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rs5758550 has been associated with enhanced transcription and suggested to be a useful marker of CYP2D6 activity. As there are limited and inconsistent data regarding the utility of this distant "enhancer" single nucleotide polymorphism (SNP), our goal was to further assess the impact of rs5758550 on CYP2D6 activity toward two probe substrates, atomoxetine (ATX) and dextromethorphan (DM), using in vivo urinary metabolite (DM; n = 188) and pharmacokinetic (ATX; n = 70) and in vitro metabolite formation (ATX and DM; n = 166) data. All subjects and tissues were extensively genotyped, the "enhancer" SNP phased with established CYP2D6 haplotypes either computationally or experimentally, and the impact on CYP2D6 activity investigated using several linear models of varying complexity to determine the proportion of variability in CYP2D6 activity captured by each model. For all datasets and models, the "enhancer" SNP had no or only a modest impact on CYP2D6 activity prediction. An increased effect, when present, was more pronounced for ATX than DM suggesting potential substate-dependency. In addition, CYP2D6*2 alleles with the "enhancer" SNP were associated with modestly higher metabolite formation rates in vitro, but not in vivo; no effect was detected for CYP2D6*1 alleles with "enhancer" SNP. In summary, it remains inconclusive whether the small effects detected in this investigation are indeed caused by the "enhancer" SNP or are rather due to its incomplete linkage with other variants within the gene. Taken together, there does not appear to be sufficient evidence to warrant the "enhancer" SNP be included in clinical CYP2D6 pharmacogenetic testing.

This study investigated the impact of SLCO1B1 genotype on rosuvastatin systemic exposure in hypercholesterolemic children and adolescents. Participants (8-21 years) with at least one allelic variant of SLCO1B1 c.521T>C (521TC, n = 13; 521CC, n = 2) and wild type controls (521TT, n = 13) completed a single oral dose pharmacokinetic study. The variability contributed by SLCO1B1 c.521 sequence variation to rosuvastatin (RVA) systemic exposure among our pediatric cohort was comparable to previous studies in adults. RVA concentration-time curve from 0-24 hours (AUC0-24 ) was 1.4-fold and 2.2-fold higher in participants with c.521TC and c.521CC genotype compared 521TT participants, respectively. Interindividual variability of RVA exposure within SLCO1B1 genotype groups exceeded the ~ 1.5-fold to 2-fold difference in mean RVA exposure observed among SLCO1B1 genotype groups, suggesting that other factors also contribute to interindividual variability in the rosuvastatin dose-exposure relationship. A multivariate model performed confirmed SLCO1B1 c.521T>C genotype as the primary factor contributing to RVA systemic exposure in this pediatric cohort, accounting for ~ 30% of the variability RVA AUC0-24 . However, of the statins investigated to date in the pediatric population, RVA has the lowest magnitude of variability in systemic exposure.

Reliability-the extent to which multiple measurements of a target yield similar results-is critical in comparing healthcare provider quality. Hospital unit fall rates are widely tracked and used for benchmarking, but their reliability is not well-studied. Our twofold purpose was to estimate fall rate reliability, both in terms of signal (between-unit variability) relative to noise (within-unit variability) and in terms of the accuracy with which units can be classified as high-fall units; and to assess reliability as a function of the number of quarters of data used to compute fall rates. Using year 2013 data from 11,765 critical care, step-down, medical, surgical, medical-surgical, and rehabilitation units in 1,552 US hospitals, we identified high-fall-rate units, computed units' signal-noise reliability, and simulated data to assess accuracy of high-fall-rate unit classification as a function of quarters of data. When critical care units were excluded, median unit type signal-noise reliabilities for annual total and injurious fall rates, respectively, ranged from .74 to .82 and from .53 to .68. In simulation, seven quarters of data were sufficient to achieve top-decile misclassification rates at or below 10% for all unit types except critical care. Top-quartile misclassification rates were higher; even 12 quarters of data did not consistently yield top-quartile misclassification rates below 10%. In the absence of long-term data, and for units with low patient volume and unit types with very low fall rates, comparison with a unit's own historical data may be more helpful for quality monitoring than attempting to rank it among its peers. © 2016 Wiley Periodicals, Inc.

It is widely thought that low staffing levels are associated with higher risk of psychiatric inpatient violence. The purpose of this study was to determine whether odds of an injurious assault are higher in months in which unit staffing levels are higher or lower relative to unit average, using a design allowing each unit to serve as its own control. Using 2011-2013 National Database of Nursing Quality Indicators data from 480 adult and 90 geriatric units in 361 US hospitals, monthly assault odds were modeled as functions of unit staffing. Monthly RN and non-RN staffing (hours per patient day) were categorized as very low, low, average, high, or very high, based on deviation from the unit's average staffing across study months. Endpoints were binary indicators for one or more injurious assaults against staff during the month and for one or more injurious assaults against patients during the month. Despite large sample sizes, neither RN nor non-RN staffing was a statistically significant predictor of odds of assault, nor was there a consistent trend of odds of assault being higher at below- or above-average staffing levels. There was little evidence that monthly deviation in unit staffing is associated with the odds of an injurious assault on a unit. This suggests that staffing-assault rate associations in previous studies of monthly data are largely attributable to between-unit rather than within-unit staffing differences. Hospitals may need to look beyond below- or above-average nurse staffing as a cause of assaults. © 2016 Wiley Periodicals, Inc.

Hospital performance reports often include rankings of unit pressure ulcer rates. Differentiating among units on the basis of quality requires reliable measurement. Our objectives were to describe and apply methods for assessing reliability of hospital‐acquired pressure ulcer rates and evaluate a standard signal‐noise reliability measure as an indicator of precision of differentiation among units. Quarterly pressure ulcer data from 8,199 critical care, step‐down, medical, surgical, and medical‐surgical nursing units from 1,299 US hospitals were analyzed. Using beta‐binomial models, we estimated between‐unit variability (signal) and within‐unit variability (noise) in annual unit pressure ulcer rates. Signal‐noise reliability was computed as the ratio of between‐unit variability to the total of between‐ and within‐unit variability. To assess precision of differentiation among units based on ranked pressure ulcer rates, we simulated data to estimate the probabilities of a unit's observed pressure ulcer rate rank in a given sample falling within five and ten percentiles of its true rank, and the probabilities of units with ulcer rates in the highest quartile and highest decile being identified as such. We assessed the signal‐noise measure as an indicator of differentiation precision by computing its correlations with these probabilities. Pressure ulcer rates based on a single year of quarterly or weekly prevalence surveys were too susceptible to noise to allow for precise differentiation among units, and signal‐noise reliability was a poor indicator of precision of differentiation. To ensure precise differentiation on the basis of true differences, alternative methods of assessing reliability should be applied to measures purported to differentiate among providers or units based on quality. © 2016 The Authors. Research in Nursing & Health published by Wiley Periodicals, Inc.

Over the past decade, a shortage of registered nurses (RNs) has been one of the most critical concerns for health care institutions, particularly hospitals (Spetz and Given 2003; Buerhaus, Auerbach, and Staiger 2009). The Bureau of Health Professions of the Health Resources and Services Administration estimated that the RN shortage would grow from 6 percent in 2002 to 20 percent by 2020 (DHHS 2002). Researchers proposed that high turnover and intent of RNs to leave the profession would further worsen the future shortage (Aiken et al. 2001; HSM Group 2002). Since 2007, however, when the U.S. economy slipped into a severe recession, immediate concern over the RN shortage abated. During the recession, RN employment and retention sharply increased compared with other professions, virtually ending the decade-long shortage of hospital RNs (Staiger, Auerbach, and Buerhaus 2012). Nevertheless, workforce researchers expect that the absence of an RN shortage may be short-lived and that increases in RN turnover and a shortage will reemerge once the economy has recovered (Buerhaus, Auerbach, and Staiger 2009; Staiger, Auerbach, and Buerhaus 2012). Thus, policy makers, administrators, and researchers must closely monitor RN turnover because it affects stability in the nursing workforce and threatens the consistency and quality of patient care. Past studies have identified an array of complex and multifaceted factors associated with nurse turnover; however, most of them have focused on the determinants of nurse turnover such as organizational factors, management style, workload, burnout, and pay/benefits (O'Brien-Pallas et al. 2006; Hayes et al. 2012). Few studies have examined the consequences of nurse turnover, particularly its effect on the quality of hospital patient care. Many researchers assume that nurse turnover will negatively affect the quality of patient care that is provided by nurses (Shortell et al. 1994; Tai, Bame, and Robinson 1998; O'Brien-Pallas et al. 2006). However, this assumption has been supported by only a few empirical studies that examined the relationship between nurse turnover and patient outcomes (Leiter, Harvie, and Frizzell 1998; O'Brien-Pallas et al. 2010; VHA 2002). Moreover, their findings could be challenged because the studies were based mostly on small sample sizes, used data from a narrow set of unit types or a limited number of hospital settings, analyzed facility-level data that might not account for differences in turnover rates among unit types, used a cross-sectional design that might make it difficult to account for changes in nurse turnover and outcomes over time, or considered turnover intention as a proxy for actual turnover instead of testing the direct effect of actual turnover on patient outcomes (Hayes et al. 2012). To address these concerns, therefore, we longitudinally examined the association between turnover of nurses, specifically RNs, and the patient outcome of pressure ulcers at the unit level.

This is a longitudinal study of the trend and seasonality in unit-level hospital-acquired pressure ulcer (HAPU) rates from 2004 to 2011, for 5447 acute care nursing units in 733 US hospitals. Unit-level HAPU rates decreased significantly during 2004-2007 (OR = 0.91, 95% CI [0.90, 0.91]) and the decreasing trend was much stronger during 2008-2011 (OR = 0.84 [0.83, 0.85]). Seasonal variations in HAPU rates were strong and consistent during 2004-2008, with the highest HAPU rate in Quarter 1 (Jan-Mar) and the lowest rate in Quarter 3 (Jul-Sep). During 2009-2011, the magnitude of this seasonality was greatly reduced, and only HAPU rate in Quarter 1 remained significantly higher than HAPU rates in other quarters.

The association between nurse staffing and patient violence on psychiatric units is unclear, and the association between nursing skill mix and violence has not been studied. Monthly data on physical/sexual assaults by patients against others were obtained for 351 adult psychiatric units. Total and injury assault rates were modeled as dependent variables using hierarchical Poisson regression, with total staffing and registered nurse (RN) mix as predictors. Assault rates were 12% higher per additional nursing hour per patient day but 6% lower per 5-point increase in percent of hours provided by RNs. In cubic spline models fit to explore nonlinear staffing-violence associations, assault rates increased with staffing through most of its range but began to decline at very high levels. © 2012 Wiley Periodicals, Inc. Res Nurs Health. Language: en