Evidence map›Paper›PMID 31655600›Full record

SynthesisBMC public health2019

Alcohol consumption's attributable disease burden and cost-effectiveness of targeted public health interventions: a systematic review of mathematical models.

Ariel Esteban Bardach, Andrea Olga Alcaraz, Agustín Ciapponi, Osvaldo Ulises Garay, Andrés Pichón Riviere, Alfredo Palacios, Mariana Cremonte, Federico Augustovski

Open access · goldAbstract readSystematic Review
In one paragraph

Synthesis in BMC public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
2.4field-weighted citation impact, top 11% of its field
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

18 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.

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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

8 authors at 2 institutions in 1 country.

Ariel Esteban BardachCentre for Research in Epidemiology and Public Health, Institute for Clinical Effectiveness and Health Policy (IECS-CONICET), Dr Emilio Ravignani 2024 (C1014CPV), Buenos Aires, Argentina. abardach@iecs.org.ar.ORCID http://orcid.org/0000-0003-4437-0073
Andrea Olga AlcarazInstitute for Clinical Effectiveness and Health Policy (IECS-CONICET), Dr Emilio Ravignani 2024 (C1014CPV), Buenos Aires, Argentina.
Agustín CiapponiCentre for Research in Epidemiology and Public Health, Institute for Clinical Effectiveness and Health Policy (IECS-CONICET), Dr Emilio Ravignani 2024 (C1014CPV), Buenos Aires, Argentina.
Osvaldo Ulises GarayInstitute for Clinical Effectiveness and Health Policy (IECS-CONICET), Dr Emilio Ravignani 2024 (C1014CPV), Buenos Aires, Argentina.
Andrés Pichón RiviereCentre for Research in Epidemiology and Public Health, Institute for Clinical Effectiveness and Health Policy (IECS-CONICET), Dr Emilio Ravignani 2024 (C1014CPV), Buenos Aires, Argentina.
Alfredo PalaciosCenter for Study of State and Society (CEDES), Sánchez de Bustamante 27 (C1173AAA) CABA, Buenos Aires, Argentina.
Mariana CremonteGroup of Psychoactive Substances and injuries due to external cause, Institute of Basic, Applied Psychology and Technology (IPSIBAT) CONICET National University of Mar del Plata, Dean Funes 3250, B7602AYJ, Mar del Plata, Buenos Aires, Argentina.
Federico AugustovskiCentre for Research in Epidemiology and Public Health, Institute for Clinical Effectiveness and Health Policy (IECS-CONICET), Dr Emilio Ravignani 2024 (C1014CPV), Buenos Aires, Argentina.
Consejo Nacional de Investigaciones Científicas y Técnicas · ARCenter for the Study of State and Society · AR

Funding

Ministerio de Salud, Argentina Becas Salud Investiga 2017
6 · The paper itself

Abstract

backgroundAround 6% of total deaths are related to alcohol consumption worldwide. Mathematical models are important tools to estimate disease burden and to assess the cost-effectiveness of interventions to address this burden.

methodsWe carried out a systematic review on models, searching main health literature databases up to July 2017. Pairs of reviewers independently selected, extracted data and assessed the quality of the included studies. Discrepancies were resolved by consensus. We selected those models exploring: a) disease burden (main metrics being attributable deaths, disability-adjusted life years, quality-adjusted life years) or b) economic evaluations of health interventions or policies, based on models including the aforementioned outcomes. We grouped models into broad families according to their common central methodological approach.

resultsOut of 4295 reports identified, 63 met our inclusion criteria and were categorized in three main model families that were described in detail: 1) State transition -i.e Markov- models, 2) Life Table-based models and 3) Attributable fraction-based models. Most studies pertained to the latter one (n = 29, 48.3%). A few miscellaneous models could not be framed into these families.

conclusionsOur findings can be useful for future researchers and decision makers planning to undertake alcohol-related disease burden or cost-effectiveness studies. We found several different families of models. Countries interested in adopting relevant public health measures may choose or adapt the one deemed most convenient, based on the availability of existing data at the local level, burden of work, and public health and economic outcomes of interest.

Indexed as

Alcohol DrinkingAlcohol-Related DisordersCost-Benefit AnalysisHealth PromotionHumansModels, TheoreticalRandomized Controlled Trials as TopicAlcoholBurden of diseaseEconomic evaluationsModelling

Identifiers

PMID31655600
PMCPMC6815367
OpenAlexW2981964186

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.