All-cause mortality during first wave of pandemic (H1N1) 2009, New South Wales, Australia, 2009

Economic and social determinants of health care utilization during the first wave of COVID-19 pandemic among adults in Ghana: a population-based cross-sectional study

BACKGROUND

The COVID-19 pandemic had socioeconomic effects in Africa. This study assessed the social and economic determinants of healthcare utilization during the first wave of COVID-19 among adults in Ghana.

METHODS

Information about individuals residing in Ghana was derived from a survey conducted across multiple countries, aiming to evaluate the impact of the COVID-19 pandemic on the mental health and overall well-being of adults aged 18 and above. The dependent variable for the study was healthcare utilization (categorized as low or high). The independent variables were economic (such as financial loss, job loss, diminished wages, investment/retirement setbacks, and non-refunded travel cancellations) and social (including food scarcity, loss of financial support sources, housing instability, challenges affording food, clothing, shelter, electricity, utilities, and increased caregiving responsibilities for partners) determinants of health. A multinomial logistic regression was conducted to identify factors associated with healthcare utilization after adjusting for confounders (age, gender, access to medical insurance, COVID-19 status, educational background, employment, and marital status of the participants).

RESULTS

The analysis included 364 responses. Individuals who encountered a loss of financial support (AOR: 9.58; 95% CI: 3.44-26.73; p < 0.001), a decrease or loss of wages (AOR: 7.44, 95% CI: 3.05-18.16, p < 0.001), experienced investment or retirement setbacks (AOR: 10.69, 95% CI: 2.60-43.88, p = 0.001), and expressed concerns about potential food shortages (AOR: 6.85, 95% CI: 2.49-18.84, p < 0.001) exhibited significantly higher odds of low healthcare utilization during the initial phase of the pandemic. Contrastingly, participants facing challenges in paying for basic needs demonstrated lower odds of low healthcare utilization compared to those who found it easy to cover basic expenses (AOR: 0.19, 95% CI: 0.06-0.67, p = 0.001).

CONCLUSION

Economic and social factors were associated with low healthcare utilization in Ghana during the first wave of the pandemic. Investment or retirement loss and financial support loss during the pandemic had the largest effect on healthcare utilization. Further research is needed to understand the connection between concerns about food shortages, welfare losses during pandemics and healthcare utilization during pandemics in Ghana.

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.

Artificial intelligence in public health: the potential of epidemic early warning systems

The use of artificial intelligence (AI) to generate automated early warnings in epidemic surveillance by harnessing vast open-source data with minimal human intervention has the potential to be both revolutionary and highly sustainable. AI can overcome the challenges faced by weak health systems by detecting epidemic signals much earlier than traditional surveillance. AI-based digital surveillance is an adjunct to-not a replacement of-traditional surveillance and can trigger early investigation, diagnostics and responses at the regional level. This narrative review focuses on the role of AI in epidemic surveillance and summarises several current epidemic intelligence systems including ProMED-mail, HealthMap, Epidemic Intelligence from Open Sources, BlueDot, Metabiota, the Global Biosurveillance Portal, Epitweetr and EPIWATCH. Not all of these systems are AI-based, and some are only accessible to paid users. Most systems have large volumes of unfiltered data; only a few can sort and filter data to provide users with curated intelligence. However, uptake of these systems by public health authorities, who have been slower to embrace AI than their clinical counterparts, is low. The widespread adoption of digital open-source surveillance and AI technology is needed for the prevention of serious epidemics.

Coronavirus disease 2019 (COVID-19) excess mortality outcomes associated with pandemic effects study (COPES): A systematic review and meta-analysis

Background and aim

With the Coronavirus Disease 2019 (COVID-19) pandemic continuing to impact healthcare systems around the world, healthcare providers are attempting to balance resources devoted to COVID-19 patients while minimizing excess mortality overall (both COVID-19 and non-COVID-19 patients). To this end, we conducted a systematic review (SR) to describe the effect of the COVID-19 pandemic on all-cause excess mortality (COVID-19 and non-COVID-19) during the pandemic timeframe compared to non-pandemic times.

Methods

We searched EMBASE, Cochrane Database of SRs, MEDLINE, Cumulative Index to Nursing and Allied Health Literature (CINAHL) and Cochrane Controlled Trials Register (CENTRAL), from inception (1948) to December 31, 2020. We used a two-stage review process to screen/extract data. We assessed risk of bias using Newcastle-Ottawa Scale (NOS). We used Critical Appraisal and Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology.

Results

Of 11,581 citations, 194 studies met eligibility. Of these studies, 31 had mortality comparisons (n = 433,196,345 participants). Compared to pre-pandemic times, during the COVID-19 pandemic, our meta-analysis demonstrated that COVID-19 mortality had an increased risk difference (RD) of 0.06% (95% CI: 0.06-0.06% p < 0.00001). All-cause mortality also increased [relative risk (RR): 1.53, 95% confidence interval (CI): 1.38-1.70, p < 0.00001] alongside non-COVID-19 mortality (RR: 1.18, 1.07-1.30, p < 0.00001). There was "very low" certainty of evidence through GRADE assessment for all outcomes studied, demonstrating the evidence as uncertain.

Interpretation

The COVID-19 pandemic may have caused significant increases in all-cause excess mortality, greater than those accounted for by increases due to COVID-19 mortality alone, although the evidence is uncertain.

Systematic review registration

[https://www.crd.york.ac.uk/prospero/#recordDetails], identifier [CRD42020201256].

All-cause excess mortality and COVID-19-related deaths in Iran

Background: Iran reported its first COVID-19 deaths on February 19, 2020 and announced 1284 deaths with a laboratory-confirmed SARS-CoV-2 infection by March 19, 2020 (end of the winter 1398 SH). We estimated all-cause excess mortality, compared to the historical trends, to obtain an indirect estimate of COVID-19-related deaths.

Methods: We assembled time series of the seasonal number of all-cause mortalities from March 21, 2013 (spring of 1392 SH) to March 19, 2020 (winter 1398 SH) for each province of Iran and nationwide with the vital statistics data from the National Organization for Civil Registration (NOCR). We estimated the expected seasonal mortality and excess mortality (the difference between the number of registered and expected deaths). Moreover, we reviewed the provincial number of confirmed cases of COVID-19 to assess their association with excess deaths.

Results: The results of our analysis showed around 7507 (95% CI: 3,350 – 11,664) and 5180 (95% CI: 1,023 – 9,337) all-cause excess mortality in fall and winter, respectively. There were 3778 excess deaths occurred in Qom, Gilan, Mazandaran, and Golestan provinces in the winter, all among the COVID-19 epicenters based on the number of confirmed cases.

Conclusion: We think most of the excess deaths in the winter were related to COVID-19. Also, we think the influenza epidemic might have been the main reason for the excess mortality in the fall and parts of excess deaths in the winter of 1398 SH. Moreover, a review of all available clinical and paraclinical records and through analyses of the surveillance data for severe acute respiratory infections (SARI) can help to obtain a more accurate estimate of COVID-19 mortality.

Health equity and COVID-19: global perspectives

The COVID-19 is disproportionally affecting the poor, minorities and a broad range of vulnerable populations, due to its inequitable spread in areas of dense population and limited mitigation capacity due to high prevalence of chronic conditions or poor access to high quality public health and medical care. Moreover, the collateral effects of the pandemic due to the global economic downturn, and social isolation and movement restriction measures, are unequally affecting those in the lowest power strata of societies. To address the challenges to health equity and describe some of the approaches taken by governments and local organizations, we have compiled 13 country case studies from various regions around the world: China, Brazil, Thailand, Sub Saharan Africa, Nicaragua, Armenia, India, Guatemala, United States of America (USA), Israel, Australia, Colombia, and Belgium. This compilation is by no-means representative or all inclusive, and we encourage researchers to continue advancing global knowledge on COVID-19 health equity related issues, through rigorous research and generation of a strong evidence base of new empirical studies in this field.

The role of pneumonia and secondary bacterial infection in fatal and serious outcomes of pandemic influenza a(H1N1)pdm09

BACKGROUND

The aim of this study was to estimate the prevalence of pneumonia and secondary bacterial infections during the pandemic of influenza A(H1N1)pdm09.

METHODS

A systematic review was conducted to identify relevant literature in which clinical outcomes of pandemic influenza A(H1N1)pdm09 infection were described. Published studies (between 01/01/2009 and 05/07/2012) describing cases of fatal or hospitalised A(H1N1)pdm09 and including data on bacterial testing or co-infection.

RESULTS

Seventy five studies met the inclusion criteria. Fatal cases with autopsy specimen testing were reported in 11 studies, in which any co-infection was identified in 23% of cases (Streptococcus pneumoniae 29%). Eleven studies reported bacterial co-infection among hospitalised cases of A(H1N1)2009pdm with confirmed pneumonia, with a mean of 19% positive for bacteria (Streptococcus pneumoniae 54%). Of 16 studies of intensive care unit (ICU) patients, bacterial co-infection identified in a mean of 19% of cases (Streptococcus pneumoniae 26%). The mean prevalence of bacterial co-infection was 12% in studies of hospitalised patients not requiring ICU (Streptococcus pneumoniae 33%) and 16% in studies of paediatric patients hospitalised in general or pediatric intensive care unit (PICU) wards (Streptococcus pneumoniae 16%).

CONCLUSION

We found that few studies of the 2009 influenza pandemic reported on bacterial complications and testing. Of studies which did report on this, secondary bacterial infection was identified in almost one in four patients, with Streptococcus pneumoniae the most common bacteria identified. Bacterial complications were associated with serious outcomes such as death and admission to intensive care. Prevention and treatment of bacterial secondary infection should be an integral part of pandemic planning, and improved uptake of routine pneumococcal vaccination in adults with an indication may reduce the impact of a pandemic.

Improving regional influenza surveillance through a combination of automated outbreak detection methods: the 2015/16 season in France

The 2014/15 influenza epidemic caused a work overload for healthcare facilities in France. The French national public health agency announced the start of the epidemic – based on indicators aggregated at the national level – too late for many hospitals to prepare. It was therefore decided to improve the influenza alert procedure through (i) the introduction of a pre-epidemic alert level to better anticipate future outbreaks, (ii) the regionalisation of surveillance so that healthcare structures can be informed of the arrival of epidemics in their region, (iii) the standardised use of data sources and statistical methods across regions. A web application was developed to deliver statistical results of three outbreak detection methods applied to three surveillance data sources: emergency departments, emergency general practitioners and sentinel general practitioners. This application was used throughout the 2015/16 influenza season by the epidemiologists of the headquarters and regional units of the French national public health agency. It allowed them to signal the first influenza epidemic alert in week 2016-W03, in Brittany, with 11 other regions in pre-epidemic alert. This application received positive feedback from users and was pivotal for coordinating surveillance across the agency’s regional units.

Emergency Department demand associated with seasonal influenza, 2010 through 2014, New South Wales, Australia

Introduction

Influenza’s impact on health and health care is underestimated by influenza diagnoses recorded in health-care databases. We aimed to estimate total and non-admitted influenza-attributable hospital Emergency Department (ED) demand in New South Wales (NSW), Australia.

Methods

We used generalized additive time series models to estimate the association between weekly counts of laboratory-confirmed influenza infections and weekly rates of total and non-admitted respiratory, infection, cardiovascular and all-cause ED visits in NSW, Australia for the period 2010 through 2014. Visit categories were based on the coded ED diagnosis or the free-text presenting problem if no diagnosis was recorded.

Results

The estimated all-age, annual influenza-attributable respiratory, infection, cardiovascular and all-cause visit rates/100 000 population/year were, respectively, 120.6 (99.9% confidence interval [CI] 102.3 to 138.8), 79.7 (99.9% CI: 70.6 to 88.9), 14.0 (99.9% CI: 6.8 to 21.3) and 309.0 (99.9% CI: 208.0 to 410.1). Among respiratory visits, influenza-attributable rates were highest among < 5-year-olds and ≥ 85-year-olds. For infection and all-cause visits, rates were highest among children; cardiovascular rates did not vary significantly by age. Annual rates varied substantially by year and age group, and statistically significant associations were absent in several years or age groups. Of the respiratory visits, 73.4% did not require admission. The non-admitted proportion was higher for the other clinical categories. Around 1 in 100 total visits and more than 1 in 10 respiratory or infection visits were associated with influenza.

Discussion

Influenza is associated with a substantial and annually varying burden of hospital-attended illness in NSW.

Influenza-Associated Excess Mortality in South Korea

INTRODUCTION

It is important to determine the health impact of influenza in order to calibrate public health measures. The objective of this study was to estimate excess mortality associated with influenza in Korea in 2003-2013.

METHODS

The authors constructed multiple linear regression models in 2014 with weekly mortality rates stratified by age, region, and cause of death against weekly surveillance data on influenza virus collected in 2003-2013. Excess mortality rates were estimated using the difference between predicted mortality rates from the fitted model versus predicted mortality rates with the influenza covariate for each strain set to 0.

RESULTS

During the study period, influenza was associated with an average of 2,900 excess deaths per year. The impact of influenza on mortality was significantly higher in older people; the overall all-cause excess annual mortality rate per 100,000 people was 5.97 (95% CI=4.89, 7.19), whereas it was 46.98 (95% CI=36.40, 55.82) for adults aged ≥65 years. It also greatly varied from year to year, ranging from 2.04 in 2009-2010 to 18.76 in 2011-2012.

CONCLUSIONS

The impact of influenza on mortality in Korea is substantial, particularly among the elderly and the rural population. More-comprehensive studies may be needed to estimate the full impact of influenza.

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.

Influenza and other respiratory viruses involved in severe acute respiratory disease in northern Italy during the pandemic and postpandemic period (2009-2011)

Since 2009 pandemic, international health authorities recommended monitoring severe and complicated cases of respiratory disease, that is, severe acute respiratory infection (SARI) and acute respiratory distress syndrome (ARDS). We evaluated the proportion of SARI/ARDS cases and deaths due to influenza A(H1N1)pdm09 infection and the impact of other respiratory viruses during pandemic and postpandemic period (2009-2011) in northern Italy; additionally we searched for unknown viruses in those cases for which diagnosis remained negative. 206 respiratory samples were collected from SARI/ARDS cases and analyzed by real-time RT-PCR/PCR to investigate influenza viruses and other common respiratory pathogens; also, a virus discovery technique (VIDISCA-454) was applied on those samples tested negative to all pathogens. Influenza A(H1N1)pdm09 virus was detected in 58.3% of specimens, with a case fatality rate of 11.3%. The impact of other respiratory viruses was 19.4%, and the most commonly detected viruses were human rhinovirus/enterovirus and influenza A(H3N2). VIDISCA-454 enabled the identification of one previously undiagnosed measles infection. Nearly 22% of SARI/ARDS cases did not obtain a definite diagnosis. In clinical practice, great efforts should be dedicated to improving the diagnosis of severe respiratory disease; the introduction of innovative molecular technologies, as VIDISCA-454, will certainly help in reducing such "diagnostic gap."

Global mortality estimates for the 2009 Influenza Pandemic from the GLaMOR project: a modeling study

BACKGROUND

Assessing the mortality impact of the 2009 influenza A H1N1 virus (H1N1pdm09) is essential for optimizing public health responses to future pandemics. The World Health Organization reported 18,631 laboratory-confirmed pandemic deaths, but the total pandemic mortality burden was substantially higher. We estimated the 2009 pandemic mortality burden through statistical modeling of mortality data from multiple countries.

METHODS AND FINDINGS

We obtained weekly virology and underlying cause-of-death mortality time series for 2005-2009 for 20 countries covering ∼35% of the world population. We applied a multivariate linear regression model to estimate pandemic respiratory mortality in each collaborating country. We then used these results plus ten country indicators in a multiple imputation model to project the mortality burden in all world countries. Between 123,000 and 203,000 pandemic respiratory deaths were estimated globally for the last 9 mo of 2009. The majority (62%-85%) were attributed to persons under 65 y of age. We observed a striking regional heterogeneity, with almost 20-fold higher mortality in some countries in the Americas than in Europe. The model attributed 148,000-249,000 respiratory deaths to influenza in an average pre-pandemic season, with only 19% in persons <65 y. Limitations include lack of representation of low-income countries among single-country estimates and an inability to study subsequent pandemic waves (2010-2012).

CONCLUSIONS

We estimate that 2009 global pandemic respiratory mortality was ∼10-fold higher than the World Health Organization's laboratory-confirmed mortality count. Although the pandemic mortality estimate was similar in magnitude to that of seasonal influenza, a marked shift toward mortality among persons <65 y of age occurred, so that many more life-years were lost. The burden varied greatly among countries, corroborating early reports of far greater pandemic severity in the Americas than in Australia, New Zealand, and Europe. A collaborative network to collect and analyze mortality and hospitalization surveillance data is needed to rapidly establish the severity of future pandemics. Please see later in the article for the Editors' Summary.

Statistical estimates of respiratory admissions attributable to seasonal and pandemic influenza for Canada

BACKGROUND

The number of admissions to hospital for which influenza is laboratory confirmed is considered to be a substantial underestimate of the true number of admissions due to an influenza infection. During the 2009 pandemic, testing for influenza in hospitalized patients was a priority, but the ascertainment rate remains uncertain.

METHODS

The discharge abstracts of persons admitted with any respiratory condition were extracted from the Canadian Discharge Abstract Database, for April 2003-March 2010. Stratified, weekly admissions were modeled as a function of viral activity, seasonality, and trend using Poisson regression models.

RESULTS

An estimated 1 out of every 6.4 admissions attributable to seasonal influenza (2003-April 2009) were coded to J10 (influenza virus identified). During the 2009 pandemic (May-March 2010), the influenza virus was identified in 1 of 1.6 admissions (95% CI, 1.5-1.7) attributed to the pandemic strain. Compared with previous H1N1 seasons (2007/08, 2008/09), the influenza-attributed hospitalization rate for persons <65 years was approximately six times higher during the 2009 H1N1 pandemic, whereas for persons 75 years or older, the pandemic rate was approximately fivefold lower.

CONCLUSIONS

Case ascertainment was much improved during the pandemic period, with under ascertainment of admissions due to H1N1/2009 limited primarily to patients with a diagnosis of pneumonia.

Trends in mortality from respiratory disease in Latin America since 1998 and the impact of the 2009 influenza pandemic

OBJECTIVE

To determine trends in mortality from respiratory disease in several areas of Latin America between 1998 and 2009.

METHODS

The numbers of deaths attributed to respiratory disease between 1998 and 2009 were extracted from mortality data from Argentina, southern Brazil, Chile, Costa Rica, Ecuador, Mexico and Paraguay. Robust linear models were then fitted to the rates of mortality from respiratory disease recorded between 2003 and 2009.

FINDINGS

Between 1998 and 2008, rates of mortality from respiratory disease gradually decreased in all age groups in most of the study areas. Among children younger than 5 years, for example, the annual rates of such mortality - across all seven study areas - fell from 56.9 deaths per 100,000 in 1998 to 26.6 deaths per 100,000 in 2008. Over this period, rates of mortality from respiratory disease were generally highest among adults older than 65 years and lowest among individuals aged 5 to 49 years. In 2009, mortality from respiratory disease was either similar to that recorded in 2008 or showed an increase - significant increases were seen among children younger than 5 years in Paraguay, among those aged 5 to 49 years in southern Brazil, Mexico and Paraguay and among adults aged 50 to 64 years in Mexico and Paraguay.

CONCLUSION

In much of Latin America, mortality from respiratory disease gradually fell between 1998 and 2008. However, this downward trend came to a halt in 2009, probably as a result of the (H1N1) 2009 pandemic.

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.

Infection fatality risk of the pandemic A(H1N1)2009 virus in Hong Kong

One measure of the severity of a pandemic influenza outbreak at the individual level is the risk of death among people infected by the new virus. However, there are complications in estimating both the numerator and denominator. Regarding the numerator, statistical estimates of the excess deaths associated with influenza virus infections tend to exceed the number of deaths associated with laboratory-confirmed infection. Regarding the denominator, few infections are laboratory confirmed, while differences in case definitions and approaches to case ascertainment can lead to wide variation in case fatality risk estimates. Serological surveillance can be used to estimate the cumulative incidence of infection as a denominator that is more comparable across studies. We estimated that the first wave of the influenza A(H1N1)pdm09 virus in 2009 was associated with approximately 232 (95% confidence interval: 136, 328) excess deaths of all ages in Hong Kong, mainly among the elderly. The point estimates of the risk of death on a per-infection basis increased substantially with age, from below 1 per 100,000 infections in children to 1,099 per 100,000 infections in those 60-69 years of age. Substantial variation in the age-specific infection fatality risk complicates comparison of the severity of different influenza strains.

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.

The impact of influenza A(H1N1)pdm09 compared with seasonal influenza on intensive care admissions in New South Wales, Australia, 2007 to 2010: a time series analysis

Background

In Australia, the 2009 epidemic of influenza A(H1N1)pdm09 resulted in increased admissions to intensive care. The annual contribution of influenza to use of intensive care is difficult to estimate, as many people with influenza present without a classic influenza syndrome and laboratory testing may not be performed. We used a population-based approach to estimate and compare the impact of recent epidemics of seasonal and pandemic influenza.

Methods

For 2007 to 2010, time series describing health outcomes in various population groups were prepared from a database of all intensive care unit (ICU) admissions in the state of New South Wales, Australia. The Serfling approach, a time series method, was used to estimate seasonal patterns in health outcomes in the absence of influenza epidemics. The contribution of influenza was estimated by subtracting expected seasonal use from observed use during each epidemic period.

Results

The estimated excess rate of influenza-associated respiratory ICU admissions per 100,000 inhabitants was more than three times higher in 2007 (2.6/100,000, 95% CI 2.0 to 3.1) than the pandemic year, 2009 (0.76/100,000, 95% CI 0.04 to 1.48). In 2009, the highest excess respiratory ICU admission rate was in 17 to 64 year olds (2.9/100,000, 95% CI 2.2 to 3.6), while in 2007, the highest excess rate was in those aged 65 years or older (9.5/100,000, 95% CI 6.2 to 12.8). In 2009, the excess rate was 17/100,000 (95% CI 14 to 20) in Aboriginal people and 14/100,000 (95% CI 13 to 16) in pregnant women.

Conclusion

While influenza was diagnosed more frequently and peak use of intensive care was higher during the epidemic of pandemic influenza in 2009, overall excess admissions to intensive care for respiratory illness was much greater during the influenza season in 2007. Thus, the impact of seasonal influenza on intensive care use may have previously been under-recognised. In 2009, high ICU use among young to middle aged adults was offset by relatively low use among older adults, and Aboriginal people and pregnant women were substantially over-represented in ICUs. Greater emphasis on prevention of serious illness in Aboriginal people and pregnant women should be a priority in pandemic planning.

Mortality burden of the 2009 A/H1N1 influenza pandemic in France: comparison to seasonal influenza and the A/H3N2 pandemic

BACKGROUND

The mortality burden of the 2009 A/H1N1 pandemic remains unclear in many countries due to delays in reporting of death statistics. We estimate the age- and cause-specific excess mortality impact of the pandemic in France, relative to that of other countries and past epidemic and pandemic seasons.

METHODS

We applied Serfling and Poisson excess mortality approaches to model weekly age- and cause-specific mortality rates from June 1969 through May 2010 in France. Indicators of influenza activity, time trends, and seasonal terms were included in the models. We also reviewed the literature for country-specific estimates of 2009 pandemic excess mortality rates to characterize geographical differences in the burden of this pandemic.

RESULTS

The 2009 A/H1N1 pandemic was associated with 1.0 (95% Confidence Intervals (CI) 0.2-1.9) excess respiratory deaths per 100,000 population in France, compared to rates per 100,000 of 44 (95% CI 43-45) for the A/H3N2 pandemic and 2.9 (95% CI 2.3-3.7) for average inter-pandemic seasons. The 2009 A/H1N1 pandemic had a 10.6-fold higher impact than inter-pandemic seasons in people aged 5-24 years and 3.8-fold lower impact among people over 65 years.

CONCLUSIONS

The 2009 pandemic in France had low mortality impact in most age groups, relative to past influenza seasons, except in school-age children and young adults. The historical A/H3N2 pandemic was associated with much larger mortality impact than the 2009 pandemic, across all age groups and outcomes. Our 2009 pandemic excess mortality estimates for France fall within the range of previous estimates for high-income regions. Based on the analysis of several mortality outcomes and comparison with laboratory-confirmed 2009/H1N1 deaths, we conclude that cardio-respiratory and all-cause mortality lack precision to accurately measure the impact of this pandemic in high-income settings and that use of more specific mortality outcomes is important to obtain reliable age-specific estimates.

Estimation of the burden of pandemic(H1N1)2009 in developing countries: experience from a tertiary care center in South India

Background

The burden of the pandemic (H1N1) 2009 influenza might be underestimated if detection of the virus is mandated to diagnose infection. Using an alternate approach, we propose that a much higher pandemic burden was experienced in our institution.

Methodology/Principal Findings

Consecutive patients (n = 2588) presenting to our hospital with influenza like illness (ILI) or severe acute respiratory infection (SARI) during a 1-year period (May 2009–April 2010) were prospectively recruited and tested for influenza A by real-time RT-PCR. Analysis of weekly trends showed an 11-fold increase in patients presenting with ILI/SARI during the peak pandemic period when compared with the pre-pandemic period and a significant (P<0.001) increase in SARI admissions during the pandemic period (30±15.9 admissions/week) when compared with pre-pandemic (7±2.5) and post-pandemic periods (5±3.8). However, Influenza A was detected in less than one-third of patients with ILI/SARI [699 (27.0%)]; a majority of these (557/699, 79.7%) were Pandemic (H1N1)2009 virus [A/H1N1/09]. An A/H1N1/09 positive test was correlated with shorter symptom duration prior to presentation (p = 0.03). More ILI cases tested positive for A/H1N1/09 when compared with SARI (27.4% vs. 14.6%, P = 0.037). When the entire study population was considered, A/H1N1/09 positivity was associated with lower risk of hospitalization (p<0.0001) and ICU admission (p = 0.013) suggesting mild self-limiting illness in a majority.

Conclusion/Significance

Analysis of weekly trends of ILI/SARI suggest a higher burden of the pandemic attributable to A/H1N1/09 than estimates assessed by a positive PCR test alone. The study highlights methodological consideration in the estimation of burden of pandemic influenza in developing countries using hospital-based data that may help assess the impact of future outbreaks of respiratory illnesses.

Comparison of the Roche RealTime ready Influenza A/H1N1 Detection Set with CDC A/H1N1pdm09 RT-PCR on samples from three hospitals in Ho Chi Minh City, Vietnam

Real-time polymerase chain reaction (PCR) can be considered the gold standard for detection of influenza viruses due to its high sensitivity and specificity. Roche has developed the RealTime ready Influenza A/H1N1 Detection Set, consisting of a generic influenza virus A PCR targeting the M2 gene (M2 PCR) and a specific PCR targeting the hemagglutinin (HA) of A/H1N1-pdm09 (HA PCR, 2009 H1N1), with the intention to make a reliable, rapid, and simple test to detect and quantify 2009 H1N1 in clinical samples. We evaluated this kit against the US Centers for Disease Control and Prevention (USCDC)/World Health Organization real-time PCR for influenza virus using 419 nose and throat swabs from 210 patients collected in 3 large hospitals in Ho Chi Minh City, Vietnam. In the per-patient analysis, when compared to CDC PCR, the sensitivity and specificity of the M2 PCR were 85.8% and 97.6%, respectively; the sensitivity and specificity of HA PCR were 88.2% and 100%, respectively. In the per-sample analysis, the sensitivity and specificity in nose swabs were higher than those in throat swabs for both M2 and HA PCRs. The viral loads as determined with the M2 and HA PCRs correlated well with the Ct values of the CDC PCR. Compared with the CDC PCR, the kit has a reasonable sensitivity and very good specificity for the detection and quantification of influenza A virus and A/H1N1-pdm09. However, given the current status of 2009 H1N1, a kit that can detect all circulating seasonal influenza viruses would be preferable.

Estimated global mortality associated with the first 12 months of 2009 pandemic influenza A H1N1 virus circulation: a modelling study

BACKGROUND

18,500 laboratory-confirmed deaths caused by the 2009 pandemic influenza A H1N1 were reported worldwide for the period April, 2009, to August, 2010. This number is likely to be only a fraction of the true number of the deaths associated with 2009 pandemic influenza A H1N1. We aimed to estimate the global number of deaths during the first 12 months of virus circulation in each country.

METHODS

We calculated crude respiratory mortality rates associated with the 2009 pandemic influenza A H1N1 strain by age (0-17 years, 18-64 years, and >64 years) using the cumulative (12 months) virus-associated symptomatic attack rates from 12 countries and symptomatic case fatality ratios (sCFR) from five high-income countries. To adjust crude mortality rates for differences between countries in risk of death from influenza, we developed a respiratory mortality multiplier equal to the ratio of the median lower respiratory tract infection mortality rate in each WHO region mortality stratum to the median in countries with very low mortality. We calculated cardiovascular disease mortality rates associated with 2009 pandemic influenza A H1N1 infection with the ratio of excess deaths from cardiovascular and respiratory diseases during the pandemic in five countries and multiplied these values by the crude respiratory disease mortality rate associated with the virus. Respiratory and cardiovascular mortality rates associated with 2009 pandemic influenza A H1N1 were multiplied by age to calculate the number of associated deaths.

FINDINGS

We estimate that globally there were 201,200 respiratory deaths (range 105,700-395,600) with an additional 83,300 cardiovascular deaths (46,000-179,900) associated with 2009 pandemic influenza A H1N1. 80% of the respiratory and cardiovascular deaths were in people younger than 65 years and 51% occurred in southeast Asia and Africa.

INTERPRETATION

Our estimate of respiratory and cardiovascular mortality associated with the 2009 pandemic influenza A H1N1 was 15 times higher than reported laboratory-confirmed deaths. Although no estimates of sCFRs were available from Africa and southeast Asia, a disproportionate number of estimated pandemic deaths might have occurred in these regions. Therefore, efforts to prevent influenza need to effectively target these regions in future pandemics.

FUNDING

None.

Attributable deaths due to influenza: a comparative study of seasonal and pandemic influenza

Influenza epidemics lead to an increase in hospitalizations and deaths. Up to now the overall impact of attributable deaths due to seasonal and pandemic influenza viruses in Austria has not been investigated in detail. Therefore we compared the number and age distribution of influenza associated deaths during ten influenza epidemic seasons to those observed during the pandemic influenza A(H1N1)2009 season. A Poisson model, relating age and daily deaths to week of influenza season using national mortality and viral surveillance data adjusted for the confounding effect of co-circulating Respiratory Syncytial Virus was used. We estimated an average of 316 influenza associated deaths per seasonal influenza epidemic (1999/2000-2008/2009) and 264 for the pandemic influenza season 2009/2010 in the area of Vienna, Austria. Comparing the mortality data for seasonal and pandemic influenza viruses in different age groups revealed a statistically significant increase in mortality for pandemic A(H1N1)2009 influenza virus in the age groups below 34 years of age and a significant decrease in mortality in those above 55 years. Our data adjusted for co-circulating RSV confirm the different mortality pattern of seasonal and pandemic influenza A(H1N1)2009 virus in different age groups.

Comparing pandemic to seasonal influenza mortality: moderate impact overall but high mortality in young children

BACKGROUND

We assessed the severity of the 2009 influenza pandemic by comparing pandemic mortality to seasonal influenza mortality. However, reported pandemic deaths were laboratory-confirmed - and thus an underestimation - whereas seasonal influenza mortality is often more inclusively estimated. For a valid comparison, our study used the same statistical methodology and data types to estimate pandemic and seasonal influenza mortality.

METHODS AND FINDINGS

We used data on all-cause mortality (1999-2010, 100% coverage, 16.5 million Dutch population) and influenza-like-illness (ILI) incidence (0.8% coverage). Data was aggregated by week and age category. Using generalized estimating equation regression models, we attributed mortality to influenza by associating mortality with ILI-incidence, while adjusting for annual shifts in association. We also adjusted for respiratory syncytial virus, hot/cold weather, other seasonal factors and autocorrelation. For the 2009 pandemic season, we estimated 612 (range 266-958) influenza-attributed deaths; for seasonal influenza 1,956 (range 0-3,990). 15,845 years-of-life-lost were estimated for the pandemic; for an average seasonal epidemic 17,908. For 0-4 yrs of age the number of influenza-attributed deaths during the pandemic were higher than in any seasonal epidemic; 77 deaths (range 61-93) compared to 16 deaths (range 0-45). The ≥75 yrs of age showed a far below average number of deaths. Using pneumonia/influenza and respiratory/cardiovascular instead of all-cause deaths consistently resulted in relatively low total pandemic mortality, combined with high impact in the youngest age category.

CONCLUSION

The pandemic had an overall moderate impact on mortality compared to 10 preceding seasonal epidemics, with higher mortality in young children and low mortality in the elderly. This resulted in a total number of pandemic deaths far below the average for seasonal influenza, and a total number of years-of-life-lost somewhat below average. Comparing pandemic and seasonal influenza mortality as in our study will help assessing the worldwide impact of the 2009 pandemic.

Excess mortality associated with the 2009 pandemic of influenza A(H1N1) in Hong Kong

Reliable estimates of the burden of 2009 pandemic influenza A(pH1N1) cannot be easily obtained because only a small fraction of infections were confirmed by laboratory tests in a timely manner. In this study we developed a Poisson prediction modelling approach to estimate the excess mortality associated with pH1N1 in 2009 and seasonal influenza in 1998–2008 in the subtropical city Hong Kong. The results suggested that there were 127 all-cause excess deaths associated with pH1N1, including 115 with cardiovascular and respiratory disease, and 22 with pneumonia and influenza. The excess mortality rates associated with pH1N1 were highest in the population aged ⩾65 years. The mortality burden of influenza during the whole of 2009 was comparable to those in the preceding ten inter-pandemic years. The estimates of excess deaths were more than twofold higher than the reported fatal cases with laboratory-confirmed pH1N1 infection.

Mortality burden of the A/H1N1 pandemic in Mexico: a comparison of deaths and years of life lost to seasonal influenza

BACKGROUND

The mortality burden of the 2009 A/H1N1 influenza pandemic remains controversial, in part because of delays in reporting of vital statistics that are traditionally used to measure influenza-related excess mortality. Here, we compare excess mortality rates and years of life lost (YLL) for pandemic and seasonal influenza in Mexico and evaluate laboratory-confirmed death reports.

METHODS

Monthly age- and cause-specific death rates from January 2000 through April 2010 and population-based surveillance of influenza virus activity were used to estimate excess mortality and YLL in Mexico. Age-stratified laboratory-confirmed A/H1N1 death reports were obtained from an active surveillance system covering 40% of the population.

RESULTS

The A/H1N1 pandemic was associated with 11.1 excess all-cause deaths per 100,000 population and 445,000 YLL during the 3 waves of virus activity in Mexico, April-December 2009. The pandemic mortality burden was 0.6-2.6 times that of a typical influenza season and lower than that of the severe 2003-2004 influenza epidemic. Individuals aged 5-19 and 20-59 years were disproportionately affected relative to their experience with seasonal influenza. Laboratory-confirmed deaths captured 1 of 7 pandemic excess deaths overall but only 1 of 41 deaths in persons >60 years of age in 2009. A recrudescence of excess mortality was observed in older persons during winter 2010, in a period when influenza and respiratory syncytial virus cocirculated.

CONCLUSIONS

Mexico experienced higher 2009 A/H1N1 pandemic mortality burden than other countries for which estimates are available. Further analyses of detailed vital statistics are required to assess geographical variation in the mortality patterns of this pandemic.

Relationship between the population incidence of febrile convulsions in young children in Sydney, Australia and seasonal epidemics of influenza and respiratory syncytial virus, 2003-2010: a time series analysis

Background

In 2010, intense focus was brought to bear on febrile convulsions in Australian children particularly in relation to influenza vaccination. Febrile convulsions are relatively common in infants and can lead to hospital admission and severe outcomes. We aimed to examine the relationships between the population incidence of febrile convulsions and influenza and respiratory syncytial virus (RSV) seasonal epidemics in children less than six years of age in Sydney Australia using routinely collected syndromic surveillance data and to assess the feasibility of using this data to predict increases in population rates of febrile convulsions.

Methods

Using two readily available sources of routinely collected administrative data; the NSW Emergency Department (ED) patient management database (1 January 2003 - 30 April 2010) and the Ambulance NSW dispatch database (1 July 2006 - 30 April 2010), we used semi-parametric generalized additive models (GAM) to determine the association between the population incidence rate of ED presentations and urgent ambulance dispatches for 'convulsions', and the population incidence rate of ED presentations for 'influenza-like illness' (ILI) and 'bronchiolitis' - proxy measures of influenza and RSV circulation, respectively.

Results

During the study period, when the weekly all-age population incidence of ED presentations for ILI increased by 1/100,000, the 0 to 6 year-old population incidence of ED presentations for convulsions increased by 6.7/100,000 (P < 0.0001) and that of ambulance calls for convulsions increased by 3.2/100,000 (P < 0.0001). The increase in convulsions occurred one week earlier relative to the ED increase in ILI. The relationship was weaker during the epidemic of pandemic (H1N1) 2009 influenza virus.

When the 0 to 3 year-old population incidence of ED presentations for bronchiolitis increased by 1/100,000, the 0 to 6 year-old population incidence of ED presentations for convulsions increased by 0.01/100,000 (P < 0.01). We did not find a meaningful and statistically significant association between bronchiolitis and ambulance calls for convulsions.

Conclusions

Influenza seasonal epidemics are associated with a substantial and statistically significant increase in the population incidence of hospital attendances and ambulance dispatches for reported febrile convulsions in young children. Monitoring syndromic ED and ambulance data facilitates rapid surveillance of reported febrile convulsions at a population level.

Controlling epidemic viral infection

PURPOSE OF THE REVIEW

Due to their different virulence and infectivity, both severe acute respiratory syndrome (SARS) and H1N1 09 revealed different strengths and weaknesses in our ability to contain new viral threats over the past decade. This review focuses on recent literature around attempts to contain the impact of these two viral epidemics that have refined our approach for the future.

RECENT FINDINGS

Attempts to contain emerging epidemics at the site of origin have so far failed, in part due to resourcing of surveillance. H1N1 09 revealed major problems with rigid pandemic planning and the need for much greater flexibility. Popular attempts to prevent international spread of pandemics have minimal efficacy. Availability of rapid diagnostic tests is critical to optimally managing epidemics and was a major problem with H1N1 09. Healthcare institutions have emerged as a major source of infection.

SUMMARY

The experience with H1N1 09 and SARS has been very useful in informing us of the strengths and weaknesses of our current approach to emerging epidemics. Key messages are a need for improved surveillance, more flexible planning, improved diagnostic testing and retaining a focus on basic hygiene measures.

Overview/reflections on the 2009 H1N1 pandemic

The Influenza A H1N1 2009 pandemic was a test of the global public health response. Strategies that worked included mass vaccine production and antivirals while quarantine and isolation proved futile. Among the lessons learned was the importance of severity in the definition of a pandemic.