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Record W2912302539 · doi:10.1111/add.14557

Commentary on Costanzo <i>et al</i>. (2019): The need for secondary analyses of prospective cohort studies—creating a better understanding of alcohol consumption and hospitalization

2019· letter· en· W2912302539 on OpenAlexafffundabout
Kevin D. Shield, Jürgen Rehm

Bibliographic record

VenueAddiction · 2019
Typeletter
Languageen
FieldMedicine
TopicAlcohol Consumption and Health Effects
Canadian institutionsPublic Health OntarioUniversity of TorontoCentre for Addiction and Mental Health
FundersCanadian Institute for Advanced Research
KeywordsMedicineCohortCohort studyEnvironmental healthHarmConsumption (sociology)Prospective cohort studyPoison controlDemographyMedical emergencyPsychologySurgery

Abstract

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The risk relationsip between alcohol consumption and disease occurrence often differs depending on whether the end-point used is mortality or morbidity. More studies using morbidity as an end-point are required to avoid incorrect results when estimating the burden of alcohol-related harm. Cohort study networks offer opportunities for conducting such studies. Alcohol consumption is a leading risk factor for the burden of disease 1, 2, and a large proportion of the alcohol-attributable burden is due to disability and impaired functioning 1-3. However, the majority of cohort studies which examine the risk relationship (RR) between alcohol consumption and disease occurrence use mortality as an end-point 4 because mortality is easier to assess as its recording is often mandated by law, while the measurement of non-fatal end-points are more resource-intensive to collect and often are not mandated by law 4, 5. For example, studies using hospitalization as an end-point require a central collection and collation of hospitalization records, and for these records to be linked to a unique personal identifier. However, in many countries and regions, hospitalization services are administered by multiple companies with different data access and data organization policies. Under such circumstances, the resources needed to collect and harmonize hospitalization data from multiple sources are often cost- and resource-prohibitive. Consequently, for meta-analyses, data on the effects of alcohol on morbidity alone are not sufficiently available to produce accurate and precise RR estimates. Accordingly, for most disease conditions, meta-analyses combine studies which use mortality and morbidity as end-points 3, 4. Previous studies have found evidence that the alcohol RRs for various chronic diseases 6-9 may differ depending on whether mortality or morbidity is used as the end-point. In line with this, the Moli-sani study differs from mortality-based studies 3, 19 in that heavy alcohol consumers were found not to be at an increased risk of hospitalization from ischaemic heart disease and cerebrovascular accidents. These differences in the RRs by disease end-point are due to potentially confounding factors (e.g. the stage at which the diseases are diagnosed and treated, access to health care, heavy drinkers avoiding hospitals due to stigmatization by health-care workers 10-14 and/or factors affected by alcohol consumption (e.g. adherence to treatment regimens 15. Therefore, the combining of mortality and morbidity RRs may lead to incorrect results in studies which utilize both these RRs, such as the 2018 Global Status Report on Alcohol and Health 1, the 2017 Global Burden of Disease study 2 and other studies which have estimated hospitalizations and hospitalization costs attributable to alcohol 16-18. The data analysis of the Moli-sani study by Costanzo and colleagues 19 demonstrates the availability of opportunities to perform analyses of cohort studies which are linked to hospital discharge registries. As a result of the observation that there are different alcohol RRs for morbidity and mortality, there is a need for additional analyses of cohort studies linked to hospitalization records to examine the association of alcohol with specific alcohol-related diseases. Such analyses could take advantage of existing cohort study networks. An example of a large cohort study network is the European Prospective Investigation into Cancer and Nutrition (of which the Moli-sani study is a participant). Although this cohort study network was originally designed to examine the RRs for various risk factor exposures and the development of cancer, such a network can be used for the secondary purpose of assessing the RR for alcohol consumption and hospitalization. These sorts of analyses will lead to improvements in our understanding of how alcohol consumption leads to disease occurrence and, further, will improve the evidence base for studies which model how best to reduce morbidity, hospitalizations and hospital costs due to alcohol consumption. None. K.D.S. receives funding from the 2018 Canadian Institutes of Health Research - Institute of Population and Public Health Trailblazer Award in Population and Public Health Research.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.489
Threshold uncertainty score0.891

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.131
GPT teacher head0.408
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2019
Admission routes3
Has abstractyes

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