Diane M. Dwyer

Serotype Distribution of Invasive Group B Streptococcal Isolates in Maryland: Implications for Vaccine Formulation

Invasive group B streptococcal (GBS) infection is a major health problem among infants and adults. The formulation of GBS vaccines depends on knowledge of the GBS serotype distribution. Serotype V GBS infection appears to have recently emerged, suggesting that the serotype distribution changes over time. GBS isolates from 210 pediatric patients, 23 pregnant women, and 314 nonpregnant adults with invasive infection in Maryland were studied. The predominant serotypes from infants with early-onset disease were as follows: serotype III, 38% of isolates; serotype Ia, 36%; serotype V, 13%; and serotype II, 11%. Although the majority (60%) of isolates among infants with late-onset infection were serotype III, serotype Ia (23%) was also common. The predominant serotype among isolates from nonpregnant adult patients was serotype V, accounting for 29% of the isolates. The serotype distribution differs between pediatric patients and adults and is changing over time. The inclusion of a relatively small number of serotypes in a GBS vaccine could provide protection against the vast majority of isolates.

Salmonella newport infections transmitted by fiberoptic colonoscopy

During a 2-week period following the colonoscopy and biopsy of a patient with acute Salmonella newport gastroenteritis, S. newport was recovered from colonic aspirates or fecal specimens of eight of 28 patients from whom specimens were cultured during or after colonoscopy. Two of the eight persons from whom S. newport was isolated developed acute gastroenteritis, two had asymptomatic infections, and four had positive aspirates collected through a colonoscope but did not become infected. Although S. newport was never recovered from the four colonoscopes used during the outbreak, cultures of one of the colonic biopsy forceps grew S. newport. Contamination of the equipment most likely occurred during colonoscopy of the index patient. Inadequate disinfection of the equipment allowed the organism to survive and possibly to cross-contaminate other colonoscopes, and the organism was then transmitted to other patients by use of the contaminated colonoscopes or the contaminated biopsy forceps. Implemented control measures terminated the outbreak.

A Framework for Capturing Clinical Data Sets from Computerized Sources

The pressure to improve health care and provide better care at a lower cost has created new needs to access clinical data for outcome analysis [1], quality assessment, guideline development [2], utilization review, pharmacoepidemiology [3], public health, benefits management, and other purposes. These needs are usually identified as data sets (that is, predefined lists of clinical questions or observations). Data sets are not new to the health care industry. The UB92 hospital billing form and UB82, its progenitor, from The Health Care Financing Administration (HCFA) have been around for some time. Recently, however, the number and richness of clinical data sets have grown dramatically. New data sets have been established by the National Center for Vital Health Statistics [4] and the National Committee for Quality Assurance [5]. The HCFA piloted an 1800-element quality-assurance data set called the Uniform Clinical Data Set System from 1989 to 1993 [6] and is working on a simpler version called the Medicare Quality Indicator System. Other HCFA data sets include the Resident Assessment Instrument for long-term health care [7] and a draft Outcome and Assessment Information Set for providers of home health care [8]. The U.S. Centers for Disease Control and Prevention (CDC) has developed Data Elements for Emergency Department Systems (DEEDS) for reporting information on visits to emergency departments [9]; the National Immunization Program for reporting data on immunizations [10]; and, in collaboration with the Council of State and Territorial Epidemiologists (CSTE) and the Association of State and Territorial Public Health Laboratory Directors (ASTPHLD), a data set that reports laboratory findings on communicable diseases [11]. Other national data sets include the Trauma Registry of American College of Surgeons [12], the Cardiovascular Data Standards for coronary arteriography [13], the Cooperative Project for coronary artery bypass graft surgery [14], and the Musculoskeletal Outcomes Data Evaluation and Management System for knee and hip replacements [15]. Cancer registries, hospitals, group practices, managed care providers, researchers, and pharmaceutical manufacturers are developing additional clinical data sets. We refer to the databases that carry data sets as analytic databases because they are usually designed for direct statistical analysis. As the need formal data sets has burgeoned, so has the use of computers to process patient information in direct support of patient care. Operational systems in the laboratory, pharmacy, patient registration area, surgical suites, and electrocardiography carts (to name a few) now include most data on laboratory procedures, prescriptions, demographics and appointments, surgical logs, and electrocardiographic measurements. Unfortunately, the two developments are occurring in independent orbits with little interaction. With a few important exceptions, developers of national data sets do not consider operational systems as sources for the contents of their data sets. Developers can find the information they want by abstracting charts. However, chart abstraction is prone to error and expensive. In one study, chart reviewers could not find 10% of the laboratory test results that were in the charts [16] and commercial chart reviews cost between

Relapsing invasive group B streptococcal infection in adults.

10 and

Invasive group B streptococcal disease in Maryland nursing home residents.

15 per admission, depending on the amount of data retrieved (Kriss E. Personal communication. Boston, MA: MediQual). Chart reviews remain the only option for retrieving some kinds of information. However, when information exists in the databases of health care providers, manually extracting it from reports that are printed from one database and reentering the information into another database is time-consuming and inefficient. In this article, we review the barriers to the direct flow of operational data into analytic databases and the technical developments that have minimized these barriers. We also suggest specific actions that can unify the two orbits as the health care industry enters the computer age. The Difference between Operational and Analytic Databases Examples of operational databases are found in hospital pharmacies, laboratories, radiology departments, critical care units, and order-processing units. The first barriers to the direct use of operational system data in analytic databases are the differences in structure and detail that obscure similarities in the content of their information. A laboratory system would typically dedicate an entire record to each observation (for example, clinical measurement or laboratory test result). An ordering or pharmacy system would do the same for each item or prescription that is ordered. Table 1 shows the structure of an operational database for a clinical reporting system. Table 1. Operational Database: One Record per Observation* In contrast, analytic databases typically carry all variables of interest (for example, the most recent hemoglobin value, whether the patient is anemic, the number of units of blood transfused, and the lowest systolic blood pressure) in a single record that describes one patient, patient encounter, or patient procedure. Table 2 shows an analytic database analogue to the operational database of Table 1. In analytic databases, the variable is identified by the name of the field (for example, most recent cholesterol level) in which its value is stored, and all variables of interest are stored horizontally as separate fields in one record. The variables in an operational database are usually defined by a code or name stored in one field (with a name such as observation ID as shown in the third column of Table 1) and their values are stored in another field (with a name such as value as shown in the fourth column of Table 1). Different variables are stacked vertically in separate records. Table 2. Revised Model of an Analytic Database: One Record per Patient Event* Operational databases often contain repeated measurements (for example, all recent hemoglobin values for a patient), whereas analytic databases often contain a single measurement (for example, the lowest hemoglobin value during the first 24 hours of a hospital stay or the first Glasgow coma score during an emergency department visit). Operational databases usually carry many items of information about each value reported (for example, its units, date and time, and where the measurement was taken) as separate fields in the same record, whereas analytic databases usually contain only the variables value. However, analytic databases may contain slightly more information. For example, an analytic database may have the value and date of the last measurement of diastolic blood pressure. Operational databases usually contain raw data [for example, the hemoglobin value], whereas analytic databases frequently carry conclusions or yes or no answers to questions, such as is the patient anemic?). Finally, the identifying codes in operational databases tend to be more detailed than the corresponding codes in analytic databases. For example, an operational database in the pharmacy might identify a prescription by the National Drug Code (NDC), which identifies the brand name, dose, and bottle size. In comparison, the corresponding variable in an analytic database might identify drugs by a more generalized code that identifies only the generic drug (such as propranolol) or drug class (such as -blockers). In many cases, operational data can be converted into analytic variables. Three simple conversion rules are worth emphasizing. First, a continuous variable, such as the hemoglobin value or cholesterol level, can be converted into a binary diagnostic variable (such as specifying yes or no to the presence of anemia) and be given a numeric threshold that defines the diagnosis (for example, a hemoglobin value < 12). Second, detailed codes can be converted into more generalized codes by using simple cross-links (for example, converting NDC codes into generic drug codes). Finally, repeated values of a variable can be converted into a single value. Conversion occurs by selecting the first, last, or worst of a series of repeated values or by combining all occurrences on the basis of some rule. Examples include taking the mean value (as might be done for blood pressure levels), the sum (as might be done for determining chemotherapy drug doses), or the count (as might be done for records of blood transfusions). It is easy to imagine more complicated conversion rules. For example, a variable that specifies yes or no for the presence of diabetes might be defined in terms of thresholds on fasting blood sugar and hemoglobin A1c or for the current use of insulin or oral agents. Variations in the Codes and Structures of Operational Systems Until recently, a second barrier to the use of operational databases has been the lack of standards for reporting data from operational systems. Each vendor structured and reported the contents of its products differently. In some cases, each implementation of a vendors product also varied. In addition, each laboratory and medical records department tended to define its own unique and idiosyncratic codes for identifying observations and findings. This cacophony presented an enormous barrier to the use of operational databases by external agencies. Today, standard message structures and formats exist for exporting patient information from operational systems. Message standards specify a uniform structure for electronically reporting clinical data from source databases to other databases. These standards also specify the format for reporting dates, times, names, numeric values, and codes. For example, the standard for date formats is CCYYMMDD (century, year, month, date). Therefore, 12 April 1979 is recorded as 19790412 and not as 4-12-79, 12-apr-79, or any other option. The American National Standards Institute Health Level 7 (HL7) standard is the most relevant to this discussion. Th

Typhoid fever in the park: Epidemiology of an outbreak at a cultural interface

Invasive infection caused by group B streptococcus (Streptococcus agalactiae) is increasingly being recognized as a substantial health problem among nonpregnant adults, particularly the elderly and those with chronic illness [1, 2]. The most common clinical presentations include skin or bone infection, bacteremia without an identified source, urosepsis, pneumonia, and peritonitis [2]. Although recurrent group B streptococcal infection has been reported to occur in both infants [3-15] and adults [16-21], no population-based data are available to quantify the risk for recurrent infection. In addition, it is unknown whether recurrent disease is caused by infection with the same strain or reinfection with another strain. After we began an active surveillance project of invasive group B streptococcal infection in Maryland, we noted that some adult patients had more than one episode of group B streptococcal infection. We therefore prospectively studied the problem of recurrent infection and supplemented our study with molecular epidemiologic methods. Methods Surveillance Active surveillance for invasive Haemophilus influenzae, meningococcal, Listeria monocytogenes, and group B streptococcal infection was initiated in November 1991 as part of the Maryland Bacterial Invasive Disease Surveillance (BIDS) project. This project is a component of the multistate National Bacterial Invasive Disease Surveillance Group, which is coordinated by the Centers for Disease Control and Prevention (CDC). The surveillance case definition is the isolation of one of the above organisms from a normally sterile body fluid, such as blood or cerebrospinal fluid, from a Maryland resident of any age with clinical signs of infection. All acute-care hospitals in Maryland participate in this project, as do hospitals in Washington, D.C., at which residents of southern Maryland frequently seek medical care. Nonhospital microbiology laboratories that process blood cultures also participate. A brief case report form is completed for each eligible case, and the bacterial isolates are submitted for species confirmation and further testing. Biweekly telephone calls are made to hospital infection control practitioners to ascertain cases not reported spontaneously. To identify unreported cases, annual on-site audits of microbiology laboratories are done by reviewing the laboratory records. Identification of Patients with Recurrent Group B Streptococcal Infection At the initiation of the surveillance project in November 1991, a computer program was developed to identify all patients with group B streptococcal infection who, on the basis of their last name and date of birth, had duplicate case report forms in the surveillance database. After noting that six adult patients had more than one admission for invasive group B streptococcal infection, we decided to prospectively study recurrent disease in all age groups, including infants. Recurrent infection was defined as two separate hospital admissions during which group B streptococcus was isolated from a normally sterile body fluid. We also included nonhospitalized patients who had an acute illness during which group B streptococcus was isolated from a normally sterile body fluid and who had a second episode. Children and adults of any age with a first episode of group B streptococcal infection after 1 November 1991 were included. To calculate the proportion of all patients with group B streptococcal infection who had more than one episode, we included only patients whose first episodes occurred by 30 September 1993; this criterion allowed a minimum follow-up of 1 year. To identify patients not detected by last name and date of birth, we also searched for duplicate street addresses or medical record numbers and matches based on the first and last name or date of birth and ZIP code. Charts were reviewed to verify that each case report form represented a distinct episode of group B streptococcal infection and to obtain clinical information. Group B streptococcal isolates received from the microbiology laboratories were confirmed for species and were serotyped. Because clinical group B streptococcal isolates are restricted to a few serotypes [22], additional subtyping methods were needed. We chose restriction endonuclease analysis of chromosomal DNA (REAC) because it has been shown to provide discriminatory power among group B streptococcal isolates of the same serotype [23]. Although multilocus enzyme electrophoresis has been shown to be of limited utility in subtyping group B streptococcal isolates obtained from infants [24, 25], we wished to evaluate the method among isolates obtained from adults. Selection of Control Isolates Fifty-three group B streptococcal BIDS isolates were selected as controls for REAC analysis to provide information on the genetic heterogeneity of group B streptococci in Maryland [26]. These isolates were obtained from sterile sites in nonpregnant adult residents of Maryland who had invasive group B streptococcal infection during the same time as the patients with recurrent infection. Laboratory Assays Serotyping was done by the Lancefield capillary precipitin method [27]; we used antisera to polysaccharide antigens Ia, Ib, II, III, and V and protein antigen c prepared at the CDC. Group B streptococcal isolates were further characterized by REAC using the restriction enzyme HhAI (Gibco-BRL, Gaithersburg, Maryland). This technique was done using previously published methods [23], although 0.7% rather than 1% gels were used to improve the resolution of the larger bands. Although REAC results are somewhat cumbersome to read because of the many bands, we chose REAC as the main subtyping method because it is relatively easy to do and because it provides greater discrimination among group B streptococcal isolates than does ribotyping [23]. Because of the difficulties in interpreting subtle differences in REAC patterns, strains with similar REAC subtypes were rerun side by side on the same gel. Group B streptococcal strain extracts were prepared, and multilocus enzyme electrophoresis analysis was done in 11.5% starch at a pH of 8.0 as previously described [28]. Gel slices were stained for the following enzymes: alcohol dehydrogenase, lactate dehydrogenase, nicotinamide-adenine dinucleotide (NAD)-dependent glyceraldehyde 3-phosphate dehydrogenase. NAD phosphate (NADP)-dependent glyceraldehyde 3-phosphate dehydrogenase, NADP-dependent glutamate dehydrogenase, reduced form of NADP diaphorase, indophenol oxidase, nucleoside phosphorylase, aspartate aminotransferase, hexokinase, carbamylate kinase, phosphoglucomutase, esterase, leucine aminopeptidase, arginine aminopeptidase, leu-gly-gly peptidase, phe-leu peptidase, aldolase, and phosphoglucose isomerase [29]. Electrophoretic variants of each enzyme activity were considered to be different alleles of that enzyme and were assigned separate allele numbers. Each strain was characterized by a list of allele numbers for the different enzymes, and each unique list of alleles was designated as an electrophoretic type and assigned a separate electrophoretic type number. Statistical Analysis The number of nonpregnant persons 18 years of age or older was estimated using 1990 census-based estimates of the 1 January 1993 population minus the total number of live births among Maryland residents in 1991, the last year for which complete birth data were available. We used the Kruskal-Wallis test to analyze continuous variables and used standard methods to calculate 95% CIs [30]. The probability of at least the number of observed intrapatient REAC subtyping matches was calculated using an independence assumption model in which each permutation of the second episode results was equally probable. We did the calculations by simulation, conditioning on the results of the first episode, and randomly permuting the results of the second episode 700 000 times. With no matches, this yields P values of less than 0.00001 with a probability of 0.999. Results Seven hundred fifty-one patients with invasive group B streptococcal infection were reported between 1 November 1991 and 30 September 1993; 449 (60%) of these were nonpregnant adults 18 years of age or older. The annual incidence of invasive group B streptococcal infection among nonpregnant Maryland residents during the study period was 6.7 per 100 000 persons, 13 times the incidence of adult invasive meningococcal disease during the same time period. Of the 449 adult patients, 54 (12%) were known to have died during the first identified episode by the time the case report forms were completed. The median duration of follow-up among the 395 survivors was 23 months (range, 12 to 35 months). Among the survivors, 17 (4.3% [95% CI, 2.6% to 6.9%]) had a second episode of invasive group B streptococcal infection. Among the 17 second episodes, 1 occurred during the last 2 months of 1991, 5 occurred during 1992, 6 occurred during 1993, and 5 occurred during the first 9 months of 1994. The patients resided in 17 ZIP code regions and in 8 of the 24 Maryland administrative jurisdictions. An additional 5 patients with recurrent infection were identified with first-episode onset dates after 30 September 1993; thus, a total of 22 adult patients with recurrent infection were identified (Table 1). For the first episodes, group B streptococcus was isolated from blood in 20 patients and from the synovial fluid of the knee in 2 patients (patients 4 and 11). Group B streptococcus was isolated from blood for 21 of the second episodes and from the synovial fluid for 1 (patient 11). Patients received at least one antibiotic agent to which their group B streptococcal isolates were susceptible. The second episode occurred an average of 24 weeks after the first episode (median, 10 weeks; range, 2 to 95 weeks). Table 1. Clinical and Group B Streptococcal Isolate Analysis for Adults with Recurrent Infection* Table 1Continued Only one patient (pa

Risk of Meningococcal Infection in College Students

Between 1991 and 1995, among 999 nonpregnant adult Maryland residents with group B Streptococcus (GBS) isolated from a normally sterile site, 84 resided in nursing homes (NHs). The age-adjusted annual incidence of GBS infection (per 100,000 population) among those > or = 65 years old was 72.3 for NH residents and 17.5 for community residents (relative risk, 4.1; P < 0.001). Thirty-four case patients resided in 11 NHs with > or = 2 cases; 1 NH had 8 case patients within 22 months. Six of 8 case patients from 3 NHs had serotype V GBS. Molecular subtyping of several isolates identified 2 case patients in 1 NH with identical subtype patterns. NH residents have a markedly higher incidence of invasive GBS than do community residents > or = 65 years old and may serve as a target group for immunization when GBS vaccines become available. Further evaluation of intra-NH transmission of GBS is warranted.

Immunization Coverage and Its Relationship to Preventive Health Care Visits Among Inner-City Children in Baltimore

The number of reported outbreaks of typhoid fever in the United States has recently increased. Only six were reported from 1980–1989, but seven outbreaks were reported in 1990. In August 1990, health officials in Montgomery County, Maryland, were notified of two cases of typhoid fever among persons who had attended both a family picnic attended by 60 persons and a Latin Food Festival attended by 100,000 people. We obtained interviews, blood and stool cultures, and Vi serologies from attendees at and food handlers for the picnic. We defined cases as culture-confirmed or probable. Of the 60 picnic attendees, 24 (40%) had cases, of which 16 were culture confirmed. Those who ate potato salad were at increased risk of disease (17/32vs. 6/28, relative risk [RR]=2.5,95% confidence interval [CI] 1.1–5.4). Picnic attendees who also attended the Latin Food Festival were not at significantly greater risk of disease than those who did not, (11/22vs. 13/38, RR=1.5, CI=0.8–2.7) and we found no evidence of disease among other festival attendees. The potato salad was prepared with intensive handling and without adequate temperature control by a recent immigrant from El Salvador who was asymptomatic, did not attend the picnic, hadSalmonella typhi (S. typhi) in her stool, and had elevated Vi antibodies, strongly suggestive of the carrier state. Outbreaks of typhoid fever are a threat for cosmopolitan communities. While currently available control measures are unlikely to prevent all outbreaks, thorough investigation can identify previously unrecognized carriers.