Evidence map›Paper›PMID 36454725›Full record

ArticlePloS one2022

Event dependent overall survival in the population-based LIFE-Adult-Study.

Samira Zeynalova, Katja Rillich, Eike Linnebank, Tina Stegmann, Michael Brosig, Matthias Reusche, Markus Loeffler

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 34% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
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 at 2 institutions in 1 country.

Samira ZeynalovaInstitute for Medical Informatics, Statistics, and Epidemiology, University of Leipzig, Leipzig, Germany.
Katja RillichInstitute for Medical Informatics, Statistics, and Epidemiology, University of Leipzig, Leipzig, Germany.ORCID 0000-0002-4734-8376
Eike LinnebankInstitute for Medical Informatics, Statistics, and Epidemiology, University of Leipzig, Leipzig, Germany.
Tina StegmannUniversity Clinic Leipzig, Leipzig, Germany.
Michael BrosigAcademic Teaching Practice, University of Leipzig, Leipzig, Germany.
Matthias ReuscheInstitute for Medical Informatics, Statistics, and Epidemiology, University of Leipzig, Leipzig, Germany.
Markus LoefflerInstitute for Medical Informatics, Statistics, and Epidemiology, University of Leipzig, Leipzig, Germany.
Leipzig University · DEUniversity Hospital Leipzig · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backroundInformation about the direct comparability of big data of epidemiological cohort studies and the general population still is lacking, especially regarding all-cause mortality rates. The aim of this study was to investigate the overall survival and the influence of several diagnoses in the medical history on survival time, adjusted to common risk factors in a populations-based cohort.

methodsFrom 10,000 subjects of the population-based cohort LIFE-Adult-Study (Leipzig Research Centre for Civilization Diseases), the medical history and typical risk factors such as age, smoking status and body-mass-index (BMI) were assessed. The survival status was identified from the saxonian population register. Univariate and multivariate analyses were used to determine the influence of the medical history and risk factors on overall survival. To develope an optimal model, the method by Collet [1] was used.

resultsThe mortality rate of the participants is approximately half the mortality rate expected for the german population. The selection bias in epidemiological studies needs to be considered whenever interpreting results of epidemiological cohort studies. Nevertheless we have shown that several diagnoses proved to have a negative influence on overall survival time even in this relatively healthy cohort. This study showed the significantly increased mortality risk if the following diseases are reported in medical history of the participants in a large population-based cohort study including adults aged 18 and over: diabetes mellitus (HR 1.533, p = 0.002), hypertension (HR 1.447, p = 0.005), liver cirrhosis (HR 4.251, p < 0.001), osteoporosis (HR 2.165, p = 0.011), chronic bronchitis (HR 2.179, p < 0.001), peptic ulcer disease (HR 1.531, p = 0.024) and cancer (HR 1.797, p < 0.001). Surprisingly, asthma has the opposite effect on survival time (HR 0.574, p = 0.024), but we believe this may be due to an overrepresentation of mild to moderate asthma and its management, which includes educating patients about a healthy lifestyle.

conclusionIn the LIFE-Adult-Study, common risk factors and several diseases had relevant effect on overall survival. However, selection bias in epidemiological studies needs to be considered whenever interpreting results of epidemiological cohort studies. Nevertheless it was shown that the general cause-and-effect principles also apply in this relatively healthy cohort.

Indexed as

AsthmaResearchAdolescentAdultCohort StudiesHumansRisk FactorsSelection Bias

Identifiers

PMID36454725
PMCPMC9714713
OpenAlexW4311070993

What OpenQuestion holds

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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.