Evidence map›Paper›PMID 27112538›Full record

ArticleBMC psychiatry2016

Quantifying the impact of chronic conditions on a diagnosis of major depressive disorder in adults: a cohort study using linked electronic medical records.

Euijung Ryu, Alanna M Chamberlain, Richard S Pendegraft, Tanya M Petterson, William V Bobo, Jyotishman Pathak

Open access · goldAbstract read
In one paragraph

Article in BMC psychiatry, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
4.5field-weighted citation impact, top 5% 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

15 citing papers in PubMed, 34 citations in OpenAlex.

  1. Observational
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Review
  12. Article
  13. Depression risk in patients with late-onset rheumatoid arthritis in Germany.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2017
    Article
  14. On Interestingness Measures for Mining Statistically Significant and Novel Clinical Associations from EMRs.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2016
    Article
  15. 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 at 3 institutions in 1 country.

Euijung RyuDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Alanna M ChamberlainDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Richard S PendegraftDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Tanya M PettersonDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
William V BoboDepartment of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA.
Jyotishman PathakDivision of Health Informatics, Department of Healthcare Policy & Research, Weill Cornell Medicine, Cornell University, New York, NY, USA. pathak@med.cornell.edu.
Mayo Clinic in Florida · USCornell University · USWinnMed · US

Funding

National Infrastructure for Standardized and Portable EHR Phenotyping AlgorithmsR01GM105688 · NIGMS · WEILL MEDICAL COLL OF CORNELL UNIV · PI LUO, YUAN, PATHAK, JYOTISHMAN · 2013 to 2020
$5.7M
Modeling Social Behavior for Healthcare Utilization in DepressionR01MH105384 · NIMH · WEILL MEDICAL COLL OF CORNELL UNIV · PI PATHAK, JYOTISHMAN, SHETH, AMIT P · 2015 to 2018
$2.1M
Understanding the Bidirectional Relationship between Depression and Heart FailureR01HS023077 · AHRQ · MAYO CLINIC ROCHESTER · PI BOBO, WILLIAM V · 2014 to 2014
$400k
AHRQ HHS R01 HS023077NIGMS NIH HHS R01 GM105688NIMH NIH HHS R01 MH105384
6 · The paper itself

Abstract

backgroundMajor depressive disorder (MDD) is often comorbid with other chronic mental and physical health conditions. Although the literature widely acknowledges the association of many chronic conditions with the risk of MDD, the relative importance of these conditions on MDD risk in the presence of other conditions is not well investigated. In this study, we aimed to quantify the relative contribution of selected chronic conditions to identify the conditions most influential to MDD risk in adults and identify differences by age.

methodsThis study used electronic health record (EHR) data on patients empanelled with primary care at Mayo Clinic in June 2013. A validated EHR-based algorithm was applied to identify newly diagnosed MDD patients between 2000 and 2013. Non-MDD controls were matched 1:1 to MDD cases on birth year (±2 years), sex, and outpatient clinic visits in the same year of MDD case diagnosis. Twenty-four chronic conditions defined by Chronic Conditions Data Warehouse were ascertained in both cases and controls using diagnosis codes within 5 years of index dates (diagnosis dates for cases, and the first clinic visit dates for matched controls). For each age group (45 years or younger, between 46 and 60, and over 60 years), conditional logistic regression models were used to test the association between each condition and subsequent MDD risk, adjusting for educational attainment and obesity. The relative influence of these conditions on the risk of MDD was quantified using gradient boosting machine models.

resultsA total of 11,375 incident MDD cases were identified between 2000 and 2013. Most chronic conditions (except for eye conditions) were associated with risk of MDD, with different association patterns observed depending on age. Among 24 chronic conditions, the greatest relative contribution was observed for diabetes mellitus for subjects aged ≤ 60 years and rheumatoid arthritis/osteoarthritis for those over 60 years.

conclusionsOur results suggest that specific chronic conditions such as diabetes mellitus and rheumatoid arthritis/osteoarthritis may have greater influence than others on the risk of MDD.

Indexed as

AdultAgedChronic DiseaseCohort StudiesComorbidityElectronic Health RecordsFemaleHumansLogistic ModelsMajor Depressive DisorderMaleMiddle AgedPrimary Health CareComorbidityDepressionMajor depressive disorderMultimorbidityRelative influenceRisk factors

Identifiers

PMID27112538
PMCPMC4845377
OpenAlexW2344535512

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.