A population-based study of cholinesterase inhibitor use for dementia.

OBJECTIVES: To examine current utilization patterns of cholinesterase inhibitor (ChEI) therapy for dementia to determine treatment duration, use in long-term care, how often patients receive these drugs until death, and frequency of switching between the available ChEIs. DESIGN: A population-based healthcare administrative database study. SETTING: Patients aged 66 and older from the Canadian province of Ontario who received a new prescription for a ChEI between June 1, 2000, and December 31, 2002. Patients were followed until discontinuation of ChEI therapy, death, or end of the observation period (March 31, 2005). PARTICIPANTS: Twenty-eight thousand nine hundred and sixty-one patients, including 4,601 residing in long-term care, mean age 80, 63% female. MEASUREMENTS: Information on diagnosis, medical comorbidity, physician visits, and concomitant medication use was obtained. Estimates of duration of continuous use were determined. The percentage of patients who remained on the initial dose prescribed, the proportion who switched to a second ChEI, and the percentage who remained on ChEIs until death were calculated. RESULTS: Patients had on average more than 26 physician visits in the year before ChEI therapy, but only 28% had seen a dementia specialist. Concomitant use of potentially inappropriate medications (strongly anticholinergic medications and benzodiazepines) was noted in 37% of patients. The average length of treatment for all patients was 866 days. Many patients (43%) remained on the initial dose prescribed, 6% switched to another ChEI, and 19% died while on ChEI therapy. CONCLUSION: Elderly patients with dementia are treated for lengthy periods of time with ChEIs in the community and in long-term care facilities. Further research is required to determine whether these utilization patterns are appropriate. It is also unclear whether these results are generalizable to other populations without universal health coverage or drug formulary benefits.

Resampling methods to reduce the selection bias in genetic effect estimation in genome-wide scans.

Using the simulated data of Problem 2 for Genetic Analysis Workshop 14 (GAW14), we investigated the ability of three bootstrap-based resampling estimators (a shrinkage, an out-of-sample, and a weighted estimator) to reduce the selection bias for genetic effect estimation in genome-wide linkage scans. For the given marker density in the preliminary genome scans (7 cM for microsatellite and 3 cM for SNP), we found that the two sets of markers produce comparable results in terms of power to detect linkage, localization accuracy, and magnitude of test statistic at the peak location. At the locations detected in the scan, application of the three bootstrap-based estimators substantially reduced the upward selection bias in genetic effect estimation for both true and false positives. The relative effectiveness of the estimators depended on the true genetic effect size and the inherent power to detect it. The shrinkage estimator is recommended when the power to detect the disease locus is low. Otherwise, the weighted estimator is recommended.

Comparison of family-based association tests in chromosome regions selected by linkage-based confidence intervals.

We use the Genetic Analysis Workshop 14 simulated data to explore the effectiveness of a two-stage strategy for mapping complex disease loci consisting of an initial genome scan with confidence interval construction for gene location, followed by fine mapping with family-based tests of association on a dense set of single-nucleotide polymorphisms. We considered four types of intervals: the 1-LOD interval, a basic percentile bootstrap confidence interval based on the position of the maximum Zlr score, and asymptotic and bootstrap confidence intervals based on a generalized estimating equations method. For fine mapping we considered two family-based tests of association: a test based on a likelihood ratio statistic and a transmission-disequilibrium-type test implemented in the software FBAT. In two of the simulation replicates, we found that the bootstrap confidence intervals based on the peak Zlr and the 1-LOD support interval always contained the true disease loci and that the likelihood ratio test provided further strong confirmatory evidence of the presence of disease loci in these regions.