Michael L. Washington

Frequency and causes of vaccine wastage

UNLABELLED Assessing the frequency of vaccine wastage and the relative magnitude of its various causes may help to target efforts to reduce these losses and to husband funds for increasingly expensive vaccines. METHODS As a preliminary overview of wastage in the United States, 64 public-sector state and local health department immunization programs were polled in 1998 and 1999 for wastage recording practices. Actual wastage data were collected from a non-random subset of five states. Data on returns of wasted vaccine to manufacturers were analyzed from routine national biologics surveillance and from an ad-hoc survey. Excise tax credit requests for such returns between 1994 and 1999 were reviewed. RESULTS Rates of wastage among the five states ranged from about 1 to 5% in 1998, with an overall rate of 2.6% among 57 immunization programs in 1999. Categories of wastage used by the health departments varied widely, with overlapping classifications. The major causes appeared to be refrigeration (cold chain) lapses, followed by expiration. Overall rates of vaccine returns varied up to 8% by manufacturer, and from 1 to 50% by vaccine type, with higher return rates generally found for lesser-used vaccines. CONCLUSIONS If these wastage estimates of 1-5% applied nationally, in 1998 there would have been approximately US dollars 6-31 million worth of unused vaccine in the public sector alone. The two most common forms of wastage reveal the potential value of developing vaccines with improved heat stability and longer shelf lives. We propose six main classifications of vaccine wastage for use in routine monitoring and reporting.

Estimating Ebola Treatment Needs, United States.

To the Editor: By December 31, 2014, the Ebola epidemic in West Africa had resulted in treatment of 10 Ebola case-patients in the United States; a maximum of 4 patients received treatment at any one time (1). Four of these 10 persons became clinically ill in the United States (2 infected outside the United States and 2 infected in the United States), and 6 were clinically ill persons medically evacuated from West Africa (Technical Appendix 1 Table 6). To plan for possible future cases in the United States, policy makers requested we produce a tool to estimate future numbers of Ebola case-patients needing treatment at any one time in the United States. Gomes et al. previously estimated the potential size of outbreaks in the United States and other countries for 2 different dates in September 2014 (2). Another study considered the overall risk for exportation of Ebola from West Africa but did not estimate the number of potential cases in the United States at any one time (3). We provide for practicing public health officials a spreadsheet-based tool, Beds for Ebola Disease (BED) (Technical Appendix 2) that can be used to estimate the number of Ebola patients expected to be treated simultaneously in the United States at any point in time. Users of BED can update estimates for changing conditions and improved quality of input data, such as incidence of disease. The BED tool extends the work of prior studies by dividing persons arriving from Liberia, Sierra Leone, and Guinea into the following 3 categories: 1) travelers who are not health care workers (HCWs), 2) HCWs, and 3) medical evacuees. This categorization helps public health officials assess the potential risk for Ebola virus infection in individual travelers and the subsequent need for post-arrival monitoring (4). We used the BED tool to calculate the estimated number of Ebola cases at any one time in the United States by multiplying the rate of new infections in the United States by length of stay (LOS) in hospital (Table). The rate of new infections is the sum of the rate of infected persons in the 3 listed categories who enter the United States from Liberia, Sierra Leone, or Guinea. For the first 2 categories of travelers, low and high estimates of Ebola-infected persons arriving in the United States are calculated by using low and high estimates of both the incidence of disease in the 3 countries and the number of arrivals per month (Table). Calculating the incidence among arriving HCWs required estimating the number of HCWs treating Ebola patients in West Africa (Technical Appendix 1, Tables 2–4). For medical evacuations of persons already ill from Ebola, we calculated low and high estimates using unpublished data of such evacuations through the end of December 2014. Table Calculated monthly rates of Ebola disease among persons arriving in the United States and additional secondary cases, 2014 Although only 1 Ebola case has caused additional cases in the United States (7), we included the possibility that each Ebola case-patient who traveled into the United States would cause either 0 secondary cases (low estimate) or 2 secondary cases (high estimate) (Table). Such transmission might occur before a clinically ill traveler is hospitalized or between a patient and HCWs treating the patient (7). To account for the possibility that infected travelers may arrive in clusters, we assumed that persons requiring treatment would be distributed according to a Poisson probability distribution. Using this distribution enables us to calculate, using the BED tool, 95% CIs around the average estimate of arriving case-patients. The treatment length used in both the low and high estimate calculations was 14.8 days, calculated as a weighted average of the LOS of hospitalized case-patients treated in West Africa through September 2014 (Technical Appendix 1 Table 5) (8). We conducted a sensitivity analysis using LOS and reduced case-fatality rate of patients treated in the United States (Technical Appendix 1 Table 6). For late 2014, the low estimate of the average number of beds needed to treat patients with Ebola at any point in time was 1 (95% CI 0–3). The high estimate was 7 (95% CI 2–13). In late 2014, the United States had to plan and prepare to treat additional Ebola case-patients. By mid-January 2015, the capacity of Ebola treatment centers in the United States (49 hospitals with 71 total beds [9]) was sufficient to care for our highest estimated number of Ebola patients. Policymakers already have used the BED model to evaluate responses to the risk for arrival of Ebola virus–infected travelers, and it can be used in future infectious disease outbreaks of international origin to plan for persons requiring treatment within the United States. Technical Appendix 1: Data inputs and assumptions; sensitivity analysis (length of stay and case-fatality rate); comparison with other published estimates; and limitations. Click here to view.(228K, pdf) Technical Appendix 2: Beds for Ebola Disease (BED) model. Click here to view.(161K, xlsx)

A Tool for the Economic Analysis of Mass Prophylaxis Operations With an Application to H1N1 Influenza Vaccination Clinics

This article uses the 2009 H1N1 influenza vaccination program experience to introduce a cost analysis approach that may be relevant for planning mass prophylaxis operations, such as vaccination clinics at public health centers, work sites, schools, or pharmacy-based clinics. These costs are important for planning mass influenza vaccination activities and are relevant for all public health emergency preparedness scenarios requiring countermeasure dispensing. We demonstrate how costs vary depending on accounting perspective, staffing composition, and other factors. We also describe a mass vaccination clinic budgeting tool that clinic managers may use to estimate clinic costs and to examine how costs vary depending on the availability of volunteers or donated supplies and on the number of patients vaccinated per hour. Results from pilot tests with school-based H1N1 influenza vaccination clinic managers are described. The tool can also contribute to planning efforts for universal seasonal influenza vaccination.

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).

Maxi-Vac: planning mass smallpox vaccination clinics.

To help emergency response planners prepare for conducting mass smallpox vaccination clinics, the Centers for Disease Control and Prevention researchers developed the Maxi-Vac software (available free from http://www.bt.cdc.gov/agent/smallpox/vaccination/maxi-vac/index.asp); it assists in designing a mass vaccination clinic with up to 9 separate stations. Users select clinic characteristics that best represent their intended setup, and the software displays the optimal placement of staff to vaccinate the maximum number of people possible. For example, for a clinic that will have 3 physicians, 30 nurses, and 10 other staff members available per 12-hour shift, Maxi-Vac shows how these staff members can best be deployed, and it projects the maximum number of persons who can be vaccinated at 8,245 per 24-hour period. Users can alter the number of available staff, which will probably be the greatest limiting factor, to determine the impact on the number of persons vaccinated per 24-hour period.

Modeling the Effect of School Closures in a Pandemic Scenario: Exploring Two Different Contact Matrices

BACKGROUND School closures may delay the epidemic peak of the next influenza pandemic, but whether school closure can delay the peak until pandemic vaccine is ready to be deployed is uncertain. METHODS To study the effect of school closures on the timing of epidemic peaks, we built a deterministic susceptible-infected-recovered model of influenza transmission. We stratified the U.S. population into 4 age groups (0-4, 5-19, 20-64, and ≥ 65 years), and used contact matrices to model the average number of potentially disease transmitting, nonphysical contacts. RESULTS For every week of school closure at day 5 of introduction and a 30% clinical attack rate scenario, epidemic peak would be delayed by approximately 5 days. For a 15% clinical attack rate scenario, 1 week closure would delay the peak by 9 days. Closing schools for less than 84 days (12 weeks) would not, however, reduce the estimated total number of cases. CONCLUSIONS Unless vaccine is available early, school closure alone may not be able to delay the peak until vaccine is ready to be deployed. Conversely, if vaccination begins quickly, school closure may be helpful in providing the time to vaccinate school-aged children before the pandemic peaks.

Public Health Modeling at the Centers for Disease Control and Prevention

At the Centers for Disease Control and Prevention, there is a growing interest in promoting the use of mathematical modeling to support public health policies. This chapter presents three examples of operations research models developed and employed by the Centers for Disease Control and Prevention. First, we discuss the Adult Immunization Scheduler, which uses dynamic programming methods to establish a personalized vaccination schedule for adults aged 19 and older. The second operations research project is a discrete event simulation model used to estimate the throughput and budget for mass vaccination clinics during the 2009–2010 H1N1 pandemic. Lastly, we describe a national HIV resource allocation model that uses nonlinear programming methods to optimize the allocation of funds to HIV prevention programs and populations.