Methodology Overview

The TCDI Nigeria website was developed by Development Gateway. The website is intended to respond to the needs of government, civil society and academic stakeholders by providing access to demand-driven, high-quality data on national tobacco prevalence, products, and policies. The design, functionality and content for the website are led by the Nigerian tobacco control community’s needs. This document outlines our overall website development methodology.

TCDI Nigeria used the 2018 Nigeria Demographic and Health Survey (NDHS) data to calculate tobacco use prevalence for different products and sub-groups of the population. To estimate the population size, we used UNDP Population estimates for 2018. The analysis was conducted using the statistical software, R. The DHS data is available for download here, and the R code here.

Nigeria DHS is a household survey that is done approximately every 5 years. The latest one was conducted in 2018 and it aimed to provide up-to-date estimates of basic demographic and health indicators. The information collected through the 2018 NDHS intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of the country’s population. The survey collected information on themes such as nutrition, HIV/AIDS and smoking, among others. Prevalence of tobacco smoking was determined by the answers of participants aged 15 years and older to the question: “Do you currently smoke tobacco every day, some days, or not at all?” Those who answered “every day/some days” were classified as current tobacco smokers, whilst those who answered “not at all” were regarded as current non-smoke.

The 2018 NDHS was nationally representative with over 42,000 households across the country’s six geopolitical zones and 36 states with a target group of women aged 15-49 and men aged 15-59. A total of 41,668 households were selected for the sample, of which 40,666 were occupied. Of the occupied households, 40,427 were successfully interviewed putting the overall response rate in 2018 at 99%. The data collected by NDHS are weighted so that proportions are those of the full population, but in order to calculate the population totals, the data needs to be reweighted. To do this, population estimates are required to calculate the number of smokers from the prevalence rates. DHS, unlike some other surveys, weights the data so that the weighted total is the same as the unweighted sample size. The weights can be rescaled using population estimates.

In total, 55,132 adults (15+ years) provided answers to the tobacco smoking question (excluding those that refused to answer or said they did not know). The tobacco smoking prevalence rates are calculated by dividing the number of tobacco smokers in the sample by the total sample and multiplying by the weights. 

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Individuals who did not answer the smoking questions were excluded from the analysis. We tested whether smoking prevalence rates were statistically significantly different from one another by comparing confidence intervals

for each estimate. The TCDI team calculated prevalence rates for the following groups: 

• Nine age categories were identified for the smoking prevalence by age analysis: The age categories were (15-19, 20-24 ,25-29, 30-34, 35-39,40-44, 45-49, 50-54, 55-59). In our analysis, we dropped ages < 15 and > 59 (men) and > 49 (women).
• For the education analysis, respondents were categorised by the highest level of education attained, unless they were in an educational institution at the time of the survey. The educational categories include: no education; primary education; secondary education and higher education. 
• For location, households were categorised into urban and rural. “Nigeria adopts a threshold population of 20,000 people as a criterion for defining an urban centre. In addition to this, all states and local government area headquarters have historically legally or administratively regarded as urban centres.”
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• For zones, the households were categorised into six zones (North Central North East, North West, South East, South South and South West)

The taxation page discusses and provides information on tobacco tax policies in Nigeria, effects of tax increase on tobacco products as well as discrediting tobacco industry arguments against tax increases. 

In addition to drawing on secondary sources, data analysis was conducted by the TCDI team to predict how increases in tax will affect consumption, government revenue, and mortality using a Tobacco Excise Tax Simulation Model (TETSiM) developed by the Research Unit on the Economics of Excisable Products (REEP). TETSiM is a “tool to quantify the likely impact of a change in the excise tax structure and/or the level of the excise tax on a number of variables, including the price of cigarettes, cigarette consumption and excise tax revenue.”

TETSiM Model

For the Tobacco Excise Tax Simulation Model (TETSiM), the TCDI team consulted with the Research Unit on the Economics of Excisable Products (REEP) and tobacco control experts, who advised on data sources and reviewed each page before publication. The TETSiM is a simulation tool that has been developed by the REEP at the University of Cape Town. The TETSiM model quantifies the likely impact of a change in the excise tax structure and/or the level of the excise tax on a number of variables, including cigarette consumption and excise revenue collected by the government. Because the model is programmed in Excel,

it can be customised to the tax system for any individual country.

For the data analysis conducted by the TCDI team, the following data and methods were used:

1. Scenario 1: Cigarette consumption and cigarette excise tax revenue projections to 2024.

We estimated the cigarette consumption by 2024 if the cigarette tax in Nigeria  increases to the real (inflation-adjusted) value of US$ 0.40 by 2024, and the ad valorem tax increases annually by 10%, from 20% of the cost, insurance, and freight (CIF) value in the baseline (2021) to  50%  by year 3 (2024).

2. Scenario 2: Cigarette consumption and cigarette excise tax revenue projections to 2024.

We estimated the cigarette consumption by 2024 if the cigarette tax in Nigeria  increases to the real (inflation-adjusted) value of US$ 1.00 by 2024, and the ad valorem tax increases annually by 10%, from 20% of the cost, insurance, and freight (CIF) value in the baseline (2021) to  50%  by year 3 (2024).

The TCDI team used constant 2021 prices for the TETSiM simulations.

For both scenarios, the TCDI team simulated an over-shift and under-shift model.

(a) Over-shift: This is when the retail selling price increases by more than the increase in the excise tax.We assumed that the industry will respond to the excise tax changes by increasing the net-of-tax price of premium brands by 25%, mid-priced and economy brands by 20%, imported and illicit brands by 17%, each year from 2022 to 2024.

(b) Under-shift: This is when the retail selling price increases by less then the increase in the excise tax.We assumed that the industry will respond to the excise tax changes by increasing the net-of-tax price of premium  brands by 8%, mid-priced, economy, and imported brands by 5%, and illicit brands by 9%, each year from 2022 to 2024. Here the net-of-tax price increases in nominal terms, but falls in real terms, i.e in constant 2021 prices, the net-of-tax price falls over time and hence the retail selling price increases, but by less than the increase in the excise tax.

The following assumptions were made for all scenarios modelled.

1. Product segments: Five brand segments, including premium, mid-price, economy, imports, and illicit.
2. Illicit cigarette market: 10% of the total cigarette market.
3. Price elasticity of demand: Range -0.4 to -0.8, with absolute elasticity being greater for lower-priced brands, and  smaller for higher priced brands.
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4. Cross-price elasticity of demand: Range 0 to 0.1, with 0 for imported brand category, and 0.1 for mid-price, economy and illicit brands.
5. Income elasticity of demand: Range  from -0.5 (illicit brands) to 0.8 (premium brands). The income elasticity was assumed to be 0. For mid-price, 0.3 for  economy and 0.5 for imported brands.

Note:

• The price elasticity of demand measures the percentage change in the demand of a product resulting from a 1% change in the retail price of that product. In the case of cigarettes, price elasticity is negative, because increases in price will result in a reduction in demand. In low- and middle-income countries (LMICs), the price elasticity of demand for cigarettes has been estimated at between -0.2 and -0.8, with many estimates clustered at around -0.5. Thus, for LMICs, a 10% increase in the retail price of cigarettes is expected to reduce consumption by between 2% and 8%.
• The cross-price elasticity of demand measures by what percentage the demand of one brand segment changes in response to a 1% increase in the price of a more expensive brand of cigarettes. Since cross-price elasticity reflects the gains to the cheaper segment (from the more expensive segment) it is a positive number. In other words, it captures how consumers substitute to cheaper products when prices increase. This also allows for movement between the legal and illegal markets, as some consumers switch from legal to (presumably cheaper) illegal cigarettes.
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  This range of elasticity is in line with international estimates for LMICs. 
• The income elasticity of demand measures by what percentage the demand changes in response to a 1% increase in income. As income rises, the demand for normal  goods increases.
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  Thus, the income elasticity for normal goods is positive, implying that the demand rises with income. Goods with income elasticities above one are perceived as luxury or superior goods. While those with income elasticities below one are perceived as necessities.
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   Most estimates of income elasticity of demand for tobacco products lie between 0 and 1. An income elasticity of demand of 0.5 means that an increase in income of 10% will result in tobacco consumption rising by 5%.
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Data from the International Monetary Fund,   The Nigeria 2018 Demographic and Health survey, and the WHO FCTC article 6 were used for the simulations.

The TCDI team conducted  data  and literature review based  on the focal topics  proposed by  stakeholders. This exercise was undertaken between May 2021 and June 2022.  Secondary data searches occurred across various platforms, including data repositories, academic journals, and online libraries. The team also reviewed trusted resources  from the World Health Organisation, World Bank Group and STOP (A Global Tobacco Industry Watchdog). The  Research Unit on the Economics of Excisable Products (REEP) and other subject matter experts, also advised on data sources and reviewed each page before publication.

The TCDI team conducted  data  and literature review based on the focal topics  proposed by stakeholders. This exercise was undertaken between May 2021 and June 2022.  Secondary data searches occurred across various platforms, including data repositories, academic journals, and online libraries.

The Industry interference theme mainly drew its data from The Global Tobacco Industry Interference Index (Global Tobacco Index) which reviews how governments respond to influences from the tobacco industry and protect their public health policies from commercial interests as required under the World Health Organization Framework Convention on Tobacco Control (WHO FCTC). The Global Tobacco Index rates countries on their response to tobacco industry interference through a survey questionnaire. The questionnaire contains 20 questions covering 7 indicators of industry interference on which countries are scored:

• Indicator 1: The industry’s participation in policy development
• Indicator 2: Tobacco industry-related corporate social responsibility activities
• Indicator 3: Benefits given to the tobacco industry
• Indicator 4: Unnecessary interaction between government and industry
• Indicator 5: Measures for transparency
• Indicator 6: Preventing conflicts of interest
• Indicator 7: Measures that prevent industry influence

The indicators score ranges from 0- 5, where 5 indicates highest level of industry interference, and 1 is low or no interference. Countries are ranked according to total scores provided by civil society groups. Hence the lower the score, the lower the overall level of interference, which augurs well for the country.

The team also reviewed resources from trusted organisations such as the World Health Organisation, World Bank Group and STOP (A Global Tobacco Industry Watchdog). The TCDI team consulted with the Research Unit on the Economics of Excisable Products (REEP) and other subject matter experts, who advised on data sources and reviewed each page before publication.

The TCDI team conducted  initial data  and literature review based on the topics proposed by  stakeholders. This exercise was undertaken between May 2021 and June 2022.  Secondary data searches occurred across various platforms, including data repositories, academic journals, and online libraries. The team also reviewed resources from trusted organisations such as the World Health Organisation, World Bank Group and STOP (A Global Tobacco Industry Watchdog). The TCDI team consulted with the Research Unit on the Economics of Excisable Products (REEP) and other subject matter experts, who advised on data sources and reviewed each page before publication.

The shisha study was a cross-sectional and non-experimental study with current and non-current shisha smokers using a computer-assisted telephone interviews (CATI) technique. The study was part of a sequential-mixed-methods primary research which also involved qualitative interviews with 78 known current shisha users from 13 states in Nigeria. The qualitative components took place before the quantitative survey. Both qualitative and quantitative studies adopted the Theory of Planned Behaviour (TPB), a framework that is useful for identifying behavioural and normative determinants of an observed behaviour such as tobacco smoking.

For the sample frame, we used the 2018/19 Nigeria Living Standard Surveys (NLSS) sample frame which is based on the National Master Sample Frame (MSF) which was originally used for the National Integrated Survey of Household 2 (NISH2) by the National Bureau of Statistics (NBS). The NLSS sample frame of households is representative at national, zonal, and state levels including Federal Capital Territories (FCTs). During the 2018/2019 NLSS which was conducted in person, phone numbers of up to 3 household members were collected. Other available information in the frame includes states, LGAs, rural/urban areas covered, and Enumeration Areas (EAs) of households surveyed, among other background information of respondents. The availability of phone numbers, other background information of household members and the existing relationship between NBS and the NLSS households made this an ideal frame for drawing samples for a national phone survey on shisha use in Nigeria.

Two states with the highest urbanisation rate were selected as the study locations in each zone. The states selected were Lagos, Oyo, FCT, Plateau, Gombe, Adamawa, Kaduna, Kano, Rivers, Edo, Anambra and Ebonyi. A two-stage stratified sample selection procedure was adopted for the survey within each state using the NLSS sample frame. In the first stage, enumeration Areas (EAs) in each of the selected states in each zone were stratified into rural or urban EAs. Seven EAs from urban EAs, and three EAs from rural EAs were randomly selected in each participating state. In the second stage, a random sample of 10 individuals (5 current shisha smokers and 5 non-smokers) were selected in every participating EA. About 3 out of every 10 individuals sampled were females. This procedure however included sampling with the replacement of EAs, and individuals to ensure relevant quotas were met within each stratum at zonal level. In a few cases, some of the replacement EAs and participants were taken from other adjoining states within the same region of the state being replaced. The reserved list of replacements, created from the sampling frame, served as backups for cases such as non-response, incomplete surveys, poor network coverage, insecurities, and other unforeseen circumstances in the sampled EAs. The telephone number of members of households obtained from the NLSS frame was used to contact the selected members for interviews. Figure 2.1.3. shows the distribution plans of the study samples at different levels.

Using simulations and rule of events per variable (EPV) approaches, a minimum sample of 1,200 survey participants, comprising approximately 600 current shisha users and 600 current shisha nonusers, was required. These approaches have been tested in extremely large populations and results have been shown to produce accurate and powered estimates derived from estimated samples that represent the target population parameters. Unlike face-to-face (FTF) surveys which have over 95% response rate in Nigeria, phone surveys through Computer-assisted telephone interviewing (CATI), have been found to have about a 15% response rate in Nigeria. Therefore, we needed to call about 8,000 phone numbers in our sample frame to achieve the desired survey sample size of approximately 1,200 respondents.

To ensure the survey estimates from the data collected are generalizable to a larger population, the data collected were multiplied by a sampling weight or expansion factor. The basic weight for each sampled individual is equal to the inverse of its probability of selection. The probability of selecting EAs and the probability of selecting eligible individuals within the EAs were obtained. The basic probability of selecting an individual was calculated by multiplying the probabilities at each sampling stage. The base weight was the weight of the sample selection process in its entirety. However, to better ensure the weighted estimates obtained from the survey truly reflect the underlying population distribution across the strata, the weights were calibrated to known population totals. The final stage of the weighting also included a post-stratification calibration to national population distribution by zone, location of residence, gender, and age groups of people aged 18 years or above.