Distressed Communities Index Is Not Associated with Mortality for Critically Ill Surgical Patients with Sepsis

Abstract

Background:

The impact of socioeconomic metrics on outcomes after sepsis is unclear. The Distressed Communities Index (DCI) is a composite score quantifying socioeconomic well-being by zip code. The primary objective of this study was to evaluate the association between DCI and mortality in patients with sepsis admitted to the surgical intensive care unit (SICU).

Patients and Methods:

All patients with sepsis admitted to the SICU (Sequential Organ Failure Assessment [SOFA] score ≥2) were reviewed retrospectively. Composite DCI scores were obtained for each patient and classified into high-distress (DCI ≥75th percentile; n = 331) and control distress (DCI <50th percentile; n = 666) groups. Baseline demographic and clinical characteristics were compared between groups. The primary outcomes were in-hospital and 90-day mortality.

Results:

The high-distress cohort was younger and more likely to be African American (19.6% vs. 6.2%), transferred from an outside facility (52% vs. 42%), have chronic obstructive pulmonary disease (25.1% vs. 18.8%), and baseline liver disease (8.2% vs. 4.2%). Sepsis presentation was comparable between groups. Compared with the control cohort, high-distress patients had similar in-house (23% vs. 24%) and 90-day mortality (30% vs. 28%) but were associated with longer hospital stay (23 vs. 19 days). High DCI failed to predict in-hospital or 90-day mortality but was an independent risk factor for longer hospital length of stay (odds ratio [OR], 2.83 ± 1.42; p = 0.047).

Conclusions:

High DCI was not associated with mortality but did independently predict longer length of stay. This may reflect limitations of DCI score in evaluating mortality for patients with sepsis. Future studies should elucidate its association with length of stay, re-admissions, and follow-up.

Sepsis is a leading cause of death in the United States, accounting for up to one-third of in-hospital deaths.¹ Substantial progress has been made in initial resuscitative and therapeutic strategies to mitigate early mortality after sepsis in the last few decades. Campaigns such as the Society of Critical Care Medicine's Surviving Sepsis¹ and the U.S. Centers for Disease Control and Prevention's Get Ahead of Sepsis² have been instrumental in standardizing treatment regimens in the way of bundled care,³ sepsis electronic medical records flags,⁴ lower thresholds for escalation of care, and robust fluid resuscitation protocols.^5,6 Although these initiatives highlight the immediate morbidity and mortality associated with septic shock, they do not include the significant long-term mortality, which can be as high as 50%.^7–9 For this reason, identifying patient and clinical factors that are associated with long-term mortality is of paramount importance, both in terms of improving our identification of at-risk patients but also in facilitating more targeted therapeutic approaches.

To this point, there is increasing evidence that socioeconomic factors impact clinical and surgical outcomes.^8–10 Variables such as household income, community poverty rates, insurance status, and access to care have been potentially associated with increased sepsis-attributable mortality.^11–13 However, quantifying socioeconomic distress in a clinically relevant variable remains challenging, and the optimal metric is controversial. The Distressed Community Index (DCI)¹⁴ is a publicly available comprehensive index from the Economic Innovation Group that factors economic, environmental, social, health care, education, and food access-related features of the population catalogued by zip code. The quantitative score is calculated across a range of zero to 100, with 100 representing the most distressed. Interestingly, higher DCI scores have been shown to be predictive of worse outcomes in cardiac, vascular, transplant, and bariatric surgical populations.^15–18 The association between high DCI scores and mortality after sepsis, however, has yet to be elucidated. Therefore, the primary objective of this study was to evaluate the association between high-DCI and in-hospital mortality and cumulative 90-day mortality. Secondary objectives evaluated included length of stay, respiratory failure, ventilator days, renal failure, days on renal replacement, and discharge disposition. We hypothesized that DCI would be associated with increased mortality and morbidity in surgical patients admitted with sepsis.

Patients and Methods

Patients and study design

All patients admitted to the Ohio State Wexner Medical Center surgical intensive care unit (SICU) within a continuous five-year period (IRB #2018EO514, 2014–2019) were reviewed retrospectively and maintained in an institutionally approved database under a waiver of consent. The inclusion criteria for this study were adult patients at least 18 years old and patients diagnosed with sepsis as stated by Sepsis-3 guidelines¹⁹ (Sequential Organ Failure Assessment [SOFA] score ≥ 2) within 48 hours of admission. Pregnant patients, prisoners, and re-admissions were excluded from this review. Additionally, patients who did not have complete mortality data up to 90 days from admission were excluded from the initial dataset. The socioeconomic metric called the Distressed Communities Index (DCI) was defined for each patient.

The DCI as a metric was developed by the public policy group Economic Innovation Group (EIG)¹⁴ and incorporates seven metrics: unemployment, education level, poverty rate, median income, business establishments, job growth, and housing vacancy. The DCI composite score (defined by patient residential zip code) ranges from zero to 100, with 100 representing severe socioeconomic distress. The DCI is typically defined by quintiles, with higher DCIs representing more socioeconomically distressed populations. In this study, we elected to categorize DCI scores into a high-distress cohort (n = 331), corresponding to the top quartile of DCI scores in our study population, and a control group (DCI score <50th percentile; n = 661 patients; Supplementary Figure S1). This grouping allowed us to compare broad differences between the high-distress subset and the control cohort without involving the nuances of different levels of socioeconomic success, which are likely to be statistically visible only at higher sample sizes. This methodology reduced our chances of a type 2 error while allowing us to answer our primary clinical question.

Data collection and outcome measures

Reported variables were obtained from our institutional electronic medical record, which is linked to many outside hospitals in our region. Any linked charts were evaluated to complete data retrieval for transfer patients, with the intent of capturing data correlating with the index admission (which in the case of transfer patients was not necessarily identical to admission data to our institution). Baseline characteristics obtained on all patients included the following: gender, race, transfer status, age, and selected comorbidities (congestive heart failure, type 2 diabetes mellitus, moderate or worse liver disease, chronic obstructive pulmonary disease (COPD), obesity, stage 3 or worse kidney disease [CKD], metastatic cancer) and Charlson comorbidity index.²⁰ Additionally, we collected descriptors related to sepsis presentation including SOFA score on admission, lactate on admission, vasopressor use on admission, and sepsis source (specified as bacteremia/line-associated, respiratory, intra-abdominal, skin/soft tissue/burns, urologic, orthopedic, head/neck/thoracic).

The primary outcomes in this study were to assess rates of in-house and 90-day cumulative mortality for patients with high socioeconomic distress (high DCI) compared to control socioeconomic distress. Mortality data was captured using any available data, including electronic medical record (EMR)-linked outside hospital data and follow-up clinic appointments. Secondary outcomes include intensive care unit (ICU) length of stay, overall length of stay (days), respiratory failure (%), ventilator days, renal failure (%), days on renal replacement, and discharge disposition (home, facility, or hospice).

Statistical analyses

All statistical analyses were performed using Stata, version 16.1 (StataCorp, College Station, TX). Continuous variables that were normally distributed were compared across groups using Student t-test, whereas non-parametric variables were compared using Mann-Whitney U test. Categorical variables were analyzed using χ² or Fisher exact tests, based on the event count. Multivariable logistic or linear regressions were created to model predictors of in-hospital, 90-day mortality, and hospital length of stay based on patient characteristics and admission presentation. We chose independent variables based on what we deemed to be clinically relevant, even if these values were not statistically significant in initial bivariable analyses. No backwards or forwards selection was utilized to achieve the final model, and all variables were run simultaneously to mitigate bias. A p value <0.05 was considered statistically significant.

Results

Baseline and clinical characteristics of patients by DCI cohort

The DCI breakdown in our dataset was distributed as follows (listed from most distressed category to least distressed category): distressed (24.80%), at-risk (27.80%); mid-tier (15.9%); comfortable (13.60%); and prosperous (17.80%) (Supplementary Figure S1). Patients with a DCI score ≥75th percentile were categorized into the high-distress cohort, whereas those with scores below the median were the control group. The breakdown of DCI quintiles represented by this categorization is shown in Supplementary Figure S1B. More than 50% of the patients included in our study were from a distressed or at-risk community, as defined by DCI quintile scores. High-DCI patients were more likely to be younger (mean age, 57.2 vs. 61.2; p = 0.001) and non-white (24.5% vs. 10.0%; p < 0.0005) and more likely to present with baseline diagnoses of chronic obstructive pulmonary disease (25.1% vs. 18.8%; p = 0.02) and liver disease (8.2% vs. 4.2%; p = 0.01). Both cohorts had similar Charlson comorbidity indices (4 [interquartile range {IQR}], 2–6) versus 3 (IQR, 2–6; p = 0.24). High-DCI patients were more likely to be transferred to our medical center from an outside hospital (51.7% vs. 41.9%; p = 0.004; Table 1).

Table 1.

Clinical and Demographic Characteristics of Patients Admitted to the Surgical ICU by DCI

Variable	DCI		p
Variable	Control DCI (n = 661)	High DCI (n = 331)	p
Transferred: yes/no	277 (41.9)	171 (51.7)	0.004
Age (years): mean SD	61.2 (14.9)	57.2 (16.2)	0.001
Geriatric population (n, %)
65 − 74 y	77 (23.3)	169 (25.6)	0.04
75 − 84 y	34 (10.3)	103 (15.6)
85 y	9 (2.7)	24 (3.6)
Female (n, %)	291 (44.0)	148 (44.7)	0.837
Race (n, %)
Non-Hispanic white	595 (90.0)	250 (75.5)	< 0.005
Black	41 (6.2)	65 (19.6)
Other	19 (2.9)	13 (3.9)
Unknown	6 (0.91)	3 (0.91)
Non-white	66 (10.0)	81 (24.5)	< 0.0005
Selected comorbidities: (n, %)
CHF	54 (8.2)	35 (10.6)	0.21
T2DM	184 (27.8)	99 (29.9)	0.50
Moderate/severe liver disease	28 (4.2)	27 (8.2)	0.01
COPD	124 (18.8)	83 (25.1)	0.02
Obesity	379 (59.7)	182 (58.0)	0.61
Stage 3 − 4 CKD	74 (11.2)	38 (11.5)	0.89
Metastatic cancer	46 (7.0)	15 (4.5)	0.13
Charlson comorbidityiIndex: p50 IQR	4 (2 − 6)	3 (2 − 6)	0.24

DCI = Distressed Communities Index; SD = standard deviation; CHF = congestive heart failure; T2DM = type 2 diabetes mellitus; COPD = chronic obstructive pulmonary disease; CKD = chronic kidney disease; IQR = interquartile range.

Differences in sepsis presentation were compared between high and control DCI cohorts, including admission SOFA score, laboratory data, sepsis source, and vasopressor use on admission. There were no differences between low- and high-DCI groups based on source of sepsis (bacteremia/line associated, respiratory, intra-abdominal, skin/soft tissue/burn, urologic, orthopedic, head/neck and thoracic). Admission SOFA score, creatinine, white blood cell count, hemoglobin, lactate, or percentage of patients on vasopressors at presentation were comparable between the high-DCI and control cohort (Table 2).

Table 2.

Sepsis Presentation by DCI Category

Variable	DCI		p
Variable	Control DCI (n = 661)	High DCI (n = 331)	p
Source of sepsis: (n, %)
Bacteremia (line-associated)	30 (4.6)	16 (4.8)	0.49
Respiratory	69 (10.5)	26 (7.9)
Intra-abdominal	406 (61.7)	213 (64.4)
Skin, soft-tissue, burn	80 (12.2)	46 (13.6)
Urologic	40 (6.1)	16 (4.8)
Orthopedic	17 (2.6)	4 (1.2)
Head and neck, thoracic	16 (2.4)	11 (3.3)
Admission Laboratory tests
SOFA: p50 (IQR)	5 (4 − 6)	6 (4 − 8)	0.75
Creatinine (mg/dL): p50 (IQR)	1.2 (0.8 − 1.9)	1.2 (0.8 − 2.1)	0.59
WBC (K/mcL): p50 (IQR)	14.0 (7.4 − 21.3)	13.3 (7.7 − 19.7)	0.40
Hemoglobin (g/dL): p50 (IQR)	9.8 (8.3 − 11.4)	9.8 (8.5 − 11.5)	0.52
Lactate (mmol/L): p50 (IQR)	2.2 (1.4 − 3.6)	2.1 (1.4 − 3.8)	0.56
Vasopressors on admission: n (%)	253 (38.3)	120 (36.3)	0.54

DCI = Distressed Communities Index; SOFA = Sequential Organ Failure Assessment score; IQR = interquartile range; WBC = white blood cell count.

Comparison of hospitalization characteristics and outcomes by DCI cohort

The overall 90-day mortality for the entire cohort was 28.6% (n = 284). Compared with the control cohort, high-DCI patients exhibited comparable in-hospital (23.9% vs. 22.5%; p = 0.64) or 90-day mortality (30.2% vs. 27.8%; p = 0.44). There were no differences in median days spent on ventilator (5 [IQR, 2–12] vs. 4 [IQR, 2–11) days; p = 0.27) and renal replacement therapy days (5 [IQR, 2–14] vs. 7 [IQR, 3–16]; p = 0.20). High-DCI patients had similar ICU lengths of stay (7.8 [IQR, 3–18.5] vs. 7.4 [IQR, 3.2–16.3] days; p = 0.56) but an overall longer median hospital stay when excluding in-house deaths (23 [IQR, 13.5–36] vs. 19 [IQR, 11–32] days; p = 0.03). There was no difference in rate of non-home discharge (71.1% vs. 70.6%; p = 0.78; Table 3.

Table 3.

Outcomes After Admission to the SICU by DCI

Variable	DCI		p
Variable	Control DCI (n = 661)	High DCI (n = 331)	p
ICU LOS (d): p50 (IQR)	7.4 (3.2 − 16.3)	7.8 (3 − 18.5)	0.55
Overall LOS (d): p50 (IQR)	18 (11 − 32)	22 (12 − 35)	0.05
LOS (excluding in-house deaths): p50 (IQR)	19 (11 − 32)	23 (13.5 − 36)	0.03
Respiratory failure: (n, %)	476 (72.01)	251 (75.8)	0.20
Days on the ventilator (d): p50 (IQR)	4 (2 − 11)	5 (2 − 12)	0.27
Renal failure: (n, %)	160 (16.2)	62 (18.7)	0.28
Days on renal replacement therapy (d): p50 (IQR)	7 (3 − 16)	5 (2 − 14)	0.20
Mortality (n, %)
In-house	149 (22.5)	79 (23.9)	0.64
90-day	184 (27.8)	100 (30.2)
Discharge disposition (n, %)
Home	147 (28.9)	74 (29.5)	0.78
SNF/LTACH	328 (64.4)	157 (62.6)
Hospice	34 (6.7)	20 (8.0)

SICU = surgical intensive care unit; DCI = Distressed Communities Index; ICU intensive care unit; LOS = length of stay; IQR = interquartile range; SNF = skilled nursing facililty; LTACH = long-term acute care hospital.

Evaluation of DCI as an independent predictor of in-hospital, cumulative ninety-day mortality, and hospital length of stay

Models for in-hospital and cumulative 90-day mortality were created using multivariable logistic regression. Independent variables utilized in these models included age, gender, race, transfer status, DCI category, body mass index (BMI), Charlson comorbidity index, admission lactate, admission SOFA, vasopressor use on admission, and source of sepsis. Positive predictors for in-hospital mortality were increasing age (OR, 1.0; 95% confidence interval [CI], 1.00–1.04; p = 0.03), transfer status (OR, 2.23; 95% CI, 1.48–3.36; p < 0.005), increasing BMI (OR, 1.05; 95% CI, 1.03–1.07; p < 0.005) and Charlson comorbidity index (OR, 1.29; 95% CI, 1.18–1.42; p = 0.005) and higher admission SOFA (OR, 1.09; 95% CI, 1.03–1.17; p = 0.005). The risk of in-hospital mortality was not increased with higher DCI scores (OR, 1.44; 95% CI, 0.95–2.2; p = 0.09; Table 5). Predictors of 90-day mortality included transfer status (OR, 2.00; 95% CI, 1.36–2.94; p < 0.005), BMI (OR, 1.05; 95% CI, 1.03–1.06; p < 0.005), Charlson comorbidity index (OR, 1.41; 95% CI, 1.29–1.54; p < 0.005), lactate on admission (OR, 1.20; 95% CI, 1.11–1.29; p < 0.005), and SOFA on admission (OR, 1.08; 95% CI, 1.01–1.14; p = 0.02). The risk of 90-day mortality was not increased with higher DCI scores (OR, 1.37; 95% CI, 0.92–2.04; p = 0.12; Table 4). Predictors of overall length of stay were modeled using multivariable linear regression. Positive predictors of increasing length of stay included high DCI (R, 2.83; 95% CI, 0.03–5.62; p = 0.047) and presenting with an orthopedic source of sepsis (compared with bacteremia) (R, 11.64; 95% CI, 0.877–22.42; p = 0.03). Transfer status was a negative predictor of length of stay (R (−4.5; 95% CI, −7.21 to −1.90; p = 0.001) (Table 4).

Table 4.

Predictors for In-Hospital and 90-Day Mortality

Variable	In-hospital death				90-day mortality
Variable	OR	SE	95th CI	p	OR	SE	95th CI	p
Age	1.02	0.009	1.00 − 1.04	0.03	1.00	0.008	0.99 − 1.02	0.73
Gender	0.73	0.15	0.49 − 1.10	0.13	0.83	0.16	0.56 − 1.21	0.33
Race (black vs. non-Hispanic white)	1.36	0.47	0.69 − 2.69	0.37	1.16	0.38	0.61 − 2.20	0.65
High DCI	1.44	0.31	0.95 − 2.2	0.09	1.37	0.28	0.92 − 2.04	0.12
Transfer status	2.23	0.47	1.48 − 3.36	<0.005	2.00	0.40	1.36 − 2.94	<0.005
Body mass index	1.05	0.01	1.03 − 1.07	<0.005	1.05	0.01	1.03 − 1.06	<0.005
Charlson comorbidity index	1.29	0.06	1.18 − 1.42	<0.005	1.41	0.06	1.29 − 1.54	<0.005
Admission lactate	1.24	0.05	1.15 − 1.33	0.005	1.20	0.045	1.11 − 1.29	<0.005
Admission SOFA	1.09	0.036	1.03 − 1.17	0.005	1.08	0.033	1.01 − 1.14	0.02
Vasopressors on admission	1.18	0.26	0.76-1.81	0.46	1.22	0.25	0.82 − 1.83	0.33
Sepsis source (compared to line-associated bacteremia)
Respiratory	2.79	1.82	0.78 − 10.0	0.12	1.57	0.90	0.51-4.81	0.43
Intra-abdominal	1.08	0.61	0.36 − 3.26	0.89	0.72	0.35	0.28 − 1.84	0.49
Skin, soft tissue, burns	0.79	0.51	0.22 − 2.79	0.71	0.50	0.28	0.17 − 1.52	0.22
Urologic	0.29	0.24	0.05 − 1.5	0.14	0.12	0.09	0.03 − 0.57	0.008
Orthopedic	0.89	0.88	0.13 − 6.21	0.91	0.68	0.58	0.13 − 3.64	0.65
Head and neck, thoracic	2.57	2.24	0.47 − 14.15	0.28	0.75	0.64	0.14 − 4.02	0.74

OR = odds ratio; SE = standard error; CI = confidence interval; DCI = Distressed Communities Index; SOFA = Sequential Organ Failure Assessment score.

Table 5.

Predictors of Length of Stay

Variable	R	SE	95th CI	p
Age	-0.11	0.06	−0.21 to 0.004	0.06
Gender	−1.42	1.33	−4.1 to 1.2	0.29
Race (black vs. non-Hispanic white)	−0.57	2.23	−4.94 to 3.80	0.80
High DCI	2.83	1.42	0.03 − 5.62	0.047
Transfer status	−4.53	1.35	−7.21 to −1.90	0.001
Body mass index	−0.02	0.063	−0.14 to 0.11	0.78
Charlson comorbidity index	−0.56	0.31	−1.17 to 0.043	0.07
Admission lactate	−0.22	0.25	−0.71 to 0.28	0.39
Admission SOFA	0.0019	0.21	−042 to 0.42	0.99
Vasopressors on admission	0.28	1.44	−2.55 to 3.11	0.845
Sepsis Source (compared with line-associated bacteremia)
Respiratory	7.22	3.97	−0.58 to 15.04	0.07
Intra-abdominal	4.72	3.27	−1.7 to 11.14	0.15
Skin, soft tissue, burns	4.53	3.71	−2.76 to 11.83	0.22
Urologic	−6.67	4.27	−15.06 to 1.71	0.12
Orthopedic	11.64	5.49	0.877–22.42	0.03
Head and neck, thoracic	8.85	5.8	−2.55 to 20.23	0.13

R = regression coefficient; SE = standard error; CI = confidence interval; DCI = Distressed Communities Index; SOFA = Sequential Organ Failure Assessment score.

Discussion

In this cohort, high DCI was not associated with increased in-hospital or 90-day mortality but was an independent predictor of length of stay. Previous literature in other surgical subtypes, including cardiac, vascular, transplant, and bariatric patients,^15,16,21 suggests that DCI does factor into surgical risk-adjustment, specifically in prediction of complications and assessment of predicted hospital cost. For example, DCI scores have been shown to be predictive of outcomes in transplant and bariatric populations,^22,23 in which there is well-documented reliance on socioeconomic infrastructures to improve follow-up, compliance with post-operative regimens, and dietary adherence.²⁴ However, in the study by Mehaffey et al.²⁴ establishing the validity of DCI in surgical risk adjustment, mortality, and length of stay were not evaluated as outcomes. Therefore, because mortality was utilized as the primary outcome in our study, it is possible that DCI failed to demonstrate significance because it is not a relevant metric in short-term mortality. Nonetheless, our study is the largest study to date evaluating DCI in surgical patients with sepsis. This directs us to consider how sepsis is unique from other populations for whom DCI was predictive of peri-operative outcomes.

One of the potential reasons for the discrepancy in our findings compared to prior surgical literature is that sepsis management has become much more algorithmic. Sepsis bundles emphasize early intervention as a way to reduce early mortality after sepsis.²⁵ Bundles for implementation at six and 24 hours were implemented in 2010²⁶ with an additional bundle addendum in 2018,²⁷ which introduced strict one-hour recommendations. Although these recommendations have subsequently received criticism, adherence to these metrics has been attributed to reductions in early mortality.²⁸ This suggests that early resuscitation and source control impact early mortality. Given adherence to bundled care is a metric for reimbursement, it is possible that differences in outcomes based on socioeconomic status are blunted by observance of these guidelines. Furthermore, in our population, we did not see differences in sepsis severity in terms of admission lactate, SOFA score, and vasopressor use, which are known predictors of mortality after sepsis.²⁹

The finding that DCI is not associated with sepsis mortality is commensurate with a previous study from our institution, which revealed that high DCI was not a risk factor for mortality for patients with necrotizing soft tissue infections (NSTI).^30,31 Necrotizing soft tissue infection is similar to sepsis as a disease entity; both are prevalent in communities with high socioeconomic distress³² and benefit from early source control (the benchmark of which is early operative debridement to achieve source control in the case of NSTI).³³ As bundled care improves mortality in sepsis, the most important factor in mitigating mortality in NSTIs is early surgical debridement.³⁴ In contrast, bariatric, cardiac, and transplant populations are elective surgical populations, in which socioeconomic factors can play a more substantial role in mediating access to care and peri-operative compliance. Importantly, long-term metrics, such as weight loss, comorbidity recidivism, follow-up adherence, and patient-centered outcomes are routinely captured with these patients^35–37 but are not the standard of care in reporting sepsis outcomes. For example, mortality in our cohort is measured at a relatively short end-point (90-days post-diagnosis of sepsis), compared with bariatric studies that report mortality at up to 10-years post-operatively.²² Long-term data on outcomes and patient functionality are lacking in sepsis literature. There are calls for systematic reviews of the topic,³⁸ with several recent studies discussing the long-term molecular mechanistic changes and the cognitive sequelae of sepsis.³⁹ It is possible that as we begin capturing longer term data, markers of socioeconomic distress may become more relevant in these analyses.⁴⁰

The characteristics of our cohort include additional potential confounders in the context of mortality as an outcome. Our cohort was more distressed: top DCI quintile patients were highly represented in our cohort (33%) compared with other large disease-specific studies^16,24 (24% −26%). In addition, mortality (in-house and 90-day), as well as length of stay, were reliably associated with transfer status. The increased likelihood of being transferred to our quaternary center and increased comorbidity indices from high-DCI communities suggest disparities in access to care for those in more distressed communities. We have shown previously that transfer status is associated with a more than 80% increase in the odds of 90-day mortality for patients admitted to the SICU with sepsis.⁴¹ This suggests that hospital distress, rather than community indices, is the more relevant socioeconomic metric in this scenario. In other words, mortality after sepsis may be more likely impacted by disparities in logistical support and capacity at rural and critical access hospitals. Recently, other groups have advocated for the use of the Area Deprivation Index (ADI) in lieu of the DCI,⁴² which includes 21 non-race measurables with greater geographic specificity. In initial studies, ADI has shown outcome associations such as decreased rate of follow-up in vascular patients⁴³ and increased re-admission risk in colorectal surgical patients⁴⁴ whereas DCI has not. This suggests that ADI may be a more sensitive metric than DCI for outcomes in surgical patients,⁴⁵ and applying this to the sepsis cohort will serve as an area of future research.

Interestingly, we did find that high DCI is an independent risk factor for longer length of stay, despite adjusting for baseline patient characteristics. This lends further credence to the theory that mortality may not be the most relevant clinical outcome to evaluate with regards to DCI. Literature has long-established that lower socioeconomic status is associated with length of stay,⁴⁶ re-admissions, and poor follow-up^35,47,48 not just in surgical patients but in a variety of patient subsets including psychiatric in-patients,⁴⁹ pregnant patients,⁵⁰ and stroke patients.⁵¹ These studies suggest that beyond initial medical outcomes, lower socioeconomic status may impact areas such as discharge education, post-discharge resource availability, and compliance with post-operative management that subsequently modulates resource utilization after discharge. In our study, this finding may represent difficulty in returning to pre-hospital status because of pre-existing housing concerns or lack of support stability, both of which are not related to illness severity. Future studies should focus on determining whether there are modifiable causes for this longer length of stay. This would allow the DCI metric to be utilized to identify at-risk individuals and create quality improvement initiatives to reduce unnecessary resource utilization.

This study is limited by its retrospective nature and sample size. The DCI is generally discussed as a population characteristic for large cohorts, and it is possible that more nuanced differences would have been seen if our study was powered to that end. Additionally, the association between length of stay and DCI may still reflect confounders that we are not accounting for in our multivariable regression models. Although we utilized the composite DCI score, it is possible that sub-components of this score are more correlative to mortality in this population. In addition, metrics of hospital distress, such as bed capacity or other key performance indicators, were not included in this study and may represent more sensitive metrics in this discussion. We also acknowledge the inherent concerns with using 90-day mortality as our primary outcomes, which is why we evaluated length of stay. Additional outcomes that will be evaluated in subsequent analyses include readmissions, post-operative follow-up, and longer assessments of functionality following discharge. Despite these limitations, this analysis is strengthened by the large cohort size and intra-institutional consistency. Finally, we believe this study provides valuable insight into how we discuss socioeconomic distress in different surgical populations and elucidates potential drawbacks to the application of DCI.

Conclusions

Socioeconomic status as a metric in risk model construction, resource utilization, hospital comparisons, and patient management decisions has been advocated broadly. Our study demonstrated that although DCI was not associated with mortality in surgical patients with sepsis, it was a risk factor for increased overall length of stay. This study highlights potential limitations to the utility of the DCI when discussing mortality in critically ill patients with sepsis and also suggests alternative outcomes that may be more relevant, such as hospital length of stay.

Footnotes

Authors' Contributions

Chloe Williams assisted in study design, statistical analysis, interpretation of data, and writing the manuscript. Whitney Kellett assisted in interpretation of data and writing the manuscript. Jonathan Wisler assisted in study design, statistical analysis, interpretation of data, and writing the manuscript. Megan Ireland assisted in study design, statistical analysis, interpretation of data, and writing the manuscript. Wendy Wahl assisted in statistical analysis, interpretation of data, and writing of manuscript. Anahita Jalilvand assisted in study design, statistical analysis, interpretation of data, and writing the manuscript. All authors have reviewed the manuscript for final approval prior to submission.

Funding Information

This research received funding from the National Institutes of Health Institute of General Medical Sciences K08GM137078.

Author Disclosure Statement

There are no conflicts of interest to report from any of the authors of this manuscript.

Supplementary Material

References

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