Evidence map›Paper›PMID 29515917›Full record

ArticleBMJ global health2018

Simulating the impact of excise taxation for disease prevention in low-income and middle-income countries: an application to South Africa.

Nicholas Stacey, Amit Summan, Aviva Tugendhaft, Ramanan Laxminarayan, Karen Hofman

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In one paragraph

Article in BMJ global health, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Nicholas StaceyPriority Cost Effective Lessons for Systems Strengthening, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.ORCID 0000-0002-3572-5314
Amit SummanCenter for Disease Dynamics Economics and Policy, Washington, District of Columbia, USA.
Aviva TugendhaftPriority Cost Effective Lessons for Systems Strengthening, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
Ramanan LaxminarayanCenter for Disease Dynamics Economics and Policy, Washington, District of Columbia, USA.
Karen HofmanPriority Cost Effective Lessons for Systems Strengthening, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionExcise taxes are policy tools that have been applied internationally with some success to reduce consumption of products adversely impacting population health including tobacco, alcohol and increasingly junk foods and sugary beverages. As in other low-income and middle-income countries, South Africa faces a growing burden of lifestyle diseases; accordingly we simulate the impact of multiple excise tax interventions in this setting.

methodsWe construct a mathematical model to simulate the health and revenue effects of increased excise taxes, which is adaptable to a variety of settings given its limited data requirements. Applying the model to South Africa, we simulate the impact of increased tax rates on tobacco and beer and of the introduction of a tax on sugar-sweetened beverages (SSB). Drawing on surveys of product usage and risk factor prevalence, the model uses a potential impact fraction to simulate the health effects of tax interventions.

resultsAdopting an excise rate of 60% on tobacco would result in a gain of 858 923 life-years (95% uncertainty interval (UI) 480 188 to 1 310 329), while adopting an excise rate of 25% on beer would result in a gain of 568 063 life-years (95% UI 412 110 to 775 560) and the adoption of a 20% tax on SSBs would result in a gain of 688 719 life-years (95% UI 321 788 to 1 079 653).

conclusionMore aggressive excise tax policies on tobacco, beer and SSBs in South Africa could result in meaningful improvements in population health and raised revenue.

Indexed as

health policyprevention strategiespublic health

Identifiers

PMID29515917
PMCPMC5838397

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.