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1.  Semiparametric estimation of the average causal effect of treatment on an outcome measured after a postrandomization event, with missing outcome data 
In the past decade, several principal stratification–based statistical methods have been developed for testing and estimation of a treatment effect on an outcome measured after a postrandomization event. Two examples are the evaluation of the effect of a cancer treatment on quality of life in subjects who remain alive and the evaluation of the effect of an HIV vaccine on viral load in subjects who acquire HIV infection. However, in general the developed methods have not addressed the issue of missing outcome data, and hence their validity relies on a missing completely at random (MCAR) assumption. Because in many applications the MCAR assumption is untenable, while a missing at random (MAR) assumption is defensible, we extend the semiparametric likelihood sensitivity analysis approach of Gilbert and others (2003) and Jemiai and Rotnitzky (2005) to allow the outcome to be MAR. We combine these methods with the robust likelihood–based method of Little and An (2004) for handling MAR data to provide semiparametric estimation of the average causal effect of treatment on the outcome. The new method, which does not require a monotonicity assumption, is evaluated in a simulation study and is applied to data from the first HIV vaccine efficacy trial.
doi:10.1093/biostatistics/kxp034
PMCID: PMC2800161  PMID: 19815692
Causal inference; HIV vaccine trial; Missing at random; Posttreatment selection bias; Principal stratification; Sensitivity analysis
2.  Association between the 2008–09 Seasonal Influenza Vaccine and Pandemic H1N1 Illness during Spring–Summer 2009: Four Observational Studies from Canada 
PLoS Medicine  2010;7(4):e1000258.
In three case-control studies and a household transmission cohort, Danuta Skowronski and colleagues find an association between prior seasonal flu vaccination and increased risk of 2009 pandemic H1N1 flu.
Background
In late spring 2009, concern was raised in Canada that prior vaccination with the 2008–09 trivalent inactivated influenza vaccine (TIV) was associated with increased risk of pandemic influenza A (H1N1) (pH1N1) illness. Several epidemiologic investigations were conducted through the summer to assess this putative association.
Methods and Findings
Studies included: (1) test-negative case-control design based on Canada's sentinel vaccine effectiveness monitoring system in British Columbia, Alberta, Ontario, and Quebec; (2) conventional case-control design using population controls in Quebec; (3) test-negative case-control design in Ontario; and (4) prospective household transmission (cohort) study in Quebec. Logistic regression was used to estimate odds ratios for TIV effect on community- or hospital-based laboratory-confirmed seasonal or pH1N1 influenza cases compared to controls with restriction, stratification, and adjustment for covariates including combinations of age, sex, comorbidity, timeliness of medical visit, prior physician visits, and/or health care worker (HCW) status. For the prospective study risk ratios were computed. Based on the sentinel study of 672 cases and 857 controls, 2008–09 TIV was associated with statistically significant protection against seasonal influenza (odds ratio 0.44, 95% CI 0.33–0.59). In contrast, estimates from the sentinel and three other observational studies, involving a total of 1,226 laboratory-confirmed pH1N1 cases and 1,505 controls, indicated that prior receipt of 2008–09 TIV was associated with increased risk of medically attended pH1N1 illness during the spring–summer 2009, with estimated risk or odds ratios ranging from 1.4 to 2.5. Risk of pH1N1 hospitalization was not further increased among vaccinated people when comparing hospitalized to community cases.
Conclusions
Prior receipt of 2008–09 TIV was associated with increased risk of medically attended pH1N1 illness during the spring–summer 2009 in Canada. The occurrence of bias (selection, information) or confounding cannot be ruled out. Further experimental and epidemiological assessment is warranted. Possible biological mechanisms and immunoepidemiologic implications are considered.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Every winter, millions of people catch influenza—a viral infection of the airways—and hundreds of thousands of people die as a result. These seasonal epidemics occur because small but frequent changes in the influenza virus mean that an immune response produced one year through infection or vaccination provides only partial protection against influenza the next year. Annual vaccination with killed influenza viruses of the major circulating strains can greatly reduce a person's risk of catching influenza. Consequently, many countries run seasonal influenza vaccination programs. In most of Canada, vaccination with a mixture of three inactivated viruses (a trivalent inactivated vaccine or TIV) is provided free to children aged 6–23 months, to elderly people, to people with long-term conditions that increase their risk of influenza-related complications, and those who provide care for them; in Ontario, free vaccination is offered to everyone older than 6 months.
In addition, influenza viruses occasionally emerge that are very different and to which human populations have virtually no immunity. These viruses can start global epidemics (pandemics) that can kill millions of people. Experts have been warning for some time that an influenza pandemic is long overdue and, in March 2009, the first cases of influenza caused by a new virus called pandemic A/H1N1 2009 (pH1N1; swine flu) occurred in Mexico. The virus spread rapidly and on 11 June 2009, the World Health Organization declared that a global pandemic of pH1N1 influenza was underway. By the end of February 2010, more than 16,000 people around the world had died from pH1N1.
Why Was This Study Done?
During an investigation of a school outbreak of pH1N1 in the late spring 2009 in Canada, investigators noted that people with illness characterized by fever and coughing had been vaccinated against seasonal influenza more often than individuals without such illness. To assess whether this association between prior vaccination with seasonal 2008–09 TIV and subsequent pH1N1 illness was evident in other settings, researchers in Canada therefore conducted additional studies using different methods. In this paper, the researchers report the results of four additional studies conducted in Canada during the summer of 2009 to assess this possible association.
What Did the Researchers Do and Find?
The researchers conducted four epidemiologic studies. Epidemiology is the study of the causes, distribution, and control of diseases in populations.
Three of the four studies were case-control studies in which the researchers assessed the frequency of prior vaccination with the 2008–09 TIV in people with pH1N1 influenza compared to the frequency among healthy members of the general population or among individuals who had an influenza-like illness but no sign of infection with an influenza virus. The researchers also did a household transmission study in which they collected information about vaccination with TIV among the additional cases of influenza that were identified in 47 households in which a case of laboratory-confirmed pH1N1 influenza had occurred. The first of the case-control studies, which was based on Canada's vaccine effectiveness monitoring system, showed that, as expected, the 2008–09 TIV provided protection against seasonal influenza. However, estimates from all four studies (which included about 1,200 laboratory-confirmed pH1N1 cases and 1,500 controls) showed that prior recipients of the 2008–09 TIV had approximately 1.4–2.5 times increased chances of developing pH1N1 illness that needed medical attention during the spring–summer of 2009 compared to people who had not received the TIV. Prior seasonal vaccination was not associated with an increase in the severity of pH1N1 illness, however. That is, it did not increase the risk of being hospitalized among those with pH1N1 illness.
What Do These Findings Mean?
Because all the investigations in this study are “observational,” the people who had been vaccinated might share another unknown characteristic that is actually responsible for increasing their risk of developing pH1N1 illness (“confounding”). Furthermore, the results reported in this study might have arisen by chance, although the consistency of results across the studies makes this unlikely. Thus, the finding of an association between prior receipt of 2008–09 TIV and an increased risk of pH1N1 illness is not conclusive and needs to be investigated further, particularly since some other observational studies conducted in other countries have reported that seasonal vaccination had no influence or may have been associated with reduced chances of pH1N1 illness. If the findings in the current study are real, however, they raise important questions about the biological interactions between seasonal and pandemic influenza strains and vaccines, and about the best way to prevent and control both types of influenza in future.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/ 10.1371/journal.pmed.1000258.
This article is further discussed in a PLoS Medicine Perspective by Cécile Viboud and Lone Simonsen
FightFlu.ca, a Canadian government Web site, provides access to information on pH1N1 influenza
The US Centers for Disease Control and Prevention provides information about influenza for patients and professionals, including specific information on H1N1 influenza
Flu.gov, a US government website, provides access to information on H1N1, avian and pandemic influenza
The World Health Organization provides information on seasonal influenza and has detailed information on pH1N1 influenza (in several languages)
The UK Health Protection Agency provides information on pandemic influenza and on pH1N1 influenza
doi:10.1371/journal.pmed.1000258
PMCID: PMC2850386  PMID: 20386731
3.  Collaborative Automation Reliably Remediating Erroneous Conclusion Threats (CARRECT) 
Objective
The objective of the CARRECT software is to make cutting edge statistical methods for reducing bias in epidemiological studies easy to use and useful for both novice and expert users.
Introduction
Analyses produced by epidemiologists and public health practitioners are susceptible to bias from a number of sources including missing data, confounding variables, and statistical model selection. It often requires a great deal of expertise to understand and apply the multitude of tests, corrections, and selection rules, and these tasks can be time-consuming and burdensome. To address this challenge, Aptima began development of CARRECT, the Collaborative Automation Reliably Remediating Erroneous Conclusion Threats system. When complete, CARRECT will provide an expert system that can be embedded in an analyst’s workflow. CARRECT will support statistical bias reduction and improved analyses and decision making by engaging the user in a collaborative process in which the technology is transparent to the analyst.
Methods
Older approaches to imputing missing data, including mean imputation and single imputation regression methods, have steadily given way to a class of methods known as “multiple imputation” (hereafter “MI”; Rubin 1987). Rather than making the restrictive assumption that the data are missing completely at random (MCAR), MI typically assumes the data are missing at random (MAR).
There are two key innovations behind MI. First, the observed values can be useful in predicting the missing cells, and thus specifying a joint distribution of the data is the first step in implementing the models. Second, single imputation methods will likely fail not only because of the inherent uncertainty in the missing values but also because of the estimation uncertainty associated with generating the parameters in the imputation procedure itself. By contrast, drawing the missing values multiple times, thereby generating m complete datasets along with the estimated parameters of the model properly accounts for both types of uncertainty (Rubin 1987; King et al. 2001). As a result, MI will lead to valid standard errors and confidence intervals along with unbiased point estimates.
In order to compute the joint distribution, CARRECT uses a bootstrapping-based algorithm that gives essentially the same answers as the standard Bayesian Markov Chain Monte Carlo (MCMC) or Expectation Maximization (EM) approaches, is usually considerably faster than existing approaches and can handle many more variables.
Results
Tests were conducted on one of the proposed methods with an epidemiological dataset from the Integrated Health Interview Series (IHIS) producing verifiably unbiased results despite high missingness rates. In addition, mockups (Figure 1) were created of an intuitive data wizard that guides the user through the analysis processes by analyzing key features of a given dataset. The mockups also show prompts for the user to provide additional substantive knowledge to improve the handling of imperfect datasets, as well as the selection of the most appropriate algorithms and models.
Conclusions
Our approach and program were designed to make bias mitigation much more accessible to much more than only the statistical elite. We hope that it will have a wide impact on reducing bias in epidemiological studies and provide more accurate information to policymakers.
PMCID: PMC3692841
Bias reduction; Missing data; Statistical model selection
4.  Monitoring the Impact of Influenza by Age: Emergency Department Fever and Respiratory Complaint Surveillance in New York City 
PLoS Medicine  2007;4(8):e247.
Background
The importance of understanding age when estimating the impact of influenza on hospitalizations and deaths has been well described, yet existing surveillance systems have not made adequate use of age-specific data. Monitoring influenza-related morbidity using electronic health data may provide timely and detailed insight into the age-specific course, impact and epidemiology of seasonal drift and reassortment epidemic viruses. The purpose of this study was to evaluate the use of emergency department (ED) chief complaint data for measuring influenza-attributable morbidity by age and by predominant circulating virus.
Methods and Findings
We analyzed electronically reported ED fever and respiratory chief complaint and viral surveillance data in New York City (NYC) during the 2001–2002 through 2005–2006 influenza seasons, and inferred dominant circulating viruses from national surveillance reports. We estimated influenza-attributable impact as observed visits in excess of a model-predicted baseline during influenza periods, and epidemic timing by threshold and cross correlation. We found excess fever and respiratory ED visits occurred predominantly among school-aged children (8.5 excess ED visits per 1,000 children aged 5–17 y) with little or no impact on adults during the early-2002 B/Victoria-lineage epidemic; increased fever and respiratory ED visits among children younger than 5 y during respiratory syncytial virus-predominant periods preceding epidemic influenza; and excess ED visits across all ages during the 2003–2004 (9.2 excess visits per 1,000 population) and 2004–2005 (5.2 excess visits per 1,000 population) A/H3N2 Fujian-lineage epidemics, with the relative impact shifted within and between seasons from younger to older ages. During each influenza epidemic period in the study, ED visits were increased among school-aged children, and each epidemic peaked among school-aged children before other impacted age groups.
Conclusions
Influenza-related morbidity in NYC was highly age- and strain-specific. The impact of reemerging B/Victoria-lineage influenza was focused primarily on school-aged children born since the virus was last widespread in the US, while epidemic A/Fujian-lineage influenza affected all age groups, consistent with a novel antigenic variant. The correspondence between predominant circulating viruses and excess ED visits, hospitalizations, and deaths shows that excess fever and respiratory ED visits provide a reliable surrogate measure of incident influenza-attributable morbidity. The highly age-specific impact of influenza by subtype and strain suggests that greater age detail be incorporated into ongoing surveillance. Influenza morbidity surveillance using electronic data currently available in many jurisdictions can provide timely and representative information about the age-specific epidemiology of circulating influenza viruses.
Don Olson and colleagues report that influenza-related morbidity in NYC from 2001 to 2006 was highly age- and strain-specific and conclude that surveillance using electronic data can provide timely and representative information about the epidemiology of circulating influenza viruses.
Editors' Summary
Background.
Seasonal outbreaks (epidemics) of influenza (a viral infection of the nose, throat, and airways) send millions of people to their beds every winter. Most recover quickly, but flu epidemics often disrupt daily life and can cause many deaths. Seasonal epidemics occur because influenza viruses continually make small changes to the viral proteins (antigens) that the human immune system recognizes. Consequently, an immune response that combats influenza one year may provide partial or no protection the following year. Occasionally, an influenza virus with large antigenic changes emerges that triggers an influenza pandemic, or global epidemic. To help prepare for both seasonal epidemics and pandemics, public-health officials monitor influenza-related illness and death, investigate unusual outbreaks of respiratory diseases, and characterize circulating strains of the influenza virus. While traditional influenza-related illness surveillance systems rely on relatively slow voluntary clinician reporting of cases with influenza-like illness symptoms, some jurisdictions have also started to use “syndromic” surveillance systems. These use electronic health-related data rather than clinical impression to track illness in the community. For example, increased visits to emergency departments for fever or respiratory (breathing) problems can provide an early warning of an influenza outbreak.
Why Was This Study Done?
Rapid illness surveillance systems have been shown to detect flu outbreaks earlier than is possible through monitoring deaths from pneumonia or influenza. Increases in visits to emergency departments by children for fever or respiratory problems can provide an even earlier indicator. Researchers have not previously examined in detail how fever and respiratory problems by age group correlate with the predominant circulating respiratory viruses. Knowing details like this would help public-health officials detect and respond to influenza epidemics and pandemics. In this study, the researchers have used data collected between 2001 and 2006 in New York City emergency departments to investigate these aspects of syndromic surveillance for influenza.
What Did the Researchers Do and Find?
The researchers analyzed emergency department visits categorized broadly into a fever and respiratory syndrome (which provides an estimate of the total visits attributable to influenza) or more narrowly into an influenza-like illness syndrome (which specifically indicates fever with cough and/or sore throat) with laboratory-confirmed influenza surveillance data. They found that emergency department visits were highest during peak influenza periods, and that the affect on different age groups varied depending on the predominant circulating viruses. In early 2002, an epidemic reemergence of B/Victoria-lineage influenza viruses caused increased visits among school-aged children, while adult visits did not increase. By contrast, during the 2003–2004 season, when the predominant virus was an A/H3N2 Fujian-lineage influenza virus, excess visits occurred in all age groups, though the relative increase was greatest and earliest among school-aged children. During periods of documented respiratory syncytial virus (RSV) circulation, increases in fever and respiratory emergency department visits occurred in children under five years of age regardless of influenza circulation. Finally, the researchers found that excess visits to emergency departments for fever and respiratory symptoms preceded deaths from pneumonia or influenza by about two weeks.
What Do These Findings Mean?
These findings indicate that excess emergency department visits for fever and respiratory symptoms can provide a reliable and timely surrogate measure of illness due to influenza. They also provide new insights into how different influenza viruses affect people of different ages and how the timing and progression of each influenza season differs. These results, based on data collected over only five years in one city, might not be generalizable to other settings or years, warn the researchers. However, the present results strongly suggest that the routine monitoring of influenza might be improved by using electronic health-related data, such as emergency department visit data, and by examining it specifically by age group. Furthermore, by showing that school-aged children can be the first people to be affected by seasonal influenza, these results highlight the important role this age group plays in community-wide transmission of influenza, an observation that could influence the implementation of public-health strategies such as vaccination that aim to protect communities during influenza epidemics and pandemics.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0040247.
• US Centers for Disease Control and Prevention provides information on influenza for patients and health professionals and on influenza surveillance in the US (in English, Spanish, and several other languages)
• World Health Organization has a fact sheet on influenza and on global surveillance for influenza (in English, Spanish, French, Russian, Arabic, and Chinese)
• The MedlinePlus encyclopedia contains a page on flu (in English and Spanish)
• US National Institute of Allergy and Infectious Diseases has a feature called “focus on flu”
• A detailed report from the US Centers for Disease Control and Prevention titled “Framework for Evaluating Public Health Surveillance Systems for Early Detection of Outbreaks” includes a simple description of syndromic surveillance
• The International Society for Disease Surveillance has a collaborative syndromic surveillance public wiki
• The Anthropology of the Contemporary Research Collaboratory includes working papers and discussions by cultural anthropologists studying modern vital systems security and syndromic surveillance
doi:10.1371/journal.pmed.0040247
PMCID: PMC1939858  PMID: 17683196
5.  Estimates of Pandemic Influenza Vaccine Effectiveness in Europe, 2009–2010: Results of Influenza Monitoring Vaccine Effectiveness in Europe (I-MOVE) Multicentre Case-Control Study 
PLoS Medicine  2011;8(1):e1000388.
Results from a European multicentre case-control study reported by Marta Valenciano and colleagues suggest good protection by the pandemic monovalent H1N1 vaccine against pH1N1 and no effect of the 2009–2010 seasonal influenza vaccine on H1N1.
Background
A multicentre case-control study based on sentinel practitioner surveillance networks from seven European countries was undertaken to estimate the effectiveness of 2009–2010 pandemic and seasonal influenza vaccines against medically attended influenza-like illness (ILI) laboratory-confirmed as pandemic influenza A (H1N1) (pH1N1).
Methods and Findings
Sentinel practitioners swabbed ILI patients using systematic sampling. We included in the study patients meeting the European ILI case definition with onset of symptoms >14 days after the start of national pandemic vaccination campaigns. We compared pH1N1 cases to influenza laboratory-negative controls. A valid vaccination corresponded to >14 days between receiving a dose of vaccine and symptom onset. We estimated pooled vaccine effectiveness (VE) as 1 minus the odds ratio with the study site as a fixed effect. Using logistic regression, we adjusted VE for potential confounding factors (age group, sex, month of onset, chronic diseases and related hospitalizations, smoking history, seasonal influenza vaccinations, practitioner visits in previous year). We conducted a complete case analysis excluding individuals with missing values and a multiple multivariate imputation to estimate missing values. The multivariate imputation (n = 2902) adjusted pandemic VE (PIVE) estimates were 71.9% (95% confidence interval [CI] 45.6–85.5) overall; 78.4% (95% CI 54.4–89.8) in patients <65 years; and 72.9% (95% CI 39.8–87.8) in individuals without chronic disease. The complete case (n = 1,502) adjusted PIVE were 66.0% (95% CI 23.9–84.8), 71.3% (95% CI 29.1–88.4), and 70.2% (95% CI 19.4–89.0), respectively. The adjusted PIVE was 66.0% (95% CI −69.9 to 93.2) if vaccinated 8–14 days before ILI onset. The adjusted 2009–2010 seasonal influenza VE was 9.9% (95% CI −65.2 to 50.9).
Conclusions
Our results suggest good protection of the pandemic monovalent vaccine against medically attended pH1N1 and no effect of the 2009–2010 seasonal influenza vaccine. However, the late availability of the pandemic vaccine and subsequent limited coverage with this vaccine hampered our ability to study vaccine benefits during the outbreak period. Future studies should include estimation of the effectiveness of the new trivalent vaccine in the upcoming 2010–2011 season, when vaccination will occur before the influenza season starts.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Following the World Health Organization's declaration of pandemic phase six in June 2009, manufacturers developed vaccines against pandemic influenza A 2009 (pH1N1). On the basis of the scientific opinion of the European Medicines Agency, the European Commission initially granted marketing authorization to three pandemic vaccines for use in European countries. During the autumn of 2009, most European countries included the 2009–2010 seasonal influenza vaccine and the pandemic vaccine in their influenza vaccination programs.
The Influenza Monitoring Vaccine Effectiveness in Europe network (established to monitor seasonal and pandemic influenza vaccine effectiveness) conducted seven case-control and three cohort studies in seven European countries in 2009–2010 to estimate the effectiveness of the pandemic and seasonal vaccines. Data from the seven pilot case-control studies were pooled to provide overall adjusted estimates of vaccine effectiveness.
Why Was This Study Done?
After seasonal and pandemic vaccines are made available to populations, it is necessary to estimate the effectiveness of the vaccines at the population level during every influenza season. Therefore, this study was conducted in European countries to estimate the pandemic influenza vaccine effectiveness and seasonal influenza vaccine effectiveness against people presenting to their doctor with influenza-like illness who were confirmed (by laboratory tests) to be infected with pH1N1.
What Did the Researchers Do and Find?
The researchers conducted a multicenter case-control study on the basis of practitioner surveillance networks from seven countries—France, Hungary, Ireland, Italy, Romania, Portugal, and Spain. Patients consulting a participating practitioner for influenza-like illness had a nasal or throat swab taken within 8 days of symptom onset. Cases were swabbed patients who tested positive for pH1N1. Patients presenting with influenza-like illness whose swab tested negative for any influenza virus were controls.
Individuals were considered vaccinated if they had received a dose of the vaccine more than 14 days before the date of onset of influenza-like illness and unvaccinated if they were not vaccinated at all, or if the vaccine was given less than 15 days before the onset of symptoms. The researchers analyzed pandemic influenza vaccination effectiveness in those vaccinated less than 8 days, those vaccinated between and including 8 and 14 days, and those vaccinated more than 14 days before onset of symptoms compared to those who had never been vaccinated.
The researchers used modeling (taking account of all potential confounding factors) to estimate adjusted vaccine effectiveness and stratified the adjusted pandemic influenza vaccine effectiveness and the adjusted seasonal influenza vaccine effectiveness in three age groups (<15, 15–64, and ≥65 years of age).
The adjusted results suggest that the 2009–2010 seasonal influenza vaccine did not protect against pH1N1 illness. However, one dose of the pandemic vaccines used in the participating countries conferred good protection (65.5%–100% according to various stratifications performed) against pH1N1 in people who attended their practitioner with influenza-like illness, especially in people aged <65 years and in those without any chronic disease. Furthermore, good pandemic influenza vaccine effectiveness was observed as early as 8 days after vaccination.
What Do These Findings Mean?
The results of this study provide early estimates of the pandemic influenza vaccine effectiveness suggesting that the monovalent pandemic vaccines have been effective. The findings also give an indication of the vaccine effectiveness for the Influenza A (H1N1) 2009 strain included in the 2010–2011 seasonal vaccines, although specific vaccine effectiveness studies will have to be conducted to verify if similar good effectiveness are observed with 2010–2011 trivalent vaccines. However, the results of this study should be interpreted with caution because of limitations in the pandemic context (late timing of the studies, low incidence, low vaccine coverage leading to imprecise estimates) and potential biases due the study design, confounding factors, and missing values. The researchers recommend that in future season studies, the sample size per country should be enlarged in order to allow for precise pooled and stratified analyses.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000388.
The World Health Organization has information on H1N1 vaccination
The US Centers for Disease Control and Prevention provides a fact sheet on the 2009 H1N1 influenza virus
The US Department of Health and Human services has a comprehensive website on flu
The European Centre for Disease Prevention and Control provides information on 2009 H1N1 pandemic
The European Centre for Disease Prevention and Control presents a summary of the 2009 H1N1 pandemic in Europe and elsewhere
doi:10.1371/journal.pmed.1000388
PMCID: PMC3019108  PMID: 21379316
6.  Characterization of Regional Influenza Seasonality Patterns in China and Implications for Vaccination Strategies: Spatio-Temporal Modeling of Surveillance Data 
PLoS Medicine  2013;10(11):e1001552.
Cécile Viboud and colleagues describe epidemiological patterns of influenza incidence across China to support the design of a national vaccination program.
Please see later in the article for the Editors' Summary
Background
The complexity of influenza seasonal patterns in the inter-tropical zone impedes the establishment of effective routine immunization programs. China is a climatologically and economically diverse country, which has yet to establish a national influenza vaccination program. Here we characterize the diversity of influenza seasonality in China and make recommendations to guide future vaccination programs.
Methods and Findings
We compiled weekly reports of laboratory-confirmed influenza A and B infections from sentinel hospitals in cities representing 30 Chinese provinces, 2005–2011, and data on population demographics, mobility patterns, socio-economic, and climate factors. We applied linear regression models with harmonic terms to estimate influenza seasonal characteristics, including the amplitude of annual and semi-annual periodicities, their ratio, and peak timing. Hierarchical Bayesian modeling and hierarchical clustering were used to identify predictors of influenza seasonal characteristics and define epidemiologically-relevant regions. The annual periodicity of influenza A epidemics increased with latitude (mean amplitude of annual cycle standardized by mean incidence, 140% [95% CI 128%–151%] in the north versus 37% [95% CI 27%–47%] in the south, p<0.0001). Epidemics peaked in January–February in Northern China (latitude ≥33°N) and April–June in southernmost regions (latitude <27°N). Provinces at intermediate latitudes experienced dominant semi-annual influenza A periodicity with peaks in January–February and June–August (periodicity ratio >0.6 in provinces located within 27.4°N–31.3°N, slope of latitudinal gradient with latitude −0.016 [95% CI −0.025 to −0.008], p<0.001). In contrast, influenza B activity predominated in colder months throughout most of China. Climate factors were the strongest predictors of influenza seasonality, including minimum temperature, hours of sunshine, and maximum rainfall. Our main study limitations include a short surveillance period and sparse influenza sampling in some of the southern provinces.
Conclusions
Regional-specific influenza vaccination strategies would be optimal in China; in particular, annual campaigns should be initiated 4–6 months apart in Northern and Southern China. Influenza surveillance should be strengthened in mid-latitude provinces, given the complexity of seasonal patterns in this region. More broadly, our findings are consistent with the role of climatic factors on influenza transmission dynamics.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Every year, millions of people worldwide catch influenza, a viral disease of the airways. Most infected individuals recover quickly but seasonal influenza outbreaks (epidemics) kill about half a million people annually. These epidemics occur because antigenic drift—frequent small changes in the viral proteins to which the immune system responds—means that an immune response produced one year provides only partial protection against influenza the next year. Annual vaccination with a mixture of killed influenza viruses of the major circulating strains boosts this natural immunity and greatly reduces the risk of catching influenza. Consequently, many countries run seasonal influenza vaccination programs. Because the immune response induced by vaccination decays within 4–8 months of vaccination and because of antigenic drift, it is important that these programs are initiated only a few weeks before the onset of local influenza activity. Thus, vaccination starts in early autumn in temperate zones (regions of the world that have a mild climate, part way between a tropical and a polar climate), because seasonal influenza outbreaks occur in the winter months when low humidity and low temperatures favor the transmission of the influenza virus.
Why Was This Study Done?
Unlike temperate regions, seasonal influenza patterns are very diverse in tropical countries, which lie between latitudes 23.5°N and 23.5°S, and in the subtropical countries slightly north and south of these latitudes. In some of these countries, there is year-round influenza activity, in others influenza epidemics occur annually or semi-annually (twice yearly). This complexity, which is perhaps driven by rainfall fluctuations, complicates the establishment of effective routine immunization programs in tropical and subtropical countries. Take China as an example. Before a national influenza vaccination program can be established in this large, climatologically diverse country, public-health experts need a clear picture of influenza seasonality across the country. Here, the researchers use spatio-temporal modeling of influenza surveillance data to characterize the seasonality of influenza A and B (the two types of influenza that usually cause epidemics) in China, to assess the role of putative drivers of seasonality, and to identify broad epidemiological regions (areas with specific patterns of disease) that could be used as a basis to optimize the timing of future Chinese vaccination programs.
What Did the Researchers Do and Find?
The researchers collected together the weekly reports of laboratory-confirmed influenza prepared by the Chinese national sentinel hospital-based surveillance network between 2005 and 2011, data on population size and density, mobility patterns, and socio-economic factors, and daily meteorological data for the cities participating in the surveillance network. They then used various statistical modeling approaches to estimate influenza seasonal characteristics, to assess predictors of influenza seasonal characteristics, and to identify epidemiologically relevant regions. These analyses indicate that, over the study period, northern provinces (latitudes greater than 33°N) experienced winter epidemics of influenza A in January–February, southern provinces (latitudes less than 27°N) experienced peak viral activity in the spring (April–June), and provinces at intermediate latitudes experienced semi-annual epidemic cycles with infection peaks in January–February and June–August. By contrast, influenza B activity predominated in the colder months throughout China. The researchers also report that minimum temperatures, hours of sunshine, and maximum rainfall were the strongest predictors of influenza seasonality.
What Do These Findings Mean?
These findings show that influenza seasonality in China varies between regions and between influenza virus types and suggest that, as in other settings, some of these variations might be associated with specific climatic factors. The accuracy of these findings is limited by the short surveillance period, by sparse surveillance data from some southern and mid-latitude provinces, and by some aspects of the modeling approach used in the study. Further surveillance studies need to be undertaken to confirm influenza seasonality patterns in China. Overall, these findings suggest that, to optimize routine influenza vaccination in China, it will be necessary to stagger the timing of vaccination over three broad geographical regions. More generally, given that there is growing interest in rolling out national influenza immunization programs in low- and middle-income countries, these findings highlight the importance of ensuring that vaccination strategies are optimized by taking into account local disease patterns.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/ 10.1371/journal.pmed.1001552.
This study is further discussed in a PLOS Medicine Perspective by Steven Riley
The UK National Health Service Choices website provides information for patients about seasonal influenza and about influenza vaccination
The World Health Organization provides information on seasonal influenza (in several languages) and on influenza surveillance and monitoring
The US Centers for Disease Control and Prevention also provides information for patients and health professionals on all aspects of seasonal influenza, including information about vaccination; its website contains a short video about personal experiences of influenza.
Flu.gov, a US government website, provides access to information on seasonal influenza and vaccination
Information about the Chinese National Influenza Center, which is part of the Chinese Center for Disease Control and Prevention: and which runs influenza surveillance in China, is available (in English and Chinese)
MedlinePlus has links to further information about influenza and about vaccination (in English and Spanish)
A recent PLOS Pathogens Research Article by James D. Tamerius et al. investigates environmental predictors of seasonal influenza epidemics across temperate and tropical climates
A study published in PLOS ONE by Wyller Alencar de Mello et al. indicates that Brazil, like China, requires staggered timing of vaccination from Northern to Southern states to account for different timings of influenza activity.
doi:10.1371/journal.pmed.1001552
PMCID: PMC3864611  PMID: 24348203
7.  The Effects of Influenza Vaccination of Health Care Workers in Nursing Homes: Insights from a Mathematical Model 
PLoS Medicine  2008;5(10):e200.
Background
Annual influenza vaccination of institutional health care workers (HCWs) is advised in most Western countries, but adherence to this recommendation is generally low. Although protective effects of this intervention for nursing home patients have been demonstrated in some clinical trials, the exact relationship between increased vaccine uptake among HCWs and protection of patients remains unknown owing to variations between study designs, settings, intensity of influenza seasons, and failure to control all effect modifiers. Therefore, we use a mathematical model to estimate the effects of HCW vaccination in different scenarios and to identify a herd immunity threshold in a nursing home department.
Methods and Findings
We use a stochastic individual-based model with discrete time intervals to simulate influenza virus transmission in a 30-bed long-term care nursing home department. We simulate different levels of HCW vaccine uptake and study the effect on influenza virus attack rates among patients for different institutional and seasonal scenarios. Our model reveals a robust linear relationship between the number of HCWs vaccinated and the expected number of influenza virus infections among patients. In a realistic scenario, approximately 60% of influenza virus infections among patients can be prevented when the HCW vaccination rate increases from 0 to 1. A threshold for herd immunity is not detected. Due to stochastic variations, the differences in patient attack rates between departments are high and large outbreaks can occur for every level of HCW vaccine uptake.
Conclusions
The absence of herd immunity in nursing homes implies that vaccination of every additional HCW protects an additional fraction of patients. Because of large stochastic variations, results of small-sized clinical trials on the effects of HCW vaccination should be interpreted with great care. Moreover, the large variations in attack rates should be taken into account when designing future studies.
Using a mathematical model to simulate influenza transmission in nursing homes, Carline van den Dool and colleagues find that each additional staff member vaccinated further reduces the risk to patients.
Editors' Summary
Background.
Every winter, millions of people catch influenza, a contagious viral disease of the nose, throat, and airways. Most people recover completely from influenza within a week or two but some develop life-threatening complications such as bacterial pneumonia. As a result, influenza outbreaks kill about half a million people—mainly infants, elderly people, and chronically ill individuals—each year. To minimize influenza-related deaths, the World Health Organization recommends that vulnerable people be vaccinated against influenza every autumn. Annual vaccination is necessary because flu viruses continually make small changes to the viral proteins (antigens) that the immune system recognizes. This means that an immune response produced one year provides only partial protection against influenza the next year. To provide maximum protection against influenza, each year's vaccine contains disabled versions of the major circulating strains of influenza viruses.
Why Was This Study Done?
Most Western countries also recommend annual flu vaccination for health care workers (HCWs) in hospitals and other institutions to reduce the transmission of influenza to vulnerable patients. However, many HCWs don't get a regular flu shot, so should efforts be made to increase their rate of vaccine uptake? To answer this question, public-health experts need to know more about the relationship between vaccine uptake among HCWs and patient protection. In particular, they need to know whether a high rate of vaccine uptake by HCWs will provide “herd immunity.” Herd immunity occurs because, when a sufficient fraction of a population is immune to a disease that passes from person to person, infected people rarely come into contact with susceptible people, which means that both vaccinated and unvaccinated people are protected from the disease. In this study, the researchers develop a mathematical model to investigate the relationship between vaccine uptake among HCWs and patient protection in a nursing home department.
What Did the Researchers Do and Find?
To predict influenza virus attack rates (the number of patient infections divided by the number of patients in a nursing home department during an influenza season) at different levels of HCW vaccine uptake, the researchers develop a stochastic transmission model to simulate epidemics on a computer. This model predicts that as the HCW vaccination rate increases from 0 (no HCWs vaccinated) to 1 (all the HCWs vaccinated), the expected average influenza virus attack rate decreases at a constant rate. In the researchers' baseline scenario—a nursing home department with 30 beds where patients come into contact with other patients, HCWs, and visitors—the model predicts that about 60% of the patients who would have been infected if no HCWs had been vaccinated are protected when all the HCWs are vaccinated, and that seven HCWs would have to be vaccinated to protect one patient. This last figure does not change with increasing vaccine uptake, which indicates that there is no level of HCW vaccination that completely stops the spread of influenza among the patients; that is, there is no herd immunity. Finally, the researchers show that large influenza outbreaks can happen by chance at every level of HCW vaccine uptake.
What Do These Findings Mean?
As with all mathematical models, the accuracy of these predictions may depend on the specific assumptions built into the model. Therefore the researchers verified that their findings hold for a wide range of plausible assumptions. These findings have two important practical implications. First, the direct relationship between HCW vaccination and patient protection and the lack of any herd immunity suggest that any increase in HCW vaccine uptake will be beneficial to patients in nursing homes. That is, increasing the HCW vaccination rate from 80% to 90% is likely to be as important as increasing it from 10% to 20%. Second, even 100% HCW vaccination cannot guarantee that influenza outbreaks will not occasionally occur in nursing homes. Because of the large variation in attack rates, the results of small clinical trials on the effects of HCW vaccination may be inaccurate and future studies will need to be very large if they are to provide reliable estimates of the amount of protection that HCW vaccination provides to vulnerable patients.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0050200.
Read the related PLoSMedicine Perspective by Cécile Viboud and Mark Miller
A related PLoSMedicine Research Article by Jeffrey Kwong and colleagues is also available
The World Health Organization provides information on influenza and on influenza vaccines (in several languages)
The US Centers for Disease Control and Prevention provide information for patients and professionals on all aspects of influenza (in English and Spanish)
The UK Health Protection Agency also provides information on influenza
MedlinePlus provides a list of links to other information about influenza (in English and Spanish)
The UK National Health Service provides information about herd immunity, including a simple explanatory animation
The European Centre for Disease Prevention and Control provides an overview on the types of influenza
doi:10.1371/journal.pmed.0050200
PMCID: PMC2573905  PMID: 18959470
8.  Influenza and Pneumococcal Vaccinations for Patients With Chronic Obstructive Pulmonary Disease (COPD) 
Executive Summary
In July 2010, the Medical Advisory Secretariat (MAS) began work on a Chronic Obstructive Pulmonary Disease (COPD) evidentiary framework, an evidence-based review of the literature surrounding treatment strategies for patients with COPD. This project emerged from a request by the Health System Strategy Division of the Ministry of Health and Long-Term Care that MAS provide them with an evidentiary platform on the effectiveness and cost-effectiveness of COPD interventions.
After an initial review of health technology assessments and systematic reviews of COPD literature, and consultation with experts, MAS identified the following topics for analysis: vaccinations (influenza and pneumococcal), smoking cessation, multidisciplinary care, pulmonary rehabilitation, long-term oxygen therapy, noninvasive positive pressure ventilation for acute and chronic respiratory failure, hospital-at-home for acute exacerbations of COPD, and telehealth (including telemonitoring and telephone support). Evidence-based analyses were prepared for each of these topics. For each technology, an economic analysis was also completed where appropriate. In addition, a review of the qualitative literature on patient, caregiver, and provider perspectives on living and dying with COPD was conducted, as were reviews of the qualitative literature on each of the technologies included in these analyses.
The Chronic Obstructive Pulmonary Disease Mega-Analysis series is made up of the following reports, which can be publicly accessed at the MAS website at: http://www.hqontario.ca/en/mas/mas_ohtas_mn.html.
Chronic Obstructive Pulmonary Disease (COPD) Evidentiary Framework
Influenza and Pneumococcal Vaccinations for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Smoking Cessation for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Community-Based Multidisciplinary Care for Patients With Stable Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Pulmonary Rehabilitation for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Long-term Oxygen Therapy for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Noninvasive Positive Pressure Ventilation for Acute Respiratory Failure Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Noninvasive Positive Pressure Ventilation for Chronic Respiratory Failure Patients With Stable Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Hospital-at-Home Programs for Patients with Acute Exacerbations of Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Home Telehealth for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Cost-Effectiveness of Interventions for Chronic Obstructive Pulmonary Disease Using an Ontario Policy Model
Experiences of Living and Dying With COPD: A Systematic Review and Synthesis of the Qualitative Empirical Literature
For more information on the qualitative review, please contact Mita Giacomini at: http://fhs.mcmaster.ca/ceb/faculty_member_giacomini.htm.
For more information on the economic analysis, please visit the PATH website: http://www.path-hta.ca/About-Us/Contact-Us.aspx.
The Toronto Health Economics and Technology Assessment (THETA) collaborative has produced an associated report on patient preference for mechanical ventilation. For more information, please visit the THETA website: http://theta.utoronto.ca/static/contact.
Objective
The objective of this analysis was to determine the effectiveness of the influenza vaccination and the pneumococcal vaccination in patients with chronic obstructive pulmonary disease (COPD) in reducing the incidence of influenza-related illness or pneumococcal pneumonia.
Clinical Need: Condition and Target Population
Influenza Disease
Influenza is a global threat. It is believed that the risk of a pandemic of influenza still exists. Three pandemics occurred in the 20th century which resulted in millions of deaths worldwide. The fourth pandemic of H1N1 influenza occurred in 2009 and affected countries in all continents.
Rates of serious illness due to influenza viruses are high among older people and patients with chronic conditions such as COPD. The influenza viruses spread from person to person through sneezing and coughing. Infected persons can transfer the virus even a day before their symptoms start. The incubation period is 1 to 4 days with a mean of 2 days. Symptoms of influenza infection include fever, shivering, dry cough, headache, runny or stuffy nose, muscle ache, and sore throat. Other symptoms such as nausea, vomiting, and diarrhea can occur.
Complications of influenza infection include viral pneumonia, secondary bacterial pneumonia, and other secondary bacterial infections such as bronchitis, sinusitis, and otitis media. In viral pneumonia, patients develop acute fever and dyspnea, and may further show signs and symptoms of hypoxia. The organisms involved in bacterial pneumonia are commonly identified as Staphylococcus aureus and Hemophilus influenza. The incidence of secondary bacterial pneumonia is most common in the elderly and those with underlying conditions such as congestive heart disease and chronic bronchitis.
Healthy people usually recover within one week but in very young or very old people and those with underlying medical conditions such as COPD, heart disease, diabetes, and cancer, influenza is associated with higher risks and may lead to hospitalization and in some cases death. The cause of hospitalization or death in many cases is viral pneumonia or secondary bacterial pneumonia. Influenza infection can lead to the exacerbation of COPD or an underlying heart disease.
Streptococcal Pneumonia
Streptococcus pneumoniae, also known as pneumococcus, is an encapsulated Gram-positive bacterium that often colonizes in the nasopharynx of healthy children and adults. Pneumococcus can be transmitted from person to person during close contact. The bacteria can cause illnesses such as otitis media and sinusitis, and may become more aggressive and affect other areas of the body such as the lungs, brain, joints, and blood stream. More severe infections caused by pneumococcus are pneumonia, bacterial sepsis, meningitis, peritonitis, arthritis, osteomyelitis, and in rare cases, endocarditis and pericarditis.
People with impaired immune systems are susceptible to pneumococcal infection. Young children, elderly people, patients with underlying medical conditions including chronic lung or heart disease, human immunodeficiency virus (HIV) infection, sickle cell disease, and people who have undergone a splenectomy are at a higher risk for acquiring pneumococcal pneumonia.
Technology
Influenza and Pneumococcal Vaccines
Trivalent Influenza Vaccines in Canada
In Canada, 5 trivalent influenza vaccines are currently authorized for use by injection. Four of these are formulated for intramuscular use and the fifth product (Intanza®) is formulated for intradermal use.
The 4 vaccines for intramuscular use are:
Fluviral (GlaxoSmithKline), split virus, inactivated vaccine, for use in adults and children ≥ 6 months;
Vaxigrip (Sanofi Pasteur), split virus inactivated vaccine, for use in adults and children ≥ 6 months;
Agriflu (Novartis), surface antigen inactivated vaccine, for use in adults and children ≥ 6 months; and
Influvac (Abbott), surface antigen inactivated vaccine, for use in persons ≥ 18 years of age.
FluMist is a live attenuated virus in the form of an intranasal spray for persons aged 2 to 59 years. Immunization with current available influenza vaccines is not recommended for infants less than 6 months of age.
Pneumococcal Vaccine
Pneumococcal polysaccharide vaccines were developed more than 50 years ago and have progressed from 2-valent vaccines to the current 23-valent vaccines to prevent diseases caused by 23 of the most common serotypes of S pneumoniae. Canada-wide estimates suggest that approximately 90% of cases of pneumococcal bacteremia and meningitis are caused by these 23 serotypes. Health Canada has issued licenses for 2 types of 23-valent vaccines to be injected intramuscularly or subcutaneously:
Pneumovax 23® (Merck & Co Inc. Whitehouse Station, NJ, USA), and
Pneumo 23® (Sanofi Pasteur SA, Lion, France) for persons 2 years of age and older.
Other types of pneumococcal vaccines licensed in Canada are for pediatric use. Pneumococcal polysaccharide vaccine is injected only once. A second dose is applied only in some conditions.
Research Questions
What is the effectiveness of the influenza vaccination and the pneumococcal vaccination compared with no vaccination in COPD patients?
What is the safety of these 2 vaccines in COPD patients?
What is the budget impact and cost-effectiveness of these 2 vaccines in COPD patients?
Research Methods
Literature search
Search Strategy
A literature search was performed on July 5, 2010 using OVID MEDLINE, MEDLINE In-Process and Other Non-Indexed Citations, EMBASE, the Cumulative Index to Nursing & Allied Health Literature (CINAHL), the Cochrane Library, and the International Agency for Health Technology Assessment (INAHTA) for studies published from January 1, 2000 to July 5, 2010. The search was updated monthly through the AutoAlert function of the search up to January 31, 2011. Abstracts were reviewed by a single reviewer and, for those studies meeting the eligibility criteria, full-text articles were obtained. Articles with an unknown eligibility were reviewed with a second clinical epidemiologist and then a group of epidemiologists until consensus was established. Data extraction was carried out by the author.
Inclusion Criteria
studies comparing clinical efficacy of the influenza vaccine or the pneumococcal vaccine with no vaccine or placebo;
randomized controlled trials published between January 1, 2000 and January 31, 2011;
studies including patients with COPD only;
studies investigating the efficacy of types of vaccines approved by Health Canada;
English language studies.
Exclusion Criteria
non-randomized controlled trials;
studies investigating vaccines for other diseases;
studies comparing different variations of vaccines;
studies in which patients received 2 or more types of vaccines;
studies comparing different routes of administering vaccines;
studies not reporting clinical efficacy of the vaccine or reporting immune response only;
studies investigating the efficacy of vaccines not approved by Health Canada.
Outcomes of Interest
Primary Outcomes
Influenza vaccination: Episodes of acute respiratory illness due to the influenza virus.
Pneumococcal vaccination: Time to the first episode of community-acquired pneumonia either due to pneumococcus or of unknown etiology.
Secondary Outcomes
rate of hospitalization and mechanical ventilation
mortality rate
adverse events
Quality of Evidence
The quality of each included study was assessed taking into consideration allocation concealment, randomization, blinding, power/sample size, withdrawals/dropouts, and intention-to-treat analyses. The quality of the body of evidence was assessed as high, moderate, low, or very low according to the GRADE Working Group criteria. The following definitions of quality were used in grading the quality of the evidence:
Summary of Efficacy of the Influenza Vaccination in Immunocompetent Patients With COPD
Clinical Effectiveness
The influenza vaccination was associated with significantly fewer episodes of influenza-related acute respiratory illness (ARI). The incidence density of influenza-related ARI was:
All patients: vaccine group: (total of 4 cases) = 6.8 episodes per 100 person-years; placebo group: (total of 17 cases) = 28.1 episodes per 100 person-years, (relative risk [RR], 0.2; 95% confidence interval [CI], 0.06−0.70; P = 0.005).
Patients with severe airflow obstruction (forced expiratory volume in 1 second [FEV1] < 50% predicted): vaccine group: (total of 1 case) = 4.6 episodes per 100 person-years; placebo group: (total of 7 cases) = 31.2 episodes per 100 person-years, (RR, 0.1; 95% CI, 0.003−1.1; P = 0.04).
Patients with moderate airflow obstruction (FEV1 50%−69% predicted): vaccine group: (total of 2 cases) = 13.2 episodes per 100 person-years; placebo group: (total of 4 cases) = 23.8 episodes per 100 person-years, (RR, 0.5; 95% CI, 0.05−3.8; P = 0.5).
Patients with mild airflow obstruction (FEV1 ≥ 70% predicted): vaccine group: (total of 1 case) = 4.5 episodes per 100 person-years; placebo group: (total of 6 cases) = 28.2 episodes per 100 person-years, (RR, 0.2; 95% CI, 0.003−1.3; P = 0.06).
The Kaplan-Meier survival analysis showed a significant difference between the vaccinated group and the placebo group regarding the probability of not acquiring influenza-related ARI (log-rank test P value = 0.003). Overall, the vaccine effectiveness was 76%. For categories of mild, moderate, or severe COPD the vaccine effectiveness was 84%, 45%, and 85% respectively.
With respect to hospitalization, fewer patients in the vaccine group compared with the placebo group were hospitalized due to influenza-related ARIs, although these differences were not statistically significant. The incidence density of influenza-related ARIs that required hospitalization was 3.4 episodes per 100 person-years in the vaccine group and 8.3 episodes per 100 person-years in the placebo group (RR, 0.4; 95% CI, 0.04−2.5; P = 0.3; log-rank test P value = 0.2). Also, no statistically significant differences between the 2 groups were observed for the 3 categories of severity of COPD.
Fewer patients in the vaccine group compared with the placebo group required mechanical ventilation due to influenza-related ARIs. However, these differences were not statistically significant. The incidence density of influenza-related ARIs that required mechanical ventilation was 0 episodes per 100 person-years in the vaccine group and 5 episodes per 100 person-years in the placebo group (RR, 0.0; 95% CI, 0−2.5; P = 0.1; log-rank test P value = 0.4). In addition, no statistically significant differences between the 2 groups were observed for the 3 categories of severity of COPD. The effectiveness of the influenza vaccine in preventing influenza-related ARIs and influenza-related hospitalization was not related to age, sex, severity of COPD, smoking status, or comorbid diseases.
safety
Overall, significantly more patients in the vaccine group than the placebo group experienced local adverse reactions (vaccine: 17 [27%], placebo: 4 [6%]; P = 0.002). Significantly more patients in the vaccine group than the placebo group experienced swelling (vaccine 4, placebo 0; P = 0.04) and itching (vaccine 4, placebo 0; P = 0.04). Systemic reactions included headache, myalgia, fever, and skin rash and there were no significant differences between the 2 groups for these reactions (vaccine: 47 [76%], placebo: 51 [81%], P = 0.5).
With respect to lung function, dyspneic symptoms, and exercise capacity, there were no significant differences between the 2 groups at 1 week and at 4 weeks in: FEV1, maximum inspiratory pressure at residual volume, oxygen saturation level of arterial blood, visual analogue scale for dyspneic symptoms, and the 6 Minute Walking Test for exercise capacity.
There was no significant difference between the 2 groups with regard to the probability of not acquiring total ARIs (influenza-related and/or non-influenza-related); (log-rank test P value = 0.6).
Summary of Efficacy of the Pneumococcal Vaccination in Immunocompetent Patients With COPD
Clinical Effectiveness
The Kaplan-Meier survival analysis showed no significant differences between the group receiving the penumoccocal vaccination and the control group for time to the first episode of community-acquired pneumonia due to pneumococcus or of unknown etiology (log-rank test 1.15; P = 0.28). Overall, vaccine efficacy was 24% (95% CI, −24 to 54; P = 0.33).
With respect to the incidence of pneumococcal pneumonia, the Kaplan-Meier survival analysis showed a significant difference between the 2 groups (vaccine: 0/298; control: 5/298; log-rank test 5.03; P = 0.03).
Hospital admission rates and median length of hospital stays were lower in the vaccine group, but the difference was not statistically significant. The mortality rate was not different between the 2 groups.
Subgroup Analysis
The Kaplan-Meier survival analysis showed significant differences between the vaccine and control groups for pneumonia due to pneumococcus and pneumonia of unknown etiology, and when data were analyzed according to subgroups of patients (age < 65 years, and severe airflow obstruction FEV1 < 40% predicted). The accumulated percentage of patients without pneumonia (due to pneumococcus and of unknown etiology) across time was significantly lower in the vaccine group than in the control group in patients younger than 65 years of age (log-rank test 6.68; P = 0.0097) and patients with a FEV1 less than 40% predicted (log-rank test 3.85; P = 0.0498).
Vaccine effectiveness was 76% (95% CI, 20−93; P = 0.01) for patients who were less than 65 years of age and −14% (95% CI, −107 to 38; P = 0.8) for those who were 65 years of age or older. Vaccine effectiveness for patients with a FEV1 less than 40% predicted and FEV1 greater than or equal to 40% predicted was 48% (95% CI, −7 to 80; P = 0.08) and −11% (95% CI, −132 to 47; P = 0.95), respectively. For patients who were less than 65 years of age (FEV1 < 40% predicted), vaccine effectiveness was 91% (95% CI, 35−99; P = 0.002).
Cox modelling showed that the effectiveness of the vaccine was dependent on the age of the patient. The vaccine was not effective in patients 65 years of age or older (hazard ratio, 1.53; 95% CI, 0.61−a2.17; P = 0.66) but it reduced the risk of acquiring pneumonia by 80% in patients less than 65 years of age (hazard ratio, 0.19; 95% CI, 0.06−0.66; P = 0.01).
safety
No patients reported any local or systemic adverse reactions to the vaccine.
PMCID: PMC3384373  PMID: 23074431
9.  Absence of influenza vaccination among high-risk older adults in Taiwan 
BMC Public Health  2010;10:603.
Background
Older adults, who often have more than one chronic disease, are at greater risk of influenza and its complications. However, because they often see physicians for other more pressing complaints, their physicians, focusing on one condition, may forget to suggest preventive measures for other diseases such as influenza. This study investigates what major factors affect an older adult with more than one chronic condition missing a vaccination opportunity.
Methods
Retrospectively reviewing a nationally representative random sample of medical claims from Taiwan's National Health Insurance Research Database during the period 2004 - 2006, we first identified patients sixty-five years or older who had visited physicians. Each patient was assigned a proxy for health status, the Charlson Comorbidity Index (CCI) score. An older claimant was defined has having "absence of a vaccination" when he or she had visited a physician during an influenza season but did not receive an influenza vaccination. Multivariate logistic regression was performed to estimate how likely it would be for older adults with various CCI scores to miss a vaccination.
Results
Out of 200,000 randomly selected claims, 20,923 older adults were included in our final analysis. We found older adults with higher CCIs to be more likely to have an absence of vaccination (p < 0.01). Our multivariate logistic regression results revealed CCI to be the greatest predictor of absence of vaccination, after controlling for individual factors and medical setting. Older adults with CCI scores three or higher were nearly five times more likely to miss a vaccination than those with a CCI of zero [OR: 4.93 (95%CI, 4.47-5.42)]. Those with CCIs of one and two were 2.53 and 3.92 times more likely to miss vaccination than those with a CCI of zero [OR 2.53 (95%CI, 2.26-2.84) and OR 3.92 (95%CI, 3.51-4.38), respectively].
Conclusions
The greater the number of certain comorbid conditions, the greater the likelihood a flu vaccination will be missed. Physicians would be well advised to not let the presenting problems of older patients distract from other possible health problems that might also need attention, in this case influenza vaccinations.
doi:10.1186/1471-2458-10-603
PMCID: PMC2964628  PMID: 20942933
10.  On Modeling HIV and T Cells In Vivo: Assessing Causal Estimators in Vaccine Trials 
PLoS Computational Biology  2006;2(6):e64.
The first efficacy trials—named STEP—of a T cell vaccine against HIV/AIDS began in 2004. The unprecedented structure of these trials raised new modeling and statistical challenges. Is it plausible that memory T cells, as opposed to antibodies, can actually prevent infection? If they fail at prevention, to what extent can they ameliorate disease? And how do we estimate efficacy in a vaccine trial with two primary endpoints, one traditional, one entirely novel (viral load after infection), and where the latter may be influenced by selection bias due to the former? In preparation for the STEP trials, biostatisticians developed novel techniques for estimating a causal effect of a vaccine on viral load, while accounting for post-randomization selection bias. But these techniques have not been tested in biologically plausible scenarios. We introduce new stochastic models of T cell and HIV kinetics, making use of new estimates of the rate that cytotoxic T lymphocytes—CTLs; the so-called killer T cells—can kill HIV-infected cells. Based on these models, we make the surprising discovery that it is not entirely implausible that HIV-specific CTLs might prevent infection—as the designers explicitly acknowledged when they chose the endpoints of the STEP trials. By simulating thousands of trials, we demonstrate that the new statistical methods can correctly identify an efficacious vaccine, while protecting against a false conclusion that the vaccine exacerbates disease. In addition to uncovering a surprising immunological scenario, our results illustrate the utility of mechanistic modeling in biostatistics.
Synopsis
In traditional biostatistics, mechanistic modeling of the relevant biology usually plays no role, because regulatory agencies will not, quite understandably, license vaccines or drugs on the basis of theories. But the second wave of trials of HIV vaccines will test two conjectures simultaneously. The theoretical possibility that these new, nonclassical, T cell–directed vaccines will prevent some infections while only ameliorating disease in others required biostatisticians to invent new ways of estimating vaccine efficacy. When only the one traditional endpoint—infection—is analyzed, the randomization to vaccine or placebo groups protects against bias. But the new techniques required input from experts on the plausible range of bias introduced by post-randomization selection (by infected state) for the second analysis. Here mechanistic modeling can play a role in evaluating the statistical methodology in biologically plausible settings. By simulating thousands of trials using their models, Wick, Gilbert, and Self were able to demonstrate that the methods protected from falsely concluding a harmful effect of the vaccine on disease. They also noted that the so-called killer T cells, as opposed to antibodies raised by a traditional vaccine, may actually be able to prevent some infections—a conclusion rather surprising for most immunologists and virologists, but which had to be allowed for when designing the vaccine trials.
doi:10.1371/journal.pcbi.0020064
PMCID: PMC1479086  PMID: 16789816
11.  A Comparative Analysis of Influenza Vaccination Programs 
PLoS Medicine  2006;3(10):e387.
Background
The threat of avian influenza and the 2004–2005 influenza vaccine supply shortage in the United States have sparked a debate about optimal vaccination strategies to reduce the burden of morbidity and mortality caused by the influenza virus.
Methods and Findings
We present a comparative analysis of two classes of suggested vaccination strategies: mortality-based strategies that target high-risk populations and morbidity-based strategies that target high-prevalence populations. Applying the methods of contact network epidemiology to a model of disease transmission in a large urban population, we assume that vaccine supplies are limited and then evaluate the efficacy of these strategies across a wide range of viral transmission rates and for two different age-specific mortality distributions.
We find that the optimal strategy depends critically on the viral transmission level (reproductive rate) of the virus: morbidity-based strategies outperform mortality-based strategies for moderately transmissible strains, while the reverse is true for highly transmissible strains. These results hold for a range of mortality rates reported for prior influenza epidemics and pandemics. Furthermore, we show that vaccination delays and multiple introductions of disease into the community have a more detrimental impact on morbidity-based strategies than mortality-based strategies.
Conclusions
If public health officials have reasonable estimates of the viral transmission rate and the frequency of new introductions into the community prior to an outbreak, then these methods can guide the design of optimal vaccination priorities. When such information is unreliable or not available, as is often the case, this study recommends mortality-based vaccination priorities.
A comparative analysis of two classes of suggested vaccination strategies, mortality-based strategies that target high-risk populations and morbidity-based strategies that target high-prevalence populations.
Editors' Summary
Background.
Influenza—a viral infection of the nose, throat, and airways that is transmitted in airborne droplets released by coughing or sneezing—is a serious public health threat. Most people recover quickly from influenza, but some individuals, especially infants, old people, and individuals with chronic health problems, can develop pneumonia and die. In the US, seasonal outbreaks (epidemics) of flu cause an estimated 36,000 excess deaths annually. And now there are fears that avian influenza might start a human pandemic—a global epidemic that could kill millions. Seasonal outbreaks of influenza occur because flu viruses continually change the viral proteins (antigens) to which the immune system responds. “Antigenic drift”—small changes in these proteins—means that an immune system response that combats flu one year may not provide complete protection the next winter. “Antigenic shift”—large antigen changes—can cause pandemics because communities have no immunity to the changed virus. Annual vaccination with vaccines based on the currently circulating viruses controls seasonal flu epidemics; to control a pandemic, vaccines based on the antigenically altered virus would have to be quickly developed.
Why Was This Study Done?
Most countries target vaccination efforts towards the people most at risk of dying from influenza, and to health-care workers who are likely come into contact with flu patients. But is this the best way to reduce the burden of illness (morbidity) and death (mortality) caused by influenza, particularly at the start of a pandemic, when vaccine would be limited? Old people and infants are much less likely to catch and spread influenza than school children, students, and employed adults, so could vaccination of these sections of the population—instead of those most at risk of death—be the best way to contain influenza outbreaks? In this study, the researchers used an analytical method called “contact network epidemiology” to compare two types of vaccination strategies: the currently favored mortality-based strategy, which targets high-risk individuals, and a morbidity-based strategy, which targets those segments of the community in which most influenza cases occur.
What Did the Researchers Do and Find?
Most models of disease transmission assume that each member of a community is equally likely to infect every other member. But a baby is unlikely to transmit flu to, for example, an unrelated, housebound elderly person. Contact network epidemiology takes the likely relationships between people into account when modeling disease transmission. Using information from Vancouver, British Columbia, Canada, on household size, age distribution, and occupations, and other factors such as school sizes, the researchers built a model population of a quarter of a million interconnected people. They then investigated how different vaccination strategies controlled the spread of influenza in this population. The optimal strategy depended on the level of viral transmissibility—the likelihood that an infectious person transmits influenza to a susceptible individual with whom he or she has contact. For moderately transmissible flu viruses, a morbidity-based vaccination strategy, in which the people most likely to catch the flu are vaccinated, was more effective at containing seasonal and pandemic outbreaks than a mortality-based strategy, in which the people most likely to die if they caught the flu are vaccinated. For highly transmissible strains, this situation was reversed. The level of transmissibility at which this reversal occurred depended on several factors, including whether vaccination was delayed and how many times influenza was introduced into the community.
What Do These Findings Mean?
The researchers tested their models by checking that they could replicate real influenza epidemics and pandemics, but, as with all mathematical models, they included many assumptions about influenza in their calculations, which may affect their results. Also, because the contact network used data from Vancouver, their results might not be applicable to other cities, or to nonurban areas. Nevertheless, their findings have important public health implications. When there are reasonable estimates of the viral transmission rate, and it is known how often influenza is being introduced into a community, contact network models could help public health officials choose between morbidity- and mortality-based vaccination strategies. When the viral transmission rate is unreliable or unavailable (for example, at the start of a pandemic), the best policy would be the currently preferred strategy of mortality-based vaccination. More generally, the use of contact network models should improve estimates of how infectious diseases spread through populations and indicate the best ways to control human epidemics and pandemics.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0030387.
US Centers for Disease Control and Prevention information about influenza for patients and professionals, including key facts on vaccination
US National Institute of Allergy and Infectious Diseases feature on seasonal, avian, and pandemic influenza
World Health Organization fact sheet on influenza, with links to information on vaccination
UK Health Protection Agency information on seasonal, avian, and pandemic influenza
MedlinePlus entry on influenza
doi:10.1371/journal.pmed.0030387
PMCID: PMC1584413  PMID: 17020406
12.  Optimizing the Dose of Pre-Pandemic Influenza Vaccines to Reduce the Infection Attack Rate 
PLoS Medicine  2007;4(6):e218.
Background
The recent spread of avian influenza in wild birds and poultry may be a precursor to the emergence of a 1918-like human pandemic. Therefore, stockpiles of human pre-pandemic vaccine (targeted at avian strains) are being considered. For many countries, the principal constraint for these vaccine stockpiles will be the total mass of antigen maintained. We tested the hypothesis that lower individual doses (i.e., less than the recommended dose for maximum protection) may provide substantial extra community-level benefits because they would permit wider vaccine coverage for a given total size of antigen stockpile.
Methods and Findings
We used a mathematical model to predict infection attack rates under different policies. The model incorporated both an individual's response to vaccination at different doses and the process of person-to-person transmission of pandemic influenza. We found that substantial reductions in the attack rate are likely if vaccines are given to more people at lower doses. These results are applicable to all three vaccine candidates for which data are available. As a guide to the magnitude of the effect, we simulated epidemics based on historical studies of immunogenicity. For example, for one of the vaccines for which data are available, the attack rate would drop from 67.6% to 58.7% if 160 out of the total US population of 300 million were given an optimal dose rather than 20 out of 300 million given the maximally protective dose (as promulgated in the US National Pandemic Preparedness Plan). Our results are conservative with respect to a number of alternative assumptions about the precise nature of vaccine protection. We also considered a model variant that includes a single high-risk subgroup representing children. For smaller stockpile sizes that allow vaccine to be offered only to the high-risk group at the optimal dose, the predicted benefits of using the homogenous model formed a lower bound in the presence of a risk group, even when the high-risk group was twice as infective and twice as susceptible.
Conclusions
In addition to individual-level protection (i.e., vaccine efficacy), the population-level implications of pre-pandemic vaccine programs should be considered when deciding on stockpile size and dose. Our results suggest that a lower vaccine dose may be justified in order to increase population coverage, thereby reducing the infection attack rate overall.
Steven Riley and colleagues examine the potential benefits of "stretching" a limited supply of vaccine and suggest that substantial reductions in the attack rate are possible if vaccines are given to more people at lower doses.
Editors' Summary
Background.
Every winter, millions of people catch influenza, a viral infection of the nose, throat, and airways. Most recover quickly, but the disease can be deadly. In the US, seasonal influenza outbreaks (epidemics) cause 36,000 excess deaths annually. And now there are fears that an avian (bird) influenza virus might trigger a human influenza pandemic—a global epidemic that could kill millions. Seasonal epidemics occur because flu viruses continually make small changes to their hemagglutinin and neuraminidase molecules, the viral proteins (antigens) that the immune system recognizes. Because of this “antigenic drift,” an immune system response (which can be induced by catching flu or by vaccination with disabled circulating influenza strains) that combats flu one year may provide only partial protection the next year. “Antigenic shift” (large changes in flu antigens) can cause pandemics because communities have no immunity to the changed virus.
Why Was This Study Done?
Although avian influenza virus, which contains a hemagglutinin type that differs from currently circulating human flu viruses, has caused a few cases of human influenza, it has not started a human pandemic yet because it cannot move easily between people. If it acquires this property, which will probably involve further small antigenic changes, it could kill millions of people before scientists can develop an effective vaccine against it. To provide some interim protection, many countries are preparing stockpiles of “pre-pandemic” vaccines targeted against the avian virus. The US, for example, plans to store enough pre-pandemic vaccine to provide maximum protection to 20 million people (including key health workers) out of its population of 300 million. But, given a limited stockpile of pre-pandemic vaccine, might giving more people a lower dose of vaccine, which might reduce the number of people susceptible to infection and induce herd immunity by preventing efficient transmission of the flu virus, be a better way to limit the spread of pandemic influenza? In this study, the researchers have used mathematical modeling to investigate this question.
What Did the Researchers Do and Find?
To predict the infection rates associated with different vaccination policies, the researchers developed a mathematical model that incorporates data on human immune responses induced with three experimental vaccines against the avian virus and historical data on the person–person transmission of previous pandemic influenza viruses. For all the vaccines, the model predicts that giving more people a low dose of the vaccine would limit the spread of influenza better than giving fewer people the high dose needed for full individual protection. For example, the researchers estimate that dividing the planned US stockpile of one experimental vaccine equally between 160 million people instead of giving it at the fully protective dose to 20 million people might avert about 27 million influenza cases in less than year. However, giving the maximally protective dose to the 9 million US health-care workers and using the remaining vaccine at a lower dose to optimize protection within the general population might avert only 14 million infections.
What Do These Findings Mean?
These findings suggest that, given a limited stockpile of pre-pandemic vaccine, increasing the population coverage of vaccination by using low doses of vaccine might reduce the overall influenza infection rate more effectively than vaccinating fewer people with fully protective doses of vaccine. However, because the researchers' model includes many assumptions, it can only give an indication of how different strategies might perform, not firm numbers for how many influenza cases each strategy is likely to avert. Before public-health officials use this or a similar model to help them decide the best way to use pre-pandemic vaccines to control a human influenza pandemic, they will need more information about the efficacy of these vaccines and about transmission rates of currently circulating viruses. They will also need to know whether pre-pandemic vaccines actually provide good protection against the pandemic virus, as assumed in this study, before they can recommend mass immunization with low doses of pre-pandemic vaccine, selective vaccination with high doses, or a mixed strategy.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0040218.
US Centers for Disease Control and Prevention provide information on influenza and influenza vaccination for patients and health professionals (in English, Spanish, Filipino, Chinese, and Vietnamese)
The World Health Organization has a fact sheet on influenza and on the global response to avian influenza (in English, Spanish, French, Russian, Arabic, and Chinese)
The MedlinePlus online encyclopedia devotes a page to flu (in English and Spanish)
The UK Health Protection Agency information on avian, pandemic, and seasonal influenza
The US National Institute of Allergy and Infectious Diseases has a comprehensive feature called “focus on the flu”
doi:10.1371/journal.pmed.0040218
PMCID: PMC1892041  PMID: 17579511
13.  Efficacy of Trivalent, Cold-Adapted, Influenza Virus Vaccine Against Influenza A (Fujian), a Drift Variant, during 2003–2004 
Vaccine  2007;25(20):4038-4045.
In the 2003–2004 influenza season, the predominant circulating influenza A (H3N2) virus in the United States was similar antigenically to A/Fujian/411/2002 (H3N2), a drift variant of A/Panama/2007/99 (H3N2), the vaccine strain. That year, a field study of trivalent live-attenuated influenza vaccine (LAIV-T) was conducted in Temple-Belton, Texas, as part of a larger community-based, non-randomized, open-label study in three communities that began in August 1998 [1, 2, 3]. Participants were healthy children aged 5 – 18 years. The analysis here concerns 6,403 children in the Scott & White Health Plan (SWHP) database living within zip codes of the Temple-Belton area, of whom 1,706 received LAIV-T and 548 received trivalent inactivated vaccine (TIV) in 2003, 983 had been previously vaccinated in 1998–2001, but not in 2002–2003 or 2003, and 3,166 had never been vaccinated. The main outcome measure was medically-attended acute respiratory illness (MAARI). Surveillance culture results were incorporated into the analysis to estimate efficacy against culture-confirmed influenza illness. Vaccine effectiveness of LAIV-T against MAARI was 26% (95% confidence interval (CI) 11,39). Vaccine efficacy of LAIV-T against culture-confirmed influenza illness including surveillance cultures of children in the SWHP database in the validation calculation was 56% (95% CI 24,84). LAIV-T was cross-protective with a drift variant strain in 2003–2004, evidence that such vaccines could be important for preparing for a pandemic and for annual influenza.
doi:10.1016/j.vaccine.2007.02.060
PMCID: PMC2883284  PMID: 17395338
efficacy; influenza; surveillance; vaccine
14.  Antiviral Resistance and the Control of Pandemic Influenza 
PLoS Medicine  2007;4(1):e15.
Background
The response to the next influenza pandemic will likely include extensive use of antiviral drugs (mainly oseltamivir), combined with other transmission-reducing measures. Animal and in vitro studies suggest that some strains of influenza may become resistant to oseltamivir while maintaining infectiousness (fitness). Use of antiviral agents on the scale anticipated for the control of pandemic influenza will create an unprecedented selective pressure for the emergence and spread of these strains. Nonetheless, antiviral resistance has received little attention when evaluating these plans.
Methods and Findings
We designed and analyzed a deterministic compartmental model of the transmission of oseltamivir-sensitive and -resistant influenza infections during a pandemic. The model predicts that even if antiviral treatment or prophylaxis leads to the emergence of a transmissible resistant strain in as few as 1 in 50,000 treated persons and 1 in 500,000 prophylaxed persons, widespread use of antivirals may strongly promote the spread of resistant strains at the population level, leading to a prevalence of tens of percent by the end of a pandemic. On the other hand, even in circumstances in which a resistant strain spreads widely, the use of antivirals may significantly delay and/or reduce the total size of the pandemic. If resistant strains carry some fitness cost, then, despite widespread emergence of resistance, antivirals could slow pandemic spread by months or more, and buy time for vaccine development; this delay would be prolonged by nondrug control measures (e.g., social distancing) that reduce transmission, or use of a stockpiled suboptimal vaccine. Surprisingly, the model suggests that such nondrug control measures would increase the proportion of the epidemic caused by resistant strains.
Conclusions
The benefits of antiviral drug use to control an influenza pandemic may be reduced, although not completely offset, by drug resistance in the virus. Therefore, the risk of resistance should be considered in pandemic planning and monitored closely during a pandemic.
Emergence of oseltamivir-resistant influenza strains during a pandemic is likely given the heightened selective pressure if the drug is widely used. Marc Lipsitch and colleagues suggest that resistance would reduce but not completely offset the drug's benefits for pandemic control.
Editors' Summary
Background.
Governments and health authorities worldwide are planning how they would best prepare for and deal with a future influenza pandemic. Seasonal influenza is thought to affect between 5% and 15% of the population worldwide each year. Most people who get influenza recover within a couple of weeks without lasting effects, but a small proportion of patients, mostly young children and elderly people, experience serious complications that can be fatal. An influenza pandemic happens when new variants of the influenza virus emerge against which little immunity exists in the general population. Pandemic influenza strains are transmitted more rapidly than seasonal strains, often sweep across several countries or continents, and make more people ill. There are drugs that can treat and prevent influenza. One of them, oseltamivir (Tamiflu) is an antiviral drug that works by preventing viral particles from being released by infected human cells. Stockpiling large amounts of oseltamivir and related drugs with the intent to treat a large fraction of the population is a key part of pandemic preparedness of many countries. However, it is known that influenza viruses can develop resistance to these drugs.
Why Was This Study Done?
It is not clear how the emergence of oseltamivir-resistant influenza strains would affect the course of any future influenza pandemic. Much research in this area has focused on how likely the new strains are to emerge in the first place, rather than on how they might spread once they had emerged. In the context of an influenza pandemic, antiviral drugs would be used in a large proportion of the population, likely driving the selection and spread of resistant viruses. For this study, the researchers wanted to estimate the likely impact of resistant strains during an influenza pandemic.
What Did the Researchers Do and Find?
These researchers set up a mathematical model (i.e., simulations done on a computer) to mimic the spread of influenza. They then fed a set of assumptions into the computer. These included information about the rate of transmission of influenza from one person to another; what proportion of people would receive antiviral drugs for prophylaxis or treatment; how likely the drugs would be to successfully treat or prevent infection; and in what proportion of people the virus might become resistant to drugs. The modeling led to three main predictions. First, it predicted that widespread use of antiviral drugs such as oseltamivir could quickly lead to the spread of resistant viruses, even if resistant strains emerged only rarely. Second, even with resistant strains circulating, prophylaxis and treatment with oseltamivir would still delay the spread of the pandemic and reduce its total size. Third, nondrug interventions (such as social isolation and school closures) would further reduce the number of cases, but a higher proportion of cases would be caused by resistant strains if these control measures were used.
What Do These Findings Mean?
These findings suggest that, in the event of a future influenza pandemic for which antiviral drugs are used, there is a risk of resistance emerging and resistant strains causing illness in a substantial number of people. This would counteract the benefits of antiviral drugs but not eliminate those benefits entirely. Like all modeling studies, this one relies on realistic assumptions being entered into the model, and it is hard to know closely the model will mimic a real-life situation until the properties of an actual pandemic strain are known. Most studies, including this one, suggest that in the event of a pandemic, antiviral drugs will have an overall beneficial impact on reducing death rates and adverse health outcomes. However, given the sizeable effects of resistance suggested here, its role should be considered in pandemic planning. This includes surveillance that can detect emergence and spread of resistant strains.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/doi:10.1371/journal.pmed.0040015.
World Health Organization: information on pandemic preparedness
World Health Organization: fact sheets on influenza
Information from the UK Health Protection Agency on pandemic influenza
US government website on both pandemic flu and avian flu (information provided by the US Department of Health and Human Services)
doi:10.1371/journal.pmed.0040015
PMCID: PMC1779817  PMID: 17253900
15.  An exploration of the missing data mechanism in an Internet based smoking cessation trial 
Background
Missing outcome data are very common in smoking cessation trials. It is often assumed that all such missing data are from participants who have been unsuccessful in giving up smoking (“missing=smoking”). Here we use data from a recent Internet based smoking cessation trial in order to investigate which of a set of a priori chosen baseline variables are predictive of missingness, and the evidence for and against the “missing=smoking” assumption.
Methods
We use a selection model, which models the probability that the outcome is observed given the outcome and other variables. The selection model includes a parameter for which zero indicates that the data are Missing at Random (MAR) and large values indicate “missing=smoking”. We examine the evidence for the predictive power of baseline variables in the context of a sensitivity analysis. We use data on the number and type of attempts made to obtain outcome data in order to estimate the association between smoking status and the missing data indicator.
Results
We apply our methods to the iQuit smoking cessation trial data. From the sensitivity analysis, we obtain strong evidence that older participants are more likely to provide outcome data. The model for the number and type of attempts to obtain outcome data confirms that age is a good predictor of missing data. There is weak evidence from this model that participants who have successfully given up smoking are more likely to provide outcome data but this evidence does not support the “missing=smoking” assumption. The probability that participants with missing outcome data are not smoking at the end of the trial is estimated to be between 0.14 and 0.19.
Conclusions
Those conducting smoking cessation trials, and wishing to perform an analysis that assumes the data are MAR, should collect and incorporate baseline variables into their models that are thought to be good predictors of missing data in order to make this assumption more plausible. However they should also consider the possibility of Missing Not at Random (MNAR) models that make or allow for less extreme assumptions than “missing=smoking”.
doi:10.1186/1471-2288-12-157
PMCID: PMC3507670  PMID: 23067272
16.  Publication Bias in Antipsychotic Trials: An Analysis of Efficacy Comparing the Published Literature to the US Food and Drug Administration Database 
PLoS Medicine  2012;9(3):e1001189.
A comparison of data held by the U.S. Food and Drug Administration (FDA) against data from journal reports of clinical trials enables estimation of the extent of publication bias for antipsychotics.
Background
Publication bias compromises the validity of evidence-based medicine, yet a growing body of research shows that this problem is widespread. Efficacy data from drug regulatory agencies, e.g., the US Food and Drug Administration (FDA), can serve as a benchmark or control against which data in journal articles can be checked. Thus one may determine whether publication bias is present and quantify the extent to which it inflates apparent drug efficacy.
Methods and Findings
FDA Drug Approval Packages for eight second-generation antipsychotics—aripiprazole, iloperidone, olanzapine, paliperidone, quetiapine, risperidone, risperidone long-acting injection (risperidone LAI), and ziprasidone—were used to identify a cohort of 24 FDA-registered premarketing trials. The results of these trials according to the FDA were compared with the results conveyed in corresponding journal articles. The relationship between study outcome and publication status was examined, and effect sizes derived from the two data sources were compared. Among the 24 FDA-registered trials, four (17%) were unpublished. Of these, three failed to show that the study drug had a statistical advantage over placebo, and one showed the study drug was statistically inferior to the active comparator. Among the 20 published trials, the five that were not positive, according to the FDA, showed some evidence of outcome reporting bias. However, the association between trial outcome and publication status did not reach statistical significance. Further, the apparent increase in the effect size point estimate due to publication bias was modest (8%) and not statistically significant. On the other hand, the effect size for unpublished trials (0.23, 95% confidence interval 0.07 to 0.39) was less than half that for the published trials (0.47, 95% confidence interval 0.40 to 0.54), a difference that was significant.
Conclusions
The magnitude of publication bias found for antipsychotics was less than that found previously for antidepressants, possibly because antipsychotics demonstrate superiority to placebo more consistently. Without increased access to regulatory agency data, publication bias will continue to blur distinctions between effective and ineffective drugs.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
People assume that, when they are ill, health-care professionals will ensure that they get the best available treatment. But how do clinicians know which treatment is likely to be most effective? In the past, clinicians used their own experience to make such decisions. Nowadays, they rely on evidence-based medicine—the systematic review and appraisal of trials, studies that investigate the efficacy and safety of medical interventions in patients. Evidence-based medicine can guide clinicians, however, only if all the results from clinical trials are published in an unbiased manner. Unfortunately, “publication bias” is common. For example, the results of trials in which a new drug did not perform better than existing drugs or in which it had unwanted side effects often remain unpublished. Moreover, published trials can be subject to outcome reporting bias—the publication may only include those trial outcomes that support the use of the new treatment rather than presenting all the available data.
Why Was This Study Done?
If only strongly positive results are published and negative results and side-effects remain unpublished, a drug will seem safer and more effective than it is in reality, which could affect clinical decision-making and patient outcomes. But how big a problem is publication bias? Here, researchers use US Food and Drug Administration (FDA) reviews as a benchmark to quantify the extent to which publication bias may be altering the apparent efficacy of second-generation antipsychotics (drugs used to treat schizophrenia and other mental illnesses that are characterized by a loss of contact with reality). In the US, all new drugs have to be approved by the FDA before they can be marketed. During this approval process, the FDA collects and keeps complete information about premarketing trials, including descriptions of their design and prespecified outcome measures and all the data collected during the trials. Thus, a comparison of the results included in the FDA reviews for a group of trials and the results that appear in the literature for the same trials can provide direct evidence about publication bias.
What Did the Researchers Do and Find?
The researchers identified 24 FDA-registered premarketing trials that investigated the use of eight second-generation antipsychotics for the treatment of schizophrenia or schizoaffective disorder. They searched the published literature for reports of these trials, and, by comparing the results of these trials according to the FDA with the results in the published articles, they examined the relationship between the study outcome (did the FDA consider it positive or negative?) and publication and looked for outcome reporting bias. Four of the 24 FDA-registered trials were unpublished. Three of these unpublished trials failed to show that the study drug was more effective than a placebo (a “dummy” pill); the fourth showed that the study drug was inferior to another drug already in use in the US. Among the 20 published trials, the five that the FDA judged not positive showed some evidence of publication bias. However, the association between trial outcome and publication status did not reach statistical significance (it might have happened by chance), and the mean effect size (a measure of drug effectiveness) derived from the published literature was only slightly higher than that derived from the FDA records. By contrast, within the FDA dataset, the mean effect size of the published trials was approximately double that of the unpublished trials.
What Do These Findings Mean?
The accuracy of these findings is limited by the small number of trials analyzed. Moreover, this study considers only the efficacy and not the safety of these drugs, it assumes that the FDA database is complete and unbiased, and its findings are not generalizable to other conditions that antipsychotics are used to treat. Nevertheless, these findings show that publication bias in the reporting of trials of second-generation antipsychotic drugs enhances the apparent efficacy of these drugs. Although the magnitude of the publication bias seen here is less than that seen in a similar study of antidepressant drugs, these findings show how selective reporting of clinical trial data undermines the integrity of the evidence base and can deprive clinicians of accurate data on which to base their prescribing decisions. Increased access to FDA reviews, suggest the researchers, is therefore essential to prevent publication bias continuing to blur distinctions between effective and ineffective drugs.
Additional Information
Please access these web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001189.
The US Food and Drug Administration provides information about drug approval in the US for consumers and health-care professionals
Detailed information about the process by which drugs are approved is on the web site of the FDA Center for Drug Evaluation and Research; also, FDA Drug Approval Packages are available for many drugs; the FDA Transparency Initiative, which was launched in June 2009, is an agency-wide effort to improve the transparency of the FDA
FDA-approved product labeling on drugs marketed in the US can be found at the US National Library of Medicine's DailyMed web page
Wikipedia has a page on publication bias (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
MedlinePlus provides links to sources of information on schizophrenia and on psychotic disorders (in English and Spanish)
Patient experiences of psychosis, including the effects of medication, are provided by the charity HealthtalkOnline
doi:10.1371/journal.pmed.1001189
PMCID: PMC3308934  PMID: 22448149
17.  Influenza Vaccination and Mortality: Differentiating Vaccine Effects From Bias 
American Journal of Epidemiology  2009;170(5):650-656.
It is widely believed that influenza (flu) vaccination of the elderly reduces all-cause mortality, yet randomized trials for assessing vaccine effectiveness are not feasible and the observational research has been controversial. Efforts to differentiate vaccine effectiveness from selection bias have been problematic. The authors examined mortality before, during, and after 9 flu seasons in relation to time-varying vaccination status in an elderly California population in which 115,823 deaths occurred from 1996 to 2005, including 20,484 deaths during laboratory-defined flu seasons. Vaccine coverage averaged 63%; excess mortality when the flu virus was circulating averaged 7.8%. In analyses that omitted weeks when flu circulated, the odds ratio measuring the vaccination-mortality association increased monotonically from 0.34 early in November to 0.56 in January, 0.67 in April, and 0.76 in August. This reflects the trajectory of selection effects in the absence of flu. In analyses that included weeks with flu and adjustment for selection effects, flu season multiplied the odds ratio by 0.954. The corresponding vaccine effectiveness estimate was 4.6% (95% confidence interval: 0.7, 8.3). To differentiate vaccine effects from selection bias, the authors used logistic regression with a novel case-centered specification that may be useful in other population-based studies when the exposure-outcome association varies markedly over time.
doi:10.1093/aje/kwp173
PMCID: PMC2728831  PMID: 19625341
aged; epidemiologic methods; influenza, human; influenza vaccines; mortality; selection bias
18.  Epidemiological Characteristics of 2009 (H1N1) Pandemic Influenza Based on Paired Sera from a Longitudinal Community Cohort Study 
PLoS Medicine  2011;8(6):e1000442.
Steven Riley and colleagues analyze a community cohort study from the 2009 (H1N1) influenza pandemic in Hong Kong, and found that more children than adults were infected with H1N1, but children were less likely to progress to severe disease than adults.
Background
While patterns of incidence of clinical influenza have been well described, much uncertainty remains over patterns of incidence of infection. The 2009 pandemic provided both the motivation and opportunity to investigate patterns of mild and asymptomatic infection using serological techniques. However, to date, only broad epidemiological patterns have been defined, based on largely cross-sectional study designs with convenience sampling frameworks.
Methods and Findings
We conducted a paired serological survey of a cohort of households in Hong Kong, recruited using random digit dialing, and gathered data on severe confirmed cases from the public hospital system (>90% inpatient days). Paired sera were obtained from 770 individuals, aged 3 to 103, along with detailed individual-level and household-level risk factors for infection. Also, we extrapolated beyond the period of our study using time series of severe cases and we simulated alternate study designs using epidemiological parameters obtained from our data. Rates of infection during the period of our study decreased substantially with age: for 3–19 years, the attack rate was 39% (31%–49%); 20–39 years, 8.9% (5.3%–14.7%); 40–59 years, 5.3% (3.5%–8.0%); and 60 years or older, 0.77% (0.18%–4.2%). We estimated parameters for a parsimonious model of infection in which a linear age term and the presence of a child in the household were used to predict the log odds of infection. Patterns of symptom reporting suggested that children experienced symptoms more often than adults. The overall rate of confirmed pandemic (H1N1) 2009 influenza (H1N1pdm) deaths was 7.6 (6.2–9.5) per 100,000 infections. However, there was substantial and progressive increase in deaths per 100,000 infections with increasing age from 0.66 (0.65–0.86) for 3–19 years up to 220 (50–4,000) for 60 years and older. Extrapolating beyond the period of our study using rates of severe disease, we estimated that 56% (43%–69%) of 3–19 year olds and 16% (13%–18%) of people overall were infected by the pandemic strain up to the end of January 2010. Using simulation, we found that, during 2009, larger cohorts with shorter follow-up times could have rapidly provided similar data to those presented here.
Conclusions
Should H1N1pdm evolve to be more infectious in older adults, average rates of severe disease per infection could be higher in future waves: measuring such changes in severity requires studies similar to that described here. The benefit of effective vaccination against H1N1pdm infection is likely to be substantial for older individuals. Revised pandemic influenza preparedness plans should include prospective serological cohort studies. Many individuals, of all ages, remained susceptible to H1N1pdm after the main 2009 wave in Hong Kong.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
From June 2009 to August 2010, the world was officially (according to specific WHO criteria—WHO phase 6 pandemic alert) in the grip of an Influenza A pandemic with a new strain of the H1N1 virus. During this time, more than 214 countries and overseas territories reported laboratory confirmed cases of pandemic influenza H1N1 2009 with almost 20,000 deaths.
While much is already known about patterns of incidence of clinical influenza, the patterns of infection incidence are much more uncertain, because many influenza infections are either asymptomatic or cause only mild symptoms. This means that it is difficult to obtain accurate estimates of risk factors for infection and the overall burden of disease using only clinical surveillance. However, without accurate estimates of infection incidence across different risk groups, it is not possible to establish the number of infections that give rise to severe disease (the per infection rate of severe disease). Consequently, it is difficult to give evidence-based advice for individuals, groups, and populations about the potential benefits of interventions including drugs and vaccines that might reduce the risk of influenza infection.
Why Was This Study Done?
During the 2009 pandemic, some countries and territories, such as Hong Kong, were able to investigate patterns of mild and asymptomatic infection using serological techniques, thus providing information that may help to fill this knowledge gap. Given the high levels of polymerase chain reaction (PCR) testing and the robust reporting of hospital episodes, the main H1N1 pandemic wave in Hong Kong (during September 2009) provided an opportunity to implement a prospective cohort study to investigate the incidence of infection.
What Did the Researchers Do and Find? The researchers collected data on the asymptomatic symptoms of influenza by randomly selecting households to participate in the study. Each member of the household willing to participate had a baseline blood sample taken before the main wave of the pandemic (July to September 2009), then, when clinical surveillance suggested that the main peak in transmission had passed, after the main wave (November 2009 to February 2010). During the study period, participants were asked to report any flu-like symptoms in three ways: to phone the study team and report symptoms in real time; to fill out a paper diary with the day and symptoms; and to report symptoms during a follow-up interview. In parallel, the researchers monitored data on every patient with H1N1 admitted to intensive care units or who died while in the hospital. The researchers then estimated the number of H1N1 infections (infection incidence) per severe case by developing a likelihood-based framework. They used a simulation model to investigate alternate study designs and to validate their estimates of the rate of severe disease per infection.
Using these methods, the researchers found that rates of H1N1 infection during the study period decreased substantially with age: for 3–19 years, the attack rate was 39%; 20–39 years, 8.9%; 40–59 years, 5.3%; and 60 years or older, 0.77%. In addition, patterns of symptom reporting indicated that children experienced symptoms more often than adults. The overall rate of confirmed H1N1 deaths was 7.6 per 100,000 infections. However, there was a substantial and progressive increase in deaths per 100,000 infections with increasing age from 0.66 for 3–19 years up to 220 for 60 years and older. Statistical modeling suggested that 56% of 3–19 year olds and 16% of people overall were infected by the pandemic strain up to the end of January 2010.
What Do These Findings Mean?
The results of this study suggest that more children were infected with H1N1 than adults but most of them did not progress to severe disease. Conversely, although fewer older adults were infected with H1N1, this group was much more likely to experience severe disease. Therefore, should H1N1 infection incidence ever increase in older adults, for example by evolving to become more infectious to this group, average rates of severe disease per infection could be much higher than for the 2009 pandemic. Revised pandemic preparedness plans should include prospective serological cohort studies, such as this one, in order to be able to estimate rates of severe disease per infection.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000442.
WHO has information about the global response to the 2009 H1N1 pandemic
WHO also provides recommendations for the H1N1 post-pandemic period
The government of Hong Kong's Centre for Health Protection provides information about H1N1 in Hong Kong
doi:10.1371/journal.pmed.1000442
PMCID: PMC3119689  PMID: 21713000
19.  Evidence for the Selective Reporting of Analyses and Discrepancies in Clinical Trials: A Systematic Review of Cohort Studies of Clinical Trials 
PLoS Medicine  2014;11(6):e1001666.
In a systematic review of cohort studies, Kerry Dwan and colleagues examine the evidence for selective reporting and discrepancies in analyses between journal publications and other documents for clinical trials.
Please see later in the article for the Editors' Summary
Background
Most publications about selective reporting in clinical trials have focussed on outcomes. However, selective reporting of analyses for a given outcome may also affect the validity of findings. If analyses are selected on the basis of the results, reporting bias may occur. The aims of this study were to review and summarise the evidence from empirical cohort studies that assessed discrepant or selective reporting of analyses in randomised controlled trials (RCTs).
Methods and Findings
A systematic review was conducted and included cohort studies that assessed any aspect of the reporting of analyses of RCTs by comparing different trial documents, e.g., protocol compared to trial report, or different sections within a trial publication. The Cochrane Methodology Register, Medline (Ovid), PsycInfo (Ovid), and PubMed were searched on 5 February 2014. Two authors independently selected studies, performed data extraction, and assessed the methodological quality of the eligible studies. Twenty-two studies (containing 3,140 RCTs) published between 2000 and 2013 were included. Twenty-two studies reported on discrepancies between information given in different sources. Discrepancies were found in statistical analyses (eight studies), composite outcomes (one study), the handling of missing data (three studies), unadjusted versus adjusted analyses (three studies), handling of continuous data (three studies), and subgroup analyses (12 studies). Discrepancy rates varied, ranging from 7% (3/42) to 88% (7/8) in statistical analyses, 46% (36/79) to 82% (23/28) in adjusted versus unadjusted analyses, and 61% (11/18) to 100% (25/25) in subgroup analyses. This review is limited in that none of the included studies investigated the evidence for bias resulting from selective reporting of analyses. It was not possible to combine studies to provide overall summary estimates, and so the results of studies are discussed narratively.
Conclusions
Discrepancies in analyses between publications and other study documentation were common, but reasons for these discrepancies were not discussed in the trial reports. To ensure transparency, protocols and statistical analysis plans need to be published, and investigators should adhere to these or explain discrepancies.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
In the past, clinicians relied on their own experience when choosing the best treatment for their patients. Nowadays, they turn to evidence-based medicine—the systematic review and appraisal of trials, studies that investigate the benefits and harms of medical treatments in patients. However, evidence-based medicine can guide clinicians only if all the results from clinical trials are published in an unbiased and timely manner. Unfortunately, the results of trials in which a new drug performs better than existing drugs are more likely to be published than those in which the new drug performs badly or has unwanted side effects (publication bias). Moreover, trial outcomes that support the use of a new treatment are more likely to be published than those that do not support its use (outcome reporting bias). Recent initiatives—such as making registration of clinical trials in a trial registry (for example, ClinicalTrials.gov) a prerequisite for publication in medical journals—aim to prevent these biases, which pose a threat to informed medical decision-making.
Why Was This Study Done?
Selective reporting of analyses of outcomes may also affect the validity of clinical trial findings. Sometimes, for example, a trial publication will include a per protocol analysis (which considers only the outcomes of patients who received their assigned treatment) rather than a pre-planned intention-to-treat analysis (which considers the outcomes of all the patients regardless of whether they received their assigned treatment). If the decision to publish the per protocol analysis is based on the results of this analysis being more favorable than those of the intention-to-treat analysis (which more closely resembles “real” life), then “analysis reporting bias” has occurred. In this systematic review, the researchers investigate the selective reporting of analyses and discrepancies in randomized controlled trials (RCTs) by reviewing published studies that assessed selective reporting of analyses in groups (cohorts) of RCTs and discrepancies in analyses of RCTs between different sources (for example, between the protocol in a trial registry and the journal publication) or different sections of a source. A systematic review uses predefined criteria to identify all the research on a given topic.
What Did the Researchers Do and Find?
The researchers identified 22 cohort studies (containing 3,140 RCTs) that were eligible for inclusion in their systematic review. All of these studies reported on discrepancies between the information provided by the RCTs in different places, but none investigated the evidence for analysis reporting bias. Several of the cohort studies reported, for example, that there were discrepancies in the statistical analyses included in the different documents associated with the RCTs included in their analysis. Other types of discrepancies reported by the cohort studies included discrepancies in the reporting of composite outcomes (an outcome in which multiple end points are combined) and in the reporting of subgroup analyses (investigations of outcomes in subgroups of patients that should be predefined in the trial protocol to avoid bias). Discrepancy rates varied among the RCTs according to the types of analyses and cohort studies considered. Thus, whereas in one cohort study discrepancies were present in the statistical test used for the analysis of the primary outcome in only 7% of the included studies, they were present in the subgroup analyses of all the included studies.
What Do These Findings Mean?
These findings indicate that discrepancies in analyses between publications and other study documents such as protocols in trial registries are common. The reasons for these discrepancies in analyses were not discussed in trial reports but may be the result of reporting bias, errors, or legitimate departures from a pre-specified protocol. For example, a statistical analysis that is not specified in the trial protocol may sometimes appear in a publication because the journal requested its inclusion as a condition of publication. The researchers suggest that it may be impossible for systematic reviewers to distinguish between these possibilities simply by looking at the source documentation. Instead, they suggest, it may be necessary for reviewers to contact the trial authors. However, to make selective reporting of analyses more easily detectable, they suggest that protocols and analysis plans should be published and that investigators should be required to stick to these plans or explain any discrepancies when they publish their trial results. Together with other initiatives, this approach should help improve the quality of evidence-based medicine and, as a result, the treatment of patients.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001666.
Wikipedia has pages on evidence-based medicine, on systematic reviews, and on publication bias (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
ClinicalTrials.gov provides information about the US National Institutes of Health clinical trial registry, including background information about clinical trials
The Cochrane Collaboration is a global independent network of health practitioners, researchers, patient advocates, and others that aims to promote evidence-informed health decision-making by producing high-quality, relevant, accessible systematic reviews and other synthesized research evidence; the Cochrane Handbook for Systematic Reviews of Interventions describes the preparation of systematic reviews in detail
PLOS Medicine recently launched a Reporting Guidelines Collection, an open-access collection of reporting guidelines, commentary, and related research on guidelines from across PLOS journals that aims to help advance the efficiency, effectiveness, and equitability of the dissemination of biomedical information
doi:10.1371/journal.pmed.1001666
PMCID: PMC4068996  PMID: 24959719
20.  The Effect of Universal Influenza Immunization on Mortality and Health Care Use 
PLoS Medicine  2008;5(10):e211.
Background
In 2000, Ontario, Canada, initiated a universal influenza immunization program (UIIP) to provide free influenza vaccines for the entire population aged 6 mo or older. Influenza immunization increased more rapidly in younger age groups in Ontario compared to other Canadian provinces, which all maintained targeted immunization programs. We evaluated the effect of Ontario's UIIP on influenza-associated mortality, hospitalizations, emergency department (ED) use, and visits to doctors' offices.
Methods and Findings
Mortality and hospitalization data from 1997 to 2004 for all ten Canadian provinces were obtained from national datasets. Physician billing claims for visits to EDs and doctors' offices were obtained from provincial administrative datasets for four provinces with comprehensive data. Since outcomes coded as influenza are known to underestimate the true burden of influenza, we studied more broadly defined conditions. Hospitalizations, ED use, doctors' office visits for pneumonia and influenza, and all-cause mortality from 1997 to 2004 were modelled using Poisson regression, controlling for age, sex, province, influenza surveillance data, and temporal trends, and used to estimate the expected baseline outcome rates in the absence of influenza activity. The primary outcome was then defined as influenza-associated events, or the difference between the observed events and the expected baseline events. Changes in influenza-associated outcome rates before and after UIIP introduction in Ontario were compared to the corresponding changes in other provinces. After UIIP introduction, influenza-associated mortality decreased more in Ontario (relative rate [RR] = 0.26) than in other provinces (RR = 0.43) (ratio of RRs = 0.61, p = 0.002). Similar differences between Ontario and other provinces were observed for influenza-associated hospitalizations (RR = 0.25 versus 0.44, ratio of RRs = 0.58, p < 0.001), ED use (RR = 0.31 versus 0.69, ratio of RRs = 0.45, p < 0.001), and doctors' office visits (RR = 0.21 versus 0.52, ratio of RRs = 0.41, p < 0.001). Sensitivity analyses were carried out to assess consistency, specificity, and the presence of a dose-response relationship. Limitations of this study include the ecological study design, the nonspecific outcomes, difficulty in modeling baseline events, data quality and availability, and the inability to control for potentially important confounders.
Conclusions
Compared to targeted programs in other provinces, introduction of universal vaccination in Ontario in 2000 was associated with relative reductions in influenza-associated mortality and health care use. The results of this large-scale natural experiment suggest that universal vaccination may be an effective public health measure for reducing the annual burden of influenza.
Comparing influenza-related mortality and health care use between Ontario and other Canadian provinces, Jeffrey Kwong and colleagues find evidence that Ontario's universal vaccination program has reduced the burden of influenza.
Editors' Summary
Background.
Seasonal outbreaks (epidemics) of influenza—a viral disease of the nose, throat, and airways—affect millions of people and kill about 500,000 individuals every year. These epidemics occur because of “antigenic drift”: small but frequent changes in the viral proteins to which the human immune system responds mean that an immune response produced one year by exposure to an influenza virus provides only partial protection against influenza the next year. Immunization can boost this natural immunity and reduce a person's chances of catching influenza. That is, an injection of killed influenza viruses can be used to prime the immune system so that it responds quickly and efficiently when exposed to live virus. However, because of antigenic drift, for influenza immunization to be effective, it has to be repeated annually with a vaccine that contains the major circulating strains of the influenza virus.
Why Was This Study Done?
Public-health organizations recommend targeted vaccination programs, so that elderly people, infants, and chronically ill individuals—the people most likely to die from pneumonia and other complications of influenza—receive annual influenza vaccination. Some experts argue, however, that universal vaccination might provide populations with better protection from influenza, both directly by increasing the number of vaccinated people and indirectly through “herd immunity,” which occurs when a high proportion of the population is immune to an infectious disease, so that even unvaccinated people are unlikely to become infected (because infected people rarely come into contact with susceptible people). In this study, the researchers compare the effects of the world's first free universal influenza immunization program (UIIP), which started in 2000 in the Canadian province of Ontario, on influenza-associated deaths and health care use with the effects of targeted vaccine programs on the same outcomes elsewhere in Canada.
What Did the Researchers Do and Find?
Using national records, the researchers collected data on influenza vaccination, on all deaths, and on hospitalizations for pneumonia and influenza in all Canadian provinces between 1997 and 2004. They also collected data on emergency department and doctors' office visits for pneumonia and influenza for Ontario, Quebec, Alberta, and Manitoba. They then used a mathematical model to estimate the baseline rates for these outcomes in the absence of influenza activity, and from these calculated weekly rates for deaths and health care use specifically resulting from influenza. In 1996–1997, 18% of the population was vaccinated against influenza in Ontario whereas in the other provinces combined the vaccination rate was 13%. On average, since 2000—the year in which UIIP was introduced in Ontario—vaccination rates have risen to 38% and 24% in Ontario and the other provinces, respectively. Since the introduction of UIIP, the researchers report, influenza-associated deaths have decreased by 74% in Ontario but by only 57% in the other provinces combined. Influenza-associated use of health care facilities has also decreased more in Ontario than in the other provinces over the same period.
What Do These Findings Mean?
These findings are limited by some aspects of the study design. For example, they depend on the accuracy of the assumptions made when calculating events due specifically to influenza, and on the availability and accuracy of vaccination and clinical outcome data. In addition, it is possible that influenza-associated deaths and health care use may have decreased more in Ontario than in the other Canadian provinces because of some unrecognized health care changes specific to Ontario but unrelated to the introduction of universal influenza vaccination. Nevertheless, these findings indicate that, compared to the targeted vaccination programs in the other Canadian provinces, the Ontarian UIIP is associated with reductions in influenza-associated deaths and health care use, particularly in people younger than 65 years old. This effect is seen at a level of vaccination unlikely to produce herd immunity so might be more marked if the uptake of vaccination could be further increased. Thus, although it is possible that Canada is a special case, these findings suggest that universal influenza vaccination might be an effective way to reduce the global burden of influenza.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0050211.
Read the related PLoSMedicine Perspective by Cécile Viboud and Mark Miller
A related PLoSMedicine Research Article by Carline van den Dool and colleagues is also available
The Ontario Ministry of Health provides information on its universal influenza immunization program (in English and French)
The World Health Organization provides information on influenza and on influenza vaccines (in several languages)
The US Centers for Disease Control and Prevention provide information for patients and professionals on all aspects of influenza (in English and Spanish)
MedlinePlus provides a list of links to other information about influenza (in English and Spanish)
The UK National Health Service provides information about the science of immunization, including a simple explanatory animation of immunity
doi:10.1371/journal.pmed.0050211
PMCID: PMC2573914  PMID: 18959473
21.  Removing the Age Restrictions for Rotavirus Vaccination: A Benefit-Risk Modeling Analysis 
PLoS Medicine  2012;9(10):e1001330.
A modeling analysis conducted by Manish Patel and colleagues predicts the possible number of rotavirus deaths prevented, and number of intussusception deaths caused, by use of an unrestricted rotavirus schedule in low- and middle-income countries.
Background
To minimize potential risk of intussusception, the World Health Organization (WHO) recommended in 2009 that rotavirus immunization should be initiated by age 15 weeks and completed before 32 weeks. These restrictions could adversely impact vaccination coverage and thereby its health impact, particularly in developing countries where delays in vaccination often occur.
Methods and Findings
We conducted a modeling study to estimate the number of rotavirus deaths prevented and the number of intussusception deaths caused by vaccination when administered on the restricted schedule versus an unrestricted schedule whereby rotavirus vaccine would be administered with DTP vaccine up to age 3 years. Countries were grouped on the basis of child mortality rates, using WHO data. Inputs were estimates of WHO rotavirus mortality by week of age from a recent study, intussusception mortality based on a literature review, predicted vaccination rates by week of age from USAID Demographic and Health Surveys, the United Nations Children's Fund (UNICEF) Multiple Indicator Cluster Surveys (MICS), and WHO-UNICEF 2010 country-specific coverage estimates, and published estimates of vaccine efficacy and vaccine-associated intussusception risk. On the basis of the error estimates and distributions for model inputs, we conducted 2,000 simulations to obtain median estimates of deaths averted and caused as well as the uncertainty ranges, defined as the 5th–95th percentile, to provide an indication of the uncertainty in the estimates.
We estimated that in low and low-middle income countries a restricted schedule would prevent 155,800 rotavirus deaths (5th–95th centiles, 83,300–217,700) while causing potentially 253 intussusception deaths (76–689). In contrast, vaccination without age restrictions would prevent 203,000 rotavirus deaths (102,000–281,500) while potentially causing 547 intussusception deaths (237–1,160). Thus, removing the age restrictions would avert an additional 47,200 rotavirus deaths (18,700–63,700) and cause an additional 294 (161–471) intussusception deaths, for an incremental benefit-risk ratio of 154 deaths averted for every death caused by vaccine. These extra deaths prevented under an unrestricted schedule reflect vaccination of an additional 21%–25% children, beyond the 63%–73% of the children who would be vaccinated under the restricted schedule. Importantly, these estimates err on the side of safety in that they assume high vaccine-associated risk of intussusception and do not account for potential herd immunity or non-fatal outcomes.
Conclusions
Our analysis suggests that in low- and middle-income countries the additional lives saved by removing age restrictions for rotavirus vaccination would far outnumber the potential excess vaccine-associated intussusception deaths.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Rotavirus causes severe diarrhea and vomiting. It is responsible for a large number of hospitalizations among young children in developed countries (an estimated 60,000 hospitalizations per year in the US in 2005, for example). In poor countries, rotavirus is a major cause of death in children under five. In 1998, the first rotavirus vaccine, called RotaShield, was approved in the US by the Food and Drug Administration. Shortly after the vaccine became widely used, doctors noticed a small increase in a problem called intussusception among the vaccinated infants. Intussusception is a rare type of bowel obstruction that occurs when the bowel telescopes in on itself. Prompt treatment of intussusception normally leads to full recovery, but some children with the condition need surgery, and when the disease is left untreated it can be fatal. Because intussusception is a serious condition and because very few children die from rotavirus infection in the United States, the US authorities stopped recommending vaccination with RotaShield in 1999. The manufacturer withdrew the vaccine from the market shortly thereafter.
Since then, two new vaccines (named Rotarix and RotaTeq) have been developed. Before they were approved in the US and elsewhere, they were extensively tested for any adverse side effects, especially intussusception. No increase in the risk for intussusception was found in these studies, and both are now approved and recommended for vaccination of infants around the world.
Why Was This Study Done?
Since 2006, hundreds of thousands of infants have been vaccinated with Rotarix or RotaTeq, with safety being closely monitored. Some countries have reported a small increase in intussusception (one to four additional cases per 100,000 vaccinated infants, compared with one per 2,000 of cases that occur in unvaccinated children). This increase is much lower than the one seen previously with RotaShield. In response to these findings, authorities in the US and other developed countries as well as the World Health Organization declared that the benefits of the vaccine outweigh the risks of the small number of additional intussusception cases in both developed and poor countries. However, because older infants have a higher risk of naturally occurring intussusception, they decided that the course of vaccination (three oral doses for Rotarix and two for RotaTeq) should be initiated before 15 weeks of age and completed before the age of 32 weeks. This is usually not a problem in countries with easy access to health facilities. However, in many poor countries where delays in infant vaccination are common, giving the vaccine only to very young children means that many others who could benefit from its protection will be excluded. In this study, the researchers examined the risks and benefits of rotavirus vaccination in poor countries where most of the rotavirus deaths occur. Specifically, they looked at the benefits and risks if the age restrictions were removed, with a particular emphasis on allowing infants to initiate rotavirus immunization even if they arrive after 15 weeks of age.
What Did the Researchers Do and Find?
The researchers used the most recent estimates for how well the vaccines protect children in Africa and Asia from becoming infected with rotavirus, how many deaths from rotavirus infection can be avoided by vaccination, how many additional cases of intussusception will likely occur in vaccinated children, and what proportion of children would be excluded from rotavirus vaccination because they are too old when they come to a health facility for their infant vaccination. They then estimated the number of rotavirus deaths prevented and the number of intussusception deaths caused by vaccination in two scenarios. The first one (the restricted scenario) corresponds to previous guidelines from WHO and others, in which rotavirus vaccination needs to be initiated before 15 weeks and the full series completed before 32 weeks. The second one (called the unrestricted scenario) allows rotavirus vaccination of children alongside current routinely administered vaccines up to three years of age, recognizing that most children receive their vaccination by 1 year of life.
The researchers estimated that removing the age restriction would prevent an additional 154 rotavirus deaths for each intussusception death caused by the vaccine. Under the unrestricted scenario, roughly a third more children would get vaccinated, which would prevent an additional approximately 47,000 death from rotavirus while causing approximately 300 additional intussusception deaths.
They also calculated some best- and worst-case scenarios. The worst-case scenario assumed a much higher risk of intussusception for children receiving their first dose after 15 weeks of life than what has been seen anywhere, and also that an additional 20% of children with intussusception would die from it than what was already assumed in their routine scenario (again, a higher number than seen in reality). In addition, it assumes a lower protection from rotavirus death for the vaccine than has been observed in children vaccinated so far. In this pessimistic case, the number of rotavirus deaths prevented was 24 for each intussusception death caused by the vaccine.
What Do These Findings Mean?
If one accepts that deaths caused by a vaccine are not fundamentally different from deaths caused by a failure to vaccinate, then these results show that the benefits of lifting the age restriction for rotavirus vaccine clearly outweigh the risks, at least when only examining mortality outcomes. The calculations are valid only for low-income countries in Africa and Asia where both vaccination delays and deaths from rotavirus are common. The risk-benefit ratio will be different elsewhere. There are also additional risks and benefits that are not included in the study's estimates. For example, early vaccination might be seen as less of an urgent priority when this vaccine can be had at a later date, leaving very young children more vulnerable. On the other hand, when many children in the community are vaccinated, even the unvaccinated children are less likely to get infected (what is known as “herd immunity”), something that has not been taken into account in the benefits here. The results of this study (and its limitations) were reviewed in April 2012 by WHO's Strategic Advisory Group of Experts. The group then recommended that, while early vaccination is still strongly encouraged, the age restriction on rotavirus vaccination should be removed in countries where delays in vaccination and rotavirus mortality are common so that more vulnerable children can be vaccinated and deaths from rotavirus averted.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001330.
The World Health Organization provides information on rotavirus
Wikipedia has information on rotavirus vaccine and intussusception (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
The US Centers for Disease Control and Prevention rotavirus vaccination page includes a link to frequently asked questions
PATH Rotavirus Vaccine Access and Delivery has timely, useful updates on status of rotavirus vaccines globally
doi:10.1371/journal.pmed.1001330
PMCID: PMC3479108  PMID: 23109915
22.  Antigenic Fingerprinting of H5N1 Avian Influenza Using Convalescent Sera and Monoclonal Antibodies Reveals Potential Vaccine and Diagnostic Targets 
PLoS Medicine  2009;6(4):e1000049.
Using whole-genome-fragment phage display libraries, Hana Golding and colleagues identify the viral epitopes recognized by serum antibodies in humans who have recovered from infection with H5N1 avian influenza.
Background
Transmission of highly pathogenic avian H5N1 viruses from poultry to humans have raised fears of an impending influenza pandemic. Concerted efforts are underway to prepare effective vaccines and therapies including polyclonal or monoclonal antibodies against H5N1. Current efforts are hampered by the paucity of information on protective immune responses against avian influenza. Characterizing the B cell responses in convalescent individuals could help in the design of future vaccines and therapeutics.
Methods and Findings
To address this need, we generated whole-genome–fragment phage display libraries (GFPDL) expressing fragments of 15–350 amino acids covering all the proteins of A/Vietnam/1203/2004 (H5N1). These GFPDL were used to analyze neutralizing human monoclonal antibodies and sera of five individuals who had recovered from H5N1 infection. This approach led to the mapping of two broadly neutralizing human monoclonal antibodies with conformation-dependent epitopes. In H5N1 convalescent sera, we have identified several potentially protective H5N1-specific human antibody epitopes in H5 HA[(-10)-223], neuraminidase catalytic site, and M2 ectodomain. In addition, for the first time to our knowledge in humans, we identified strong reactivity against PB1-F2, a putative virulence factor, following H5N1 infection. Importantly, novel epitopes were identified, which were recognized by H5N1-convalescent sera but did not react with sera from control individuals (H5N1 naïve, H1N1 or H3N2 seropositive).
Conclusion
This is the first study, to our knowledge, describing the complete antibody repertoire following H5N1 infection. Collectively, these data will contribute to rational vaccine design and new H5N1-specific serodiagnostic surveillance tools.
Editors' Summary
Background
Every winter, millions of people catch influenza, a viral infection of the airways. Most recover quickly but seasonal influenza outbreaks (epidemics) kill about half a million people annually. These epidemics occur because small but frequent changes in the viral proteins (antigens) to which the human immune system responds mean that an immune response produced one year by infection or through vaccination provides only partial protection against influenza the next year. Influenza viruses also occasionally appear that contain major antigenic changes. Human populations have little or no immunity to such viruses (which often originate in animals or birds), so they can start deadly global epidemics (pandemics ). Worryingly, the last influenza pandemic occurred in 1968 and many experts fear that another pandemic is now overdue. The trigger for such a pandemic, they think, could be the avian (bird) H5N1 influenza virus, which first appeared in 1996 in a goose in China. The name indicates the types of two major influenza antigens present in the virus: H5N1 carries type 5 hemagglutinin and type 1 neuraminidase.
Why Was This Study Done?
H5N1 has caused about 400 confirmed cases of human influenza and more than 250 deaths in the past decade but it has not started a human pandemic because it cannot pass easily between people. However, it could possibly acquire this ability at any time, so it is a priority to develop both vaccines that will provide protection against a pandemic H5N1 viral strain, as well as antibody-based antiviral therapies for people not protected by vaccination (antibodies are proteins produced by the immune system that help to fight infections; people can sometimes be protected from infection by injecting them with pre-prepared antibodies). To do this, scientists need to know how the human immune system responds to the H5N1 virus. In particular, they need to know which parts of the virus the immune system can detect and make antibodies against. In this study, therefore, the researchers characterize the specific antibody responses found in people recovering from infection with H5N1.
What Did the Researchers Do and Find?
The researchers made several “genome-fragment phage display libraries”, collections of bacterial viruses (phages) engineered so that each phage makes one of many possible short pieces (polypeptides) of a nonphage protein. Such “libraries” can be used to investigate which fragments are recognized by antibodies from a given source. In this case, several libraries were made that contained fragments of the genome of the H5N1 strain responsible for an outbreak of human influenza in Vietnam in 2004–2005 (A/Vietnam/1203/2004). The researchers used these libraries to analyze the antibodies made by five Vietnamese people recovering from infection with A/Vietnam/1203/2004. H5N1 convalescent blood samples, the researchers report, contained antibodies that recognized small regions (“epitopes”) in several viral proteins, including hemagglutinin, neuraminidase, a structural protein called M2, and a viral protein called PB1-F2 that is partly responsible for the severity of H5N1 infections. Several of the novel epitopes identified were not recognized by antibodies in blood taken from people recovering from infection with other influenza viruses. The researchers also used their phage display libraries to analyze two neutralizing human monoclonal antibodies generated from patients infected with A/Vietnam/1203/2004 (neutralizing antibodies protect mice against normally lethal challenge with H5N1; monoclonal antibodies are generated in the laboratory by creating continuously growing cell lines that produce a single type of antibody). Importantly, both of the neutralizing monoclonal antibodies recognized “noncontinuous conformation-dependent epitopes”—protein sequences that are not adjacent to one another in the polypeptide sequence of the protein, but that lie close together in space because of the way the protein is folded up.
What Do These Findings Mean?
Although some aspects of the antibody repertoire produced in people exposed to the H5N1 influenza virus may have been missed in this analysis, these findings provide important and detailed new information about how the human immune system responds to infection with this virus. In particular, they show that people recovering from H5N1 infection make a diverse range of antibodies against several viral proteins for at least six months and identify specific parts of H5N1 that may be particularly good at stimulating a protective immune response. This information can now be used to help design vaccines against H5N1 and antibody-based therapies for the treatment of H5N1 infections, and to develop new tools for monitoring outbreaks of avian influenza in human populations.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000049.
This study is further discussed in a PLoS Medicine Perspective by Malik Peiris
The US Centers for Disease Control and Prevention provides information for about influenza for patients and professionals, including specific information on avian and pandemic influenza (in several languages)
The World Health Organization provides information on influenza (in several languages) and on H5N1 avian influenza (in several languages), and a global timeline about H5N1 avian influenza infection in birds and people
The UK Health Protection Agency provides information on avian, pandemic, and epidemic (seasonal) influenza
MedlinePlus provides a list of links to other information about influenza and bird flu (in English and Spanish)
doi:10.1371/journal.pmed.1000049
PMCID: PMC2661249  PMID: 19381279
23.  Comparison of Filtering Methods for the Modeling and Retrospective Forecasting of Influenza Epidemics 
PLoS Computational Biology  2014;10(4):e1003583.
A variety of filtering methods enable the recursive estimation of system state variables and inference of model parameters. These methods have found application in a range of disciplines and settings, including engineering design and forecasting, and, over the last two decades, have been applied to infectious disease epidemiology. For any system of interest, the ideal filter depends on the nonlinearity and complexity of the model to which it is applied, the quality and abundance of observations being entrained, and the ultimate application (e.g. forecast, parameter estimation, etc.). Here, we compare the performance of six state-of-the-art filter methods when used to model and forecast influenza activity. Three particle filters—a basic particle filter (PF) with resampling and regularization, maximum likelihood estimation via iterated filtering (MIF), and particle Markov chain Monte Carlo (pMCMC)—and three ensemble filters—the ensemble Kalman filter (EnKF), the ensemble adjustment Kalman filter (EAKF), and the rank histogram filter (RHF)—were used in conjunction with a humidity-forced susceptible-infectious-recovered-susceptible (SIRS) model and weekly estimates of influenza incidence. The modeling frameworks, first validated with synthetic influenza epidemic data, were then applied to fit and retrospectively forecast the historical incidence time series of seven influenza epidemics during 2003–2012, for 115 cities in the United States. Results suggest that when using the SIRS model the ensemble filters and the basic PF are more capable of faithfully recreating historical influenza incidence time series, while the MIF and pMCMC do not perform as well for multimodal outbreaks. For forecast of the week with the highest influenza activity, the accuracies of the six model-filter frameworks are comparable; the three particle filters perform slightly better predicting peaks 1–5 weeks in the future; the ensemble filters are more accurate predicting peaks in the past.
Author Summary
Influenza, or the flu, is a significant public health burden in the U.S. that annually causes between 3,000 and 49,000 deaths. Predictions of influenza, if reliable, would provide public health officials valuable advanced warning that could aid efforts to reduce the burden of this disease. For instance, medical resources, including vaccines and antiviral drugs, can be distributed to areas in need well in advance of peak influenza incidence. Recent applications of statistical filtering methods to epidemiological models have shown that accurate and reliable influenza forecast is possible; however, many filtering methods exist, and the performance of any filter may be application dependent. Here we use a single epidemiological modeling framework to test the performance of six state-of-the-art filters for modeling and forecasting influenza. Three of the filters are particle filters, commonly used in scientific, engineering, and economic disciplines; the other three filters are ensemble filters, frequently used in geophysical disciplines, such as numerical weather prediction. We use each of the six filters to retrospectively model and forecast seasonal influenza activity during 2003–2012 for 115 cities in the U.S. We report the performance of the six filters and discuss potential strategies for improving real-time influenza prediction.
doi:10.1371/journal.pcbi.1003583
PMCID: PMC3998879  PMID: 24762780
24.  Joint generalized estimating equations for multivariate longitudinal binary outcomes with missing data: An application to AIDS data 
SUMMARY
In a large, prospective longitudinal study designed to monitor cardiac abnormalities in children born to HIV-infected women, instead of a single outcome variable, there are multiple binary outcomes (e.g., abnormal heart rate, abnormal blood pressure, abnormal heart wall thickness) considered as joint measures of heart function over time. In the presence of missing responses at some time points, longitudinal marginal models for these multiple outcomes can be estimated using generalized estimating equations (GEE) (Liang and Zeger, 1986), and consistent estimates can be obtained under the assumption of a missing completely at random (MCAR) mechanism. When the missing data mechanism is missing at random (MAR), that is the probability of missing a particular outcome at a time-point depends on observed values of that outcome and the remaining outcomes at other time points, we propose joint estimation of the marginal models using a single modified GEE based on an EM-type algorithm. The proposed method is motivated by the longitudinal study of cardiac abnormalities in children born to HIV-infected women and analyses of these data are presented to illustrate the application of the method. Further, in an asymptotic study of bias, we show that under an MAR mechanism in which missingness depends on all observed outcome variables, our joint estimation via the modified GEE produces almost unbiased estimates, provided the correlation model has been correctly specified, whereas estimates from standard GEE can lead to substantial bias.
doi:10.1111/j.1467-985X.2008.00564.x
PMCID: PMC2888330  PMID: 20585409
EM-type algorithm; generalized estimating equations; missing at random; missing completely at random
25.  Mortality in Iraq Associated with the 2003–2011 War and Occupation: Findings from a National Cluster Sample Survey by the University Collaborative Iraq Mortality Study 
PLoS Medicine  2013;10(10):e1001533.
Based on a survey of 2,000 randomly selected households throughout Iraq, Amy Hagopian and colleagues estimate that close to half a million excess deaths are attributable to the recent Iraq war and occupation.
Please see later in the article for the Editors' Summary
Background
Previous estimates of mortality in Iraq attributable to the 2003 invasion have been heterogeneous and controversial, and none were produced after 2006. The purpose of this research was to estimate direct and indirect deaths attributable to the war in Iraq between 2003 and 2011.
Methods and Findings
We conducted a survey of 2,000 randomly selected households throughout Iraq, using a two-stage cluster sampling method to ensure the sample of households was nationally representative. We asked every household head about births and deaths since 2001, and all household adults about mortality among their siblings. We used secondary data sources to correct for out-migration. From March 1, 2003, to June 30, 2011, the crude death rate in Iraq was 4.55 per 1,000 person-years (95% uncertainty interval 3.74–5.27), more than 0.5 times higher than the death rate during the 26-mo period preceding the war, resulting in approximately 405,000 (95% uncertainty interval 48,000–751,000) excess deaths attributable to the conflict. Among adults, the risk of death rose 0.7 times higher for women and 2.9 times higher for men between the pre-war period (January 1, 2001, to February 28, 2003) and the peak of the war (2005–2006). We estimate that more than 60% of excess deaths were directly attributable to violence, with the rest associated with the collapse of infrastructure and other indirect, but war-related, causes. We used secondary sources to estimate rates of death among emigrants. Those estimates suggest we missed at least 55,000 deaths that would have been reported by households had the households remained behind in Iraq, but which instead had migrated away. Only 24 households refused to participate in the study. An additional five households were not interviewed because of hostile or threatening behavior, for a 98.55% response rate. The reliance on outdated census data and the long recall period required of participants are limitations of our study.
Conclusions
Beyond expected rates, most mortality increases in Iraq can be attributed to direct violence, but about a third are attributable to indirect causes (such as from failures of health, sanitation, transportation, communication, and other systems). Approximately a half million deaths in Iraq could be attributable to the war.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
War is a major public health problem. Its health effects include violent deaths among soldiers and civilians as well as indirect increases in mortality and morbidity caused by conflict. Unlike those of other causes of death and disability, however, the consequences of war on population health are rarely studied scientifically. In conflict situations, deaths and diseases are not reliably measured and recorded, and estimating the proportion caused, directly or indirectly, by a war or conflict is challenging. Population-based mortality survey methods—asking representative survivors about deaths they know about—were developed by public health researchers to estimate death rates. By comparing death rate estimates for periods before and during a conflict, researchers can derive the number of excess deaths that are attributable to the conflict.
Why Was This Study Done?
A number of earlier studies have estimated the death toll in Iraq since the beginning of the war in March 2003. The previous studies covered different periods from 2003 to 2006 and derived different rates of overall deaths and excess deaths attributable to the war and conflict. All of them have been controversial, and their methodologies have been criticized. For this study, based on a population-based mortality survey, the researchers modified and improved their methodology in response to critiques of earlier surveys. The study covers the period from the beginning of the war in March 2003 until June 2011, including a period of high violence from 2006 to 2008. It provides population-based estimates for excess deaths in the years after 2006 and covers most of the period of the war and subsequent occupation.
What Did the Researchers Do and Find?
Interviewers trained by the researchers conducted the survey between May 2011 and July 2011 and collected data from 2,000 randomly selected households in 100 geographical clusters, distributed across Iraq's 18 governorates. The interviewers asked the head of each household about deaths among household members from 2001 to the time of the interview, including a pre-war period from January 2001 to March 2003 and the period of the war and occupation. They also asked all adults in the household about deaths among their siblings during the same period. From the first set of data, the researchers calculated the crude death rates (i.e., the number of deaths during a year per 1,000 individuals) before and during the war. They found the wartime crude death rate in Iraq to be 4.55 per 1,000, more than 50% higher than the death rate of 2.89 during the two-year period preceding the war. By multiplying those rates by the annual Iraq population, the authors estimate the total excess Iraqi deaths attributable to the war through mid-2011 to be about 405,000. The researchers also estimated that an additional 56,000 deaths were not counted due to migration. Including this number, their final estimate is that approximately half a million people died in Iraq as a result of the war and subsequent occupation from March 2003 to June 2011.
The risk of death at the peak of the conflict in 2006 almost tripled for men and rose by 70% for women. Respondents attributed 20% of household deaths to war-related violence. Violent deaths were attributed primarily to coalition forces (35%) and militia (32%). The majority (63%) of violent deaths were from gunshots. Twelve percent were attributed to car bombs. Based on the responses from adults in the surveyed households who reported on the alive-or-dead status of their siblings, the researchers estimated the total number of deaths among adults aged 15–60 years, from March 2003 to June 2011, to be approximately 376,000; 184,000 of these deaths were attributed to the conflict, and of those, the authors estimate that 132,000 were caused directly by war-related violence.
What Do These Findings Mean?
These findings provide the most up-to-date estimates of the death toll of the Iraq war and subsequent conflict. However, given the difficult circumstances, the estimates are associated with substantial uncertainties. The researchers extrapolated from a small representative sample of households to estimate Iraq's national death toll. In addition, respondents were asked to recall events that occurred up to ten years prior, which can lead to inaccuracies. The researchers also had to rely on outdated census data (the last complete population census in Iraq dates back to 1987) for their overall population figures. Thus, to accompany their estimate of 460,000 excess deaths from March 2003 to mid-2011, the authors used statistical methods to determine the likely range of the true estimate. Based on the statistical methods, the researchers are 95% confident that the true number of excess deaths lies between 48,000 and 751,000—a large range. More than two years past the end of the period covered in this study, the conflict in Iraq is far from over and continues to cost lives at alarming rates. As discussed in an accompanying Perspective by Salman Rawaf, violence and lawlessness continue to the present day. In addition, post-war Iraq has limited capacity to re-establish and maintain its battered public health and safety infrastructure.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001533
This study is further discussed in a PLOS Medicine Perspective by Salman Rawaf.
The Geneva Declaration on Armed Violence and Development website provides information on the global burden of armed violence.
The International Committee of the Red Cross provides information about war and international humanitarian law (in several languages).
Medact, a global health charity, has information on health and conflict.
Columbia University has a program on forced migration and health.
Johns Hopkins University runs the Center for Refugee and Disaster Response.
University of Washington's Health Alliance International website also has information about war and conflict.
doi:10.1371/journal.pmed.1001533
PMCID: PMC3797136  PMID: 24143140

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