{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T17:35:44Z","timestamp":1791999344147,"version":"4.4.0"},"reference-count":35,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T00:00:00Z","timestamp":1703203200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["K22 CA225705"],"award-info":[{"award-number":["K22 CA225705"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Medicine"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Healthcare costs in the U.S. are high and variable, which can hinder access and impact health outcomes across communities. This study examined hospital\u2010 and county\u2010level characteristics to identify factors that explain geographic variation in prices for four cancer\u2010related procedures.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Data sources included Turquoise Health, which compiles publicly\u2010available price data from U.S. hospitals. We examined list prices for four procedures: abdominal ultrasound, diagnostic colonoscopy, brain MRI, and pelvis CT scan, which we linked to characteristics of hospitals (e.g., number of beds) and counties (e.g., metropolitan status). We used multilevel linear regression models to assess multivariable relationships between prices and hospital\u2010 and county\u2010level characteristics. Supplementary analyses repeated these models using procedures prices for commercial insurance plans.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>For each procedure, list prices varied across counties (intraclass correlation: abdominal ultrasound\u2009=\u200923.2%; colonoscopy\u2009=\u200917.1%; brain MRI\u2009=\u200937.2%; pelvis CT\u2009=\u200950.9%). List prices for each procedure were associated with hospital ownership (all <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001) and percent of population without health insurance (all <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.05). For example, list prices for abdominal ultrasound were higher for proprietary versus Government\u2010owned hospitals (<jats:italic>\u03b2<\/jats:italic>\u2009=\u2009539.10, 95% confidence interval [CI]: 256.12, 822.08, <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001) and for hospitals in counties with more uninsured residents (<jats:italic>\u03b2<\/jats:italic>\u2009=\u200923.44, 95% CI: 2.55, 44.33, <jats:italic>p<\/jats:italic>\u2009=\u20090.03). Commercial insurance prices were negatively associated with metropolitan status.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Prices for cancer\u2010related healthcare procedures varied substantially, with considerable heterogeneity associated with county location as well as county\u2010level social determinants of health (e.g., health insurance coverage). Interventions and policy changes are needed to alleviate the financial burden of cancer care among patients, including geographic variation in prices for cancer\u2010related procedures.<\/jats:p><\/jats:sec>","DOI":"10.1002\/cam4.6792","type":"journal-article","created":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T13:18:36Z","timestamp":1703251116000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Hospital\u2010 and county\u2010level characteristics explain geographic variability in prices of cancer\u2010related procedures: Implications for policy and interventions"],"prefix":"10.1002","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3794-1344","authenticated-orcid":false,"given":"Jennifer L.","family":"Moss","sequence":"first","affiliation":[{"name":"Department of Family and Community Medicine Penn State College of Medicine, The Pennsylvania State University  Hershey Pennsylvania USA"},{"name":"Department of Public Health Sciences Penn State College of Medicine, The Pennsylvania State University  Hershey Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Savanna G.","family":"Ledford","sequence":"additional","affiliation":[{"name":"Department of Public Health Sciences Penn State College of Medicine, The Pennsylvania State University  Hershey Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Veronica","family":"Bernacchi","sequence":"additional","affiliation":[{"name":"Department of Family and Community Medicine Penn State College of Medicine, The Pennsylvania State University  Hershey Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chan","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Public Health Sciences Penn State College of Medicine, The Pennsylvania State University  Hershey Pennsylvania USA"},{"name":"Department of Surgery Penn State College of Medicine, The Pennsylvania State University  Hershey Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,12,22]]},"reference":[{"key":"e_1_2_12_2_1","unstructured":"Congress of the United States Congressional Budget Office.Geographic variation in health care spending.https:\/\/www.cbo.gov\/sites\/default\/files\/cbofiles\/ftpdocs\/89xx\/doc8972\/02\u201015\u2010geoghealth.pdf2008."},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13104-022-06014-2"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1377\/hlthaff.2014.0168"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2013.278139"},{"issue":"1","key":"e_1_2_12_6_1","first-page":"51","article-title":"Rural health care costs: are they higher and why might they differ from urban health care costs?","volume":"79","author":"Williams D","year":"2018","journal-title":"N C Med J"},{"key":"e_1_2_12_7_1","volume-title":"Health Care Is Local: Impact of Income and Geography on Premiums and Premium Support","author":"Riley T","year":"2017"},{"key":"e_1_2_12_8_1","volume-title":"Americans' Challenges with Health Care Costs","author":"Montero A","year":"2021"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/jso.25374"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.1093\/jnci\/djab192"},{"key":"e_1_2_12_11_1","unstructured":"Centers for Medicare & Medicaid Services.Hospital price transparency.https:\/\/www.cms.gov\/hospital\u2010price\u2010transparency2021."},{"key":"e_1_2_12_12_1","unstructured":"WheelerC TaylorR.New Year New CMS Price Transparency Rule For Hospitals. Health Affairs Blog2021."},{"key":"e_1_2_12_13_1","unstructured":"Turquoise Health.Price transparency data for researchers.https:\/\/turquoise.health\/researchers2022."},{"key":"e_1_2_12_14_1","volume-title":"For\u2010Profit Enterprise in Health Care","author":"Gray BH","year":"1986"},{"key":"e_1_2_12_15_1","unstructured":"U.S. Department of Health and Human Services.Area Health Resources Files.https:\/\/data.hrsa.gov\/topics\/health\u2010workforce\/ahrf2021."},{"key":"e_1_2_12_16_1","unstructured":"U.S. Census Bureau.Census Regions and Divisions of the United States.https:\/\/www2.census.gov\/geo\/pdfs\/maps\u2010data\/maps\/reference\/us_regdiv.pdf"},{"key":"e_1_2_12_17_1","unstructured":"U.S. Department of Agriculture.Rural\u2010Urban Continuum Codes: Overview.http:\/\/www.ers.usda.gov\/data\u2010products\/rural\u2010urban\u2010continuum\u2010codes.aspx2019."},{"key":"e_1_2_12_18_1","unstructured":"County Health Rankings & Roadmaps.Explore Health Rankings.https:\/\/www.countyhealthrankings.org\/explore\u2010health\u2010rankings2021."},{"key":"e_1_2_12_19_1","unstructured":"National Institute of Standards and Technology.Current FIPS.https:\/\/www.nist.gov\/itl\/current\u2010fips2018."},{"key":"e_1_2_12_20_1","first-page":"316","volume-title":"Methods in Social Epidemiology","author":"Blakely TA","year":"2006"},{"key":"e_1_2_12_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0277-9536(03)00415-5"},{"key":"e_1_2_12_22_1","unstructured":"BellBA EneM SmileyW SchoenebergerJA.A multilevel model primer using SAS PROC MIXED. 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