Introduction Viral load (VL) monitoring programs have been scaled up rapidly, but are now facing the challenge of providing access to the most remote facilities (the “last mile”). For the hardest-to-reach facilities in Zambia, we compared the cost of placing point of care (POC) viral load instruments at or near facilities to the cost of an expanded sample transportation network (STN) to deliver samples to centralized laboratories. Methods We extended a previously described geospatial model for Zambia that first optimized a STN for centralized laboratories for 90% of estimated viral load volumes. Amongst the remaining 10% of volumes, facilities were identified as candidates for POC placement, and then instrument placement was optimized such that access and instrument utilization is maximized. We evaluated the full cost per test under three scenarios: 1) POC placement at all facilities identified for POC; 2)an optimized combination of both on-site POC placement and placement at facilities acting as POC hubs; and 3) integration into the centralized STN to allow use of centralized laboratories. Results For the hardest-to-reach facilities, optimal POC placement covered a quarter of HIV-treating facilities. Scenario 2 resulted in a cost per test of $39.58, 6% less than the cost per test of scenario 1, $41.81. This is due to increased POC instrument utilization in scenario 2 where facilities can act as POC hubs. Scenario 3 was the most costly at $53.40 per test, due to high transport costs under the centralized model ($36 per test compared to $12 per test in scenario 2). Conclusions POC VL testing may reduce the costs of expanding access to the hardest-to-reach populations, despite the cost of equipment and low patient volumes. An optimal combination of both on-site placement and the use of POC hubs can reduce the cost per test by 6–35% by reducing transport costs and increasing instrument utilization.

IntroductionThe World Health Organization recommends viral load (VL) monitoring at six and twelve months and then annually after initiating antiretroviral treatment for HIV. In many African countries, expansion of VL testing has been slow due to a lack of efficient blood sample transportation networks (STN). To assist Zambia in scaling up testing capacity, we modelled an optimal STN to minimize the cost of a national VL STN.MethodsThe model optimizes a STN in Zambia for the anticipated 1.5 million VL tests that will be needed in 2020, taking into account geography, district political boundaries, and road, laboratory and facility infrastructure. We evaluated all‐inclusive STN costs of two alternative scenarios: (1) optimized status quo: each district provides its own weekly or daily sample transport; and (2) optimized borderless STN: ignores district boundaries, provides weekly or daily sample transport, and reaches all Scenario 1 facilities.ResultsUnder both scenarios, VL testing coverage would increase to from 10% in 2016 to 91% in 2020. The mean transport cost per VL in Scenario 2 was $2.11 per test (SD $0.28), 52% less than the mean cost/test in Scenario 1, $4.37 (SD $0.69), comprising 10% and 19% of the cost of a VL respectively.ConclusionsAn efficient STN that optimizes sample transport on the basis of geography and test volume, rather than political boundaries, can cut the cost of sample transport by more than half, providing a cost savings opportunity for countries that face significant resource constraints.

Introduction Routine viral load testing is the WHO‐recommended method for monitoring HIV‐infected patients on ART, and many countries are rapidly scaling up testing capacity at centralized laboratories. Providing testing access to the most remote populations and facilities (the “last mile”) is especially challenging. Using a geospatial optimization model, we estimated the incremental costs of accessing the most remote 20% of patients in Zambia by expanding the transportation network required to bring blood samples from ART clinics to centralized laboratories and return results to clinics. Methods The model first optimized a sample transportation network (STN) that can transport 80% of anticipated sample volumes to centralized viral load testing laboratories on a daily or weekly basis, in line with Zambia's 2020 targets. Data incorporated into the model included the location and infrastructure of all health facilities providing ART, location of laboratories, measured distances and drive times between the two, expected future viral load demand by health facility, and local cost estimates. We then continued to expand the modelled STN in 5% increments until 100% of all samples could be collected. Results and Discussion The cost per viral load test when reaching 80% patient volumes using centralized viral load testing was a median of $18.99. With an expanded STN, the incremental cost per test rose to $20.29 for 80% to 85% and $20.52 for 85% to 90%. Above 90% coverage, the incremental cost per test increased substantially to $31.57 for 90% to 95% and $51.95 for 95% to 100%. The high numbers of kilometres driven per sample transported and large number of vehicles needed increase costs dramatically for reaching the clinics that serve the last 5% of patients. Conclusions Providing sample transport services to the most remote clinics in low‐ and middle‐income countries is likely to be cost‐prohibitive. Other strategies are needed to reduce the cost and increase the feasibility of making viral load monitoring available to the last 10% of patients. The cost of alternative methods, such as optimal point‐of‐care viral load equipment placement and usage, dried blood/plasma spot specimen utilization, or use of drones in geographically remote facilities, should be evaluated.

Background: Viral load (VL) testing is recommended for monitoring people on ART. The National Health Laboratory Service (NHLS) in South Africa conducts >5million laboratory-based VL tests but faces challenges with specimen integrity and results delivery. Point-of-care (POC) VL monitoring may improve VL suppression (VLS). We assessed the cost-effectiveness of different strategies for POC testing in South Africa. Methods: We developed a cost-outcome model utilizing NHLS data, including facility-level annual VL volumes, proportion with VLS, specimen rejection rates, turnaround time , and the cost/test. We assessed the impact of adopting POC VL technology under 4 strategies: (1) status-quo; (2) targeted POC testing at facilities with high levels of viral failure; (3) targeted POC testing at low-performing facilities; (4) complete POC adoption. For each strategy, we determined the total cost, effectiveness (expected number of virally suppressed people) and incremental cost-effectiveness ratio (ICER) based on expected (>10%) VLS improvement. Findings: Existing laboratory-based VL testing costs $126 m annually and achieves 85.2% VLS. Strategy 2 was the most cost-effective approach, with 88.5% VLS and $40/additional person suppressed, compared to the status-quo. Should resources allow, complete POC adoption may be cost-effective (ICER: $136/additional person suppressed), requiring an additional $49 m annually and achieving 94.5% VLS. All other strategies were dominated in the incremental analysis. Interpretation: Assuming POC VL monitoring confers clinical benefits, the most cost-effective strategy for POC adoption in South Africa is a targeted approach with POC VL technologies placed at facilities with high level of viral failure. Funding: Funding support from the Bill & Melinda Gates Foundation.

Background Patient interruption of antiretroviral therapy (ART) continues to limit HIV programs’ progress toward epidemic control. Multiple factors have been associated with client interruption in treatment (IIT)— including age, gender, CD4 count, and education level. In this paper, we explore the factors associated with IIT in people living with HIV (PLHIV) in United States Agency for International Development (USAID)-supported facilities under the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR) program in Nigeria. Methods We conducted cross-sectional analyses on data obtained from Nigeria’s National Data Repository (NDR), representing a summarized record of 573 630 ART clients that received care at 484 PEPFAR/USAID-supported facilities in 16 states from 2000–2020. IIT was defined as no clinical contact for 28 days or more after the last expected clinical contact. Univariate and multivariate logistic regression models were computed to explore the factors associated with IIT. The variables included in the analysis were sex, age group, zone, facility level, regimen line, multi-month dispensing (MMD), and viral load category. Results Of the 573 630 clients analysed in this study, 32% have been recorded as having interrupted treatment. Of the clients investigated, 66% were female (32% had interrupted treatment), 39% were aged 25–34 at their last ART pick-up date (with 32% of them interrupted treatment), 59% received care at secondary level facilities (37% interrupted treatment) and 38% were last receiving between three- to five-month MMD (with 10% of these interrupted treatment). Those less likely to interrupt ART were males (aOR = 0.91), clients on six-month MMD (aOR = 0.01), adults on 2nd line regimen (aOR = 0.09), and paediatrics on salvage regimen (aOR = 0.02). Clients most likely to interrupt ART were located in the South West Zone (aOR = 1.99), received treatment at a tertiary level (aOR = 12.34) or secondary level facilities (aOR = 4.01), and had no viral load (VL) on record (aOR =10.02). Age group was not significantly associated with IIT. Conclusions Sex, zone, facility level, regimen line, MMD, and VL were significantly associated with IIT. MMD of three months and longer (especially six months) had better retention on ART than those on shorter MMD. Not having a VL on record was associated with a considerable risk of IIT.

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.

ObjectivesIdentify factors (demographic and clinical) associated with a non-suppressed viral load (VL) of people living with HIV (PLHIV) on antiretroviral therapy (ART) in Nigeria.DesignCross-sectional study.SettingSixteen US Agency for International Development supported states in Nigeria.Participants585 632 PLHIV on ART.Primary outcome measuresVL non-suppression (defined as having a VL of at least 1000 HIV RNA copies per mL of plasma). χ2testing and multivariable modified Poisson regression with robust variance estimates were conducted on routinely collected ART programme data.ResultsSixty-six per cent of the study population were females. The largest age groups were 25–34 and 35–44, accounting for 32.1% and 31.1%, respectively. Males had a 9% greater likelihood (adjusted prevalence ratio, APR=1.09) of being non-suppressed. The age groups below 60+ (APR=0.67) had a higher likelihood of a non-suppressed VL, with the highest in the 0–14 age group (APR=2.38). Clients enrolled at tertiary and secondary level facilities had the greatest likelihood of a non-suppressed VL. Clients who started ART between 2010 and 2015 had the greatest likelihood of viral non-suppression (APR=6.19). A shorter time on ART (<1 year (APR=3.92)) was associated with a higher likelihood of a non-suppressed VL. Clients receiving care at private facilities had a lower likelihood of viral non-suppression in the adjusted model. Clients in the Edo (APR=2.66) and Niger (APR=2.54) states had the greatest likelihood of viral non-suppression.ConclusionsTargeting males, clients of younger age, those on treatment for less than 3 years, clients at tertiary and secondary health facilities, small and medium facilities, and clients in the Edo, Niger and Borno states for interventions could lead to improvements in VL suppression in Nigeria. The independent factors associated with a non-suppressed VL can guide improvements in ART programme development and VL suppression of PLHIV on ART in Nigeria.

ObjectiveThe variation in the anatomy of the iliopsoas tendon is important information for orthopaedic surgeons operating around the hip. The aim of this study was to identify the prevalence of bifid iliopsoas tendons in children on magnetic resonance imaging (MRI).MethodsMRI hip and pelvis images of 50 sequential children aged 7–15 years were retrieved from our radiology database at the Evelina London Children’s Hospital from 2007 to 2013. Included were 37 children with imaging of both hips and 13 children with imaging of one hip only. Therefore, our study was based on a total of 87 hips.ResultsAt least 1 bifid tendon was noted in 13 children (26 %). Five children from a total of 37 (14 %) with both hips adequately imaged had bilateral bifid tendons. Among all 87 adequately imaged hips, 18 (21 %) were found to have two discrete distal iliopsoas tendons.ConclusionsBifid iliopsoas tendon is noted anecdotally by surgeons but was only reported in scattered case reports and a few anatomical studies until very recently. Our finding is that a bifid iliopsoas tendon with two distinct tendinous components at the level of the hip joint is quite common. This has clinical significance, particularly in children’s orthopaedic surgery when an adequate iliopsoas release is important.