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Community level socioeconomic status association with surgical outcomes and resource utilisation in a regional cohort: a prospective registry analysis
  1. J Hunter Mehaffey1,
  2. Robert B Hawkins1,
  3. Eric J Charles1,
  4. Florence E Turrentine1,
  5. Brian Kaplan2,
  6. Sandy Fogel3,
  7. Charles Harris3,
  8. David Reines4,
  9. Jorge Posadas5,
  10. Gorav Ailawadi1,
  11. John B Hanks1,
  12. Peter T Hallowell1,
  13. R Scott Jones1
  1. 1 Surgery, University of Virginia, Charlottesville, Virginia, USA
  2. 2 Department of Surgery, Virginia Commonwealth University, Richmond, Virginia, USA
  3. 3 Department of Surgery, Carilion Clinic, Roanoke, Virginia, USA
  4. 4 Department of Surgery, Inova Mount Vernon Hospital, Alexandria, Virginia, USA
  5. 5 Department of Surgery, Winchester Medical Center, Winchester, Virginia, USA
  1. Correspondence to Dr J Hunter Mehaffey, Surgery, University of Virginia, Charlottesville, VA 22903, USA; jhm9t{at}


Background Socioeconomic status affects surgical outcomes, however these factors are not included in clinical quality improvement data and risk models. We performed a prospective registry analysis to determine if the Distressed Communities Index (DCI), a composite socioeconomic ranking by zip code, could predict risk-adjusted surgical outcomes and resource utilisation.

Methods All patients undergoing surgery (n=44,451) in a regional quality improvement database (American College of Surgeons-National Surgical Quality Improvement Program ACS-NSQIP) were paired with DCI, ranging from 0–100 (low to high distress) and accounting for unemployment, education level, poverty rate, median income, business growth and housing vacancies. The top quartile of distress was compared to the remainder of the cohort and a mixed effects modeling evaluated ACS-NSQIP risk-adjusted association between DCI and the primary outcomes of surgical complications and resource utilisation.

Results A total of 9369 (21.1%) patients came from severely distressed communities (DCI >75), who had higher rates of most medical comorbidities as well as transfer status (8.4% vs 4.8%, p<0.0001) resulting in higher ACS-NSQIP predicted risk of any complication (8.0% vs 7.1%, p<0.0001). Patients from severely distressed communities had increased 30-day mortality (1.8% vs 1.4%, p=0.01), postoperative complications (9.8% vs 8.5%, p<0.0001), hospital readmission (7.7 vs 6.8, p<0.0001) and resource utilisation. DCI was independently associated with postoperative complications (OR 1.07, 95% CI 1.04 to 1.10, p<0.0001) as well as resource utilisation after adjusting for ACS-NSQIP predicted risk

Conclusion Increasing Distressed Communities Index is associated with increased postoperative complications and resource utilisation even after ACS-NSQIP risk adjustment. These findings demonstrate a disparity in surgical outcomes based on community level socioeconomic factors, highlighting the continued need for public health innovation and policy initiatives.

  • health policy
  • healthcare quality improvement
  • patient safety
  • surgery
  • quality improvement

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Socioeconomic status (SES) has been demonstrated to be strongly associated with both short and long-term health outcomes in the American population.1–4 Similarly, low SES has been tied to worse outcomes in cardiovascular disease and even hospital selection in European countries with universal healthcare.5 6 Socioeconomic disparities have important patient level effects, which have been demonstrated using surrogates including race, insurance status and income.7–10 However, community level factors are critical to understand when assessing the impact of SES on clinical outcomes.11 12 Community level factors include availability of in home health support, access to primary care, as well as travel distance to higher level or complex care.13 14 Currently there is no composite measure of community level SES to identify regions at highest risk for postoperative complications related to community level socioeconomic distress.

The Distressed Communities Index (DCI) is a tool created by the Economic Innovation Group (EIG) for measuring the relative economic distress of United States communities using zip-code level Census Bureau data from 2011 to 2015.15 DCI uses seven local metrics to quantify socioeconomic risk including the proportion of the population (age ≥25 years) without a high school diploma or equivalent, housing vacancy rate, adult (age ≥16 years) unemployment rate, poverty rate, median income ratio and local change in employment and number of business establishments. Based on this data, scores ranging from 0 (no distress) to 100 (severe distress) are obtained by standardising all zip codes on each of those metrics, averaging them and normalising data to generate a relative measure of socioeconomic distress. Recent work by our group has demonstrated DCI correlates with postoperative complications and hospital costs after general surgery cases at a single academic institution.16

The purpose of this study was to assess the risk-adjusted association between DCI, a composite metric of community level SES and surgical outcomes in a regional American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP database to confirm our prior findings. We hypothesise a higher DCI score will predict worse risk-adjusted outcomes and higher resource utilisation in our cohort.



This observational cohort study was performed using a prospective registry surgical registry. The Health Sciences Institutional Review Board approved the study with waiver of patient consent (Protocol #18801) since this was a deidentified analysis of a multicentre database. A Surgical Quality Collaborative includes all institutions (6) in the region that submit validated data to the ACS-NSQIP for both inpatient and outpatient operations. Standard ACS-NSQIP variables were used to compare baseline and demographic factors between our patient populations. Transfer status refers to a patient being transferred from another hospital to the hospital where they underwent surgery. While we are unable to assess the reason for the transfer, generally it is because of acuity or complexity of surgical problem that requires a higher level of care. Previously validated 30 day outcomes including mortality, postoperative complications (Pneumonia, Cardiac Complication, Surgical Site Infection, Urinary Tract Infection, Venous Thromboembolism, Renal Failure, Return to the OR, Sepsis), readmission and discharge to a nursing or rehab facility were also compared.17–20 Standard operation definitions by Current Procedural Terminology code from the ACS-NSQIP were used to describe the case mix. The DCI score was merged with case records from each institution based on patient’s zip code and then submitted for analysis to maintain deidentification of the data. Proportion and type of cases captured by ACS-NSQIP as well as rates of missing zip code (0% to 30.7%) varied by centre (online supplementary table 1). All patients without a zip code were excluded (n=5721).

Supplemental material

Distressed communities index

The DCI score is available for all zip codes in the United States in which more than 500 people reside, capturing 99% of the American population.21 It is a composite score based on the following seven metrics: no high school degree, housing vacancy rate, adults not working, poverty rate, median income ratio, change in employment and change in business establishments. The seven evenly weighted variables are used to calculate a zip code’s rank compared with its geographic peers and then normalised to obtain a distress score that ranges from 0 (no distress) to 100 (severe distress). The seven SES indicators were obtained from the American Communities Survey 2014 5-year Estimates and the Census Bureau County and Zip Code Business Patterns. Recent work has demonstrated patients from the top quartile of distressed communities by DCI have the highest rates of adverse events and worse long-term survival after surgical procedures.16 22

Statistical analysis

Patients in the highest DCI quartile (Distressed) were compared to the remainder of the cohort. Categorical variables are presented as n (%) and compared using χ2 analysis. Continuous variables were assessed for normality and presented as Median (IQR) and compared using Mann-Whitney U test for non-normal distributions. Mixed effects models were used to evaluate the association between DCI and outcomes. Primary outcomes were 30-day mortality, surgical complications, length of stay, discharge to nursing facility and hospital readmission. Multivariable linear (continuous outcomes) or logistic (binary outcomes) models were fit including DCI as a continuous variable; year of surgery and ACS-NSQIP predicted risk score as fixed effects and institution as a random effect. Statistical significance was defined as p<0.05. All analyses were performed using SAS V.9.1 (SAS Institute).


Baseline demographics and comorbid disease

After exclusion of 5721 patients with missing zip code, a total of 44 451 surgical operations with complete 30-day follow-up were included in the study. The cohort had a median age of 58 (23) with 25 415 (57.2%) female patients and a majority of patients were white 36 186 (81.4%). These cases include a variety of operations in ten different surgical subspecialties (table 1). The study population had a total of 9369 (21.1%) patients from distressed communities (DCI >75). Patients from distressed communities had higher rates of medical comorbidities including hypertension (55.8% vs 46.8%, p<0.0001) and diabetes (20.7% vs 16.1%, p<0.0001) resulting in significantly higher proportion of cases with American Society of Anesthesiology classification greater than 2 (54.9% vs 45.6%, p<0.0001) (table 1). Similarly, patients from distressed communities had higher rates of transfer from outside hospital (8.4% vs 4.8%, p<0.0001) but fewer outpatient procedures (42.2 vs 46.0%, p<0.0001) and emergency status operations (5.4% vs 9.3%, p<0.0001)(table 1). These factors resulted in a higher ACS-NSQIP predicted risk of complication (8.0% vs 7.1%, p<0.0001) in patients from the top quartile most distressed communities. The subcomponents of DCI correlate with higher distress score as patients from the most distressed communities having the largest disparities (table 2)

Table 1

Baseline demographics and comorbidities

Table 2

Subcomponents of DCI by quartile

Unadjusted outcomes and resource utilisation

Patients from severely distressed communities had longer operative times (106 minutes vs 96 minutes, p<0.0001) and longer median hospital length of stay (2 days vs 1 day, p<0.0001) as well as higher rates of discharge to a skilled nursing facility (11.1% vs 8.2%, p<0.0001)(table 3). These patients had an increased incidence of postoperative complications (9.8% vs 8.5%, p<0.0001) including urinary tract infections (2.0% vs 1.5%, p=0.0006), pneumonia (1.7% vs 1.2%, p<0.0001) and pulmonary embolism (0.3% vs 0.5%, p=0.017). Additionally, patients from distressed communities had higher rates of 30-day hospital readmission (7.7 vs 6.8, p<0.0001) and all-cause mortality (1.8% vs 1.4%, p=0.01) (table 3).

Table 3

Postoperative outcomes

Risk-adjusted outcomes and resource utilisation

After risk adjustment with mixed effects models, DCI was independently associated with postoperative complications (OR 1.07; 95% CI 1.04, 1.10; p<0.0001) but not 30-day mortality (figure 1). The DCI was also independently associated with common measures of resource utilisation including hospital length of stay (+0.06; 95% CI 0.04, 0.08; p=0.026), discharge to a facility (OR 1.20; 95% CI 1.16, 1.24; p<0.0001) and 30-day readmission (OR 1.06; 95% CI 1.03, 1.10; p=0.0004) (table 4).

Figure 1

Risk-adjusted association of DCI with outcomes. DCI, Distressed communities index.

Table 4

Risk-adjusted effect of DCI on 30 Day surgical outcomes


The present study demonstrates the association of community level socioeconomic factors on surgical outcomes and resource utilisation in a regional cohort. Patients from distressed communities have higher rates of medical comorbidities portending higher preoperative risk for complications. As expected this high-risk population has higher rates of complications and greater resource utilisation compared to the lower risk patients with lower DCI. However, after risk adjustment with the ACS-NSQIP risk score, the DCI score independently predicts worse outcomes and higher healthcare expenditures. These data support the DCI score as a valid measure of community level SES, which is strongly associated with healthcare outcomes. Finally, these findings corroborate previous work demonstrating a strong relationship between DCI and surgical outcomes in a single institution study.16

Surgical outcomes are affected by a plethora of factors including clinical variables, patient effects, as well as physician and hospital influences; however this study demonstrates a strong correlation with community level SES.10 23 These geographical effects highlight the interplay between health resources, population characteristics and regional disparities in access. Our findings identify populations across the state of Virginia with high risk for adverse outcomes but community level disparities in healthcare compound these effects. Racial differences across geographic regions have long been identified as a critical factor in predicting disparities in health outcomes.7 24 25 However, as populations shift overtime the disparities still exist despite a narrowing of the racial gap in health care while the gap in health outcomes by income inequality continues to grow.1 26 27 Correction of these disparate outcomes is going to require identification of the populations most at risk as well as innovative solutions. Technology has enabled more efficient delivery of health care and may represent an opportunity to overcome these regional disparities in healthcare.28

The community level association with surgical outcomes demonstrated in the present study is likely multifactorial including access to care, hospital quality and community health resources. Several studies of access to surgical care focus on patient travel distance as a surrogate however a more important factor is timing to diagnosis, referral and surgical treatment.13 29–32 Insurance status has repeatedly been reported as one of the most important factors affecting a patients’ access to surgical care.33–35 Another critical factor in determining healthcare outcomes in surgical disease is hospital quality and operative volume.36–38 Several national programs have been developed to identify and improve quality healthcare delivery at the hospital level but these efforts currently have limited ability to effect change.39 40 However, many patients from distressed communities rely on these safety net hospitals to receive care at late stages of disease that results in a unique challenge for surgeons.12 14 41 The best opportunity to effect change in these populations is earlier diagnosis and access to surgical care as well as improved baseline health care to prevent and manage comorbid diseases.

Several studies have identified postoperative complications and resource utilisation as the largest drivers of healthcare cost.42–44 To offer optimal value in healthcare we must provide the highest quality care at the lowest cost. However, a patient’s medical factors and SES dictate their cost of care and these must be accounted for when creating cost models.45 Current cost models focus on clinical factors to determine quality and predict cost for bundled payments while omitting the critical socioeconomic factors that affect both resource utilisation and cost.46 The DCI score is Health Insurance Portability and Accountability Act compliant composite surrogate for community level SES that can be integrated into cost models with clinical data to provide appropriate risk adjustment for value based payment models. These data demonstrate the strong association between DCI and surgical outcomes as well as resource utilisation in this regional cohort making it the ideal metric for integration into clinical databases for surgical risk adjustment and prediction.

The limitations of the present study include the retrospective design precluding demonstration of causality. While this analysis includes a regional cohort a majority of our patients were white and these results may not directly translate to other populations nationally. Approximately 10% of the total population was excluded for missing Zip code, which may introduce bias, however, due to the nature of the ACS-NSQIP database there was no missing clinical data and no additional patients were excluded. The ACS-NSQIP is a clinical database verified with a rigorous validation process using a systematic sampling protocol demonstrating >98% accuracy, however the follow-up is limited to 30-days postoperatively. Finally, we elected to use the ACS-NSQIP risk score for risk adjustment in our models rather than the classic approach of refitting individual covariates on our smaller population because these scores are based on hierarchical models with millions of patients and are highly predictive.

In conclusion the Distressed Communities Index, an established metric for community level socioeconomic distress, highlights disparities in health status and comorbid disease in a regional cohort. Furthermore, DCI predicts surgical outcomes and resource utilisation beyond the gold standard ACS-NSQIP risk score to better stratify patients and allocate resources. These findings demonstrate a disparity in surgical outcomes based on community level socioeconomic factors and highlight an area of focus for future health policy initiatives.



  • Funding This study was supported by National Heart, Lung, and Blood Institute (T32HL00784).

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement Data are available upon reasonable request.