Assessing Adult Tobacco Smoking Cessation in Low-And-Middle

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the Global Adult Tobacco Survey Data, 2009 – 2012

______

A dissertation presented to the faculty of the Department of Biostatistics and Epidemiology East Tennessee State University

In partial fulfillment of the requirements for the degree Doctor of Public Health with concentration in Epidemiology ______

Daniel Owusu

May 2016

______

Dr. Megan Quinn, Chair

Dr. Hadii M. Mamudu

Dr. Ke-Sheng Wang

Dr. Sreenivas P. Veeranki

Keywords: Tobacco smoking cessation, low-and-middle income countries, cessation assistance, home smoking rule, intention to quit, advice to quit

ABSTRACT

Assessing Adult Tobacco Smoking Cessation in Low-and-Middle Income Countries: Analysis of

the Global Adult Tobacco Survey Data, 2009 – 2012

Daniel Owusu

Smoking cessation can reduce health risk and prevent millions of tobacco-related deaths.

However, cessation rates are low in low-and-middle income countries (LMICs), with only a small proportion of smokers intending to quit. Given the paucity of literature to support tobacco cessation programs in LMICs, this study aimed to: 1) identify factors associated with intention to quit smoking, 2) assess the relationship between health care provider quit advice/tobacco screening and utilization of cessation assistance, and 3) examine the relationship between home smoking rule and smoking intensity across three stages of smoking cessation (precontemplation, contemplation and preparation) in LMICs. Data were obtained from the Global Adult Tobacco

Survey, 2009-2012, a nationally representative household survey of noninstitutionalized civilians aged 15 years and older. Weighted multivariable regression analyses were conducted using SAS version 9.4. Adjusted odds ratios (OR), percent change in smoking intensity and associated 95% confidence intervals (CI) were estimated. Home smoking rule and exposure to anti-smoking messages were the important factors associated with contemplation and preparation to quit smoking. Approximately 1%, 7%, 9% and 15% used quitline, medical treatment, counseling/cessation clinic and cessation assistance (all three combined), respectively, in the past year. Quit advice was significantly associated with utilization of counseling/cessation clinic

(OR=3.89, 95% CI=2.8–5.5), medical treatment (OR=1.71, 95% CI=1.2–2.4) and cessation

2 assistance (OR=2.60, 95% CI=2.0–3.4). Tobacco screening was associated with utilization of counseling/cessation clinic (OR=2.60, 95% CI=1.1–5.9) and medical treatment (OR=1.71, 95%

CI=1.2–2.4). Living in a completely smoke-free home was associated with a 22.5% (95%

CI=17.1%–28.0%), an 18.6% (95% CI=9.0%–28.2%), and a 19.4% (95% CI=3.9%–34.9%) significant reduction in smoking intensity among smokers in precontemplation, contemplation and preparation, respectively. In conclusion, the results suggest that smoke-free home, anti- smoking campaigns, and health care provider intervention promote smoking cessation in LMICs.

Therefore, comprehensive smoke-free policies, anti-smoking media campaigns and integration of tobacco screening and quit advice into the health care system are important for tobacco cessation in LMICs, suggesting the need for full implementation of the World Health Organization

Framework Convention for Tobacco Control Articles 8 and 11 – 13.

DEDICATION

I dedicate this dissertation to my brothers, Nicholas Obeng, Martin Owusu and Stephen

Owusu who contributed generously to my formal education. I owe my educational accomplishments to their financial support, inspiration, and encouragement.

ACKNOWLEDGEMENTS

I give glory, honor and praises to the Almighty God in the name of our Lord and Savior

Jesus Christ. I sincerely acknowledge the favor of God that has seen me through to this level of my educational pursuit.

I am indebted to all the faculty and staff of the College of Public Health, East Tennessee

State University (ETSU), especially, my Dissertation Committee: Dr. Megan Quinn, Dr.

Kesheng Wang, Dr. Hadii M. Mamudu and Dr. Sreenivas P. Veeranki for their immense support.

My special appreciation to my Advisor and Dissertation Committee Chair, Dr. Megan Quinn for her wonderful guidance throughout my program. My special thanks also go to my research mentors, Dr. Kesheng Wang and Dr. Mamudu for their great mentoring which has greatly helped to improve my research skills. Dr. Wang’s mentoring in data management and analysis, and manuscript writing since the beginning of my program proved very consequential in this dissertation. Dr. Mamudu has made immeasurable contribution to my training by providing great mentoring in tobacco control research which has resulted in this dissertation. I also want to acknowledge Dr. Shimin Zheng for opening his doors for me, even outside school hours. Dr.

Zheng never hesitated to assist with my statistical needs any time I called on him.

I sincerely thank Dr. Lazarous Mbulo of the Center for Disease Control and Prevention, and the Global Adult Tobacco Survey (GATS) Collaborative Group for their support and guidance in using the GATS data.

My appreciations also go to Ms. Jocelyn Aibangbee, Ms. Oluwatosin Ariyo and Mr.

Joseph Kusi, graduate students of the College of Public of Health, ETSU for proofreading parts

6 of the dissertation for me. Their corrections and suggestions were very useful for the improvement of this research.

I want to express my heartfelt appreciations to my brothers, Nicholas Obeng, Martin

Owusu and Stephen Owusu, who have been the main financiers of my formal education. May

God richly bless them.

Last but not the least, I am very grateful to my wife, Edith Frimpong for her support and patience. She did not only encourage and inspire me to pursue doctoral degree but also provided material and emotional supports throughout the program.

TABLE OF CONTENTS

Page

ABSTRACT ...... 2

DEDICATION ...... 5

ACKNOWLEDGEMENTS ...... 6

LIST OF TABLES ...... 12

LIST OF FIGURES ...... 13

ACRONYMS AND ABBREVIATIONS ...... 14

Chapter

1. INTRODUCTION ...... 15

Problem Statement ...... 15

Significance of the Study ...... 18

History of Tobacco Use...... 19

Health Impact of Tobacco Use ...... 21

Economic Impact of Tobacco Use ...... 23

Tobacco Control ...... 25

Tobacco Cessation...... 29

Benefits of Tobacco Cessation ...... 29

Tobacco Cessation Interventions ...... 32

Intention to Quit Smoking ...... 35

Health Care Providers’ Role in Tobacco Cessation ...... 37

Smoke-Free Policies and Smoking Cessation ...... 40

Tobacco Cessation in Low-and-Middle Income Countries (LMICs) ...... 42

Theoretical Framework ...... 45

The Ecological Model ...... 45

Transtheoretical Model (TTM) ...... 48

The WHO Framework Convention for Tobacco Control (FCTC) ...... 49

The MPOWER Package ...... 51

Global Tobacco Surveillance System (GTSS) ...... 54

Methods ...... 54

2. FACTORS ASSOCIATED WITH INTENTION TO QUIT TOBACCO SMOKING IN 14

LOW-AND-MIDDLE INCOME COUNTRIES ...... 60

Abstract ...... 61

INTRODUCTION ...... 62

METHODS...... 63

Data ...... 63

Variables ...... 65

Statistical Analysis ...... 66

RESULTS...... 67

DISCUSSION ...... 77

CONCLUSION ...... 82

REFERENCES ...... 83

3. ASSOCIATION BETWEEN HEALTH CARE PROVIDER INTERVENTION AND

UTILIZATION OF CESSATION ASSISTANCE IN LOW-AND-MIDDLE INCOME

COUNTRIES ...... 90

Abstract ...... 91

Introduction ...... 92

Methods ...... 95

Measures ...... 96

Statistical Analysis ...... 97

Results ...... 98

Discussion ...... 104

Conclusion ...... 107

Reference ...... 109

4. IMPACT OF SMOKE-FREE HOME ON SMOKING INTENSITY IN LOW-AND-MIDDLE

INCOME COUNTRIES ...... 115

Abstract ...... 116

Introduction ...... 117

Methods ...... 118

Data ...... 118

Measures ...... 120

Statistical Analysis ...... 121

Results ...... 122

Discussion ...... 127

Limitations ...... 129

Conclusion ...... 129

References ...... 131

5. CONCLUSION ...... 137

REFERENCES ...... 143

APPENDIX: Human Subject Protection ...... 166

VITA ...... 167

LIST OF TABLES

Table Page

1.1: Constructs of the ecological model………………………………………………………....46

1.2: Study measures and survey items with responses, Global Adult Tobacco Survey………....58

2.1: Proportions of current smokers in the stages of tobacco cessation by independent

variables (N=43,542) ……………………………………………………………………….69

2.2: Factors associated with intention to quit smoking in the 14 countries combined

(N=43,542)………………………………………………………………………………….71

2.3: Factors associated with intention to quit smoking by country……………………………...73

3.1: Demographic characteristics of study participants (N=5848)…………………………...... 100

3.2: Prevalence of cessation assistance utilization among adults (N=5848)...………………....101

3.3: Relationship between health care provider behavioral intervention and utilization of

cessation assistance (N=5848)..…………………………………………………………....103

4.1: Demographic characteristics of study participants (N=39204)…………………………....123

4.2: Means of smoking intensity in independent variables by stage (N=39204)………………124

4.3: Association between home smoking rule and smoking intensity among adults

(N=39204)…...... 125

4.4: Association between home smoking rule and smoking intensity by stages of cessation

(N=39204)...... 126

LIST OF FIGURES

Figure Page

1.1: A summary of cessation studies in LMICs………………………………………………....17

1.2: The Ecological Model……………………………………………………………………....48

1.3: Stages of the Transtheoretical Model……………………………………………………....49

2.1: Current smoker's cessation stages by country………………………………………………68

3.1: Prevalence of cessation assistance utilization by country…………………………………102

ACRONYMS AND ABBREVIATIONS

CDC Center for Disease Control and Prevention

EM Ecological Model

FCTC Framework Convention for Tobacco Control

GATS Global Adult Tobacco Survey

IOM Institute of Medicine

IUATLD International Union Against Tuberculosis and Lung Diseases

JHSPH John Hopkins Bloomberg School of Public Health

LIMICs Low-and-middle income countries

NCI National Cancer Institute

TTM Transtheoretical model

UNC University of North Carolina

US United States

WHO World Health Organization

CHAPTER 1

INTRODUCTION

Problem Statement

Tobacco use has been increasing in low-and-middle income countries (LMICs) where about 80% of smokers live (World Health Organization (WHO), 2015). It is projected that over

80% of the projected eight million tobacco-related deaths will occur in LMICs by 2030 (WHO,

2008) if the current trend continues. The main vector of this tobacco epidemic in LMICs is the tobacco industry (Cairney, Studlar, & Mamudu, 2011; Lee, Ling, & Glantz, 2012; Hadii M.

Mamudu, Hammond, & Glantz, 2008; Otanez, Mamudu, & Glantz, 2009). In a systematic review of 114 published studies on tobacco industry activities in LMICs, it was observed that transnational tobacco companies used economic activity (smuggling and investment) to enter new markets; political activities (lobbying, offering voluntary, self-regulatory codes, and mounting corporate social responsibility campaigns); deceptive activities (science manipulation and third party allies) to promote tobacco use, resist smoke-free policies and delay others; and marketing/promotion through advertisements and tailoring tobacco brands to specific environment to make tobacco use acceptable (Lee et al., 2012). The tobacco industry strategically targets specific populations such as youth and women, and introduces new tobacco products that are able to evade marketing restrictions and taxes, while maintaining tobacco use acceptability in the society to counter tobacco control efforts (Lee et al., 2012).

One of the major public health concerns is that, the increasing trend of tobacco smoking prevalence has not been met with increasing capacity to support cessation in LMICs. A Study that assessed tobacco dependence treatment in 121 countries that are parties to the WHO

Framework Convention for Tobacco Control (WHO FCTC), the first international public health treaty negotiated under the leadership of WHO, reported that, LMICs provided less dependence treatment than high-income countries, and majority of LMICs had not yet implemented the

FCTC Article 14 recommendations (Piné-Abata et al., 2013). This study reported that, as of

2012, about 66% of LMICs had no national treatment strategy, with only about 11% having treatment guidelines. Moreover, only 5% of LMICs had quitlines, with no LMIC having nationwide specialized treatment facilities.

The full implementation of the WHO FCTC, with adoption of the recommendations of the

WHO MPOWER (WHO, 2008) will be required to address the rising tobacco epidemic in

LMICs. This will involve prevention of tobacco use initiation, protection against secondhand smoke (SHS) exposure, and cessation. Tobacco control programs may be population based such as smoke-free policies, taxation, mass media education, regulation of tobacco production and marketing, prohibition of tobacco advertisements and promotion, and restriction of access to tobacco; or individual-based interventions that promote tobacco cessation through counseling, cessation clinics, pharmacological treatment, and quitlines or telephone based support services

(Centers for Disease Control and Prevention (CDC), 2014a; Mamudu, Gonzalez, & Glantz,

2011). Although all aspects of tobacco control are important, this dissertation focuses on tobacco smoking cessation in LMICs.

It is important to enact and implement effective control measures in LMICs not only to prevent initiation of tobacco use among non-users but to also promote cessation among users and to prevent SHS exposure. This will require studies to provide evidence to inform policy development and intervention planning and implementation as well as advocacy initiatives.

Although evidence on tobacco cessation in LMICs is beginning to accumulate, a thorough

16 review of the literature on tobacco cessation in LMICs with available public use data from the

Global Adult Tobacco Survey (GATS) since 2009 revealed that, the availability of evidence varied greatly in scope within countries and across countries, with a higher proportion being sub- national, and in special populations such as pregnant women and hospital patients. Figure 1.1 describes the studies found in LMICs. Of the cessation factors of interest (health care providers’ advice, home smoke-free policy and tobacco quit intentions), in about 50% of the countries, less than 5 studies have been conducted, mostly sub-national. Despite the scarce literature, the existing studies provide vital information on factors that are related to tobacco cessation in

LMICs, including health care providers’ advice (Abdullah et al., 2013; Gong et al., 2012); age, awareness of smoking harm, and gender (Kaleta et al., 2012; Kaleta, Usidame, Dziankowska- zaborszczyk, & Makowiec-d, 2014); educational attainment (Kaleta et al., 2012); and socioeconomic background (Yong et al., 2013).

41 papers on intention to quit, smoke-free policies and cessation, health care providers' advice, and utilization of cessation assistance Cross-country (including at least one National Sub-national of the interested Systematic Randomized LMICs) reviews trials 1 1 Systematic reviews Follow-up studies Follow-up studies 1 1 2 Cross-sectional Cross-sectional Cross-sectional studies studies studies 18 11 4 Online searches and correspondence 2

Figure 1.1: A summary of Cessation Studies in LMICs

The existing literature also revealed paucity of research on cessation in general in LMICs, and the need for national and cross-country studies to provide comprehensive assessment of tobacco cessation in LMICs. This observation is in line with an earlier report that identified monitoring and evaluation of tobacco dependence treatment as research priority areas in LMICs

(McRobbie, Raw, & Chan, 2013). In this respect, there is a gap in the literature to understand factors that promote tobacco cessation in LMICs. Therefore, the main goal of this study is to increase tobacco cessation in LMICS by identifying factors that promote intention to quit, use of cessation assistance and reduction in smoking intensity among adults. This goal will be achieved through the following three specific study aims:

Aim #1: Delineate the major factors associated with different levels of intention (pre

contemplation, contemplation and preparation) to quit smoking in LMICs.

Aim #2: Evaluate the relationship between health provider behavioral intervention and

utilization of cessation assistance in LMICs.

Aim #3: Assess the relationship between home smoking rule and smoking intensity among

smokers at different stages of the cessation process (precontemplation, contemplation

and preparation).

Significance of the Study

The results of this study will inform policy and intervention planning and advocacy initiatives to improve population health through effective tobacco dependence treatment that results in cessation. Particularly, it will serve as an important resource for the implementation of the WHO FCTC Article 14, which requires all parties to provide support to reduce tobacco dependence, and increase cessation (WHO FCTC, 2005). Since the study questions have not 18 been addressed in the literature on tobacco smoking cessation in LMICs, coupled with sparse literature across the globe, this will be the first major comparative and population based study of tobacco smoking cessation in LMICs. Thus, the study will serve as an important resource for

LMICs where there is a research desert on tobacco control (Warner, 2005). As the first cross- country study to comprehensively assess tobacco cessation in LMICs, this study will serve as baseline for future research.

History of Tobacco Use

Gately (2007) has provided a detailed account of the history of tobacco use by mankind.

Briefly, the tobacco genus, Nicotiana, comprises 64 species of which two, Nicotiana rustica and

Nicotiana tabacum are the most commonly used by mankind. These two species are native to only the Americas and have been known to humans for thousands of years. However, tobacco was unknown to the first inhabitants of the American continent, who were of Asiatic origin and had spread southwards through the continent after crossing the Bering Strait land bridge. Those who settled in the south cultivated vegetables, while the others continued their nomadic lifestyle.

They augmented their knowledge of herbs with new plants they encountered, including tobacco.

Tobacco’s center of origin has been traced to the Peruvian/Ecuadorean Andes by plant geneticists, and it is estimated to have been first cultivated from 5000 to 3000 BC. Its use then spread northwards and it had reached every corner of the continent and offshore islands such as

Cuba by the time of Christopher Columbus’s arrival in 1492.

The exact time humans started smoking tobacco is unknown but it is believed that smoking itself evolved from snuffing, given that snuffing tubes are among the oldest tobacco- related artifacts found in the Americas. Tobacco was consumed in several ways including

19 sniffing, chewing, drinking, smearing over bodies, smoking, applying to the eye, and for enema.

Tobacco was blown to the faces of warriors for fortification, offered to gods and accepted as gifts from the gods. It was both used as insecticide on crops and applied to skin to kill lice and other parasites. It then gained mythical properties and was associated with cleansing and fertility.

It also served as medicine in South America where it was applied in the treatment of ailments such as toothache, wounds, fever, snakebite and even cancer. It also became a powerful commodity for witch doctors who used tobacco for training in the form of fortification.

As tobacco spread to the Central America, its use became less diverse and smoking became more prominent. Smoking became an integral part of the Mayans’ culture, and it was even used for relaxation and contemplation. The Mayans left elegant depictions of smoking which speak to their devotion to the practice.

Tobacco use concentrated in the Americas until the arrival of European explorers, led by

Christopher Columbus, whose crew became the first known Europeans to have smoked tobacco in 1492. Due to its supposed medicinal properties, these Europeans carried the tobacco seeds with them to Spain and Portugal for cultivation. It then spread to Britain and other European countries and to the rest of the world. Cultivation then became widespread due to its economic and ‘medicinal’ values. Though tobacco became widely accepted and even given royalty status by Queen Elizabeth I’s actions, King James I of England described tobacco as unhealthy practice with much indignation (Gately, 2001).

The invention of safety machines in 1852 and the Bonsack cigarette rolling machine in

1884 that permitted commercial production and mass consumer marketing led tobacco use to skyrocket by the end of the 19th century (Proctor, 2001). According to Proctor (2001), cigarette

20 became a good source of revenue to governments from sales regulation and taxation. By the late

19th century, cigarette was the preferred tobacco because of the fermentation procedure that prevented cough associated with smoking. Packaging also made cigarette easy to carry and use when needed. Proctor has stated that, the rise in tobacco consumption saw an explosion in lung cancer incidence globally, and in Germany, lung cancer became the second leading cause of deaths. Consequently, German pathologists in the 1920 produced adequate statistics to show that lung cancer was epidemic. The first statistically good evidence was published by Fritz Lickint of

Dresden to demonstrate the link between tobacco and lung cancer (Proctor, 2001). Beginning from the 1950s, strong evidence has linked tobacco to several health problems in humans (Royal

College of Physicians of London (RCP), 1962; USDHEW, 1964; USDHHS, 2014).

Health Impact of Tobacco Use

Tobacco use has a long history, and available evidence suggests that it was as early as the

18th century that the link between tobacco and cancer was suggested (Boyle, 1997). In the early part of the 20th century, evidence began to accumulate on the deleterious effects of tobacco use on health (Musk & De Klerk, 2003; Proctor, 2001; Schairer & Schöniger, 1944). However, strong evidence of smoking and cancer started emerging in the early 1950s (Doll & Hill, 1950;

Hammond, Horn, & Jan, 1955; Levin, Goldstein, & Gerhardt, 1950; RCP, 1962). In 1962, the

Royal College of Physicians of London report, “Smoking and Health” provided comprehensive information on the link between tobacco smoking and lung cancer and other diseases (RCP,

1962). In the United States (US), the 1964 US Surgeon General report also implicated smoking as a probable cause of the lung cancer epidemic globally and a probable cause of cancers of other sites (USDHEW, 1964). Among the over 7000 chemicals in tobacco, 70 are carcinogenic (IARC

& WHO, 2009; USDHHS, 2006, 2014). The International Association of Research on Cancer

(IARC) has declared tobacco as carcinogenic based on information synthesized from several study results (IARC & WHO, 2009; WHO & IARC, 2002). Tobacco has been described as the only legal drug that can harm any exposed person and is able to kill about 50% of those who use it as intended (WHO, 2008). It is now known that smokers on average die more than 10 years earlier than non-smokers (Prabhat Jha et al., 2013; USDHHS, 2014). As a risk factor for six of the eight leading causes of global deaths, tobacco is lethal in several diverse ways ( WHO,

2008).

The health impact of tobacco use has been well reported. It has been estimated that tobacco use contributes to more than 480,000 deaths annually in the US alone (USDHHS,

2014). The 2014 US Surgeon General report showed that tobacco smoking has killed more than

20 million people in the US from 1965 to 2014 inclusive (USDHHS, 2014). This figure included over two million non-smokers who died from SHS exposure. Another 100, 000 deaths were tobacco-related sudden death syndrome in babies. More than 87%, 61%, and 31% deaths from cancer, pulmonary diseases and coronary artery disease, respectively in the US have been attributed to tobacco smoking and SHS exposure (USDHHS, 2014) and it is projected that if the current trend continues, over five million children under 18 years today in the US will die prematurely in adulthood from smoking (USDHHS, 2014).

Globally, it has been estimated that tobacco killed about 100 million people in the 20th century and it remains the leading cause of preventable deaths worldwide (WHO, 2008) .

Currently, tobacco is estimated to kill nearly six million people annually, including 600,000 deaths due to SHS exposure ( WHO, 2011). The estimated annual tobacco-related deaths far exceeded projections made in the 1990s that tobacco would kill two million people annually

(Peto et al., 1996). The heaviest burden of tobacco-related illness and deaths is found in LMICs

(WHO, 2008). For instance, in china where one-third of the world smokers reside, an analysis of two prospective studies of adults revealed that smoking would account for 20% of all male adult deaths in the 2010s (Chen et al., 2015). This study indicated that if no extensive cessation programs are implemented, smoking is projected to cause two million deaths by 2030 and three million by 2050 (Chen et al., 2015).

The increase in tobacco-induced mortalities is not surprising given that in 2009, global tobacco prevalence among persons aged 15 years and above was estimated to be 36% among males and 8% among females (WHO, 2012). It is estimated that, if the current trend of tobacco use continues, annual tobacco-related deaths will exceed eight million by 2030 (Mathew, 2005;

WHO, 2008), with others putting the figure at a low of 7.4 million and as high as 9.7 million

(Mathers & Loncar, 2006). The tobacco death toll in the 21st century is projected to be over one billion (WHO, 2009). About half of these premature deaths will be people who are alive today

(WHO, 2009). This evidence suggests the need for tobacco use cessation; hence this study.

Economic Impact of Tobacco Use

Tobacco use also comes with high economic burden to the user and society. In the US for instance, from 2009 to 2012, smoking alone cost $289–332.5 billion ($132.5–175.9 billion in direct medical care, $151 billion for productivity loss as a result of premature deaths, and another

$5.6 billion for lost productivity as a result of SHS exposure (USDHHS, 2014). Smoking presents direct medical cost to the smoker and society at large (Jha & Chaloupka, 1999).

Smokers pay for the health care cost that tobacco use inflicts on them and part of the cost of tobacco use is also shared by the citizens, including non-smokers (Jha & Chaloupka, 1999). In spite of the fact that smokers die earlier than non-smokers, the literature suggests that the lifetime

23 health care cost of smokers is higher than non-smokers (Jha & Chaloupka, 2000; Jha &

Chaloupka, 1999). Studies suggest that the gross healthcare costs of smoking in LMICs is similar to that of high-income countries (between 0.1% and 1.1% of gross domestic products (GDP))

(Jha & Chaloupka, 2000). Loss of productivity due to tobacco use negatively affects a country’s economy due to a reduction in tax revenues (Block & Webb, 2009; Max, 2004). In India, where the second largest proportion of smokers reside, it was estimated that 15 million people were impoverished by tobacco smoking (John, Sung, Max, & Ross, 2011). The economic cost of tobacco use has several dimensions. Tobacco use is known to be more common among the poor

(Pu, Lan, Chou, & Lan, 2008) and poor smokers are known to spend significant proportion of their income on smoking (ASH, 2015), crowding out expenditure on vital family needs. In

Bangladesh, it was observed that about 10.5 million more people would have adequate nourishment if money spent on tobacco were spent on food instead (Efroymson et al., 2001).

Efroymson et al. (2001) found that a typical smoker could afford additional 500 calories a day if he or she did not spend money to buy tobacco. It has also been shown that spending on tobacco reduces investment in education in China (Wang, Sindelar, & Busch, 2006; Xin et al., 2009) and

India (John, 2006). Decreased education is associated with increased poverty (Cascio & Reber,

2013) and may impact the economy negatively, since quality and quantity of education impact a nation’s economy positively (Duflo, 2001). Tobacco use therefore is a developmental issue as well (Reddy, Yadav, Arora, & Nazar, 2012). Again, tobacco related deaths bring about loss in economic opportunity and this is expected to be high in developing countries since tobacco- related deaths occur at the economic prime age of the smoker (WHO, 2008). This carries great effect on manpower in the LMICs, where 4 in 5 tobacco deaths will occur by 2030 (WHO,

2008). Consequently, the WHO has predicted that tobacco-related deaths and the cost associated

24 with the tobacco epidemic will severely hurt the economy of LMICs in the next few decades

(WHO, 2008). As such, effective tobacco control measures are required to reduce the health and economic impacts of tobacco in these countries.

Tobacco Control

Tobacco use is a complex public health problem with underlying biological, behavioral and social factors (Garrett, Dube, Babb, & McAfee, 2015; King, Dube, Kaufmann, Shaw, &

Pechacek, 2011). It consists of pharmacological aspect which induces addiction or dependence that makes cessation very difficult for the user (RCP, 2000; USDHHS, 1988), and the behavioral aspect which involves the social context of tobacco use, including acceptability and cultural practices (Institute of Medicine (IOM), 2001). Two main forces act on the tobacco user: availability of resources at individual’s disposal (demand) and accessibility and availability of tobacco products (supply) (Jha & Chaloupka, 1999; USDHHS, 2014). Thus, an effective tobacco control programs must take this complexity into consideration.

Tobacco control has three pillars: protection against SHS exposure; prevention of tobacco use initiation and promotion of tobacco cessation (CDC, 2014a). Several evidence-based guidelines have been developed to address these issues at both individual and population levels.

These best practice guidelines include the Centers for Disease Control and Prevention (CDC)’s

Best Practices for Comprehensive Tobacco Control Programs (CDC, 2014a), the Institute of

Medicine’s Ending the Tobacco Problem: A Blueprint for the Nation (IOM, 2007), Monograph

12 of the National Cancer Institute (NCI) (National Cancer Institute (NCI), 2000), the Public

Health Service’s Treating Tobacco Use and Dependence: 2008 Update (Fiore, Baker, et al.,

2008).

The CDC best practice for comprehensive tobacco control acknowledges that, for a tobacco control program to be effective, it must be a multi-component effort and the individual components must work together to produce a synergy in the statewide control program (CDC,

2014a). The CDC has identified five overarching components of effective comprehensive tobacco control program: 1) State and Community Interventions (programs and policies that influence society, systems and networks to develop smoke-free norms in individuals); 2) Mass-

Reach Health Communication Interventions (promotion of cessation, countermarketing, health communications, etc.); 3) Cessation Interventions (integration of tobacco cessation into routine care, insurance coverage of tobacco dependence treatment, and supporting quitline capacity); 4)

Surveillance and Evaluation (short, intermediate and long term outcomes to inform programs, policy, measure effectiveness and progress, and for accountability); and 5) Infrastructure,

Administration, and Management (fully functioning infrastructure, sufficient capacity and adequate number of staff with requisite skills are necessary for effective comprehensive tobacco control programs).

The CDC also provides vital information on tobacco cessation interventions as a component of comprehensive tobacco control program. Three main goals have been identified for effective cessation programs: promoting health system change, expanding insurance coverage and utilization of cessation treatments, and supporting quitline capacity. Changes are required in the health system to institutionalize tobacco cessation and make cessation as part of the routine care of the state. This will ensure consistency in health care provider screening of patients of tobacco use and intervening appropriately. To achieve substantive quit rate, the health system must strive to intervene with every tobacco user on each visit to health care facilities (Fiore,

Baker, et al., 2008). The CDC also recommends cost containment in tobacco cessation

26 treatments. For comprehensive cessation programs, insurance coverage is important to eliminate or minimize costs sharing and other barriers. This requires ongoing promotion and education to create awareness among smokers and health care providers. The third goal of the comprehensive tobacco cessation recommended by CDC involves supporting state quitline capacity partly through increasing funding to increase coverage of quitline services to tobacco users by 8% annually. Increasing quitline reach can be accomplished by increasing health care provider screening and referrals (including effort to generate electronic referrals), media promotion of tobacco cessation and where to seek help, and cessation medicine giveaways. Other policies such as increasing tobacco prices and smoke-free laws can generate interest in quitting with consequent decision to seek help through the state quitline. The quitline should be able to provide a basic service to all callers and should be accessible to all tobacco users wishing to quit.

Text messaging, web, and social media are emerging technologies that can help in expanding the reach and impact of quitline.

The US Public Health Service has also produced evidence based clinical guidelines for tobacco dependence treatment (Fiore, Baker, et al., 2008). The guideline is based on evidence from more than 8700 peer-reviewed articles and abstract published in English between 1975 and

2007. Among others, the guideline requires health care providers to assess tobacco use, advice to quit, assess willingness to make a quit attempt, assist patients in quitting through counseling and medication, and then arrange a follow-up contact with the patients.

Monograph 12 of the US National Cancer Institute (NCI, 2000) highlights population smoking cessation interventions. Aside the aforementioned tobacco dependence treatments, smoke-free policies, raising the cost of tobacco products, self-help materials and mass media campaigns are some population based tobacco control measures that can aid cessation at the

27 population level. The monograph also discusses the social context of tobacco smoking and the need to implement community-wide interventions that denormalize smoking. This will include countermarketing and tobacco advertising ban. NCI recommends a local community intervention that is tailored to the needs and the concerns of the community. It is believed that such intervention will best speed up the desired change in the social norms of tobacco smoking.

Though these best practices have been developed for country-specific programs in a high income country, several of their recommendations can be adopted and used for tobacco control in

LMICs.

Globally, tobacco control has also seen recommendations and best practices including the

International Agency for Research in Cancer’s Handbooks of Cancer Prevention (IARC &

WHO, 2009), WHO / The UNion monograph on TB and tobacco control (WHO/The UNion,

2007) and the WHO FCTC (WHO FCTC, 2005) and MPOWER Package (WHO, 2008).

The International Union Against Tuberculosis and Lung Diseases (IUATLD) has classified tobacco control programs into those tackling the demand side of tobacco use and those tackling the supply side of tobacco use (WHO/The UNion, 2007). Raising prices and taxes of tobacco products, instituting smoke-free policies in public places and workplaces, placing a ban on tobacco advertising, promotions and sponsorships, regulating packaging of tobacco products to include visible health warnings, anti-tobacco use mass media campaigns, and tobacco dependence treatment are interventions that target reduction in demand. Controlling tobacco products illicit trade targets the supply aspect of the tobacco use problem. Without control, illicit tobacco trading will lead to proliferation of the market with low-priced tobacco products that are affordable to low-income consumers. Law enforcement and custom agencies can help to reduce tobacco smuggling across national borders. The IUATLD also recommends a mix of the core

28 interventions to achieve comprehensive tobacco control programs within countries (WHO/The

UNion, 2007). The Union also emphasized that the most cost effective way to reduce tobacco consumption is the use of price measures and smoke-free policies, followed by advertising and promotion ban, warning labels and mass media campaigns in LMICs.

In summary, all the best practice guidelines emphasize comprehensive tobacco control programs that are multi-component in nature. These programs should include population-based policies such as smoke-free, health care system changes, raising prices of tobacco products, and advertising and promotion ban policies. Individual-based programs such as tobacco dependence treatment, including advice to quit, individual counseling and the use of cessation aids or pharmacological treatment for nicotine addiction should be part of any effective tobacco control program. This suggests that tobacco cessation can be promoted by both individual and population based interventions.

Tobacco Cessation

Benefits of Tobacco Cessation

Tobacco cessation (stopping tobacco use or smoking) is a core component of comprehensive tobacco control programs (CDC, 2014a; Fiore, Jaén, et al., 2008; WHO FCTC,

2005). Health benefits of tobacco use cessation were well discussed in the 21st report of the US

Surgeon General on tobacco and health (USDHHS, 1990). The report described tobacco cessation effort as a primary prevention as it prevents morbidity and mortality in healthy people, as well as prevents passive smoking in non-smokers. It was observed that people who quit smoking before 50 years of age have 50% of the risk of death in those who continue to smoke in

29 the next 15 years (USDHHS, 1990). The risk of lung cancer in those who achieve a 10 year smoking abstinence also declines to about 30% to 50% of the risk for continuing smokers

(USDHHS, 1990).

The benefit of cessation is not limited by age or by current health status. The US Surgeon

General report on the benefits of cessation indicated that adults aged 60-64 who smoked at least one pack a day could reduce the risk of dying in the next 15 years by 10% if he or she quit smoking (USDHHS, 1990). It was also reported that cessation could reduce the risk of death by about 50% in persons diagnosed with congenital heart disease (CHD), and smokers who had already developed lung cancer and other diseases equally stood to benefit from cessation

(USDHHS, 1990). Abdullah and Husten (2004) have demonstrated how tobacco cessation could benefit LMICs through a reduction in deaths and improvement in the overall population health.

Cessation also has important benefit for fetuses and children. Quitting smoking among pregnant smokers reduces the risk of perinatal death, low birth weight and preterm delivery

(USDHHS, 1990). In children, parental smoking leads to passive smoking with consequent negative health impact such as otitis media, pneumonia, bronchitis, persistent middle ear effusions and ischemic heart disease (Oberg, Jaakkola, Woodward, Peruga, & Pruss-Ustun,

2011; USDHHS, 1990). Cessation of smoking among parents reduces SHS exposure in children and negative health effects associated with the exposure.

Several studies have supported the 1990 US Surgeon General report. A randomized controlled trial has demonstrated that smoking cessation significantly slowed decline in forced expiratory volume in 1 second (FEV1) in patient with mild obstructive pulmonary disease

(Anthonisen et al., 1994). A systematic review of six randomized controlled trials revealed that

30 quitting smoking reduced post-operative complications by 41%, and every week of continuous abstinence was associated with 19% increase in the magnitude of reduction in postoperative complications (Mills et al., 2011). One study linked the National Health Interview Survey 1997-

2004 data to the National Health Index in the US and examined mortality among smokers in a sample of 216, 917 noninstitutionalized adults. Compared to continuing smokers, former smokers who quit at ages 25 to 34, 35 to 44, or 45 to 54 gained approximately 10, 9, and 6, years of life, respectively (Prabhat Jha et al., 2013). Another study that measured 50 year trends in deaths related to smoking in the US observed that, cessation at any age drastically reduced mortality rates (Thun et al., 2013). After 22 years of follow-up, physicians who quit smoking had a 40% reduction in the risk of dying after 10 years of cessation, and the risk of mortality further reduced to the level of non-smokers after 20 years of quitting (Cao et al., 2011).

Quitting tobacco reduces the risk of death from tobacco-related conditions. Cardiovascular deaths have been found to continuously reduce significantly with increasing number of years of smoking cessation based on data from 25 cohorts that participated in the Consortium on Health and Ageing: Network of Cohorts in Europe and the United States (CHANCES) (Mons et al.,

2015). Even in people aged 50 years and above, quitting tobacco smoking has been found to normalize heart rate dynamics within 15 years of cessation for light smokers but may require 15 to 25 years to achieve this normalization in former heavy smokers (Girard et al., 2015).

Mortalities from other diseases such as renal failure, breast cancer, intestinal ischemia, among others, also decline significantly with increasing years of smoking cessation (Carter et al., 2015).

In sum, several studies have provided evidence of significant health benefits associated with smoking cessation. These benefits provide support for evidence based policies and interventions to promote tobacco smoking cessation.

Tobacco Cessation Interventions

There are several tobacco cessation programs worldwide and extensive literature of these programs and their impact on behavior and health outcomes exists. Population level intervention programs for tobacco cessation include raising tobacco product prices to reduce demand, smoke- free policies, mass campaigns to promote tobacco cessation, and health warning labels on tobacco packages (WHO/The UNion, 2007). Individual level interventions such as pharmacological treatment and counseling, when complemented with population level measures, can increase quit rate among smokers (Fiore, Jaén, et al., 2008).

It has been shown that comprehensive control programs are more effective in achieving the desired results than individual programs (CDC, 2014a). The California Smoker’s Helpline, a telephone-based program for tobacco cessation among users in California implemented in 1992, is an example of comprehensive tobacco cessation program. This state-wide intervention uses media campaigns, tobacco control programs, healthcare providers, and public schools to refer tobacco users/smokers to the Helpline for tobacco cessation support (Zhu, Anderson, Johnson,

Tedeschi, & Roeseler, 2000). The California Smoking Helpline provides a multilingual (English,

Spanish, Vietnamese, Korean and Chinese) cessation assistance such as telephone counseling, self-help quit kits, medications among other relevant supports. This initiative is part of a

Comprehensive Tobacco Control Program in California. Another example is a collaborative work between state health departments, service providers or organizations and other national organizations to set up state-based quitlines across the United States. The state-based quitlines are telephone based program which offers telephone counseling, medication, information, among other relevant supports for tobacco cessation among tobacco users (Lemaire, Bailey, &

Leischow, 2015). Currently, the program has been implemented in the 50 states, the District of

Columbia, Guam and Puerto Rico.

In the US, one noteworthy achievement is the provision the US Affordable Care Act, makes for tobacco cessation treatment. The varied insurance coverage for tobacco cessation assistance and the low utilization of clinical cessation interventions by smokers and health care providers due to costs (co-payments) and prior authorization limited the effectiveness of the tobacco cessation programs in the US (McAfee, Babb, McNabb, & Fiore, 2015). Recognizing this problem, the US government in 2014 made amendments to the Affordable Care Act to ensure that the financial responsibility of tobacco cessation treatment, which falls under preventive services be borne by health plans and health insurers with no co-pays by patients for up to two quit attempts per year (McAfee et al., 2015). Removing these barriers of access and utilization of tobacco cessation program is one commendable achievement as it provides an enabling environment for quitting smoking among users as well as making it easier for providers to assist with tobacco cessation (McAfee et al., 2015).

Smoking interventions may also be workplace based. Workplace interventions may aim at individuals (counseling, pharmacological treatment, self-help and social support) or the worksite as a whole (incentives and environmental cues) (Cahill & Lancaster, 2014).

There are currently behavioral and therapeutic cessation methods for tobacco dependence treatment and cessation in the health care settings. Approved pharmacologic interventions include nicotine replacement therapy (NRT), varenicline, credible interval, nicotine nasal spray, and bupropion (WHO, 2008; Wu, Wilson, Dimoulas, & Mills, 2006). First-line medications for tobacco dependence treatment include NRTs, bupropion SR, and varenicline. Nortriptyline and

33 clonidine are second-line medications (Fiore, Jaén, et al., 2008). The behavioral approaches include counseling, quitlines and health professional advice. Motivational interviewing treatment, a technique in which clinicians support patients’ self-efficacy in order to help them transit from desire to quit to quitting, is another type of a behavioral intervention for tobacco cessation (Lindson‐Hawley, Thompson, & Begh, 2015). This applies to patients who have a desire to quit but lack self-efficacy in quitting.

Cessation programs are affordable and effective in discontinuing reliance on tobacco, and preventing associated chronic diseases and deaths (West et al., 2015). A meta-analysis of 86 randomized controlled trials on the efficacy of therapeutic or pharmacologic treatment for tobacco dependence found NRT, bupropion and varenicline to be very efficacious in tobacco smoking cessation (Wu et al., 2006). Another study systematically identified 70 publications of

69 trials with a total of 32,908 and conducted a meta-analysis to examine efficacy of seven approved pharmacologic interventions of tobacco smoking (Eisenberg et al., 2008). Eisenberg et al., (2008) found that all the seven drugs were potent in promoting abstinence from smoking at six and 12 months.

Behavioral interventions such as advice to quit, media campaigns and counseling have been found to be effective in achieving tobacco smoking cessation. Gorin and Heck (2004) reviewed 37 studies on health care professional counseling effects on tobacco cessation, published from 1990 to 2004, and concluded that, a brief encounter with a healthcare professional could increase tobacco cessation. Mottillo et al. (2009) conducted a meta-analysis of

50 randomized controlled trials which examined behavioral interventions of smoking cessation in the US. Their results indicated that intensive behavioral intervention can increase smoking cessation, but the result was inconclusive for minimal interventions. In particular, individual

34 counseling, group counseling and telephone counseling each increased rates of tobacco quitting.

In 2012, another review reported that offering behavioral support to quit by physicians achieved higher quit rate than brief advice to quit (Aveyard, Begh, Parsons, & West, 2012).

For successful implementation of these interventions, and to increase cessation rates, the role of individual, interpersonal, institutional, community and policy factors cannot be ignored.

For the purpose of this study, a review of the roles of intention to quit, the health care provider and smoke-free policies is provided.

Intention to Quit Smoking

Tobacco cessation is a process that starts with a decision to quit and ends will successfully quitting (Fiore, Jaén, et al., 2008). Cessation is an individual level process that has several biological and social determinants (USDHHS, 1990). The process of smoking cessation has been widely studied by scientists and intention to quit has served as an important tool for classifying smokers into stages of the cessation process (DiClemente et al., 1991; Pallonen, Prochaska,

Velicer, Prokhorov, & Smith, 1998; USDHHS, 1990). Intention to quit therefore is an integral component of the smoking cessation process.

Intention to quit has been significantly linked to attempt to quit in different populations.

Among psychiatric patients in the US, intention to quit was found to be a predictor of quit attempt regardless of psychiatric symptoms and other substance use (Tzilos, Strong, Abrantes,

Ramsey, & Brown, 2014). A longitudinal study in England also found intention to quit as independent predictor of quit attempt (Smit, Fidler, & West, 2011). In the same study, it was observed that a larger proportion of smokers admitted they ought to stop smoking than those who intended to quit. The PRIME theory (Plans, Responses, Impulses, Motives, Evaluations) has

35 provided a distinction between a desire to quit and intention to quit, and has argued that intention to quit can lead to quit attempt only through a desire to quit smoking (Smit et al., 2011). In the

US, it has been reported that approximately 70% of smokers intended to quit, 52% had made a quit attempt but only 6% successfully quit in the past year (CDC, 2011). The addictive nature of nicotine makes it difficult for smokers to quit and they may require 8-11 quit attempts before successfully quitting smoking (CDC, 2013a).

Several factors are associated with intentions to quit smoking. Intention to quit has been found to change over a relatively long period of time (3 to 12 months) (Hughes, Keely,

Fagerstrom, & Callas, 2005). A study that examined changes in intention to quit among US and

Swedish participants observed that intention to quit changes over a short period of time, and this change did not differ by country (Hughes et al., 2005). In India, it was reported that 20% of smokers intended to quit smoking, and higher education, doctor’s advice, and anti-tobacco messages were positive predictors of intention to quit smoking (Dhumal et al., 2014). While intention to quit is known to predict quit attempt, the reverse has also been found. Among

Chinese secondary students in Hong Kong, a previous quit attempt was found to be positively associated with intention to quit smoking (Wong, Chan, Ho, Fong, & Lam, 2010). In these students, other factors such as light smoking, disapproval, gender and awareness of health hazard due to smoking were associated with intention to quit. While poor social image of tobacco users may lead to intention to quit (Hughes, Naud, Fingar, Callas, & Solomon, 2015), limited knowledge on the detrimental effect of tobacco on health and acceptability of tobacco smoking by some population groups may prolong the development of quit intentions among tobacco smokers (Athamneh, Sansgiry, Essien, & Abughosh, 2015; Bethea, Murtagh, & Wallace, 2015).

It has been reported that anti-smoking messages were associated with intention to quit in

17 countries (CDC, 2013b). This finding suggests that mass education on harm associated with smoking may increase the number of smokers intending to quit. Another study also reported that noticing anti-smoking messages had the same impact on intention to quit among people with high and low education, however, those with low education were less likely to have noticed anti- smoking messages (Springvloet et al., 2015). In Greece, one study attributed the increase in the number of smokers intending to quit to, possibly, tobacco control policies and austerity measures implemented in that country (Schoretsaniti et al., 2014). This increase was obvious in all socioeconomic strata, suggesting that policy measures can have a great impact on intention to quit.

In summary, intention to quit is important in smoking cessation and many smokers intend to quit but only a few make quit attempt. Factors such as socioeconomic status, education, gender, knowledge of smoking harm, smoke-free policies, and anti-smoking messages have been found to predict intention to quit smoking.

Health Care Providers’ Role in Tobacco Cessation

With all the best practice guidelines on tobacco cessation advocating for tobacco dependence treatment as part of routine care in health care facilities ( Fiore, Baker, et al., 2008;

WHO/The UNion, 2007; WHO, 2008), health care professionals have crucial role to play in the cessation process. Consequently, the US Public Health Service (USPHS) has developed a guideline for treating tobacco dependence in the primary care setting (Fiore, Jaén, et al., 2008).

The guidelines provide information on the use of the 5As (Ask, Advice, Assist, Aid and Arrange

37 follow-up) to assist smokers to cease smoking. The guideline also requires providers to screen every patient at every clinic visit for tobacco use and offer advice to quit.

Over three decades ago, it was reported that even brief physician advice significantly increased quit rate, motivation and intention to quit smoking (Russell, Wilson, Taylor, & Baker,

1979). Russell et al. (1979) asserted that if all general practitioners in the United Kingdom provided advice to their patients to quit smoking, over half a million former smokers would be recorded annually. An increase in the number of initiatives which sought to enjoin physicians to provide cessation support has been observed since Russell et al. (1979 reported their findings

(Raw, McNeill, & Murray, 2010; WHO, n.d.a). In January 2004, the WHO developed a code of practice for health professionals, highlighting their roles in providing tobacco dependence treatment services and tobacco cessation assistance, and direction on organizational changes and activities that can be engaged in to reduce tobacco smoking (WHO, n.d.a). Physicians render services to tobacco users who want to quit in the form of advice, referral advice to patients for counseling and support, and pharmacological therapy

Lancaster (2011) emphasized that, while physicians could make a significant impact on tobacco smoking reduction through all the six mechanisms stipulated by the WHO, the clinical setting provides a direct opportunity where physicians counsel and support tobacco users to quit.

According to Lancaster (2011), physician advice is an inexpensive but effective tobacco cessation measure which mostly occurs at the primary care setting. A systematic review of 17 trials that examined quit rates in physician advice versus no advice showed 66% greater increased rate of smoking cessation associated with quit advice from physicians (Stead et al.,

2008). Health professional advice has also been reported to be associated with intention to quit smoking in India (Dhumal et al., 2014). Another meta-analysis of 35 trials that compared nursing

38 advice to quit with normal care (no advice to quit) reported that, nursing intervention could increase the chances of quitting by 29% (Rice, Hartmann-Boyce, & Stead, 2013).

Health care providers also play role in cessation counseling, which may be face-to-face or telephone-based. Counseling may also be done individually or in a group. In a systematic review of 30 randomized or quasi-randomized trials with over 7000 participants, counseling was found to significantly increase tobacco cessation rate (Stead et al., 2008). The review also did not detect any significant difference in cessation between intensive and brief counseling.

While physicians and other health care providers have a significant role to play in smoking cessation, there are barriers that prevent them from doing so. Health care providers who are themselves smokers are less likely to advice their patients to quit smoking ( Abdullah et al.,

2013, 2014) and this is common in LMICs (Abdullah et al., 2014). Lack of training has been reported as one of the barriers to providing quit advice by physicians in some LMICs such as

Indonesia (Ng et al., 2007) and Vietnam (Shelley et al., 2014).

Since physician/ health provider advice is effective but inexpensive way of promoting tobacco cessation, it could be integrated into the health care system in LMICs without much cost.

However, more studies are required to provide information on the barriers and how to ensure success of this integration. In China for example, a systematic review of cessation intervention studies in 2012 revealed the need for more studies and long term follow-up to assess physician advice and Traditional Chinese Medicine before their adoption as evidence-based interventions

(Kim et al., 2012).

Smoke-Free Policies and Smoking Cessation

Smoke-free policies are one of the key recommendations of the best practice guidelines for comprehensive tobacco control (CDC, 2014a; WHO, 2008). The WHO FCTC and

MPOWER urge member countries to implement a ban on smoking in public places and private workplaces (WHO FCTC, 2005; WHO, 2008). National smoke-free policies may cover public places such as hospitals, government buildings, public transports, malls, restaurants, schools, bars, movie theaters and sport centers. Private business may enact or be required to enact smoking restrictions in indoor workplaces. Again, smoking restrictions may be implemented in private homes and other residential buildings. Currently, many countries have implemented smoke-free policies to some extent (Islami, Stoklosa, Drope, & Jemal, 2015), but only about

16% of the world’s population were fully protected by smoke-free policies (WHO, 2013).

Smoke-free policies do not only protect nonsmokers against involuntary/passive smoking, but they also serve as disincentives to encourage cessation or reduction in the intensity of tobacco smoking among smokers. Several studies have examined impact of smoke-free policies on smoking behavior and tobacco cessation. One study reported a decrease in the prevalence of smoking in Spain between 2006 and 2011 after an introduction of a complete national smoke- free policy in 2006 (Perez-Rios, Fernandez, Schiaffino, Nebot, & Lopez, 2015), however, around the same period, other tobacco control policies were implemented, making it difficult to attribute the reduction in prevalence to the smoke-free policy (Perez-Rios et al., 2015). Using series of cross-sectional data, Heloma, Jaakkola, Kähkönen, and Reijula (2001) demonstrated that a national workplace smoke-free policy was associated with reductions in smoking prevalence and the number of daily cigarette consumptions in Finland. However, in Italy, a recent study which examined a long-term impact of the national smoke-free law failed to find any long-term

40 reductions in the prevalence of smoking and the number of daily cigarette consumption (Gualano et al., 2014). This suggests that impact of a national smoke-free policy may be affected by time.

Hopkins et al. (2010) identified 57 studies that examined impact of smoke-free policies and restrictions on tobacco use, published in English from 1980 through 2005. Thirty-seven of these studies met the inclusion criteria and were analyzed for the impact of smoke-free policies on self-reported tobacco use. The results showed that workplace smoke-free policies significantly reduced smoking among workers. This result corroborated an earlier systematic review that included 26 studies conducted prospectively, retrospectively or series of cross-sectional designs, which assessed the impact of a complete workplace smoke-free policy on smoking behavior

(Fichtenberg & Glantz, 2002). The results indicated that a complete workplace smoke-free policy was associated with absolute reduction of 3.5% in smoking prevalence, and a reduction in daily consumption of 3.1 cigarettes per continuing smoking. However, a quasi-experimental study reported in 2001 that worksite smoke-free policy increased quit rate in the intervention group but relapse rate was similar in both intervention and control groups (Longo, Johnson, Kruse,

Brownson, & Hewett, 2001), raising question about the long time impact of smoke-free policies on cessation. In an isolated community in the US, a public and workplace smoking ban was associated with a significant reduction in the number of admissions for acute myocardial infarction six months after the ban (Sargent, Shepard, & Glantz, 2004).

Smoke-free policy at home has also been examined for its impact on smoking behavior.

More than a decade ago, a nationally representative cross-sectional study reported that a total smoking ban at home was associated with quit attempt, light smoking and six-month smoking cessation (Farkas, Gilpin, Distefan, & Pierce, 1999). It has been reported that, even with no parental smoking, home smoking restrictions are strongly associated with adolescent smoking

41 behavior (Emory, Saquib, Gilpin, & Pierce, 2010). Among employed women, smoke-free policy at home has been found to increase the odds of becoming a former smoker by about 7 times of the odds in those living in homes where smoking is permitted (Shopland, Anderson, & Burns,

2006). A longitudinal analysis of evolution and impact of home smoke-free rules found that smoke-free homes were associated with increased cessation and reduction in relapse (Hyland et al., 2009).

Overall, evidence of smoke-free policies’ positive impact on cessation, quit attempt, reduction in smoking intensity, and reduction in smoking prevalence is promising. Hoffman and

Tan, (2015), conducted systematic overview of all systematic reviews published up to 2014 on health-related effects of tobacco control policies. They reviewed 12 systematic reviews (3 strong,

8 moderate, and 1 either moderate or strong in quality) of health-related impact of smoking bans and restriction in public places, workplaces and residences. Most of the reviews on smoking behavior reported increased cessation and reductions in prevalence of smoking and cigarette consumption. However, three reviews produced inconsistent results on prevalence of smoking or cessation.

Tobacco Cessation in Low-and-Middle Income Countries (LMICs)

Tobacco use is increasing in LMICs with 80% of tobacco-related deaths expected to occur in these countries by 2030 (Jha & Chaloupka, 1999). Tobacco smoking is therefore a major public health issue that requires attention in LMICs.

Tobacco cessation in LMICs carries the same health and economic benefits as discussed previously. According to the World Bank, 180 million tobacco-related deaths by 2050 could be avoided if consumption of tobacco by adults decreased by half by 2020 (Jha & Chaloupka,

1999). Therefore, cessation is important to save lives, reduce poverty, stimulate economic development and increase global security (Abdullah & Husten, 2004).

Frameworks for smoking cessation in developing countries exist. One framework recommended that cessation intervention should target health professionals in LMICs as a first step to make them role models for others and also for them to be able to promote cessation in health facilities (Abdullah & Husten, 2004). Tobacco cessation in health professionals is important because evidence suggests that health professionals who smoke are less likely to initiate smoking cessation interventions in their patients (Abdullah et al., 2014; Pipe, Sorensen,

& Reid, 2009). In addition, integration of cessation treatment into the health care system, specifying the roles of health care professionals and developing a cessation model for LMICs have been recommended (Abdullah & Husten, 2004). Thus, health professionals are seen as critical for tobacco cessation in LMICs.

Analysis of data from the International Tobacco Control (ITC) policy Evaluation Surveys in 15 countries showed that quit attempt rates, health professional advice and use of cessation assistance varied widely across countries (Borland et al., 2012). The use of medication to quit smoking was higher than behavioral interventions. In China and Malaysia, less than 20% of smokers as against about 50% of smokers in Thailand had made quit attempt in the past year

(Borland et al., 2012). While about half of smokers who visited health facilities were advised to quit smoking in china and Mexico, more than two-thirds received advice to quit smoking in

Thailand and Malaysia. Another study, using the Global Adult Tobacco Survey (GATS) data from 16 LMICs, reported great variations in the utilization of smoking cessation assistance (4%

– 27% in lower middle-income countries, and 5% – 18% in upper middle-income countries)

(Wang, Jin, Lu, & Ferketich, 2015). The use of cessation assistance was generally low in these

43 countries: counseling, 1% to 15%; pharmacotherapy, 1% to 27%; and quitline, 0.1% to 1.5%.

The authors recommended promotion of use of cessation assistance to improve quit rates. In nine countries of the former Soviet Union, including Russia and Ukraine, 62.7% of smokers were willing to quit, and 64.9% had taken action to stop smoking. However, only 12.6% had sought cessation assistance (Footman et al., 2013).

Tobacco control policies may have significant impact on tobacco cessation in LMICs. It was estimated that 10% increase in taxes on cigarettes resulted in 1.6% to 2.3% increase in the probability of smoking cessation in Poland, Russia and Ukraine (Ross, Kostova, Stoklosa, &

Leon, 2014). In Russia, the SimSmoke model demonstrated that smoke-free and other policies could reduce smoking rate by 30% by 2020 (Maslennikova et al., 2014). Other factors have been found to correlate with smoking cessation in LMICs, including workplace smoking ban, health warning labels, tobacco price, and antismoking messages (Shang, Chaloupka, & Kostova, 2014); perceived risk and self-efficacy (Schnoll, Subramanian, Martinez, & Engstrom, 2011); education, income and employment status (Siahpush, Borland, Yong, Kin, & Sirirassamee,

2008); awareness of smoking harm (Sansone et al., 2012); age, age of initiation, education, years of smoking, and nicotine dependence (Islami et al., 2015); and education, doctor’s advice, and anti-tobacco messages (Dhumal et al., 2014).

In summary, several factors have been found to be associated with tobacco smoking cessation in LMICs. However, cessation rates in these countries are generally low compared to the developed countries (Abdullah & Husten, 2004; Borland et al., 2012). More studies are required to inform policy and intervention planning and advocacy initiatives in these countries

(Kim et al., 2012; McRobbie et al., 2013; Warner, 2005).

Theoretical Framework

Several models have been developed for health promotion and education (National Cancer

Institute (NCI), 2005). Commonly used ones include the Health Belief Model (HBM),

Transtheoretical Model (TTM), Theory of Planned Behavior (TPB), Theory of Reasoned Action

(TRA), Social Learning/Social Cognitive Theory (SCT) and the Ecological Model (EM) (NCI,

2005). With the exception of EM and SCT, all the aforementioned models involve individual level behavior changes. HBM seeks to reduce barriers; increase perceived susceptibility, severity and benefits, provide cues to action and increase self-efficacy to achieve desirable behavior in an individual and TTM recognizes stages of behavioral change and suitable interventions are planned for each stage of the behavior change process (NCI, 2005). TPB and TRA consider perceived control and intentions as potent factors to achieve behavioral change. Central to SCT is the notion of reciprocal determinism (interaction between environmental factors, personal factors and the behavior itself). The EM goes beyond the individual and interpersonal factors to include broad social and environmental factors that affect behavior (NCI, 2005).

To address the overall research goal of this study, the approach conforms to the ecological model (EM) (Pantaewan et al., 2012), however, with the focus of individual study aims on cessation, the TTM or stages model (Aveyard, Massey, Parsons, Manaseki, & Griffin,

2009; DiClemente et al., 1991; Prochaska & Norcross, 2001; Schumann et al., 2007) will be used to guide the study aims.

The Ecological Model

This study adapted the socio-ecological model of health (Figure 1.2) to develop its theoretical framework. The Ecological Model (EM) is based on the evidence that no single factor

45 explains differences in health risk in the population (WHO, n.d.b). The concept of reciprocal causation is expressed in the model: behavior affects and is affected by multiple levels of influence, and behavior shapes and it is shaped by the social environment (Glanz & Rimer,

2005). Glanz and Rinner (2005) have discussed the four principles of the model. These are (1) multiple levels of factors influence health behaviors (2) influences interact across levels (3) multi-level interventions should be most effective in changing behavior and (4) Ecological models are most powerful when they are behavior-specific. The model was developed to redirect public health promotion to factors outside the individual that affect individual’s behavior (WHO, n.d.b). The EM generally has five constructs; intrapersonal, interpersonal, institutional, and community factors and public policy. Table 1.1 is a summary of the EM constructs.

Table 1.1: Constructs of the ecological model Concepts Definition Intrapersonal Individual characteristics that influence behavior, such as knowledge, Level attitudes, beliefs, and personality traits

Interpersonal Level Interpersonal processes and primary groups, including family, friends, and peers that provide social identity, support, and role definition Institutional Rules, regulations, policies, and informal structures, which may Factors constrain or promote recommended behaviors

Community Social networks and norms, or standards, which exist as formal or Factors informal among individuals, groups, and organizations

Public Policy Local, state, and federal policies and laws that regulate or support healthy actions and practices for disease prevention, early detection, control, and management Adapted from National Cancer Institute (NCI) (2005)

Tobacco smoking and cessation have multi-level influences, including intrapersonal factors (genetics, addiction), interpersonal (peer, parental smoking), community level factors

(advertisement, socio-economic status) and organization/policy level factors (smoke-free policies, taxation). Glanz and Rimer, (2005) offered a good demonstration of how the factors can

46 interact with one another: genetics and addiction can create excellent markets for tobacco products, profit from sales will drive advertisement and campaigns to promote the high-mood benefits of tobacco and, due to their wealth, tobacco companies can have strong political influences to resist any restrictions on their products. Therefore, it is important to consider multiple factors that promote tobacco smoking, and tailor intervention to each of the factors to control tobacco smoking.

Cessation interventions such as counseling, pharmacological treatment and health professional advice to quit are examples of individual level interventions, whereas smoke-free policies, tobacco access control, increase taxation and regulation tobacco manufacturing are examples of community/organization/policy level interventions. A combination of these types of interventions to achieve a comprehensive national level program has proven very effective in reducing tobacco smoking in the US (Glanz & Rimer, 2005). The WHO FCTC relies heavily on the ecological model. The MPOWER (monitor, protection through smoke-free laws, offer assistance to quit, warning labels, enforcing advertisement ban, and raising taxes on tobacco product) approach for tobacco control considers multiple levels of influences.

Public Policy (e.g. smoke-free policies, taxation, regulation of tobacco products and package, etc.) Community factors (e.g. built environment, business, community leaders, etc.) Institutional factors (e.g. health directorates, law enforcement agents, coorporate organization, etc.) Interpersonal factors (e.g. health providers, family, colleagues, etc.) Intrapersonal factors (e.g. genes, beliefs, addiction, etc.)

Figure 1.2: The Ecological Model (adapted from NCI, 2005)

Transtheoretical Model (TTM)

The TTM, also known as Stages of Change Model was developed by Prochaska and

DiClemente from studies that compared smoking cessation with assistance to quitting without assistance (Glanz & Rimer, 2005). The theory is premised on the idea that behavior change is a process but not an event. Behavior change undergoes five main stages: precontemplation, contemplation, preparation, action, and maintenance. People in different stages of the continuum have different needs and benefit from the stage-specific interventions. The Model is circular, allowing people to enter at any stage, relapse to an earlier stage and restart the process

(Glanz & Rimer, 2005). Figure 1.3 summarizes the stages of the TTM.

Precontemplation Has no intention of taking action within the next six months

Maintenance Contemplation Has changed behavior for Intends to take action in the more than six months next six months

Preparation Action Intends to take action within Has changed behavior for the next thirty days and has less than six months taken some behavioral steps in this direction

Figure 1.3: Stages of the Transtheoretical Model (adapted from NCI, 2005)

The TTM has largely been applied to smoking cessation. It has been applied in different settings, including homes, schools, worksite, and primary health care (Glanz & Rimer, 2005). It was reported that differences in stages predicted attempt to quit and smoking cessation success

(DiClemente et al., 1991).

The WHO Framework Convention for Tobacco Control (FCTC)

Global effort to control tobacco epidemic is evidenced by the adoption of the FCTC by the

WHO General Assembly (WHO FCTC, 2005). Recognizing the impact of global tobacco epidemic, the World Health Assembly of the United Nations (UN) unanimously adopted the

WHO FCTC in 2003, the world’s first treaty against tobacco. The WHO FCTC is considered as one of the most widely accepted treaty of the UN, signaling the importance of tobacco control in

49 the world (WHO FCTC, 2005). The treaty aims at neutralizing the health and economic impact of tobacco use to prevent the projected one billion tobacco-related deaths in the 21st century. The

FCTC was opened for signatures on June, 2003 and closed in June 2004 (WHO FCTC, 2005).

As of 2015, 180 countries were parties to the convention since it came into force in 2005 (WHO

FCTC, 2015). The development of WHO FCTC was based on globalization of tobacco consumption, which has been facilitated by complex factors, including trade liberalization, transnational tobacco advertising, promotion and sponsorship, and direct foreign investment

(WHO FCTC, 2005). Countries which have signed or acceded to the convention, in principle, agree to strive to ratify, accept and demonstrate political commitment not to undermine its objectives (WHO FCTC, 2005). Articles 6 to 14 contain provisions on the following measures to reduce demand for tobacco:

 Price and tax measures;

 Non-price measures to reduce the demand for tobacco

 Protection from exposure to tobacco smoke;

 Regulation of the contents of tobacco products;

 Regulation of tobacco product disclosures;

 Packaging and labeling of tobacco products;

 Education, communication, training and public awareness;

 Tobacco advertising, promotion and sponsorship; and,

 Demand reduction measures concerning tobacco dependence and cessation.

And articles 15 to 17 contain the following core provisions on measures to reduce tobacco supply:

 Illicit trade in tobacco products;

 Sales to and by minors; and

 Provision of support for economically viable alternative activities.

Article 14 of the FCTC is devoted to tobacco dependence treatment and cessation. It requires parties to provide support to reduce tobacco dependence, and increase cessation through counseling, psychological support, nicotine replacement therapy (NRT), and other programs. To this end, parties were to develop infrastructure to promote quit attempts and to offer support to those willing to quit smoking. The Article 14 guidelines also encourage parties to include cessation treatment in the health care system and national tobacco control programs (FCTC,

2010). Actions necessary to promote cessation include mass communication to encourage quitting and provide information on availability of support, brief advice from healthcare professionals, and quitlines to provide support to those wishing to quit. In addition, tobacco dependence treatment needs to be integrated into the healthcare systems, and affordable medications for treatment dependence should be made available.

The MPOWER Package

To assist with the implementation of the FCTC, the WHO introduced the MPOWER, a package of six proven tobacco control policies: (1) Monitor tobacco use and prevention (2)

Protect people from tobacco smoke (3) Offer help to quit tobacco use (4) Warn about the dangers of tobacco (5) Enforce bans on tobacco advertising, promotion and sponsorship, and (6) Raise taxes on tobacco (WHO, 2008). It is believed that this package has a very high potential to avert the current trend of smoking-related mortalities, and it is also cost-efficient.

The MPOWER package offers details of the tobacco control policies (WHO, 2008).

Briefly, monitoring is important to ensure the success of the other five components of the

MPOWER package. Comprehensive monitoring provides information about the extent of tobacco use and consumption by age, sex and other demographic characteristics, and geographically. A good monitoring system should be able to track several indicators, such as prevalence of tobacco use, impact of policy interventions, and tobacco industry marketing, promotion and lobbying. The WHO has committed to working with countries to develop and expand both national and global monitoring systems.

The second policy of the package is protection against SHS exposure. This involves legislation to ensure smoke-free public places, including workplaces, health facilities, schools, government buildings and public transports. To be effective, it is recommended that all indoor environments should be made smoke-free. Legislations should be fully enforced and appropriate sanctions applied to offenders, including business owners and individuals who smoke in prohibited areas. It is recommended that, protection of people from SHS be done in a step-by- step approach. The MPOWER package recommends educational campaigns about the dangers of

SHS and to elicit public support as the first step. Then a draft of the legislation should be made available to the public for comments. Governments are advised to continue to maintain strong public support after implementation of the legislation.

The next component of the package which is the central issue of this study, involves offering assistance to quit tobacco use. To achieve this, the package makes recommendation for three types of treatment that must be included in any tobacco control program: (i) tobacco cessation advice incorporated into primary health care services; (ii) easily accessible and free quitlines; and (iii) access to low-cost pharmacological therapy. Integrating cessation advice into

52 the existing health care system makes it inexpensive to implement and repeated advice reinforces the need to quit. Since countries already have networks of primary health care, this intervention is easy to implement but may require training of care workers on cessation counseling and development of information materials for implementation. The package also recommends establishment of well-staffed quitline services to provide confidential assistance to smokers to quit. It also recommends extension of quitlines to remote areas and tailoring them to specific populations. Pharmacological treatments with approved medications, including nicotine replacement therapy in the form of patches, lozenges, gum and nasal sprays, and prescription medications such as bupropion and varenicline, are also recommended to be part of tobacco dependence treatment. It is recommended that the cost of these medications be affordable.

The MPOWER package also advocates for comprehensive warning about the dangers of tobacco to change the image of tobacco, especially among adolescents. Warning about tobacco will lead to association of tobacco with addictiveness and dangerous health consequences to serve as disincentives to initiation and continuous smoking. Health warnings on tobacco packages can reach all smokers. Warning may include words and graphic depictions, but graphic warnings are generally more effective than word.

The last two components recommend banning tobacco advertisement, promotion and sponsorship, and raising taxes on tobacco products. The package recommends a complete ban of all forms of tobacco advertisement and promotion. Policy makers are required to announce this ban well in advance to allow enough time for media houses to look for alternative sponsors. It also suggests periodical revision of this ban to include innovations and tactics tobacco industries develop to market their products. Countries are also recommended to raise taxes on tobacco to reduce affordability and use. Higher taxes can be disincentive to both the poor and the youth. To

53 prevent smuggling due to taxation, the package recommends affixing stamps to all tobacco packages intended for retail.

The WHO has shown a commitment to helping countries monitor tobacco control by partnering with several institutions to develop the Global Tobacco Surveillance System (GTSS).

Global Tobacco Surveillance System (GTSS)

The WHO established the Tobacco Free Initiated (TFI) in 1998 to focus attention and resources on global tobacco epidemic. Among other objectives, the TFI was to promote the ratification of the WHO FCTC. In the same year, WHO, CDC and the Canadian Public Health

Association (CPHA) initiated the GTSS to help countries establish tobacco control surveillance and monitoring (Warren et al., 2009). GTSS includes three school-based surveys: Global Youth

Tobacco Survey (GYTS), Global School Personnel Survey (GSPS), and Global Health

Professions Student Survey (GHPSS), and one household survey: Global Adult Tobacco Survey

(GATS). For the purpose of this study, GATS data will be analyzed.

Methods

This study used data from nationally representative cross-sectional surveys (GATS) conducted from 2009 to 2012 in LMICs. Though clinical trials are ideal and will establish cause and effects in cessation studies, there is currently no known ongoing cross-country trial in the population of interest (LMICs). McRobbie et al. (2013) observed that such cross-sectional surveys with standard protocols, produce valuable data to monitor tobacco cessation, and recommended GATS as a one of the standard tools that all countries should use for monitoring tobacco dependence treatment. Therefore, GATS is suitable for this study.

Details of GATS have been published elsewhere (Global Adult Tobacco Survey

Collaborative, 2010; Warren et al., 2009). Briefly, GATS is a multi-partner project with involvement of global, regional and national partners. The main partners are WHO, CDC, CDC

Foundation, the John Hopkins Bloomberg School of Public Health (JHSPH). RTI International and the University of North Carolina (UNC) Survey Research Unit are associate partners. The

WHO headquarters and regional offices provide coordination, technical and administrative support, plan, organize, and manage GATS for member countries, serve as centers for dissemination, promotion of political commitment, and urge members to implement and disseminate the GATS results. The CDC also provides technical support to GATS and serves as data coordinating center, while CDC Foundation provides administration, funding and coordination for GATS. JHSPH assist in validation of the GATS questionnaire and data analysis by providing technical expertise. Bloomberg Initiative to Reduce Tobacco Use provides both financial and technical support for the survey. National governments commit resources, ally with national sponsors, choose agency to conduct the survey, facilitate all phases of GATS in their countries and assist in the use of the results for policy development, tobacco control programs and monitoring. RTI supports data collection through software provision and training. UNC

Survey Research Unit provides support to countries on GATS methodology.

Initiated in 2007, GATS is a nationally representative household survey of noninstitutionalized adults aged 15 years and above. The survey uses a standard protocol for questionnaire, sampling and data collection. The protocol is reviewed and approved by experts across the world. GATS is a face-to-face personal interview and it uses electronic data collection procedures using handheld machines. It uses a multi-stage geographically clustered sampling design to adequately cover the target population. Countries are required to design the survey in a

55 way that allows for precise cross-sectional estimates by gender and rural/urban indicator.

However, countries may also add additional domain of interest.

In conducting the survey, primary sampling units (PSU) are defined. Sampling units are expected to be many enough (>1000 per country) so that selected PSUs would not be greater than 10% of all PSUs a country is partitioned into. This requirement translates into a selection of at least 100 PSUs in the country and 400 sample segments. A probability sampling technique is used at each stage of the selection process. Partitioning of units is based on size measure constructed from a recent census and/or administrative records. In general, this should be equal to the total population of adults aged 15 years and above or the number of households in the area

(Warren et al., 2009). A sample size of 2,000 is recommended for each key domain to meet the survey standards of statistical quality for the domain estimate. Consequently, a sample size of

8,000 is required for national estimates to be reported concurrently by gender and residence

(rural versus urban). Each survey is required to address non-response and ineligibility at each stage, and to achieve a combined response rate of 80%.

The GATS Sampling Manual (GATS Collaborative Group, 2010) has provided detailed sampling design of the survey. Eligible participants are non-institutionalized civilians 15+ years who are citizens and reside in the country, or non-citizens who are usual residents of the country

(who have resided in the country for at least half of the time during the past 12 months). There is also a household membership requirement for participation in the survey. To meet the household membership requirement, an eligible participant from a sampled household has no other residence or has more than one residence but has been living in the selected household for at least half of the time of the past 12 months. A person who has recently moved into the selected household as the sole place of residence with no intention of returning to the previous household

56 meets the household requirement. In contrast, a person who has recently moved out of the household with no intention of returning is not considered a member of the household. All students living in dormitories meet the household requirement.

The GATS has core questionns, consisting of eight sections: background characteristics, tobacco smoking, smokeless tobacco, cessation, SHS smoke exposure economics, media, and knowledge, attitude and perceptions. Countries are required to add as many of the core questions as possible. Optional questions have also been developed for countries who wish to include them. The participating countries may include other questions they deem relevant to their situation. Data used in this research were obtained by core questions. Table 1.2 shows the definitions of the variables used in this study.

Table 1. 2: Study measures and survey items with responses, Global Adult Tobacco Survey (GATS)

Study GATS survey items GATS item Categorization measure responses Status determination Smoking Do you *currently* smoke tobacco on a daily Daily status basis, less than daily, or not at all? Less than daily Not at all Dependent variable Intention to During the past 12 months, have you tried to stop Yes/No Quit someday but not quit smoking? <12 months within the next 12 smoking Thinking about the last time you tried to quit, how months, or not long did you stop smoking? Quit within the interested in quitting= Which of the following best describes your next one month Precontemplation thinking about quitting smoking? Quit within the Quit within the next 12 next 12 months months=Contemplation Quit someday Quit within the next but not within one month and had the next 12 stopped smoking for at months, Not least 1 day but less than interested in 12 months in the past quitting year=Preparation

Utilization During the past 12 months, did you use any of the Yes Counseling (Yes=1, of cessation following to try to stop smoking tobacco? No No=0) assistance [Counseling, including at a smoking cessation Medical treatment clinic, nicotine replacement therapy, such as the (Yes=1, No=0) patch or gum ,other prescription medications, Quitline (Yes=1, No=0) traditional medicine, a quitline or a smoking telephone support line] Smoking On average, how many of the following products Total number of intensity do you currently smoke each day? [Manufactured tobacco products cigarettes, hand-rolled cigarettes, kreteks, pipes smoked daily full of tobacco, cigars (cheroots, cigarillos), water pipe sessions, others) Independent variables Knowledge Based on what you know or believe, does smoking Yes No or Don’t know = 0 of smoking tobacco cause serious illness? No Yes = 1 harm Don’t know Exposure to In the last 30 days, have you noticed *information* Yes No/not applicable to anti- about the dangers of smoking cigarettes or that No all=0 smoking encourages quitting in any of the following places? Not Applicable Yes to only one=1 media Yes to more than one=2 messages Home Which of the following best describes the rules Allowed Allowed or no rules=0 smoking about smoking inside of your home? Not allowed but (smoking allowed) rule exceptions Not allowed but Never allowed exceptions= 1 (smoking No rules restriction) Never allowed=2 (smoke-free) Education What is the highest level of education you have Below high school=0 completed? High school=1 Above high school=2 58

Table 1.2 cont’d

Employment Which of the following best describes your Government Unemployed=0 status *main* work status over the past 12 months? employee, non- Employed=1 government employee, self- employed, student, homemaker, retired, unemployed-able to work, or unemployed- unable to work Health care Yes (ii) No=0 (no provider (i) Have you visited a doctor or other health care No intervention) behavioral provider in the past 12 months? intervention (ii) During any visit to a doctor or health care (ii) Yes and iii) No=1 provider in the past 12 months, were you asked if (Tobacco screening) you smoke tobacco? (iii) Yes=2 (Advice to (iii) During any visit to a doctor or health care quit) provider in the past 12 months, were you advised to quit smoking tobacco? Age How old are you? 15-24 years 25-44 years 45-64 years 65+ years Sex Male Female Residence Rural Urban

CHAPTER 2

FACTORS ASSOCIATED WITH INTENTION TO QUIT TOBACCO SMOKING IN 14

LOW-AND-MIDDLE INCOME COUNTRIES

Daniel Owusu, DrPHc1; Megan Quinn, DrPH1; Sreenivas P. Veeranki, MBBS, DrPH2; Ke- Sheng Wang, PhD1; Hadii M. Mamudu, PhD, MPA3

1Department of Biostatistics and Epidemiology, East Tennessee State University, Johnson City, Tennessee 2Division of Epidemiology, Department of Preventive Medicine and Community Health, University of Texas Medical Branch, Galveston, Texas 3Department of Health Services Management and Policy, East Tennessee State University, Johnson City, Tennessee

*Correspondent author: Daniel Owusu, Box 13166, Johnson City, TN 37614. Tel: +1 404 491 8940 Email: [email protected]

What is known on this subject

Intention to quit predicts quit attempt and tobacco smoking cessation. Determinants of intention to quit include education, sex, age, anti-smoking messages, and socio-economic status.

What this study adds

. The study estimates intention to quit among adult smokers in low- and middle-income

countries (LMICs)

. The study provides determinants of three levels of intention to quit among adult smokers

in LMICs.

Word count: 3766

Keywords: Intention to quit, tobacco smoking, transtheoretical model, Global Adult Tobacco Survey (GATS), low-and-middle income countries.

Abstract

Introduction: Understanding factors that promote intention to quit is important for policy and programs to control the tobacco epidemic in low-and-middle income countries (LMICs). This study evaluated factors associated with three levels of intention to quit smoking among adults in

LMICs.

Method: Data from 43,542 participants of the Global Adults Tobacco Survey (GATS) in 14

LMICs were analyzed. Intention to quit was categorized into precontemplation (referent category), contemplation and preparation stages. Multinomial logit models were built for pooled data, and for each country. Odds ratios (ORs) with 95% confidence intervals (CIs) were estimated.

Results: Approximately 82%, 14% and 4% of smokers were in precontemplation, contemplation and preparation, respectively. Males were less likely to be in contemplation (OR=0.70, 95%

CI=0.6–0.9) and preparation (OR=0.72, 95% CI=0.5–1.0) than females. Rural dwellers were more likely to be in contemplation (OR=1.41, 95% CI=1.1–1.8) and preparation (OR=1.28, 95%

CI=1.0–1.6) than urban dwellers. Smoke-free homes were associated with increased odds of contemplation and preparation (OR=1.77, 95% CI=1.5–2.1 and OR=2.18, 95% CI=1.8–2.7, respectively). Exposure to anti-smoking message in more than one channel was associated with contemplation (OR=1.60, 95% CI=1.3–1.9) and preparation (OR=1.73, 95% CI=1.2–2.4).

Factors associated with intention to quit varied across the countries.

Conclusion: Home smoking rule and anti-smoking messages were the major determinants of intention to quit. The results suggest that comprehensive anti-smoking campaigns and smoke-

61 free policies, by implementing the Framework Convention for Tobacco Control Articles 11 – 13, will promote intention to quit smoking in LMICs.

INTRODUCTION

Tobacco is expected to kill half a billion of all persons alive today by the end of the century.1,2

Given that about 80% of all smokers reside in low-and-middle income countries (LMICs),3,4 over

80% of the projected eight million tobacco-related deaths globally are expected to occur in

LMICs by 20303 if current trend is not curtailed.5

Promoting tobacco cessation is one of the key components of a comprehensive tobacco control program6 and intention to quit is integral part of the cessation process.7 Intention to quit has been found to be significantly associated with quit attempt.8 Therefore, understanding factors that promote quit intention is important for intervention planning.

While extensive literature exists on factors associated with intention to quit elsewhere,9–12 there is paucity of literature in LMICs on quit intention. The few studies that have characterized intention to quit smoking in LMICs were either sub-national or did not consider different levels of intention to quit or both.13–15 To the best of knowledge, only one study has examined quit intention in 17 countries simultaneously using the Global Adult Tobacco Survey (GATS) data.16

However, this report concentrated on anti-smoking media messages exposure and quit intention and failed to consider differences in smokers’ intention to quit. Given that differences in cessation stages may predict quit attempt and cessation,17 it is important to understand characteristics of adults at different stages of the cessation process for stage-specific intervention planning. To fill this gap, this study’s aim was to use the transtheoretical model of behavior change to classify study participants into precontemplation, contemplation and preparation groups and identify individual and broad social characteristics of these groups.

The proposed study will provide comprehensive information on intention to quit smoking in about half of smokers worldwide and will serve as evidence to inform policy and intervention planning.

Several factors may influence the development of intention to quit smoking. Evidence on educational and wealth-related differences on the intention to quit cigarette smoking among smokers has also been reported.9 Springvloet et al. (2015) observed that educated smokers were more likely to have noticed anti-tobacco information, and noticing anti-tobacco information increased intention to quit.10 Indeed, environmental cues have been found to promote quit attempts,11 and quit attempt is known to be preceded by quit intention,18 which provides evidence that environmental cues are also important in the development of quit intentions. Health warnings on cigarette packages serve as direct communication with smokers and offer education about smoking harm and lead to cessation.12 Poor social image of tobacco users may increase quit intentions,11 while limited knowledge on the detrimental effect of tobacco on health and acceptability of tobacco by some populations may prolong the development of quit intentions among tobacco smokers.19,20

Our study will add to the body of knowledge on intention to quit smoking, while providing the baseline for future assessment of intention to quit in the 14 countries.

METHODS

Data

The Global Adult Tobacco Survey (GATS) data from 14 LMICs, between 2009 and 2012 inclusive were analyzed. Details of GATS design, implementation and data collection have been published elsewhere.21–23 Briefly, GATS is part of the Global Tobacco Surveillance system,

63 developed to monitor global tobacco use and control. GATS is a nationally representative survey of civilian noninstitutionalized adults aged 15 years or older. The survey uses multi-stage clustered probability sampling technique to obtain nationally representative respondents to answer survey questions. Data collection is through face-to-face household interviews. It employs consistent and standard protocol which allows for cross-country comparisons. The standard protocol is in respect to questionnaire, sampling, data collection and data management procedures. The GATS has core questionnaire consisting of eight sections: background characteristics, tobacco smoking, smokeless tobacco, cessation, secondhand smoke (SHS) exposure, economics, media, and knowledge, attitude and perceptions. Countries are required to add as many of the core questions as possible. In addition, optional questions have been designed for countries who wish to include them. Again, each country may include other questions that are relevant to the country’s own situation.

To allow for pooled data analysis, data included in this study were obtained by standard core questions common to all the countries. Pooling data from several countries for analysis is consistent with literature.24–26 Included in this analysis were current smokers at the time of interview, who had information on quit attempt within 12 months prior to the interview and who provided information on their intentions to quit smoking. Smoking status was determined from the question: ‘Do you *currently* smoke tobacco on a daily basis, less than daily, or not at all?’

Those who answered ‘daily basis’ or ‘less than daily basis’ were considered to be current smokers and were included in this analysis. Countries with sufficient data for the multinomial logit model and information on all the selected variables were included in the analysis. Data were obtained from GATS conducted in Bangladesh (2009), China (2010), Egypt (2009), India

(2010), Indonesia (2011), Malaysia (2011), Nigeria (2012), Philippines (2009), Russia (2009),

Thailand (2011), Turkey (2012), Ukraine (2010), Uruguay (2009) and Vietnam (2010). Though

Russia Federation is currently a high-income country, it was included because at the time of the survey in 2009, it was classified as middle-income country. In Thailand, the survey has been conducted twice within the period, but the more recent data (2011) were used.

Variables

Outcome variable. Intention to quit tobacco smoking was categorized into precontemplation, contemplation and preparation stages based on the transtheoretical model. First, current smokers were asked if they attempted to quit smoking in the past 12 months. Those who answered ‘Yes’ were further asked how long they stopped smoking. Attempt to quit was defined as abstinence from smoking for at least one day in the past year. Future quit intentions were determined for all participants by the question: “Which of the following best describes your thinking about quitting smoking? I am planning to quit within the next month, I am thinking about quitting within the next 12 months, I will quit someday but not within the next 12 months, or I am not interested in quitting?”

To be consistent with literature, 27 adults who had no intention to quit smoking within 12 months were classified as being in precontemplation. Respondents who had made an attempt to quit smoking in the past 12 months but who had no intention to quit within the next 12 months were considered to have relapsed into the precontemplation stage. All those planning to quit within one year, except those who plan to quit within the next month and who had attempted to quit in the past 12 months, were classified as being in contemplation to quit smoking. Lastly, respondents who planned to quit in the next one month and who had attempted to quit in the past

12 months were assumed to be in preparation to quit smoking.

Independent variables. Independent variables included in the analysis were age, sex, exposure to anti-smoking media messages, exposure to health warnings on cigarette packages, knowledge of smoking harm, home smoking rule, employment status, educational level and residence. Age was categorized into four age groups (15-24, 25-44, 45-64 and 65+ years old) in consistence with the recommendations of the GATS Collaborative Group.28 Exposure to anti-smoking media messages was determined from the question, “In the last 30 days, have you noticed

*information* about the dangers of smoking cigarettes or that encourages quitting in any of the following places?” Exposure to anti-smoking messages through four main channels (newspapers or magazines, television, radio and billboards) was analyzed in this study. Study participants were categorized into ‘no exposure’, ‘exposure to only one channel’, and ‘exposure to more than one channel’ in consistence with literature.16 Exposure to health warnings on cigarette packages

(Yes/No) was determined from the question, “In the last 30 days, did you notice any health warnings on cigarette packages?” (Yes/No). Educational level was classified into: below high school, high school, and above high school education. Participants were also categorized into two groups based on their self-reported employment status: ‘employed’ and

‘unemployed/student. Participants were considered to be knowledgeable of smoking harm if they answered ‘Yes’ to the question, “Based on what you know or believe, does smoking tobacco cause serious illness?” Lastly, home smoking rule was categorized into ‘smoking allowed’ (no rule or smoking allowed); smoking restriction (smoking generally not allowed but with exception); and smoke-free (smoking never allowed).

Statistical Analysis

SAS version 9.4 (SAS Institute, Cary, NC, USA) was used to conduct data management and statistical analyses. Sampling weights were used in all estimations so that estimates will be

66 representative of the populations from which the samples were drawn. SAS survey procedure syntaxes were used to obtain weighted estimates of different categories of intention to quit and chi-square test was used to examine differences in the three levels of intention to quit by each independent variable. Multinomial logit model (MNLM) was built to evaluate factors associated with intention to quit smoking. The MNLM, also known as polytomous logistic regression model, is an extension of the logistic model which models outcomes with more than two categories and employs the maximum likelihood estimation method to estimate model parameters. Current smokers at the precontemplation stage were used as referent category for the

MNLM. MNLM was built for pooled data from all the 14 countries and for each country separately. All models were examined for model diagnostics and no significant correlation that warranted deletion or adjustment of variables was detected. Odds ratios (OR) and 95% confidence intervals (CI) with associated significance levels were reported. For inferential purposes, P-value <0.05 was considered statistically significant.

RESULTS

A total of 43,542 current smokers, representing approximately 580 million adults aged 15+ years from 14 LMICs were included in the study. Less than a quarter of the participants were females in each country, except Russia Federation (30%) and Uruguay (42%). Approximately 15%, 47%,

32% and 6% were in the ages of 15-24, 25-44, 45-64 and 65+ years, respectively.

Overall, approximately 82%, 14% and 4% of adult smokers were in precontemplation, contemplation and preparation to quit smoking, respectively. By country, proportions in precontemplation, contemplation and preparation ranged from approximately 61% (Bangladesh) to 90% (Indonesia), 7% (Indonesia) to 27% (Bangladesh) and 2% (China) to 13% (Nigeria),

67 respectively (Figure 2.1). Table 2.1 presents the differences in three categories of adults’ intention to quit smoking by independent variables.

Figure 2.1: Current smokers' cessation stages by country

Precontemplation Contemplation

Preparation Country of survey of Country

Proportion (%)

Table 2.1: Proportions of current smokers in the stages of tobacco cessation by independent variables

Variable Precontemplation Contemplation Preparation P-value Sample n (%) % (95% CI) % (95% CI) % (95% CI) Gender 0.0033 Female 77.9 (75.5–80.3) 17.3 (15.0–19.5) 4.8 (3.8–5.9) 5041 (7.9) Male 82.0 (80.9–83.1) 14.0 (13.0–15.0) 4.0 (3.6–4.5) 38501 (92.1) 15-24 years 78.0 (73.8–82.3) 15.4 (11.5–19.3) 6.6 (4.4–8.7) 4460 (15.3) 25-44 years 82.2 (80.9–83.5) 14.3 (13.0–15.5) 3.5 (3.1–3.9) 20903 (46.8) 45-64 years 82.2 (80.7–83.6) 14.1 (12.7–15.4) 3.7 (3.2–4.3) 14536 (31.7) 65+ years 84.0 (81.6–86.5) 11.9 (9.6–14.3) 4.1 (3.1–5.1) 3643 (6.3) Education 0.0017 Below high school 81.4 (80.6–82.3) 13.9 (13.1–14.6) 4.7 (4.3–5.1) 29713 (53.3) High school 83.1 (80.6–85.5) 14.0 (11.7–16.3) 2.9 (2.0–3.9) 7240 (35.6) Above high school 78.5 (75.9–81.0) 16.6 (14.3–9.0) 4.9 (3.8–6.0) 6589 (11.1) Residence 0.0215 Urban 83.2 (81.6–84.8) 12.9 (11.4–14.5) 3.8 (3.3–4.3) 20004 (41.5) Rural 80.6 (79.3–81.9) 15.1 (13.9–16.3) 4.3 (3.6–4.9) 23538 (58.5) Employment status 0.0015 Unemployed/student 79.1 (76.9–81.2) 15.2 (13.3–17.1) 5.7 (4.8–6.7) 9034 (13.8) Employed 82.1 (81.0–83.2) 14.1 (13.0–15.1) 3.8 (3.4–4.3) 34508 (86.2) Seen warning label 0.2318 No 82.0 (79.7–84.2) 14.7 (12.5–17.0) 3.3 (2.7–3.9) 6314 (16.5) Yes 81.6 (80.5–82.8) 14.1 (13.1–15.2) 4.2 (3.7–4.7) 37228 (83.5) Know smoking harm <.0001 No 91.7 (90.3–93.2) 6.9 (5.5–8.2) 1.4 (0.9–1.9) 4588 (15.6) Yes 79.8 (78.7–81.0) 15.6 (14.5–16.7) 4.6 (4.1–5.1) 38954 (84.4) Home smoking rule <.0001 Smoking allowed 84.2 (83.0–85.4) 12.6 (11.5–13.7) 3.2 (2.7–3.7) 28975 (75.8) Smoking restriction 76.3 (73.7–79.0) 17.8 (15.2–20.3) 5.9 (4.9–6.9) 6651 (12.4) Smoke-free 71.0 (68.7–73.3) 21.1 (19.0–23.2) 7.9 (6.8 –8.9) 7916 (11.8) Exposure to anti-smoking <.0001 media message No 85.4 (84.0–86.7) 11.5 (10.4–12.7) 3.1 (2.3–3.8) 15404 (43.2) One channel 81.2 (79.0–83.5) 14.9 (12.6–17.2) 3.9 (3.3–4.5) 11529 (27.9) More than one channel 76.6 (74.8–78.4) 17.6 (16.0–19.3) 5.8 (5.1–6.4) 16609 (28.9) Total population 81.7 (80.7–82.7) 14.2 (13.3–15.2) 4.1 (3.7–4.5 ) 43542 Note: CI, Confidence interval. P-values are based on X2 test. Data source: GATS 2009-2012

Table 2.2 shows the results of the pooled data analysis. The adjusted estimates of MLNM

method with adults in the precontemplation group as a referent category showed that, males were

30% (OR=0.70, 95% CI=0.6–0.9) and 28% (OR=0.72, 95% CI=0.5–1.0) significantly less likely

to be in contemplation and preparation, respectively than females. Participants residing in rural

69 areas were more likely to be in contemplation (OR=1.41, 95% CI=1.1–1.8) and preparation

(OR=1.28, 95% CI=1.0–1.6) stages compared to adult smokers residing in urban areas.

Knowledge of smoking harm was associated with an increased probability of being in contemplation and preparation (OR=2.23, 95% CI=1.7–2.8 and OR=2.57, 95% CI= 1.7–3.8, respectively). Home smoking restriction was associated with 49% and 76% elevated odds of contemplation and preparation, respectively. Smoke-free homes were associated with increased likelihood of contemplation and preparation (OR=1.77%, 95% CI=1.5–2.1 and OR=2.18, 95%

CI=1.8–2.7, respectively). Exposure to anti-smoking messages in one media channel was associated with only contemplation (OR=1.37, 95% CI=1.1–1.7) but exposure to more than one channel was associated with both contemplation (OR=1.60, 95% CI=1.3–1.9) and preparation

(OR=1.73, CI=1.2– 2.4).

Table 2.2: Factors associated with intention to quit smoking in the 14 countries combined (N=43542) Contemplation Preparation Variable OR 95% CI OR 95% CI Gender Female 1.00 1.00 Male 0.70*** 0.6 – 0.9 0.72* 0.5 – 1.0 Age 15-24 years 1.00 1.00 25-44 years 0.84 0.6 – 1.2 0.49*** 0.3 – 0.7 45-64 years 0.91 0.6 – 1.3 0.60* 0.4 – 0.9 65+ years 0.81 0.6 – 1.2 0.66 0.4 – 1.0 Education Below high school 1.00 1.00 High school 1.31** 1.1 – 1.6 1.14 0.8 – 1.6 Above high school 1.33 0.9 – 1.6 1.23 0.9 – 1.6 Residence Urban 1.00 1.00 Rural 1.41** 1.1 – 1.8 1.28* 1.0 – 1.6 Employment status Unemployed/student 1.00 1.00 Employed 1.06 0.9 – 1.3 0.91 0.7 – 1.2 Seen warning label No 1.00 1.00 Yes 0.76* 0.6 – 1.0 1.17 0.9 – 1.5 Know smoking harm No 1.00 1.00 Yes 2.23*** 1.7 – 2.8 2.57*** 1.7 – 3.8 Home smoking rule Smoking allowed 1.00 1.00 Smoking restriction 1.49*** 1.2 – 1.9 1.76*** 1.4 – 2.2 Smoke-free 1.77*** 1.5 – 2.1 2.18*** 1.8 – 2.7 Exposure to anti-smoking media message No 1.00 1.00 One channel 1.37** 1.1 – 1.7 1.3 0.9 – 1.9 More than one channel 1.60*** 1.3 – 1.9 1.7 3*** 1.2 – 2.4 Note: OR, Odds ratio; C, Contemplation; P, Preparation; CI, Confidence interval; *p<0.05; **p<0.01; ***p<0.001. Intention to quit was categorized into precontemplation, contemplation and preparation, based on the Transtheoretical model. Odds ratios were derived from weighted multiple multinomial logistic regression model. Referent for the multinomial model was precontemplation. Estimates were adjusted for survey country. Data source: GATS 2009 – 2012.

Table 2.3 shows country-specific determinants of intention to quit smoking. The significant determinants in the countries were home smoking rule (12 countries), exposure to anti-smoking messages (11 countries), knowledge of smoking harm (9 countries), age (8 countries), education

(7 countries), residence (6 countries), warning label (5 countries), sex (3 countries) and employment status (1 country). The most frequent factor associated with contemplation was exposure to anti-smoking messages in more than one channel. It was associated with increased odds of contemplation in 11 countries (OR ranged from 1.30 (95% CI=1.0–1.6) in India to 2.96

(95% CI=1.1–8.0) in Nigeria). The second frequent factor associated with contemplation was smoke-free home, which showed positive association in 9 countries (OR ranged from 1.46 (95%

CI=1.1–1.9) in Turkey to 2.56 (95% CI=1.7–3.9) in Bangladesh). For preparation, the most frequent associated factor was smoke-free home; it was significant in 9 countries (significant OR ranged from 1.95 (95% CI=1.3–3.0) in Philippines to 3.90 (95% CI=1.6–9.8) in Indonesia). The second best determinant of preparation was home smoking restriction (significant in 9 countries;

OR ranged from 1.50 (95% CI=1.0–2.2) in India to 3.31 (95% CI=1.9–5.7) in Indonesia.

Table 2.3: Factors associated with intention to quit smoking by country Country (N=43542) Bangladesh (N=1929) China (N=3578) Egypt (N=4123) India (N=10371) Determinants C (OR, CI) P (OR, CI) C (OR, CI) P (OR, CI) C (OR, CI) P (OR, CI) C (OR, CI) P (OR, CI) Male vs Female 2.17 1.26 0.73 0.42 0.66 1.18 0.61** 0.76 (0.7–7.0) (0.3–5.0) (0.4–1.5) (0.1–1.7) (0.3– 1.4) (0.4–3.8) (0.4–0.8) (0.4–1.3) 0.63 1.26 0.82 0.30 0.92 1.26 0.91 0.40*** 25-44 years vs 15-24 years (0.4–1.0) (0.7–2.4) (0.4–1.9) (0.1–1.0) (0.6–1.3) (0.7–2.3) (0.6–1.3) (0.3–0.6) 0.81 1.8 1.15 0.67 0.73 0.68 0.74 0.41*** 45-64 years vs 15-24 years (0.5–1.3) (0.9–3.5) (0.5–2.6) (0.2–2.0) (0.5–1.0) (0.4–1.3) (0.5–1.1) (0.3–0.6) 1.33 1.38 1.08 0.72 0.49** 2.28* 0.55** 0.44** 65+ years vs 15-24 years (0.7–2.7) (0.5–4.1) (0.5–2.6) (0.2–3.4) (0.3–0.8) (1.0–5.0) (0.4–0.9) (0.3–0.8) 1.96 0.87 1.61 1.57 0.89 0.42* 1.07 1.11 High school vs below high school (0.9–4.2) (0.3–2.6) (1.1–2.3) (0.6–3.9) (0.6–1.4) (0.2–0.8) (0.8–1.4) (0.7–1.9) 0.85 0.98 1.92 1.89 1.17 0.82 1.01 0.93 Above high school vs below high school (0.4–1.8) (0.4–2.4) (1.1–3.5) (0.6–6.4) (0.92–1.5) (0.5–1.3) (0.7–1.4) (0.6–1.4) 1.03 0.94 1.13 1.81 1.02 1.32 1.02 1.34 Rural vs Urban (0.8–1.4) (0.6–1.5) (0.4–3.0) (1.1–3.1) (0.8–1.3) (0.9–1.9) (0.8–1.2) (1.0–1.8) 0.83 0.46 1.00 0.53 0.97 0.92 1.00 1.19 Employed vs Unemployed/student (0.4–1.0) (0.2–1.2) (0.5–2.1) (0.2–1.7) (0.7–1.4) (0.5–1.6) (0.8–1.3) (0.8–1.8) 0.92 1.19 0.52* 1.44 0.54 1.55 0.91 0.97 Seen warning label (yes vs no) (0.6–1.4) (0.6–2.4) (0.3–0.9) (0.4– 4.8) (0.3–1.0) (0.3–7.7) (0.7–1.2) (0.7–1.3) 2.62 2.28 2.63*** 3.41* 1.72 1.21 2.2*** 1.89* Know smoking harm (yes vs no) (0.9–8.0) (0.7–7.6) (1.7–4.1) (1.2–10.1) (0.9–3.4) (0.4–3.4) (1.5–3.2) (1.2–3.1) Home smoking restriction vs smoking 2.61*** 2.56*** 1.50 1.35 1.96*** 1.79* 1.35* 1.50* allowed (1.8–3.8) (1.5–2.5) (0.8–2.8) (0.4 – 4.0) (1.5–2.6) (1.1–2.9) (1.0–1.8) (1.0–2.2) 2.58*** 2.88*** 1.87* 1.03 2.51*** 1.76 1.71*** 2.47*** Smoke-free home vs smoking allowed (1.7–3.9) (1.8–4.7) (1.0–3.5) (0.3–3.7) (1.8–3.5) (1.0–3.1) (1.3–2.2) (1.8–3.4) Antismoking message in One media 1.19 1.76* 1.63* 0.67 1.03 1.17 1.06 1.88** channel vs No exposure (0.8–1.7) (1.0–3.0) (1.0–2.6) (0.3–1.7) (0.8–1.3) (0.7–1.9) (0.8–1.3) (1.2–2.8) Antismoking message in >One media 2.11*** 2.65*** 1.79** 1.14 1.40* 2.68*** 1.30* 2.38*** channel vs No exposure (1.5–3.0) (1.6–4.5) (1.2–2.7) (0.5–2.7) (1.1–1.8) (1.6–4.4) (1.0–1.6) (1.7–3.3) Note: OR= Odds ratio; C=Contemplation; P=Preparation; CI= 95% confidence interval; *p<0.05; **p<0.01; ***p<0.001. Intention to quit was categorized into precontemplation, contemplation and preparation, based on the Transtheoretical model. Odds ratios were derived from weighted multiple multinomial logistic regression model. Referent for the multinomial model was precontemplation. Data source: GATS 2009 – 2012.

Table 2.3 cont’d

Country (N) Determinants Indonesia (N=2720) Malaysia (N=940) Nigeria (N=393) Philippines (N=2727) C (OR, CI) P (OR, CI) C (OR, CI) P (OR, CI) C (OR, CI) P (OR, CI) C (OR, CI) P (OR, CI) 1.04 0.65 2.40 0.45 1.33 3.36 0.90 0.73 Male vs Female (0.4–2.5) (0.2–2.2) (0.4–14.3) (0.1–2.5) (0.2–10.3) (0.2–51.9) (0.6–1.4) (0.4–1.2) 1.07 0.48* 0.78 0.76 0.23* 0.85 0.85 0.78 25-44 years vs 15-24 years (0.6–1.9) (0.2–0.9) (0.3–2.2) (0.3–2.2) (0.1–0.7) (0.2–4.5) (0.6–1.3) (0.5–1.2) 1.48 0.70 0.51 1.34 0.20* 0.78 0.76 0.92 45-64 years vs 15-24 years (0.8–2.7) (0.32–1.5) (0.2–1.4) (0.4–4.2) (0.1–0.8) (0.1–5.6) (0.4–1.6) (0.6–1.5) 1.38 0.85 0.64 0.53 1.51 2.88 0.78 2.09 65+ years vs 15-24 years (0.7–2.9) (0.3–2.5) (0.1–4.8) (0.1–3.4) (0.2–9.2) (0.3–26.5) (0.4–1.6) (1.0–4.4) 1.11 0.51* 1.37 12.8** 1.42 0.91 1.15 1.80** High school vs below high school (0.8–1.7) (0.3–1.0) (0.3–5.7) (2.6–63.2) (0.5–3.7) (0.3–3.3) (0.8–1.7) (1.2–2.7) 0.96 2.09 1.04 2.57 0.59 0.98 1.17 1.45 Above high school vs below high school (0.5–2.0) (1.0–4.4) (0.3–3.2) (0.8–8.8) (0.2–1.9) (0.2–3.9) (0.8–1.8) (0.9–2.3) 1.50* 1.21 0.99 0.91 0.81 0.94 1.50* 1.02 Rural vs Urban (1.0–2.2) (0.7–2.0) (0.5–1.9) (0.4–2.1) (0.4–1.8) (0.3–2.7) (1.1–2.1) (0.7–1.5) 1.23 0.92 0.99 0.49 3.04 0.76 0.79 0.92 Employed vs unemployed/student (0.6–2.4) (0.4–2.1) (0.3–3.5) (0.2–1.2) (0.7–14.1) (0.1–4.0) (0.5–1.2) (0.6–1.4) 1.02 1.99* 0.82 4.64 0.81 3.06* 1.39 0.76 Seen warning label (yes vs no) (0.7–1.5) (1.0–3.9) (0.2–3.2) (0.5–44.5) (0.4–1.8) (1.3–7.4) (0./8–2.5) (0.5–1.3) 0.97 4.65** 1.38 1.40 2.80* 4.96** 1.82 2.70* Know smoking harm (yes vs no) (0.6–1.5) (1.7–12.6) (0.4–4.3) (0.3–6.3) (1.2–6.7) (1.5–16.2) (0.9–3.5) (1.2–6.0) Home smoking restriction vs smoking allowed 1.79** 3.31*** 1.07 0.66 1.89 2.44 1.89** 1.67* (1.2–2.7) (1.9–5.7) (0.4–2.9) (0.6–4.5) (0.6–5.9) (0.7–8.8) (1.3–2.8) (1.0–2.7) Smoke-free home vs smoking allowed 2.51** 3.90** 1.49 1.17 1.10 3.56* 1.41 1.95** (1.3–4.8) (1.6–9.8) (0.7–3.1) (0.4–3.3) (0.5–2.7) (1.3–9.7) (1.0–2.1) (1.3–3.0) Antismoking message in One media channel vs 1.67* 1.93* 0.38 0.24 1.04 2.28 2.41*** 1.21 No exposure (1.1–2.5) (1.0–3.5) (0.1–2.5) (0.0–1.8) (0.4–2.7) (0.8–6.3) (1.5–3.8) (0.7–2.0) Antismoking message in >One media channel 1.96** 1.41 1.47 0.60 2.96* 0.97 2.55*** 1.13 vs No exposure (1.3–3.0) (0.7–2.8) (0.4–5.1) (0.1–2.8) (1.1–8.0) (0.3–3.2) (1.7–3.8) (0.7–1.7) Note: OR= Odds ratio; C, Contemplation; P, Preparation; CI, 95% confidence interval; *p<0.05; **p<0.01; ***p<0.001. Intention to quit was categorized into precontemplation, contemplation and preparation, based on the Transtheoretical model. Odds ratios were derived from weighted multiple multinomial logistic regression model. Referent for the multinomial model was precontemplation. Data source: GATS 2009 – 2012.

Table 2.3 cont’d

Country (N) Determinants Russia F. (N=4488) Thailand (N=4141) Turkey (N=2350) Ukraine (N=2253) C (OR, CI) P (OR, CI) C (OR, CI) P (OR, CI) C (OR, CI) P (OR, CI) C (OR, CI) P (OR, CI) 0.64** 1.11 0.82 0.53* 0.98 0.77 0.85 0.79 Male vs Female (0.5–0.9) (0.6–2.0) (0.5–1.3) (0.3–1.0) (0.8–1.3) (0.5–1.2) (0.6–1.2) (0.4–1.6) 0.74 0.86 1.03 1.32 1.75** 1.00 0.65* 0.26*** 25-44 years vs 15-24 years (0.5–1.1) (0.5–1.6) (0.6–1.7) (0.5–3.4) (1.2–2.6) (0.6–1.7) (0.4–1.0) (0.1–0.5) 0.46** 0.52 0.92 0.85 1.48 0.80 0.64* 0.32** 45-64 years vs 15-24 years (0.3–0.7) (0.3–1.0) (0.6–1.5) (0.3–2.1) (1.0–2.3) (0.4–1.5) (0.4–1.0) (0.1–0.7) 0.45 0.47 1.23 1.70 1.27 0.62 0.67 0.21* 65+ years vs 15-24 years (0.2–1.0) (0.1–1.6) (0.7–2.2) (0.6–5.3) (0.7–2.5) (0.3–1.5) (0.4–1.2) (0.1–0.7) 1.89* 2.23 1.15 1.61 1.01 1.01 1.32 0.52* High school vs below high school (1.1–3.1) (1.0–5.1) (0.8–1.7) (0.8–3.4) (0.8–1.3) (0.7–1.5) (1.0–1.8) (0.3–1.0) 1.65** 0.97 0.98 1.68 1.18 0.94 1.67* 1.64 Above high school vs below high school (1.2–2.3) (0.5–1.9) (0.6–1.7) (0.9–3.2) (0.8–1.7) (0.6–0.6) (1.1–2.5) (0.7–4.0) 1.39* 1.24 0.81 0.59* 1.11 1.04 1.33* 0.91 Rural vs Urban (1.1–1.8) (0.8–1.9) (0.6–1.1) (0.4–1.0) (0.9–1.4) (0.7–1.5) (1.0–1.8) (0.5–1.6) 1.06 0.71 0.85 1.15 0.95 0.89 1.42* 0.85 Employed vs unemployed/student (0.7–1.6) (0.4–1.3) (0.5–1.4) (0.4–2.9) (0.7–1.2) (0.6–1.3) (1.1–1.9) (0.5–1.6) 1.49 0.45 1.02 1.19 1.88* 2.19 0.97 7.5 Seen warning label (yes vs no) (0.8–2.9) (0.1–1.4) (0.6–1.6) (0.5–3.0) (1.1–3.3) (0.9–5.6) (0.5–2.0) (0.9–63.7) 2.20** 1.76 1.21 1.04 1.97 1.34 2.67*** 4.24* Know smoking harm (yes vs no) (1.4–3.4) (0.7–4.4) (0.5–2.9) (0.2–6.3) (1.0–3.9) (0.5–3.7) (1.6–4.6) (1.2–14.5) Home smoking restriction vs smoking 1.45* 1.31 0.79 0.3 1.19 1.61* 1.35 1.85 allowed (1.0–2.1) (0.7–2.4) (0.3–2.1) (0.0–2.3) (0.9–1.6) (1.0–2.6) (0.9–2.0) (0.7–5.0) Smoke-free home vs smoking allowed 1.67** 1.18 1.12 0.97 1.46** 2.30*** 1.54* 2.86* (1.2–2.4) (0.6–2.2) (0.8–1.5) (0.6–1.7) (1.1–1.9) (1.5–3.4) (1.1–2.2) (1.2–7.0) Antismoking message in One media 1.27 1.49 1.29 1.66 1.58 3.42* 1.07 1.80 channel vs No exposure (0.9–1.8) (0.8–2.7) (0.8–2.0) (0.6–4.5) (1.0–2.8) (1.2–10.2) (0.8–1.5) (0.9–3.8) Antismoking message in >One media 1.50* 1.57 1.74** 2.98* 1.55 4.69** 1.58** 3.17*** channel vs No exposure (1.1–2.1) (0.9–2.7) (1.2–2.5) (1.2–7.5) (1.0–2.7) (1.6–13.7) (1.2–2.2) (1.7–5.9) Note: OR, Odds ratio; C, Contemplation; P, Preparation; CI, 95% confidence interval; *p<0.05; **p<0.01; ***p<0.001. Intention to quit was categorized into precontemplation, contemplation and preparation, based on the Transtheoretical model. Odds ratios were derived from weighted multiple multinomial logistic regression model. Referent for the multinomial model was precontemplation. Data source: GATS 2009 – 2012.

Table 2.3 cont’d

Country (N) Determinants Uruguay (N=1360) Vietnam (N=2169) C (OR, CI) P (OR, CI) C (OR, CI) P (OR, CI) 1.16 1.17 0.78 0.70 Male vs Female (0.8–1.17) (0.7–2.0) (0.4–1.7) (0.2–2.2) 1.14 0.70 0.91 0.66 25-44 years vs 15-24 years (0.7–1.9) (0.4–1.4) (0.7–1.5) (0.4–1.2) 1.35 0.59 0.89 0.42* 45-64 years vs 15-24 years (0.8–2.3) (0.3–1.2) (0.5–1.4) (0.2–0.8) 0.99 0.49 0.64 0.66 65+ years vs 15-24 years (0.5–2.1) (0.1–1.6) (0.3–1.2) (0.6–1.6) 1.46 0.58 2.22*** 1.15 High school vs below high school (0.9–2.3) (0.3–1.3) (1.5–3.2) (0.6–2.2) 1.74 1.29 1.82** 1.67 Above high school vs below high school (1.0–3.1) (0.5–3.3) (1.2–2.7) (1.0–2.9) 1.13 1.11 1.82*** 1.66* Rural vs Urban (0.8–1.6) (0.7–1.8) (1.4–2.4) (1.1–2.5) 0.69 1.57 0.98 1.13 Employed vs unemployed/student (0.5–1.0) (0.9–2.9) (0.6–1.6) (0.6–2.3) 1.53 0.89 1.84* 1.41 Seen warning label (yes vs no) (0.6–3.9) (0.2–3.5) (1.0–3.3) (0.6–3.1) 4.65* 1.68 3.61*** 4.05 Know smoking harm (yes vs no) (1.2–18.5) (0.3–11.1) (1.7–4.6) (0.8–21.0) Home smoking restriction vs smoking 2.40*** 2.84** 1.20 2.93*** allowed (1.5–3.8) (1.5–5.4) (0.8–1.8) (1.7–5.1) Smoke-free home vs smoking allowed 2.07*** 2.97*** 1.20 3.10*** (1.4–3.1) (1.7–5.3) (0.7–2.1) (1.6–5.9) Antismoking message in One media 0.82 1.04 1.26 0.77 channel vs No exposure (0.5–1.5) (0.4–2.6) (0.6–2.5) (0.3–2.1) Antismoking message in >One media 1.00 1.48 1.58 1.77 channel vs No exposure (0.6–1.6) (0.7–3.1 ) (0.8–3.2) (0.7–4.5) Note: OR, Odds ratio; C, Contemplation; P, Preparation; CI, 95% confidence interval; *p<0.05; **p<0.01; ***p<0.001. Intention to quit was categorized into precontemplation, contemplation and preparation, based on the Transtheoretical model. Odds ratios were derived from weighted multiple multinomial logistic regression model. Referent for the multinomial model was precontemplation. Data source: GATS 2009 – 2012.

DISCUSSION

Tobacco smoking has been established to have serious health2,29,30 and economic2,31,32 consequences, yet more than one billion people continue to smoke33 with about 80% living in

LMICs.3 In spite of the fact that smoking cessation can prevent millions of deaths,34 cessation rates in LMICs remain low.35,36 To inform policy and intervention planning, this study was conducted to examine factors that are associated with three levels of intention to quit smoking

(precontemplation, contemplation and preparation) among adults in 14 LMICs using data from the GATS. The study included 43,542 participants, representing about 580 million adults from

LMICs, and the results showed that, overall, approximately 4 in 5, 1 in 10 and 1 in 25 adult smokers were in precontemplation, contemplation and preparation, respectively (Table 2.1), though the proportions varied across countries (Figure 2.1).

In contrast with literature,13,37 the pooled data results (Table 2.2) showed that, males were less likely to be either in contemplation or preparation than females. This might be due to the definition of intention to quit in the studies. In the previous studies, intention to quit was broadly defined, while the current study’s definition is specific and based on the transtheoretical model.

Our results suggest the need for gender-specific programs tailored to increase the proportion of both males and females who are in contemplation or preparation to quit smoking. In most of the

LMICs, prevalence of smoking is significantly higher in males than females,21 hence, to achieve significant cessation rates in general, cessation in males will play a very important role. It is also worth mentioning that, while sex was significant in the pooled data, it was significant in only three countries in the country-specific analysis, but in each case, males were less likely to be in contemplation or preparation than females. These results suggest the need for gender-specific programs to increase tobacco cessation in respective countries.

Our results also confirm age differences in intention to quit smoking previously reported in the literature.14,15 However, our results provided further information about the age difference by indicating that age differences occur only in preparation to quit in the pooled data. Compared to participants aged 15-24 years, smokers in the ages of 25-44 and 45-64 years were less likely to be in preparation to quit smoking. This creates a great public health concern since almost 4 in 5 smokers were within these age brackets. The stratified analysis generally confirmed that older age is associated with a reduced probability of being in either contemplation or preparation to quit smoking. In all countries where age was significant, adults older than 15-25 years were less likely to be in contemplation or preparation to quit smoking except Egypt (65+ years significantly more likely than 15-25 years to be in preparation) and Turkey (25-44 years associated positively with contemplation). This calls for intervention to create awareness that smoking cessation at all ages are beneficial, despite the fact that the benefit is greater in those who cease at early age.

The effects of smoke-free policies on the number of daily tobacco smoking and cessation rates have been reported.38,39 In this study, home smoking rule was significantly associated with increased odds of contemplation and preparation in the pooled data, and either contemplation or preparation in about 80% of the countries (12 out of 14 countries). Compared to homes where smoking is allowed, home smoking restriction and smoke-free homes showed association with increased probability of contemplation or preparation stage. A dose-response relationship was observed between home smoking rule and intention to quit smoking. While home smoking restriction showed 49% and 76% increase odds of contemplation and preparation, respectively, smoke-free homes showed 77% and 118% increase odds of contemplation and preparation, respectively. By country, home smoking restriction was associated with contemplation and

78 preparation in 7 countries each, while smoke-free home was associated with contemplation and preparation in 9 countries each.

Smoke-free policy is one of the key policy measures in the FCTC for global tobacco control.40 It has been reported that people who work in a smoke-free environment are likely to also live in smoke-free homes,41 suggesting that public or workplace smoking ban may have a trickle-down effect on home smoking rule. Consequently, public smoking ban will not only protect vulnerable non-smokers, but it will also help to promote smoking cessation by serving as a disincentive to smokers. National governments should therefore fully implement and enforce the Article 8 of the

WHO FCTC.40

In consistence with literature,10,16 anti-smoking messages were found to be associated with intention to quit in the pooled analysis and in about 80% of the countries (11/14). One major issue of tobacco control in LMICS is the invasion of transnational tobacco companies.42 These companies employ several marketing strategies to sell their products, including sponsorships and tobacco advertisement.42 To counter these strategies, the WHO recommends not only banning all forms of tobacco advertisement but also to use counter-messages that encourage quitting.40 The results of this study indicate that these counter-messages could promote contemplation to quit smoking in more countries than preparation to quit smoking (10 vs 7 countries), hence the need for other programs and policies that complement one another for comprehensive tobacco control.6 The results also indicate that anti-smoking messages from different media channels may be more potent in promoting contemplation and preparation to quit smoking than from a single channel (significant in 11 vs 6 countries). The effectiveness of mass media campaigns (MMC) for tobacco control has been reported, and factors such as reach, channel, frequency and nature of the messages are key determinants.43 The use of multiple channels to deliver anti-smoking

79 messages may help increase intention to quit in LMICs, but national governments need to address key challenges of MMC such as reach and frequency. These challenges may not be limited to the rural residents as our pooled and stratified analyses showed that rural residents were more likely to be in contemplation or preparation than urban dwellers. In all countries where residence was significant, rural residence was associated with increased odds of being in contemplation or preparation to quit smoking, except Thailand where rural residence was associated with significant reduction in the odds of preparation to quit smoking.

In contrast to existing literature,44 exposure to health warnings showed significant reduction in the odds of contemplation, and no association with preparation in the pooled data analysis.

However, in all countries where exposure to warning labels was statistically significant (5/14), it was associated with elevated odds of contemplation or preparation, except China where it was associated with a 48% reduction in the odds of contemplation. Hence, the results generally showed that warning label may be an important predictor of intention to quit smoking, in line with literature.44 Warning labels have been found to be effective in communicating dangers of smoking to smokers and they are known to induce quit intentions and increase tobacco cessation.12 Labelling tobacco products packs has been described as a direct communication to the smokers about the harm of the products, and it effectively counters tobacco industry marketing.12

Warning label and anti-smoking messages communicate harm associated tobacco smoking, and this knowledge has been found to promote smoking cessation.11,19 Our results showed that knowledge of harm doubles the chances of being in contemplation and preparation in the pooled data (Table 2.2). The results were replicated in 7 countries (contemplation) and 6 countries

(preparation) (Table 2.3). Related to this knowledge is educational level which has been reported

80 to determine quit intentions and cessation rate.10,13 We found educational level of participants to associated with contemplation but showed no association with preparation. By country, educational level showed significance with contemplation or preparation in 7 countries, where higher education than basic school generally showed increased odds of contemplation or preparation. The overall results suggest that tailoring intervention to smokers’ educational level may be necessary to improve intention to quit across educational levels in the population. For instance, pictorial warning labels, which have been found to be more potent than written labels,45 can bridge the gap between the highly educated and those with low or no formal education.

Our study has important strengths that need to be emphasized. By pooling nationally representative data from 14 tobacco high burden countries, the estimates can be generalized to more than one half of all global tobacco smokers aged 15 years and older. Unlike other studies, this study considered the inherent differences in the levels of intention to quit smoking by classifying adults’ intention into the first three stages of the transtheoretical model of change. By stratifying the analysis, country differences and country-specific determinants of intention to quit smoking have been provided to help national level program planning, as well as cross-country collaboration to control the tobacco epidemic.

The study also has some limitations that must be taken into consideration in interpreting the results. Since the data were from cross-sectional surveys, temporality cannot be established.

Again, in some countries, the analyses were underpowered due to small sample size. This limitation implies that, some variables that were not significant could be significant or effect sizes could increase if the samples were increased. Therefore, this limitation when rectified will provide support for the findings. Also, all information used in the analysis was self-reported and could be affected by recall or social desirability bias. This bias is more likely in the information

81 on quit attempt. However, since quit attempt was measured within the past 12 months, recall bias is expected to be minimal if it occurred.

CONCLUSION

Given that intention to quit smoking predicts quit attempt and smoking cessation, we conducted this study to examine factors that determine different levels of intention to quit in 14 high burden countries, where more than 50% of global tobacco users reside. The results showed that a very small proportion of adults were in preparation stage of the TTM, and home smoking policy and anti-smoking messages were the major factors associated with both contemplation and preparation stages of tobacco cessation. The results suggest that comprehensive anti-smoking campaigns and smoke-free policies, through full implementation of the Framework Convention for Tobacco Control Articles 11 – 13 will promote intention to quit smoking in LMICs.

Funding Source

No funding was received for this study.

Conflict of interest

Authors declare that they have no conflict of interest.

Acknowledgement

Authors will like to thank the Global Adult Tobacco Survey Collaborative Group for providing the data and offering technical advice for this study. We also acknowledge East Tennessee State

University College of Public Health for providing logistic support.

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CHAPTER 3

ASSOCIATION BETWEEN HEALTH CARE PROVIDER INTERVENTION AND

UTILIZATION OF CESSATION ASSISTANCE IN LOW-AND-MIDDLE INCOME

COUNTRIES

Daniel Owusu, DrPHc1; Ke-Sheng Wang, PhD1; Megan Quinn, DrPH1; Sreenivas P. Veeranki , DrPH2; Hadii M. Mamudu , PhD3

*Correspondent author: Daniel Owusu, Box 13166, Johnson City, TN 37614. Tel: +1 404 491 8940 Email: [email protected] What is known on this subject

Health care provider advice to quit is associated with increased smoking quit rates, and increased satisfaction with care in patients.

What important gaps in knowledge exist on this topic

. Relationship between health care provider intervention and utilization of cessation

assistance not evaluated in LMICs.

. There is paucity of literature on utilization of cessation assistance in low-and-middle

countries.

What this study adds

. The study provides information on relationship between health care provider intervention

and utilization of cessation assistance in 11 tobacco high burden countries

. Utilization of cessation assistance is independent of residence (rural vs. urban)

Word count: 3237

Conflict of interest: Authors declare that they have no conflict of interest.

Keywords: Health care provider intervention, tobacco smoking, quit advice, cessation assistance, low-and-middle income countries.

Abstract

Background: The psychological and physiological addictive nature of tobacco smoking makes it difficult for some smokers to quit without assistance. Tobacco cessation and utilization of cessation assistance rates are low in low-and-middle income countries (LMICs). It is not clear if health care provider tobacco screening and quit advice promote utilization of assistance to quit tobacco.

Purpose: To evaluate the relationship between health care provider intervention and utilization of cessation assistance in LMICs.

Method: Data from 5848 participants of the Global Adults Tobacco Survey (GATS) in 11

LMICs were analyzed. Outcome variables were utilization of counseling/cessation clinic, medical treatment, quitline and any cessation assistance. Health care provider intervention (‘no intervention’, ‘tobacco screening’ and ‘quit advice’) was the main independent variable. Four multiple logistic regression models were completed to evaluate the relationship between the independent variable and each outcome, adjusting for covariates. All analyses were conducted

91 using SAS version 9.4. Adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were estimated.

Results: Approximately 45%, 6%, and 49% of participants received no intervention, tobacco screening, and advice to quit, respectively, and approximately 1%, 7%, 9% and 15% used quitline, counseling/cessation clinic, medical treatment, and any cessation assistance, respectively. Quit advice was associated with increased utilization of counseling (OR=3.89, 95%

CI=2.8–5.5), medical treatment (OR=1.71, 95% CI=1.2–2.4) and any assistance (OR=2.60, 95%

CI=2.0–3.4).

Conclusion: A comprehensive tobacco control program, with frequent tobacco screening and quit advice by health care providers may improve utilization of cessation assistance in LMICs.

Introduction

Deaths attributed to smoking are preventable, yet smoking continues to be the leading preventable cause of deaths in the world.[1] More than one billion smokers aged 15 years and above exist in the world over.[2] About 100 million people died from tobacco consumption in the 20th century and one billion deaths will be recorded in the 21st century if current consumption pattern persists.[3] The increasing tobacco epidemic in low-and-middle income countries is not only a threat to health and lives[4] but also a major setback to sustainable economic development in light of the loss of potential years of life attributed to smoking[5] and direct and indirect medical cost due to smoking.[4] Smoking cessations is one of the pillars of tobacco control which can save millions of lives in a few decades.[6] However, the addictive nature of

92 tobacco smoking makes it difficult for smokers to quit,[7] hence the need for support and dependence treatment (cessation assistance).

Cessation interventions are affordable and effective [8] and they could help to control tobacco smoking in LMICs if they are readily available and acceptable to the population. However, utilization of cessation assistance is low in LMICs.[9] Consequently, increase utilization of cessation assistance is needed to increase cessation rates.[10] Therefore, it is important to understand factors that promote utilization of cessation assistance in LMICs for effective tobacco dependence treatment.

Health care professionals play a crucial role in tobacco cessation interventions. Lancaster (2011) emphasized that while physicians could make a significant impact on tobacco smoking reduction through all the six mechanisms stipulated by the World Health Organization (WHO), the clinical setting provides a direct opportunity where physicians counsel and support tobacco users to quit.[11] Brief clinician interventions can make a difference in tobacco cessation and there is established relationship between the intensity of the intervention and tobacco cessation.[12] For instance, the results of a systematic review of 17 trials that examined quit rates in physician advice versus no advice showed 66% greater rate of smoking cessation among participants who received quit advice from physicians.[13] Another meta-analysis of 35 trials that compared nursing advice to quit with normal care (no advice to quit), reported that nursing intervention could increase the chances of quitting by 29%.[14] It is now known that even smokers who receive physician advice to quit report higher satisfaction with health care than those who do not.[12]

The WHO MPOWER package, which aims at assisting countries with the implementation of the

Framework Convention for Tobacco Control (FCTC), recognizes the significant role of health

93 care professionals by encouraging routine tobacco screening and advice to quit smoking by these professionals.[4] Though more than 180 countries, including several LMICs, have ratified the

FCTC [15] and subsequent WHO reports have shown satisfactory progress in implementation by most of the countries,[1,16,17] tobacco smoking quit rate in LMICs is still low.[18,19] Health care providers’ screening for tobacco smoking in some LMICs was reported to range from

34.9% to 82.1%,[20] and utilization of cessation assistance in 16 LMICs has recently been reported to range from 4% to 27%.[9]

While several socio-demographic and economic factors[21–24] may affect utilization of cessation assistance among smokers, literature on the association between health providers behavioral intervention and utilization of cessation assistance in LMICs is sparse. To the best of our knowledge, no study has comprehensively evaluated the association between health care provider behavioral intervention (tobacco screening and advice to quit smoking) and utilization of cessation assistance in LMICs. To fill this gap, our study therefore used data from the Global

Adult Tobacco Survey (GATS), which is considered to be the global standard for monitoring tobacco control[25], to comprehensively evaluate the association between health care provider behavioral intervention and utilization of cessation assistance in 11 tobacco high burden LMICs.

We hypothesized that advice to quit will be associated with increased utilization of cessation assistance, irrespective of residence (rural vs. urban) or a country’s income level. The results of this study will serve as evidence for strengthening health care provider screening and advice to quit smoking in LMICs.

Methods

Data from the Global Adult Tobacco Survey (GATS) 2009-2012 were analyzed. GATS is a component of the Global Tobacco Surveillance System which aims at helping countries to monitor key indicators of tobacco use and control programs. Details of the survey have been published elsewhere.[26–29] Briefly, it is a multi-stage area clustered probability sampling of civilian noninstitutionalized adults aged 15 years and older in each participating countries.

Firstly, countries are divided into primary sampling units (PSU) in proportion to size, based on a recent census data and/or administrative records. Households are then sampled and one adult is randomly selected from eligible adults in each household to answer survey questions. The survey is designed to achieve a nationally representative data with a response rate of at least 80%. The use of standardized study protocol allows for cross-country comparisons of the core indicators and pooled data analysis. The survey questionnaire contains core questions from eight different sections: background characteristics, tobacco smoking, smokeless tobacco use, tobacco cessation, media, secondhand smoke exposure, knowledge and perceptions, and tobacco economics. Each country is allowed to add additional questions relevant to the country’s own situation.

We analyzed publicly available GATS data from Bangladesh (2009), Egypt (2009), Mexico

(2009), China (2010), India (2010), Vietnam (2010), Romania (2011), Thailand (2011),

Argentina (2012) and Turkey (2012). Eligible participants were those who had abstained from smoking for at least one day but less than 12 months within 12 months prior to the interview.

Participants were included if they reported to have seen a doctor or a health care provider within the past 12 months.

Measures

The outcome variables were counseling/cessation clinic, medical treatment, quit line/telephone support, and cessation assistance (any of the three types of assistance) in the past year. These were determined by the question “During the past 12 months, did you use any of the following to try to stop tobacco smoking?” Medical treatment was defined as the use of approved pharmacological products, traditional medicine, and other therapies such as acupuncture for tobacco cessation.

The main independent variable was health care provider behavioral intervention (No intervention, tobacco screening, and advice to quit smoking). This variable was created from responses to the question, “During any visit to a doctor or health care provider in the past 12 months, were you asked if you smoke tobacco? Those who answered ‘Yes’ were further asked,

“During any visit to a doctor or health care provider in the past 12 months, were you advised to quit smoking tobacco?”. If a doctor or health care provider did not ask about tobacco smoking, it was classified as ‘no intervention’. If a doctor or health care provider asked about smoking status but did not offer quit advice, it was categorized as ‘tobacco screening’ and if a doctor or health care provider advised to quit smoking, it was considered to be ‘advice to quit smoking’.

Other variables included in the analysis were sex, age, educational level, employment status, exposure to health warnings on cigarette packages, knowledge of smoking harm, home smoking rule, and exposure to anti-smoking media messages. Age was classified into 4 age groups (15-24,

25-44, 45-64 and 65+ years old) as recommended by the GATS Collaborative Group.[29]

Educational level was categorized into below high school, high school, and above high school education. Employment status was categorized into ‘employed’ and

‘unemployed/student/homemaker’. Exposure to health warnings on cigarette packages was

96 evaluated by the question, “In the last 30 days, did you notice any health warnings on cigarette packages?” (Yes/No). Exposure to anti-smoking media messages was assessed by the question,

“In the last 30 days, have you noticed *information* about the dangers of smoking cigarettes or that encourages quitting in any of the following places?”(Newspapers or magazines, television, radio and billboards). We categorized exposure to anti-smoking media messages into ‘no exposure’, ‘exposure to only one of the media’, and ‘exposure to more than one media’ to be consistent with literature[30]. Home smoking rule was classified into ‘smoking allowed’

(participants reported that there was no rule or smoking was allowed), ‘smoking restriction’

(smoking generally not allowed but with exception) and ‘smoke-free’ (smoking completely not allowed at home).

Statistical Analysis

Data management and statistical analyses were conducted in SAS version 9.4 (SAS Institute,

Cary, NC, USA). Sampling weights, accounting for sampling effects and nonresponses, were used in each analysis to ensure that the estimates would be generalizable. Chi-square test was used to conduct bivariate analysis of each outcome variable by each independent variable. We conducted multi-level analyses to assess random effect of residence (rural vs. urban) and fixed effect of country’s income (low/lower middle income vs. upper middle income) on each outcome variable. Guided by literature,[31] unconditional models (model without any predictor) were first conducted to calculate intraclass correlation coefficient (ICC) for estimation of variability between rural and urban residence in each outcome. Using 3.29 as the error variance, ICC was calculated as follows:

휏 ICC = 00 .[31] 휏00 + 3.29

Where 휏00= the parameter estimate of the random effect of residence.

We then gradually included individual level variables while checking for model improvement.

Finally, country income level was added as fixed effect. There was no significant random effect on the outcomes: Counseling/cessation clinic (ICC=0.005, Z=0.90, p>0.18); medical treatment

(ICC=0.008, Z=1.02, p>0.15) and quitline (ICC=0.017, Z=0.97, p>0.16). Addition of other predictors to the models did not improve the random effect of residence. After ruling out random effect of residence and fixed effect of country’s income level, four multiple logistic regression models were built to examine the relationship between health care provider behavioral intervention and utilization of each of the four outcome variables (counseling/cessation clinic, quit line/telephone support, medical treatment and any cessation assistance). In each model, a country dummy variable was added to adjust for country effect on the results. For each model, model diagnostics were evaluated and no significant correlation was found that warranted deletion of any variable. P-value<0.05 was considered statistically significant. Odds ratios and associated 95% confidence intervals were reported.

Results

Table 3.1 shows the characteristics of the study participants. A total of 5848 participants, representing 57295060 adults aged 15 years and above from 11 LMICs were included in the analysis. Of the participants included, approximately 88% were males, and about 14%, 41%,

34% and 10% were in the ages of 15-24, 25-44, 45-64 and 65+ years, respectively. Overall, approximately 45%, 6% and 49% of participants received no intervention, tobacco screening and advice to quit, respectively.

Table 3.2 shows the results of the bivariate analysis. Overall, prevalence of cessation assistance utilization was approximately 9%, 7%, 1% and 15% for counseling/cessation clinic, medical treatment, quitline and any cessation assistance, respectively. Among participants who reported no intervention, tobacco screening and advice to quit, about 4%, 8% and 14%, respectively used counseling/cessation clinic to quit smoking. Prevalence of medical treatment utilization was 6%,

7% and 9% in no intervention, tobacco screening and quit advice groups, respectively.

Prevalence of quitline utilization was about 1%, 4% and 1% in those who received no intervention, tobacco screening and quit advice, respectively. Among participants who received no intervention, tobacco screening and quit advice, prevalence of any cessation assistance utilization was approximately 9%, 14% and 20%, respectively.

Figure 3.1 shows the prevalence of cessation assistance utilization by country. Prevalence of any cessation assistance utilization ranged from approximately 5% in China to 27% in Bangladesh.

Table 3.1: Demographic characteristics of study participants (N=5848)

Country Year of Sample Female Male 15-24 25-44 years 45 -64 years 65+ years NI TS AQ Survey size (N) n (%) n (%) years n (%) n (%) n (%) n (%) n (%) n (%) n (%) Argentina 2012 403 218 (49.1) 185 (50.9) 75 (25.2) 180 (30.9) 114 (30.6) 34 (13.3) 95 (26.7) 67 (18.8) 241 (54.4) Bangladesh 2009 446 13 (1.8) 433 (98.2) 39 (12.4) 242 (50.4) 137 (30.6) 28 (6.7) 174 (42.4) 9 (2.0) 263 (56.6) China 2010 259 27 (7.9) 232 (92.1) 11 (14.2) 90 (35.2) 109 (40.2) 49 (10.4) 134 (53.9) 11(2.2) 114 (43.8) Egypt 2009 587 20 (2.5) 567 (97.5) 44 (11.7) 285 (47.1) 192 (30.9) 66 (10.4) 129 (22.1) 33 (5.8) 425 (72.1) India 2010 1902 226 (11.2) 1696 (88.8) 137 (12.9) 915 (41.3) 643 (34.7) 207 (11.1) 736 (42.4) 104 (5.3) 1062 (52.3) Mexico 2009 295 91 (35.0) 204 (65.0) 65 (31.2) 126 (44.6) 76 (17.6) 28 (6.6) 90 (29.8) 148 (47.1) 57 (23.2) Romania 2011 234 106 (46.0) 128 (54.0) 21 (13.9) 88 (41.5) 97 (37.5) 28 (17.1) 41 (17.9) 34 (16.6) 159 (65.5) Thailand 2011 703 83 (8.6) 620 (91.4) 57 (17.3) 228 (35.8) 295 (35.9) 123 (11.1) 200 (32.1) 61 (7.7) 442 (60.3) Turkey 2012 585 200 (30.6) 385 (69.4) 51 (12.2) 331 (58.5) 160 (25.0) 43 (4.2) 257 (45.4) 46 (8.4) 282 (46.2) Vietnam 2010 434 18 (3.7) 416 (96.3) 43 (15.7) 175 (45.3) 155 (29.5) 61 (9.5) 268 (65.3) 24 (4.6) 142 (30.1) Total 5848 1002 (12.4) 4846 (87.6) 543 (14.3) 2660 (41.3) 1978 (34.4) 667 (9.9) 2124 (44.8) 537 (6.2) 3187 (48.9) Note: NI=No intervention; TS= Tobacco screening; AQ=Advice to quit. Data source: GATS 2009-2012

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Table 3.2: Prevalence of cessation assistance utilization among adults (N=5848)

Counseling/cessation Medical treatment Quitline or Cessation Assistance clinic telephone Variable % (95% CI) P % (95% CI) P % (95% CI) P % (95% CI) P Health provider 0.00 0.04 0.02 0.00 intervention No intervention 3.6 (2.5–4.8) 5.9 (4.1–7.7) 0.7 (0.4–1.1) 8.8 (6.7–10.9) Tobacco 8.0 (2.5–13.5) 7.3 (4.3–10.3) 3.9 (0.0–8.5) 13.9 (8.0–19.0) screening Advice to quit 13.9 (11.6–16.2) 8.7 (7.0–10.4) 1.4 (0.7–2.0) 20.2 (17.4–23.0) Gender 0.60 0.21 0.24 0.78 Female 8.2 (4.5–11.8) 9.1 (5.9–12.2) 0.8 (0.2–1.4) 15.2(10.5–20.0) Male 9.0 (7.5–10.5) 7.1 (5.8–8.4) 1.3 (0.8–1.8) 14.6 (12.7–16.6) Age 0.08 0.06 0.64 0.10 15-24 years 6.2 (2.9–9.4) 5.5 (2.8–8.2) 1.6 (0.0–3.8) 10.6 (6.4–14.9) 25-44 years 8.6 (6.8–10.3) 9.1 (6.9–11.2) 1.3 (0.7–1.8) 16.1 (13.4–18.8) 45-64 years 9.5 (7.3–11.6) 6.2 (4.6–7.8) 1.3 (0.6–2.1) 14.0 (11.4–16.6) 65+ years 12.6 (8.5–16.8) 7.0 (3.6–10.4) 0.4 (0.0–0.8) 17.4 (12.4–22.4) Education 0.00 0.16 0.21 Below high 10.8 (9.0–12.6) 7.2 (5.8–8.6) 1.3 (0.6–1.9) 16.4 (14.2–18.6) 0.00 school High school 4.1 (2.5–5.8) 6.4 (4.0–8.8) 0.8 (0.3–1.3) 9.1 (6.2–12.1) Above high 7.7 (4.9 – 10.6) 10.3 (6.1–14.6) 1.9 (0.6–3.3) 16.2 (11.2–21.1) school Employment 0.76 0.09 0.42 0.35 status Unemployed/ 8.6 (5.9–11.3) 9.2 (6.4 – 12.0) 1.0 (0.5–1.5) 16.1 (12.1–20.1) student Employed 9.0 (7.5–10.6) 6.9 (5.5–8.2) 1.3 (0.7–1.9) 14.3 (12.3–16.3) Seen warning 0.08 0.62 0.11 0.36 label No 11.2 (7.5–15.0) 8.0 (5.1–10.8) 2.1 (0.3–4.0) 16.2 (11.7–20.8) Yes 8.4 (7.0–9.7) 7.2 (5.9–8.5) 1.0 (0.7–1.4) 14.3 (12.4–16.2) Know smoking 0.02 0.87 0.50 0.27 harm No 4.5 (1.5–7.5) 7.9 (1.2–14.6) 1.7 (0.0–3.7) 10.5 (3.6–17.3) Yes 9.3 (7.9–10.8) 7.3 (6.2–8.5) 1.2 (0.7–1.7) 15.1 (13.2–17.0) Home smoking 0.00 0.02 0.00 0.00 rule Allowed 6.9 (5.5–8.2) 6.4 (4.9–7.9) 0.5 (0.2–0.8) 12.4 (10.3–14.4) Restriction 12.3 (8.2–16.4) 8.3 (5.4–11.1) 3.3 (0.9–5.8) 18.3 (13.3–23.3) Smoke-free 13.6 (10.5 –16.8) 10.1 (7.3–12.8) 2.3 (1.1–3.5) 20.3 (16.5–24.0) Exposure to 0.25 0.00 0.02 0.00 anti-smoking media message No 8.2 (5.9–10.4) 4.0 (2.4–5.5) 0.4 (0.0 –0.7) 11.1 (8.3–13.8) One channel 8.2 (5.8–10.5) 8.07 (5.8–11.6) 1.9 (0.5–3.2) 15.3 (11.6–18.9) More than one 10.2 (8.2–12.2) 9.7 (7.9–11.6) 1.7 (0.9–2.4) 17.9 (15.2–20.5) channel Total population 8.9 (7.5–10.3) 7.4 (6.1–8.6) 1.2 (0.8–1.7) 14.7 (12.8–16.6) CI=95% confidence interval; P=p-value. P-values were obtained for X2 test, and they are corrected to 2 decimal places. Data source: GATS 2009-2012

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Figure 3.1: Prevalence of cessation assistance utilization by country

Cessation assistance (any assistance used) Counseling/cessation clinic

Medical treatment

Country of Survey of Country Quitline

Prevalence (%)

Table 3.3 illustrates the results of the multiple logistic regression analyses for the relationship between health provider intervention and utilization of cessation assistance. Compared to no intervention, odds of counseling/cessation clinic utilization were significantly increased in tobacco screening (OR=2.60, 95% CI=1.1–5.9) and advice to quit (OR=3.89, 95% CI=2.8–5.5).

Odds of medical treatment utilization was 71% (OR=1.71, 95% CI=1.2–2.4) higher in those who received quit advice than those who received no intervention. Odds of quitline utilization did not differ between those who received no intervention and those who were advised to quit. However, the probability of quitline utilization was higher in those who received tobacco screening

(OR=3.89, 95% CI=1.2–13.0). Utilization of any cessation assistance among those who received

102

tobacco screening did not significantly differ from those who received no intervention (p>0.05).

For those who received advice to quit smoking, there was approximately a twofold increase in

the odds of any cessation assistance utilization compared to those who received no intervention

(OR=2.60, 95% CI=2.0–3.4).

Table 3.3: Relationship between health care provider behavioral intervention and utilization of cessation assistance (N=5848)

Counseling/ Medical treatment Quitline or Cessation cessation clinic telephone Assistance Variable OR 95% CI OR 95% CI OR 95% CI OR 95% CI Health provider intervention No intervention 1.00 1.00 1.00 1.00 Tobacco screening 2.60* 1.1 – 5.9 0.98 0.6 – 1.7 3.95* 1.2 – 13.0 1.52 0.9 – 2.7 Advice to quit 3.89** 2.8 – 5.5 1.71** 1.2 – 2.4 1.82 0.9 – 3.7 2.60** 2.0 – 3.4 Gender Female 1.00 1.00 1.00 1.00 Male 0.76 0.5 – 1.3 0.95 0.6 – 1.5 1.55 0.7 – 3.7 0.86 0.6 – 1.2 Age 15-24 years 1.00 1.00 1.00 1.00 25-44 years 1.08 0.6 – 2.0 1.64 1.0 – 2.8 0.87 0.3 – 2.4 1.34 0.9 – 2.1 45-64 years 1.25 0.7 – 2.3 1.07 0.6 – 1.9 1.10 0.4 – 3.1 1.15 0.7 – 1.8 65+ years 1.77 0.9 – 3.5 1.12 0.5 – 2.3 0.31 0.1 – 1.6 1.46 0.8 – 2.5 Education Below high school 1.00 1.00 1.00 1.00 High school 0.84 0.5 – 1.4 0.95 0.6 – 1.6 0.90 0.4 – 2.1 0.83 0.5 – 1.2 Above high school 0.92 0.6 – 1.4 1.37 0.8 – 2.3 1.28 0.6 – 2.9 1.13 0.7 – 1.7 Employment status Unemployed/student 1.00 1.00 Employed 1.10 0.7 – 1.6 0.83 0.6 – 1.2 1.88 0.9 – 4.0 0.91 0.7 – 1.2 Seen warning label No 1.00 1.00 1.00 1.00 Yes 0.67 0.4 – 1.0 0.61* 0.4 – 1.0 0.30 0.1 – 0.7 0.70 0.5 – 1.0 Know smoking harm No 1.00 1.00 1.00 1.00 Yes 1.40 0.7 – 3.0 0.58 0.2 – 1.6 0.28 0.1 – 1.1 0.94 0.4 – 2.2 Home smoking rule Allowed 1.00 1.00 1.00 1.00 Restriction 1.94** 1.3 – 2.9 1.13 0.7 – 1.7 2.41** 2.4 – 12.0 1.47* 1.1 – 2.0 Smoke-free 2.10** 1.5 – 3.0 1.31 0.9 – 2.0 3.67** 1.4 – 9.7 1.56** 1.2 – 2.1 Exposure to anti- smoking media message No 1.00 1.00 1.00 1.00 One channel 1.15 0.8 – 1.7 2.14* 1.2 – 3.9 6.03** 1.6 – 22.7 1.53* 1.0 – 2.3 More than one channel 1.56* 1.1 – 2.2 2.01** 1.2 – 3.4 4.51* 1.1 – 18.6 1.67** 1.2 – 2.4 Note: OR= Odds ratio; CI=Confidence interval; *p<0.05; **p<0.01; ***p<0.001. Odds ratios were derived from weighted multiple logistic regression models. Estimates were adjusted for country of survey. Data source: GATS 2009 – 2012.

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Discussion

Strong evidence exists about the health and economic benefits of tobacco smoking cessation.

[12,32,33] Cessation can save millions of lives and prevent premature deaths.[34] However, tobacco smoking prevalence is still high in LMICs[4] and it is projected to increase in most of these countries.[35] Tobacco smoking cessation can help to reverse the rising trend of smoking prevalence in LMICs. However, the addictive nature makes quitting difficult for the majority of smokers.[7] Though significant number of smokers quit unaided,[36] there are many who will require assistance to successfully give up smoking due to physical and psychological dependence.[37] In the United States for instance, while more than 40% of smokers try to quit smoking annually,[36] less than 10% are able to quit without assistance.[38] To increase quit rates therefore, tobacco dependence treatment is recommended and needs to be integrated into the health care system.[12] In spite of the availability of evidenced based cessation assistance,[12] utilization remains low in LMICs.[9] To help find ways of improving utilization of cessation assistance for tobacco smoking cessation, this study was conducted to examine the relationship between health care provider tobacco screening/quit advice and utilization of cessation assistance in LMICs.

Overall, close to half of the participants (45%) were not advised to quit nor screened for tobacco smoking upon seeing a doctor or a health care provider, and 6% of participants were asked about their smoking status but were not advised to quit smoking in the past year (Table 3.1). Of the total sample of 5848, representing about 57.3 million people, 9 in 100, 7 in 100, 1 in 100, and 3 in 20 adults used counseling/cessation clinic, medical treatment, quitline and any cessation assistance to attempt to quit smoking in the past 12 months. In all types of cessation assistance used, the proportion of participants who used was highest among those who received quit advice

104 compared to those who did not receive any intervention and those who were screened only, except for quitline utilization in which the highest proportion was seen in those who were screened. Generally, the most commonly reported cessation assistance across countries was medical treatment (pharmacological treatment and medical procedures), followed by counseling/cessation clinic. However, the overall results show that counseling/cessation clinic was the commonly used cessation assistance in the sample. Quitline was less likely to be reported across countries and in the pooled data. Though quitline has been found to be effective in tobacco cessation in high income countries, its effectiveness in the developing world is not well evaluated.[39] It has been argued that since the rise in tobacco smoking has paralleled an increase in telecommunication access, quitline could be used to offer cessation counseling in developing countries where there may be limited resources to provide cessation medications.[40]

However, the results suggest that quitline utilization is very low in all the countries, hence, the need for scientific investigations into ways by which utilization of telephone based cessation counseling can be increased in LMICs.

In support of the study hypothesis, the results showed approximately twofold and threefold increase in the odds of utilization of counseling/cessation clinic in those who received tobacco screening and participants who received quit advice, respectively, compared to those who received no intervention. For medical treatment however, increased utilization was seen in only those who were advised to quit smoking. Interestingly, while there was no significant difference between the no intervention group and the quit advice group, there was significant increase in quitline utilization in smokers who received only tobacco screening. Again, the results show about twofold increased probability of using any type of smoking assistance to attempt quitting among those who received advice to quit compared to those who received no intervention. The

105 results suggest added benefit of tobacco screening or quit advice in comprehensive tobacco control.

While the results of this study cannot explain the relationship between health care provider intervention and utilization of cessation assistance, health care provider intervention (screening and advice to quit) has been shown to increase patients’ satisfaction with care[12] and satisfaction with care is associated with utilization of impatient care.[41,42] This suggests that increased utilization of cessation assistance in smokers who received advice to quit may be mediated by satisfaction with care. However, further studies are required to confirm the finding and explain this association.

Health care provider behavioral intervention (tobacco screening and advice to quit) has been found to increase the chances of quitting smoking.[12,14] It is an effective but inexpensive tobacco use intervention which can easily be integrated into the health care system,[11] and best practices have been developed to guide implementation of this intervention.[12,43] The FCTC

Article 14 requires integration of tobacco dependence treatment into the health care system and emphasizes health care provider tobacco screening and advice to quit.[44] Our results provide evidence and support for full implementation of health care provider intervention in LMICs.

However, since a minority of the population in LMICs report seeing health care provider in the past 12 months,[20] it is important to also implement other tobacco control policies which encourage utilization of cessation assistance and successful quitting.

Our results provide additional support that in comprehensive control program, components can complement one another.[45] In all types of cessation assistance, utilization was significantly increased in participants who reported exposure to anti-smoking messages in the media, especially those who reported exposure to these messages in more than one media channel.

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Again, with the exception of utilization of medical treatment, those who reported living in homes with either smoking restriction or complete smoke-free rule were more likely to have used assistance to attempt quitting smoking. Both home smoking rule and anti-smoking messages were associated with utilization of any cessation assistance. This suggests the need for comprehensive tobacco control program in the LMICs, in which cessation assistance is readily available and affordable for those who require help to quit smoking.

The study has some limitations that must be considered in the interpretation of the results.

Firstly, smoking status and quit attempts were both measured by self-report and may be subject to recall bias or social desirability. Secondly, being a cross-sectional study, temporality cannot be established. It may happen that cessation assistance was utilized before visiting health care provider and vice versa. However, we strongly believe that health care providers would not selectively screened or offer advice to quit to those who had utilized cessation assistance to attempt to quit smoking in the past year. Thirdly, the sample for quitline was small so the analysis was underpowered.

Conclusion

Of the three types of cessation assistance considered in this study (counseling/cessation clinic, medical treatment and quitline), health care provider intervention (tobacco screening and quit advice) was significantly associated with increased utilization. In a combined analysis, health care provider advice was associated with increased utilization of any cessation assistance. In addition, home smoking rule (restriction and smoke-free) and exposure to anti-smoking messages were associated with increased utilization of cessation assistance. In addition to other

107 control measures, health care provider intervention should be integrated into the health care system, and be made part of routine health care procedure for all care seekers in LMICs

Acknowledgements

The authors will like to thank the Global Adult Survey Collaborative Group for providing the data and offering technical advice for this study.

108

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CHAPTER 4

IMPACT OF SMOKE-FREE HOME ON SMOKING INTENSITY IN LOW-AND-MIDDLE

INCOME COUNTRIES

Daniel Owusu, DrPHc1; Sreenivas P. Veeranki, DrPH2; Megan Quinn, DrPH1; Ke-Sheng Wang, PhD1; Hadii M. Mamudu , PhD3

*Correspondent author: Daniel Owusu, Box 13166, Johnson City, TN 37614. Tel: +1 404 491 8940 Email: [email protected]

Pages: 21 Tables: 4

Conflict of interest: Authors declare that they have no conflict of interest.

Funding source: No funding was received for this study

Keywords: smoking intensity, home smoking rule, transtheoretical model, GATS, low-and- middle income countries.

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Abstract

Introduction: Smoke-free policy is associated with smoking reduction, however, there is a paucity of literature on the relationship between home smoking rule and smoking intensity in low-and-middle income countries (LMICs). The aim of this study was to conduct a cross-country evaluation of the relationship between home smoking rule and smoking intensity among smokers in different stages of smoking cessation.

Methods: Data from 39,204 current smokers from the Global Adult Survey (GATS), 2009-2012 from 17 LMICs were analyzed. Weighted multiple linear regression analyses were conducted using the log of smoking intensity as the outcome variable with home smoking rule as the main independent variable. Adjusted regression coefficients (β) with associated 95% confidence intervals (CI) were estimated.

Results: Overall, the average smoking intensity was approximately 15, 14, and 13 for home smoking allowed, smoking restriction and smoke-free rules, respectively. There was a 12.7%

(95% CI=7.6%–17.8%) and a 22.5% (95% CI=17.1%–28.0%) reduction in smoking intensity among adults in precontemplation from homes with smoking restriction and smoke-free rules, respectively. Among adults in contemplation, smoking restriction and smoke-free rules were associated with a 21.5% (95% CI=6.0%–36.9%) and an 18.6% (95% CI=9.0%–28.2%) reduction in smoking intensity, respectively. For adults in preparation, smoke-free rule was associated with a 19.4% (95% CI=3.9%–34.9%) reduction in smoking intensity.

Conclusion: Smoke-free homes are associated with a significant reduction in smoking intensity across the first three stages of the transtheoretical model. This suggests that smoke-free policies will benefit smokers irrespective of their intention to quit.

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Introduction

Unlike before when smoking was a burden to mainly industrialized countries, the tobacco epidemic has been increasing in low-and-middle income countries (LMICs).1,2 The death toll from tobacco 3–5 and its economic implications 6,7 make tobacco a major public health concern globally, but tobacco cessation among adults can prevent millions of tobacco-related deaths.8

Smoke-free policies are one of the key tobacco control measures known to affect the demand aspect of smoking and promote tobacco cessation.2,9,10 While many LMICs have embraced the

World Health Organization’s (WHO) Framework Convention for Tobacco Control (FCTC),11 and with the current evidence suggesting association between workplace smoke-free policy and smoke-free homes,12 questions remain as to whether smoke-free homes are associated with reduced smoking and whether this association cuts across the stages of the tobacco cessation process in this population. Answers to these questions will serve as evidence to support implementation and enforcement of public smoking ban which may result in smoke-free homes as a trickle-down effect, with consequent impact on smoking behavior in LMICs.

Smoke-free policy is one of the six policy measures in the WHO MPOWER package for tobacco control.2 Smoke-free policy has been associated with smoking reduction, reduction in environmental tobacco smoke, and improved health outcomes.13,14 It has been projected that a national comprehensive smoking restriction could results in 9% and 12% reductions in the prevalence of smoking by 2015 and 2025, respectively in Russia.15 A systematic review of 37 studies reported that smoke-free policies implemented at workplaces and in communities significantly reduce tobacco use.16 A recent meta-analysis of studies that examined impact of smoke-free campus policies reported that a year following implementation of policies saw a reduction of students smoking prevalence from 16.5% to 12.8%,17 suggesting that smoking ban

117 in settings that affect the youth may prevent initiation or promote cessation of tobacco smoking.

It has also been reported recently that home smoking ban/restriction is associated with reduced initiation among nonsmoking adolescents and a reduction in the number of daily cigarettes smoked among adolescent smokers in Canada.18

The overall support for tobacco-free legislation has been promising.14 However, there is paucity of literature on the impact of smoke-free policies on tobacco cessation in LMICs where about

80% of smokers reside.2 Our study aimed to fill this gap by evaluating the association between home smoking rule and smoking intensity by stages of the cessation process as described by the transtheoretical model (TTM) (precontemplation, contemplation and preparation). The study will provide information on the extent to which people are protected from environmental smoke at home and how this protection may lead to a reduction in the number of daily tobacco smoking among smokers. Reducing the number of daily smoking is important because, previous studies have found evidence of association between reduction in the number of daily smoking and cessation of smoking.19–21 We hypothesized that smoke-free home will be associated with a reduction in smoking intensity irrespective of the stage of tobacco cessation, and that the effect size will be similar across the stages. This work will serve as the first comprehensive evaluation of smoke-free home in tobacco cessation in 17 tobacco high-burden LMICs.

Methods

Data

We used data from the Global Adult Tobacco Survey (GATS), a nationally representative survey of noninstitutionalized adults aged ≥15 years old. Details of GATS design have been published elsewhere. 22–24 GATS is a cross-sectional face-to-face household survey to monitor key

118 indicators of tobacco use and control. It is considered to be a global standard for monitoring tobacco control. GATS uses multi-stage cluster probability sampling design to select a nationally representative sample for the study. To ensure cross-country comparison of the data, standard protocol in design, sampling, questionnaire, interview, and data analysis and reporting is used in each participating country. Questionnaire for the survey contains core questions, and any other country-specific questions. Core questions are grouped into sections, including household information, background characteristics, tobacco smoking, smokeless tobacco use, secondhand smoke (SHS) exposure, cessation, knowledge and perception, media and tobacco economics.

GATS has been recommended as a standard survey that can be used for monitoring tobacco cessation in LMICs.25

This study included data obtained from core questions from 17 LMICs. Participants included were those who reported smoking daily at the time of interview. Using standardized approach,26–

28 pooled data analysis was conducted, and in Thailand where the survey has been conducted twice within the period, only the more recent data (2011) were included.

All participants were categorized into precontemplation, contemplation and preparation based on the TTM.29 First, current smokers were asked if they attempted to quit smoking in the past 12 months. Attempt to quit was defined as abstinence from smoking for at least one day in the past

12 months. Intention to quit smoking was determined for all participants by the question: “Which of the following best describes your thinking about quitting smoking? I am planning to quit within the next month, I am thinking about quitting within the next 12 months, I will quit someday but not within the next 12 months, or I am not interested in quitting?” In consistence with literature,30 adults who had no intention to quit smoking within 12 months were classified as being in precontemplation. Participants who indicated their intention to quit within the next one

119 year, except those who plan to quit within the next month and who had attempted to quit in the past 12 months, were classified as being in contemplation to quit smoking. Lastly, participants who had attempted to quit in the past 12 months and intended to quit in the next one month were assumed to be in preparation to quit smoking.

Measures

The main outcome variable was smoking intensity, defined as the average number of tobacco products smoked daily. It was evaluated by the question: “On average, how many of the following products do you currently smoke each day?” [Manufactured cigarettes, hand-rolled cigarettes, pipes full of tobacco, cigars, shisha, etc]. Responses were combined to obtain the average number of tobacco products smoked daily for each current daily smoker. Current daily smoker is defined as a self-reported daily smoking and it excludes those who had stopped smoking at the time of interview.

Home smoking rule was the main independent variable. This was evaluated by the questions: "I would now like to ask you a few questions about smoking in various places.

Which of the following best describes the rules about smoking inside of your home: Smoking is allowed inside of your home, smoking is generally not allowed inside of your home but there are exceptions, smoking is never allowed inside of your home, or there are no rules about smoking in your home?" ‘Smoking is never allowed’ was considered complete smoke-free home, and

‘smoking generally not allowed but with exception’ was classified as home smoking restriction.

Homes were considered not smoke-free if smoking was allowed or there were no rules about smoking inside the home.

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Sex, age, educational level, employment status, exposure to health warnings on cigarette packages, knowledge of smoking harm, and exposure to anti-smoking media messages were included in the analysis as covariates based on literature.17,31–33 Age was grouped into 4 age groups (15-24, 25-44, 45-64 and 65+ years old) in conformity with the recommendations of the

GATS Collaborative Group.34 Educational level was categorized into below high school, high school, and above high school education. Participants were also categorized into ‘employed’, and

‘unemployed/student’. Exposure to health warnings on cigarette packages (Yes/No) was determined from the question, “In the last 30 days, did you notice any health warnings on cigarette packages?” (Yes/No). Participants were considered to know smoking harm if they answered ‘Yes’ to the question, “Based on what you know or believe, does smoking tobacco cause serious illness?” Exposure to anti-smoking media messages was evaluated by the question,

“In the last 30 days, have you noticed *information* about the dangers of smoking cigarettes or that encourages quitting in any of the following places?” (newspapers or magazines, television, radio and billboards). We classified exposure to anti-smoking media messages into ‘no exposure’, ‘exposure to only one of the media’, and ‘exposure to more than one media’ in consistence with literature.35

Statistical Analysis

Data management and analyses were conducted using SAS version 9.4 (SAS Institute, Cary, NC,

USA). In all analyses, sample weights which accounts for the complex survey design and nonresponses were used for estimations so that the results will be generalizable to the population from which the sample was drawn. Age and sex characteristics of the sample, as well as distribution of participants by stages of tobacco cessation were estimated by weighted frequencies. Weighted means of smoking intensity were estimated in each independent variable.

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Before model buildings, model diagnostics were conducted to check for the assumptions of linear regression. Extreme values of smoking intensity were excluded from the analysis. Log transformation was completed to improve the normality of the distribution of smoking intensity.

Data were found to be adequate for the linear regression in each model. Four multiple linear regression models were built using PROC SURVEYREG procedure: (1) combined model (all stages), (2) a model for those in precontemplation, (3) a model for those in contemplation and (4) a model for those in preparation. In all models, country of survey was added to adjust for its possible effect on the estimates. STB option was used to obtain standardized beta coefficients for comparison of effects sizes within and across the three stages of tobacco cessation. Adjusted regression coefficients (β) with associated 95% confidence intervals (CI) were estimated.

Regression coefficients were multiplied by 100 and reported as percentage change in smoking intensity. For the purpose of statistical inferences, p-values≤0.05 were considered statistically significant.

Results

Table 4.1 shows characteristics of study participants. A total of 39,204 participants were included in the analysis; approximately 92% were males, and 14%, 46%, 33% and 7% were in the ages of 15-24, 25-44, 45-64 and 65+ years, respectively. Overall, approximately 82%, 14% and 3% of participants were in precontemplation, contemplation and preparation, respectively.

Table 4.2 illustrates the weighted means of tobacco smoking intensity by independent variables.

The average smoking intensity was 15, 14, 12 and 15 for smokers in precontemplation, contemplation, preparation, and all smokers combined, respectively. Overall, the average smoking intensity was approximately 15, 14, and 13 for smoking allowed, smoking restriction and smoke-free homes, respectively. For precontemplation, the average smoking intensity was

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15, 14 and 13 in smoking allowed, smoking restriction and smoke-free rules, respectively. Among participants in contemplation, smoking intensity average was 14, 12, and 12 in smoking allowed, smoking restriction and smoke-free home rules, respectively. This number was 13, 12 and 11 for smoking allowed, smoking restriction and smoke-free home rules, respectively among participants in preparation.

Table 4.1: Demographic characteristics of study participants (N=39204)

Country Year N Female Male 15-24 years 25 -44 years 45 -64 years 65+ years PC n (%) C P n (%) n (%) n (%) n (%) n (%) n (%) n (%) n (%) Argentina 2012 1168 500 (39.0) 668 (61.0) 188 (20.1) 555 (41.8) 350 (33.2) 75 (4.9) 902 (80.7) 198 (15.3) 68 (4.0) Bangladesh 2009 1183 15 (1.2) 1168 (98.2) 143 (19.4) 676 (50.7) 318 (25.9) 46 (4.0) 728 (59.2) 312 (28.9) 143 (11.9) China 2010 311 167 (3.8) 2944 (96.2) 135 (14.0) 1224 (43.3) 1360 (35.0) 372 (5.6) 2694 (85.8) 367 (12.7) 50 (1.6) Egypt 2009 3871 51 (1.2) 3820 (98.8) 403 (18.3) 2061 (48.3) 1151 (27.8) 256 (5.6) 2894 (73.9) 792 (20.6) 185 (5.5) India 2010 7907 810 (11.0) 7097 (89.0) 481 (7.8) 3885 (43.2) 2746 (38.2) 795 (10.9) 6366 (784.) 1142 (16.9) 399 (4.6) Indonesia 2011 2293 89 (3.2) 2204 (96.8) 250 (16.2) 1168 (50.6) 696 (27.2 179 (6.0) 2115 (92.4) 133 (5.6) 45 (2.0) Malaysia 2011 854 24 (1.6) 830 (98.4) 114 (19.4) 418 (52.0) 272 (24.3) 50 (4.3) 759 (87.9) 62 (7.3) 33 (4.8) Mexico 2009 792 181 (25.2) 611 (74.8) 136 (24.2) 304 (40.3) 272 (29.1) 80 (6.3) 515 (66.9) 196 (23.9) 81 (9.2) Nigeria 2012 302 11 (5.0) 291 (95) 23 (10.6) 188 (56.2) 74 (25.3) 17 (8.0) 222 (68.3) 52 (21.0) 28 (10.7) Philippines 2009 2203 353 (15.2) 1850 (84.8) 284 (18.4 ) 1159 (49.2) 604 (26.5) 156 (5.8) 1834 (84.4) 251 (10.5) 118 (5.1) Romania 2011 918 289 (31.2) 629 (68.8) 82 (12.5) 404 (51.1) 359 (31.6) 73 (4.8) 709 (77.3) 169 (18.6) 40 (4.1) Russia 2009 3958 723 (25.3) 3235 (74.7) 512 (15.9) 1710 (44.1) 1470 (34.2) 266 (6.9) 3529 (88.0) 352 (9.9) 77 (2.1) Federation Thailand 2011 3704 327 (5.3) 3377 (94.7) 326 (16.1) 1404 (42.9) 1501 (33.5) 473(7.5) 3177 (86.3) 426 (11.2) 101 (2.5) Turkey 2012 2035 503 (22.7) 1532 (77.3) 191 (15.3) 1138 (55.5) 598 (26.0) 108 (3.2) 1326 (66.2) 553 (26.6) 156 (7.2) Ukraine 2010 2018 268 (18.7) 1750 (81.3) 211 (16.8) 921 (47.6) 723 (30.2) 163 (5.4) 1563 (76.5) 383 (19.4) 72 (4.1) Uruguay 2009 1128 481 (43.0) 647 (57.0) 136 (17.7) 476 (42.9) 408 (33.7) 108 (5.7) 762 (67.2) 283 (26.2) 83 (6.7) Vietnam 2010 1759 66 (3.1) 1693 (96.9) 139(12.6) 825 (49.8) 654 (32.0) 141 (5.6) 1306 (73.0) 348 (20.8) 105 (6.2) Total 39204 4858 34346 3754 18516 13576 3358 31401 6019 1784 (8.2) (91.8) (14.0) (46.1) (33.4) (6.5) (83.2) (13.7) (3.0) N, sample size; PC, Precontemplation; C, contemplation; P, preparation. Data source: GATS 2009-2012

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Table 4.2: Means of smoking intensity in independent variables by stage (N=39204)

Precontemplation Contemplation Preparation (n=1784) Overall (N=39204) (n=31401) (n=6019) Mea Variable 95% CI Mean 95% CI Mean 95% CI Mean 95% CI n Home smoking rule Smoking allowed 15.4 15.1 – 15.7 14.2 13.4 – 15.0 13.0 11.5 – 14.4 15.2 14.9 – 15.5 Home smoking 14.3 13.7 – 14.8 12.0 10.6 – 13.4 11.8 9.9 – 13.8 13.8 13.3 – 14.3 restriction Smoke-free 13.0 12.5 – 13.6 11.8 11.0– 12.6 10.7 9.5 – 11.9 12.7 12.2 – 13.1 Gender Female 11.0 10.5 – 11.5 11.7 10.3 – 13.1 11.1 9.1 – 13.2 11.1 10.7 – 11.6 Male 15.4 15.1 – 15.7 13.7 13.1 – 14.3 12.5 11.4 – 13.5 15.1 14.8 – 15.3 Age 15-24 years 12.0 11.1 – 12.8 10.6 9.6 – 11.7 8.8 7.6 – 10.0 11.6 10.9 – 12.3 25-44 years 14.9 14.5 – 15.2 13.4 12.6 – 14.1 13.3 12.0 – 14.6 14.6 14.3 – 14.9 45-64 years 16.9 16.4 – 17.3 15.1 13.9 – 16.2 14.6 12.4 – 16.7 16.6 16.1 – 17.0 65+ years 13.5 12.8 – 14.2 13.3 11.2 – 15.5 9.3 5.7 – 12.9 13.3 12.7 – 14.0 Education Below high school 14.7 14.4 – 15.0 13.0 12.4 – 13.5 12.3 11.1 – 13.4 14.4 14.1 – 14.7 High school 15.9 15.3 – 16.5 14.4 13.1 – 15.7 11.9 9.7 – 14.2 15.6 15.1 – 16.1 Above high school 14.0 13.4 – 14.7 13.0 12.0 – 14.1 13.6 11.7 – 15.6 13.9 13.3 – 14.4 Employment status Employed 14.0 13.5 – 14.6 12.7 11.3 – 14.1 11.0 8.9 – 13.0 13.7 13.2 – 14.2 Unemployed/ 15.2 14.9 – 15.5 13.6 13.0 – 14.3 12.6 11.5 – 13.8 14.9 14.6 – 15.2 student Seen warning label No 13.5 13.0 – 14.0 11.8 10.3 – 13.3 9.8 7.8 – 11.9 13.2 12.8 – 13.7 Yes 15.4 15.0 – 15.7 13.8 13.2 – 14.4 12.6 11.5 – 13.7 15.1 14.8 – 15.3 Know smoking harm No 15.6 15.0 – 16.3 14.7 12.6 – 16.8 20.3 11.2 – 29.3 15.6 15.0 – 16.3 Yes 14.9 14.6 – 15.2 13.4 12.8 – 14.0 12.0 11.1 – 13.0 14.6 14.3 – 14.9 Exposure to anti- smoking media message No 14.9 14.7 – 15.3 13.8 12.6 – 15.0 11.5 9.8 – 12.3 14.7 14.3 – 15.1 One channel 15.4 14.9 – 15.9 14.1 13.0 – 15.3 12.6 11.0 – 14.2 15.2 14.7 – 15.6 More than one 15.0 14.4 – 15.5 12.7 12.0 – 13.5 12.8 11.2 – 14.3 14.5 14.0 – 14.9 channel Total population 15.1 14.8 – 15.3 13.5 12.9 – 14.1 12.3 11.4 – 13.3 14.8 14.5 – 15.0 P-values are based on X2 test. Data source: GATS 2009-2012

Table 4.3 shows the adjusted results of the relationship between smoking intensity and home smoking rule in all stages combined. There was approximately 15% (β CI=10%–19.6%) and

22% (CI=17.7%–26.9%) reduction in smoking intensity in home smoking restriction and smoke- free home, respectively, compared to homes in which smoking was allowed.

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Table 4.3: Association between home smoking rule and smoking intensity among adults (N=39204)

Variable Change in 95% CI Standardized t Value P-value smoking estimates intensity (%) Home smoking restriction vs smoking allowed -14.8 -19.6 – -10.0 -0.089 -6.0 <.0001 Smoke-free home vs smoking allowed -22.3 -26.9 – -17.7 -0.139 -9.5 <.0001 Male vs Female 39.8 34.3 – 45.2 -0.019 14.3 <.0001 25-44 years vs 15-24 years -3.0 -8.1 – 2.1 -0.042 -1.2 0.2452 45-64 years vs 15-24 years -10.6 -15.8 – -5.4 -0.174 -4.0 <.0001 65+ years vs 15-24 years 27.4 20.0 – 34.7 0.219 7.3 <.0001 High school vs below high school 36.3 28.7 – 43.9 0.051 9.4 <.0001 Above high school vs below high school 16.2 7.1 – 25.3 0.018 3.5 0.0005 Employed vs unemployed/student 4.1 -1.3 – 9.4 0.037 1.5 0.1377 Seen warning label (yes vs no) 8.0 3.0 – 13.0 0.009 3.1 0.0018 Know smoking harm (yes vs no) 1.8 -3.0 – 6.7 -0.002 -0.7 0.4605 Antismoking message in One media channel vs -0.3 -4.3 – 3.7 -0.020 -0.2 0.8801 No exposure Antismoking message in >One media channel vs -3.5 -8.2 – 1.2 -0.060 -1.5 0.1446 No exposure CI, Confidence interval. Percent change smoking intensity was estimated by PROC SURVEYREG procedure using the log of the number of daily smoking as an outcome variable. Beta coefficients were multiplied by 100. Negative sign (-) means percentage reduction in daily smoking. Estimates were adjusted for country of survey. Data source: GATS 2009 – 2012.

Table 4.4 illustrates the relationship between home smoking rule and smoking intensity across the first three stages of TTM. There was a 12.7% (CI=7.6%–17.8%) and a 22.5% (CI=17.1%–

28.0%) reduction is smoking intensity among adults in precontemplation from smoking restricted and smoke-free homes, respectively compared to those in smoking allowed homes. Among adults in contemplation, smoking restriction and smoke-free homes were associated with a

21.5% (CI=6.0%–36.9%) and an 18.6% (CI=9.0%–28.2%) reduction in smoking intensity, respectively. For adults in preparation, there was a 19.4% (CI=3.9%–34.9%) reduction in smoking intensity in those who resided in smoke-free homes compared to smoking allowed homes. There was no significant difference in smoking intensity between smoking allowed and smoking restriction homes among smokers in preparation.

125

Table 4.4: Association between home smoking rule and smoking intensity by stages of cessation (N=39204)

Precontemplation (n=31401) Contemplation (n=6019) Preparation (n=1784) Determinants Change Change in Change in in smoking smoking 95% CI STB 95% CI STB smoking 95% CI STB intensity intensity intensity (%) (%) (%) Home smoking restriction -12.7*** -17.8 – -7.6 -0.051 -21.5** -36.9 – -6.0 -0.093 -16.3 -38.7 – 6.1 -0.071 vs. smoking allowed Smoke-free home vs. -22.5*** -28.0 – -17.1 -0.087 -18.6*** -28.2 – -9.0 -0.085 -19.4* -34.9 – -3.9 -0.087 smoking allowed Male vs. Female 43.3*** 37.4 – 49.2 0.150 26.0*** 10.8 – 41.2 0.098 13.2 -6.2 – 32.5 0.044 25-44 years vs. 15-24 27.8** 19.1 – 36.6 0.179 17.6* 3.9 – 31.2 0.110 41.4*** 19.1 – 63.8 0.234 years 45-64 years vs. 15-24 36.7*** 27.7 – 45.6 0.224 29.0*** 15.8 – 42.2 0.170 41.4*** 17.9 – 64.9 0.216 years 65+ years vs. 15-24 years 16.4** 6.2 – 26.5 0.053 21.2 -1.8 – 44.1 0.060 -13.3 -65.3 – 38.7 -0.038 High school vs. below high -1.3 -6.9 – 4.4 -0.008 -9.4 -20.0 – 1.1 -0.057 15.7 -4.6 – 36.1 0.069 school Above high school vs. -11.1*** -17.1 – -5.0 -0.044 -10.8 -21.6 – 0.1 -0.045 -16.1 -42.1 – 9.9 -0.084 below high school Employed vs. 2.7 -2.7 – 8.1 0.012 10.5 -6.3 – 27.2 0.046 6.6 -13.1 – 26.3 0.025 unemployed/student Seen warning label (yes vs. 5.6* 0.6 – 10.6 0.027 19.0* 3.7 – 34.2 0.086 17.7 -8.3 – 43.7 0.060 no) Know smoking harm (yes 2.8 -2.3 – 7.9 0.014 2.1 16.5 – 20.7 0.007 -40.5* -73.4 – -7.6 -0.088 vs. no) Antismoking message in -4.6 – 4.1 One media channel vs. No -0.2 -0.001 -0.7 -0.004 6.9 -12.5 – 26.2 0.034 -12.1 – 10.8 exposure Antismoking message in -7.3 – 3.4 >One media channel vs. -2.0 -0.012 -8.6 -19.8 – 2.6 -0.052 2.7 -18.6 – 23.9 0.015

No exposure CI, Confidence interval; STB, Standardized beta. Boldface means statistically significant (*p<0.05, **p<0.01, ***p<0.001). Percent change intensity was estimated by PROC SURVEYREG procedure using the log of number of daily smoking as an outcome. Beta coefficients were multiplied by 100. Negative sign means percentage reduction in daily smoking. Estimates were adjusted for survey country. Data source: GATS 2009 – 2012.

126

Discussion

Tobacco smoking in LMICs is high and it is predicted that LMICs will contribute 80% to the projected eight million tobacco-related deaths by 2030.2 Tobacco cessation is one of the key interventions to reverse the global tobacco epidemic,2 however, cessation rates are low in

LMICs.36,37 Since smoking reduction has been found to predict future tobacco cessation,19–21 factors that promote smoking reduction may increase cessation in LMICs. This study was conducted to assess relationship between home smoking rule and smoking intensity among

39,204 (≈500 million) adult smokers in LMICs. Overall, about 4 in 5 adults were in precontemplation and less than 1 in 25 adults was in preparation to quit smoking, though the proportion of adults in different stages of cessation varied across countries (Table 4.1). The average number of tobacco products smoked daily in the study sample was approximately 15 tobacco products per day, and in all stages, smoking intensity was greatest in adults living in homes where smoking was allowed, followed by those in smoking restricted homes.

Consistent with literature,15,17,33,38 home smoking restriction and smoke-free home were associated with reduction in tobacco smoking (Table 4.3). This significant reduction in smoking intensity was observed across all the three stages of tobacco cessation (Table 4.4) which is consistent with the study hypothesis. Though the absolute percent reduction in smoking intensity was highest in precontemplation (approximately 23%) and lowest in preparation (about 19%) among those living in smoke-free homes, the standardized betas suggest that the effect sizes were similar across all stages (Table 4.4). For those living in smoking restriction homes, significant reduction in intensity was seen in those in precontemplation and those in contemplation. However, there was 16% non-significant reduction in intensity in those in preparation. This non-significance seems to be due to the smallness of the sample as evidenced

127 by the wide confidence interval (-38.7% – 6.1%). The overall results suggest that, living in homes with smoking restriction or smoke-free homes could facilitate tobacco cessation through a reduction in tobacco smoking among those in contemplation and preparation. For those in precontemplation (no intention to quit smoking), the impact of the observed reduction in tobacco smoking on health is unknown,20 however, it may have economic benefit by reducing expenditure on tobacco and diverting resources to essential commodities.39

Smoke-free policy is one key policy measure espoused by the WHO FCTC10 to protect non- smokers from SHS exposure which kills approximately 600,000 people annually worldwide.2

Article 8 of FCTC recommends that all public places and government buildings be made smoke- free. While many countries have embraced the FCTC and have made progress in its implementation,5 it was reported that only about 16% of the world non-smokers were fully protected from environmental smoke.3 A recent report shows that SHS exposure is still very high in LMICs who are parties to the FCTC.30 This finding suggests that smoke-free policies are not fully implemented or enforced in these countries in spite of the strong evidence for smoke-free policies. Our results add to the growing literature on the effect of smoke-free policy on smoking cessation globally, and provide, to the best of our knowledge, the first cross-country evidence on the association between home smoking rule and smoking intensity among adults in LMICs. Our findings, together with other literature can provide support for full implementation and enforcement of smoke-free policies in the countries included in the analysis. While a national home smoke-free policy may be difficult to enforce, evidence has shown that working in a smoke-free environment correlates with living in smoke-free homes.12 Consequently, complete public smoking ban, including public and private business places may have trickle-down effect

128 on home smoking with consequent increase in cessation rates through a reduction in tobacco smoking. There is therefore the need to enforce smoking ban in these LMICs.

Limitations

This study has some limitations that should be considered in interpreting the findings. Firstly, information on daily tobacco smoking and home smoking rule were self-reported, and therefore subject to recall and social desirability bias. Again, other factors such as tobacco-related morbidities may affect smoking intensity but since the survey protocol does not include this information, we could not adjust for their effects. Also, temporality cannot be established because of possibility of reverse causation as a result of using cross-sectional data.

Conclusion

Home smoking rule is associated with intensity of tobacco smoking, with significant lower intensity of smoking in adults residing in smoke-free homes than those living in homes where smoking is allowed or where there are no rules. The results suggest that smoke-free homes are associated with a reduction in the number of tobacco products smoked daily across the first three stages of change (precontemplation, contemplation and preparation). Home smoking restriction however, was associated with a reduced intensity only in adults who were in precontemplation and contemplation to quit smoking. Consequently, smoke-free homes may promote tobacco cessation among adults in contemplation and preparation through a reduction in tobacco smoking. However, research is required to estimate health and economic benefits of smoking reduction in smokers in precontemplation, and to determine whether this reduction could promote quit intentions.

129

Acknowledgement

Authors will like to thank the Global Adult Tobacco Survey Collaborative Group for providing the data and offering technical advice for this study.

130

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CHAPTER 5

CONCLUSION

Tobacco contains more than 7,000 chemicals, with about 70 of them found to be harmful to humans (IARC & WHO, 2009; USDHHS, 2014). The large number of chemical composition means that tobacco can affect humans in several ways (WHO, 2008). It has been described as the only legal drug that is capable of killing half of all those who use it as intended (WHO, 2008).

As a risk factor for six of the eight leading causes of deaths worldwide (WHO, 2008), it is able to cause users to lose more than 10 years of their potential lives (Prabhat Jha et al., 2013;

USDHHS, 2014). Tobacco kills through diseases such as cancers, cardiovascular diseases, chronic respiratory diseases and diabetes, (USDHHS, 2014) and facilitate occurrence and poor prognosis of other conditions such as tuberculosis (Gajalakshmi, Peto, Kanaka, & Jha, 2003).

Tobacco use also has economic implication in the form of loss of manpower through early deaths, productivity loss, and direct health care cost (USDHHS, 2014; WHO, 2008). Tobacco users and smokers spend a significant amount of their income on tobacco products which reduces expenditure on essential commodities and services such as education, food and health care

(Efroymson et al., 2001; John et al., 2011). Consequently, widespread tobacco use is a threat to health and economic development.

Despite the health and economic threat posed, tobacco use has reached global epidemic level. It has been estimated that over one and a quarter of a billion people smoke, (Roemer,

Taylor, & Lariviere, 2005) and 4 in 5 of these people live in LMICs (WHO, 2008). Tobacco use in LMICs has been increasing and it is paralleled with increasing prevalence of non- communicable diseases and its mortalities (Bilano et al., 2015). For instance, it has been

137 projected that tobacco use will increase by 37% by 2025 in Sub-Saharan Africa which is currently at stage I of the tobacco epidemic (Bilano et al., 2015). Non-communicable diseases in the Sub-Saharan Africa sub-region are also expected to increase by 27% by 2025 (Bilano et al.,

2015). The increase in tobacco use has been attributed to tobacco industry activities in these countries (Lee et al., 2012). If the current trend of tobacco use is not checked, tobacco alone will kill approximately eight million annually by 2030 worldwide (Mackay, J., Eriksen, M., &

Shafey, 2009; WHO, 2008). However, globalization has made individual country’s effort to control tobacco virtually impossible (WHO FCTC, 2005). This has necessitated urgent public health intervention for global tobacco control (WHO FCTC, 2005).

In response to the global tobacco epidemic, the WHO World Health Assembly adopted the FCTC in May 2003 which entered into force in 2005 (WHO FCTC, 2005). In the history of the United Nations, the FCTC is considered the most widely embraced treaty (WHO FCTC,

2005). This reflects global commitment to controlling the tobacco epidemic. Central to tobacco control is prevention of initiation, promotion of cessation and protection of non-smokers against

SHS exposure. The FCTC calls for several policy measures for comprehensive control in each country, including smoke-free policy, taxation and dependence treatment and promotion of cessation. Several LMICs have shown commitment to tobacco control by ratifying or acceding to the convention (WHO FCTC, 2015).

In spite of the commitment to tobacco control and progress made in implementation of

FCTC, tobacco cessation rates are low in LMICs (Storr et al., 2010; Yang et al., 2011). For the improvement of tobacco cessation rates in LMICs, this study was conducted to assess factors associated with intention to quit smoking, utilization of cessation assistance and smoking intensity in LMICs using data from the GATS, which is considered the global standard for

138 monitoring tobacco control among adults (Palipudi et al., 2012). In a sample of 43,542 current smokers, representing approximately 580 million adults aged 15+ years from Bangladesh, China,

Egypt, India, Indonesia, Malaysia, Nigeria, Philippines, Russia, Thailand, Turkey, Ukraine,

Uruguay and Vietnam, it was observed that more than 4 in 5 adult smokers had no intention to quit smoking in the next 12 months, and as low as 1 in 25 adults was in preparation to quit smoking. These proportions, however, vary widely in individual countries. Proportion of smokers in preparation to quit smoking ranged from as low as 2% to 13% in the 14 countries, suggesting that a few number of smokers have intention to quit smoking in the short term and the larger proportion do not see themselves giving up smoking anytime soon. In this population also, it was observed that factors associated with contemplation and preparation to quit smoking vary across countries, however, in majority of the countries, living in homes with either smoking restriction or complete smoke-free rule and exposure to antismoking messages were associated with increased probability of being in contemplation or preparation to quit smoking.

From a sample of 5,848 adult smokers from Argentina, Bangladesh, China, Egypt, India,

Mexico, Romania, Thailand, Turkey and Vietnam who had abstained from smoking for at least a day but less than 12 months in past year, and had seen a doctor or health care provider in the past year, utilization of cessation assistance to quit smoking was approximately 9%, 7% and 1% for counseling/cessation clinic, medical treatment, and quitline, respectively. Close to half of the participants (45%) were not asked about tobacco smoking when they saw a doctor or health care provider in the past year. The results also showed that health care provider behavioral intervention (tobacco screening or quit advice) was associated with increased probability of using cessation assistance to attempt quitting tobacco smoking in the past year. There was a general increase in the odds of cessation assistance utilization for participants residing in

139 smoking restricted or smoke-free homes, and for participants exposed to anti-smoking media messages.

Among 39,204 (≈500 million) adult smokers from Argentina, Bangladesh, China, Egypt,

India, Indonesia, Malaysia, Mexico, Nigeria, Philippines, Romania, Russia, Thailand, Turkey,

Ukraine, Uruguay and Vietnam, less than 1 in 25 was in preparation to quit smoking and more than 4 in 5 had no intention to quit smoking, at least, not within the next 12 months. On average,

15 tobacco products were smoked daily in this sample. It was found that living in homes with smoking restriction rule and smoke-free rule was associated with approximately 15% and 22% reduction in smoking intensity, respectively. Again, complete smoke-free homes were associated with a significant reduction in smoking across all the three stages of TTM examined

(precontemplation, contemplation and preparation), while home smoking restriction was associated with a significant reduction in smoking among smokers in precontemplation and contemplation only.

The overall results of this research reflect the importance of home smoking restriction/smoke-free homes, health care provider behavioral intervention and antismoking messages in the media in promoting tobacco cessation among adults. Home smoking rule showed association with the three known indicators of tobacco cessation (intention to quit, utilization of cessation assistance, and a reduction in smoking intensity) examined in this study.

This provides further support for smoke-free policies in LMICs, and calls for a full implementation and enforcement of FCTC Article 8 in those countries. The results suggest that, in addition to protection of non-smokers, implementation of complete ban on public smoking can promote tobacco smoking cessation. Even in people who have no intention to quit smoking, the

140 results suggest that smoke-free policies could lead to a reduction in smoking with consequent economic benefit by reducing expenditure on tobacco products.

The results also suggest the importance of mass media campaigns about the harm of tobacco smoking to encourage quitting. Exposure to anti-smoking messages appears to promote contemplation to quit smoking in more countries than preparation to quit smoking. Again, exposure to anti-smoking messages in more than one channel was associated with intention to quit smoking in more countries than exposure to anti-smoking messages in only one channel.

This observation highlights the importance of delivering anti-smoking campaigns through different channels. In implementing anti-smoking media campaigns, it is important to consider other evidence, especially, on content of the campaign, reach and duration which were not assessed in this study. While anti-smoking messages are promising in tobacco cessation promotion, the full implementation of Article 13 of the FCTC to completely ban all forms of tobacco advertising which could counter anti-smoking messages is important.

Lastly, the study results indicate the need for full integration of tobacco screening and quit advice into the health care system, which requires the full implementation of the FCTC

Article 14. Given that adults who received health care provider behavioral intervention were more likely to use cessation service to quit smoking, cessation assistance need to be available for those who need them to quit smoking. Unexpectedly, quitline utilization was low in spite of the hike in telecommunication access in LMICs (Croyle, 2010). Therefore, it is important for public health professionals to explore ways by which telephone based cessation assistance could be embraced by the target population.

In summary, the overall results support the need for comprehensive tobacco control with components that complement one another. A full implementation of tobacco policies espoused

141 by the WHO FCTC, with guidelines from the MPOWER package may help to promote tobacco cessation, aside from preventing initiation and protecting non-smokers against SHS.

142

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APPENDIX

Human Subject Protection

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VITA

DANIEL OWUSU

Education: DrPH in Epidemiology, East Tennessee State University (ETSU), Johnson City, 2016 MPhil in Public Health, University of Cambridge, Cambridge, 2010 B.A. in Nursing and Psychology, University of Ghana, Legon- Accra, 2008 Teacher’s Certificate “A”, Presbyterian Training College, Akropong, 2002 Professional experience: Graduate Research Assistant, Department of Biostatistics and Epidemiology, ETSU, 2013-2016 Research Assistant (volunteer), Tobacco Policy Research, Department of Health Services Management and Policy, ETSU, 2013-2016 Intern, DrPH Field Experience with World Health Organization (WHO) African Region, 2014 Health Coordinator, Sastod Health Consult, Ghana, 2011-2012 Research Assistant, Amend.org’s program evaluation study in Ghana, 2009 Teaching Assistant, Department of Psychology, University of Ghana, Legon, 2008-2009

Publications: Owusu D, Mamudu HM, John RM, Ibrahim A, Ouma AEO, Veeranki SP (2015). Never-smoking adolescents’ exposure to secondhand smoke in Africa. Am J Prev Med. Under review Owusu D, Quinn M, Wang KS. Alcohol Consumption, Depression, Insomnia and Colorectal Cancer Screening: Racial Differences. Int J High Risk Behav Addict. 2015;4(2): e23424 Wang KS, Liu X, Owusu D, Pan Y and Xie C (2015) Polymorphisms in the ANKS1B gene are associated with cancer, obesity, and type 2 diabetes. In Special Issue: Genetic Epidemiology. AIMS Genetics 2(3):192-203 Owusu D, Longcoy J, Quinn M, Wang K. Relationship between Chronic Disease Conditions and Colorectal Cancer Screening: Results from the 2012 National Health Interview Survey Data. Am J Cancer Epidemiol and Prev. 2014; 2(1), 1-11 Wang K, Wang L, Liu X, Owusu D, Zhang M. Associations of binge drinking and smoking with type 2diabetes among

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adults in California: Age and race differences. Int J Diabetol Vasc Dis. 2014; 2(4): 57-62 Wang KS, Owusu D, Pan Y, Xu C. Common Genetic Variants in the Hnf1b Gene Contribute to Diabetes and Multiple Cancers. Austin Biomark Diagn. 2014;1(1):1-5 Wang K, Zuo L, Owusu D, Pan Y, Luo X. Prostate Cancer Related JAZF1 Gene is Associated with Schizophrenia. J Schizophr Res. c2014;1(1): 1-6 Selected presentations: Owusu D, Collins C, Aibangbee J, Robertson C, Littleton MA, Veeranki SP, Boghozian R, Wang L, Mamudu HM. E- cigarette use among high school students in Northeast Tennessee. Appalachian Students Research Forum, Johnson City, TN, 2016 Owusu D, Collins C, Aibangbee J, Robertson C, Littleton MA, Veeranki SP, Boghozian R, Wang L, Mamudu HM. E- cigarette use among high school students in Northeast Tennessee. Appalachian Students Research Forum, Johnson City, TN, 2016 Owusu D, Kioko D, , Rijo MJ , Ibrahim A, Ouma AEO, Mamudu HM, Veeranki SP. Regional differences and determinants of secondhand smoke exposure among never-smoking youth. American Public Health Association annual meeting, Chicago, 2015 Owusu D, Aibangbee J, Quinn M, Mamudu HM, Veeranki SP, Wang K. Relationship between cigarette smoking and self- rated health among adults in the United States. Tennessee Public Health Association annual meeting, 2015 Honors and Awards: Outstanding DrPH Epidemiology Student, ETSU, 2016 APHA Leadership Challenge Scholarship Winner, 2015 Top 5 Poster Presenter, Tennessee Public Health Association Annual Meeting, 2015 2nd Prize Winner, Appalachian Student Research Forum poster presentation, 2015 Overall Best Graduating Nursing Student, University of Ghana, Accra, Ghana, 2008 Overall Best Student in Academics, Presbyterian Training College, Akropong Akuapim, Ghana, 2002

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