An adapted algorithm for patient engagement in care for young people living with perinatal HIV in England

Abstract

Abstract Background Evidence suggests that engagement in care (EIC) may be worse in young people living with perinatal HIV (YPLPHIV) compared to adults or children living with HIV. We took a published EIC algorithm for adults with HIV, which takes patients’ clinical scenarios into account, and adapted it for use in YPLPHIV in England, to measure their EIC. Methods The adult algorithm predicts when in the next 6 months the next clinic visit should be scheduled, based on routinely collected clinical indicators at the current visit. We updated the algorithm based on the latest adult guidelines at the time, and modified it for young people in paediatric care using the latest European paediatric guidelines. Paediatric/adolescent HIV consultants from the UK reviewed and adapted the resulting flowcharts. The adapted algorithm was applied to the Adolescent and Adults Living with Perinatal HIV (AALPHI) cohort in England. Data for 12 months following entry into AALPHI were used to predicted visits which were then compared to appointment attendances, to measure whether young people were in care in each month. Proxy markers (e.g. dates of CD4 counts, viral loads (VL)) were used to indicate appointment attendance. Results Three hundred sixteen patients were in AALPHI, of whom 41% were male, 82% of black African ethnicity and 58% born abroad. At baseline (time of AALPHI interview) median [IQR] age was 17 [15–18] years, median CD4 was 597 [427, 791] cells/µL and 69% had VL ≤50c/mL. 10 patients were dropped due to missing data. 306 YPLPHIV contributed 3,585 person months of follow up across the 12 month study in which a clinic visit was recorded for 1,204 months (38/1204 dropped due to missing data). The remaining 1,166 months were classified into 3 groups: Group-A: on ART, VL ≤ 50c/mL—63%(734/1,166) visit months, Group-B: on ART, VL > 50c/mL—27%(320/1,166) Group-C: not on ART-10%(112/1,166). Most patients were engaged in care with 87% (3,126/3,585) of months fulfilling the definition of engaged in care. Conclusions The adapted algorithm allowed the varying clinical scenarios of YPLPHIV to be taken into account when measuring EIC. However availability of good quality surveillance data is crucial to ensure that EIC can be measured well.