Integrating HIV Care into Primary Care Services: Quantifying Progress of an Intervention in South Africa

Background

Integration of human immunodeficiency virus (HIV) care into primary care services is one strategy proposed to achieve universal access to antiretroviral treatment (ART) for HIV-positive patients in high burden countries. There is a need for controlled studies of programmes to integrate HIV care with details of the services being integrated.

Methods

A semi-quantitative questionnaire was developed in consultation with clinic staff, tested for internal consistency using Cronbach''s alpha coefficients and checked for inter-observer reliability. It was used to conduct four assessments of the integration of HIV care into referring primary care clinics (mainstreaming HIV) and into the work of all nurses within ART clinics (internal integration) and the integration of pre-ART and ART care during the Streamlining Tasks and Roles to Expand Treatment and Care for HIV (STRETCH) trial in South Africa. Mean total integration and four component integration scores at intervention and control clinics were compared using one way analysis of variance (ANOVA). Repeated measures ANOVA was used to analyse changes in scores during the trial.

Results

Cronbach''s alpha coefficients for total integration, pre-ART and ART integration and mainstreaming HIV and internal integration scores showed good internal consistency. Mean total integration, mainstreaming HIV and ART integration scores increased significantly at intervention clinics by the third assessment. Mean pre-ART integration scores were almost maximal at the first assessment and showed no further change. There was no change in mean internal integration score.

Conclusion

The questionnaire developed in this study is a valid tool with potential for monitoring integration of HIV care in other settings. The STRETCH trial interventions resulted in increased integration of HIV care, particularly ART care, by providing HIV care at referring primary care clinics, but had no effect on integrating HIV care into the work of all nurses with the ART clinic.     

A Bayesian approach for inferring the dynamics of partially observed endemic infectious diseases from space-time-genetic data

We describe a statistical framework for reconstructing the sequence of transmission events between observed cases of an endemic infectious disease using genetic, temporal and spatial information. Previous approaches to reconstructing transmission trees have assumed all infections in the study area originated from a single introduction and that a large fraction of cases were observed. There are as yet no approaches appropriate for endemic situations in which a disease is already well established in a host population and in which there may be multiple origins of infection, or that can enumerate unobserved infections missing from the sample. Our proposed framework addresses these shortcomings, enabling reconstruction of partially observed transmission trees and estimating the number of cases missing from the sample. Analyses of simulated datasets show the method to be accurate in identifying direct transmissions, while introductions and transmissions via one or more unsampled intermediate cases could be identified at high to moderate levels of case detection. When applied to partial genome sequences of rabies virus sampled from an endemic region of South Africa, our method reveals several distinct transmission cycles with little contact between them, and direct transmission over long distances suggesting significant anthropogenic influence in the movement of infected dogs.     

Task shifting and integration of HIV care into primary care in South Africa: The development and content of the streamlining tasks and roles to expand treatment and care for HIV (STRETCH) intervention

Background

The Veterans Health Administration (VHA) oversees the largest integrated healthcare system in the United States. The feasibility of a large-scale, nationwide, group-randomized implementation trial of VHA outpatient practices has not been reported. We describe the recruitment and enrollment of such a trial testing a clinician-directed, Internet-delivered intervention for improving the care of postmyocardial infarction patients with multiple comorbidities.

Methods

With a recruitment goal of 200 eligible community-based outpatient clinics, parent VHA facilities (medical centers) were recruited because they oversee their affiliated clinics and the research conducted there. Eligible facilities had at least four VHA-owned and -operated primary care clinics, an affiliated Institutional Review Board (IRB), and no ongoing, potentially overlapping, quality-improvement study. Between December 2003 and December 2005, in two consecutive phases, we used initial and then intensified recruitment strategies.

Results

Overall, 48 of 66 (73%) eligible facilities were recruited. Of the 219 clinics and 957 clinicians associated with the 48 facilities, 168 (78%) clinics and 401 (42%) clinicians participated. The median time from initial facility contact to clinic enrollment was 222 days, which decreased by over one-third from the first to the second recruitment phase (medians: 323 and 195 days, respectively; p < .001), when more structured recruitment with physician recruiters was implemented and a dedicated IRB manager was added to the coordinating center staff.

Conclusions

Large group-randomized trials benefit from having dedicated physician investigators and IRB personnel involved in recruitment. A large-scale, nationally representative, group-randomized trial of community-based clinics is feasible within the VHA or a similar national healthcare system.     

Identifying and prioritizing potential human-infecting viruses from their genome sequences

Determining which animal viruses may be capable of infecting humans is currently intractable at the time of their discovery, precluding prioritization of high-risk viruses for early investigation and outbreak preparedness. Given the increasing use of genomics in virus discovery and the otherwise sparse knowledge of the biology of newly discovered viruses, we developed machine learning models that identify candidate zoonoses solely using signatures of host range encoded in viral genomes. Within a dataset of 861 viral species with known zoonotic status, our approach outperformed models based on the phylogenetic relatedness of viruses to known human-infecting viruses (area under the receiver operating characteristic curve [AUC] = 0.773), distinguishing high-risk viruses within families that contain a minority of human-infecting species and identifying putatively undetected or so far unrealized zoonoses. Analyses of the underpinnings of model predictions suggested the existence of generalizable features of viral genomes that are independent of virus taxonomic relationships and that may preadapt viruses to infect humans. Our model reduced a second set of 645 animal-associated viruses that were excluded from training to 272 high and 41 very high-risk candidate zoonoses and showed significantly elevated predicted zoonotic risk in viruses from nonhuman primates, but not other mammalian or avian host groups. A second application showed that our models could have identified Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) as a relatively high-risk coronavirus strain and that this prediction required no prior knowledge of zoonotic Severe Acute Respiratory Syndrome (SARS)-related coronaviruses. Genome-based zoonotic risk assessment provides a rapid, low-cost approach to enable evidence-driven virus surveillance and increases the feasibility of downstream biological and ecological characterization of viruses.

Surveillance of emerging viruses is one of the first steps to avoid the next pandemic. This study uses machine learning to identify many zoonotic viruses directly from their genomes. This allows rapid assessment of research priorities as soon as new viruses are discovered, focusing research and surveillance efforts on the viruses most likely to infect humans.