Development and Assessment of a Systematic Approach for Detecting Disparities in Surgical Access | Health Disparities | JAMA Surgery | JAMA Network
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Table 1.  Summary of Characteristics of the Lowest-Ranked and Highest-Ranked Counties by Health Outcome in North Carolina
Summary of Characteristics of the Lowest-Ranked and Highest-Ranked Counties by Health Outcome in North Carolina
Table 2.  Observed and Expected Number of General Surgery Procedures Performed for the 5 Lowest-Ranked Counties in North Carolina
Observed and Expected Number of General Surgery Procedures Performed for the 5 Lowest-Ranked Counties in North Carolina
Table 3.  Emergency/Urgent Cholecystectomies and Herniorrhaphies for the 5 Lowest-Ranked Counties in North Carolina
Emergency/Urgent Cholecystectomies and Herniorrhaphies for the 5 Lowest-Ranked Counties in North Carolina
Table 4.  Elective Inpatient Cholecystectomies for the 5 Lowest-Ranked Counties in North Carolina
Elective Inpatient Cholecystectomies for the 5 Lowest-Ranked Counties in North Carolina
Table 5.  Obesity and Bariatric Surgery Procedures for the 5 Lowest-Ranked Counties in North Carolina
Obesity and Bariatric Surgery Procedures for the 5 Lowest-Ranked Counties in North Carolina
1.
A model for monitoring access. In: Institute of Medicine (US) Committee on Monitoring Access to Personal Health Care Services; Millman  M, eds.  Access to Health Care in America. National Academies Press; 1993:31-45.
2.
Office of Disease Prevention and Health Promotion. About Healthy People. Accessed April 8, 2020. https://www.healthypeople.gov/2020/About-Healthy-People
3.
Kim  J, Artinyan  A, Mailey  B,  et al.  An interaction of race and ethnicity with socioeconomic status in rectal cancer outcomes.   Ann Surg. 2011;253(4):647-654. doi:10.1097/SLA.0b013e3182111102PubMedGoogle ScholarCrossref
4.
Yang  R, Cheung  MC, Byrne  MM,  et al.  Do racial or socioeconomic disparities exist in lung cancer treatment?   Cancer. 2010;116(10):2437-2447. doi:10.1002/cncr.24986PubMedGoogle Scholar
5.
Bennett  KM, Scarborough  JE, Pappas  TN, Kepler  TB.  Patient socioeconomic status is an independent predictor of operative mortality.   Ann Surg. 2010;252(3):552-557. doi:10.1097/SLA.0b013e3181f2ac64PubMedGoogle ScholarCrossref
6.
Brookfield  KF, Cheung  MC, Lucci  J, Fleming  LE, Koniaris  LG.  Disparities in survival among women with invasive cervical cancer: a problem of access to care.   Cancer. 2009;115(1):166-178. doi:10.1002/cncr.24007PubMedGoogle ScholarCrossref
7.
Lapar  DJ, Bhamidipati  CM, Walters  DM,  et al.  Primary payer status affects outcomes for cardiac valve operations.   J Am Coll Surg. 2011;212(5):759-767. doi:10.1016/j.jamcollsurg.2010.12.050PubMedGoogle ScholarCrossref
8.
Kelz  RR, Gimotty  PA, Polsky  D, Norman  S, Fraker  D, DeMichele  A.  Morbidity and mortality of colorectal carcinoma surgery differs by insurance status.   Cancer. 2004;101(10):2187-2194. doi:10.1002/cncr.20624PubMedGoogle ScholarCrossref
9.
Lathan  CS, Neville  BA, Earle  CC.  The effect of race on invasive staging and surgery in non-small-cell lung cancer.   J Clin Oncol. 2006;24(3):413-418. doi:10.1200/JCO.2005.02.1758PubMedGoogle ScholarCrossref
10.
Murphy  MM, Simons  JP, Hill  JS,  et al.  Pancreatic resection: a key component to reducing racial disparities in pancreatic adenocarcinoma.   Cancer. 2009;115(17):3979-3990. doi:10.1002/cncr.24433PubMedGoogle ScholarCrossref
11.
Jolly  S, Vittinghoff  E, Chattopadhyay  A, Bibbins-Domingo  K.  Higher cardiovascular disease prevalence and mortality among younger blacks compared to whites.   Am J Med. 2010;123(9):811-818. doi:10.1016/j.amjmed.2010.04.020PubMedGoogle ScholarCrossref
12.
Kershaw  KN, Diez Roux  AV, Burgard  SA, Lisabeth  LD, Mujahid  MS, Schulz  AJ.  Metropolitan-level racial residential segregation and black-white disparities in hypertension.   Am J Epidemiol. 2011;174(5):537-545. doi:10.1093/aje/kwr116PubMedGoogle ScholarCrossref
13.
Norris  KC, Agodoa  LY.  Unraveling the racial disparities associated with kidney disease.   Kidney Int. 2005;68(3):914-924. doi:10.1111/j.1523-1755.2005.00485.xPubMedGoogle ScholarCrossref
14.
Morris  AM, Wei  Y, Birkmeyer  NJ, Birkmeyer  JD.  Racial disparities in late survival after rectal cancer surgery.   J Am Coll Surg. 2006;203(6):787-794. doi:10.1016/j.jamcollsurg.2006.08.005PubMedGoogle ScholarCrossref
15.
Sosa  JA, Mehta  PJ, Wang  TS, Yeo  HL, Roman  SA.  Racial disparities in clinical and economic outcomes from thyroidectomy.   Ann Surg. 2007;246(6):1083-1091. doi:10.1097/SLA.0b013e31812eecc4PubMedGoogle ScholarCrossref
16.
Aranda  MA, McGory  M, Sekeris  E, Maggard  M, Ko  C, Zingmond  DS.  Do racial/ethnic disparities exist in the utilization of high-volume surgeons for women with ovarian cancer?   Gynecol Oncol. 2008;111(2):166-172. doi:10.1016/j.ygyno.2008.08.009PubMedGoogle ScholarCrossref
17.
Bristow  RE, Palis  BE, Chi  DS, Cliby  WA.  The National Cancer Database report on advanced-stage epithelial ovarian cancer: impact of hospital surgical case volume on overall survival and surgical treatment paradigm.   Gynecol Oncol. 2010;118(3):262-267. doi:10.1016/j.ygyno.2010.05.025PubMedGoogle ScholarCrossref
18.
Nathan  H, Frederick  W, Choti  MA, Schulick  RD, Pawlik  TM.  Racial disparity in surgical mortality after major hepatectomy.   J Am Coll Surg. 2008;207(3):312-319. doi:10.1016/j.jamcollsurg.2008.04.015PubMedGoogle ScholarCrossref
19.
de Jager  E, Levine  AA, Udyavar  NR,  et al.  Disparities in surgical access: a systematic literature review, conceptual model, and evidence map.   J Am Coll Surg. 2019;228(3):276-298. doi:10.1016/j.jamcollsurg.2018.12.028PubMedGoogle ScholarCrossref
20.
Levine  AA, de Jager  E, Britt  LD.  Perspective: identifying and addressing disparities in surgical access: a health systems call to action.   Ann Surg. 2020;271(3):427-430.PubMedGoogle ScholarCrossref
21.
Kilbourne  AM, Switzer  G, Hyman  K, Crowley-Matoka  M, Fine  MJ.  Advancing health disparities research within the health care system: a conceptual framework.   Am J Public Health. 2006;96(12):2113-2121. doi:10.2105/AJPH.2005.077628PubMedGoogle ScholarCrossref
22.
Debas  HT, Laxminarayan  R, Straus  SE. Complementary and alternative medicine. In: Jamison  DT, Breman  JG, Measham  AR,  et al, eds.  Disease Control Priorities in Developing Countries. 2nd ed. The International Bank for Reconstruction and Development/The World Bank; 2006:1281-1292.
23.
University of Wisconsin Population Health Institute. County health rankings & roadmaps: North Carolina. Accessed April 7, 2020. https://www.countyhealthrankings.org/app/north-carolina/2019/overview
24.
Agency for Healthcare Research and Quality. Healthcare Cost and Utilization Project (HCUP). Accessed April 8, 2020. https://www.ahrq.gov/data/hcup/index.html
25.
Office of State Budget and Management. State demographer. Accessed August 12, 2020.https://www.osbm.nc.gov/facts-figures/demographics
26.
Institute of Medicine (US) Committee on Quality of Health Care in America.  Crossing the Quality Chasm: A New Health System for the 21st Century. National Academies Press; 2001.
27.
Breslow  NE, Day  NE.  Statistical methods in cancer research. volume II—the design and analysis of cohort studies.   IARC Sci Publ. 1987;(82):1-406.PubMedGoogle Scholar
28.
Haider  AH, Dankwa-Mullan  I, Maragh-Bass  AC,  et al.  Setting a national agenda for surgical disparities research: recommendations from the National Institutes of Health and American College of Surgeons Summit.   JAMA Surg. 2016;151(6):554-563. doi:10.1001/jamasurg.2016.0014PubMedGoogle ScholarCrossref
29.
American College of Surgeons. ACS statement: there is no quality without access. Accessed April 13, 2020. https://www.facs.org/health-care-disparities
30.
Shrime  MG, Bickler  SW, Alkire  BC, Mock  C.  Global burden of surgical disease: an estimation from the provider perspective.   Lancet Glob Health. 2015;3(suppl 2):S8-S9. doi:10.1016/S2214-109X(14)70384-5PubMedGoogle ScholarCrossref
31.
Bickler  SN, Weiser  TG, Kassebaum  N,  et al. Global burden of surgical conditions. In: Debas  HT, Donkor  P, Gawande  A, Jamison  DT, Kruk  ME, Mock  CN, eds.  Essential Surgery: Disease Control Priorities, Third Edition (Volume 1). The International Bank for Reconstruction and Development/The World Bank; 2015:19-40.
32.
Harvard T.H. Chan School of Public Health. Zip code better predictor of health than genetic code. Accessed April 13, 2020. https://www.hsph.harvard.edu/news/features/zip-code-better-predictor-of-health-than-genetic-code/
33.
Robert Wood Johnson Foundation. Life expectancy: could where you life influence how long you live? Accessed April 13, 2020. https://www.rwjf.org/en/library/interactives/whereyouliveaffectshowlongyoulive.html
34.
Mullen  MG, Michaels  AD, Mehaffey  JH,  et al.  Risk associated with complications and mortality after urgent surgery vs elective and emergency surgery: implications for defining “quality” and reporting outcomes for urgent surgery.   JAMA Surg. 2017;152(8):768-774. doi:10.1001/jamasurg.2017.0918PubMedGoogle ScholarCrossref
35.
Wier  LM, Steiner  CA, Owens  PL. Surgeries in hospital-owned outpatient facilities, 2012. Accessed September 1, 2020. https://hcup-us.ahrq.gov/reports/statbriefs/sb188-Surgeries-Hospital-Outpatient-Facilities-2012.jsp
36.
Kind  AJH, Buckingham  WR.  Making neighborhood-disadvantage metrics accessible—the Neighborhood Atlas.   N Engl J Med. 2018;378(26):2456-2458. doi:10.1056/NEJMp1802313PubMedGoogle ScholarCrossref
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    Original Investigation
    December 16, 2020

    Development and Assessment of a Systematic Approach for Detecting Disparities in Surgical Access

    Author Affiliations
    • 1Division of Surgical Oncology, Department of Surgery, Brody School of Medicine at East Carolina University, Greenville, North Carolina
    • 2Division of Surgical Research, Department of Surgery, Brody School of Medicine at East Carolina University, Greenville, North Carolina
    • 3Department of Public Health, East Carolina University, Greenville, North Carolina
    • 4Division of General Minimal Invasive and Bariatric Surgery, Department of Surgery, Brody School of Medicine at East Carolina University, Greenville, North Carolina
    • 5Division of Trauma and Critical Care, Department of Surgery, Brody School of Medicine at East Carolina University, Greenville, North Carolina
    • 6Division of Surgical Immunology and Transplantation, Department of Surgery, Brody School of Medicine at East Carolina University, Greenville, North Carolina
    JAMA Surg. 2021;156(3):239-245. doi:10.1001/jamasurg.2020.5668
    Key Points

    Question  Is there a reproducible method to detect and measure disparities in surgical access?

    Findings  In this cross-sectional study of more than 2 million residents residing in the 5 highest-ranked and lowest-ranked counties of North Carolina by health outcome rank as defined by the Robert Wood Johnson Foundation, the proportion of individuals receiving similar access to surgical care was significantly different than a reference healthy population.

    Meaning  A framework using a priori the healthiest populations at the county level as the reference provides a systematic approach to detect and measure disparities in surgical access.

    Abstract

    Importance  Although optimal access is accepted as the key to quality care, an accepted methodology to ascertain potential disparities in surgical access has not been defined.

    Objective  To develop a systematic approach to detect surgical access disparities.

    Design, Setting, and Participants  This cross-sectional study used publicly available data from the Health Cost and Utilization Project State Inpatient Database from 2016. Using the surgical rate observed in the 5 highest-ranked counties (HRCs), the expected surgical rate in the 5 lowest-ranked counties (LRCs) in North Carolina were calculated. Patients 18 years and older who underwent an inpatient general surgery procedure and patients who underwent emergency inpatient cholecystectomy, herniorrhaphy, or bariatric surgery in 2016 were included. Data were collected from January to December 2016, and data were analyzed from March to July 2020.

    Exposures  Health outcome county rank as defined by the Robert Wood Johnson Foundation.

    Main Outcomes and Measures  The primary outcome was the proportional surgical ratio (PSR), which was the disparity in surgical access defined as the observed number of surgical procedures in the 5 LRCs relative to the expected number of procedures using the 5 HRCs as the standardized reference population.

    Results  In 2016, approximately 1.9 million adults lived in the 5 HRCs, while approximately 246 854 lived in the 5 LRCs. A total of 28 924 inpatient general surgical procedures were performed, with 4521 being performed in those living in the 5 LRCs and 24 403 in those living in the 5 HRCs. The rate of general surgery in the 5 HRCs was 13.09 procedures per 1000 population. Using the 5 HRCs as the reference, the PSR for the 5 LRCs was 1.40 (95% CI, 1.35-1.44). For emergent/urgent cholecystectomy, the PSR for the 5 LRCs was 2.26 (95% CI, 2.02-2.51), and the PSR for emergent/urgent herniorrhaphy was 1.83 (95% CI, 1.33-2.45). Age-adjusted rate of obesity (body mass index [calculated as weight in kilograms divided by height in meters squared] greater than 30), on average, was 36.6% (SD, 3.4) in the 5 LRCs vs 25.4% (SD, 4.6) in the 5 HRCs (P = .002). The rate of bariatric surgery in the 5 HRCs was 33.07 per 10 000 population with obesity. For the 5 LRCs, the PSR was 0.60 (95% CI, 0.51-0.69).

    Conclusions and Relevance  The PSR is a systematic approach to define potential disparities in surgical access and should be useful for identifying, investigating, and monitoring interventions intended to mitigate disparities in surgical access that effects the health of vulnerable populations.

    Introduction

    Access, defined as “the timely use of personal health services to achieve the best possible health outcomes,”1 continues to be a major challenge in the US and remains a priority of the Healthy People 2020 initiative.2 Personal health services may include access to surgical services in a substantial proportion of the population. Surgical access is influenced by the complex interaction of numerous factors, including race/ethnicity, social economic status,3-5 insurance,6-8 and even the willingness to undergo surgery,9,10 while outcomes and access may be influenced by disparities in disease burden and comorbid conditions,11-13 surgeon volume,14-16 and hospital volume,17,18 all of which may result in surgical health care disparities.19 However, limited access to health care is closely associated with surgical health care disparities.19,20

    Kilbourne and colleagues21 proposed a conceptual framework in which disparities research should be conducted in the context of the health care system. The first phase in this framework is detecting health disparities. Although indicators of access to health services can provide insight into the utilization of health services and are intended to sense where or when access problems occur,1 it is important to recognize that access to health services broadly and surgical services specifically is not an end in and of itself. Access to surgical services is just one facet of health care services that contribute to the wellness of a population.

    Determining the burden of surgical disease in a population is complex.22 Most estimates of surgical disease burden are based solely on procedures performed and do not account for surgical conditions that affect health but never require a surgical procedure. To address this shortcoming, we defined an expected utilization of surgical services as that observed in a healthy population and used that as a reference to determine whether a difference in utilization observed in a vulnerable population could be broadly applied to detect potential disparities in surgical access using North Carolina as the modeling framework.

    Methods
    Quantifying the Health of a Population

    We used the county health rankings of the University of Wisconsin Population Health Institute Robert Wood Johnson Foundation (RWJF) to define the health rankings of the 100 counties in North Carolina.23 The rankings are derived from models that use the domains of health behaviors, clinical care, social and economic factors, and physical environment where an individual lives to determine the impact on the length of life (ie, years of potential life lost before age 75 years) and quality of life. Health outcomes ranks demonstrated a cluster in the highest-ranked counties (HRCs) and the lowest-ranked counties (LRCs), with substantial overlap of the midquartiles of health outcome scores.23 For this reason, we used the 5 HRCs as the reference for surgical access and compared surgical access with the 5 LRCs. The study used data that are publicly available on the internet from Healthcare Cost and Utilization Project (HCUP).24 Based on guidance from the Office of Human Research Protection, this study was determined not to require institutional review board review under 45 CFR 46.

    Data Sources

    We used data from the 2016 North Carolina State Inpatient Database (SID) to determine the use of inpatient surgical services. The SID are part of the family of databases and software tools developed for the HCUP24 and capture hospital inpatient stays in a given state. SID contains more than 100 clinical and nonclinical variables, such as the principal and secondary diagnoses, procedures, and patient demographic characteristics. Census data were obtained from the North Carolina Office of Budget and Management25 to determine population size projections by county for 2016.

    Procedures

    Surgical procedures were identified using International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Procedure Coding System (ICD-10-PCS) codes and diagnostic-related group (DRG) codes (DRG versions 33 and 34) where the primary reason for hospitalization was procedural. We excluded surgical procedures generally provided by subspecialty services, such as cardiac and peripheral vascular, surgery of the head and neck, transplant, and orthopedic procedures. Inpatient surgical procedures were categorized as emergent/urgent or elective according to the HCUP reporting structure. In addition, we studied cholecystectomy and hernia repairs as representative of commonly performed general (nonspecialized) surgical procedures that are performed on both an elective and emergent basis. We studied bariatric surgery as an example of an electively scheduled surgery. To identify cholecystectomy procedures, we used ICD-10-PCS codes FB40ZZ, 0FB44ZZ, 0FB43ZZ, 0FB48ZZ, 0FT40ZZ, and 0FT44ZZ. To identify herniorrhaphy (femoral repair, inguinal repair, and anterior wall repair), we used DRG codes 350 to 355. To identify bariatric procedures, we used DRG codes 619 to 621.

    Assumptions, Definitions, and Hypothesis

    Access to timely, safe, effective, equitable, evidence-based, and patient-centered26 surgical care for those who require surgical care is a key contributor to the overall health of a population. We assumed that surgical care in the urgent or emergent setting should be minimized and surgical care, when needed, should be maximized in the elective setting. Furthermore, we assumed there is a disease burden defined as the number of individuals (N) with conditions of such duration and/or severity who meet accepted guidelines for surgical care as part of maintaining their health. In this paradigm, the observed surgical prevalence (R1) is defined as R1 = m/N, where m is the number of surgical procedures for a given disease or condition. For comparison, we assumed that there is a theoretical surgical prevalence, R2, that reflects the true or ideal rate of surgery for population N. Since R2 is unknown, we assumed the rate of surgery in the healthiest population23 would best approximate R2. The rationale for this is that individuals in the healthiest population are more likely to have access to health services, including surgical care, in a timely fashion. Furthermore, in the absence of prevalence data for a given disease or condition, ie, where there are insufficient data to estimate N, we assumed that a given population was at similar risk for developing conditions that require surgical intervention. Thus, we hypothesized that if R1 is much less than R2, then access to surgical care is insufficient for elective access to surgical care, and if R1 is much greater than R2, then there is either overutilization or emergency/urgent access to surgical care. In using the healthiest population to approximate R2, it is important to recognize that this approximation may not define the ideal rate of surgical care for the population.

    We defined disparity as a statistically significant difference in the observed number of individuals undergoing a surgical procedure in a less healthy population relative to the expected number of procedures based on the healthiest (reference) population. We termed this the proportional surgical ratio (PSR). Although unique to surgery, the PSR is similar in concept to the standardized mortality ratio used in epidemiologic research.27

    Statistical Analysis

    Using the county health rankings,23 we estimated rates of inpatient surgical services in the 5 LRCs in North Carolina as well as in the 5 HRCs, which were defined a priori. These 5 HRCs served as the reference for R2 and the PSR calculation. PSR was obtained by dividing the observed number of procedures in the 5 LRCs by the expected number of procedures. The expected number is the number of procedures that would occur in the 5 LRCs if the surgical rate in the HRC reference population (R2) occurred in that cohort.

    The expected number was calculated by multiplying the surgery rate of the 5 HRCs by the population size of each county in the 5 LRCs and then adding up the results. If the observed number of procedures equaled the expected number, the PSR is 1. If more procedures were observed than expected, the PSR is greater than 1. If fewer procedures were observed than expected, the PSR is less than 1. 95% CIs around the PSR were calculated using Byar approximation.27 Calculations were performed for each type of surgical procedure. Characteristics of the 5 LRCs and HRCs were compared using the standard 2-sampled t test. All analyses were performed using SAS version 9.4 (SAS Institute). All tests were 2-tailed. P values less than .05 were considered statistically significant.

    Results
    Study Population

    The adult population in the 5 HRCs in North Carolina in 2016, as defined by the RWJF, numbered approximately 1.86 million individuals, while 246 854 individuals resided in the 5 LRCs. In the 5 HRCs, on average, 32.5% of the residents lived in a rural setting compared with 60.1% of residents in the 5 LRCs. Residents in the 5 LRCs were typically older, had less education and lower income, were more likely to be uninsured, and had higher rates of preexisting health conditions compared with residents in the 5 HRCs (Table 1).

    General Surgery

    In 2016, a total of 28 924 inpatient general surgical procedures were performed in the 10 counties studied. Of these, 4521 procedures (373 emergent/urgent and 4148 elective procedures) were performed in the 5 LRCs (18.31 procedures per 1000 population) and 24 403 procedures (1213 emergent/urgent and 23190 elective procedures) were performed in the 5 HRCs (13.09 procedures per 1000 population). The observed and expected number of inpatient surgical procedures in the 5 LRCs are presented in Table 2. Residents in the 5 LRCs were 40% more likely to undergo an inpatient general surgical procedure than residents in the 5 HRCs (PSR, 1.40; 95% CI, 1.35-1.44). For emergent/urgent procedures, the PSR for the 5 LRCs was 2.72 (95% CI, 2.09-2.57).

    Emergent/Urgent Cholecystectomy and Herniorrhaphy

    The rate of emergent/urgent cholecystectomy ranged from 8.39 to 19.75 per 10 000 population, and the rate of emergent/urgent herniorrhaphies ranged from 7.30 to 29.18 per 100 000 population in the 5 LRCs. The observed and expected number of emergency/urgent cholecystectomy and herniorrhaphy performed in the 5 LRCs are presented in Table 3. Using HRCs as the reference, the PSR for the 5 LRCs was 2.26 (95% CI, 2.02-2.51) for cholecystectomy and 1.83 (95% CI, 1.33-2.45) for herniorrhaphy.

    Elective Inpatient Cholecystectomy

    The number of elective in-patient cholecystectomies performed was substantially less than the number of emergent/urgent cholecystectomies (1425 emergent/urgent vs 156 elective procedures). The rate of elective cholecystectomy ranged from 1.16 to 2.44 per 10 000 population in the 5 LRCs and from 0.34 to 0.83 per 10 000 population in the 5 HRCs. The expected number of inpatient cholecystectomies was 15.35 for the 5 LRCs. The PSR for the 5 LRCs was 2.60 (95% CI, 1.78-3.41) (Table 4).

    Elective Bariatric Surgery

    Obesity was defined as a body mass index (calculated as weight in kilograms divided by height in meters squared) of 30 or greater, and the percentage of adult obesity was obtained from the county health rankings of the RWJF.23 On average, 36.6% (SD, 3.4) of individuals in the 5 LRCs had obesity compared with 25.4% (SD, 4.6) in the 5 HRCs (P = .002). Based on these estimates, 91 475 individuals in the 5 LRCs and 456 264 individuals in the 5 HRCs would be classified as having obesity. For the 5 HRCs, the rate of bariatric surgery (R2) was 33.07 per 10 000 population with obesity. The observed and expected number of bariatric surgery procedures in the 5 LRCs are presented in Table 5. Using the 5 HRCs as the reference, the PSR for the 5 LRCs was 0.60 (95% CI, 0.51-0.69).

    Discussion

    Mitigating surgical disparities is a priority of the American College of Surgeons and the National Institute of Health.28 Optimizing access is considered the key to quality surgical care.29 We hypothesized that surgical access disparities could be defined as the difference between the observed usage of surgical services in a vulnerable, less healthy population and that observed in a healthy reference population. In this report, we used routinely collected population-level information and focused on observed utilization of inpatient surgery as an indicator of healthiness. We observed that individuals residing in the 5 LRCs (less healthy) were significantly less likely to receive purely elective surgical care (eg, bariatric surgery) while being at a substantially greater likelihood to have emergency or urgent surgical interventions than those living in the 5 HRCs (more healthy). We designated this difference between individuals residing in LRCs and HRCs as the PSR and propose that it represents a useful indicator to identify potential surgical access disparity between populations.

    A substantial volume of observational and cross-sectional data19 demonstrate that certain segments of the population receive less than optimal surgical access as defined by certain processes of care and/or specified outcomes. These reports have used existing retrospective databases and are limited to the availability of the variables in the database and generally to patients who have undergone a surgical procedure.19 For this reason, the disparity domains examined are largely confined to race/ethnicity and, to a lesser extent, insurance status and level of income.

    To understand disparities in surgical access, it is incumbent to define the burden of surgical disease within a population. Estimating the population burden of surgical disease is extremely challenging. Although the number of inpatient and ambulatory surgery procedures performed can be obtained from large data repositories, such as the nationwide HCUP database,24 defining the surgical disease burden solely on these data alone likely underestimates what constitutes surgical care. Certain types of surgical care would not be captured, such as preoperative assessments of whether it is appropriate to operate and intraoperative anesthetic management30 that is critical to successfully treat a surgical condition or disease that never results in a surgical procedure but requires the expertise of surgeons, such as nonoperative management of blunt abdominal trauma. For these reasons, previous reports likely fail to account for the true burden of surgical disease and may not be truly representative of global disparities in access to surgical care.

    Our approach to this fundamental question was a conceptual model in which access to surgical care was not characterized by disparity metrics19 but instead was defined as an integral part of a health care system that contributes to the overall health and wellness of a population.31 In this conceptual model, the healthiest populations were hypothesized to be most likely to have the best access to surgical care. Because a vulnerable subpopulation is not solely based on race/ethnicity but on a broad range of characteristics, such as socioeconomic status, age, sex, level of education, and place of residence,32,33 incorporating these variables into the model is crucial to understand the determinants of the surgical access disparity.

    To test this conceptual model, we examined whether the county health rankings model of the RWJF,23 which incorporates multiple factors beyond race/ethnicity a priori, defines a population at the county level who have the fewest barriers to surgical care. The county health rankings model is a measure both of length of life (years of potential life lost before age 75 years) and quality of life. We opted to use the county of residence as the unit of analysis rather than the county of surgical care, as the RWJF ranking incorporates access to care. Our results demonstrate that the PSR is a dynamic platform that can identify differences in the use of surgical services relative to a reference healthy population. We suggest these differences can be used to globally define potential disparities in surgical access.

    One of the strengths of this model is that it can account for both overuse and underuse of surgical services relative to a reference healthy population. Our results demonstrate that surgical conditions that ideally should be performed electively, such as cholecystectomy or herniorrhaphy, are significantly more likely to have emergent/urgent surgical care with the attended greater morbidity and mortality34 in the 5 LRCs. In contrast, an elective surgical procedure with defined surgical indications, such as obesity, are less frequently performed in those living in LRCs. We observe that the populations in the 5 LRCs were significantly more likely to undergo inpatient elective cholecystectomy (PSR, 2.60; 95% CI, 1.78-3.41) than those living in the 5 HRCs. This is not unanticipated, as most cholecystectomies are now performed in an ambulatory setting35 in which patients are generally healthy while inpatient procedures are reserved for those with significant comorbidities. Further research is needed to determine the reasons for the disparities, which may include more complex statistical modeling as an initial step.

    Limitations

    This study had limitations. This report did not account for care by surgeons that does not involve an inpatient surgical procedure or surgical procedures performed in the outpatient setting. We recognize that in our evolving health care system, increasingly, many surgical procedures are now conducted in the outpatient setting, and for this reason, SID data alone may not account for the actual prevalence of clinical conditions within populations. In this report, we assumed that our populations of comparison were at similar risk of developing conditions of surgical importance, which may not be accurate. Combining both inpatient and outpatient data will give a more comprehensive perspective of disparities in surgical access as well as the prevalence of disease. However, it is important to note that the PSR methodology proposed in this report will remain useful to detect potential disparities in surgical access that warrant further investigation.

    In the 3 phases of health disparities research described by Kilbourne and colleagues,21 detecting and defining vulnerable populations predicates understanding and subsequently intervening and reducing health disparities. This report describes a systematic method to define and detect potential disparities in surgical access. Because we used the county health rankings in the model, the PSR incorporates differences in health outcomes or health status and minimizes the effects of selection bias and other potential confounding factors. Although the calculation of the PSR presented in this report is simple, more complex statistical models can be performed considering not only the variability in access across counties but variability within a county as well. In this report, we used the county of residence as the unit of measure; however, there is no discernable reason not to use other units, such as the county in which surgical care is delivered, the Census tract, or the area deprivation index,36 as long as data are available, to ascertain the population size and the number of operative procedures.

    Conclusions

    The PSR is a methodologic framework in which populations can be determined to have potential surgical access disparity for very specific surgical conditions. Future investigations will use increasingly granular, publicly available Census data on health, behavioral, and social determinants down to the level of the zip code, which will allow us to develop more robust statistical models that will allow more precise estimates of the PSR. This will allow us to better understand variables at the patient, physician, clinical encounter, and health care system level that contribute to these disparities and can help unravel how these variables are intertwined. We anticipate this will allow a systematic development of interventions to address and reduce surgical access disparities.

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    Article Information

    Accepted for Publication: September 21, 2020.

    Published Online: December 16, 2020. doi:10.1001/jamasurg.2020.5668

    Corresponding Author: Jan H. Wong, MD, Division of Surgical Oncology, Department of Surgery, Brody School of Medicine at East Carolina University, 600 Moye Blvd, Greenville, NC 27834 (wongj@ecu.edu).

    Author Contributions: Drs Wong and Irish had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Study concept and design: All authors.

    Acquisition, analysis, or interpretation of data: Wong, Irish, DeMaria, Vohra, Pories, Brownstein.

    Drafting of the manuscript: Wong, Irish, DeMaria, Altieri, Akram, Haisch.

    Critical revision of the manuscript for important intellectual content: Wong, Irish, DeMaria, Vohra, Pories, Brownstein, Altieri, Leeser, Tuttle.

    Statistical analysis: Wong, Irish.

    Administrative, technical, or material support: Wong, Pories, Akram, Leeser, Tuttle.

    Study supervision: Wong.

    Conflict of Interest Disclosures: Dr DeMaria has received speaking fees from Medtronic and WL Gore as well as nonfinancial support from Intuitive. No other disclosures were reported.

    References
    1.
    A model for monitoring access. In: Institute of Medicine (US) Committee on Monitoring Access to Personal Health Care Services; Millman  M, eds.  Access to Health Care in America. National Academies Press; 1993:31-45.
    2.
    Office of Disease Prevention and Health Promotion. About Healthy People. Accessed April 8, 2020. https://www.healthypeople.gov/2020/About-Healthy-People
    3.
    Kim  J, Artinyan  A, Mailey  B,  et al.  An interaction of race and ethnicity with socioeconomic status in rectal cancer outcomes.   Ann Surg. 2011;253(4):647-654. doi:10.1097/SLA.0b013e3182111102PubMedGoogle ScholarCrossref
    4.
    Yang  R, Cheung  MC, Byrne  MM,  et al.  Do racial or socioeconomic disparities exist in lung cancer treatment?   Cancer. 2010;116(10):2437-2447. doi:10.1002/cncr.24986PubMedGoogle Scholar
    5.
    Bennett  KM, Scarborough  JE, Pappas  TN, Kepler  TB.  Patient socioeconomic status is an independent predictor of operative mortality.   Ann Surg. 2010;252(3):552-557. doi:10.1097/SLA.0b013e3181f2ac64PubMedGoogle ScholarCrossref
    6.
    Brookfield  KF, Cheung  MC, Lucci  J, Fleming  LE, Koniaris  LG.  Disparities in survival among women with invasive cervical cancer: a problem of access to care.   Cancer. 2009;115(1):166-178. doi:10.1002/cncr.24007PubMedGoogle ScholarCrossref
    7.
    Lapar  DJ, Bhamidipati  CM, Walters  DM,  et al.  Primary payer status affects outcomes for cardiac valve operations.   J Am Coll Surg. 2011;212(5):759-767. doi:10.1016/j.jamcollsurg.2010.12.050PubMedGoogle ScholarCrossref
    8.
    Kelz  RR, Gimotty  PA, Polsky  D, Norman  S, Fraker  D, DeMichele  A.  Morbidity and mortality of colorectal carcinoma surgery differs by insurance status.   Cancer. 2004;101(10):2187-2194. doi:10.1002/cncr.20624PubMedGoogle ScholarCrossref
    9.
    Lathan  CS, Neville  BA, Earle  CC.  The effect of race on invasive staging and surgery in non-small-cell lung cancer.   J Clin Oncol. 2006;24(3):413-418. doi:10.1200/JCO.2005.02.1758PubMedGoogle ScholarCrossref
    10.
    Murphy  MM, Simons  JP, Hill  JS,  et al.  Pancreatic resection: a key component to reducing racial disparities in pancreatic adenocarcinoma.   Cancer. 2009;115(17):3979-3990. doi:10.1002/cncr.24433PubMedGoogle ScholarCrossref
    11.
    Jolly  S, Vittinghoff  E, Chattopadhyay  A, Bibbins-Domingo  K.  Higher cardiovascular disease prevalence and mortality among younger blacks compared to whites.   Am J Med. 2010;123(9):811-818. doi:10.1016/j.amjmed.2010.04.020PubMedGoogle ScholarCrossref
    12.
    Kershaw  KN, Diez Roux  AV, Burgard  SA, Lisabeth  LD, Mujahid  MS, Schulz  AJ.  Metropolitan-level racial residential segregation and black-white disparities in hypertension.   Am J Epidemiol. 2011;174(5):537-545. doi:10.1093/aje/kwr116PubMedGoogle ScholarCrossref
    13.
    Norris  KC, Agodoa  LY.  Unraveling the racial disparities associated with kidney disease.   Kidney Int. 2005;68(3):914-924. doi:10.1111/j.1523-1755.2005.00485.xPubMedGoogle ScholarCrossref
    14.
    Morris  AM, Wei  Y, Birkmeyer  NJ, Birkmeyer  JD.  Racial disparities in late survival after rectal cancer surgery.   J Am Coll Surg. 2006;203(6):787-794. doi:10.1016/j.jamcollsurg.2006.08.005PubMedGoogle ScholarCrossref
    15.
    Sosa  JA, Mehta  PJ, Wang  TS, Yeo  HL, Roman  SA.  Racial disparities in clinical and economic outcomes from thyroidectomy.   Ann Surg. 2007;246(6):1083-1091. doi:10.1097/SLA.0b013e31812eecc4PubMedGoogle ScholarCrossref
    16.
    Aranda  MA, McGory  M, Sekeris  E, Maggard  M, Ko  C, Zingmond  DS.  Do racial/ethnic disparities exist in the utilization of high-volume surgeons for women with ovarian cancer?   Gynecol Oncol. 2008;111(2):166-172. doi:10.1016/j.ygyno.2008.08.009PubMedGoogle ScholarCrossref
    17.
    Bristow  RE, Palis  BE, Chi  DS, Cliby  WA.  The National Cancer Database report on advanced-stage epithelial ovarian cancer: impact of hospital surgical case volume on overall survival and surgical treatment paradigm.   Gynecol Oncol. 2010;118(3):262-267. doi:10.1016/j.ygyno.2010.05.025PubMedGoogle ScholarCrossref
    18.
    Nathan  H, Frederick  W, Choti  MA, Schulick  RD, Pawlik  TM.  Racial disparity in surgical mortality after major hepatectomy.   J Am Coll Surg. 2008;207(3):312-319. doi:10.1016/j.jamcollsurg.2008.04.015PubMedGoogle ScholarCrossref
    19.
    de Jager  E, Levine  AA, Udyavar  NR,  et al.  Disparities in surgical access: a systematic literature review, conceptual model, and evidence map.   J Am Coll Surg. 2019;228(3):276-298. doi:10.1016/j.jamcollsurg.2018.12.028PubMedGoogle ScholarCrossref
    20.
    Levine  AA, de Jager  E, Britt  LD.  Perspective: identifying and addressing disparities in surgical access: a health systems call to action.   Ann Surg. 2020;271(3):427-430.PubMedGoogle ScholarCrossref
    21.
    Kilbourne  AM, Switzer  G, Hyman  K, Crowley-Matoka  M, Fine  MJ.  Advancing health disparities research within the health care system: a conceptual framework.   Am J Public Health. 2006;96(12):2113-2121. doi:10.2105/AJPH.2005.077628PubMedGoogle ScholarCrossref
    22.
    Debas  HT, Laxminarayan  R, Straus  SE. Complementary and alternative medicine. In: Jamison  DT, Breman  JG, Measham  AR,  et al, eds.  Disease Control Priorities in Developing Countries. 2nd ed. The International Bank for Reconstruction and Development/The World Bank; 2006:1281-1292.
    23.
    University of Wisconsin Population Health Institute. County health rankings & roadmaps: North Carolina. Accessed April 7, 2020. https://www.countyhealthrankings.org/app/north-carolina/2019/overview
    24.
    Agency for Healthcare Research and Quality. Healthcare Cost and Utilization Project (HCUP). Accessed April 8, 2020. https://www.ahrq.gov/data/hcup/index.html
    25.
    Office of State Budget and Management. State demographer. Accessed August 12, 2020.https://www.osbm.nc.gov/facts-figures/demographics
    26.
    Institute of Medicine (US) Committee on Quality of Health Care in America.  Crossing the Quality Chasm: A New Health System for the 21st Century. National Academies Press; 2001.
    27.
    Breslow  NE, Day  NE.  Statistical methods in cancer research. volume II—the design and analysis of cohort studies.   IARC Sci Publ. 1987;(82):1-406.PubMedGoogle Scholar
    28.
    Haider  AH, Dankwa-Mullan  I, Maragh-Bass  AC,  et al.  Setting a national agenda for surgical disparities research: recommendations from the National Institutes of Health and American College of Surgeons Summit.   JAMA Surg. 2016;151(6):554-563. doi:10.1001/jamasurg.2016.0014PubMedGoogle ScholarCrossref
    29.
    American College of Surgeons. ACS statement: there is no quality without access. Accessed April 13, 2020. https://www.facs.org/health-care-disparities
    30.
    Shrime  MG, Bickler  SW, Alkire  BC, Mock  C.  Global burden of surgical disease: an estimation from the provider perspective.   Lancet Glob Health. 2015;3(suppl 2):S8-S9. doi:10.1016/S2214-109X(14)70384-5PubMedGoogle ScholarCrossref
    31.
    Bickler  SN, Weiser  TG, Kassebaum  N,  et al. Global burden of surgical conditions. In: Debas  HT, Donkor  P, Gawande  A, Jamison  DT, Kruk  ME, Mock  CN, eds.  Essential Surgery: Disease Control Priorities, Third Edition (Volume 1). The International Bank for Reconstruction and Development/The World Bank; 2015:19-40.
    32.
    Harvard T.H. Chan School of Public Health. Zip code better predictor of health than genetic code. Accessed April 13, 2020. https://www.hsph.harvard.edu/news/features/zip-code-better-predictor-of-health-than-genetic-code/
    33.
    Robert Wood Johnson Foundation. Life expectancy: could where you life influence how long you live? Accessed April 13, 2020. https://www.rwjf.org/en/library/interactives/whereyouliveaffectshowlongyoulive.html
    34.
    Mullen  MG, Michaels  AD, Mehaffey  JH,  et al.  Risk associated with complications and mortality after urgent surgery vs elective and emergency surgery: implications for defining “quality” and reporting outcomes for urgent surgery.   JAMA Surg. 2017;152(8):768-774. doi:10.1001/jamasurg.2017.0918PubMedGoogle ScholarCrossref
    35.
    Wier  LM, Steiner  CA, Owens  PL. Surgeries in hospital-owned outpatient facilities, 2012. Accessed September 1, 2020. https://hcup-us.ahrq.gov/reports/statbriefs/sb188-Surgeries-Hospital-Outpatient-Facilities-2012.jsp
    36.
    Kind  AJH, Buckingham  WR.  Making neighborhood-disadvantage metrics accessible—the Neighborhood Atlas.   N Engl J Med. 2018;378(26):2456-2458. doi:10.1056/NEJMp1802313PubMedGoogle ScholarCrossref
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