Applied Econometrics for Health Economists A Practical Guide · ⇛ The price elasticity of demand measures the change in demand as a result of a 1 percent decrease in prices. For

2

Objectives

Objectives Methodology Results Discussion

How are changes in the prices of drugs (either through the

introduction of generics or subsidization by a third party) will affect

the demand?

⇰ To forecast demand for HCV medication under several

scenarios (over a 10-year period)

⇰ To assess the responsivenness of the demand for HCV

medication to changes in prices of the medicines

3

Outline

⇰ Methodological Approach

o Mathematical Model

⇰ Two Base Cases for Treatment Cascade

o Current Best Regimen vs. Future Best Regimen

⇰ Two Financing Scenarios

o Free-Market vs. Third Party Coverage

⇰ Working Assumptions ⇰ Main Results:

o Low-income, Lower-Middle & Upper-middle income countries

⇰ Sensitivity Analysis

⇰ Discussion & Policy Recommendations


4

Methodological Approach


⇰ A Susceptible-Infected-Removed (SIR) Model (e.g. Elbasha 2013):

⟿ New infections are assumed to be of the naïve nature (No re-

infection)

⟿ Population is assumed to mix homogeneously

N

Susceptible S

Acutely infected I

Immune R

Chronically infected Fm

5


x

Acute

HCV

I(t)

μ

Recovered

R(t) Mortality Unrelated to HCV

μ

Susceptible

S(t)

ν

μ

F0(t)

μ

ε1 F1(t)

μ

ε2 F2(t)

μ

ε3 F3(t)

μ

ε4 F4(t) ε0

μ

σ

λ


6


⇛ The chronically infected go through the diagnosis cascade starting with HCV

detection and ending once treatment is accepted.


Outline of the Stages of Detection, Referral to Care and Treatment

Health

System

Absorption

Capacity

Ability-to-

Pay

Price

Elasticity

of Demand

(Among

those with

ATP)

Treatment

Received

α'5

Chronically

Infected

α1

HCV

Detected

α2

Referred

to Care

α3 Fibrosis

Staging

α4

Medically

Eligible

Patients

(based on

scenarios)

α"5

α'"5

α5

7

Methodological Approach: Diagnosis Rate (r0 = α1*α2*α3)

⇛ Diagnosis rate reflects the probability that a chronically infected individual would have ⟿ had HCV detected ⟿ been referred to care and ⟿ gone through fibrosis staging. ⇛ For the current best case, this rate (r0) is assumed to be 5% in the year 2013

⇛ For the future best case, this rate (r0) is assumed to be 15% in the year 2013


Stage 2013 to

2014 2014 to

2015 2015 to

2016 2016 to

2017 2017 to

2018 2018 to

2019 2019 to

2020 2020 to

2021 2021 to

2022 2022 to

2023 Acute 0,1% 0,1% 0,1% 0,2% 0,2% 0,2% 0,2% 0,3% 0,3% 0,4%

F0 0,5% 0,6% 0,7% 0,8% 0,9% 1,0% 1,2% 1,3% 1,5% 1,8% F1 1,0% 1,2% 1,3% 1,5% 1,7% 2,0% 2,3% 2,7% 3,1% 3,5% F2 2,5% 2,9% 3,3% 3,8% 4,4% 5,0% 5,8% 6,7% 7,6% 8,8% F3 10,0% 11,5% 13,2% 15,2% 17,5% 20,1% 23,1% 26,6% 30,6% 35,2% F4 15,0% 17,3% 19,8% 22,8% 26,2% 30,2% 34,7% 39,9% 45,9% 52,8%

Stage 2013 to

2014 2014 to

2015 2015 to

2016 2016 to

2017 2017 to

2018 2018 to

2019 2019 to

2020 2020 to

2021 2021 to

2022 2022 to

2023 Acute 5,0% 5,8% 6,6% 7,6% 8,7% 10,1% 11,6% 13,3% 15,3% 17,6% F0 10,0% 11,5% 13,2% 15,2% 17,5% 20,1% 23,1% 26,6% 30,6% 35,2% F1 15,0% 17,3% 19,8% 22,8% 26,2% 30,2% 34,7% 39,9% 45,9% 52,8% F2 20,0% 23,0% 26,5% 30,4% 35,0% 40,2% 46,3% 53,2% 61,2% 70,4% F3 25,0% 28,8% 33,1% 38,0% 43,7% 50,3% 57,8% 66,5% 76,5% 80,0% F4 35,0% 40,3% 46,3% 53,2% 61,2% 70,4% 80,0% 80,0% 80,0% 80,0%

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Methodological Approach: Eligibility(α4)

⇛ The probability that an individual will be offered treatment is based on their eligibility for treatment.

⇛ All individuals eligible for treatment are offered treatment after fibrosis staging.

⟿ α4 is the probability that the individual will fulfil the eligibility criteria (depending on WHO (2014) guidelines).

⇛ Scope of eligibility for treatment:

⟿ Initial analysis: only fibrosis stage F3 & F4

⟿ Sensitivity analysis: treatment of everyone who is chronically infected

(F0 to F4).


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Methodological Approach: Treatment Rate (α5)

⇛ Individuals are at liberty to either accept or refuse treatment based on conditions:

⟿ Side effects of the drug (acceptability of treatment)

⟿ Costs involved (Ability-to-Pay (ATP))

⇛ Sensitivity analysis to take into account differential acceptability of treatment &

various thresholds for ATP


In the Two Cases: ⟿ Number of people willing and able to accept treatment depends on

three factors:

oHealth System’s Absorption Capacity (AC) 𝛂’5

o Individuals’ Ability-To-Pay (ATP) 𝛂’’5

oPrice elasticity of demand (𝛏dq) 𝛂’’’5

Treatment rate 𝛂5

1

0

Methodological Approach: Treatment Rate (α5)


⇛ For the current best case, the initial absorbtion capacity of the health system is assumed to be 5% in the year 2013 and increasing (annually) by 5% over time, i.e. AC t = AC t-1+0.05.

⇛ For the future best case, the initial absorbtion capacity of the health system is

assumed to be 5% in the year 2013 and increasing (annually) by 15% over time, i.e. AC t = AC t-1+0.15 (with a maximum 80% capacity at end of period)

⇛ The price elasticity of demand measures the change in demand as a result of a 1

percent decrease in prices. ⟿For the current best case, it is assumed that elasticity is 0.9 for the

highest income quintile and reduces to 0.5 for the lowest income quintile in each country.

⟿For the future best case, it is assumed that elasticity is 0.9 irrespective of the income quintile.

⇛ Individuals are considered as able-to-pay for medication if the cost of the regimen represents at most 40% of their per-capita GDP.

1

1

Methodological Approach: Prices & Cost of Treatment


⇛ For the initial analysis drugs are assumed to cost the same in all countries irrespective of their income levels. ⟿ Current lowest prices are used as the reference prices ⟿ Prices are assumed to fall by an annual rate of 3.4% as in the price of brand ARVs. ⟿The total cost of medication will depend on the genotype.

Genotype Medication Cost (12 Weeks) in

USD Recommended

Duration (Weeks)

1, 4 Sofosbuvir 840

12

Current Best WHO (2014)

Ribavirin 53

Peg-IFN 500

2 Sofosbuvir 840 12

Ribavirin 53

3 Sofosbuvir 840 24

Ribavirin 53

All Genotypes Sofosbuvir 840

12 Future Best Another DAA 420

1

2

⊛ Treatment Module: Two FINANCING Scenarios: (Free Market vs. Subsidised)


⧆ Free Market Prices: ↪ Demand for drugs depends on ATP ⧇ Subsidised:

↪ Third party willing to cover a fraction of the avg. costs (say, e.g., 50%) ↪ Demand for drugs based on ATP after subsidies have been applied.

Major sensitivity analysis

⟿ Changes in Eligibility Criteria

⟿ Changes in third-party subsidies

⟿ Changes in ATP thresholds

⟿ Changes in price reduction rates

1

3

⊛ Countries Used in Analysis


Low Income

Countries

($664)

Lower-Middle Income

Countries

($2751)

Upper Middle Income

Countries

($9025)

Included Benin, Burkina Faso, Gambia, Guinea, Guinea Bissau,

Liberia, Mali, Mauritania, Niger, Sierra Leone, Togo, Central African Republic,

Chad, Democratic Republic of Congo, Burundi, Comoros,

Ethiopia, Kenya, Madagascar, Malawi, Mozambique,

Rwanda, Tanzania, Uganda, Zimbabwe, Haiti, Afghanistan,

Bangladesh, Nepal, Cambodia, Kyrgyz Republic,

Tajikistan

Cape Verde, Côte d’Ivoire, Ghana, Nigeria, Senegal, Cameroon, Congo, São Tomé & Principe, Lesotho, Swaziland, Djibouti, Zambia, Egypt, Morocco, Iraq,

Sudan, Yemen, Bolivia, Paraguay, Belize, El Salvador, Guatemala, Honduras, Nicaragua, Guyana,

Pakistan, India, Bhutan, Sri Lanka, Indonesia, Lao People’s Democratic Republic, Philippines, Vietnam, Fiji,

Papua New Guinea, Armenia, Georgia, Moldova, Ukraine,

Uzbekistan, Albania, Mongolia

Angola, Gabon, Botswana, Namibia, South Africa,

Seychelles, Algeria, Islamic Republic of Iran, Jordan, Tunisia,

Argentina, Brazil, Chile, Colombia, Ecuador, Peru,

Uruguay, Venezuela, Costa Rica, Mexico, Panama, Dominican Republic, Jamaica, Suriname, Maldives, Malaysia, Thailand,

China, Azerbaijan, Belarus, Kazakhstan, Turkmenistan,

Russia, Bosnia & Herzegovina, Serbia, Montenegro, The Former Yugoslav Republic of Macedonia,

Turkey, Bulgaria, Latvia, Lithuania, Romania

Excluded Eritrea, Democratic Republic of Korea, Myanmar, Somalia

Kiribati, Kosovo, Marshall Islands, Federal States of Micronesia,

Samoa, Solomon Islands, South Sudan, Syrian Arab Republic,

Timor-Leste, Tonga, Vanuatu, West Bank & Gaza

Cuba, Dominica, Grenada, Lebanon, Libya, Mauritius, Palau,

St. Lucia, St. Vincent and the Grenadine, Tuvalu

1

4

⊛ Data Sources


Data & Variables Data Sources

⌛ Economic-related Data ⟿ Share of GDP held by each quintile (income per capita (share of GDP /# of i's in each q) ⟿ GDP ⬆ overtime

World Development Indicators (WDI) International Monetary Fund’s World Economic

Outlook Database (April 2014)

⌛ Population related Data: ⟿ size, mortality, growth rate

World Development Indicators (WDI) WB Projection Tables; World Health Statistics (2013)

⌛ Prices of medication ⌛ Recommended therapy per genotype

WHO guidelines (2014), Open Society pharmaceutical companies & other media sources

⌛ Transition probabilities exogenous & given by the natural history of the disease

⌛ Incidence rates exogenous and obtained from literature

⌛ Sensitivity Parameters: ⟿ treatment option, rate & type of third party coverage, & rate of price decrease)

exogenously determined but allowed to vary based on assumptions made

NA: extrapolated using geography, genotypes, GDP

15

⌛Main Results⌛

Some Definitions

•Medication Expenditure: This refers to the total (cumulative) expenditure on drugs at the end

of the period.

•Cost of Regimen: This is the average cost of the drug regimen at the end of the period for end-

user (patient).


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Results: Base case of Low Income Countries (LICs) [price decrease = 3.4, subsidy = 50%, Treatment Group = F3 & F4, ATP =40%]


Current Best Future Best

LIC LIC Cumulative

Number Treated

Medication Expenditure ($)

Cost of Regimen

($)

Cumulative Number Treated


Cost of Regimen ($)

By 2018 32 075 37 798 657 1 166 Free Market

By 2018 137 451 154 849 029 1 097 Free Market

By 2023 382 208 401 162 603 981 By 2023 950 173 948 104 095 923 By 2018 299 055 362 184 232 583 Subsidised

50% By 2018 693 806 798 787 520 549 Subsidised

50% By 2023 1 600 895 1 716 199 586 490 By 2023 2 171 704 2 245 180 280 461

Number of Individuals Treated (Annual) in ‘000s Cumulative Number of Individuals Treated in ‘000s

⟿ LIC markets are and will remain limited in size - there are fewer and fewer LICs

- HVC prevalence is relatively low

⟿ In the most pessimistic scenario (current best regimen, free market) there will still be

over 380.000 patients treated by 2023, for 400 Million USD over 10 years

⟿ In the most optimistic scenario (future best regimen, 50% subsidy) there would be

almost 2.2 million people treated by 2013, for 2.2 Billion USD over 10 years.

⟿ In none of 4 scenarios we see the number of new patients treated annually decline

over time.

⟿ Alternative actions (steeper price declines, higher levels of subsidy, improved

diagnostic access and health system performance are needed to realize meaningful

impact on the epidemic in LIC.

17

Lessons from LIC Base Case Scenarios


18

Results: Base case of Lower-Middle Income Countries (LMICs) [price decrease = 3.4, subsidy = 50%, Treatment Group = F3 & F4, ATP =40%]



LMIC LMIC Cummulative

Number Treated


Cost of Regimen

($)

Cummulative Number Treated


Cost of Regimen ($)

By 2018 2 996 937 3 815 860 410 1 233 Free Market

By 2018 8 613 412 9 965 213 852 1 097 Free Market

By 2023 13 085 821 14 923 572 220 1 037 By 2023 19 660 396 20 902 839 829 923 By 2018 6 606 014 8 505 262 636 617 Subsidised

50% By 2018 16 193 401 18 830 253 427 549 Subsidised

50% By 2023 24 539 091 28 684 844 550 519 By 2023 30 070 869 32 627 246 899 461


⟿ LMIC markets will be massive

⟿ In the most pessimistic scenario (current best regimen, free market) over 13 Million

patients will have been treated by 2023, for a total of almost 15 Billion USD over 10

years.

⟿ In the most optimistic scenario (future best regimen, 50% subsidy) over 30 Million

patients will have been treated, for a total of almost 33 Billion USD over 10 years

⟿ The number of new patients treated decline over time only in the future best regimen

case scenarios with 50% subsidy (most optimistic)

⟿ For public health impact either lower prices than projected, or subsidy for treatment

will be needed, along with improved access to diagnostics and health system

performance.

19

Lessons from LMIC Base Case Scenarios


20


Results: Base case of Upper-Middle Income Countries (UMICs) [price decrease = 3.4, subsidy = 50%, Treatment Group = F3 & F4, ATP =40%]


UMIC UMIC Cumulative

Number Treated


Cost of Regimen ($)



Cost of Regimen ($)

By 2018 7 930 697 10 095 540 115 1 204 Free Market

By 2018 19 553 810 22 930 786 148 1 097 Free Market


50% By 2018 23 552 170 27 660 696 916 549 Subsidised

50% By 2023 25 650 001 29 882 590 436 506 By 2023 29 540 482 33 692 538 796 461


⟿ UMIC markets will be massive too and may reach saturation of demand by 2023

⟿ In the most pessimistic scenario (current best regimen, free market) over 23 Million

patients will have been treated by 2023, for a total of over 26 Billion USD over 10

years.

⟿ In the most optimistic scenario (future best regimen, 50% subsidy) over 29 Million

patients will have been treated, for a total of over 33 Billion USD over 10 years

⟿ The number of new patients treated declines over time in all scenarios.

⟿ For impact on the epidemic, better performance in case finding and health system

organization will be needed than in the current best regimen scenario. Subsidy would

improve the impact marginally.

21

Lessons from UMIC Base Case Scenarios


22


Results: Base case of All Countries [price decrease = 3.4, subsidy = 50%, Treatment Group = F3 & F4, ATP =40%]


Low-to-Middle Income Low-to-Middle Income Cumulative

Number Treated


Cost of Regimen ($)

CumUlative Number Treated


Cost of Regimen

($) By 2018 10 959 710 13 949 199 182 1 208 Free

Market By 2018 28 304 673 33 050 849 029 1 107 Free

Market By 2023 36 590 525 42 037 556 862 1 016 By 2023 49 070 361 53 612 003 039 931 By 2018 16 680 529 21 288 476 768 604 Subsidised

50% By 2018 40 439 377 47 289 737 863 553 Subsidised

50% By 2023 51 789 987 60 283 634 573 508 By 2023 61 783 055 68 564 965 975 465


⟿LIC markets will be small - additional price decreases, better

diagnostic/health system performance and high levels of subsidy are

needed to have a meaningful public health impact

⟿ LMIC and UMIC markets will be massive

⟿ In LMIC, third party coverage would boost demand (X2.0 to X3.0)

In LMIC, impact on the epidemic will require both greater price decreases

than projected, better access to diagnostics and health system organization;

⟿ In UMIC saturation of demand, high coverage, and impact on the epidemic

may be reached if diagnostics and health system organization can be

improved, and subsidy will impact demand in a limited way.

⟿ Comparison of "current" and "future" best regimen case scenarios suggests

that simplification in drug regimens and access to diagnostics might boost

LIC, LMIC and UMIC markets in the next 5 years (X 2.5 to X4.0) and

increase demand at the 2023 horizon.

23

Summary: Lessons from Base Case Scenarios


24

Sensitivity Analysis:

Expand eligibility for treatment

from F3-F4 to F0-F4


Main Lesson

In all cases, demand would be very sensitive to an extension of current

treatment eligibility guidelines.

If it happened now, there would be, in L+MIC combined, by the end of 2023:

57 million people treated, compared to 36.5 million in the current best regimen

case/free market baseline scenario

198 million people treated, compared to 62 million in the future best regimen

case/50% subsidy baseline scenario

25

Results: Base case of Lower-Middle Income Countries (LMICs) [price decrease = 3.4, subsidy = 50%, Treatment Group = F0 to F4, ATP =40%]



LMIC LMIC Cumulative

Number Treated


Cost of Regimen ($)



Cost of Regimen ($)

By 2018 4 164 082 5 280 333 248 1 233 Free Market

By 2018 25 093 120 28 829 997 752 1 097 Free Market By 2023 22 677 558 25 448 559 537 1 037 By 2023 79 299 298 82 336 253 704 923

By 2018 9 370 928 11 997 202 592 617 Subsidised 50%

By 2018 51 745 204 59 670 005 575 549 Subsidised 50% By 2023 43 763 614 50 128 875 779 519 By 2023 131 398 116 138 651 692 810 461

Sensitivity F0 to F4

Number of Individuals Treated (Annual) in ‘000s

Base Case F3 to F4


26


Results: Base case of All Countries [price decrease = 3.4, subsidy = 50%, Treatment Group = F0 to F4, , ATP =40%]



Number Treated


Cost of Regimen

($)



Cost of Regimen ($)

By 2018 14 329 479 18 194 647 956 1 208 Free Market

By 2018 66 775 765 77 468 459 288 1 107 Free Market


50% By 2018 105 133 961 122 000 893 547 553 Subsidised

50% By 2023 84 458 350 96 800 688 559 508 By 2023 197 920 762 204 900 065 654 465

Sensitivity F0 to F4


Base Case F3 to F4


27

Sensitivity Analysis: Subsidy Rate 0% - 100% (25% Interval)


Main Lesson

Free of charge drugs for end-users at the point of delivery (100% subsidy)

would be required to have a significant impact on the number of annual cases

in need of treatment in LIC.

High-level of third-party coverage (≥ 75% subsidy) would be needed to boost

demand (X2.0 or more) in LMIC.

This is shown in the current best regimen case scenarios in the next 3 slides.

28

Results: Sensitivity Analysis of Low Income Countries (LICs) [price decrease = 3.4, subsidy = 0% - 100%, Treatment Group = F3 to F4, ATP =40%] Current Best


Current Best Current Best

By 2018 LIC By 2023 LIC

Subsidy Cumulative

Number Treated

Medication Expenditure

($)

Cost of Regimen ($)

Subsidy Cumulative

Number Treated


Cost of Regimen ($)

0% 32 609 38 423 765 1 166 0% 397 495 416 914 125 981 25% 102 772 123 913 117 874 25% 775 667 820 740 040 735 50% 299 055 362 184 232 583 50% 1 600 895 1 716 199 586 490 75% 931 776 1 130 756 487 291 75% 4 201 765 4 540 084 705 245 100% 4 122 755 4 978 565 682 0 100% 10 705 603 11 836 440 879 0


29

Results: Sensitivity Analysis of Lower-Middle Income Countries (LMICs) [price decrease = 3.4, subsidy = 0% - 100%, Treatment Group = F3 & F4] Current Best



By 2018 LMIC By 2023 LMIC

Subsidy Cumulative

Number Treated


Cost of Regimen ($)

Subsidy Cumulative

Number Treated


Cost of Regimen ($)

0% 3 060 995 3 894 338 396 1 233 0% 13 593 409 15 468 255 426 1 037 25% 4 609 513 5 926 929 458 925 25% 17 594 130 20 272 215 366 778 50% 6 606 014 8 505 262 636 617 50% 24 539 091 28 684 844 550 519 75% 11 070 840 14 581 070 999 308 75% 34 377 849 40 968 808 505 259 100% 14 072 595 18 545 573 989 0 100% 36 955 349 44 499 146 459 0


30

Sensitivity Analysis: Rate of Price Reduction 25% (2014-

2016) then 5.2%


Main Lesson

•⟿ In LIC, an initial “price shock” would help create a significant market

The value of the LIC market will reach almost 1billion US$ in the most pessimistic scenario (current best case/free market) compared to 400 Million over 10 years in the base case, without "price shock“

⟿ In LMIC, it will increase access to treatment by a factor of 1.5 to 2.0, while

maintaining total value of market over the next ten years

⟿ In UMIC, it will increase demand by 30% to 50% in the next five years

(without affecting total $ value of market). It will reach similar levels of

coverage at the 2023 horizon (with circa 15% to 25% decrease in total $

market value)

⟿ Prices would still be well above the marginal costs at the 2023 horizon for

DAA-only treatment

31

Results: Base case of Low Income Countries (LICs) [price decrease = 25% (2014-2016) then 5.2%, subsidy = 50%, Treatment Group = F3 & F4, ATP =40%]



LIC LIC Cumulative

Number Treated


Cost of Regimen

($)



Cost of Regimen ($)

By 2018 227 882 166 402 918 677 Free Market

By 2018 626 556 435 200 510 637 Free Market

By 2023 1 617 499 951 020 996 518 By 2023 2 743 006 1 570 501 423 488 By 2018 913 270 680 810 757 338 Subsidised

50% By 2018 1 859 570 1 350 129 733 318 Subsidised

50% By 2023 4 302 163 2 612 181 020 259 By 2023 5 202 940 3 157 593 207 244

Sensitivity 25% decrease (2014-2016) then 5.2%

price decrease


Base Case 3.4% price decrease


32

Results: Base case of Lower-Middle Income Countries (LMICs) [price decrease = 25% (2014-2016) then 5.2%, subsidy = 50%, Treatment Group = F3 & F4, ATP =40%]



LMIC LMIC Cumulative

Number Treated


Cost of Regimen

($)



Cost of Regimen ($)

By 2018 6 602 310 5 283 687 069 716 Free Market

By 2018 14 943 103 11 281 629 789 637 Free Market

By 2023 24 157 270 16 113 287 505 548 By 2023 30 654 679 19 885 788 946 488 By 2018 12 599 899 10 426 181 482 358 Subsidised

50% By 2018 24 119 479 18 849 357 477 318 Subsidised

50% By 2023 35 089 176 24 503 565 392 274 By 2023 38 503 177 26 776 480 939 244


price decrease




33


Results: Base case of Upper-Middle Income Countries (UMICs) [price decrease = 25% (2014-2016) then 5.2%, subsidy = 50%, Treatment Group = F3 & F4, ATP =40%]


UMIC UMIC Cumulative

Number Treated


Cost of Regimen

($)



Cost of Regimen ($)

By 2018 11 777 910 9 553 864 619 699 Free Market

By 2018 23 494 442 19 227 713 680 637 Free Market


50% By 2018 23 756 967 20 081 649 149 318 Subsidised

50% By 2023 26 418 170 18 844 086 380 268 By 2023 29 467 175 23 268 590 605 244


price decrease




34


Results: Base case of All Countries [price decrease = 25% (2014-2016) then 5.2%, subsidy = 50%, Treatment Group = F3 & F4, ATP =40%]



Number Treated


Cost of Regimen

($)



Cost of Regimen ($)

By 2018 18 608 102 15 003 954 605 701 Free Market

By 2018 39 064 101 30 944 543 980 642 Free Market


50% By 2018 49 736 015 40 281 136 359 321 Subsidised

50% By 2023 65 809 509 45 959 832 793 268 By 2023 73 173 292 53 202 664 751 246


price decrease




35

Sensitivity Analysis: Ability-to-Pay 10% - 50% (10% Interval)


Main Lesson

⟿ Results are quite robust to economic uncertainties on the demand-side :

they are only moderately sensitive to assumptions about elasticity of demand

to price under households’ budgetary constraints (ability-to-pay).

⟿ However, monitoring of patients’ actual preferences and behaviors will

remain key for scaling up access to treatment.

36

Results: Base case of Lower-Middle Income Countries (LMICs) [price decrease = 3.4, Treatment Group = F3 & F4, ATP = 10% - 50%] Free Market Current Best




ATP Cumulative Number

Treated Medication Expenditure

($) Cost of

Regimen ($) ATP



Cost of Regimen ($)

10% 102 412 129 393 235 1 233 10% 1 600 011 1 726 601 121 1 037 20% 878 543 1 106 010 591 1 233 20% 5 112 034 5 690 018 998 1 037 30% 1 601 150 2 007 741 934 1 233 30% 9 667 006 10 861 429 547 1 037 40% 2 996 937 3 815 860 410 1 233 40% 13 085 821 14 923 572 220 1 037 50% 4 154 053 5 326 640 212 1 233 50% 16 246 509 18 686 068 921 1 037


37

Results: Base case of Lower-Middle Income Countries (LMICs) [price decrease = 3.4, Treatment Group = F3 & F4, ATP = 10% - 50%] Free Market Future Best


Future Best Future Best


ATP Cumulative

Number Treated Medication Expenditure

($) Cost of

Regimen ($) ATP



Cost of Regimen ($)

10% 141 202 160 422 457 1 097 10% 3 640 066 3 591 103 257 923 20% 2 492 557 2 890 841 608 1 097 20% 9 952 737 10 158 847 036 923 30% 5 299 542 6 072 426 421 1 097 30% 15 790 197 16 455 983 462 923 40% 8 613 412 9 965 213 852 1 097 40% 19 660 396 20 902 839 829 923 50% 11 013 613 12 800 267 111 1 097 50% 23 318 367 24 966 796 915 923


⟿ Model does not take into account potential re-infections and is limited to initial

treatment initiation.

⟿ Model does not fully take into account the impact of treatment on rates of

transmission (incidence remains exogenous).

⟿ There is an intrinsic difficulty in predicting ex-ante human behaviours and market

dynamics

⟿ Heterogeneity and uncertainty on epidemiological data used ?

⟿ Some assumptions on price dynamics derived from the experience of HIV/AIDS

drugs ?

38

Some limitations of the forecasting exercise


⟿There is a significant opportunity for a massive scale up of access to HCV treatment

↪ ↑ in health system absorption capacity will be required.

↪ ↑ in treatment eligibility from F3-F4 to F0-F4 would greatly increase demand.

↪ a significant ↓ in initial drug prices is needed for LIC and LMIC, and should be

possible because it would increase the total value of the LIC and LMIC markets

over a 10 year horizon.

↪ significant subsidy will be needed in LIC markets and some subsidy in LMIC to

decrease the number of new cases needing treatment. The willingness to

subsidize will be greater if the drugs are more affordable – and would increase the

value of the market.

⟿ There is a rationale for adapting policies for HCV treatment access according to

countries’ level of development.

39

Some major lessons of the forecasting exercise


Documents

Applied Econometrics for Health Economists A Practical Guide · ⇛ The price elasticity of demand measures the change in demand as a result of a 1 percent decrease in prices. For