CONFERENCE OF TOBACCO CONTROL SCHOOL OF ECONOMICS, UNIVERSITY OF CAPE TOWN JULY 2014 Socio-economic determinants of tobacco use in the Southern African Customs Union Socio-economic determinants of tobacco use in the Southern African Customs Union Linda Nyabongo presented by Corne van Walbeek

Background to the study Determinants of smoking prevalence is fairly well understood in many (mainly developed) countries Studies about smoking prevalence in Africa has lagged behind Pampel (2008) investigated socio-economic determinants of smoking in many African countries, using a general, undifferentiated model This study adds to the literature by looking at countries in a region with similar price and tax regimes

The data CountryWomen in DHS Women in final sample (age 15-49) Men in DHS Men in final sample (age 15-49) Lesotho (2009) 7624 (age 15-49) (age 15-59) 2988 Namibia (2006-7) 9804 (age 15-49) (age 15-49) 3899 Swaziland (2006-7) 4987 (age 15-49) (age 15-49) 4149 South Africa (2008) Not declared 6499Not declared 4649

The descriptive statistics (an example of Lesotho) Men (n = 2988) Women (n = 7621) n Weighted % n Weighted % Age , , , Residence Rural , Urban , Education No school Primary , Secondary , Post-Secondary Occupation Not Working , Agriculture Service-Manual , Non-Manual , Religion Other Catholic , Protestant ,

Tobacco use prevalence amongst males

Tobacco use prevalence amongst females

Cigarette smoking prevalence by males by age

Pipe smoking prevalence amongst males by age

Cigarette smoking prevalence by females by age

Snuff use prevalence by females by age

Cigarette smoking prevalence by education, males

Pipe smoking prevalence by education, males

Cigarette smoking prevalence by education, females

Snuff use prevalence by education, females

The empirical model Logistic regression Results are presented in the form of odds ratios  Odds ratio > 1: more likely to consume tobacco than base category  Odds ratio < 1: less likely to consume tobacco than base category P(Tob = 1) = f(Age, Residence, Education, Occupation, Religion)

Example of regression output

Another example: females in Namibia

Combined regression output for cigarettes, males

Combined regression output for cigarettes, females

Summary of main findings Cigarette smoking more likely in the urban areas amongst females Pipe smoking, chewing tobacco and snuff more concentrated in rural areas for both males and females Generally, as education increases, prevalence of tobacco use decreases  Exceptions: cigarette smoking among females No clear relationship between occupation and smoking prevalence

Limitations of the study Age restricted to Price not included in the analysis Ethnicity not asked in DHS (although this is only really relevant in Namibia)

Implications and conclusion Negative relationship between SES and prevalence of tobacco use Many studies (not this one) have shown that people with lower SES are more price responsive An increase in the price of tobacco products will be more effective in reducing tobacco use amongst people with lower SES and will thus decrease inequalities in tobacco use