Person:

Olson, Karen

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.

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.

This study aimed to comprehensively describe prevalence and temporal dispensing patterns for medications prescribed to children and adolescents in the United States. Participants were 1.6 million children (49% female) under 18 years old enrolled in a nation-wide, employer-provided insurance plan. All medication claims from 1999–2006 were reviewed retrospectively. Drugs were assigned to 16 broad therapeutic categories. Effects of trend over time, seasonality, age and gender on overall and within category prevalence were examined. Results: Mean monthly prevalence for dispensed medications was 23.5% (range 19.4–27.5), with highest rates in winter and lowest in July. The age group with the highest prevalence was one-year-old children. On average each month, 17.1% of all children were dispensed a single drug and 6.4% were dispensed two or more. Over time, prevalence for two or more drugs did not change, but the proportion of children dispensed a single drug decreased (slope -.02%, p = .001). Overall, boys had higher monthly rates than girls (average difference 0.9%, p = .002). However, differences by gender were greatest during middle childhood, especially for respiratory and central nervous system agents. Contraceptives accounted for a large proportion of dispensed medication to older teenage girls. Rates for the drugs with the highest prevalence in this study were moderately correlated (average Pearson r.66) with those from a previously published national survey. Conclusion: On average, nearly one quarter of a population of insured children in the United States was dispensed medication each month. This rate decreased somewhat over time, primarily because proportionally fewer children were dispensed a single medication. The rate for two or more drugs dispensed simultaneously remained steady.

Background: Personally controlled health records (PCHRs) are accessible over the Internet and allow individuals to maintain and manage a secure copy of their medical data. These records provide a new opportunity to provide customized health recommendations to individuals based on their record content. Health promotion programs using PCHRs can potentially be used in a variety of settings and target a large range of health issues. Objectives: The aim was to assess the value of a PCHR in an employee health promotion program for improving knowledge, beliefs, and behavior around influenza prevention. Methods: We evaluated a PCHR-based employee health promotion program using a randomized controlled trial design. Employees at Hewlett Packard work sites who reported reliable Internet access and email use at least once every 2 days were recruited for participation. PCHRs were provided to all participants for survey administration, and tailored, targeted health messages on influenza illness and prevention were delivered to participants in the intervention group. Participants in the control group received messages addressing cardiovascular health and sun protection. The main outcome measure was improvement in knowledge, beliefs, and behavior around influenza prevention. Secondary outcomes were influenza vaccine rates among household members, the impact of cardiovascular health and sun protection messages on the control group, and the usability and utility of the PCHR-based program for employees. Results: The intervention did not have a statistically significant effect on the influenza knowledge elements we assessed but did impact certain beliefs surrounding influenza. Participants in the intervention group were more likely to believe that the influenza vaccine was effective (OR = 5.6; 95% CI = 1.7-18.5), that there were actions they could take to prevent the flu (OR = 3.2; 95% CI = 1.1-9.2), and that the influenza vaccine was unlikely to cause a severe reaction (OR = 4.4; 95% CI = 1.3-15.3). Immunization rates did not differ between the intervention and control groups. However, participants in the intervention group were more likely to stay home during an infectious respiratory illness compared with participants in the control group (39% [16/41] vs 14% [5/35], respectively; P = .02). The program also succeeded in improving recognition of the signs of heart attack and stroke among participants in the control group. Overall, 78% of participants rated the PCHR as “extremely/very” easy to use, and 73% responded that they would be “extremely/very” likely to participate again in a PCHR-based health promotion system such as this one. Conclusions: With a small sample size, this study identified a modest impact of a PCHR-based employee health program on influenza prevention and control. Employees found the PCHR acceptable and easy to use, suggesting that it should be explored as a common medium for health promotion in the workplace.

Purpose: Physicians require information on the comparative benefits and harms of medications for optimal treatment decisions. However, this type of data is limited, especially for pediatric patients. Objective: Our aim was to use observational data to measure and compare medication utilization patterns in a pediatric patient population. Methods: Using pharmacy claims data from a large, national-scale insurance program in the US, we identified all patients with a diagnosis of epilepsy treated with a first-generation (carabamazepine, ethosuximide, phenobarbital, phenytoin, valproate) or second-generation (carbamazepine XR, gabapentin, lamotrigine, levetiracetam, oxcarbazepine, tiagabine, topiramate, valproate XR, zonisamide) antiepileptic drug. Treatment periods were defined based on prescription fill dates and medication days supplied. Medication use was measured for individual antiepileptic drugs and for first-generation and second-generation drugs as groups. Results: There were 2527 (54%) patients who initiated therapy with first-generation and 2139 (46%) with second-generation antiepileptics. First- and second-generation drugs had the same one-year retention rates (26% [95%CI 24–28] and 26% [95%CI 25–28], respectively). A total of 26% (95%CI 25–28) and 29% (95%CI 27–31) of patients who started on a first- or second-generation antiepileptic medication, respectively, resumed treatment with the initial drug after discontinuation. Overall, 73% (95%CI 71–74) of patients were treated with only one antiepileptic drug, with similar rates for patients started on first- and second-generation drugs (71% [95%CI 69–73] vs 74% [95%CI 72–76]). Conclusions: Comparing drug utilization patterns in a pediatric population using observational data, we found similar rates of retention and therapeutic changes. These findings are consistent with available comparative data and demonstrate an approach that could be extended to other drug classes and conditions in pediatric populations to examine drug effectiveness.

Background: Elderly patients represent the greatest consumers of healthcare per capita but have historically been underrepresented in clinical trials. It is unknown how many trials are designed to focus exclusively on elderly patients. Objective: To define the prevalence of interventional trials that study exclusively elderly persons and describe the characteristics of these trials, including their distribution across conditions most prevalent in the elderly. Design: All interventional clinical trials enrolling exclusively elderly patients (≥65 years), conducted primarily in high-income countries, and initiated between 2006 and 2014, identified through ClincialTrials.gov. Main Measures Trials were identified and characterized according to design features and disease categories studied. Across disease categories we examined the burden of disease in the elderly in high-income countries (measured in disability-adjusted life years [DALYs]) and compared to the number of trials conducted exclusively in the elderly. Results: Among 80,965 interventional trials, 1,112 (1.4%) focused on elderly patients. Diverse types of interventions were studied in these trials (medications 33%, behavioral interventions 18%, and dietary supplements 10%) and the majority was funded by non-profit organizations (81%). Studies tended to be small (median sample size 122 participants [IQR 58, 305]), single-center studies (67%). Only 43% of 126 disease categories affecting elderly persons were studied in trials focused on the elderly. Among these disease categories, there was a 5162-fold range in the ratio of DALYs per trial. Across 5 conditions where over 80% of DALYs are in the elderly, there were a total of only 117 trials done exclusively in the elderly. Conclusions: Very few and mostly small studies are conducted exclusively in elderly persons, even for conditions that affect almost exclusively the elderly.