1. Maternal Smoking & the 1998 Master Settlement Agreement Douglas E. Levy, Ph.D. Ellen R. Meara, Ph.D. Dept. Health Care Policy, Harvard Medical School Support Provided by AHRQ T32-HS00055 & NIA T32-AG00186

2. Price Responsiveness of Smoking Elasticity of smoking participation Elasticity of smoking participation General population  -0.25 General population  -0.25 Pregnant women Pregnant women -0.5 (Evans & Ringel; Natality Files 1989-1992) -0.5 (Evans & Ringel; Natality Files 1989-1992) -0.7 (Ringel & Evans; Natality Files 1989-1995) -0.7 (Ringel & Evans; Natality Files 1989-1995) -1.0 (Colman et al.; PRAMS data 1993-1999) -1.0 (Colman et al.; PRAMS data 1993-1999) The Master Settlement Agreement led to an immediate 22% (45 ¢ ) increase in cigarette prices The Master Settlement Agreement led to an immediate 22% (45 ¢ ) increase in cigarette prices Price increase from MSA could reduce Price increase from MSA could reduce Smoking among pregnant women 10-20% Smoking among pregnant women 10-20% Low birth weight 2-4% Low birth weight 2-4% DID IT? DID IT?

3. Study Design Examined four outcomes Examined four outcomes Maternal smoking participation and intensity Maternal smoking participation and intensity (# of cigarettes/day) LBW and birth weight in grams LBW and birth weight in grams Used interrupted time-series regressions to determine whether there were important changes in outcomes following the MSA Used interrupted time-series regressions to determine whether there were important changes in outcomes following the MSA Subgroup analyses by mothers’ age, race, education Subgroup analyses by mothers’ age, race, education

4. Study Data National Vital Statistics Natality Detail Files from 1996 to 2000 National Vital Statistics Natality Detail Files from 1996 to 2000 We exclude We exclude States not reporting maternal smoking (CA, IN, SD, upstate NY) States not reporting maternal smoking (CA, IN, SD, upstate NY) Non-singleton birth Non-singleton birth Mother 44 years old Mother 44 years old Incomplete data on outcomes and demographics Incomplete data on outcomes and demographics 4% had missing data given first three criteria 4% had missing data given first three criteria Control variables: Control variables: calendar month, state, excise taxes, maternal demographics, pregnancy characteristics, prenatal care, heavy alcohol consumption calendar month, state, excise taxes, maternal demographics, pregnancy characteristics, prenatal care, heavy alcohol consumption N = 9,638,863 N = 9,638,863

5. Study Period Infants conceived from January 1996 to February 2000 Infants conceived from January 1996 to February 2000 Adequately capture pre- and post-MSA trends without picking up later non-price effects of MSA Adequately capture pre- and post-MSA trends without picking up later non-price effects of MSA Exclude infants conceived from March 1998 through November 1998 Exclude infants conceived from March 1998 through November 1998 Avoid pregnancies only partially affected by MSA Avoid pregnancies only partially affected by MSA

6. Summary Statistics OutcomeAllSmokers Non- Smokers Smoking12.1%100%0% #Cigs/day (sd) 1.3 (4.4) 11.0 (7.4) 0 (0) LBW5.9%10.1%5.3% Birth weight, g (sd) 3352 (572) 3161 (575) 3378 (567)

7. Maternal Smokers Are… More likely to be white More likely to be white Less likely to have attended college Less likely to have attended college Younger Younger Less likely to be married Less likely to be married Likely to have fewer and later prenatal care visits More likely to have had a prior preterm birth More likely to drink heavily during pregnancy

8. Regression Analyses Y its =  0 +  time t +  MSA t +  time t *MSA t +  Month t +  Demographics i + u s + e ist Y its =  0 +  time t +  MSA t +  time t *MSA t +  Month t +  Demographics i + u s + e ist Time is monthly count; captures secular trend Time is monthly count; captures secular trend MSA captures immediate changes beginning Nov. 1998 MSA captures immediate changes beginning Nov. 1998 Time*MSA captures changes in trend following MSA Time*MSA captures changes in trend following MSA Month captures seasonal variation in outcome Month captures seasonal variation in outcome

9. Predicted Smoking Prevalence MSA, Nov. 1998

10. Predicted Smoking Prevalence 15mos Post-MSA Population w/o MSA w/MSARel. %  Pr(  MSA =  MSA*time = 0) All11.7%11.4%-2.2%p<0.0197 Hispanic 3.23.0-6.3p=0.1064 15-19yo 17.816.3-8.2p<0.0001

11. Predicted LBW Prevalence 15mos Post-MSA Population w/o MSA w/MSARel. %  Pr(  MSA =  MSA*time = 0) All6.3%6.2%-1.8%p=0.0402 Hispanic 5.75.5-4.8p=0.3726 15-19yo9.08.7-3.5p=0.1077

12. Why So Little Effect? Perhaps remaining smokers are less likely to change habits in response to price hikes Perhaps remaining smokers are less likely to change habits in response to price hikes Perhaps earlier estimates based on changes in state excise taxes were confounded Perhaps earlier estimates based on changes in state excise taxes were confounded Simultaneous changes in attitude towards smoking Simultaneous changes in attitude towards smoking Simultaneous tobacco control legislation Simultaneous tobacco control legislation

13. Limitations No control group No control group Assumes trend is linear Assumes trend is linear Maternal smoking is self-reported Maternal smoking is self-reported Major states were excluded from the analysis Major states were excluded from the analysis 20% of births during this period 20% of births during this period

14. Implications Pros of price increases for tobacco control Pros of price increases for tobacco control Young women and their infants may benefit Young women and their infants may benefit Price changes affect most smokers Price changes affect most smokers Cons of price increases for tobacco control Cons of price increases for tobacco control Less effective than commonly believed Less effective than commonly believed Regressive taxation Regressive taxation Next steps Next steps Results in other populations? Results in other populations? Relate to new evidence re: long-run price-responsiveness Relate to new evidence re: long-run price-responsiveness

15. Predicted Smoking Prevalence 15mos Post-MSA Population w/o MSA w/MSARel. %  Pr(  MSA =  MSA*time = 0) All11.7%11.4%-2.2%p<0.0197  HS 17.417.0-2.3p<0.0240 > HS 5.15.0-1.8p=0.1059 15-19yo 17.816.3-8.2p<0.0001 20-44yo10.910.8p=0.0219

16. Predicted Cigarettes/Day 15mos Post-MSA Population w/o MSA w/MSARel. %  Pr(  MSA =  MSA*time = 0) All10.810.5-2.8%p<0.0001 White11.411.1-2.7p<0.0001 Black8.07.8-3.1p=0.0027 Hispanic7.37.2-1.8p=0.5581

17. Predicted Cigarettes/Day 15mos Post-MSA Population w/o MSA w/MSARel. %  Pr(  MSA =  MSA*time = 0) All10.810.5-2.8%p<0.0001  HS 11.010.6-2.8p<0.0001 > HS 10.19.8-2.8p=0.0038 15-19yo9.39.0-3.3p<0.0101 20-44yo11.110.8-2.7p<0.0001

18. Predicted LBW Prevalence 15mos Post-MSA Population w/o MSA w/MSARel. %  Pr(  MSA =  MSA*time = 0) All6.3%6.2%-1.8%p=0.0402 White5.05.0-0.9p=0.1913 Black 11.511.3-1.4 p=0.4135 Hispanic 5.75.5-4.8p=0.3726

19. Predicted LBW Prevalence 15mos Post-MSA Population w/o MSA w/MSARel. %  Pr(  MSA =  MSA*time = 0) All6.3%6.2%-1.8%p=0.0402  HS 7.57.3-2.3p=0.0406 >HS4.94.9-0.9p=0.9014 15-19yo9.08.7-3.5p=0.1077 20-44yo5.95.8-1.5p=0.1640

20. Predicted Birth Weight 15mos Post-MSA Population w/o MSA w/MSARel. %  Pr(  MSA =  MSA*time = 0) All333733410.1%p<0.0050 White340234060.1p<0.0029 Black 313731390.1p=0.2859 Hispanic 332633290.1p=0.6212

21. Predicted Birth Weight 15mos Post-MSA Population w/o MSA w/MSARel. %  Pr(  MSA =  MSA*time = 0) All333733410.1%p<0.0050  HS 328232840.1p=0.0421 > HS 340134060.2p<0.0302 15-19yo318831980.3p=0.0098 20-44yo335733600.1p<0.0092

22. Cigarette Prices and Taxes