Evidence map›Paper›PMID 19763823›Full record

ArticleCommunity mental health journal2009

Antidepressant adequacy and work status among medicaid enrollees with disabilities: a restriction-based, propensity score-adjusted analysis.

Eric G Smith, Alexis D Henry, Jianying Zhang, Fred Hooven, Steven M Banks

Abstract read
PubMed Publisher
In one paragraph

Article in Community mental health journal, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
3.1field-weighted citation impact, top 9% 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, 13 citations in OpenAlex.

  1. Gains in employment status following antidepressant medication or cognitive therapy for depression.The British journal of psychiatry : the journal of mental science · 2015
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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

5 authors at 1 institution in 1 country.

Eric G SmithCenter for Psychopharmacologic Research and Treatment, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA. Eric.Smith@umassmed.edu
Alexis D Henry
Jianying Zhang
Fred Hooven
Steven M Banks
University of Massachusetts Chan Medical School · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Abstract This cross-sectional study of adult survey respondents with disability and depression (n = 199) enrolled in Massachusetts' Medicaid program examined the association of adequately or inadequately prescribed antidepressant treatment and self-reported work status using conditional logistic regression, controlling for age, gender, race, marital status, education, receipt of SSI/SSDI, self-reported disabling condition, and health status. Confounding by severity was addressed by two methods: restriction of our sample and subsequent stratification by propensity score. Individuals receiving adequate antidepressant treatment had an increased odds of working compared to individuals receiving inadequate treatment, both in analyses in which restriction was used to limit confounding (OR = 3.45, 95% CI = 1.15-10.32, P < .03), and in analyses which combined restriction with adjustment by propensity score stratification (OR = 3.04, 95% CI = 1.01-9.62, P < .05). Among this sample of Medicaid enrollees with disability and depression, those receiving adequate antidepressant treatment were significantly more likely to report working.

Indexed as

EmploymentMedicaidPropensity ScoreAdultAntidepressive AgentsCross-Sectional StudiesDepressionFemaleHumansMaleMassachusettsMiddle AgedPersons with DisabilitiesTreatment OutcomeUnited StatesAntidepressive Agents

Identifiers

PMID19763823
OpenAlexW1999507167

What OpenQuestion holds

Textmetadata
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.