Evidence map›Paper›PMID 36716082›Full record

ArticleJMIR mental health2023

Leveraging Symptom Search Data to Understand Disparities in US Mental Health Care: Demographic Analysis of Search Engine Trace Data.

Ben Rochford, Sachin Pendse, Neha Kumar, Munmun De Choudhury

Open access · goldAbstract read
In one paragraph

Article in JMIR mental health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
2.0field-weighted citation impact, top 13% 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

2 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. 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

4 authors at 1 institution in 1 country.

Ben Rochford *School of Interactive Computing, College of Computing, Georgia Institute of Technology, Atlanta, GA, United States.ORCID https://orcid.org/0000-0002-3405-2189
Sachin Pendse *School of Interactive Computing, College of Computing, Georgia Institute of Technology, Atlanta, GA, United States.ORCID https://orcid.org/0000-0001-6925-3258
Neha KumarSchool of Interactive Computing, College of Computing, Georgia Institute of Technology, Atlanta, GA, United States.ORCID https://orcid.org/0000-0002-7014-5585
Munmun De ChoudhurySchool of Interactive Computing, College of Computing, Georgia Institute of Technology, Atlanta, GA, United States.ORCID https://orcid.org/0000-0002-8939-264X
Georgia Institute of Technology · US

Funding

Leveraging Social Media Data and Machine Learning to Optimize Treatment Paradigms for Youth with SchizophreniaR01MH117172 · NIMH · GEORGIA INSTITUTE OF TECHNOLOGY · PI DE CHOUDHURY, MUNMUN · 2019 to 2022
$2.6M
NIMH NIH HHS R01 MH117172
6 · The paper itself

Abstract

backgroundIn the United States, 1 out of every 3 people lives in a mental health professional shortage area. Shortage areas tend to be rural, have higher levels of poverty, and have poor mental health outcomes. Previous work has demonstrated that these poor outcomes may arise from interactions between a lack of resources and lack of recognition of mental illness by medical professionals.

objectiveWe aimed to understand the differences in how people in shortage and nonshortage areas search for information about mental health on the web.

methodsWe analyzed search engine log data related to health from 2017-2021 and examined the differences in mental health search behavior between shortage and nonshortage areas. We analyzed several axes of difference, including shortage versus nonshortage comparisons, urban versus rural comparisons, and temporal comparisons.

resultsWe found specific differences in search behavior between shortage and nonshortage areas. In shortage areas, broader and more general mental health symptom categories, namely anxiety (mean 2.03%, SD 0.44%), depression (mean 1.15%, SD 0.27%), fatigue (mean 1.21%, SD 0.28%), and headache (mean 1.03%, SD 0.23%), were searched significantly more often (Q<.0003). In contrast, specific symptom categories and mental health disorders such as binge eating (mean 0.02%, SD 0.02%), psychosis (mean 0.37%, SD 0.06%), and attention-deficit/hyperactivity disorder (mean 0.77%, SD 0.10%) were searched significantly more often (Q<.0009) in nonshortage areas. Although suicide rates are consistently known to be higher in shortage and rural areas, we see that the rates of suicide-related searching are lower in shortage areas (mean 0.05%, SD 0.04%) than in nonshortage areas (mean 0.10%, SD 0.03%; Q<.0003), more so when a shortage area is rural (mean 0.024%, SD 0.029%; Q<2 × 10

conclusionsThis study demonstrates differences in how people from geographically marginalized groups search on the web for mental health. One main implication of this work is the influence that search engine ranking algorithms and interface design might have on the kinds of resources that individuals use when in distress. Our results support the idea that search engine algorithm designers should be conscientious of the role that structural factors play in expressions of distress and they should attempt to design search engine algorithms and interfaces to close gaps in care.

Indexed as

digital mental healthhealth equitymental healthsearch engine algorithms

Identifiers

PMID36716082
PMCPMC9926343
OpenAlexW4309442889

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.