Dataset Information

Potential revenue from taxing e-cigarettes and comparison of annual costs of daily e-cigarette use versus daily cigarette smoking among South African adults.

ABSTRACT:

Introduction

To inform policy making under the proposed The Control of Tobacco and Electronic Delivery Systems Bill, we compared annual costs of using e-cigarettes versus cigarettes, and estimated revenue from e-cigarette taxation.

Methods

We extracted e-cigarette retail prices from 231 South African e-cigarette vendor websites. We compared annual costs associated with daily cigarette smoking (self-reports from daily smokers in the 2018 South African Social Attitudes Survey, SASAS) versus daily e-cigarette use (based on cumulative costs of consumables plus device costs). We estimated revenue from excise tax if e-cigarettes were taxed at 75% (the rate proposed by the government) and 37.5% (half of the government's proposal as a hypothetical scenario) of the cigarette excise rate. We applied the different rates to e-cigarette consumption in 2018 SASAS and projected for 2021.

Results

Mean annual cost associated with daily use was ZAR 6693 (US$460.32, based on an exchange rate of about 69 US$ to 1000 ZAR) for manufactured cigarettes; for e-cigarettes, this ranged from ZAR 8574.69/year (with price minimizing strategies) to ZAR 19780.83/year (retail products exclusively). Expected revenue from e-cigarette excise tax at 75% of the cigarette tax rate was up to ZAR 2.20 billion (95% CI: 0.96-3.44). If taxed at 37.5% of the cigarette tax rate - half of the government's proposed rate - the projected revenue was up to ZAR 1.10 billion (95% CI: 0.48-1.72). Of the projected revenue from e-cigarette excise tax at 75% of the cigarette rate, the portion attributable to hardware (device and batteries) was 61% (ZAR 1.35 billion), while the portion attributable to e-liquid was 39% (ZAR 0.86 billion).

Conclusions

Calculated daily costs were higher for e-cigarettes than cigarettes. We recommend an e-cigarette excise tax. The government's proposed tax rate may reduce youth e-cigarette access, while allowing adult smokers wishing to switch exclusively to e-cigarettes to reduce their tobacco-related harm.

SUBMITTER: Agaku IT

PROVIDER: S-EPMC7842579 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Potential revenue from taxing e-cigarettes and comparison of annual costs of daily e-cigarette use versus daily cigarette smoking among South African adults.

Agaku Israel T IT Egbe Catherine O CO Ayo-Yusuf Olalekan A OA

Tobacco induced diseases 20210129

<h4>Introduction</h4>To inform policy making under the proposed <i>The Control of Tobacco and Electronic Delivery Systems Bill</i>, we compared annual costs of using e-cigarettes versus cigarettes, and estimated revenue from e-cigarette taxation.<h4>Methods</h4>We extracted e-cigarette retail prices from 231 South African e-cigarette vendor websites. We compared annual costs associated with daily cigarette smoking (self-reports from daily smokers in the 2018 South African Social Attitudes Survey ...[more]

PMID: 33542679

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Project description:BackgroundCigarette smoking is an established cause of preventable death and often initiated during adolescence. We estimated the short- and long-term costs of cigarette smoking among currently smoking adolescents in Nigeria.MethodsA cross-sectional survey among adolescents in Oyo state, Nigeria and a review of mortality records of patients managed for lung cancer in a tertiary facility in Ibadan, Nigeria were conducted. Short-term costs estimated were: (a) average weekly costs of purchasing cigarettes by currently smoking adolescents, and (b) costs of managing at least an episode of chronic cough occurring within 12 months of the survey. Long-term costs were limited to: (a) life-time expenditure on purchasing cigarettes, and (b) direct medical and non-medical (transportation) costs of managing lung cancer. Long-term costs were first projected to the approximate year when the adolescents (mean age:16.0 ± 1.8 years) might be diagnosed with lung cancer based on the average age at presentation with symptoms of lung cancer obtained from the records (59.8 years), and then discounted to 2020 prices. This was estimated as 44 years from the base year (2020). Costs were reported in 2020 prices in Nigerian Naira (NGN) and US dollar (USD) equivalent using the Central Bank of Nigeria, June 2020 exchange rate of USD 1: NGN 360.50.ResultsApproximately 3.8% of the adolescents were current cigarette smokers. Average weekly expenditure on cigarettes was NGN 306.82 ± 5.74 (USD 0.85 ± 0.02). About 26% had experienced at least an episode of chronic cough which cost them an average of NGN 1226.81 ± 6.18 (USD 3.40 ± 0.02) to manage. Total future costs of cigarette smoking in 2020 prices for the 43 adolescents who were current smokers in the event that they develop lung cancer were approximately NGN 175.7 million (USD 487.3 thousand), NGN 871.8 million (USD 2.4 million) and NGN 4.6 trillion (USD 12.7 million) at assumed annual inflation rates of 10%, 15%, and 20% respectively and discount rate of 4.25%.ConclusionThe estimated economic costs of smoking were very high. Efforts to prevent smoking initiation among adolescents in our study area should be intensified. Interventions to subsidize the medical cost of health-related consequences of cigarette smoking are also required, especially as treatment costs are currently largely borne out-of-pocket.Supplementary informationThe online version contains supplementary material available at 10.1007/s10389-021-01644-5.

Project description:A total of 2453 smokers were interviewed in townships over two rounds of data collection. Townships are low-income, urban areas characterised by overpopulation, poor service delivery, crime, and poor socioeconomic outcomes. Township residents typically live in poverty. Data were collected from six townships in four of South Africa's nine provinces, namely Gauteng (Eldorado Park and Ivory Park), Western Cape (Khayelitsha and Mitchell's Plain), Free State (Thabong) and KwaZulu-Natal (Umlazi). These townships were chosen to represent both the geographical and racial spread of low socioeconomic areas in South Africa. Round 1 data (n = 1260) were collected from October to November 2017, and round 2 data (n = 1193) were collected from July to August 2018. The sample includes two of South Africa's four population groups: African and mixed race (locally referred to as "Coloured", which describes people of mixed Khoisan, Malay, European, and black African ancestry). Since few Whites and Asians live in townships, they were not sampled. Households were selected via a random walk through each township. One smoker per household was interviewed (if a household contained at least one available smoker). We aimed to interview 200 adult smokers (aged 18+ years) per township per round. If a household had more than one smoker, a random selection determined which smoker to interview. Respondents were asked about their most recent cigarette purchase, specifically packaging type (single stick, pack, or carton), number of items purchased, brand, type of outlet where the cigarettes were bought, and the total amount paid for cigarettes. Respondents were also asked about other tobacco use in the household, and about their perceptions regarding illegal cigarettes. Socioeconomic and demographic information was collected at the individual and household level. The data has been used to estimate illicit trade (https://tobaccocontrol.bmj.com/content/early/2020/03/10/tobaccocontrol-2019-055136.info), and to analyse the determinants of smoking intensity (https://www.sciencedirect.com/science/article/pii/S2211335520300590).

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Dataset Information

Potential revenue from taxing e-cigarettes and comparison of annual costs of daily e-cigarette use versus daily cigarette smoking among South African adults.

Introduction

Methods

Results

Conclusions

Publications

Potential revenue from taxing e-cigarettes and comparison of annual costs of daily e-cigarette use versus daily cigarette smoking among South African adults.

Similar Datasets

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