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ObjectiveTo describe content domains and applications of the IeDEA Data Exchange Standard, its development history, governance structure, and relationships to other established data models, as well as to share open source, reusable, scalable, and adaptable implementation tools with the informatics community.MethodsIn 2012, the International Epidemiology Databases to Evaluate AIDS (IeDEA) collaboration began development of a data exchange standard, the IeDEA DES, to support collaborative global HIV epidemiology research. With the HIV Cohorts Data Exchange Protocol as a template, a global group of data managers, statisticians, clinicians, informaticians, and epidemiologists reviewed existing data schemas and clinic data procedures to develop the HIV data exchange model. The model received a substantial update in 2017, with annual updates thereafter.FindingsThe resulting IeDEA DES is a patient-centric common data model designed for HIV research that has been informed by established data models from US-based electronic health records, broad experience in data collection in resource-limited settings, and informatics best practices. The IeDEA DES is inherently flexible and continues to grow based on the ongoing stewardship of the IeDEA Data Harmonization Working Group with input from external collaborators. Use of the IeDEA DES has improved multiregional collaboration within and beyond IeDEA, expediting over 95 multiregional research projects using data from more than 400 HIV care and treatment sites across seven global regions. A detailed data model specification and REDCap data entry templates that implement the IeDEA DES are publicly available on GitHub.ConclusionsThe IeDEA common data model and related resources are powerful tools to foster collaboration and accelerate science across research networks. While currently directed towards observational HIV research and data from resource-limited settings, this model is flexible and extendable to other areas of health research.HighlightsThe IeDEA Data Exchange Standard is a data model for HIV epidemiology research.The model has expedited 95 projects using data from >400 HIV clinics worldwide.A browsable and adaptable version and data collection templates are available online.

ObjectivesWe extend the method of Significant Zero Crossings of Derivatives (SiZer) to address within-subject correlations of repeatedly collected longitudinal biomarker data and the computational aspects of the methodology when analyzing massive biomarker databases. SiZer is a powerful visualization tool for exploring structures in curves by mapping areas where the first derivative is increasing, decreasing or does not change (plateau) thus exploring changes and normalization of biomarkers in the presence of therapy.MethodsWe propose a penalized spline SiZer (PS-SiZer) which can be expressed as a linear mixed model of the longitudinal biomarker process to account for irregularly collected data and within-subject correlations. Through simulations we show how sensitive PS-SiZer is in detecting existing features in longitudinal data versus existing versions of SiZer. In a real-world data analysis PS-SiZer maps are used to map areas where the first derivative of weight change after antiretroviral therapy (ART) start is significantly increasing, decreasing or does not change, thus exploring the durability of weight increase after the start of therapy. We use weight data repeatedly collected from persons living with HIV initiating ART in five regions in the International Epidemiologic Databases to Evaluate AIDS (IeDEA) worldwide collaboration and compare the durability of weight gain between ART regimens containing and not containing the drug stavudine (d4T), which has been associated with shorter durability of weight gain.ResultsThrough simulations we show that the PS-SiZer is more accurate in detecting relevant features in longitudinal data than existing SiZer variants such as the local linear smoother (LL) SiZer and the SiZer with smoothing splines (SS-SiZer). In the illustration we include data from 185,010 persons living with HIV who started ART with a d4T (53.1%) versus non-d4T (46.9%) containing regimen. The largest difference in durability of weight gain identified by the SiZer maps was observed in Southern Africa where weight gain in patients treated with d4T-containing regimens lasted 52.4 weeks compared to 94.4 weeks for those with non-d4T-containing regimens. In the other regions, persons receiving d4T-containing regimens experienced weight gains lasting 51-61 weeks versus 59-77 weeks in those receiving non-d4T-based regimens.DiscussionPS-SiZer, a SiZer variant, can handle irregularly collected longitudinal data and within-subject correlations and is sensitive in detecting even subtle features in biomarker curves.

BackgroundIn resource-constrained settings, people living with HIV (PLWH) treated for tuberculosis (TB) despite negative bacteriologic tests have a higher mortality than those treated with positive tests. Many PLWH are treated without bacteriologic testing; their mortality compared to those with bacteriologic testing is uncertain.MethodsWe conducted an observational cohort study among PLWH ≥ 15 years of age who initiated TB treatment at clinical sites affiliated with four regions of the International epidemiology Databases to Evaluate AIDS (IeDEA) consortium from 2012-2014: Caribbean, Central and South America, and Central, East, and West Africa. The primary exposure of interest was the TB bacteriologic test status at TB treatment initiation: positive, negative, or no test result. The hazard for death in the 12 months following TB treatment initiation was estimated using the Cox proportional hazard model, adjusted for patient- and site-level factors. Missing covariates were multiply imputed.ResultsAmong 2,091 PLWH included, the median age at TB treatment initiation was 36 years, 44% were female, 53% had CD4 counts ≤ 200 cells/mm3, and 52% were on antiretroviral treatment (ART). Compared to patients with positive bacteriologic tests, the adjusted hazard for death was higher among patients with no test results (HR 1.56, 95% CI 1.08-2.26) but not different than those with negative tests (HR 1.28, 95% CI 0.91-1.81). Older age was also associated with a higher hazard for death, while being on ART, having a higher CD4 count, West Africa region, and tertiary facility level were associated with lower hazards for death.ConclusionPLWH treated for TB with no bacteriologic test results were more likely to die than those treated with positive tests, underscoring the importance of TB bacteriologic diagnosis in resource-constrained settings. Research is needed to understand the causes of death among PLWH treated for TB in the absence of positive bacteriologic tests.