Academic journal article Health Care Financing Review

Evaluating the Predictive Validity of Nursing Home Pre-Admission Screens

Academic journal article Health Care Financing Review

Evaluating the Predictive Validity of Nursing Home Pre-Admission Screens

Article excerpt


Nursing home pre-admission screening has evolved as an integral component of State Medicaid home and community-based services waiver programs for the elderly. The Health Care Financing Administration (HCFA) requires that services provided under the auspices of this program be targeted to those at risk of nursing home admission. Pre-admission screens are supposed to identify those "who but for" waiver services would be at high risk of institutionalization.

But how well do pre-admission screens actually perform in identifying this at-risk population? According to a 1987 U.S. General Accounting Office (GAO) report, targeting efforts were not resulting in the kind of cost-effectiveness originally envisioned when the waiver programs were implemented (U.S. General Accounting Office, 1987). GAO voiced concern that community-based services were being provided to persons who were supplementing their care in the community with waiver services, rather than using waiver services as a substitute for nursing home care. As a result of their investigation, GAO recommended that HCFA fund research to find better ways of discriminating between those who authentically substitute waiver services for institutionalization and those who use such services as an add-on to their current community-based service package and who are therefore not at risk of imminent institutionalization.

How might a State evaluate the ability of its screen to target the intended population? What method might a State, considering a series of alternative screen decision rules, use in assessing them relative to each other? This article proposes a method for evaluating the predictive validity of nursing home pre-admission screens by using measures of predictive validity adapted from the field of epidemiology. Our approach estimates how well a PAS performs in identifying the "who but for" population. We also demonstrate this methodology's usefulness in screen revision.


Analytic Approach

We rely on epidemiological techniques traditionally used to assess the efficacy of screening for disease. This approach relies on a series of measures that relate screen-detected disease to disease which is subsequently identified through diagnostic evaluation. Our analytic framework considers the outcome of a PAS - either eligible or ineligible for services - as analogous to a positive or negative screen for disease. Using longitudinal data, we apply a screen's decision rules to baseline measurements, and track the eligible and ineligible groups for 6 months to determine the proportion of each group that eventually enters a nursing home.

Figure 1 graphically depicts our analytic approach. Sample members were classified by screen decision rules (Screen Outcome) and by whether they had been admitted to a nursing home at any point within 6 months. The contingency table presented in Figure 1 includes four cells, two representing correct" screen predictions, (a and d), and two representing "incorrect" screen predictions (b and c). The concordant cells represent true positives and true negatives, i.e., those deemed in need of nursing home care (eligible) who actually enter a nursing facility (cell a), and those deemed ineligible for admission who remain in the community (cell d), respectively. Discordant cells (b and c) denote false negatives and false positives, i.e., persons classified by the screen as ineligible but who are admitted to a nursing home (b), and those deemed eligible but who remain in the community (c), respectively.

                     Figure 1
Framework for Predictive Validity Analyses

                        Screen Outcome
                     Eligible   Not Eligible
Nursing Home   Yes      a            b
Within 6       No       c            d

Screening Measures:
Sensitivity = al(a + b)
Specificity = d(c+d)
Proportion False Positives =  c/(c + d) or (1 specificity)
Proportion False Negatives =  b/(a+b) or (1-sensitivity)
SOURCE: Jackson, M. … 
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