Abstract
ObjectiveTo develop and disseminate tools for interactive visualization of HIV cohort data.Design and MethodsIf a picture is worth a thousand words, then an interactive video, composed of a long string of pictures, can produce an even richer presentation of HIV population dynamics. We developed an HIV cohort data visualization tool using open-source software (R statistical language). The tool requires that the data structure conform to the HIV Cohort Data Exchange Protocol (HICDEP), and our implementation utilized Caribbean, Central and South America network (CCASAnet) data.ResultsThis tool currently presents patient-level data in three classes of plots: (1) Longitudinal plots showing changes in measurements viewed alongside event probability curves allowing for simultaneous inspection of outcomes by relevant patient classes. (2) Bubble plots showing changes in indicators over time allowing for observation of group level dynamics. (3) Heat maps of levels of indicators changing over time allowing for observation of spatial-temporal dynamics. Examples of each class of plot are given using CCASAnet data investigating trends in CD4 count and AIDS at antiretroviral therapy (ART) initiation, CD4 trajectories after ART initiation, and mortality.ConclusionsWe invite researchers interested in this data visualization effort to use these tools and to suggest new classes of data visualization. We aim to contribute additional shareable tools in the spirit of open scientific collaboration and hope that these tools further the participation in open data standards like HICDEP by the HIV research community.
Highlights
In the practice of epidemiology, data visualization has been of great importance historically [1], whether for exploration of data structures preparatory to analysis [2], for interpreting patterns of events in populations over space and time [3], or for more clearly communicating inferences drawn from completed analyses [4]
The collaboration was established in 2006 as part of the International Epidemiologic Databases to Evaluate AIDS (IeDEA; www.iedea.org) with the purpose of collecting retrospective clinical HIV data to describe the unique characteristics of the epidemic in the region [17]
In an effort to reduce the workload of data extraction and speed up the time to analysis, an HIV Cohort Data Exchange Protocol (HICDEP, available at http://www.hicdep.org/) was developed and widely disseminated in 2004 [18]
Summary
In the practice of epidemiology, data visualization has been of great importance historically [1], whether for exploration of data structures preparatory to analysis [2], for interpreting patterns of events in populations over space and time [3], or for more clearly communicating inferences drawn from completed analyses [4]. Data animations can improve figures by allowing the display of a temporal dimension [3]. The requisite data dimensions consume all the display space precluding the opportunity to add the temporal dimension without compromising the clarity and effectiveness of conveyed information. Static snapshots taken of plots at regular time intervals can be strung together to form frames in a video animation, the direction and speed of which can be altered by the user. Recent work elucidated the CD4 and viral load response to antiretroviral therapy using a dynamic visual display [7]
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