CONFERENCE OF TOBACCO CONTROLSCHOOL OF ECONOMICS, UNIVERSITY OF

CAPE TOWN16-18 JULY 2014

Socio-economic determinants of tobacco use in the Southern African Customs Union

Linda Nyabongopresented 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

Country Women 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)

7621 3317 (age 15-

59)

2988

Namibia (2006-7)

9804 (age 15-

49)

9779 3915 (age 15-

49)

3899

Swaziland (2006-7)

4987 (age 15-

49)

4977 4156 (age 15-

49)

4149

South Africa (2008)

Not declared

6499 Not declared

4649

The descriptive statistics (an example of Lesotho)

Men (n = 2988) Women (n = 7621) n Weighted % n Weighted %

Age 15-19 838 27.8 1,840 23.4 20-24 631 21.1 1,555 20.4 25-29 462 15.4 1,203 16.3 30-34 370 13.2 960 12.9 35-39 282 9.7 755 10.0 40-44 204 6.5 663 8.6 45-49 201 6.4 645 8.4 Residence Rural 2313 71.9 5,646 66.3 Urban 675 28.1 1,975 33.7 Education No school 393 11.2 114 1.1 Primary 1494 48.8 3,863 46.6 Secondary 955 34.1 3,276 46.4 Post-Secondary 146 5.9 368 5.8 Occupation Not Working 936 31.8 4,285 54.9 Agriculture 1143 34.8 951 9.9 Service-Manual 662 24.2 1,367 21.2 Non-Manual 247 9.2 1,018 14.0 Religion Other 348 10.2 583 7.0 Catholic 1217 42.4 3,217 42.6 Protestant 11423 47.4 3,821 50.2

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 regressionResults 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 15-49

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