Catherine J. Staes

Sources of Infection Among Persons With Acute Hepatitis A and No Identified Risk Factors During a Sustained Community-Wide Outbreak

Context. Hepatitis A is a common vaccine-preventable disease in the United States. Most cases occur during community-wide outbreaks, which can be difficult to control. Many case-patients have no identified source. Objective. To identify foodborne and household sources of hepatitis A during a community-wide outbreak. Design. Serologic and descriptive survey. Setting. Salt Lake County, Utah. Participants. A total of 355 household contacts of 170 persons reported with hepatitis A during May 1996 to December 1996, who had no identified source of infection; and 730 food handlers working in establishments where case-patients had eaten. Main Outcome Measure. Prevalence of immunoglobulin M antibodies to hepatitis A virus (IgM anti-HAV) among household and food service contacts. Results. Overall, 70 household contacts (20%) were IgM anti-HAV-positive, including 52% of children 3 to 5 years old and 30% of children <3 years old. In multivariate analysis, the presence of a child <3 years old (odds ratio [OR]: 8.8; 95% confidence limit [CL]: 2.1,36) and a delay of ≥14 days between illness onset and reporting (OR: 7.9; 95% CL: 1.7,38) were associated with household transmission. Of 18 clusters of infections linked by transmission between households, 13 (72%) involved unrecognized infection among children <6 years old. No food handlers were IgM anti-HAV-positive. Conclusion. During a community-wide outbreak, HAV infection among children was common, was frequently unrecognized, and may have been an important source of transmission within and between households. Transmission from commercial food establishments was uncommon. Ongoing vaccination of children may prevent future outbreaks.

Value Driven Outcomes (VDO): a pragmatic, modular, and extensible software framework for understanding and improving health care costs and outcomes

Objective To develop expeditiously a pragmatic, modular, and extensible software framework for understanding and improving healthcare value (costs relative to outcomes). Materials and methods In 2012, a multidisciplinary team was assembled by the leadership of the University of Utah Health Sciences Center and charged with rapidly developing a pragmatic and actionable analytics framework for understanding and enhancing healthcare value. Based on an analysis of relevant prior work, a value analytics framework known as Value Driven Outcomes (VDO) was developed using an agile methodology. Evaluation consisted of measurement against project objectives, including implementation timeliness, system performance, completeness, accuracy, extensibility, adoption, satisfaction, and the ability to support value improvement. Results A modular, extensible framework was developed to allocate clinical care costs to individual patient encounters. For example, labor costs in a hospital unit are allocated to patients based on the hours they spent in the unit; actual medication acquisition costs are allocated to patients based on utilization; and radiology costs are allocated based on the minutes required for study performance. Relevant process and outcome measures are also available. A visualization layer facilitates the identification of value improvement opportunities, such as high-volume, high-cost case types with high variability in costs across providers. Initial implementation was completed within 6 months, and all project objectives were fulfilled. The framework has been improved iteratively and is now a foundational tool for delivering high-value care. Conclusions The framework described can be expeditiously implemented to provide a pragmatic, modular, and extensible approach to understanding and improving healthcare value.

Deaths related to lead poisoning in the United States, 1979-1998.

This study was conducted to describe trends in US lead poisoning-related deaths between 1979 and 1998. The predictive value of relevant ICD-9 codes was also evaluated. Multiple cause-of-death files were searched for records containing relevant ICD-9 codes, and underlying causes and demographic characteristics were assessed. For 1979-1988, death certificates were reviewed; lead source information was abstracted and accuracy of coding was determined. An estimated 200 lead poisoning-related deaths occurred from 1979 to 1998. Most were among males (74%), Blacks (67%), adults of age >/=45 years (76%), and Southerners (70%). The death rate was significantly lower in more recent years. An alcohol-related code was a contributing cause for 28% of adults. Only three of nine ICD-9 codes for lead poisoning were highly predictive of lead poisoning-related deaths. In conclusion, lead poisoning-related death rates have dropped dramatically since earlier decades and are continuing to decline. However, the findings imply that moonshine ingestion remains a source of high-dose lead exposure in adults.

Comparison of computerized surveillance and manual chart review for adverse events.

OBJECTIVE To understand how the source of information affects different adverse event (AE) surveillance methods. DESIGN Retrospective analysis of inpatient adverse drug events (ADEs) and hospital-associated infections (HAIs) detected by either a computerized surveillance system (CSS) or manual chart review (MCR). MEASUREMENT Descriptive analysis of events detected using the two methods by type of AE, type of information about the AE, and sources of the information. RESULTS CSS detected more HAIs than MCR (92% vs 34%); however, a similar number of ADEs was detected by both systems (52% vs 51%). The agreement between systems was greater for HAIs than ADEs (26% vs 3%). The CSS missed events that did not have information in coded format or that were described only in physician narratives. The MCR detected events missed by CSS using information in physician narratives. Discharge summaries were more likely to contain information about AEs than any other type of physician narrative, followed by emergency department reports for HAIs and general consult notes for ADEs. Some ADEs found by MCR were detected by CSS but not verified by a clinician. LIMITATIONS Inability to distinguish between CSS false positives and suspected AEs for cases in which the clinician did not document their assessment in the CSS. CONCLUSION The effect that information source has on different surveillance methods depends on the type of AE. Integrating information from physician narratives with CSS using natural language processing would improve the detection of ADEs more than HAIs.

Description of outbreaks of health-care-associated infections related to compounding pharmacies, 2000-12.

PURPOSE Outbreaks of health-care-associated infections related to compounding pharmacies from 2000 through 2012 are described. METHODS PubMed and the websites for the Centers for Disease Control and Prevention and the Food and Drug Administration were searched to identify infectious outbreaks associated with compounding pharmacies outside the hospital setting between January 2000 and November 2012. RESULTS Between January 2000 and before the 2012 fungal meningitis outbreak, 11 outbreaks were identified, involving 207 infected patients and 17 deaths after exposure to contaminated compounded drugs. The 2012 meningitis outbreak had a similar mortality rate but increased these totals almost fivefold. Half of the outbreaks involved patients in more than one state. Three outbreaks involved ophthalmic drugs. The remaining outbreaks involved corticosteroids, heparin flush solutions, cardioplegia solution, i.v. magnesium sulfate, total parenteral nutrition, and fentanyl. The outbreaks were caused by pathogens commonly associated with health-care-associated infections, common skin commensals, and organisms that rarely cause infection. Morbidity was substantial, including vision loss. Half the outbreaks resulted in recall of all sterile drugs from the pharmacy due to systemic problems with sterile procedures. CONCLUSION Before the nationwide 2012 fungal meningitis outbreak, drugs produced by compounding pharmacies were associated with 11 other smaller, but equally serious, outbreaks that occurred sporadically over the past 12 years. Lapses in sterile compounding procedures led to contamination of compounded drugs, exposure to patients, and a threat to public health in these outbreaks. Recognition and subsequent public health investigation were usually triggered by the occurrence of illness among multiple patients in a single health care setting.

Development of an electronic public health case report using HL7 v2.5 to meet public health needs.

Clinicians are required to report selected conditions to public health authorities within a stipulated amount of time. The current reporting process is mostly paper-based and inefficient and may lead to delays in case investigation. As electronic medical records become more prevalent, electronic case reporting is becoming increasingly feasible. However, there is no existing standard for the electronic transmission of case reports from healthcare to public health entities. We identified the major requirements of electronic case reports and verified that the requirements support the work processes of the local health departments. We propose an extendable standards-based model to electronically transmit case information and associated laboratory information from healthcare to public health entities. The HL7 v2.5 message model is currently being implemented to transmit electronic case reports from Intermountain Healthcare to the Utah Department of Health.

Predicting the start week of respiratory syncytial virus outbreaks using real time weather variables

BackgroundRespiratory Syncytial Virus (RSV), a major cause of bronchiolitis, has a large impact on the census of pediatric hospitals during outbreak seasons. Reliable prediction of the week these outbreaks will start, based on readily available data, could help pediatric hospitals better prepare for large outbreaks.MethodsNaïve Bayes (NB) classifier models were constructed using weather data from 1985-2008 considering only variables that are available in real time and that could be used to forecast the week in which an RSV outbreak will occur in Salt Lake County, Utah. Outbreak start dates were determined by a panel of experts using 32,509 records with ICD-9 coded RSV and bronchiolitis diagnoses from Intermountain Healthcare hospitals and clinics for the RSV seasons from 1985 to 2008.ResultsNB models predicted RSV outbreaks up to 3 weeks in advance with an estimated sensitivity of up to 67% and estimated specificities as high as 94% to 100%. Temperature and wind speed were the best overall predictors, but other weather variables also showed relevance depending on how far in advance the predictions were made. The weather conditions predictive of an RSV outbreak in our study were similar to those that lead to temperature inversions in the Salt Lake Valley.ConclusionsWe demonstrate that Naïve Bayes (NB) classifier models based on weather data available in real time have the potential to be used as effective predictive models. These models may be able to predict the week that an RSV outbreak will occur with clinical relevance. Their clinical usefulness will be field tested during the next five years.

A Case for Manual Entry of Structured, Coded Laboratory Data from Multiple Sources into an Ambulatory Electronic Health Record

Laboratory results provide necessary information for the management of ambulatory patients. To realize the benefits of an electronic health record (EHR) and coded laboratory data (e.g., decision support and improved data access and display), results from laboratories that are external to the health care enterprise need to be integrated with internal results. We describe the development and clinical impact of integrating external results into the EHR at Intermountain Health Care (IHC). During 2004, over 14,000 external laboratory results for 128 liver transplant patients were added to the EHR. The results were used to generate computerized alerts that assisted clinicians with managing laboratory tests in the ambulatory setting. The external results were sent from 85 different facilities and can now be viewed in the EHR integrated with IHC results. We encountered regulatory, logistic, economic, and data quality issues that should be of interest to others developing similar applications.

Identification of pneumonia and influenza deaths using the death certificate pipeline

BackgroundDeath records are a rich source of data, which can be used to assist with public surveillance and/or decision support. However, to use this type of data for such purposes it has to be transformed into a coded format to make it computable. Because the cause of death in the certificates is reported as free text, encoding the data is currently the single largest barrier of using death certificates for surveillance. Therefore, the purpose of this study was to demonstrate the feasibility of using a pipeline, composed of a detection rule and a natural language processor, for the real time encoding of death certificates using the identification of pneumonia and influenza cases as an example and demonstrating that its accuracy is comparable to existing methods.ResultsA Death Certificates Pipeline (DCP) was developed to automatically code death certificates and identify pneumonia and influenza cases. The pipeline used MetaMap to code death certificates from the Utah Department of Health for the year 2008. The output of MetaMap was then accessed by detection rules which flagged pneumonia and influenza cases based on the Centers of Disease and Control and Prevention (CDC) case definition. The output from the DCP was compared with the current method used by the CDC and with a keyword search. Recall, precision, positive predictive value and F-measure with respect to the CDC method were calculated for the two other methods considered here. The two different techniques compared here with the CDC method showed the following recall/ precision results: DCP: 0.998/0.98 and keyword searching: 0.96/0.96. The F-measure were 0.99 and 0.96 respectively (DCP and keyword searching). Both the keyword and the DCP can run in interactive form with modest computer resources, but DCP showed superior performance.ConclusionThe pipeline proposed here for coding death certificates and the detection of cases is feasible and can be extended to other conditions. This method provides an alternative that allows for coding free-text death certificates in real time that may increase its utilization not only in the public health domain but also for biomedical researchers and developers.Trial RegistrationThis study did not involved any clinical trials.

A case for using grid architecture for state public health informatics: the Utah perspective.

This paper presents the rationale for designing and implementing the next-generation of public health information systems using grid computing concepts and tools. Our attempt is to evaluate all grid types including data grids for sharing information and computational grids for accessing computational resources on demand. Public health is a broad domain that requires coordinated uses of disparate and heterogeneous information systems. System interoperability in public health is limited. The next-generation public health information systems must overcome barriers to integration and interoperability, leverage advances in information technology, address emerging requirements, and meet the needs of all stakeholders. Grid-based architecture provides one potential technical solution that deserves serious consideration. Within this context, we describe three discrete public health information system problems and the process by which the Utah Department of Health (UDOH) and the Department of Biomedical Informatics at the University of Utah in the United States has approached the exploration for eventual deployment of a Utah Public Health Informatics Grid. These three problems are: i) integration of internal and external data sources with analytic tools and computational resources; ii) provide external stakeholders with access to public health data and services; and, iii) access, integrate, and analyze internal data for the timely monitoring of population health status and health services. After one year of experience, we have successfully implemented federated queries across disparate administrative domains, and have identified challenges and potential solutions concerning the selection of candidate analytic grid services, data sharing concerns, security models, and strategies for reducing expertise required at a public health agency to implement a public health grid.