Estimating treated prevalence and service utilization rates: assessing disparities in mental health.
ABSTRACT There is considerable public concern about health disparities among different cultural/racial/ethnic groups. Important process measures that might reflect inequities are treated prevalence and the service utilization rate in a defined period of time. We have previously described a method for estimating N, the distinct number who received service in a year, from a survey of service users at a single point in time. The estimator is based on the random variable 'time since last service', which enables the estimation of treated prevalence. We show that this same data can be used to estimate the service utilization rate, E(J), the mean number of services in the year. If the sample is typical with respect to the time since last visit, the MLE of E(J) is asymptotically unbiased. Confidence intervals and a global test of equality of treated prevalence and service utilization rates among several groups are given. A data set of outpatient mental health services from a county in New York State for which the true values of the parameters are known is analyzed as an illustration of the methods and an appraisal of their accuracy.
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ABSTRACT: We have designed a statistical test that eliminates the assumption of equal group variances from one-way analysis of variance. This test is preferable to the standard technique of trial-and-error transformation and can be shown to be an extension of the Behrens-Fisher T test to the case of three or more means. We suggest that this procedure be used in most applications where the one-way analysis of variance has traditionally been applied to biological data.Proceedings of the National Academy of Sciences 12/1989; 86(21):8183-4. · 9.81 Impact Factor
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ABSTRACT: A survey is conducted at w of K selection units or lists, e.g. health care institutions or weeks in a year, to estimate N, the total number of individuals with particular characteristics. Our estimator utilizes two items determined for each survey participant: the number, u, among the w lists in S and the number, j, among all K lists on which each survey participant appears. In its traditional form, selection units are chosen using probability sampling and the statistical properties of the estimator derive from the sampling mechanism. Here, selection units are purposively chosen to maximize the chance that they are 'typical' and a model-based analysis is used for inference. If the sample is typical, the ML estimators of N and E(J) are unbiased. If a condition on the second moment of U/J is satisfied, the model-based variance of the estimator of N based on a purposively chosen typical sample is smaller than one based on a randomly chosen sample. Methods to test whether the typical assumption is valid using data from the survey are not yet available. The importance of proper selection of the sample to maximize the chance that it is typical and model breakdown does not occur must be emphasized.Statistics in Medicine 08/2009; 28(17):2230-52. · 2.04 Impact Factor
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ABSTRACT: The NKI Cultural Competency Assessment Scale measures organizational CC in mental health outpatient settings. We describe its development and results of tests of its psychometric properties. When tested in 27 public mental health settings, factor analysis discerned three factors explaining 65% of the variance; each factor related to a stage of implementation of CC. Construct validity and inter-rater reliability were satisfactory. In tests of predictive validity, higher scores on items related to linguistic and service accommodations predicted a reduction in service disparities for engagement and retention outcomes for Hispanics. Disparities for Blacks essentially persisted independent of CC scores.Administration and Policy in Mental Health and Mental Health Services Research 02/2011; 38(2):120-30. · 3.44 Impact Factor