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Biomedical subjects

Kenneth D Mandl

Publications and source records attributed to Kenneth D Mandl.

At least 19 recordsLinked to original sources
PubMed2025

Genetic Landscape of Opsoclonus-Myoclonus-Ataxia Syndrome in Children.

BACKGROUND: Opsoclonus-myoclonus-ataxia syndrome (OMAS) is a rare neurological disorder, with involuntary rapid saccadic conjugate eye movements as one of characteristics, primarily affecting the cerebellum. While the exact pathogenesis remains unclear, genetic and autoimmune factors have been suggested to contribute to its development. METHODS: We enrolled patients diagnosed with OMAS before the age of 18 years at a pediatric neuroimmunology clinic in Boston, United States, using the 2004 Genoa Criteria. Whole genome sequencing was conducted for the patients and their biological parents in all cases, with one case including an unaffected twin sibling. RESULTS: De novo germline variants (DNVs) in probands were identified and validated and analyses of structural variants, recessive variants in neuroimmune-associated genes, and high-resolution human leukocyte antigen (HLA) typing were performed. Our study included 42 patients, 23 of whom had neuroblastoma. We found 12 confirmed DNVs in protein-coding regions in nine patients (29.0% of 31 from 30 trios and 1 quartet). Ten patients (23.8% of 42) had rare homozygous or compound heterozygous variants known to alter protein function, affecting 11 genes. Notably, the major histocompatibility complex, class II, DR beta 1 (HLA-DRB1) &#x2217;01 allele was observed in 27 out of 84 (32.1%) alleles in the patients, significantly higher than that in the general population (chi-square test, P < 0.0001). In one case, a potential genetic modifier of OMAS with severe cerebellar atrophy was identified, associated with a protein-truncating DNV in the CACNA2D2 gene. CONCLUSIONS: This first genome sequencing study reveals potential genetic contributors to OMAS, implicating polygenic predisposition-with HLA-DRB1&#x2217;01 as a possible factor-combined with nongenetic risk factors like neuroblastoma.

Humans↗
PubMed2007

Linking surveillance to action: incorporation of real-time regional data into a medical decision rule.

OBJECTIVE: Broadly, to create a bidirectional communication link between public health surveillance and clinical practice. Specifically, to measure the impact of integrating public health surveillance data into an existing clinical prediction rule. We incorporate data about recent local trends in meningitis epidemiology into a prediction model differentiating aseptic from bacterial meningitis. DESIGN AND MEASUREMENTS: Retrospective analysis of a cohort of all 696 children with meningitis admitted to a large urban pediatric hospital from 1992 to 2000. We modified a published bacterial meningitis score by adding a new epidemiological context adjustor variable. We examined 540 possible rules for this adjustor, varying both the number of aseptic meningitis cases that needed to be seen, and the recent time window in which they were seen. We performed sensitivity analyses with each of 540 possibilities in order to identify the optimal rule--namely, the one that included the most cases of aseptic meningitis without missing additional cases of bacterial meningitis, as compared with the published prediction model. We used bootstrap methods to validate this new score. RESULTS: The optimal rule was found to be: "at least four cases of aseptic meningitis in the previous 10 days." The epidemiological context adjustor based on surveillance of recent cases of meningitis allowed the correct identification of an additional 47 cases (7%) of aseptic meningitis without missing any additional cases of bacterial meningitis. The epidemiological context adjustor was validated, showing significance in 84% of 1,000 bootstrap samples. CONCLUSION: Epidemiological contextual information can improve the performance of a clinical prediction rule. We provide a methodological framework for leveraging regional surveillance data to improve medical decision-making.

Child↗
PubMed2006

An unsupervised classification method for inferring original case locations from low-resolution disease maps.

BACKGROUND: Widespread availability of geographic information systems software has facilitated the use of disease mapping in academia, government and private sector. Maps that display the address of affected patients are often exchanged in public forums, and published in peer-reviewed journal articles. As previously reported, a search of figure legends in five major medical journals found 19 articles from 1994-2004 that identify over 19,000 patient addresses. In this report, a method is presented to evaluate whether patient privacy is being breached in the publication of low-resolution disease maps. RESULTS: To demonstrate the effect, a hypothetical low-resolution map of geocoded patient addresses was created and the accuracy with which patient addresses can be resolved is described. Through georeferencing and unsupervised classification of the original image, the method precisely re-identified 26% (144/550) of the patient addresses from a presentation quality map and 79% (432/550) from a publication quality map. For the presentation quality map, 99.8% of the addresses were within 70 meters (approximately one city block length) of the predicted patient location, 51.6% of addresses were identified within five buildings, 70.7% within ten buildings and 93% within twenty buildings. For the publication quality map, all addresses were within 14 meters and 11 buildings of the predicted patient location. CONCLUSION: This study demonstrates that lowering the resolution of a map displaying geocoded patient addresses does not sufficiently protect patient addresses from re-identification. Guidelines to protect patient privacy, including those of medical journals, should reflect policies that ensure privacy protection when spatial data are displayed or published.

Boston↗
PubMed2006

Privacy protection versus cluster detection in spatial epidemiology.

OBJECTIVES: Patient data that includes precise locations can reveal patients' identities, whereas data aggregated into administrative regions may preserve privacy and confidentiality. We investigated the effect of varying degrees of address precision (exact latitude and longitude vs the center points of zip code or census tracts) on detection of spatial clusters of cases. METHODS: We simulated disease outbreaks by adding supplementary spatially clustered emergency department visits to authentic hospital emergency department syndromic surveillance data. We identified clusters with a spatial scan statistic and evaluated detection rate and accuracy. RESULTS: More clusters were identified, and clusters were more accurately detected, when exact locations were used. That is, these clusters contained at least half of the simulated points and involved few additional emergency department visits. These results were especially apparent when the synthetic clustered points crossed administrative boundaries and fell into multiple zip code or census tracts. CONCLUSIONS: The spatial cluster detection algorithm performed better when addresses were analyzed as exact locations than when they were analyzed as center points of zip code or census tracts, particularly when the clustered points crossed administrative boundaries. Use of precise addresses offers improved performance, but this practice must be weighed against privacy concerns in the establishment of public health data exchange policies.

Algorithms↗
PubMed2006

A Bayesian dynamic model for influenza surveillance.

The severe acute respiratory syndrome (SARS) epidemic, the growing fear of an influenza pandemic and the recent shortage of flu vaccine highlight the need for surveillance systems able to provide early, quantitative predictions of epidemic events. We use dynamic Bayesian networks to discover the interplay among four data sources that are monitored for influenza surveillance. By integrating these different data sources into a dynamic model, we identify in children and infants presenting to the pediatric emergency department with respiratory syndromes an early indicator of impending influenza morbidity and mortality. Our findings show the importance of modelling the complex dynamics of data collected for influenza surveillance, and suggest that dynamic Bayesian networks could be suitable modelling tools for developing epidemic surveillance systems.

Bayes Theorem↗
PubMed2006

Lumbar puncture ordering and results in the pediatric population: a promising data source for surveillance systems.

BACKGROUND: The Centers for Disease Control and Prevention is incorporating laboratory data into real-time surveillance systems. When normal patterns of laboratory test orders and results are modeled, aberrations can be detected. Because many test orders are available electronically well before results, atypical patterns of test ordering may signal outbreaks. OBJECTIVES: The authors sought to characterize baseline patterns in the ordering and early results of lumbar punctures, motivated by the possibility of using these data for real-time surveillance for early detection of meningitis or encephalitis outbreaks. METHODS: Retrospective cohorts of pediatric emergency department patients at a single hospital (1993-2003) and from the National Hospital and Ambulatory Medical Care Survey (1992-2000) were used for analysis. RESULTS: Test ordering exhibits seasonal patterns, with monthly peaks in January and August (p < 0.0001). For the hospital cohort, the rate of cerebrospinal fluid pleocytosis exhibits seasonal patterns (p < 0.0001), with a peak from August to October. This is strongly associated with the rate and pattern of clinical neurologic disease (p < 0.0001). A long-term secular decline in daily test ordering is evident, dropping from 5.3 to 2.9 in the hospital sample, and from 371.8 to 185.3 in the national sample (p < 0.001). The long-term rate of pleocytosis has declined (p < 0.0001), though the yield of testing for pleocytosis has improved (p = 0.0104). CONCLUSIONS: Laboratory test patterns correspond with those of clinical disease and are a promising source of surveillance data. Using such data for real-time monitoring requires specific adjustments for patient age, periodicities, and secular trends.

Adolescent↗
PubMed2006

A susceptible-infected model of early detection of respiratory infection outbreaks on a background of influenza.

The threat of biological warfare and the emergence of new infectious agents spreading at a global scale have highlighted the need for major enhancements to the public health infrastructure. Early detection of epidemics of infectious diseases requires both real-time data and real-time interpretation of data. Despite moderate advancements in data acquisition, the state of the practice for real-time analysis of data remains inadequate. We present a nonlinear mathematical framework for modeling the transient dynamics of influenza, applied to historical data sets of patients with influenza-like illness. We estimate the vital time-varying epidemiological parameters of infections from historical data, representing normal epidemiological trends. We then introduce simulated outbreaks of different shapes and magnitudes into the historical data, and estimate the parameters representing the infection rates of anomalous deviations from normal trends. Finally, a dynamic threshold-based detection algorithm is devised to assess the timeliness and sensitivity of detecting the irregularities in the data, under a fixed low false-positive rate. We find that the detection algorithm can identify such designated abnormalities in the data with high sensitivity with specificity held at 97%, but more importantly, early during an outbreak. The proposed methodology can be applied to a broad range of influenza-like infectious diseases, whether naturally occurring or a result of bioterrorism, and thus can be an integral component of a real-time surveillance system.

Algorithms↗
PubMed2006

Validation of syndromic surveillance for respiratory infections.

STUDY OBJECTIVE: A key public health question is whether syndromic surveillance data provide early warning of infectious outbreaks. One cause for skepticism is that biological correlates of the administrative and clinical data used in these systems have not been rigorously assessed. This study measures the value of respiratory data currently used in syndromic surveillance systems to detect respiratory infections by comparing it against criterion standard viral testing within a pediatric population. METHODS: We conducted a longitudinal study with prospective validation in the emergency department (ED) of a tertiary care children's hospital. Children aged 7 years or younger who presented with a respiratory syndrome or who were tested for respiratory syncytial virus (RSV), influenza virus, parainfluenza virus, adenovirus, or enterovirus between January 1993 and June 2004 were included. We assessed the predictive ability of the viral tests by fitting generalized linear models to respiratory syndrome counts. RESULTS: Of 582,635 patient visits, 89,432 (15.4%) were for respiratory syndromes, and of these, 7,206 (8.1%) patients were tested for the viruses of interest. RSV was significantly related to respiratory syndrome counts (adjusted rate ratio [RR] 1.33; 95% confidence interval [CI] 1.04 to 1.71). In multivariate models including all viruses tested, influenza virus was also a significant predictor of respiratory syndrome counts (RR 1.47; 95% CI 1.03 to 2.10). This model accounted for 81.6% of the observed variability in respiratory syndrome counts. CONCLUSION: Respiratory syndromic surveillance data strongly correlate with virologic test results in a pediatric population, providing evidence of the biologic validity of such surveillance systems. Real-time outbreak detection systems relying on syndromic data may be an important adjunct to the current set of public health systems for the detection and surveillance of respiratory infections.

Boston↗
PubMed2006

Empirical evidence for the effect of airline travel on inter-regional influenza spread in the United States.

BACKGROUND: The influence of air travel on influenza spread has been the subject of numerous investigations using simulation, but very little empirical evidence has been provided. Understanding the role of airline travel in large-scale influenza spread is especially important given the mounting threat of an influenza pandemic. Several recent simulation studies have concluded that air travel restrictions may not have a significant impact on the course of a pandemic. Here, we assess, with empirical data, the role of airline volume on the yearly inter-regional spread of influenza in the United States. METHODS AND FINDINGS: We measured rate of inter-regional spread and timing of influenza in the United States for nine seasons, from 1996 to 2005 using weekly influenza and pneumonia mortality from the Centers for Disease Control and Prevention. Seasonality was characterized by band-pass filtering. We found that domestic airline travel volume in November (mostly surrounding the Thanksgiving holiday) predicts the rate of influenza spread (r(2) = 0.60; p = 0.014). We also found that international airline travel influences the timing of influenza mortality (r(2) = 0.59; p = 0.016). The flight ban in the US after the terrorist attack on September 11, 2001, and the subsequent depression of the air travel market, provided a natural experiment for the evaluation of flight restrictions; the decrease in air travel was associated with a delayed and prolonged influenza season. CONCLUSIONS: We provide the first empirical evidence for the role of airline travel in long-range dissemination of influenza. Our results suggest an important influence of international air travel on the timing of influenza introduction, as well as an influence of domestic air travel on the rate of inter-regional influenza spread in the US. Pandemic preparedness strategies should account for a possible benefit of airline travel restrictions on influenza spread.

Aircraft↗
PubMed2006

Influenza and other respiratory virus-related emergency department visits among young children.

BACKGROUND: Influenza and other winter respiratory viruses cause substantial morbidity among children. Previous estimates of the burden of illness of these viruses have neglected to include the emergency department, where a large number of patients seek acute care for respiratory illnesses. This study provides city- and statewide population estimates of the burden of illness attributable to respiratory viruses for children receiving emergency department-based care for respiratory infections during the winter months. METHODS: The number of patients < or = 7 years of age presenting to the emergency department of an urban tertiary care pediatric hospital with acute respiratory infections was estimated by using a classifier based on presenting complaints. The rates of specific viral infections in this population were estimated by using the rates of positivity for respiratory syncytial virus, influenza virus, parainfluenza virus, adenovirus, and enterovirus. Local emergency department market share and US Census data enabled determination of the rates of emergency department visits in the Boston, Massachusetts, area and in Massachusetts. RESULTS: During the 11-year study period, the mean yearly number of patients < or = 7 years of age presenting to the study emergency department during the winter season was 17397. On the basis of the respiratory classifier, the mean number of patients with an acute respiratory infection was 6923, or 398 per 1000 emergency department visits. In the city population, the mean number of emergency department visits for acute respiratory infections was 17906, which is equivalent to 113.9 per 1000 children residing in the city, and in the state population the mean number was 61529, or 94.5 per 1000 children residing in the state. At the state level, 23114 of the visits were for respiratory syncytial virus, 5650 for influenza, 1751 for parainfluenza virus, 2848 for adenovirus, and 798 for enterovirus. For patients 6 to 23 months of age in the state population, there were 19860 emergency department visits for acute respiratory infections, or 168 per 1000 children in this age group, with 6235 visits resulting from respiratory syncytial virus and 2112 resulting from influenza. CONCLUSION: There is a high incidence of emergency department visits for infectious respiratory illnesses among children. This important component of health care use should be included in estimates of the burden of illness attributable to influenza and other winter respiratory viruses.

Boston↗
PubMed2006

Incorporating vaccine-preventable disease surveillance into the National Health Information Network: leveraging children's hospitals.

OBJECTIVE: Development of national biosurveillance systems to advance regional and national data exchange among sites of clinical care and public health authorities is a top federal priority, creating the opportunity to develop a unified national network for tracking and responding to cases of vaccine-preventable diseases. The purpose of this study was to assess the current practice and feasibility of developing a nationwide network of children's hospitals to conduct surveillance for vaccine preventable diseases. METHODS: In 2004-2005, Web-based surveys were sent to 506 key hospital personnel from 119 pediatric hospitals, identified by the National Association of Children's Hospitals and Related Institutions. Surveys measured attitudes toward public health initiatives, willingness to join a surveillance network of children's hospitals, knowledge of mandated reporting requirements, methods of disease detection and reporting, and data sources available for surveillance. RESULTS: A total of 395 (78%) respondents from 119 hospitals completed the survey. Surveillance at pediatric hospitals is largely passive and driven by unreimbursed efforts of infection control staff. It is vulnerable to missing cases that occur in the outpatient setting and are diagnosed clinically without laboratory confirmation or are never diagnosed by clinicians. Nearly 90% of hospital leaders are interested in participating in public health programs, and most are interested in a national network to conduct active surveillance for vaccine-preventable diseases, dependent on the provision of sufficient funding. Pediatric hospitals store records relevant to surveillance in an electronic fashion accessible to query, but <20% of these hospitals use automated methods to report cases of disease. CONCLUSIONS: There is both the will and capability to create a robust active pediatric hospital-based reporting system for vaccine preventable diseases. This effort would dovetail well with the national priority to bolster surveillance, as well as with the goal of reducing morbidity and mortality from vaccine-preventable diseases.

Child↗
PubMed2005

A context-sensitive approach to anonymizing spatial surveillance data: impact on outbreak detection.

OBJECTIVE: The use of spatially based methods and algorithms in epidemiology and surveillance presents privacy challenges for researchers and public health agencies. We describe a novel method for anonymizing individuals in public health data sets by transposing their spatial locations through a process informed by the underlying population density. Further, we measure the impact of the skew on detection of spatial clustering as measured by a spatial scanning statistic. DESIGN: Cases were emergency department (ED) visits for respiratory illness. Baseline ED visit data were injected with artificially created clusters ranging in magnitude, shape, and location. The geocoded locations were then transformed using a de-identification algorithm that accounts for the local underlying population density. MEASUREMENTS: A total of 12,600 separate weeks of case data with artificially created clusters were combined with control data and the impact on detection of spatial clustering identified by a spatial scan statistic was measured. RESULTS: The anonymization algorithm produced an expected skew of cases that resulted in high values of data set k-anonymity. De-identification that moves points an average distance of 0.25 km lowers the spatial cluster detection sensitivity by less than 4% and lowers the detection specificity less than 1%. CONCLUSION: A population-density-based Gaussian spatial blurring markedly decreases the ability to identify individuals in a data set while only slightly decreasing the performance of a standardly used outbreak detection tool. These findings suggest new approaches to anonymizing data for spatial epidemiology and surveillance.

Algorithms↗
PubMed2005

Identifying pediatric age groups for influenza vaccination using a real-time regional surveillance system.

Evidence is accumulating that universal vaccination of schoolchildren would reduce the transmission of influenza. The authors sought to identify target age groups within the pediatric population that develop influenza the earliest and are most strongly linked with mortality in the population. Patient visits for respiratory illness were monitored, using real-time syndromic surveillance systems, in six Massachusetts health-care settings, including ambulatory care sites and emergency departments at tertiary-care and community hospitals. Visits from January 1, 2000, to September 30, 2004, were segmented into age group subpopulations. Timeliness and prediction of each subpopulation were measured against pneumonia and influenza mortality in New England with time-series analyses and regression models. Study results show that patient age significantly influences timeliness (p = 0.026), with pediatric age groups arriving first (p < 0.001); children aged 3-4 years are consistently the earliest (p = 0.0058). Age also influences the degree of prediction of mortality (p = 0.036), with illness among children under age 5 years, compared with all other patients, most strongly associated with mortality (p < 0.001). Study findings add to a growing body of support for a strategy to vaccinate children older than the currently targeted age of 6-23 months and specifically suggest that there may be value in vaccinating preschool-age children.

Adolescent↗
PubMed2005

A software tool for creating simulated outbreaks to benchmark surveillance systems.

BACKGROUND: Evaluating surveillance systems for the early detection of bioterrorism is particularly challenging when systems are designed to detect events for which there are few or no historical examples. One approach to benchmarking outbreak detection performance is to create semi-synthetic datasets containing authentic baseline patient data (noise) and injected artificial patient clusters, as signal. METHODS: We describe a software tool, the AEGIS Cluster Creation Tool (AEGIS-CCT), that enables users to create simulated clusters with controlled feature sets, varying the desired cluster radius, density, distance, relative location from a reference point, and temporal epidemiological growth pattern. AEGIS-CCT does not require the use of an external geographical information system program for cluster creation. The cluster creation tool is an open source program, implemented in Java and is freely available under the Lesser GNU Public License at its Sourceforge website. Cluster data are written to files or can be appended to existing files so that the resulting file will include both existing baseline and artificially added cases. Multiple cluster file creation is an automated process in which multiple cluster files are created by varying a single parameter within a user-specified range. To evaluate the output of this software tool, sets of test clusters were created and graphically rendered. RESULTS: Based on user-specified parameters describing the location, properties, and temporal pattern of simulated clusters, AEGIS-CCT created clusters accurately and uniformly. CONCLUSION: AEGIS-CCT enables the ready creation of datasets for benchmarking outbreak detection systems. It may be useful for automating the testing and validation of spatial and temporal cluster detection algorithms.

Algorithms↗
PubMed2005

Real time spatial cluster detection using interpoint distances among precise patient locations.

BACKGROUND: Public health departments in the United States are beginning to gain timely access to health data, often as soon as one day after a visit to a health care facility. Consequently, new approaches to outbreak surveillance are being developed. When cases cluster geographically, an analysis of their spatial distribution can facilitate outbreak detection. Our method focuses on detecting perturbations in the distribution of pair-wise distances among all patients in a geographical region. Barring outbreaks, this distribution can be quite stable over time. We sought to exemplify the method by measuring its cluster detection performance, and to determine factors affecting sensitivity to spatial clustering among patients presenting to hospital emergency departments with respiratory syndromes. METHODS: The approach was to (1) define a baseline spatial distribution of home addresses for a population of patients visiting an emergency department with respiratory syndromes using historical data; (2) develop a controlled feature set simulation by inserting simulated outbreak data with varied parameters into authentic background noise, thereby creating semisynthetic data; (3) compare the observed with the expected spatial distribution; (4) establish the relative value of different alarm strategies so as to maximize sensitivity for the detection of clustering; and (5) measure factors which have an impact on sensitivity. RESULTS: Overall sensitivity to detect spatial clustering was 62%. This contrasts with an overall alarm rate of less than 5% for the same number of extra visits when the extra visits were not characterized by geographic clustering. Clusters that produced the least number of alarms were those that were small in size (10 extra visits in a week, where visits per week ranged from 120 to 472), diffusely distributed over an area with a 3 km radius, and located close to the hospital (5 km) in a region most densely populated with patients to this hospital. Near perfect alarm rates were found for clusters that varied on the opposite extremes of these parameters (40 extra visits, within a 250 meter radius, 50 km from the hospital). CONCLUSION: Measuring perturbations in the interpoint distance distribution is a sensitive method for detecting spatial clustering. When cases are clustered geographically, there is clearly power to detect clustering when the spatial distribution is represented by the M statistic, even when clusters are small in size. By varying independent parameters of simulated outbreaks, we have demonstrated empirically the limits of detection of different types of outbreaks.

Ambulatory Care↗
PubMed2005

Wireless technology infrastructures for authentication of patients: PKI that rings.

As the public interest in consumer-driven electronic health care applications rises, so do concerns about the privacy and security of these applications. Achieving a balance between providing the necessary security while promoting user acceptance is a major obstacle in large-scale deployment of applications such as personal health records (PHRs). Robust and reliable forms of authentication are needed for PHRs, as the record will often contain sensitive and protected health information, including the patient's own annotations. Since the health care industry per se is unlikely to succeed at single-handedly developing and deploying a large scale, national authentication infrastructure, it makes sense to leverage existing hardware, software, and networks. This report proposes a new model for authentication of users to health care information applications, leveraging wireless mobile devices. Cell phones are widely distributed, have high user acceptance, and offer advanced security protocols. The authors propose harnessing this technology for the strong authentication of individuals by creating a registration authority and an authentication service, and examine the problems and promise of such a system.

Cell Phone↗