How Does Technological Change A ect Quality-Adjusted ... Jena and Philipson (2008) { incentives for innovation Hult, Ja e, and Philipson Technological Change and Quality-Adjusted Prices

How Does Technological Change Affect Quality-AdjustedPrices in Health Care?

Evidence from thousands of innovations

Sonia Jaffejoint with Kristopher J. Hult and Tomas J. Philipson

The Health Sector and the EconomySeptember 30, 2016

Hult, Jaffe, and Philipson Technological Change and Quality-Adjusted Prices 1

Motivation


Motivation


Motivation

Health care spending is increasing

I Not just quantities

New innovations are expensive

I Is spending growth accompanied by larger benefits inhealth?

⇒ What is the effect of technological change onquality-adjusted prices?


Motivation







Motivation







Evidence

Existing studies of specific treatment or disease

I Cancer drugs, heart disease, ...

Missing systematic analysis.

We analyze thousands of new technologies

I Comparing quality-adjusted prices to incumbenttechnologies for the same indication.


Evidence

Existing studies of specific treatment or disease

I Cancer drugs, heart disease, ...

Missing systematic analysis.

We analyze thousands of new technologies

I Comparing quality-adjusted prices to incumbenttechnologies for the same indication.


New Evidence

I Reinterpret the medical “cost-effectiveness” literature inan economic framework of quality-adjusted-prices

I Use Tufts Cost Effectiveness Analysis Registry (CEAR)

I Comparisons of new innovations to ‘standard-of-care’implicitly provides the necessary data.


Overview of Findings

I The median (mean) difference between innovations andincumbents is

I Quality-adjusted Prices (QAP): 4% (120%)I Prices: 8% (147%)I Quality: 1% (26%)

I We investigate theoretically and empirically conditionsunder which quality adjusted prices fall or rise due totechnological change

I If incumbent prices had fallen 24% (75%) prior to thenew innovation, then 75% (95%) of indications would seeprices fall over time




















Literature

Quality-adjusted prices for specific indications, e.g.

I Cutler et al (1998) – prices of heart attack treatments

I Frange et al (1999) – prices for treatment of depression

I Howard et al (2015) – prices of anti-cancer drugs

Explaining general expenditure increases

I Newhouse 1992 – innovation as the residual

I Weisbrod 1991 – insurance & innovation incentives

Theory on how to value innovations

I Hall and Jones (2007)

I Dow et al (1999) – complementarity of competing risks

I Jena and Philipson (2008) – incentives for innovation


Literature













Literature













Why health is different

Consumer cannot buy as much as they want at a given price

Price

Health (q)qs

pspsqs




Price

Health (q)qs

pspsqs

U




Price

Health (q)qs

pspsqs

q

p

pq

U




Price

Health (q)qs

pspsqs

q

p

pq

UU ′


Quality

Quality-adjusted life years (QALYs)

I Treatment prolongs life by x years at full health

I Treatment prolongs life by 1 year at fraction-x of fullhealth

I Treatment improves quality of life by fraction-x of fullhealth for a year

I Widely used measure

$/QALY is the quality-adjusted price.

I Used in some countries to determine coverage


Data

Cost-Effectiveness Analysis Registry (CEAR) at Tufts

I Database of “all” articles in medical journals thatcompare the costs and effectiveness of treatments.

I Each articleI Compares new innovation to ‘standard of care’

(incumbent)I Price and quality (QALYs) for each treatment for articles

since 2002I For older articles ‘Incremental Cost Effectiveness Ratio’

ICER =p − psq − qs

?? The interpretation very different depending on q ≷ qs??




Price

Health (q)qs

pspsqs

q

p

pq ICER

UU ′


Data

Cost-Effectiveness Analysis Registry (CEAR) at Tufts

I Database of “all” articles in medical journals thatcompare the costs and effectiveness of treatments.

I Each articleI Compares new innovation to ‘standard of care’

(incumbent)I Price and quality (QALYs) for each treatment for articles

since 2002I For older articles ‘Incremental Cost Effectiveness Ratio’

ICER =p − psq − qs

?? The interpretation very different depending on q ≷ qs??


Summary

Mean Min Max S.D. ObsYear 2010 1976 2014 5 12,560US dummy 0.39 0 1 0.49 12,560EU dummy 0.43 0 1 0.49 12,560Prevention Stage 2.43 1 3 0.76 12,560Reliability Score 4.69 1 7 1.00 12,557

Median 5th 95th S.D. ObsQuality 7.7 0.2 27.3 10.7 6,597SOC Quality 7.3 0.1 27.0 10.7 6,572Innovator Price 21,506 263 310,397 303,734 6,886SOC Price 16,682 84 274,861 282,271 6,854Innovator $/QALY 4,532 36 88,345 162,054 6,504SOC $/QALY 3,755 20 83,505 196,667 6,391ICER 17,415 -111,268 419,635 621,768 12,483


Ratio of Quality-adjusted Prices

Wide range of baseline prices

I Look at percent change instead of differenceI 100 · p−pspsI 100 · q−qsqs

I And the quality-adjusted price ratio

∆ =p

q

/psqs

I The percent increase in quality-adjusted price is

100 · (∆− 1)


Price and Quality Changes


Distribution of Quality-adjusted Price Ratios


% Change in QAP by Intervention Type

All High ScoreObs Median Mean St. Dev. Obs Median Mean St. Dev.

Education 319 0.78 13.70 79.69 217 2.14 16.15 59.30

Diagnostic 528 1.35 19.16 88.48 297 1.55 27.89 106.68

Care Delivery 498 1.49 28.18 99.09 255 1.71 20.03 70.00

Screening 1109 2.59 31.57 122.83 676 3.17 43.72 149.00

Surgical 697 3.43 25.54 93.79 294 9.77 37.09 100.33

Immunization 193 3.99 64.37 163.49 128 2.25 41.05 138.41

Device 491 5.23 29.22 94.56 250 7.76 26.80 57.56

Pharmaceutical 3448 5.67 40.36 128.43 2220 4.93 37.80 120.62

Procedure 696 6.38 34.24 100.19 351 7.49 38.82 102.23

Omits top 1% of of QAP ratios


% Change in QAP by Disease Type


Maternal/Child 65 -0.17 -7.60 37.73 23 -0.17 -9.60 39.74

Genito-Urinary 277 0.25 17.44 75.21 148 0.55 14.56 53.85

Neuro-Psychiatric/Neurological

490 1.57 21.05 104.59 311 1.55 13.33 74.61

Endocrine Disorders 413 1.71 22.14 118.30 234 3.11 22.09 98.59

Cardiovascular 1176 2.87 28.48 98.04 796 2.76 21.57 72.67

Musculoskeletal/Rheumatologic

633 3.00 31.25 124.46 356 3.55 36.95 135.27

Digestive 300 6.37 44.25 116.14 136 0.99 31.81 93.37

Respiratory 158 7.41 42.86 159.29 120 7.47 42.12 152.17

MalignantNeoplasms

1064 7.58 42.37 122.31 637 7.49 47.70 137.01

Sense Organ 108 8.19 57.50 125.11 62 11.59 72.02 127.88

Infectious 973 8.91 42.26 126.71 626 9.87 47.03 134.39



What might we expect to lead to higher quality-adjusted priceratios?

I Costs?

I Break-through Innovations (quality)?

I Absence of price controls?


Cost & Quality Effects

Innovator’s Price

Incumbent’s Price (ps)

p(ps)

p = ps

d∆dc

= ∆

∂p∂c

p︸︷︷︸>0

−∂ps∂c

ps︸︷︷︸≥0



Innovator’s Price


p(ps)

Oligopolyps(p)

p = ps

d∆dc

= ∆

∂p∂c

p︸︷︷︸>0

−∂ps∂c

ps︸︷︷︸≥0



Innovator’s Price


p(ps)

Competitiveps(p)

Oligopolyps(p)

p = ps

d∆dc

= ∆

∂p∂c

p︸︷︷︸>0

−∂ps∂c

ps︸︷︷︸≥0



Innovator’s Price


p(ps)

Competitiveps(p)

Oligopolyps(p)

p = ps

d∆dc

= ∆

∂p∂c

p︸︷︷︸>0

−∂ps∂c

ps︸︷︷︸≥0



Innovator’s Price


p(ps)

Competitiveps(p)

Oligopolyps(p)

p = ps

d∆dc

= ∆

∂p∂c

p︸︷︷︸>0

−∂ps∂c

ps︸︷︷︸≥0

d∆dq

= ∆

∂p∂q

p︸︷︷︸>0

−∂ps∂q

ps︸︷︷︸<0

− 1q


% Change in QAP by Intervention Type


Education 319 0.78 13.70 79.69 217 2.14 16.15 59.30

Diagnostic 528 1.35 19.16 88.48 297 1.55 27.89 106.68

Care Delivery 498 1.49 28.18 99.09 255 1.71 20.03 70.00

Screening 1109 2.59 31.57 122.83 676 3.17 43.72 149.00

Surgical 697 3.43 25.54 93.79 294 9.77 37.09 100.33

Immunization 193 3.99 64.37 163.49 128 2.25 41.05 138.41

Device 491 5.23 29.22 94.56 250 7.76 26.80 57.56

Pharmaceutical 3448 5.67 40.36 128.43 2220 4.93 37.80 120.62

Procedure 696 6.38 34.24 100.19 351 7.49 38.82 102.23



PharmaceuticalsQuantile Regression

Pharmaceutical 0.109∗∗∗ 0.0328∗∗∗

(0.0306) (0.00593)

2006-2011 0.117 -0.0151 -0.0263(0.0687) (0.0136) (0.0175)

2012-2014 -0.0136 -0.0155 -0.0245(0.0687) (0.0136) (0.0182)

Pharma and 2002-2005 0.162∗ 0.00407 0.0248(0.0768) (0.0152) (0.0187)

Pharma and 2006-2011 0.0180 0.0275∗∗ 0.0560∗∗∗

(0.0478) (0.00948) (0.0122)

Pharma and 2012-2014 0.179∗∗∗ 0.0415∗∗∗ 0.0647∗∗∗

(0.0468) (0.00929) (0.0113)

Controls for Disease & Spon-sor Type

X

Observations 6262 6262 6326 6326 6325Adjusted R2 0.002 0.003

Standard errors in parentheses

Omits top 1% of QAP ratios∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001Hult, Jaffe, and Philipson Technological Change and Quality-Adjusted Prices 23

Pharmaceuticals (Highly rated studies)Quantile Regression

Pharmaceutical 0.0501 0.0167∗

(0.0392) (0.00715)

2006-2011 0.174 -0.0318 -0.0152(0.108) (0.0205) (0.0307)

2012-2014 -0.0154 -0.0430∗ -0.0362(0.104) (0.0198) (0.0304)

Pharma and 2002-2005 0.128 -0.0326 0.000270(0.119) (0.0228) (0.0332)

Pharma and 2006-2011 -0.115 0.00507 0.0199(0.0657) (0.0125) (0.0190)

Pharma and 2012-2014 0.145∗∗ 0.0321∗∗ 0.0581∗∗∗

(0.0534) (0.0102) (0.0142)

Controls for Disease & Spon-sor Type

X

Observations 3777 3777 3821 3821 3821Adjusted R2 0.000 0.002


Omits top 1% of QAP ratios∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001


‘Break-through Innovations’

Larger quality increase

Effect on ∆Quantile Regression

50th-75th Percentile Quality Change 0.0435 0.0363 0.00837 0.00706(0.0377) (0.0400) (0.00879) (0.0106)

> 75th Percentile Quality Change 0.0682 0.0458 0.0160 -0.0105(0.0378) (0.0409) (0.00882) (0.0108)

Controls:Year Dummies X X X XIntervention, Disease &Sponsor Type

X X

Observations 6263 6262 6263 6262Adjusted R2 0.005 0.034


Linear regressions omit top 1% of QAP ratios∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001


‘Break-through Innovations’




> 75th Percentile Quality Change 0.0682 0.0458 0.0160 -0.0105(0.0378) (0.0409) (0.00882) (0.0108)


X X





‘Break-through Innovations’ (Highly rated studies)




> 75th Percentile Quality Change 0.145∗∗ 0.146∗∗ 0.0595∗∗∗ 0.0344∗

(0.0479) (0.0522) (0.0104) (0.0143)


X X





Geography (Price Controls?)

Even if controls lower price levels, effect on ∆ is ambiguous.

Effect on ∆ (Relative to the rest of the world)Quantile Regression

US dummy -0.271∗∗∗ -0.233∗∗∗ -0.0636∗∗∗ -0.0544∗∗∗

(0.0451) (0.0483) (0.00876) (0.0119)

EU dummy -0.265∗∗∗ -0.201∗∗∗ -0.0791∗∗∗ -0.0513∗∗∗

(0.0477) (0.0516) (0.00926) (0.0127)

UK dummy -0.00196 0.0150 0.0269∗∗ 0.0232(0.0472) (0.0490) (0.00915) (0.0121)

Japan -0.0183 0.0438 -0.0354 -0.0258(0.111) (0.114) (0.0215) (0.0280)

Controls:Year Dummies X X X XIntervention, Disease, & Sponsor Type X X





Geography (Price Controls?)



US dummy -0.271∗∗∗ -0.233∗∗∗ -0.0636∗∗∗ -0.0544∗∗∗

(0.0451) (0.0483) (0.00876) (0.0119)

EU dummy -0.265∗∗∗ -0.201∗∗∗ -0.0791∗∗∗ -0.0513∗∗∗

(0.0477) (0.0516) (0.00926) (0.0127)

UK dummy -0.00196 0.0150 0.0269∗∗ 0.0232(0.0472) (0.0490) (0.00915) (0.0121)

Japan -0.0183 0.0438 -0.0354 -0.0258(0.111) (0.114) (0.0215) (0.0280)






Geography (Price Controls?) Highly rated studies



US dummy -0.290∗∗∗ -0.250∗∗∗ -0.0782∗∗∗ -0.0771∗∗∗

(0.0571) (0.0615) (0.0121) (0.0160)

EU dummy -0.320∗∗∗ -0.171∗∗ -0.0881∗∗∗ -0.0538∗∗

(0.0587) (0.0636) (0.0124) (0.0166)

UK dummy 0.0558 0.0591 0.0220 0.0104(0.0579) (0.0616) (0.0123) (0.0161)

Japan -0.324∗ -0.284 -0.0408 -0.00352(0.142) (0.145) (0.0301) (0.0378)






Price trends

To what extent is innovation driving price increases over time?

I This evidence is cross-sectional

I Incumbent’s price may have changed since entry

I May have decreased to accommodate innovation’sentrance


Or think about the price-quality curve in each yearp

qq1 q2 q3

2000

20012002

Decreasing pq

2001

2002Increasing pq



qq1 q2 q3

2000

2001

2002

Decreasing pq

2001

2002Increasing pq



qq1 q2 q3

2000

20012002

Decreasing pq

2001

2002Increasing pq



qq1 q2 q3

2000

20012002

Decreasing pq

2001

2002Increasing pq



qq1 q2 q3

2000

20012002

Decreasing pq

2001

2002Increasing pq


Another way to see this

QAP

Time

4%p2

q2

p1

q1

QAP

Time

4%

p2

q2

p1

q1

Upward Trend in Prices Downward Trend in Prices


Change in incumbent’s price

To see overall price change

I consider the SOC as a composite of pre-entry incumbents,

I let p̃s be the SOC’s price prior to entry

I let se be the entrant’s market share (after entry)

The change in the market-average quality-adjusted price is

QAP t − QAP t−1 = sep

q+ (1− se)

psqs− p̃s

qs.

The change is negative if

p̃s > ps · ((1− se) + se ·∆) ,

where ∆ = pq/psqs

is the ratio of quality-adjusted prices.









q+ (1− se)

psqs− p̃s

qs.


p̃s > ps · ((1− se) + se ·∆) ,

where ∆ = pq/psqs










q+ (1− se)

psqs− p̃s

qs.


p̃s > ps · ((1− se) + se ·∆) ,

where ∆ = pq/psqs




For each innovation we calculate

I The minimum pre-entry price p̃s and

I the percent price drop 100 · p̃s−pp̃s

such that the overall price change is negative.

I For 30% of innovations, pq

/psqs

< 1 so no price drop isnecessary

I If the entrant gets 100% market shareI Min drop is 1− 1

∆I A price drop of 4.1% would lead to an overall decrease

for 50% of innovations

I 80% market share → 3.2%




For each innovation we calculate

I The minimum pre-entry price p̃s and

I the percent price drop 100 · p̃s−pp̃s

such that the overall price change is negative.

I For 30% of innovations, pq

/psqs

< 1 so no price drop isnecessary

I If the entrant gets 100% market shareI Min drop is 1− 1

∆I A price drop of 4.1% would lead to an overall decrease

for 50% of innovations




CDF of 100 · p̃s−pp̃s

100% marketshareMean: 9%75 Percentile: 25%95 Percentile: 74%









Highly rated



Conclusion

I The medical cost effectiveness literature can speak toeconomic questions about quality-adjusted prices

I New technologies have higher quality-adjusted prices thanincumbents

I Across a range of technologies and diseasesI Somewhat less so in the US and EUI Recently, more so for pharmaceuticalsI Maybe, more so for innovations with quality ¿6.6%

above the incumbent

I Time trend unknownI Modest competitive effects on incumbents’ prices

indicate downward trend


Conclusion




above the incumbent




Conclusion




above the incumbent




Conclusion




above the incumbent




Conclusion

Future Work

I Weight by quantities

I FDA drug approvals / biologics?

I What causes higher QAPs?I costs, market power, cost-offsets, competing risks

I Suggestions?


Conclusion

Future Work




I Suggestions?


Conclusion

Future Work




I Suggestions?


Conclusion

Future Work




I Suggestions?

Thanks


Documents

How Does Technological Change A ect Quality-Adjusted ... Jena and Philipson (2008) { incentives for innovation Hult, Ja e, and Philipson Technological Change and Quality-Adjusted Prices