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Cassa, Grannis, Overhage, Mandl. A context-sensitive approach to anonymizing spatial surveillance data: impact on outbreak detection. J Am Med Inform Assoc. 2006;13:160–5.
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.

B

Dasgupta, Mandl, Brownstein. Breaking the news or fueling the epidemic? Temporal association between news media report volume and opioid-related mortality. PLoS One. 2009;4:e7758.
BACKGROUND: Historical studies of news media have suggested an association between reporting and increased drug abuse. Period effects for substance use have been documented for different classes of legal and illicit substances, with the suspicion that media publicity may have played major roles in their emergence. Previous analyses have drawn primarily from qualitative evidence; the temporal relationship between media reporting volume and adverse health consequences has not been quantified nationally. We set out to explore whether we could find a quantitative relationship between media reports about prescription opioid abuse and overdose mortality associated with these drugs. We assessed whether increases in news media reports occurred before or after increases in overdose deaths. METHODOLOGY/PRINCIPAL FINDINGS: Our ecological study compared a monthly time series of unintentional poisoning deaths involving short-acting prescription opioid substances, from 1999 to 2005 using multiple cause-of-death data published by the National Center for Health Statistics, to monthly counts of English-language news articles mentioning generic and branded names of prescription opioids obtained from Google News Archives from 1999 to 2005. We estimated the association between media volume and mortality rates by time-lagged regression analyses. There were 24,272 articles and 30,916 deaths involving prescription opioids during the seven-year study period. Nationally, the number of articles mentioning prescription opioids increased dramatically starting in early 2001, following prominent coverage about the nonmedical use of OxyContin. We found a significant association between news reports and deaths, with media reporting preceding fatal opioid poisonings by two to six months and explaining 88% (p0.0001, df 78) of the variation in mortality. CONCLUSIONS/SIGNIFICANCE: While availability, structural, and individual predispositions are key factors influencing substance use, news reporting may enhance the popularity of psychoactive substances. Albeit ecological in nature, our finding suggests the need for further evaluation of the influence of news media on health. Reporting on prescription opioids conforms to historical patterns of news reporting on other psychoactive substances.
Sebastiani, Mandl, Szolovits, Kohane, Ramoni. A Bayesian dynamic model for influenza surveillance. Stat Med. 2006;25:1803–16; discussion 1817.
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.

A

Sunyaev, Dehling, Taylor, Mandl. Availability and quality of mobile health app privacy policies. J Am Med Inform Assoc. 2014.

Mobile health (mHealth) customers shopping for applications (apps) should be aware of app privacy practices so they can make informed decisions about purchase and use. We sought to assess the availability, scope, and transparency of mHealth app privacy policies on iOS and Android. Over 35 000 mHealth apps are available for iOS and Android. Of the 600 most commonly used apps, only 183 (30.5%) had privacy policies. Average policy length was 1755 (SD 1301) words with a reading grade level of 16 (SD 2.9). Two thirds (66.1%) of privacy policies did not specifically address the app itself. Our findings show that currently mHealth developers often fail to provide app privacy policies. The privacy policies that are available do not make information privacy practices transparent to users, require college-level literacy, and are often not focused on the app itself. Further research is warranted to address why privacy policies are often absent, opaque, or irrelevant, and to find a remedy.

Wieland, Brownstein, Berger, Mandl. Automated real time constant-specificity surveillance for disease outbreaks. BMC Med Inform Decis Mak. 2007;7:15.
BACKGROUND: For real time surveillance, detection of abnormal disease patterns is based on a difference between patterns observed, and those predicted by models of historical data. The usefulness of outbreak detection strategies depends on their specificity; the false alarm rate affects the interpretation of alarms. RESULTS: We evaluate the specificity of five traditional models: autoregressive, Serfling, trimmed seasonal, wavelet-based, and generalized linear. We apply each to 12 years of emergency department visits for respiratory infection syndromes at a pediatric hospital, finding that the specificity of the five models was almost always a non-constant function of the day of the week, month, and year of the study (p 0.05). We develop an outbreak detection method, called the expectation-variance model, based on generalized additive modeling to achieve a constant specificity by accounting for not only the expected number of visits, but also the variance of the number of visits. The expectation-variance model achieves constant specificity on all three time scales, as well as earlier detection and improved sensitivity compared to traditional methods in most circumstances. CONCLUSION: Modeling the variance of visit patterns enables real-time detection with known, constant specificity at all times. With constant specificity, public health practitioners can better interpret the alarms and better evaluate the cost-effectiveness of surveillance systems.
Bourgeois, Olson, Ioannidis, Mandl. Association between pediatric clinical trials and global burden of disease. Pediatrics. 2014;133:78–87.
BACKGROUND: The allocation of research resources should favor conditions responsible for the greatest disease burden. This is particularly important in pediatric populations, which have been underrepresented in clinical research. Our aim was to measure the association between the focus of pediatric clinical trials and burden of disease and to identify neglected clinical domains. METHODS: We performed a cross-sectional study of clinical trials by using trial records in ClinicalTrials.gov. All trials started in 2006 or after and studying patient-level interventions in pediatric populations were included. Age-specific measures of disease burden were obtained for 21 separate conditions for high-, middle-, and low-income countries. We measured the correlation between number of pediatric clinical trials and disease burden for each condition. RESULTS: Neuropsychiatric conditions and infectious diseases were the most studied conditions globally in terms of number of trials (874 and 847 trials, respectively), while intentional injuries (5 trials) and maternal conditions (4 trials) were the least studied. Clinical trials were only moderately correlated with global disease burden (r = 0.58, P = .006). Correlations were also moderate within each of the country income levels, but lowest in low-income countries (r = .47, P = .03). Globally, the conditions most understudied relative to disease burden were injuries (-260 trials for unintentional injuries and -160 trials for intentional injuries), nutritional deficiencies (-175 trials), and respiratory infections (-171 trials). CONCLUSIONS: Pediatric clinical trial activity is only moderately associated with pediatric burden of disease, and least associated in low-income countries. The mismatch between clinical trials and disease burden identifies key clinical areas for focus and investment.
D’Amore, Mandel, Kreda, Swain, Koromia, Sundareswaran, Alschuler, Dolin, Mandl, Kohane, et al. Are Meaningful Use Stage 2 certified EHRs ready for interoperability? Findings from the SMART C-CDA Collaborative. J Am Med Inform Assoc. 2014.

BACKGROUND AND OBJECTIVE: Upgrades to electronic health record (EHR) systems scheduled to be introduced in the USA in 2014 will advance document interoperability between care providers. Specifically, the second stage of the federal incentive program for EHR adoption, known as Meaningful Use, requires use of the Consolidated Clinical Document Architecture (C-CDA) for document exchange. In an effort to examine and improve C-CDA based exchange, the SMART (Substitutable Medical Applications and Reusable Technology) C-CDA Collaborative brought together a group of certified EHR and other health information technology vendors. MATERIALS AND METHODS: We examined the machine-readable content of collected samples for semantic correctness and consistency. This included parsing with the open-source BlueButton.js tool, testing with a validator used in EHR certification, scoring with an automated open-source tool, and manual inspection. We also conducted group and individual review sessions with participating vendors to understand their interpretation of C-CDA specifications and requirements. RESULTS: We contacted 107 health information technology organizations and collected 91 C-CDA sample documents from 21 distinct technologies. Manual and automated document inspection led to 615 observations of errors and data expression variation across represented technologies. Based upon our analysis and vendor discussions, we identified 11 specific areas that represent relevant barriers to the interoperability of C-CDA documents. CONCLUSIONS: We identified errors and permissible heterogeneity in C-CDA documents that will limit semantic interoperability. Our findings also point to several practical opportunities to improve C-CDA document quality and exchange in the coming years.

Murthy, Mandl, Bourgeois. Analysis of pediatric clinical drug trials for neuropsychiatric conditions. Pediatrics. 2013;131:1125–31.
BACKGROUND AND OBJECTIVE: Neuropsychiatric conditions represent a large and increasing disease burden in children. A number of drugs are available for the treatment of these conditions, but most drugs have not been adequately tested in children, and off-label drug use remains widespread. We sought to define and quantify recent and ongoing clinical research on the use of neuropsychiatric drugs in children. METHODS: Drug trials registered in ClinicalTrials.gov between 2006 and 2011 and studying neuropsychiatric conditions were selected and classified based on the drug's Food and Drug Administration (FDA) approval status in children. We measured the proportion of trials seeking to expand the use of a drug to pediatric patients and the proportion of available drugs studied in children. RESULTS: Only 10% of neuropsychiatric trials focused on children. Of 303 drugs studied in both pediatric and adult populations, 90% lacked FDA approval in children and 97% were not approved in children for the indication studied. However, only 19% of all neuropsychiatric drugs were under study in pediatric populations, with as few as 8% of either antidepressant or antipsychotic drugs. Overall, 76% of pediatric drug trials examined a drug previously unapproved in children and 26% explored the use of a drug for a new indication. CONCLUSIONS: Despite the rising prevalence of neuropsychiatric disease and the paucity of FDA-approved pediatric drugs, only a small proportion of trials focus on pediatric populations and these trials cover only a fraction of available drugs. This deficiency is most pronounced for depression and schizophrenia.
Reis, Kirby, Hadden, Olson, McMurry, Daniel, Mandl. AEGIS: a robust and scalable real-time public health surveillance system. J Am Med Inform Assoc. 2007;14:581–8.
In this report, we describe the Automated Epidemiological Geotemporal Integrated Surveillance system (AEGIS), developed for real-time population health monitoring in the state of Massachusetts. AEGIS provides public health personnel with automated near-real-time situational awareness of utilization patterns at participating healthcare institutions, supporting surveillance of bioterrorism and naturally occurring outbreaks. As real-time public health surveillance systems become integrated into regional and national surveillance initiatives, the challenges of scalability, robustness, and data security become increasingly prominent. A modular and fault tolerant design helps AEGIS achieve scalability and robustness, while a distributed storage model with local autonomy helps to minimize risk of unauthorized disclosure. The report includes a description of the evolution of the design over time in response to the challenges of a regional and national integration environment.
Bourgeois, Shannon, Valim, Mandl. Adverse drug events in the outpatient setting: an 11-year national analysis. Pharmacoepidemiol Drug Safe. 2010;19:901–10.
PURPOSE: Adverse drug events (ADEs) are a common complication of medical care resulting in high morbidity and medical expenditure. Population level estimates of outpatient ADEs are limited. Our objective was to provide national estimates and characterizations of outpatient ADEs and determine risk factors associated with these events. METHODS: Data are from the National Center for Health Statistics which collects information on patient visits to outpatient clinics and emergency departments throughout the United States. We examined visits between 1995 and 2005 and measured the national annual estimates of and risk factors for outpatient ADEs requiring medical treatment. RESULTS: The national annual number of ADE-related visits was 4 335,990 (95%CI: 4 326 872-4 345 108). Visits for ADEs to outpatient clinics increased over the study period from 9.0 to 17.0 per 1000 persons (p-value for trend 0.001). In multivariate analyses, factors associated with ADE visits included patient age (OR: 2.13; 95%CI: 1.63-2.79 for 65 years and older), number of medications taken by patient (OR: 1.88; 95%CI: 1.58-2.25 for five medications or more), and female gender (OR: 1.51; 95%CI: 1.34-1.71). Overall, outpatient ADEs resulted in 107,468 (95%CI: 89 011-125 925) hospital admissions annually, with older patients at highest risk for hospitalization (p-value for trend 0.001). CONCLUSIONS: Both patient age and polypharmacy use are risk factors for ADE-related healthcare visits, which have substantially increased in outpatient clinics between 1995 and 2005. The incidence of ADEs has particularly increased among patients 65 years and older with as many as 1 in 20 persons seeking medical care for an ADE.