Alyssa L Arey1, Hope Cassidy-Stewart2, Pamela L Kurowski3, Jeffrey C Hitt3, Colin P Flynn1. 1. Maryland Department of Health, Center for HIV Surveillance, Epidemiology and Evaluation, Baltimore, MD. 2. Maryland Department of Health, Center for HIV Prevention and Health Services, Baltimore, MD. 3. Maryland Department of Health, Infectious Disease Prevention and Health Services Bureau, Baltimore, MD.
Abstract
BACKGROUND: HIV surveillance is essential to quantifying the impact of the epidemic and shaping HIV programs. The Maryland Department of Health (MDH) historically conducted HIV Data to Care (D2C) activities using surveillance data to identify individuals who were not in HIV care; however, most case investigations concluded that the individuals in question were currently engaged in care. This suggests that delays and gaps in laboratory reporting to HIV surveillance exist and the proportion of HIV-positive Marylanders who are truly in care is underestimated. Therefore, solely relying on surveillance data might not be an efficient method for identifying not in care HIV cases. SETTING: Through the Partnerships for Care (P4C) project, MDH conducted targeted D2C efforts on HIV patients from 4 health centers. METHODS: The expanded D2C model that MDH created during P4C integrated clinical data as a secondary data source to enhance the surveillance data used to estimate HIV patient care engagement. MDH matched and compared health center electronic health records with HIV surveillance data to assess completeness of HIV case and laboratory reporting. RESULTS: HIV case ascertainment was high (99.9%) for the P4C cohort (N = 927), but differences in estimated care engagement and viral suppression between data sources revealed incomplete laboratory reporting and that patients received care from multiple providers. Analyzing the clinical data leads to the resolution of several reporting gaps, which improved surveillance data quality over time. CONCLUSIONS: Health departments should validate their HIV surveillance completeness. Triangulating surveillance data with clinical data generated more accurate depictions of care engagement and increased D2C efficiency.
BACKGROUND: HIV surveillance is essential to quantifying the impact of the epidemic and shaping HIV programs. The Maryland Department of Health (MDH) historically conducted HIV Data to Care (D2C) activities using surveillance data to identify individuals who were not in HIV care; however, most case investigations concluded that the individuals in question were currently engaged in care. This suggests that delays and gaps in laboratory reporting to HIV surveillance exist and the proportion of HIV-positive Marylanders who are truly in care is underestimated. Therefore, solely relying on surveillance data might not be an efficient method for identifying not in care HIV cases. SETTING: Through the Partnerships for Care (P4C) project, MDH conducted targeted D2C efforts on HIV patients from 4 health centers. METHODS: The expanded D2C model that MDH created during P4C integrated clinical data as a secondary data source to enhance the surveillance data used to estimate HIV patient care engagement. MDH matched and compared health center electronic health records with HIV surveillance data to assess completeness of HIV case and laboratory reporting. RESULTS: HIV case ascertainment was high (99.9%) for the P4C cohort (N = 927), but differences in estimated care engagement and viral suppression between data sources revealed incomplete laboratory reporting and that patients received care from multiple providers. Analyzing the clinical data leads to the resolution of several reporting gaps, which improved surveillance data quality over time. CONCLUSIONS: Health departments should validate their HIV surveillance completeness. Triangulating surveillance data with clinical data generated more accurate depictions of care engagement and increased D2C efficiency.
Authors: Riley D Shearer; William J Lundberg; Jason V Baker; Katherine Diaz Vickery Journal: AIDS Patient Care STDS Date: 2022-02-17 Impact factor: 5.078
Authors: Jessica P Ridgway; Joseph A Mason; Eleanor E Friedman; Samantha Devlin; Junlan Zhou; David Meltzer; John Schneider Journal: JAMIA Open Date: 2022-05-17