The influence of constraints on the efficient allocation of resources for HIV prevention

Abstract

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Effect of Universal Testing and Treatment on HIV Incidence — HPTN 071 (PopART)

“…ART coverage data from HPTN 071 (PopART), like data from other studies, draws special attention to the challenges in achieving ART coverage targets in young people, men, and communities with high mobility. 31-33 If HIV transmission is concentrated in these subgroups, impact of UTT on HIV transmission may be compromised. Special efforts will be needed to address these coverage gaps to realize the full impact of UTT on HIV epidemic control.…”

Building resource constraints and feasibility considerations in mathematical models for infectious disease: A systematic literature review

“…Costs estimated from the One Health Tool and from the literature. Progress towards 2030 targets adjusted by level of 'strength' of the health system (conflict, vulnerable, low-income, lower middle-income, upper middle-income) Two financial space scenarios in each country, reflecting uncertainty around health systems' absorption capacity: i) ambitious, strengthening system towards global benchmarks and expanding coverage of full service package to 95%; ii) progress, not all SDG targets met by 2030 but improvements can be achieved by scaling up services delivered through the lower platforms Stopard et al (2019 ) Efficient resource allocation - incidence minimizing Political constraints on decision making (earmarking, externally imposed targets, minimising change to current program) Assumption Assumption Transmission model-based estimation - Calculate intervention costs and impact Constraints are modelled through initial conditions in each scenario representing minimum coverage by subgroups within the transmission model Four scenarios of real-world constraints: 1) earmarking, where the first intervention funded would be PrEP for heterosexual women (excluding FSWs); 2) targets, where 90 % of PLHIV must receive UTT; 3) minimising change, baseline allocation represents an allocation at national level; and 4) all constriants simultaneously Verma et al (2020 ) Feasibility assessment - produce realistic intervention impact estimates given health system constraints Hospital beds, ICU beds and mechanical ventilation equipment Assumption Secondary data Transmission model-based estimation - Limit effects and calculate resource requirements along the cascade Available capacity estimated from public records, including for private sector. Capacity needs calculated based on requirements per case and turnover times from the literature.…”

“…Non-financial constraints influencing health providers’ ability to deliver health services were considered in two thirds of included studies (n = 28) ( Adisasmito et al, 2015 ; Alistar et al, 2013 ; Bärnighausen et al, 2016 ; Barker et al, 2017 ; Bottcher et al, 2015 ; Bozzani et al, 2018 , 2020 ; Chen et al, 2019 ; Cruz-Aponte et al, 2011 ; Curran et al, 2016 ; Dalgiç et al, 2017 ; Ferrer et al, 2014 ; Krumkamp et al, 2011 ; Langley et al, 2014 ; Lin et al, 2011 ; Martin et al, 2011 ; McKay et al, 2018 ; Peak et al, 2020 ; Putthasri et al, 2009 ; Rudge et al, 2012 ; Salomon et al, 2006 ; Sébille and Valleron, 1997 ; Shattock et al, 2016 ; Stopard et al, 2019 ; Sumner et al, 2019 ; Verma et al, 2020 ; Zhang et al, 2020 ), while only two studies considered constraints to the demand for health services ( Hecht and Gandhi, 2008 ; Shim et al, 2011 ), and six articles considered both demand- and supply-side factors ( Anderson et al, 2014 , 2018 ; Hontelez et al, 2016 ; Marks et al, 2017 ; Martin et al, 2015a , b ; Stenberg et al, 2017 ). The models that exclusively include demand-side constraints both focus on vaccines: one study projected the public and private demand for an AIDS vaccine candidate under different vaccine characteristics (efficacy, duration of protection, price), performance (acceptability, compliance) and country-level profile scenarios (including political ability and motivation to implement HIV/AIDS prevention programmes) ( Hecht and Gandhi, 2008 ); the second study subdivided model compartments based on individual decisions to vaccinate against seasonal influenza, to assess the effects of vaccine hesitancy on coverage and to derive optimal vaccine allocation across age groups under a Nash (own interest) versus a utilitarian strategy (optimal for the population) ( Shim et al, 2011 ).…”

“…A second set of supply-side non-financial constraints groups are distal factors deriving from political and social values and practices that determine how budgets are allocated, what activities are considered feasible or acceptable, and broader societal policy objectives that the system can pursue. Examples of models that allow for considering these constraints include the Resource Allocation for Controlling HIV tool, which allows users to specify interventions that cannot be implemented due to social, political or ethical concerns, or that have to receive a minimum/maximum level of funding for historical or strategic reasons ( Alistar et al, 2013 ); the Optima model, which lets users analyse different budget allocation scenarios (constant, front-loaded, rear-loaded or initially scaled-up/down then later scaled-down/up over the funding cycle) ( Shattock et al, 2016 ); and model developed by Stopard and colleagues, that examines different constraints to the efficient allocation of resources for HIV prevention, including externally imposed targets or limited capacity to modify existing programmes ( Stopard et al, 2019 ). One set of studies in this group considers policy constraints both on the funding cycle (varying the flexibility of spending and the time horizon over which choices are to be optimised) and on how funds are allocated across key populations and geographical areas ( Anderson et al, 2014 , 2018 ).…”

“…The first category includes constrained estimation exercises, where intervention implementation is modelled at the maximum attainable coverage given the constraints. Effects (and costs) are thus limited at the level of the specific intervention, the disease cascade or the health system as a whole ( Bottcher et al, 2015 ; Chen et al, 2019 ; Cruz-Aponte et al, 2011 ; Ferrer et al, 2014 ; Hecht and Gandhi, 2008 ; Marks et al, 2017 ; Peak et al, 2020 ; Salomon et al, 2006 ; Sébille and Valleron, 1997 ; Shattock et al, 2016 ; Shim et al, 2011 ; Stopard et al, 2019 ; Zhang et al, 2020 ).…”