Using laboratory-based surveillance data for prevention: an algorithm for detecting Salmonella outbreaks

Defining and detecting malaria epidemics in the highlands of western Kenya

Epidemic detection algorithms are being increasingly recommended for malaria surveillance in sub-Saharan Africa. We present the results of applying three simple epidemic detection techniques to routinely collected longitudinal pediatric malaria admissions data from three health facilities in the highlands of western Kenya in the late 1980s and 1990s. The algorithms tested were chosen because they could be feasibly implemented at the health facility level in sub-Saharan Africa. Assumptions of these techniques about the normal distribution of admissions data and the confidence intervals used to define normal years were also investigated. All techniques identified two "epidemic" years in one of the sites. The untransformed Cullen method with standard confidence intervals detected the two "epidemic" years in the remaining two sites but also triggered many false alarms. The performance of these methods is discussed and comments made about their appropriateness for the highlands of western Kenya.

Threat of a biological terrorist attack on the US food supply: the CDC perspective

Deliberate contamination of food with biological agents has already been perpetrated in the USA. The US food supply is increasingly characterised by centralised production and wide distribution of products. Deliberate contamination of a commercial food product could cause an outbreak of disease, with many illnesses dispersed over wide geographical areas. Dependent on the biological agent and contaminated food, such an outbreak could either present as a slow, diffuse, and initially unremarkable increase in sporadic cases, or as an explosive epidemic suddenly producing many illnesses. Preparedness for a bioterrorist event affecting the food supply, therefore, entails augmentation of the traditional public-health infrastructure to enhance disease surveillance, accelerate capacity of laboratory detection, rapidly investigate and control outbreaks, and develop capacity for response to mass-casualty disasters.

Infectious disease surveillance in North Rhine-Westphalia: first steps in the development of an early warning system

It is often difficult to detect warning signals on infectious disease outbreaks from raw surveillance data. Data need to be used for a timely generation and distribution of information on the current state of infectious diseases. This offers the opportunity to detect outbreaks and to initiate preventive measures. To improve the surveillance system in North Rhine-Westphalia we have introduced an infectious disease barometer, a simple tool based on weekly notification data. The aim of this tool is not to provide in depth data analysis, but to help to detect clusters or outbreaks. This can be the first step in the development of an early warning system and can support epidemiological investigation and policy making.

Use of molecular subtyping in surveillance for Salmonella enterica serotype typhimurium

BACKGROUND

Because Salmonella enterica serotype typhimurium is the most common serotype isolated from persons with salmonellosis in the United States, it is difficult to detect unusual clusters or outbreaks. To determine whether molecular subtyping could be useful in public health surveillance for S. enterica serotype typhimurium, the Minnesota Department of Health initiated the routine use of pulsed-field gel electrophoresis (PFGE) of isolates.

METHODS

Beginning in 1994, all S. enterica serotype typhimurium isolates submitted by clinical laboratories to the Department of Health were subtyped by PFGE. A standard questionnaire was used to interview patients about possible sources of infection.

RESULTS

From 1994 through 1998, 998 cases of infection with S. enterica serotype typhimurium were reported to the Minnesota Department of Health (4.4 cases per 100,000 person-years). PFGE was performed on 958 of the isolates (96 percent), and 174 different patterns were identified. Sixteen outbreaks with a common source were identified, accounting for 154 cases. PFGE subtyping made it possible to confirm 10 outbreaks that involved small numbers of cases in institutional settings. Of six larger, community-based outbreaks, four would probably not have been recognized without PFGE subtyping. These four outbreaks accounted for 96 of the 154 culture-confirmed outbreak cases (62 percent). Fifty-six of 209 isolates tested for antimicrobial susceptibility (27 percent) were resistant to at least five antimicrobial agents. The multidrug-resistant isolates identified had unique PFGE patterns.

CONCLUSIONS

Routine molecular subtyping of S. enterica serotype typhimurium by PFGE can improve the detection of outbreaks and aid in the identification of multidrug-resistant strains. Combining routine molecular subtyping with a method of rapid communication among public health authorities can improve surveillance for S. enterica serotype typhimurium infections.

Value of ICD-9 coded chief complaints for detection of epidemics

To assess the value of ICD-9 coded chief complaints for early detection of epidemics, we measured sensitivity, positive predictive value, and timeliness of Influenza detection using a respiratory set (RS) of ICD-9 codes and an Influenza set (IS). We also measured inherent timeliness of these data using the cross-correlation function. We found that, for a one-year period, the detectors had sensitivity of 100% (1/1 epidemic) and positive predictive values of 50% (1/2) for RS and 25% (1/4) for IS. The timeliness of detection using ICD-9 coded chief complaints was one week earlier than the detection using Pneumonia and Influenza deaths (the gold standard). The inherent timeliness of ICD-9 data measured by the cross-correlation function was two weeks earlier than the gold standard.

Implementing a network for electronic surveillance reporting from public health reference laboratories: an international perspective

Electronic data reporting from public health laboratories to a central site provides a mechanism for public health officials to rapidly identify problems and take action to prevent further spread of disease. However, implementation of reference laboratory systems is much more complex than simply adopting new technology, especially in international settings. We describe three major areas to be considered by international organizations for successful implementation of electronic reporting systems from public health reference laboratories: benefits of electronic reporting, planning for system implementation (e.g., support, resources, data analysis, country sovereignty), and components of system initiation (e.g., authority, disease definition, feedback, site selection, assessing readiness, problem resolution). Our experience with implementation of electronic public health laboratory data management and reporting systems in the United States and working with international organizations to initiate similar efforts demonstrates that successful reference laboratory reporting can be implemented if surveillance issues and components are planned.

Practical risk-adjusted quality control charts for infection control

BACKGROUND

Control chart methodology has been widely touted for monitoring and improving quality in the health care setting. P charts and U charts are frequently recommended for rate and ratio statistics, but their practical value in infection control may be limited because they (1) are not risk-adjusted, and (2) perform poorly with small denominators. The Standardized Infection Ratio is a statistic that overcomes both these obstacles. It is risk-adjusted, and it effectively increases denominators by combining data from multiple risk strata into a single value.

SETTING

The AICE National Database Initiative is a voluntary consortium of US hospitals ranging in size from 50 to 900 beds. The infection control professional submits monthly risk-stratified data for surgical site infections, ventilator-associated pneumonia, and central line-associated bacteremia.

METHODS

Run charts were constructed for 51 hospitals submitting data between 1996 and 1998. Traditional hypothesis tests (P values <.05) flagged 128 suspicious points, and participating infection control professionals investigated and categorized each flag as a "real problem" or "background variation." This gold standard was used to compare the performance of 5 unadjusted and 11 risk-adjusted control charts.

RESULTS

Unadjusted control charts (C, P, and U charts) performed poorly. Flags based on traditional 3-sigma limits suffered from sensitivity <50%, whereas 2-sigma limits suffered from specificity <50%. Risk-adjusted charts based on the Standardized Infection Ratio performed much better. The most consistent and useful control chart was the mXmR chart. Under optimal conditions, this chart achieved a sensitivity and specificity >80%, and a receiver operating characteristic area of 0. 84 (P <.00001).

CONCLUSIONS

These findings suggest a specific statistic (the Standardized Infection Ratio) and specific techniques that could make control charts valuable and practical tools for infection control.

Infections associated with eating seed sprouts: an international concern

Recent outbreaks of Salmonella and Escherichia coli O157:H7 infections associated with raw seed sprouts have occurred in several countries. Subjective evaluations indicate that pathogens can exceed 107 per gram of sprouts produced from inoculated seeds during sprout production without adversely affecting appearance. Treating seeds and sprouts with chlorinated water or other disinfectants fails to eliminate the pathogens. A comprehensive approach based on good manufacturing practices and principles of hazard analysis and critical control points can reduce the risk of sprout-associated disease. Until effective measures to prevent sprout-associated illness are identified, persons who wish to reduce their risk of foodborne illness from raw sprouts are advised not to eat them; in particular, persons at high risk for severe complications of infections with Salmonella or E. coli O157:H7, such as the elderly, children, and those with compromised immune systems, should not eat raw sprouts.

Association rules and data mining in hospital infection control and public health surveillance

OBJECTIVES

The authors consider the problem of identifying new, unexpected, and interesting patterns in hospital infection control and public health surveillance data and present a new data analysis process and system based on association rules to address this problem.

DESIGN

The authors first illustrate the need for automated pattern discovery and data mining in hospital infection control and public health surveillance. Next, they define association rules, explain how those rules can be used in surveillance, and present a novel process and system--the Data Mining Surveillance System (DMSS)--that utilize association rules to identify new and interesting patterns in surveillance data.

RESULTS

Experimental results were obtained using DMSS to analyze Pseudomonas aeruginosa infection control data collected over one year (1996) at University of Alabama at Birmingham Hospital. Experiments using one-, three-, and six-month time partitions yielded 34, 57, and 28 statistically significant events, respectively. Although not all statistically significant events are clinically significant, a subset of events generated in each analysis indicated potentially significant shifts in the occurrence of infection or antimicrobial resistance patterns of P. aeruginosa.

CONCLUSION

The new process and system are efficient and effective in identifying new, unexpected, and interesting patterns in surveillance data. The clinical relevance and utility of this process await the results of prospective studies currently in progress.

Emerging foodborne diseases: an evolving public health challenge

The epidemiology of foodborne disease is changing. New pathogens have emerged, and some have spread worldwide. Many, including Salmonella, Escherichia coli O157:H7, Campylobacter, and Yersinia enterocolitica, have reservoirs in healthy food animals, from which they spread to an increasing variety of foods. These pathogens cause millions of cases of sporadic illness and chronic complications, as well as large and challenging outbreaks over many states and nations. Improved surveillance that combines rapid subtyping methods, cluster identification, and collaborative epidemiologic investigation can identify and halt large, dispersed outbreaks. Outbreak investigations and case-control studies of sporadic cases can identify sources of infection and guide the development of specific prevention strategies. Better understanding of how pathogens persist in animal reservoirs is also critical to successful long-term prevention. In the past, the central challenge of foodborne disease lay in preventing the contamination of human food with sewage or animal manure. In the future, prevention of foodborne disease will increasingly depend on controlling contamination of feed and water consumed by the animals themselves.