Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco 2013. This work is written by (a) US Government employee(s) and is in the public domain in the US.

doi:10.1093/ntr/ntt179

ORIGINAL INVESTIGATION

Who Quits? An Overview of Quitters in Low- and Middle-Income Countries

Ce Shang PhD1, Frank J. Chaloupka PhD1,2, Deliana Kostova PhD3

1Health Policy Center, Institute for Health Research and Policy, University of Illinois at Chicago, Chicago, IL; 2Department of Economics, University of Illinois at Chicago, Chicago, IL; 3Centers for Disease Control and Prevention, Atlanta, GA

Corresponding Author: Ce Shang, PhD, Health Policy Center, Institute for Health Research and Policy, University of Illinois at Chicago, Room 422, 1747 W. Roosevelt Road, Chicago, IL 60608, USA. Telephone: 312-996-0774; Fax: 312-996-2703; E-mail: cshang@uic.edu

Received April 29, 2013; accepted October 8, 2013

ABSTRACT

Introduction: Using the Global Adult Tobacco Surveys from 14 primarily low- and middle-income countries, we describe the asso-ciation between the probability of being a recent quitter and a number of demographic and policy-relevant factors such as exposure to warning labels, work-site smoking bans, antismoking media messaging, tobacco marketing, and current cigarette and bidi prices.

Methods: Logistic regressions were used to examine the potential correlates of recent quitting and recent quit attempts.

Results: After accounting for country-specific attributes in pooled analyses, we found that higher rates of exposure to work-site smoking bans are associated with higher odds of being a quitter (odds ratio [OR] with 95% confidence interval [CI] = 1.13 [1.04, 1.22]). Exposure to antismoking media messaging (OR with 95% CI = 1.08 [1.00, 1.17]), work-site smoking bans (OR with 95% CI = 1.11 [0.99, 1.26]), and warning labels (OR with 95% CI = 1.03 [1.01, 1.05]); cigarette prices (OR with 95% CI = 1.01 [1.00, 1.02]); and bidi prices (OR with 95% CI =1.17 [1.11, 1.22]) are factors associated with higher odds of recent quit attempts in the pooled analysis. These effects vary by country. Exposure to warning labels is found to be associated with greater likelihood of recent quitting in Egypt (OR with 95% CI = 3.20 [1.53, 6.68]), and the positive association between exposure to work-site smoking bans and quitting is particularly strong for Southeast Asia (OR with 95% CI = 1.20 [1.06, 1.35]) and Asia Pacific countries (OR with 95% CI = 1.85 [0.93, 3.68]). Additionally, exposure to tobacco industry marketing is significantly associated with smaller odds of quitting in Asia Pacific (OR with 95% CI = 0.83 [0.79, 0.87]) and Latin American countries (OR with 95% CI = 0.78 [0.74, 0.82]).

Conclusions: Although our results vary by country, they generally suggest that greater exposure to tobacco control polices is significantly associated with quitting.

INTRODUCTION

Tobacco use is one of the leading causes of preventable death worldwide. Although the majority of the world’s smokers reside in low- and middle-income countries (LMICs), the quit rate among smokers in LMICs is relatively low (Jha et  al., 2008; Rani, Bonu, Jha, Nguyen, & Jamjoum, 2003). To address this, the World Health Organization (WHO) has identified tobacco cessation as a major goal in its published guidelines of the Framework Convention on Tobacco Control 2006 (WHO FCTC) (WHO, 2010b).

Although smoking cessation is recognized as an important aspect of tobacco control in LMICs, less is known about the factors linked to cessation in these countries. Using individual-level data on smokers from 13 LMICs and Poland obtained from the Global Adult Tobacco Survey (GATS) 2008–2010, we examine potential correlates of quitting within and across

this group of countries. (Countries were classified into income groups according to their 2011 per capita gross national income following the World Bank Atlas method. Countries are classified as high income if they have a gross national income per capita of $12,476 or more. Poland has a gross national income per capita of $12,480 which is slightly above the cutoff. Therefore, we included Poland in our analyses alongside 13 LMICs.) We describe the association between recent quitting and a comprehensive set of policy-relevant and individual-spe-cific factors. Using individual GATS responses, we construct a number of location-specific index variables, which reflect the local prevalence of work-site smoking bans, cigarette warning labels, tobacco advertising, tobacco promotion, antismoking information, and prices paid for cigarettes (and bidis for India and Bangladesh). By exploring these factors as correlates of quitting, we evaluate their potential as cessation-promoting mechanisms among smokers in LMICs.

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10.1093/ntr/ntt179© The Author 2012. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Over the past decade, a growing body of research has focused on examining the impacts of price and tobacco control policies on tobacco use in LMICs, building upon the evidence demon-strating the effectiveness of these interventions in high-income countries (HICs) (Chaloupka et al., 2011; Chaloupka & Warner, 2000; Guindon, Perucic, & Boisclair, 2003; Kostova, Ross, Blecher, & Markowitz, 2011; Ranson et  al., 2002). However, there are few studies that examine the factors in promoting smoking cessation in LMICs. Kostova (2012) examines the impact of price on smoking transitions before the age of 15 in a set of 40 LMICs and finds that in early adolescence, prices are more effective in driving initiation rather than cessation. Ross et al. (in press) find that higher taxes have effectively increased cessation among adults in three Eastern European countries dur-ing the transitional period of the 1990s and 2000s. Existing stud-ies of U.S. data have estimated that the short-run price elasticity of smoking cessation, which reflects smokers’ initial efforts to quit in response to price, increases ranges from 0.3 to 0.9 and falls in the longer run as some quitters relapse (DeCicca, Kenkel, & Mathios, 2008; Tauras, 2004). Graphic warning labels and mass-media antismoking campaigns that encourage quitting have been shown to play a role in increasing interest in cessa-tion and quit attempts in HICs (Centers for Disease Control and Prevention, 2012; Davis, Nonnemaker, Farrelly, & Niederdeppe, 2011; Hammond, Fong, McNeill, Borland, & Cummings, 2006). However, the effect of these nonfiscal approaches on cessation in LMICs has not been extensively studied.

The relationship between cessation and tobacco control measures such as taxes in LMICs is likely to be different from that in HICs. On the one hand, complicated/tiered tax struc-tures in many LMICs can widen the range of cigarette prices within countries (Chaloupka, Kostova, & Shang, 2013; Shang, Chaloupka, Fong, & Zahra, 2013). This provides an incentive for smokers to switch between cigarette brands or tobacco prod-ucts in response to higher taxes, potentially reducing the full impact of tax increases on lowering prevalence and consump-tion. Similarly, economic growth in some LMICs that results in significant income increases can make tobacco products more affordable, encouraging further shifts in consumption (Blecher & van Walbeek, 2004, 2009; Kostova et al., 2012). On the other hand, given the relatively low awareness of tobacco health risks in some LMICs (King et al., 2010), informational policy tools such as graphic warning labels and mass media antismoking campaigns may have a relatively larger impact on cessation in LMICs.

DATA AND METHODS

The GATS is an ongoing nationally representative household survey of adults aged 15 years or older, which has been con-ducted in 14 countries between 2008 and 2010. It collects infor-mation on respondents’ demographic characteristics, tobacco use, exposure to tobacco control policies, and tobacco market-ing. In GATS, respondents who identify themselves as past smokers are asked to report how long it had been since they quit smoking. This allows us to describe measures of quitting within the past 12 months, which can be evaluated in the context of recent exposure to tobacco control policies and tobacco market-ing, as well as to demographic characteristics. These contempo-raneous quitting measures include an indicator of respondents who quit in the past 12  months (the ratio of the number of

respondents who quit in the past 12 months to the number of smokers 12 months ago) and an indicator of smokers who made at least one unsuccessful quit attempt (the ratio of the number of current smokers who attempted to quit in the past 12 months to the number of current smokers). The sample of smokers 12  months ago consists of both current smokers and those who quit in the past 12 months, and that the combined quitting and quit attempt rates can be derived as a weighted average of the two indicators. We present these quitting measures along with smoking prevalence in the studied countries in Figure 1. Countries with relatively high smoking prevalence rates such as China and Russia tend to have relatively low recent quit rates, whereas Latin American countries have the highest quit rates.

The GATS asks smokers to report expenditures on their last purchase of cigarettes (and bidis for respondents from India and Bangladesh), as well as the number of sticks purchased. Using this information, the price paid per cigarette (bidi) can be derived for each smoker. Since individual-level prices and indi-vidual smoking intensity are likely to be simultaneously deter-mined (heavier smokers are more likely to seek out lower prices while lower prices encourage heavier smoking), individual-level prices would be endogenous in models of smoking cessation. To address this simultaneity bias, our analyses use market-level prices, derived as the primary sampling unit (PSU)-specific consumption weighted average cigarette (bidi) price paid per 20 sticks. This approach has been detailed in the International Agency for Research on Cancer (IARC) Handbook (IARC Handbooks of Cancer Prevention, Tobacco Control, 2008) and Economics of Tobacco Toolkit (WHO, 2010a). In order to make prices comparable across countries, we convert them into a common international dollar currency using purchasing power parity adjustment factors, and then into constant 2010 interna-tional dollars using the index of average consumer prices pub-lished by the International Monetary Fund (Table 1).

Individual-level demographic controls include age, gender, education, rural residence, wealth, household size and occupa-tion type (Tables 1 and 2). Age is defined by binary indicator variables for four age categories (15–24, 25–39, 40–64, 65 and older). Education level is described through five categories: no education/less than primary, primary, secondary, high school, and college or higher. Occupation type is described through three categories: indoor, outdoor, and unemployed/unspeci-fied. Wealth is measured from survey questions that inquire about the possession of certain personal and household items (electricity, flush toilet, fixed telephone, cellular phone, televi-sion, radio, refrigerator, car/bike/boat, moped/scooter/motor-cycle, washing machine, and any other surveyed assets) and is defined as the fraction of surveyed items, which the respond-ent has in their possession, weighted by the per capita gross domestic product of the respondent’s country.

Besides individual demographic controls, our study employs a number of indices constructed from the individual responses of GATS participants. These include exposure to tobacco con-trol policies, exposure to antismoking media messaging, and exposure to tobacco marketing (Tables 1 and 2). These indices are constructed as PSU-level aggregates, which has a num-ber of advantages over using the underlying individual-level exposure status. First, individual exposure may have a reverse causality link to quitting behavior—for instance, antismoking messaging may target and be observed disproportionately more by the type of person who is more prone to quitting in the first place or tobacco marketing may be disproportionately targeting

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Quitters in low- and middle-income countries

Figure 1. Quitting and quit attempts in the past 12 months and smoking prevalence by country

individuals who are committed smokers and less likely to quit in the first place. The reverse causality bias is reduced by aggre-gating individual exposure responses at the PSU level. Second, aggregating individual exposure responses at the PSU level reduces misclassification bias occurring when some respond-ents underreport and some overreport personal exposure. Third, the PSU-level indices may also capture subnational dif-ferences in policy implementation and enforcement. They are defined as follows. The work-site smoking ban index is con-structed as the PSU-level average of individual responses that designate the presence of smoking restrictions at the respond-ent’s work-site (0, no restriction; 1, some restriction; 2, full restriction). The warning label index is constructed at the PSU level as the fraction of respondents who report noticing warn-ing labels among those who have seen cigarette packs in the past 30 days, scaled from 1 to 10. For developing the antismok-ing information index, we first estimated, for each respondent, the fraction of media outlets (newspapers or magazines, televi-sion, radio, billboard, and any other outlets) that have exposed the respondent to antismoking information in the past 30 days. These individual-specific fractions were then averaged across all respondents in a PSU and scaled from 1 to 10 to produce the index. For developing the tobacco promotion index, we first estimated, for each respondent, the fraction of promotion approaches (free samples, clothing with brand names, and any other approaches) that have been observed by the respondent in the past 30 days. Similarly to the antismoking information index, these individual-specific fractions were then averaged across respondents in a PSU and scaled from 1 to 10 to produce

the tobacco promotion index. The tobacco advertising index was constructed using a similar formula: first, we estimated the fraction of advertising outlets (stores, television, newspa-pers or magazines, and any other outlets) that have exposed each respondent to tobacco advertising in the past 30 days, then averaged these fractions at the PSU level and scaled them from 1 to 10 to produce the tobacco advertising index.

Logistic regressions were used to examine the potential correlates of recent quitting (the probability of quitting in the past 12  months) and recent quit attempts (unsuccessful quit attempts in the past 12 months). For individual country esti-mates, the standard errors were clustered at the PSU level; in the pooled analysis, they were clustered at the country level. All models include individual demographic controls for age, gender, education, wealth, household size, rural residence, occupation type, and PSU-level indices for antismoking infor-mation, tobacco promotion, tobacco advertising, and warning labels, and prices paid for cigarettes. Bidi prices were included in models for India and Bangladesh. To examine how work-place smoking bans may impact quitting depending on the type of employment, we included interaction terms between the workplace smoking ban index and the indicators for indoor and “other” occupation (“other” refers to outdoor and unemployed/unspecified occupation). Pooled models include country fixed effects to control for unmeasured country-specific factors that may impact cessation behavior. The analysis sample for the models of recent quitting consists of current smokers and those who quit in the past year and the sample for models of recent quit attempts consists of current smokers only.

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RESULTS

In Table  2, we list the means of the outcome variables and their potential correlates by country. Warning labels are most often observed in Egypt, exposure to antismoking mass-media messages is highest in Vietnam. Smokers are most frequently exposed to work-site smoking bans in Brazil and Mexico—countries with relatively high quit rates, and smokers’ exposure to tobacco advertising and promotion is greatest in Bangladesh, Russia, and the Philippines—countries with relatively low quit rates.

Although the associations between recent quitting and its correlates can vary by country, several patterns emerge (Table 3). Men and those older than 24 years are less likely to be former smokers in all countries. Rural smokers and those with more education are more likely to quit in most countries. The association between wealth and quitting varies across coun-tries, with greater wealth associated with increased quitting in Bangladesh, Brazil, Uruguay, Russia, and Ukraine but less

quitting in India and Turkey. This finding suggests that wealth-ier smokers in a majority of LMICs may have more incentives to quit, which has been shown theoretically in the health capital model developed by Grossman (1972) and empirically shown by others (Fagan et al., 2007; Siahpush, McNeill, Borland, & Fong, 2006). Given intention to quit, wealthier smokers also tend to have more access to professional services and drugs that help quitting (Kotz & West, 2009).

Higher cigarette prices are significantly associated with increased odds of being a recent quitter in the Philippines, and higher bidi prices are significantly associated with increased odds of being a recent quitter in Bangladesh, with a margin-ally significant association seen for India. Greater exposure to mass-media antismoking information is significantly associ-ated with increased odds of quitting in Poland. Greater expo-sure to tobacco advertising and promotion is significantly associated with less quitting in Bangladesh and the Philippines. And greater awareness of warning labels is associated with higher quit rates in Egypt. Mixed results are obtained for

Table 1. Variable Descriptions and Definitions

Individual-level variables

Quit in past 12 months Indicator equals 1 if the respondent has quit smoking in the past 12 months, 0 if smokes at the time of survey

Quit attempt in past 12 months Indicator equals 1 if the smoker at the time of survey attempted and failed to quit in the past 12 months, 0 otherwise

Age Binary indicators for four age categories: 15–24, 25–39, 40–64, 65+ Education Binary indicators for five education categories: no education/less than primary,

primary, secondary, high school, college or higher Wealth The fraction of GATS-surveyed household items (electricity, flush toilet, and any other

surveyed assets) that the respondents has in their possession, weighted by the per capita gross domestic product of the respondent’s country

Household size Number of household members Rural residence Indicator equals 1 if the respondent lives in rural area, 0 otherwise Indoor occupation Indicator equals 1 if the respondent works indoors, 0 otherwise Outdoor occupation Indicator equals 1 if the respondent works outdoors, 0 otherwise

PSU-level variables

Work-site smoking ban index The average of individual responses that designate the presence of smoking restrictions at the respondent’s work-site (0 = no restriction, 1 = some restriction, 2 = full restriction)

Warning label index The fraction of respondents who report noticing warning labels among those who have seen cigarette packs in the past 30 days, scaled from 1 to 10

Antismoking information indexa Out of a number of possible antismoking media outlets (newspapers or magazines, television, radio, billboard, and any other outlets), the fraction that each respondent was exposed to in the past 30 days was calculated. These individual-specific fractions were then averaged and scaled from 1 to 10.

Tobacco promotion indexa Out of a number of possible promotion approaches (free samples, clothing with brand names, and any other approaches), the fraction that each respondent observed in the past 30 days was calculated. These individual-specific fractions were then averaged and scaled from 1 to 10.

Tobacco advertising indexa Out of a number of possible advertising outlets (stores, television, newspapers or magazines, and any other outlets), the fraction that each respondent was exposed to tobacco advertising in the past 30 days was calculated. These individual-specific fractions were then averaged and scaled from 1 to 10.

Cigarette price Average price paid per 20 cigarettes in constant 2010 international dollars Bidi price Average price paid per 20 bidis in constant 2010 international dollars

Note. GATS = Global Adult Tobacco Survey; PSU = primary sampling unit.aThe indices are imputed for individuals as the average of indicators of items listed. Take antismoking information index as the example, index = (newspapers or magazines + television + radio + billboard + any other outlets)/5, and then aggregated into the PSU-level index.

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Quitters in low- and middle-income countries

Tab

le 2

. D

escr

iptiv

e S

tatis

tics

BR

MX

UY

INB

DT

HC

NV

NPH

PLR

UT

RU

AE

GA

ll

% Q

uit i

n pa

st

12 m

onth

s11

.5(3

1.9)

15.9

(36.

5)11

.4(3

1.8)

3.10

(17.

3)4.

29(2

0.3)

5.34

(22.

5)4.

52(2

0.8)

7.57

(26.

5)6.

77(2

5.1)

7.43

(26.

2)5.

11(2

2.0)

9.85

(29.

8)8.

89(2

8.5)

5.50

(22.

8)6.

81(2

5.2)

% Q

uit a

ttem

pts

in p

ast

12 m

onth

s41

.8(4

9.3)

46.2

(49.

9)44

.0(4

9.7)

30.5

(46.

0)47

.7(5

0.0)

47.4

(49.

9)30

.9(4

6.2)

51.7

(50.

0)46

.5(4

9.9)

30.4

(46.

0)29

.2(4

5.5)

41.1

(49.

2)34

.8(4

7.6)

38.0

(48.

6)38

.3(4

8.6)

Hou

seho

ld s

ize

3.26

(1.7

9)4.

53(2

.13)

3.15

(1.8

6)5.

14(2

.37)

4.49

(1.8

7)3.

53(1

.69)

2.78

(1.3

0)3.

89(1

.64)

5.00

(2.3

5)3.

35(1

.61)

3.04

(1.3

9)4.

11(2

.06)

3.08

(1.3

8)4.

80(2

.42)

3.98

(2.1

3)%

Rur

al17

.9(3

8.4)

36.5

(48.

1)33

.7(4

7.3)

68.3

(46.

5)52

.4(5

0.0)

44.9

(49.

7)62

.1(4

8.5)

51.9

(50.

0)60

.2(4

9.0)

47.0

(49.

9)46

.0(4

9.8)

44.1

(49.

7)47

.2(4

9.9)

41.1

(49.

2)48

.0(5

0.0)

% M

ale

56.8

(49.

5)75

.8(4

2.8)

57.2

(49.

5)88

.4(3

2.1)

96.5

(18.

3)91

.0(2

8.7)

93.8

(24.

1)96

.0(1

9.7)

82.8

(37.

7)58

.8(4

9.2)

78.3

(41.

2)74

.3(4

3.7)

84.0

(36.

7)98

.5(1

2.3)

81.4

(38.

9)W

ealth

8.57

(2.3

8)9.

63(2

.72)

11.3

(2.9

4)1.

22(0

.82)

0.53

(0.2

6)6.

55(1

.75)

4.26

(1.5

8)2.

82(1

.25)

1.85

(1.0

8)16

.2(2

.74)

11.1

(2.3

3)11

.5(1

.55)

4.84

(1.2

6)4.

81(1

.01)

6.05

(4.6

1)%

Ind

oor

occu

patio

n22

.6(4

1.8)

22.3

(41.

6)30

.2(4

5.9)

15.6

(36.

2)16

.8(3

7.4)

17.0

(37.

6)24

.7(4

3.2)

20.7

(40.

5)11

.3(3

1.7)

34.1

(47.

4)41

.5(4

9.3)

29.1

(45.

4)27

.7(4

4.7)

22.8

(42.

0)22

.9(4

2.0)

% O

utdo

or o

ccup

atio

n36

.6(4

8.2)

47.4

(49.

9)33

.3(4

7.1)

56.7

(49.

5)74

.7(4

3.5)

58.3

(49.

3)41

.4(4

9.3)

50.6

(50.

0)65

.9(4

7.4)

23.3

(42.

3)32

.2(4

6.7)

33.0

(47.

0)28

.0(4

4.9)

59.5

(49.

1)47

.1(4

9.9)

% A

ge 2

5–39

33.1

(47.

1)36

.0(4

8.0)

33.5

(47.

2)37

.2(4

8.3)

42.8

(49.

5)30

.4(4

6.0)

21.2

(40.

9)35

.0(4

7.7)

39.4

(48.

9)33

.0(4

7.0)

33.8

(47.

3)42

.6(4

9.5)

37.4

(48.

4)39

.1(4

8.8)

34.9

(47.

7)%

Age

40–

6444

.7(4

9.7)

32.1

(46.

7)42

.1(4

9.4)

45.3

(49.

8)40

.0(4

9.0)

48.1

(50.

0)58

.2(4

9.3)

47.5

(49.

9)37

.5(4

8.4)

50.8

(50.

0)44

.1(4

9.7)

41.1

(49.

2)41

.8(4

9.3)

43.4

(49.

6)44

.9(4

9.7)

% A

ge 6

5+8.

91(2

8.5)

7.02

(25.

6)8.

46(2

7.8)

9.25

(29.

0)6.

34(2

4.4)

11.9

(32.

4)16

.7(3

7.3)

8.22

(27.

5)7.

68(2

6.6)

6.09

(23.

9)6.

02(2

3.8)

4.81

(21.

4)7.

72(2

6.7)

6.80

(25.

2)8.

73(2

8.2)

% P

rim

ary

scho

ol18

.9(3

9.2)

25.2

(43.

4)43

.3(4

9.6)

29.5

(45.

6)26

.6(4

4.2)

53.8

(49.

8)27

.3(4

4.5)

26.8

(44.

3)40

.5(4

9.1)

13.6

(34.

2)2.

17(1

4.6)

51.6

(50.

0)6.

45(2

4.5)

18.1

(38.

5)26

.8(4

4.3)

% S

econ

dary

sch

ool

39.3

(48.

8)29

.6(4

5.6)

21.5

(41.

1)25

.6(4

3.6)

20.4

(40.

3)17

.4(3

7.9)

39.1

(48.

8)26

.0(4

3.9)

16.9

(37.

5)35

.8(4

7.9)

6.99

(25.

5)10

.4(3

0.5)

35.9

(47.

9)11

.2(3

1.5)

24.5

(43.

0)%

Hig

h sc

hool

19.4

(39.

6)14

.6(3

5.3)

15.3

(36.

0)6.

63(2

4.9)

2.83

(16.

6)14

.8(3

5.5)

16.8

(37.

4)25

.6(4

3.6)

19.7

(39.

8)36

.1(4

8.0)

69.3

(46.

1)20

.2(4

0.2)

41.5

(49.

2)8.

14(2

7.4)

20.8

(40.

6)

(Con

tinu

ed)

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niversity of Cape Tow

n user on 28 June 2019

Nicotine & Tobacco Research, Volume 16, Supplement 1 (January 2014)

BR

MX

UY

INB

DT

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NV

NPH

PLR

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GA

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r gr

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7.59

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5)4.

16(2

0.0)

9.48

(29.

3)8.

81(2

8.3)

0.37

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6)19

.0(3

9.3)

14.3

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0)21

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1.0)

8.71

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2)16

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6.6)

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4)12

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Ant

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4)5.

38(0

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4.72

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9)3.

45(1

.94)

2.50

(1.1

9)5.

86(0

.64)

2.85

(1.4

8)6.

17(1

.52)

4.75

(2.0

2)3.

76(1

.53)

2.75

(1.3

3)4.

08(1

.08)

2.90

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87(0

.42)

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bacc

o pr

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32(0

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0.52

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20(1

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0.38

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3)0.

27(0

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0.28

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58(1

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0.44

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0.26

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0.24

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0.47

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73(1

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0.60

(0.4

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80(1

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0.22

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War

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8.65

(0.5

6)6.

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8.70

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33(1

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8.54

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8.15

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97(1

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5)–

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(0.6

3)0.

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1.46

(0.2

1)0.

76(0

.31)

0.80

(0.5

2)1.

29(0

.60)

1.32

(0.3

0)1.

15(0

.26)

1.27

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0.84

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Cig

aret

te p

rice

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(1.6

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2.79

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1.83

(0.4

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2.50

(1.1

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4.36

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3.29

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4.61

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ces

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0.29

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––

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(0.2

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1,57

311

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184

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2,63

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51

Not

e. I

n th

e co

lum

n he

ader

s, B

R, M

X, U

Y, I

N, B

D, T

H, C

N, V

N, P

H, P

L, R

U, T

R, U

A, a

nd E

G r

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sent

Bra

zil,

Mex

ico,

Uru

guay

, Ind

ia, B

angl

ades

h, T

haila

nd, C

hina

, Vie

tnam

, th

e Ph

ilipp

ines

, Pol

and,

Rus

sia,

Tur

key,

Ukr

aine

, and

Egy

pt, r

espe

ctiv

ely.

The

sam

ples

are

res

tric

ted

to c

urre

nt s

mok

ers

and

past

yea

r qu

itter

s. F

or q

uit a

ttem

pt r

ates

, the

sam

ples

for

de

nom

inat

ors

are

curr

ent s

mok

ers.

Sta

ndar

d de

viat

ion

is in

the

pare

nthe

sis.

Tab

le 2

. C

ont

inue

d

S49

Dow

nloaded from https://academ

ic.oup.com/ntr/article-abstract/16/Suppl_1/S44/1220611 by U

niversity of Cape Tow

n user on 28 June 2019

Quitters in low- and middle-income countries

Tab

le 3

. C

oun

try-

Sp

ecifi

c M

od

els

of

Rec

ent

Qui

ttin

g (Q

uitt

ing

in t

he P

ast

12 M

ont

hs)

OR

95%

CI

OR

95%

CI

OR

95%

CI

OR

95%

CI

OR

95%

CI

BR

(N

= 7

,915

)M

X (

N =

2,1

64)

UY

(N

= 1

,573

)IN

(N

= 1

,196

7)B

D (

N =

2,3

33)

Hou

seho

ld s

ize

0.97

0.93

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11.

07**

1.01

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30.

930.

84–1

.02

1.00

0.95

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51.

08*

0.99

–1.1

8R

ural

1.14

0.92

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31.

130.

82–1

.57

1.58

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0.90

0.70

–1.1

80.

810.

40–1

.65

Mal

e0.

83**

0.71

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70.

69**

0.49

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70.

70**

0.48

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00.

950.

58–1

.55

1.02

0.36

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0W

ealth

1.05

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01–1

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0.98

0.92

–1.0

41.

09**

1.02

–1.1

60.

79*

0.61

–1.0

23.

26**

1.17

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2In

door

occ

upat

ion

3.85

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11–1

3.4

1.12

0.21

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60.

810.

54–1

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0.63

0.30

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50.

410.

12–1

.38

Out

door

occ

upat

ion

0.72

***

0.60

–0.8

60.

880.

63–1

.24

0.87

0.56

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60.

66**

0.47

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20.

47**

0.24

–0.9

1A

ge

25–3

90.

57**

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47–0

.70

0.64

***

0.46

–0.9

00.

58**

0.37

–0.9

00.

69*

0.45

–1.0

40.

28**

*0.

15–0

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40

–64

0.41

***

0.33

–0.5

10.

47**

*0.

34–0

.66

0.49

***

0.32

–0.7

70.

56**

0.35

–0.8

90.

52**

0.29

–0.9

5

65+

0.46

***

0.33

–0.6

40.

61*

0.34

–1.0

80.

48**

0.24

–0.9

50.

920.

48–1

.78

0.87

0.40

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9E

duca

tion

Pr

imar

y0.

900.

69–1

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1.20

0.80

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92.

01**

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41.

46**

1.06

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30.

760.

44–1

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Se

cond

ary

0.82

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67–0

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1.27

0.83

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51.

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1.80

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60.

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40–1

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H

igh

scho

ol0.

890.

71–1

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1.09

0.65

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12.

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91.

260.

75–2

.11

0.85

0.26

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5

Col

lege

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abov

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140.

82–1

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01.

010.

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.98

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21.

280.

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-lev

el in

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s

Ant

ism

okin

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form

atio

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970.

87–1

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0.86

0.64

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50.

950.

72–1

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0.99

0.91

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81.

200.

96–1

.49

To

bacc

o pr

omot

ion

0.84

0.57

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20.

790.

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0.90

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150.

91–1

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0.81

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To

bacc

o ad

vert

isin

g1.

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81.

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0.94

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10.

990.

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arni

ng la

bel

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10.

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170.

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0.78

0.51

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8

Cig

aret

te p

rice

0.98

0.87

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0.84

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8

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5

PLa

(N =

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04)

RU

a (N

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R (

N =

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96)

UA

(N

 = 2

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95)

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seho

ld s

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41.

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0.96

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80.

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21.

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door

occ

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0.80

0.46

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80.

68**

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80.

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Age

25

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0.90

0.60

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40.

46**

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0.90

0.57

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1

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40.

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0.41

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60.

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250.

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65

+0.

680.

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0.49

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1.09

0.61

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48**

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41.

200.

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catio

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1.00

1.00

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60.

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6

(Con

tinu

ed)

S50

Dow

nloaded from https://academ

ic.oup.com/ntr/article-abstract/16/Suppl_1/S44/1220611 by U

niversity of Cape Tow

n user on 28 June 2019

Nicotine & Tobacco Research, Volume 16, Supplement 1 (January 2014)

OR

95%

CI

OR

95%

CI

OR

95%

CI

OR

95%

CI

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95%

CI

BR

(N

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N =

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= 1

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cond

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ning

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0.50

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0.35

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0.54

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67–1

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1.78

0.69

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0.35

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340.

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1.54

0.55

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Hig

h sc

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1.31

0.63

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20.

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1.64

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83–1

.24

1.00

0.90

–1.1

31.

000.

92–1

.09

To

bacc

o pr

omot

ion

1.49

0.91

–2.4

51.

080.

59–1

.97

0.82

0.50

–1.3

70.

81**

*0.

71–0

.91

To

bacc

o ad

vert

isin

g1.

070.

46–2

.46

0.72

0.45

–1.1

70.

890.

65–1

.24

1.09

0.98

–1.2

0

War

ning

labe

l0.

910.

77–1

.08

0.99

0.85

–1.1

51.

140.

97–1

.34

0.98

0.87

–1.0

9

Wor

k ba

n* in

door

occ

upat

ion

0.70

0.22

–2.2

26.

37**

1.28

–31.

71.

550.

79–3

.05

1.35

0.67

–2.7

2

Wor

k ba

n* o

ther

occ

upat

ion

1.41

0.72

–2.7

31.

90*

0.94

–3.8

11.

070.

78–1

.49

0.93

0.70

–1.2

3

Cig

aret

te p

rice

1.01

0.76

–1.3

61.

020.

99–1

.04

1.02

0.87

–1.1

91.

20**

*1.

04–1

.38

Not

e. a T

here

are

ver

y fe

w r

espo

nden

ts in

Pol

and

and

Rus

sia

who

hav

e no

t rec

eive

d fo

rmal

edu

catio

n. T

here

fore

the

base

cat

egor

y of

edu

catio

n in

dica

tors

is p

rim

ary

educ

atio

n fo

r Po

land

an

d R

ussi

a. O

R =

 odd

s ra

tio; C

I =

 con

fiden

ce in

terv

al.

*.05

 < p

≤ .1

, **.

01 <

p ≤

.05,

***

p ≤

.01.

Tab

le 3

. C

ont

inue

d

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Quitters in low- and middle-income countries

Tab

le 4

. P

oo

led

Mo

del

s o

f R

ecen

t Q

uitt

ing

(Qui

ttin

g in

the

Pas

t 12

Mo

nths

)

Lat

in A

mer

ica

(N =

 11,

652)

Sout

heas

t Asi

a (N

 = 1

9,48

4)A

sia

Paci

fic

(N =

 9,6

15)

Eur

ope

(N =

 13,

303)

All

(N =

 58,

451)

OR

95%

CI

OR

95%

CI

OR

95%

CI

OR

95%

CI

OR

95%

CI

Hou

seho

ld s

ize

1.00

0.93

–1.0

70.

990.

95–1

.03

1.01

0.96

–1.0

70.

95**

*0.

93–0

.98

0.99

0.96

–1.0

2R

ural

1.21

**1.

02–1

.45

1.05

0.91

–1.2

01.

12*

1.00

–1.2

61.

28**

*1.

16–1

.41

1.18

***

1.10

–1.2

6M

ale

0.79

***

0.72

–0.8

70.

840.

63–1

.13

0.68

***

0.58

–0.8

10.

72**

*0.

59–0

.88

0.78

***

0.71

–0.8

6W

ealth

1.04

0.99

–1.0

90.

990.

88–1

.12

1.04

0.97

–1.1

01.

05**

1.01

–1.1

01.

04**

1.00

–1.0

7In

door

occ

upat

ion

2.41

*0.

85–6

.82

0.66

***

0.56

–0.7

80.

43**

0.19

–0.9

90.

800.

49–1

.32

0.78

0.57

–1.0

9O

utdo

or o

ccup

atio

n0.

77**

*0.

67–0

.89

0.60

***

0.52

–0.7

00.

68**

*0.

61–0

.75

0.77

**0.

61–0

.97

0.70

***

0.64

–0.7

7A

ge

25–3

90.

59**

*0.

55–0

.63

0.58

***

0.43

–0.7

90.

61**

*0.

56–0

.67

0.59

***

0.46

–0.7

60.

58**

*0.

53–0

.64

40

–64

0.43

***

0.38

–0.4

90.

60**

*0.

53–0

.68

0.65

***

0.53

–0.8

10.

47**

*0.

32–0

.69

0.52

***

0.43

–0.6

2

65+

0.48

***

0.41

–0.5

60.

83**

0.70

–0.9

81.

080.

84–1

.38

0.58

***

0.39

–0.8

70.

68**

*0.

53–0

.87

Edu

catio

n

Prim

ary

1.03

0.80

–1.3

31.

27**

1.02

–1.5

81.

220.

86–1

.73

––

1.04

0.86

–1.2

6

Seco

ndar

y0.

900.

70–1

.16

1.43

***

1.09

–1.8

81.

070.

62–1

.82

0.98

0.82

–1.1

61.

010.

80–1

.27

H

igh

scho

ol0.

990.

74–1

.33

1.35

***

1.15

–1.6

01.

380.

88–2

.16

0.96

0.83

–1.1

11.

070.

87–1

.32

C

olle

ge o

r ab

ove

1.10

***

1.04

–1.1

61.

87**

*1.

53–2

.28

1.54

*0.

98–2

.42

1.11

*1.

00–1

.24

1.26

**1.

03–1

.52

PSU

-lev

el in

dice

s

Ant

ism

okin

g in

form

atio

n0.

96*

0.92

–1.0

10.

990.

93–1

.05

1.01

1.00

–1.0

21.

080.

96–1

.21

1.03

0.98

–1.0

8

Toba

cco

prom

otio

n0.

78**

*0.

74–0

.82

1.06

0.89

–1.2

70.

83**

*0.

79–0

.87

1.08

0.90

–1.3

00.

980.

85–1

.12

To

bacc

o ad

vert

isin

g1.

05**

*1.

02–1

.08

0.96

*0.

92–1

.00

1.04

0.94

–1.1

60.

980.

92–1

.04

1.01

0.97

–1.0

4

War

ning

labe

l0.

980.

88–1

.09

0.94

***

0.93

–0.9

51.

000.

94–1

.07

1.03

*1.

00–1

.06

0.99

0.95

–1.0

3

Wor

k ba

n* in

door

occ

upat

ion

0.66

0.35

–1.2

41.

20**

*1.

06–1

.35

1.85

*0.

93–3

.68

1.16

0.90

–1.4

91.

180.

94–1

.46

W

ork

ban*

oth

er o

ccup

atio

n1.

29**

*1.

17–1

.43

1.12

*1.

00–1

.25

1.09

0.84

–1.4

11.

050.

87–1

.25

1.13

***

1.04

–1.2

2C

igar

ette

pri

ce1.

010.

99–1

.03

0.94

***

0.92

–0.9

71.

03**

*1.

01–1

.05

0.99

0.90

–1.0

90.

990.

97–1

.01

Bid

i pri

ce–

–1.

07**

*1.

04–1

.11

––

––

––

Not

e. A

ll re

gres

sion

s al

so c

ontr

ol f

or c

ount

ry fi

xed

effe

cts.

In

the

last

two

colu

mns

, the

sam

ple

incl

udes

all

14 c

ount

ries

. The

poo

led

sam

ple

of A

mer

ica

incl

udes

Mex

ico,

Bra

zil,

and

Uru

guay

. The

poo

led

sam

ple

of S

outh

Eas

t Asi

a in

clud

es I

ndia

, Ban

glad

esh,

and

Tha

iland

, whe

re th

e bi

di p

rice

for

Tha

iland

is r

epla

ced

by th

e m

ean

of th

e bi

di p

rice

for

Ind

ia a

nd

Ban

glad

esh.

The

poo

led

sam

ple

of A

sia

Paci

fic in

clud

es C

hina

, Vie

tnam

, and

the

Phili

ppin

es. T

he p

oole

d sa

mpl

e of

Eur

ope

incl

udes

Pol

and,

Rus

sia,

Ukr

aine

, and

Tur

key.

The

info

rmat

ion

on in

door

wor

k-si

te s

mok

ing

polic

y in

Uru

guay

is n

ot a

vaila

ble,

and

its

wor

k-si

te s

mok

ing

ban

inde

x is

rep

lace

d by

the

mea

n in

dex

of th

e ot

her

coun

trie

s in

the

pool

ed m

odel

s of

Am

eric

a an

d al

l cou

ntri

es. C

I =

 con

fiden

ce in

terv

al; O

R =

 odd

s ra

tio.

*.05

 < p

≤ .1

, **.

01 <

p ≤

.05,

***

p ≤

.01.

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Nicotine & Tobacco Research, Volume 16, Supplement 1 (January 2014)

Tab

le 5

. P

oo

led

Mo

del

s o

f Q

uit

Att

emp

ts (U

nsuc

cess

ful Q

uit

Att

emp

t in

the

Pas

t 12

Mo

nths

)

Lat

in A

mer

ica

(N =

 10,

215)

Sout

heas

t Asi

a (N

 = 1

8,60

6)A

sia

Paci

fic

(N =

 6,6

07)

Eur

ope

(N =

 12,

307)

All

(N =

 51,

890)

OR

95%

CI

OR

95%

CI

OR

95%

CI

OR

95%

CI

OR

95%

CI

Hou

seho

ld s

ize

0.99

0.96

–1.0

31.

010.

97–1

.06

0.99

0.96

–1.0

21.

010.

98–1

.04

1.00

0.98

–1.0

2R

ural

1.02

0.81

–1.2

90.

95*

0.90

–1.0

01.

26**

*1.

13–1

.39

1.19

***

1.11

–1.2

81.

10**

1.01

–1.1

9M

ale

0.75

***

0.69

–0.8

20.

87**

*0.

79–0

.96

0.78

**0.

62–0

.99

0.86

**0.

75–0

.99

0.83

***

0.77

–0.8

9W

ealth

1.00

0.99

–1.0

00.

990.

89–1

.10

1.02

0.95

–1.1

01.

03**

1.00

–1.0

51.

000.

98–1

.02

Indo

or o

ccup

atio

n1.

79**

*1.

46–2

.19

1.33

0.93

–1.8

80.

970.

68–1

.38

1.13

0.82

–1.5

51.

18**

1.00

–1.3

9O

utdo

or o

ccup

atio

n1.

000.

95–1

.05

1.18

***

1.08

–1.2

80.

85**

*0.

79–0

.91

0.95

0.86

–1.0

41.

000.

89–1

.13

Age

25

–39

1.01

0.96

–1.0

60.

89**

0.80

–1.0

01.

010.

84–1

.22

0.75

***

0.68

–0.8

20.

89**

*0.

82–0

.97

40

–64

0.83

***

0.73

–0.9

50.

900.

78–1

.03

0.91

0.67

–1.2

30.

65**

*0.

53–0

.80

0.81

***

0.72

–0.9

2

65+

0.67

***

0.57

–0.7

80.

880.

64–1

.23

0.82

***

0.71

–0.9

40.

55**

*0.

41–0

.73

0.75

***

0.61

–0.9

2E

duca

tion

Pr

imar

y1.

100.

84–1

.44

1.05

0.98

–1.1

31.

16*

0.98

–1.3

8–

–1.

09**

1.02

–1.1

7

Seco

ndar

y1.

100.

96–1

.26

1.30

***

1.17

–1.4

31.

50**

*1.

24–1

.81

1.01

0.99

–1.0

41.

23**

*1.

14–1

.32

H

igh

scho

ol1.

000.

75–1

.34

1.22

***

1.12

–1.3

31.

53**

*1.

18–1

.98

0.97

0.80

–1.1

71.

17**

*1.

06–1

.30

C

olle

ge o

r ab

ove

0.81

*0.

66–1

.00

1.25

***

1.11

–1.4

01.

39**

*1.

21–1

.58

0.95

0.74

–1.2

31.

12**

1.00

–1.2

5PS

U-l

evel

indi

ces

A

ntis

mok

ing

info

rmat

ion

1.05

*1.

00–1

.11

1.01

0.94

–1.0

71.

09**

*1.

07–1

.12

1.20

***

1.11

–1.3

01.

08**

1.00

–1.1

7

Toba

cco

prom

otio

n0.

980.

95–1

.01

1.09

*0.

98–1

.20

1.04

0.99

–1.1

01.

03**

1.00

–1.0

51.

05**

1.00

–1.1

0

Toba

cco

adve

rtis

ing

1.01

0.98

–1.0

41.

020.

99–1

.05

1.02

0.94

–1.1

00.

970.

91–1

.04

1.01

0.98

–1.0

4

War

ning

labe

l0.

990.

96–1

.02

1.04

***

1.02

–1.0

71.

000.

96–1

.04

1.05

**1.

01–1

.09

1.03

***

1.01

–1.0

5

Wor

k ba

n* in

door

occ

upat

ion

0.75

***

0.63

–0.8

91.

090.

90–1

.32

1.02

0.72

–1.4

50.

810.

62–1

.05

0.97

0.83

–1.1

2

Wor

k ba

n* o

ther

occ

upat

ion

1.11

***

1.09

–1.1

31.

21**

*1.

08–1

.37

0.98

0.87

–1.1

00.

960.

89–1

.05

1.11

*0.

99–1

.26

Cig

aret

te p

rice

0.96

*0.

92–1

.00

0.99

***

0.98

–1.0

01.

010.

99–1

.03

1.04

***

1.04

–1.0

51.

01**

*1.

00–1

.02

Bid

i pri

ce–

–1.

17**

*1.

11–1

.22

––

––

––

Not

e. S

ee N

ote

for

Tabl

e 4.

OR

 = o

dds

ratio

; CI 

= c

onfid

ence

inte

rval

.*.

05 <

p ≤

.1, *

*0.0

1 <

p ≤

.05,

***

p ≤

.01.

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Quitters in low- and middle-income countries

exposure to indoor work-site smoking bans. The estimates indicate that working in an outdoor occupation is associated with lower odds of quitting for most countries, and working indoors is associated with higher odds of quitting in Brazil and lower odds in China. Exposure to work-site smoking bans is associated with increased odds of quitting for Chinese smokers who work indoors, and the association is particularly strong, which may reflect the social norms around smoking as a way of networking at work in the country.

In the pooled models of recent quitting (Table  4), living in rural areas, having higher education, and more wealth are factors associated with higher odds of being a recent quitter, whereas being male, over 24  years old, and working in an outdoor occupation are factors associated with lower odds. Although exposure to work-site smoking bans increases the odds of being a recent quitter, greater exposure to cigarette advertising is associated with lower odds of quitting in the Southeast Asian region. Similarly, greater exposure to tobacco promotion is associated with lower odds of quitting in Latin America and Asia Pacific regions. The warning label index is associated with higher odds of quitting in European countries but lower odds of quitting in Southeast Asia.

The associations between individual demographic charac-teristics and the probability of making a recent quit attempt are quite similar to the associations observed in the models of recent quitting (Table  5). Living in rural areas is associ-ated with higher odds of quit attempts in most regions other than Southeast Asia. Having formal education is associated with higher odds of quit attempts in most regions other than Latin American. Being male and being older than 24 years are associated with lower odds in all models. Greater exposure to antismoking mass-media messages, higher cigarette prices, and greater exposure to warning labels are significantly asso-ciated with increased odds of making a recent quit attempt. Although working indoors is associated with higher odds of a quit attempt, the exposure to work-site smoking bans also affects those who do not usually work indoors in increasing quit attempts.

DISCUSSION

In this study, we use GATS data from 14 countries to describe the factors associated with quitting and quit attempts. We find that living in rural areas, having more education, and being wealthier are factors associated with higher odds of being a quitter and trying to quit. Men are less likely to have quit or tried to quit than women. Greater exposure to work-site smok-ing bans is associated with higher odds of recent quitting. Although higher cigarette prices are associated with higher probability of quit attempts, higher bidi prices are associated with higher probabilities of both quitting and quit attempts in Southeast Asian countries where bidi use is common.

Greater exposure to work-site bans is strongly associated with quitting in China where smoking at indoor work-sites is prevalent. Our estimates also call attention to the potential influence of tobacco marketing in the Asia Pacific and Latin American regions where greater exposure to tobacco promo-tion is linked to reduced likelihood of quitting.

Our findings are in line with the existing limited literature that investigates quitting and quit attempts in LMICs. For example, we consistently find that quitting and quit attempts

in high tobacco-using LMICs are low, which has been docu-mented in a series of reports using GATS (http://nccd.cdc.gov/gtssdata/Ancillary/DataReports) and reports from the Bloomberg Global Initiative to Reduce Tobacco Use (www.tobaccofreeunion.org/content/en/217). In this study, we fur-ther explore how quitting and quit attempts are associated with individual and environmental risk factors. Our findings show that although the associations between these factors and quit-ting may vary by countries, the results of pooled analyses that take account of unobserved country-specific attributes tend to indicate that tobacco control polices such as work-site smok-ing bans, warning labels, and antismoking media messaging can be linked to either quitting or quit attempts. Meanwhile, we have observed that not all LMICs are at the same stage of employing these tobacco control policies or are not applying them at the same level so that there are substantial differences across countries in exposure to them. The potential of these policies in encouraging quitting may be especially relevant to policy makers in countries where they do not appear to reach enough smokers.

There are some limitations in this analysis. We use cross-sectional data from 14 countries to study cessation. The ces-sation measures, prices, and indices for policy and marketing exposures are constructed using self-reported information. In addition, these prices and indices are contemporaneous meas-ures; while most previous literature has shown that it is the change of prices or policies over time that drives quitting, we cannot estimate the effects of changes over time in this study. However, given that most tobacco policies are recently adopted in LMICs, we find their associations with quitting to be significant and strong even when these policies are con-temporaneously measured. This study takes the initial steps in investigating the associations between determinants and quit-ting across LMICs, but future research that employs longitu-dinal surveys in many countries is needed to better understand the effectiveness and cost-effectiveness of tobacco control interventions in LMICs.

FUNDING

Funding for the Global Adult Tobacco Survey (GATS) is pro-vided by the Bloomberg Initiative to Reduce Tobacco Use, a program of Bloomberg Philanthropies. Governments of Brazil and India contributed to GATS implementation in their respec-tive countries. The Bill and Melinda Gates Foundation pro-vided additional funding for GATS implementation in China and for analysis.

DECLARATION OF INTERESTS

The conclusions in this paper are those of the authors and do not necessarily represent the official position of their affiliated organizations.

ACKNOWLEDGMENTS

We thank Nahleen Zahra, Pavel Dramski, and William Ridgeway for excellent research assistance. The findings of this study are those of the authors and do not represent the offi-cial position of the Centers for Disease Control and Prevention.

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Nicotine & Tobacco Research, Volume 16, Supplement 1 (January 2014)

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