Correction: Estimating retention in HIV care accounting for patient transfers: A national laboratory cohort study in South Africa

Record linkage without patient identifiers: proof of concept using data from South Africa's national HIV program

Background

Linkage between health databases typically requires identifiers such as patient names and personal identification numbers. We developed and validated a record linkage strategy to combine administrative health databases without the use of patient identifiers, with application to South Africa's public sector HIV treatment program.

Methods

We linked CD4 counts and HIV viral loads from South Africa's HIV clinical monitoring database (TIER.Net) and the National Health Laboratory Service (NHLS) for patients receiving care between 2015-2019 in Ekurhuleni District (Gauteng Province). We used a combination of variables related to lab results contained in both databases (result value; specimen collection date; facility of collection; patient year and month of birth; and sex). Exact matching linked on exact linking variable values while caliper matching applied exact matching with linkage on approximate test dates (± 5 days). We then developed a sequential linkage approach utilising specimen barcode matching, then exact matching, and lastly caliper matching. Performance measures were sensitivity and positive predictive value (PPV); share of patients linked across databases; and percent increase in data points for each linkage approach.

Results

We attempted to link 2,017,290 lab results from TIER.Net (representing 523,558 unique patients) and 2,414,059 lab results from the NHLS database. Linkage performance was evaluated using specimen barcodes (available for a minority of records in TIER.net) as a "gold standard". Exact matching achieved a sensitivity of 69.0% and PPV of 95.1%. Caliper-matching achieved a sensitivity of 75.7% and PPV of 94.5%. In sequential linkage, we matched 41.9% of TIER.Net labs by specimen barcodes, 51.3% by exact matching, and 6.8% by caliper matching, for a total of 71.9% of labs matched, with PPV=96.8% and Sensitivity = 85.9%. The sequential approach linked 86.0% of TIER.Net patients with at least one lab result to the NHLS database (N=1,450,087). Linkage to the NHLS Cohort increased the number of laboratory results associated with TIER.Net patients by 62.6%.

Conclusions

Linkage of TIER.Net and NHLS without patient identifiers attained high accuracy and yield without compromising patient privacy. The integrated cohort provides a more complete view of patients' lab history and could yield more accurate estimates of HIV program indicators.

The proportion of loss to follow-up from antiretroviral therapy (ART) and its association with age among adolescents living with HIV in sub-Saharan Africa: A systematic review and meta-analysis

Background

Human immunodeficiency virus (HIV) remains a global health threat, especially in developing countries. The successful scale-up of antiretroviral therapy (ART) programs to address this threat is hindered by a high proportion of patient loss to follow-up (LTFU). LTFU is associated with poor viral suppression and increased mortality. It is particularly acute among adolescents, who face unique adherence challenges. Although LTFU is a critical obstacle on the continuum of care for adolescents, few regional-level studies report the proportion of LTFU among adolescents receiving ART. Therefore, a systematic review and meta-analysis were conducted to estimate the pooled LTFU in ART programs among adolescents living with HIV in sub-Saharan Africa (SSA).

Methods

We searched five databases (PubMed, Embase (Elsevier), PsycINFO, CINAHL, and Scopus) for articles published between 2005 and 2020 and reference lists of included articles. The PRISMA guidelines for systematic reviews were followed. A standardised checklist to extract data was used. Descriptive summaries were presented using narrative tables and figures. Heterogeneity within the included studies was examined using the Cochrane Q test statistics and I² test. Random effect models were used to estimate the pooled prevalence of LTFU among ALHIV. We used Stata version 16 statistical software for our analysis.

Results

Twenty-nine eligible studies (n = 285,564) were included. An estimated 15.07% (95% CI: 11.07, 19.07) of ALHIV were LTFU. Older adolescents (15–19 years old) were 43% (AOR = 0.57, 95% CI: 0.37, 0.87) more likely to be LTFU than younger (10–14 years old) adolescents. We find an insignificant relationship between gender and LTFU (AOR = 0.95, 95% CI: 0.87, 1.03). A subgroup analysis found that regional differences in the proportion of adolescent LTFU were not statistically significant. The trend analysis indicates an increasing proportion of adolescent LTFU over time.

Conclusions and recommendations

The proportion of LTFU among HIV-positive adolescents in SSA seems higher than those reported in other regions. Older adolescents in the region are at an increased risk for LTFU than younger adolescents. These findings may help policymakers develop appropriate strategies to retain ALHIV in ART services. Such strategies could include community ART distribution points, appointment spacing, adherence clubs, continuous free access to ART, and community-based adherence support.

Applying machine learning and predictive modeling to retention and viral suppression in South African HIV treatment cohorts

HIV treatment programs face challenges in identifying patients at risk for loss-to-follow-up and uncontrolled viremia. We applied predictive machine learning algorithms to anonymised, patient-level HIV programmatic data from two districts in South Africa, 2016-2018. We developed patient risk scores for two outcomes: (1) visit attendance ≤ 28 days of the next scheduled clinic visit and (2) suppression of the next HIV viral load (VL). Demographic, clinical, behavioral and laboratory data were investigated in multiple models as predictor variables of attending the next scheduled visit and VL results at the next test. Three classification algorithms (logistical regression, random forest and AdaBoost) were evaluated for building predictive models. Data were randomly sampled on a 70/30 split into a training and test set. The training set included a balanced set of positive and negative examples from which the classification algorithm could learn. The predictor variable data from the unseen test set were given to the model, and each predicted outcome was scored against known outcomes. Finally, we estimated performance metrics for each model in terms of sensitivity, specificity, positive and negative predictive value and area under the curve (AUC). In total, 445,636 patients were included in the retention model and 363,977 in the VL model. The predictive metric (AUC) ranged from 0.69 for attendance at the next scheduled visit to 0.76 for VL suppression, suggesting that the model correctly classified whether a scheduled visit would be attended in 2 of 3 patients and whether the VL result at the next test would be suppressed in approximately 3 of 4 patients. Variables that were important predictors of both outcomes included prior late visits, number of prior VL tests, time since their last visit, number of visits on their current regimen, age, and treatment duration. For retention, the number of visits at the current facility and the details of the next appointment date were also predictors, while for VL suppression, other predictors included the range of the previous VL value. Machine learning can identify HIV patients at risk for disengagement and unsuppressed VL. Predictive modeling can improve the targeting of interventions through differentiated models of care before patients disengage from treatment programmes, increasing cost-effectiveness and improving patient outcomes.

Antiretroviral therapy uptake and predictors of virological failure in patients with HIV receiving first-line and second-line regimens in Johannesburg, South Africa: a retrospective cohort data analysis

OBJECTIVE

This study described the demographics, treatment information and identified characteristics associated with virological failure and being lost to follow-up (LTFU) for patients with HIV on first-line and second-line antiretroviral therapy (ART) regimens in a large South African cohort.

DESIGN

A quantitative retrospective cohort study using secondary data analysis.

SETTING

Seven Johannesburg inner city facilities.

PARTICIPANTS

Unique records of 123 002 people with HIV receiving ART at any point in the period 1 April 2004 to 29 February 2020 were included.

MEASURES

Demographic characteristics, ART status, CD4 count information and retention status were collected and analysed as covariates of outcomes (viral load (VL) and LTFU).

RESULTS

Of the total study patients, 95% (n=1 17 260) were on a first-line regimen and 5% (n=5742) were on a second-line regimen. Almost two-thirds were female (64%, n=79 226). Most patients (60%, n=72 430) were initiated on an efavirenz-based, tenofovir disoproxil fumarate-based and emtricitabine-based regimen (fixed-dose combination). 91% (n=76 737) achieved viral suppression at least once since initiating on ART and 60% (n=57 981) remained in care as at the end of February 2020. Patients from the community health centre and primary healthcare clinics were not only more likely to be virally suppressed but also more likely to be LTFU. Patients on second-line regimens were less likely to reach viral suppression (adjusted OR (aOR)=0.26, CI=0.23 to 0.28) and more likely to be LTFU (aOR=1.21, CI=1.09 to 1.35). Being older (≥25 years) and having a recent CD4 cell count≥100 cells/µL were predictors of viral suppression and retention in patients on ART.

CONCLUSION

Patients on first-line regimens had higher VL suppression rates and were more likely to remain in care than those on a second-line regimen. Being younger and having low CD4 cell counts were associated with poor outcomes, suggesting priority groups for ART adherence support.

Towards achieving the 90-90-90 HIV targets: results from the south African 2017 national HIV survey

Background

Measuring progress towards the Joint United Nations Programme on HIV/AIDS (UNAIDS) 90–90–90 treatment targets is key to assessing progress towards turning the HIV epidemic tide. In 2017, the UNAIDS model estimated that 75% of people living with HIV (PLHIV) globally knew their HIV positive status, 79% of those who knew their status were on antiretroviral therapy (ART), and 81% of those who knew their HIV status and were on ART had a suppressed viral load. The fifth South African national HIV sero-behavioural survey collected nationally representative data that enabled the empirical estimation of these 90–90–90 targets for the country stratified by a variety of key factors.

Methods

To evaluate progress towards achievement of the 90–90–90 targets for South Africa, data obtained from a national, representative, cross-sectional population-based multi-stage stratified cluster random survey conducted in 2017 were analysed. The Fifth South African National HIV Prevalence, Incidence, Behaviour and Communication Survey (SABSSM V), collected behavioural and biomarker data from individuals residing in households from 1000 randomly selected Small Area Layers (SALs), across all nine provinces of the country. Structured questionnaires were used to collect socio-demographic data, knowledge and perceptions about HIV, and related risk behaviours. Blood samples were collected to test for HIV infection, antiretroviral use, and viral suppression (defined as < 1000 copies/ml). Weighted proportions of study participants aged 15 years and older who tested HIV positive were computed for those who reported awareness of their status (1st 90), and among these, those who were currently on ART (2nd 90) and of these, those who were virally suppressed (3rd 90).

Results

Among persons 15 years and older who were HIV positive, 84.8% were aware of their HIV positive status, of whom 70.7% were currently on ART, with 87.4% of these estimated to have suppressed viral load at the time of the survey. These estimates varied by sex, age, and geo-location type. Relatively higher percentages across all three indicators for women compared to men were observed: 88.7% versus 78.2% for those aware of their status, 72.3% versus 67.7% for on ART, and 89.8% versus 82.3% for viral suppression. Knowing one’s positive HIV status increased with age: 74.0, 85.8, and 88.1% for age groups 15–24 years old, 25–49 years old and 50–64 years old, although for those 65 years and older, 78.7% knew their HIV positive status. A similar pattern was observed for the 2nd 90, among those who knew their HIV positive status, 51.7% of 15 to 24 year olds, 70.5% of those aged 25–49 years old, 82.9% of those aged 50–64 years old and 82.4% of those aged 65 years or older were currently on ART. Viral suppression for the above mentioned aged groups, among those who were on ART was 85.2, 87.2, 89.5, and 84.6% respectively. The 90–90–90 indicators for urban areas were 87.7, 66.5, and 87.2%, for rural settings was 85.8, 79.8, and 88.4%, while in commercial farming communities it was 56.2, 67.6 and 81.4%.

Conclusions

South Africa appears to be on track to achieve the first 90 indicator by 2020. However, it is behind on the second 90 indicator with ART coverage that was ~ 20-percentage points below the target among people who knew their HIV status, this indicates deficiencies around linkage to and retention on ART. Overall viral suppression among those on ART is approaching the target at 87.4%, but this must be interpreted in the context of low reported ART coverage as well as with variation by age and sex. Targeted diagnosis, awareness, and treatment programs for men, young people aged 15–24 years old, people who reside in farming communities, and in specific provinces are needed. More nuanced 90–90–90 estimates within provinces, specifically looking at more granular sub-national level (e.g. districts), are needed to identify gaps in specific regions and to inform provincial interventions.

Recreational ART use among individuals living with HIV/AIDS in South Africa: Examining longitudinal ART initiation and viral suppression

BACKGROUND

South Africa has the highest number of people living with HIV (PLWH) and one of the largest antiretroviral therapy (ART) programs globally. High rates of substance use comorbidity exist, including speculation of recreational ART use (i.e., mixing ART with other illicit drugs). Recreational ART use may affect viral load among PLWH due to ART nonadherence and/or viral resistance; however, prior quantitative research has not examined rates of recreational ART use, nor associations with HIV treatment outcomes longitudinally.

METHODS

Data were drawn from a prospective, observational cohort study (n = 500) of ART-eligible adults recruited from two HIV voluntary counseling and testing centers in Cape Town, and Johannesburg, South Africa. Multiple logistic regression models assessed recreational ART use as a predictor of ART initiation over six months and viral load suppression over nine months, above and beyond other substance use (binge drinking and illicit drug use).

RESULTS

Approximately 5% (n = 24) reported recreational ART use, which was less frequent in Cape Town compared to Johannesburg (AOR = 0.025; 95%CI: 0.003-0.19; p < 0.001). Recreational ART use was not significantly associated with ART initiation or viral suppression. Other substance use, but not recreational ART use, was significantly associated with lower odds of ART initiation (AOR = 0.54; 95%CI: 0.33-0.87; p = .01) and viral suppression (AOR = 0.47; 95%CI: 0.25-0.89; p = .02).

CONCLUSIONS

Recreational ART use was infrequent and not uniquely associated with ART initiation or viral suppression. Findings suggest that comorbid use of other substances is ultimately what may make recreational ART use problematic for ongoing engagement in care and viral suppression.

Retention in clinic versus retention in care during the first year of HIV care in Nairobi, Kenya: a prospective cohort study

INTRODUCTION

When measuring the success of HIV programmes to retain patients in care, few studies distinguish between retention in clinic (individual returns to the same clinic) and retention in care (individual is active in care at initial site or elsewhere). The objectives of this study were to quantify retention in clinic versus retention in care and determine risk factors associated with attrition from care in low-income settings in Nairobi, Kenya.

METHODS

Between April 2013 and June 2015, adults testing positive for HIV were recruited at two comprehensive care clinics in informal urban settlements. Participants were followed from the time of a positive HIV test for up to 14 months. Participants who did not return to the clinic for their 12-month appointment between 10 and 14 months after their baseline visit were traced by telephone or community outreach to determine whether they were still receiving HIV care. We used generalized linear regression to determine the association between clinical and socio-demographic factors and attrition from care at 12 months.

RESULTS

Of the 1068 individuals screened for study participation, 775 individuals newly presenting to HIV care were included in this study. Between 10 and 14 months, 486 participants (62.7%, 95% confidence intervals [CI], 59.2% to 66.1%) returned to the clinic for their 12-month appointment (retained in clinic). After telephone tracing and community outreach, an additional 123 of 289 participants were found to be active in care at other HIV clinics (42.6%, 95% CI, 36.8% to 48.5%). Overall, 609 (78.6%, 95% CI, 75.7% to 81.5%) participants were retained in care at any HIV clinic at 12 months. Participants in higher baseline CD4 count categories were more likely to be retained than those whose baseline CD4 count was <200 cells/mm3 .

CONCLUSIONS

Retention in clinic substantially underestimated retention in care 12 months after presenting to care in this high-prevalence and low-income urban setting. Improved systems to track patients between clinics are required to accurately estimate retention in care in resource-limited settings. Although the proportion of patients retained in care was greater than expected, interventions to improve retention in care are needed to meet global targets to end the AIDS epidemic.