Biomedical subjects

Maureen C O'Keeffe-Rosetti

Publications and source records attributed to Maureen C O'Keeffe-Rosetti.

3 recordsLinked to original sources

PubMed2006

Prevalence of prostatitis-like symptoms in a managed care population.

PURPOSE: We calculated the prevalence of symptoms typically associated with chronic prostatitis/chronic pelvic pain syndrome in men in a managed care population in the Pacific Northwest. MATERIALS AND METHODS: A questionnaire mailing to 5,000 male enrollees 25 to 80 years old in the Kaiser Permanente Northwest (Portland, Oregon) health plan was performed. The questionnaires included screening questions about the presence, duration and severity of pelvic pain, and the National Institutes of Health Chronic Prostatitis Symptom Index. Chronic prostatitis/chronic pelvic pain syndrome symptoms were defined in 2 ways: 1) presence of any of the following for a duration of 3 or more months: pain in the perineum, testicles, tip of penis, pubic or bladder area, dysuria, ejaculatory pain; and 2) perineal and/or ejaculatory pain, and a National Institutes of Health Chronic Prostatitis Symptom Index total pain score of 4 or more. Prevalence estimates were age adjusted to the total Kaiser Permanente Northwest male population. RESULTS: A total of 1,550 questionnaires were returned. The prevalence of chronic prostatitis/chronic pelvic pain syndrome symptoms was 7.5% for definition 1 and 5.9% for definition 2. Mean National Institutes of Health Chronic Prostatitis Symptom Index scores were 17 for definitions 1 and 2. Of those with prostatitis-like symptoms, 30% met criteria for having both definitions present. The prevalence of prostatitis-like symptoms using either of the 2 diagnoses was 11.2%. CONCLUSIONS: This population based study indicates that approximately 1 in 9 men have prostatitis-like symptoms. Application of 2 different definitions for prostatitis-like symptoms identified unique groups of men, with limited overlap in the groups.

Adult↗

PubMed2003

Using risk-adjustment models to identify high-cost risks.

BACKGROUND: We examine the ability of various publicly available risk models to identify high-cost individuals and enrollee groups using multi-HMO administrative data. METHODS: Five risk-adjustment models (the Global Risk-Adjustment Model [GRAM], Diagnostic Cost Groups [DCGs], Adjusted Clinical Groups [ACGs], RxRisk, and Prior-expense) were estimated on a multi-HMO administrative data set of 1.5 million individual-level observations for 1995-1996. Models produced distributions of individual-level annual expense forecasts for comparison to actual values. Prespecified "high-cost" thresholds were set within each distribution. The area under the receiver operating characteristic curve (AUC) for "high-cost" prevalences of 1% and 0.5% was calculated, as was the proportion of "high-cost" dollars correctly identified. Results are based on a separate 106,000-observation validation dataset. MAIN RESULTS: For "high-cost" prevalence targets of 1% and 0.5%, ACGs, DCGs, GRAM, and Prior-expense are very comparable in overall discrimination (AUCs, 0.83-0.86). Given a 0.5% prevalence target and a 0.5% prediction threshold, DCGs, GRAM, and Prior-expense captured $963,000 (approximately 3%) more "high-cost" sample dollars than other models. DCGs captured the most "high-cost" dollars among enrollees with asthma, diabetes, and depression; predictive performance among demographic groups (Medicaid members, members over 64, and children under 13) varied across models. CONCLUSIONS: Risk models can efficiently identify enrollees who are likely to generate future high costs and who could benefit from case management. The dollar value of improved prediction performance of the most accurate risk models should be meaningful to decision-makers and encourage their broader use for identifying high costs.

Adolescent↗

PubMed2002

Issues in pooling administrative data for economic evaluation.

Managed care, in particular the health maintenance organization (HMO), now dominates US healthcare delivery, and economic evaluation is receiving increasing attention as a management tool that can be tailored to its perceived business needs. This encourages use of HMO administrative data as an efficient source of resource utilization and cost measures. Use of administrative data coincides with growing research interest in multisite analyses that increase external validity. The best alternative to a nationally representative data set is to pool administrative data from multiple sites within one database. However, pooling administrative data is problematic because HMO data sources reflect differences in systems of care, costing, and coding. This paper describes issues inherent in the pooling of HMO administrative cost data for use in multisite economic evaluations. We describe the attributes of administrative data that are relevant to costing and discuss their implications for multisite economic evaluations. We then briefly describe our experience with pooling multisite cost data, discuss lessons learned, and offer suggestions for researchers working with such data, followed by concluding comments. Multisite administrative data provide unique opportunities to conduct population-based clinical and economic research.

Community Health Planning↗