Currently Viewing:
The American Journal of Managed Care March 2013
Rates of Guideline Adherence Among US Community Oncologists Treating NSCLC
Zhaohui Wang, MD, PhD; Inga Aksamit, RN, MBA; Lisa Tuscher, BA; and Kim Bergstrom, PharmD
Effectiveness and Cost-Effectiveness of Diabetes Prevention Among Adherent Participants
William H. Herman, MD, MPH; Sharon L. Edelstein, ScM; Robert E. Ratner, MD; Maria G. Montez, RN, MSHP; Ronald T. Ackermann, MD, MPH; Trevor J. Orchard, MD; Mary A. Foulkes, PhD; Ping Zhang, PhD; Christopher D. Saudek, MD†; and Morton B. Brown, PhD; The Diabetes Prevention Program Research Group
Mental Health in ACOs: Missed Opportunities and Low-Hanging Fruit
Allison N. O'Donnell, MPH; Brent C. Williams, MD; Daniel Eisenberg, PhD; and Amy M. Kilbourne, PhD, MPH
Drug Adherence After Price Changes in a Previously Compliant Population
James J. Hill III, MD, MPH; Deron Galusha, MS; Martin D. Slade, MPH; and Mark R. Cullen, MD
Measuring Quality in the Early Years of Health Insurance Exchanges
Ledia M. Tabor, MPH; Phyllis Torda, MA; Sarah S. Thomas, MS; and Jennifer L. Zutz, MHSA
Multilevel Predictors of Colorectal Cancer Screening Use in California
Salma Shariff-Marco, PhD, MPH; Nancy Breen, PhD; David G. Stinchcomb, MS, MA; and Carrie N. Klabunde, PhD
Engaging Providers in Underserved Areas to Adopt Electronic Health Records
Cleo A. Samuel, BS; Jennifer King, PhD; Fadesola Adetosoye, MS; Leila Samy, MPH; and Michael F. Furukawa, PhD
Factors Associated With Primary Hip Arthroplasty After Hip Fracture
Ishveen Chopra, MS; Khalid M. Kamal, PhD; Jayashri Sankaranarayanan, MPharm, PhD; and Gibbs Kanyongo, PhD
Measuring Concurrent Oral Hypoglycemic and Antidepressant Adherence and Clinical Outcomes
Hillary R. Bogner, MD, MSCE; Heather F. de Vries, MSPH; Alison J. O'Donnell, BA; and Knashawn H. Morales, ScD
Currently Reading
Computed Tomography Scan Use Variation: Patient, Hospital, and Geographic Factors
Eric A. Vance, PhD; Xiaojin Xie, MS; Andrew Henry, BS; Christian Wernz, PhD; and Anthony D. Slonim, MD, DrPH
Trends in Inpatient Hospital Prices, 2008 to 2010
Jeff Lemieux, MA; and Teresa Mulligan, MHSA

Computed Tomography Scan Use Variation: Patient, Hospital, and Geographic Factors

Eric A. Vance, PhD; Xiaojin Xie, MS; Andrew Henry, BS; Christian Wernz, PhD; and Anthony D. Slonim, MD, DrPH
We observed small-area variation in computed tomography scan use for inpatients in New York State, even after controlling for relevant patient and hospital characteristics.
The results of a logistic regression analysis that included county as an independent variable, along with the patient and hospital characteristics, can be found in Appendix E. After controlling for the other factors, the results show that males, whites, and Medicare beneficiaries were not more likely to receive a CT scan, contrary to the results of the univariate analysis above. The OR for males compared with females was 0.977 (95% CI, 0.970-0.983). Compared with whites, the OR for Asians was 1.145 (95% CI, 1.123-1.168) and the OR for blacks was 1.128 (95% CI, 1.118-1.139). Medicare beneficiaries had an OR of 1.052 (95% CI, 1.041-1.068) compared with inpatients paying with private insurance. Inpatients who self-paid (OR = 1.228; 95% CI, 1.209-1.248) or were not charged (OR= 1.619; 95% CI, 1.505-1.743) were the most likely to have received a CT scan. An inpatient’s odds of receiving a CT scan generally increased with age. Compared with the reference group of 30- to 39-year-olds, the youngest inpatients (birth to 1 year) had the lowest odds of receiving a CT scan (OR = 0.109; 95% CI, 0.104-0.115), and the oldest patients (85 years and older) had the highest odds (OR = 1.604; 95% CI, 1.576-1.633). Trauma was the diagnostic category most likely to result in a CT scan (OR compared with the reference category of Endocrine, Nutritional and Metabolic System was 24.531; 95% CI, 21.888-27.493), while Pregnancy, Childbirth and Puerperium was the least likely category (OR = 0.049; 95% CI, 0.047-0.051). Inpatients at nonteaching hospitals were slightly less likely to receive a CT scan than those at teaching hospitals (OR = 0.971; 95% CI, 0.962-0.981). Inpatients at large hospitals (OR = 0.975; 95% CI, 0.967-0.984) or small hospitals (OR = 0.842; 95% CI, 0.832-0.852) were less likely to receive a CT scan than those at medium-sized hospitals.

We determined how efficient and effective each variable was in explaining CT scan use rates by using the Schwarz criterion. The Table presents Schwarz criterion values for the full model and the submodels. The greater the difference in Schwarz criterion values (Δ Schwarz criterion) between the full model and the submodel, the greater the efficiency and effectiveness of the corresponding categorical factor of the submodel in describing CT scan use rates. Results indicate that MDC was the variable associated with the greatest variation in CT scan use, followed by age group and county. Race, hospital bed size, and method of payment had little explanatory value, and sex and hospital teaching status played virtually no role in predicting CT scan use.

DISCUSSION

Geographic analysis can be a helpful tool for understanding care patterns, technology use, and small-area variations in healthcare. Several broad-based studies have demonstrated differences in health outcomes on a geographic level.10,11,17-20 However, the use of geographic analysis to determine the variability in radiology testing, specifically the use of CT scans, has not been performed. Therefore, we conducted a geographic analysis of CT scan use and examined how patient, hospital, and geographic factors affect the use of this technology for inpatients in New York State. We found signifi cant variations due to patient characteristics (age, race, insurance status, sex, MDC). In addition, we found that hospital characteristics (bed size, hospital teaching status) had a weak infl uence on CT scan use. Finally, after controlling for patient and hospital factors, unexplained variability remained at the level of the county. Identifying additional factors that may explain the residual variability is important for developing interventions and managing care to ensure the appropriate use of this important technology.

Since Wennberg and Gittelsohn10 fi rst developed their conceptual approach to understanding the effect of geographic variation on the use of medical services for surgical procedures, others have advanced their work both in content and methodology for a broad range of procedures and populations.10,11,17-20 One of the major fi ndings of these studies was that procedural variation is greatly infl uenced by patient and hospital factors. Without controlling for these factors, a geographic analysis on medical procedures could be biased. Our study also found several important patient and population level characteristics—including age, insurance status, and diagnosis—that influenced the geographic variation of CT scans. Although certain characteristics such as age or diagnosis cannot be modifi ed, interventions informed by evidencebased guidelines or protocols can help to enforce appropriate CT scan use and can ultimately reduce variation and improve healthcare value.

Teaching hospitals perform important societal functions including the provision of direct care for patients and the education of the next generation of healthcare professionals. Surprisingly, we found little difference in CT scan use rates between teaching and nonteaching hospitals. This finding was interesting given that teaching hospitals have medical trainees who are less experienced in identifying the appropriate indications for ordering these diagnostic tests. In addition, teaching hospitals often serve a population who have worse access to healthcare, more comorbid conditions, and fewer resources to care for them. Hence, overuse of certain technologies and therapies was expected. Since teaching hospitals are involved in the education of medical trainees and evidencebased medicine principles have become foundational in this setting, we speculate that our observation is related to better adherence to protocols and guidelines for CT scan prescribing. If true, generalizing these findings outside of teaching institutions to a broad geographic catchment area may help to address the important issues of guideline adherence and reduce variability.

Finally, after controlling for patient and hospital characteristics, we found that geographic variation persisted at the level of the county. This remaining variation may be related to the number of CT scanners available for use,21 the number of radiologists and other physicians in the region, differences in provider practice preferences,22 or the economic status of a county. Regulatory mechanisms such as the Certificate of Need program are in place in New York State to assist with the appropriate allocation of expensive technologies. The major drivers of CT scan use are prescribing physicians. Hence, variability in the geographic distribution of physicians generally and in specific specialties like radiology may be as responsible for CT scan use patterns as the presence of CT scanners. Practice pattern differences can be ameliorated through continuing education and peer review programs. Unfortunately, the relationship between these factors and CT use cannot be understood by using the existing data elements in SID, but can certainly be investigated using other data sources and methods.

Several limitations in our study must be considered. The SID includes only inpatient data and does not include CT scans performed on outpatients even if performed at the same hospital. This limitation does not affect the conclusions for inpatient CT scan use, but may influence conclusions at thepopulation level. In addition, while we used literature-defined approaches for identifying the performance of CT scans during inpatient stays in the SID, misclassification biases associated with poor documentation may exist. We believe that we have overcome this limitation to the extent possible by using complementary approaches for acquiring CT scan use rates. Finally, we included all factors potentially affecting CT scan use that were available through the data set. However, we are humbled by the number of other potential explanatory variables that may be operational at the level of the county but that are not contained in the SID.

Despite these limitations, we believe that this study has significant strengths because it demonstrates that area variation exists for an expensive medical imaging technology like CT. In addition, the relationship between CT scan use and age is an important finding and can serve as guidance to alter physician practices, particularly for those patients who may be harmed by CT scan overuse like children and adolescents, in whom lifelong radiation dosage may adversely affect health.3-5 Further, as acute care hospitals and physicians consider methods for integrating inpatient and outpatient care for the benefit of patient populations, analyses that incorporate geography are likely to provide practical insights that ensure the continuity of service while avoiding oversupply of an expensive resource. These results can help to inform Certificate of Need programs both inside and outside of New York State for regulating CT scan deployment. Finally, we believe our study provides guidance for future research investigating racial and ethnic disparities in the provision of healthcare services because it identifies other factors that may account for county-level geographic variation in CT scan use or the use of other technologies.

Acknowledgment

We gratefully acknowledge assistance from Virginia Tech’s Laboratory for Interdisciplinary Statistical Analysis.

Author Affiliations: From Department of Statistics (EAV), Grado Department of Industrial and Systems Engineering (XX, AH, CW, ADS), Virginia Tech, Blacksburg, VA; Barnabas Health (ADS), West Orange, NJ.

Funding Source: This research was supported by the 2009 Research Acceleration Program at Carilion Clinic.

Author Disclosures: The authors (EAV, XX, AH, CW, ADS) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (EAV, AH, CW, ADS); acquisition of data (CW); analysis and interpretation of data (EAV, XX, AH, CW, ADS); drafting of the manuscript (EAV, XX, AH, CW, ADS); critical revision of the manuscript for important intellectual content (EAV, XX, CW, ADS); statistical analysis (EAV, XX); provision of study materials or patients (CW); obtaining funding (EAV, AH, CW, ADS); administrative, technical, or logistic support (ADS); and supervision (EAV, CW, ADS).

Address correspondence to: Eric A. Vance, PhD, 212 Hutcheson Hall (0439), Blacksburg, VA 24061. E-mail: ervance@vt.edu.
1. Mettler FA Jr, Thomadsen BR, Bhargavan M, et al. Medical radiation exposure in the US in 2006: preliminary results. Health Phys. 2008; 95(5):502-507.


2. Brenner DJ, Hall EJ. Computed tomography—an increasing source of radiation exposure. N Engl J Med. 2007;357(22):2277-2284.


3. Berrington de González A, Mahesh M, Kim KP, et al. Projected cancer risks from computed tomographic scans performed in the United States in 2007. Arch Intern Med. 2009;169(22):2071-2077.


4. Smith-Bindman R, Lipson J, Marcus R, et al. Radiation dose associated with common computed tomography examinations and the associated lifetime attributable risk of cancer. Arch Intern Med. 2009; 169(22):2078-2086.


5. Redberg RF. Cancer risks and radiation exposure from computedtomographic scans: how can we be sure that the benefits outweigh the
 risks? Arch Intern Med. 2009;169(22):2049-2050.


6. Wu SR, Shakibai S, McGahan JP, Richards JR. Combined head and abdominal computed tomography for blunt trauma: which patients with minor head trauma benefit most? Emerg Radiol. 2006;13(2):61-67.


7. Lysdahl KB, Hofmann BM. What causes increasing and unnecessary use of radiological investigations? a survey of radiologists’ perceptions. BMC Health Serv Res. 2009;9:155.


8. Lee SL, Walsh AJ, Ho HS. Computed tomography and ultrasonography do not improve and may delay the diagnosis and treatment of acute appendicitis. Arch Surg. 2001;136(5):556-562.


9. Partrick DA, Janik JE, Janik JS, Bensard DD, Karrer FM. Increased CT scan utilization does not improve the diagnostic accuracy of appendicitis in children. J Pediatr Surg. 2003;38(5):659-662.


10. Wennberg J, Gittelsohn A. Small area variations in health care delivery. Science. 1973;182(4117):1102-1108.


11. Epstein AJ, Nicholson S. The Formation and evolution of physician treatment styles: an application to cesarean sections. J Health Econ. 2009;28(6):1126-1140.


12. Brenner D, Elliston CD, Hall E, Berdon W. Estimated risks of radiation- induced fatal cancer from pediatric CT. AJR Am J Roentgenol. 2001;176(2):289-296. 13. Agency for Healthcare Research and Quality. Utilization flags. http://www.hcup-us.ahrq.gov/toolssoftware/util_flags/utilflag.jsp. Published November 2010. Accessed August 4, 2012.


14. Utah Department of Health. Major Diagnostic Categories (MDC). http://health.utah.gov/opha/IBIShelp/codes/MDC.htm. Updated September 2005. Accessed July 13, 2012.


15. New York State Department of Health. New York State hospital profile. http://hospitals.nyhealth.gov/. Published 2011. Accessed November 28, 2011.


16. Healthcare Cost and Utilization Project. NIS description of data elements: bedsize of hospital. http://www.hcup-us.ahrq.gov/db/vars/ hosp_bedsize/nisnote.jsp. Published September 2008. Accessed November 28, 2011.


17. Chassin MR, Kosecoff J, Park RE, et al. Does inappropriate use explain geographic variations in the use of health care services? a study of three procedures. JAMA. 1987;258(18):2533-2537.


18. Volinn E, Diehr P, Ciol MA, Loeser JD. Why does geographic variation in health care practice matter? (and seven questions to ask in evaluating studies on geographic variation). Spine (Phila Pa 1976). 1994;19(18)(suppl):2092S-2100S.


19. Rao VM, Parker L, Levin DC, Sunshine J, Bushee G. Use trends and geographic variation in neuroimaging: nationwide Medicare data for 1993 and 1998. AJNR Am J Neuroradiol. 2001;22(9):1643-1649.


20. Havranek EP, Wolfe P, Masoudi FA, Rathore SS, Krumholz HM, Ordin DL. Provider and hospital characteristics associated with geographic variation in the evaluation and management of elderly patients with heart failure. Arch Intern Med. 2004;164(11):1186-1191.


21. Niagara Health Quality Coalition. MRI Services in Western New York 2003. http://www.myhealthfinder.com/hcac/MRIreport03.pdf. Published February 2004. Accessed November 28, 2011.


22. Gawande A. The cost conundrum: what a Texas town can teach us about health care [Annals of Medicine]. The New Yorker. http://www .newyorker.com/reporting/2009/06/01/090601fa_fact_gawande. Published June 1, 2009. Accessed November 28, 2011.
PDF
 
Copyright AJMC 2006-2019 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
x
Welcome the the new and improved AJMC.com, the premier managed market network. Tell us about yourself so that we can serve you better.
Sign Up