Evidence map›Paper›PMID 30291640›Full record

ArticleAmerican journal of industrial medicine2018

Decision rule approach applied to estimate occupational lead exposure in a case-control study of kidney cancer.

Catherine L Callahan, Sarah J Locke, Pamela J Dopart, Patricia A Stewart, Kendra Schwartz, Julie J Ruterbusch, Barry I Graubard, Nathaniel Rothman, Jonathan N Hofmann, Mark P Purdue and 1 more

Abstract read
In one paragraph

Article in American journal of industrial medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Review
  5. Article
  6. 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

11 authors.

Catherine L CallahanOccupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology Genetics, National Cancer Institute, Bethesda, Maryland.ORCID 0000-0002-6835-0571
Sarah J LockeOccupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology Genetics, National Cancer Institute, Bethesda, Maryland.
Pamela J DopartOccupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology Genetics, National Cancer Institute, Bethesda, Maryland.
Patricia A StewartStewart Exposure Assessments, LLC, Arlington, Virginia.
Kendra SchwartzDepartment of Family Medicine and Public Health Sciences, Karmanos Cancer Institute, Wayne State University, Detroit, Michigan.
Julie J RuterbuschDepartment of Family Medicine and Public Health Sciences, Karmanos Cancer Institute, Wayne State University, Detroit, Michigan.
Barry I GraubardBiostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland.
Nathaniel RothmanOccupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology Genetics, National Cancer Institute, Bethesda, Maryland.
Jonathan N HofmannOccupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology Genetics, National Cancer Institute, Bethesda, Maryland.
Mark P PurdueOccupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology Genetics, National Cancer Institute, Bethesda, Maryland.
Melissa C FriesenOccupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology Genetics, National Cancer Institute, Bethesda, Maryland.

Funding

Tumor Biology and Microenvironment (Program 1)P30CA022453 · NCI · WAYNE STATE UNIVERSITY · PI PAUL M STEMMER · 1985 to 2026
$68.4M
Intramural Research ProgramNCI NIH HHS P30 CA022453NIH HHS
6 · The paper itself

Abstract

backgroundWe developed a systematic, data-driven approach to estimate metrics of occupational exposure to lead to aid in epidemiologic analyses in a case-control study of kidney cancer.

methodsProbability of exposure to ten lead sources was assigned using decision rules developed from an extensive literature review and expert judgement. For jobs with >50% probability of exposure, we assigned source-specific frequency based on subjects' self-reported task frequencies or means of subjects' job-groups and source-specific intensity estimates of blood lead (μg/dL).

resultsIn our study, 18.7% of employed person-years were associated with high (≥80%) probability of exposure to any lead source. The most common medium (>50%) or high probability source of lead exposure was leaded gasoline (2.5% and 11.5% of employed person-years, respectively). The median blood lead attributed to occupational exposure was 3.1 μg/dL.

conclusionsThese rules can aid in future studies after population-specific adaption for geographic differences and different exposure scenarios.

Indexed as

Decision Support TechniquesAdultCase-Control StudiesFemaleHumansKidney NeoplasmsLeadLead PoisoningMaleMiddle AgedOccupational DiseasesOccupational ExposureProbabilityLeaddecision-rulesexposure assessmentleadoccupational exposurepopulation-based studies

Identifiers

PMID30291640
PMCPMC6849374

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.