Pandemic influenza A(H1N1)v in New Zealand: the experience from April to August 2009

How Do Past Immunization Strategies Compare With the COVID-19 Immunization Rollout: A New Zealand Analysis

OBJECTIVE

The aim of this study was to compare past New Zealand immunization strategies with the New Zealand coronavirus disease 2019 (COVID-19) immunization roll-out.

METHODS

Using the READ document analysis method, 2 New Zealand immunization strategies (for influenza and measles) were analyzed for how the disease, context, vaccine supply and demand, ethical principles (equity, individual autonomy, and maximizing benefits), and the Treaty of Waitangi impacted the immunization programs. The findings were compared with the ongoing COVID-19 mass immunization program in New Zealand, as of October 15, 2021.

RESULTS

Several themes common to the case-studies and the COVID-19 pandemic were identified including the importance of equity, obligations under the Treaty of Waitangi, ethical mandates, and preparedness.

CONCLUSIONS

Future emergency planning should integrate learnings from other infectious disease responses and immunization programs to avoid repeating mistakes and to create better health outcomes. This study has provided a basis for ongoing research into how an appropriate immunization plan can be developed that incorporates ethical values, the Treaty of Waitangi (in the NZ context), and evidence-based research to increase trust, equity, health, and preparedness for future outbreaks.

Cohort profile: Ngā Kawekawe o Mate Korona | Impacts of COVID-19 in Aotearoa - a prospective, national cohort study of people with COVID-19 in New Zealand

PURPOSE

The COVID-19 pandemic has had significant health, social and economic impacts around the world. We established a national, population-based longitudinal cohort to investigate the immediate and longer-term physical, psychological and economic impacts of COVID-19 on affected people in Aotearoa New Zealand (Aotearoa), with the resulting evidence to assist in designing appropriate health and well-being services for people with COVID-19.

PARTICIPANTS

All people residing in Aotearoa aged 16 years or over, who had a confirmed or probable diagnosis of COVID-19 prior to December 2021, were invited to participate. Those living in dementia units were excluded. Participation involved taking part in one or more of four online surveys and/or in-depth interviews. The first wave of data collection took place from February to June 2022.

FINDINGS TO DATE

By 30 November 2021, of 8735 people in Aotearoa aged 16+ who had COVID-19, 8712 were eligible for the study and 8012 had valid addresses so were able to be contacted to take part. A total of 990 people, including 161 Tāngata Whenua (Māori, Indigenous peoples of Aotearoa) completed one or more surveys; in addition, 62 took part in in-depth interviews. Two hundred and seventeen people (20%) reported symptoms consistent with long COVID. Key areas of adverse impacts were experiences of stigma, mental distress, poor experiences of health services and barriers to healthcare, each being significantly more pronounced among disabled people and/or those with long COVID.

FUTURE PLANS

Further data collection is planned to follow-up cohort participants. This cohort will be supplemented by the inclusion of a cohort of people with long COVID following Omicron infection. Future follow-ups will assess longitudinal changes to health and well-being impacts, including mental health, social, workplace/education and economic impacts of COVID-19.

Surveillance of communicable diseases using social media: A systematic review

BACKGROUND

Communicable diseases pose a severe threat to public health and economic growth. The traditional methods that are used for public health surveillance, however, involve many drawbacks, such as being labor intensive to operate and resulting in a lag between data collection and reporting. To effectively address the limitations of these traditional methods and to mitigate the adverse effects of these diseases, a proactive and real-time public health surveillance system is needed. Previous studies have indicated the usefulness of performing text mining on social media.

OBJECTIVE

To conduct a systematic review of the literature that used textual content published to social media for the purpose of the surveillance and prediction of communicable diseases.

METHODOLOGY

Broad search queries were formulated and performed in four databases. Both journal articles and conference materials were included. The quality of the studies, operationalized as reliability and validity, was assessed. This qualitative systematic review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

RESULTS

Twenty-three publications were included in this systematic review. All studies reported positive results for using textual social media content to surveille communicable diseases. Most studies used Twitter as a source for these data. Influenza was studied most frequently, while other communicable diseases received far less attention. Journal articles had a higher quality (reliability and validity) than conference papers. However, studies often failed to provide important information about procedures and implementation.

CONCLUSION

Text mining of health-related content published on social media can serve as a novel and powerful tool for the automated, real-time, and remote monitoring of public health and for the surveillance and prediction of communicable diseases in particular. This tool can address limitations related to traditional surveillance methods, and it has the potential to supplement traditional methods for public health surveillance.

Dynamic Propagation and Impact of Pandemic Influenza A (2009 H1N1) in Children: A Detailed Review

Influenza is a highly contagious respiratory infection caused by the circulating Swine flu virus. According to the World Health Organization (WHO), the unique blending strain of influenza A H1N1 2009 (Swine Flu) is a pandemic affecting several geographical regions, including India. Previous literature indicates that children are "drivers" of influenza pandemics. At present, satisfactory data were not available to accurately estimate the role of children in the spread of influenza (in particular 2009 pandemic influenza). However, the role of children in the spread of pandemics influenza is unclear. Several studies in children have indicated that the immunization program decreased the occurrence of influenza, emphasizing the significance of communities impacted by global immunization programs. This article provides a brief overview on how children are a key contributor to pandemic Influenza A (2009 H1N1) and we would like to draw your attention to the need for a new vaccine for children to improve disease prevention and a positive impact on the community.

Socioeconomic bias in influenza surveillance

Individuals in low socioeconomic brackets are considered at-risk for developing influenza-related complications and often exhibit higher than average influenza-related hospitalization rates. This disparity has been attributed to various factors, including restricted access to preventative and therapeutic health care, limited sick leave, and household structure. Adequate influenza surveillance in these at-risk populations is a critical precursor to accurate risk assessments and effective intervention. However, the United States of America’s primary national influenza surveillance system (ILINet) monitors outpatient healthcare providers, which may be largely inaccessible to lower socioeconomic populations. Recent initiatives to incorporate Internet-source and hospital electronic medical records data into surveillance systems seek to improve the timeliness, coverage, and accuracy of outbreak detection and situational awareness. Here, we use a flexible statistical framework for integrating multiple surveillance data sources to evaluate the adequacy of traditional (ILINet) and next generation (BioSense 2.0 and Google Flu Trends) data for situational awareness of influenza across poverty levels. We find that ZIP Codes in the highest poverty quartile are a critical vulnerability for ILINet that the integration of next generation data fails to ameliorate.

Public health agencies maintain increasingly sophisticated surveillance systems, which integrate diverse data streams within limited budgets. Here we develop a method to design robust and efficient forecasting systems for influenza hospitalizations. With these forecasting models, we find support for a key data gap namely that the USA’s public health surveillance data sets are much more representative of higher socioeconomic sub-populations and perform poorly for the most at-risk communities. Thus, our study highlights another related socioeconomic inequity—a reduced capability to monitor outbreaks in at-risk populations—which impedes effective public health interventions.

Mixed polysaccharides derived from Shiitake mushroom, Poriacocos, Ginger, and Tangerine peel enhanced protective immune responses in mice induced by inactivated influenza vaccine

Influenza viruses are responsible for severe respiratory tract infections of individuals and may cause pandemics with a high risk of mortality and morbidity. Although vaccination is a primary means for prevention of influenza virus infections, poor vaccine performance or inadequate immune responses limits the efficacy of current vaccines and raises question regarding whether a better correlates of protection procedures should be performed. Here, we want to evaluate whether mixed polysaccharides (MPs) derived from shiitake mushroom, poriacocos, ginger, and dried tangerine peel could promote the immune response of inactivated influenza vaccine. Firstly, MPs were given to mice each day and for a total of 30 days, during which two immunizations were performed on mice on days 14 and 21. The results showed that serum total IgG and IgG2a levels were increased in MPs-treated mice on day 30. Following A/WSN/33 (H1N1) virus challenge, we found that MPs pretreatment in mice could increase mice weight gain and attenuate their clinical symptoms. Additional protective factors were also observed including prevention of excessive lung inflammation, promotion of CD19+ and CD278+ cell proportions in lung, elimination of virus in lung, and elevation of IFN-γ levels in serum. The current study demonstrate that MPs from shiitake mushroom, poriacocos, ginger, and dried tangerine peel could promote the immune efficacy and alleviate lung inflammation in mice with vaccines against H1N1 virus infection by activating both humoral and cellular immunity.

One Health Aotearoa: a transdisciplinary initiative to improve human, animal and environmental health in New Zealand

There is increased recognition that complex health challenges at the human-animal-environmental interface require a transdisciplinary, "whole-of-society" approach. This philosophy is particularly pertinent in Aotearoa-New Zealand because of the country's relatively isolated island ecosystem, economic reliance on agriculture and its intensification, and existing indigenous worldview that emphasises holism and interconnectivity between humans, animals and the environment. In New Zealand, the One Health Aotearoa (OHA) alliance was established in order to better connect researchers and to address a growing number of infectious diseases challenges. The emphasis of OHA is to bring together and facilitate interactions between people from diverse disciplines, link to stakeholders and communities, and engage with policy-makers, government operational agencies, and funders, thus providing a holistic and integrative systems-thinking approach to address priority questions and achieve desired outcomes in One Health. The initial focus of OHA has been on infectious diseases, but there is increasing recognition of the potential benefits of the alliance to address broader complex issues. Greater involvement and overlap of the environmental sciences, human and animal health sciences, social science, and indigenous kaupapa Māori research is particularly critical for ensuring its success within the New Zealand context. Given the economic and cultural importance of New Zealand's "clean, green" image, a One Health approach that draws strongly on the environmental sciences makes particular sense. Furthermore, as the global environment becomes increasingly stressed by anthropogenic pressures our research may hold potential solutions for similar challenges elsewhere.

Using Clinical Research Networks to Assess Severity of an Emerging Influenza Pandemic

Background

Early clinical severity assessments during the 2009 influenza A H1N1 pandemic (pH1N1) overestimated clinical severity due to selection bias and other factors. We retrospectively investigated how to use data from the International Network for Strategic Initiatives in Global HIV Trials, a global clinical influenza research network, to make more accurate case fatality ratio (CFR) estimates early in a future pandemic, an essential part of pandemic response.

Methods

We estimated the CFR of medically attended influenza (CFR_MA) as the product of probability of hospitalization given confirmed outpatient influenza and the probability of death given hospitalization with confirmed influenza for the pandemic (2009–2011) and post-pandemic (2012–2015) periods. We used literature survey results on health-seeking behavior to convert that estimate to CFR among all infected persons (CFR_AR).

Results

During the pandemic period, 5.0% (3.1%–6.9%) of 561 pH1N1-positive outpatients were hospitalized. Of 282 pH1N1-positive inpatients, 8.5% (5.7%–12.6%) died. CFR_MA for pH1N1 was 0.4% (0.2%–0.6%) in the pandemic period 2009–2011 but declined 5-fold in young adults during the post-pandemic period compared to the level of seasonal influenza in the post-pandemic period 2012–2015. CFR for influenza-negative patients did not change over time. We estimated the 2009 pandemic CFR_AR to be 0.025%, 16-fold lower than CFR_MA.

Conclusions

Data from a clinical research network yielded accurate pandemic severity estimates, including increased severity among younger people. Going forward, clinical research networks with a global presence and standardized protocols would substantially aid rapid assessment of clinical severity.

Clinical Trials Registration

NCT01056354 and NCT010561.

Health Challenges of the Pacific Region: Insights From History, Geography, Social Determinants, Genetics, and the Microbiome

The Pacific region, also referred to as Oceania, is a geographically widespread region populated by people of diverse cultures and ethnicities. Indigenous people in the region (Melanesians, Polynesians, Micronesians, Papuans, and Indigenous Australians) are over-represented on national, regional, and global scales for the burden of infectious and non-communicable diseases. Although social and environmental factors such as poverty, education, and access to health-care are assumed to be major drivers of this disease burden, there is also developing evidence that genetic and microbiotic factors should also be considered. To date, studies investigating genetic and/or microbiotic links with vulnerabilities to infectious and non-communicable diseases have mostly focused on populations in Europe, Asia, and USA, with uncertain associations for other populations such as indigenous communities in Oceania. Recent developments in personalized medicine have shown that identifying ethnicity-linked genetic vulnerabilities can be important for medical management. Although our understanding of the impacts of the gut microbiome on health is still in the early stages, it is likely that equivalent vulnerabilities will also be identified through the interaction between gut microbiome composition and function with pathogens and the host immune system. As rapid economic, dietary, and cultural changes occur throughout Oceania it becomes increasingly important that further research is conducted within indigenous populations to address the double burden of high rates of infectious diseases and rapidly rising non-communicable diseases so that comprehensive development goals can be planned. In this article, we review the current knowledge on the impact of nutrition, genetics, and the gut microbiome on infectious diseases in indigenous people of the Pacific region.

Hospitalization and death among patients with influenza, Guatemala, 2008-2012

Background

Influenza is a major cause of respiratory illness resulting in 3–5 million severe cases and 291,243-645,832 deaths annually. Substantial health and financial burden may be averted by annual influenza vaccine application, especially for high risk groups.

Methods

We used an active facility-based surveillance platform for acute respiratory diseases in three hospitals in Guatemala, Central America, to estimate the incidence of laboratory-confirmed hospitalized influenza cases and identify risk factors associated with severe disease (defined as admission to the intensive care unit (ICU) or death). We enrolled patients presenting with signs and symptoms of acute respiratory infection (ARI) and obtained naso- and oropharyngeal samples for real-time reverse transcriptase polymerase chain reaction (RT-PCR). We used multivariable logistic regression to identify risk factors for ICU admission or death, adjusted for age and sex.

Results

From May 2008 to July 2012, among 6326 hospitalized ARI cases, 446 (7%) were positive for influenza: of those, 362 (81%) had influenza A and 84 (18%) had influenza B. Fifty nine percent of patients were aged ≤ 5 years, and 10% were aged ≥ 65 years. The median length of hospitalization was 5 days (interquartile range: 5). Eighty of 446 (18%) were admitted to the ICU and 28 (6%) died. Among the 28 deaths, 7% were aged ≤ 6 months, 39% 7–60 months, 21% 5–50 years, and 32% ≥ 50 years. Children aged ≤ 6 months comprised 19% of cases and 22% of ICU admissions. Women of child-bearing age comprised 6% of cases (2 admitted to ICU; 1 death). In multivariable analyses, Santa Rosa site (adjusted odds ratio [aOR] = 10, 95% confidence interval [CI] = 2–50), indigenous ethnicity (aOR = 4, 95% CI = 2–13, and radiologically-confirmed pneumonia (aOR = 5, 95% CI = 3–11) were independently associated with severe disease. Adjusted for hospital utilization rate, annual incidence of hospitalized laboratory-confirmed influenza was 24/100,000 overall, 93/100,000 for children aged < 5 years and 50/100,000 for those ≥ 65 years.

Conclusions

Influenza is a major contributor of hospitalization and death due to respiratory diseases in Guatemala. Further application of proven influenza prevention and treatment strategies is warranted.

Optimal multi-source forecasting of seasonal influenza

Forecasting the emergence and spread of influenza viruses is an important public health challenge. Timely and accurate estimates of influenza prevalence, particularly of severe cases requiring hospitalization, can improve control measures to reduce transmission and mortality. Here, we extend a previously published machine learning method for influenza forecasting to integrate multiple diverse data sources, including traditional surveillance data, electronic health records, internet search traffic, and social media activity. Our hierarchical framework uses multi-linear regression to combine forecasts from multiple data sources and greedy optimization with forward selection to sequentially choose the most predictive combinations of data sources. We show that the systematic integration of complementary data sources can substantially improve forecast accuracy over single data sources. When forecasting the Center for Disease Control and Prevention (CDC) influenza-like-illness reports (ILINet) from week 48 through week 20, the optimal combination of predictors includes public health surveillance data and commercially available electronic medical records, but neither search engine nor social media data.

A review of the 1918 herald pandemic wave: importance for contemporary pandemic response strategies

Mounting epidemiological evidence supports the occurrence of a mild herald pandemic wave in the spring and summer of 1918 in North America and Europe, several months before the devastating autumn outbreak that killed an estimated 2% of the global population. These epidemiological findings corroborate the anecdotal observations of contemporary clinicians who reported widespread influenza outbreaks in spring and summer 1918, with sporadic occurrence of unusually severe clinical manifestations in young adults. Initially seen as controversial, these findings were eventually confirmed by retrospective identification of influenza specimens collected from U.S. soldiers who died from acute respiratory infections in May-August 1918. Other studies found that having an episode of influenza illness during the spring herald wave was highly protective in the severe autumn wave. Here, we conduct a systematic review of the clinical, epidemiological, and virological evidence supporting the global occurrence of mild herald waves of the 1918 pandemic and place these historic observations in the context of pandemic preparedness. Taken together, historic experience with the 1918 and subsequent pandemics shows that increased severity in second and later pandemic waves may be the rule rather than the exception. Thus, a sustained pandemic response in the first years following a future pandemic is critical; conversely, multiwave pandemic patterns allow for more time to rollout vaccines and antivirals.

The impact of regular school closure on seasonal influenza epidemics: a data-driven spatial transmission model for Belgium

BACKGROUND

School closure is often considered as an option to mitigate influenza epidemics because of its potential to reduce transmission in children and then in the community. The policy is still however highly debated because of controversial evidence. Moreover, the specific mechanisms leading to mitigation are not clearly identified.

METHODS

We introduced a stochastic spatial age-specific metapopulation model to assess the role of holiday-associated behavioral changes and how they affect seasonal influenza dynamics. The model is applied to Belgium, parameterized with country-specific data on social mixing and travel, and calibrated to the 2008/2009 influenza season. It includes behavioral changes occurring during weekend vs. weekday, and holiday vs. school-term. Several experimental scenarios are explored to identify the relevant social and behavioral mechanisms.

RESULTS

Stochastic numerical simulations show that holidays considerably delay the peak of the season and mitigate its impact. Changes in mixing patterns are responsible for the observed effects, whereas changes in travel behavior do not alter the epidemic. Weekends are important in slowing down the season by periodically dampening transmission. Christmas holidays have the largest impact on the epidemic, however later school breaks may help in reducing the epidemic size, stressing the importance of considering the full calendar. An extension of the Christmas holiday of 1 week may further mitigate the epidemic.

CONCLUSION

Changes in the way individuals establish contacts during holidays are the key ingredient explaining the mitigating effect of regular school closure. Our findings highlight the need to quantify these changes in different demographic and epidemic contexts in order to provide accurate and reliable evaluations of closure effectiveness. They also suggest strategic policies in the distribution of holiday periods to minimize the epidemic impact.

National assessment of Canadian pandemic preparedness: Employing InFluNet to identify high-risk areas for inter-wave vaccine distribution

BACKGROUND

Influenza pandemics emerge at irregular and unpredictable intervals to cause substantial health, economic and social burdens. Optimizing health-system response is vital to mitigating the consequences of future pandemics.

METHODS

We developed a mathematical model to assess the preparedness of Canadian health systems to accommodate pandemic-related increases in patient demand. We identify vulnerable areas, assess the potential of inter-wave vaccination to mitigate impacts and evaluate the association between demographic and health-system characteristics in order to identify predictors of pandemic consequences.

RESULTS

Modelled average attack rates were 23.7-37.2% with no intervention and 2.5-6.4% with pre-vaccination. Peak acute-care demand was 7.5-19.5% of capacity with no intervention and 0.6-2.6% with pre-vaccination. The peak ICU demand was 39.3-101.8% with no intervention and 2.9-13.3% with pre-vaccination. Total mortality was 2258-7944 with no intervention and 88-472 with pre-vaccination. Regions of Southern Ontario were identified as most vulnerable to surges in patient demand. The strongest predictors of peak acute-care demand and ICU demand were acute-care bed capacity (R = -0.8697; r2 = 0.7564) and ICU bed capacity (R = -0.8151; r2 = 0.6644), respectively. Demographic characteristics had mild associations with predicted pandemic consequences.

CONCLUSION

Inter-wave vaccination provided adequate acute-care resource protection under all scenarios; ICU resource adequacy was protected under mild disease assumptions, but moderate and severe diseases caused demand to exceed expected availability in 21% and 49% of study areas, respectively. Our study informs priority vaccine distribution strategies for pandemic planning, emphasizing the need for targeted early vaccine distribution to high-risk individuals and areas.

Characteristics and outcomes of a cohort hospitalized for pandemic and seasonal influenza in Germany based on nationwide inpatient data

RATIONALE

From June of 2009 to August of 2010 the influenza subtype H1N1pdm09 caused a worldwide pandemic. The impact on populations and health care systems around the globe evolved differently. Substantial data come from the German national surveillance network in an outpatient and private practice setting, while information on hospitalized patients in Germany is rather limited.

METHODS

Data from the Federal Statistics Office comprising health insurance claims of the entire nationwide inpatient sample from 2005 to 2012 were used to identify patients who were hospitalized for laboratory-confirmed influenza and to analyse demographical aspects, comorbidities, hospitalization duration, outcomes and ventilator use during the pandemic and seasonal waves of influenza.

MEASUREMENTS AND MAIN RESULTS

A number of 34,493 admissions for laboratory-confirmed influenza occurred during waves between 2005 and 2012. During the pandemic seasonal waves, the number of hospitalizations vastly surpassed the level that was seen in any of the seasonal waves. A major demographic shift was seen with respect to patient age, as younger patients (< 60 years old) were more frequently hospitalized. Mean length of stay was shorter (149 vs. 193 hours), mean time on ventilation tended to be shorter (261 vs. 305 hours) in young children (< 4 years old) and longer (393 vs. 339 hours) in the elderly (> 60 years old). Time to ventilation was shorter in non-fatal cases (328 vs. 349 hours) and longer in fatal cases (419 vs. 358 hours). Logistic regression was used to show the impact of comorbidities and co-diagnoses on mortality and the need for ventilation, as well as differences between pandemic and seasonal influenza.

CONCLUSIONS

Inpatient data suggest differences in patient populations during pandemic and seasonal influenza. Younger patients were more frequently hospitalized. Differences with respect to the presence of certain comorbidities and co-diagnoses, length of stay, time to ventilation and ventilation time could be identified.

Modelled seasonal influenza mortality shows marked differences in risk by age, sex, ethnicity and socioeconomic position in New Zealand

OBJECTIVES

Influenza is responsible for a large number of deaths which can only be estimated using modelling methods. Such methods have rarely been applied to describe the major socio-demographic characteristics of this disease burden.

METHODS

We used quasi Poisson regression models with weekly counts of deaths and isolates of influenza A, B and respiratory syncytial virus for the period 1994 to 2008.

RESULTS

The estimated average mortality rate was 13.5 per 100,000 people which was 1.8% of all deaths in New Zealand. Influenza mortality differed markedly by age, sex, ethnicity and socioeconomic position. Relatively vulnerable groups were males aged 65-79 years (Rate ratio (RR) = 1.9, 95% CI: 1.9, 1.9 compared with females), Māori (RR = 3.6, 95% CI: 3.6, 3.7 compared with European/Others aged 65-79 years), Pacific (RR = 2.4, 95% CI: 2.4, 2.4 compared with European/Others aged 65-79 years) and those living in the most deprived areas (RR = 1.8, 95% CI: 1.3, 2.4) for New Zealand Deprivation (NZDep) 9&10 (the most deprived) compared with NZDep 1&2 (the least deprived).

CONCLUSIONS

These results support targeting influenza vaccination and other interventions to the most vulnerable groups, in particular Māori and Pacific people and men aged 65-79 years and those living in the most deprived areas.

Infectious Disease Surveillance in the Big Data Era: Towards Faster and Locally Relevant Systems

While big data have proven immensely useful in fields such as marketing and earth sciences, public health is still relying on more traditional surveillance systems and awaiting the fruits of a big data revolution. A new generation of big data surveillance systems is needed to achieve rapid, flexible, and local tracking of infectious diseases, especially for emerging pathogens. In this opinion piece, we reflect on the long and distinguished history of disease surveillance and discuss recent developments related to use of big data. We start with a brief review of traditional systems relying on clinical and laboratory reports. We then examine how large-volume medical claims data can, with great spatiotemporal resolution, help elucidate local disease patterns. Finally, we review efforts to develop surveillance systems based on digital and social data streams, including the recent rise and fall of Google Flu Trends. We conclude by advocating for increased use of hybrid systems combining information from traditional surveillance and big data sources, which seems the most promising option moving forward. Throughout the article, we use influenza as an exemplar of an emerging and reemerging infection which has traditionally been considered a model system for surveillance and modeling.

Prevention and Control of Seasonal Influenza with Vaccines

This report updates the 2015-16 recommendations of the Advisory Committee on Immunization Practices (ACIP) regarding the use of seasonal influenza vaccines (Grohskopf LA, Sokolow LZ, Olsen SJ, Bresee JS, Broder KR, Karron RA. Prevention and control of influenza with vaccines: recommendations of the Advisory Committee on Immunization Practices, United States, 2015-16 influenza season. MMWR Morb Mortal Wkly Rep 2015;64:818-25). Routine annual influenza vaccination is recommended for all persons aged ≥6 months who do not have contraindications. For the 2016-17 influenza season, inactivated influenza vaccines (IIVs) will be available in both trivalent (IIV3) and quadrivalent (IIV4) formulations. Recombinant influenza vaccine (RIV) will be available in a trivalent formulation (RIV3). In light of concerns regarding low effectiveness against influenza A(H1N1)pdm09 in the United States during the 2013-14 and 2015-16 seasons, for the 2016-17 season, ACIP makes the interim recommendation that live attenuated influenza vaccine (LAIV4) should not be used. Vaccine virus strains included in the 2016-17 U.S. trivalent influenza vaccines will be an A/California/7/2009 (H1N1)-like virus, an A/Hong Kong/4801/2014 (H3N2)-like virus, and a B/Brisbane/60/2008-like virus (Victoria lineage). Quadrivalent vaccines will include an additional influenza B virus strain, a B/Phuket/3073/2013-like virus (Yamagata lineage).Recommendations for use of different vaccine types and specific populations are discussed. A licensed, age-appropriate vaccine should be used. No preferential recommendation is made for one influenza vaccine product over another for persons for whom more than one licensed, recommended product is otherwise appropriate. This information is intended for vaccination providers, immunization program personnel, and public health personnel. Information in this report reflects discussions during public meetings of ACIP held on October 21, 2015; February 24, 2016; and June 22, 2016. These recommendations apply to all licensed influenza vaccines used within Food and Drug Administration-licensed indications, including those licensed after the publication date of this report. Updates and other information are available at CDC's influenza website (http://www.cdc.gov/flu). Vaccination and health care providers should check CDC's influenza website periodically for additional information.

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

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

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

Southern Hemisphere Influenza and Vaccine Effectiveness Research and Surveillance

The 2009 influenza A(H1N1)pdm09 pandemic highlighted the need for improved scientific knowledge to support better pandemic preparedness and seasonal influenza control. The Southern Hemisphere Influenza and Vaccine Effectiveness Research and Surveillance (SHIVERS) project, a 5-year (2012–2016) multiagency and multidisciplinary collaboration, aimed to measure disease burden, epidemiology, aetiology, risk factors, immunology, effectiveness of vaccination and other prevention strategies for influenza and other respiratory infectious diseases of public health importance. Two active, prospective, population-based surveillance systems were established for monitoring influenza and other respiratory pathogens among those hospitalized patients with acute respiratory illness and those enrolled patients seeking consultations at sentinel general practices. In 2015, a sero-epidemiological study will use a sample of patients from the same practices. These data will provide a full picture of the disease burden and risk factors from asymptomatic infections to severe hospitalized disease and deaths and related economic burden. The results during the first 2 years (2012–2013) provided scientific evidence to (a) support a change to NZ's vaccination policy for young children due to high influenza hospitalizations in these children; (b) contribute to the revision of the World Health Organization's case definition for severe acute respiratory illness for global influenza surveillance; and (c) contribute in part to vaccine strain selection using vaccine effectiveness assessment in the prevention of influenza-related consultations and hospitalizations. In summary, SHIVERS provides valuable international platforms for supporting seasonal influenza control and pandemic preparedness, and responding to other emerging/endemic respiratory-related infections.

Identifying influenza-like illness presentation from unstructured general practice clinical narrative using a text classifier rule-based expert system versus a clinical expert

BACKGROUND

We designed and validated a rule-based expert system to identify influenza like illness (ILI) from routinely recorded general practice clinical narrative to aid a larger retrospective research study into the impact of the 2009 influenza pandemic in New Zealand.

METHODS

Rules were assessed using pattern matching heuristics on routine clinical narrative. The system was trained using data from 623 clinical encounters and validated using a clinical expert as a gold standard against a mutually exclusive set of 901 records.

RESULTS

We calculated a 98.2 % specificity and 90.2 % sensitivity across an ILI incidence of 12.4 % measured against clinical expert classification. Peak problem list identification of ILI by clinical coding in any month was 9.2 % of all detected ILI presentations. Our system addressed an unusual problem domain for clinical narrative classification; using notational, unstructured, clinician entered information in a community care setting. It performed well compared with other approaches and domains. It has potential applications in real-time surveillance of disease, and in assisted problem list coding for clinicians.

CONCLUSIONS

Our system identified ILI presentation with sufficient accuracy for use at a population level in the wider research study. The peak coding of 9.2 % illustrated the need for automated coding of unstructured narrative in our study.

Potential Biases in Estimating Absolute and Relative Case-Fatality Risks during Outbreaks

Estimating the case-fatality risk (CFR)-the probability that a person dies from an infection given that they are a case-is a high priority in epidemiologic investigation of newly emerging infectious diseases and sometimes in new outbreaks of known infectious diseases. The data available to estimate the overall CFR are often gathered for other purposes (e.g., surveillance) in challenging circumstances. We describe two forms of bias that may affect the estimation of the overall CFR-preferential ascertainment of severe cases and bias from reporting delays-and review solutions that have been proposed and implemented in past epidemics. Also of interest is the estimation of the causal impact of specific interventions (e.g., hospitalization, or hospitalization at a particular hospital) on survival, which can be estimated as a relative CFR for two or more groups. When observational data are used for this purpose, three more sources of bias may arise: confounding, survivorship bias, and selection due to preferential inclusion in surveillance datasets of those who are hospitalized and/or die. We illustrate these biases and caution against causal interpretation of differential CFR among those receiving different interventions in observational datasets. Again, we discuss ways to reduce these biases, particularly by estimating outcomes in smaller but more systematically defined cohorts ascertained before the onset of symptoms, such as those identified by forward contact tracing. Finally, we discuss the circumstances in which these biases may affect non-causal interpretation of risk factors for death among cases.

An outbreak of influenza A (H1N1) virus in a remote Aboriginal community post-pandemic: implications for pandemic planning and health service policy

OBJECTIVE

To describe a 2013 outbreak of pandemic influenza A (H1N1) virus in a remote Western Australian Aboriginal community; inform outbreak prevention and control measures and discuss the community susceptibility to H1N1, three years after the A(H1N1)pdm09 pandemic.

METHODS

Records at the local clinic were used to classify cases as 'confirmed' (laboratory test positive for H1N1 or temperature >38°C with cough and/or sore throat) or 'probable' (self-reported fever with cough and/or sore throat). Additional data were collected from medical records and public health databases.

RESULTS

A total of 108 individuals met case definitions. Clinical attack rate was 23%. Children under five years of age had the highest age-specific attack rate (545 per 1,000 population). Thirty cases (28%) experienced complications with six (5.6%) requiring aero-evacuation. Only 7% of the community had received H1N1-containing vaccine during the previous year. No H1N1 cases from the community were previously reported.

CONCLUSIONS

This is the first description of the effects of a novel influenza strain on a remote Australian Aboriginal community. Isolation and low vaccination are likely explanations for the apparent naivety to H1N1.

IMPLICATIONS

There may be other remote communities at risk of H1N1. High attack and complication rates confirm that Aboriginal Australians should be prioritised in pandemic planning.

Estimating influenza-associated mortality in New Zealand from 1990 to 2008

This study used Poisson regression modelling to estimate influenza-associated mortality in New Zealand for 1990-2008. Inputs were weekly numbers of deaths and influenza and RSV isolates. Seasonal influenza was associated with an average of 401 medical deaths annually from 1990 to 2008, a rate of 10.6 (95% CI: 7.9, 13.3) per 100,000 persons per year, which is 17 times higher than recorded influenza deaths. The majority (86%) of deaths occurred in those 65 years and over. There was no clear decline in influenza-associated mortality in this age group over the course of the study period.

Implementing hospital-based surveillance for severe acute respiratory infections caused by influenza and other respiratory pathogens in New Zealand

BACKGROUND

Recent experience with pandemic influenza A(H1N1)pdm09 highlighted the importance of global surveillance for severe respiratory disease to support pandemic preparedness and seasonal influenza control. Improved surveillance in the southern hemisphere is needed to provide critical data on influenza epidemiology, disease burden, circulating strains and effectiveness of influenza prevention and control measures. Hospital-based surveillance for severe acute respiratory infection (SARI) cases was established in New Zealand on 30 April 2012. The aims were to measure incidence, prevalence, risk factors, clinical spectrum and outcomes for SARI and associated influenza and other respiratory pathogen cases as well as to understand influenza contribution to patients not meeting SARI case definition.

METHODS/DESIGN

All inpatients with suspected respiratory infections who were admitted overnight to the study hospitals were screened daily. If a patient met the World Health Organization's SARI case definition, a respiratory specimen was tested for influenza and other respiratory pathogens. A case report form captured demographics, history of presenting illness, co-morbidities, disease course and outcome and risk factors. These data were supplemented from electronic clinical records and other linked data sources.

DISCUSSION

Hospital-based SARI surveillance has been implemented and is fully functioning in New Zealand. Active, prospective, continuous, hospital-based SARI surveillance is useful in supporting pandemic preparedness for emerging influenza A(H7N9) virus infections and seasonal influenza prevention and control.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Pandemic influenza A(H1N1)pdm09: risk of infection in primary healthcare workers

BACKGROUND

Healthcare workers in primary care are at risk of infection during an influenza pandemic. The 2009 influenza pandemic provided an opportunity to assess this risk.

AIM

To measure the prevalence of seropositivity to influenza A(H1N1)pdm09 among primary healthcare workers in Canterbury, New Zealand, following the 2009 influenza pandemic, and to examine associations between seropositivity and participants' sociodemographic characteristics, professional roles, work patterns, and seasonal influenza vaccination status.

DESIGN AND SETTING

An observational study involving a questionnaire and testing for influenza A(H1N1)pdm09 seropositivity in all primary healthcare workers in Canterbury, New Zealand between December 2009 and February 2010. Method Participants completed a questionnaire that recorded sociodemographic and professional data, symptoms of influenza-like illness, history of seasonal influenza vaccination, and work patterns. Serum samples were collected and haemagglutination inhibition antibody titres to influenza A(H1N1)pdm09 measured.

RESULTS

Questionnaires and serum samples were received from 1027 participants, from a workforce of 1476 (response rate 70%). Seropositivity was detected in 224 participants (22%). Receipt of seasonal influenza vaccine (odds ratio [OR] = 2.0, 95% confidence interval [CI] = 1.2 to 3.3), recall of influenza (OR = 1.9, 95% CI = 1.3 to 2.8), and age ≤45 years (OR = 1.4, 95% CI = 1.0 to 1.9) were associated with seropositivity.

CONCLUSION

A total of 22% of primary care healthcare workers were seropositive. Younger participants, those who recalled having influenza, and those who had been vaccinated against seasonal influenza were more likely to be seropositive. Working in a dedicated influenza centre was not associated with an increased risk of seropositivity.

Case fatality risk of influenza A (H1N1pdm09): a systematic review

BACKGROUND

During the 2009 influenza pandemic, uncertainty surrounding the seriousness of human infections with the H1N1pdm09 virus hindered appropriate public health response. One measure of seriousness is the case fatality risk, defined as the probability of mortality among people classified as cases.

METHODS

We conducted a systematic review to summarize published estimates of the case fatality risk of the pandemic influenza H1N1pdm09 virus. Only studies that reported population-based estimates were included.

RESULTS

We included 77 estimates of the case fatality risk from 50 published studies, about one-third of which were published within the first 9 months of the pandemic. We identified very substantial heterogeneity in published estimates, ranging from less than 1 to more than 10,000 deaths per 100,000 cases or infections. The choice of case definition in the denominator accounted for substantial heterogeneity, with the higher estimates based on laboratory-confirmed cases (point estimates = 0-13,500 per 100,000 cases) compared with symptomatic cases (point estimates = 0-1,200 per 100,000 cases) or infections (point estimates = 1-10 per 100,000 infections). Risk based on symptomatic cases increased substantially with age.

CONCLUSIONS

Our review highlights the difficulty in estimating the seriousness of infection with a novel influenza virus using the case fatality risk. In addition, substantial variability in age-specific estimates complicates the interpretation of the overall case fatality risk and comparisons among populations. A consensus is needed on how to define and measure the seriousness of infection before the next pandemic.

Severe acute respiratory infections caused by 2009 pandemic influenza A (H1N1) among American Indians--southwestern United States, May 1-July 21, 2009

BACKGROUND

During April-July 2009, U.S. hospitalization rates for 2009 pandemic influenza A (H1N1) virus (H1N1pdm09) infection were estimated at 4·5/100 000 persons. We describe rates and risk factors for H1N1pdm09 infection among American Indians (AIs) in four isolated southwestern U.S. communities served by the Indian Health Service (IHS).

METHODS

We reviewed clinical and demographic information from medical records of AIs hospitalized during May 1-July 21, 2009 with severe acute respiratory infection (SARI). Hospitalization rates were determined using denominator data provided by IHS. H1N1pdm09 infection was confirmed with polymerase chain reaction, rapid tests, or convalescent serology. Risk factors for more severe (SARI) versus milder [influenza-like illness (ILI)] illness were determined by comparing confirmed SARI patients with outpatients with ILI.

RESULTS

Among 168 SARI-hospitalized patients, 52% had confirmed H1N1pdm09 infection and 93% had >1 high-risk condition for influenza complications. The H1N1pdm09 SARI hospitalization rate was 131/100 000 persons [95% confidence interval (CI), 102-160] and was highest among ages 0-4 years (353/100 000; 95% CI, 215-492). Among children, asthma (adjusted odds ratio [aOR] 3·2; 95% CI, 1·2-8·4) and age<2 years (aOR 3·8; 95% CI, 1·4-10·0) were associated with H1N1pdm09 SARI-associated hospitalization, compared with outpatient ILI. Among adults, diabetes (aOR 3·1; 95% CI, 1·5-6·4) was associated with hospitalization after controlling for obesity.

CONCLUSIONS

H1N1pdm09 hospitalization rates among this isolated AI population were higher than reported for other U.S. populations. Almost all case patients had high-risk health conditions. Prevention strategies for future pandemics should prioritize AIs, particularly in isolated rural areas.

Pandemic influenza A (H1N1) in non-vaccinated, pregnant women in Spain (2009-2010)

The aim of this study was to investigate the main characteristics of non-vaccinated pregnant women who were hospitalised for influenza A (H1N1) pdm09 pandemic versus pregnant women hospitalised for non-influenza-related reasons in Spain, and to characterise the clinical presentation of the disease in this population to facilitate early diagnosis and future action programmes. Understanding influenza infection during pregnancy is important as pregnant women are a high-risk population for increased morbidity from influenza infection. We investigated the socio-demographic and clinical features of 51 non-vaccinated, pregnant women infected with the pandemic influenza A (H1N1) virus in Spain (cases) and compared them to 114 controls (non-vaccinated and non-infected pregnant women) aged 15-44 years. Substantial and significant odd ratios (ORs) for pandemic influenza A (H1N1) were found for the pregnant women who were obese compared with controls (body mass index > 30) (OR 3.03; 95% confidence intervals 1.13-8.11). The more prevalent symptoms observed in pandemic influenza-infected pregnant women were high temperature, cough (82.4%), malaise (80.5%), myalgia (56.1%), and headaches (54.9%). Our results suggest that the initial symptoms and risk factors for infection of pregnant women with the influenza A (H1N1) pdm09 virus are similar to the symptoms and risk factors for seasonal influenza, which make early diagnosis difficult, and reinforces the need to identify and protect high-risk groups.

Estimating the burden of A(H1N1)pdm09 influenza in Finland during two seasons

In Finland, the pandemic influenza virus A(H1N1)pdm09 was the dominant influenza strain during the pandemic season in 2009/2010 and presented alongside other influenza types during the 2010/2011 season. The true number of infected individuals is unknown, as surveillance missed a large portion of mild infections. We applied Bayesian evidence synthesis, combining available data from the national infectious disease registry with an ascertainment model and prior information on A(H1N1)pdm09 influenza and the surveillance system, to estimate the total incidence and hospitalization rate of A(H1N1)pdm09 infection. The estimated numbers of A(H1N1)pdm09 infections in Finland were 211 000 (4% of the population) in the 2009/2010 pandemic season and 53 000 (1% of the population) during the 2010/2011 season. Altogether, 1·1% of infected individuals were hospitalized. Only 1 infection per 25 was ascertained.

Effects of vaccine program against pandemic influenza A(H1N1) virus, United States, 2009-2010

Vaccination likely prevented 700,000–1,500,000 clinical cases, 4,000–10,000 hospitalizations, and 200–500 deaths.

In April 2009, the United States began a response to the emergence of a pandemic influenza virus strain: A(H1N1)pdm09. Vaccination began in October 2009. By using US surveillance data (April 12, 2009–April 10, 2010) and vaccine coverage estimates (October 3, 2009–April 18, 2010), we estimated that the A(H1N1)pdm09 virus vaccination program prevented 700,000–1,500,000 clinical cases, 4,000–10,000 hospitalizations, and 200–500 deaths. We found that the national health effects were greatly influenced by the timing of vaccine administration and the effectiveness of the vaccine. We estimated that recommendations for priority vaccination of targeted priority groups were not inferior to other vaccination prioritization strategies. These results emphasize the need for relevant surveillance data to facilitate a rapid evaluation of vaccine recommendations and effects.

The challenges of global case reporting during pandemic A(H1N1) 2009

During the 2009 A(H1N1) influenza pandemic, the World Health Organization (WHO) asked all Member States to provide case-based data on at least the first 100 laboratory-confirmed influenza cases to generate an early understanding of the pandemic and provide appropriate guidance to affected countries. In reviewing the pandemic surveillance strategy, we evaluated the utility of case-based data collection and the challenges in interpreting these data at the global level. To do this, we assessed compliance with the surveillance recommendation and data completeness of submitted case records and described the epidemiological characteristics of up to the first 110 reported cases from each country, aggregated into regions. From April 2009 to August 2011, WHO received over 18 000 case records from 84 countries. Data reached WHO at different time intervals, in different formats and without information on collection methods. Just over half of the 18 000 records gave the date of symptom onset, which made it difficult to assess whether the cases were among the earliest to be confirmed. Descriptive epidemiological analyses were limited to summarizing age, sex and hospitalization ratios. Centralized analysis of case-based data had little value in describing key features of the pandemic. Results were difficult to interpret and would have been misleading if viewed in isolation. A better approach would be to identify critical questions, standardize data elements and methods of investigation, and create efficient channels for communication between countries and the international public health community. Regular exchange of routine surveillance data will help to consolidate these essential channels of communication.

A Simulation Optimization Approach to Epidemic Forecasting

Reliable forecasts of influenza can aid in the control of both seasonal and pandemic outbreaks. We introduce a simulation optimization (SIMOP) approach for forecasting the influenza epidemic curve. This study represents the final step of a project aimed at using a combination of simulation, classification, statistical and optimization techniques to forecast the epidemic curve and infer underlying model parameters during an influenza outbreak. The SIMOP procedure combines an individual-based model and the Nelder-Mead simplex optimization method. The method is used to forecast epidemics simulated over synthetic social networks representing Montgomery County in Virginia, Miami, Seattle and surrounding metropolitan regions. The results are presented for the first four weeks. Depending on the synthetic network, the peak time could be predicted within a 95% CI as early as seven weeks before the actual peak. The peak infected and total infected were also accurately forecasted for Montgomery County in Virginia within the forecasting period. Forecasting of the epidemic curve for both seasonal and pandemic influenza outbreaks is a complex problem, however this is a preliminary step and the results suggest that more can be achieved in this area.

Age-dependent estimates of the epidemiological impact of pandemic influenza (H1N1-2009) in Japan

The total number of influenza cases with medical attendance has been estimated from sentinel surveillance data in Japan under a random sampling assumption of sentinel medical institutions among the total medical institutions. The 2009 pandemic offered a research opportunity to validate the sentinel-based estimation method using the estimated proportion of infections measured by the population-wide seroepidemiological survey employing hemagglutinin inhibition (HI) assay. For the entire population, we estimated the age-standardized proportion of infections at 28.5% and 23.5% using cut-off values of HI titer at 1 : 20 and 1 : 40, respectively. Investigating the age profiles, we show that the estimated influenza-like illness (ILI) cases with medical attendance exceeded the estimated infections among those aged from 0 to 19 years, indicating an overestimation of the magnitude by sentinel-based estimation method. The ratio of estimated cases to estimated infections decreased as a function of age. Examining the geographic distributions, no positive correlation was identified between the estimated cases and infections. Our findings indicate a serious technical limitation of the so-called multiplier method in appropriately quantifying the risk of influenza due to limited specificity of ILI and reporting bias. A seroepidemiological study should be planned in advance of a pandemic.

School closures and influenza: systematic review of epidemiological studies

OBJECTIVE

To review the effects of school closures on pandemic and seasonal influenza outbreaks.

DESIGN

Systematic review.

DATA SOURCES

MEDLINE and EMBASE, reference lists of identified articles, hand searches of key journals and additional papers from the authors' collections.

STUDY SELECTION

Studies were included if they reported on a seasonal or pandemic influenza outbreak coinciding with a planned or unplanned school closure.

RESULTS

Of 2579 papers identified through MEDLINE and EMBASE, 65 were eligible for inclusion in the review along with 14 identified from other sources. Influenza incidence frequently declined after school closure. The effect was sometimes reversed when schools reopened, supporting a causal role for school closure in reducing incidence. Any benefits associated with school closure appeared to be greatest among school-aged children. However, as schools often closed late in the outbreak or other interventions were used concurrently, it was sometimes unclear how much school closure contributed to the reductions in incidence.

CONCLUSIONS

School closures appear to have the potential to reduce influenza transmission, but the heterogeneity in the data available means that the optimum strategy (eg, the ideal length and timing of closure) remains unclear.

Teacher led school-based surveillance can allow accurate tracking of emerging infectious diseases - evidence from serial cross-sectional surveys of febrile respiratory illness during the H1N1 2009 influenza pandemic in Singapore

BACKGROUND

Schools are important foci of influenza transmission and potential targets for surveillance and interventions. We compared several school-based influenza monitoring systems with clinic-based influenza-like illness (ILI) surveillance, and assessed the variation in illness rates between and within schools.

METHODS

During the initial wave of pandemic H1N1 (pdmH1N1) infections from June to Sept 2009 in Singapore, we collected data on nation-wide laboratory confirmed cases (Sch-LCC) and daily temperature monitoring (Sch-DTM), and teacher-led febrile respiratory illness reporting in 6 sentinel schools (Sch-FRI). Comparisons were made against age-stratified clinic-based influenza-like illness (ILI) data from 23 primary care clinics (GP-ILI) and proportions of ILI testing positive for pdmH1N1 (Lab-ILI) by computing the fraction of cumulative incidence occurring by epidemiological week 30 (when GP-ILI incidence peaked); and cumulative incidence rates between school-based indicators and sero-epidemiological pdmH1N1 incidence (estimated from changes in prevalence of A/California/7/2009 H1N1 hemagglutination inhibition titers ≥ 40 between pre-epidemic and post-epidemic sera). Variation in Sch-FRI rates in the 6 schools was also investigated through a Bayesian hierarchical model.

RESULTS

By week 30, for primary and secondary school children respectively, 63% and 79% of incidence for Sch-LCC had occurred, compared with 50% and 52% for GP-ILI data, and 48% and 53% for Sch-FRI. There were 1,187 notified cases and 7,588 episodes in the Sch-LCC and Sch-DTM systems; given school enrollment of 485,723 children, this represented 0.24 cases and 1.6 episodes per 100 children respectively. Mean Sch-FRI rate was 28.8 per 100 children (95% CI: 27.7 to 29.9) in the 6 schools. We estimate from serology that 41.8% (95% CI: 30.2% to 55.9%) of primary and 43.2% (95% CI: 28.2% to 60.8%) of secondary school-aged children were infected. Sch-FRI rates were similar across the 6 schools (23 to 34 episodes per 100 children), but there was widespread variation by classrooms; in the hierarchical model, omitting age and school effects was inconsequential but neglecting classroom level effects led to highly significant reductions in goodness of fit.

CONCLUSIONS

Epidemic curves from Sch-FRI were comparable to GP-ILI data, and Sch-FRI detected substantially more infections than Sch-LCC and Sch-DTM. Variability in classroom attack rates suggests localized class-room transmission.

Differential pathological and immune responses in newly weaned ferrets are associated with a mild clinical outcome of pandemic 2009 H1N1 infection

Young children are typically considered a high-risk group for disease associated with influenza virus infection. Interestingly, recent clinical reports suggested that young children were the smallest group of cases with severe pandemic 2009 H1N1 (H1N1pdm) influenza virus infection. Here we established a newly weaned ferret model for the investigation of H1N1pdm infection in young age groups compared to adults. We found that young ferrets had a significantly milder fever and less weight loss than adult ferrets, which paralleled the mild clinical symptoms in the younger humans. Although there was no significant difference in viral clearance, disease severity was associated with pulmonary pathology, where newly weaned ferrets had an earlier pathology improvement. We examined the immune responses associated with protection of the young age group during H1N1pdm infection. We found that interferon and regulatory interleukin-10 responses were more robust in the lungs of young ferrets. In contrast, myeloperoxidase and major histocompatibility complex responses were persistently higher in the adult lungs; as well, the numbers of inflammation-prone granulocytes were highly elevated in the adult peripheral blood. Importantly, we observed that H1N1pdm infection triggered formation of lung structures that resembled inducible bronchus-associated lymphoid tissues (iBALTs) in young ferrets which were associated with high levels of homeostatic chemokines CCL19 and CXCL13, but these were not seen in the adult ferrets with severe disease. These results may be extrapolated to a model of the mild disease seen in human children. Furthermore, these mechanistic analyses provide significant new insight into the developing immune system and effective strategies for intervention and vaccination against respiratory viruses.

Respiratory support during the influenza A (H1N1) pandemic flu in Sweden

BACKGROUND

Acute respiratory insufficiency characterised critically ill patients during the influenza A (H1N1) pandemic 2009-2010. Detailed understanding of disease progression and outcome in relation to different respiratory support strategies is important.

METHODS

Data collected between August 2009 and February 2010 for a national intensive care unit influenza registry were combined with cases identified by the Swedish Institute for Infectious Disease Control.

RESULTS

Clinical data was available for 95% (126/136) of the critically ill cases of influenza. Median age was 44 years, and major co-morbidities were present in 41%. Respiratory support strategies were studied among the 110 adult patients. Supplementary oxygen was sufficient in 15% (16), non-invasive ventilation (NIV) only was used in 20% (22), while transition from NIV to invasive ventilation (IV) was seen in 41% (45). IV was initiated directly in 24% (26). Patients initially treated with NIV had a higher arterial partial pressure of oxygen/fraction of oxygen in inspired gas ratio compared with those primarily treated with IV. Major baseline characteristics and 28-day mortality were similar, but 90-day mortality was higher in patients initially treated with NIV 17/67 (25%) as compared with patients primarily treated with IV 3/26 (12%), relative risk 1.2 (95% confidence interval 0.3-4.0).

CONCLUSIONS

Critical illness because of 2009 influenza A (H1N1) in Sweden was dominated by hypoxic respiratory failure. The majority of patients in need of respiratory support were initially treated with NIV. In spite of less severe initial hypoxemia, initiation of ventilatory support with NIV was not associated with improved outcome.

Impact of H1N1 on socially disadvantaged populations: systematic review

BACKGROUND

The burden of H1N1 among socially disadvantaged populations is unclear. We aimed to synthesize hospitalization, severe illness, and mortality data associated with pandemic A/H1N1/2009 among socially disadvantaged populations.

METHODS/PRINCIPAL FINDINGS

Studies were identified through searching MEDLINE, EMBASE, scanning reference lists, and contacting experts. Studies reporting hospitalization, severe illness, and mortality attributable to laboratory-confirmed 2009 H1N1 pandemic among socially disadvantaged populations (e.g., ethnic minorities, low-income or lower-middle-income economy countries [LIC/LMIC]) were included. Two independent reviewers conducted screening, data abstraction, and quality appraisal (Newcastle Ottawa Scale). Random effects meta-analysis was conducted using SAS and Review Manager.

CONCLUSIONS/SIGNIFICANCE

Sixty-two studies including 44,777 patients were included after screening 787 citations and 164 full-text articles. The prevalence of hospitalization for H1N1 ranged from 17-87% in high-income economy countries (HIC) and 11-45% in LIC/LMIC. Of those hospitalized, the prevalence of intensive care unit (ICU) admission and mortality was 6-76% and 1-25% in HIC; and 30% and 8-15%, in LIC/LMIC, respectively. There were significantly more hospitalizations among ethnic minorities versus non-ethnic minorities in two studies conducted in North America (1,313 patients, OR 2.26 [95% CI: 1.53-3.32]). There were no differences in ICU admissions (n = 8 studies, 15,352 patients, OR 0.84 [0.69-1.02]) or deaths (n = 6 studies, 14,757 patients, OR 0.85 [95% CI: 0.73-1.01]) among hospitalized patients in HIC. Sub-group analysis indicated that the meta-analysis results were not likely affected by confounding. Overall, the prevalence of hospitalization, severe illness, and mortality due to H1N1 was high for ethnic minorities in HIC and individuals from LIC/LMIC. However, our results suggest that there were little differences in the proportion of hospitalization, severe illness, and mortality between ethnic minorities and non-ethnic minorities living in HIC.

Impact of antiviral treatment and hospital admission delay on risk of death associated with 2009 A/H1N1 pandemic influenza in Mexico

BACKGROUND

Increasing our understanding of the factors affecting the severity of the 2009 A/H1N1 influenza pandemic in different regions of the world could lead to improved clinical practice and mitigation strategies for future influenza pandemics. Even though a number of studies have shed light into the risk factors associated with severe outcomes of 2009 A/H1N1 influenza infections in different populations, analyses of the determinants of mortality risk spanning multiple pandemic waves and geographic regions are scarce. Between-country differences in the mortality burden of the 2009 pandemic could be linked to differences in influenza case management, underlying population health, or intrinsic differences in disease transmission. Additional studies elucidating the determinants of disease severity globally are warranted to guide prevention efforts in future influenza pandemics.In Mexico, the 2009 A/H1N1 influenza pandemic was characterized by a three-wave pattern occurring in the spring, summer, and fall of 2009 with substantial geographical heterogeneity. A recent study suggests that Mexico experienced high excess mortality burden during the 2009 A/H1N1 influenza pandemic relative to other countries. However, an assessment of potential factors that contributed to the relatively high pandemic death toll in Mexico are lacking. Here, we fill this gap by analyzing a large series of laboratory-confirmed A/H1N1 influenza cases, hospitalizations, and deaths monitored by the Mexican Social Security medical system during April 1 through December 31, 2009 in Mexico. In particular, we quantify the association between disease severity, hospital admission delays, and neuraminidase inhibitor use by demographic characteristics, pandemic wave, and geographic regions of Mexico.

METHODS

We analyzed a large series of laboratory-confirmed pandemic A/H1N1 influenza cases from a prospective surveillance system maintained by the Mexican Social Security system, April-December 2009. We considered a spectrum of disease severity encompassing outpatient visits, hospitalizations, and deaths, and recorded demographic and geographic information on individual patients. We assessed the impact of neuraminidase inhibitor treatment and hospital admission delay (≤ > 2 days after disease onset) on the risk of death by multivariate logistic regression.

RESULTS

Approximately 50% of all A/H1N1-positive patients received antiviral medication during the Spring and Summer 2009 pandemic waves in Mexico while only 9% of A/H1N1 cases received antiviral medications during the fall wave (P < 0.0001). After adjustment for age, gender, and geography, antiviral treatment significantly reduced the risk of death (OR = 0.52 (95% CI: 0.30, 0.90)) while longer hospital admission delays increased the risk of death by 2.8-fold (95% CI: 2.25, 3.41).

CONCLUSIONS

Our findings underscore the potential impact of decreasing admission delays and increasing antiviral use to mitigate the mortality burden of future influenza pandemics.

Optimizing provider recruitment for influenza surveillance networks

The increasingly complex and rapid transmission dynamics of many infectious diseases necessitates the use of new, more advanced methods for surveillance, early detection, and decision-making. Here, we demonstrate that a new method for optimizing surveillance networks can improve the quality of epidemiological information produced by typical provider-based networks. Using past surveillance and Internet search data, it determines the precise locations where providers should be enrolled. When applied to redesigning the provider-based, influenza-like-illness surveillance network (ILINet) for the state of Texas, the method identifies networks that are expected to significantly outperform the existing network with far fewer providers. This optimized network avoids informational redundancies and is thereby more effective than networks designed by conventional methods and a recently published algorithm based on maximizing population coverage. We show further that Google Flu Trends data, when incorporated into a network as a virtual provider, can enhance but not replace traditional surveillance methods.

Public health agencies use surveillance systems to detect and monitor chronic and infectious diseases. These systems often rely on data sources that are chosen based on loose guidelines or out of convenience. In this paper, we introduce a new, data-driven method for designing and improving surveillance systems. Our approach is a geographic optimization of data sources designed to achieve specific surveillance goals. We tested our method by re-designing Texas' provider-based influenza surveillance system (ILINet). The resulting networks better predicted influenza associated hospitalizations and contained fewer providers than the existing ILINet. Furthermore, our study demonstrates that the integration of Internet source data, like Google Flu Trends, into surveillance systems can enhance traditional, provider-based networks.

Lessons from a one-year hospital-based surveillance of acute respiratory infections in Berlin- comparing case definitions to monitor influenza

Background

Surveillance of severe acute respiratory infections (SARI) in sentinel hospitals is recommended to estimate the burden of severe influenza-cases. Therefore, we monitored patients admitted with respiratory infections (RI) in 9 Berlin hospitals from 7.12.2009 to 12.12.2010 according to different case definitions (CD) and determined the proportion of cases with influenza A(H1N1)pdm09 (pH1N1). We compared the sensitivity and specificity of CD for capturing pandemic pH1N1 cases.

Methods

We established an RI-surveillance restricted to adults aged ≤ 65 years within the framework of a pH1N1 vaccine effectiveness study, which required active identification of RI-cases. The hospital information-system was screened daily for newly admitted RI-patients. Nasopharyngeal swabs from consenting patients were tested by PCR for influenza-virus subtypes. Four clinical CD were compared in terms of capturing pH1N1-positives among hospitalized RI-patients by applying sensitivity and specificity analyses. The broadest case definition (CD1) was used for inclusion of RI-cases; the narrowest case definition (CD4) was identical to the SARI case definition recommended by ECDC/WHO.

Results

Over the study period, we identified 1,025 RI-cases, of which 283 (28%) met the ECDC/WHO SARI case definition. The percentage of SARI-cases among internal medicine admissions decreased from 3.2% (calendar-week 50-2009) to 0.2% (week 25-2010). Of 354 patients tested by PCR, 20 (6%) were pH1N1-positive. Two case definitions narrower than CD1 but -in contrast to SARI- not requiring shortness of breath yielded the largest areas under the Receiver-Operator-Curve. Heterogeneity of proportions of patients admitted with RI between hospitals was significant.

Conclusions

Comprehensive surveillance of RI cases was feasible in a network of community hospitals. In most settings, several hospitals should be included to ensure representativeness. Although misclassification resulting from failure to obtain symptoms in the hospital information-system cannot be ruled out, a high proportion of hospitalized PCR-positive pH1N1-patients (45%) did not fulfil the SARI case-definition that included shortness of breath or difficulty breathing. Thus, to assess influenza-related disease burden in hospitals, broader, alternative case definitions should be considered.

Increasing incidence of serious infectious diseases and inequalities in New Zealand: a national epidemiological study

BACKGROUND

Although the burden of infectious diseases seems to be decreasing in developed countries, few national studies have measured the total incidence of these diseases. We aimed to develop and apply a robust systematic method for monitoring the epidemiology of serious infectious diseases.

METHODS

We did a national epidemiological study with all hospital admissions for infectious and non-infectious diseases in New Zealand from 1989 to 2008, to investigate trends in incidence and distribution by ethnic group and socioeconomic status. We extended a recoding system based on the ninth revision of international classification of diseases (ICD-9) to the tenth revision (ICD-10), and applied this to data for hospital admissions from the New Zealand Ministry of Health, National Minimum Dataset. We filtered results to account for changes in health-care practices over time. Acute overnight admissions were the events of interest.

FINDINGS

Infectious diseases made the largest contribution to hospital admissions of any cause. Their contribution increased from 20·5% of acute admissions in 1989-93, to 26·6% in 2004-08. We noted clear ethnic and social inequalities in infectious disease risk. In 2004-08, the age-standardised rate ratio was 2·15 (95% CI 2·14-2·16) for Māori (indigenous New Zealanders) and 2·35 (2·34-2·37) for Pacific peoples compared with the European and other group. The ratio was 2·81 (2·80-2·83) for the most socioeconomically deprived quintile compared with the least deprived quintile. These inequalities have increased substantially in the past 20 years, particularly for Māori and Pacific peoples in the most deprived quintile.

INTERPRETATION

These findings support the need for stronger prevention efforts for infectious diseases, and reinforce the need to reduce ethnic and social inequalities and to address disparities in broad social determinants such as income levels, housing conditions, and access to health services. Our method could be adapted for infectious disease surveillance in other countries.

FUNDING

New Zealand Ministry of Health, New Zealand Health Research Council.