2022
DOI: 10.1186/s12879-022-07047-5
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Studying patterns and predictors of HIV viral suppression using A Big Data approach: a research protocol

Abstract: Background Given the importance of viral suppression in ending the HIV epidemic in the US and elsewhere, an optimal predictive model of viral status can help clinicians identify those at risk of poor viral control and inform clinical improvements in HIV treatment and care. With an increasing availability of electronic health record (EHR) data and social environmental information, there is a unique opportunity to improve our understanding of the dynamic pattern of viral suppression. Using a stat… Show more

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Cited by 15 publications
(16 citation statements)
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“…Details of the SC eHARS information have been described elsewhere. 23 Briefly, the individual variables we will derive from SC eHARS include date of HIV diagnosis, sociodemographic (eg, age, race/ethnicity, risk exposure (men who have sex with men (MSM), drug user), CD4 count and viral load). This HIV cohort will be updated through at least December 2022.…”
Section: Methods and Analysismentioning
confidence: 99%
See 3 more Smart Citations
“…Details of the SC eHARS information have been described elsewhere. 23 Briefly, the individual variables we will derive from SC eHARS include date of HIV diagnosis, sociodemographic (eg, age, race/ethnicity, risk exposure (men who have sex with men (MSM), drug user), CD4 count and viral load). This HIV cohort will be updated through at least December 2022.…”
Section: Methods and Analysismentioning
confidence: 99%
“…In our ongoing project, around 15 years (2005–2020) of healthcare encounter data are extracted from longitudinal EHR from a variety of state agencies (figure 2). Details of the SC eHARS information have been described elsewhere 23. Briefly, the individual variables we will derive from SC eHARS include date of HIV diagnosis, sociodemographic (eg, age, race/ethnicity, risk exposure (men who have sex with men (MSM), drug user), CD4 count and viral load).…”
Section: Methods and Analysismentioning
confidence: 99%
See 2 more Smart Citations
“…The de-identified EHR data from SC DHEC's eHARS and all payers' claim data were linked by SC Revenue and Fiscal Affairs Office (SC RFA). Details of data sources and data linkage are described elsewhere [41,42].…”
Section: Study Cohortmentioning
confidence: 99%