Evidence map›Paper›PMID 30395603›Full record

ArticlePLoS neglected tropical diseases2018

The economic value of identifying and treating Chagas disease patients earlier and the impact on Trypanosoma cruzi transmission.

Sarah M Bartsch, Cameron M Avelis, Lindsey Asti, Daniel L Hertenstein, Martial Ndeffo-Mbah, Alison Galvani, Bruce Y Lee

Abstract read
In one paragraph

Article in PLoS neglected tropical diseases, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 2 pooled it
–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

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

27 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. The economic burden of Chagas disease: A systematic review.PLoS neglected tropical diseases · 2023
    Pooled it
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  7. Identification of highly conservedEmerging microbes & infections · 2024
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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

7 authors.

Sarah M BartschPublic Health Computational and Operations Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America.
Cameron M AvelisPublic Health Computational and Operations Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America.ORCID 0000-0001-9909-5246
Lindsey AstiPublic Health Computational and Operations Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America.ORCID 0000-0003-4095-0660
Daniel L HertensteinPublic Health Computational and Operations Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America.
Martial Ndeffo-MbahCenter for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT, United States of America.
Alison GalvaniCenter for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT, United States of America.
Bruce Y LeePublic Health Computational and Operations Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America.ORCID 0000-0003-2633-8651

Funding

MIDAS Informatics Services Group (ISG)U24GM110707 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI ESPINO, JESSI U, WAGNER, MICHAEL MATTHEW · 2014 to 2018
$11.6M
Regional Healthcare Ecosystem Analyst for Healthcare Associated InfectionsR01HS023317 · AHRQ · JOHNS HOPKINS UNIVERSITY · PI LEE, BRUCE Y · 2014 to 2014
$2.4M
AHRQ HHS R01 HS023317NIGMS NIH HHS U24 GM110707
6 · The paper itself

Abstract

backgroundThe World Health Organization's 2020 Goals for Chagas disease include access to antiparasitic treatment and care of all infected/ill patients. Policy makers need to know the economic value of identifying and treating patients earlier. However, the economic value of earlier treatment to cure and prevent the Chagas' spread remains unknown.

methodsWe expanded our existing Chagas disease transmission model to include identification and treatment of Chagas disease patients. We linked this to a clinical and economic model that translated chronic Chagas disease cases into health and economic outcomes. We evaluated the impact and economic outcomes (costs, cost-effectiveness, cost-benefit) of identifying and treating different percentages of patients in the acute and indeterminate disease states in a 2,000-person village in Yucatan, Mexico.

resultsIn the absence of early treatment, 50 acute and 22 new chronic cases occurred over 50 years. Identifying and treating patients in the acute stage averted 0.5-5.4 acute cases, 0.6-5.5 chronic cases, and 0.6-10.8 disability-adjusted life years (DALYs), saving $694-$7,419 and $6,976-$79,950 from the third-party payer and societal perspectives, respectively. Treating in the indeterminate stage averted 2.2-4.9 acute cases, 6.1-12.8 chronic cases, and 11.7-31.1 DALYs, saving $7,666-$21,938 from the third-party payer perspective and $90,530-$243,068 from the societal perspective. Treating patients in both stages averted ≤9 acute cases and ≤15 chronic cases. Identifying and treating patients early was always economically dominant compared to no treatment. Identifying and treating patients earlier resulted in a cumulative cost-benefit of $7,273-$224,981 at the current cost of identification and treatment.

conclusionsEven when identifying and treating as little as 5% of cases annually, treating Chagas cases in the acute and indeterminate stages reduces transmission and provides economic and health benefits. This supports the need for improved diagnostics and access to safe and effective treatment.

Indexed as

AnimalsAntiprotozoal AgentsChagas DiseaseCost-Benefit AnalysisHumansMexicoSecondary PreventionTreatment OutcomeTrypanosoma cruziAntiprotozoal Agents

Identifiers

PMID30395603
PMCPMC6237415

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.