Demonstrating the value of the RN in ambulatory care

During 2003, an estimated 906 million visits were made to physician offices in the United States (Hing, Cherry, & Woodwell, 2005). Overall, 42% of visits to outpatient settings were attended by a registered nurse (Middleton & Hing, 2005). Despite ambulatory care being the fastest growing site for care, it is the least studied. The purpose of this article is to provide an overview of the role of the RN in ambulatory care and describe the direct and indirect economic value of RNs in ambulatory care settings

An application of principal stratification to control for institutionalization at follow-up in studies of substance abuse treatment programs

Participants in longitudinal studies on the effects of drug treatment and criminal justice system interventions are at high risk for institutionalization (e.g., spending time in an environment where their freedom to use drugs, commit crimes, or engage in risky behavior may be circumscribed). Methods used for estimating treatment effects in the presence of institutionalization during follow-up can be highly sensitive to assumptions that are unlikely to be met in applications and thus likely to yield misleading inferences. In this paper we consider the use of principal stratification to control for institutionalization at follow-up. Principal stratification has been suggested for similar problems where outcomes are unobservable for samples of study participants because of dropout, death or other forms of censoring. The method identifies principal strata within which causal effects are well defined and potentially estimable. We extend the method of principal stratification to model institutionalization at follow-up and estimate the effect of residential substance abuse treatment versus outpatient services in a large scale study of adolescent substance abuse treatment programs. Additionally, we discuss practical issues in applying the principal stratification model to data. We show via simulation studies that the model can only recover true effects provided the data meet strenuous demands and that there must be caution taken when implementing principal stratification as a technique to control for post-treatment confounders such as institutionalization.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS179 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Measuring nurse workload in ambulatory care

Nurses and adequate nurse staffing are critical to the delivery of safe, cost-effective, and quality patient care in every health care setting. This has been proven time and again through various research studies and recognized by various accrediting bodies such as JCAHO. However, the information available on required or optimal ambulatory care nurse staffing is limited and varies across ambulatory care settings. An overview of instruments for measuring nursing workload in ambulatory care, a critical prerequisite when identifying best nurse staffing models for diverse ambulatory care settings, is provided

Linking nursing workload and performance indicators in ambulatory care

More and more ambulatory care organizations are using nursing report cards to monitor and evaluate the quality and effectiveness of nursing care in the ambulatory setting. Nurse staffing levels is usually one of the items included in a nursing report card and the one most scrutinized by ambulatory care administrators. One strategy employed by the nursing leadership at the South Texas Veterans Healthcare System to justify nurse staffing levels is linking administrative staffing monitors with nurse-sensitive outcomes via workload and performance indicators. Through this approach, nurse leaders are able to justify nurse staffing level changes, needed technology changes, process improvements, and/or workflow needs to administrators with positive results and support

In What Ways do Religious Congregations Address HIV? Examining Predictors of Different Types of Congregational HIV Activities

This is an accepted manuscript, postprint version.Religious congregations play an important role in HIV prevention and care. However, most research on congregation-based HIV activities has focused on prevention. Using data from a nationally representative survey of U.S. congregations, this study found that 18.6% of congregations were engaged in some type of HIV activity; 8.7% engaged in prevention; 7.6% offered support to people with HIV; 7.4% raised awareness; and 7.6% provided donations for other organizations’ HIV activities. Among congregations that participate in some type of HIV activities, having more educated clergy is associated with higher odds of engaging in support, raising awareness, and giving donations relative to prevention activities. Being a predominantly African-American congregation is associated with lower odds of these other three types of HIV activities compared to prevention activities. Understanding the factors associated with specific types of HIV activities helps inform policy and practice related to congregation-based HIV programming

How balance and sample size impact bias in the estimation of causal treatment effects: a simulation study

Observational studies are often used to understand relationships between exposures and outcomes. They do not, however, allow conclusions about causal relationships to be drawn unless statistical techniques are used to account for the imbalance of confounders across exposure groups. Propensity score and balance weighting (PSBW) are useful techniques that aim to reduce the imbalances between exposure groups by weighting the groups to look alike on the observed confounders. Despite the plethora of available methods to estimate PSBW, there is little guidance on what one defines as adequate balance, and unbiased and robust estimation of the causal treatment effect is not guaranteed unless several conditions hold. Accurate inference requires that 1. the treatment allocation mechanism is known, 2. the relationship between the baseline covariates and the outcome is known, 3. adequate balance of baseline covariates is achieved post-weighting, 4. a proper set of covariates to control for confounding bias is known, and 5. a large enough sample size is available. In this article, we use simulated data of various sizes to investigate the influence of these five factors on statistical inference. Our findings provide evidence that the maximum Kolmogorov- Smirnov statistic is the proper statistical measure to assess balance on the baseline covariates, in contrast to the mean standardised mean difference used in many applications, and 0.1 is a suitable threshold to consider as acceptable balance. Finally, we recommend that 60-80 observations, per confounder per treatment group, are required to obtain a reliable and unbiased estimation of the causal treatment effect

Early detection of influenza outbreaks using the DC Department of Health's syndromic surveillance system

<p>Abstract</p> <p>Background</p> <p>Since 2001, the District of Columbia Department of Health has been using an emergency room syndromic surveillance system to identify possible disease outbreaks. Data are received from a number of local hospital emergency rooms and analyzed daily using a variety of statistical detection algorithms. The aims of this paper are to characterize the performance of these statistical detection algorithms in rigorous yet practical terms in order to identify the optimal parameters for each and to compare the ability of two syndrome definition criteria and data from a children's hospital versus vs. other hospitals to determine the onset of seasonal influenza.</p> <p>Methods</p> <p>We first used a fine-tuning approach to improve the sensitivity of each algorithm to detecting simulated outbreaks and to identifying previously known outbreaks. Subsequently, using the fine-tuned algorithms, we examined (i) the ability of unspecified infection and respiratory syndrome categories to detect the start of the flu season and (ii) how well data from Children's National Medical Center (CNMC) did versus all the other hospitals when using unspecified infection, respiratory, and both categories together.</p> <p>Results</p> <p>Simulation studies using the data showed that over a range of situations, the multivariate CUSUM algorithm performed more effectively than the other algorithms tested. In addition, the parameters that yielded optimal performance varied for each algorithm, especially with the number of cases in the data stream. In terms of detecting the onset of seasonal influenza, only "unspecified infection," especially the counts from CNMC, clearly delineated influenza outbreaks out of the eight available syndromic classifications. In three of five years, CNMC consistently flags earlier (from 2 days up to 2 weeks earlier) than a multivariate analysis of all other DC hospitals.</p> <p>Conclusions</p> <p>When practitioners apply statistical detection algorithms to their own data, fine tuning of parameters is necessary to improve overall sensitivity. With fined tuned algorithms, our results suggest that emergency room based syndromic surveillance focusing on unspecified infection cases in children is an effective way to determine the beginning of the influenza outbreak and could serve as a trigger for more intensive surveillance efforts and initiate infection control measures in the community.</p