Application of CEA and BCA Tuberculosis Control ... · including 0.4 million deaths among people with human immunodeficiency virus (HIV). TB is the leading cause of death in South

Comparing the Application of CEA and BCA to Tuberculosis Control Interventions in South Africa

Thomas Wilkinson, Fiammetta Bozzani, Anna Vassall, Michelle Remme, and Edina Sinanovic

June 2018

Guidelines for Benefit‐Cost Analysis Project Working Paper No. 12 Prepared for the Benefit‐Cost Analysis Reference Case Guidance Project Funded by the Bill and Melinda Gates Foundation Visit us on the web: https://sites.sph.harvard.edu/bcaguidelines/

Comparing the application of CEA and BCA to tuberculosis control

interventions in South Africa.

Wilkinson T1, Bozzani F2, Vassall A2, Remme M2, Sinanovic E1

1. Health Economics Unit, School of Public Health and Family Medicine, University of Cape

Town, South Africa

2. Department of Global Health and Development, London School of Hygiene and Tropical

Medicine, UK

Abstract

Introduction: Achieving ambitious targets for the identification and successful treatment of patients

with tuberculosis (TB) requires consideration of the likely impact of potential interventions. Cost

Effectiveness Analysis (CEA) and Benefit Cost Analysis (BCA) are two approaches to economic

evaluation that assess the costs and effects of competing alternatives, however the differing

theoretical basis and methodological approach to CEA and BCA is likely to result in alternative

analytical outputs and potentially different policy interpretations.

Methods: A BCA was conducted under the Guidelines for Benefit‐Cost Analysis Project by converting

an existing CEA analysis on 10 mutually exclusive TB control interventions in the South African setting.

Local estimates of Value of Statistical Life (VSL) and Value of Statistical Life Year (VSLY) were calculated

using the benefits transfer approach and were applied to expected mortality and morbidity

respectively. Impact was modelled over a 30‐year time‐period.

Results: BCA analysis indicates that all interventions are likely to result in substantial gains compared

to the status quo, ranging from ZAR16.9 billion to ZAR751 billion (Int$3.0 ‐Int$135 billion) over the 30‐

year period and depending on choice of VSL method. CEA and BCA results identified Intervention 5 as

cost saving and yielding positive health benefits, and intervention 10 was estimated to have the

highest net benefit.

Discussion: The differing analytical outputs reflect the theoretical underpinnings of BCA and CEA.

Further work is required to guide appropriate interpretation and policy recommendations in the South

African policy perspective and context.

Introduction

The aim of this case study is to assess the expected impact of investing in various tuberculosis (TB)

control interventions in the South African context using a benefit cost‐analysis (BCA) approach. The

analysis is informed by the Guidelines for BCA project (1) and converts outputs of a cost effectiveness

analysis (CEA). This approach will enable comparison of CEA and BCA analysis of the same

interventions in the same context and is intended to assist in further refinement of methodological

guidance provided in the Guidelines for BCA project and the International Decision Support Initiative

(IDSI) Reference Case for economic evaluation (2).

Policy context

TB remains a significant policy priority globally. In 2016, 1.7 million people died as a result of TB

including 0.4 million deaths among people with human immunodeficiency virus (HIV). TB is the leading

cause of death in South Africa with a mortality rate of 181 per 100,000 in HIV+ patients and 41 per

100,000 in patient without HIV (3). Since 1990, globally there has been a 47% decline in the TB

mortality rate and the HIV‐related TB deaths have reduced by 32% since 2005. (3)

Between 2015 and 2030, the Sustainable Development Goals (SDG) aim to reduce the global number

of TB deaths by 90%, reduce the TB incidence rate by 80%, and eliminate catastrophic costs on

households as a result of TB. Pillar 1 of the World Health Organisation’s (WHO) End TB strategy

focusses on integrated, patient‐centred TB care and prevention, and includes “early diagnosis of TB

including universal drug‐susceptibility testing and systematic screening of contacts and high‐risk

groups” and “collaborative TB/HIV activities”(4).

South Africa’s National Strategic Plan (NSP) for HIV, TB and STIs 2017‐2022 (NSP) outlines the roadmap

for a national response to HIV, TB and Sexually transmitted infections (STIs), recognising the need for

a comprehensive and integrated response (5). Aligned to the SDG targets, the NSP aims to reduce

national TB incidence from 450,000 to less than 315,000 per year by 2022, including diagnosis of 90%

of people with TB, treating 100% of those diagnosed and achieving successful treatment for 90% of

patients with drug‐susceptible TB.

Goal 2 of the NSP aims to reduce morbidity and mortality by providing treatment, care and adherence

support for all, specifying increased need for screening and testing programmes to appropriately

identify patients in order to initiate treatment. The TB Targets project assessed the impact of

interventions aimed at reaching the End TB strategy target and demonstrated that a range of

interventions would be required across the spectrum TB care (6). Central to achieving the ambitious

targets for reducing TB incidence and mortality involves optimal use of available resources, and

developing innovative ways of managing the identification, diagnosis and treatment of TB tailored to

local country context and constraints. The TB Targets analysis found that Intensive Case Finding (ICF)

was the single most effective intervention for reaching NSP targets, but was also the most expensive,

demonstrating the need for increased resource allocation and further research on the optimal

approach to implementation of case finding strategies in the South African context. (7)

After receiving conditional programmatic recommendation from WHO in 2010, a new diagnostic test

Xpert MTB/RIF was rolled out in South Africa in 2012, marking a milestone development in the

identification of patients with TB (8). Compared to existing diagnostic regimens, multiple economic

evaluations predicted that Xpert MTB/RIF would be cost effective in a range of settings, including

South Africa (9). However, a cost‐effectiveness analysis investigated the effect of Xpert utilising data

from a pragmatic cluster randomised trial (XTEND study) conducted during roll‐out of the new

technology in South Africa, and found that Xpert was highly unlikely to have improved the cost‐

effectiveness of TB diagnosis at a range of cost‐effectiveness thresholds during its early stage of roll‐

out (9). This finding demonstrated the need to explore implementation constraints and fully explore

the relative value of different options for identification of persons needing screening and diagnostics

for TB, leading to the analysis used for this case study.

Approach to the case study

The original CEA assessing the TB control interventions (Bozzani et al, in press) was developed

combining impact estimates generated using the TIME epidemiological transmission model with cost

estimates from the literature and micro‐costing of TB control at sites across South Africa, to estimate

the cost‐effectiveness of specific interventions detailed in Figure 1.

The TIME model (a deterministic epidemiological model) was developed by the London School of

Hygiene and Tropical Medicine in collaboration with Avenir Health as a user‐friendly tool to predict

the impact of interventions along the TB transmission, diagnosis and treatment pathway in high‐

burden settings. The cost‐effectiveness analysis that this case study is based on assessed specific

interventions related to screening and diagnosis, and utilised the TIME model to estimate likely

outcomes.

In completing the case study, the recommended BCA methodological approaches were applied to the

original analysis where feasible, and no additional primary analysis or data collection was conducted.

In addition to applying the methodological specifications developed under the Guidelines for Benefit‐

Cost Analysis project, the case study attempts to conform with recommendations of the linked

initiatives the IDSI Reference Case (2), and guidance from the Global Health Cost Consortium Project

(10).

Policy options

The NSP for TB in South Africa requires a substantial and rapid scale‐up of approaches to identify

patients with TB and effectively initiate them on the right treatment. There are a range of policy

options that can be applied individually or in combination to improve TB patient identification, and by

comparing the expected costs and benefits associated with each option, the optimal combination can

be identified. The analysis compares ten mutually exclusive interventions (status quo plus six unique

interventions and three intervention combinations) for improving TB control as shown in Figure 1 and

Figure 2 below. The potential interventions in Figure 1 were identified in discussion with policy makers

and represent the realistic and immediate policy options.

The current measure to identify patients to initiate the TB diagnostic pathway include 1) passive

screening, which relies on patients actively seeking care, 2) cough triage, which includes a simple

question to patients about history of coughing symptoms, and 3) a structured questionnaire with

specific questions related to patient symptoms and clinical history developed by the World Health

Association (WHO symptoms screening tool). Currently 40% of patients who are HIV positive are

screened for TB symptoms using the WHO symptoms screening tool, and staff at Primary Health Clinics

(PHC) passively screen all patients for TB. The available options to scale up TB control involve six

potential interventions, each of which have associated costs and expected benefits. The options

include increasing Xpert coverage from 80% to 100%; increasing microscopy follow‐up of those who

have a negative Xpert result from 14% to 90%; triaging all HIV+ and 90% of all PHC patients for cough

assessment; and performing WHO symptoms screening in 100% of HIV+ patients and 90% of all PHC

patients, respectively. In addition, a further three combinations were assessed that consisted of the

Xpert interventions (100% Xpert coverage and 90% follow‐up of negative results), the Xpert

interventions combined with cough triage in 90% of all PHC patients, or Xpert interventions combined

with WHO symptoms screening in 90% of all PHC patients.

Figure 1. Intervention scenarios

The impact of the interventions strategies was estimated utilising the TIME model, with costs and

effects modelled at specific stages in the causal pathway of TB as shown in Figure 2.

Figure 2. Intervention scenarios within the TIME model, modified from Menzies N, 2016

Perspective

The CEA was conducted from the perspective of the health service provider, which in this instance is

the South African government. In the South African public health sector, TB care is provided free of

charge to patients at the point of use. This means that the government finances all direct health

systems costs and patients are not required to make any financial contribution to the direct health

services costs associated with accessing TB treatment beyond general government taxation.

In terms of indirect and non‐health systems costs, a costing analysis involving TB patients in South

Africa found 69% of those who were confirmed to have TB reported no income, and a further 5%

accessed government cash transfers as their main source of income (receipt of which would not be

impacted by disease). This indicates that patients in South Africa suffer relatively low levels of income

loss from TB due to the context of high unemployment rates. In addition, transport costs were

relatively low as many patients in the study sample were able to access facilities within walking

distance or a short journey from their home (11). The low transport costs may not be representative

of the total population in South Africa, as some patients in rural communities may need to travel

substantial distances to access care. Further research is required to fully assess the indirect costs faced

by this patient population in the South African context.

This analysis will present costs from a government healthcare provider perspective, and the welfare

gains to individuals will be estimated according to methodological specifications in the BCA project.

Additional sensitivity analysis, including costs incurred by 3rd parties (such as carers assisting patients

to access health services), will be included in the analysis. Benefits incurred by patients in terms of

mortality and morbidity reduction will be incorporated and valued according to recommendations of

the BCA reference case.

Baseline Conditions

The baseline comparators are detailed in Figure 1 and consist of Xpert coverage for 80% of cases,

limited follow‐up (14%) of those that receive negative Xpert result, WHO symptoms screening for less

than half of patients with HIV, and passive screening of patients in PHCs. An important aspect of this

analysis is that it is not assessing whether or not to introduce a new individual technology, but

assessing the costs and benefits of investing additional resources in order to achieve target levels of

TB patient identification. Therefore, the current baseline comparator is the existing screening

algorithm, with associated levels of staffing, equipment and technologies at South African health

facilities.

As the interventions represent up‐scaling of existing interventions, it is not predicted that there would

be major societal shifts or structural changes to the economy as a result of implementing the

interventions, beyond of course the potential significant mortality and morbidity benefits of improved

management of TB.

The IDSI RC recommended that, while current practice should be used in base case analysis, additional

analysis should be conducted using best supportive, non‐interventional care as a comparator where

appropriate to the decision problem. This case study does not incorporate a non‐interventional

comparator as the current policy decision that this analysis seeks to answer is restricted to utilising

existing diagnostic technologies and processes available in South Africa. In addition, applying a non‐

interventional (“do‐nothing”) comparator for diagnostic interventions would require substantial

assumptions about the down‐stream management of TB that may limit the usefulness of any findings

of such an analysis.

Expected impact

The expected impacts of the different policy options are differing levels of resource use, largely

because of staffing requirements to carry out the scaled‐up interventions, and a corresponding

improvement in TB patient identification with downstream impact on TB care and ultimately reduced

TB‐related mortality and morbidity. The use of more intensive screening interventions incurs more

nurse time, and improved sensitivity generates more diagnostic tests downstream with associated

costs but improved patient outcomes. A central assumption in predicting impact is related to the

causal pathway from diagnosis to appropriate treatment, and then treatment to patient outcomes. In

this analysis, common assumptions about treatment outcomes are applied consistently to all

interventions and are based on outputs from the TIME epidemiological model. The major impact areas

modelled include numbers of patients screened for TB using the passive and WHO approaches, the

number of smear microscopy and Xpert diagnostic tests completed, the number of patients initiated

on first‐line and multi‐drug resistant (MDR) regimens, and reduction in the number of total person‐

years of untreated active disease.

Table 1 shows the expected impact of each of the interventions under consideration on health system

outcomes (in ‘000s) over the 20‐year period from 2015‐2035 compared to the status quo. Importantly,

all interventions are expected to substantially reduce the number of person‐years of untreated active

disease – a key indicator for reduction in TB transmission. Intervention 7 (100% screening of all

patients who are HIV+) is expected to result in more than 447 million patient screening events using

the structured WHO survey, with a resultant reduction in number of patients passively screened and

increase in diagnostic tests performed and patients initiated on treatment. Intervention 10 (increased

Xpert coverage and follow‐up, and symptoms screening of 90% of all PHC attendees) is expected to

yield the greatest reduction in untreated active disease, more than 833 million additional screens and

181 million additional Xpert diagnostic tests over the 20‐year period.

Table 1 Intervention impact on health system outcomes 2015‐2035 (in ‘000s)

Intervention* Untreated active disease

(person years)

No. patients screened (passive)

No. of patients screened (WHO)

No. of smear microscopies completed

No. of Xpert tests completed

No. patients initiated on 1st line treatment

No. of patients initiated on

MDR treatment

2 ‐1 951 ‐404 ‐22 ‐21 409 21 008 ‐288 8

3 ‐1 132 ‐231 24 ‐57 ‐228 59 10

4 ‐3 134 ‐646 ‐7 ‐21 411 20 710 ‐228 19

5 ‐344 ‐65 ‐319 226 ‐5 658 ‐22 633 ‐219 1

6 ‐1 233 ‐261 18 3 701 14 803 102 ‐4

7 ‐6 384 ‐1 364 447 597 21 036 84 146 823 17

8 ‐4 409 ‐942 833 124 32 354 129 417 1 156 ‐14

9 ‐4 274 ‐887 6 ‐21 285 39 095 ‐189 15

10 ‐7 171 ‐1 510 833 238 ‐20 517 181 649 385 3

*Intervention results are presented incremental to intervention 1, which is the status quo or baseline.

Costs

This case study adopts the recommendations of the BCA RC guidance in determining costs and

benefits. Estimates of net costs of the interventions followed guidance of the Global Health Costing

Consortium Reference Case (10) and involved combining the output of the TIME epidemiological

model with costing parameters derived from local South African data.

The cost of key elements within the care pathway were estimated using a micro costing approach. For

example, costs of drug regimens were estimated by calculating total number of tablets/injections

required over the course of treatment multiplied by their unit cost, and screening costs involved the

unit cost of the test per patient plus health professional time. Compared to the status quo scenario,

the cost‐impact of key elements of the management pathway for the period 2015‐2035 (discounted

at an annual rate of 3%) are detailed in Table 2 and Table 3.

Table 2 Cost of interventions by element of the treatment pathway 2015‐2035 (in Int$, ‘000s)

Intervention Passive screening costs

Xpert test costs

Smear microscopy

costs

Patient follow up costs*

Cough triage costs

WHO screening costs

1st line treatment

costs

MDR treatment

costs

IPTtreatment costs**

2 ‐$485 $1 195 079 ‐$410 609 ‐$4 584 $80 ‐$43 ‐$64 720 $121 358 ‐$43

3 ‐$278 ‐$13 076 ‐$1 102 $27 769 $21 $52 $13 993 $155 441 $50

4 ‐$776 $1 178 004 ‐$410 654 $29 439 $100 ‐$8 ‐$50 267 $295 990 ‐$10

5 ‐$95 ‐$1 251 274 ‐$105 470 ‐$115 $925 061 ‐$780 079 ‐$48 473 $19 429 ‐$88

6 ‐$308 $844 873 $71 214 ‐$181 $423 585 $39 $23 834 ‐$49 097 $37

7 ‐$1 635 $4 776 620 $402 620 ‐$1 864 $179 $1 071 585 $190 693 $290 588 $527

8 ‐$1 111 $7 356 267 $620 058 ‐$655 ‐$591 834 $2 031 633 $264 360 ‐$184 706 $130

9 ‐$1 062 $2 225 230 ‐$407 526 $29 185 $423 758 $18 ‐$40 742 $240 255 $15

10 ‐$1 798 $10 310 882 ‐$388 511 $28 524 ‐$591 834 $2 031 889 $91 890 $88 461 $79

*cost of following up patients who have a negative Xpert result

** People living with HIV receiving isoniazid preventative therapy

Table 3 Cost of interventions by element of the treatment pathway 2015‐2035 (in ZAR, ‘000s)

Intervention Passive screening costs

Xpert test costs

Smear microscopy

costs

Patient follow up costs*

Cough triage costs

WHO screening costs

1st line treatment

costs

MDR treatment

costs

IPT treatment costs**

2 ‐R2 697 R6 649 417 ‐R2 284 627 ‐R25 507 R443 ‐R238 ‐R360 100 R675 236 ‐R241

3 ‐R1 548 ‐R72 754 ‐R6 132 R154 505 R117 R292 R77 859 R864 875 R281

4 ‐R4 316 R6 554 412 ‐R2 284 880 R163 801 R558 ‐R47 ‐R279 685 R1 646 886 ‐R58

5 ‐R528 ‐R6 962 089 ‐R586 832 ‐R642 R5 147 037 ‐R4 340 357 ‐R269 706 R108 102 ‐R487

6 ‐R1 715 R4 700 873 R396 235 ‐R1 007 R2 356 829 R218 R132 614 ‐R273 174 R208

7 ‐R9 095 R26 577 114 R2 240 177 ‐R10 369 R997 R5 962 297 R1 061 016 R1 616 832 R2 931

8 ‐R6 184 R40 930 270 R3 450 000 ‐R3 644 ‐R3 292 964 R11 304 003 R1 470 898 ‐R1 027 706 R721

9 ‐R5 908 R12 381 182 ‐R2 267 477 R162 385 R2 357 790 R102 ‐R226 690 R1 336 781 R84

10 ‐R10 003 R57 369 746 ‐R2 161 677 R158 707 ‐R3 292 964 R11 305 430 R511 274 R492 195 R441

*cost of following up patients who have a negative Xpert result

** People living with HIV receiving isoniazid preventative therapy

The Xpert diagnostic test is a driver of cost under most intervention scenarios. Xpert costs are

estimated in excess of $4 billion over the 20‐year period, with an additional $71 million for 1st line

and MDR TB treatment costs. Intervention 5 (cough triage for 100% of HIV+ patients) is expected to

result in savings in most elements of care due to a reduction in TB cases over time.

Benefits

In line with the recommendations of the BCA Reference Case guidance, this case study adopts a

“benefits transfer” approach to estimate the monetary value of mortality and morbidity risk reduction

(12) (13). A benefits transfer approach applies the benefit valuation observed in one county or

jurisdiction to another with relevant adjustments, and was selected as a literature search did not

identify literature of sufficient quality to estimate the Value of Statistical Life (VSL) in South Africa

directly. Equation 1 shows the approach to calculating the values used in the benefits transfer, where

the VSLtarget is the estimated VSL in South Africa, VSLbase is the value in the originating country, and

income is the GNI per capita adjusted for purchasing power parity. Equation 2 is derived from

Equation 1 and represents the VSLtarget in proportion to Incometarget which is more convenient to work

with.

VSLtarget = VSLbase * (Incometarget/ Incomebase) elasticity Equation 1

(VSLtarget / Incometarget )= (VSLbase / Incomebase)* (Incometarget/ Incomebase) (elasticity – 1) Equation 2

The values to estimate the VSL for the South African target population used in this case study are

informed by the BCA reference case guidance (12), and include three approaches for parameters in

Equation 2 above. The VSL was adjusted for expected economic growth in future years by applying

equation 1 above. Assuming an elasticity of 1, the VSL in any year (VSLtarget) will be proportional to the

per capita Income in the target year divided by per capita Income in the base year multiplied by the

VSL used in the base year. The International Monetary Fund average projected per capita GDP growth

to 2023 for South Africa (2.52%) (14) was assumed to represent a reasonable estimation of the annual

expected change in per capita income year to year and was used to estimate annual VSL growth to

2035.

Table 4 Estimated VSL monetary values for South Africa ($Int)

Approach 1: GNI per capita*160 (elasticity 1)


Approach 3: GNI per capita*160 (elasticity 1.5)

$2 054 400 $1 284 000 $ 981 652

Incometarget =$12,840 (GNI pc for South Africa, adjusted for ppp, 2015) Incomebase =$57,900 (GNI pc for United States, adjusted for ppp, 2015) VSLbase =$9.4 million (derived from primary literature)

Value of mortality risk reduction

The projected deaths avoided as a result of the different interventions over the period 2015‐2035

(undiscounted) are shown in Table 5. All interventions are expected to avoid a substantial number of

deaths relative to the status quo, with intervention 10 expected to yield the largest reduction in

mortality at over 73,000 deaths avoided over the 20‐year period. The valuation of the mortality risk

reduction is also shown in Table 5, using VSL detailed in Table 4 and discounted at an annual rate of

3%.

Table 5 Total deaths avoided and value of mortality risk reduction (in Int$ millions)

Intervention Total deaths avoided*

Value of mortality risk reduction (approach 1)



2 17 913 $34 583 $21 614 $16 525

3 5 170 $9 999 $6 250 $4 778

4 22 780 $43 993 $27 496 $21 021

5 1 769 $3 513 $2 196 $1 679

6 15 571 $30 055 $18 785 $14 361

7 35 775 $69 248 $43 280 $33 089

8 55 429 $106 968 $66 855 $51 112

9 37 237 $71 905 $44 940 $34 358

10 73 970 $142 810 $89 256 $68 239 *Total deaths avoided incremental to the status quo (intervention 1)

Value of morbidity risk reduction

The approach to valuing morbidity follows the BCA Reference Case guidance (13). Ideally the approach

to estimating the VSLY would be based on locally‐derived and high‐quality willingness to pay estimates

for the target population (i.e. patients with TB in South Africa), or a valuation function. However, these

are currently not available so as a proxy, a constant Value of Statistical Life Year (VSLY) was derived

from a monetised disability‐adjusted life year (DALY). The proposed VSLY estimates are detailed in

Table 6 and are derived from the different approaches to estimate the VSL in Table 4 and divided by

32.83 years (the mean expected numbers of years of life remaining for the average patient who was

15+ years old in the target population). This approach relies on strong assumptions as detailed in the

BCA RC guidance, including that 1) the VSLY is constant, 2) the VSLY as calculated is equivalent to a

DALY, and 3) the value per DALY is constant.

Table 6 Estimated VSLY monetary values for South Africa for target population


Approach 2:GNI per capita*100 (elasticity 1)

Approach 3: GNI per capita*160 (elasticity 1.5)

$62 577 $39 111 $29 901

Using established disability weights from the literature for the relevant health states (with and without

active TB in combination with different HIV states) (15), the total estimated morbidity reduction

associated with each intervention over the period 2015‐2035 is detailed in Table 7 and discounted at

3% annually. Monetised benefit of the morbidity reduction is calculated by multiplying the morbidity‐

related DALYs averted by the VSLY values detailed in Table 6, with and without 3rd‐party costs added.

Table 7 shows that Intervention 10 is expected to avert the highest number of DALYs averted (58, 609)

at a monetised value of Int$3.6 billion over the 20‐year period using approach 1 and incorporating 3rd

party costs.

The BCA RC guidance notes that VSLY estimates are assumed to incorporate non‐health systems costs

incurred by the individual, and so these costs are not added to the VSLY estimates. There is some

uncertainty as to whether it is appropriate to add 3rd‐party costs (e.g. those included by household or

family members). An analysis of the economic costs of TB in South Africa (11) found the mean

guardian/carer costs per diagnostic and treatment episode to be US$114.10. Assuming this cost would

be incurred by 3rd parties for all patient initiating 1st line or MDR treatment under the different

interventions, the impact of this 3rd‐party cost on the value of morbidly reduction (discounted at 3%

annually) is also incorporated in Table 7 for comparison. Interventions that will result in a net

reduction in initiations of TB treatments (e.g. Intervention 2), the inclusion of 3rd party costs increase

the net morbidity related value, whereas interventions that increase the numbers initiating treatment

(e.g. intervention 6) result in decreased value as additional treatment initiations result in higher 3rd

party costs. In this scenario, the inclusion of 3rd‐party costs represents a small proportion of the benefit

compared to VSLY, with the mean increase in valuation across the interventions ranging from 0.98%

(when using Approach 1) to 2.05% (when using Approach 3).

Table 7 Total DALYs (morbidity only) and corresponding monetized benefit by VSL estimation approach with

and without 3rd party costs (in Int$, millions).

Intervention Total DALYs

(morbidity only) averted

Monetized benefit

(approach 1)

Monetizedbenefit

(approach 1 + 3rd‐party costs)

Monetized benefit

(approach 2)

Monetized benefit (approach 2 with 3rd‐party costs)

Monetized benefit

(approach 3)

Monetizedbenefit

(approach 3 + 3rd‐party costs)

2 17 011 $1 065 $1 087 $665 $688 $509 $531

3 6 549 $410 $404 $256 $250 $196 $190

4 24 039 $1 504 $1 521 $940 $956 $719 $735

5 3 802 $238 $255 $149 $166 $114 $131

6 10 345 $647 $639 $405 $396 $309 $301

7 37 872 $2 370 $2 301 $1 481 $1 412 $1 132 $1 063

8 36 977 $2 314 $2 221 $1 446 $1 353 $1 106 $1 013

9 33 812 $2 116 $2 129 $1 322 $1 336 $1 011 $1 024

10 58 609 $3 668 $3 634 $2 292 $2 259 $1 752 $1 719

*Total DALYs averted incremental to the status quo (intervention 1)

Net benefits

The net benefits calculation followed the BCA RC guidance by subtracting total costs from total

monetized benefits to estimate net benefits as detailed in Table 8 and Table 9. All interventions are

estimated to result in positive net benefits compared to status quo. Regardless of approach to

estimate the VSL, intervention 10 (improved Xpert access and follow up, WHO symptoms screening in

90% of PHC patients) appears to offer the greatest net benefit over the 20‐year period, at between

Int$135 billion (Approach 1) and $58.4 billion (Approach 3). Intervention 5 (cough triage in all HIV+

patients) represented the lowest net benefits, ranging from Int$4.9 billion (Approach 1) to Int$3.0

billion (Approach 3).

Table 8 Net benefit by intervention, compared to status quo (in Int$ millions)

Intervention Net benefit

(VSL estimation approach 1)

Net benefit


Net benefit


2 $34 812 $21 444 $16 197

3 $10 226 $6 323 $4 791

4 $44 455 $27 394 $20 698

5 $4 992 $3 585 $3 033

6 $29 389 $17 875 $13 357

7 $64 888 $38 032 $27 492

8 $99 788 $58 807 $42 724

9 $71 551 $43 794 $32 900

10 $134 908 $79 979 $58 422

Table 9 Net benefit by intervention, compared to status quo (in ZAR millions)

Intervention Net benefit


Net benefit


Net benefit


2 R193 692 R119 313 R90 123

3 R56 899 R35 181 R26 657

4 R247 350 R152 420 R115 164

5 R27 775 R19 949 R16 878

6 R163 519 R99 458 R74 317

7 R361 039 R211 608 R152 964

8 R555 218 R327 202 R237 715

9 R398 111 R243 668 R183 055

10 R750 629 R445 003 R325 059

Return on Investment

The BCA RC guidance notes that return on investment (ROI) calculations may be presented, however

results should be interpreted with caution as ROI is influenced by the allocation of costs as either

inputs or outputs (1).

ROI calculations for the interventions are shown in Table 10. Intervention 5 was estimated to be cost

saving to the health system (i.e. had negative input costs) and so the ROI cannot be calculated but is

represented in the table as greater than 100 to give an indication of relative favourable returns.

Further analysis of intervention 5 would enable a more accurate estimation of the ROI. Intervention

10, which was estimated to have the greatest net benefits of all the interventions, has a relatively low

estimated ROI, reflecting the large costs associated with implementation.

Table 10 Return on Investment

Intervention ROI


ROI


ROI


2 43 27 20

3 57 36 27

4 44 27 21

5 >100 >100 >100

6 23 15 11

7 11 7 5

8 12 7 6

9 30 19 14

10 13 8 6

Distribution of effects

Incidence of TB is heavily influenced by income and socioeconomic status. Despite significant

reductions in the rate of poverty1 from 1996, the poverty rate in South Africa has increased to 18.9%

in 2015 from 16.9% in 2008. As TB is both a cause and effect of poverty, the distributional of the social

benefits and costs associated with the TB control interventions across the South African population is

highly relevant to the policy recommendation (13).

Even though TB care in South Africa is largely free at the point of use, patients experience direct and

indirect costs associated with the disease and in accessing TB diagnosis and treatment. An extended

cost‐effectiveness analysis that utilised the same epidemiological model (TIME) as this case study

estimated the impact of expanded TB services on households in South Africa and India (16). The study

found that in the South African base case scenario, 1.1 to 1.2 million households would experience

catastrophic costs related to TB over the period 2015‐2030, with 80% of catastrophic costs

experienced in the bottom quintile, and zero households in the top quintile experiencing catastrophic

costs. Expanded access to TB services in South Africa was estimated to reduce TB‐related catastrophic

costs by 5‐20%, with the majority of benefits accruing to poorest households.

All interventions within this case study reduce the amount of untreated active TB and avert significant

morbidity and mortality. This case study was unable to make accurate quantitative estimations of the

distributional impacts of the different interventions as although the socioeconomic status of patients

passively screen for TB is known, it is uncertain precisely how the benefits of more intensified case

finding and screening will be distributed. It is expected however that the intervention effects will

mainly be experienced by poor and impoverished households and that interventions with larger

reductions in TB‐associated morbidity and mortality are likely to have a greater impact on households

in lower‐income quintiles. As TB interventions in South Africa are largely delivered in the public sector

and are free at the point of use to patients, the cost of the interventions falls on government revenue,

through a broadly progressive taxation system.

1 Poverty defined as below $1.90 per day (ppp)

Discussion

This case study aimed to demonstrate the methodological specifications of the BCA RC guidance and

was applied to an existing cost‐effectiveness analysis of 10 interventions to improve access and

diagnosis of TB in the South African setting.

The interventions all demonstrated positive net benefits and although this case study provides a useful

estimate of the different interventions using a benefits transfer approach to the valuation of statistical

lives and statistical life‐years, further analysis would be required to establish a policy recommendation

in the South African setting. The CEA results found that Intervention 5 (cough triage in 100% of HIV+

patients) is expected to result in net health service savings and generate positive health outcomes,

and under a CEA framework would be described as dominating the status quo (Intervention 1), and

would likely receive a positive policy recommendation even under scenarios where the cost‐

effectiveness threshold is either extremely low or unknown. Interventions 3, 4, 6, 7 and 8 are either

strongly or extendedly dominated, indicating that for any given cost effectiveness threshold, an

alternative intervention exists that represents a more favourable use of resources2. Intervention 10

(100% Xpert coverage, 90% follow‐up of Xpert negatives and WHO symptoms screen in 90% of all PHC

patients) is expected to have the highest Incremental Cost Effectiveness Ratio (ICER). It represents a

potentially viable policy option but the ICER for intervention 10 is likely to be higher than a recent

cross‐country analysis of cost‐per DALY ranges in number of countries that estimated the cost‐per

DALY threshold in South Africa at between US$1,175 – US$4,714 per DALY averted (17). Intervention

5 (cough triage in all HIV+ patients) and Intervention 9 (100% Xpert coverage and follow up of 90% of

patients with a negative Xpert result) are also potential policy options depending on the cost

effectiveness threshold used in the South African setting to guide health system investments.

Under the BCA framework, intervention 5 represents the lowest net benefit as although the costs are

negative, the per patient health impact is relatively low. With net savings to the health system,

Intervention 5 offers the most favourable ROI. The BCA framework indicates that Intervention 10 has

the highest net benefit under all approaches to VSL calculation, but a relatively low return on

investment given the high implementation costs. A decision framework for the interpretation of net

benefits and ROI is not currently available to represent the South African policy perspective and health

system context so this report is unable to definitively recommend Interventions as policy options

beyond Intervention 5 (which is cost saving and has positive outcomes).

Contrasting the results utilising the CEA and BCA frameworks highlight the differing theoretical

underpinnings of the approaches. The CEA provides a series of ICERs estimating incremental health

system costs and DALYs accrued to individual patients, while the BCA case study provides monetized

net benefits of the same health system costs and estimations of individual willingness to pay for

mortality and morbidity risk reduction, and estimates of return on investment. Both approaches

identified intervention 5 as cost saving and providing positive health effects relative to status quo.

Applying a VSL of between Int$0.98 – Int$2.1 million to lives saved reduced the relative importance of

small changes in input costs between interventions, resulting in the intervention with the greatest

health impact yielding the greatest net benefit (Intervention 10). A more granular approach to

estimating return on investment than provided in this case study would improve understanding of the

relative efficiency of each intervention under the BCA framework, but the incremental approach

adopted in CEA allows the ruling out of dominated interventions that was not applied in the BCA

approach.

2 See Chapter 4 Drummond 2015, Chapter 4 pp98‐102 (19)

The CEA and BCA approaches in this case study reflect a judgement on whether social values imbedded

in economic evaluation ought to reflect those implied by the outcome of legitimate processes (in this

case a democratically‐elected government setting budgets for health care) or a notion of welfare

founded on individual preferences or an explicit welfare function (18). However, a key consideration

for the interpretation of the results of either the CEA or BCA is health system affordability. Simplistic

decision rules to implement policies based on analytical outputs that are not linked to available

funding has the potential to result in net population loss of health if more efficient interventions are

pushed out to fund new investments. In the South African context, completion of ongoing work to

accurately estimate the marginal productivity of the public health system will assist in interpretation

of CEA results, while further consideration of the appropriate interpretation of BCA results in the

context of South Africa’s progress towards Universal Health Coverage is required.

The results of this case study may contribute to further understanding of the nature and relationship

of the costs and benefits of the different TB control interventions and the appropriate analytical

technique to demonstrate value relative or other health system priorities. Ultimately, the validity of

the differing approaches rests on the requirements, understanding, and informational needs of the

intended decision maker, and the realities of local perspective and context.

References

1. Robinson LA, Hammitt JK, O ’keeffe L, Munk C, Patenaude B, Geng F. Benefit‐Cost Analysis in Global Health and Development: Current Practices and Opportunities for Improvement (Scoping Report). 2017 [cited 2018 May 29]; Available from: https://cdn2.sph.harvard.edu/wp‐content/uploads/sites/94/2017/05/BCA‐Guidelines‐Scoping‐Report‐Review‐Draft.pdf

2. Wilkinson T, Sculpher MJ, Claxton K, Revill P, Briggs A, Cairns JA, et al. The International Decision Support Initiative Reference Case for Economic Evaluation: An Aid to Thought. Value Health [Internet]. 2016 Dec 1 [cited 2018 Apr 30];19(8):921–8. Available from: http://www.ncbi.nlm.nih.gov/pubmed/27987641

3. WHO | Global tuberculosis report 2017. WHO [Internet]. 2017 [cited 2018 May 3]; Available from: http://www.who.int/tb/publications/global_report/en/

4. World Health Organisation. The End TB strategy. 2015.

5. National Strategic Plan for HIV, TB and STIs 2017‐2022 [Internet]. 2017 [cited 2018 May 11]. Available from: http://sanac.org.za/wp‐content/uploads/2017/05/NSP_FullDocument_FINAL.pdf

6. Houben R, Menzies N, Sumner T, Huynh G. Feasibility of achieving the 2025 WHO global tuberculosis targets in South Africa, China, and India: a combined analysis of 11 mathematical models. Lancet Glob Heal. 2016;http://dx.(4):e806–15.

7. Menzies NA, Gomez GB, Bozzani F, Chatterjee S, Foster N, Baena IG, et al. Cost‐effectiveness and resource implications of aggressive action on tuberculosis in China, India, and South Africa: a combined analysis of nine models. Lancet Glob Heal [Internet]. 2016 Nov 1 [cited 2018 May 18];4(11):e816–26. Available from: http://www.ncbi.nlm.nih.gov/pubmed/27720689

8. Health Organization W. New Diagnostic test changes tuberculosis landscape. Bull World Heal Organ [Internet]. 2013 [cited 2018 May 3];91:163–4. Available from: http://www.who.int/bulletin/volumes/91/3/13‐020313.pdf

9. Vassall A, Siapka M, Foster N, Cunnama L, Ramma L, Fielding K, et al. Cost‐effectiveness of Xpert MTB/RIF for tuberculosis diagnosis in South Africa: a real‐world cost analysis and economic evaluation. Lancet Glob Heal [Internet]. 2017 Jul 1 [cited 2018 May 3];5(7):e710–9. Available from: https://www.sciencedirect.com/science/article/pii/S2214109X1730205X

10. Vassall A, Sweeney S, Kahn J, Gomez J. GHCC | Global Heath Cost Consortium [Internet]. 2017 [cited 2018 May 11]. Available from: https://ghcosting.org/pages/standards/reference_case

11. Foster N, Vassall A, Cleary S, Cunnama L, Churchyard G, Sinanovic E. The economic burden of TB diagnosis and treatment in South Africa. Soc Sci Med [Internet]. 2015 Apr 1 [cited 2018 May 11];130:42–50. Available from: https://www.sciencedirect.com/science/article/pii/S0277953615000726

12. Robinson LA, Hammitt JK, O ’keeffe L. Valuing Mortality Risk Reductions in Global Benefit‐Cost Analysis. Guidelines for Benefit‐Cost Analysis Project. Working Paper No.7. 2018 [cited 2018 May 29]; Available from: https://cdn2.sph.harvard.edu/wp‐content/uploads/sites/94/2017/01/Robinson‐Hammitt‐OKeeffe‐VSL.2018.03.23.pdf

13. Robinson LA, Hammitt JK. Valuing Nonfatal Health Risk Reductions in Global Benefit‐Cost Analysis. Guidelines for Benefit‐Cost Analysis Project. Working Paper No.2. 2018 [cited 2018 May 29]; Available from: https://cdn2.sph.harvard.edu/wp‐

content/uploads/sites/94/2017/01/Robinson‐Hammitt‐Nonfatal‐Risks.2018.03.121.pdf

14. International Monetary Fund. World Economic Outlook (April 2018) [Internet]. [cited 2018 Jun 5]. Available from: http://www.imf.org/external/datamapper/datasets/WEO

15. Salomon JA, Vos T, Hogan DR, Gagnon M, Naghavi M, Mokdad A, et al. Common values in assessing health outcomes from disease and injury: disability weights measurement study for the Global Burden of Disease Study 2010. Lancet [Internet]. 2012 Dec 15 [cited 2018 May 4];380(9859):2129–43. Available from: https://www.sciencedirect.com/science/article/pii/S0140673612616808

16. Verguet S, Kim JJ, Jamison DT. Extended Cost‐Effectiveness Analysis for Health Policy Assessment: A Tutorial. Pharmacoeconomics. 2016 Sep;34(9):913–23.

17. Woods B, Revill P, Sculpher M, Claxton K. Country‐Level Cost‐Effectiveness Thresholds: Initial Estimates and the Need for Further Research. Value Health [Internet]. 2016 Dec 1 [cited 2018 May 28];19(8):929–35. Available from: http://www.ncbi.nlm.nih.gov/pubmed/27987642

18. Claxton K. Accounting for the Timing of Costs and Benefits in the Evaluation of Health Projects Relevant to LMICs Guidelines for Benefit Cost Analysis Project. Working Paper No.8. 2018 [cited 2018 May 28]; Available from: https://cdn2.sph.harvard.edu/wp‐content/uploads/sites/94/2017/01/Claxton_discounting_2018.03.21‐final.pdf

19. Drummond M.F., Sculpher, M.J., Claxton K. SGL and TG. Methods for the economic evaluation of health care programmes. Oxford University Press; 2015.

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Application of CEA and BCA Tuberculosis Control ... · including 0.4 million deaths among people with human immunodeficiency virus (HIV). TB is the leading cause of death in South