Impact of defined clinical population and missing data on temporal trends in HIV viral load estimation within a health care system1. Issue 6 (17th February 2015) - Edelman, EJ; Tate, JP; Fiellin, DA; Brown, ST; Bryant, K; Gandhi, N; Gibert, CL; Goetz, MB; Gordon, KS; Rodriguez‐Barradas, MC; Braithwaite, RS; Rimland, D; et al.
<x xml:space="preserve">Abstract</x> Objectives Community viral load (CVL) estimates vary based on analytic methods. We extended the CVL concept and used data from the Veterans Health Administration (VA) to determine trends in the health care system viral load (HSVL) and its sensitivity to varying definitions of the clinical population and assumptions regarding missing data. Methods We included HIV‐infected patients in the Veterans Aging Cohort Study, 2000−2010, with at least one documented CD4 count, HIV‐1 RNA or antiretroviral prescription (n = 37 318). We created 6‐month intervals including patients with at least one visit in the past 2 years. We assessed temporal trends in clinical population size, patient clinical status and mean HSVL and explored the impact of varying definitions of the clinical population and assumptions about missing viral load. Results The clinical population size varied by definition, increasing from 16 000–19 000 patients in 2000 to 23 000–26 000 in 2010. The proportion of patients with suppressed HIV‐1 RNA increased over time. Over 20% of patients had no viral load measured in a given interval or the past 2 years. Among patients with a current HIV‐1 RNA, mean HSVL decreased from 97 800 HIV‐1 RNA copies/mL in 2000 to 2000 copies/mL in 2010. When current HIV‐1 RNA data were unavailable and the HSVL was recalculated using the last available HIV‐1 RNA, HSVL decreased from 322 300 to 9900 copies/mL. HSVL was underestimated when using only current data in each interval. Conclusions The CVL concept can be applied to a health care system, providing a measure of health care quality. Like CVL, HSVL estimates depend on definitions of the clinical population and assumptions about missing data.