The Long-Run Impacts of Biomedical Innovation: Evidence from the Sulfa

Drug Era

Work in progress, December 2010

Sonia Bhalotra, University of Bristol (Bristol, UK)Atheendar Venkataramani, Washington University School of Medicine (St. Louis,USA)

Introduction Growing literature on the impact of the in

utero and early childhood environment on health and economic outcomes later in life (Almond and Currie, 2010; Heckman 2007).

Focus on natural disasters and epidemics- Famine e.g. Dutch, Chinese Pollution e.g. Chernobyl Disease e.g. influenza epidemic

A few recent studies analyse the long-run effects of public programs

malaria & hookworm eradication; water quality improvements – Lucas, 2010; Cutler, et al, 2010; Bleakley, 2010; Venkataramani, 2010

In quantifying the longer run benefits of intervention, these studies have clearer policy implications.

What We Do Examine the long-run impacts of early life exposure to

infectious disease using the sulfa drug innovation as an instrument for infectious disease.

Sulfa drugs (anti-microbial sulfonomides) were the first pharmaceuticals effective at treating infectious diseases.

Contribute evidence on- Impact of birth-year exposure to infection on health,

cognitive and economic outcomes in adulthood- Differences by sex *race - Reinforcing (v compensating) parental investments- The [undocumented] long run returns to medical innovation

Sulfa Drugs- timing Discovered in a German lab in 1932, evidence for their anti-

microbial potential first published in 1935, first clinical trials in 1936, 1937 (London, NY)

Jayachandran, Lleras-Muney and Smith (JLS 2010) identify a structural break in trend in 1937 for diseases treatable by sulfa drugs: strep infections, pneumonia, meningitis

The US witnessed unprecedented declines in mortality in the 20th century. There were no significant advances in treatment of infectious disease before sulfa arrived. And nothing else on the stage till antibiotics appeared in the mid-1940s.

We sample cohorts born 1930-1943.

Short run impact JLS attribute a 25 % decline in maternal mortality [puerperal

fever] and a 13 % decline in pneumonia and influenza mortality* between 1937 and 1943 to sulfa.

These declines a/c for 40-75% of the total decline in deaths from these causes during the period.

No significant change in 1937 in rate of decline of mortality from “control diseases” such as TB, diarrhea, cancer, heart disease.

* pneumonia responded to sulfa but influenza did not. Some 75% of deaths from (p+i) were on account of p.

Prevalence and infections of childrenPre v post sulfa mortality rates per 1000 (JLS) Maternal mortality 6.5 – 3.6 Influenza-pneumonia 1.2 – 0.8

Pre v post neonatal mortality rates per 1000- All causes: 3.6 - 2.4 Pneumonia: 1.6 – 1.1 Influenza: 0.2 – 0.2

Pneumonia was the leading cause of child death (8% v 44% pre)Mortality rates proxy wider morbidity rates.We analyse LR impact of exogenous declines in pneumonia and

maternal mortality rates at birth [and all-cause infant mortality rate.]

Why Long-Run Effects?Mechanisms: Pneumonia-exposureInfectious disease results in the body redirecting nutritional

resources from physical and mental growth to fighting infection.

Long run outcomes most sensitive to exposure in early childhood:

(a) rapid growth- greater nutritional demands(b) immune system not fully developed

Under-researched potential role of (a) reinforcing or compensating parental investments(b) dynamic complementarities resulting in multiplicative

deficits if early life brain development is impaired together with physiological growth.

Mechanisms- maternal mortalityMaternal mortality rates fell with sulfa because of

control of puerperal sepsis, a post-birth infection.

Likely paths for impact on offspring are increase in investments in girls as their life

expectancy improves (Jayachandran and Lleras-Muney, 2009; Albanesi and Olivetti, 2010)

Increased investment in both genders as more mothers survive

Why gender and race heterogeneity The pre-sulfa incidence of pneumonia and MMR was about

twice as high in the black population- so they stood to gain more.

But there was racial segregation in medical care and black Americans were more rural. For both reasons they were less likely to benefit from new technology.

Boys are more sensitive to resource deprivation in the pre and postnatal period (Waldron 1983, Stinson 1985). So they may show greater gains in general – biological reasons.

Girls may show greater gains from improvements in maternal mortality – parental investment reasons.

Extant Empirical Approaches JLS 2010: Structural break in national trend in

treated-disease mortality at time of intervention- Mdt = α + β treatedd*postt*yeart + ..

Bleakley 2007: Intervention creates a decline in mortality that varies across regions, decreasing (continuously) in the pre-intervention level of mortality-

Mjt= α + β postt*Mj(pre) + ..

Our Empirical Strategy We effectively combine these approaches, exploiting

variation across treated/untreated diseases in states with high/low pre-intervention mortality pre/post sulfa.

Individual data from the US census files for 1970-2000. Cohorts born in 1937 are aged 33, 43, 53, 63.

Data collapsed to state*sex*race averages. Later: longitudinal micro data on offspring of

exposed cohorts.

Estimated Equations First stageMdst =αf +βf postt*Mds(pre)+ δs

f + γtf +µr

f + εst

f

[treatment] Second stageYrst = αs + βs Mdst + Xst

s´π +δrss + γrt

s +µra

s + εsts

Marginal impact on outcome of sulfa-induced decline in mortality. Xst includes control disease mortality rates.

Reduced formYrst = α + β*postt*Ms(pre) + Xst

s´π + θrs + ηrt + λra + ergst ; βs = β / βf

Postt = 1 for birth cohorts 1937-43 Ms(pre) is the state-specific pre-intervention mortality

rate (1930-35). Outcome equations include fixed effects for race*birth

state, race*birth year and race*census year.Heterogeneity in treatment effects by gender*race.

Threats to Identification State * cohort macroeconomic or disease shocks Pre-existing trends

We assess stability of our results to inclusion of birth state * birth year data on mortality rates from other infectious and non-infectious diseases, state macroeconomic characteristics and state specific time trends.

TB and diarrhoea measure state-year variation in sanitation and poverty. Heart disease and cancer deaths capture trends in medical technology.

Mortality rates for sulfa-treated diseases- trend break (JLS 2010)

Trend Breaks: treated v control diseases

TREATED UNTREATED  (1) (2) (3) (4) (5) (6) (7)

VARIABLESInfluenza & Pneumonia

Maternal mortality

rate

Infant mortality rate TB

Diarrhea. Cancer Heart

               

(post==1)*year -0.0999*** -0.214***

-1.063***

0.0117*** 9.826**

-0.0108*

**

-0.0406*

**

(0.00592) (0.0252) (0.168)(0.0038

8) (4.971)(0.0019

5)(0.0058

3)

Observations 667 667 667 667 655 667 667R-squared 0.635 0.851 0.764 0.544 0.297 0.720 0.799Number of state 48 48 48 48 47 48 48Mean of D.V. 0.937 4.923 54.72 0.541 289.6 1.038 2.402S.D. of D.V. 0.298 1.959 16.77 0.328 318.8 0.326 0.767Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1, Sample period: 1930-1943. State fixed effects included. IPR, TB, Cancer, Heart.:#deaths/1,000 pop. IMR, MMR: infant/maternal deaths per 1,000 births.

Convergence in pneumonia-influenza mortality rate post-sulfa

Convergence in pneumonia-influenza mortality compared to tuberculosis

-250

-200

-150

-100

-50

0

0 100 200 300 400i_disease

Flu & Pneumonia Flu & PneumoniaTB fit line Tuberculosis

Flu & Pneumonia vs. TuberculosisConvergence by treatability

Convergence RegressionsTREATED UNTREATED

  (1) (2) (3) (4) (5) (6) (7)VARIABLES IPR MMR IMR TB Diarr. Cancer Heart               

(post==1)*base mortality

-0.336**

*

-0.227**

*

-0.152**

*

-0.369**

*

-0.343*

*0.0225

*0.169**

*(0.0480

)(0.0396

)(0.0198

)(0.0146

)(0.024

3)(0.0123

)(0.0165

)

Observations 667 667 667 667 655 667 667R-squared 0.792 0.863 0.810 0.784 0.510 0.743 0.845Number of state 48 48 48 48 47 48 48Mean of D.V. 0.937 4.923 54.72 0.541 289.6 1.038 2.402S.D. of D.V. 2.980 1.959 16.77 3.283 318.8 3.265 7.665

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1. State and Year FE, Period: 1930 – 1943, Base: 1930 to 1935. IPR, TB, Cancer, Heart dis.: no. of deaths among 1000 pop. IMR/MMR per 1000 births.

Data Outcomes

log family income, educational attainment, college attendance, current employment, number of children born, disability preventing work, difficulty with mobility, self-care, cohort size

We compute (weighted) means for race X gender X birth state X birth year X census year cells

State-year varying controls Diarrhea, TB, heart disease and cancer mortality rates from US Vital

Statistics State income per capita, number of hospitals, physicians, schools and

educational spending from various sources

  (1) (2) (3) (4) (5) (6)  Education Education Education College College CollegeMales Post*Base(Pneumonia) 0.245** 0.247** 0.151** -0.0154 -0.00840 0.0151

(0.114) (0.119) (0.0718) (0.0150) (0.0151) (0.0132)

Post*Base(Maternal Mortality) 0.00466 -0.00596-

0.0238*** -0.00224 -0.00179 -0.00306*(0.0167) (0.0171) (0.00791) (0.00197) (0.00195) (0.00171)

N 4,860 4,763 4,763 4,860 4,763 4,763  Education Education Education College College CollegeFemalesPost*Base(Pneumonia)

0.214** 0.201** -0.0454 -0.0164 -0.00928 0.000167(0.0872) (0.0917) (0.0717) (0.0153) (0.0119) (0.0117)

Post*Base(Maternal Mortality) 0.0154 0.0137 0.00141 -0.00338* -0.00129 -0.00176(0.0104) (0.0118) (0.00927) (0.00182) (0.00144) (0.00196)

N 4,887 4,790 4,790 4,887 4,790 4,790Controls- Every column includes birth state and birth year fixed effects (interacted with race); base: 1930-1935, disease is per 1000.Census Year x Race FE Y Y Y Y Y YDisease Controls (level) N Y Y N Y YEcon. Control and State Specific Trends N N Y N N Y

Educational Outcomes

  (7) (8) (9) (10) (11) (12)

 Ln(FamInc

)Ln(FamInc

)Ln(FamIn

c) Employed Employed EmployedMales Post*Base(Pneumonia) 0.0471*** 0.0468*** 0.0404* 0.0139** 0.0152** 0.0202*

(0.0176) (0.0168) (0.0213) (0.00671) (0.00746) (0.0104)Post*Base(Maternal Mortality) -0.000554 -0.00132 -0.00392 0.000460 -0.000100 -0.00319**

(0.00181) (0.00188) (0.00370) (0.000911) (0.000798) (0.00156)

N 4,839 4,743 4,743 3,718 3,641 3,641

 Ln(FamInc

)Ln(FamInc

)Ln(FamIn

c) Chborn Chborn ChbornFemalesPost*Base(Pneumonia) 0.0406** 0.0346* 0.00262 0.114 0.105 0.0518

(0.0179) (0.0183) (0.0223) (0.0821) (0.0840) (0.0570)Post*Base(Maternal Mortality) 0.00464** 0.00310 0.00196 0.00910 0.00876 -0.00252

(0.00210) (0.00210) (0.00345) (0.0116) (0.0115) (0.00751)

N 4,875 4,778 4,778 3,652 3,577 3,577ControlsEvery column includes birth state and birth year fixed effects (interacted with race) ; control dis: level ; base: 1930-1935Census Year x Race FE Y Y Y Y Y YDisease Controls (level) N Y Y N Y YEcon. Control & State Specific Trends N N Y N N Y

Income, Employment, Fertility

  (1) (2) (3) (4) (5) (6)

  Work Disability

Work Disability

Work Disability

Difficulty with Mobility

Difficulty with Mobility

Difficulty with Mobility

Males Post*Base(Pneumonia) -0.0188** -0.0194*** -0.0141 -0.0106** -0.00928* -0.0122

(0.00757) (0.00635) (0.00895) (0.00515) (0.00493) (0.00760)

Post*Base(MMR) -0.00262*** -0.00182** 0.000585 -0.00178** -0.00154* 0.000723(0.000929) (0.000839) (0.00117) (0.000807) (0.000887) (0.000972)

N 3,718 3,641 3,641 2,440 2,392 2,392

 Work

DisabilityWork

DisabilityWork

DisabilityDifficulty

with MobilityDifficulty

with MobilityDifficulty

with Mobility

FemalesPost*Base(Pneumonia) -0.0103 -0.00854 0.00884 -0.00787 -0.00517 0.00538

(0.00964) (0.00871) (0.00717) (0.00750) (0.00747) (0.00912)

Post*Base(MMR) -0.00365*** -0.00261** -0.000606 -0.00198 -0.00195 -0.000309

(0.00127) (0.00126) (0.00113) (0.00134) (0.00149) (0.00170)

N 3,739 3,663 3,663 2,460 2,416 2,416ControlsEvery column includes birth state and birth year fixed effects (interacted with race) PNA, MMR: no. of death among 1,000pop/births; base: 30-35Census Year x Race FE Y Y Y Y Y YDisease Controls (level) N Y Y N Y YEcon. Control & State Specific Time Trends N N Y N N Y

Disability

  (7) (8) (9)

  Difficulty with Self-Care

Difficulty with Self-Care

Difficulty with Self-Care

Males Post*Base(Pneumonia) -0.00158 0.001000 -0.00373

(0.00370) (0.00340) (0.00673)Post*Base(Maternal Mortality)

-0.00168** -0.00125** 0.000938(0.000657) (0.000582) (0.000899)

N 2,440 2,392 2,392

 

Difficulty with Self-Care

Difficulty with Self-Care

Difficulty with Self-Care

FemalesPost*Base(Pneumonia) -0.00305 -0.00211 0.00175

(0.00486) (0.00482) (0.00684)Post*Base(Maternal Mortality) -0.00149* -0.00130 -0.000284

(0.000790) (0.000882) (0.00121)

N 2,460 2,416 2,416ControlsEvery column includes birth state and birth year fixed effects (interacted with race), PNA, MMR: no. of death among 1,000 pop/births) ; base: 30-35Census Year x Race FE Y Y YDisease Controls (level) N Y Y

Econ. Control & State Specific Linear Time Trends N N Y

Disability

IV Estimates  (1) (2) (3) (4) (5) (6) (7)

Educ. College Log Income Employed Disab. Work Diffi. Mob Diff. Care Male              Pneumonia -1.016** 0.0591 -0.190** -0.0655*** 0.0904 0.0594** 0.0110

(0.495) (0.0770) (0.0817) (0.0252) (0.0596) (0.0287) (0.0145)MMR -0.0624 0.0114 -0.0109 -0.00506 0.0193** 0.0105** 0.00548**

(0.0845) (0.0101) (0.0122) (0.00431) (0.00854) (0.00488) (0.00251)

N 4,763 4,763 4,743 3,641 3,641 2,392 2,392

Educ. College Log Income Child born Disab. Work Diffi. Mob Diff. Care Female              Pneumonia -1.023** 0.0550 -0.185** -0.559 0.00954 0.0445 0.0239

(0.444) (0.0611) (0.0834) (0.461) (0.0566) (0.0307) (0.0246)MMR -0.138** 0.00943 -0.0271** -0.0800 0.0169** 0.0109* 0.00679*

(0.0658) (0.00850) (0.0124) (0.0711) (0.00708) (0.00576) (0.00395)N 4,790 4,790 4,778 3,577 3,663 2,416 2,416Controls all includes control diseases mortality (level) and birth state/year fixed effect (interacted with race). F test ~10

Simulation- womenReduced form coefficients- Educ. College Log income Chborn

Pneumonia 0.201** -0.00928 0.0346* 0.105

Maternal mortality 0.0137 -0.00129 0.0031 0.0087610th percent pneumonia/1000 0.82510th percentile MMR/1000 4.990th percentile IPR/1000 1.3990th percentile MMR/1000 7.91Effect at 10th percentile for both 0.233 -0.014 0.044 0.130Effect at 90th percentile for both 0.388 -0.023 0.073 0.215

Simulation- men

Reduced form coefficients- Educ. College Log incomeEmploye

d

Pneumonia 0.247** -0.0084 0.0468*** 0.015**

Maternal mortality -0.00596 -0.00179 -0.0013 -1E-0410th percentile pneumonia/1000 0.825

10th percentile MMR/1000 4.990th percentile pneumonia/1000 1.39

90th percentile MMR/1000 7.91Change at 10th percentile from both 0.175 -0.016 0.032 0.012Change at 90th percentile from both 0.296 -0.026 0.055 0.020

Comparison of effect sizesA state with the mean pre-sulfa pneumonia mortality rate saw a

post-sulfa education increase of 0.25 years and an income increase of 4%.

Influenza and pneumonia death rate pre/post 1937: 1.1 – 0.79

Almond (2006) estimates that the cohort exposed to the influenza epidemic of 1918 had 0.25 years less education and income lower by 6% percent

Influenza &pneumonia death rate 1917-1918 in %: 1.16- 4.91 Influenza death rate 1917-1918 in %: 0.17 – 2.9Suggests pneumonia more scarring than influenza.

ITT<ATT We are estimating the intent to treat (ITT) i.e. the

effect of sulfa averaged across the population it is supposed to help

This will be smaller than the ATT to the extent that not everybody could afford or access sulfa drugs. e.g r/u, m/f.

The cost of a complete course was $28-$100 (in 2008 US $) or $4.3 per patient per day.

Pooled sample: coefs on cohort*base

white females

white males

black females

black males

Other Results Stratifying by race

Coefficients in preferred specification (with all controls and state trends) generally smaller in magnitude for blacks vis-à-vis whites among males; no consistent pattern with females

Consistent with whites having preferred access to medical treatment (JLS).

Falsification check – placebo interventions in 1935 and 1939 Precisely estimates zeros for most outcome variables

Mechanisms? Endowments alone? Or endowments + compensating or

reinforcing endowments? Effects only in adulthood or differences seen in adolescence?

We examine impact of program on whether child attended school in the two months prior to the enumeration date of the 1950 census (“marginal” cohort is13 years old) Schooling is an outcome in itself; but mechanism for earnings, empl,

fertility. Caveat 1: compulsory school laws generally in place by early 1930s,

so lack of attendance could be due to variety of factors and may be thought of as an outcome in addition to a mechanism

Caveat 2: School attendance only available for a subset of the sample so sibling FE not possible

Schooling of children 7-18 years old  (1) (2) (3) (4) (5) (6)

  School School School High GradeHigh

GradeHigh Grade

Males

Post*Base(pneumonia) 0.00173** 0.00171**0.00201**

* 0.141*** 0.129*** 0.0535

(0.000704) (0.000681)(0.000547

) (0.0355) (0.0337) (0.0496)

N 957 943 943 957 943 943

  School School School High GradeHigh

GradeHigh Grade

Females

Post*Base(pneumonia)0.00163**

*0.00163**

* 0.00137** 0.0534** 0.0510* 0.0165

(0.000465) (0.000461)(0.000636

) (0.0261) (0.0293) (0.0418)

N 939 923 923 939 923 923ControlsEvery column includes birth state and birth year fixed effects (interacted with race); control dis.: log; base: 1930-1936. Pneumonia is deaths per 100,000pop; period: 1932-1943Census Year x Race FE Y Y Y Y Y YEconomic and Disease Controls N Y Y N Y YState Specific Linear Time Trends N N Y N N Y

Conclusions There is some evidence of sulfa-induced declines in mortality

in early childhood exerting positive long-run effects on income, educational attainment, employment and work disability

The effects are fairly substantial though in cases they are sensitive to controls for to state-year varying variables

The evidence is more robust evidence for men, especially white men.

Black men record stronger effects on prob(poverty).

Conclusions contd Impact from pneumonia reduction > impact from MMR

reduction for SES

MMR has more of an impact on disability; more of an impact on black people for eg. their education.

Results for adolescent school attendance suggest reinforcing parental investments

Implications for developing countries, where childhood pneumonia remains a leading cause of death

Work in progress Intergenerational effects

Currently looking at data from Collaborative Perinatal Project: longitudinal data for the early 1970s that include information on offspring of pre/post sulfa cohorts. Rich set of indicators.

Preliminary findings show association between conditions faced by mothers during birth year on the birth weight, motor development and IQ of their children

Mechanisms These data allow us to look more carefully at parental investments

The (provisional) endPlease email us with any questions or

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