National HIV Surveillance System [NHSS]), claims (e.g., Medicaid Statistical Information System), and programmatic (e.g., Ryan White HIV/AIDS Program) data sources, as well as epidemiologic studies of PLWHA (e.g, the Medical Monitoring Project). Efficient analysis of the indicators will require overcoming challenges to combining data across these disparate systems.
One analytic challenge to the efficient analysis of indicators relates to differences in the way that data systems operationalize data elements or define concepts to allow them to be measured. An area in which this may be relevant is in the calculation of indicators for subgroups of PLWHA, because data systems may vary in how they define certain demographic data such as income, geographic marker of residence, race or ethnicity, and sex or gender. Another challenge is differences across data systems in the periodicity for particular data elements. Although claims systems will have continuous data on dispensing of antiretroviral drugs, the Ryan White HIV/AIDS Program collects information on whether antiretroviral drugs were prescribed within a 12-month reporting period. This presents an obstacle to combining data from these systems for purposes of estimating the proportion of PLWHA who were or were not on antiretroviral therapy (ART) during a given period. Although technically difficult, there are approaches to deal with the analytic challenges of combining data, as discussed below.
Additional impediments to the efficient analysis of the indicators by federal agencies that relate to combining data from multiple systems include the current lack of an infrastructure to support the secure exchange of health information across health information technology systems (e.g., electronic health records) and organizations, and other barriers to data sharing. These issues are discussed in Chapters 4 and 5 of this report.
An Example of Challenges to the Efficient Analysis
of an Indicator for Clinical HIV Care
One of the core indicators for clinical HIV care recommended by the committee (see Recommendation 2-1 in Chapter 2) is the proportion of people with diagnosed HIV infection and a CD4+ cell count <500 cells/mm3 who are not on ART among all patients who receive such counts. To define this indicator more precisely, one must take timing into account. For example, one might ask: What proportion of individuals who received a CD4+ cell count measurement of <500 cells/mm3 in 2011 also received ART at any point in 2011. Although this definition is clear, it suffers from the problem that those who received such a count late in 2011 had less opportunity to receive ART in that year. Therefore, it may make more sense to rephrase the question: How many individuals who received a CD4+ cell count <500 cells/mm3 in 2011 received ART treatment within a fixed window of time (e.g., 6 months) of receipt of that measurement. In addition