Automatically extracting social determinants of health for suicide: a narrative literature review

Abstract

Suicide is a complex phenomenon that is often not preceded by a diagnosed mental health condition, therefore making it difficult to study and mitigate. Artificial Intelligence has increasingly been used to better understand Social Determinants of Health factors that influence suicide outcomes. In this review we find that many studies use limited SDoH information and minority groups are often underrepresented, thereby omitting important factors that could influence risk of suicide.

Full text

npj | mentalhealth research Review
https://doi.org/10.1038/s44184-024-00087-6
Automatically extracting social
determinants of health for suicide: a
narrative literature review
Check for updates
Annika M. Schoene1, Suzanne Garverich2,ImanIbrahim
2,SiaShah
2, Benjamin Irving1&
Clifford C. Dacso3,4,5
Suicide is a complex phenomenon that is often not preceded by a diagnosed mental health condition,
therefore making it difficult to study and mitigate. Artificial Intelligence has increasingly been used to
better understand Social Determinants of Health factors that influence suicide outcomes. In this review
we find that many studies use limited SDoH information and minority groups are often
underrepresented, thereby omitting important factors that could influence risk of suicide.
There has been an increased use of Natural Language Processing (NLP) and
Machine Learning (ML), sub-disciplines in AI, in understanding and
identifying suicidal ideation, behavior, risk and attempts in both public and
clinical contexts. Suicide is one of the leading causes of death for people aged
15-29; over 700,000 people die every year as of 20191. For every person who
has died by suicide, there are many more who attempt suicide, which
increases their risk of dying by suicide in the future2.
The onset and progression of mental health issues have been linked to a
person’s social, economic, political, and physical circumstances3.These
circumstances have been summarized in a framework of social factors
referred to as Social Determinants of Health (SDoH)4where each factor is
considered a driving force behind adverse health outcomes and inequalities
(e.g.: hospitalization, increased mortality and lack of access to treatment)5.
SDoH also affects people living with mental health conditions6,where
addressing these inequalities across all stages of a person’s life at an indi-
vidual, local and national level are vital to reduce the number of suicide
attempts and deaths by suicide overall7.Recentwork
6highlights that there is
a set of factors and circumstances that are unique to individuals living with
mental health concerns, calling for an expansion of traditional SDoH
categories to a new subset called Social Determinants of Mental Health
(SDoMH). The use of NLP and ML to extract SDoH in the contex t of suicide
is sparsely investigated. Work in this space has predominantly focused on
reviewing (i) AI for mental health dete ction and understanding8,9,(ii)theuse
of NLP and ML to predict suicide, suicidal ideation or attempts without
considering social factors10–25, (iii) the use of NLP to extract SDoH without
focusing on specific mental health incidents26,27, where suicide is one of
many possible health outcomes or (iv) focus on suicidality in context of
specificSDoMH’swithouttheuseofAI
13,28–30. It is also well understood that
there are some groups that are underrepresented, which leads to greater
health disparities31, which include but are not limited to groups of racial and
ethnic minorities, underserved rural communities, sexual and gender
minorities and people with disabilities. This underrepresentation can have
great impacts on NLP and ML methods (e.g.: issues of bias and fairness).
In this literature review we analyze 94 studies at the intersection of
suicide and SDoH and aim to answer the following research questions:
•What NLP and ML methods are used to extract SDoH from textual
data and what are the most common data sources?
•What are the most commonly identified SDoH factors for suicide?
•What socio-demographic groups are these algorithms developed from
and for?
•What are the most frequently used health factors and behaviors in NLP
and ML algorithms?
Methodology
Data extraction and categories for review
For each paper in this review, we extracted metadata to identify overall
trends in the collection. Data extraction is divided into four main categories,
aiming to capture (i) general information about the NLP or ML method and
data used, (ii) SDoH and SDoMH variables, (iii) socio-demographic factors,
and (iv) physical, mental, and behavioral health factors.
General information. The year of publication, the data source for the
presented study, and what type of method has been used (e.g.: NLP or ML)
are captured.
Social determinants of (Mental) health. In this category we draw on
previous and include general SDoH categories32 and categories that dis-
proportionately affect people living with mental health disorders
(SDoMH)5,6. For this, we grouped factors into broader categories called
Social, Psychosocial and Economic, where within each category we
1Northeastern University, Institute for Experiential AI, Boston, USA. 2Northeastern University, Institute for Health Equity and Social Justice Research, Boston, USA.
3Medicine Baylor College of Medicine, Houston, USA. 4Electrical and Computer Engineering Rice University, Houston, USA. 5Knox Clinic, Rockland, Maine, USA.
e-mail: a.schoene@northeastern.edu
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capture more granular factors as outlined in Table 1. In this work, we use
SDoH as the overarching term to describe such factors, where SDoMH
factors are included and our categories were developed empirically
through the wording and descriptions used in the works we reviewed.
However, we would like to note that as we attempted to categorize each
SDoH in the context of mental health we were not able to find clear
distinctions between some terminology/categories in the literature. For
example, many works use references to adverse life events and trauma as
mutually exclusive categories and others use it as the same category. It is
beyond the scope of this review, to make such a distinction but should be
examined by further research.
Sociodemographic factors. We capture basic demographic informa-
tion about research participants, such as age, sex, and marital status.In
addition to this, we also include factors for groups that are at greater risk
of health disparities, such as gender, sexuality, disability, race and
ethnicity31.
Health factors. In addition to the aforementioned categories, we also
label each paper for a set of health conditions, treatments, behaviors and
outcomes that were frequently mentioned as referenced in the original
paper. This includes references to Physical, neurocognitive and mental
health conditions, the use of psychiatric medications,intreatments (e.g.:
stay in hospital, outpatient, ED visits, admissions), previous attempts,
history of self-harm, aggressive/antisocial behavior, substance abuse, level
of physical activity/overall health (e.g.: BMI, weight changes) and risky
behavior. Prior research33–35 has shown that such factors can significantly
increases a persons’risk of suicide.
Search strategy and query. We retrieved 1,661 bibliographic records in
December 2023 from two scientific databases (PubMed and the
Anthology of the Association for Computational Linguistics). We use the
following search queries on two popular scientific database (PubMed and
ACL Anthology) and retrieved 1,585 and 76 papers respectively:
(Natural Language Processing OR Information Extraction OR Infor-
mation Retrieval OR Text Mining OR Machine Learning OR Deep Learning)
AND (Suicide OR Suicidality OR Suicidal) AND (Social Determinants of
Health OR behavioral determinants of health)
Due to the different layout of the ACL Anthology (https://aclanthology.
org/) we use a combination of terms to identify related literature:
Table 1 | Description of Social Determinants of Health categories
Category Sub category Examples
Social Relationships and social support •Being in a romantic relationship
•Level of support in relations (incl. Romantic, platonic, family and at work)
•Breakdown of relationships
•References to family life
Social Exclusion
Stigmatization
Religion / Culture
•Discrimination based on protected characteristics
•Cultural or family attitudes towards suicide and mental health
•The influence of a person’s belief system on mental health (e.g.: religious views on suicide)
Level of socialization •Lack of social connections
•No or low social support
•Feeling lonely
•Social isolation / loneliness (e.g.: Beck’s scale of Belonging67)
Psychosocial Adverse life experiences •Abortion
•Bullying
•Responsible of enemy death
Abuse / Trauma [adult] •Sexual, physical harassment and/ or abuse
•Military
•Death of family or loved one / witnessing suicide
•Full lista
Legal issues [adult] •Incarceration
•Ongoing criminal investigations
Abuse / Trauma [childhood] •Parents’divorce or losing a caregiver
•Foster care
•Bullying
Adverse childhood experiences •Unspecified (e.g.: the reviewed literature does not give detailed examples)
Imprisonment/criminal behavior [teens] •Trouble with the law during childhood/ teens
•Time spent in jail
Economic Housing Insecurity •Living with others
•Having housing insecurity
•Homelessness (in the past or present)
Build environment / Neighborhood •Urban vs Rural
Access to healthcare / insurance •Type of payer
•Level of coverage
Education •Level of education
Occupation •Type of job held now or in the past (e.g.: doctor, soldier, legal professional…)
Employment status •Unemployment, lack of security in employment
•Level of employment (e.g.: military: active duty, deployed, etc.)
Socioeconomic •Income level
•Financial difficulties or pressures
Food Insecurity •Lack of access to food, changes in food intake
ahttps://www.ptsd.va.gov/professional/assessment/documents/LEC5_Standard_Self-report.PDF.
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Suicide +keyword or Suicide +related keyword
Our queries were constructed to capture as many relevant SDoH fac-
tors by name and also include related terms (see Table 2for a full overview of
keywords). Similarly to26,wefirst surveyed existing literature for SDOH
related keywords, where we identified a total of 17 relevant keywords for our
search.
Filtering and review strategy. First, we removed duplicates and inclu-
ded all papers that have been peer-reviewed, published as a full text, in
English between 2013 and 2023. Next, we screened both title and abstract
using RobotAnalyst36 to reduce human workload. RobotAnalyst is a web-
based software system that uses both text mining and machine learning
methods to prioritize papers for their relevance based on human feedback
(Free access to RobotAnalyst can be requested to reproduce this work
here: http://www.nactem.ac.uk/robotanalyst/). For this, an iterative
classification approach is used and RobotAnalyst was retrained six times
during our screening process. We developed a set of inclusion and
exclusion criteria to screen 452 papers that were predicted to be relevant
by RobotAnalyst. Both title and abstract were screened for each paper
based on the following exclusion and inclusion criteria:
Inclusion criteria:
I. Papers that focus on the use of NLP or ML to extract SDOHs related to
suicide
II. Published work and studies that take place in English-speaking
countries, including USA, United Kingdom, Australia, and Ireland
Exclusion criteria:
I. Papers that only focus on suicide and SDOH without NLP or ML
methods
II. All retracted papers
III. Workshop proceedings, commentaries, proposals, previous literature
reviews
IV. Papers focusing on unintentional death or homicides, methods of
suicide
V. Research that proposes new ideas, policies or intervention for suicide
with or without the use of Artificial Intelligence
Based on our inclusion and exclusion criteria, we retrieved 94 papers
for a full review. In Fig. 1we show the full workflow of our search and filter
strategy.
Findings
General Information
We generated a Sankey diagram37 to give a comprehensive overview of how
many articles in our collection were assigned to each category. Figure 2
shows each rectangle node as a metadata category, where the node height
represents the value and each line is proportional to the value. For example,
we can see that both age and ethnicity are often considered as important
factor alongside the economic SDoH, and mental health as a health factor.
Figure 3illustratestheoverallnumberofpublicationsandtypeof
methodsusedovertime,reflecting trends over the last 10 years. Here, we can
see (i) that research is increasingly focusing on SDOH factors to better
understand suicide and (ii) that Machine Learning (ML) based methodol-
ogy are used more over time. This highlights not just the rising popularity of
NLP and ML methods, but also their usefulness to understand and extract
additional information at scale . At the same time, it is important to note that
research in Information Extraction has also grown over recent with the
advancements of new AI and NLP methods.
Data types and methods. In our collection of papers, two main
approaches are typically used to predict a person’s risk of suicide or
suicidal behavior. Feature-based approaches utilize information, such as a
person’s demographic characteristics, frequency of treatments or other
related health behaviors as input into an algorithm, whereas NLP
approaches take advantage of language (e.g.: written online posts or
EHRs) to gain insight into how suicidal ideation is expressed to predict
risk. Within these approaches, we further distinguish between (i) tradi-
tional machine learning methods, such as linear/logistic regression,
Decision Trees, or Support Vector Machines (SVM), (ii) deep learning
methods (e.g.: artificial neural networks (ANN), CNNs (Convolutional
Neural Networks) or Transformers) and (iii) unsupervised learning
methods, such as topic modeling, to discover patterns from unlabeled
data. Finally, some studies have utilized existing out-of-the box tools and
software to conduct experiments and analysis and two papers did not
disclose their full approach. In Table 3, we categorize each paper in our
collection according to the methodology used.
Table 2 | Overview of keywords used in our literature search
Keywords Related keywords
smoking Cigarette, cigarette use, tobacco, tobacco use, nicotine
Alcohol Alcohol abuse, alcohol withdrawal, alcohol consume,
alcoholism, alcohol disorder
Drug Drug use, drug abuse
Substance abuse Substance use, Substance misuse
Diet Nutrition, food
Exercise Activity, physical activity
Sexual activity Sex, sexual
Child abuse Child, children
Abuse Abusive, trauma
Healthcare Quality care, access to care, access to primary care, access
health care, insurance
Education Educational stress, studying
Income Financial condition, financial constraint, economic condition
Work Occupation, working condition, employment,
unemployment
Social support Social isolation, social connections, social cohesion
Relationships Family, friends, friendship, relationship, mother, father,
sister, brother, sibling
Food security Food insecurity
Housing Homeless, homelessness, living condition
Fig. 1 | Overview of article selection process.
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Furthermore, we find that the most commonly used data source to
extract data from are Electronic Health Records (EHR) among the reviewed
papers, closely followed by surveys and interviews (see Fig. 4). Multiple data
sources refer to work that utilizes more than one type of data in their work,
such as text data (e.g.: insurance claims) in combination with traditional
clinical data38. Other data sources include audio recordings39, newspaper
articles40, and mobile data collected via an app or smart device41.
Social Determinants of (Mental) Health and socio-demographic
factors
For each paper in our collection, we extracted SDoH and socio-
demographic information for the population studied, where in Fig. 5
shows a heatmap that indicates the number of papers with combinations of
SDoH and socio-demographic information. The diagonal indicates the total
number of papers focusing on a single variable and there are a total of 18
SDoHs and 8 socio-demographic variables shown.
For SDoH variables the majority of papers focus on different types of
abuse or trauma that has been experienced in adulthood (57.44%), followed
by socioeconomic issues (36.17%), issues in relationships, and type of
occupation held (31.91%). Very few works investigate the importance of
legal issues experienced either in childhood (3.19%)oradulthood(7.44%),
or the impact of discrimination (2.12%) and food insecurity (2.21%). For
socio-demographic information we have found that the vast majority of
papers focus on age (22.9%), race (19.3%)andethnicity(19.3%). Only very
few papers focus on other variables, such as sex (12.7%), gender (11.3%),
marital status (10.5%), sexual orientation (2.5%) and disability (1.5%).
However, the vast majority of papers focus on the intersection of multiple
socio-demographic factors. This is particularly important as previous
research has shown42 how multiple elements of a person’sidentity(e.g.,
gender, race and age) can lead to compounded discrimination. Here it is
noticeable that (i) most papers only control for age, race/ethnicity, sex/
gender factors and (ii) very little research is investigating the impact of
sexual orientation and disability in relation to any of the other frequently
investigated factors. It is important to note that not all papers disclosed
socio-demographic information in their study or did not have this
Fig. 2 | Sankey diagram of all reviewed articles. A Sankey diagram37, showing the methods, socio-demographic information, SDoH, and other health factors for our selected
articles.
Fig. 3 | Number of publications per year. Number of papers published between
2014 - 2024 and categorized by methodology.
Table 3 | All reviewed papers categorized by ML and NLP
method used
Approach Method Papers
Feature based Traditional ML 41,68–109
Neural Network 100,109–114
Tools 115
NLP Traditional ML 39,51,116–126
Neural Network 27,39,48,51,127–133
Topic modeling 116,134–137
Tools 40,138–147
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information available to them. We find similar patterns when considering
how many studies look at a combination of socio-demographic factors and
SDoH information, where there are considerable gaps in research working
on the intersection. These gaps may be due to a lack of data, where studies
collecting such data points from participants or databases are leaving out
this type of information by design. For example, for people with disabilities
there are no considerations of education level, experiencing legal issues in
adulthood, discrimination, belonging, having recorded negative lifetime
events or trauma and abuse in childhood in our set of papers. There is also the
question about how generalizable the findings are in relation to 1) who is
represented in our healthcare system; 2) who these findings apply to; and 3)
that by design some minority groups (e.g.: transgender people) are not
represented in these studies. In Supplementary Table 1 we categorized each
paper according to their focus on SDoH categories and in section 4 we
provide a full discussion detailing the implications of our findings.
Social Determinants of Health and physical, mental, and beha-
vioral health factors
Based on our findings, we can see that the most commonly researched
health factors are existing mental health conditions (84.04%), substance use
(60.63%), physical health (54.25%) and previous attempts (47.87%). How-
ever, few works consider levels of physical activity (10.63%) or aggression
(8.51%). Similar to section 3.2 we also compared SDoH information to
physical, mental and behavioral health factors, where in Fig. 6we show a
heatmap of the most frequently co-occurring factors in every paper. We find
that existing mental health conditions and substance use are most often
considered as a variable for SDoHs related to psychosocial (e.g.: abuse/
trauma in adulthood (30.85% and 30.85%), negative lifetime experiences
(22.34% and 21.27%) and economic factors (e.g.: socioeconomic status
(34.04% and 24.46%), occupation (25.53% and22.34%)andbuildenvir-
onment/neighborhood (28.72% and 23.40%). Very few studies look at social
SDoHs, such as discrimination (2.12%) or sexual orientation (7.44%). These
results also illustrate that there is a gap in the research related to disability
status.
Discussion
The increased use of ML and NLP methods to successfully extract SDoH
related to suicide brings new challenges that require multidisciplinary
solutions. In the following section we highlight three key areas, based on the
information from the review of the literature, that need to be further
explored in future research as the algorithms and subsequent tools become
more sophisticated.
Fig. 4 | Type of data source used in each ML/NLP experiment . Type of data sources
in % .
Fig. 5 | Heatmap showing the number of papers for each socio-demographic factor.
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Data sources
•The majority of papers in this review utilize data that is typically not
accessible to the wider research community to protect patient privacy
and adhere to HIPPA (Health Insurance Portability and Account-
ability Act) legal requirements, which means that any kind of experi-
mental results are hard to reproduce. In addition to this, many datasets
are small in comparison to the treatment population and therefore
findings may not be generalizable to larger or more diverse
populations.
•Similar concerns apply to data that is more readily available to
researchers and is often sourced from social media. In such cases, (i)
researchers usually do not have any ground truth information about
the real mental health status of a user, (ii) data is sourced from plat-
forms that have limited demographics (e.g.: Reddit’suserpopulationis
70% male43) and (iii) often use a single post to assess risk of suicide24.
•Previous work has called for the use of whole user timelines from social
media to predict risk44,45. However, this also raises concerns around
clinical validity of public social media data, which can be taken out of
context and the risks to a user’s privacy may not warrant such an
intrusive approach. In order to address concerns around ground truth
and clinical validity of such approaches, researchers often ask human
annotators to label data for levels of perceived suicide risk, however in
few cases annotators have any kind of medical, psychological, or health
science training that would enable them to make a more informed
judgment46–48. However, this also increases the risk of annotation bias,
which can develop when throughout the data annotation process
systematic errors are introduced that impact the tool’s performance
and fairness.
•Developing tools to predict suicide from either clinical or social media
can lead to a number of ethical questions, including but not limited to:
Who is responsible for the tools decision making? And what do we do
when a user receives a high suicide risk score? Therefore, we recom-
mend that future research takes an interdisciplinary approach that
incorporates perspectives from bioethics, law and policy, computer
science and health science to outline how such technologies can be
developed and deployed responsibly.
Bias and Fairness concerns
•Existing disparities in health research49 also lead to inequalities in who
is represented in the data that is used to develop methods and tools.
Therefore, many vulnerable populations (e.g.: transgender people,
people who are hard of hearing to name just two) are left out of this type
of research by design, leading to an increased risk of biased and unfair
automated diagnosis, treatment and possible health outcomes for
different groups of people. Therefore, extracting SDoH information
from public and clinical records can lead to further amplification of
health disparities, biases and fairness concerns.
•Recent research has proposed a variety of bias and fairness metrics to
measure and mitigate biases, however; biases are not mutually exclusive
and each method comes with its own benefits and disadvantages50.This
ultimately means the person or group designing the tool decides what is
fair. Future research in this field should carefully consider all aspects of
the machine learning lifecycle (e.g.: data collection and preparation,
algorithm choices) to reduce harm. For example, examining the dataset
prior to training a new model, rebalancing training samples, and
involving a diverse group of people in the development process to get
feedback should be essential for any new algorithm or tool
development.
Computational tasks on suicide
•There have been a number of different tasks on detecting suicidal
ideation51–53 or attempts14,54 often with the goal to predict risk24,55,56,
using additional information that is not related to SDoH such as
emotions or emojis44,57.Typically,suchtasksareformulatedusing
categorical labels that are meant to reflect levels of risk and whilst, ML
models have been able to produce accurate predictions of suicide
ideation, attempts and death58, they are rarely grounded in clinically
Fig. 6 | A heatmap showing both SDoH and physical, mental, and behavioral health factors.
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theories of suicide as they are hard to implement due to their
complexity59.
•Furthermore, a diagnosed mental health condition is not a necessary
precursor to dying by suicide60 nor is expressing suicidal ideation61,62,
which can be used as a form of self-regulation63. Therefore, using AI/
ML based tools to predict risk, especially from public sources, can lead
to an increased risk of discrimination and stigmatization for those
affected. This is particularly concerning given that some research
proposes to use of such algorithms to predict individual risk of suicide
and therefore making people with stigmatized conditions publicly
identifiable.
•At the same time, healthcare systems are under more pressure than
ever to provide adequate mental health care64 and AI/ML based tools
have often been sold as promising solutions, but often overlook real-
world challenges of such systems (e.g.: clinical applicability, general-
izability, methodological issues)20,65. Similar to66, we caution against
overenthusiasm for the use of such technologies in the real world as it is
yet to be determined whether these tools are “competing, comple-
mentary, or merely duplicative”21. Especially, given the current use of
and task set up for suicide related tasks using AI/ML it is vital that we
have multi-stakeholder conversations (clinicians, patient advocate
groups, developers) to establish guidelines and regulations that ensures
safeguarding of those most affected by such technologies. Subse-
quently, leading to the development of more promising tools and
technologies that could aid in preventing suicide.
Conclusion
In this work, we have reviewed and manually categorized 94 papers that
use ML or NLP to extract SDoH information on suicide, including but
not limited to suicide risk and attempt prediction and ideation
detection. We find that ML and NLP methods are increasingly used to
extract SDoH information and the majority of current research focuses
on (i) clinical records as a data source, (ii) traditional ML approaches
(e.g.: SVMs, Regression), and (iii) a limited number of SDoH factors
(e.g.: trauma and abuse as experienced in adulthood, socio-economic
factors etc.) as they relate to demographic information (e.g.: binary
gender, age and race) and other health factors (e.g.: existing mental
health diagnosis and current or historic substance abuse) compared to
the general population and those most in need of care. In our discus-
sion we have highlighted challenges and necessary next steps for future
research, which include not using AI to predict suicide, but rather uses
it as a tool of many to aid in suicide prevention. Finally, this work is
limited in that it focuses on only English-speaking countries and
western nations, where SDoHs identified in this research may not be
applicable in different contexts. Furthermore, we have chosen to only
report broad categories of methods and dataset in order to identify
general trends and patterns.
In spite of these limitations, this review highlights the need for future
research to focus on not only on the responsible development of technol-
ogies in suicide prevention, but also more modern machine learning
approaches that incorporate existing social science and psychology research.
Data availability
No datasets were generated or analysed during the current study.
Received: 25 June 2024; Accepted: 9 September 2024;
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Acknowledgements
The study was supported by a grant from the Institute for Collaboration in
Health.
Author contributions
A.M.S., S.S., and I.I. searched the literature, and categorized each paper
according to predefined categories. B.I. generated visualizations. A.M.S.
wrote theinitial draft andA.M.S., S.G., and C.D.revised the paper.All authors
reviewed the paper.
Competing interests
The authors declare no competing interests.
Additional information
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Annika M. Schoene.
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