Monitoring HIV Care in the United States Indicators and Data Systems (2012) / Chapter Skim
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6 Efficient Analysis of HIV Care Indicators and Dissemination of Data by Federal Agencies
6 Efficient Analysis of HIV Care Indicators and Dissemination of Data by Federal Agencies
Pages 299-318
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From page 299... ...
EFFICIENT ANALYSIS OF HIV CARE INDICATORS BY FEDERAL AGENCIES As discussed in Chapter 3, no single data system can be used to gauge the impact of the National HIV/AIDS Strategy (NHAS) and the Patient Protection and Affordable Care Act (ACA)
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From page 300... ...
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)
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From page 301... ...
, and federal prisons provide all of the relevant information needed, permitting a relatively straightforward estimation for subsets of the population. However, analytic issues arise from the fact that patients may leave these systems at any point -- possibly after a CD4+ cell count <500 cells/mm3 is measured but before the prescription is provided or 6 months have elapsed.
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From page 302... ...
Issues in Combining Information Many problems can bedevil analyses of data sets that are derived from clinical program or public health systems and from which treatment or intervention effects are being estimated; these include missing data, unknown population sizes and denominators, and sampling bias. Analysis of randomized studies generally also suffers from these challenges, since they are subject to some level of participant attrition, unplanned crossovers, and inadvertent unblinding.
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From page 303... ...
In addition to the problem of missing data, analyses of observational data intended to produce causal estimates of the impact of factors, such as demographics or insurance status, on outcomes must take into account confounding factors. There is an enormous literature on adjustment for confounding factors, as well as increased interest in causal modeling for
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From page 304... ...
resources can be wasted -- for example, when 95 percent of data are collected on someone, but due to the 5 percent missing data, the entire data block is left unused; and (4) ethical obligations to research subjects can be compromised when they have inconvenienced themselves under the assumption that they were doing this for biomedical or behavioral research, but the investigator discards their data due to missing variables.
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From page 305... ...
In human biology and epidemiology, the completeness of population ascertainment can be indirectly estimated using capture-recapture, as with estimations of persons who need HIV therapy, drug addiction services, or other social or medical services. Thus, persons must be "captured" and "marked," to borrow the ecology model, such that they are available for recapture after release.
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is an approach to handling missing data that reweights observations by the inverse of the probability that they are made. Marginal structural models use IPW to deal with the unobserved ("missing")
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Yet, analysis of indicators may occur at a local level, such as to disseminate information to local health departments and HIV care providers on the status of the HIV epidemic in their jurisdictions. In some communities of the United States, the number of individuals who comprise a specific demographic group (e.g., racial and ethnic minority men who have sex with men)
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From page 308... ...
While Bayesian methods provide posterior distributions for any subgroup, no matter its size, the inference for that group will rest most heavily on the mean and on underlying assumptions if the subgroup is small. More advanced statistical methods, such as those that do not require parametric assumptions for the distributions of the random effects, can provide more reliable and robust results in this setting.
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From page 309... ...
Such investment could speed the provision of effective treatment to all communities and thereby improve control of HIV transmission. DISSEMINATION OF DATA TO IMPROVE HIV CARE QUALITY Analysis of the HIV care and related indicators identified by the committee will generate data of interest to a number of stakeholders, including federal and state agencies and policy makers, state and local health departments, health care systems (e.g., HMOs, VA, prisons)
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From page 310... ...
to issue guidance to federal agencies to ensure the "quality, objectivity, utility, and integrity" of information disseminated to the public. In response, the OMB issued Guidelines for Ensuring and Maximizing the Quality, Objectivity, Utility, and Integrity of Information Disseminated by Federal Agencies, effective October 2001.
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From page 311... ...
As one HIV-specific example, the types of information disseminated by HRSA listed in its guidelines include HRSA HIV/ AIDS Bureau State Profiles that describe spending and service information for Ryan White HIV/AIDS Programs, including provider characteristics (e.g., the number and types of organizations in the state that receive Ryan White HIV/AIDS Program funding) , client demographic information, service utilization information (e.g., number of patient visits for core medical services)
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. Potential audiences for data derived from the full set of HIV care indicators identified by the committee already have been identified (federal and state agencies and policy makers, state and local health departments, health care systems, individual providers, consumers [patients]
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Data Quality and Interpretation As discussed, the IQA mandates that federal agencies develop quality assurance guidelines for information releases to the public, and a number of HHS agencies have issued Guidelines for Ensuring the Quality of Information Disseminated to the Public. Although it is important for agencies to present the message clearly, concisely, and in language that is understood by and resonates with the target audience, it is also important that they include information about the quality of the data that support the message and the methods used to interpret them (Sofaer and Hibbard, 2010a)
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From page 314... ...
. One approach is to include with the disseminated information a summary, presented in language accessible to the target audience, of the data and the data analysis, including discussion of limitations or gaps in the data and any other relevant information that would enhance the audience's understanding and evaluation of the data (HHS, 2006b; Marriott et al., 2000; Sofaer and Hibbard, 2010a)
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From page 315... ...
At least once every 2 years, the Department of Health and Human Services should reevaluate mechanisms for combining data elements to estimate key indicators of HIV care and access to supportive services, analyze the combined data, and identify and address barriers to the efficient analysis of such data, including relevant statistical methodologies. To facilitate this pro cess, HHS should engage a center of excellence representing broad areas of expertise that include information technology, statistical methodologies for combining data, and data system content.
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From page 316... ...
To facilitate understanding and use of the indicator information by stakehold ers, dissemination products and strategies may vary depending on the target audience and message to be conveyed. Information about the quality of the indicator data (e.g., confidence ranges for indicators estimates, use of proxy data elements)
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2006b. Guidelines for Ensuring the Quality of Information Disseminated to the Public.
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From page 318... ...
2008. Marginal structural models might overcome confounding when analyzing multiple treatment effects in observational stud ies.
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