T o b a c c o C o n t r o l R e s e a r c h C o n f e r e n c e

1 6 - 1 8 J u l y 2 0 1 4

Determinants of smoking initiation in South Africa

Nicole Vellios and Corné van Walbeek

PURPOSE OF RESEARCH

• To investigate individual and household variables that influence the smoking onset decision

• This is done using survival analysis analysis of time to events

• Event occurrence represents an individual’s transition from one “state” to another “state”

• Within the context of this study, a person is a non-smoker until he/she becomes a smoker

SURVIVAL ANALYSIS

• Survival data are described and modelled in terms of two related probabilities, namely survival and hazard

• Hazard function assesses the risk associated with each time period, given that respondent has not yet experienced the event. Hazard rate = dependent variable in survival analysis

• Survivor function cumulates the risk of event occurrence to assess the probability that a randomly selected individual will “survive” (not experience the event). Survive Does not start smoking

LITERATURE REVIEW

• Guindon (2013) reviewed 27 studies that examine the impact of tobacco prices on smoking onset and concludes that existing studies do not provide strong evidence that tobacco prices impact smoking onset. He points to serious methodological issues (e.g. price not treated as a time-varying covariate), as well as data and measurement issues (e.g. current location may not match location at time of decision)

• These studies typically use cross-sectional or panel surveys within a survival analysis framework, and allow one to measure the impact of various factors on when smoking is initiated, if at all

• The existing literature is dominated by studies performed in high-income countries. Only two studies consider the determinants of smoking initiation in a non-high-income country (Guindon, 2014 and Laxminarayan & Deolalikar, 2004)

• Useful studies include: Guindon (2014), Cawley et al (2006), Cawley et al (2006), Forster and Jones (2004), Kidd and Hopkins (2004) Grignon (2007), López Nicolás (2002) and Madden (2007)

DATA

• NIDS wave 1 (2008 – representative of SA population), wave 2 (2010) and wave 3 (2012)

• Although the data is longitudinal, we could not use this approach, because the change in the real price between the waves was small and only 224 respondents indicated that they started smoking between the first and third wave

• Instead we include new respondents in wave 2 (n=5127) and wave 3 (3931) to the original sample (n=10 864). Final sample n=19 922 (males: 8810 , females: 11 112 )

• 15 < Age < 48. Excludes respondents > 48 years at the time of the interview to reduce the recall error of older respondents and because of price data restrictions

• We then created a pseudo-panel based on respondents’ responses on when they started smoking

SMOKING ONSET AGE

Source: NIDS wave1 (2008), wave 2 (2010) and wave 3 (2012) data

SMOKING PREVALENCE (EVER SMOKER) BY RACE AND GENDER

• Smoking prevalence varies by both gender and race. Prevalence amongst males is much higher at 39% compared to females at 11%. Mixed race and Whites are heavy smokers. There is a very low uptake of smoking amongst African females

  Male Female 

African 34.8% 3.2%Mixed race 61.8% 49.6%

Asian 49.5% 15.1%

White 50.9% 44.0%

Total 39.1%   10.7%

AGGREGATE CIGARETTE CONSUMPTION AND PRICE OF CIGARETTES, 1970 - 2013

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Real price of cigarettes (base 2008) (secondary axis) Excise tax per pack (base 2008) (secondary axis)Aggregate cigarette consumption

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Source: Van Walbeek 2005, Statistics South Africa (various is-sues)

EXPANDING THE DATA

Person ID

Year AgePeriod

(t)Event (start)

Gender

Price (R)

Base: 2010

x 1978 10 1 0 M 8.96x 1979 11 2 0 M 8.40x 1980 12 3 0 M 7.71x 1981 13 4 0 M 7.20x 1982 14 5 0 M 7.37x 1983 15 6 0 M 7.01x 1984 16 7 0 M 7.02x 1985 17 8 0 M 6.86x 1986 18 9 0 M 6.47x 1987 19 10 0 M 6.45x 1988 20 11 1 M 6.40

An artificial panel is created from cross-sectional data.

Individual x aged 40 years in 2008. Started smoking at age 20 (in 1988). A separate observational record is created for each year that individual x is known to be at risk. Starts smoking in year 11 (1988) “failure”. Once the event is experienced, the person drops out of the risk set.

Individual y aged 44 in 2008 but has not started smoking by 2012. Individual y is censored after 35 years (the last time period when the event could have occurred). “Failure” not observed.

Observational records: original sample of 19 922 expanded to 289 064 rows of data

Person ID

Year Age Period (t)Event (start)

Gender Price (R)

y 1978 10 1 0 F 8.96y 1979 11 2 0 F 8.40y 1980 12 3 0 F 7.71y 1981 13 4 0 F 7.20y 1982 14 5 0 F 7.37y 1983 15 6 0 F 7.01y 1984 16 7 0 F 7.02y 1985 17 8 0 F 6.86y 1986 18 9 0 F 6.47y 1987 19 10 0 F 6.45y 1988 20 11 0 F 6.40y 1989 21 12 0 F 6.32y 1990 22 13 0 F 6.61y 1991 23 14 0 F 5.94y 1992 24 15 0 F 6.77y 1993 25 16 0 F 7.08y 1994 26 17 0 F 7.24y 1995 27 18 0 F 8.17y 1996 28 19 0 F 8.46y 1997 29 20 0 F 10.00y 1998 30 21 0 F 11.45y 1999 31 22 0 F 13.07y 2000 32 23 0 F 13.62y 2001 33 24 0 F 14.13y 2002 34 25 0 F 14.34y 2003 35 26 0 F 14.85y 2004 36 27 0 F 15.26y 2005 37 28 0 F 16.84y 2006 38 29 0 F 17.61y 2007 39 30 0 F 18.30y 2008 40 31 0 F 18.17y 2009 41 32 0 F 19.39y 2010 42 33 0 F 20.96y 2011 43 34 0 F 21.08y 2012 44 35 0 F 20.93

REGRESSION MODEL

Discrete time•

Continuous time•

• Odds ratio = 1.0 odds of event occurrence in two groups are equal• Odds ratio > 1.0 event is more likely to occur in second group • Odds ratio < 1.0 event is less likely to occur in second group

• We test robustness of results by running a split population duration model which takes cognizance of the fact that not all people start smoking

 Males 1. Logit model 2. Logit model 3. Split pop. 4. Logit model 5. Logit modelIndependent variables (Discrete time) (Cont. time) (Discrete time) (Cont. time) (Cont. time)Price of cigarettes 0.989*** 0.989*** 0.990** 0.976** 0.971***  (0.004) (0.004) (0.00447) (0.009) (0.009)White 1.000 1.000 1.000 1.000 1.000 Asian 1.023 1.023 1.184 1.504 1.316  (0.182) (0.182) (0.317) (0.541) (0.478) Mixed race 1.394*** 1.389*** 1.339* 1.342 1.186  (0.150) (0.149) (0.201) (0.339) (0.302) African 0.605*** 0.604*** 0.489*** 0.665 0.696  (0.062) (0.062) (0.0711) (0.167) (0.176)Rural 1.000 1.000 1.000 1.000 1.000 Urban 1.266*** 1.266*** 1.325*** 1.557*** 1.476***  (0.053) (0.053) (0.0616) (0.147) (0.140)Either parent’s highest level of education - Primary / no education 1.000 1.000 1.000 1.000 1.000 Incomplete secondary education 1.003 1.002 1.021 0.817** 0.873  (0.047) (0.047) (0.0537) (0.080) (0.087) Complete secondary 0.905 0.905 0.905 0.967 1.050  (0.071) (0.070) (0.0798) (0.158) (0.172) At least some tertiary education 0.738*** 0.737*** 0.714*** 0.664** 0.738  (0.070) (0.069) (0.0772) (0.123) (0.138)Illiterate 1.000 1.000 1.000 1.000 1.000 Literate 0.602*** 0.603*** 0.590*** 0.660*** 0.691***  (0.029) (0.029) (0.0320) (0.073) (0.077)Mother alive when respondent was 15 1.000 1.000 1.000 1.000 1.000 Mother died before respondent was 15 1.029 1.032 1.017 1.619 1.383  (0.095) (0.095) (0.103) (0.549) (0.469)Neither parent was ever a smoker 1.000 Either parent was ever a smoker         1.924***          (0.186)Controls for age Yes  Yes Yes  Yes  YesConstant 0.106*** 0.000*** 0.158*** 0.000*** 0.000***  (0.013) (0.000) (0.0266) (0.000) (0.000)Observations 96 448 96 448 96 448 18 262 18 262Pseudo R-squared 0.100 0.0961   0.107 0.115

Standard error in parentheses. Source: NIDS wave1 (2008), wave 2 (2010) and wave 3 (2012) data. *** p<0.01, ** p<0.05, * p<0.1

SMOKING ONSET AGE BY RACE (MALES)

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AfricanMixed raceWhite and Asian

Smoking initiation age

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Females 1. Logit model 2. Logit model 3. Split pop. 4. Logit model 5. Logit modelIndependent variables (Discrete time) (Cont. time) (Discrete time) (Cont. time) (Cont. time)Price of cigarettes 1.002 1.002 1.010 0.977* 0.977*  (0.007) (0.007) (0.00873) (0.013) (0.013)White 1.000 1.000 1.000 1.000 1.000 Asian 0.316*** 0.317*** 0.262*** 0.831 0.757  (0.083) (0.083) (0.0762) (0.473) (0.432) Mixed race 1.278** 1.275** 1.406** 1.842** 1.650*  (0.154) (0.153) (0.212) (0.516) (0.463) African 0.061*** 0.061*** 0.0469*** 0.097*** 0.104***  (0.008) (0.008) (0.00790) (0.031) (0.034)Rural 1.000 1.000 1.000 1.000 1.000 Urban 1.532*** 1.534*** 1.682*** 1.586*** 1.490**  (0.132) (0.132) (0.160) (0.276) (0.260)Either parent’s highest level of education - Primary / no education 1.000 1.000 1.000 1.000 1.000 Incomplete secondary education 1.037 1.037 1.045 1.085 1.107  (0.085) (0.085) (0.105) (0.158) (0.162) Complete secondary 0.943 0.944 0.935 1.217 1.279  (0.121) (0.121) (0.149) (0.291) (0.309) At least some tertiary education 1.119 1.118 1.111 1.302 1.530*  (0.156) (0.156) (0.184) (0.329) (0.389)Illiterate 1.000 1.000 1.000 1.000 1.000 Literate 0.583*** 0.584*** 0.490*** 0.733* 0.767  (0.050) (0.050) (0.0506) (0.133) (0.140)Mother alive when respondent was 15 1.000 1.000 1.000 1.000 1.000 Mother died before respondent was 15 1.253 1.255 1.097 1.993 1.815  (0.191) (0.191) (0.185) (1.077) (0.978)Neither parent was ever a smoker 1.000 Either parent was ever a smoker 2.085***  (0.435)Controls for age Yes Yes Yes Yes YesConstant 0.060*** 0.000*** 0.0932*** 0.000*** 0.000***  (0.011) (0.000) (0.0186) (0.000) (0.000)Observations 161,071 161,071 161,071 25,546 25,546Pseudo R-squared 0.236 0.234   0.236 0.241

Standard error in parentheses. Source: NIDS wave1 (2008), wave 2 (2010) and wave 3 (2012) data. *** p<0.01, ** p<0.05, * p<0.1

Source: NIDS wave1 (2008), wave 2 (2010) and wave 3 (2012) data.

PROBABILITY OF INITIATING SMOKING AMONGST MIXED RACE MALES AND FEMALES (DISCRETE AND CONTINUOUS TIME)

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Mixed race males (Discrete time) Mixed race females (Continuous time)

Mixed race females (Discrete time) Mixed race females (Continuous time)

Smoking initiation age

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In all cases, it is assumed that that either parent has completed secondary school, the person is literate, lives in an urban area and the price of cigarettes is R18.17 (price in 2008).

RESULTS

• At all ages, smoking initiation amongst males is much higher than among females. For both males and females, the probability of starting smoking is highest amongst the mixed race population. African females have a very low uptake of smoking.

• Males are more responsive to price changes than females. Depending on the specification, a R1 increase in the price of cigarettes reduces the risk of smoking onset by between 1.0% and 2.9% for males

• Children of parents with limited education are more likely to start smoking than children of parents with more education. As education levels in South Africa improve over time, smoking initiation is likely to decrease amongst the next generation

• Literate people are less likely to initiate smoking than illiterate people. As education levels improve, illiteracy recedes, with positive long-term tobacco control consequences.

• Children of parents where at least one smokes are about twice as likely to initiate smoking as children of parents where neither smokes. Children of parents who do not smoke are less likely to initiate smoking. Smoking prevalence among adults in South Africa has been decreasing steadily for at least 20 years. As non-smoking becomes the norm, smoking initiation amongst the next generation is expected to decrease.

CONCLUSION

• The results reported in this paper are generally positive from a tobacco control perspective. However, the effect of price is not as strong as we would have hoped

• Tobacco taxation should remain a major public policy instrument to discourage smoking

• Further increases in the excise tax on cigarettes are likely to discourage smoking habit and to delay onset for those who decide to start