Effective Data Sources for Re-Linkage to HIV Care Programs
Christopher J. Hoffmann, MD, MPH, Johns Hopkins University School of Medicine
U.S. federal government efforts to end the HIV epidemic in the United States
aim to reduce new infections by 74% by 2025 and 90% by 2030. Achievement of these goals rests on 4 pillars of targeted intervention:
1. Diagnose all people with HIV.
2. Treat HIV rapidly and effectively to achieve sustained viral suppression.
3. Prevent new infections through use of proven interventions.
4. Respond quickly to new HIV outbreaks.
Success in treating HIV rapidly and effectively requires linkage to and retention in care and re-linkage when necessary. The U.S. Centers for Disease Control and Prevention’s (CDC) framework for identifying individuals who are not in care (NIC) is a data-driven approach that applies a variety of case management and navigation approaches to re-link people with HIV to medical care. This approach is referred to as “data to care” or “D2C.”
A major challenge with this approach is determining which data are meaningful and how to apply them to guide intervention. The CDC currently recommends use of 3 types of data: public health department surveillance data, reporting data from clinics, and combined surveillance and clinic-level data.
New York State experience:
In 2018, researchers from New York State (NYS) published a study comparing use of health department surveillance and clinic-level data to identify individuals NIC for D2C efforts in selected NYS jurisdictions [Hart-Malloy R, et al. AIDS Care. 2018;30(3):391-396
]. Between January 1, 2015, and September 1, 2016, health department surveillance identified 1,735 individuals meeting criteria for NIC, 348 (26%) of whom were confirmed to be NIC. Of those confirmed as NIC, 273 (78%) were successfully re-linked to care. During this same period, clinic reporting identified 261 individuals who were potentially NIC; 73 (28%) met the surveillance definition of NIC and were traced for linkage. Of these, 19 were confirmed NIC. Of the 19, 12 (63%) were re-linked to care. The findings from this study highlight the challenge of accurately identifying individuals NIC and suggest that there is little difference in outcomes based on health department surveillance and clinic reporting data, although the small number of individuals reported by clinics limit the value of this comparison.
San Francisco experience:
Researchers from the San Francisco Department of Health recently expanded understanding of meaningful data sources for D2C by comparing 3 parallel and simultaneous approaches in place from 2015 to 2017 [Sachdev DD, et al. Open Forum Infect Dis. 2020 Aug 21;7(9):ofaa369]
. Surveillance data were used to identify individuals NIC based on no viral load result in the previous 15 months or viral load >1,500 copies/mL in the previous 4 months. Healthcare provider referrals included patients who had no evidence of care post-diagnosis, did not access care, or were believed to be non-adherent to antiretroviral therapy. The third approach used a combination of laboratory surveillance data and electronic medical record data from public clinics. Linkage navigators were assigned to use all available contact information to locate NIC individuals within 30 days and enroll them in a care linkage navigation program.
Results: In the San Francisco study, 954 patients were referred for navigation; 44% were identified with surveillance data, 43% with clinic reporting data, and 13% with combination data, as summarized below.
- Surveillance data source outcomes:
- Referred for re-linkage: 422
- Not located: 92 (22%)
- Ineligible*: 250 (59%)
- Enrolled: 38 (9%)
- Declined: 41 (10%)
- Suppressed viral load within 12 months of linkage: 24 (6%)
- Clinic reporting data source outcomes:
- Referred for re-linkage: 413
- Not located: 77 (19%)
- Ineligible*: 111 (27%)
- Enrolled: 167 (40%)
- Declined: 58 (14%)
- Suppressed viral load within 12 months of linkage: 92 (22%)
- Combination data source outcomes:
- Referred for re-linkage: 119
- Not located: 33 (28%)
- Ineligible*: 45 (38%)
- Enrolled: 28 (24%)
- Declined: 13 (11%)
- Suppressed viral load within 12 months of linkage: 8 (7%)
*406 of the 954 (43%) of NIC individuals were ineligible for referral because they were found to be already enrolled in care, or they had moved away, were incarcerated, had severe medical or psychiatric barriers to navigation, or were deceased.
Among the NIC individuals enrolled in navigation, the majority were referred by clinics (72%). When compared with those referred through surveillance or combined data sources, a greater proportion of those identified with clinic data were experiencing homelessness, were men who have sex with men, or used injection drugs. A lower proportion of individuals referred through surveillance reported methamphetamine use (16% compared to 55% and 53% for clinic and combination referrals, respectively).
Among individuals enrolled in the navigation program, retention in care and viral suppression increased from 35% to 58% and 18% to 53%, respectively. The improvement in viral suppression was greatest among surveillance referrals (60%) compared to referrals from clinics or combined data referral. This was possibly because of a lower proportion of individuals experiencing homelessness compared to those identified through clinic referrals; those experiencing homelessness also had lower success with viral suppression in this study.
Conclusions: These results demonstrate that D2C programs can successfully re-link individuals with HIV to care and help them to achieve viral load suppression for at least 12 months after re-engagement in care. They also show that, at least in San Francisco, surveillance data are less effective for identifying individuals who are NIC and eligible for linkage activities. Furthermore, only 6% of individuals identified through surveillance ultimately enrolled in linkage navigation and achieved viral suppression; this was partly because many of the individuals identified through surveillance were already in care. Importantly, each of the approaches to identifying NIC individuals reached a slightly different population.
The most successful re-linkage programs may be those that use data from multiple sources and accept substantial inefficiency to achieve ending the HIV epidemic goals.