fer in their final numbers even for such apparently clear-cut characteristics as the age distribution of the population of the United States for the year 2009. In collecting U.S. population data, for example, at least three potential sources were consulted: the United Nations Population Division, the World Health Organization (WHO), and the U.S. Census Bureau, all of which contain age-specific estimates of the U.S. population (and of the populations of many other nations) by gender. However, the sources differ in minor ways even for such apparently simple data. The United States conducts a complete census only once a decade, and many other nations do so even less frequently. The U.S. Census Bureau often adjusts final estimates to allow for under-reporting by various groups. Thus, even such apparently “hard” data as population demographics may have differences across sources. For example, data are adjusted differently and may be either extrapolated or interpolated differently across years. As part of its testing, the committee used population data for the United States and South Africa drawn from the WHO Global Health Observatory Data Repository (see Appendix B), even though these data differ in some detail from U.S. Census Bureau data.
2. The second type of data relate to disease burden and costs. These data will have a relatively “hard” basis in some nations based on various survey programs, surveillance systems, and one-time research efforts. The committee used such sources to estimate disease burden and treatment costs for the United States and South Africa (see related data tables and sources in Appendix B). For many other settings, especially developing countries, such data will be unavailable immediately and will likely be supplied by a process that relies primarily on expert opinion. Given the uncertainties about these key assumptions, sensitivity analyses will be important to test the robustness of the model’s results. Committee members often relied on their own areas of expertise and judgment to identify potential errors in the data, with the result being a reevaluation of the data checked against the original sources. Because the focus of this study is the development and testing of the model, the committee did not use other possible methods of checking data accuracy; however, the committee acknowledges the value of further data verification to optimize the use and accuracy of the model.
3. The third type of data contains assumptions about the characteristics of each vaccine, including efficacy under ideal circumstances, effectiveness in real-life settings, duration of immunity, and risk of adverse events. Some of these characteristics are approximations.