Editorial
Annals of Internal Medicine
Exploring Variation in Care: Alternative Conceptual Models
I
n this issue, McWilliams and colleagues (1) report an assessment of variation in the use of imaging services for men with prostate, lung, or colorectal cancer receiving care in the Veteran Affairs (VA) health care system or Medicare. They asked, “Does a health care system with lower use of health care services exhibit less geographic variation in the use of these services?” Surprisingly, the researchers found the opposite of what they expected. Despite lower use within the VA system, they observed greater geographic variation in the use of cancer-related imaging tests within the VA system than within Medicare. Specifically, they found that both the absolute and relative differences in overall intensity of imaging services in the highest versus lowest quintiles of hospital referral regions was 240% ($247 per patient) in the VA cohort compared with only 47% ($141 per patient) in the Medicare cohort. This study provides evidence that contradicts the growing and previously untested assumption in health services research that high variation in care reflects overuse. These findings raise a question: What is the underlying cause of geographic variability in use of medical services that drives the more than double overall per capita costs of care for Medicare beneficiaries residing in Manhattan, New York ($11 744), or Miami, Florida ($15 909), versus Lynchburg, Virginia ($6022), or Honolulu, Hawaii ($5293) (2)? Critical in the discussion of geographic variation is how we define geographic granularity. At 1 extreme, patient-level differences in preferences might drive variation in different geographic regions. Arguing against this hypothesis are survey studies that have concluded that patient preferences do not seem to drive systematic regional differences in use of health care services, whereas broader cultural markers, such as race or physician supply, do (3). At the other extreme, a fascinating investigation by Dartmouth researchers (4) suggests that a broader perspective of technology diffusion is needed to explain some aspects of geographic variation. This work explored common, underlying regional factors involved in the adoption of a diverse set of technologies, including hybrid corn and tractors in the early 1900s, computers in the 1990s, and the treatment of heart attacks with -blockers in the 1990s to 2000s. Of interest, some states consistently adopted new, effective technology across all fields. These “early adopting” states possessed social capital and education but not necessarily higher income, population density, or spending. In the United States, for example, hospital referral regions with higher overall costs are often associated with a more general increased utilization across multiple areas of health care spending (5). This generalized concept of technology diffusion is also supported by the present study, in which McWilliams and colleagues observed similar patterns in regional imaging variability among both cancer-specific and overall imaging utilization.
Between the extremes of individual patient preferences and a generalized regional inclination to adopt new technologies, other potential drivers of regional variation may come from the level of physicians, providers, and individual facilities, and these drivers may act at multiple levels. A poignant example of these middle factors comes from studies examining the use of single-fraction radiation treatments for the palliation of painful bone metastases, which is recommended by national and international guidelines for the management of uncomplicated bone metastases. Nonetheless, only 5% of eligible Medicare beneficiaries (and perhaps up to 8% to 10% of patients in U.S. academic centers) receive single-fraction radiation compared with 49% in Canada (6). However, variation also exists within the single-payer Canadian system, with the proportion of eligible patients receiving this recommended intervention varying by facility from 25% to 73% (7). Analogous to McWilliams and colleagues’ study, this shows that a 2- to 3-fold variation in care may be present even within a system with homogeneous financial incentives. The Canadian study concluded that regional variation suggests that physicians’ practice patterns are influenced by their colleagues’ practice, an idea that is further supported by observation of similar health care use among physicians who treat overlapping networks of patients. Perhaps the most controversial hypothesis that McWilliams and colleagues address is that variation in health care use is influenced by financial incentives. To this point, Ariely (8) has described a body of work that suggests that even when people are provided the opportunity to massively cheat to reap the maximum award with no chance of getting caught, they do not do so. Instead, they cheat a little, with the most potent factor affecting the magnitude of cheating being the influence of perceived social norms. In other words, variation can be based on an individual’s perception of the behavior of others within the community. Work by Freeman and colleagues (9) further supports the idea that “cheating” may not purely reflect a desire for financial gain, and that most physicians would be willing to lie to obtain coverage for patients with real medical need, whereas fewer than 3% would do so to obtain coverage for cosmetic surgery (9). Lastly, we should be careful when we attempt to quantify “geographic variation.” A balance must be considered between the granularity of how we define geographic regions and sample sizes per geographic unit. In addition, we must precisely define the outcome of interest. For example, costs are typically not normally distributed and the use of means and SDs may violate assumptions of inference. McWilliams and colleagues examined imaging in the 1 to 3 years after diagnosis. However, costs associated with a new cancer diagnosis are by far the greatest during the initial evaluation and management. Aggregating imaging beyond © 2014 American College of Physicians 835
Downloaded From: https://annals.org/pdfaccess.ashx?url=/data/journals/aim/931772/ by a Tufts University User on 07/27/2017
Editorial
Exploring Variation in Care
this period may obscure trends present during the initial work-up of these patients. Finally, we know that the use of diagnostic imaging expanded greatly from 2003 to 2005 (10), especially with respect to positron emission tomography, which is the imaging modality that represented 90% of the variation reported in this study. The present analysis may therefore represent a snapshot of a period during which imaging in cancer was rapidly changing and may not reflect current steady-state use of these technologies. In conclusion, McWilliams and colleagues provide reasonable evidence that contradicts the assumption that high variation in care reflects overuse, setting the stage for future research to identify the underlying causes of variation in use of medical technology. Michaela A. Dinan, PhD Kevin A. Schulman, MD Duke Clinical Research Institute, Duke University School of Medicine Durham, North Carolina Disclosures: Authors have disclosed no conflicts of interest. Forms can
be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms .do?msNum⫽M14-2382. Requests for Single Reprints: Kevin A. Schulman, MD, Duke Clinical
Research Institute, PO Box 17969, Durham, NC 27715; e-mail, [email protected]. Current author addresses are available at www.annals.org. Ann Intern Med. 2014;161:835-836. doi:10.7326/M14-2382
References 1. McWilliams JM, Dalton JB, Landrum MB, Frakt AB, Pizer SD, Keating NL. Geographic variation in cancer-related imaging: Veterans Affairs health care system versus Medicare. Ann Intern Med. 2014;161:794-802. doi:10.7326 /M14-2382 2. Gottlieb DJ, Zhou W, Song Y, Andrews KG, Skinner JS, Sutherland JM. Prices don’t drive regional Medicare spending variations. Health Aff (Millwood). 2010;29:537-43. [PMID: 20110290] doi:10.1377/hlthaff.2009.0609 3. Yasaitis LC, Bynum JP, Skinner JS. Association between physician supply, local practice norms, and outpatient visit rates. Med Care. 2013;51:524-31. [PMID: 23666491] doi:10.1097/MLR.0b013e3182928f67 4. Skinner J, Staiger D. Technology adoption from hybrid corn to beta blockers. Working Paper 11251. Cambridge, MA: National Bureau of Economic Research; 2005. Accessed at www.nber.org/papers/w11251.pdf on 23 October 2014. 5. Zuckerman S, Waidmann T, Berenson R, Hadley J. Clarifying sources of geographic differences in Medicare spending. N Engl J Med. 2010;363:54-62. [PMID: 20463333] doi:10.1056/NEJMsa0909253 6. Olson RA, Tiwana MS, Barnes M, Kiraly A, Beecham K, Miller S, et al. Use of single- versus multiple-fraction palliative radiation therapy for bone metastases: population-based analysis of 16,898 courses in a Canadian province. Int J Radiat Oncol Biol Phys. 2014;89:1092-9. [PMID: 25035213] doi:10.1016/j.ijrobp .2014.04.048 7. Landon BE, Keating NL, Barnett ML, Onnela JP, Paul S, O’Malley AJ, et al. Variation in patient-sharing networks of physicians across the United States. JAMA. 2012;308:265-73. [PMID: 22797644] doi:10.1001/jama.2012.7615 8. Ariely D. The (Honest) Truth About Dishonesty. New York: HarperCollins; 2012. 9. Freeman VG, Rathore SS, Weinfurt KP, Schulman KA, Sulmasy DP. Lying for patients: physician deception of third-party payers. Arch Intern Med. 1999; 159:2263-70. [PMID: 10547165] 10. Dinan MA, Curtis LH, Hammill BG, Patz EF Jr, Abernethy AP, Shea AM, et al. Changes in the use and costs of diagnostic imaging among Medicare beneficiaries with cancer, 1999-2006. JAMA. 2010;303:1625-31. [PMID: 20424253] doi:10.1001/jama.2010.460
836 2 December 2014 Annals of Internal Medicine Volume 161 • Number 11
Downloaded From: https://annals.org/pdfaccess.ashx?url=/data/journals/aim/931772/ by a Tufts University User on 07/27/2017
www.annals.org
Annals of Internal Medicine Current Author Addresses: Drs. Dinan and Schulman: Duke Clinical
Research Institute, PO Box 17969, Durham, NC 27715.
www.annals.org
2 December 2014 Annals of Internal Medicine Volume 161 • Number 11
Downloaded From: https://annals.org/pdfaccess.ashx?url=/data/journals/aim/931772/ by a Tufts University User on 07/27/2017
Annals of Internal Medicine
Exploring Variation in Care: Alternative Conceptual Models
I
n this issue, McWilliams and colleagues (1) report an assessment of variation in the use of imaging services for men with prostate, lung, or colorectal cancer receiving care in the Veteran Affairs (VA) health care system or Medicare. They asked, “Does a health care system with lower use of health care services exhibit less geographic variation in the use of these services?” Surprisingly, the researchers found the opposite of what they expected. Despite lower use within the VA system, they observed greater geographic variation in the use of cancer-related imaging tests within the VA system than within Medicare. Specifically, they found that both the absolute and relative differences in overall intensity of imaging services in the highest versus lowest quintiles of hospital referral regions was 240% ($247 per patient) in the VA cohort compared with only 47% ($141 per patient) in the Medicare cohort. This study provides evidence that contradicts the growing and previously untested assumption in health services research that high variation in care reflects overuse. These findings raise a question: What is the underlying cause of geographic variability in use of medical services that drives the more than double overall per capita costs of care for Medicare beneficiaries residing in Manhattan, New York ($11 744), or Miami, Florida ($15 909), versus Lynchburg, Virginia ($6022), or Honolulu, Hawaii ($5293) (2)? Critical in the discussion of geographic variation is how we define geographic granularity. At 1 extreme, patient-level differences in preferences might drive variation in different geographic regions. Arguing against this hypothesis are survey studies that have concluded that patient preferences do not seem to drive systematic regional differences in use of health care services, whereas broader cultural markers, such as race or physician supply, do (3). At the other extreme, a fascinating investigation by Dartmouth researchers (4) suggests that a broader perspective of technology diffusion is needed to explain some aspects of geographic variation. This work explored common, underlying regional factors involved in the adoption of a diverse set of technologies, including hybrid corn and tractors in the early 1900s, computers in the 1990s, and the treatment of heart attacks with -blockers in the 1990s to 2000s. Of interest, some states consistently adopted new, effective technology across all fields. These “early adopting” states possessed social capital and education but not necessarily higher income, population density, or spending. In the United States, for example, hospital referral regions with higher overall costs are often associated with a more general increased utilization across multiple areas of health care spending (5). This generalized concept of technology diffusion is also supported by the present study, in which McWilliams and colleagues observed similar patterns in regional imaging variability among both cancer-specific and overall imaging utilization.
Between the extremes of individual patient preferences and a generalized regional inclination to adopt new technologies, other potential drivers of regional variation may come from the level of physicians, providers, and individual facilities, and these drivers may act at multiple levels. A poignant example of these middle factors comes from studies examining the use of single-fraction radiation treatments for the palliation of painful bone metastases, which is recommended by national and international guidelines for the management of uncomplicated bone metastases. Nonetheless, only 5% of eligible Medicare beneficiaries (and perhaps up to 8% to 10% of patients in U.S. academic centers) receive single-fraction radiation compared with 49% in Canada (6). However, variation also exists within the single-payer Canadian system, with the proportion of eligible patients receiving this recommended intervention varying by facility from 25% to 73% (7). Analogous to McWilliams and colleagues’ study, this shows that a 2- to 3-fold variation in care may be present even within a system with homogeneous financial incentives. The Canadian study concluded that regional variation suggests that physicians’ practice patterns are influenced by their colleagues’ practice, an idea that is further supported by observation of similar health care use among physicians who treat overlapping networks of patients. Perhaps the most controversial hypothesis that McWilliams and colleagues address is that variation in health care use is influenced by financial incentives. To this point, Ariely (8) has described a body of work that suggests that even when people are provided the opportunity to massively cheat to reap the maximum award with no chance of getting caught, they do not do so. Instead, they cheat a little, with the most potent factor affecting the magnitude of cheating being the influence of perceived social norms. In other words, variation can be based on an individual’s perception of the behavior of others within the community. Work by Freeman and colleagues (9) further supports the idea that “cheating” may not purely reflect a desire for financial gain, and that most physicians would be willing to lie to obtain coverage for patients with real medical need, whereas fewer than 3% would do so to obtain coverage for cosmetic surgery (9). Lastly, we should be careful when we attempt to quantify “geographic variation.” A balance must be considered between the granularity of how we define geographic regions and sample sizes per geographic unit. In addition, we must precisely define the outcome of interest. For example, costs are typically not normally distributed and the use of means and SDs may violate assumptions of inference. McWilliams and colleagues examined imaging in the 1 to 3 years after diagnosis. However, costs associated with a new cancer diagnosis are by far the greatest during the initial evaluation and management. Aggregating imaging beyond © 2014 American College of Physicians 835
Downloaded From: https://annals.org/pdfaccess.ashx?url=/data/journals/aim/931772/ by a Tufts University User on 07/27/2017
Editorial
Exploring Variation in Care
this period may obscure trends present during the initial work-up of these patients. Finally, we know that the use of diagnostic imaging expanded greatly from 2003 to 2005 (10), especially with respect to positron emission tomography, which is the imaging modality that represented 90% of the variation reported in this study. The present analysis may therefore represent a snapshot of a period during which imaging in cancer was rapidly changing and may not reflect current steady-state use of these technologies. In conclusion, McWilliams and colleagues provide reasonable evidence that contradicts the assumption that high variation in care reflects overuse, setting the stage for future research to identify the underlying causes of variation in use of medical technology. Michaela A. Dinan, PhD Kevin A. Schulman, MD Duke Clinical Research Institute, Duke University School of Medicine Durham, North Carolina Disclosures: Authors have disclosed no conflicts of interest. Forms can
be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms .do?msNum⫽M14-2382. Requests for Single Reprints: Kevin A. Schulman, MD, Duke Clinical
Research Institute, PO Box 17969, Durham, NC 27715; e-mail, [email protected]. Current author addresses are available at www.annals.org. Ann Intern Med. 2014;161:835-836. doi:10.7326/M14-2382
References 1. McWilliams JM, Dalton JB, Landrum MB, Frakt AB, Pizer SD, Keating NL. Geographic variation in cancer-related imaging: Veterans Affairs health care system versus Medicare. Ann Intern Med. 2014;161:794-802. doi:10.7326 /M14-2382 2. Gottlieb DJ, Zhou W, Song Y, Andrews KG, Skinner JS, Sutherland JM. Prices don’t drive regional Medicare spending variations. Health Aff (Millwood). 2010;29:537-43. [PMID: 20110290] doi:10.1377/hlthaff.2009.0609 3. Yasaitis LC, Bynum JP, Skinner JS. Association between physician supply, local practice norms, and outpatient visit rates. Med Care. 2013;51:524-31. [PMID: 23666491] doi:10.1097/MLR.0b013e3182928f67 4. Skinner J, Staiger D. Technology adoption from hybrid corn to beta blockers. Working Paper 11251. Cambridge, MA: National Bureau of Economic Research; 2005. Accessed at www.nber.org/papers/w11251.pdf on 23 October 2014. 5. Zuckerman S, Waidmann T, Berenson R, Hadley J. Clarifying sources of geographic differences in Medicare spending. N Engl J Med. 2010;363:54-62. [PMID: 20463333] doi:10.1056/NEJMsa0909253 6. Olson RA, Tiwana MS, Barnes M, Kiraly A, Beecham K, Miller S, et al. Use of single- versus multiple-fraction palliative radiation therapy for bone metastases: population-based analysis of 16,898 courses in a Canadian province. Int J Radiat Oncol Biol Phys. 2014;89:1092-9. [PMID: 25035213] doi:10.1016/j.ijrobp .2014.04.048 7. Landon BE, Keating NL, Barnett ML, Onnela JP, Paul S, O’Malley AJ, et al. Variation in patient-sharing networks of physicians across the United States. JAMA. 2012;308:265-73. [PMID: 22797644] doi:10.1001/jama.2012.7615 8. Ariely D. The (Honest) Truth About Dishonesty. New York: HarperCollins; 2012. 9. Freeman VG, Rathore SS, Weinfurt KP, Schulman KA, Sulmasy DP. Lying for patients: physician deception of third-party payers. Arch Intern Med. 1999; 159:2263-70. [PMID: 10547165] 10. Dinan MA, Curtis LH, Hammill BG, Patz EF Jr, Abernethy AP, Shea AM, et al. Changes in the use and costs of diagnostic imaging among Medicare beneficiaries with cancer, 1999-2006. JAMA. 2010;303:1625-31. [PMID: 20424253] doi:10.1001/jama.2010.460
836 2 December 2014 Annals of Internal Medicine Volume 161 • Number 11
Downloaded From: https://annals.org/pdfaccess.ashx?url=/data/journals/aim/931772/ by a Tufts University User on 07/27/2017
www.annals.org
Annals of Internal Medicine Current Author Addresses: Drs. Dinan and Schulman: Duke Clinical
Research Institute, PO Box 17969, Durham, NC 27715.
www.annals.org
2 December 2014 Annals of Internal Medicine Volume 161 • Number 11
Downloaded From: https://annals.org/pdfaccess.ashx?url=/data/journals/aim/931772/ by a Tufts University User on 07/27/2017
