Clinical trial registries can provide important information about relevant studies for a given condition to other researchers and the public. We developed a computerized informatics based approach to provide an overview and analysis of COVID-19 studies registered on ClinicalTrials.gov registry. Using the perspective of analyzing active or completed COVID-19 studies, we identified 401 interventional clinical trials, 287 observational studies and 64 registries. We analyzed features of each study type separately such as location, design, interventions and update history. Our results show that the United States had the most COVID-19 interventional trials, France had the most COVID-19 observational studies and France and the United States tied for the most COVID-19 registries on ClinicalTrials.gov. The majority of studies in all three study types had a single study site. For update history “Study Status” is the most updated information and we found that studies located in Canada (2.70 updates per study) and the United States (1.76 updates per study) update their studies more often than studies in any other country. Using normalization and mapping techniques, we identified Hydroxychloroquine (92 studies) as the most common drug intervention, while convalescent plasma (20 studies) is the most common biological intervention. The primary purpose of most interventional trials is for treatment with 298 studies (74.3%). For COVID-19 registries we found the most common proposed follow-up time is 1 year (15 studies). Of specific importance and interest is COVID-19 vaccine trials, of which 12 were identified. Our informatics based approach allows for constant monitoring and updating as well as multiple applications to other conditions and interests.

Background Efforts to define research Common Data Elements try to harmonize data collection across clinical studies. Objective Our goal was to analyze the quality and usability of data dictionaries of HIV studies. Methods For the clinical domain of HIV, we searched data sharing platforms and acquired a set of 18 HIV related studies from which we analyzed 26 328 data elements. We identified existing standards for creating a data dictionary and reviewed their use. To facilitate aggregation across studies, we defined three types of data dictionary (data element, forms, and permissible values) and created a simple information model for each type. Results An average study had 427 data elements (ranging from 46 elements to 9 945 elements). In terms of data type, 48.6% of data elements were string, 47.8% were numeric, 3.0% were date and 0.6% were date-time. No study in our sample explicitly declared a data element as a categorical variable and rather considered them either strings or numeric. Only for 61% of studies were we able to obtain permissible values. The majority of studies used CSV files to share a data dictionary while 22% of the studies used a non-computable, PDF format. All studies grouped their data elements. The average number of groups or forms per study was 24 (ranging between 2 and 124 groups/forms). An accurate and well formatted data dictionary facilitates error-free secondary analysis and can help with data de-identification. Conclusion We saw features of data dictionaries that made them difficult to use and understand. This included multiple data dictionary files or non-machine-readable documents, data elements included in data but not in the dictionary or missing data types or descriptions. Building on

The objective of this paper is to determine the temporal trend of the association of 66 comorbidities with human immunodeficiency virus (HIV) infection status among Medicare beneficiaries from 2000 through 2016. We harvested patient level encounter claims from a 17-year long 100% sample of Medicare records. We used the chronic conditions warehouse comorbidity flags to determine HIV infection status and presence of comorbidities. We prepared 1 data set per year for analysis. Our 17 study data sets are retrospective annualized patient level case histories where the comorbidity status reflects if the patient has ever met the comorbidity case definition from the start of the study to the analysis year. We implemented one logistic binary regression model per study year to discover the maximum likelihood estimate (MLE) of a comorbidity belonging to our binary classes of HIV+ or HIV– study populations. We report MLE and odds ratios by comorbidity and year. Of the 66 assessed comorbidities, 35 remained associated with HIV– across all model years, 19 remained associated with HIV+ across all model years. Three comorbidities changed association from HIV+ to HIV– and 9 comorbidities changed association from HIV– to HIV+. The prevalence of comorbidities associated with HIV infection changed over time due to clinical, social, and epidemiological reasons. Comorbidity surveillance can provide important insights into the understanding and management of HIV infection and its consequences.

Background:: Patient registries represent a long-term data collection system that is a platform for performing multiple research studies to generate real-world evidence. Many of these registries use common data elements (CDEs) and link data from Electronic Health Records. Objective:: This study evaluated HIV registry features that contribute to the registry’s usability for retrospective analysis of existing registry data or new prospective interventional studies. Methods:: We searched PubMed and ClinicalTrials.gov (CTG) to generate a list of HIV registries. We used the framework developed by the European Medical Agency (EMA) to evaluate the registries by determining the presence of key research features. These features included information about the registry, request and collaboration processes, and available data. We acquired data dictionaries and identified CDEs. Results: We found 13 HIV registries that met our criteria, 11 through PubMed and 2 through CTG. The prevalence of the evaluated features ranged from all 13 (100%) having published key registry information to 0 having a research contract template. We analyzed 6 data dictionaries and identified 14 CDEs that were present in at least 4 of 6 (66.7%) registry data dictionaries. Conclusion:: The importance of registries as platforms for research data is growing and the presence of certain features, including data dictionaries, contributes to the reuse and secondary research capabilities of a registry. We found some features such as collaboration policies were in the majority of registries while others such as, ethical support, were in a few and are more for future development.