# Publications

2015
Murthy S, Mandl KD, Bourgeois FT. Industry-sponsored clinical research outside high-income countries: an empirical analysis of registered clinical trials from 2006 to 2013. Health Res Policy Syst. 2015;13 :28.Abstract

BACKGROUND: Industry-sponsored clinical trials, in the past performed almost exclusively in more developed countries, now often recruit participants globally. However, recruitment from outside high-income countries may not represent the ultimate target population for the intervention. Clinical trial registries provide an opportunity to quantify and examine the type of clinical research performed in various geographic regions. We sought to characterize industry-sponsored randomized controlled trials conducted in high-income countries and to compare these trials to those performed outside high-income countries. METHODS: Clinical trial data on all industry-funded randomized controlled trials conducted between 2006 and 2014 were obtained from the registry ClinicalTrials.gov. Trials were classified according to their study sites as conducted in high or non-high income countries, and data on trial characteristics were collected. RESULTS: Of 22,511 relevant trials, a total of 6,085 (27.0 %) trials included study sites outside a high-income country, and 2,045 (9.1 %) were conducted exclusively outside high-income countries. Of country groups, Central Europe had the greatest number of trials (3,127), followed by Eastern Europe (2,075). The percentage of trials with study sites outside high-income countries remained relatively constant over the study period. Studies with sites outside high-income countries tended to recruit more participants (median enrolled participants 265 vs. 71, P <0.001), to be longer (median study duration 20 vs. 13 months, P <0.05), and to study more advanced phase interventions (Phase 3 or 4 trial 58 % vs. 33 %, P <0.001). CONCLUSIONS: More than a quarter of industry-sponsored trials include participants from outside high-income countries and this rate remained stable over the 7-year study period. Trials conducted outside high-income countries tend to be larger, have a longer duration, and study later phase interventions compared to studies performed exclusively in high-income countries.

Mandl KD, Kohane IS. Federalist principles for healthcare data networks. Nat Biotechnol. 2015;33 :360-3.Abstract

Applying federalist principles to networked health record data could facilitate realization of the potential of shared health data.

Ong MS, Mandl KD. National expenditure for false-positive mammograms and breast cancer overdiagnoses estimated at $4 billion a year. Health Aff (Millwood). 2015;34 :576-83.Abstract Populationwide mammography screening has been associated with a substantial rise in false-positive mammography findings and breast cancer overdiagnosis. However, there is a lack of current data on the associated costs in the United States. We present costs due to false-positive mammograms and breast cancer overdiagnoses among women ages 40-59, based on expenditure data from a major US health care insurance plan for 702,154 women in the years 2011-13. The average expenditures for each false-positive mammogram, invasive breast cancer, and ductal carcinoma in situ in the twelve months following diagnosis were$852, $51,837 and$12,369, respectively. This translates to a national cost of $4 billion each year. The costs associated with false-positive mammograms and breast cancer overdiagnoses appear to be much higher than previously documented. Screening has the potential to save lives. However, the economic impact of false-positive mammography results and breast cancer overdiagnoses must be considered in the debate about the appropriate populations for screening. 2014 Mandl KD. Ebola in the United States: EHRs as a Public Health Tool at the Point of Care. JAMA. 2014. Mandl KD, Kohane IS, McFadden D, Weber GM, Natter M, Mandel J, Schneeweiss S, Weiler S, Klann JG, Bickel J, et al. Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS): Architecture. J Am Med Inform Assoc. 2014.Abstract We describe the architecture of the Patient Centered Outcomes Research Institute (PCORI) funded Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS, http://www.SCILHS.org) clinical data research network, which leverages the$48 billion dollar federal investment in health information technology (IT) to enable a queryable semantic data model across 10 health systems covering more than 8 million patients, plugging universally into the point of care, generating evidence and discovery, and thereby enabling clinician and patient participation in research during the patient encounter. Central to the success of SCILHS is development of innovative 'apps' to improve PCOR research methods and capacitate point of care functions such as consent, enrollment, randomization, and outreach for patient-reported outcomes. SCILHS adapts and extends an existing national research network formed on an advanced IT infrastructure built with open source, free, modular components.

Ong MS, Kohane IS, Cai T, Gorman MP, Mandl KD. Population-Level Evidence for an Autoimmune Etiology of Epilepsy. JAMA Neurol. 2014.Abstract
IMPORTANCE Epilepsy is a debilitating condition, often with neither a known etiology nor an effective treatment. Autoimmune mechanisms have been increasingly identified. OBJECTIVE To conduct a population-level study investigating the relationship between epilepsy and several common autoimmune diseases. DESIGN, SETTING, AND PARTICIPANTS A retrospective population-based study using claims from a nationwide employer-provided health insurance plan in the United States. Participants were beneficiaries enrolled between 1999 and 2006 (N = 2 518 034). MAIN OUTCOMES AND MEASURES We examined the relationship between epilepsy and 12 autoimmune diseases: type 1 diabetes mellitus, psoriasis, rheumatoid arthritis, Graves disease, Hashimoto thyroiditis, Crohn disease, ulcerative colitis, systemic lupus erythematosus, antiphospholipid syndrome, Sjogren syndrome, myasthenia gravis, and celiac disease. RESULTS The risk of epilepsy was significantly heightened among patients with autoimmune diseases (odds ratio, 3.8; 95% CI, 3.6-4.0; P < .001) and was especially pronounced in children (5.2; 4.1-6.5; P < .001). Elevated risk was consistently observed across all 12 autoimmune diseases. CONCLUSIONS AND RELEVANCE Epilepsy and autoimmune disease frequently co-occur; patients with either condition should undergo surveillance for the other. The potential role of autoimmunity must be given due consideration in epilepsy so that we are not overlooking a treatable cause.
Ong MS, Umetsu DT, Mandl KD. Consequences of antibiotics and infections in infancy: bugs, drugs, and wheezing. Ann Allergy Asthma Immunol. 2014.Abstract
BACKGROUND: The prevalence of asthma has increased alarmingly in the past 2 to 3 decades. Increased antibiotic use in infancy has been suggested to limit exposure to gastrointestinal microbes and to predispose to asthma in later life. OBJECTIVE: To evaluate the association between antibiotic exposure during the first year of life and the development of asthma up to the age of 7 years. METHODS: A retrospective population-based study of a cohort of children enrolled in a nationwide employer-provided health insurance plan from January 1, 1999, through December 31, 2006, in the United States (n = 62,576). We evaluated the association between antibiotic exposure during the first year of life and subsequent development of 3 asthma phenotypes: transient wheezing (began and resolved before 3 years of age), late-onset asthma (began after 3 years of age), and persistent asthma (began before 3 years of age and persisted through 4-7 years of age). RESULTS: Antibiotic use in the first year of life was associated with the development of transient wheezing (odds ratio [OR], 2.0; 95% confidence interval [CI], 1.9-2.2; P < .001) and persistent asthma (OR, 1.6; 95% CI, 1.5-1.7; P < .001). A dose-response effect was observed. When 5 or more antibiotic courses were received, the odds of persistent asthma doubled (OR, 1.9; 95% CI, 1.5-2.6; P < .001). There is no association between antibiotic use and late-onset asthma. CONCLUSION: Antibiotic use in the first year life is associated with an increased risk of early-onset childhood asthma that began before 3 years of age. The apparent effect has a clear dose response. Heightened caution about avoiding unnecessary use of antibiotics in infants is warranted.
Natter MD, Ong MS, Ilowite NT, Mandl KD, Mieszkalski KL, Sandborg CI, Wallace C, Schanberg LE, Investigators CR. A167: variations in patterns of care across pediatric rheumatic diseases in the childhood arthritis & rheumatology alliance network registry. Arthritis Rheumatol. 2014;66 Suppl 11 :S215-6.Abstract
BACKGROUND/PURPOSE: In 2009, the Childhood Arthritis and Rheumatology Research Alliance (CARRA) established a longitudinal multi-center, multiple disease U.S. national registry (CARRA Registry) for pediatric rheumatology with the intent of providing a new framework to drive observational clinical research and best practices, evidence-based care. Simultaneously, recognizing that widely variable therapeutic approaches hinder the ability to conduct meaningful comparative effectiveness studies and pragmatic trials in pediatric rheumatic diseases, CARRA investigators convened expert groups to formulate new consensus-based treatment plans (CTPs) in 5 major pediatric rheumatic disease areas. As the CTP approaches are adopted, it is important to establish baseline treatment variability across pediatric rheumatic diseases and clinical sites in the CARRA network. Using longitudinal data from the CARRA Registry, we provide a first description of variability of care across the network. METHODS: We examine variations of medication usage across 55 clinical sites in the treatment of 8 rheumatic conditions, including juvenile idiopathic arthritis (JIA), SLE and mixed connective tissue disease (MCTD), juvenile dermatomyositis (JDM), localized scleroderma, systemic sclerosis, juvenile primary fibromyalgia syndrome (JPFS), sarcoidosis, and vasculitis. Management of uveitis in JIA patients was also assessed. Study participants include all CARRA registry subjects enrolled in May 2010 through December 2013. Medications were categorized into 4 major classes: biologics, DMARDs, steroids and NSAIDs. We compare the percentage of patients exposed to each medication class at each; care variations were quantified using dispersion measures of standard deviation and range. A subgroup analysis was conducted to assess care variations among the largest group of subjects with similar characteristics of and low disease activity (JIA subjects with an average active joint count of 0 to 1 averaged over the enrolment period), where treatment were hypothesized to be most similar. RESULTS: 8,869 subjects were included in data analysis. Therapeutic approaches were highly variable for all 8 rheumatic diseases (Table 1, Fig 1). Subgroup analysis for JIA showed persistence of variability (Fig 2). CONCLUSION: We quantify a substantial degree of therapeutic practice variability across sites, persisting across disease-severity-matched cohorts. Although enrollment bias is a significant limitation, the magnitude of the variability for the largest cohort (JIA) and persistence across multiple diseases and subtypes supports a widespread effect. This baseline quantification and methods developed for assessing variability will support ongoing efforts to monitor new consensus treatment protocol-based standardization efforts across the CARRA network.
D'Amore JD, Mandel JC, Kreda DA, Swain A, Koromia GA, Sundareswaran S, Alschuler L, Dolin RH, Mandl KD, Kohane IS, 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.Abstract

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.

Weber GM, Mandl KD, Kohane IS. Finding the missing link for big biomedical data. JAMA. 2014;311 :2479-80.
Mandl KD, Olson KL, Mines D, Liu C, Tian F. Provider Collaboration: Cohesion, Constellations, and Shared Patients. J Gen Intern Med. 2014.Abstract

BACKGROUND: There is a natural assumption that quality and efficiency are optimized when providers consistently work together and share patients. Diversity in composition and recurrence of groups that provide face-to-face care to the same patients has not previously been studied. OBJECTIVE: Claims data enable identification of the constellation of providers caring for a single patient. To indirectly measure teamwork and provider collaboration, we measure recurrence of provider constellations and cohesion among providers. DESIGN: Retrospective analysis of commercial healthcare claims from a single insurer. PARTICIPANTS: Patients with claims for office visits and their outpatient providers. To maximize capture of provider panels, the cohort was drawn from the four regions with the highest plan coverage. Regional outpatient provider networks were constructed with providers as nodes and number of shared patients as links. MAIN MEASURES: Measures of cohesion and stability of provider constellations derived from the networks of providers to quantify patient sharing. RESULTS: For 10,325 providers and their 521,145 patients, there were 2,641,933 collaborative provider pairs sharing at least one patient. Fifty-four percent only shared a single patient, and 19 % shared two. Of 15,449,835 unique collaborative triads, 92 % shared one patient, 5 % shared two, and 0.2 % shared ten or more. Patient constellations had a median of four providers. Any precise constellation recurred rarely-89 % with exactly two providers shared just one patient and only 4 % shared over two; 97 % of constellations with exactly three providers shared just one patient. Four percent of constellations with 2+ providers were not at all cohesive, sharing only the hub patient. In the remaining constellations, a median of 93 % of provider pairs shared at least one additional patient beyond the hub patient. CONCLUSION: Stunning variability in the constellations of providers caring for patients may challenge underlying assumptions about the current state of teamwork in healthcare.

Bourgeois FT, Olson KL, Ioannidis JP, Mandl KD. Association between pediatric clinical trials and global burden of disease. Pediatrics. 2014;133 :78-87.Abstract
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.
Fine AM, Nizet V, Mandl KD. Participatory medicine: a home score for streptococcal pharyngitis. Ann Intern Med. 2014;160 :289.
Sunyaev A, Dehling T, Taylor PL, Mandl KD. Availability and quality of mobile health app privacy policies. J Am Med Inform Assoc. 2014.Abstract

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.

Pfiffner PB, Oh J, Miller TA, Mandl KD. ClinicalTrials.gov as a data source for semi-automated point-of-care trial eligibility screening. PLoS OnePLoS OnePLoS One. 2014;9 :e111055.Abstract
BACKGROUND: Implementing semi-automated processes to efficiently match patients to clinical trials at the point of care requires both detailed patient data and authoritative information about open studies. OBJECTIVE: To evaluate the utility of the ClinicalTrials.gov registry as a data source for semi-automated trial eligibility screening. METHODS: Eligibility criteria and metadata for 437 trials open for recruitment in four different clinical domains were identified in ClinicalTrials.gov. Trials were evaluated for up to date recruitment status and eligibility criteria were evaluated for obstacles to automated interpretation. Finally, phone or email outreach to coordinators at a subset of the trials was made to assess the accuracy of contact details and recruitment status. RESULTS: 24% (104 of 437) of trials declaring on open recruitment status list a study completion date in the past, indicating out of date records. Substantial barriers to automated eligibility interpretation in free form text are present in 81% to up to 94% of all trials. We were unable to contact coordinators at 31% (45 of 146) of the trials in the subset, either by phone or by email. Only 53% (74 of 146) would confirm that they were still recruiting patients. CONCLUSION: Because ClinicalTrials.gov has entries on most US and many international trials, the registry could be repurposed as a comprehensive trial matching data source. Semi-automated point of care recruitment would be facilitated by matching the registry's eligibility criteria against clinical data from electronic health records. But the current entries fall short. Ultimately, improved techniques in natural language processing will facilitate semi-automated complex matching. As immediate next steps, we recommend augmenting ClinicalTrials.gov data entry forms to capture key eligibility criteria in a simple, structured format.
Pfiffner PB, Oh J, Miller TA, Mandl KD. ClinicalTrials.gov as a Data Source for Semi-Automated Point-Of-Care Trial Eligibility Screening. PLoS One. 2014;9 :e111055.Abstract

BACKGROUND: Implementing semi-automated processes to efficiently match patients to clinical trials at the point of care requires both detailed patient data and authoritative information about open studies. OBJECTIVE: To evaluate the utility of the ClinicalTrials.gov registry as a data source for semi-automated trial eligibility screening. METHODS: Eligibility criteria and metadata for 437 trials open for recruitment in four different clinical domains were identified in ClinicalTrials.gov. Trials were evaluated for up to date recruitment status and eligibility criteria were evaluated for obstacles to automated interpretation. Finally, phone or email outreach to coordinators at a subset of the trials was made to assess the accuracy of contact details and recruitment status. RESULTS: 24% (104 of 437) of trials declaring on open recruitment status list a study completion date in the past, indicating out of date records. Substantial barriers to automated eligibility interpretation in free form text are present in 81% to up to 94% of all trials. We were unable to contact coordinators at 31% (45 of 146) of the trials in the subset, either by phone or by email. Only 53% (74 of 146) would confirm that they were still recruiting patients. CONCLUSION: Because ClinicalTrials.gov has entries on most US and many international trials, the registry could be repurposed as a comprehensive trial matching data source. Semi-automated point of care recruitment would be facilitated by matching the registry's eligibility criteria against clinical data from electronic health records. But the current entries fall short. Ultimately, improved techniques in natural language processing will facilitate semi-automated complex matching. As immediate next steps, we recommend augmenting ClinicalTrials.gov data entry forms to capture key eligibility criteria in a simple, structured format.

Bourgeois FT, Kim JM, Mandl KD. Premarket safety and efficacy studies for ADHD medications in children. PLoS One. 2014;9 :e102249.Abstract