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  • Presenting this work on behalf of Gesine Meyer-Rath Mead Over will take the second half of the presentation
  • Things in the world of HIV prevention have been changing for a number of years – no longer ABCCurrently treatment is being touted as one of the best prevention methods with the chance of stopping the disease in its tracks and being cost effective
  • Epi: Biological consequences of early treatment initiation can be beneficial (reduced transmission) and adverse (more resistance); Recent review summarizes epidemiological considerations.Eco: The cost of recruiting and retaining people is likely to suffer from diseconomies of large scale and tenuous accountability. Focus of this presentation is on the cost function.
  • Cost accounting identity: assume a single constant unit cost per patient year / per patient year by regimen across a large population and many years.Cost function: Can handle substituting one input for another, changing scale and scope of operations, eligibility criteria, task shifting etc. Feedback mechanism to unit cost which may change.
  • Excluded those that looked at PMTCT onlyExcluded editorials, letters, articles without quantitative data or those without a modelled estimateInput cost – determined whether it was constant or had been varied by determinants such as type of regimen, health state, time on treatment and mode of delivery, either in main or sensitivity
  • Although not included in the original literature review the most recent publication on treatment as prevention should be included – Granich 2012
  • Argue – these are not the only variables that should affect input cost and in some instances their impact on total costs may be overwhelmed in situations of rapid scale up or large scale changes to program delivery such as task shifting to lower levels of facilities and healthcare cadres
  • The prices of factors of production, including labour, supplies, utilities, transportation, equipment and buildings, clearly affect the cost of health services. By varying the cost of treatment regimen and / or lab prices they have taken into account factor prices.ARV – largest component of cost and varied dramatically over the last ten years.Chart shows the cost of the most common 1st line dropped 13 fold from $10,439 to $331 between June 2000 and Sept 2001; further drop of 120% between 2001 and 2008. Scope for further drops limited.Target other factors: service delivery, lab tests and overheads – targets by UNAIDS treatment 2.0 initiative
  • None of the reviewed papers considered the impact of scale – in particular those looking at treatment as prevention which often model dramatic increases in scaleMost economic theory suggests use shaped relationship between scale and average cost – this may be the case in ART clinics: increasing the number of patients generates a less than proportionate increase in cost Economies of scale have been found in HIV prevention: Marseille 2007 HIV prevention and program scale – PANCEA project; Guinness 2007 Does scale matter – sex workers in Inda; Guinness Cost function of HIV prevention services: is there a U shape.Modelled cost of hygiene outreach interventions in this slide – u shaped relationship between average or marginal cost.
  • Usually assume that there is a benefit from “learning by doing” resulting in a decrease in avg cost.Often coincides with scale up and so it is difficult to untangle the exact cause of reduction in cost.Menzies examined data from PEPFAR ART sites and found that the median per patient cost decreased with each successive 6 month period from the start of the ART program biggest decrease between 1st and 2nd.Facility experience was not considered in any of the published papers.
  • Cost will also be determined by scope (PHC vs. specialised ART clinics at 2nd hospitals) and distribution (public or private sector – for profit + not for profit)Generally large facilities like hospitals can achieve economics of scope – spread the cost of infrastructure across the production of multiple health services-Rosen et al – 12 months on treatment compared public hospital, private GP, NGO HIV and NGO PHC, costs varied significantly between sites as a result of differences in service delivery. Since patient mix was comparable across the 4 sites only a small portion of the difference could be attributed to differences in disease severity-None of those papers examining treatment as prevention considered differences in level of care and only 3 of all those reviewed included it. -Future costing should include the distribution of population across different delivery models particularly where rapid scale up will require this spread in order to handle the volume of patients
  • QoC difficult to measure – in ART retention in care and improvement in health indicators seems reasonable proxySame analysis by Rosen et al. – cost per quality adjusted output – used routinely collected data to determine retention in care and response to treatmentDepending on the quality of care in each clinic and the resulting levels of loss to followup and treatment failure , the production cost per patient in care and responding was 22% and 48% higher because of the resources spent on patient either leaving care or not responding to care
  • Technical efficiency: production of good or service without wastePublic and private face constraints in the availability and quality of staffi.e. StaffingPublic sector: suffers from lower wages, low morale and staff absenteeismPrivate sector: fee for service which deters patientAs donor programs give control back to NGOs and government management will become an even bigger player in technical efficiencyBest approach may be to use a function that improves technical efficiency over time
  • The solid dark green piecewise linear curve accurately matches the observed size-rank distribution of the largest 800 ART facilities in South Africa in 2010.The other solid line slightly modifies the observed distribution to characterize the full set of 1,095 facilities in 2010 which were used to deliver the actual amount of ART services in that year.The dashed lines are the authors’ projections of the size-rank distributions that are consistent with the total number of patient-years that are consistent with the amount of ART that will be required for UTT 6 years after scale-up (2016) and in the years 2030 and 2050
  • The authors’ projections of the time-path of size-rank distributions can also be characterized by the total number of facilities in each year and by the number of patient-years of ART delivered in the smallest facility in each year. Both the number of facilities and the size of the smallest one increase at first to accommodate the year of maximum treatment delivery approximately six years after the beginning of scale up. Then both the number of facilities and the size of the smallest one decline as need declines.
  • In our model, economies of scale are a characteristic of the individual treatment facility. A simple characterization of economies of scale is given by a log-linear average cost function. Any such log-linear function can be characterized by its slope and its intercept. By assuming a constant average cost of $800, Granich et al implicitly assumed the flat average cost function in this slide, which has an intercept of $800 and a a slope of zero. Slide 28 (one of the Annex slides) gives the intercept associated with each of a range slopes between 0 and -0.5 (i.e. scale elasticities between 1.0 and 0.5). This slide plots this family of average cost functions. In the worked example, we focus on the average cost function with scale elasticity of 0.7 (i.e. slope in log-log space of -0.3).

Meyer Meyer Presentation Transcript

  • Modelling the cost of ART for prevention Gesine Meyer-Rath1,2, Mead Over3, Lawrence Long21 Center for Global Health and Development, Boston University, Boston, US. 2 Health Economics and Epidemiology Research Office, University of Witwatersrand, Johannesburg, South Africa. 3 Center for Global Development, Washington DC, US. Health Economics and Epidemiology Research Office HE RO 2 Wits Health Consortium University of the Witwatersrand
  • PreventionThings are changing = Prevention Health Economics and Epidemiology Research Office HE RO 2 Health Economics and Epidemiology Research Office Wits Health Consortium University of the Witwatersrand
  • What’s in a projection model?• Epidemiological function – captures the impact of medical policies on the biological consequences, both beneficial and adverse• Cost function – captures the economic consequences of the policy Kahn, Marseille, Bennett, Williams & Granich, October 14, 2011 Health Economics and Epidemiology Research Office HE RO 2Health Economics and Epidemiology Research Office Wits Health Consortium University of the Witwatersrand
  • Identities vs. functions• Cost accounting identity – Too rigid to model large scale changes over periods of more than a few years – Not appropriate to model ART as prevention• Cost function – More plausible characterisation and projection of cost Health Economics and Epidemiology Research Office HE RO 2Health Economics and Epidemiology Research Office Wits Health Consortium University of the Witwatersrand
  • The cost accounting identity tends toover-estimate costs at different prices on Economizing Total Cost accounting the higher Cost identity priced input saves costs TCAI TCF TC0 Cost function Price of i’th input (e.g. Tenofovir)
  • The cost accounting identity tends tounder-estimate costs at different scales Total Diminishing Cost Cost returns function eventually increase costs TCF TCAI TC0 Cost accounting Fixed identity cost Annual output (e.g. patient-years)
  • Use of cost functions in the literature• Reviewed 8 literature databases from1988-2011 + References + Grey literature for ART costing• Included all with a modelled cost• Compared by: economic evaluation method, type of model, time horizon, outcome metric, input cost Health Economics and Epidemiology Research Office HE RO 2Health Economics and Epidemiology Research Office Wits Health Consortium University of the Witwatersrand
  • Results: Literature Review• 45 published articles, 1 conference abstract and 4 reports – 38 for single countries – 4 for wider regions – 8 were global• 5, all for single countries, considered the impact of ART on transmission Health Economics and Epidemiology Research Office HE RO 2Health Economics and Epidemiology Research Office Wits Health Consortium University of the Witwatersrand
  • Results: Literature Review - including transmissionPaper, year (country) AnalysisOver 2004 (India) HIV/AIDS treatment and prevention in India: Modelling the costs and consequencesGranich 2009 (South Africa) Impact of universal voluntary testing and immediate treatment (UTT) on HIV incidence and prevalence and annual costLong EF 2010 (United States) The cost effectiveness and population outcomes of expanded HIV screening and ART in the USHontelez 2011 (South Africa) Incremental cost benefit of ART initiation at CD4 cell count threshold < 200 vs. <350Schwartländer 2011 (Int.) Incremental cost effectiveness of “investment approach” to achieving universal access to HIV prevention, treatment, care and support by 2015Granich 2012 (South Africa) Expanding ART for Treatment and Prevention of HIV in South Africa: Estimated Cost and Cost-Effectiveness 2011-2050 Health Economics and Epidemiology Research Office HE RO 2 Health Economics and Epidemiology Research Office Wits Health Consortium University of the Witwatersrand
  • Factors influencing costPaper Factors influencing input cost (Including in sensitivity analysis, SA)Over (2004) Time on treatment (first 3 years vs. year before death); health state (symptomatic, non-AIDS | AIDS); unstructured vs. structured treatment provision; SA: Cost not includedGranich (2009) Drug cost by FL/ SL, otherwise constant unit cost; No SALong EF (2010) One regimen cost only; health state (untreated symptomatic | untreated symptomatic | treated symptomatic | untreated AIDS | treated AIDS); SA: Cost not includedHontelez (2011) On ART cost by baseline CD4 cell count (100|200|350) for first 3 years, then uniform; drug cost by FL/ SL; SA: Cost varied by +/- 33%Schwartländer (2011) “Average cost per patient of antiretroviral therapy is assumed to decline by about 65% between 2011 and 2020, with a large proportion of the cost savings after 2015 coming from an increasing shift to primary care and community-based approaches and cheaper point-of-care diagnostics”; No SAGranich (2012) Drug cost by FL/SL; Laboratory cost by first year on regimen or > 1 year; Inpatient / outpatient cost based on treatment status; SA: Varied ART, monitoring, inpatient costs based on data available for South Africa. Health Economics and Epidemiology Research Office HE RO 2 Health Economics and Epidemiology Research Office Wits Health Consortium University of the Witwatersrand
  • Potential determinants of a cost function• Most modelled estimates of ART to date use cost accounting identities, with minimal use of cost functions• If a more flexible cost function where to be used, which variables should be included? Health Economics and Epidemiology Research Office HE RO 2Health Economics and Epidemiology Research Office Wits Health Consortium University of the Witwatersrand
  • Treatment characteristics• Regimens, health states and time on treatment• More complex = higher treatment costs• Distribution into first and second line• Distribution across CD4 count strata• Time on treatment dictating likelihood of an event Health Economics and Epidemiology Research Office HE RO 2Health Economics and Epidemiology Research Office Wits Health Consortium University of the Witwatersrand
  • Factor pricesThe development of the price of d4T+3TC+NVP 2000 - 2008MSF Campaign for Access to Essential Medicines: Untangling the Web of Antiretroviral PriceReductions. 11th edition, July 2008 Health Economics and Epidemiology Research Office HE RO 2 Health Economics and Epidemiology Research Office Wits Health Consortium University of the Witwatersrand
  • Scale• Marginal and average cost for hygiene outreach in 2000 Int’l $• Adjustment for scale used in WHO- CHOICE generalized CEA• Modelled on world-wide GPS data (clinic and population density)• Calculated transport cost of goods, fixed and supervision costs; health centre cost excludedJohns B, Baltussen R: Accounting for the cost of scaling-up health interventions.Health Econ. 13: 1117–1124 (2004) Health Economics and Epidemiology Research Office HE RO 2 Health Economics and Epidemiology Research Office Wits Health Consortium University of the Witwatersrand
  • Experience of facility and program Menzies et al, 2011, PEPFAR data. Health Economics and Epidemiology Research Office HE RO 2 Health Economics and Epidemiology Research Office Wits Health Consortium University of the Witwatersrand
  • Scope and distribution• Analysis of cost of ART provision amongst different models of care• 4 settings in South Africa (GP/ MP/ EC)• Annual per patient Rosen et al: The outcomes and outpatient costs of different models cost in each of antiretroviral treatment delivery in South Africa. Trop Med Intern Health 13(8):1005-15 setting (2008) Health Economics and Epidemiology Research Office HE RO 2 Health Economics and Epidemiology Research Office Wits Health Consortium University of the Witwatersrand
  • Quality of care• “In care and (not)responding”defined by VL, CD4and new WHOstage 3/ 4conditions• “No longer incare” pt died orwas lost to follow-up in the first 12months Rosen et al: The outcomes and outpatient costs of different models of antiretroviral treatment delivery in South Africa. Trop Med Intern Health 13(8):1005-15 (2008) Health Economics and Epidemiology Research Office HE RO 2 Health Economics and Epidemiology Research Office Wits Health Consortium University of the Witwatersrand
  • Technical efficiency• Production of good/service without waste• Incentives: Salaries (private vs. public)• Non financial incentives: Encouragement and supervision• Technical changes: take into account things not currently used / invented Health Economics and Epidemiology Research Office HE RO 2Health Economics and Epidemiology Research Office Wits Health Consortium University of the Witwatersrand
  • Worked example of how a flexiblefunction can alter cost projections• Use the example of Granich et al’s 1999 article on Universal Test and Treat in South Africa• Change only one assumption: – Instead of constant returns to scale, allow for increasing returns to scale at the facility level• Requires data or theory on the size distribution of ART facilities Health Economics and Epidemiology Research Office HE RO 2 Health Economics and Epidemiology Research Office Wits Health Consortium University of the Witwatersrand
  • Steps in the analysis• Use empirical size-rank distribution of South African ART treatment facilities in 2010• Project the size-rank distribution of facilities to expand to full-coverage and then to shrink as need declines• Generate a family of facility-specific average cost functions scale elasticities < 1.0• Project future cost at each scale elasticity Health Economics and Epidemiology Research Office HE RO 2Health Economics and Epidemiology Research Office Wits Health Consortium University of the Witwatersrand
  • Current and projected sizedistributions of ART facilities in SA Health Economics and Epidemiology Research Office HE RO 2 Health Economics and Epidemiology Research Office Wits Health Consortium University of the Witwatersrand
  • Health Economics and Epidemiology Research Office HE RO 2Health Economics and Epidemiology Research Office Wits Health Consortium University of the Witwatersrand
  • Family of South African facility-specific averagecost curves with scale-elasticities from 0.5 to 1.0 Health Economics and Epidemiology Research Office HE RO 2 Health Economics and Epidemiology Research Office Wits Health Consortium University of the Witwatersrand
  • With a scale–elasticity of 0.7, peak costsand cumulated costs will be 40% greater Health Economics and Epidemiology Research Office HE RO 2 Health Economics and Epidemiology Research Office Wits Health Consortium University of the Witwatersrand
  • Conclusions on the potential value of flexible cost functions • A flexible cost function can give very different cost projections over the long run • Depending on the elasticity of scale alone, the cost of UTT could be up to 75% greater than projected under the constant returns assumption • It behooves modelers to pay as much attention to their cost specifications as to their epidemiologic ones. Health Economics and Epidemiology Research Office HE RO 2 Health Economics and Epidemiology Research Office Wits Health Consortium University of the Witwatersrand
  • Annex slides
  • Peak costs and cumulated costs vary with the assumed scale-elasticity Health Economics and Epidemiology Research Office HE RO 2 Health Economics and Epidemiology Research Office Wits Health Consortium University of the Witwatersrand
  • Calibration of the average cost function to South African data for 2010/11:How we fit the family of average cost functions Value of σ Value of (σ – 1) Percent increase in total Percent decrease Cost of using an entire ART facility to treat a cost associated with a in average total single patient 1% increase in output cost associated (Scale elasticity) with a 1% increase in Derived from Meyer- Deflated to match output Rath et al Granich et al costs Constant returns 1.0 0 $924 $800 to scale 0.9 -0.1 $1,976 $1,711 0.8 -0.2 $4,187 $3,625 Increasing returns 0.7 -0.3 $8,791 $7,611 to scale 0.6 -0.4 $18,296 $15,840 0.5 -0.5 $37,763 $32,695 Health Economics and Epidemiology Research Office HE RO 2 Health Economics and Epidemiology Research Office Wits Health Consortium University of the Witwatersrand
  • Impact on peak-year and cumulated cost of a Universal Test andTreat policy in South Africa of alternative assumptions regarding economies of scale in ART service delivery Value of σ Costs of Universal Test and Treat policy Total cumulated cost without discounting in Per cent increase in total cost constant 2010 USD associated with a one per cent Per cent of total increase in output (Scale Peak cost in billions Total cost in billions above constant elasticity) of USD of USD returns to scale Constant returns to 1.0 $3.5 $74.6 0.0% scale 0.9 $83.6 12.0% $3.8 0.8 $93.6 25.4% $4.1 Increasing returns to 0.7 $104.8 40.4% scale $4.4 0.6 $117.2 57.0% $4.7 0.5 $131.0 75.4% $5.1 Health Economics and Epidemiology Research Office HE RO 2 Health Economics and Epidemiology Research Office Wits Health Consortium University of the Witwatersrand