Accounting for biases in survey-based estimates of population attributable fractions.

Abstract read

In one paragraph

Article in Population health metrics, 2019. 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

–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

1 citing paper in PubMed.

Sources and severity of bias in estimates of the BMI-mortality association.Population studies · 2023
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

2 authors.

Ryan MastersDepartment of Sociology, Population Program and Health & Society Program, Institute of Behavioral Sciences, University of Colorado Population Center, Boulder, CO, 80309, USA. ryan.masters@colorado.edu.ORCID 0000-0002-6285-4445

Eric ReitherDepartment of Sociology, Social Work, and Anthropology, Utah State University, Logan, USA.

Funding

University of Colorado Population CenterP2CHD066613 · NICHD · UNIVERSITY OF COLORADO · PI JANE A MENKEN · 2015 to 2026

$3.7M

NICHD NIH HHS P2C HD066613

6 · The paper itself

Abstract

backgroundThis paper discusses best practices for estimating fractions of mortality attributable to health exposures in survey data that are biased by observed confounders and unobserved endogenous selection. Extant research has shown that estimates of population attributable fractions (PAF) from the formula using the proportion of deceased that is exposed (PAF

methodsWe review equations for calculating PAF based on either the proportion of deceased (pd) or the proportion of the entire sample (pe) that receives the exposure. We explore how estimates from each equation are affected by confounding bias and selection bias using hypothetical data and real-world survey data from the National Health Interview Survey-Linked Mortality Files, 1987-2011. We examine the association between cigarette smoking and all-cause mortality risk in the US adult population as an example.

resultsWe show that both PAF

conclusionWe recommend that researchers use the PAF

Indexed as

Health StatusSelection BiasBiasChronic DiseaseData CollectionEpidemiologic MethodsHumansRisk AssessmentAttributable fractionsConfounding biasMortalitySelection bias

Identifiers

PMID31830997

DOI10.1186/s12963-019-0196-6

PMCPMC6909532

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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.