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Fisher  ES, Wennberg  DE, Stukel  TA, Gottlieb  DJ, Lucas  FL, Pinder  EL.  The implications of regional variations in Medicare spending—part 1: the content, quality, and accessibility of care.   Ann Intern Med. 2003;138(4):273-287. doi:10.7326/0003-4819-138-4-200302180-00006PubMedGoogle ScholarCrossref
Kibria  A, Mancher  M, McCoy  MA, Graham  RP, Garber  AM, Newhouse  JP.  Variation in Health Care Spending: Target Decision Making, Not Geography. National Academies Press; 2013.
Newhouse  JP, Garber  AM.  Geographic variation in health care spending in the United States: insights from an Institute of Medicine report.   JAMA. 2013;310(12):1227-1228. doi:10.1001/jama.2013.278139PubMedGoogle ScholarCrossref
Newhouse  JP, Garber  AM.  Geographic variation in Medicare services.   N Engl J Med. 2013;368(16):1465-1468. doi:10.1056/NEJMp1302981PubMedGoogle ScholarCrossref
Baker  LC, Bundorf  MK, Kessler  DP.  Patients’ preferences explain a small but significant share of regional variation in Medicare spending.   Health Aff (Millwood). 2014;33(6):957-963. doi:10.1377/hlthaff.2013.1184PubMedGoogle ScholarCrossref
Finkelstein  A, Gentzkow  M, Williams  H.  Sources of geographic variation in health care: evidence from patient migration.   Q J Econ. 2016;131(4):1681-1726. doi:10.1093/qje/qjw023PubMedGoogle ScholarCrossref
Skinner  J. Causes and consequences of regional variations in health care. In: Pauly  MV, Mcguire  TG, Barros  PP, eds.  Handbook of Health Economics. Vol 2. Elsevier; 2011:45-93.
Reschovsky  JD, Hadley  J, Romano  PS.  Geographic variation in fee-for-service Medicare beneficiaries’ medical costs is largely explained by disease burden.   Med Care Res Rev. 2013;70(5):542-563. doi:10.1177/1077558713487771PubMedGoogle ScholarCrossref
Wennberg  JE, Cooper  MM.  The Quality of Medical Care in the United States: A Report on the Medicare Program: the Dartmouth Atlas of Health Care in the United States. Amer Hospital Pub; 1999.
Zhang  Y, Li  J.  Geographic variation in Medicare per capita spending narrowed from 2007 to 2017.   Health Aff (Millwood). 2020;39(11):1875-1882. doi:10.1377/hlthaff.2020.00188PubMedGoogle ScholarCrossref
Zhang  Y, Ancker  JS, Hall  J, Khullar  D, Wu  Y, Kaushal  R.  Association between residential neighborhood social conditions and health care utilization and costs.   Med Care. 2020;58(7):586-593. doi:10.1097/MLR.0000000000001337PubMedGoogle ScholarCrossref
Kind  AJ, Jencks  S, Brock  J,  et al.  Neighborhood socioeconomic disadvantage and 30-day rehospitalization: a retrospective cohort study.   Ann Intern Med. 2014;161(11):765-774. doi:10.7326/M13-2946PubMedGoogle ScholarCrossref
Centers for Medicare & Medicaid Services. Medicare Data for the Geographic Variation Public Use File: A Methodological Overview. Updated March 2021. Accessed March 20, 2021.
Health Resources and Services Administration. Area Health Resources Files. Updated July 31, 2020. Accessed January 31, 2020.
Centers for Medicare & Medicaid Services. Provider of Services Current Files. Updated April 16, 2021. Accessed January 31, 2020.
United States Census Bureau. 2020 American Community Survey. Accessed January 31, 2020.
Robert Wood Johnson Foundation. 2019 County Health Rankings. Accessed January 31, 2020.
North Carolina Department of Health and Human Services. The COVID-19 Support Services Program. Accessed March 23, 2021.
Indiana Family and Social Services Administration. Hoosier Health & Well-Being Atlas. Accessed March 23, 2021.
Centers for Medicare and Medicaid Services. Geographic Variation Public Use File: Technical Supplement on Standardization. Updated March 24, 2021. Accessed April 28, 2021.
National Academies of Sciences, Engineering, and Medicine.  Accounting for Social Risk Factors in Medicare Payment. National Academies Press; 2017.
Patrick  SW, Choi  H, Davis  MM.  Increase in federal match associated with significant gains in coverage for children through Medicaid and CHIP.   Health Aff (Millwood). 2012;31(8):1796-1802. doi:10.1377/hlthaff.2011.0988PubMedGoogle ScholarCrossref
Gettens  J, Lei  P-P, Henry  AD.  Accounting for geographic variation in Social Security disability program participation.   Social Security Bulletin. 2018;78(2):29-47. Accessed online April 28, 2021. Scholar
Pope  GC, Kautter  J, Ellis  RP,  et al.  Risk adjustment of Medicare capitation payments using the CMS-HCC model.   Health Care Financ Rev. 2004;25(4):119-141.PubMedGoogle Scholar
Reschovsky  JD, Hadley  J, Saiontz-Martinez  CB, Boukus  ER.  Following the money: factors associated with the cost of treating high-cost Medicare beneficiaries.   Health Serv Res. 2011;46(4):997-1021. doi:10.1111/j.1475-6773.2011.01242.xPubMedGoogle ScholarCrossref
Zuckerman  S, Waidmann  T, Berenson  R, Hadley  J.  Clarifying sources of geographic differences in Medicare spending.   N Engl J Med. 2010;363(1):54-62. doi:10.1056/NEJMsa0909253PubMedGoogle ScholarCrossref
Mays  GP, Smith  SA.  Geographic variation in public health spending: correlates and consequences.   Health Serv Res. 2009;44(5 Pt 2):1796-1817. doi:10.1111/j.1475-6773.2009.01014.xPubMedGoogle ScholarCrossref
Donohue  JM, Morden  NE, Gellad  WF,  et al.  Sources of regional variation in Medicare Part D drug spending.   N Engl J Med. 2012;366(6):530-538. doi:10.1056/NEJMsa1104816PubMedGoogle ScholarCrossref
Alamian  A, Paradis  G.  Individual and social determinants of multiple chronic disease behavioral risk factors among youth.   BMC Public Health. 2012;12:224. doi:10.1186/1471-2458-12-224PubMedGoogle ScholarCrossref
Cockerham  WC, Hamby  BW, Oates  GR.  The social determinants of chronic disease.   Am J Prev Med. 2017;52(1S1):S5-S12. doi:10.1016/j.amepre.2016.09.010Google ScholarCrossref
Hill  J, Nielsen  M, Fox  MH.  Understanding the social factors that contribute to diabetes: a means to informing health care and social policies for the chronically ill.   Perm J. 2013;17(2):67-72. doi:10.7812/TPP/12-099Google ScholarCrossref
Falagas  ME, Zarkadoulia  EA, Pliatsika  PA, Panos  G.  Socioeconomic status (SES) as a determinant of adherence to treatment in HIV infected patients: a systematic review of the literature.   Retrovirology. 2008;5:13. doi:10.1186/1742-4690-5-13PubMedGoogle ScholarCrossref
Arpey  NC, Gaglioti  AH, Rosenbaum  ME.  How socioeconomic status affects patient perceptions of health care: a qualitative study.   J Prim Care Community Health. 2017;8(3):169-175. doi:10.1177/2150131917697439PubMedGoogle ScholarCrossref
Zhang  Y, Baik  SH, Fendrick  AM, Baicker  K.  Comparing local and regional variation in health care spending.   N Engl J Med. 2012;367(18):1724-1731. doi:10.1056/NEJMsa1203980PubMedGoogle ScholarCrossref
Song  Y, Skinner  J, Bynum  J, Sutherland  J, Wennberg  JE, Fisher  ES.  Regional variations in diagnostic practices.   N Engl J Med. 2010;363(1):45-53. doi:10.1056/NEJMsa0910881PubMedGoogle ScholarCrossref
Finkelstein  A, Gentzkow  M, Hull  P, Williams  H.  Adjusting risk adjustment–accounting for variation in diagnostic intensity.   N Engl J Med. 2017;376(7):608-610. doi:10.1056/NEJMp1613238PubMedGoogle ScholarCrossref
Keating  NL, Huskamp  HA, Kouri  E,  et al.  Factors contributing to geographic variation in end-of-life expenditures for cancer patients.   Health Aff (Millwood). 2018;37(7):1136-1143. doi:10.1377/hlthaff.2018.0015PubMedGoogle ScholarCrossref
Sheiner  L.  Why the Geographic Variation in Health Care Spending Cannot Tell Us Much About the Efficiency or Quality of Our Health Care System. Brookings Papers on Economic Activity. Brookings Institution Press;2014. doi:10.1353/eca.2014.0012
Adhikari  S, Pantaleo  NP, Feldman  JM, Ogedegbe  O, Thorpe  L, Troxel  AB.  Assessment of community-level disparities in coronavirus disease 2019 (COVID-19) infections and deaths in large US metropolitan areas.   JAMA Netw Open. 2020;3(7):e2016938. doi:10.1001/jamanetworkopen.2020.16938PubMedGoogle Scholar
Azar  KMJ, Shen  Z, Romanelli  RJ,  et al.  Disparities in outcomes among COVID-19 patients in a large health care system in California.   Health Aff (Millwood). 2020;39(7):1253-1262. doi:10.1377/hlthaff.2020.00598PubMedGoogle ScholarCrossref
Chandra  A, Fisher  ES, Skinner  J. Pitfalls in the analysis of regional variation in health care: a response to Hadley, Berenson, Waidmann, and Zuckerman. Unpublished manuscript. Dartmouth Institute for Health Policy and Clinical Practice. Published online September 21, 2007. Accessed November 29, 2020.
Brewster  AL, Wilson  TL, Frehn  J, Berish  D, Kunkel  SR.  Linking health and social services through area agencies on aging is associated with lower health care use and spending.   Health Aff (Millwood). 2020;39(4):587-594. doi:10.1377/hlthaff.2019.01515PubMedGoogle ScholarCrossref
National Academies of Sciences, Engineering, and Medicine.  Accounting for Social Risk Factors in Medicare Payment: Criteria, Factors, and Methods. National Academies Press; 2016.
Medicare Payment Advisory Commission. Applying the Commission’s principles for measuring quality: Population-based measures and hospital quality incentives. In: Report to the Congress: Medicare and the Health Care Delivery System. June 2018. Accessed April 04, 2021.
Edwards  RD.  Public transit, obesity, and medical costs: assessing the magnitudes.   Prev Med. 2008;46(1):14-21. doi:10.1016/j.ypmed.2007.10.004PubMedGoogle ScholarCrossref
Romley  JA, Hackbarth  A, Goldman  DP.  The impact of air quality on hospital spending.   Rand Health Q. 2012;2(3):6.PubMedGoogle Scholar
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    Original Investigation
    Health Policy
    June 10, 2021

    Social Determinants of Health and Geographic Variation in Medicare per Beneficiary Spending

    Author Affiliations
    • 1Division of Health Policy and Economics, Department of Population Health Sciences, Weill Cornell Medical College, New York, New York
    JAMA Netw Open. 2021;4(6):e2113212. doi:10.1001/jamanetworkopen.2021.13212
    Key Points

    Question  How much variation in Medicare per beneficiary spending across counties was associated with social determinants of health (SDoH)?

    Findings  In this cross-sectional study, SDoH were associated with 37.7% of variation in price-adjusted Medicare per beneficiary spending between counties in the highest and lowest quintiles of spending in 2017, including both direct contributions and indirect contributions through other factors. SDoH’s direct contribution accounted for 5.8% of the variation after controlling for patient demographic characteristics, clinical risk, and supply of health care resources.

    Meaning  These findings suggest that addressing SDoH is important for reducing geographic spending variation and improving the value of health care.


    Importance  Despite substantial geographic variation in Medicare per beneficiary spending in the US, little is known about the extent to which social determinants of health (SDoH) are associated with this variation.

    Objective  To determine the associations between SDoH and county-level price-adjusted Medicare per beneficiary spending.

    Design, Setting, and Participants  This cross-sectional study used county-level data on 2017 Medicare fee-for-service (FFS) spending, patient demographic characteristics (eg, age and gender) and clinical risk score, supply of health care resources (eg, number of hospital beds), and SDoH measures (eg, median income and unemployment rate) from multiple sources. Multivariable regressions were used to estimate the association of the variation in spending across quintiles with SDoH.

    Main Outcomes and Measures  2017 county-level price-adjusted Medicare Parts A and B spending per beneficiary. SDoH measures included socioeconomic position, race/ethnicity, social relationships, and residential and community context.

    Results  Among 3038 counties with 33 495 776 Medicare FFS beneficiaries (18 352 336 [54.8%] women; mean [SD] age, 72 [1.5] years), mean Medicare price-adjusted per beneficiary spending for counties in the highest spending quintile was $3785 (95% CI, $3706-$3862) higher, or 49% higher, than spending for bottom-quintile counties (mean [SD] spending per beneficiary, $11 464 [735] vs $7679 [522]; P < .001). The total contribution (including through both direct and indirect pathways) of SDoH was 37.7% ($1428 of $3785) of this variation, compared with 59.8% ($2265 of $3785) by patient clinical risk, 14.5% ($549 of $3785) by supply of health care resources, and 19.8% ($751 of $3785) by patient demographic characteristics. When all factors were included within the same model, the direct contribution of SDoH was associated with 5.8% of the variation, compared with 4.6% by supply, 4.7% by patient demographic characteristics, and 62.0% by patient clinical risk.

    Conclusions and Relevance  These findings suggest social determinants of health are associated with considerable proportions of geographic variation in Medicare spending. Policies addressing SDoH for disadvantaged patients in certain regions have the potential to contain health care spending and improve the value of health care; patient SDoH may need to be accounted for in publicly reported physician performance, and in value-based purchasing incentive programs for health care professionals.