Evidence map›Paper›PMID 36936582›Full record

ArticleBMJ medicine2022

Environmental risk factors for non-Hodgkin's lymphoma: umbrella review and comparison of meta-analyses of summary and individual participant data.

Xiaoting Shi, Haoran Zhuo, Yuxuan Du, Kate Nyhan, John Ioannidis, Joshua D Wallach

Abstract read
In one paragraph

Article in BMJ medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Experimental and therapeutic medicine · 2026
    Article
  3. Review
  4. Article
  5. Review
  6. Article
  7. Article
  8. 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

6 authors.

Xiaoting ShiDepartment of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA.ORCID 0000-0002-5256-0646
Haoran ZhuoDepartment of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA.
Yuxuan DuDepartment of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA.
Kate NyhanDepartment of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA.ORCID 0000-0001-5397-2303
John IoannidisDepartment of Medicine, of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, and Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA, USA.ORCID 0000-0003-3118-6859
Joshua D WallachDepartment of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA.ORCID 0000-0002-2816-6905

Funding

Identifying Patient Subgroups That Are Most Likely To Benefit From Medications Used To Treat Alcohol Use DisorderK01AA028258 · NIAAA · YALE UNIVERSITY · PI WALLACH, JOSHUA DAVID · 2021 to 2025
$892k
NIAAA NIH HHS K01 AA028258
6 · The paper itself

Abstract

Objectives: To summarise the range, strength, and validity of reported associations between environmental risk factors and non-Hodgkin's lymphoma, and to evaluate the concordance between associations reported in meta-analyses of summary level data and meta-analyses of individual participant data. Design: Umbrella review and comparison of meta-analyses of summary and individual participant level data. Data sources: Medline, Embase, Scopus, Web of Science Core Collection, Cochrane Library, and Epistemonikos, from inception to 23 July 2021. Eligibility criteria for selecting studies: English language meta-analyses of summary level data and of individual participant data evaluating associations between environmental risk factors and incident non-Hodgkin's lymphoma (overall and subtypes). Data extraction and synthesis: Summary effect estimates from meta-analyses of summary level data comparing ever versus never exposure that were adjusted for the largest number of potential confounders were re-estimated using a random effects model and classified as presenting evidence that was non-significant, weak (P<0.05), suggestive (P<0.001 and >1000 cases), highly suggestive (P<0.000001, >1000 cases, largest study reporting a significant association), or convincing (P<0.000001, >1000 cases, largest study reporting a significant association, I Results: We identified 85 meta-analyses of summary level data reporting 257 associations for 134 unique environmental risk factors and 10 subtypes of non-Hodgkin's lymphoma nearly all (79, 93%) were classified as having critically low quality. Most associations (225, 88%) presented either non-significant or weak evidence. The 11 (4%) associations presenting highly suggestive evidence were primarily for autoimmune or infectious disease related risk factors. Only one association, between history of coeliac disease and risk of non-Hodgkin's lymphoma, presented convincing evidence. Of 40 associations reported in meta-analyses of summary level data that were also evaluated in InterLymph meta-analyses of individual participant data, 22 (55%) pairs were in the same direction, had the same level of statistical significance, and had overlapping 95% confidence intervals; 28 (70%) pairs had summary effect sizes from the meta-analyses of individual participant data that were more conservative. Conclusion: This umbrella review suggests evidence of many meta-analyses of summary level data reporting weak associations between environmental risk factors and non-Hodgkin's lymphoma. Improvements to primary studies as well as evidence synthesis in evaluations of evironmental risk factors and non-Hodgkin's lymphoma are needed. Review registration number: PROSPERO CRD42020178010.

Indexed as

epidemiologylymphoma

Identifiers

PMID36936582
PMCPMC9978687

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.