Toby L. Merlin

Estimating the Burden of 2009 Pandemic Influenza A (H1N1) in the United States (April 2009–April 2010)

To calculate the burden of 2009 pandemic influenza A (pH1N1) in the United States, we extrapolated from the Centers for Disease Control and Preventions Emerging Infections Program laboratory-confirmed hospitalizations across the entire United States, and then corrected for underreporting. From 12 April 2009 to 10 April 2010, we estimate that approximately 60.8 million cases (range: 43.3-89.3 million), 274,304 hospitalizations (195,086-402,719), and 12,469 deaths (8868-18,306) occurred in the United States due to pH1N1. Eighty-seven percent of deaths occurred in those under 65 years of age with children and working adults having risks of hospitalization and death 4 to 7 times and 8 to 12 times greater, respectively, than estimates of impact due to seasonal influenza covering the years 1976-2001. In our study, adults 65 years of age or older were found to have rates of hospitalization and death that were up to 75% and 81%, respectively, lower than seasonal influenza. These results confirm the necessity of a concerted public health response to pH1N1.

Comparison of cytology proficiency testing: glass slides vs. virtual slides.

OBJECTIVE To compare proficiency testing in gynecologic cytology using glass slides vs. virtual slides. STUDY DESIGN To compare performance, a sample of 111 individuals (pathologists = 52, cytotechnologists = 59) from participating in-state laboratories were administered 2 proficiency tests. The annual test of the Maryland Cytology Proficiency Testing Program (MCPTP) was administered to individuals in their laboratories following normal work practice (i.e., using microscopes and equipment with which they were familiar). The other test was CytoView II (Centers for Disease Control and Prevention, Atlanta, Georgia, U.S.A.), a computer-based test composed of virtual slides captured from the MCPTPs glass slides, which test administration personnel transported to the individuals laboratory and administered using 1 of 2 laptop computers. ANOVA was used to compare the performance on the 2 tests and the effect of various potential confounding variables. The slides were evaluated by comparing the performance average for each glass slide to that of the matching virtual slides. All data analysis was performed at the 95% confidence interval. RESULTS The mean score of the individuals (n = 111) on the MCPTP test was 99.2% (SD = 2.2, range = 90-100%). The mean score of the individuals (n = 111) on CytoView II was 96.8% (SD = 5.8, range = 70-100%). No individual scored < 90% on the glass slide test (pass rate = 100%). Eight individuals (pathologists = 3, cytotechnologists = 5) scored < 90% on the CytoView II (pass rate = 93.8%). Comparison of an individuals performance on the 2 tests demonstrated a significant difference. When virtual slides that did not attain a 90% consensus were excluded from the scoring, a comparison of individual pass rate for the glass slide test (100%) and computer-based test (99.1%) did not demonstrate significant difference. CONCLUSION Each slide (glass or virtual) must be field validated by cytotechnologists and pathologists. If field validation and Clinical Laboratory Improvement Amendment referencing of virtual slides are comparable to those of glass slides, computer-based testing can be equivalent.

Protecting Vulnerable Populations From Pandemic Influenza in the United States: A Strategic Imperative

Protecting vulnerable populations from pandemic influenza is a strategic imperative. The US national strategy for pandemic influenza preparedness and response assigns roles to governments, businesses, civic and community-based organizations, individuals, and families. Because influenza is highly contagious, inadequate preparedness or untimely response in vulnerable populations increases the risk of infection for the general population. Recent public health emergencies have reinforced the importance of preparedness and the challenges of effective response among vulnerable populations. We explore definitions and determinants of vulnerable, at-risk, and special populations and highlight approaches for ensuring that pandemic influenza preparedness includes these populations and enables them to respond appropriately. We also provide an overview of population-specific and cross-cutting articles in this theme issue on influenza preparedness for vulnerable populations.

A primer on strategies for prevention and control of seasonal and pandemic influenza.

The United States has made considerable progress in pandemic preparedness. Limited attention, however, has been given to the challenges faced by populations that will be at increased risk of the consequences of the pandemic, including challenges caused by societal, economic, and health-related factors. This supplement to the American Journal of Public Health focuses on the challenges faced by at-risk and vulnerable populations in preparing for and responding to an influenza pandemic. Here, we provide background information for subsequent articles throughout the supplement. We summarize (1) seasonal influenza epidemiology, transmission, clinical illness, diagnosis, vaccines, and antiviral medications; (2) H5N1 avian influenza; and (3) pandemic influenza vaccines, antiviral medications, and nonpharmaceutical interventions.

Estimating Effect of Antiviral Drug Use during Pandemic (H1N1) 2009 Outbreak, United States

From April 2009 through March 2010, during the pandemic (H1N1) 2009 outbreak, ≈8.2 million prescriptions for influenza neuraminidase-inhibiting antiviral drugs were filled in the United States. We estimated the number of hospitalizations likely averted due to use of these antiviral medications. After adjusting for prescriptions that were used for prophylaxis and personal stockpiles, as well as for patients who did not complete their drug regimen, we estimated the filled prescriptions prevented ≈8,400–12,600 hospitalizations (on the basis of median values). Approximately 60% of these prevented hospitalizations were among adults 18–64 years of age, with the remainder almost equally divided between children 0–17 years of age and adults >65 years of age. Public health officials should consider these estimates an indication of success of treating patients during the 2009 pandemic and a warning of the need for renewed planning to cope with the next pandemic.

Peramivir Use for Treatment of Hospitalized Patients with Influenza A(H1N1)pdm09 under Emergency Use Authorization, October 2009 – June 2010

BACKGROUND In response to the influenza A(H1N1)pdm09 (pH1N1) pandemic, peramivir, an investigational intravenous neuraminidase inhibitor, was made available for treatment of hospitalized patients with pH1N1 in the United States under an Emergency Use Authorization (EUA). The Centers for Disease Control and Prevention (CDC) implemented a program to manage peramivir distribution to requesting clinicians under EUA. We describe results of the CDCs peramivir program and 3 related surveys. METHODS We analyzed data on peramivir requests made by clinicians to the CDC through an electronic request system. Three surveys were administered to enhance clinician compliance with adverse event reporting, to conduct product accountability, and to collect data on peramivir-treated patients. Descriptive analyses were performed, and 2-source capture-recapture analysis based on the 3 surveys was used to estimate the number of patients who received peramivir through the EUA. RESULTS From 23 October 2009 to 23 June 2010, CDC received 1371 clinician requests for peramivir and delivered 2129 five-day adult treatment course equivalents of peramivir to 563 hospitals. Based on survey responses, at least 1274 patients (median age, 43 years; range, 0-92 years; 49% male) received ≥1 doses of peramivir (median duration, 6 days). Capture-recapture analysis yielded estimates for the potential total number of peramivir recipients ranging from 1185 (95% confidence interval [CI], 1076-1293) to 1490 (95% CI, 1321-1659). CONCLUSIONS Approximately 1274 hospitalized patients received peramivir through EUA program during the pH1N1 pandemic. Further analyses are needed to assess the clinical effectiveness of peramivir treatment of hospitalized patients with pH1N1.

Regional Spread of Ebola Virus, West Africa, 2014

To explain the spread of the 2014 Ebola epidemic in West Africa, and thus help with response planning, we analyzed publicly available data. We found that the risk for infection in an area can be predicted by case counts, population data, and distances between affected and nonaffected areas.

Modeling in Real Time During the Ebola Response

To aid decision-making during CDCs response to the 2014-2016 Ebola virus disease (Ebola) epidemic in West Africa, CDC activated a Modeling Task Force to generate estimates on various topics related to the response in West Africa and the risk for importation of cases into the United States. Analysis of eight Ebola response modeling projects conducted during August 2014-July 2015 provided insight into the types of questions addressed by modeling, the impact of the estimates generated, and the difficulties encountered during the modeling. This time frame was selected to cover the three phases of the West African epidemic curve. Questions posed to the Modeling Task Force changed as the epidemic progressed. Initially, the task force was asked to estimate the number of cases that might occur if no interventions were implemented compared with cases that might occur if interventions were implemented; however, at the peak of the epidemic, the focus shifted to estimating resource needs for Ebola treatment units. Then, as the epidemic decelerated, requests for modeling changed to generating estimates of the potential number of sexually transmitted Ebola cases. Modeling to provide information for decision-making during the CDC Ebola response involved limited data, a short turnaround time, and difficulty communicating the modeling process, including assumptions and interpretation of results. Despite these challenges, modeling yielded estimates and projections that public health officials used to make key decisions regarding response strategy and resources required. The impact of modeling during the Ebola response demonstrates the usefulness of modeling in future responses, particularly in the early stages and when data are scarce. Future modeling can be enhanced by planning ahead for data needs and data sharing, and by open communication among modelers, scientists, and others to ensure that modeling and its limitations are more clearly understood. The activities summarized in this report would not have been possible without collaboration with many U.S. and international partners (http://www.cdc.gov/vhf/ebola/outbreaks/2014-west-africa/partners.html).

Enhancing Laboratory Response Network Capacity in South Korea

Laboratory Response Network (LRN) laboratories help protect populations from biological and chemical public health threats. We examined the role of LRN biological laboratories in enhancing capacity to detect and respond to public health infectious disease emergencies in South Korea. The model for responding to infectious disease emergencies leverages standardized laboratory testing procedures, a repository of standardized testing reagents, laboratory testing cooperation among hospital sentinel laboratories and reference laboratories, and maintenance of a trained workforce through traditional and on-demand training. Cooperation among all network stakeholders helps ensure that laboratory response is an integrated part of the national response. The added laboratory testing capacity provided by the US Centers for Disease Control and Prevention LRN assets helps protect persons who reside in South Korea, US military personnel and civilians in South Korea, and those who reside in the continental United States.

How do advanced molecular tests compare to routine clinical laboratory evaluation of CSF in meningoencephalitis? A Study in 10 Urban Emergency Departments across the United States.

Abstract Background The EMERGEncy ID Net Study Group is investigating whether advanced molecular tests (AMT) increase the detection of causative agents in the CSF of patients presenting with meningoencephalitis (ME). We report findings from a pilot study using AMT on 18 CSF samples from 10 US Urban Emergency Departments. The purpose of the pilot was to compare the performance of these four AMT to established clinical laboratory methods. Methods We investigated four AMT: (1) BioFire FilmArray ME Panel targeting 14 causative agents; (2) an in-house target-directed next generation sequencing assay targeting 25 agents; (3) a microarray capable of detecting >2,500 agents; and (4) deep metagenomic next generation sequencing. For targeted sequencing, loci from 12 DNA-based and 13 RNA-based pathogens were amplified from the extracts by multiplex PCR. All sequencing was performed on an Illumina MiSeq using 500 cycle v2 Reagent Kits. Reads from the targeted sequencing were aligned to the 25 specific reference target sequences using Bowtie2 while metagenomics reads were processed with the taxonomic sequence classifying software Kraken. For microarray analysis, Lawrence Livermore Microbial Detection Array v2 arrays were hybridized with Cy3-labeled DNA or cDNA. Scanned images of arrays were analyzed by CLiMax 3.1. Results Eight CSF samples had results positive for well-established causes of ME from prior testing (Table). The pilot study demonstrated none of the four AMT detected all causative agents in the eight CSF samples known to have well-established causes of ME. BioFire and targeted sequencing performed best, both detecting 6/8, metagenomics deep sequencing detected 3/8, and microarray detected 1/8. Conclusion Despite the sophistication of AMT, they cannot detect pathogens they do not target, that are present in small numbers, or that have been eliminated from the CSF by the immune response. Despite the theoretical potential for microarray and metagenomic sequencing to detect thousands of different agents, the agents probably must be present at high levels for detection. Clinical laboratory evaluation BioFire Film Array Targeted sequencing Microarray Metagenomic sequencing Cryptococcus spp. 4 3 3 0 2 Streptococcus pneumoniae 1 1 1 1 1 Enterovirus 2 2 2 0 West Nile Virus 1 N/Aa 0 0 0 aWest Nile Virus is not on the BioFire Panel. Disclosures All authors: No reported disclosures.