Tobacco Control Research Conference July 2014 Determinants of smoking initiation in South Africa 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= (males: 8810, females: ) 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 MaleFemale African 34.8%3.2% Mixed race 61.8%49.6% Asian 49.5%15.1% White 50.9%44.0% Total39.1% 10.7%

AGGREGATE CIGARETTE CONSUMPTION AND PRICE OF CIGARETTES,

EXPANDING THE DATA Person ID YearAge Period (t) Event (start) Gender Price (R) Base: 2010 x M 8.96 x M 8.40 x M 7.71 x M 7.20 x M 7.37 x M 7.01 x M 7.02 x M 6.86 x M 6.47 x M 6.45 x M 6.40 An artificial panel is created from cross-sectional data. Individual x  aged 40 years in 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 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 expanded to rows of data Person ID YearAgePeriod (t) Event (start) GenderPrice (R) y F 8.96 y F 8.40 y F 7.71 y F 7.20 y F 7.37 y F 7.01 y F 7.02 y F 6.86 y F 6.47 y F 6.45 y F 6.40 y F 6.32 y F 6.61 y F 5.94 y F 6.77 y F 7.08 y F 7.24 y F 8.17 y F 8.46 y F y F y F y F y F y F y F y F y F y F y F y F y F y F y F y F 20.93

REGRESSION MODEL

Males 1. Logit model2. Logit model3. Split pop.4. Logit model5. Logit model Independent variables (Discrete time)(Cont. time)(Discrete time)(Cont. time) Price of cigarettes0.989*** 0.990**0.976**0.971*** (0.004) ( )(0.009) White1.000 Asian (0.182) (0.317)(0.541)(0.478) Mixed race1.394***1.389***1.339* (0.150)(0.149)(0.201)(0.339)(0.302) African0.605***0.604***0.489*** (0.062) (0.0711)(0.167)(0.176) Rural1.000 Urban1.266*** 1.325***1.557***1.476*** (0.053) (0.0616)(0.147)(0.140) Either parent’s highest level of education - Primary / no education1.000 Incomplete secondary education **0.873 (0.047) (0.0537)(0.080)(0.087) Complete secondary (0.071)(0.070)(0.0798)(0.158)(0.172) At least some tertiary education0.738***0.737***0.714***0.664**0.738 (0.070)(0.069)(0.0772)(0.123)(0.138) Illiterate1.000 Literate0.602***0.603***0.590***0.660***0.691*** (0.029) (0.0320)(0.073)(0.077) Mother alive when respondent was Mother died before respondent was (0.095) (0.103)(0.549)(0.469) Neither parent was ever a smoker1.000 Either parent was ever a smoker 1.924*** (0.186) Controls for ageYes Constant0.106***0.000***0.158***0.000*** (0.013)(0.000)(0.0266)(0.000) Observations Pseudo R-squared 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)

Females 1. Logit model2. Logit model3. Split pop.4. Logit model5. Logit model Independent variables (Discrete time)(Cont. time)(Discrete time)(Cont. time) Price of cigarettes * (0.007) ( )(0.013) White Asian 0.316***0.317***0.262*** (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*** ***0.097***0.104*** (0.008) ( )(0.031)(0.034) Rural Urban 1.532***1.534***1.682***1.586***1.490** (0.132) (0.160)(0.276)(0.260) Either parent’s highest level of education - Primary / no education Incomplete secondary education (0.085) (0.105)(0.158)(0.162) Complete secondary (0.121) (0.149)(0.291)(0.309) At least some tertiary education * (0.156) (0.184)(0.329)(0.389) Illiterate Literate 0.583***0.584***0.490***0.733*0.767 (0.050) (0.0506)(0.133)(0.140) Mother alive when respondent was Mother died before respondent was (0.191) (0.185)(1.077)(0.978) Neither parent was ever a smoker Either parent was ever a smoker 2.085*** (0.435) Controls for age Yes Constant 0.060***0.000***0.0932***0.000*** (0.011)(0.000)(0.0186)(0.000) Observations 161,071 25,546 Pseudo R-squared 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) 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