Edward F. Brown

Weight Outcomes Among Antidepressant Users in Nursing Facilities

OBJECTIVES: Depression is a common and treatable condition among nursing facility residents, with low body weight being a frequent concomitant concern. A common prescribing dictum is that older tricyclic antidepressants (TCAs) enhance appetite and may facilitate weight gain, while newer selective serotonin reuptake inhibitors (SSRIs) cause anorexia and resultant weight loss in older adults. Evidence is lacking on whether the small weight changes noted during short‐term antidepressant efficacy trials translate into larger weight changes during prolonged treatment periods. Our main objective was to compare weight outcomes at 6 months among users of three different antidepressant groups with a control group of non‐antidepressant users. A secondary objective was to determine whether antidepressant selection was associated with weight pattern before drug initiation, to capture possible prescribing bias that would affect study inferences.

Incidence of Venous Thromboembolic Events Among Nursing Home Residents

Chronic care facility stay has been shown to be an independent risk factor for venous thromboembolism. Review of the literature, however, reveals a paucity of data addressing the issue of venous thromboembolism in nursing home residents. The purpose of this study was to determine the incidence of venous thromboembolic events among nursing home residents. A retrospective cohort study was derived from data compiled in the State of Kansas Minimum Data Set (MDS) for nursing home residents from July 1, 1997 to July 1, 1998. A total of 18,661 residents (median age, 85 years, 74% female, 95% white) satisfied the study criteria. The outcome measures of the primary endpoint—development of a venous thromboembolic event (VTE)—were obtained from the MDS quarterly health assessments and the Medicare ICD-9 codes. We determined the incidence of VTE among nursing home residents as 1.30 events per 100 person-years of observation.

Urban-rural patterns of increasing antidepressant use among nursing facility residents.

OBJECTIVE To characterize changing patterns of antidepressant use in nursing facilities across the urban-rural continuum during the mid-1990s. DESIGN Retrospective analysis of antidepressant drug codes and demographic/clinical data from the Minimum Data Set (MDS) 1994 to 1997. SETTING Kansas nursing facilities. PARTICIPANTS Facility residents aged 65 and older. MEASUREMENTS We examined (1) admission use and (2) after-admission use for newer and older antidepressants for each year separately, using a 4-stratum system to classify nursing facility location by county, from urban to most rural. Incidence rate ratios were determined for antidepressant use in each stratum using the urban strata as the reference. RESULTS Remarkable increases in use of newer antidepressants were seen over time in all strata, but use was highest in the urban area. Modest urban-rural gradient effects attenuated over time and were not consistently seen across analyses and years. Tricyclic antidepressant (TCA) use remained largely unchanged over time and at much lower rates than newer agents. However, TCA use was also modestly higher in urban areas. Differences were clearer for use after admission, in contrast with admission use patterns. CONCLUSIONS Newer antidepressants were rapidly adopted across the urban-rural continuum from 1994 to 1997. Marked increases in both admission and after-admission use of newer antidepressants were seen. Both newer and older antidepressants were used at modestly higher rates in urban areas. Further work is needed to elucidate the patient, prescriber, and facility factors that explain these prescribing patterns.

Best Linear Unbiased Estimators for Normal Distribution Quantiles for Sample Sizes up to 20

This paper provides tables for estimating the quantiles of the normal distribution by using order statistics in small samples. Linear unbiased estimators with minimum variance and based on ordered observations are constructed for sample sizes from 2 through 20.

Quantile Estimates in Complete and Censored Samples From Extreme-Value and Weibull Distributions

This paper deals with the problem of estimating the quantiles (¿) = 0.01, 0.05(0.05)0.95, 0.99 of the largest extreme-value distribution by using order statistics in small samples. Linear unbiased estimators, with minimum variance, based on ordered observations are constructed for sample sizes 5(5)20 for both complete and right-censored samples.

Estimation and testing of quantiles of the extreme-value distribution

This paper deals with the ABLUE (Asymptotically Best Linear Unbiased Estimators) and a test of the hypothesis quantile Q(η) (0<η<1) of the location-scale parameters of the extreme-value distribution for maxima (largest value) based on k (≤n) optimally chosen order statistics in a large sample of size n. Tables of optimum spacings, coefficients of the ABLUE, and the ARE (Asymptotic Relative Efficiency) of the estimates compared to the corresponding full sample maximum likelihood estimators (m.l.e.) are provided for k=2(1)10 and η=0.01, 0.05(0.05) 0.95, 0.99. It is shown that the same spacings may be used for both testing and confidence intervals.

Optimum spacings for the joint estimation and tests of hypothesis of location and scale parameters of the cauchy distribution

A simultaneous test for the location and scale parameters of the Cauchy distribution is considered based on selected order statistics.. It is shown that optimum spacings that maximise the Pitman ARE of the test coincide with that of the optimum spacings for the estimation problem.

Best linear unbiased quantile estimators of the logistic distribution using order statistics

This paper deals with the problem of estimating the Quantiles Q(ξ), with ξ=0.01, 0.05(0.05)0.95, 0.99 of the logistic distribution by using order statistics in small samples. Linear unbiased estimators with minimum variance BLUE based on ordered observations are constructed for sample size n = 2(1)10, 15 and 20.

Urban-Rural Differences in Amitriptyline Use Among Nursing Facility Residents

OBJECTIVE To characterize patterns of amitriptyline use across the urban-rural continuum. DESIGN Retrospective analysis of antidepressant drug codes and demographic and clinical data from the Minimum Data Set (MDS), 1994 to 1997. SETTING Kansas nursing facilities. PARTICIPANTS Facility residents aged 65 and older. MEASURES A four-strata system was used to classify nursing facility location by county, from urban to frontier. We examined admission use and after-admission use of amitriptyline across strata for each year separately. Unadjusted and adjusted odds ratios were determined for each stratum, using the urban stratum as the point of reference. RESULTS Admission use of amitriptyline occurred in 2.3 to 4% of all admissions, and although such use was highest in the most rural stratum, no clear urban-rural gradient was found. In contrast, amitriptyline use 30 days or more after admission demonstrated modest urban-rural gradients in unadjusted and adjusted analyses. In 1997, when adjusted for demographic factors, odds ratios for amitriptyline use were 2.10 (1.54-2.87), 1.68 (1.33-2.13), and 1.49 (1.17-1.90) for the Frontier, Rural, and Densely Settled Rural categories as compared with the Urban reference group. CONCLUSIONS After admission to Kansas nursing facilities, rural practice patterns for amitriptyline use are less favorable than those in urban areas, and an urban-rural gradient is identified. Further work is needed to identify explanatory patient, facility, and prescriber factors.

Small Sample Quantile Estimation of the Normal Distribution Using Optimal Order Statistics

Abstract BLUE of the quantile-function Q(ξ), 0<ξ<1 of a two-parameter normal distribution is considered based on k(≤n) selected order statistics from a finite sample of size n (up to 20). Tables of optimum ranks, coefficients and relative efficiencies (RE) (compared to the uniformly minimum variance unbiased estimator of Q(ξ)) are tabulated for ξ=.01, .05( 05). 15, 20(.10).50, k=2(l)4 and n = 5(1)20.