Clinical and HIV Surveillance Data to Determine Care Status for People With HIV in Washington, DC.

Clinical and HIV Surveillance Data to Determine Care Status for People with HIV in Washington, DC - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice J Acquir Immune Defic Syndr . Author manuscript; available in PMC: 2026 Jun 1. Published in final edited form as: J Acquir Immune Defic Syndr. 2026 Jun 1;101(6):620–632. doi: 10.1097/QAI.0000000000003846 Clinical and HIV Surveillance Data to Determine Care Status for People with HIV in Washington, DC Lauren O’Connor 1 , Shannon Hammerlund 1 , Lisa Mele 2 , Amanda Castel 1 , Alan Greenberg 1 , Rachel Denyer 3 , Anne Monroe 1 , on behalf of DC Cohort Executive Committee Author information Copyright and License information 1 The Milken Institute School of Public Health, George Washington University, Washington, DC 2 Department of Biostatistics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA 3 Infectious Diseases Section, DC Veterans Administration Medical Center, Washington, DC, USA Previously presented data at: Conferences on Retroviruses and Opportunistic Infections (CROI), Poster #1335, San Francisco, CA, March 9-12, 2025 ✉ Corresponding Author: Shannon Hammerlund, 950 New Hampshire Ave NW Suite 500, Washington, DC 20052, skhammerlund@gwmail.gwu.edu PMC Copyright notice PMCID: PMC13158979  NIHMSID: NIHMS2165805  PMID: 41677241 The publisher's version of this article is available at J Acquir Immune Defic Syndr Abstract Background We examined factors related to being out of care (OOC) or re-entering care after clinic transfer among people with HIV (PWH) in Washington, DC using clinical and linked surveillance data from the DC Department of Health (DC Health). Methods Using DC Cohort HIV care encounters and labs, linked surveillance data, and chart review, we determined if participants were (1) engaged in care, (2) OOC, (3) died, (4) withdrew, (5) clinic-reported transfer, (6) surveillance-based transfer (using DC Health data), or (7) undetermined. Of the clinic-reported transfers, we used surveillance data to determine whether participants re-entered care after transfer. We evaluated associations between participant characteristics, being OOC and re-entering care using logistic regression. Results We evaluated 12,563 DC Cohort participants, 6,557 (52.5%) were engaged in care, 748 (6.0%) OOC, 1,893 (15.0%) clinic-reported transfers, 1,692 (13.5%) surveillance-based transfers, 1,418 (11.0%) died, 41 (0.3%) withdrew, and 214 (1.7%) were undetermined. Of clinic-reported transfers, 832 (44.0%) re-entered care, with 80.9% achieving viral suppression (VS) (< 200 copies/mL). In the adjusted analysis, females, older age, non-Hispanic Black race, longer time since HIV diagnosis, CD4 ≥ 500 cells/μL, former or never smoking, and public insurance were associated with being engaged in care. Similarly, non-Hispanic Black race, non-Hispanic White race, non-VS, Ryan White clinic, former alcohol abuse, and temporary/unstable housing were associated with re-entering care among the clinic-reported transfers, after adjusting for covariates. Conclusions Individual, clinical, and structural factors were associated with being OOC or care re-entry. Targeted interventions addressing these factors may improve sustained HIV care and reduce transmission. Keywords: Surveillance, engagement in care, cohort, clinical services, HIV, health department, linkage, care continuum INTRODUCTION The HIV care continuum is a framework for improving health outcomes among people with HIV (PWH) including linkage to care, engagement in care, and maintenance of viral suppression (VS). 1 In 2021, UNAIDS adopted the 95-95-95 targets aiming for 95% of PWH knowing their HIV status, 95% receiving treatment, and 95% maintaining VS. 2 However, as of 2022, only 75.6% of PWH in the United States were linked to care. 3 While efforts have been made to improve linkage to care, many patients linked to care are not retained in care and significant disparities in engagement in care have been found. 2 – 6 However, a limitation of many studies of engagement in care is that they are limited to one data source, such as a single clinic’s electronic health record (EHR). This is problematic given that patients often move between clinics and patient mobility between clinics is associated with worse health outcomes among PWH, perhaps due to fragmentation in care. 7 Novel approaches are needed to more accurately characterize PWH who are engaged in care to better understand who is at risk for falling OOC. There are several interventions to help identify PWH who are OOC and improve re-engagement in care including patient navigation, real-time alert systems, appointment reminders, transportation support, and data to care (D2C). 8 Surveillance-based D2C is a public health strategy that uses surveillance systems, such as health departments, to identify those out of care and re-engage them. 9 – 12 Cohort studies follow enrolled participants and track participants’ care status longitudinally to close the OOC gap. 13 – 15 However, both of these approaches have weaknesses that do not provide the full spectrum of care engagement. D2C approaches face challenges with incomplete or inaccurate data, reporting delays, and barriers to data sharing. 9 , 16 Cohort studies alone result in significant gaps due to patients receiving care across multiple clinics. 17 , 18 The DC Cohort, a longitudinal cohort of PWH receiving care at 14 clinics in Washington, DC, provides a unique opportunity to use both Cohort data and clinician relationships alongside health department surveillance data to improve measurement and reengagement of OOC PWH. In contrast to previous cohort studies based solely off clinic data, the DC Cohort links data extracted from EHRs to DC health HIV surveillance data to allow for a more comprehensive and accurate picture of care engagement. While over 12,000 PWH were enrolled in the DC Cohort by the end of 2023, almost half have not accessed HIV care in over 18 months based on Cohort data. A better understanding of whether these patients were transferred, deceased, or truly OOC was warranted. Therefore, we sought to determine whether PWH who appeared to be OOC based on EHR records were truly out of care, to describe the differences among PWH who were actively in care compared to those OOC, and to identify characteristics associated with re-engagement in care. METHODS Study Setting The DC Cohort study is a longitudinal observational cohort study of PWH receiving care in Washington, D.C. Participants are recruited from 14 DC-based HIV care sites and provide informed consent during routine HIV care clinic visits or while receiving care from one participating site where consent for data use for research purposes is part of clinical care and explained annually in the explanation of benefits. Enrollment in the cohort began in 2011 and there were over 12,000 participants enrolled as of December 31, 2023. The DC Cohort database includes manual data abstraction (13 sites) or automatic abstraction from a clinical registry at baseline (1 site), subsequent automated extraction of electronic health record (EHR) data (14 sites), and linkage with HIV surveillance data from the DC Department of Health (DC Health) (13 sites). Data are collected on demographics, medications, procedures, labs, care encounters, and social determinants of health and stored in a secure remote database. The database also includes participant status changes including patient deaths, transfers, and withdrawals from the study. Additional details on DC Cohort methods have been described previously. 19 The linkage to DC Health data has been described previously and includes confidential name-based HIV reporting, laboratory data reporting of CD4 and viral load (VL) values, and death data from the Vital Statistics Registry. 17 , 20 In this US-based study setting, private insurance was defined as insurance coverage through a private entity, such as an employer or spouse’s employer, while public insurance was defined as insurance coverage through a government-funded insurance program. Outcomes of Interest DC Cohort participants who had at least one HIV care encounter or lab in the DC Cohort EHR data in the 18 months prior to December 31, 2023 were considered Engaged in Care . Those who did not have an HIV care encounter or lab were considered Inactive. Among the Inactive participants, further investigation of a participant’s status was performed. First, lists of Inactive participants were sent to site RAs who manually reviewed EHRs of Inactive participants for documentation of a transfer to another HIV care site or the participant’s death and subsequent termination of their medical record. The RAs compiled these lists and reported results to the DC Cohort team through a secure file transfer. Second, DC Health linkage data for labs and mortality were evaluated. Using the findings from these sources, Inactive participants were further classified into one of the following, mutually exclusive, categories: Died , defined as a death reported from the RA or determined through the DC Health linkage Withdrew , defined as a participant withdrawal from the DC Cohort study Undetermined , defined as someone with insufficient evidence to categorize Clinic-reported transfer , defined as a patient who informed their care provider at a participating DC Cohort clinic they would be transferring their care to another healthcare facility Surveillance-based transfer , defined as someone who did not report to their DC Cohort clinic that they were transferring care but had evidence of care at another HIV care site through the DC Health linkage (evidence of care at another HIV care site was defined as a CD4 or VL lab at least 6 months after their date of last contact with their DC Cohort site) Out of care, defined as any participants in the Inactive group who did not meet any of the 5 criteria above Predictors of Interest Using DC Cohort data extracted from participant EHRs, we evaluated participant race/ethnicity, gender, age, HIV transmission factor, time since HIV diagnosis, CD4, VL, history of AIDS, type of HIV care site, substance use (smoking, alcohol abuse, illicit drug use), insurance status, employment status, and housing status. All variables were measured as of the date of last contact with the cohort or December 31, 2023, whichever came first. Statistical Analysis Our out of care participant list included DC Cohort participants that had not withdrawn from the study, had no evidence of transfer or death in their medical record at their DC Cohort site, had no confirmed death in the DC Health linkage, and had no evidence of HIV care elsewhere in DC per the DC Health linkage. We compared this out of care group to our engaged in care participant group to evaluate factors related to engagement in care. A detailed list of definitions for each status can be found in Supplemental Table 1 . Once we had classified all participants, we further classified participants considered clinic-reported transfers based on whether they re-entered care locally or not. Specifically, we evaluated whether participants had evidence of a CD4 or VL lab at another site at least 6 months after their last date of contact with their initial DC Cohort site. Sites could be other DC Cohort sites or non-DC Cohort sites, but all sites were in Washington, DC. Participants with at least one lab 6 or more months after their last contact were considered to have successfully re-entered care while all others were not considered to have re-entered care. Due to significant differences in baseline characteristics, the surveillance-based transfers were excluded from this portion of the analysis. We used descriptive statistics to describe the distribution of participant statuses and characteristics. Chi-squared and Wilcoxon rank-sum tests were used to evaluate significant differences between being engaged in care or out of care . Then, we used simple logistic regression to evaluate whether certain individual and structural level factors were associated with engagement in HIV care. Stepwise selection procedures were used to determine which covariates contribute to the model. We then developed a final multivariable logistic regression model to evaluate the adjusted associations between each participant characteristic and being engaged in care. Next, we evaluated all clinic-reported transfers and used health department linkage data to determine whether participants successfully re-entered care locally after transfer, as mentioned above. We then evaluated significant differences between those who re-entered care and those who did not using chi-squared and Wilcoxon rank-sum tests. We used simple logistic regression to evaluate whether the factors listed above were associated with re-engagement in care. Stepwise selection procedures were used to determine which covariates should remain in the model and a final multivariable logistic regression model was used to evaluate the adjusted association between each covariate and re-entry into care. All analyses were conducted using SAS 9.4 (Cary, NC) and p-values < 0.05 were considered statistically significant. RESULTS We evaluated 12,563 DC Cohort participants as of December 31, 2023. Of the total participants, 6,557 (52.5%) were engaged in care and 6,006 (47.8%) were inactive . We then categorized the inactive participants as 748 (6.0%) out of care , 1,893 (15.0%) clinic-reported transfers , 1,692 (13.5%) surveillance-based transfers , 1,418 (11.0%) died , 41 (0.3%) withdrew , and 214 (1.7%) were undetermined ( Figure 1 ). Of the clinic-reported transfers , 832 (44.0%) re-entered care after transfer; 1,061 (56.0%) did not. Figure 1. Open in a new tab Flow chart of identification of out-of-care participants (eHARS = enhanced HIV/AIDS reporting system) (Please see Supplemental Table 1 for definitions of care statuses ) Comparison of Engaged in Care versus Out of Care Participants We compared the 6,557 participants who were engaged in care to the 748 participants who were out of care . Most participants were non-Hispanic Black (5,537; 75.8%), male (5,125; 70.2%), men who have sex with men (MSM) (3,111; 42.6%), and the average age as of date of last contact was 52.8 years old (St. Dev. 13.4). Participant demographics and HIV-related clinical characteristics differed significantly between those who were engaged in care versus those who were out of care (p < 0.05) ( Table 1 ). Table 1. Distribution of participant characteristics by status as of December 31, 2023. Chi-Square tests and Wilcoxon Rank Sum tests used to calculate p-values for categorical and continuous variables, respectively. Demographic a Total Participants (N = 7,305) Engaged in Care (N = 6,557) Out of Care (N = 748) p-value Race/Ethnicity 0.0000 Non-Hispanic Black 5537(75.8) 5057(77.12) 480(64.17) Non-Hispanic White 932(12.76) 780(11.9) 152(20.32) Hispanic 501(6.86) 454(6.92) 47(6.28) Other 106(1.45) 91(1.39) 15(2.01) Unknown 229(3.13) 175(2.67) 54(7.22) Gender 0.0000 Male 5125(70.16) 4536(69.18) 589(78.74) Female 2007(27.47) 1861(28.38) 146(19.52) Transgender: male-to-female 146(2) 134(2.04) 12(1.6) Transgender: female-to-male 16(0.22) 15(0.23) 1(0.13) Unknown 11(0.15) 11(0.17) Age [years](Mean [St.Dev.]) 52.78(13.4) 53.44(13.3) 46.98(12.9) 0.0000 HIV Transmission Factor 0.0000 Men who have sex with Men (MSM) 3111(42.59) 2736(41.73) 375(50.13) People who Inject Drugs (PWID) 350(4.79) 324(4.94) 26(3.48) MSM and PWID 88(1.2) 81(1.24) 7(0.94) Heterosexual Sex 2358(32.28) 2174(33.16) 184(24.6) Perinatal 131(1.79) 121(1.85) 10(1.34) Other 1260(17.25) 1114(16.99) 146(19.52) Unknown 7(0.1) 7(0.11) Time Since HIV Diagnosis 0.0000 0-5 years 575(7.87) 419(6.39) 156(20.86) 6-10 years 975(13.35) 800(12.2) 175(23.4) 11+ years 5710(78.17) 5309(80.97) 401(53.61) Unknown 45(0.62) 29(0.44) 16(2.14) CD4 [cells/uL] 0.0000 < 200 387(5.3) 316(4.82) 71(9.49) [200,500) 1822(24.94) 1599(24.39) 223(29.81) 500+ 5039(68.98) 4609(70.29) 430(57.49) Unknown 57(0.78) 33(0.5) 24(3.21) Viral Suppression (< 200 copies/mL) 0.0000 Virally Suppressed 6516(89.2) 5958(90.86) 558(74.6) Not Virally Suppressed 646(8.84) 485(7.4) 161(21.52) Unknown 143(1.96) 114(1.74) 29(3.88) AIDS 0.0004 AIDS Dx 2832(38.77) 2587(39.45) 245(32.75) No AIDS Dx 4473(61.23) 3970(60.55) 503(67.25) Type of HIV Care Site 0.0000 Community 4401(60.25) 4063(61.96) 338(45.19) Hospital 2904(39.75) 2494(38.04) 410(54.81) Ryan White Clinic 3836 (52.51) 3470 (52.92) 366 (48.93) 0.0384 Smoking Status 0.0000 Current 2179(29.83) 1931(29.45) 248(33.16) Former 1284(17.58) 1185(18.07) 99(13.24) Never 3120(42.71) 2822(43.04) 298(39.84) Unknown 722(9.88) 619(9.44) 103(13.77) Alcohol Abuse 0.0018 Current 832(11.39) 766(11.68) 66(8.82) Former 756(10.35) 699(10.66) 57(7.62) Never 4209(57.62) 3760(57.34) 449(60.03) Unknown 1508(20.64) 1332(20.31) 176(23.53) Illicit Drug Use 0.0001 Current 1094(14.98) 992(15.13) 102(13.64) Former 1227(16.8) 1115(17) 112(14.97) Never 3311(45.33) 2998(45.72) 313(41.84) Unknown 1673(22.9) 1452(22.14) 221(29.55) Insurance 0.0000 Private 2476(33.89) 2132(32.51) 344(45.99) Public 4170(57.08) 3854(58.78) 316(42.25) Other 147(2.01) 122(1.86) 25(3.34) Unknown 512(7.01) 449(6.85) 63(8.42) Employment 0.0481 Working Full-Time 2106(28.83) 1857(28.32) 249(33.29) Working Part-Time 229(3.13) 212(3.23) 17(2.27) Unemployed 1507(20.63) 1365(20.82) 142(18.98) Other 519(7.1) 470(7.17) 49(6.55) Unknown 2944(40.3) 2653(40.46) 291(38.9) Housing 0.0552 Permanent 6300(86.24) 5676(86.56) 624(83.42) Temporary/Unstable 383(5.24) 339(5.17) 44(5.88) Homeless 70(0.96) 65(0.99) 5(0.67) Other 12(0.16) 11(0.17) 1(0.13) Unknown 540(7.39) 466(7.11) 74(9.89) Open in a new tab a All demographics measured as of the participant’s date of last contact with the DC Cohort Specifically, participants who were engaged in care were more likely to be non-Hispanic Black (77.1% vs. 64.2%), female (28.4% vs. 19.5%), inject drugs (4.9% vs. 3.5%), and acquire HIV through heterosexual sex (33.2% vs. 24.6%). Furthermore, those who were engaged in care were older (mean: 53 years vs. 47 years), more likely to be VS (90.9% vs. 74.6%), have a history of AIDS (39.5% vs. 32.8%), receive care at a Community site clinic (62.0% vs. 45.2%), and receive care at a Ryan White clinic (52.9% vs. 48.9%). Alternatively, people who were engaged in care were less likely to be diagnosed with HIV in the past 5 years (6.4% vs. 20.9%) or have a CD4 < 200 cells/μL (4.8% vs. 9.5%). Substance use, insurance status, and employment status also significantly differed by care status ( Table 1 ). Based on simple logistic regression, females were significantly more likely than males to be engaged in care (OR (95% CI): 1.66 (1.37, 2.00). People who inject drugs (PWID) (1.71 (1.13, 2.58)) and those who acquired HIV through heterosexual sex (2.62 (1.35, 1.95)) were also more likely than MSM to be engaged in care. Similarly, those diagnosed with HIV more than 5 years ago as well as those with a CD4 count ≥ 200 cells/μL were likely to be engaged. On the other hand, non-Hispanic White participants were less likely to be engaged in care than non-Hispanic Black participants (0.49 (0.40, 0.59)), as well as those with non-VS (0.28 (0.23, 0.34)). Age, history of AIDS, type of HIV care site, Ryan White clinic, substance use, insurance, and employment status were also all associated with engagement in care ( Table 2 ). Table 2. Logistic regression odds ratios and their 95% confidence intervals (CI) describing the association between each covariate and being engaged in care. Covariate a , b Unadjusted Odds Ratio (95% CI) Adjusted Odds Ratio (95% CI) c Race/Ethnicity Non-Hispanic Black REF REF Non-Hispanic White 0.49 (0.40, 0.59) 0.43 (0.34, 0.54) Hispanic 0.92 (0.67, 1.26) 1.19 (0.85, 1.69) Other 0.58 (0.33, 1.00) 0.77 (0.43, 1.39) Gender Male REF REF Female 1.66 (1.37, 2.00) 1.34 (1.05, 1.71) Transgender 1.49 (0.84, 2.64) 1.78 (0.96, 3.31) Age [years](Mean [St.Dev.]) 1.04 (1.03, 1.04) 1.02 (1.01, 1.03) HIV Transmission Factor Men who have sex with Men (MSM) REF REF People who Inject Drugs (PWID) 1.71 (1.13, 2.58) 0.77 (0.48, 1.23) MSM and PWID 1.59 (0.73, 3.46) 1.57 (0.69, 3.56) Heterosexual Sex 1.62 (1.35, 1.95) 0.90 (0.70, 1.15) Perinatal 1.66 (0.86, 3.19) 1.87 (0.90, 3.86) Other 1.05 (0.85, 1.28) 0.77 (0.61, 0.99) Time Since HIV Diagnosis 0-5 years REF REF 6-10 years 1.70 (1.33, 2.18) 1.52 (1.16, 1.98) 11+ years 4.93 (4.00, 6.08) 3.56 (2.75, 4.62) CD4 [cells/uL] < 200 REF REF [200,500) 1.61 (1.20, 2.16) 1.26 (0.91, 1.75) 500+ 2.41 (1.83, 3.17) 1.71 (1.24, 2.36) Viral Suppression (< 200 copies/mL) Virally Suppressed REF REF Not Virally Suppressed 0.28 (0.23, 0.34) 0.30 (0.24, 0.39) AIDS N/A AIDS Dx REF No AIDS Dx 0.75 (0.64, 0.88) Type of HIV Care Site N/A Community REF Hospital 0.51 (0.44, 0.59) Ryan White Clinic 1.17 (1.01, 1.37) 0.96 (0.79, 1.16) Smoking Status Current REF REF Former 1.54 (1.20, 1.96) 1.49 (1.15, 1.95) Never 1.22 (1.02, 1.45) 1.56 (1.27, 1.91) Alcohol Abuse N/A Current REF Former 1.06 (0.73, 1.53) Never 0.72 (0.55, 0.95) Illicit Drug Use N/A Current REF Former 1.02 (0.77, 1.36) Never 0.99 (0.78, 1.25) Insurance Private REF REF Public 1.97 (1.67, 2.31) 1.86 (1.52, 2.27) Other 0.79 (0.51, 1.23) 0.82 (0.50, 1.34) Employment N/A Working Full-Time REF Working Part-Time 1.67 (1.00, 2.79) Unemployed 1.29 (1.04, 1.60) Other 1.29 (0.93, 1.78) Housing N/A Permanent REF Temporary/Unstable 0.85 (0.61, 1.17) Homeless 1.43 (0.57, 3.56) Other 1.21 (0.16, 9.38) Open in a new tab a All demographics measured as of the participant’s date of last contact with the DC Cohort; b Unknown values are not shown; c Adjusted for race/ethnicity, gender, age, HIV transmission factor, time since HIV diagnosis, CD4, VL,Ryan White site, smoking status, and insurance status Race/ethnicity, gender, age, HIV transmission factor, time since HIV diagnosis, CD4, VL, Ryan White site, smoking status, and insurance status were included in the multivariable logistic regression model. Based on the adjusted regression model, females were still significantly more likely to be engaged in care (1.34 (1.05, 1.71)), as were those with a CD4 ≥ 500 cells/μL (vs. < 200 cells/μL) (1.71 (1.24, 2.36)). Non-Hispanic White participants were less likely to be engaged in care compared to non-Hispanic Black participants (0.43 (0.34, 0.54)) and those with non-VS were less likely to be engaged in care (0.30 (0.24, 0.39)). The associations between age, time since HIV diagnosis, smoking status, insurance, and engagement in care also remained significant in the adjusted model ( Table 2 ). Comparison of Transfers who Re-entered Care versus did not Re-enter Care Of the 1,893 clinic-reported transfers, 832 (44.0%) re-entered care after transfer. Of those who re-entered care with a known CD4 or VL after their last contact, 61.0% had a CD4 count >500 cells/μL and 80.9% had VS. Most clinic-reported transfers were non-Hispanic Black (1,313; 69.4%), male (1,463; 77.3%), MSM (1,015; (53.6%), and the average age was 42.7 years (St. Dev. 14.9). Most participant demographics and clinical factors differed significantly across groups (p < 0.05) ( Table 3 ). For instance, those who re-entered care were more likely to be non-Hispanic Black (73.4% vs. 66.2%), female (25.4%), older, perinatal HIV transmission (14.2% vs. 3.4%), receive care at a Ryan White clinic (76.2% vs. 23.8%), have a CD4 < 200 cells/μL (9.3% vs. 5.5%), and have temporary or unstable housing (8.8% vs. 2.2%). On the other hand, those who re-entered care were less likely to have been diagnosed with HIV 11 or more years ago (48.6%, vs. 55.05%) ( Table 3 ). Table 3. Distribution of participant characteristics who were a clinic-reported transfer stratified by those who restarted care elsewhere or not, as of December 31, 2023. Chi-Square tests and Wilcoxon Rank Sum tests used to calculate p-values for categorical and continuous variables, respectively. Demographic Total Clinic-reported Transfers (N = 1,893) Evidence of Re-engagement in Care (N = 832) No Evidence of Re-engagement in Care (N = 1,061) p-value Race/Ethnicity 0.0006 Non-Hispanic Black 1313(69.36) 611(73.44) 702(66.16) Non-Hispanic White 323(17.06) 119(14.3) 204(19.23) Hispanic 160(8.45) 53(6.37) 107(10.08) Other 30(1.58) 14(1.68) 16(1.51) Unknown 67(3.54) 35(4.21) 32(3.02) Gender 0.0001 Male 1463(77.28) 607(72.96) 856(80.68) Female 396(20.92) 211(25.36) 185(17.44) Transgender: male-to-female 24(1.27) 13(1.56) 11(1.04) Transgender: female-to-male 7(0.37) 1(0.12) 6(0.57) Unknown 3(0.16) 0(0.00) 3(0.28) Age [years](Mean [St.Dev.]) 42.72(14.91) 39.37(15.18) 45.39(14.15) 0.0000 HIV Transmission Factor 0.0000 Men who have sex with Men (MSM) 1015(53.62) 395(47.48) 620(58.44) People who Inject Drugs (PWID) 80(4.23) 24(2.88) 56(5.28) MSM and PWID 23(1.22) 6(0.72) 17(1.6) Heterosexual Sex 410(21.66) 174(20.91) 236(22.24) Perinatal 154(8.14) 118(14.18) 36(3.39) Other 211(11.15) 115(13.82) 96(9.05) Time Since HIV Diagnosis 0.0000 0-5 years 456(24.09) 228(27.4) 228(21.49) 6-10 years 428(22.61) 199(23.92) 229(21.58) 11+ years 988(52.19) 404(48.56) 584(55.04) Unknown 21(1.11) 1(0.12) 20(1.89) CD4 [cells/uL] 0.0000 < 200 135(7.13) 77(9.25) 58(5.47) [200,500) 517(27.31) 237(28.49) 280(26.39) 500+ 1208(63.81) 515(61.9) 693(65.32) Unknown 33(1.74) 3(0.36) 30(2.83) Viral Suppression (< 200 copies/mL) 0.0000 Virally Suppressed 1530(80.82) 624(75) 906(85.39) Not Virally Suppressed 330(17.43) 203(24.4) 127(11.97) Unknown 33(1.74) 5(0.6) 28(2.64) AIDS 0.7234 AIDS Dx 579(30.59) 258(31.01) 321(30.25) No AIDS Dx 1314(69.41) 574(68.99) 740(69.75) Type of HIV Care Site 0.0000 Community 1075(56.79) 329(39.54) 746(70.31) Hospital 818(43.21) 503(60.46) 315(29.69) Ryan White Clinic 810 (42.79) 617 (76.17) 193 (23.83) 0.0000 Smoking Status 0.0000 Current 446(23.56) 246(29.57) 200(18.85) Former 268(14.16) 102(12.26) 166(15.65) Never 949(50.13) 373(44.83) 576(54.29) Unknown 230(12.15) 111(13.34) 119(11.22) Alcohol Abuse 0.0000 Current 195(10.3) 109(13.1) 86(8.11) Former 178(9.4) 98(11.78) 80(7.54) Never 1170(61.81) 412(49.52) 758(71.44) Unknown 350(18.49) 213(25.6) 137(12.91) Illicit Drug Use 0.0000 Current 339(17.91) 142(17.07) 197(18.57) Former 267(14.1) 137(16.47) 130(12.25) Never 970(51.24) 340(40.87) 630(59.38) Unknown 317(16.75) 213(25.6) 104(9.8) Insurance 0.0000 Private 849(44.85) 225(27.04) 624(58.81) Public 828(43.74) 476(57.21) 352(33.18) Other 44(2.32) 34(4.09) 10(0.94) Unknown 172(9.09) 97(11.66) 75(7.07) Employment 0.0000 Working Full-Time 525(27.73) 202(24.28) 323(30.44) Working Part-Time 81(4.28) 55(6.61) 26(2.45) Unemployed 257(13.58) 165(19.83) 92(8.67) Other 200(10.57) 115(13.82) 85(8.01) Unknown 830(43.85) 295(35.46) 535(50.42) Housing 0.0000 Permanent 1564(82.62) 614(73.8) 950(89.54) Temporary/Unstable 96(5.07) 73(8.77) 23(2.17) Homeless 18(0.95) 13(1.56) 5(0.47) Other 1(0.05) 0(0.00) 1(0.09) Unknown 214(11.3) 132(15.87) 82(7.73) Open in a new tab From the simple logistic regression analysis, females were significantly more likely to have re-entered care than males (OR (95% CI): 1.61 (1.29, 2.01)), as were those with perinatal HIV transmission compared to MSM (5.15 (3.47, 7.63)). Those at a Ryan White clinic were especially more likely to re-enter care than those who did not attend a Ryan White clinic (12.91 (10.36, 16.08)), as well as those who had temporary/unstable housing (4.91 (3.04, 7.93)) or were homeless (4.02 (1.43, 11.34)) compared to those with permanent housing. On the other hand, non-Hispanic White (0.67 (0.52, 0.86)) and Hispanic (0.57 (0.40, 0.81)) participants were less likely to re-enter care than non-Hispanic Black participants. Those who had HIV for 11 or more years and those who had a CD4 ≥ 200 cells/μL were also less likely to re-enter care. Age, VS, type of HIV care site, smoking status, alcohol abuse, illicit drug use, insurance, and employment were also associated with re-entering care ( Table 4 ). Table 4. Logistic regression odds ratios and their 95% confidence intervals (CI) describing the association between each covariate and re-entering care after a clinic-reported transfer. Covariate a , b Unadjusted Odds Ratio (95% CI) Adjusted Odds Ratio (95% CI) d Race/Ethnicity Non-Hispanic Black REF REF Non-Hispanic White 0.67 (0.52, 0.86) 1.58 (1.13, 2.19) Hispanic 0.57 (0.40, 0.81) 0.40 (0.26, 0.62) Other (0.49, 2.08) 2.00 (0.81, 4.93) Gender Male REF REF Female 1.61 (1.29, 2.01) 0.95 (0.66, 1.37) Transgender 1.16 (0.57, 2.37) 0.44 (0.19, 1.03) Age [years](Mean [St.Dev.]) 0.97 (0.97, 0.98) 0.99 (0.99, 1.00) HIV Transmission Factor Men who have sex with Men (MSM) REF REF People who Inject Drugs (PWID) 0.67 (0.41, 1.10) 0.72 (0.38, 1.36) MSM and PWID 0.55 (0.22, 1.42) 0.55 (0.17, 1.74) Heterosexual Sex 1.16 (0.92, 1.46) 0.92 (0.63, 1.33) Perinatal 5.15 (3.47, 7.63) 0.95 (0.57, 1.57) Other 1.88 (1.39, 2.54) 1.19 (0.80, 1.77) Time Since HIV Diagnosis N/A 0-5 years REF 6-10 years 0.87 (0.67, 1.13) 11+ years 0.69 (0.55, 0.87) CD4 [cells/uL] N/A < 200 REF [200,500) 0.64 (0.44, 0.93) 500+ 0.56 (0.39, 0.80) Viral Suppression (< 200 copies/mL) Virally Suppressed REF REF Not Virally Suppressed 2.32 (1.82, 2.96) 1.47 (1.08, 2.01) AIDS N/A AIDS Dx REF No AIDS Dx 0.97 (0.79, 1.18) Type of HIV Care Site N/A Community REF Hospital 3.62 (2.99, 4.38) Ryan White Clinic 12.91 (10.36, 16.08) 13.76 (10.31, 18.37) Smoking Status N/A Current REF Former 0.50 (0.37, 0.68) Never 0.53 (0.42, 0.66) Alcohol Abuse Current REF REF Former 0.97 (0.64, 1.46) 3.17 (1.88, 5.33) Never 0.43 (0.32, 0.58) 1.05 (0.71, 1.54) Illicit Drug Use N/A Current REF Former 1.46 (1.06, 2.02) Never 0.75 (0.58, 0.96) Insurance N/A Private REF Public 3.75 (3.05, 4.61) Other 9.43 (4.58, 19.40) Employment N/A Working Full-Time REF Working Part-Time 3.38 (2.06, 5.57) Unemployed 2.87 (2.10, 3.91) Other 2.16 (1.55, 3.01) Housing c Permanent REF REF Temporary/Unstable 4.91 (3.04, 7.93) 2.69 (1.53, 4.73) Homeless 4.02 (1.43, 11.34) 1.92 (0.61, 6.04) Open in a new tab a All demographics measured as of the participant’s date of last contact with the DC Cohort; b Unknown values are not shown; c Does not include “Other” housing status; d Adjusted for race/ethnicity, gender, age, HIV transmission factor, viral load, Ryan White site, alcohol abuse status, and housing status Race/ethnicity, gender, age, HIV transmission factor, VS, Ryan White clinic, alcohol abuse, and housing status were retained in the multivariable logistic regression model. In the adjusted model, participants who were non-Hispanic White (OR 1.58 (1.13, 2.19)) were significantly more likely to re-enter care than those who were non-Hispanic Black, as well as those with temporary/unstable housing compared to those with permanent housing (2.69 (1.53, 4.73)). Once again, there was a strong association between attending a Ryan White clinic and re-entering care (13.76 (10.31, 18.37)). Alternatively, Hispanic participants were less likely than non-Hispanic Black participants to re-enter care (0.40 (0.26, 0.62)). VS and alcohol abuse were also significantly associated with whether or not someone re-entered care ( Table 4 ). DISCUSSION Using data from the DC Cohort and DC Health, we determined that 52.5% of enrolled participants were engaged in care and only 6.0% were truly out of care. Furthermore, only about half of participants who reported to their primary DC Cohort site that they were transferring clinics successfully re-entered care locally after transfer. By linking clinical and surveillance data, we were able to more accurately assess PWH’s care status. Using only the DC Cohort database and RA manual review, without the linked surveillance data, would have resulted in classifying 2,439 participants as OOC. Of those, 1,691 were ultimately determined to have transferred using surveillance data and 748 deemed OOC. This means only 31% of people who would have been classified as OOC were truly OOC in our study. Another study conducted in New York City that used a D2C approach similarly found that only 26% of individuals believed to be OOC were truly OOC after incorporating surveillance data. 21 These findings emphasize the importance of linked surveillance data, in addition to clinic-reported sources. The Louisiana Public Health Information Exchange (LaPHIE) was a novel, bidirectional health care delivery-public health information exchange that demonstrated effective re-engagement of OOC PWH, however its success required the collaboration of emergency departments, inpatient units, ambulatory clinics alongside the Louisiana public health authority. 22 The combination of clinic and health department data has been used by one other HIV cohort in North America, the Ontario HIV Treatment Network Cohort Study (OCS). 23 , 24 However, this study was conducted in Canada, where healthcare systems and insurance coverage are different than in the US. Future US-based studies should incorporate linked surveillance data to more accurately identify and re-engage OOC PWH. These approaches are labor and resource intensive, face data-sharing barriers, privacy concerns, and surveillance data may still contain inaccuracies or missingness. 9 , 25 More efficient and streamlined processes are needed to implement this approach in broader settings. Despite the high proportion of participants who were engaged in care, we found significant disparities by gender, age, HIV transmission factor, time since HIV diagnosis, CD4, VL, smoking status, and insurance status. Disparities by insurance status could be due to differences in care coverage. For instance, some private insurance plans may not have adequate care coverage or there may be more gaps in coverage due to its dependence on employment status. Similarly, those who were OOC may be more likely to be non-VS since they are likely not on regular treatment. Further studies will be needed to elucidate the reasons behind these associations. These findings also suggest that although a majority of PWH were found to be linked to care, there may be significant disparities in who is linked to care nation-wide. 3 As the United States aims to meet the 95-95-95 targets set by UNAIDS, effort should be made to ensure these targets are met equally across PWH, regardless of gender, age, HIV transmission factor, clinical factors, and insurance status. Similar trends have been found previously with regards to VL, substance use, and insurance status. For instance, prior studies have found similar associations between age, substance use, VS, social determinants of health, and being OOC. 26 , 27 Multiple studies have also found similar trends with regards to insurance and engagement in care. Specifically, one study by Kerr et. al. found that people on Medicare, which was classified as public insurance in our study, were also more likely to be engaged in primary HIV care. 28 Another nation-wide study among MSM in the US found that lower income and lack of health insurance was associated with being OOC. 29 This study also found that private insurance and stimulant use were associated with not being on an ART regimen, which could be used as a proxy measure of care engagement. These disparities by insurance status found both in the literature and in our results suggest that public insurance may sufficiently provide coverage for HIV care needs, though gaps may exist for individuals with private insurance. Future interventions should evaluate how these gaps in coverage can be filled. Although nearly half of the clinic-reported transfers in our study successfully re-entered care, significant disparities by race/ethnicity, VS, type of HIV care site, alcohol abuse, and housing status were observed. A 2018 analysis of DC Cohort participants found that individuals re-engaged through the city-wide Recapture Blitz – which leveraged surveillance and clinical data to identify persons OOC – were significantly more likely to be engaged in care at 18 months and to achieve VS at 6, 12, and 18 months, compared to those not re-engaged. These findings highlight the critical role of targeted re-engagement efforts in improving HIV care outcomes. 30 Another study conducted in Seattle and King County, WA used a D2C strategy and also found that half of participants who were believed to be poorly engaged in care had successfully re-entered care. 31 Interestingly, this study found that VS was a significant predictor of re-entry into care, which is contrary to our findings here. Our study has several limitations. First, it may be limited by generalizability. A prior study on DC Cohort participants found that PWH in the DC Cohort were more engaged in care than the general population of PWH. 18 Therefore, our findings may not be applicable to the general population of PWH. Similarly, the low proportion of people who were truly OOC may reflect the fact that enrollment in the DC Cohort occurs during routine clinical care visits - meaning all participants had previously visited or were newly attending a participating HIV clinic, potentially underestimating the true proportion of OOC PWH in Washington, DC. Furthermore, we considered participants engaged in care if they had at least one HIV care encounter or lab in the 18 months prior to 12/31/2023. However, the HIV care continuum defines retention in care as receiving two or more labs at least 3 months apart, meaning some participants classified as engaged in care in our study may not have met this criteria. 1 Moreover, our study evaluated engagement in care at one point in time. However, a prior study by Colasanti et. al. found this measure may be suboptimal for evaluating long-term treatment success. 32 Future studies should consider a longitudinal approach for classifying engagement in care. Lastly, our linkage data included only clinics in Washington, DC, potentially missing participants who re-engaged in care elsewhere. This limitation may affect the accuracy of our clinic-reported transfers who did not re-enter care. Furthermore, this limitation did not allow us to distinguish between participants who dropped out of care and participants who moved away from Washington, DC. Thus, factors associated with re-entering care in our study should be interpreted with caution, as our measure could be a proxy for relocation outside of the Washington, DC area rather than re-engagement. Finally, we evaluated factors associated with care across the entire time period of the DC Cohort (2011-2023). Of note, when we restricted analysis to only those participants who enrolled from 2018 to 2023 (Engaged in Care N = 1,474; Out of Care N = 63), we found that only age, time since HIV diagnosis, CD4, viral suppression, and alcohol use disorder were associated with care status. These differences could be due to differences in sample size or changes in the associations over time. Nonetheless, since living participants who enrolled at any point in the study should be engaged in care, we retained all participants in our analysis, regardless of consent date. Despite these limitations, our study offers notable strengths. This is one of the first and largest U.S.-based studies to integrate clinic-reported and surveillance data in classifying care status. Combining data from the DC Cohort, manual RA review, and surveillance data allows confident OOC classification. Additionally, leveraging EHR data strengthens the objectivity of the covariate measures in our analysis. Finally, DC Health’s ability to identify specific HIV-related lab results ensures accurate classification of care engagement and prevents misclassification due to non-HIV care. Future Directions Integrating cohort data with surveillance-based D2C strategies is a promising approach for identifying and re-engaging OOC PWH. However, manual RA review is time-consuming and labor-intensive. Updated EHR systems capable of detecting transfers and deaths, and notifying study teams to such changes, could reduce the need for manual review. Advances in technology make machine learning models that can predict patients at risk of falling OOC a feasible tool worth considering. Clinics should also be encouraged to use health department data to determine which patients have re-entered care elsewhere and which should be prioritized for re-engagement efforts. Furthermore, programs to ensure patients remain in care and adherent to treatment, such as peer support programs, should be evaluated to ensure people remain in care after being linked. Supplementary Material Supplemental Material NIHMS2165805-supplement-Supplemental_Material.docx (33.7KB, docx) Acknowledgements: Data in this manuscript were collected by the DC Cohort Study Group with investigators and research staff located at: Children’s National Hospital Pediatric clinic (Natella Rakhmanina); the Senior Deputy Director of the DC Department of Health HAHSTA (Clover Barnes); Family and Medical Counseling Service (Rita Aidoo); Georgetown University (Princy Kumar); The George Washington University Biostatistics Center (Tsedenia Bezabeh, Vinay Bhandaru, Asare Buahin, Nisha Grover, Lisa Mele, Alla Sapozhnikova, Greg Strylewicz, and Marinella Temprosa); The George Washington University Department of Epidemiology (Shannon Barth, Morgan Byrne, Amanda Castel, Alan Greenberg, Shannon Hammerlund, Paige Kulie, Anne Monroe, Lauren O’Connor, James Peterson, Su Yadana, and Martin Zavala) and Department of Biostatistics and Bioinformatics; The George Washington University Medical Faculty Associates (Jose Lucar); Howard University Adult Infectious Disease Clinic (Jhansi L. Gajjala) and Pediatric Clinic (Sohail Rana); Kaiser Permanente Mid-Atlantic States (Michael Horberg); La Clinica Del Pueblo (Carrington Koebele); MetroHealth (Duane Taylor); Washington Health Institute, formerly Providence Hospital (Jose Bordon); Unity Health Care (Gebeyehu Teferi); Veterans Affairs Medical Center (Debra Benator and Rachel Denyer); Washington Hospital Center (Adam Klein); and Whitman-Walker Institute (Stephen Abbott). The authors also wish to thank Maria Jaurretche for her contributions to this work. Funding Research reported in this publication was supported by the National Institute Of Allergy And Infectious Diseases of the National Institutes of Health under Award Number R24AI152598. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Footnotes Institutional Review Board Statement: The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of George Washington University (071029). Informed Consent Statement: Informed consent was obtained from all participants involved in the study. Conflicts of Interest: The authors declare no conflicts of interest. The views expressed in this article are those of the authors and do not necessarily reflect the position or the policy of the Department of Veterans Affairs or the United States government. Data Availability Statement: Per DC Cohort protocols, data are available upon request and approval of the DC Cohort Executive Committee. Interested parties should email the Principal Investigator at acastel@gwu.edu . REFERENCES 1. HIV Care Continuum. HIV.gov Accessed April 3, 2025. https://www.hiv.gov/federal-response/other-topics/hiv-aids-care-continuum 2. 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