were developed, modelling weekly influenza activity through multiplying clinical influenza-like illness consultation rates with proportion of samples positive for influenza A or B. To adjust for confounding factors, a similar activity indicator was calculated for Respiratory Syncytial Virus. Extreme temperature and seasonal trend were controlled for. Following a severe influenza season in 2008/09 in 65+yr olds (estimated excess of 13,058 influenza A all-cause deaths), attributed all-cause mortality was not significant during the 2009 pandemic in this age group and comparatively low levels of influenza A mortality were seen in post-pandemic seasons. The age shift of the burden of seasonal influenza from the elderly to young adults during the pandemic continued into 2010/11; a comparatively larger impact was seen with the same circulating A(H1N1)pdm09 strain, with the burden of influenza A all-cause excess mortality in 15-64 yr olds the largest reported during 2008/09-2011/12 (436 deaths in 15-44 yr olds and 1,274 in 45-64 yr olds). On average, 76% of seasonal influenza A all-age attributable deaths had a cardiovascular or respiratory cause recorded (average of 5,849 influenza A deaths per season), with nearly a quarter reported for other causes (average of 1,770 influenza A deaths per season), highlighting the importance of all-cause as well as cause-specific estimates. No significant influenza B attributable mortality was detected by season, cause or age group. This analysis forms part of the preparatory work to establish a routine mortality monitoring system ahead of introduction of the UK universal childhood seasonal influenza vaccination programme in 2013/14.

Estimating influenza deaths in Canada, 1992-2009

Background

Poisson regression modelling has been widely used to estimate the disease burden attributable to influenza, though not without concerns that some of the excess burden could be due to other causes. This study aims to provide annual estimates of the mortality and hospitalization burden attributable to both seasonal influenza and the 2009 A/H1N1 pandemic influenza for Canada, and to discuss issues related to the reliability of these estimates.

Methods

Weekly time-series for all-cause mortality and regression models were used to estimate the number of deaths in Canada attributable to influenza from September 1992 to December 2009. To assess their robustness, the annual estimates derived from different parameterizations of the regression model for all-cause mortality were compared. In addition, the association between the annual estimates for mortality and hospitalization by age group, underlying cause of death or primary reason for admission and discharge status is discussed.

Results

The crude influenza-attributed mortality rate based on all-cause mortality and averaged over 17 influenza seasons prior to the 2009 A/H1N1 pandemic was 11.3 (95%CI, 10.5 - 12.1) deaths per 100 000 population per year, or an average of 3,500 (95%CI, 3,200 - 3,700) deaths per year attributable to seasonal influenza. The estimated annual rates ranged from undetectable at the ecological level to more than 6000 deaths per year over the three A/Sydney seasons. In comparison, we attributed an estimated 740 deaths (95%CI, 350–1500) to A(H1N1)pdm09. Annual estimates from different model parameterizations were strongly correlated, as were estimates for mortality and morbidity; the higher A(H1N1)pdm09 burden in younger age groups was the most notable exception.

Interpretation

With the exception of some of the Serfling models, differences in the ecological estimates of the disease burden attributable to influenza were small in comparison to the variation in disease burden from one season to another.

Regional variation in mortality impact of the 2009 A(H1N1) influenza pandemic in China

BACKGROUND

Laboratory-confirmed deaths grossly underestimate influenza mortality burden, so that reliable burden estimates are derived from indirect statistical studies, which are scarce in low- and middle-income settings.

OBJECTIVES

Here, we used statistical excess mortality models to estimate the burden of seasonal and pandemic influenza in China.

METHODS

We modeled data from a nationally representative population-based death registration system, combined with influenza virological surveillance data, to estimate influenza-associated excess mortality for the 2004-2005 through 2009-2010 seasons, by age and region.

RESULTS

The A(H1N1) pandemic was associated with 11·4-12·1 excess respiratory and circulatory (R&C) deaths per 100,000 population in rural sites of northern and southern China during 2009-2010; these rates were 2·2-2·8 times higher than those of urban sites (P<0·01). Influenza B accounted for a larger proportion of deaths than pandemic A(H1N1) in 2009-2010 in some regions. Nationally, we attribute 126,200 (95% CI, 61,000-248,400) excess R&C deaths (rate of 9·4/100,000) and 2,323,000 (1,166,000-4,533,000) years of life lost (YLL) to the first year of A(H1N1)pdm circulation.

CONCLUSIONS

The A(H1N1) pandemic posed a mortality and YLL burden comparable to that of interpandemic influenza in China. Our high burden estimates in rural areas highlight the need to enhance epidemiological surveillance and healthcare services, in underdeveloped and remote areas.

Mortality burden of the 2009-10 influenza pandemic in the United States: improving the timeliness of influenza severity estimates using inpatient mortality records

BACKGROUND

Delays in the release of national vital statistics hinder timely assessment of influenza severity, especially during pandemics. Inpatient mortality records could provide timelier estimates of influenza-associated mortality.

METHODS

We compiled weekly age-specific deaths for various causes from US State Inpatient Databases (1990-2010) and national vital statistics (1990-2009). We calculated influenza-attributable excess deaths by season based on Poisson regression models driven by indicators of respiratory virus activity, seasonality, and temporal trends.

RESULTS

Extrapolations of excess mortality from inpatient data fell within 11% and 17% of vital statistics estimates for pandemic and seasonal influenza, respectively, with high year-to-year correlation (Spearman's rho = 0.87-0.90, P < 0.001, n = 19). We attribute 14,800 excess respiratory and cardiac deaths (95% CI: 10,000-19,650) to pandemic influenza activity during April 2009-April 2010, 79% of which occurred in people under 65 years.

CONCLUSIONS

Modeling inpatient mortality records provides useful estimates of influenza severity in advance of national vital statistics release, capturing both the magnitude and the age distribution of pandemic and epidemic deaths. We provide the first age- and cause-specific estimates of the 2009 pandemic mortality burden using traditional 'excess mortality' methods, confirming the unusual burden of this virus in young populations. Our inpatient-based approach could help monitor mortality trends in other infectious diseases.