Abstract
Background
The COVID-19 pandemic created interruptions in healthcare for many people with chronic illnesses, including people with HIV (PWH). Our objective was to assess the association between individual and clinic-level factors and virological suppression throughout the COVID-19 pandemic.
Methods
The DC Cohort, a longitudinal cohort study of PWH in Washington, DC, conducted a survey to examine the impact of the COVID-19 pandemic on HIV clinical services between 2019 and 2021. This longitudinal study used clinic and patient characteristics throughout the pandemic to explore changes in virologic suppression (≤ 200 copies/mL) status, comparing pre- (closest lab prior to March 1, 2020) and peri-pandemic time periods (March 1, 2020–December 31, 2021). We assessed prevalence of demographic and clinical characteristics of DC Cohort Study participants, by maintenance of viral suppression peri-pandemic. Multilevel multivariable logistic regression analyses were performed to compare clinic characteristics of those who did and did not maintain virologic suppression during the pandemic.
Results
Data were collected from 4018 participants from 14 DC Cohort clinics. Differences in prevalence estimates were found based on clinic-level characteristics. Compared to PWH who were virally suppressed, among PWH with loss of virologic suppression, 14.5% were at a clinic with modified substance abuse counseling (vs. 8.5% of those who were virally suppressed, p < 0.05), 22.4% were at a clinic with reduced clinic hours (vs. 15.1% of virally suppressed, p < 0.05), 51.7% attended a clinic with reduction of clinic hours (vs. 42.6% of virally suppressed, p < 0.05). PWH experiencing loss of virologic suppression during the pandemic had greater prevalence of care at a clinic with lower telehealth utilization. These findings were not significant in multivariable logistic regression models.
Conclusion
These findings help identify the impact of pandemic disruptions on clinic operations and PWH’s ability to maintain virologic suppression. Reduction in clinic services during the pandemic were associated with loss of virologic suppression status while uptake of telehealth services was associated with maintenance of virologic suppression. The results may help prepare for future pandemic or public health emergencies that disrupt healthcare.
Keywords: Viral suppression, HIV infection, Clinic-level factors, COVID-19 pandemic, HIV care continuum, HIV service delivery
Introduction
The COVID-19 pandemic lead to vast interruptions in healthcare, leading to lack of access to proper treatment and care for persons with chronic conditions, including persons with HIV (PWH). Medication adherence and visit retention are important factors related to virological suppression among PWH. Due to their decreased immune function and increased susceptibility to illness, it is crucial to consider the enormous effect the pandemic had on PWH regardless of viral suppression status. Pre-pandemic data from 2019 estimated that only 66% of PWH in the United States were retained in care, defined as having an HIV-related medical visit within a 4-month period [1]. However, the COVID-19 pandemic magnified gaps in the HIV care continuum [2].
Findings regarding the impact of the pandemic on viral suppression have been mixed [3]. One study at a safety-net HIV clinic in San Francisco found that viral suppression rates fell significantly when the pandemic stay-at-home order and telemedicine were instituted [4]. That clinic implemented an intervention called the “POP-UP” program, a model for low barrier access to care for unstably housed patients within an HIV clinic, resulting in better care engagement and viral suppression among program participants [5]. A study focused on PWH in Ontario found increased likelihood of antiretroviral therapy adherence and viral load suppression among those who participated in virtual care during the pandemic [6]. A systematic review describing the impact of COVID-19 pandemic on HIV outcomes found that four of five studies reported no impact of the pandemic on viral suppression among PWH [7]. Discrepancies in viral suppression findings may be attributable to geographic location studied (two studies were non-US), time period of viral load collection comparing before/during the pandemic, and definition of viral suppression (some studies used 200 copies/mL as the cut off where another used 50 copies/mL). Similarly, a review article reported that challenges to antiretroviral therapy access during the COVID-19 pandemic did not result in large-scale reductions in viral suppression [8].
Given these findings, there is a need to further investigate the relationship between the interruptions in the HIV care continuum caused by the COVID-19 pandemic and maintenance of viral suppression among PWH. Gaining an understanding of how individual and clinic-level changes during the COVID-19 pandemic affected PWH’s ability to remain virally suppressed may help illuminate and potentially ameliorate gaps in HIV healthcare delivery. Therefore, the objectives of this analysis are to describe pandemic era HIV clinic service changes and assess the association between the availability of and/or changes to HIV services throughout the pandemic and their impact on PWH’s ability to remain virally suppressed.
Methods
Data sources
Data for this analysis come from the DC Cohort study, a longitudinal prospective cohort study of approximately 13,000 PWH receiving HIV care at one of 14 clinics in Washington, DC. Enrollment in the cohort began in 2011 and is ongoing. Previous publications have described the methods of the DC Cohort in detail [9]. In short, sociodemographic, clinical, and laboratory data are abstracted monthly from participants’ outpatient electronic medical record (EMR) systems into the DC Cohort database prospectively from the date of cohort enrollment. Additionally, data from the DC Cohort Site Assessment Survey, administered to the Site Principal Investigators of clinics participating in the DC Cohort Study, were also utilized in this analysis.
The DC Cohort Site Assessment Survey characterizes service delivery and resources available at each participating Cohort site to better understand the context in which HIV care is delivered. The most recent survey was conducted in Spring 2022 and was updated to include questions designed to investigate which clinic-level services were halted, modified, and continued during the COVID-19 pandemic. Detailed information about the methods of this survey have been published previously [10]. Site Principal Investigators received an electronic questionnaire that addressed clinic characteristics and the impact of the COVID-19 pandemic on the clinic, such as whether the clinic experienced a closure, a reduction in providers, discontinuation of services, mitigation strategies, and use of telehealth. Survey questions were adapted from several validated questionnaires and reports that investigated the impact of COVID-19 on preparedness and resources, HIV services, telehealth use, and vaccine rollout [11–19]. The survey was determined to be non-human subjects research by the George Washington University (GWU) Institutional Review Board (IRB); the DC Cohort longitudinal study is fully approved by the GWU IRB and external site IRBs. DC Cohort participant level data were linked to Provider Survey responses by clinic site. Survey responses were not validated, and missing data was not imputed. Selected results from the survey have previously been published. However, the current analysis focuses on patient-level outcomes impacted by site-level pandemic modifications [10].
Participants
Eligibility criteria for this study included being enrolled in the DC Cohort study participants by March 1, 2019, still active as of June 1, 2020 (defined by having at least one HIV encounter visit at the clinic within the prior 18 months), aged 18 as of March 1, 2020, having at least one CD4 and HIV viral load lab both before and after March 1, 2020. Due to missing HIV lab data, two of the 14 clinic sites were excluded from analyses incorporating individual-level data. For these analyses, pre-pandemic is before March 1, 2020 and peri-pandemic is between March 1, 2020 and December 31, 2021.
Measures
The primary outcome of this analysis was viral suppression status peri-pandemic compared to pre-pandemic (i.e., remaining virally suppressed or experiencing loss of viral suppression). Viral suppression was defined as having HIV RNA ≤ 200 copies/mL. Clinic-level variables were constructed from Provider Survey responses. Individual-level predictors were abstracted from the DC Cohort database. Individual-level variables included sociodemographic characteristics (e.g., age, sex at birth, gender, race/ethnicity, and state of residence), primary mode of HIV transmission, pre-pandemic CD4 count (before March 1, 2020), peri-pandemic CD4 count (March 1, 2020 through December 31, 2021), pre-pandemic (before March 1, 2020) HIV viral load suppression, peri-pandemic (March 1, 2020 through December 31, 2021) HIV viral load suppression, and whether the participant received primary care at the clinic. The most recent HIV RNA labs and CD4 labs closest to the pre-pandemic era date (before March 1, 2020) and most recent during the peri-pandemic era (March 1, 2020, through December 31, 2021) were chosen for each participant.
Statistical analyses
Descriptive statistics (frequency counts, prevalence estimates, and chi-square tests) were calculated for clinic-level and individual-level factors. The participants were stratified based on viral suppression status change from pre- to peri-pandemic. To examine the relationship between maintaining a suppressed viral load and clinic-level factors, considering site variability, we applied a multilevel multivariable logistic regression analysis [20]. This method organized the data into two levels: individual characteristics and clinic factors as level one and each site as level two. A generalized linear mixed model (GLMM) was fitted due to the dichotomous dependent variable. PROC GLIMMIX in SAS was used to estimate variable parameters and to assess the extent of random variation across sites. P was set at p < 0.05 for statistical significance. Statistical Analysis Software (SAS, Cary, NC) version 9.4 was used for all analyses [21].
Results
The current analysis included 4018 DC Cohort participants with complete pre- and peri-pandemic lab data. Table 1 summarizes the descriptive statistics of the demographic and socioeconomic variables among PWH, stratified by peri-pandemic viral status. Among the 4018 participants included in the analysis, 205 (5.1%) experienced loss of viral suppression. Those who experienced loss of viral suppression peri-pandemic were younger (mean age 48.5 years vs. 52 years, p < 0.0001). Those who remained virologically suppressed peri-pandemic were 13% non-Hispanic white, 77% non-Hispanic Black, and 6% Hispanic compared to those who did not remain virologically suppressed who were 3% non-Hispanic White, 89% non-Hispanic Black, and 6% Hispanic (p = 0.0002). The proportion of PWH remaining virally suppressed differed by HIV risk factor with men who have sex with men (MSM) representing 39% of the virally suppressed group and 32% of the non-suppressed group (p = 0.0041). HIV care at a Ryan White-funded clinic was prevalent among 50% of participants who did not remain virally suppressed compared to 40% of those who were virologically suppressed (p = 0.0089).
Table 1.
Demographic and clinical characteristics of DC cohort study participants, by maintenance of viral suppression peri-pandemic, 1/1/2011-12/31/2021, N = 4018
| Total cohort N = 4018 |
Loss of viral suppression peri-pandemic n = 205 (5.1%)a | Remained virally suppressed peri-pandemic n = 3813 (94.9%)a |
p-value | |
|---|---|---|---|---|
| n (%) | n (%) | n (%) | ||
| Age, median (IQR) (as of March 1st, 2020) | 54 (44, 61) | 48.5 (38, 58) | 52 (44, 61) | < 0.0001 |
| Gender | ||||
| Male | 2694 (67.1%) | 134 (65.4%) | 2560 (67.2%) | 0.4623 |
| Female | 1235 (30.8%) | 64 (31.2%) | 1171 (30.7%) | |
| Transgender: female to male | 5 (0.1%) | 0 (0%) | 5 (0.1%) | |
| Transgender: male to female | 80 (2%) | 7 (3.4%) | 73 (1.9%) | |
| Race/ethnicity | ||||
| Non-Hispanic Black | 3180 (77.4%) | 182 (88.8) | 2926 (76.8%) | 0.0002 |
| Non-Hispanic white | 497 (12.4%) | 6 (2.9%) | 491 (12.9%) | |
| Hispanic | 250 (6.2%) | 13 (6.3%) | 237 (6.2%) | |
| Other | 48 (1.2%) | 2 (1.0%) | 46 (1.2%) | |
| Unknown | 111 (2.8%) | 2 (1.0%) | 109 (2.9%) | |
| State of residence | ||||
| District of Columbia | 3107 (77.3%) | 175 (85.4%) | 2932 (76.9%) | 0.0287 |
| Maryland | 708 (17.6%) | 26 (12.7%) | 682 (17.9%) | |
| Virginia | 173 (4.3%) | 3 (1.5%) | 170 (4.5%) | |
| Other | 30 (1.0%) | 1 (0.1%) | 29 (1.0%) | |
| Mode of HIV transmission | ||||
| Men who have sex with men | 1531 (38.1%) | 66 (32.2%) | 1465 (38.5%) | 0.0041 |
| Heterosexual | 1387 (34.6%) | 74 (36.1%) | 1313 (34.5%) | |
| IDU | 209 (5.2%) | 9 (4.4%) | 200 (5.3%) | |
| IDU/MSM | 43 (1.1%) | 2 (1.0%) | 41 (1.1%) | |
| Perinatal | 38 (1.0%) | 7 (3.4%) | 31 (1.0%) | |
| Other/unknown | 807 (20.1%) | 47 (22.9%) | 760 (20.0%) | |
| History of substance use | ||||
| Yes | 2539 (63.2%) | 141 (68.8%) | 2398 (62.9%) | 0.0893 |
| CD4 count | ||||
| CD4 count pre-pandemic, mean (IQR)a | 713 (479, 893) | 574 (322, 786) | 721 (488, 898) | <0.0001 |
| CD4 count peri-pandemic, mean (IQR)b | 725 (480, 911) | 495 (254, 684) | 738 (495, 918) | <0.0001 |
| Receive primary care at clinic | 3073 (76.5%) | 167 (81.5%) | 2906 (76.2%) | 0.3768 |
| Clinic type | ||||
| Community-based | 2845 (70.8%) | 145 (70.7%) | 2700 (70.8%) | 0.9807 |
| Hospital-based | 1173 (29.2%) | 60 (29.3%) | 1113 (29.2%) | |
| Attend clinic receiving Ryan White funding | 1645 (41%) | 102 (49.8%) | 1543 (40.47%) | 0.0089 |
a. Peri-pandemic defined as 3/2/2020–12/31/2021 using latest lab value
b. Pre-pandemic defined as 1/1/2011–3/1/2020 using most recent lab value
Table 2 presents the prevalence estimates of participants impacted by clinic-level factors during the pandemic by viral status. There were greater percentages of participants experiencing loss of viral suppression at sites with modified substance abuse counseling (14.2% vs. 8.5%, p = 0.0054), reduced clinic hours (22.4% vs. 15.1%, p = 0.0047), reorganization of appointments to only allow scheduled visits (51.7% vs. 42.6%, p = 0.0102), and prioritization of in-person visits for patients with acute/urgent medical conditions (14.2% vs. 25.1%, p = 0.0004). Differences in maintenance of viral suppression were found by percentages of providers providing telehealth during the pandemic (i.e., 11.7% not virally suppressed vs. 7.7% virally suppressed amongst those with only 10–24% of clinicians offering telehealth services compared to 57.6% of those with 75% or more offering telehealth, p = 0.0223). In unadjusted models from the multilevel modeling analyses, there were no statistically significant findings comparing those with and without maintenance of viral suppression (Table 2). Findings remained the same after controlling for age, sex at birth, race/ethnicity, HIV transmission risk factor, and clinic size in multilevel multivariable models.
Table 2.
Prevalence estimates and unadjusted and adjusted odds ratios and 95% confidence intervals of clinic-level factors by maintenance of viral suppression during the pandemic among DC cohort participants, 1/1/2011–12/31/2021, N = 4018
| Clinic-level factor | Loss of viral suppression peri-pandemic (N = 205)a | Remained virally suppressed peri-pandemic (N = 3813)a | Chi-sq p value | Multilevel modeling | |
|---|---|---|---|---|---|
| N (column %) | N (column %) | Unadjusted Odds Ratio (95% CI) | Adjusted Odds Ratio (95% CI)b |
||
| Reduction in peer navigators (ref = no) | 68 (33.17) | 1089 (28.56) | 0.1556 | 1.31 (0.62, 2.80) | 0.99 (0.37, 2.70) |
| Modified substance abuse counseling (ref = no) | 29 (14.15) | 324 (8.50) | 0.0054 | 1.68 (0.82, 3.43) | 1.82 (0.85, 3.89) |
| Multi-month dispensation of ART medication (ref = yes) | 199 (97.07) | 3745 (98.22) | 0.2356 | 1.72 (0.51–5.87) | 1.37 (0.40, 4.67) |
| Alternative drug delivery (i.e. delivery via postal/courier, home/community delivery, or pick-up) (ref = yes) | 199 (97.07) | 3745 (98.22) | 0.2356 | 1.72 (0.51, 5.87) | 1.37 (0.40, 4.67) |
| Reduced clinic hours (ref = no) | 46 (22.44) | 576 (15.11) | 0.0047 | 1.69 (0.93, 3.06) | 1.79 (0.87, 3.67) |
| Reorganization of appointments, only allowing scheduled visits (ref = no) | 106 (51.71) | 1624 (42.59) | 0.0102 | 1.38 (0.78, 2.42) | 1.48 (0.84, 2.60) |
| Use of staff working at home to contact patients remotely to encourage appointment attendance (ref = yes) | 184 (89.76) | 3239 (84.95) | 0.0589 | 0.71 (0.34, 1.51) | 0.82 (0.36, 1.87) |
| Use of staff working at home to contact patients remotely to inquire about perceived barriers to regimen maintenance throughout the pandemic (ref = yes) | 14 (69.27) | 2468 (64.73) | 0.1842 | 0.87 (0.46, 1.65) | 1.04 (0.44, 2.43) |
| Provision of appointment reminders to patients with missing viral load measures (w/in 6-month window) (ref = yes) | 100 (48.78) | 1905 (49.96) | 0.7420 | 0.94 (0.49 ,1.82) | 1.12 (0.57, 2.19) |
| Prioritization of appointments to patients without viral load measures (w/in 6-month window) (ref = yes) | 95 (46.34) | 1840 (48.26) | 0.5930 | 1.01 (0.53, 1.94) | 1.17 (0.61, 2.22) |
| Prioritization of appointments for those with changes in their health (ref = yes) | 83 (40.49) | 1612 (42.28) | 0.6135 | 0.93 (0.48, 1.80) | 1.14 (0.62, 2.11) |
| Prioritization of appointments for those without symptoms (ref = no) | 63 (30.73) | 1101 (28.87) | 0.5681 | 1.18 (0.560, 2.50) | 1.05 (0.5, 2.21) |
| Patient prioritization of in-person visits for those newly diagnosed (ref = yes) | |||||
| There was no clinic-wide standard; individual providers decided | 54 (26.34) | 1052 (27.59) | 0.5976 | 0.97 (0.44, 2.15) | 1.16 (0.54, 2.52) |
| Protocol did not change, patients were not prioritized | 60 (29.27) | 994 (26.07) | 1.05 (0.35, 3.16) | 1.16 (0.35, 3.90) | |
| Patient prioritization of in-person visits for those with VL > 200 copies/mL (ref = yes) | |||||
| There was no clinic-wide standard; individual providers decided | 140 (68.29) | 2754 (72.23) | 0.4108 | 0.72 (0.17, 2.99) | 0.81 (0.19, 3.47) |
| Protocol did not change, patients were not prioritized | 60 (29.27) | 994 (26.07) | 0.79 (0.15, 4.14) | 0.88 (0.14, 5.53) | |
| Patient prioritization of in-person visits for those with CD4 < 200 cells/uL or other AIDS-defining illness (ref = yes) | |||||
| There was no clinic-wide standard; individual providers decided | 136 (66.34) | 2667 (69.94) | 0.5487 | 0.94 (0.32, 2.78) | 0.90 (0.29, 2.74) |
| Protocol did not change, patients were not prioritized | 60 (29.27) | 994 (26.07) | 1.01 (0.25, 4.05) | 0.96 (0.20, 4.75) | |
| Patient prioritization of in-person visits for those with acute /urgent medical conditions (ref = yes) | 29 (14.15) | 956 (25.07) | 0.0004 | 0.48 (0.28, 0.82) | 0.56 (0.278 ,1.13) |
| Patient prioritization of in-person visits for those with symptoms (ref = yes) | 74 (36.10) | 1238 (32.47) | 0.2803 | 1.22 (0.65, 2.28) | 1.42 (0.81, 2.50) |
| Percent of providers now utilizing telehealth during the pandemic. (ref = > 75%) | |||||
| <10% | 13 (6.34) | 225 (5.90) | 0.0223 | 0.94 (0.30, 2.95) | 0.99 (0.31, 3.24) |
| 10–24% | 24 (11.71) | 293 (7.68) | 1.43 (0.51, 4.04) | 1.46 (0.45, 4.72) | |
| 25–49% | 39 (19.02) | 848 (22.24) | 0.75 (0.27, 2.04) | 0.67 (0.21, 2.17) | |
| 50–74% | 11 (5.37) | 419 (10.99) | 0.425 (0.13, 1.38) | 0.57 (0.17, 1.93) | |
| ≥75% | 118 (57.56) | 2028 (53.19) | ref | ref | |
a. Peri-pandemic defined as 3/2/2020-12/31/2021
b. Adjusted multilevel models adjust for gender, race/ethnicity, HIV transmission risk factor, site, and site size
Discussion
These study findings identified several pandemic-era factors that impacted care delivery at HIV clinics and subsequent patient maintenance of HIV viral load suppression, including modified substance abuse counseling, reduced clinic hours, only allowing scheduled visits, prioritization of urgent health issues, and lower uptake of telehealth. However, loss of viral suppression peri-pandemic was minimal at 5% among individuals who had pre- and peri-pandemic lab results available. Our findings show age and race/ethnicity disparities in maintenance of viral suppression, with younger people and non-Hispanic Black PWH experiencing loss of viral suppression compared to older people and non-Hispanic white PWH. PWH seen at clinics receiving Ryan White funding had a greater prevalence of peri-pandemic loss of viral suppression. When examining clinic-level factors, significantly greater loss of viral suppression was found for PWH at clinics that reported modified substance abuse counseling, reduced clinic hours, and reorganization of appointments to only allow for scheduled visits. However, when evaluating individual-level data, those with a history of substance use (including alcohol, smoking, and other drugs) did not have increased loss of viral suppression status. PWH at clinics that prioritized in-person visits for patients with acute/urgent medical concerns experienced greater prevalence of maintaining viral suppression. However, these results did not remain significant in multilevel multivariable logistic regression models controlling for site level and demographic covariates. Previous studies have shown mixed results for loss of viral suppression among PWH during the pandemic. Our findings are aligned with some systematic review studies indicating maintenance of virologic suppression status [7, 8].
The COVID-19 pandemic changed delivery of healthcare services, requiring providers to identify new ways to safely offer care to patients. This was particularly important for PWH, due to increased susceptibility to COVID-19. Maintaining service delivery for PWH became a crucial goal. Within transmission risk categories, MSM experienced high prevalence of viral suppression maintenance.
We found no statistically significant differences in viral suppression status by telehealth utilization in multivariable logistic regression models. During the pandemic, one unique aspect of care delivery used by some clinics included adapting their infrastructure to support technology-based platforms such as telehealth to encourage continuity of care; noting that some clinics in the DC Cohort already had this capability. Telehealth increased the capacity of providers to assist patients who could not physically come into clinic, whether due to individual reasons or clinic closures. Recent research has demonstrated that telehealth is a beneficial form of differentiated care delivery and should remain as permanent infrastructure to aid in the expansion of care for PWH [2, 22]. One of the survey questions sought to understand the percentage of providers at each clinic using telehealth throughout the pandemic. Patients receiving HIV care at clinics with a greater percentage of clinicians providing telehealth care during the pandemic had a lower prevalence of virologic failure, though these findings did not remain significant in multilevel models.
Some possible explanations for our unexpected findings related to telehealth might include decreased availability of laboratory testing, difficulty in receiving HIV medications, or prioritization of the sickest patients at clinics with greater telehealth visits. PWH experiencing adverse social determinants of health are at greater risk for barriers to care, lack of visit adherence, and challenges reaching or maintaining viral suppression [23]. It is well documented that PWH at greatest risk for loss of viral suppression due to adverse social determinants of health are those that are also most likely to have challenges accessing telehealth consistently [22, 24]; however, we were unable to determine which patients utilized telehealth in these analyses. Further exploration and better understanding of the relationship between telehealth use and social determinants of health could benefit care delivery for PWH at greatest risk for loss of viral suppression and visit adherence. A previous mixed-methods study exploring telehealth experiences among PWH in the DC Cohort at the patient level reported some challenges to telehealth use including unequal access to telehealth technology (including devices and internet), patient preference for in-person visits with their providers, and differing opinions about effectiveness of telehealth visits [22].
We found associations in bivariable comparisons for several site level measures. Clinics reporting reduced substance use counseling, reduced clinic hours, and reorganization of appointments to allow only scheduled visits had greater prevalence of loss of viral suppression. Clinics reporting prioritization of in-person visits for those with acute or urgent medical conditions had a greater prevalence of patients who maintained viral suppression pre- versus peri-pandemic. These associations, while not significant in logistic regression models, indicate direct patient related outcomes associated with clinic level decisions during the pandemic, which can be used to inform decision making for future similar situations.
This analysis was subject to several limitations. As the analysis was cross-sectional in design, causal inference cannot be ascertained. However, the association of these variables is critical to our understanding of intersecting epidemics of COVID and HIV and can help to inform future research and interventions. Selection bias may be present as the PWH included in the analyses were those who were actively engaged in care within the last 18 months, though this was necessary to capture the primary outcome of virologic suppression. Loss to follow-up may have impacted outcomes. Lab data were missing for two of the sites, resulting in exclusion of those sites from analyses. Additionally, the provider survey was completed by one provider per clinic which may have limited the generalizability of the site survey findings within a clinic and introduced information bias. Certain provider characteristics may confound the association which may be associated with telehealth utilization and/or modality of telehealth used (telephone only versus video call).
Nevertheless, there were several strengths to this analysis, including the ability to capture temporality of the pandemic impact due to the design of pre- and peri-pandemic eras. We were also able to link clinic-level responses to patient-level EMR data. Our survey captured city-wide data on how HIV providers experienced service delivery changes during the pandemic, adding important data to the sparse research on this topic.
This analysis found patient-level characteristics that were significantly associated with loss of viral suppression during the pandemic. Results from these analyses may help identify the potential impact of pandemic disruptions on PWH’s ability to remain virally suppressed. By illuminating the structural barriers that were associated with individual virologic failure, these analyses will help us prepare for future pandemic or public health emergencies that disrupt healthcare. Our findings demonstrate that maintaining availability of care and social support services are crucial for PWH to remain virally suppressed. Systems need to be instituted to prepare for future healthcare disruptions and alternative methods of HIV care delivery should be readily accessible, such as expanding telehealth options and maintaining accessibility to labs and medications. We recommend that clinics adopt standard protocols for sustaining provision of healthcare and access to social services in emergency situations, in which typical service and delivery may be disrupted.
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, Megan O’Brien, Lauren O’Connor, James Peterson, Jonathon Rendina, 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 (Suyanna Barker); Washington Health Institute, formerly Providence Hospital (Jose Bordon); Unity Health Care (Gebeyehu Teferi); Us Helping Us, People Into Living, Inc. (DeMarc Hickson); Veterans Affairs Medical Center (Rachel Denyer); Washington Hospital Center (Adam Klein); and Whitman-Walker Institute (Stephen Abbott).
Author contributions
Nicole Barish contributed to the main manuscript text, study design, background reserach, and data analyses. Shannon Barth contributed to the main manuscript text, statistical analysis plan and data analyses.Yun Ji contributed to the statistical analysis plan and data analyses.Anne Monroe contributed to study conceptualization, survey design, data collection, and study design.Amanda Castel contributed to main manuscript text, study conceptualization, survey design, and study design.Alan Greenberg contributed to study conceptualization and survey design.
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.
Data availability
The datasets used and/or analyzed during the current study are available from the DC Cohort Longitudinal HIV Study following approval of request(s) from the study team and DC Cohort Executive Committee. Access is restricted in this way to protect the privacy of Cohort participants, who have consented to inclusion of their data according to this restricted access policy. Requests for access can be made by contacting Amanda Castel at acastel@gwu.edu and will be followed by a screening process first by the study team, then by the DC Cohort Executive Committee.
Declarations
Ethics approval and consent to participate
All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. This research study adhered to the Helsinki Declaration. The DC Cohort received approval from the George Washington University Institutional Review Board (IRB) (#071029). External participating sites with their own IRBs also obtained additional approval. Informed consent was provided by all participants during a routine clinical visit 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.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the DC Cohort Longitudinal HIV Study following approval of request(s) from the study team and DC Cohort Executive Committee. Access is restricted in this way to protect the privacy of Cohort participants, who have consented to inclusion of their data according to this restricted access policy. Requests for access can be made by contacting Amanda Castel at acastel@gwu.edu and will be followed by a screening process first by the study team, then by the DC Cohort Executive Committee.