Predicting hospital surge after a large-scale anthrax attack: a model-based analysis of CDC's cities readiness initiative prophylaxis recommendations.

BACKGROUND: A CRI-compliant prophylaxis campaign starting 2 days after exposure would protect from 86% to 87% of exposed individuals from illness (assuming, in the base case, 90% antibiotic effectiveness and a 95% attack rate). Each additional day needed to complete the campaign would result in, on average, 2.4% to 2.9% more hospitalizations in the exposed population; each additional day's delay to initiating prophylaxis beyond 2 days would result in 5.2% to 6.5% additional hospitalizations. These population protection estimates vary roughly proportionally to antibiotic effectiveness but are relatively insensitive to variations in anthrax incubation period. CONCLUSION: . Delays in detecting and initiating response to large-scale, covert aerosol anthrax releases in a major city would render even highly effective CRI-compliant mass prophylaxis campaigns unable to prevent unsustainable levels of surge hospitalizations. Although outcomes may improve with more rapid epidemiological identification of affected subpopulations and increased collaboration across regional public health and hospital systems, these findings support an increased focus on prevention of this public health threat.

Can modeling of HIV treatment processes improve outcomes? Capitalizing on an operations research approach to the global pandemic.

BACKGROUND: Mathematical modeling has been applied to a range of policy-level decisions on resource allocation for HIV care and treatment. We describe the application of classic operations research (OR) techniques to address logistical and resource management challenges in HIV treatment scale-up activities in resource-limited countries. METHODS: We review and categorize several of the major logistical and operational problems encountered over the last decade in the global scale-up of HIV care and antiretroviral treatment for people with AIDS. While there are unique features of HIV care and treatment that pose significant challenges to effective modeling and service improvement, we identify several analogous OR-based solutions that have been developed in the service, industrial, and health sectors. RESULTS: HIV treatment scale-up includes many processes that are amenable to mathematical and simulation modeling, including forecasting future demand for services; locating and sizing facilities for maximal efficiency; and determining optimal staffing levels at clinical centers. Optimization of clinical and logistical processes through modeling may improve outcomes, but successful OR-based interventions will require contextualization of response strategies, including appreciation of both existing health care systems and limitations in local health workforces. CONCLUSION: The modeling techniques developed in the engineering field of operations research have wide potential application to the variety of logistical problems encountered in HIV treatment scale-up in resource-limited settings. Increasing the number of cross-disciplinary collaborations between engineering and public health will help speed the appropriate development and application of these tools.

Hospital "self-prophylaxis": strategies for efficient protection of the workforce in the face of infectious disease threats.

Hospital preparedness for nosocomial or community-wide outbreaks of communicable disease includes the capability for rapid, self-reliant administration of prophylaxis to its workforce, with the goal of minimal disruption of patient care, here called hospital "self-prophylaxis." We created a new discrete-event simulation model of a hypothetical hospital wing to compare the operational charateristics of standard single-line, "first-come, first-served" dispensing clinics with those of 2 staff management strategies that can dramatically reduce staff waiting time while centralizing dispensing around existing pharmacy-distribution points.

Is overtriage associated with increased mortality? Insights from a simulation model of mass casualty trauma care.

PURPOSE: To examine the relationship between overtriage and critical mortality after a mass casualty incident (MCI) using a simulation model of trauma system response. METHODS: We created a discrete event simulation model of trauma system management of MCIs involving individual patient triage and treatment. Model variables include triage performance, treatment capability, treatment time, and time-dependent mortality of critically injured patients. We model triage as a variable selection process applied to a hypothetical population of critically and noncritically injured patients. Treatment capability is represented by staffed emergency department trauma bays with associated staffed operating rooms that are recycled after each use. We estimated critical and noncritical patient treatment times and time-dependent mortality rates from the trauma literature. RESULTS: In this simulation model, overtriage, the proportion of noncritical patients among all of those labeled as critical, has a positive, negative, or variable association with critical mortality depending on its etiology (ie, related to changes in triage sensitivity or to changes in the prevalence and total number of critical patients). In all of the modeled scenarios, the ratio of critical patients to treatment capability has a greater impact on critical mortality than overtriage level or time-dependent mortality assumption. CONCLUSIONS: Increasing overtriage may have positive, negative, or mixed effects on critical mortality in this trauma system simulation model. These results, which contrast with prior analyses describing a positive linear relationship between overtriage and mortality, highlight the need for alternative metrics to describe trauma system response after MCIs. We explore using the relative number of critical patients to available and staffed treatment units, or the critical surge to capability ratio, which exhibits a consistent and nonlinear association with critical mortality in this model.