Commentary: Increasing uptake of HIV testing: gifts are good but more is needed
Bibliographic record
Abstract
The drive to increase coverage of HIV testing has never been greater. Global targets aim to have tested 90% of people who are HIV-positive by 2020, and updated guidance from the World Health Organization (WHO) recommends treating all who are HIV-positive irrespective of disease status.1 Current practice globally falls well short of these targets: globally only 54% of people who are HIV-positive know their status in 2014, whereas antiretroviral treatment coverage is around 40%.2 For most of the past two decades, HIV testing has mainly been offered within clinic services. While provider-initiated HIV testing and counselling remain a cornerstone of test provision, they are limited to detecting HIV mainly in sick patients and pregnant women who are willing and able to access health services. Recent years have seen an increase in evaluations supporting a broader range of options including community-, home- and self-testing,3 and these community-based approaches are now recommended by WHO.3,4 Availability and uptake are not the same thing, however, and, whereas all these approaches have led to increased availability of HIV testing across a range of settings and study designs, uptake remains variable and suboptimal. Interventions aimed at motivating individuals to accept the offer of an HIV test are needed, irrespective of where and how the HIV test is provided. There has been recent enthusiasm for incentive-based interventions to improve health-seeking behaviours among people with HIV.5 In this issue of IJE, a study from rural KwaZulu-Natal in South Africa reports positive findings of the estimated causal effect of providing a gift (a US$5 food voucher for families) to increase uptake of home-based HIV testing.6 The study found that, overall, gift provision resulted in a 25% increase in the proportion of household members consenting to a test overall, whereas consent rates in the control group were largely unchanged. Importantly, whereas there was no difference in the effect of the intervention by sex, men across both control and intervention groups were less likely to consent to testing than women. Also of note was the relatively low uptake of home-based testing overall: with the exception of women who received the intervention, fewer than 50% of participants consented to an HIV test. Interventions that demonstrate a positive effect in increasing HIV-testing uptake are urgently needed, particularly if the intervention can also serve to improve social protection in impoverished settings7; around the time of the study, around a quarter of households in KwaZulu-Natal were reported to have inadequate or severely inadequate food access.8 Nevertheless, the levels of HIV-testing acceptance achieved by the intervention remain far from global targets and what is required to have a major impact on the epidemic.9 It will be important to better understand reasons for test refusal in general, and among men in particular, in order to further define interventions, which may include other incentives. A critical unanswered question this study raises is what happened next. Increasing uptake of HIV testing is a pressing public health priority as a necessary first step in directing people to appropriate services but, in most settings, there are substantial losses between testing HIV-positive, starting treatment and achieving successful virological suppression.10 A recent randomized trial assessing strategies to improve linkage between HIV testing and uptake of antiretroviral therapy and male circumcision achieved high linkage to care for HIV-positive individuals (93%), but still only around a third of patients (37%) started treatment.11 There is a growing evidence base supporting a range of interventions along the cascade of care to support improvements in uptake of HIV testing, linkage to care, initiation of treatment, and adherence and retention in care.11–13 Stemming from the evidence-based movement, there is natural preference for randomized trials designed to study the effect of single interventions on single outcomes. However, clinical trials are resource-intensive, frequently burdensome and may be hindered by their design, conduct and timeliness.14 Other designs, such as the difference-in-differences design employed in the KwaZulu-Natal study, represent options that provide potentially strong inferences on treatment effects. The difference-in-differences approach has not been commonly used in medical research, but is often employed in other social fields, such as economics and demography.15 Yet, even with strong inferences, the ability to act on evidence is going to be context-specific and there will be a preference for inexpensive interventions that are able to impact across multiple parts of the care cascade. For example, evidence supports the use of gifts for testing,6 peer counsellors for linkage,16 point-of-care CD4 machines for treatment initiation,17 SMS text messages for adherence12 and prize-bowl vouchers for retention.18 It is unrealistic that programme managers in resource-limited settings would be able to introduce all these interventions in settings where even the most basic elements of service delivery such as ensuring adequate drug supply are challenged.19 More recently, studies have aimed to assess the effect of packaged interventions across several steps in the cascade, e.g. linkage and treatment initiation,11 or initiation and retention.20 Whereas this is a positive evolution in thinking about how interventions need to work within programme settings, the assessment of packaged interventions present challenges for both interpretation (in particular comparability between different packages of interventions) and implementation (if not all aspects of the package can be implemented, is it still worthwhile to introduce some parts?). Increasing uptake of HIV testing is an important priority and the findings of this study suggesting a way to do so in settings of high HIV prevalence and low test acceptance. Future research is encouraged to assess the influence of interventions across multiple points across the cascade of care on critical clinical and public health outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.122 | 0.089 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".