Jean B Nachega1, Ethan D Borre2, David W Dowdy3, Pascalina Chanda-Kapata4, Susan Cleary5, Elvin H Geng6. 1. Infectious Diseases and Microbiology, University of Pittsburgh, Pittsburgh, PA, USA; Center for Global Health, University of Pittsburgh, Pittsburgh, PA, USA; Department of Medicine and Centre for Infectious Diseases, Stellenbosch University Faculty of Medicine and Health Sciences, Cape Town 7505, South Africa; Department of Epidemiology and Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. Electronic address: jbn16@pitt.edu. 2. Department of Medicine, Duke University School of Medicine, Durham, NC, USA. 3. Department of Epidemiology and Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. 4. Ministry of Health, Lusaka, Zambia. 5. Health Economics Unit, School of Public Health and Family Medicine, University of Cape Town, Cape Town, South Africa. 6. Division of Infectious Diseases, Department of Medicine, Washington University, St Louis, MO, USA; Center for Dissemination and Implementation, Institute for Public Health, Washington University, St Louis, MO, USA.
The HPTN 071 (PopART) trial and others have shown that a combination HIV
prevention package, including universal testing and treatment (UTT), can reduce the
population-level incidence of HIV compared with standard care.[1,2] However,
evidence on the cost and cost-effectiveness of this strategy has been limited. In
The Lancet Global Health, Ranjeeta Thomas and colleagues[3] report on a cost-effectiveness analysis
model, projecting that combination HIV prevention including UTT (ie, PopART) is
cost-effective at thresholds greater than US$800 per disability-adjusted life year
(DALY) averted in individuals older than 14 years. Their incremental cost-effectiveness
ratios (ICERs) are lower than those in previous modelling studies, suggesting that
population-level combination HIV prevention might be more cost-effective than initially
suggested.In a 2018 study, country-level thresholds for cost per DALY averted, based on
per-capita gross domestic product, were estimated at $2480–3334 in South Africa
and $417–575 in Zambia (2015 US$).[4] These estimates suggest that annual implementation of PopART until
2030, as modelled by Thomas and colleagues, would be cost-effective in South Africa
($645 [95% credible interval 538–757] per DALY averted), but not necessarily in
Zambia ($593 [526-674] per DALY averted).[3] In addition, the budget required to implement this
intervention—at a mean cost of $6·53 (SD 0·29) per person per year
in Zambia and $7·93 (0·16) per person per year in South Africa[3]—would represent nearly 10% of
total health expenditure in many low-income countries. These considerations are
important. If not cost-effective, such investments might reduce population health and
increase inequalities in settings such as Zambia. Some stakeholders might argue that
international donors can or should implement higher cost-effectiveness thresholds than
national governments, whereas others believe that donors should support programmes that
are most beneficial to local communities.[5]Nevertheless, the findings of Thomas and colleagues, via a methodologically
rigorous cost-effectiveness analysis, will assist policy makers in sub-Saharan Africa in
identifying the most worthwhile investments towards achieving the UNAIDS 90-90-90 target
of AIDS elimination by 2030. The analysis is specific to the highprevalence, peri-urban
communities in which PopART was studied; future research might aim to identify the
minimum community HIV prevalence at which PopART is cost-effective. Additionally, the
budgetary outlays required for PopART include the intervention itself and the costs of
HIV treatment, laboratory monitoring, and other medical costs for people testing
positive for HIV and their linkage to care. These additional costs approach or exceed
the annual intervention-only costs in Zambia and South Africa.[3] As a result, based on overall budgetary outlays
and World Bank population estimates, the annual incremental cost of PopART could exceed
$1 billion if scaled to the population of Zambia (for all individuals aged >14
years), and $7 billion to cover the population of South Africa. Although the analysis
makes clear that the studied intervention could provide substantial health gains in
populations similar to those studied in the HPTN 071 trial, policy makers are now faced
with obtaining funding to implement the PopART intervention and other health
interventions at a broader scale.Considering the available evidence, what should be the next steps for researchers
and policy makers? Thomas and colleagues’ analysis provides three considerations.
First, health system and context-specific factors (eg, what would be needed to implement
PopART within different health system platforms and in diverse populations) should be
considered in cost-effectiveness analyses. Such analyses might also seek to evaluate
quality metrics in scaling up complex models of care. For example, if a cadre of
community-based health-care workers were trained in HIV prevention activities,
management structures to monitor and maintain quality would be needed; the costs of
these structures should not be ignored when compiling a realistic picture of the
investments required.Second, components of organisational structure, such as morale, staffing, and
performance feedback, are crucial to both implementation and incremental costs. In the
USA, for example, a modelling study of the optimal package of HIV prevention activities
suggested widely differing costs across six cities.[6] In studies from eastern Africa and Zambia,[7,8] a 4-times
difference was observed in HIV-related mortality among people on treatment in facilities
with the lowest mortality versus facilities with the highest mortality, even across
geographically comparable and similarly staffed facilities. These differences suggest
that health system performance is uneven, and such heterogeneities (including
epidemiological, cultural, and demographic factors) should be considered if the results
of cost-effectiveness analyses are to translate into optimal evidence-based decisions in
the real world.Finally, policy makers do not make decisions to buy a given strategy or policy,
but rather seek to optimise population health via selection of the optimal bundle of
practices and policies. This process involves not only comparing the cost-effectiveness
of a range of interventions, but also considering the opportunity costs and the extent
to which investments in particular resources can be leveraged across systems or disease
areas. These considerations emerge at scale and are not easy to capture in any one
study. For example, many countries have invested substantially in cadres of community
health-care workers to improve maternal and child health and decrease mortality in
children younger than 5 years.[9] Could
HIV prevention with PopART be incorporated into the existing cadres, thus reducing
incremental costs while potentially expanding benefits? Or would PopART community
health-care workers represent a competing model of service delivery that could undermine
the value of investments in other community-based cadres, increasing inefficiencies?
These questions are outside the scope of any one study, but as the HIV elimination
agenda converges with growing momentum for universal health coverage and synergy across
disease conditions,[10] such integrated
considerations demand urgent exploration by novel research methods.In summary, the PopART trial and Thomas and colleagues bring into focus three
emerging considerations for cost-effectiveness analyses of health interventions in
resource-limited settings. Such analyses should be systems-focused, context-specific,
organisationally minded, and broad in their scope. By promoting economic evaluations in
these directions, we can ensure that the results are relevant to health policy decision
making in settings of limited resources.
Authors: Dean T Jamison; Lawrence H Summers; George Alleyne; Kenneth J Arrow; Seth Berkley; Agnes Binagwaho; Flavia Bustreo; David Evans; Richard G A Feachem; Julio Frenk; Gargee Ghosh; Sue J Goldie; Yan Guo; Sanjeev Gupta; Richard Horton; Margaret E Kruk; Adel Mahmoud; Linah K Mohohlo; Mthuli Ncube; Ariel Pablos-Mendez; K Srinath Reddy; Helen Saxenian; Agnes Soucat; Karen H Ulltveit-Moe; Karene H Ulltveit-Moe; Gavin Yamey Journal: Lancet Date: 2013-12-03 Impact factor: 79.321
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Authors: Bohdan Nosyk; Xiao Zang; Emanuel Krebs; Benjamin Enns; Jeong E Min; Czarina N Behrends; Carlos Del Rio; Julia C Dombrowski; Daniel J Feaster; Matthew Golden; Brandon D L Marshall; Shruti H Mehta; Lisa R Metsch; Ankur Pandya; Bruce R Schackman; Steven Shoptaw; Steffanie A Strathdee Journal: Lancet HIV Date: 2020-03-05 Impact factor: 12.767
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