Factors Associated With the Trend of Physical and Chemical Restraint Use Among Long-Term Care Facility Residents in Hong Kong: Data From an 11-Year Observational Study

Dependency in activities of daily living (ADLs) is a reality within nursing homes, and we describe ADL measurement strategies based on items in the Minimum Data Set (MDS) and the creation and distributional properties of three ADL self-performance scales and their relationship to other measures. Information drawn from four data sets for a multistep analysis was guided by four study objectives: (1) to identify the subcomponents of ADLs that are present in the MDS battery; (2) to demonstrate how these items could be aggregated within hierarchical and additive ADL summary scales; (3) to describe the baseline and longitudinal distributional properties of these scales in a large, seven-state MDS database; and (4) to evaluate how these scales relate to two external criteria. Prevalence and factor structure findings for seven MDS ADL self-performance variables suggest that these items can be placed into early, middle, and late loss ADL components. Two types of summary ADL self-performance measures were created: additive and hierarchical. Distributional properties of these scales are described, as is their relationship to two external ADL criteria that have been reported in prior studies: first as an independent variable predicting staff time involved in resident care; second as a dependent variable in a study of the efficacy of two programs to improve resident functioning. The new ADL summary scales, based on readily available MDS data, should prove useful to clinicians, program auditors, and researchers who use the MDS functional self-performance items to determine a resident's ADL status.

Atypical antipsychotic medications are widely used to treat delusions, aggression, and agitation in people with Alzheimer disease and other dementia; however, concerns have arisen about the increased risk for cerebrovascular adverse events, rapid cognitive decline, and mortality with their use. To assess the evidence for increased mortality from atypical antipsychotic drug treatment for people with dementia. MEDLINE (1966 to April 2005), the Cochrane Controlled Trials Register (2005, Issue 1), meetings presentations (1997-2004), and information from the sponsors were searched using the terms for atypical antipsychotic drugs (aripiprazole, clozapine, olanzapine, quetiapine, risperidone, and ziprasidone), dementia, Alzheimer disease, and clinical trial. Published and unpublished randomized placebo-controlled, parallel-group clinical trials of atypical antipsychotic drugs marketed in the United States to treat patients with Alzheimer disease or dementia were selected by consensus of the authors. Trials, baseline characteristics, outcomes, all-cause dropouts, and deaths were extracted by one reviewer; treatment exposure was obtained or estimated. Data were checked by a second reviewer. Fifteen trials (9 unpublished), generally 10 to 12 weeks in duration, including 16 contrasts of atypical antipsychotic drugs with placebo met criteria (aripiprazole [n = 3], olanzapine [n = 5], quetiapine [n = 3], risperidone [n = 5]). A total of 3353 patients were randomized to study drug and 1757 were randomized to placebo. Outcomes were assessed using standard methods (with random- or fixed-effects models) to calculate odds ratios (ORs) and risk differences based on patients randomized and relative risks based on total exposure to treatment. There were no differences in dropouts. Death occurred more often among patients randomized to drugs (118 [3.5%] vs 40 [2.3%]. The OR by meta-analysis was 1.54; 95% confidence interval [CI], 1.06-2.23; P = .02; and risk difference was 0.01; 95% CI, 0.004-0.02; P = .01). Sensitivity analyses did not show evidence for differential risks for individual drugs, severity, sample selection, or diagnosis. Atypical antipsychotic drugs may be associated with a small increased risk for death compared with placebo. This risk should be considered within the context of medical need for the drugs, efficacy evidence, medical comorbidity, and the efficacy and safety of alternatives. Individual patient analyses modeling survival and causes of death are needed.

Generalized Estimating Equation (GEE) is a marginal model popularly applied for longitudinal/clustered data analysis in clinical trials or biomedical studies. We provide a systematic review on GEE including basic concepts as well as several recent developments due to practical challenges in real applications. The topics including the selection of “working” correlation structure, sample size and power calculation, and the issue of informative cluster size are covered because these aspects play important roles in GEE utilization and its statistical inference. A brief summary and discussion of potential research interests regarding GEE are provided in the end.