Geospatial Evaluation of Disparities in Access to Cervical Spine Fusion in Metropolitan Areas Across the United States

被引:1
作者
Peterman, Nicholas [1 ]
Shivdasani, Krishin [1 ,2 ]
Naik, Anant [1 ,3 ]
Dharnipragada, Rajiv [3 ]
Harrop, James [4 ]
Vaccaro, Alexander R. [5 ]
Arnold, Paul M. [1 ,6 ,7 ]
机构
[1] Univ Illinois, Carle Illinois Coll Med, Urbana, IL USA
[2] Dept Orthopaed Surg & Rehabil, Loyola Med, Maywood, IL USA
[3] Univ Minnesota Twin Cities, Dept Neurosurg, Minneapolis, MN USA
[4] Thomas Jefferson Hosp, Dept Neurosurg, Philadelphia, PA USA
[5] Rothman Orthoped Inst, Dept Orthoped Surg, Philadelphia, PA USA
[6] Carle Fdn Hosp, Dept Neurosurg, Urbana, IL USA
[7] Carle Fdn Hosp, Carle Illinois Coll Med, 610 N Lincoln Ave, Urbana, IL 61801 USA
来源
CLINICAL SPINE SURGERY | 2024年 / 37卷 / 05期
关键词
geospatial; cervical spinal fusions; hotspot analysis; Medicare; health care disparities; RACIAL DISPARITIES; INSURANCE STATUS; SURGERY; DISKECTOMY; TRENDS; MORTALITY; OUTCOMES; RATES; CARE; RACE;
D O I
10.1097/BSD.0000000000001564
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
Study Design:Retrospective study with epidemiologic analysis of public Medicare data.Objective:The purpose of this study is to use geospatial analysis to identify disparities in access to cervical spine fusions in metropolitan Medicare populations.Summary of Background Data:Cervical spine fusion is among the most common elective procedures performed by spine surgeons and is the most common surgical intervention for degenerative cervical spine disease. Although some studies have examined demographic and socioeconomic trends in cervical spine fusion, few have attempted to identify where disparities exist and quantify them at a community level.Methods:Center for Medicare and Medicaid Services physician billing and Medicare demographic data sets from 2013 to 2020 were filtered to contain only cervical spine fusion procedures and then combined with US Census socioeconomic data. The Moran Index geospatial clustering algorithm was used to identify statistically significant hotspot and coldspots of cervical spine fusions per 100,000 Medicare members at a county level. Univariate and multivariate analysis was subsequently conducted to identify demographic and socioeconomic factors that are associated with access to care.Results:A total of 285,405 cervical spine fusions were analyzed. Hotspots of cervical spine fusion were located in the South, while coldspots were throughout the Northern Midwest, the Northeast, South Florida, and West Coast. The percent of Medicare patients that were Black was the largest negative predictor of cervical spine fusions per 100,000 Medicare members (beta=-0.13, 95% CI: -0.16, -0.10).Conclusions:Barriers to access can have significant impacts on health outcomes, and these impacts can be disproportionately felt by marginalized groups. Accounting for socioeconomic disadvantage and geography, this analysis found the Black race to be a significant negative predictor of access to cervical spine fusions. Future studies are needed to further explore potential socioeconomic barriers that exist in access to specialized surgical care.Level of Evidence:Level III-retrospective.
引用
收藏
页码:E208 / E215
页数:8
相关论文
共 33 条
  • [1] Insurance Status, Geography, Race, and Ethnicity as Predictors of Anterior Cervical Spine Surgery Rates and In-Hospital Mortality An Examination of United States Trends From 1992 to 2005
    Alosh, Hassan
    Riley, Lee H., III
    Skolasky, Richard L.
    [J]. SPINE, 2009, 34 (18) : 1956 - 1962
  • [2] Trends in Outpatient Cervical Spine Surgery: Are There Emerging Disparities?
    Amen, Troy B.
    Bovonratwet, Patawut
    Rudisill, Samuel S.
    Barber, Lauren A.
    Jordan, Yusef J.
    Chatterjee, Abhinaba
    Mok, Jung K.
    Varady, Nathan H.
    Qureshi, Sheeraz A.
    [J]. SPINE, 2023, 48 (09) : E116 - E121
  • [3] [Anonymous], 2021, American Community Survey 5-year estimate
  • [4] [Anonymous], 2021, Medicare Physician & Other Practitioners - by Provider
  • [5] [Anonymous], 2022, Medicare Geographic Variation-by National, State & County
  • [6] GeoDa:: An introduction to spatial data analysis
    Anselin, L
    Syabri, I
    Kho, Y
    [J]. GEOGRAPHICAL ANALYSIS, 2006, 38 (01) : 5 - 22
  • [7] Area Deprivation Index, 2022, Neighborhood Atlas
  • [8] New Approaches for Calculating Moran's Index of Spatial Autocorrelation
    Chen, Yanguang
    [J]. PLOS ONE, 2013, 8 (07):
  • [9] Cromartie J, 2020, RURAL URBAN CONTINUU
  • [10] Inequality and the health-care system in the USA
    Dickman, Samuel L.
    Himmelstein, David U.
    Woolhandler, Steffie
    [J]. LANCET, 2017, 389 (10077) : 1431 - 1441