Evidence map›Paper›PMID 30095403›Full record

ArticlePreventing chronic disease2018

Adverse Health Outcomes Among Industrial and Occupational Sectors in Michigan.

Ling Wang, Kenneth Rosenman

Open access · diamondAbstract read
In one paragraph

Article in Preventing chronic disease, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
1.2field-weighted citation impact, top 21% 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Observational
  5. 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 at 1 institution in 1 country.

Ling WangDepartment of Medicine, Michigan State University, 909 Fee Rd, Room 120 West Fee, East Lansing, Michigan 48824. Email: ling.wang@hc.msu.edu.
Kenneth RosenmanDepartment of Medicine, Michigan State University, East Lansing, Michigan.
Michigan State University · US

Funding

Expanded Program in Occupational Injury and Illness SurveillanceU60OH008466 · OH · MICHIGAN STATE UNIVERSITY · PI HARDUAR MORANO, LAUREL · 2005 to 2025
$16.4M
6 · The paper itself

Abstract

introductionWe used data from the Michigan Behavioral Risk Factor Surveillance System (MIBRFSS) to estimate the prevalence of adverse health outcomes by industry and occupation and to examine the association of adverse health outcomes with industry and occupation while controlling for demographics and personal lifestyle behaviors.

methodsWe calculated the prevalence of adverse health outcomes by industry by using data from the 2013-2015 MIBRFSS. Adjusted prevalence of adverse health outcomes was calculated by industry and occupation by using logistic regression for survey design, adjusting for demographics and health behaviors, and was compared with the overall prevalence in all industries and occupations.

resultsThree industries had a significantly higher prevalence of current asthma, diabetes, and depression compared with prevalence among workers employed in all industries. After controlling for confounding factors, only Health Care and Social Assistance had significantly higher prevalence of a health outcome, depression (20.1%). Three occupations had significantly higher prevalence of chronic obstructive pulmonary disease, current asthma, depression, high blood pressure, and diabetes compared with workers employed in all occupations. After adjusting for all confounding factors, only one occupation, protective service, had a significantly higher prevalence of high blood pressure (37.3%) and diabetes (12.8%).

conclusionAdverse health outcomes varied significantly by industry and occupation in Michigan. Employers, policy makers, and health promotion practitioners can use results based on BRFSS to target and prioritize worksite wellness programs. MIBRFSS data also suggested the need for further research to identify why some industries had higher risks for diabetes, hypertension, and depression after controlling for covariates.

Indexed as

Health StatusAdolescentAdultAsthmaBehavioral Risk Factor Surveillance SystemBinge DrinkingFemaleHealth BehaviorHumansHypertensionIndustryMaleMichiganMiddle AgedObesityOccupations

Identifiers

PMID30095403
PMCPMC6093263
OpenAlexW2885120347

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.