Evidence map›Paper›PMID 34564795›Full record

ArticleEuropean journal of epidemiology2021

Strength in causality: discerning causal mechanisms in the sufficient cause model.

Etsuji Suzuki, Eiji Yamamoto

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In one paragraph

Article in European journal of epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06666647 (Ambulance and Helicopter Response Times in Danish Emergency Medical Services), which is not on this map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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.

NCT06666647 active not recruitingnot on this mapstarted 2025, after this paper: background citation

Ambulance and Helicopter Response Times in Danish Emergency Medical Services: Protocol for an Epidemiological Study. The AHRTEMIS Study

TypeobservationalSponsorPeter Martin HansenRan2025 to 2026Enrolled2,500,000ConditionsEmergency Medical Services, Response, Triage, Survival OutcomesArmsTreatment by ambulance or helicopter services with response times
3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
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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

2 authors.

Etsuji SuzukiDepartment of Epidemiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata-cho, Kita-ku, Okayama, 700-8558, Japan. etsuji-s@cc.okayama-u.ac.jp.ORCID http://orcid.org/0000-0002-1290-5793
Eiji YamamotoOkayama University of Science, 1-1 Ridai-cho, Kita-ku, Okayama, 700-0005, Japan.

Funding

Japan Society for the Promotion of Science JP18K10104Japan Society for the Promotion of Science JP19KK0418Japan Society for the Promotion of Science JP20K10471Japan Society for the Promotion of Science JP20K10499
6 · The paper itself

Abstract

The assessment of causality is fundamental to epidemiology and biomedical sciences. One well-known approach to distinguishing causal from noncausal explanations is the nine Bradford Hill viewpoints. A recent article in this journal revisited the viewpoints to incorporate developments in causal thinking, suggesting that the sufficient cause model is useful in elucidating the theoretical underpinning of the first of the nine viewpoints-strength of association. In this article, we discuss how to discern the causal mechanisms of interest in the sufficient cause model, which pays closer attention to the relationship between the sufficient cause model and the Bradford Hill viewpoints. To this end, we explicate the link between the sufficient cause model and the potential-outcome model, both of which have become the cornerstone of causal thinking in epidemiology and biomedicine. A clearer understanding of the link between the two models provides significant implications for interpretation of the observed risks in the subpopulations defined by exposure and confounder. We also show that the concept of potential completion times of sufficient causes is useful to fully discerning completed sufficient causes, which leads us to pay closer attention to the fourth of the nine Bradford Hill viewpoints-temporality. Decades after its introduction, the sufficient cause model may be vaguely understood and thus implicitly used under unreasonably strict assumptions. To strengthen our assessment in the face of multifactorial causality, it is significant to carefully scrutinize the observed associations in a complementary manner, using the sufficient cause model as well as its relevant causal models.

Indexed as

Biomedical ResearchCausalityForecastingModels, TheoreticalEpidemiologic StudiesHumansResearch DesignBradford HillCausal inferenceCausal mechanismsCounterfactualPotential-outcome modelSufficient cause model

Identifiers

PMID34564795

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Registered trials

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.