# Evaluating the Impact of Social Determinants
on Health Prediction

Ming Ying Yang<EMAIL_ADDRESS>Massachusetts Institute of TechnologyUSA ,
Gloria Hyunjung Kwak<EMAIL_ADDRESS>Harvard Medical
SchoolMassachusetts General HospitalUSA ,  Tom Pollard<EMAIL_ADDRESS>Massachusetts Institute of TechnologyUSA ,  Leo Anthony Celi<EMAIL_ADDRESS>Massachusetts Institute of TechnologyBeth Israel Deaconess Medical
CenterHarvard UniversityUSA and  Marzyeh Ghassemi<EMAIL_ADDRESS>Massachusetts Institute of TechnologyUSAVector InstituteCanada

(2023)

###### Abstract.

Social determinants of health (SDOH) – the conditions in which people live,
grow, and age – play a crucial role in a person’s health and well-being. There
is a large, compelling body of evidence in population health studies showing
that a wide range of SDOH is strongly correlated with health outcomes. Yet, a
majority of the risk prediction models based on electronic health records
(EHR) do not incorporate a comprehensive set of SDOH features as they are
often noisy or simply unavailable. Our work links a publicly available EHR
database, MIMIC-IV, to well-documented SDOH features. We investigate the
impact of such features on common EHR prediction tasks across different
patient populations. We find that community-level SDOH features do not improve
model performance for a general patient population, but can improve data-
limited model fairness for specific subpopulations. We also demonstrate that
SDOH features are vital for conducting thorough audits of algorithmic biases
beyond protective attributes. We hope the new integrated EHR-SDOH database
will enable studies on the relationship between community health and
individual outcomes and provide new benchmarks to study algorithmic biases
beyond race, gender, and age.

health disparities, social determinants of health, electronic health records,
machine learning

††copyright: acmcopyright††journalyear: 2023††doi:
XXXXXXX.XXXXXXX††conference: Proceedings of the 2023 AAAI/ACM Conference on
AI, Ethics, and Society; August 09–11, 2023; Montreal, CA††price: 15.00††isbn:
978-1-4503-XXXX-X/18/06††ccs: Applied computing Health informatics††ccs:
Social and professional topics User characteristics††ccs: Computing
methodologies Machine learning††ccs: Computing methodologies Natural language
processing

##  1\. Introduction

The increasing adoption of electronic health records (EHRs) in modern
healthcare systems has facilitated the development of machine learning (ML)
models to predict the progression of diseases and patient outcomes. Many such
models (Lehman et al., 2012; Ghassemi et al., 2014; Harutyunyan et al., 2019)
incorporate clinical factors (e.g., labs, vitals, medication, procedures) and
basic demographic features (e.g., age, gender, and race) to identify high-risk
patients. However, a patient’s clinical profile only offers a partial view of
all the risk factors that affect their health. Understanding the conditions of
their living environment may help to fill in the missing pieces and benefit
patients’ health and medical care. Human health is affected by many non-
clinical factors, commonly known as social determinants of health (SDOH).

The Healthy People 2030 initiative (Healthy People 2030, 2020), developed by
the US Department of Health and Human Services, describes SDOH as ”the
conditions in the environments where people are born, live, learn, work, play,
worship, and age that affect a wide range of health, functioning, and quality-
of-life outcomes and risks.” They grouped SDOH into five key domains: (1)
economic stability (Shain and Kramer, 2004; Brooker and Eakin, 2001), (2)
education access and quality (Hahn and Truman, 2015; Krueger et al., 2015),
(3) health care and quality (Hood et al., 2016), (4) neighborhood and built
environment (Kruger et al., 2007; Mitchell et al., 2007), and (5) social and
community context (Chang, 2019; Meddings et al., 2017).

Across all five domains, SDOH can have either a direct or indirect impact on
one’s health. At a high level, they can be viewed as individual-level
determinants or community-level determinants (Cantor and Thorpe, 2018). The
former determinants are specific to a person, and examples include education
level, annual income, and family dynamics. Access to individual-level SDOH is
limited due to the lack of standardized and validated SDOH screening questions
(Cantor and Thorpe, 2018) and privacy concerns (McGraw, 2015). In contrast,
community-level SDOH measure broader socioeconomic, neighborhood, and
environmental characteristics such as unemployment rate, access to public
transportation, and air pollution levels. They serve as “community vital sign”
(Bazemore et al., 2016) that reflect complex societal factors and health
disparities that influence one’s health (Borrell et al., 2021; Adler et al.,
2016).

Population health studies have identified many SDOH to be strongly correlated
with acute and chronic conditions (de Boer et al., 1997; Amrollahi et al.,
2022; Pfohl et al., 2019; Galobardes et al., 2006; Dugravot et al., 2020).
SDOH are also underlying, contributing factors of health disparities (e.g.,
poverty (Garcia Popa-Lisseanu et al., 2005; Cooper et al., 2012; Vutien et
al., 2019), unequal access to health care (Chang, 2019; Cristancho et al.,
2008), low educational attainment (Assari et al., 2018; Cutler and Lleras-
Muney, 2006; Hahn and Truman, 2015), and segregation (PRRAC, 2017; Carter and
Zimmerman, 2022)). However, to date, there has been less focus in the ML
community to include SDOH in common EHR prediction tasks because many SDOH
measures are poorly collected, lack granularity, or are simply unavailable. An
American Health Information Management Association (AHIMA) survey (NORC at the
University of Chicago and American Health Information Management Association,
2023) finds that most healthcare organizations are collecting SDOH data, but
they face challenges with a lack of standardization and integration of the
SDOH data into EHR and patient distrust in sharing the data. Thus, while SDOH
are being increasingly studied in population health (Hood et al., 2016; Yu et
al., 2020; Purtle et al., 2019) and primary care settings (Katz et al., 2018;
NORC at the University of Chicago and American Health Information Management
Association, 2023), data limitations have left the association between SDOH
and critical care outcomes largely unexplored.

In this work, we investigate the impact of incorporating SDOH features on
common EHR prediction tasks in the intensive care unit (ICU). We first link
MIMIC-IV (Johnson et al., 2022), a publicly available EHR database, to
external SDOH databases based on patient zip code. We then train models on the
common tasks of mortality and readmission risk, evaluating the contribution of
SDOH as compared to the EHR data alone. We find that adding SDOH does not
improve model performance in the general patient population. We do note that,
as compared to the EHR data alone, incorporating SDOH can lead to better-
calibrated and fairer models in specific subgroups, with varying levels of
contribution depending on the population and predictive task. Finally, we
illustrate that fairness audits based on both protective attributes and SDOH
features help to connect the commonly observed disparities to the underlying
mechanisms that drive adverse health outcomes downstream.

Our work makes three main contributions.

  * •

We release a publicly accessible database that combines EHR data with SDOH
measures. To the best of our knowledge, this is the first public EHR database
that contains structural features that span all five defined SDOH domains. The
database will enable new studies on the relationship between community health
and individual clinical outcomes.

  * •

We investigate the impact of incorporating SDOH in predictive models across
three tasks, three model classes, and six patient populations. We find that
the inclusion of SDOH can improve performance for certain vulnerable
subgroups.

  * •

We demonstrate that SDOH features enable more fine-grain audits of algorithmic
fairness, reporting the FPR parity – the difference in false positive rates
(FPR) – across intersectional patient subgroups.

##  2\. Related Work

###  2.1. SDOH in Health Prediction

A number of studies in population health have attempted to assess the impact
of social factors on health (Project, 2003; Marmot and Wilkinson, 2005;
Braveman and Gottlieb, 2014; Adler et al., 2016). There is a large, compelling
body of evidence showing that a wide range of SDOH is strongly correlated with
health outcomes, such as sepsis (Amrollahi et al., 2022), heart failure (Chin
and Goldman, 1997), pneumonia (Meddings et al., 2017), cardiovascular disease
(Galobardes et al., 2006), and diabetes (Ye et al., 2020; Walker et al.,
2014). A particular study found that 40% of deaths in the United States are
caused by behavior patterns that could be modified by preventive interventions
and suggested that only 10-15% of preventable mortality could be avoided by
higher-quality medical care (McGinnis, 1993). Other studies have also
indicated that the effect of medical care may be more limited than commonly
believed (Hood et al., 2016; Mackenbach et al., 1989; Mackenbach, 1996).
However, there are active controversies regarding the strength of the evidence
that suggests a causal relationship between SDOH and well-being. These
researchers are increasingly utilizing SDOH to predict individual health
outcomes (Shadmi et al., 2015; Özyılmaz et al., 2022).

While several studies have shown that machine learning models can predict
individual patient outcomes, such as in-hospital mortality (Ghassemi et al.,
2014; Grnarova et al., 2016; Lehman et al., 2012; Ye et al., 2020; Moon et
al., 2022) and readmission (Lehman et al., 2012; Golmaei and Luo, 2021;
Rumshisky et al., 2016), very few have incorporated SDOH into the models due
to the lack of granular and high-quality SDOH data at the individual level.

Due to the limited availability of individual-level SDOH data, many studies
are limited to community-level SDOH data (Chen et al., 2020). Most found that
community-level SDOH do not lead to improvement in model performance (Bhavsar
et al., 2018; Jamei et al., 2017; Vest and Ben-Assuli, 2019), partly due to
the low data resolution. In contrast, researchers who are able to access
individual-level SDOH generally report improvements in the model’s predictive
performance (Chen et al., 2020; Feller et al., 2018; Molfenter et al., 2012).
These studies often focus on a specific outcome for a specific patient group,
such as HIV risk assessment (Feller et al., 2018; Nijhawan et al., 2019) and
suicide attempts (Walsh et al., 2018; Zheng et al., 2020). There have also
been studies of model improvements for readmission and mortality prediction in
specific subgroups, such as the elderly and obese (Zhang et al., 2020b). One
has shown that integrating SDOH into predictive models can improve the
fairness of the prediction in underserved heart failure subpopulations (Li et
al., 2022).

Despite a growing body of SDOH-focused research, the relationship between SDOH
and critical care outcomes is unclear. While some have argued that the ICU is
not an appropriate setting to collect and identify SDOH, there are several
reasons why it could be essential. For example, critical conditions place high
demands on the patient and their social network (McPeake et al., 2022;
Turnbull et al., 2022). Social isolation may increase the risk of adverse
outcomes, such as mortality (Turnbull et al., 2022). By incorporating SDOH
into MIMIC-IV, our work investigates the contribution of community-level SDOH
on common EHR prediction tasks in a multi-year cohort in the critical care
setting.

###  2.2. SDOH in EHR

In order for SDOH features to be readily incorporated into risk prediction
models, they need to be collected and documented with individual health
outcomes. EHRs contain clinical information about patients, such as medical
history, vital signs, laboratory data, immunizations, and medications (Centers
for Medicare & Medicaid Services, 2021). In the United States, few SDOH
features are currently documented in structured EHR data fields due to the
lack of adoption of standardized and validated SDOH screening questions
(Cantor and Thorpe, 2018). The set of SDOH features available for research use
is typically limited to insurance type, preferred language, and smoking and
alcohol use, but SDOH information can also be extracted from unstructured EHR
data (i.e. clinical notes) (Navathe et al., 2018; Brown et al., 2022; Feller
et al., 2018). SDOH may also be captured in billing codes (American Hospital
Association, 2022), but they have not been widely utilized by providers
(Jessica L Maksut et al., 2021).

The integration of SDOH in EHRs is further delayed due to concerns regarding
privacy and misuse (McGraw, 2015). The United States Public Health Services
Syphilis Study at Tuskegee (Tuskegee Study) among African Americans
(Washington, 2006) and efforts to sterilize American Indians (Lawrence, 2000;
Davis and Reid, 1999) are examples of a dark history of structural inequities
in healthcare and unethical medical experimentation against racial and ethnic
minorities. As a result, mistrust of the healthcare system and medical
research has been well documented among minority groups (Boag et al., 2018;
Dula, 1994; Corbie-Smith et al., 2002). The collection and utilization of SDOH
require the consent and trust of the patients. Patients who identify with
populations that medical establishments and medical researchers historically
mistreat might not want to share any personal or sensitive information.

Overall, current EHR-derived SDOH data do not constitute a comprehensive set
of SDOH domains. In this study, we link a large, multi-year EHR dataset to
public SDOH datasets covering _all five SDOH domains_ to comprehensively study
the relationship between the community-level SDOH and patient outcomes.

###  2.3. Fairness and Bias in Healthcare

While much work has been done in algorithmic fairness and bias in health, most
of the studies that focused on group-based fairness have only examined bias
from the lens of protected attributes, namely age, gender, and race (Chen et
al., 2019, 2018; Hashimoto et al., 2018; Obermeyer et al., 2019; Seyyed-
Kalantari et al., 2020; Balagopalan et al., 2022; Zhang et al., 2020a; Seyyed-
Kalantari et al., 2021; Zhang et al., 2022; Suriyakumar et al., 2023; Adam et
al., 2022; Hebert et al., 2017; Maness et al., 2021). Recent fairness
literature has underscored the importance of measuring biases from
multidimensional perspectives, focusing on social processes that produce the
biases (Hanna et al., 2020; Selbst et al., 2018; Hoffmann, 2019).

There is strong evidence that intersectional social identities are related to
a patient’s health outcomes (Katz et al., 2018; Seyyed-Kalantari et al.,
2021). Capturing social context beyond protected attributes in the form of
SDOH is thus vital for this cause. For example, in the primary care setting,
researchers have observed a negative correlation between the odds of receiving
appropriate prevention and screening and the number of social risk factors
experienced by the patient (Katz et al., 2018). The more factors a patient was
living with, the less likely they were to receive care such as a mammogram
screening or vaccinations. This is not something that can be detected through
race or gender alone.

Moreover, a recent meta-analysis (Vince et al., 2023) ranked 47 studies using
a self-developed SDOH scoring system based on the type and number of SDOH
features used. The researchers found that Black patients had significantly
higher prostate cancer–specific mortality (PCSM) than White patients when
there was minimal accounting for SDOH. In contrast, studies with greater
consideration for SDOH showed the opposite: Black patients had significantly
lower PCSM compared to White patients. The findings of this meta-analysis
should not be interpreted as suggesting that racial disparities do not exist.
Rather, it suggests that there is a significant interaction between race and
SDOH, and health equity researchers should incorporate SDOH features into data
collection and analyses to better address the long-standing disparities in
healthcare (Washington, 2006).

We hope the new integrated MIMIC-IV-SDOH dataset will enable more studies that
follow the complex hierarchical system that defined advantaged or
disadvantaged subjects in the first place. Our work serves as a first effort,
and we demonstrate how SDOH features allow for more granular, actionable
algorithmic audits.

##  3\. EHR-SDOH Database: MIMIC-IV-SDOH

The MIT Laboratory for Computational Physiology (LCP) developed and maintains
the publicly available Medical Information Mart for Intensive Care (MIMIC), a
database on patients admitted to the emergency department (ED) and intensive
care units (ICU) at the Beth Israel Deaconess Medical Center (BIDMC) in Boston
(Johnson et al., 2018). The database is used by researchers in over 30
countries for clinical research studies, exploratory analyses, and the
development of decision-support tools (Pollard and Celi, 2017). The current
version, MIMIC-IV, contains detailed, de-identified data associated with over
70,000 ICU stays from 2008 to 2019 and over 400,000 ED stays from 2011 and
2019. Yet, due to the lack of high-quality SDOH data, none of the studies or
tools built based on MIMIC account for SDOH measures beyond basic demographics
such as insurance, and language. To enable the study of the relationship
between community characteristics and individual health outcomes, we create
the MIMIC-IV-SDOH database by linking MIMIC-IV to three public SDOH databases
(Table 1):

  1. (1)

County Health Rankings (CHR) (County Health Rankings & Roadmaps, 2022)

  2. (2)

Social Vulnerability Index (SVI) (CDC/ATSDR, 2022)

  3. (3)

Social Determinants of Health Database (SDOHD) (Agency for Healthcare Research
and Quality, 2022)

This database will be made available on PhysioNet (Goldberger et al., 2000).

A Pearson correlation coefficients between basic demographic features and
selected features in CHR (top), SVI (middle), and SDOHD (bottom).

B Distribution of features in each SDOH database by domain.

Figure 1. Comparison of features in the MIMIC-IV-SDOH databases. SDOHD is the
most comprehensive and granular SDOH database of the three. Its features are
more correlated with race than CHR and SVI indices. Each database emphasizes a
set of SDOH domains more than others. Table 1. Characteristics of the final
MIMIC-IV-SDOH tables where $d$ and $n$ are the number of SDOH features and the
number of distinct rows or sets of features, respectively. Note that both the
SVI and SDOHD are available at the county level; SDOHD is also available at
the zip code level. We use the most granular data for each database, that is
county-level data for CHR and census tract-level data for SVI and SDOHD.

| SDOH
---
Database
Data Version/Year |  | Geographic
---
Level
$d$
CHR | 2010-2020 | County |  106
SVI | 2008, 2014, 2016, 2018 | Census Tract |  162
SDOHD | 2009-2020 | Census Tract |  1329

###  3.1. Public SDOH Databases

While there exist other SDOH databases, such as Area Deprivation Index (Kind
and Buckingham, 2018) and Atlas of Rural and Small-Town America (U.S.
Department of Agriculture Economic Research Service, 2022), they are either
domain-specific or not frequently updated. Because MIMIC-IV contains ICU stays
from 2008 to 2019, we focus on databases with SDOH variables that span
multiple years and all five SDOH domains, as defined by Healthy People 2030
(Healthy People 2030, 2020).

####  3.1.1. County Health Rankings (CHR)

CHR evaluates counties within each state in the United States based on
modifiable health determinants and is updated annually. CHR estimates that
clinical care only accounts for 20% of all contributors to long-term health
outcomes, specifically the length and quality of life. The remaining 80% stems
from health behaviors (30%), physical environment (10%), and social and
economic factors (40%) (Hood et al., 2016).

####  3.1.2. Social Vulnerability Index (SVI)

Based on data from the American Community Survey (ACS), SVI evaluates social
factors across four themes: socioeconomic status, household composition and
disability, minority status and language, and housing type and transportation.
Although the index was designed to assess community preparedness and
resilience in face of natural hazards, SVI has been used in many population
health and health equity studies (Karmakar et al., 2021; Tipirneni et al.,
2022; Lehnert et al., 2020; Yu et al., 2020; Azap et al., 2020). For example,
communities with higher levels of social vulnerability experienced greater
COVID-19 incidence and mortality (Karmakar et al., 2021; Tipirneni et al.,
2022; Howell et al., 2022). Unlike CHR, SVI is available at both the county
level and census tract level.

####  3.1.3. Social Determinants of Health Database (SDOHD)

To incorporate more granular SDOH data into MIMIC-IV, the last database used
in the integration is the Social Determinants of Health Database (SDOHD),
which is available at the county, census tract, and zip code levels. The
database was recently developed to provide a range of well-documented, readily
linkable SDOH variables across domains without having to access multiple
source files. SDOHD is curated based on the five key SDOH domains defined by
Healthy People 2030: economic stability, education access and quality, health
care and quality, neighborhood and built environment, and social and community
context.

###  3.2. EHR-SDOH Integration

The creation of the integrated MIMIC-IV-SDOH database is carried out in three
steps.

##### Step 1: SDOH Data Acquisition

For each SDOH database, we concatenate all datasets released between 2008 and
2020. We map each feature to one of the five SDOH domains and provide detailed
documentation.

##### Step 2: Geographic Crosswalk

Although SVI and SDOHD are available at the census tract level, we only use
county-level data to minimize the risk of patients being identified. Each
patient’s zip code is mapped to a county using the crosswalk files provided by
the United States Department of Housing and Urban Development (HUD) (Wilson,
2018). The files contain a residential ratio column, the ratio of residential
addresses in the zip-county area to the total number of residential addresses
in the entire zip. Because the mapping is many-to-many, the residential ratio
is treated as the probability that the patient with zip code $z$ lives in the
census tract $t$ or county $c$, as suggested by the HUD (Din and Wilson,
2020). Note that only patients with Massachusetts zip codes that are in the
HUD crosswalk files are included in the final MIMIC-IV-SDOH dataset.

##### Step 3: Data Merging

MIMIC-IV is merged with each of the three SDOH databases using the geographic
location and the SDOH data year closest to the year of admission.

###  3.3. Comparison of SDOH Features

The demographic features in MIMIC-IV, such as race and gender, are sometimes
used as proxies for SDOH features, such as socioeconomic status and health
behaviors (Chen et al., 2019; Röösli et al., 2022). We find that many
community-level SDOH features are weakly correlated with race in MIMIC-IV. For
example, three SDOHD features, the percentage of households that receive food
stamps, the percentage of workers taking public transportation, and the
percentage of the population with educational attainment less than high school
are all weakly and positively correlated with the Black race (Figure 1).

Though to a lesser extent, subindices from SVI (e.g., socioeconomic) and CHR
(e.g. health outcomes, quality of life, and social and economic factors) are
also weakly associated with race. There are no strong correlations between
SDOH features and other tabular features such as labs, risk scores, and
Charlson comorbidities.

To better illustrate the type of features in each SDOH database, we manually
map each feature to one of the five SDOH domains (Figure 1). SDOHD is arguably
the most detailed and comprehensive SDOH database out of the three used in the
integration and has the largest feature space. As shown in Figure 1, the
features in SDOHD are predominantly of the economic stability domain. While
SVI shares a similar distribution as SDOHD, it emphasizes the neighborhood and
the built environment more. Conversely, more than 40% of the features in CHR
are related to health outcomes. The types of features in CHR make it well-
suited for health predictions, but it has very low resolution, with only 56
distinct sets of values in the integrated MIMIC-IV-CHR table.

##  4\. Data and Methods

Our primary goal is to determine how incorporating SDOH in ML models could
impact predictions of acute and longitudinal outcomes. Leveraging the newly
created MIMIC-IV-SDOH database, we assess the impact from the perspective of
classification performance and group fairness. We also provide a preliminary
investigation of the possible mechanisms behind the contributions of SDOH to
model performance or the lack thereof.

###  4.1. Data

In this study, we analyze five patient populations in the MIMIC-IV-SDOH
database to assess the impact of SDOH across three tasks. MIMIC-IV data comes
from a single EHR system in one geographic location, so the variation in the
community-level SDOH features might be too low to be informative. Many past
studies that used community-level SDOH features with EHR data from a single
hospital or region ended with similar conclusions (Chen et al., 2020). To
examine the generalizability of our finding, we compare the MIMIC-IV cohort to
a patient population in the All of Us Controlled Tier Dataset v6 (The All of
Us Research Program Investigators, 2019) for the task of 30-day readmission.
Unlike MIMIC-IV, which comes from a single hospital in Boston, the All of Us
dataset contains patient-level data from more than 35 hospitals across the
United States. Because of this difference, the variation in the SDOH data in
All of Us is much greater than that in MIMIC-IV (Figure B1). In addition,
based on the distribution of SDOH features, we note that the patients in
MIMIC-IV are on average more affluent and more educated than the patients in
All of Us.

####  4.1.1. MIMIC-IV-SDOH

Our analysis only includes adult ICU patients (i.e. over 18) from MIMIC-IV
v2.2 with a hospital length of stay of at least 3 days. The cohort contains
42,665 patients and a total of 54,380 admissions.

##### Task Definition

We focus on three common classification tasks: (1) in-hospital mortality, (2)
30-day readmission, and (3) one-year mortality. Patients who have expired
during a stay are excluded from predictions of 30-day readmission and one-year
mortality. For 30-day readmission, we only consider non-elective readmissions.

##### Patient Population Definition

A recent study suggests that the impact of incorporating SDOH in predictive
models varies by subpopulation – vulnerable populations like Black patients
and the elderly are likely to benefit more from the inclusion of SDOH
(Belouali et al., 2022). Moreover, several studies have suggested that SDOH
are strongly associated with glycemic control (Walker et al., 2014), as well
as diabetic risk, morbidity, and mortality (Hill-Briggs et al., 2022).
Diabetic patients also use significantly more healthcare resources compared to
patients with other chronic diseases (Fuchs et al., 2012). In fact, they
account for 31% of all ICU patients in MIMIC-IV.

Thus, in addition to the cohort of all ICU patients, we evaluate five
subgroups:

  1. (1)

Diabetic patients

  2. (2)

Black diabetic patients

  3. (3)

Elderly diabetic patients over 75 years old

  4. (4)

Female diabetic patients

  5. (5)

Non-English speaking diabetic patients

On average, all five of these subgroups have more comorbidities compared to
the general ICU patients (Table B1).

##### Data Pre-processing

To better understand the contribution of different types of features to model
performance, we divide the entire dataset into a total of 15 feature sets
(Table 2) and train separate models on each. These feature sets can be broadly
classified into three categories: SDOH features alone, EHR features alone, and
SDOH features combined with EHR features.

Table 2. Breakdown of feature sets by the type of EHR data contained EHR Data Type | Feature Set
---|---
No EHR Data/SDOH Alone | CHR
SVI
SDOHD
Structured EHR Data (Tabular) | Tabular
Tabular+CHR
Tabular+SVI
Tabular+SDOHD
Unstructured EHR Data (Clinical Notes) | Notes
Notes+CHR
Notes+SVI
Notes+SDOHD
All EHR Data | All
All+CHR
All+SVI
All+SDOHD

For the EHR features, we include two different data modalities: tabular data
and discharge notes. To enable fair comparison across the three tasks, we use
the same tabular features and sections of the discharge notes in all
prediction tasks. Tabular features include basic demographics, Charlson
comorbidities, labs from the first 24 hours of stay, and risk scores (APSIII,
SAPS-II, SOFA, and OASIS). The following sections from discharge notes are
included: chief complaint, present illness, medical history, medications on
admission, allergies, major surgical or invasive procedure, physical exam on
admission, pertinent results on admission, and family history.

Before separating patients into different patient populations, we use median
imputation for numerical features before performing standard scaling and
constant imputation for categorical features before one-hot encoding. Median
imputation is used instead of mean imputation in consideration of skewed data.
We also apply principal component analysis (PCA) to reduce the dimensionality
of the SDOH data, which is particularly useful as many of the SDOH features
are strongly correlated. We retain principal components that explain at least
0.99 of the variance in the data.

Discharge notes are stripped of explicit indicators of in-hospital mortality
before being tokenized and lemmatized. Corpus-specific stop words are removed
by filtering terms with a document frequency greater than 0.7. Terms with a
document frequency less than 0.001 are also removed. Lastly, the notes are
converted into a Term Frequency-Inverse Document Frequency (TF-IDF)
representation with a vocabulary of size $|V|=11,751$ words.

####  4.1.2. All of Us Cohort

Because the All of Us dataset is made up of primarily living participants, we
focus on the prediction of 30-day readmission. We only include adult
participants who had an in-patient hospital stay between 2014 and 2021. Unlike
MIMIC-IV, the hospital stays in All of Us are not limited to the ICU, so these
patients have fewer comorbidities on average (Table B1).

We exclude patients who stayed less than 3 days in the hospital and didn’t
have any lab results in the first 24 hours; these are the same labs used in
MIMIC-IV. We have 13,324 patients and 21,555 admissions in the final All of Us
cohort, representing all 50 US states but Kentucky. More than 50% of the
patients come from the Northeast region.

The All of Us dataset only has 7 community-level SDOH features sourced from
the 2017 ACS via a three-digit zip code linkage (subsection A.3). Tabular
features in the All of Us dataset are the same as those in MIMIC-IV except for
clinical risk scores, which are not available. We apply the same data pre-
processing techniques on both datasets. Because the All of Us dataset has no
clinical notes, we only train models on three feature sets: (Tabular, SDOH,
and Tabular+SDOH).

###  4.2. Models Benchmarked

We train three types of machine learning models – logistic regression
(Pedregosa et al., 2011), random forest classifier (Pedregosa et al., 2011),
and XGBoost classifier (Chen and Guestrin, 2016) – for each task, patient
population, and feature set combination. Each dataset is partitioned into
70:30 train-test splits. To prevent data leakage, no patient appears in both
the training set and the test set. Each model is tuned through random
hyperparameter search (Bergstra and Bengio, 2012) under broad parameter
distributions. See subsection A.1 for additional training details.

Figure 2. Comparison of the performance of XGBoost classifiers trained on 15
different feature sets to predict in-hospital mortality, 30-day readmission,
and one-year mortality in the general ICU population. There are no detectable
differences between AUROC and AUPRC of models that do not incorporate SDOH
features and those that do. However, when combining SDOH features with all EHR
data, we observe significant impacts on ECE, FPR, and recall. We highlight
such occurrences with asterisks. The error bars denote the 95% confidence
intervals obtained through 1000 bootstrap samples.

###  4.3. Evaluation

####  4.3.1. Classification Performance

We evaluate the models in terms of three primary metrics: 1) area under the
receiver operating characteristic curve (AUROC), 2) area under the precision-
recall curve (AUPRC), and 3) expected calibration error (ECE). While AUROC is
a standard metric to assess accuracy, we include AUPRC to account for class
imbalance and ECE to measure the reliability of the prediction. We also use
recall as a secondary metric. While threshold selection is complex, cost-
dependent, and application-specific, we use a classification threshold of 0.5
for demonstration purposes. 95% confidence intervals are constructed for all
metrics by sampling the test set for 1000 bootstrap iterations (DiCiccio and
Efron, 1996).

####  4.3.2. Group Fairness

In addition to classification parity, we evaluate the FPR parity – based on
the equality of opportunity definition of group fairness (Hardt et al., 2016).

$\hat{Y}\perp\\!\\!\\!\perp G\mid Y=0$

In other words, the probability of the model predicting a negative outcome is
independent of group attribute $G$, conditional on the outcome $Y$ being a
true negative.

We examine the differences in TPR across subgroups defined based on the
following attributes: (1) race, (2) age, discretized into four bins, (3)
gender, (4) median household income, (5) percentage of workers commuting via
public transportation, (6) percentage of the population with educational
attainment less than high school, (7) percentage of the population receiving
food stamps, and (8) the percentage of non-citizens. The SDOH features are
discretized into quartiles.

##  5\. Impact of SDOH on Clinical Prediction Tasks

###  5.1. Impact of SDOH on the General Population

We first examine the impact of SDOH on the general ICU patient population in
MIMIC-IV. We find that the inclusion of community-level SDOH in models does
not help with predictive performance, measured by AUROC and AUPRC, but can
lead to better-calibrated models with a lower FPR. We validate this finding on
a more geographically diverse dataset: the All of Us dataset.

####  5.1.1. No Improvement in Model Accuracy

First, SDOH features alone, without any EHR data, are not predictive of
individual patient outcomes. The mean test AUC of the XGBoost models, the best
models in terms of AUROC, trained on SDOH alone is below 0.60 across all
tasks, substantially lower than those trained on tabular EHR features and the
TF-IDF representation of discharge notes (Figure 2). This is not particularly
surprising as most studies that utilize community-level SDOH have arrived at
similar conclusions (Chen et al., 2019). One possible explanation is that
community-level estimates are either imprecise or biased, especially if the
within-community variance of a feature is high. Moreover, when a patient is
critically ill, information on their upstream risk factors might not be as
useful as their current state of health.

Table 3. Comparison of model performance for XGBoost classifiers trained with
and without SDOH for the task of 30-day readmission in MIMIC-IV and the All of
Us dataset. In both datasets, the addition of SDOH has no effect on model
performance.

Feature Set | MIMIC-IV | All of Us
---|---|---
AUROC | AUPRC | ECE | FPR | Recall | AUROC | AUPRC | ECE | FPR | Recall
SDOH | 0.57 | 0.08 | 0.42 | 0.42 | 0.54 | 0.53 | 0.21 | 0.30 | 0.48 | 0.50
Tabular | 0.67 | 0.11 | 0.40 | 0.38 | 0.63 | 0.60 | 0.26 | 0.27 | 0.35 | 0.47
Tabular+SDOH | 0.67 | 0.11 | 0.40 | 0.37 | 0.62 | 0.60 | 0.26 | 0.27 | 0.34 | 0.46

Table 4. Combinations of EHR and SDOH features that influence performance of
the best models for each patient population in terms of AUPRC. We report the
number of occurrences in which incorporating SDOH features significantly
improves or worsens performance in the form of (# improves/# worsens) across
the three prediction tasks (total number of occurrences is 3). Significance is
evaluated using a 1000-sample bootstrap hypothesis test at the 5% significance
level.

Patient Population | Metric | Tabular | Notes | All
---|---|---|---|---
CHR | SVI | SDOHD | CHR | SVI | SDOHD | CHR | SVI | SDOHD
All Diabetic | ECE | 0/1 | 0/0 | 0/0 | 0/0 | 0/0 | 0/1 | 0/0 | 0/0 | 0/0
FPR | 0/1 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0
Recall | 1/0 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0
Black Diabetic | ECE | 1/0 | 0/0 | 0/0 | 0/0 | 1/0 | 0/1 | 0/0 | 0/1 | 0/1
FPR | 1/0 | 0/1 | 0/0 | 0/0 | 1/0 | 0/1 | 0/0 | 0/1 | 1/0
Recall | 0/1 | 1/0 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | 1/0 | 0/0
Elderly Diabetic | ECE | 0/0 | 2/0 | 0/0 | 0/0 | 0/0 | 1/0 | 0/0 | 0/0 | 0/0
FPR | 0/0 | 1/0 | 0/0 | 0/0 | 0/0 | 0/1 | 0/0 | 0/0 | 0/0
Recall | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | 1/0 | 0/0 | 0/0 | 0/0
Female Diabetic | ECE | 0/0 | 0/0 | 0/0 | 1/0 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0
FPR | 0/0 | 0/0 | 0/0 | 1/0 | 0/0 | 0/0 | 1/0 | 0/0 | 0/0
Recall | 0/0 | 0/0 | 0/0 | 0/1 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0
Non-English Speaking Diabetic | ECE | 0/0 | 1/0 | 1/0 | 0/1 | 0/0 | 0/0 | 0/0 | 0/0 | 1/0
FPR | 0/0 | 0/0 | 0/0 | 0/1 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0
Recall | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0

Similarly, combining SDOH with tabular EHR data and discharge notes does not
improve the AUROC and AUPRC of the model. Again, this trend is observed in all
model classes and SDOH databases. This suggests that the added SDOH features
do not provide additional information beyond what is already captured in the
EHR. However, SDOH features have some influence on other metrics as such ECE,
TPR, and recall. For example, for the task of in-hospital mortality
prediction, combining CHR features with all EHR features significantly reduces
ECE from 0.11 to 0.03 and FPR from 0.09 to 0.04 (Table B2). Likewise, for the
task of 30-day readmission, combining SVI features with all EHR features
significantly reduces ECE from 0.39 to 0.35 and FPR from 0.35 to 0.27.
However, these improvements are at the expense of a lower recall.

####  5.1.2. Generalizability of the Finding

Using the All of Us dataset, we validate that our finding generalizes to a
more geographically diverse cohort. Unlike MIMIC-IV, which represents
critically ill patient stays at a hospital in Boston, the All of Us cohort
includes all in-patient stays across the United States, and many patients do
not have any comorbidities (Table B1). The AUROC of the XGBoost classifiers
for All of Us is lower than that in MIMIC due to the lack of detailed, hourly
lab and vitals. The AUPRC is higher for All of Us because the 30-day
readmission rate is 18% in the All of Us cohort and only 6% in the MIMIC-IV
cohort.

Because the two cohorts are drastically different, a direct comparison of
model performance is not meaningful, but we can examine the trend in the added
value of SDOH. Consistent with our results in MIMIC-IV, we see no significant
performance differences in models trained with tabular EHR data and tabular
EHR data combined with SDOH data in the All of Us cohort (Table 3).

###  5.2. Varying Impact of SDOH on Vulnerable Patient Populations

In this section, we investigate whether including SDOH in predictive models
can lead to better performance for specific patient populations. For each
task, we compare the best model that incorporates SDOH data to the best
baseline model trained on only EHR data.

####  5.2.1. Limited Improvement in Model Performance in Vulnerable Patient
Populations

Similar to the general ICU patients, we find that incorporating SDOH has some
impact on model performance in the more vulnerable patient populations.
Although predictive performance metrics such as AUROC and AUPRC are largely
unaffected, we observe significant improvements in ECE, FPR, or recall in
selected models trained on SDOH data. In Table 4, we report the aggregated
number of occurrences in which incorporating SDOH features significantly
improves or worsens model performance across three prediction tasks. See Table
B2 for more granular results.

We find that the added value of SDOH features varies by patient population,
prediction task, and the EHR features they are combined with. Even for the
same patient population and task, SDOH features are not equally informative or
useful. For example, for models trained on all diabetic patients, the only
observed performance boost is in recall when CHR features are combined with
tabular features. However, the improvement in the recall is at the expense of
higher ECE and FPR. In contrast, for models trained on female diabetic
patients, incorporating CHR improves model performance in at least one of the
three metrics when combined with discharge notes or all EHR data but not
tabular EHR data alone.

This varying effect of the inclusion of SDOH by patient population precisely
captures why SDOH should be collected and incorporated in analyses. Although
individuals in a neighborhood are exposed to the same community-level SDOH,
they have varying social needs (Agency for Healthcare Research and Quality,
2020). Incorporating SDOH into predictive models may be helpful to identify
patients with specific needs and reduce health disparities associated with
poor social conditions (Andermann, 2016; Adler et al., 2016; Chen et al.,
2019).

##  6\. SDOH As Fairness Audit Categories

A 2014 report by the National Academies of Medicine (NAM) argued that the
integration of SDOH into the EHR would better enable healthcare providers to
address health disparities (Committee on the Recommended Social and Behavioral
Domains and Measures for Electronic Health Records et al., 2015). Extending on
a previous study on the integration of SDOH features and model fairness in
patients with heart failure (Li et al., 2022), we conduct a thorough audit of
FPR parity in all ICU patients using SDOH features in addition to protected
attributes such as race, age, and gender. To enable evaluation based on SDOH
features, they are binned into quartiles, and the bin edges are documented in
subsection A.2.

In Figure 3, we report the FPR of classifiers with the highest AUPRC for
30-day readmission prediction. We focus on the models trained on all EHR data
(All) and all EHR data combined with the most helpful SDOH features (All+SVI)
across different subgroups. See Figure B2 for the other two prediction tasks.

###  6.1. SDOH Features Enable More Granular Audits

In this setting, a high FPR indicates that the model is overdiagnosing or
falsely claiming that the patient is high-risk, which has both medical and
economic costs (Hoffman and Cooper, 2012). A high FPR disparity means that
members of a protected subgroup would not be given the correct diagnosis or
appropriate intervention at the same rate as the other patients.

An audit of the FPR based on protective attributes confirms findings from
prior work that algorithms exhibit biases against underserved patient
populations (Seyyed-Kalantari et al., 2021; Zhang et al., 2020a, 2022). We
find that patients who are older, Black, or female have higher FPRs. (Figure
3). These differences are among the most commonly reported findings in health
disparities research; often, these studies stop there without connecting the
observed disparities to mechanisms of systemic biases that drive downstream
adverse health outcomes (Lett et al., 2022). This is partially due to the lack
of additional information on the patients beyond basic demographics.

Figure 3. Comparison of the FPR of XGBoost classifiers trained on all EHR data
(All) and all EHR data combined with SVI features for 30-day readmission
prediction in all MIMIC-IV ICU patients. FPR is reported for subgroups defined
by race, gender, age, and five SDOH features, which are binned into quartiles.
The bin edges are documented in subsection A.2. The error bars denote the 95%
confidence intervals obtained through 1000 bootstrap samples.

The fairness audit based on SDOH features provided additional insight and
raised more questions. We find that the FPR is elevated for patients residing
in communities where the median household income is low, a larger proportion
of individuals commute to work using public transit, and a larger proportion
of individuals did not complete high school (Figure 3).

We hypothesize that the FPR disparity is a result of bias propagation, which
has been suggested by previous studies (Seyyed-Kalantari et al., 2021; Adam et
al., 2022). While future work is needed to validate the hypothesis, one
interpretation of the FPR disparity between patients in quartiles defined
based on the percent of workers commuting via public transit is that patients
in the fourth quartile likely do not own a car and hence have higher
transportation barriers and limited access to healthcare (Cristancho et al.,
2008; Garcia Popa-Lisseanu et al., 2005; Syed et al., 2013; Towne et al.,
2021; Cooper et al., 2012; Ney et al., 2013). Additionally, a lower household
income and lower educational attainment could represent socioeconomic and
linguistic disparities in access to care (Gottlieb et al., 1995; Towne et al.,
2021; Cristancho et al., 2008; Chang, 2019).

##  7\. Discussion

###  7.1. The Need for Better Data

Overall, our analysis validates previous findings that community-level SDOH
features do not improve the accuracy of clinical prediction tasks (Chen et
al., 2020) in both a multi-year cohort and a geographically diverse cohort. We
expect individual-level SDOH to be better predictors of outcomes, as prior
studies that incorporated them all reported significantly improved performance
(Takahashi et al., 2015; Nijhawan et al., 2019; Amrollahi et al., 2022).
However, this data are not readily available. For example, although
individual-level SDOH can be extracted from participant surveys in the All of
Us dataset, less than 15% of the participants have completed the SDOH survey.
Moreover, the survey responses were collected only once for each participant,
so these survey-based SDOH features may not accurately reflect the lived
experience of the respondents beyond the period the survey was conducted. In
light of our findings, we call for further efforts to standardize the routine
collection of SDOH data and integration into EHR.

The healthcare system plays a vital role in collecting, using, and sharing
actionable SDOH data (NORC at the University of Chicago and American Health
Information Management Association, 2023). To facilitate this effort,
providers and operations staff across care settings should focus on actions
that enhance the standardization and integration of SDOH data. Organizations
such as the Office of National Coordinator for Health IT (ONC), the Joint
Commission, and Health Level Seven International (HL7) are all leading efforts
to further SDOH interoperability and standards (Ribick and Moriarty, 2019;
Office of the National Coordinator for Health Information Technology, 2023).
It should also be a focus to provide sufficient training and education for the
staff who are collecting and encoding the data from the patients while
adhering to cultural competency, privacy, and confidentiality standards
(McGraw, 2015).

As the research community awaits access to the more granular EHR-SDOH data, we
hope the MIMIC-IV-SDOH database will serve as a starting point for studies on
the relationship between community risk factors and patient outcomes and those
looking to understand the needs of vulnerable subpopulations.

###  7.2. On More Actionable Audits

Despite spending a higher percentage of our GDP on medical care expenditures
than other developed countries, health outcomes in the United States are among
the worst for developed countries (OECD, 2021). Numerous studies have
confirmed the potential of AI in improving health outcomes, but very few tools
that were developed have actually helped (Will Douglas Heaven, 2021; Avi
Goldfarb and Florenta Teodoridis, 2022). A promising direction forward is to
look beyond the clinical walls and understand the conditions that affect the
health of the people upstream (McGinnis et al., 2002).

The growing evidence around the association between SDOH and health outcomes
calls for targeted action, but there is a lack of consensus on what
interventions would work (McGinnis et al., 2002). Progress in evidence-
informed policymaking requires a commitment to enhancing our current
understanding of how SDOH affect different populations and ways to measure the
effectiveness of interventions targeting specific SDOH domains. Thus,
community-level SDOH features are essential for evaluating and monitoring
health disparities (Purtle et al., 2019).

By evaluating fairness using intersectional social identities, we could better
account for the socially constructed nature of protected attributes such as
race and gender. Capturing SDOH provides information on the social processes
that created health disparities in the first place (Chang, 2019; Hahn and
Truman, 2015; Lett et al., 2022). Audits of biases from the lens of SDOH are
also more actionable because these features are not social constructs but
modifiable factors that can be addressed (Purtle et al., 2019). Consider
transportation, which is one of the SDOH features we used in the fairness
audit. Surveys and audits have identified transportation barriers as one of
the leading causes of missed or delayed medical appointments, especially in
the elderly and those in rural areas (Syed et al., 2013; Health Research &
Educational Trust, 2017; Towne et al., 2021). Health insurance and healthcare
delivery organizations are addressing the issue through partnerships with
popular ride-share companies to provide non-emergency medical transportation
(NEMT) services (Powers et al., 2018; Unite Us, 2020). These programs have
decreased costs (Powers et al., 2016) and the frequency of urgent care visits
(PatientEngagementHIT, 2019). The development of this intervention would not
be possible without an understanding of the underlying SDOH and the population
affected.

###  7.3. Future Work on SDOH and Health Predictions

While our work shows that the inclusion of SDOH has minimal impact on three
common EHR prediction tasks, they could be more helpful in other tasks and
patient groups. Specifically, we did not include any phenotype prediction
tasks. Given the associations between SDOH and chronic diseases (Amrollahi et
al., 2022; Chin and Goldman, 1997), it is possible that SDOH features are good
risk predictors for specific comorbidities. In addition, SDOH could be
important to account for in the estimation of treatment effects, which several
studies have done using the MIMIC database but without SDOH data (Hsu et al.,
2015; Zhang et al., 2015; Lan et al., 2019). Likewise, although our study
utilized three different model classes, they are all relatively simple. Neural
networks could potentially uncover more underlying relationships between SDOH
and health outcomes (Zheng et al., 2020).

Regardless of the predictive value of SDOH, it is a good idea to account for
them in analyses for more granular benchmarking and evaluation of fairness.
For example, MIMIC-IV-SDOH can be mapped to MIMIC-CXR, a large database of
chest radiographs with radiology reports. There have been many works that
focus on group fairness in the field of medical imaging (Zhang et al., 2022;
Seyyed-Kalantari et al., 2020, 2021), which the inclusion of SDOH could
contribute to.

##  8\. Conclusion

This work advances our understanding of the impact of SDOH on health
prediction. First, we develop a new EHR-SDOH dataset by linking a popular EHR
database, MIMIC-IV, to public community-level SDOH databases. This dataset can
be used to uncover underlying trends between community health and individual
health outcomes and provide more benchmarks for evaluating bias and fairness.
Second, we demonstrate that incorporating SDOH features in certain vulnerable
subgroups can improve model performance. The value of adding SDOH features,
however, is dependent on the characteristics of the cohort and the prediction
task. Third, we highlight that algorithmic audits conducted through the lens
of SDOH are more comprehensive and actionable. However, the lack of access to
high-resolution, individual SDOH data is a limitation of the study. To address
this, future work should focus on collecting individual-level SDOH features
and accounting for them in analyses to address patient needs better and
promote health equity.

###### Acknowledgements.

This project is supported by the National Institute of Biomedical Imaging and
Bioengineering (NIBIB) of the National Institutes of Health (NIH) under grant
number R01-EB017205. We would also like to acknowledge the All of Us Research
Program, which is supported by the National Institutes of Health, Office of
the Director: Regional Medical Centers: 1 OT2 OD026549; 1 OT2 OD026554; 1 OT2
OD026557; 1 OT2 OD026556; 1 OT2 OD026550; 1 OT2 OD 026552; 1 OT2 OD026553; 1
OT2 OD026548; 1 OT2 OD026551; 1 OT2 OD026555; IAA #: AOD 16037; Federally
Qualified Health Centers: HHSN 263201600085U; Data and Research Center: 5 U2C
OD023196; Biobank: 1 U24 OD023121; The Participant Center: U24 OD023176;
Participant Technology Systems Center: 1 U24 OD023163; Communications and
Engagement: 3 OT2 OD023205; 3 OT2 OD023206; and Community Partners: 1 OT2
OD025277; 3 OT2 OD025315; 1 OT2 OD025337; 1 OT2 OD025276. In addition, the All
of Us Research Program would not be possible without the partnership of its
participants. Finally, we would like to thank Hammaad Adams, Aparna
Balagopalan, Hyewon Jeong, Qixuan (Alice) Jin, Intae Moon, Vinith Suriyakumar,
Yuxin Xiao, Haoran Zhang, Dana Moukheiber, Lama Moukheiber, and Mira
Moukheiber for their valuable feedback and constructive review of this work.

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##  Appendix A Additional Information on Model Training and Data Pre-
processing

###  A.1. Model Training

Due to the class imbalance in all the prediction tasks, we use AUROC for model
selection during hyperparameter tuning. The distributions of parameters
sampled during the randomized search for logistic regression (lr), random
forest (rf), and XGBoost (xgb) classifiers are as followed:

⬇

lr_param_grid = {

"C" : [1e-5, 1e-4, 1e-3, 1e-2, 0.1, 1, 5],

"solver" : ["liblinear"],

}

rf_param_grid = {

"n_estimators": [50,100,200,500],

"max_depth": scipy.stats.randint(2, 10),

"min_samples_split": scipy.stats.randint(2, 10),

"min_samples_leaf": scipy.stats.randint(1, 10),

}

xgb_param_grid = {

"n_estimators": [50,100,200,500],

"max_depth": scipy.stats.randint(2, 10),

"learning_rate": (0.01,0.05,0.1,0.2,0.3),

"min_child_weight": scipy.stats.randint(2, 10),

"colsample_bytree": [0.5,1],

"subsample" : [0.3,0.6,0.9],

"reg_alpha" : scipy.stats.randint(0, 10),

"reg_lambda": scipy.stats.randint(0, 10),

}

###  A.2. Binning SDOHD Features

The quartile bin edges for SDOH features used in the fairness audit are as
followed:

  1. (1)

Percentage of non-citizens:
0, 0.32, 1.09, 2.54, 30.58

  2. (2)

Median household income in dollars:
10446, 60698.5, 74902, 92381, 250001

  3. (3)

Percentage with less than high school education:
0, 4.04, 6.79, 11.64, 67.49

  4. (4)

Percentage of households receiving food stamps:
0, 4.15, 6.85, 12.1, 78.43

  5. (5)

Percentage of workers taking public transit:
0, 4.74, 10.5, 20.44, 77.61

###  A.3. SDOH Features in the All of Us Dataset

The community-SDOH features used include the following:

  1. (1)

Percentage of households receiving food stamps

  2. (2)

Percentage of the population with at least a high school
diploma

  3. (3)

Median household income

  4. (4)

Percentage of the population with no health insurance

  5. (5)

Percentage of the population in poverty

  6. (6)

Percentage of houses that are vacant

  7. (7)

Deprivation index

##  Appendix B Additional Figures and Tables

Figure B1. Comparison of selected SDOH features between the MIMIC-IV and the
All of Us patient cohorts. Because the All of Us dataset is more
geographically diverse, the variation in its SDOH data is much greater than
that in MIMIC-IV.

A All ICU Patients: In-hospital Mortality

B All ICU Patients: 30-Day Readmission

C All ICU Patients: One-Year Mortality

Figure B2. Comparison of the FPR of XGBoost classifiers trained on all EHR
data (All) and all EHR data combined with best SDOH features for the three
tasks in all MIMIC-IV ICU patients. FPR is reported for subgroups defined by
race, gender, age, and five SDOH features, which are binned into quartiles.
The bin edges are documented in subsection A.2. The error bars denote the 95%
confidence intervals obtained through 1000 bootstrap samples. Table B1.
Charactertics of the MIMIC-IV and All of Us cohorts. The MIMIC-IV cohort has
six patient populations: all ICU patients, diabetic patients, black diabetic
patients, elderly diabetic patients, female diabetic patients, and non-English
speaking diabetic patients. The All of Us cohort contains all in-patient
hospital stays. $N$ is the number of patients in each group.

Attribute | Subgroup | MIMIC-IV | All of Us
---|---|---|---
| All ICU
---
(N=42,665)
| All Diab.
---
(N=12,651)
| Black Diab.
---
(N=1,710)
| Elderly Diab.
---
(N=4,520)
| Female Diab.
---
(N=5,251)
| Non-English
---
Speaking Diab.
(N=1,675)
| All Inpatient
---
(N=13,324)
Age | 17-55 | 10,136 (23%) | 1,806 (14%) | 337 (19%) | 0 (0%) | 681 (12%) | 158 (9%) | 6,481 (48%)
55-65 | 8,773 (20%) | 2,759 (21%) | 407 (23%) | 0 (0%) | 1,008 (19%) | 332 (19%) | 3,134 (23%)
65-75 | 10,013 (23%) | 3,706 (29%) | 473 (27%) | 0 (0%) | 1,457 (27%) | 415 (24%) | 2,430 (18%)
75+ | 13,742 (32%) | 4,380 (34%) | 493 (28%) | 4,520 (100%) | 2,105 (40%) | 770 (45%) | 1,279 (9%)
Gender | Female | 18,677 (43%) | 5,251 (41%) | 927 (54%) | 2,169 (47%) | 5,251 (100%) | 801 (47%) | 8,226 (61%)
Male | 23,988 (56%) | 7,400 (58%) | 783 (45%) | 2,351 (52%) | 0 (0%) | 874 (52%) | 4,800 (36%)
Other | – | – | – | – | – | – | 298 (2%)
Race | White | 29,148 (68%) | 8,033 (63%) | 0 (0%) | 3,033 (67%) | 3,150 (59%) | 455 (27%) | 6,168 (46%)
Black | 3,880 (9%) | 1,700 (13%) | 1,710 (100%) | 523 (11%) | 920 (17%) | 192 (11%) | 3,589 (26%)
Other | 9,637 (22%) | 2,918 (23%) | 0 (0%) | 964 (21%) | 1,181 (22%) | 1,028 (61%) | 3,567 (26%)
Insurance Type | Medicaid | 2,976 (6%) | 763 (6%) | 153 (8%) | 68 (1%) | 370 (7%) | 249 (14%) | 7,109 (53%)
Medicare | 18,844 (44%) | 6,453 (51%) | 781 (45%) | 3,169 (70%) | 2,828 (53%) | 721 (43%) | 3,270 (24%)
Other | 20,845 (48%) | 5,435 (42%) | 776 (45%) | 1,283 (28%) | 2,053 (39%) | 705 (42%) | 2,945 (22%)
Language | English | 38,291 (89%) | 11,018 (87%) | 1,523 (89%) | 3,744 (82%) | 4,463 (84%) | 0 (0%) | –
Other | 4,374 (10%) | 1,633 (12%) | 187 (10%) | 776 (17%) | 788 (15%) | 1,675 (100%) | –
Charlson Comorbidity Index | 0 | 2,856 (6%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 5,977 (44%)
1 | 3,208 (7%) | 301 (2%) | 63 (3%) | 0 (0%) | 110 (2%) | 27 (1%) | 1,315 (9%)
2 | 4,232 (9%) | 626 (4%) | 106 (6%) | 0 (0%) | 254 (4%) | 56 (3%) | 1,240 (9%)
3 | 5,151 (12%) | 1,039 (8%) | 131 (7%) | 0 (0%) | 397 (7%) | 109 (6%) | 1,174 (8%)
4 | 5,576 (13%) | 1,434 (11%) | 176 (10%) | 173 (3%) | 561 (10%) | 174 (10%) | 855 (6%)
5 | 5,329 (12%) | 1,793 (14%) | 212 (12%) | 511 (11%) | 777 (14%) | 245 (14%) | 651 (4%)
6 | 4,558 (10%) | 1,713 (13%) | 215 (12%) | 653 (14%) | 720 (13%) | 230 (13%) | 554 (4%)
7+ | 11,755 (27%) | 5,745 (45%) | 807 (47%) | 3,183 (70%) | 2,432 (46%) | 834 (49%) | 1,558 (11%)

Table B2. Comparison of the performance of models trained with and without
SDOH feature to predict in-hospital mortality, 30-day readmission, and one-
year mortality for the six patient populations. The evaluation is done only on
the best model for each feature set category and task. In general,
incorporating SDOH features has a limited impact on model performance. Values
in bold indicate significantly better performance compared to the baseline
model trained without SDOH, evaluated using a 1000-sample bootstrap hypothesis
test at the 5% significance level.

Patient Group | Feature Set | In-hospital Mortality | 30-Day Readmission | One-Year Mortality
---|---|---|---|---
AUROC ($\uparrow$) | AUPRC ($\uparrow$) | ECE ($\downarrow$) | FPR ($\downarrow$) | Recall ($\uparrow$) | AUROC ($\uparrow$) | AUPRC ($\uparrow$) | ECE ($\downarrow$) | FPR ($\downarrow$) | Recall ($\uparrow$) | AUROC ($\uparrow$) | AUPRC ($\uparrow$) | ECE ($\downarrow$) | FPR ($\downarrow$) | Recall ($\uparrow$)
All ICU | Tabular | 0.863 | 0.43 | 0.133 | 0.13 | 0.583 | 0.663 | 0.123 | 0.43 | 0.373 | 0.583 | 0.833 | 0.563 | 0.153 | 0.213 | 0.713
Tabular+SDOH | 0.862 | 0.42 | 0.132 | 0.12 | 0.562 | 0.673 | 0.123 | 0.393 | 0.363 | 0.623 | 0.832 | 0.562 | 0.142 | 0.22 | 0.692
Notes | 0.843 | 0.383 | 0.183 | 0.13 | 0.523 | 0.693 | 0.143 | 0.393 | 0.323 | 0.63 | 0.833 | 0.573 | 0.173 | 0.23 | 0.683
Notes+SDOH | 0.843 | 0.373 | 0.183 | 0.13 | 0.513 | 0.693 | 0.143 | 0.393 | 0.323 | 0.63 | 0.832 | 0.572 | 0.172 | 0.22 | 0.682
All | 0.93 | 0.493 | 0.113 | 0.093 | 0.613 | 0.73 | 0.153 | 0.393 | 0.353 | 0.653 | 0.853 | 0.613 | 0.143 | 0.23 | 0.743
All+SDOH | 0.891 | 0.491 | 0.031 | 0.041 | 0.471 | 0.712 | 0.152 | 0.352 | 0.272 | 0.572 | 0.862 | 0.622 | 0.142 | 0.22 | 0.742
All Diabetic | Tabular | 0.863 | 0.43 | 0.093 | 0.073 | 0.453 | 0.653 | 0.153 | 0.383 | 0.373 | 0.553 | 0.783 | 0.523 | 0.133 | 0.233 | 0.623
Tabular+SDOH | 0.852 | 0.392 | 0.082 | 0.062 | 0.422 | 0.642 | 0.142 | 0.382 | 0.362 | 0.582 | 0.781 | 0.521 | 0.191 | 0.321 | 0.741
Notes | 0.823 | 0.333 | 0.213 | 0.183 | 0.653 | 0.673 | 0.173 | 0.373 | 0.33 | 0.553 | 0.783 | 0.513 | 0.143 | 0.193 | 0.583
Notes+SDOH | 0.811 | 0.321 | 0.211 | 0.181 | 0.631 | 0.663 | 0.173 | 0.393 | 0.33 | 0.553 | 0.781 | 0.521 | 0.131 | 0.181 | 0.561
All | 0.893 | 0.473 | 0.073 | 0.053 | 0.483 | 0.683 | 0.183 | 0.373 | 0.33 | 0.543 | 0.813 | 0.573 | 0.113 | 0.23 | 0.653
All+SDOH | 0.892 | 0.472 | 0.072 | 0.052 | 0.472 | 0.672 | 0.182 | 0.372 | 0.312 | 0.522 | 0.812 | 0.572 | 0.112 | 0.192 | 0.642
Black Diabetic | Tabular | 0.833 | 0.363 | 0.273 | 0.223 | 0.683 | 0.593 | 0.143 | 0.373 | 0.193 | 0.313 | 0.743 | 0.493 | 0.183 | 0.273 | 0.593
Tabular+SDOH | 0.832 | 0.352 | 0.262 | 0.22 | 0.672 | 0.592 | 0.162 | 0.352 | 0.382 | 0.562 | 0.721 | 0.461 | 0.081 | 0.171 | 0.411
Notes | 0.763 | 0.153 | 0.163 | 0.063 | 0.193 | 0.63 | 0.173 | 0.33 | 0.013 | 0.023 | 0.753 | 0.533 | 0.213 | 0.093 | 0.353
Notes+SDOH | 0.762 | 0.192 | 0.052 | 0.012 | 0.072 | 0.573 | 0.153 | 0.263 | 0.173 | 0.23 | 0.741 | 0.521 | 0.211 | 0.081 | 0.321
All | 0.833 | 0.343 | 0.273 | 0.213 | 0.683 | 0.583 | 0.153 | 0.263 | 0.173 | 0.243 | 0.793 | 0.593 | 0.213 | 0.133 | 0.523
All+SDOH | 0.822 | 0.332 | 0.262 | 0.212 | 0.662 | 0.63 | 0.183 | 0.373 | 0.063 | 0.143 | 0.782 | 0.582 | 0.272 | 0.262 | 0.662
Elderly Diabetic | Tabular | 0.793 | 0.383 | 0.163 | 0.063 | 0.353 | 0.633 | 0.143 | 0.373 | 0.363 | 0.583 | 0.733 | 0.553 | 0.143 | 0.313 | 0.643
Tabular+SDOH | 0.82 | 0.372 | 0.082 | 0.062 | 0.362 | 0.631 | 0.161 | 0.371 | 0.351 | 0.531 | 0.732 | 0.542 | 0.082 | 0.252 | 0.552
Notes | 0.763 | 0.293 | 0.093 | 0.033 | 0.173 | 0.573 | 0.13 | 0.373 | 0.033 | 0.053 | 0.723 | 0.543 | 0.093 | 0.233 | 0.513
Notes+SDOH | 0.763 | 0.293 | 0.093 | 0.033 | 0.183 | 0.553 | 0.13 | 0.333 | 0.23 | 0.243 | 0.722 | 0.552 | 0.092 | 0.232 | 0.532
All | 0.843 | 0.433 | 0.053 | 0.043 | 0.313 | 0.613 | 0.123 | 0.253 | 0.183 | 0.333 | 0.763 | 0.593 | 0.073 | 0.223 | 0.563
All+SDOH | 0.841 | 0.431 | 0.051 | 0.041 | 0.311 | 0.61 | 0.121 | 0.251 | 0.21 | 0.361 | 0.762 | 0.592 | 0.072 | 0.212 | 0.582
Female Diabetic | Tabular | 0.843 | 0.43 | 0.263 | 0.223 | 0.733 | 0.613 | 0.133 | 0.373 | 0.363 | 0.553 | 0.753 | 0.443 | 0.23 | 0.323 | 0.73
Tabular+SDOH | 0.832 | 0.382 | 0.262 | 0.222 | 0.712 | 0.571 | 0.121 | 0.351 | 0.341 | 0.471 | 0.753 | 0.453 | 0.223 | 0.353 | 0.713
Notes | 0.763 | 0.283 | 0.073 | 0.023 | 0.123 | 0.623 | 0.153 | 0.43 | 0.153 | 0.273 | 0.753 | 0.443 | 0.213 | 0.33 | 0.683
Notes+SDOH | 0.762 | 0.272 | 0.072 | 0.012 | 0.112 | 0.611 | 0.151 | 0.391 | 0.131 | 0.231 | 0.741 | 0.441 | 0.121 | 0.151 | 0.411
All | 0.863 | 0.443 | 0.043 | 0.033 | 0.383 | 0.623 | 0.163 | 0.393 | 0.163 | 0.263 | 0.773 | 0.493 | 0.113 | 0.183 | 0.533
All+SDOH | 0.862 | 0.442 | 0.042 | 0.032 | 0.372 | 0.621 | 0.141 | 0.381 | 0.081 | 0.171 | 0.773 | 0.493 | 0.13 | 0.173 | 0.523
Non-English Speaking Diabetic | Tabular | 0.83 | 0.343 | 0.273 | 0.243 | 0.683 | 0.613 | 0.123 | 0.363 | 0.273 | 0.463 | 0.83 | 0.553 | 0.133 | 0.133 | 0.513
Tabular+SDOH | 0.792 | 0.352 | 0.262 | 0.232 | 0.712 | 0.583 | 0.133 | 0.33 | 0.313 | 0.483 | 0.792 | 0.542 | 0.072 | 0.142 | 0.512
Notes | 0.743 | 0.273 | 0.053 | 0.023 | 0.083 | 0.633 | 0.113 | 0.293 | 0.183 | 0.273 | 0.713 | 0.483 | 0.133 | 0.183 | 0.463
Notes+SDOH | 0.751 | 0.261 | 0.051 | 0.011 | 0.061 | 0.561 | 0.091 | 0.371 | 0.321 | 0.381 | 0.722 | 0.512 | 0.132 | 0.182 | 0.492
All | 0.823 | 0.383 | 0.093 | 0.113 | 0.443 | 0.613 | 0.123 | 0.363 | 0.273 | 0.463 | 0.773 | 0.573 | 0.123 | 0.173 | 0.553
All+SDOH | 0.812 | 0.392 | 0.12 | 0.112 | 0.412 | 0.583 | 0.133 | 0.33 | 0.313 | 0.483 | 0.772 | 0.562 | 0.122 | 0.182 | 0.562
1 Trained on CHR, 2 Trained on SVI, 3 Trained on SDOHD