James C. Benneyan

Statistical Control Charts Based on a Geometric Distribution

In some production processes and administrative processes, the occurrence of certain events is best described by a geometric distribution. Control charts are developed for the total number of events and for the average number of events in a fixed number..

Foundations of Opioid Risk Management

Increased abuse and diversion of prescription opioids has been a consequence of the increased availability of opioids to address the widespread problem of undertreated pain. Opioid risk management refers to the effort to minimize harms associated with opioid therapy while maintaining appropriate access to therapy. Management of these linked public health issues requires a coordinated and balanced effort among a disparate group of stakeholders at the federal, state, industry, practitioner, and patient levels. This paper reviews the principles of opioid risk management by examining the epidemiology of prescription opioid abuse in the United States; identifying key stakeholders involved in opioid risk management and their responsibilities for managing or monitoring opioid abuse and diversion; and summarizing the mechanisms currently used to monitor and address prescription opioid abuse. Limitations of current approaches, and emerging directions in opioid risk management, are also presented.

Statistical Quality Control Methods in Infection Control and Hospital Epidemiology, Part I Introduction and Basic Theory

This article is the first in a two-part series discussing and illustrating the application of statistical process control (SPC) to processes often examined by hospital epidemiologists. The basic philosophical and theoretical foundations of statistical quality control and their relation to epidemiology are emphasized in order to expand mutual understanding and cross-fertilization between these two disciplines. Part I provides an overview of quality engineering and SPC, illustrates common types of control charts, and provides references for further information or statistical formulae. Part II discusses statistical properties of control charts, issues of chart design and optimal control limit widths, alternate possible SPC approaches to infection control, some common misunderstandings, and more advanced issues. The focus of both articles is mostly non-mathematical, emphasizing important concepts and practical examples rather than academic theory and exhaustive calculations.

Impact of emulsion-based drug delivery systems on intestinal permeability and drug release kinetics

Lipid based drug delivery systems, and in particular self-emulsifying drug delivery systems (SEDDS), show great potential for enhancing oral bioavailability but have not been broadly applied, largely due to lack of general formulation guidance. To help understand how formulation design influences physicochemical emulsion properties and associated function in the gastrointestinal environment, a range of twenty-seven representative self-emulsifying formulations were investigated. Two key functions of emulsion-based drug delivery systems, permeability enhancement and drug release, were studied and statistically related to three formulation properties - oil structure, surfactant hydrophilic liphophilic balance (HLB) values, and surfactant-to-oil ratio. Three surfactants with HLB values ranging from 10 to 15 and three structurally different oils (long chain triglyceride, medium chain triglyceride, and propylene glycol dicaprylate/dicaprate) were combined at three different weight ratios (1:1, 5:1, 9:1). Unstable formulations of low HLB surfactant (HLB=10) had a toxic effect on cells at high (1:1) surfactant concentrations, indicating the importance of formulation stability for minimizing toxicity. Results also indicate that high HLB surfactant (Tween 80) loosens tight junction at high (1:1) surfactant concentrations. Release coefficients for each emulsion system were calculated. Incorporation of a long chain triglyceride (Soybean oil) as the oil phase increased the drug release rate constant. These results help establish an initial foundation for relating emulsion function to formulation design and enabling bioavailability optimization across a broad, representative range of SEDDS formulations.

National addictions vigilance intervention and prevention program (NAVIPPRO): a real-time, product-specific, public health surveillance system for monitoring prescription drug abuse.

The National Addictions Vigilance Intervention and Prevention Program (NAVIPPRO™) is a scientific, comprehensive risk management program for scheduled therapeutics. NAVIPPRO™ provides post‐marketing surveillance, signal detection, signal verification and prevention and intervention programs. Here we focus on one component of NAVIPPRO™ surveillance, the Addiction Severity Index‐Multimedia Version® (ASI‐MV®) Connect, a continuous, real‐time, national data stream that assesses pharmaceutical abuse by patients entering substance abuse treatment by collecting product‐specific, geographically‐detailed information.

Number-between g-type statistical quality control charts for monitoring adverse events.

Alternate Shewhart-type statistical control charts, called “g” and “h” charts, are developed and evaluated for monitoring the number of cases between hospital-acquired infections and other adverse events, such as heart surgery complications, catheter-related infections, surgical site infections, contaminated needle sticks, and other iatrically induced outcomes. These new charts, based on inverse sampling from geometric and negative binomial distributions, are simple to use and can exhibit significantly greater detection power over conventional binomial-based approaches, particularly for infrequent events and low “defect” rates. A companion article illustrates several interesting properties of these charts and design modifications that significantly can improve their statistical properties, operating characteristics, and sensitivity.

Controlling methicillin-resistant Staphylococcus aureus: A feedback approach using annotated statistical process control charts

OBJECTIVESnTo investigate the benefit of a hospitalwide feedback program regarding methicillin-resistant Staphylococcus aureus (MRSA), using annotated statistical process control charts.nnnDESIGNnRetrospective and prospective analysis of MRSA rates using statistical process control charts.nnnPARTICIPANTSnTwenty-four medical, medical specialty, surgical, intensive care, and cardiothoracic care wards and units at four Glasgow Royal Infirmary hospitals.nnnMETHODSnAnnotated control charts were applied to prospective and historical monthly data on MRSA cases from each ward and unit during a 46-month period from January 1997 through September 2000. Results were fed back from December 1999 and then on a regular monthly basis to medical staff, ward managers, senior managers, and hotel services.nnnRESULTSnMonthly reductions in the MRSA acquisition rate started 2 months after the introduction of the feedback program and have continued to the present time. The overall MRSA rate currently is approximately 50% lower than when the program began and has become more consistent and less variable within departments throughout Glasgow Royal Infirmary. The control charts have helped to detect rate changes and manage resources more effectively. Medical and nursing staff and managers also report that they find this the most positive form of MRSA feedback they have received.nnnCONCLUSIONSnFeedback programs that provide current information to front-line staff and incorporate annotated control charts can be effective in reducing the rate of MRSA.

Performance of Number-Between g-type Statistical Control Charts for Monitoring Adverse Events

Alternate Shewhart-type statistical control charts, called “g” and “h” charts, have been developed for monitoring the number of cases between hospital-acquired infections and other adverse events, such as heart surgery complications, catheter-related infections, surgical site infections, contaminated needle sticks, medication errors and other care induced concerns. This article investigates the statistical properties of these new charts and illustrates several design considerations that significantly can improve their operating characteristics and sensitivity, including the use of with-in limit rules, a new in-control rule, redefined Bernoulli trials, and probability-based limits. These new charts are based on inverse sampling from geometric and negative binomial distributions, are simple for practitioners to use, and in some cases exhibit significantly greater detection power over conventional binomial-based approaches, particularly for infrequent events and low “defect” rates.

Predicting Emergency Department Inpatient Admissions to Improve Same-day Patient Flow

OBJECTIVESnThe objectives were to evaluate three models that use information gathered during triage to predict, in real time, the number of emergency department (ED) patients who subsequently will be admitted to a hospital inpatient unit (IU) and to introduce a new methodology for implementing these predictions in the hospital setting.nnnMETHODSnThree simple methods were compared for predicting hospital admission at ED triage: expert opinion, naïve Bayes conditional probability, and a generalized linear regression model with a logit link function (logit-linear). Two months of data were gathered from the Boston VA Healthcare Systems 13-bed ED, which receives approximately 1,100 patients per month. Triage nurses were asked to estimate the likelihood that each of 767 triaged patients from that 2-month period would be admitted after their ED treatment, by placing them into one of six categories ranging from low to high likelihood. Logit-linear regression and naïve Bayes models also were developed using retrospective data and used to estimate admission probabilities for each patient who entered the ED within a 2-month time frame, during triage hours (1,160 patients). Predictors considered included patient age, primary complaint, provider, designation (ED or fast track), arrival mode, and urgency level (emergency severity index assigned at triage).nnnRESULTSnOf the three methods considered, logit-linear regression performed the best in predicting total bed need, with a receiver operating characteristic (ROC) area under the curve (AUC) of 0.887, an R(2) of 0.58, an average estimation error of 0.19 beds per day, and on average roughly 3.5 hours before peak demand occurred. Significant predictors were patient age, primary complaint, bed type designation, and arrival mode (p < 0.0001 for all factors). The naïve Bayesian model had similar positive predictive value, with an AUC of 0.841 and an R(2) of 0.58, but with average difference in total bed need of approximately 2.08 per day. Triage nurse expert opinion also had some predictive capability, with an R(2) of 0.52 and an average difference in total bed need of 1.87 per day.nnnCONCLUSIONSnSimple probability models can reasonably predict ED-to-IU patient volumes based on basic data gathered at triage. This predictive information could be used for improved real-time bed management, patient flow, and discharge processes. Both statistical models were reasonably accurate, using only a minimal number of readily available independent variables.

Thresholds and accuracy in screening tools for early detection of psychopathology

BACKGROUNDnThe accuracy of any screening instrument designed to detect psychopathology among children is ideally assessed through rigorous comparison to gold standard tests and interviews. Such comparisons typically yield estimates of what we refer to as standard indices of diagnostic accuracy, including sensitivity, specificity, positive predictive value (PPV), and negative predictive value. However, whereas these statistics were originally designed to detect binary signals (e.g., diagnosis present or absent), screening questionnaires commonly used in psychology, psychiatry, and pediatrics typically result in ordinal scores. Thus, a threshold or cut score must be applied to these ordinal scores before accuracy can be evaluated using such standard indices. To better understand the tradeoffs inherent in choosing a particular threshold, we discuss the concept of threshold probability. In contrast to PPV, which reflects the probability that a child whose score falls at or above the screening threshold has the condition of interest, threshold probability refers specifically to the likelihood that a child whose score is equal to a particular screening threshold has the condition of interest.nnnMETHODnThe diagnostic accuracy and threshold probability of two well-validated behavioral assessment instruments, the Child Behavior Checklist Total Problem Scale and the Strengths and Difficulties Questionnaire total scale were examined in relation to a structured psychiatric interview in three de-identified datasets.nnnRESULTSnAlthough both screening measures were effective in identifying groups of children at elevated risk for psychopathology in all samples (odds ratios ranged from 5.2 to 9.7), children who scored at or near the clinical thresholds that optimized sensitivity and specificity were unlikely to meet criteria for psychopathology on gold standard interviews.nnnCONCLUSIONSnOur results are consistent with the view that screening instruments should be interpreted probabilistically, with attention to where along the continuum of positive scores an individual falls.