Spine-Related Conditions in MEPS 2020: Association Between Care Fragmentation and Outpatient MRI/CT Use and Radiology Expenditures

Spine-Related Conditions in MEPS 2020: Association Between Care Fragmentation and Outpatient MRI/CT Use and Radiology Expenditures | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Spine-Related Conditions in MEPS 2020: Association Between Care Fragmentation and Outpatient MRI/CT Use and Radiology Expenditures View ORCID Profile Andrew Bouras doi: https://doi.org/10.1101/2025.08.24.25334318 Andrew Bouras 1 Nova Southeastern University Kiran C. Patel College of Osteopathic Medicine OMS-II Roles: Research Fellow Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Andrew Bouras For correspondence: ab4646{at}mynsu.nova.edu Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Outpatient advanced imaging is central to the evaluation of spine-related conditions, yet concerns persist about coordination and potential overuse. We assessed whether higher care fragmentation is associated with increased MRI/CT use and radiology expenditures among U.S. adults with spine-related conditions using 2020 Medical Expenditure Panel Survey (MEPS) data. A spine cohort was identified from ICD-10-CM and CCSR codes; care fragmentation was categorized using fixed cutpoints on full-year utilization. Outcomes included MRI/CT-only imaging in office-based care and radiology expenditures analyzed with survey-weighted regression models. Compared with Low fragmentation, Medium and High fragmentation were associated with higher odds of advanced imaging and higher odds of any radiology spending; conditional spending was directionally higher for High vs Low but imprecise. Effects appeared strongest among privately insured. Findings suggest opportunities to improve coordination and value in ambulatory spine care. 1 Introduction Spine-related conditions are common and often evaluated with advanced imaging in ambulatory care. While MRI and CT can be pivotal for diagnosis, concerns persist regarding coordination and potential overuse when imaging occurs outside of red-flag indications. Appropriateness frameworks emphasize restraint for uncomplicated back pain and timely imaging for red-flags [ 2 , 3 , 4 , 5 ]. We examine whether greater care fragmentation is associated with increased outpatient MRI/CT use and higher radiology expenditures in a nationally representative sample of U.S. adults with spine-related conditions. We used 2020 Medical Expenditure Panel Survey (MEPS) data to build a person-year dataset, identified a spine cohort using diagnosis codes, and categorized care fragmentation using fixed, reproducible cutpoints on annual utilization [ 6 ]. Outcomes included MRI/CT-only imaging in office-based care and radiology expenditures, analyzed with survey-weighted models. Our goal is to provide policy-relevant, population-level evidence to inform efforts to improve coordination and value in spine imaging. 2 Methods 2.1 Data source and cohort We used MEPS 2020: Full Year Consolidated (HC-224), Office-Based Medical Provider Visits (HC-220G), and Medical Conditions (HC-222) [ 6 ]. Adults (AGELAST ≥ 18) with non-missing survey design variables were included. The spine cohort was defined by ICD-10-CM (M48, M50–M54) and/or CCSR categories for spinal stenosis, intervertebral disc disorders, spondylosis, or low back pain. 2.2 Fragmentation exposure Care fragmentation ( fragmentation_score_cat ) was categorized on fixed, reproducible cutpoints using annual utilization variables ( OBTOTV20, ERTOT20, IPDIS20 ). View this table: View inline View popup Download powerpoint Table 1: Fragmentation category definitions 2.3 Outcomes and covariates Primary outcome: any MRI/CT-only imaging in office-based (OB) care in 2020. Advanced imaging flags were derived from HC-220G service indicators ( MRI_M18, SONOGRAM_M18, XRAYS_M18, MAMMOG_M18 ). Secondary outcomes: any advanced imaging (MRI/CT±ultrasound), radiology expenditures (two-part: any spend; positive spend), and count of imaging-positive OB visits. Covariates: age group, sex, race/ethnicity, poverty status, insurance type, total OB visits ( OBTOTV20 ), multimorbidity ( mcc_2plus ), and design variables ( PERWT20F, VARSTR, VARPSU ). 2.4 Statistical analysis Survey-weighted logistic regression was used for binary outcomes; quasi-Poisson for counts; two-part model for expenditures (quasibinomial for any spend; quasi-Poisson with log link for positive spend). Survey design used Taylor-series linearization with strata VARSTR , PSU VARPSU , and weight PERWT20F . Payer interaction (fragmentation×insurance) was tested for MRI/CT-only; within-payer contrasts (Medium vs Low; High vs Low) were estimated. Unstable figure cells (CV>30% or unweighted n<50) were suppressed. Imaging flags missingness was set to FALSE/0 when no OB imaging records were observed; no imputation for covariates. Software: R 4.4.1; survey 4.4.2; emmeans 1.11.2; ggplot2 3.5.2. 3 Results 3.1 Cohort characteristics We identified a spine cohort of 1,988 adults (weighted ∼22.8 million). The weighted probability of MRI/CT-only imaging in the spine cohort was ∼13.6%, with higher rates among those with multimorbidity (≥2 conditions) and increasing fragmentation levels. View this table: View inline View popup Download powerpoint Table 2: Spine cohort overview 3.2 MRI/CT-only imaging by fragmentation and multimorbidity Compared with Low fragmentation, the odds of advanced imaging were higher in Medium (OR 2.47, 95% CI 1.54–3.97) and High (OR 3.44, 95% CI 2.09–5.66) groups after adjustment. Download figure Open in new tab Figure 1. Survey-weighted probability (95% CI) of ≥1 MRI/CT in OB care by fragmentation level (Low →Medium →High) and multimorbidity (mcc 2plus). Cells with CV>30% or unweighted n<50 suppressed; unweighted cell Ns available in the appendix CSV. 3.3 Radiology expenditures by fragmentation and insurance In the two-part model, any radiology spend was more likely in Medium (OR 1.71, 95% CI 1.10–2.67) and High (OR 2.00, 95% CI 1.27–3.15) vs Low fragmentation. Among those with positive spend, High vs Low showed a directionally higher expenditure (exp(beta) 1.56, 95% CI 0.96–2.55), though imprecise. Download figure Open in new tab Figure 2. Survey-weighted mean radiology spending (USD; 95% CI) by fragmentation level (Low →Medium →High) and insurance. Cells with CV>30% or unweighted n<50 suppressed; unweighted cell Ns available in the appendix CSV. 3.3 Payer interaction for MRI/CT-only Within-payer contrasts indicated the strongest association in private insurance (High vs Low OR 3.74, 95% CI 1.16–12.08). Medicare showed a similar trend (High vs Low OR 3.07, 95% CI 0.89–10.55) but was borderline; Medicaid and dual-eligible strata were imprecise. View this table: View inline View popup Download powerpoint Table 3: Payer-specific contrasts for MRI/CT-only (reference=Low) Global interaction (fragmentation×insurance) Wald F=72.53, p<0.001 (survey-weighted). View this table: View inline View popup Download powerpoint Table 4: Global interaction test for MRI/CT-only 4 Discussion In a nationally representative cohort of U.S. adults with spine-related conditions, higher care fragmentation was associated with greater outpatient MRI/CT use and higher radiology spending. Associations were monotonic across fragmentation categories and appeared strongest among privately insured individuals, with similar (but less precise) trends in Medicare. These findings are consistent with concerns that fragmented ambulatory care can increase imaging intensity without necessarily improving downstream outcomes and complement operating-room evidence in which intraoperative monitoring benefits are uncertain [ 1 ]. Strengths include the use of survey-weighted methods, a prespecified fragmentation definition, and transparent suppression of unstable figure cells. This analysis has limitations. First, MEPS public-use files lack procedural identifiers and intraoperative monitoring details; clinical severity and red-flag status are not directly observed. Second, results reflect office-based imaging only; emergency/outpatient department imaging is not included. Third, diagnosis-based cohorting may misclassify some conditions, and residual confounding may persist despite adjustment. Finally, 2020 utilization patterns occurred during the COVID-19 pandemic, which may affect health-seeking behavior and care patterns. Taken together, the results support efforts to improve coordination in ambulatory spine care and to align imaging more closely with appropriateness frameworks—avoiding routine imaging for uncomplicated back pain while ensuring timely MRI for red-flag presentations [ 2 , 4 , 5 , 3 ]. Payer differences suggest that benefit design, referral pathways, and authorization processes may shape imaging decisions in fragmented care. 5 Conclusions Among U.S. adults with spine-related conditions, higher care fragmentation is associated with greater outpatient MRI/CT use and higher radiology spending. Effects appear most pronounced among privately insured individuals. Future work should extend beyond office-based settings and evaluate longitudinal episodes linking imaging to interventions and outcomes. Data Availability All data produced in the present study are available upon reasonable request to the authors Data and code availability The analysis uses the Medical Expenditure Panel Survey (MEPS) public-use files for 2020, which are de-identified and publicly available from the Agency for Healthcare Research and Quality (AHRQ) [ 6 ]. This study involved secondary analysis of public-use data and did not constitute human subjects research. All analysis code (data processing, modeling, and figure generation) and a reproducible snapshot of derived outputs will be deposited in an open-access repository (e.g., Zenodo/OSF) under a versioned release and persistent DOI at the time of publication. The repository will include software environment details (R 4.4.1; survey 4.4.2; emmeans 1.11.2; ggplot2 3.5.2) and instructions to reproduce figures and tables from public MEPS inputs. Supplementary materials provide: Table S1: Fragmentation category definitions (cutpoints). Tables S2–S6: Full model estimates for primary and secondary outcomes. Table S7: Listing of suppressed figure cells (high relative standard error or small unweighted cell counts). Supplementary material Table S1. Fragmentation cutpoints View this table: View inline View popup Download powerpoint Table 5: Table S1. Fragmentation cutpoints Table S2. Full mod/CT-only (odds ratios) View this table: View inline View popup Download powerpoint Table 6: Full model: MRI/CT-only (odds ratios) Table S3. Full model: Any advanced imaging (odds ratios) View this table: View inline View popup Download powerpoint Table 7: Full model: Any advanced imaging (odds ratios) Table S4. Two-part model: Any radiology spend (odds ratios) View this table: View inline View popup Download powerpoint Table 8: Two-part model: Any radiology spend (odds ratios) Table S5. Two-part model: Positive radiology spend (exp( β )) View this table: View inline View popup Download powerpoint Table 9: Two-part model: Positive radiology spend (exp( β )) Table S6. Imaging-visit counts (rate ratios) View this table: View inline View popup Download powerpoint Table 10: Imaging-visit counts (rate ratios) Table S7. Suppressed figure cells View this table: View inline View popup Download powerpoint Table 11: Suppressed figure cells References [1]. ↵ Frazzetta JN , Hofler RC , Adams W , Schneck MJ , Jones GA . The Significance of Motor Evoked Potential Changes and Utility of Multimodality Intraoperative Monitoring in Spinal Surgery: A Retrospective Analysis of Consecutive Cases at a Single Institution . Cureus . 2020 Dec 13; 12 ( 12 ): e12065 . doi: 10.7759/cureus.12065 . PMID: 33489485 ; PMCID: PMC7806190 . OpenUrl CrossRef PubMed [2]. ↵ Patel ND , Broderick DF , Burns J , Deshmukh TK , Fries IB , Harvey HB , Holly L , Hunt CH , Jagadeesan BD , Kennedy TA , O’Toole JE , Perlmutter JS , Policeni B , Rosenow JM , Schroeder JW , Whitehead MT , Cornelius RS , Corey AS . ACR Appropriateness Criteria Low Back Pain . J Am Coll Radiol . 2016 Sep ; 13 ( 9 ): 1069 – 1078 . doi: 10.1016/j.jacr.2016.06.008 . Epub 2016 Aug 3. PMID: 27496288 . OpenUrl CrossRef PubMed [3]. ↵ Expert Panel on Neurological Imaging ; Shah VN , Parsons MS , Boulter DJ , Burns J , Callaghan B , Eldaya R , Hanak M , Hassankhani A , Hutchins TA , Jackson CD , Khan MA , Mullin J , Ortiz AO , Reitman C , Sampson C , Sandstrom CK , Timpone VM , Trout AT , Policeni B. ACR Appropriateness Criteria Thoracic Back Pain . J Am Coll Radiol . 2024 Nov ; 21 ( 11S ): S504 – S517 . doi: 10.1016/j.jacr.2024.08.016 . PMID: 39488357 . OpenUrl CrossRef PubMed [4]. ↵ American Association of Neurological Surgeons/Congress of Neurological Surgeons . Choosing Wisely: Imaging for nonspecific acute low back pain . Available at: https://www.choosingwisely.org/clinician-lists/american-association-of-neurological-surgeons-imaging-for-nonspecific-acute-low-back-pain/ [5]. ↵ Choosing Wisely Canada . Imaging Tests for Lower Back Pain: When you need them and when you don’t . Available at: https://choosingwiselycanada.org/pamphlet/imaging-tests-for-lower-back-pain/ [6]. ↵ Agency for Healthcare Research and Quality. MEPS HC-224: 2020 Full Year Consolidated Data File . Available at: https://meps.ahrq.gov/data_stats/download_data/pufs/h224/h224doc.shtml View the discussion thread. Back to top Previous Next Posted August 28, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. 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