Impact of Saudi Arabia’s Sugary Drink Tax on Prices and Purchases (P10-066-19)
Impact of Saudi Arabia’s Sugary Drink Tax on Prices and Purchases (P10-066-19) Reem Alsukait,1 Parke Wilde,2 Sara Bleich,3 Gitanjali Singh,2 and Sara Folta2 1Tufts University; 2Friedman School of Nutrition Science & Policy, Tufts University; and 3Harvard T.H. Chan School of Public Health
Objectives: Consumption of sugar-sweetened beverages (SSBs) has been associated with weight gain and an increased risk of type 2 diabetes and cardiovascular disease. Use of governmental policies, such as taxation, to reduce SSB consumption, has been successful in a number of global settings. However, the impact of such tax has not been examined in Arab Gulf countries where prevalence of obesity is high, and a unified excise tax was adopted in 2016. This tax increased the price of soda and energy drinks by 50% and 100% respectively, making it the largest beverage tax to date. date. Saudi Arabia was the first of the six Arab Gulf countries to implement these taxes in June 2017, followed by the United Arab Emirates, Bahrain, Oman, and Qatar. Saudi Arabia additionally added a 5% value added tax (VAT) to their beverage tax in 2018. We describe the impact of these excise taxes by examining price changes and purchases of taxed beverages pre-post taxation in Saudi Arabia Methods: This is an observational study of a natural experiment with a pre-post design. The Saudi General Authority for Statistics’ national-level monthly survey of average prices for soda from 2009– 2018 was used to describe the changes in the prices of the taxed beverage before and after the tax’s implementation. The 2004–2018 Euromonitor annual volume sales data for Saudi Arabia was used to describe the changes in soda and energy drink sales. Results: Post beverage and VAT implementation, annual pruchases (volume per capita) of soda and energy drinks were reduced by 41% and 58%, respectively in 2018 compared to 2016. During the same time period, soda prices prices increased by 67% compared to the announced 55% tax rate (price per can from 1.5 to 2.5 Saudi Riyals). Prior to the implementation of excise taxes, soda prices have been consistent in Saudi Arabia, except for one price increase by manufacturers in 2010 (Figure 1). Conclusions: These results highlight the substantial impact of excise taxation on the reduction of soda and energy drink sales in Saudi Arabia and contribute to the growing body of global evidence on the effects of SSB taxation on consumption, especially in other Arab Gulf countries that have implemented a similar tax structure. Funding Sources: Reem Alsukait is a doctoral student supported by King Saud University, Saudi Arabia.
Diabetes is a major health issue that has reached alarming levels. Today, more than half a billion people are living with diabetes worldwide.
The IDF Diabetes Atlas is an authoritative source of evidence on the prevalence of diabetes, related morbidity and mortality, as well as diabetes-related health expenditures at global, regional and national levels. The IDF Diabetes Atlas also introduces readers to the pathophysiology of diabetes, its classification and its diagnostic criteria. It presents the global picture of diabetes for different types of diabetes and populations and provides information on specific actions that can be taken, such as proven measures to prevent type 2 diabetes and best management of all forms of diabetes to avoid subsequent complications.
An overview of current approaches and the
potential benefits for children
Implementing Taxes on
An overview of current approaches and the
potential benefits for children
Why this is
Many countries are looking for ways to promote healthy
diets as a vital priority in the drive to prevent and control
non-communicable diseases (NCDs). To support this goal,
the World Health Organization (WHO) issued a technical
meeting report in 2016. Its guidance on how to design
fiscal policies aiming to reduce rates of obesity concluded
that the strongest health effects will result from taxes that
raise the retail price of beverages with added sugar by at
least 20 per cent.1
While illness and deaths resulting from NCDs occur mainly
in adults, the exposure to risks begins in childhood.2
Extensive evidence associates consumption of added
sugars with multiple health risks for children, including
diabetes, tooth decay and obesity.3
As of 2016, an estimated 340 million of the world’s
children and adolescents aged 5–19 were overweight
or obese, affecting 18 per cent of this population – up
from 4 per cent in 1975.4
Children with the highest intake
of sugar-sweetened beverages (SSBs) are more likely to
be overweight or obese than children with a low intake.5
Scientific standards specify that children aged 2–18 should
have less than 25 grams, or 6 teaspoons, of added sugars
a day and children under age 2 should not have any at
all.6 The average can of sugary drink contains around
40 grams of free sugars, equivalent to 10 teaspoons of table
sugar – and consumption is increasing among children
As noted in the 2018 Global Nutrition
Report, although 30 per cent of all school-age children do
not eat any fruit daily, 44 per cent drink soda every day
. World Health Organization, ‘Fiscal Policies for Diet and
Prevention of Noncommunicable Diseases’, Technical Meeting
Report, 5–6 May 2015, WHO, Geneva, 2016, pp. 9, 24, available
2. World Health Organization, ‘Global Action Plan for the Prevention
and Control of Noncommunicable Diseases, 2013–2020’,
Resolution WHA66.10, WHO, Geneva, 2013, p. 7, available at
3. See, for example: Delli Bovi, et al., ‘Obesity and Obesity Related
Diseases, Sugar Consumption and Bad Oral Health: A fatal
epidemic mixture’, Translational Medicine @ UniSA, vol. 16, no.
2, 2017, pp. 11–16.
4. World Health Organization, ‘Obesity and Overweight’, WHO,
Geneva, 16 February 2018, <www.who.int/news-room/factsheets/detail/obesity-and-overweight>. See, also: World Obesity
Federation and World Health Organization, ‘Taking Action on
Childhood Obesity’, WHO, Geneva, 2018, available at <www.
5. World Health Organization, ‘Information Note about Intake of
Sugars Recommended in the WHO Guideline for Adults and
Children’, WHO, Geneva, 2015, p. 1, available at <www.who.int/
6. American Heart Association, ‘Added Sugars and Cardiovascular
Disease Risk in Children’, Circulation, vol. 135, no. 19, 9 May
2017, pp. e1017–e1034. For further technical information, see:
World Health Organization, ‘Guideline: Sugars intake for adults
and children’, WHO, Geneva, 2015.
7. World Health Organization, ‘Taxes on Sugary Drinks: Why do it?’,
WHO, Geneva, 2017, p. 1, open PDF from <http://apps.who.int/
8. Independent Expert Group of the Global Nutrition Report,
2018 Global Nutrition Report: Shining a light to spur action on
nutrition, Development Initiatives Poverty Research Ltd., Bristol,
UK, 2018, p. 16, available at <https://globalnutritionreport.org/
9. Commission on Ending Childhood Obesity, ‘Report of the
Commission on Ending Childhood Obesity’, World Health
Organization, Geneva, 2016, p. 18, available at <www.who.
also: Childhood Obesity Intervention Cost-Effectiveness Study
(CHOICES), ‘Brief: Cost-effectiveness of a sugar-sweetened
beverage excise tax in 15 U.S. cities’, Harvard T. H. Chan School
of Public Health, Boston, Mass., December 2016, p. 4, available
10. USD amounts in the case studies reflect currency conversions
carried out in January 2019, at <www.xe.com>.
11. Landon, Jane, and Hannah Graff, ‘What is the Role of HealthRelated Food Duties?’, National Heart Forum, London, 2012, p.
12. Power Up for Health, ‘Taxing Sweetened Drinks in France’,
July 2015, p. 2, open PDF from <https://powerupforhealth.
13. Spiegel Online, ‘French “Cola Tax” Approved: Paris vows to
fight deficit and obesity’, 29 December 2011, <www.spiegel.de/
international/europe/french-cola-tax-approved-paris-vows-to-fightdeficit-and-obesity-a-806194.html>. Note: Currency conversions
to USD in the case studies reflect rates in January 2019, as
calculated at <www.xe.com>.
14. European Competitiveness and Sustainable Industrial Policy
Consortium, Food Taxes and Their Impact on Competitiveness
in the Agri-Food Sector: Annexes to the main report, Ecorsys,
Rotterdam, The Netherlands, 12 July 2014, p. 204.
15. European Competitiveness and Sustainable Industrial Policy
Consortium, Food Taxes and Their Impact on Competitiveness
in the Agri-Food Sector: Final report, Ecorsys, Rotterdam, The
Netherlands, 12 July 2014, p. 36, available at <www.ecorys.
Food and Agriculture Organization of the United Nations
Ministry of Planning Development and Reform, Government of Pakistan
The Pakistan Dietary Guidelines for Better Nutrition (PDGN) are comprehensive country specific guidelines
for the general public to adopt healthy eating practices, and prevent and reduce the risk of infectious and
chronic diseases. These guidelines are simple to adopt, provide age and gender appropriate and culturally
acceptable options to choose nutritious foods.
The PDGN provide a thorough review of the food security, health and nutrition situation of the population
and is cognizant of the fact that about half of the population is deficient in one or more of essential
nutrients, reflecting unhealthy dietary practices compounded by poor hygiene and sanitation. Women and
young children are more vulnerable to nutritional deficiencies, morbidity and mortality due to their
compromised health and nutritional status. The PDGN highlight the predominant consumption of energy
dense foods and monotonous diets by majority of the population.
The emphasis of the PDGN is on the consumption of a variety of safe and nutritious foods including milk and
milk products, meat and pulses, wholegrain cereals, vegetables and fruits and decreasing consumption of
energy dense foods such as deep fried foods, bakery products, processed foods and reducing the amount of
fat specifically saturated fat, oil, sugar and salt in cooking, as there is a strong relationship between diet and
disease, specially non-communicable diseases such as diabetes, cardiovascular diseases and others that are
associated with poor dietary habits. Moreover, physical activity is critical for all population groups to remain
physically fit and healthy.
The PDGN are a rich resource for general public, decision and policy makers, planners, researchers,
academicians, food and pharmaceutical industries, hospitals, agriculture extension and allied health
professionals for integration into their own plan of actions and interventions. These guidelines will benefit
communities, clients and consumers in making healthy food choices and well suited to break the vicious
cycle of malnutrition when integrated into nutrition sensitive and the social development programmes of
I appreciate the efforts of the contributors and hope that the use of these guidelines, would serve as an
effective tool for improving nutrition and well being of the population.
28 May, 2018 Planning Commission of Pakistan
Pakistan Dietary Guidelines for Better Nutrition
The Ministry of Planning Development and Reform, gratefully acknowledges the generous support of the
Food and Agriculture Organization of the United Nations and Scaling up Nutrition (SUN) Movement for
developing the Pakistan Dietary Guidelines for Better Nutrition (PDGN). Mr. Francisco Gamarro (Late),
Ex-Deputy Representative FAO, and Mr. Nasar Hayat, Assistant Representative FAO, deserve appreciation
for their continuous support in accomplishing the task of developing PDGN.
The efforts of Dr. Parvez Iqbal Paracha, Nutrition Specialist in preparing the document and great
contributions of Dr. Nomeena Anis, Nutritionist and Gender Focal Person, FAO, Islamabad in developing a
comprehensive document are highly appreciated. The dedicated efforts, overall coordination and
commitment of the Nutrition Section/ SUN Secretariat are commendable. Special thanks to Dr. M. Azeem
Khan, Member Food Security and Climate Change, Planning Commission for his overall guidance in finalizing
of this document. The Nutrition Section appreciates the valuable contribution of Dr. Mubrik Ali, Ex-Member
Food Security and Climate Change, Planning Commission Pakistan.
The Ministry of Planning Development and Reform is highly indebted to distinguished and renowned
academicians, researchers, programme managers, policy makers, development partners and other
stakeholders for their valuable inputs in formulation of PDGN, which will serve as a source of guidance for
planning and implementation of nutrition related programmes aimed at promoting healthy diets and
preventing malnutrition and diseases.
M. Aslam Shaheen
Chief Nutrition/SUN Focal Person
Ministry of Planning Development & Reform Pakistan
Smokers and vapers may be at greater risk for severe illness when confronted with COVID-19.
Smoking Harms Lung Health Smoking damages the lungs and negatively impacts how well they function.
• The lungs of smokers produce more and thicker mucus than the lungs of nonsmokers.
This mucus is both difficult to remove and makes the lungs prone to infection
.1 • Smoking also inhibits and eventually destroys the cilia, the small hair-like projections on the surfaces of cells in the breathing airway that brush away dirt and other particles to protect the lungs.
2 • Exposure to cigarette smoke causes airway inflammation. This inflammation and the resulting scar tissue damage the membranes that pass oxygen to the bloodstream.
1 Smoking causes lung cancer, chronic obstructed pulmonary disease (COPD), asthma, and other respiratory diseases. • The lung diseases caused by smoking occur among smokers and non-smokers exposed to tobacco smoke.
1 • The lung diseases caused by smoking are among the underlying conditions known to place people at greater risk of more severe illness when diagnosed with COVID-19
.3,4 Smoking Impairs Immunity Smoking harms the immune system and, therefore, the body’s ability to fight infection. This impairment occurs in two different ways.
• The chemicals in tobacco smoke suppress the activity of different types of immune cells that are involved in general and targeted immune responses.
1 • The components in tobacco smoke also over-activate immune cells, which are recruited to combat the toxins that are inhaled and their effects. Over time, this pro-inflammatory effect can damage different tissues throughout the body and result in a number of chronic diseases including various autoimmune diseases, cardiovascular disease, cancer, diabetes, and chronic obstructive pulmonary disease (COPD.)1,5 Smoking increases susceptibility to respiratory infections.1 • There is overwhelming evidence that people who smoke are at higher risk of getting viral and bacterial respiratory infections: o Smokers have two to four times the risk of pneumococcal diseases like pneumonia and meningitis than nonsmokers. o Influenza risk is twice as high in smokers compared with nonsmokers. o Smokers have about twice the risk of contracting tuberculosis.6 In light of smoking’s negative impacts on the immune system and smokers’ increased susceptibility to other respiratory infections, it is likely that smoking is associated with increased risk of infection with the novel coronavirus. Smoking, Vaping, and COVID-19 Emerging Evidence • COVID-19 attacks the lungs and behaviors that weaken the lungs put individuals at greater risk. The harmful impact of smoking on the lungs is well-documented, and there is evidence that e-cigarette use (vaping) can also harm lung health. • It is not surprising that there is mounting concern among leading public health organizations and experts that smokers face a higher risk for severe illness from COVID-19. As vaping impacts the immune system and can harm lung health, e-cigarette users may also face higher risks. We urge all smokers and e-cigarette users to quit in order to protect their health, especially at this critical time. • In several countries, rumors have been spread that smoking or vaping will protect tobacco users from COVID-19. These are unproven and dangerous. Tobacco kills over 8 million users each year and its harms are scientifically proven. WWW.TOBACCOFREEKIDS.ORG MAY 2020 SMOKING, VAPING, AND COVID-19: EMERGING EVIDENCE Vaping Impacts Health Early studies into the effects of e-cigarette use show detrimental effects on the lungs, as well as the immune and cardiovascular systems. This research, considered alongside emerging evidence that patients with compromised respiratory, immune, and cardiovascular systems are at higher risk for severe COVID-19 infection, has led health authorities and others to caution against using e-cigarettes, particularly amidst the coronavirus pandemic.14,15,16 • Lungs: Exposure to e-cigarette aerosol can have negative effects on various types of lung cells, including those involved in maintaining normal, healthy lung function.17 • Immune Response: E-cigarette aerosol also inhibits and can kill several kinds of immune cells in the lungs, compromising the lungs ability to fight infection.17 Smoking is a Leading Risk Factor for Noncommunicable Diseases (NCDs) Additionally, nicotine, a critical component of e-cigarette aerosol, is known to suppress immune function throughout the body.1 • Cardiovascular System: E-cigarette use can have shortterm effects in reducing the function of cardiovascular tissue that controls blood flow.18,19 Although it is too early to draw conclusions on the long-term effects of e-cigarette use, this dysfunction is commonly observed early in the development of cardiovascular disease.20 Researchers have not yet found a direct link between e-cigarette use and likelihood of COVID-19 infection or severity of disease in those who are infected. However, given the early evidence of potential health risks from using e-cigarettes, there is mounting concern that people who use e-cigarettes may be at greater risk for severe illness when confronted with COVID-19. The World Health Organization has emphasized that smoking requires repeated hand-to-face motion, which increases the risk of viral transmission from fingers and cigarettes to the mouth.3,7 Along the same lines, many have raised concerns that waterpipe use, which often involves using shared mouthpieces in social settings, contributes to the spread of the novel coronavirus.8,9 According to the WHO, available research suggests that smokers are at greater risk of developing severe disease and dying from COVID-19,11 including the following two studies: • Smoking causes cancer, COPD and other lung diseases, cardiovascular disease, and diabetes.1 • Conditions like respiratory and cardiovascular diseases increase risk of severe disease in patients infected with other known coronaviruses, including those that cause MERS and SARS.10 • The World Health Organization has stated that people with NCDs appear to be at higher risk for experiencing more severe forms of COVID-19.3 ■ One of the largest early studies investigating associations between smoking and COVID-19 examined clinical outcomes from 1,099 patients with lab-confirmed COVID-19 infection from 552 hospitals across China. This study reports that 12.4% of current smokers died, were admitted to an intensive care unit or required mechanical ventilation, compared with 4.7% of nonsmokers. Along similar lines, 21.2% of current smokers had severe symptoms, as opposed to 14.5% of nonsmokers.12 ■ A recently published study analyzed data from 8,910 patients hospitalized with COVID-19, collected from 169 hospitals across 11 countries in Asia, Europe and North America. The authors controlled for age and sex, two factors that influence smoking rates. They found that smoking was among the factors independently associated with in-hospital death; 9.4% of smokers hospitalized with COVID-19 died, as opposed to 5.6% of former smokers or nonsmokers. Smokers in the study were 1.79 times more likely to die in the hospital of COVID-19 than former smokers or nonsmokers.13 WWW.TOBACCOFREEKIDS.ORG MAY 2020 • As countries around the world work to limit the impact of the coronavirus, there has never been a better or more urgent time for people to quit smoking and vaping. • In order to protect their health and reduce their risk of severe COVID-19 symptoms, we urge all those who smoke or vape to quit. Research has shown that quitting smoking rapidly improves lung function. SMOKING, VAPING, AND COVID-19: EMERGING EVIDENCE REFERENCES We urge all smokers and e-cigarette users to make every effort to quit. Quitting smoking improves lung function, immune response, and cardiovascular health, putting former smokers in a stronger position to fight severe infections like COVID-19. • Within two weeks of quitting smoking, lung function improves.21 Cilia, the hair-like projections that protect the lungs, regrow and return to normal activity levels, making it easier to fight infection.22 Many smokers begin to notice a decrease in respiratory symptoms like coughing and shortness of breath within one month of quitting smoking.
23 • After quitting, the immune inflammation caused by smoking decreases, white blood cell counts return to normal, and immune function improves.22 Rates of respiratory infections, including pneumonia and bronchitis, are significantly lower among former smokers than current smokers.23 • Quitting smoking lowers blood pressure and heart rate almost immediately. Twenty-four hours after quitting smoking, the risk of heart disease begins to decline.22 There has never been a better time to quit smoking. According to Dr. Tedros Adhanom Ghebreyesus, Director General of the World Health Organization, “quitting tobacco is one of the best things any person can do for their own health.” 24 Quitting Smoking Rapidly Improves Lung Health 1. U.S. Department of Health and Human Services. The Health Consequences of Smoking: 50 Years of Progress. A Report of the Surgeon General. Atlanta, GA: U.S. 2014. 2. U.S. Department of Health and Human Services. How Tobacco Smoke Causes Disease: The Biology and Behavioral Basis for Smoking-Attributable Disease: A Report of the Surgeon General. Atlanta, GA: U.S. 2010. 3. World Health Organization (WHO). Information Note: COVID-19 and NCDs. Published 23 March 2020. 4. U.S. Centers for Disease Control and Prevention (CDC). Morbidity and Mortality Weekly Report: Preliminary Estimates of the Prevalence of Selected Underlying Health Conditions Among Patients with Coronavirus Disease 2019 — United States, February 12–March 28, 2020. 69(13);382–386. 3 April, 2020. 5. Pahwa R, Goyal A, Bansal P, et al. Chronic Inflammation. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2020. 6. Arcavi L and Benowitz NL. Cigarette Smoking and Infection. Arch Intern Med. 2004;164(20):2206-2216. 7. Simons D, Perski O, Brown J. Covid-19: The role of smoking cessation during respiratory virus epidemics. British Medical Journal: Opinion. Published 20 March 2020. 8. WHO Regional Office of the Eastern Mediterranean. Tobacco and waterpipe use increases the risk of suffering from COVID-19. Tobacco Free Initiative. 2020. 9. Kalan et al. Waterpipe Tobacco Smoking: A Potential Conduit of COVID-19. Tobacco Control: Blog. Published 23 March, 2020. 10. Volkow ND. COVID-19: Potential Implications for Individuals with Substance Use Disorders. Nora’s Blog: National Institute for Drug Abuse. Published 6 April, 2020. 11. WHO. WHO statement: Tobacco use and COVID-19. Published 11 May, 2020. 12. Guan W, Ni Z, Hu Y, et al. Clinical Characteristics of Coronavirus Disease 2019 in China. N Engl J Med 2020. 13. Mehra MR, et al. Cardiovascular Disease, Drug Therapy, and Mortality in Covid-19. N Engl J Med 2020. 14. Volkow ND. Collision of the COVID-19 and Addiction Epidemics. Ann Intern Med. 2020; [Epub ahead of print 2 April 2020]. 15. Glantz SA. Reduce your risk of serious lung disease caused by corona virus by quitting smoking and vaping. UCSF Center for Tobacco Control Research and Education: Blog. Accessed 13 April, 2020. 16. Mayo Clinic. What smokers need to know about coronavirus. Blog post by Vivien Williams. Published 24 March, 2020. 17. Gotts JE, et al., What are the respiratory effects of e-cigarettes? British Medical Journal. 2019, 366:l5275. 18. U.S. National Academies of Sciences, Engineering, and Medicine. Public health consequences of e-cigarettes. Washington, DC: The National Academies Press. 2018. 19. Caporale A, et al. Acute Effects of Electronic Cigarette Aerosol Inhalation on Vascular Function Detected at Quantitative MRI. Radiology. 2019 :190562. 20. Bonetti PO, et al. Endothelial Dysfunction: A Marker of Atherosclerotic Risk. Arteriosclerosis, Thrombosis, and Vascular Biology. 2003;23:168–175. 21. National Institutes of Health, National Cancer Institute, Smokefree.gov, What Happens When You Quit Smoking?, viewed 30 March 2020. 22. National Cancer Institute. “Benefits of Quitting.” 23. U.S. Department of Health and Human Services. Smoking Cessation. A Report of the Surgeon General. Atlanta, GA: U.S. 2020. Page 311. 24. WHO. “WHO launches new report on the global tobacco epidemic.” Published 26 July 2019. News release. Geneva/Rio de Janerio.
1 The Pakistan Institute of Development Economics (PIDE) is funded by the University of Illinois Chicago’s (UIC) Institute for Health Research and Policy to conduct economic research on tobacco taxation in Pakistan. UIC is a partner of the Bloomberg Initiative to Reduce Tobacco Use. The views expressed in this document cannot be attributed to, nor do they represent, the views of UIC, the Institute for Health Research and Policy, or Bloomberg Philanthropies 2 Executive Summary Pakistan with 24 million active tobacco users is among the world’s top tobacco-consuming countries. Tobacco use is associated with many adverse health effects, but the tax revenue it generates cause tobacco tax policy inertia in Pakistan and other countries. Despite evidence that higher tobacco taxation discourages tobacco consumption, Pakistan’s tax policy is among the weakest action areas in the country’s fight against tobacco. One explanation could be that the policymakers, who consider the tobacco industry a major contributor to government coffers, are reluctant to raise taxes fearing the revenue loss. However, when the government abolished the third tax tier in 2019 which effectively reduced the tobacco industry’s maneuvering space to sell cheaper cigarettes by avoiding taxes, the tax contribution of the industry actually increased to 120 billion Pakistani rupees (Rs) compared to Rs 92 billion in 2016. This raised the tobacco industry’s share of total tax collection to 3 percent from 2.15 percent in FY16.The government’s reluctance to change tobacco tax policy is partly due to its failure to fully appreciate the smoking-attributable fraction (SAF) of health and social costs. This makes its benefit-cost analysis of tax revenue faulty and compromised over e health outcomes. In reality, tobacco use incurs huge direct and indirect cost to its subjects. Direct costs include in- and out-patient hospital expenses, whereas, the indirect cost include the caregiving costs, opportunity cost of the lost workdays of the patients and their caregivers. By using the cost of illness (COI) approach, this study estimates the economic burden of three major smokinginduced diseases (cancer, cardiovascular, and respiratory) in Pakistan, based on a nationally representative sample of 12,298 households and their smoking members. The study also estimates total economic costs from all smoking-attributable diseases and deaths in Pakistan in 2019. The survey results show that smoking prevalence in Pakistan is 8.8 percent. Prevalence is highest in Balochistan (14.43 percent) and in the age category of 65 and older (15.90 percent), though the 35–64 age group is not far behind (15.07 percent). Nationally, cardiovascular diseases are the most prevalent in the year 2019. Cardiovascular diseases are also most prevalent, followed by cancer, in urban regions, across both genders, and in Punjab and Khyber Pakhtunkhwa (KP) provinces. The total smoking-attributable fraction of the direct cost of three diseases is Rs 100.3 billion ($0.63 billion) of which the medical cost is 96 percent (Rs 96.24 billion or US$ 0.60 billion) and non-medical cost is four percent (Rs. 4.06 billion US$0.03 billion. Smoking-attributable indirect morbidity cost is Rs 56.32 billion ($0.35 billion). The morbidity cost is 56 percent of the smoking-attributable medical and non-medical expenses. Mortality due to smoking, on the other hand, costs Rs 281.1 billion ($1.76 billion), with rural areas contributing 59 percent to the total morbidity cost. At the disease level, cancer has the highest share (56 percent) of the mortality cost. The mortality cost for males is higher than females in both age groups and also 3 within and across regions. Overall, the mortality cost for males is Rs 259 billion ($1.62 billion), which is 92 percent of the total. Following are the sobering insights and messages from this study: • The total costs attributable to all smoking-related diseases and deaths in Pakistan for 2019 are Rs 615.07 billion ($3.85 billion), and the indirect costs (morbidity and mortality) make up 70 percent of the total cost. Rural residents bear 61 percent, males bear 77 percent and 35–64 age group bears 86% of the total cost. The total tax contribution of tobacco industry (120 billion in 2019) is only around 20 percent of the total cost of smoking. • Smoking-attributable total direct and indirect cost of cancer, cardiovascular and respiratory diseases amount to a total of Rs 437.76 billion (US$ 2.74 billion) which is 3.65 times higher than the overall tax revenue from the tobacco industry (120 billion in 2019). Of this, the direct cost is 23 percent and indirect mortality cost is 64 percent. Rural residents bear 65 percent, males bear 87 percent and 35–64 years age group bears 82 percent of this cost. • The major share (71 percent) of the smoking-induced costs come from cancer, cardiovascular and respiratory diseases. The total smoking-attributable costs are 1.6 percent of the GDP, whereas the smoking-attributable costs of cancer, cardiovascular and respiratory diseases are 1.15 percent of the GDP. • The share of morbidity and mortality costs for females is underestimated because of their lower rates of labor force participation and difficulties in putting monitory value on their informal contribution to household production. • The smoking-attributable direct cost is 8.3 percent of the total health expenditures, which is very high. Keeping in mind the tax elasticity of cigarette demand and the enormous economic and health costs of smoking, more effective use of taxation policy is recommended to reduce tobacco consumption in the interest of public health. The taxes should be increased at least to the level that meets the World Health Organization’s recommended threshold or to the level that covers the health and economic costs due to smoking-induced diseases and deaths. In the short run, the rates of the two existing tax tiers on cigarettes should be increased with a higher increase for the second tier in order to narrow the gap between them. In the long run, however, the twotier system should be abolished to have a single-tier system for tobacco taxation. This would help to bring the poor out of the vicious cycle of poverty in addition to reducing the smokingrelated disease burden. 4 Table of Contents INTRODUCTION 7 DATA NEEDS AND SOURCES 9 2.1 APPROACH 9 2.2 DATA 9 2.3 SAMPLING AND DATA COLLECTION 10 2.4 DATA DESCRIPTION 13 ESTIMATION METHODS 14 3.1 SMOKING-ATTRIBUTABLE FRACTION (SAF) 14 3.2 DIRECT COST OF SMOKING 15 3.3 INDIRECT MORBIDITY COSTS 16 3.4 INDIRECT MORTALITY COST 16 RESULTS AND DISCUSSION 18 4.1 PREVALENCE, RELATIVE RISKS, AND SAFS 18 4.2 COST ESTIMATIONS FOR THREE MAJOR DISEASES 19 4.3 CONTEXT AND IMPLICATIONS 22 4.4 SENSITIVITY ANALYSIS 23 4.5 TOTAL COST FOR ALL SMOKING-INDUCED DISEASES AND DEATHS 24 4.6 LIMITATIONS OF THE STUDY 25 CONCLUSIONS AND POLICY IMPLICATIONS 26 REFERENCES 28 APPENDICES 30 5 List of Tables Table 1. Relative risks, smoking prevalence, and smoking-attributable fractions 18 Table 2. Direct cost (billion Rs) 19 Table 3. Morbidity cost (billion Rs) 20 Table 4. Mortality cost (billion Rs) 21 Table 5. Comparison of economic cost of smoking with outcome indicators 23 Table 6. Direct cost for all smoking-induced diseases and deaths (billion Rs) 24 Table 7. Morbidity cost for all smoking-induced diseases and deaths (billion Rs) 24 Table 8. Mortality cost for all smoking-induced diseases and deaths (billion Rs) 25 List of Figures Figure 1. Cost of illness approach 9 Figure 2. Content and flow chart of the survey questionnaire 12 Figure 3. Major components of total cost of tobacco use to Pakistani economy 22 List of Annex Tables Table A1. Sampling details 30 Table A2. Population and sample distribution across different administrative units 30 Table C1. Descriptive analysis……………………………………………………………………………………32 Table C2. Prevalence of tobacco and smoking………………………………………………………………32 Table C3. Disease prevalence……………………………………………………………………………………..32 Table D1. Present discounted value of lifetime earnings (million Rs) 34 Table D2. Cost estimation using RRs from India and China 34 Table D3. Cost estimation using disaggregated RR for Pakistan from survey data 34 Table E1. RRs, smoking prevalence, and SAF for all-cause mortality 35 6 Acronyms COI Cost of Illness FATA Federally Administered Tribal Areas GATS Global Adult Tobacco Survey GDP Gross Domestic Product HIES Household Integrated Economic Survey HIICS Household Integrated Income and Consumption Survey KP Khyber Pakhtunkhwa LFS Labour Force Survey PBS Pakistan Bureau of Statistics PIDE Pakistan Institute of Development Economics PSLM Pakistan Social and Living Measurement RME Relative Margin of Error RR Relative Risk Rs Pakistani Rupees SAE Smoking-attributable Expenditures SAF Smoking-attributable Fraction WDI World Development Indicators WHO World Health Organization 7 INTRODUCTION The success of the tobacco industry hinges on the ignorance of real economics at work behind the scenes. Though tobacco use is associated with many adverse health effects (Saha et al., 2007), increased health costs (John et al., 2020; Sung et al., 2006), and overburdened health systems (Amin et al., 2017), the tax revenues it generates often encourage policy inertia in poor economies such as Pakistan. As a country of 24 million active tobacco users, Pakistan stands as the one of the world’s top tobacco-consuming countries. Although 86 percent of its adult population know that tobacco use damages human health (GATS, 2014), some 45 percent households report tobacco use. Smoking prevalence varies across gender (male 32.4 percent, female 5.7 percent), region (rural 13.9 percent, urban ten percent), and age group (adults 19.1 percent, adolescents 6–14 percent). This situation requires the government to make corrective policies to nudge public behavior in the greater interest of society. One way to alter public behavior is through taxation policies. Increasing tobacco taxes has been found to reduce tobacco consumption, including in Pakistan (Nayab et al., 2018). Despite empirical evidence that tax policy is effective in reducing tobacco consumption and improving public health outcomes, the country’s taxation policy is among the weakest action areas in its fight against tobacco. One potential reason for this could be that the government considers tobacco industry as major tax contributor and is therefore reluctant to increase taxes for fear of revenue losses. The first part of this argument is correct: the tobacco industry contributes considerably to the national exchequer. In fiscal year 2015–16, its contribution to the total tax collection was around 2.15 percent. The second part of the argument, however, does not carry weight. Nayab et al. (2018) projected that abolishing the third tobacco tax tier and increasing taxes would raise the tax revenue, which is indeed what happened. In fiscal year 2018–19 when tax policy was simplified along the lines suggested by Nayab et al. (2018), the tax revenue increased to Rs 120 billion from the baseline revenue of Rs 92 billion in 2016. Another reason for the government’s reluctance to use tax policy effectively to curb tobacco consumption could be the absence of a reliable estimate of the true economic costs of smoking. Both government and the general public are aware that smoking causes various diseases such as cancer, cardiovascular problems, and respiratory complications, among others. What is unknown, however, are the costs that smoking-induced diseases impose on society. In other words, the absence of a monetized value of smoking-attributable costs makes it difficult for the government to account for the tax-induced improvements in public health outcomes. These costs consist of direct medical and non-medical expenditures as well as indirect morbidity and mortality costs. 8 The international evidence suggests that smoking-related illnesses impose enormous costs on society. Various studies have found these costs to be in the range of 0.5 to 2 percent of the respective country’s gross domestic product (GDP). Studies that estimate the economic costs of smoking-induced disease burden have been conducted for America (Warner et al., 1999), Germany (Welte et al., 2000), India (John et al., 2009; John, 2019; John et al., 2020), China (Sung et al., 2006; Yang et al., 2016), Vietnam (Ross et al., 2007), and Sri Lanka (Amarasinghe et al., 2018). In all these studies, indirect costs (morbidity and mortality) constitute the major shares. Moreover, in all these studies the costs of smoking outweigh the tax contribution of the tobacco industry. Keeping in mind the evidence from multiple countries as well as the government’s hesitation to use tax policy more aggressively to curb tobacco consumption, there is a need to explore the true economic cost of smoking-induced diseases in Pakistan. To the best of the authors’ knowledge, there is only one study that attempted an estimation of the economic cost of smoking in Pakistan (Saqib et al., 2020). The study found the smoking-attributable economic costs to be Rs 192 billion, or 0.4 percent of the country’s GDP. However, the study uses a sample of hospital patients and, therefore, suffers from sample selection bias. It excludes those patients who visit as outpatients or who might have died at home. Moreover, the study does not provide estimates at any level of disaggregation such as by region, province, gender and age groups. To fill the gap identified above, the current study estimates the true cost of three major smoking-induced diseases (cancer, cardiovascular, and respiratory) in Pakistan by conducting a nationally representative survey of smokers in the country. This study provides estimates not only across diseases but also by gender, region, age group, and type of service (inpatient or outpatient). Furthermore, both direct (medical and non-medical) and indirect (morbidity and mortality) costs are estimated. In addition, this study also estimates total economic costs from all smoking-attributable diseases and deaths in Pakistan in the year 2019 using relative risk of all-cause mortality from smoking. These estimates provide a broader view of smoking-induced illnesses for the government and facilitate the development of more informed tobacco taxation policies. 9 DATA NEEDS AND SOURCES 2.1 APPROACH This study uses the annual cost of illness (COI) approach to estimate tobacco consumption’s economic and health costs to Pakistan’s economy. The COI essentially aggregates smoking’s impact across all economic agents to determine the annual macroeconomic consequences of tobacco consumption. This approach totals the excess costs of smoking-induced diseases and deaths suffered by smokers (current, past, or ever) compared to never smokers during a year. Although these costs are incurred at selected points in time during the cure of smoking-related illnesses, these costs, in fact, accumulate over the years of exposure to tobacco. Hence, this approach is also termed as a prevalence-based approach. This approach is used to calculate the economic costs of smoking of those who (i) are recently diagnosed with or (ii) are in advanced stages of smoking-induced illness, and (iii) those who die of such illnesses in a particular year, irrespective of their smoking initiation or cessation date. Normally a nationally representative sample is required to estimate the cost of tobacco use at various levels of disaggregation, as shown in Figure 1. Figure 1. Cost of illness approach 2.2 DATA There is no existing data set that satisfies the requirements of COI to estimate the economic costs of smoking at the required levels of disaggregation. Of the two most relevant national data sets, Pakistan Social and Living Standards Measurements (PSLM)/ Household Integrated Economic Survey (HIES)/ Household Integrated Income and Consumption Survey (HIICS) provide information aggregated at the household level instead of at the level of individual smokers; while the National Health Accounts ignore some very important tobacco-related 10 illnesses. This situation demands primary data collection through a nationally representative survey of smokers that could cover relevant details for the estimation of smoking-attributable costs at national and disaggregated levels. This study used the World Health Organization’s Economics of Tobacco Toolkit (WHO, 2011) as a guide to design a comprehensive questionnaire. Figure 2 summarizes the scheme and content of the sample survey questionnaire. To rationalize the field costs, the authors broadened the survey scope and included another module obtaining the information required for another planned study (Figure 2, Section D4). 2.3 SAMPLING AND DATA COLLECTION This study uses the National Sampling Frame 2017, the latest edition available from the Pakistan Bureau of Statistics (PBS). Being the official statistics arm of the government, PBS was engaged to help with the sampling exercise. The authors’ previous work exhibited significant variations in smoking behavior across rural and urban regions (Nayab et al., 2018). Besides using the region as a primary sampling consideration, the sampling was performed based on the following parameters: the predicted value of the indicator (r), design effect, relative margin of error (RME), the base population in total population according to the census conducted in 2017, average household size, and response rate. The resulting sample size of 12,298 households from 615 blocks was determined based on the cluster sampling technique (Table A1 in Appendix A). The authors also expected some refusals to participate in the survey, which is addressed through a replacement sample of 702 households (about 6 percent of the sample size); the sample size and replacement sample add up to 13,000 households. To ensure true representativeness, the sample was distributed across all administrative units in proportion to their share in the country’s population. Table A2 presents these proportions. Punjab and Sindh account for slightly more than half and slightly more than one-fourth of the sample, respectively. The remaining one-fifth adds up as Khyber Pakhtunkhwa (KP) 12 percent, Balochistan 5.5 percent, Islamabad one percent, and Federally Administered Tribal Areas (FATA) 1.7 percent. The survey was conducted from October 2019 to March 2020. Out of the sampled 615 blocks, the survey teams were able to cover 607 blocks. The remaining eight blocks could not be surveyed due to extreme weather conditions, non-permission in security-sensitive areas, and lockdown due to COVID-19. Fortunately, the response rate from households in the surveyed blocks was 100 percent. The 607 surveyed blocks covered 12,140 households. In rural areas, data from 312 blocks (6,240 households) were collected, whereas 295 blocks (5,900 households) were surveyed in urban regions. The provincial distribution of households was as follows: Punjab (5,240 households), Sindh (3,420 households), Khyber Pakhtunkhwa (1,980 households), and Balochistan (1,500 households). Since the sample provided by the PBS is nationally representative at the rural-urban level, it came with sampling weights, which allows the estimates to be generalized for the entire country with a certain degree of confidence. With sampling weights, the sum of health care expenditures for all diseases in the study sample should ideally derive the total health care 11 expenditures for all non-institutionalized populations in Pakistan. Further description of field activities is provided in Appendix B. 12 Figure 2. Content and flow chart of the survey questionnaire 13 2.4 DATA DESCRIPTION The overall sample size consists of 82,889 individuals. Since estimation of the economic costs of smoking requires restricting the sample to individuals aged 35 years and above (see, for instance, Sung et al., 2006), the relevant sample for the current study consists of 22,823 individuals. Among these, 11,700 are from rural areas and 11,123 are urban residents. Regarding gender distribution, males make up 11,875 of the study sample, or slightly less than half. Similarly, 19,660 individuals belong to the 35–64 age group, and the rest are in the age group of 65 years and older. The descriptive statistics for the sample (overall, 15 and above, and 35 and above) are reported in Table C1 in Appendix C. The average age in the sample of interest is 49.5 years. The proportion of males (52 percent) and average years of education (8 years) are in line with the overall sample. Forty-three percent of the respondents are employed in this sample, which is higher than the overall sample average. The prevalence of tobacco and smoking across different dimensions (region, gender, province, age group) are reported in Table C2. The table includes the prevalence figures from GATS (2014) for comparison. The tobacco prevalence in the study sample is exactly equal to that of GATS (19.1 percent). The smoking prevalence (8.8 percent) in the study sample is, however, slightly lower than that of GATS. Broadly speaking, the prevalence of tobacco and smoking are relatively similar in the two surveys. Smoking prevalence is highest in Balochistan (14.43 percent) and in the age category of 65 and older (15.90 percent), although the 35–64 age group is not far behind (15.07 percent). In terms of disease prevalence in the year prior to the interview, cardiovascular diseases are the most prevalent nationally, in urban regions, across both genders, and in the Punjab and Khyber Pakhtunkhwa (KP) provinces (Table C3). The second most prevalent disease is cancer. Prevalence was also highest for cardiovascular diseases during the last 15 days prior to the interview across regions, genders, and in Punjab and Sindh. Overall, the prevalence of these chronic diseases is high across all dimensions. 14 ESTIMATION METHODS Calculating the economic costs of tobacco consumption requires estimation of three components. The first component is direct cost, consisting of direct medical expenditures incurred by individuals for treating smoking-related diseases and direct non-medical expenses, such as transportation charges and caregiving expenses. The second component is morbidity cost, also called cost in the form of productivity loss due to sickness or disability caused by smoking. Mortality cost is the third component that is required for estimating the value of lives lost due to premature deaths brought on by smoking. The first component is called the direct cost of smoking, whereas the last two components (morbidity and mortality) constitute the indirect costs. Following Sung et al. (2006), the economic cost of smoking is estimated forever smokers, two age groups (35–64 and 65 and older), two regions (rural and urban), two service types (inpatient and outpatient), and two genders (male and female). The direct costs and the indirect morbidity and mortality costs are estimated by employing a prevalence-based attributable-risk approach (Rice et al., 1985; Rice et al., 1986). For estimating the cost of premature deaths attributable to smoking, the present value of lost earnings is estimated by employing the human capital approach (Rice & Cooper, 1967; Max et al., 2004). This study considers three broader categories of diseases, namely cancer, cardiovascular diseases, and respiratory diseases. 1 3.1 SMOKING-ATTRIBUTABLE FRACTION (SAF) SAF is the proportion of health care utilization, health care expenditures, productivity loss, deaths, and other health outcomes attributable to smoking. Each component of smokingattributable cost requires an estimation of its SAF. This study uses the epidemiological approach to estimate SAF (WHO, 2011). Hence, following Rice et al. (1986), SAF is estimated through the following formula: ���!”#$ = ��!”#$’��!”#$ − 1+ ��!”#$’��!”#$ − 1+ + 1 (�) Where ���!”#$ is the smoking-attributable fraction for diseases �, gender �, region �, and age group �. PE is the prevalence of smoking for ever-smokers. RR is the relative risk of premature death compared to non-smokers (mortality ratio) or work loss of employed smokers due to illness/disease relative to employed non-smokers (WHO, 2011). One of the challenges for estimating SAFs in Pakistan is the absence of estimates for RR for different diseases, especially at the required levels of disaggregation such as gender, region, 1 Cancer includes lip, oral cavity, pharynx, esophagus, stomach (gastric), pancreas, larynx, trachea, lung, bronchus, cervix, uteri, kidney and renal pelvis, urinary bladder, acute myeloid leukemia, liver, colon, rectum. Cardiovascular diseases include ischemic heart disease, cerebrovascular disease (stroke), atherosclerosis, aortic aneurysm, peripheral vascular disease, arterial embolism and thrombosis. Respiratory diseases include chronic bronchitis, emphysema, chronic airways obstruction, asthma, and pneumonia. 15 and age group. One method to address this lack of data is to use estimated RRs for other countries with similar economic environments and tobacco use patterns as proxies. These, however, may not reflect the actual relative risks as there are several other factors, including some that are unobservable, that differ across countries. Another option is to estimate RRs for a country when the data permits. In this study’s survey, the information required to estimate RRs was collected. There are four approaches to estimate RR, which are: (i) the medical cost ratio approach, (ii) the utilization ratio approach, (iii) the disease incidence ratio approach, and (iv) the mortality ratio approach. The methods are listed from the most preferred method to the least preferred. However, this study uses the mortality ratio approach because of two issues with the first three methods. First, there are several sub-categories with no observations on the relevant variables due to multilevel disaggregation, which prevents the calculation of RR and consequently SAF. Second, several of the RRs calculated using medical cost ratio or utilization ratio approaches have a value of less than one, thereby resulting in zero SAF. Therefore, RRs are estimated by using the mortality ratio approach, which is then used to estimate the SAFs. These SAFs are utilized in the calculation of direct medical (and non-medical) costs as well as indirect morbidity and mortality costs, multiplied by direct/indirect health expenditures for obtaining direct/indirect health care costs. The details on these methods are provided in the WHO toolkit (WHO, 2011). 3.2 DIRECT COST OF SMOKING Following Equation 2, total direct medical cost attributable to smoking is estimated for both inpatient hospitalization and outpatient visits. �����!”#$ = ������!”#$ ∗ ���!”#$ = :�����!”#$ + ������!”#$= ∗ ���!”#$ = :’����!”#$ ∗ ��!”#$ + ����!”#$ ∗ ��!”#$ ∗ 26+ + ‘�����!”#$ ∗ ��!”#$ + �����!”#$ ∗ ��!”#$ ∗ 26+= ∗ ���!”#$ ∗ ���!”#$ (2) In the above equation, �����!”#$ is smoking-attributable health expenditures for disease �, gender �, region �, and age group �; ������ is total health expenditures. Total health expenditures are the sum of two subtotals: a) direct medical expenditures incurred by the patients ����� and b) direct non-medical expenditures on informal or formal caregivers ������. ���� is the mean expenditures per hospitalization, and �� is the average number of hospitalizations per person during the last 365 days. Similarly, ���� is the average of outof-pocket expenditures per outpatient visit, and �� is the average number of outpatient visits per person for two weeks before the date of the interview. ����� and ����� are the average expenditures on transportation and food of formal/informal caregivers per hospitalization and 16 per outpatient visit, respectively. Finally, POP is the total population in the age groups of 35– 64 and 65 and older in 2019 for the respective gender and region. Expenditures on inpatient hospitalization and outpatient visits include insurance, doctor/consultation fee, cost of medicine, surgery and laboratory tests, transport charges, admission fee, and food expenditures. Average expenditures on outpatient visits are converted into annual average expenditures by multiplying them by 26 (fortnights). 3.3 INDIRECT MORBIDITY COSTS The smoking-attributable indirect morbidity cost is estimated by multiplying the indirect cost of lost productivity due to smoking-related specific diseases with the SAF as in Equation 3. ����!”#$ = �����!”#$ ∗ ���!”#$ = (����!”#$ ∗ ��!”#$ + ����!”#$ ∗ ��!”#$ ∗ 26) ∗ ���!”#$ ∗ ���!”#$ (3) In Equation 3, ����� represents indirect morbidity cost and ���� is the average number of workdays lost in a year per employed individual due to hospitalization caused by smokinginduced diseases. �� is the average daily earnings of the respective population group. Similarly, ���� is the average number of lost workdays per employed individual in the last two weeks. These average values are annualized as described in the previous section. �� is the mean daily earnings of the relevant population group. Data on employment rates are obtained from the Pakistan Economic Survey (2018–19). Data on annual earnings for respective groups are obtained from the Labour Force Survey (LFS, 2017–18) and are converted into daily earnings. 3.4 INDIRECT MORTALITY COST The smoking-attributable mortality cost requires the estimation of smoking-attributable deaths and the present discounted value of lifetime earnings. The product of these two variables provides the smoking-attributable mortality cost. ����!”#$ = ���!”#% ∗ ∑ (��!”#% ∗ ����!”#%) &$’% %(&)*% (4) In Equation 4, ���� is the smoking-attributable mortality cost, �� is the total number of deaths from disease �, and PVLE is the present discounted value of lifetime earnings. It should be noted that � represents 5-year age intervals starting from age 35. � is different from subscript �, which represents the two age groups (35–64, 65 and older). The total number of deaths is obtained by multiplying the death rate with the total population in the respective category of gender and region. For estimating the death rate, the ratio of total deaths due to specific disease to the total number of respondents (including smokers and non-smokers) is applied to the respective 5-year age interval. The estimation of the discounted present value of lifetime earnings requires life expectancy for different age groups (or probability of survival until the 17 life expectancy of respective age interval, �) average annual earnings, 2 labor productivity, and discount rate (Max et al., 2004). ����!”#$ = ∑ ����!”#$(�) ∗ (�!”#$(�) ∗ ���!”#$(�) &$’% %(&)*% ) ∗ (,-.#/)!”# (,-#)!”# (5) In Equation 5, ���� is the present discounted value of lifetime earnings, ���� is the probability of survival, � is average annual earning of employed individuals computed from the survey data, ��� is the proportion of the employed population obtained from the Labour Force Survey (LFS, 2017–18), ��� is productivity growth, � is discount interest rate, and � is the age of the person at death. Productivity growth accounts for growth in future earnings and is assumed to be 4.1 percent, based on the country’s average GDP growth rate between 2000–2001 to 2018–2019. An average value of two percent of the real interest rate for the same time period, taken from World Development Indicators (WDI), is used as a discounting factor for converting a future stream of earning into its present value. The data on life expectancy and probability of survival are taken from the latest life tables of the World Health Organization (2011). Equation 5 calculates the present discounted value of lifetime earnings for all 5-year age groups from 35 years and older. These are then used in Equation 4 to estimate the indirect mortality cost across diseases, gender, region, and the two age groups (35–64, 65 and older). 2 The original formula also requires calculation of average imputed value of household production. However, this study omits that part due to unavailability of data. 18 RESULTS AND DISCUSSION 4.1 PREVALENCE, RELATIVE RISKS, AND SAFS Table 1 provides details of the relative risk for the three major diseases – estimated by mortality ratio approach – along with smoking prevalence (Pe) and their respective smoking-attributable fractions. Information on these variables is provided for the three diseases by gender, age group, and region. Due to sample size limitations, the relative risks are calculated for the three diseases across two genders, and the same relative risks are used in the estimation of SAF for the two age groups (35–64, 65 and older) across the two regions (rural, urban). Table 1 reveals the relative risk estimates calculated by the mortality ratio approach. These RRs are used in the estimation of direct medical costs (Table 2) and the indirect morbidity and mortality costs (tables 3 and 4). Hence, the estimated SAF, for instance, of rural males in the age group 35–64 suggests that 16 percent of the deaths from cancer in this category are attributable to smoking. These estimates, translated to expenditure terms, suggest that for every Rs 100 spent on treatment of male cancer patients from rural areas, smoking is responsible for Rs 16. In terms of morbidity cost, this would mean that 16 percent of the work lost due to cancer is attributable to smoking. It is worth mentioning here that if the value of an RR is less than 1, its corresponding SAF becomes zero (Sung et al., 2006). Table 1 further shows that there are considerable variations in the male SAFs across diseases but not much between the two age groups in urban areas. For rural females, SAF increased significantly for all the diseases (except cancer) when they enter the 65 and older age group. This implies that rural females are more vulnerable to diseases due to smoking in their old age. This could be due to the severity of disease owing to the absence of health care facilities in rural regions. Similarly, there are significant SAF differentials across rural and urban regions for females. The trends generally show higher SAFs in the rural region for both age groups. For instance, the SAF for respiratory diseases for rural females in 65 and older age group is almost twice the SAF of their counterparts in urban areas. Overall, smoking-attributable fractions vary across diseases, genders, regions, and age groups. The SAFs are lower for females primarily because of their lower prevalence rates. Table 1. Relative risks, smoking prevalence, and smoking-attributable fractions Region Disease Group Male Female 35+ 35-64 65+ 35+ 35-64 65+ RR Pe SAF Pe SAF RR Pe SAF Pe SAF Mortality Ratio Approach Rural Cancer 1.68 28.85 16.38 25.72 14.87 1.64 1.31 0.83 1.84 1.16 Cardiovascular 1.33 28.85 8.76 25.72 7.88 3.72 1.31 3.43 1.84 4.77 Respiratory 1.86 28.85 19.94 25.72 18.17 3.76 1.31 3.49 1.84 4.85 Urban Cancer 1.68 27.81 15.88 28.23 16.09 1.64 0.92 0.58 1.06 0.67 Cardiovascular 1.33 27.81 8.46 28.23 8.58 3.72 0.92 2.43 1.06 2.8 Respiratory 1.86 27.81 19.36 28.23 19.59 3.76 0.92 3.47 1.06 2.84 Source: Authors’ calculations using the survey data Note: The mortality ratio approaches are used for the calculation of relative risks (RR). 19 4.2 COST ESTIMATIONS FOR THREE MAJOR DISEASES 4.2.1 Direct cost Using the SAFs reported in the previous section, the smoking-attributable expenditures (SAEs) for inpatient hospitalizations and outpatient visits are estimated for different genders, regions, age groups, and disease categories (Table 2). These consist of medical costs (Panel A: Table 1) and non-medical or caregivers’ expenses (Panel B: Table 2). The sum of these two, or total direct medical cost, is Rs 96.24 billion (US$ 0.60 billion). The estimated outpatient visit cost is Rs 87.58 ($0.54 billion), or 91 percent of the total medical expenses. Among the disease categories, cancer accounts for the highest treatment cost estimated at Rs 47 billion ($0.29), followed by cardiovascular diseases with an estimate of Rs 32.4 billion ($0.20 billion). Males account for 85 percent of the total direct medical costs, and rural areas account for 77 percent or Rs 74 billion. Counterintuitively, the age group of 65 and older accounts for only 21 percent of the medical costs. Household expenditures on tobacco (and smoking) are higher in rural areas and among the lower-income groups (Nayab et al., 2018), due to their concentration in rural areas. Spending on tobacco also crowds out health and education expenditures in Pakistan, especially among the lower-income groups (Saleem & Iqbal, 2020). Moreover, smoking prevalence is higher in rural areas, and so are their SAFs. Therefore, it is logical to hypothesize that smoking traps households into a vicious cycle of poverty. Table 2. Direct cost (billion Rs) Region Diseases Inpatient Hospitalization Outpatient visits Male Female Male Female Total 35-64 65+ 35-64 65+ 35-64 65+ 35-64 65+ Panel A. Medical Expenditures Rural Cancer 2.42 0.56 0.12 0.04 30.33 4.21 1.53 0.33 39.56 Cardiovascular 1.37 0.18 0.54 0.11 12.52 1.85 4.91 1.12 22.60 Respiratory 0.50 0.21 0.09 0.06 6.79 2.45 1.19 0.65 11.94 Urban Cancer 0.65 0.14 0.02 0.01 4.17 2.17 0.15 0.09 7.40 Cardiovascular 0.53 0.49 0.15 0.16 3.54 2.97 1.02 0.97 9.83 Respiratory 0.11 0.02 0.01 0.16 2.98 1.08 0.38 0.16 4.90 Both Total 5.58 1.60 0.94 0.54 60.34 14.74 9.18 3.32 96.24 Panel B. Non-Medical Expenditures Rural Cancer 0.11 0.01 0.01 0.00 0.84 0.17 0.04 0.01 1.19 Cardiovascular 0.09 0.03 0.04 0.02 0.52 0.17 0.20 0.10 1.16 Respiratory 0.05 0.03 0.01 0.01 0.30 0.27 0.05 0.07 0.79 Urban Cancer 0.04 0.03 0.00 0.00 0.13 0.12 0.00 0.01 0.34 Cardiovascular 0.03 0.05 0.01 0.02 0.06 0.17 0.02 0.06 0.41 Respiratory 0.00 0.00 0.00 0.00 0.08 0.07 0.01 0.01 0.18 Both Total 0.32 0.16 0.06 0.05 1.92 0.98 0.33 0.26 4.07 Source: Authors’ calculations using the survey data The non-medical expenditures (Panel B) are Rs 4.07 billion. Once again, the majority of this part of the direct cost is for outpatient visits (86 percent), males (83 percent), and rural areas (77 percent). Unlike with the medical cost, cancer and cardiovascular diseases contribute equally (38 percent each) to direct non-medical expenses. This is likely due to the fact that the 20 treatment of these two diseases requires more frequent outpatient visits. However, the older age group has a lower share (35 percent) in the non-medical expenses mostly because care is given by the younger people. The total direct smoking-attributable expenditures are Rs 100.3 billion ($0.63 billion), of which the share of non-medical expenses is only four percent. That is, for every Rs 96 in medical spending on treatment, Rs 4 are caregiver expenses. Hence, smoking also increases out-ofpocket expenditures on non-medical activities. 4.2.2 Indirect morbidity cost The smoking-attributable indirect morbidity cost comes to Rs 56.32 billion ($0.35 billion) (Table 3). The morbidity cost is 56 percent of the smoking-attributable medical and nonmedical expenses. One plausible reason for this relatively lower morbidity cost could be the use of national RRs for the disaggregated (regional and age groups) analysis of the cost. It is pertinent to reiterate here that the RRs used in the calculation of morbidity cost are also the ones estimated through the mortality ratio approach. Table 3. Morbidity cost (billion Rs) Region Disease groups Inpatient hospitalization Outpatient visits Male Female Male Female Total 35-64 65+ 35-64 65+ 35-64 65+ 35-64 65+ Rural Cancer 3.34 0.03 0.11 0.04 3.82 3.83 0.50 1.21 12.88 Cardiovascular 0.54 0.01 1.61 0.50 5.10 0.88 1.86 5.24 15.73 Respiratory 0.21 0.05 0.18 0.70 5.62 2.97 0.51 2.05 12.29 Urban Cancer 0.19 0.18 0.03 0.02 1.89 0.69 0.07 0.14 3.20 Cardiovascular 0.22 0.14 0.42 0.23 1.87 1.31 0.93 1.87 7.01 Respiratory 0.03 0.01 0.04 0.04 3.65 0.45 0.29 0.70 5.20 Both Total 4.54 0.42 2.40 1.52 21.96 10.12 4.15 11.21 56.32 Source: Authors’ calculations using the survey data The share of indirect morbidity cost is higher in rural areas (72 percent) and for outpatient visits (84 percent). Two points demand attention here. First, the nature of jobs in rural areas is vulnerable, as most people make their living as daily-wagers which implies that making an outpatient visit would results in the loss of their income for that day. Second, the lack of local health care facilities forces rural patients to seek health care in urban areas, making their outpatient visits costlier in terms of expenses, time, and loss of income. Similarly, the morbidity cost for males is Rs 41.6 billion ($0.23 billion), which is 66 percent of the total estimated morbidity cost. The lower share of morbidity cost for females is due to their lower labor force participation rates compared to their male counterparts. Nonetheless, the authors believe that the female morbidity cost is severely understated. The inclusion of household production activities in the cost estimation would have significantly increased female shares and thus the overall costs. The absence of data on these activities, however, restricts researchers from estimating the value of household production. 21 Table 3 further reveals that the cost of outpatient visits for rural males (Rs 22.2 billion) is significantly higher than outpatient visit cost not only for rural females (Rs11.4 billion) but also for their counterparts in urban regions (Rs 9.8 billion). However, the costs of inpatient hospitalizations remain almost the same in both regions. Cancer has the highest share of morbidity cost (40 percent) in disease category, followed by respiratory diseases (17.5 percent). 4.2.3 Indirect mortality cost Table 4 provides the estimates for indirect mortality costs, which amount to Rs 281.1 billion ($1.76 billion). Rural areas contribute 59 percent to the total mortality cost. Cancer has the highest share (55 percent) of the cost in the rural areas, and it also tops the list in the urban region with 58 percent share. Overall, however, cancer diseases have the highest share (56 percent) of the mortality cost. Unlike direct cost and indirect morbidity cost, the mortality cost cannot be estimated by service types. The mortality cost for female cancer patients is zero because the value for RR for this category is less than one. The mortality cost for males is higher than for females in both age groups, both within and across the regions. Overall, the mortality cost for males is Rs 259 billion ($1.62 billion), which is 92 percent of the total. Again, the reason for the lower share of female mortality cost is the exclusion of household production activities from cost estimations. Similarly, 88 percent of the indirect mortality cost is borne by the age group of 35–64 years. Although death rates are higher in the age group of 65 and older, deaths in the younger age bracket result in the loss of higher forgone future income because of more years of productive life lost (see Table D1 in Appendix D, which contains the present discounted value of lifetime earnings for males and females across two regions). The most productive age group for males in both regions is 35–39. From there onwards, there is a gradual decline in the earnings. The trends are almost similar for females, though their age-group earnings in absolute terms are lower than their male counterparts. The earnings drop significantly for the age group of 65 and older. The high estimated indirect mortality cost shows that smoking related premature deaths have an enormous economic burden on Pakistani families. Though data do not permit the inclusion of household production in the calculation of the present discounted value of lifetime earnings, current estimates may also be an underestimation of the actual indirect mortality costs of tobacco use in Pakistan. Table 4. Mortality cost (billion Rs) Region Disease Male Female Total 35-64 65+ 35-64 65+ Rural Cancer 80.38 8.06 2.16 0.21 90.80 Cardiovascular 47.56 7.04 5.48 1.28 61.36 Respiratory 6.84 3.71 2.02 0.43 13.01 Urban Cancer 55.65 6.20 5.10 0.15 67.10 Cardiovascular 33.86 2.66 4.02 0.19 40.73 Respiratory 3.73 3.65 0.62 0.14 8.14 Both Total 228.03 31.31 19.39 2.40 281.13 Source: Authors’ calculations using the survey data 22 4.3 CONTEXT AND IMPLICATIONS The smoking-attributable direct and indirect expenditures amount to a total of Rs 437.76 billion ($2.74 billion) (Figure 3). The direct cost is 23 percent of these expenditures. The indirect mortality cost is the biggest component, with a 64-percent share. Sixty-five percent of the total cost is borne by rural residents, and males account for Rs 382 billion ($2.39 billion) or 87 percent of the overall cost. Lastly, 82 percent of the total cost is accounted for by the 35–64 years age group. Figure 3. Major components of total cost of tobacco use to Pakistani economy The question these data provoke is how relatively high this cost is. Is the cost high enough to alarm the policymakers in Pakistan? To put these cost estimates in perspective, they are compared with various outcome indicators (Table 5). The total revenue collected from tobacco (primarily cigarettes) taxation in the fiscal year 2018–19 was Rs 120 billion. Hence, the economic and health cost imposed by smoking on society is 3.65 times higher than the overall tax collected from the tobacco industry. Similarly, the smoking-attributable direct cost is 8.3 percent of the total health expenditures, which is significantly high. Likewise, the total economic cost of smoking is almost equal (1.03 times) to the public sector health spending (both federal and provincial). Even if it is assumed (100.3 B) Direct (23%) or Cancer (48.5 B) Medical (46.9 B) In patient Out patient (34.0 B) CVD Medical (32.4 B) Out patient RD (17.8 B) Out patient Cancer (174.0 B) Morbidity (16.1 B) In patient Out patient Mortality (157.9 B) CVD (124.8 B) Morbidity (22.7 B) In patient Out patient Mortality (102.1 B) RD (38.7 B) Morbidity Out patient (17.5 B) Mortality (21.2 B) (16.8 B) Medical Indirect (337.5 B) (77%) or 437.8 Billion Rs. 23 that the entire tax collection from tobacco goes into the health sector, its contribution to improving the health of society is substantially lower than its attributable damage. Table 5. Comparison of economic cost of smoking with outcome indicators Outcome indicator Unit Value – The total economic cost of smoking from three major diseases (2019) Billion Rs 437.8 – The total direct cost of smoking from three major diseases (2019) Billion Rs 100.3 – Revenue from tobacco (smoking) taxation (2018–19)* Billion Rs 120 – Public sector health expenditure on health Billion Rs 421.8 – Total health expenditures ** Billion Rs 1208.5 – GDP at current prices (2018–19) Billion Rs 37972 – Cost of smoking (% of GDP) % 1.15% – Tobacco revenue (% of the cost of smoking) % 27% – Direct cost as (% of total health expenditures) % 8.3% Sources: Data sources include Pakistan Economic Survey (2019–20); Pakistan Bureau of Statistics, National Health Accounts (2015–16) and Ministry of Finance, Government of Pakistan. Notes * The major taxed tobacco product in Pakistan is cigarettes. Hence, the revenue from tobacco taxation primarily comes from cigarette taxation. ** The data for Total Health Expenditure is not officially available for the year 2018–19. The last National Health Account report is available for 2015–16. The Total Health Expenditure according to that report was Rs 908 billion, which was a 20 percent increase (in nominal terms) over the value reported in 2013–14. Assuming the same rate of growth, the authors project the Total Health Expenditure for 2018–19 as reported in the table above. The economic cost of smoking is 1.15 percent of the country’s GDP. This estimated cost is in line with the literature on the disease burden of smoking, which suggests that this cost is in the range of 0.5–2.0 percent. In a country where the public sector health spending historically has remained less than one percent of the GDP, this cost of 1.15 percent should concern not only public health institutions but also the tax authorities whose policies can play a vital role in avoiding this cost. 4.4 SENSITIVITY ANALYSIS For sensitivity analysis, the estimated RRs are replaced with the ones used for India and China (see Table D2 in Appendix D). The total cost using India’s RRs is estimated to be Rs 711.2 billion, which is 1.6 times higher than this study’s estimated cost. Using China’s RR produces an estimated total cost of Rs 287.2 billion. This is 35 percent less than this study’s estimated cost of Rs 438 billion. The probable reason for these differences in costs is coming from the differences in RRs. India’s RRs are relatively higher, whereas those of China are lower compared to Pakistan. That is, compared to non-smoking individuals, the probability of death among smokers is higher in India than in China. The lower death ratio in China could either be because of (i) better health care systems or (ii) treatment-seeking (risk-averse) behavior of smokers or both. To check the robustness of the analysis, RR is estimated at more disaggregated levels (region and age group) and these are used in the cost estimation (see Table D3 in Appendix D). The cost using these RRs is estimated to be Rs 349.5 billion. The reason this is lower than the originally estimated cost is that the values for some of the disaggregated RRs are lower because of zero or fewer observations for smokers. The lower-than-one values of RR turn off the 24 corresponding SAFs and therefore the cost for those categories into zero.3 Consequently, the total cost estimated using these RRs is approximately 20 percent lower than the original estimated cost. Had some of the SAFs not equaled zero (due to RR<1), the total cost value obtained might have been closer to the originally estimated cost. This suggests that the estimated cost of Rs 438 billion may not be far off the mark.
4.5 TOTAL COST FOR ALL SMOKING-INDUCED DISEASES AND DEATHS In addition to estimating the economic costs of smoking for the three major diseases in 2019, the study also estimates total economic costs from all smoking-attributable diseases and deaths in Pakistan for the same year using relative risk of all-cause mortality from smoking. Due to sampling constraints, the relative risk for all-cause mortality is calculated only for the two genders. These RRs are then used to calculate the SAFs for the two regions and age groups (see Table E1 in Appendix E for details). Tables 6–8 show the direct, morbidity, and mortality costs from all smoking-attributable disease and deaths. The total cost amounts to Rs 615.07 billion ($3.85 billion). The indirect cost (morbidity and mortality) constitutes 70 percent of the total cost. Rural residents bear 61 percent (Rs 376 billion) and males account for 77 percent (Rs 474 billion) of the total cost. Moreover, most of this cost (86 percent) is borne by individuals in 35–64 age group. There are differences in the shares of cost across different dimensions between economic burdens from the three major diseases and the total cost from all smoking-attributable diseases and deaths. Table 6. Direct cost for all smoking-induced diseases and deaths (billion Rs) Region Diseases Inpatient hospitalization Outpatient visits Male Female Male Female Total 35-64 65+ 35-64 65+ 35-64 65+ 35-64 65+ Panel A. Medical Expenditures Rural All diseases 5.96 1.27 1.57 0.52 69.19 24.46 18.16 9.99 131.12 Urban All diseases 2.49 1.03 0.48 0.22 24.60 7.58 4.71 1.65 42.76 Both Total 8.45 2.30 2.05 0.74 93.79 32.04 22.87 11.64 173.88 Panel B. Non-Medical Expenditures Rural All diseases 0.40 0.11 0.10 0.05 4.43 1.06 1.16 0.45 7.76 Urban All diseases 0.21 0.22 0.04 0.05 0.82 0.44 0.16 0.13 2.07 Both Total 0.61 0.33 0.14 0.10 5.25 1.50 1.32 0.58 9.83 Source: Authors’ calculations using the survey data Table 7. Morbidity cost for all smoking-induced diseases and deaths (billion Rs) Region Disease groups Inpatient hospitalization Outpatient visits Male Female Male Female Total 35-64 65+ 35-64 65+ 35-64 65+ 35-64 65+ Rural All diseases 3.49 0.02 3.25 0.21 35.65 1.00 47.46 2.02 93.09 Urban All diseases 1.12 0.02 0.95 0.06 20.86 0.20 16.37 0.37 39.95 Both Total 4.61 0.04 4.20 0.27 56.51 1.20 63.83 2.39 133.04 Source: Authors’ calculations using the survey data 3 On the other hand, some of the RRs have values as high as 4, which seems unrealistic and therefore pushed the authors to rely on the aggregated RRs for the main analysis. 25 Table 8. Mortality cost for all smoking-induced diseases and deaths (billion Rs) Region Disease Male Female Total 35-64 65+ 35-64 65+ Rural All diseases 110.34 16.88 12.94 3.49 143.65 Urban All diseases 125.52 14.41 14.04 0.70 154.67 Both Total 235.86 31.29 26.98 4.19 298.32 Source: Authors’ calculations using the survey data It is evident that a major share (71 percent) of the smoking-induced cost comes from the three major diseases analyzed in this study. Making use of the information in Table 5, the total smoking-attributable cost is 1.6 percent of the GDP as compared to 1.15 percent from the three major diseases. Similarly, the total cost is five times higher than the total revenue collected from the tobacco sector and 1.45 times the total public sector health spending. This indicates that smoking puts a tremendous burden on the country’s health infrastructure – especially by increasing the number of patients with cancer, cardiovascular, and respiratory diseases – and the tobacco industry’s tax contribution does not even adequately compensate for it. 4.6 LIMITATIONS OF THE STUDY It is important to note that there are some limitations that may have played a role in making these estimates conservative. First, the RRs are calculated using the survey data to make them as close to the country’s context as possible. The sample size, however, is not large enough for a disaggregated analysis of the RR. Consequently, the authors have to use the estimated RR for both regions and both age groups. This may underestimate the SAF and consequently the cost for some categories. For instance, the RR for the age group of 65 and older may be higher compared to the 35–64 age group. But using the same RR for both age groups could result in lower costs. Second, the unavailability of data on household production activities prevents the authors from calculating the present discounted values of lifetime earnings from these activities, especially for females. Consequently, the indirect mortality cost is highly underestimated. Third, the labor force participation rate for females is low in Pakistan. Females are mostly involved in household production activities. Since these activities are not considered a part of the labor market, the indirect morbidity – as well as mortality – costs are underestimated. Fourth, the reported loss of income due to inpatient hospitalizations or outpatient visits could undervalue the productivity loss because people who are unemployed or self-employed may avoid reporting a loss of income. Despite these limitations, the current study provides the first comprehensive estimates of smoking-attributable costs at the national and disaggregated levels. 26 CONCLUSIONS AND POLICY IMPLICATIONS This study estimates the annual economic cost of smoking in Pakistan. For this purpose, a nationally representative survey was conducted. The combined smoking-attributable cost for cancer, cardiovascular, and respiratory diseases is estimated to be Rs 437.8 billion in 2018–2019, which is 71 percent of the total cost from all smoking-induced diseases and deaths. Interestingly, the indirect costs are found to constitute the major share (77 percent) of smokingimposed expenditures. A disaggregated analysis is also conducted by disease category, gender, region, age group, and service type. Men, rural residents, and people in the 35–64 age bracket bear the major share of this cost. Cancer comes out to be the costliest disease (51 percent share) due to tobacco use. The smoking-induced cost for the three major diseases is 1.15 percent of the GDP. A previous study on Pakistan for the same diseases found this cost to be 0.40 percent of the GDP, which shows the extent of underestimation of the cost using a non-representative sample. The tobacco industry argument that it is one of the major contributors to the public exchequer becomes contestable when the smoking-induced health cost is monetized. The industry opposes increases in tobacco taxes using the argument of illicit trade and subsequent reduction in revenue collection from tobacco taxation. This argument, however, is not valid for the reasons stated below. First, the tobacco-industry-cited figure of 40 percent4 for the market share of illicit trade is exaggerated. A recent survey found the size to be 16 percent. 5 Second, the simulations done by Nayab et al. (2018) show that abolishing the third tobacco tax tier would increase tax revenue and improve public health outcomes. The study further shows that tax on cigarettes is highly elastic (1.06), suggesting that increased taxation reduces tobacco consumption. Combining those projections with the cost estimates of this study, one may suggest that even a tax rate at which the total revenue falls to zero (because of zero consumption) should be acceptable because the economic gains through improvement in public health outcomes of such a rate would outweigh the loss of revenues. 6 This study reveals that the total revenue collected from the tobacco sector is only around 20 percent of the total cost of smoking. The cost is even higher than the total public sector spending on health in the country. Hence, the revenue collected from the tobacco industry does not even cover the harm it causes to the public health care system. This imbalance forces the government to reallocate resources from other productive sectors to help meet the health care 4 https://illicittobacco.oxfordeconomics.com/markets/pakistan/ 5 https://drive.google.com/file/d/1OniBwQyAkmM5SWmUcnSgCxmWIAJAGSz0/view 6 One can bring in the argument regarding employment generation by tobacco industry. However, the employment share by tobacco sector in the total agricultural employment is only 0.4-0.5 percent in Pakistan. moreover, this loss can simply be outweighed by saving the smoking attributable expenditures from other diseases as we have included only three here. 27 needs of the nation. Moreover, further burdening a resource-constrained public health sector through increases in the number of patients suffering from cancer, cardiovascular, and respiratory disease could collapse the system. Similarly, it is known that tobacco spending crowds out expenditure on food and education in lower-income households in Pakistan (Saleem & Iqbal, 2020). It is also a fact that tobacco use is more prevalent among this group of households. Hence, consumers’ direct spending on tobacco use and tobacco-induced health care costs will push more people into poverty. Hence, keeping in mind the tax elasticity of cigarette demand and the enormous economic and health costs of smoking, this study recommends a more effective use of taxation policy to reduce tobacco consumption in the interest of public health. The taxes should be increased at least to meet the WHO’s recommended threshold of 70 percent of the retail price or the level required to cover the costs tobacco makes the country incur. In the latter case, the increase would be four to five times what the tax rate is now. In the short-run, the rates on the two tax tiers should be increased with a higher increase for the second tier so that the gap between them is minimized. In the long run, however, the two-tier system should be abolished to have a single-tier system. This would help in bringing the poor out of the vicious cycle of poverty in addition to reducing the smoking-related disease burden. 28 REFERENCES Amarasinghe, H., Ranaweera, S., Ranasinghe, T., Chandraratne, N., Kumara, D. R., Thavorncharoensap, M., Abeykoon, P., & de Silva, A. (2018). Economic cost of tobacco-related cancers in Sri Lanka. Tobacco Control, 27, 542-46. Amin, H. S., Alomair, A. N. A., Alhammad, A. H. A., Altwijri, F. A. A., Altaweel, A. A., & Alandejani T. A. (2017). Prevalence of tobacco product consumption and exposure among healthcare students in King Saud University in Riyadh, Saudi Arabia. Journal of Community Medical Health Education, 7, 567. doi: 10.4172/2161-0711.1000567. John, R. M. (2019). Economic costs of diseases and deaths attributable to bidi smoking in India, 2017. Tobacco Control, 28, 513-18. John, R. M., Sinha, P., Munish, V. G., & Tullu, F. T. (2021). Economic costs of diseases and deaths attributable to tobacco use in India, 2017-18. Nicotine and Tobacco Research, 23, 294-301. John, R. M., Sung, H. Y., & Max, W. (2009). Economic cost of tobacco use in India, 2004. Tobacco Control, 18, 138-43. John, R. M., Rout, S. K., Kumar, B. R., & Arora, M. (2015). Economic burden of tobacco related diseases in India. New Delhi: Ministry of Health and Family Welfare, Government of India. Max, W., Rice, D. P., Sung, H. Y., & Michel, M. (2004). Valuing human life: Estimating the present value of lifetime earnings, 2000. UCSF: Center for Tobacco Control Research and Education. Retrieved from https://escholarship.org/uc/item/82d0550k. Nayab, D., Nasir, M., Memon, J. A., Khalid, M. & Hussain, A. (2020). Estimating the price elasticity for cigarette and chewed tobacco in Pakistan: Evidence from microlevel data. Tobacco Control, 29(s5), 319-325. Rice, D. P., & Cooper, B. S. (1967). The economic value of human life. American Journal of Public Health, 57, 1954-66. Rice, D. P., Hodgson, T. A., & Kopstein, A. N. (1985). The economic costs of illness: A replication and update. Health Care Financing Review, 7, 61. Rice, D. P., Hodgson, T. A., Sinsheimer, P., Browner, W., & Kopstein, A. N. (1986). The economic costs of the health effects of smoking, 1984. The Milbank Quarterly, 489- 547. Ross, H., Trung, D. V., & Phu, V. X. (2007). The costs of smoking in Vietnam: The case of inpatient care. Tobacco Control, 16, 405-09. Saha, S. P., Bhalla, D. K., Whayne Jr., T. F., & Gairola, C. G. (2007). Cigarette smoke and adverse health effects: An overview of research trends and future needs. International Journal of Angiology, 16(3),77-83. Saleem, W. & Iqbal. M. A. (2020). ‘The impact of tobacco use on household consumption pattern in Pakistan’, SPDC Research Report. Siddique, O. (2020). Total factor productivity and economic growth in Pakistan: A five decade overview. Pakistan Institute of Development Economics. Sung, H. Y., Wang, L., Jin, S., Hu, T. W., & Jiang, Y. (2006). Economic burden of smoking in China, 2000. Tobacco Control, 15, i5-i11. Warner, K. E., Hodgson, T. A., & Carroll, C. (1999). Medical costs of smoking in the United States: Estimates, their validity, and their implications. Tobacco Control, 8, 290-300. 29 Welte, R., König, H. H., & Leidl, R. (2000). The costs of health damage and productivity losses attributable to cigarette smoking in Germany. The European Journal of Public Health, 10, 31-38. WHO. (2011). Economics of tobacco toolkit: assessment of the economic costs of smoking. World Health Organization, Geneva, Switzerland WHO. (2011). Global Health Observatory Data Repository; Life Expectancy: Life Tables Pakistan. World Health Organization, http://apps.who.int/gho/data/view.main.61230. Yang, L., Sung, H. Y., Mao, Z., Hu, T. W., & Rao, K. (2016). Economic costs attributable to smoking in China: Update and an 8-year comparison, 2000–2008. In Economics of Tobacco Control in China: From Policy Research to Practice. World Scientific. 30 APPENDICES APPENDIX A Table A1. Sampling details Base Sampling Parameters Sample Size r Design effect RME pb Avg. HH Size Response Rate HHs Clusters Blocks Urban 0.124 2 0.05 0.6401 6.2 0.95 5996 20 300 Rural 0.124 2 0.05 0.5721 6.6 0.95 6302 20 315 Pakistan 12298 ≈ 13000 615 Table A2. Population and sample distribution across different administrative units Administrative Unit Population (Households) Sample No. (in thousands) percent No. percent Punjab 17103.84 53.11 6904 53.11 Khyber Pakhtunkhwa (KP) 3845.17 11.94 1552 11.94 Sindh 8585.61 26.66 3466 26.66 Balochistan 1775.94 5.51 717 5.51 FATA 558.38 1.73 225 1.73 Islamabad 336.18 1.04 136 1.04 Pakistan 32205.11 100 13000 100 31 APPENDIX B Field activity for this survey involved the collection of data from a nationally representative sample of households in Pakistan. PIDE itself collected the data. Based on its vast experience of conducting nationally representative surveys, PIDE recruited its own field team. A two-daylong training session was conducted at selected locations throughout Pakistan to orient the field teams with the nature of the survey, survey tools, ethical considerations, and other field issues. They used most of these two days to understand the questionnaire, practice questionnaire filling, and clarifying the questions they had. Once considered suitably trained, these teams were mobilized in the field. While supervisors managed field teams and operations, the training of the survey and monitoring were performed by the core team of PIDE. The research protocols were approved by the Graduate Research Management Committee (GRMC) of the Pakistan Institute of Development Economics (PIDE).
GLOBAL YOUTH TOBACCO SURVEY FACT SHEET PAKISTAN 2013[/caption]
Country fact sheets The World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC), Atlanta, developed the Global Youth Tobacco Survey to track tobacco use among youth across countries using a common methodology and core questionnaire. Information from the Survey is compiled within the participating country by a Research Coordinator nominated by the Ministry of Health, and technically reviewed by WHO and CDC. The content has not otherwise been edited by WHO or CDC. All reasonable precautions have been taken by the World Health Organization to verify the information contained in this publication. However, the published material is being distributed without warranty of any kind, either expressed or implied. The responsibility for the interpretation and use of the material lies with the reader. In no event shall the World Health Organization be liable for damages arising from its use.
GYTS│GLOBAL YOUTH TOBACCO SURVEY FACT SHEET PAKISTAN 2013 GYTS Objectives The Global Youth Tobacco Survey (GYTS), a component of the Global Tobacco Surveillance System (GTSS), is a global standard for systematically monitoring youth tobacco use (smoking and smokeless) and tracking key tobacco control indicators. GYTS is a nationally representative school-based survey of students in grades associated with age 13 to 15 years and is designed to produce cross-sectional estimates for each country. GYTS uses a standard core questionnaire, sample design, and data collection protocol. It assists countries in fulfilling their obligations under the World Health Organization (WHO) Framework Convention on Tobacco Control (FCTC) to generate comparable data within and across countries. WHO has developed MPOWER, a package of selected demand reduction measures contained in the WHO FCTC: GYTS Methodology GYTS uses a global standardized methodology that includes a two-stage sample design with schools selected proportional to enrollment size. The classrooms within selected schools are chosen randomly and all students in selected classes are invited to participate in the survey. The survey uses a standard global core questionnaire with a set of optional questions that permits adaptation to meet the needs of the country on tobacco use and key tobacco control indicators. The questionnaire consists of the following topics: tobacco use (smoking and smokeless), cessation, secondhand smoke (SHS), pro- and anti-tobacco media and advertising, access and availability to obtain tobacco products, and knowledge and attitudes regarding tobacco. The questionnaire is self-administered; using scannable, paper-based bubble sheets, it is anonymous to ensure confidentiality. In Pakistan, GYTS was conducted in 2013 jointly by the Tobacco Control Cell and Pakistan Medical Research Council, under the coordination of the WHO Pakistan Office. A total of 8,723 eligible students in grades 7-10 completed the survey, of which 5,832 were aged 13-15 years. The overall response rate of all students surveyed was 61.5%. GYTS Highlights TOBACCO USE 10.7% overall, 13.3% of boys, and 6.6% of girls currently used any tobacco products. 7.2% overall, 9.2% of boys, and 4.1% of girls currently smoked tobacco. 3.3% overall, 4.8% of boys, and 0.9% of girls currently smoked cigarettes. 5.3% overall, 6.4% of boys, and 3.7% of girls currently used smokeless tobacco. Almost 2 in 5 ever smokers initiated cigarette smoking before the age of 10. 11.2% of never tobacco users are susceptible to tobacco use in the future. CESSATION 6 in 10 current smokers show signs of smoking dependence. 6 in 10 current smokers tried to stop smoking in the past 12 months. SECONDHAND SMOKE 21.0% of students were exposed to tobacco smoke at home. 37.8% of students were exposed to tobacco smoke inside enclosed public places. ACCESS & AVAILABILITY 87.6% of current cigarette smokers obtained cigarettes by buying them from a store, shop, street vendor, kiosk, school canteen, or pharmacy. Among current cigarette smokers who bought cigarettes, 44.9% were not prevented from buying them because of their age. MEDIA 5 in 10 students noticed anti-tobacco messages in the media. 3 in 10 students noticed tobacco advertisements or promotions when visiting points of sale. 1 in 10 students own something with a tobacco brand logo on it. KNOWLEDGE & ATTITUDES 75.9% of students definitely thought other people’s tobacco smoking is harmful to them. 76.5% of students favor banning smoking inside enclosed public places. Last updated 10 Sept 2014 GYTS│GLOBAL YOUTH TOBACCO SURVEY FACT SHEET PAKISTAN 2013 TOBACCO USE SMOKED TOBACCO OVERALL (%) BOYS (%) GIRLS (%) Current tobacco smokers1 7.2 9.2 4.1 Current cigarette smokers2 3.3 4.8 0.9 Frequent cigarette smokers3 0.2 0.3 0.1 Current smokers of other tobacco4 4.2 4.9 3.1 Ever tobacco smokers5 18.7 22.6 12.7 Ever cigarette smokers6 13.7 17.8 7.2 Ever smokers of other tobacco7 7.3 7.9 6.5 SMOKELESS TOBACCO Current smokeless tobacco users8 5.3 6.4 3.7 Ever smokeless tobacco users9 10.0 11.4 8.0 TOBACCO USE (smoked and/or smokeless) Current tobacco users10 10.7 13.3 6.6 Ever tobacco users11 24.5 28.7 17.9 SUSCEPTIBILITY Never tobacco users susceptible to tobacco use in the future12 11.2 12.3 9.9 Never smokers who thought they might enjoy smoking a cigarette13 15.2 15.7 14.7 CESSATION OVERALL (%) BOYS (%) GIRLS (%) Current smokers who tried to stop smoking in the past 12 months 58.4 57.1 — Current smokers who want to stop smoking now 57.9 62.7 — Current smokers who thought they would be able to stop smoking if they wanted to 63.2 65.8 — Current smokers who have ever received help/advice from a program or professional to stop smoking 27.0 27.6 22.2 SECONDHAND SMOKE OVERALL (%) BOYS (%) GIRLS (%) Exposure to tobacco smoke at home†† 21.0 22.9 18.2 Exposure to tobacco smoke inside any enclosed public place†† 37.8 44.5 27.8 Exposure to tobacco smoke at any outdoor public place†† 30.0 37.1 19.3 Students who saw anyone smoking inside the school building or outside on school property† 20.3 22.2 17.3 ACCESS & AVAILABILITY OVERALL (%) BOYS (%) GIRLS (%) Current cigarette smokers who obtained cigarettes by buying them from a store, shop, street vendor, kiosk, school canteen, or pharmacy14 87.6 86.2 — Current cigarette smokers who were not prevented from buying cigarettes because of their age15 44.9 46.6 — Current cigarette smokers who bought cigarettes as individual sticks16 35.2 37.0 — MEDIA TOBACCO INDUSTRY ADVERTISING OVERALL (%) BOYS (%) GIRLS (%) Noticing tobacco advertisements or promotions at points of sale17 32.6 31.7 33.8 Students who saw anyone using tobacco on television, videos, or movies18 63.3 63.2 63.5 Students who were ever offered a free tobacco product from a tobacco company representative 9.3 9.7 8.5 Students who own something with a tobacco brand logo on it 13.4 14.6 11.5 ANTI-TOBACCO ADVERTISING Noticing anti-tobacco messages in the media† 52.7 50.7 55.5 Noticing anti-tobacco messages at sporting or community events19 36.2 36.4 35.7 Current smokers who thought about quitting because of a warning label20 32.6 36.8 12.2 Students who were taught in school about the dangers of tobacco use in the past 12 months 49.7 54.8 42.2 KNOWLEDGE & ATTITUDES OVERALL (%) BOYS (%) GIRLS (%) Students who definitely thought it is difficult to quit once someone starts smoking tobacco 29.6 26.6 34.2 Students who thought smoking tobacco helps people feel more comfortable at celebrations, parties, and social gatherings 59.8 56.0 65.5 Students who definitely thought other people’s tobacco smoking is harmful to them 75.9 75.8 76.2 Students who favor banning smoking inside enclosed public places 76.5 76.1 77.1 Students who favor banning smoking at outdoor public places 77.0 74.8 80.3 1 Smoked tobacco anytime during the past 30 days. 2 Smoked cigarettes anytime during the past 30 days. 3 Smoked cigarettes on 20 or more days of the past 30 days. 4 Smoked tobacco other than cigarettes anytime during the past 30 days. 5 Ever smoked any tobacco, even one or two puffs. 6 Ever smoked cigarettes, even one or two puffs. 7 Ever smoked tobacco other than cigarettes, even one or two puffs. 8 Used smokeless tobacco anytime during the past 30 days. 9 Ever used smokeless tobacco. 10 Smoked tobacco and/or used smokeless tobacco anytime during the past 30 days. 11 Ever smoked tobacco and/or used smokeless tobacco. 12 Susceptible to future tobacco use includes those who answered “Definitely yes”, “Probably yes”, or “Probably not” to using tobacco if one of their best friends offered it to them or those who answered “Definitely yes”, “Probably yes”, or “Probably not” to using tobacco during the next 12 months. 13 Those who answered “Agree” or “Strongly agree” to the statement: “I think I might enjoy smoking a cigarette”. 14 How cigarettes were obtained the last time respondents smoked cigarettes in the past 30 days. 15 Of those who tried to buy cigarettes during the past 30 days. 16 Based on the last purchase, of those who bought cigarettes during the past 30 days. 17 Among those who visited a point of sale in the past 30 days. 18 Among those who watched television, videos, or movies in the past 30 days. 19 Among those who attended sporting or community events in the past 30 days. 20 Among those who noticed warning labels on cigarette packages in the past 30 days. 212008 data is from Karachi region only. † During the past 30 days. †† During the past 7 days. NOTE: Students refer to persons aged 13-15 years who are enrolled in school. Data have been weighted to be nationally representative of all students aged 13-15 years. Percentages reflect the prevalence of each indicator in each group, not the distribution across groups. –Indicates estimate based on less than 35 unweighted cases and has been suppressed.
The Opinion Poll on SSBs in Pakistan 2021 was successfully completed with the efforts and involvement of numerous organizations and individuals at different stages of the Poll. We would like to thank everyone who participated and made this Poll a success. First of all, we are grateful to the Ministry of National Health Services, Regulations and Coordination (NHSR&C) for its support.
We wish to express our special thanks to Global Health Advocacy Incubator (GHAI) and Pakistan National Heart Association (PANAH) for their support and facilitation in this Poll. We are thankful to Pakistan Telecommunication Authority (PTA) for supporting in this national Poll. In addition, the untiring efforts of data collectors in the current COVID-19 situation are highly acknowledged. Last but not least, we would like to express our sincere thanks and gratitude to the Poll Coordinators, Poll team members, data entry operators, and above all, the household members/community who participated in this Poll.
COLLABORATING ORGANIZATIONS: Ministry of National Health Services, Regulations and Coordination (NHSR&C) Islamabad, Pakistan Pakistan Health Research Council (PHRC) Islamabad, Pakistan Pakistan National Heart Association (PANAH) Rawalpindi, Pakistan The Solutions Pakistan- Contract Research Organization (SP-CRO) Rawalpindi, Pakistan
2021 OPINION POLL: NATIONAL VIEWS ON SUGAR SWEETENED BEVERAGES IN PAKISTAN
Published: 2021 This document is published by Pakistan Health Research Council, Ministry of National Health Services, Regulations and Coordination. The document may be freely reviewed, abstracted, reproduced and translated, in part or in whole, but is not for sale or use in conjunction with commercial purposes.