The Assessment of Social Determinants of Health in Postsepsis Mortality and Readmission: A Scoping Review : Critical Care Explorations

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Systematic Review

The Assessment of Social Determinants of Health in Postsepsis Mortality and Readmission: A Scoping Review

Hilton, Ryan S. BS1; Hauschildt, Katrina PhD2; Shah, Milan MD3; Kowalkowski, Marc PhD4; Taylor, Stephanie MD5,6

Author Information
Critical Care Explorations 4(8):p e0722, August 2022. | DOI: 10.1097/CCE.0000000000000722


Sepsis, life-threatening organ dysfunction caused by a dysregulated host response to infection, is responsible for significant acute and chronic morbidity and mortality (1–4). Two commonly studied adverse outcomes for sepsis survivors are: 1) rehospitalization and 2) long-term mortality (5–7). Existing reports suggest that sepsis survivors are at increased risk for both adverse outcomes, including an estimated 40% rate of hospital readmission rate at 90 days and 28–44% mortality rate at 1 year after sepsis discharge (6–9). Research developing risk models and identifying predictors of adverse outcomes among sepsis survivors has proliferated in the last decade (6,10–14).

Despite the increased attention to recovery after sepsis, an important topic that remains understudied is the association of social determinants of health (SDH) with adverse outcomes. The clinical significance of SDH has been demonstrated in numerous other settings (15–19), and SDH may be a particularly salient contributor to the risk of hospital readmission (20,21). Sepsis has specifically been identified as a condition that is affected by a combination of medical and social forces, implying that identification and mitigation of social factors is necessary to improve outcomes (22).

Although past attempts to summarize the literature on predictors of adverse events after sepsis have been made (12), we found no systematic exploration of the relationship between SDH and risk for rehospitalization and mortality after sepsis in adults. Understanding the social determinants that impact adverse outcomes after sepsis is paramount to inform interventions that adequately address the whole-person needs of sepsis survivors. The purpose of this review is to summarize knowledge and identify gaps in evidence about the relationship between social determinants and postsepsis outcomes.


This study conformed to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for scoping reviews (23) (Supplemental Digital Content, The study protocol is available in Supplemental Digital Content,

Inclusion Criteria

We searched for observational studies and randomized clinical trials published from 1992 (the year of the first consensus sepsis definition) to the date of the literature search (May 31, 2021) in Medical Literature Analysis and Retrieval System Online, Cochrane Library and its associated databases, and EMBASE. With the guidance of a medical librarian, we developed a search strategy using controlled vocabulary terms and text words for sepsis and postsepsis mortality or hospital readmission, and the search set was limited to humans and English language. The full electronic search strategy for Medical Literature Analysis and Retrieval System Online is available in Online Supplement Table 1 ( and modified for other databases. We updated the search on February 16, 2022, to identify additional studies published since the first search date.

Study Selection

Two reviewers (R.S.H., S.T.) independently screened citations for those evaluating a cohort of sepsis patients and reporting either of the primary outcomes after index sepsis discharge: 1) all-cause mortality or 2) hospital readmission in the title or abstract. The full text of any citation considered potentially relevant by either reviewer was retrieved. Eligible studies: 1) had a cohort, case-control, or randomized controlled trial design, 2) enrolled survivors of a hospital admission for sepsis, and 3) reported all-cause readmission or postdischarge mortality as a primary outcome. For inclusion into the review, sepsis was defined as infection-related organ dysfunction managed in hospital setting including studies using terminology of sepsis, severe sepsis, and septic shock. To maximize the generalizability of the results, we excluded studies restricted to children and to special populations such as those with HIV, cancer, and other immunocompromised states. We also excluded studies enrolling survivors of uncomplicated infections, such as pneumonia, without referring to organ dysfunction or to International Classification of Diseases codes for sepsis, severe sepsis, or septic shock in their index sepsis case definitions. We screened reference lists of included studies, related review articles, and editorials.

Data Collection and Validity Assessment

Four authors (R.S.H., K.H., M.S., S.T.) extracted data from the included studies, and issues of uncertainty were resolved by consensus. We included full articles and conference abstracts for assessing the count of studies assessing social determinants but only the full manuscripts for assessing social determinants as independent risk factors. We defined social determinants as factors belonging to the five key domains for SDH as defined by Healthy People 2020 (neighborhood and built environment, economic stability, education, health and healthcare, and social and community context). Uncertainty about whether a measure should be included as SDH was resolved by discussion of all authors. From each of the included studies, we extracted data on study design, number of patients, duration of follow-up, description of index sepsis admission, rehospitalization events, mortality events, and social determinants assessed as independent risk factors for rehospitalization or mortality.

Assessment of Methodological Quality

For studies reported as full-text articles, we determined cohort data source and duration of follow-up for outcome. We assessed the following characteristics and quality of SDH data: 1) type of SDH, 2) source of SDH data, 3) reporting and handling of SDH data missingness, 3) validity checks of SDH data (e.g., cross-checking multiple data sources or use of a validated data quality assessment software tool) (24), and 4) level of SDH assessment (e.g., individual, neighborhood, and county). The SDH reported as independent risk factors for mortality and rehospitalization were identified only from studies that used methods to account for confounders.

Data Analysis

Study characteristics are reported as number (%). We described the characteristics of social determinants included among studies and report those social determinants identified as increasing the risk of rehospitalization or mortality in sepsis survivors between studies.


The initial bibliographical database search identified 3,371 records (Fig. 1). After exclusion of duplicates, we screened 2,077 records. Following screening, 154 records were sought for retrieval and were accessible. Based on the initial full-text evaluation, 99 records met our specified inclusion criteria (Online Supplement Table 2, The second search revealed six additional eligible articles for a total of 105 records meeting eligibility. Of these, 28 (27%) of studies evaluated one or more SDH as a risk factor for postsepsis adverse events (23 full articles and five conference abstracts) (6,9,11,25–49). There were no major differences in study characteristics between studies that included SDH and those that did not (Online Supplement Table 3 and 4, The proportion of studies including SDH increased over time (Fig. 2).

Figure 1.:
PRISMA diagram showing study selection for inclusion in the review. EMBASE = Excerpta Medica database, MEDLINE = Medical Literature Analysis and Retrieval System Online, PRISMA = Preferred Reporting Items for Systematic Reviews and Meta-Analyses, RCT = randomized controlled trial, SDH = social determinants of health.
Figure 2.:
Trend in proportion of studies evaluating postsepsis adverse events thvat included social determinants of health (SDH) over time.

Study Characteristics

Table 1 shows the characteristics of the eligible studies grouped by their inclusion of SDH data. Of the 28 articles that included one or more SDH, 12 (43%) were published between 2010 and 2015 (6, 27, 28, 31, 33, 35, 37, 39, 41, 42, 44, and 46), 13 (46%) were published between 2016 and 2020 (29, 30, 32, 34, 36, 38, 40, 43, 45, 47, 48, 50, and 51), and three (11%) were published after 2020 (25, 26, and 49). Cohort size varied from eight studies (29%) of 100–1,000 patients (27,32,37,38,40,41,43,45) to 14 (50%) with more than 10,000 patients (6,9,11,25,28,29,34,36,39,42,47–49). Nineteen (68%) included patients from multiple centers (6,9,11,25,28,29,31,34,36,38–40,42–44,46–49). The primary outcome was postdischarge mortality in 14 (50%) (9,25,26,32–38,40,46–48) and readmission in 18 (64%) studies (6,11,25–32,39,41–45,47,49); four (14%) studies evaluated both mortality and readmission (25,26,32,47). For timing of follow-up, 15 (53%) studies evaluated the primary outcome at 7–30 day postdischarge (6,11,27–29,31,35,39–45,49), seven (25%) evaluated outcomes at 90 days to 1-year postdischarge (9,27,32,34,36,46,47), and 10 (36%) evaluated outcomes at greater than 1-year postdischarge (25,26,33,34,36–38,40,46,48).

Table 1. - Characteristics of Studies Including Greater Than 1 Social Determinants of Health in Evaluation of Postsepsis Adverse Outcomes (n = 28)
Variable n (%)
 2010–2015 12 (43)
 2016–2020 13 (46)
 2020 to present 3 (11)
Cohort size 14 (50)
 < 100 0 (0)
 100–1,000 8 (29)
 1,000–10,000 6 (21)
 > 10,000 19 (68)
Postdischarge outcome evaluated
 Mortality 14 (50)
 Readmission 18 (64)
Outcome follow-up
 7–30 d 15 (53)
 30–90 d 0 (0)
 90 d to 1 yr 7 (25)
 >1 yr 10 (36)

Methodologic Results

Table 2 shows the characteristics of SDH evaluation and reporting among included studies. The sources of SDH data were documented as coming from the electronic health record (EHR) in six studies (21%) (25–27,30,40,41) and from linkage to administrative dataset such as census or database in 20 studies (71%) (6,9,11,25,26,28,29,31,32,34,36,37,39,42,44–49) with four studies (14%) not reporting the source of SDH data (33,35,38,43). None of the studies reported any validity checks on SDH data. Eight (29%) reported missingness of SDH data as low (9,11,25,32,34,40,41,49), but none of the studies discussed methods for handling missing data.

Table 2. - Characteristics of Included Social Determinants of Health
SDH feature (n = 28) n (% Hilton)
Type of SDH
 Race/ethnicity 21 (75)
 Economic stability
  Income/wealth 8 (29)
  Education level attained 1 (4)
 Healthcare access/quality
  Payer type 10 (36)
 Neighborhood and built environment
  Population setting (rural/urban) 6 (21)
  Neighborhood socioeconomic status 6 (21)
  Preillness living situation 1 (4)
 Social community
  Marital status 5 (18)
SDH level
 Individual 28 (100)
 Neighborhood 10 (36)
SDH sourcing
 Electronic health record 6 (21)
 Linkage to other administrative dataset 20 (71)
 Patient-reported 0 (0)
 Not reported 4 (14)
Handling of SDH missing data
 Reported as “low” 8 (29)
 Not reported 20 (71)
SDH = social determinants of health.

The most common SDH evaluated was race/ethnicity (n = 21, 75%) (6,9,11,25–27,30,31,33–35,37,39–47), followed by payer type (n = 10, 36%) (6,26,29,31,39,42–45,49), and income/wealth (n = 8, 29%) (9,25,26,31,32,36,46,48). Of those studies evaluating race/ethnicity, nine (32%) evaluated no other SDH (11,27,30,33–35,37,40,41). One study including race/ethnicity discussed the use of this variable as a surrogate for social disadvantage (26), and none specifically discussed systemic or structural racism (50). Education (32) and preillness living situation (e.g., homelessness) (28) were both evaluated by one article (4%) each.

All articles included at least one SDH measured at the individual level, and 10 (36%) included determinants measured at both the aggregate and individual levels (25,26,29,36,39,42,45,47–49). Among the articles that included aggregated measures, geographic level included three out of 10 (30%) using areas smaller than a ZIP code (e.g., a census tract) (39,45,47) and seven out of 10 (70%) using ZIP code–level measures (25,26,29,36,42,48,49).

Several studies reported independent associations between SDH and outcomes after sepsis discharge; however, these findings were mixed across studies (Table 3). Five studies reported an independent association between race or ethnicity and readmission after sepsis discharge, of which three report that Black race had a higher risk of readmission (6,26,42), and two reported that Indigenous (including Native American and Australian Aboriginal) ethnicity had a higher risk of readmission (33,42). Payer type was also reported to be independently associated with postsepsis readmission although in inconsistent directions: one study reporting higher readmission rate among Medicaid beneficiaries (6), one study reporting decreased readmission in uninsured patients (26), and two studies reporting decreased readmission for private insurance, self-pay, and “other” (39,49). One study reported lower income and metropolitan residence as factors increasing the risk of postsepsis readmission (42). Three studies reported that lower neighborhood socioeconomic status was associated with increased risk for postsepsis readmission (45,47,49). Studies reporting independent associations between SDH and mortality following sepsis discharge were more varied, with marital status reported as a protective factor (38), and rural residence (48), low neighborhood socioeconomic status (47), low income (32), and Asian or “other” race reported as increasing risk for mortality (34).

Table 3. - Full-Text Studies Reporting Independent Associations Between Social Determinants of Health and Outcomes After Sepsis Discharge
Study Study Cohort Outcome Assessment Social Determinants Evaluated Risk Association Between Social Determinant and Study Outcome
Lopes et al (37) Centro Hospitalar Lisboa Norte, Infectious Disease ICU, 2002–2007; n = 234 2-yr mortality Race No association reported
Davis et al (35) Royal Darwin Hospital, Tiwi, Darwin, NT, 2007–2008; n = 1090 28-d mortality Race No association reported
Lemay et al (46) Department of Veterans’ Affairs Healthcare databases, 2001–2007; n = 2,727 90–365-d mortality Race, marital status, and income No association reported
> 365-d mortality
Davis et al (33) Royal Darwin Hospital, Tiwi, Darwin, 2007–2008; n = 1028 5-yr mortality Race Increased risk for Indigenous [Aboriginal Australian] race
Ortego et al (41) Hospital of the University of Pennsylvania, Dec 2007 to Jan 2010; n = 997 30-d readmission Race No association reported
Chang et al (42) HCUP—California State Inpatient Database, 2009–2011; n = 240,198 30-d readmission Race, income, urbanicity, and payer Increased risk for Black and Native American race, lower income, and metropolitan residence
Jones et al (44) University of Pennsylvania Health System, 2010–2012; n = 3,620 30-d readmission Race, marital status, and payer No association reported
Goodwin et al (6) HCUP State Inpatient Databases (CA, FL, and NY), 2011; n = 43,452 30-d readmission Race and payer Increased risk for Black race, Medicare, and/or Medicaid insurance
Donnelly et al (39) United Healthcare clinical database; n = 345,657 30-d readmission Race, payer, population setting, and census region Decreased risk for private insurance, self-pay, and “other” with Medicare as reference
Sun et al (43) University of Pennsylvania Health System, 2012; n = 444 30-d readmission Race, marital status, and payer No association reported
Chao et al (36) Taiwan’s National Health Insurance Research Database, 1995–2011; n = 272,879 1-yr morality Income and urbanization No association reported
2-yr morality
5-yr morality
Schnegelsberg et al (32) Aarhus University Hospital, Denmark, 2008–2010; n = 387 180-d readmission Education level, income, and cohabitation status Increased risk of 30-d mortality for low income
30-d mortality
180-d mortality
Abu-Kaf et al (38) Israeli Sepsis Group database, 2003–2011; n = 409 2-yr mortality Marital status Increased risk for married marital status
Gadre et al (49) Healthcare Cost and Utilization Project National Readmission Data, 2013–2014; n = 1,030,335 30-d readmission Payer and income Decreased risk for private insurance/self-pay, and higher neighborhood SES
Shankar-Hari et al (34) ICNARC Case Mix Programme, 2009–2014; n = 94,748 Up to 6-yr mortality Race Decreased risk for Asian race and “other” race
Bowles et al (11) Medicare beneficiaries national dataset, 2013–2014; n = 165,228 30-d readmission Race No association reported
Courtright et al (9) Medicare Beneficiaries, 2013–2014; n = 87,581 1-yr mortality Race and Medicaid eligibility No association reported
Gameiro et al (40) Division of Intensive Medicine of the Centro Hospitalar Universitário Lisboa Norte, 2008–2014; n = 256 30-d mortality Race No association reported
5-yr mortality
Shankar-Hari et al (47) ICNARC Case Mix Programme database & Hospital Episode Statistics database and Office for National Statistics death registrations, 2009–2014; n = 94,748 1-yr readmission Race and neighborhood socioeconomic status Increased risk for lower neighborhood SES for both readmission and mortality
1-yr mortality
Galiatsatos et al (45) Johns Hopkins Bayview Medical Center, 2017; n = 647 30-d readmission Race, payer, and area deprivation index Increased risk for higher neighborhood disadvantage
Lizza et al (26) Barnes Jewish Hospital, 2010–2017; n = 3390 1-yr readmission Race, insurance, and income Increased risk for Black race and decreased risk for uninsured
Oh et al (48) National Health Insurance database, South Korea, 2011–2014; n = 45,826 5-yr mortality Population setting and income Increased risk for residence in metropolitan city other than Seoul, and “other area” in South Korea
Farrah et al (25) National dataset in Canada; n = 196,922 > 1 yr readmission Urbanicity, income, and Ontario marginalization index No associations reported
>1 yr mortality
HCUP = Healthcare Costs and Utilization Project, ICNARC = Intensive Care National Audit & Research Center, SES = socioeconomic status.


SDH are increasingly recognized as critical upstream drivers of poor outcomes and higher costs (51–53). However, our review of studies assessing risk factors for adverse outcomes following sepsis indicates that SDH are infrequently evaluated and, even when evaluated, vulnerable to measurement error and interpretation challenges.

Low Utilization of SDH in Studies of Postsepsis Adverse Events

Our review found that only one-quarter of studies evaluating postsepsis events included SDH data in their analysis. Notably, 35% of these studies were classified as evaluating SDH based only on their inclusion of race or ethnicity. How race is defined and used is a critically important question for medical research (54–56). “Race” is widely acknowledged to be an indistinct, nonbiologic construct that is weakly measured, poorly analyzed, and inadequately reported (54,57–59). We chose to include race and ethnicity as SDH variables to explore how this construct is used in research on postsepsis adverse events.

Measurement Error

Significant progress has been made in the recognition of SDH as key influencers of health, and a long list of variables have emerged, which are proposed to capture socioeconomic aspects such as health insurance, access to care, deprivation, geography, education, social support, financial mobility, and health behaviors. Unfortunately, these variables remain nonstandardized and are often inaccurately measured. Many SDH data included in the studies were obtained through EHRs. Despite widespread acknowledgement that SDH data are frequently missing or inaccurate in EHRs and large datasets (60), SDH data quality, including characteristics of completeness, correctness, and consistency, was seldom addressed in the studies included in this review—three out of four studies failed to report handling of missing data, and no study reported applying any validation method for SDH data. Measurement error may explain the mixed findings of independent associations between some SDH and postsepsis outcomes reported among our studies. Because data related to SDH are often not accessible through structured data fields, but embedded in free-text fields (61), continued development and application of novel clinical natural language processing (NLP) methods are needed to harness valuable SDH data from unstructured EHR data (62,63).

However, even with improved extraction techniques such as NLP, EHR-derived SDH data are not likely sufficient to constitute a complete and accurate set of SDH domains, as many social and behavioral determinants that may influence health and mortality such as living arrangement and economic stability are not reliably captured and recorded (64). Researchers and practitioners face multiple challenges related to documenting SDH, including a lack of standardization, knowledge and buy-in from providers and staff members, and time to discuss SDH (65–68). Further, many SDH are time-varying, meaning that this information must be regularly updated to effectively inform research on outcomes and interventions to improve care (54). In our review, SDH data were often organized at city or national level rather than individual or neighborhood level, indicating the challenges in obtaining person-level SDH data.

Handling of “Race” as a Social Determinant of Health

The quality and validity of race as an included SDH in risk factor studies deserve special attention. Although race was the most commonly included SDH, its inclusion may not be informative. Misclassification of race and ethnicity variables in administrative sources has been identified as a limitation in health disparities research (69). Importantly, none of the studies identified in our review clearly defined race or justified its inclusion as a surrogate for sociologic constructs, for example, racism. This is consistent with other reports that racism is rarely explicitly named in published articles (54,70,71). The use of race as a variable in medical research is evolving, with the goal of ensuring that its use does not perpetuate inequities but rather intentionally seeks to resolve them. Specifically, journals have called for authors to clearly define race and justify inclusion in analyses (54,72,73), explicitly name racism and explore the contributions of racism and other race-related social constructs to study findings. Of note, efforts to control for other SDH may obscure rather than reveal the impact of race on health outcomes (55). If naming racism as a determinant of health is necessary for racial health equity, our review suggests that additional work is needed in the field of postsepsis research.

Associations Between Social Determinants and Postsepsis Outcomes

Due to heterogeneous definitions and study procedures, most associations between SDH factor and postsepsis outcomes were mixed across studies. The results for payer type were particularly mixed, possibly due to different healthcare payer models (e.g., single payer vs for-profit healthcare systems) in different geographic regions. Overall, neighborhood disadvantage was one factor that was consistently associated with increased readmission after sepsis and should be the focus of additional research.

Our review has important limitations. Although we employed an exhaustive search strategy, some relevant articles may have been missed. Additionally, data extraction was complicated by variability in reporting among studies. For example, SDH validity checks may have been done in studies but not reported in the article. Finally, the heterogeneity of SDH data prevented a meaningful statistical synthesis of the results. Nonetheless, to the best of our knowledge, this is the first in-depth review describing utilization and quality of SDH data in understanding risk of adverse outcomes after sepsis.


The discovery of which social determinants affect recovery after sepsis is pivotal to advancing knowledge to further guide support and management approaches in this vulnerable population. Although our review suggests that more recent studies of postsepsis adverse events are more likely to include social risk factors than older studies, SDH remain underutilized in risk models, and SDH data are often of uncertain quality. Transparent and explicit ontogenesis and data models for SDH data are urgently needed to support research and clinical applications with specific attention to advancing our understanding of the role racism and racial health inequities in postsepsis outcomes.


We thank Laura Leach, MLIS, for assistance developing the search strategy for this study.


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mortality; readmission; sepsis; social determinants

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