Area Deprivation Index Predicts Mortality for Critically Ill Surgical Patients With Sepsis

Abstract

Background:

The impact of socioeconomic status on outcomes after sepsis has been challenging to define, and no polysocial metric has been shown to predict mortality in sepsis. The primary objective of this study was to evaluate the association between the Area Deprivation Index (ADI) and mortality in patients admitted to the surgical intensive care unit (SICU) with sepsis.

Patients and Methods:

All patients admitted to the SICU with sepsis (Sequential Organ Failure Assessment [SOFA] score ≥2) were retrospectively reviewed. The ADI scores were obtained and classified as “high ADI” (≥85th percentile, n = 400, representative of high socioeconomic deprivation) and “control ADI” (ADI <85th percentile, n = 976). Baseline demographic and clinical characteristics were compared between groups. The primary outcome was 90-day mortality.

Results:

High ADI patients were younger (mean age 58.5 vs. 60.8; p = 0.01) and more likely to be non-white (23.7% vs. 10.0%; p < 0.0005) and to present with chronic obstructive pulmonary disease (26.5% vs. 19.0%; p = 0.002). High ADI patients had increased in-hospital (27.3% vs. 21.6%; p = 0.025) and 90-day mortality (35.0% vs. 28.9%; p = 0.03). High ADI patients also had increased rates of renal failure (20.3% vs. 15.3%; p = 0.02). Both cohorts had similar intensive care unit (ICU) lengths of stay and median hospital stay, Charlson comorbidity index, and rate of discharge to home. High ADI is an independent risk factor for 90-day mortality after admission for surgical sepsis (odds ratio [OR], 1.39 ± 0.24; p = 0.014).

Conclusions:

High ADI is an independent predictor of 90-day mortality in patients with surgical sepsis. Targeted community interventions are needed to reduce sepsis mortality for these at-risk patients.

The interplay between socioeconomic status (SES) and health outcomes is a crucial area of investigation currently. The impact of economic and education status on access to care, health literacy, and nutrition are well documented, and socioeconomic distress remains an important factor in analyzing disparities in health outcomes.¹ Many studies have demonstrated that lower SES is associated with higher prevalence of baseline comorbidities and increasing morbidity and mortality once hospitalized.^2,3 Similarly, the surgical literature has contributed to this narrative by demonstrating the association between a variety of socioeconomic metrics and surgical outcomes and mortality, including worse outcomes, such as post-operative complication and re-admission rates after cardiac, vascular, transplant, and bariatric surgery.^1,4–6 Interestingly, although the incidence of sepsis has been linked to lower SES, the implications of markers of distress on outcomes after sepsis remain controversial, with mixed data. One possible reason for this is that not all metrics of SES are validated for health outcomes research. Different metrics may not actually capture the characteristics of socioeconomic distress that translate into limitations on access to care, health literature, and nutritional support.

An example of one such polysocial metric is the Distressed Communities Index (DCI), a comprehensive index encompassing economic, environmental, social, healthcare, education, and food access based on zip code.⁷ Whereas DCI has been linked to poorer outcomes in a variety of surgical subspecialties, a recent study by our institution failed to demonstrate an association between high DCI (high distress) and outcomes for surgical patients with sepsis. In fact, longer length of stay was the only outcome associated with high distress. We hypothesized that the DCI may not be the most accurate socioeconomic tool for analyzing mortality in this specific cohort and sought to find a more comprehensive metric for this analysis.

The Area Deprivation Index (ADI) is a publicly accessible measure of socioeconomic disadvantage that considers factors such as income, education, employment status, and housing quality when averaged across a U.S. census block group.⁸ The ADI has been shown to improve the socioeconomic risk adjustment in the surgical population and to independently predict the rate of post-operative complications in the general surgery population.⁹ Recent literature in several medical and surgical patient subtypes has revealed that high ADI (high distress) predicts complication rates, cost expenditure, re-admission rates, disability burden, and rates of discharge to facilities.^10,11 Additionally high ADI is a risk stratification tool reflecting both increased readmission rates as well as high overall cost of care in the general surgery population¹² Given this, we elected to determine the association of ADI on sepsis outcomes for surgical patients admitted to the surgical ICU with sepsis. We hypothesized that ADI would be associated with increased mortality for in-hospital and cumulative 90-day mortality.

Patients and Methods

Study design

All patients admitted to the Ohio State Wexner Medical Center surgical intensive care unit within a continuous five-year period (Institutional Review Board [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 as follows: adult patients (minimum 18 years of age) and diagnosed with sepsis as stated by SEPSIS III guidelines (Sequential Organ Failure Assessment [SOFA] score ≥2) within 48 hours of admission.¹³ Pregnant patients, prisoners, and re-admissions (without index admission data in the study time frame) were excluded from this review. Additionally, patients who lacked complete mortality data up to 90 days from admission were excluded. This mortality review was completed three years after the final study participant exited enrollment, to provide ample time after initial admission to record any known mortality information. For patients who were admitted more than once, and whose subsequent admissions met criteria for inclusion otherwise, the final admission date only was used in calculating time-to-mortality. The Area Deprivation Index (ADI) was defined for each patient enrolled.

The ADI metric was developed by the Health Resources and Service Administration (HRSA) and has been adapted for validation at the neighborhood (census block group) level by Dr. Amy Kind's group at the University of Wisconsin School of Medicine and Public Health's Center for Health Disparities Research.¹⁴ The ADI incorporates data for several socioeconomically relevant domains: income, education, employment, and housing quality. The ADI composite score, which is recalled using patient home physical address alone, is defined at both the national level (percentages 1–100) and the state level (deciles 1–10), with the highest percentage (100) and decile (10) corresponding to the highest level of socioeconomic distress. Differences in the DCI and ADI are listed in Table 1.

Table 1.

Comparison of Area Deprivation Index (ADI) and Distressed Communities Index (DCI)

	ADI	DCI
Education	• % population aged 25 or older with less than 9 y of education	• % population aged 25 or older with a high school diploma or equivalent
	• % population aged 25 or older with at least a high school diploma
	• % employed aged 16 or older in white-collar occupations
Income/employment	• Median family income • Income disparity • % families below federal poverty level • % population below 150% of federal poverty level • % civilian labor force age 16 or older who are unemployed	• % of adults age 25–54 not currently employed • % population living under federal poverty line • Median household income as a % of metro area median household income • % change in the number of jobs • % change in the number of business establishments
Housing	• Median home value	• % habitable housing that is unoccupied
	• Median gross rent
	• Median monthly mortgage
	• % owner-occupied housing
	• % occupied housing units without complete plumbing
Household characteristics	• % single-parent households with children younger than 18
	• % households without a motor vehicle
	• % households without a telephone
	• % households with more than 1 person per room

The ADI is defined by census block group and generated by searching search address into a publicly available database. For our study, street address was defined by the stated address of domicile on intake. If this was unavailable, other documentation was used to establish this address, including the unexpired government-issued photo ID submitted at the time of admission, ambulance run location, durable healthcare power of attorney address of record, or address as recorded by case management or social work during discharge suitability inquiry. If ADI could not be defined due to unknown address after thorough search for the above documents, or if there was no ADI associated with the address found (as is the case in ultra-high–density populations), we did not include these patients in the analysis.

The ADI scores were categorized into a high-deprivation cohort (n = 400), corresponding to 85th percentile or greater on the national indices, and a control group (ADI score 84th percentile or less; n = 976 patients). This categorization of ADI has been utilized in multiple studies of different populations and a variety of clinical outcomes and allows us to address our primary clinical question, which is the effect of high area deprivation distress on clinical outcomes.^5,15,16

Data collection and outcome measures

Reported variables were obtained from our institutional electronic medical record (EMR), 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 demographics and clinical data were obtained on all patients included the following: gender, race, transfer status, age, and selected comorbidities (congestive heart failure, type 2 diabetes mellitus [T2DM], moderate or worse liver disease, chronic obstructive pulmonary disease [COPD], obesity, stage 3 or worse kidney disease [CKD], metastatic cancer) and Charlson comorbidity index, SOFA score on admission, serum lactate (mmol/L) on admission, vasopressor use, and sepsis source (categorized as bacteremia/line-associated, respiratory, intra-abdominal, skin/soft tissue/burns, urologic, orthopedic, head/neck/thoracic).¹⁷

The primary outcomes in this study were in-house and 90-day cumulative mortality for patients with high ADI compared with control ADI. Mortality data were captured using any available data, including EMR-linked outside hospital data and follow-up clinic appointments. Secondary outcomes included ICU length of stay (days), overall length of stay (days), respiratory failure requiring invasive mechanical ventilation (%), ventilator duration (days), renal failure (%), renal replacement duration (days), and discharge disposition (home, facility, or hospice).

Statistical analyses

All statistical analyses were performed using STATA/SE 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. Categorical variables were analyzed using χ² or Fisher exact tests, based on the event count. Multivariable logistical regressions were created to model predictors of 90-day mortality 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 ADI

In our cohort, high ADI patients (ADI classified as ≥85th percentile nationally) represented 29.1% of cohort (n = 400) and control ADI (<85th percentile nationally) represented 70.9% of cohort (n = 976). The specific distribution of ADIs in this cohort is displayed in a histogram (Fig. 1). Our cohort was predominantly composed of high ADI individuals, with 80.7% (n = 1127) at or above the national median; 19.3% of the cohort (n = 250) were below the national median. High ADI patients were more likely to be younger (mean age 58.5 ± 15.4 vs. 60.8 ± 15.2; p = 0.01), non-white (23.7% vs. 10.0%; p < 0.0005), and more likely to have COPD on admission (26.5% vs. 19.0%; p = 0.002). High-ADI patients were more likely to be transferred to our medical center from an outside hospital (52.0% vs. 44.8%; p = 0.015; Table 2).

FIG. 1.

Distribution of Area Deprivation Index (ADI) values in our cohort (national percentile, with 100 representing maximum area deprivation, with total number of patients within each five-percentile-wide grouping shown on the y-axis). Our cohort disproportionally reflects the high-ADI (high deprivation, shaded bars) population compared with the low-ADI (lower levels of deprivation) population. The ADI breakdown in our dataset was distributed as follows (listed from most distressed category to least distressed category): high ADI (≥85th percentile nationally) represented 29.1% of cohort (n = 400) and control ADI (<85th percentile nationally) represented 70.9% of cohort (n = 976).

Table 2.

Clinical and Demographic Characteristics of Patients Admitted to the SICU by ADI

Variable	ADI		p
Variable	Control ADI (n = 976)	High ADI (n = 400)	p
Transferred: yes/no	437, 44.8	208, 52.0	0.015
Age (years): mean SD	60.8 (15.2)	58.5 (15.4)	0.01
Female (n, %)	443, 45.4	168, 42.0	0.25
Race (n, %)
Non-Hispanic white	874, 90.0	305, 76.3	< 0.005
Black	61, 6.3	78, 19.5
Other	31, 3.2	12, 3.0
Unknown	10, 1.0	5, 1.3
Selected comorbidities: (n, %)
CHF	82, 8.4	41, 10.3	0.28
T2DM	266, 27.3	116, 29.0	0.511
Moderate/severe liver disease	58, 5.9	29 7.3	0.37
COPD	185, 19.0	106, 26.5	0.002
Obesity	542, 57.6	214, 57.1	0.86
Stage III-IV CKD	110, 11.3	41, 10.3	0.58
Metastatic cancer	60, 6.2	20, 5.0	0.41
Charlson comorbidity index: p50 IQR	4 (2-6)	3 (2-6)	0.19

SICU = surgical intensive care unit; ADI = Area Deprivation 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 groups of high ADI and control ADI, including admission SOFA score, laboratory data, sepsis source, and vasopressor use on admission. Skin and soft tissue sources of sepsis were more common in the high ADI cohort compared with the control group (17.5% vs. 10.9%; p = 0.006). There was no significant difference between the high ADI and control cohorts when comparing rates of other presenting sources of sepsis (bacteremia/line associated, respiratory, intra-abdominal, 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 groups.

Comparison of hospitalization characteristics and outcomes by ADI cohorts and predictors of 90-day mortality

The overall 90-day mortality for the entire cohort was 30.6% (n = 422). Compared with the control cohort, high ADI patients exhibited increased in-hospital (27.3% vs. 21.6%; p = 0.025) and 90-day mortality (35.0% vs. 28.9%; p = 0.03). The high ADI cohort also exhibited increased rates of renal failure (35.0% vs. 28.9%; p = 0.03), comparable median ICU lengths of stay (7.0 [interquartile range {IQR}, 3.1–18.1) vs. 7.3 [IQR, 3.0–15.3] days; p = 0.62), overall hospital lengths of stay (20 [IQR, 10–33] vs. 18 [IQR, 10–32] days; p = 0.03), and equivocal median hospital stay when excluding in-house deaths (21 [IQR, 12–34] vs. 20 [IQR,12–33] days; p = 0.19). There was no difference in rate of discharge to home for high ADI patient compared with control (28.3% vs. 28.8%; p = 0.99; Table 3).

Table 3.

Outcomes After Admission to the SICU by ADI

Variable	ADI		p
Variable	ControL ADI (n = 976)	High ADI (n = 400)	p
ICU LOS (d): p50 (IQR)	7.3 (3–15.3)	7 (3.1–18.1)	0.62
Overall LOS (d): p50 (IQR)	18 (10–32)	20 (10–33)	0.30
LOS (excluding in-house deaths): p50 (IQR)	20 (12–33)	21 (12–34)	0.19
Respiratory failure: (n, %)	696, 71.3	301, 75.3	0.14
Days on the ventilator (d): p50 (IQR)
Renal failure: (n, %)	149, 15.3	81, 20.3	0.02
Days on renal replacement therapy (d): p50 (IQR)
Mortality (n, %)
In-house	211, 21.6	109, 27.3	0.025
90-d	282, 28.9	140, 35.0	0.03
Discharge disposition (n, %)
Home	219, 28.8	82, 28.3	0.99
SNF/LTACH	492, 64.7	189, 65.2
Hospice	50, 6.6	19, 6.6

SICU = surgical intensive care unit; ADI = Area Deprivation Index; LOS = length of stay; ICU intensive care unit; IQR = interquartile range; SNF = skilled nursing facility; LTACH = long-term acute care hospital.

A multiple logistic regression model was created for cumulative 90-day mortality with the following independent variables: age, gender, race, transfer status, body mass index (BMI), ADI, Charlson comorbidity index, admission lactate, admission SOFA, vasopressor use on admission, and sepsis source. Positive predictors of 90-day mortality were high ADI scores (OR, 1.39; 95% confidence interval [CI], 1.09–2.07; p = 0.014), transfer status (OR, 1.74; 95% CI: 1.28–2.35; p < 0.0005), increasing BMI (OR, 1.03; 95% CI: 1.02–1.04; p < 0.0005) and Charlson comorbidity index (OR, 1.35; 95% CI, 1.26–1.44; p < 0.0005), higher admission lactate (OR, 1.17; 95% CI: 1.10–1.24; p < 0.0005), SOFA (OR, 1.07; 95% CI, 1.02–1.12; p = 0.005; Table 4). Finally, when source of sepsis was compared between high ADI and control ADI groups, urologic sources of sepsis were independent predictors of decreased 90-day mortality rate (OR, 0.15; 95% CI, 0.03–0.062; p = 0.009).

Table 4.

Predictors for In-Hospital and 90-Day Mortality

Variable	90-d mortality
Variable	OR	SE	95% CI	p
Age	1.01	0.01	0.99–1.02	0.07
Gender	0.87	0.13	0.64–1.17	0.36
Race (black vs non-Hispanic white)	1.37	0.31	0.88–2.12	0.16
High ADI	1.39	0.24	1.09–2.07	0.014
Transfer status	1.74	0.27	1.28–2.35	< 0.0005
BMI	1.03	0.007	1.02–1.04	< 0.0005
Charlson comorbidity index	1.35	0.05	1.26–1.44	< 0.0005
Admission lactate	1.17	0.03	1.10–1.24	< 0.0005
Admission SOFA	1.07	0.03	1.02–1.12	0.005
Vasopressors on admission	1.10	0.18	0.79–1.51	0.58
Sepsis source (compared to line-associated bacteremia)
Respiratory	2.23	1.10	0.89–5.89	0.09
Intra-abdominal	2.03	0.86	0.89–4.63	0.09
Skin, soft tissue, burns	1.12	0.56	0.47–3.01	0.72
Urologic	0.15	0.11	0.03–0.062	0.009
Orthopedic	0.87	0.69	0.19–4.1	0.869
Head and neck, thoracic	2.22	1.67	0.51–9.7	0.29

OR = odds ratio; SE = standard error; CI = confidence interval; ADI = Area Deprivation Index; BMI = body mass index; SOFA = Sequential Organ Failure Assessment.

Discussion

Socioeconomic status affects outcomes in medical and surgical patients alike. However, specific SES metrics have proven disease and outcome specific. In previous studies, high ADI has been associated with reduced rates of follow-up of vascular patients, increased re-admission risk in colorectal surgical patients, and increased mortality in certain high-risk emergency general surgery patients.^10,18,19 In other surgical subtypes, including general surgery patients, ADI has a role in surgical risk-adjustment, as it predicts complication rates and cost expenditure.^10,16 This study is the first to show that high ADI is an independent predictor of 90-day mortality in a large cohort of patients with surgical sepsis. In our population, almost 30% of patients were classified as high ADI. This is consistent with existing studies in the literature.^20,21 Our study adds to the surgical sepsis literature by identifying a polysocial metric that is predictive of higher mortality in this patient population.

In addition to high ADI, there were other predictors of 90-day mortality in surgical sepsis patients in our study. These factors include increasing BMI, transfer status, increased Charlson comorbidity index, higher admission lactate, and higher admission SOFA scores, which are consistent with recent literature on factors contributing to longer term mortality in sepsis survivors.^22,23 In this cohort, high BMI was predictive of 90-day mortality, which is found in many studies of patients with sepsis, although there is some literature debate as to this relation.²⁴ Finally, the high ADI cohort also had increased rates of renal failure. It is well-established that the risk of kidney disease and progression to renal failure is higher in patients with more socioeconomic risk,²⁵ and our study specifically identifies renal failure as an independent predictor of 90-day mortality in this cohort of patients with surgical sepsis.

Our group has previously published that another polysocial metric, DCI, is not an independent predictor for mortality in surgical sepsis.²⁶ This may reflect disease-specific or metric-specific factors. When considering disease-specific factors, sepsis can be compared to the elective surgical presentations. The DCI has been shown to be predictive of outcomes in elective surgical populations. For example, DCI is predictive in transplant and bariatric surgical populations, where clinic infrastructure demands reliable follow-up and compliance in both the pre- and post-operative periods as a component of undergoing surgery.^27–29 The utility of ADI for sepsis and DCI for planned surgeries is a reflection on the utility of specific metrics for specific types of surgical presentations.

Now that we have established ADI as an independent predictor of 90-day mortality in patients with surgical sepsis, we must leverage this to inspire correlative laboratory investigations. Specifically, molecular characteristics of patients with high ADI should be investigated to explore the physiologic allostatic load in these patients. Additionally, this data can be leveraged to design clinical improvements in care delivery. Individual ADI components may be addressed by interventions in the post-discharge setting. In order to continue to explore the 90-day mortality metric in this study, we should pair these data with additional patient subsets and outcomes measures inspired by the multitude of literature that has shown low SES associations with increased length of stay, re-admissions, and poor follow-up in surgical patients and multiple medical patient subsets.^30–36

Equipped with this new association between high ADI and risk of 90-day mortality, we next should target discharge education, post-discharge resource availability, and compliance with post-operative management for these patients. Many institutions including our own are developing multi-disciplinary transition of care clinics for post-critical illness patients. These clinics can serve as powerful tools to attempt to ameliorate the effects of socioeconomic distress including poor social support, transportation to follow-up, and establishment of long-term primary care.³⁷ Emergency department utilization in the immediate post-discharge period should be explored, as this represents high-cost resource utilization and simultaneously indicates a potential failure, or area requiring targeted improvement, in our discharge planning for any individual patient. Ultimately, these efforts may coincide with our previous data regarding increased length of stay.

Finally, when considering interventions, treatment of “psychosocial” risk factors includes institutional, structural, and policy change initiatives to improve outcomes for patients with high ADI. Both patients and caregivers experience a range of challenges across transitions of care, and hospital systems can look to remedy some of these recognizable challenges through improving interactions with the healthcare system through improved communication, identifying recovery limitations such as lack of transportation, and assistance with establishment of long-term care.³⁷

Socioeconomic status is a difficult target for metric construction because it is challenging to account for the mandatory amalgam of factors including patient, family, community, clinical, and hospital-specific comparisons. This study demonstrates that the ADI metric is predictive of 90-day mortality in our patients. This, combined with previous data on the relation between another polysocial metric (DCI) and increased length-of-stay in patients with sepsis, bolsters our call to advocacy on behalf of at-risk high-ADI patient populations.

Conclusions

Socioeconomic status has been broadly advocated to improve surgical risk stratification and more efficient resource allocation. In contrast to prior studies from our institution using the DCI, we demonstrate that high ADI is an independent risk factor for 90-day mortality in patients admitted to our SICU with sepsis. This study highlights that ADI may be a more clinically relevant variable when evaluating mortality following sepsis. Finally, these findings open avenues for future research to determine potentially modifiable risk factors within the high ADI cohort that can be targeted to mitigate mortality disparities in these groups.

Footnotes

Authors' Contributions

Study design: Kellett, Jalilvand, Wisler. Data collection: Kellett. Data organization: Kellett. Interpretation of data: Kellett, Jalilvand, Wisler. Writing of manuscript: Kellett, Jalilvand, Wisler. Statistical analysis: Jalilvand, Wisler. Database management: Baselice. Critical review of the manuscript: Baselice. Writing and revisions: Collins. Database analysis: Collins. Writing and critical review of manuscript: Ireland.

All authors have reviewed the manuscript for final approval prior to submission.

Funding Information

This research was funded in part through the National Institutes of Health award R35GM150968.

Author Disclosure Statement

The authors have no relevant conflicts of interest to disclose.

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