5
Estimating Resource Needs

The Ryan White Comprehensive AIDS Resources Emergency (CARE) Act (RWCA) attempts to direct funds to areas in the greatest need of financial assistance through several of its discretionary grant programs, including Title I supplemental awards, Title II AIDS Drug Assistance Program (ADAP) supplemental awards, and Title III and IV awards. In contrast to formula awards, which are based exclusively on estimates of living AIDS cases (ELCs), these grants attempt to take into account other factors affecting severity of need. The Health Resources and Services Administration’s HIV/AIDS Bureau (HRSA/HAB) defines severity of need as “the degree to which providing primary medical care to people with HIV disease in any given area is more complicated and costly than in other areas based on a combination of the adverse health and socio-economic circumstances of the populations to be served” (HRSA, 2003).

In the 2000 reauthorization, Congress asked the Institute of Medicine (IOM) Committee to examine “existing and needed epidemiological data and other analytic tools for resource planning and allocation decisions, specifically for estimating severity of need of a community and the relationship to the allocations process” (Ryan White CARE Act. 42 U.S.C. § 300ff-11 [2003]). The Committee focused its analysis on the application of severity-of-need criteria in determining Title I supplemental awards, the largest of these discretionary grant awards, because of the specific requests for assistance by Congress and HRSA/HAB in this area. Although this chapter does not discuss other discretionary grant programs that use



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Measuring What Matters: Allocation, Planning, and Quality Assessment for the Ryan White CARE Act 5 Estimating Resource Needs The Ryan White Comprehensive AIDS Resources Emergency (CARE) Act (RWCA) attempts to direct funds to areas in the greatest need of financial assistance through several of its discretionary grant programs, including Title I supplemental awards, Title II AIDS Drug Assistance Program (ADAP) supplemental awards, and Title III and IV awards. In contrast to formula awards, which are based exclusively on estimates of living AIDS cases (ELCs), these grants attempt to take into account other factors affecting severity of need. The Health Resources and Services Administration’s HIV/AIDS Bureau (HRSA/HAB) defines severity of need as “the degree to which providing primary medical care to people with HIV disease in any given area is more complicated and costly than in other areas based on a combination of the adverse health and socio-economic circumstances of the populations to be served” (HRSA, 2003). In the 2000 reauthorization, Congress asked the Institute of Medicine (IOM) Committee to examine “existing and needed epidemiological data and other analytic tools for resource planning and allocation decisions, specifically for estimating severity of need of a community and the relationship to the allocations process” (Ryan White CARE Act. 42 U.S.C. § 300ff-11 [2003]). The Committee focused its analysis on the application of severity-of-need criteria in determining Title I supplemental awards, the largest of these discretionary grant awards, because of the specific requests for assistance by Congress and HRSA/HAB in this area. Although this chapter does not discuss other discretionary grant programs that use

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Measuring What Matters: Allocation, Planning, and Quality Assessment for the Ryan White CARE Act severe-need criteria in allocating resources, the Committee’s findings and recommendations may also be relevant to those programs. In the remainder of this chapter, the Committee uses the term “resource needs” instead of “severity of need” to reflect Congress’ interest in the relationship between need and resource allocation. The Committee uses the term “severity of need,” however, when referencing the specific severity-of-need component of the Title I application. Congress specified that “[Title I] supplemental awards are to be directed principally to those eligible areas with ‘severe need,’ or the greatest or expanding public health challenges in confronting the epidemic” (U.S. Congress, 2000). Reflecting this notion, Congress increased the weight assigned to severity of need in determining the supplemental award from 25 percent to 33 percent in the 2000 reauthorization (Ryan White CARE Act. 42. U.S.C. § 300ff-13 [2003]). In determining severity of need, Congress directed HRSA/HAB to consider factors such as: “(I) STDs, substance abuse, tuberculosis, severe mental illness, or other co-morbid factors; (II) new or growing populations of individuals with HIV; (III) homelessness; (IV) current prevalence of HIV; (V) increasing need for HIV services including the relative rates of increase in the number of cases of HIV disease; [and] (VI) unmet need for services” (Ryan White CARE Act. 42 U.S.C. §. 300ff-13 [2003]). Congress further directed HRSA/HAB to “employ standard, quantitative measures to the maximum extent possible in lieu of narrative self-reporting when awarding supplemental awards” (U.S. Congress, 2000). In addressing its charge, the Committee organized its work into the following tasks: Developing a conceptual framework for factors affecting resource needs; Defining criteria for assessing measures of resource needs; Evaluating the process and data currently used to award Title I supplemental funds; Proposing a new way of identifying predictors of resource needs; and Making recommendations to evaluate and implement this approach. Although HRSA/HAB uses explicit criteria to evaluate resource needs and allocate Title I supplemental grants, no consistent indicators are used to evaluate relative need, and much of the evaluation process is subjective. The Committee also found that the process for awarding Title I supplemental grants focuses on the characteristics of individuals, such as the prevalence of comorbid conditions that often accompany HIV disease,

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Measuring What Matters: Allocation, Planning, and Quality Assessment for the Ryan White CARE Act and does not account for other important factors affecting resource needs, such as the cost of providing services and the availability of local resources. The Committee proposes a potential new approach to allocating Title I supplemental awards that is based on standardized, quantitative indicators of resource needs of different jurisdictions. FACTORS AFFECTING RESOURCE NEEDS A broad array of individual and social factors determines an area’s resource needs. The Committee groups these factors into three categories: disease burden, the costs of providing care, and available resources. Resource needs can be viewed as a product of disease burden and cost of care minus available resources: Resource needs = (Disease burden * Costs of providing care) − Available resources Table 5-1 provides some examples of the types of measures that could be used to assess resource needs. TABLE 5-1 Examples of Measures of Resource Needs Factors Affecting Resource Needs Example Measuresa Disease Burden   Reported HIV cases Reported AIDS cases Incident reported HIV cases Incident reported AIDS cases Prevalent HIV infections (including undiagnosed) Incident HIV infections (including undiagnosed) Case mix Costs   Case mix Wages of health care workers Costs of medical supplies Transportation costs Resources   Generosity of state Medicaid program Number of primary care or HIV specialists per capita Rates of insurance coverage Per capita income Poverty rate aThis is not intended to be a comprehensive list.

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Measuring What Matters: Allocation, Planning, and Quality Assessment for the Ryan White CARE Act Disease Burden Disease burden is commonly measured by the incidence or prevalence of a disease.1 Incidence refers to the number of new cases of a condition during a specified period of time in a population at risk for developing the disease. Prevalence can be assessed in terms of either point prevalence or period prevalence. Point prevalence is defined as the proportion of persons in the population with the condition at a specific point in time (Gordis, 1996). Period prevalence is the proportion of people who have had the disease at any time during a certain period (e.g., a calendar year). Some people may have developed the disease during that time while others may have had the disease and died during that period (Gordis, 1996). Incident, or new, HIV infection is the ideal measure for understanding the dynamics, spread, and success of prevention programs. Prevalent known HIV infection is appropriate for estimating the clinical burden to apportion care resources. Current RWCA allocation formulas are based on estimated AIDS prevalence, based on data from states through their AIDS case-reporting systems. It is important to note that the medical and financial significance of an AIDS diagnosis has changed as treatments have evolved. Thus, unlike the early period of the epidemic when the effects of therapy were small and transient, many people who currently have a diagnosis of AIDS have responded well to highly active antiretroviral therapy and are now at relatively low risk for opportunistic infections and are able to maintain an active and productive life. Estimates of HIV cases using a uniform methodology are not available (see Chapter 4). Costs of Care Costs of care may be driven by several factors, including the complexity of a person’s medical condition. For instance, HIV-infected individuals who are at a later stage of disease generally require more resources for their care than HIV-infected people who are at earlier stages of disease (Bozzette et al., 2001).2 The costs of care also depend on the cost of obtaining and providing services, such as the prevailing wages of health care 1   Other measures of morbidity, mortality, or natality may be used to assess disease burden, but incidence and prevalence are the two most relevant for this report. 2   While the rate of resource use is higher for HIV-infected individuals with more advanced disease, lifetime resource use is higher for individuals who receive state-of-the-art care from the earliest stages of their disease (Freedberg et al., 2001).

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Measuring What Matters: Allocation, Planning, and Quality Assessment for the Ryan White CARE Act workers and the local costs of medical supplies. To receive comparable care, a patient in a costly metropolitan area may require greater financial resources than one residing in a less costly locality. Even with comparable patients and costs, some areas may have fewer resource needs because they are more efficient at providing care. Available Resources Available resources or fiscal capacity across regions or states also affect resource needs. Such resource disparities are important in many policy arenas. Formula allocations for Temporary Assistance to Needy Families (formerly Aid to Families with Dependent Children) and other federal programs have long been designed to assist less affluent states (NRC, 2003). Defining “available resources” equitably is an extremely difficult task, however. The purchasing power of a dollar varies greatly across areas and many resources that affect the difficulty and cost of providing HIV care are not measured. Some programs use per capita income as a proxy to account for such variations. For example, Medicaid and several other formula allocation programs use a formula that adjusts allocations based on the ratio between the state per capita income and the national per capita income when determining what proportion of state program expenditures will be reimbursed by the federal government (NRC, 2003). A 1975 study of alternative formulas for the General Revenue Sharing (GRS) program recommended inclusion of a poverty factor in the intrastate allocation and allocations on a per capita basis for governmental jurisdictions for which reliable estimates of income and poverty were available (NRC, 2001). In the arena of health services, advantages that accrue from higher per capita income are partly offset by higher labor costs and by higher costs of other resources required in patient care. Medicare addresses these issues through the use of adjusted average per capita cost (AAPCC) in determining capitation rates in different medical markets (CMS, 1999; MEDPAC, 1999). This method, which relies upon historical average reimbursements, is imperfect and controversial. The AAPCC is based on past Medicare reimbursements rather than differences in input prices. Thus, the AAPCC methodology appears to penalize cities and states that have historically made most efficient use of medical resources (Society of Actuaries, 1997). Despite these limitations, data regarding regional variation in medical costs and prices could provide a useful complement to existing data in determining RWCA formula allocations and supplemental awards. Similarly, the coverage of private and public health insurance pro-

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Measuring What Matters: Allocation, Planning, and Quality Assessment for the Ryan White CARE Act grams is a major factor affecting states’ and Eligible Metropolitan Areas’ (EMAs) resource needs. Medicaid programs in particular—by far the largest payer of care for people with HIV/AIDS—vary substantially in the benefits they cover and their eligibility criteria across states. For example, in some states, being medically needy is not an eligibility criterion for Medicaid; many states also have limitations on Medicaid drug coverage (Kaiser Family Foundation, 2000). The relative “generosity” of Medicaid programs can greatly influence regions’ reliance on CARE Act funds, including its ADAP. All else equal, areas with a greater proportion of insured residents with HIV/AIDS should require fewer CARE Act funds than areas with a high proportion of uninsured patients with HIV/AIDS. States further differ in the resources they devote to addressing the HIV/AIDS epidemic. For instance, some states have imposed one or more restrictions on ADAP, such as enrollment caps, limits on access to antiretroviral treatments, and expenditure caps (NASTAD et al., 2003). Furthermore, states vary a great deal in how much they contribute to ADAP programs. In some cases, lack of political will and emphasis on other priorities have contributed to these restrictions. Title I supplemental awards, along with Title I and II formulas, do not take into account variations in the costs of providing care or other resources available to states and metropolitan areas. Including such information in allocation decisions could have a substantial impact on RWCA funding across states and EMAs. For example, an EMA in a state that has poor Medicaid coverage may choose to use more of its Title I funds on primary care services than an EMA in a state with more generous Medicaid coverage. The EMA in the state with poor Medicaid coverage will therefore have relatively fewer resources to devote to support services, since their RWCA funds must be used to cover basic primary medical care. TITLE I SUPPLEMENTAL AWARD PROCESS Congress divides Title I funds into two components, designating half for the formula-based awards and half for supplemental awards. After HRSA/HAB deducts funds for the Minority AIDS Initiative and the hold-harmless provision,3 approximately 80 percent of the supplemental award amount remains available for distribution among EMAs. 3   The hold-harmless provision is designed to prevent large reductions in funding from year to year.

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Measuring What Matters: Allocation, Planning, and Quality Assessment for the Ryan White CARE Act The Review Process and Scoring Guide Each application for a supplemental award can receive a maximum of 100 points (Box 5-1). Applications submitted for fiscal year (FY) 2002 could achieve up to the following number of points in each category: BOX 5-1 Scoring of FY2002 Supplemental Application Compliance with FY2000–2001 Title I requirements 26 points Grant administration 5 points Severe need 33 points Impact of Title I funds 6 points Planning council mandated roles/responsibilities 10 points Update on assuring quality of services and evaluation activities 10 points Progress in implementing the FY2001 plan 5 points Plan for FY2002 5 points MAXIMUM TOTAL 100 points HRSA/HAB originally used an external review process to score Title I supplemental applications. However, beginning with the FY1999 review process, HRSA/HAB relied on Title I project officers as the primary reviewers, given their familiarity with grantees’ programs. Because RWCA operates on a 5-year budget period, early reviews of applications for supplemental funding set the standard for the remaining budget period. HRSA still uses an external review process for the first year of the budget cycle. At least two HRSA/HAB project officers review and score each application; the scores are then averaged. A guide helps reviewers assign scores to each component but also states clearly that such guidance is not definitive: “Reviewers should use their own judgment and expertise in determining a final score” (HRSA, 2001b). Hence, the subjective scores can deviate significantly from empirical indicators of need. HRSA/HAB also uses an algorithm that may vary from year to year, which may reduce disparities among supplemental allocations (HRSA, 2001c). The detailed algorithm for determining final supplemental awards is not made public. It is important to note that even though severity of need accounts for one-third of the total points, it may not account for one-third of the variation in total points. That depends on both the relative variation in severity-of-need scores and the relative variation in other components of

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Measuring What Matters: Allocation, Planning, and Quality Assessment for the Ryan White CARE Act the application. If, for example, EMAs received identical scores for all items other than severity of need, 100 percent of the variation in scores—and thus in awards—would stem from severity of need. If, in contrast, all EMAs received similar severity-of-need scores, almost all the variation in scores and awards would stem from other components. Severity-of-Need Component of the Application Scoring of severity of need in the Title I application is based on three equally weighted components: (1) HIV/AIDS epidemiology; (2) comorbidity, poverty, and insurance status; and (3) assessment of populations with special needs. HIV/AIDS Epidemiology For this component, grantees supply data on AIDS incidence, AIDS prevalence, and HIV prevalence. Grantees also provide narrative detail on three issues: Trends and compositional changes in caseloads, based on a comparison of the estimated number of people living with HIV, the number of people living with AIDS, and the number of new AIDS cases reported within the last 2 years. The demographics of cases, based on populations in the EMA with disproportionately high HIV/AIDS prevalence compared with the general population. The level of unmet need among populations who are underrepresented in the CARE-funded system, based on utilization data for all covered services (HRSA, 2001a).4 Comorbidity, Poverty, and Insurance Status The EMA must also provide information on the incidence of six comorbid conditions: tuberculosis, syphilis, gonorrhea, intravenous drug use, other substance use, and homelessness. The EMA also reports the 4   In 2000, HRSA convened an unmet needs consultation with participants from HRSA, the Centers for Disease Control and Prevention (CDC), grantees, and researchers to assist HRSA in developing measures to estimate unmet need for HIV primary medical care. A number of issues emerged from that meeting including the need for common terms and definitions, methods and models for assessing unmet need, easy-to-use formulas to estimate the number of individuals not in care, and flexibility in meeting state and local needs and capabilities (HRSA, 2000; Kahn et al., 2003).

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Measuring What Matters: Allocation, Planning, and Quality Assessment for the Ryan White CARE Act number and percentage of residents with incomes below 300 percent of the federal poverty line during the prior fiscal year, and the number and percentage of residents without public or private health insurance. Applicants must describe the overall effect of these components on their populations, and explain how they affect the cost of service and the complexity of providing care. Populations with Special Needs In the final severity-of-need component, applicants respond to 10 questions regarding six special populations. These populations are youth 13–24 years of age, injection drug users (IDUs), substance users other than IDUs, men of color who have sex with men, white/Anglo men who have sex with men, and women of childbearing age (13 years of age and older). Applicants can also report on other populations they deem to have special needs. Applicants are requested to provide information on HIV and AIDS prevalence, trends, and service needs for each special population (Box 5-2). BOX 5-2 Information Requested for Special Populations The estimated number of people in these populations in the EMA, regardless of HIV status. The estimated number of people in each special population living with AIDS. The estimated number of persons in these special populations with HIV infection, including AIDS. The HIV prevalence rate for each special population. A brief description of the special population, including its geographic distribution in the EMA, income level, language barriers, and other characteristics. HIV infection and risk trends in the special population. HIV/AIDS service needs of individuals in each special population who know their status and who are in primary care. The extent to which members of this special population are not in HIV/ AIDS care, and efforts by the planning council to identify and address their service needs. HIV/AIDS service needs of individuals who know their status and who are not in primary care. Information on the planning council’s efforts to include this special population in its need assessment, used to determine its service priorities and allocate funds.

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Measuring What Matters: Allocation, Planning, and Quality Assessment for the Ryan White CARE Act CRITERIA FOR ASSESSING MEASURES OF RESOURCE NEEDS The Committee reviewed all 51 Title I applications submitted by grantees in FY2002. In reviewing the applications, the Committee considered whether the measures used by different jurisdictions were important, scientifically sound, and feasible for national use. Specifically, the Committee identified nine criteria to evaluate current measures that are similar to those used by previous IOM and National Research Council (NRC) committees (NRC, 1997; IOM, 2001a) (Box 5-3). Measures should reflect important resource needs. A measure’s importance encompasses its meaningfulness, the prevalence and seriousness of the needs being measured, the potential for changing the situation, and the overall impact of providing the resources under consideration. BOX 5-3 Criteria for Selecting Measures of Resource Needs Importance of the measure: Meaningfulness: Is the measure meaningful to policy makers and grantees? Prevalence and seriousness of the problem: How common is the problem being assessed? Is there a general deficit or significant variation in the need being measured? Potential for improvement: How susceptible to improvement is the area being assessed? Potential impact: Considering the prevalence, and seriousness of the problem, and the potential improvement, how much of an impact, in aggregate, would improvement have on resource needs? Scientific soundness of the measure: Validity: Does the measure capture what it purports to measure? Reliability: Does the measure produce similar results when applied across the same populations and settings? Evidence: Is there an evidence base to support this measure? Feasibility of the measure: Data availability: Are data collected in a timely manner with reasonable periodicity? Cost and burden of measurement: Can the data be collected at a reasonable cost and with reasonable effort?

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Measuring What Matters: Allocation, Planning, and Quality Assessment for the Ryan White CARE Act Meaningfulness: There should be consensus among clinicians, patients, or policy makers that the measure reflects an important area of need. Prevalence and seriousness of the problem: Measures should focus on major problems that affect a sizable proportion of RWCA clients. Measures should focus on aspects of need that are unmet or for which there is significant variation across grantees. Potential for improvement: Measures should reflect areas of need that can be improved most by additional resources. Potential impact: Considering the prevalence and seriousness of the problem and the potential for improvement, measures of resources that could have the greatest impact on persons living with HIV infection are desirable. Measures should be scientifically sound. Scientific soundness entails three major components: reliability, validity, and evidence base. Reliability: Reliability can be enhanced by using standard data collection methods across EMAs, collecting data in a way that minimizes manipulation, and employing a common definition of the population of interest and the time period. Validity: Measures should capture what they purport to measure. Measures should make sense logically (face validity), should correlate well with other measures of resource needs (construct validity), and should capture meaningful aspects of resource needs (content validity) (IOM, 2001a). If one is developing predictors of needs, then the measures should have predictive validity. That is, one should be able to show that they predict measured needs. As indicated elsewhere in the report, when comparable assessments across regions are important in determining allocations and absolute levels are not, valid measures can have a bias as long as the bias (e.g., a given percentage of underreporting) is consistent across allocation regions. Evidence base: Measures should have strong empirical support. For instance, indicators of resource needs should be empirically linked to needs or costs. Measures should be feasible. Feasibility includes the availability of data and the cost and burden of measurement. Availability of data: Data should be available at the appropriate level. For example, for assessments at the EMA level, each EMA should have the same data. Data should also be available in a timely manner and collected with reasonable periodicity.

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Measuring What Matters: Allocation, Planning, and Quality Assessment for the Ryan White CARE Act   Funding Information Locations Year Total Michigan 2001 $328,367 New Jersey 2002 $240,000 Seattle, WA     Atlanta, GA 2000 $2,005,836 Bayamon, PR 2001 $2,371,683 Dallas, TX 2002 $2,094,018 Denver, CO   Detroit, MI Houston, TX Los Angeles County, CA New Orleans, LA New York, City, NY Seattle, WA

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Measuring What Matters: Allocation, Planning, and Quality Assessment for the Ryan White CARE Act Project Start Date Description HIV Testing Survey (HITS) 1996 Some public health and community groups remain concerned that implementing HIV case reporting may deter some at-risk persons from seeking HIV testing. The primary objective of the HITS is to identify the reasons that persons at risk for HIV infection may seek or defer HIV testing and HIV-related health care, and the role state HIV testing and reporting policies play in the decision, to assess whether HIV case reports underrepresent some populations and to improve HIV prevention planning. Additional objectives for HITS are to evaluate the influence of recent events, such as availability of drug therapies and new testing methodologies, on persons’ decisions to seek HIV testing. HITS is an anonymous, cross-sectional survey of persons at risk for HIV. Project areas use a standardized protocol based on targeted venue-based sampling methods. Specific recruitment sites and methods will be developed locally, in order to provide a generalizable understanding of HIV testing patterns in at-risk racial/ethnic minority populations.

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Measuring What Matters: Allocation, Planning, and Quality Assessment for the Ryan White CARE Act   Funding Information Locations Year Total Arizona 2000 $199,999 California 2001 $1,252,205 Colorado 2002 $1,581,088 Florida   Houston, TX Illinois Kansas Los Angeles, County, CA Louisiana Maryland Michigan Mississippi Missouri Nevada New Jersey New Mexico New York New York City, NY North Carolina Ohio Oregon Philadelphia, PA Portland, OR San Francisco, CA Seattle, WA Texas Vermont Washington

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Measuring What Matters: Allocation, Planning, and Quality Assessment for the Ryan White CARE Act Project Start Date Description Enhanced Perinatal Surveillance (EPS) 1990 EPS activities include two main activities in addition to those activities that are considered core pediatric surveillance. Participating areas are expected to match birth registries to HIV/AIDS registries in order to improve ascertainment of mother-infant pairs, and to collect supplemental information on both mothers and infants from a variety of medical records, including mother’s prenatal care chart, labor and delivery chart, and the infant’s birth chart and pediatric chart. In areas where HIV infection is not reportable by name a hospital-based approach to identify mother-infant pairs is pursued rather than the population-based approach which is feasible in HIV-reporting states only. Twenty-six areas were funded with 1999 supplemental funds to participate in EPS. AIDS Progression Study (APS) 2001 The APS was designed to understand the characteristics of people who are infected with HIV who progress to or die from AIDS and to explain why progression to AIDS occurs. In addition, this time-limited study examines reasons for progression from AIDS to death among deceased AIDS cases. Abstracted from medical records during the 12-month period preceding AIDS diagnosis, the data include patient characteristics, HIV/AIDS-related history, testing history, AIDS defining conditions, HIV exposure, and laboratory data.   SOURCE: CDC, 2003b.

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Measuring What Matters: Allocation, Planning, and Quality Assessment for the Ryan White CARE Act   Funding Information Locations Year Total Alabama 2000 $1,866,553 California 2001 $1,874,431 Chicago, IL 2002 $1,814,324 Connecticut   District of Columbia Houston, TX Los Angeles, CA Louisiana Maryland Massachusetts Michigan New Jersey New York New York City, NY North Carolina Ohio Pennsylvania Philadelphia, PA Puerto Rico South Carolina Tennessee Texas Virginia Boston, MA 2000 $199,999 Chicago, IL 2001 $319,593 Denver, CO   Hartford, CT Los Angeles, CA San Francisco, CA

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Measuring What Matters: Allocation, Planning, and Quality Assessment for the Ryan White CARE Act therapy (HAART).12 Details of the sample and analyses are represented in Appendix D. Below we summarize the approach and results. As examples of indicators of need, the Committee used the number of needs reported by the patients and whether the patients had been treated with HAART drugs. In the HCSUS interview, each patient was asked the following questions: Did you need income assistance such as SSI, SSDI, AFDC, or health care benefits from Medicaid or the Veterans Administration in the last 6 months? Did you need to find a place to live in the last 6 months? Did you need home health care in the last 6 months? Did you need mental health or emotional care or counseling in the last 6 months? Did you need drug or alcohol treatment in the last 6 months? Using these data, the Committee calculated a variable called “number of needs,” which is simply the number of these questions that the respondent answered affirmatively. The Committee selected several county characteristics that it considered representative of the kinds of variables that are likely to be related to resource needs for HIV care. Examples of indicators of medical resources are: Total general practitioners in 1996 divided by the total population in 1990, Total number of medical specialists in 1996 divided by the total population in 1990. Examples of area sociodemographic characteristics are: Percent of the population that was African American in 1990, Percent of the population that is foreign born in 1990, Percent of population that lives in urban areas in 1990, Percent of population who live in poverty in 1990, Percent of population who are college graduates in 1990. 12   At the time of the HCSUS study, the recommended therapy for HIV disease was HAART. Recommendations for therapy change over time are updated in the treatment guidelines published by the Department of Health and Human Services (http://www.aidsinfo.nih.gov/) and others.

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Measuring What Matters: Allocation, Planning, and Quality Assessment for the Ryan White CARE Act Using publicly available data (the U.S. Census and a compilation of information called the Area Resource File [ARF]), the Committee compiled data on several characteristics of each county. The Committee then used the data to estimate models that assessed how well these area characteristics and regions predicted the number of reported needs and receipt of HAART. The models (Appendix D) indicate that the education in an area, the number of general practice physicians, and the number of specialists in an area are statistically significant predictors of the number of needs reported by individuals. Specifically, persons reported more needs if they lived in areas with fewer college educated persons, fewer general practitioners, and more medical specialists. The relationships with education and general practitioners seem reasonable. The relationship for medical specialists is counterintuitive, but it might be a reflection of an emphasis on more expensive care at the expense of more basic services. The strongest predictors of not receiving HAART therapy were living in a county with a high percent of African Americans, a high percentage of families below the poverty level, and an area with more general practitioners. Some of these effects imply striking differences. For example, the average percentage of persons with a college education in the counties studied was 16 percent, with a standard deviation of 5 percent. The coefficient in the model for percent of persons with a college education implies that if one went from a county with a percentage of college educated persons that is a standard deviation below the mean (11 percent) to a county that is a standard deviation above the mean (21 percent), the average need score, which has a range of 0 to 5 and a mean of 1.29 would increase by 0.33. This model illustrates an approach that could be used to select and calibrate variables that predict various types of resource needs. This example has several limitations. For example, the HCSUS sample was not designed to support the analysis of county effects. The Committee selected variables that were readily available for modeling. Thus, the Committee mainly included variables representing the general availability of medical personnel in an area and its socioeconomic characteristics. Undoubtedly, other variables are more likely to be related to the needs of HIV persons. For example, the health provider shortage areas and medically underserved areas (designated by HRSA’s Bureau of Primary Care) may have particularly high resource needs. The predictive value of such variables needs to be tested. However, it is important to recognize that many of the measures that grantees now report on their supplemental applications might not be related to resource needs. None of the applications the Committee reviewed provided empirical support for the association between resource needs and a specific “need factor.” An alternative approach would be to develop a set of direct indica-

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Measuring What Matters: Allocation, Planning, and Quality Assessment for the Ryan White CARE Act tors. There currently are no such indicators that are comparable across regions. It might be possible, however, to coordinate and/or consolidate current efforts conducted by HRSA/HAB and CDC. One would need to assess the tradeoff between scientific accuracy and cost. Surveying a scientifically valid random sample of HIV-infected persons would probably produce the most accurate assessment of needs and would allow one to develop measures directly related to the conceptualization of need proposed by HRSA. However, such an approach would be difficult and expensive to implement. The HCSUS study was able to identify and survey a probability sample of HIV-infected persons in treatment, but it was a complicated and expensive study. Presumably this would be less so for entities with legal surveillance authority. An indirect modeling approach could use available measures. The limitations of such a model are that it might be a poor predictor of interregional variations in needs and might be relatively insensitive to changes over time, if the predictor variables are not updated with sufficient frequency. RECOMMENDATIONS Recommendation 5-1 HRSA should modify the Title I supplemental application process. The severity-of-need component of the Title I supplemental award should be based on two components: Quantitatively defined need, based on a small number of measures that can be calculated by HRSA/HAB. Locally defined need described in a short narrative by the applicant. Recommendation 5-2 A predominance of the weight for determining Title I awards should be given to the quantitative measure of resource needs that reflect variations in costs of care and fiscal capacity across EMAs. Recommendation 5-3 HRSA/HAB should evaluate the feasibility and usefulness of using social area indicator models based on publicly available data that are collected in standardized ways across jurisdictions, to estimate EMA-level resource needs for the Title I supplemental award. This approach also might be useful in assessing resource needs for other RWCA discretionary grant programs. Such an evaluation would entail several steps: First, HRSA/HAB should review with additional experts the po-

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Measuring What Matters: Allocation, Planning, and Quality Assessment for the Ryan White CARE Act tential data sources and develop recommendations for additional measures to be considered. HRSA/HAB should determine the availability of data to support these measures. The potential measures and corresponding data sources should be evaluated according to their importance, scientific soundness, and feasibility. Second, HRSA/HAB should determine an appropriate definition of resource needs that could be measured directly. Examples used in this report were needs and unmet needs reported by persons living with HIV infection and total costs of care. However, none of these measures captures the resource needs that are most appropriately provided by RWCA funds. Such a definition might take into account whether an individual had alternative sources of public or private health insurance, generosity of that insurance, and/or the cost of providing services in a given area. Thus, estimating need might involve determining both the needs of individuals living with HIV as well as the costs of meeting those needs in a particular area. Third, HRSA/HAB should develop a strategy for directly determining need in an adequate number of areas so the relationship between social area indicators and actual need can be estimated. The Committee knows of no such measures now available and so this step will involve consultation with survey experts and statisticians skilled in developing and estimating such models. Finally, HRSA/HAB should develop models and assess the association between social area indicators and direct measures of need. It likely will be not be feasible to collect enough data to do this at the EMA level, but models for county variability should be created and data from those models used to assess their ability to explain between-EMA variability. It is important to evaluate whether a periodic direct assessment of needs or a model-based approach would be more feasible and useful. Resource needs change rapidly and many area-level predictors (e.g., census data) do not change frequently enough to capture such changes. Thus, it may be better to periodically review and update the quantitative indicators used to allocate Title I supplemental award funds. Recommendation 5-4 The Secretary of Health and Human Services (HHS) should evaluate the cost and utility of redesigning and coordinating studies conducted by HRSA/HAB and CDC to assess the specific needs and circumstances of people living with HIV. These data can be used to estimate resource needs and as part of quality assessment activities. The Secretary of HHS should also assess the cost and utility of the indirect modeling approach described in Recommendation 5-3 for assessing regional variations in resource requirements.

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