Trends in use of adjuvant multi-agent chemotherapy and tamoxifen for breast cancer in the United States: 1975-1999.

BACKGROUND: Understanding trends in the dissemination of findings from clinical research can help in estimating their population-level benefits. We evaluated trends in the use of adjuvant multi-agent chemotherapy, tamoxifen, and the combination of both treatments for early-stage breast cancer in the United States from 1975 through 1999. METHODS: Data on treatment of 217 508 patients diagnosed from 1975 through 1999 with stages I, II, and IIIA breast cancer were obtained from eight registries of the Surveillance, Epidemiology, and End Results (SEER) Program. Models of dissemination were developed from these data after adjustment based on information from a series of population-based Patterns of Care (POC) studies that randomly selected case patients from the SEER registries. The POC studies included 7116 patients diagnosed from 1987 through 1991 and in 1995 who were eliminated from the SEER data used in this analysis. RESULTS: The modeled disseminations were generally compatible with the POC-observed proportions of each treatment. The use of multi-agent chemotherapy was higher among premenopausal women, and the use of tamoxifen was higher among postmenopausal women. The use of multi-agent chemotherapy for postmenopausal women diagnosed with lymph node-positive stage II+ or stage IIIA cancer reached a peak in 1983 and then decreased through 1986, indicating its substitution with tamoxifen. After 1986, the combined use of multi-agent chemotherapy and tamoxifen increased for almost all stages and ages. After the early 1990s, tamoxifen use in postmenopausal women with stage II+ or stage III breast cancer declined. CONCLUSIONS: The observed dissemination patterns suggest that the results of clinical trials are disseminated fairly rapidly to community-based physicians and their patients.

On modelling response propensity for dwelling unit (DU) level non-response adjustment in the Medical Expenditure Panel Survey (MEPS).

Non-response is a common problem in household sample surveys. The Medical Expenditure Panel Survey (MEPS), sponsored by the Agency for Healthcare Research and Quality (AHRQ), is a complex national probability sample survey. The survey is designed to produce annual national and regional estimates of health-care use, expenditures, sources of payment, and insurance coverage for the U.S. civilian non-institutionalized population. The MEPS sample is a sub-sample of respondents to the prior year's National Health Interview Survey (NHIS) conducted by the National Center for Health Statistics (NCHS). The MEPS, like most sample surveys, experiences unit, or total, non-response despite intensive efforts to maximize response rates. This paper summarizes research on comparing alternative approaches for modelling response propensity to compensate for dwelling unit (DU), i.e. household level non-response in the MEPS.Non-response in sample surveys is usually compensated for by some form of weighting adjustment to reduce the bias in survey estimates. To compensate for potential bias in survey estimates in the MEPS, two separate non-response adjustments are carried out. The first is an adjustment for DU level non-response at the round one interview to account for non-response among those households subsampled from NHIS for the MEPS. The second non-response adjustment is a person level adjustment to compensate for attrition across the five rounds of data collection. This paper deals only with the DU level non-response adjustment. Currently, the categorical search tree algorithm method, the chi-squared automatic interaction detector (CHAID), is used to model the response probability at the DU level and to create the non-response adjustment cells. In this study, we investigate an alternative approach, i.e. logistic regression to model the response probability. Main effects models and models with interaction terms are both evaluated. We further examine inclusion of the base weights as a covariate in the logistic models. We compare variability of weights of the two alternative response propensity approaches as well as direct use of propensity scores. The logistic regression approaches produce results similar to CHAID; however, using propensity scores from logistic models with interaction terms to form five classification groups for weight adjustment appears to perform best in terms of limiting variability and bias. Published in 2007 by John Wiley & Sons, Ltd.

Regression-based sampling for persons with high health expenditures: evaluating accuracy and yield with the 1997 MEPS.

BACKGROUND: Given the high concentration of health care expenditures among a relatively small percentage of the population, the 1997 Medical Expenditure Panel Survey was designed to learn more about these high expenditure individuals by oversampling them. OBJECTIVE: Oversampling high expenditure individuals enables more precise estimation of what the nation's health care dollar buys and who pays it. It also enhances the ability to discern the causes of high health care expenses and the characteristics of the individuals who incur them. METHOD: Using the 1987 National Medical Expenditure Survey, a probabilistic model was developed to select households from the 1996 National Health Interview Survey likely to contain individuals incurring high levels of medical expenditures in the 1997 MEPS. The accuracy of the selection model, and the degree to which the high expenditure population was oversampled, are assessed with the 1997 MEPS data. RESULTS: Over half of the persons selected by the regression model were expected to have high health expenditures. Of the 456 persons selected by the model for oversampling, 257 individuals or 56.4% did, in fact, have high expenditures. Regression-based sampling increased the proportion of MEPS individuals with high expenditures from 14.3% without oversampling to 17.2% of the total cohort with oversampling (or from 938-1,126 persons). CONCLUSION: This paper demonstrates that a model-based approach to oversampling a high expenditure population, or any population with dynamic characteristics, can be highly successful in terms of sampling yield and accuracy.