Discrimination and Suicidality Among Hispanic Mental Health Patients, 2010–2020: A Natural Language Processing Approach

Goldstein, Evan V.; Bailey, Elise Vandersteen; Wilson, Fernando A.

doi:10.1176/appi.ps.20220240

Cited by 4 publications

(13 citation statements)

References 3 publications

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“…More than half of the papers were from the US or incorporated populations based in the US, four were from Europe, and the rest were from Asia. Among the 13 articles, ve focused on refugee populations (32)(33)(34)(35)(36), three focused on Hispanic populations in the US (31,37,38), two focused on black individuals (39, 40), one on Native Americans (41), and the last two articles focused on Korean immigrants in the US (42) and immigrant populations in Europe (43). The areas of mental health focus included stress (35), ADHD (39,40), trauma (32,33), depression (36,38,40), PTSD (34), psychological distress (42), schizophrenia (43), suicidal ideation (37,41), and substance abuse (31).…”

Section: Publication Characteristicsmentioning

confidence: 99%

“…Surveys (32,34,36,42), drawings (33), secondary data sets (including EHR data, surveillance data, and national sample sets) (31,33,37,41,43), internet-based posts (35,38), and genomic sequencing data (39,40) were analyzed in the included publications (see Table 2). Various populations were considered, and sample sizes varied widely due to the type of data collected and analyzed.…”

Section: Publication Characteristicsmentioning

confidence: 99%

“…Table 3 outlines a summary of ML characteristics and model performance. This review found that all 13 included publications fell into three categories: classi cation (31,35,37,(39)(40)(41)(42)(43), regression (32)(33)(34)36), and unsupervised topic modeling (38).…”

Section: Machine Learning Model Performance and Characteristicsmentioning

confidence: 99%

“…Two studies did not report the best algorithm performance (37,38). For the other studies, accuracy and AUC were commonly reported.…”

Section: Machine Learning Model Performance and Characteristicsmentioning

confidence: 99%

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Machine Learning Applications in Studying Mental Health Among Immigrants and Racial and Ethnic Minorities: An Exploratory Review

Park,

Ahmed,

Al-Garadi

2023

Preprint

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Background The use of machine learning (ML) in mental health (MH) research is increasing, especially as new, more complex data types become available to analyze. By examining the published literature, this review aims to explore potential gaps in the current use of ML to study MH in vulnerable populations of immigrants, refugees, migrants, and racial and ethnic minorities. Methods From October 2022 to June 2023, Google Scholar, EMBASE and PubMed were queried. ML-related terms, MH-related terms, and population-of-focus search terms were strung together with Boolean operators. Backward reference searching was also conducted. Included peer-reviewed studies reported using a method or application of ML in an MH context and focused on the populations of interest. We did not have date cutoffs. Publications were excluded if they were narrative or did not exclusively focus on a minority population from the respective country. Data including study context, the focus of mental healthcare, sample, data type, type of ML algorithm used, and algorithm performance was extracted from each. Results Ultimately, 13 peer-reviewed publications were included. All the articles were published within the last 6 years, and over half of them studied populations within the US. Most reviewed studies used supervised learning to explain or predict MH outcomes. Some publications used up to 16 models to determine the best predictive power. Almost half of the included publications did not discuss their cross-validation method. Conclusions The included studies provide proof-of-concept for the potential use of ML algorithms to address MH concerns in these special populations, few as they may be. Our review finds that the clinical application of these models for classifying and predicting MH disorders is still under development.

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Section: Publication Characteristicsmentioning

confidence: 99%

Section: Publication Characteristicsmentioning

confidence: 99%

Section: Machine Learning Model Performance and Characteristicsmentioning

confidence: 99%

“…Two studies did not report the best algorithm performance (37,38). For the other studies, accuracy and AUC were commonly reported.…”

Section: Machine Learning Model Performance and Characteristicsmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning Applications in Studying Mental Health Among Immigrants and Racial and Ethnic Minorities: An Exploratory Review

Park,

Ahmed,

Al-Garadi

2023

Preprint

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“…Suicide risks experienced by Hispanic populations are often driven by social determinants, including mental health stigma, discrimination, acculturation, and language or literacy challenges (Eghaneyan & Murphy, 2020; Flavin et al, 2018; Goldmann et al, 2017; Goldstein et al, 2022). Suicide risks can also vary across nationalities and immigration status (Goldstein & Wilson, 2022; Silva & Van Orden, 2018), as do lifetime suicidality rates (Borges et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Poverty and Suicidal Ideation Among Hispanic Mental Health Care Patients Leading up to the COVID-19 Pandemic

Goldstein

Bailey

Wilson

2023

Hisp Health Care Int

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Introduction: Suicide rates have risen in Hispanic communities since 2015, and poverty rates among Hispanics often exceed the national average. Suicidality is a complex phenomenon. Mental illness may not alone explain whether suicidal thoughts or behaviors will occur; it remains uncertain how poverty affects suicidality among Hispanic persons with known mental health conditions. Our objective was to examine whether poverty was associated with suicidal ideation among Hispanic mental healthcare patients from 2016 to 2019. Methods: We used de-identified electronic health record (EHR) data from Holmusk, captured using the MindLinc EHR system. Our analytic sample included 4,718 Hispanic patient-year observations from 13 states. Holmusk uses deep-learning natural language processing (NLP) algorithms to quantify free-text patient assessment data and poverty for mental health patients. We conducted a pooled cross-sectional analysis and estimated logistic regression models. Results: Hispanic mental health patients who experienced poverty had 1.55 greater odds of having suicidal thoughts in a given year than patients who did not experience poverty. Conclusion: Poverty may put Hispanic patients at greater risk for suicidal thoughts even when they are already receiving treatment for psychiatric conditions. NLP appears to be a promising approach for categorizing free-text information on social circumstances affecting suicidality in clinical settings.

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Critical gaps in understanding firearm suicide in Hispanic communities: demographics, mental health, and access to care

Goldstein

Brenes

Wilson

2023

Health Affairs Scholar

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Suicide rates increased by 26.7% among Hispanics from 2015-2020, driven at least in part by highly-lethal firearm suicide deaths. However, there are critical gaps in characterizing firearm suicide risks and prevention opportunities in Hispanic communities. We examined Hispanic adult firearm suicide decedents reported through the National Violent Death Reporting System from 2013-2019, focusing on demographic characteristics, firearm choices, suicidal thoughts/behaviors, mental health, and mental health treatment, compared to non-Hispanic adult firearm suicide decedents. Only 13.8% of Hispanic firearm suicide decedents were known to be undergoing treatment for a mental health or substance use problem prior to death, compared to 18.8% of non-Hispanic firearm suicide decedents. On average, Hispanic firearm suicide decedents were 19.0% less likely than non-Hispanic firearm suicide decedents known to have been treated for a mental health or substance use problem. These results may underscore the critical need for public health agencies and policymakers to promote initiatives integrating mental health screening into medical care, reducing mental health stigma among Hispanics, and expanding mental health treatment capacity in Hispanic communities.

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Discrimination and Suicidality Among Hispanic Mental Health Patients, 2010–2020: A Natural Language Processing Approach

Cited by 4 publications

References 3 publications

Machine Learning Applications in Studying Mental Health Among Immigrants and Racial and Ethnic Minorities: An Exploratory Review

Machine Learning Applications in Studying Mental Health Among Immigrants and Racial and Ethnic Minorities: An Exploratory Review

Poverty and Suicidal Ideation Among Hispanic Mental Health Care Patients Leading up to the COVID-19 Pandemic

Critical gaps in understanding firearm suicide in Hispanic communities: demographics, mental health, and access to care

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