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Geographic Variationin Health Care

Tom Rosenthal

David Geffen School of Medicine, University of California, Los Angeles,California 90095-7400; email: [email protected]

Annu. Rev. Med. 2012. 63:493509

First published online as a Review in Advance onNovember 4, 2011

TheAnnual Review of Medicineis online atmed.annualreviews.org

This articles doi:10.1146/annurev-med-050710-134438

Copyright c 2012 by Annual Reviews.All rights reserved

0066-4219/12/0218-0493$20.00

Keywordssmall-area variation, ecologic fallacy, supply-sensitive care, health

disparities

Abstract

Measurements of health care spending and outcomes in a geograarea and comparisons of one area to another have been used to m

observations about health delivery systems and guide health care

icy. Medicare claims files are a ready source of data about healthutilization and have served as the basis for a large number of studi

the United States. If ecologic studies are to accurately reflect local p

tices, potential variables must be accounted for. In the United Stdifferences in disease burden and socioeconomic factors are impovariables affecting health care spending and outcomes. The asse

that regional variation in Medicare spending in the last two years ois indicative of widespread waste in the U.S. health care system bec

a controversial part of the health care reform debate in 2009201

493

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The basic premise of these studies was that

if the inputs are the samethat is, the popu-lation demographics and health are generally

similar or their differences can be accounted forstatisticallyandoutcomes such as mortality or

other measures of quality are the same, then thevariation in utilization and cost above the mini-

mum represents waste in the health care system.

FROM NEW ENGLAND TOTHE UNITED STATES

The next step in the work was to expand

the analysis across the United States. To do

so, Wennberg and collaborators developed theconcept of hospital referral regions (HRRs).

They aggregated 42,000 zip codes into 3,436hospital service areas, which were further ag-

gregated into 306 small areas covering the en-tire United States (11). Each area was drawn to

encompass an HRRa region where the ma-jority of people get their hospital care, which

has at least one large referral hospital, so majorsurgery can be accurately attributed to the re-

gion. The purpose and value in creating HRRsis to make conclusions from regional data ac-

curate about actual local practice, which is

not possible when larger areas such as statesor countries are compared. Medicare claims

data were then used to compare cost and uti-

lization across the entire United States. Thefirst Dartmouth Atlas of Health Care was self-published in 1996 (10), a massive undertaking

which showed widespread variation in rates ofMedicare spending, supply of physicians and

hospitals, rates of surgical procedures, and hos-pitalizations for medical conditions across the

306 HRRs.

UTILIZATION AND COST IN

THE LAST TWO YEARS OF LIFEOne continued challenge was the problemof comparing outcomes. If the mortality rate

is comparatively low in a region with highcost and utilization, then the higher cost and

utilization could be justified. Risk adjustinglarge populations using Medicare claims data

is imperfect. To obviate this problem, the

2006 Atlas compared Medicare cost by HRRlooking only at Medicare beneficiaries who had

died, reasoning that since the outcome, death,was identical in every case, there could be no

assertion that higher cost in one area overanother could have made any difference (this

reasoning is critiqued below); hence, all theutilization above the minimum was unneces-

sary and therefore waste (12). The 1,200,000deaths across 306 HRRs (a 20% sample of the

Medicare data base) were extrapolated to the

remaining 44,000,000 Medicare beneficiaries.The results were then extrapolated to the

entire U.S. population of 300,000,000.

GEOGRAPHIC VARIATIONAND THE CASE FOR HEALTH

CARE REFORMIn the 2008 Atlas, Wennberg and colleaguesdrew increasingly dire conclusions about the

U.S. health care system based on the amountand causes of geographic variation (13):

These findings have several implications for

patients with chronic disease, and for the cost

of Medicare. First and foremost, overtreat-

ment harms patients, and it contributes to the

chaotic quality of American health care. Sec-

ond, overtreatment wastes taxpayer dollars.

Various estimates for the amount we waste

on overtreatment in this country range be-

tween 20 to 30 cents on every health care dol-

lar spent. And because of the way Medicare is

financed, overtreatment also entails a system-

atic transfer of tax dollars from residents of

low-cost regions to high-cost regions, where

those dollars fund the useless, and potentially

harmful, care that is being delivered.

In 20092010, the conclusions articulated inthe Dartmouth Atlas became a centerpiece ar-gument in the health care reform debate. Even

though the focus of the bill was achieving uni-versal insurance coverage, concerns over health

care cost inflation generally and the potentialadditional cost of universal coverage specifically

www.annualreviews.org Geographic Variation in Health Care 495


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were of wide concern. The methods of actu-

ally controlling health care spending, such ascost controls, technology controls, and the uti-

lization management of managed care, seem tobe politically unacceptable. A politically attrac-

tive solution to health care inflation is createdby identifying 30% of healthcare spending as

waste, which could be eliminated easily if doc-tors in inefficient regions could be incentivized

to practice like doctors in efficient regions. This30% is also more than enough to pay for uni-

versal coverage.

The White House budget director, PeterOrzag, testified to Congress when he was Di-

rector of the Congressional Budget Office (14):

Overuse of supply-sensitive services and

differences in social norms among local physi-

cians seem to drive regional approaches in

the use of innovations and treatments. Some

regions appear more prone to adopt low-cost,

highly effective patterns of care, whereas oth-

ersare more prone to adopt high-costpatterns

of care and to deliver treatments that provide

little benefit or are even harmful. Researchers

have estimated [ John E. Wennberg and oth-

ers, Geography and the Debate Over Medi-

care Reform, Health Affairs,Web Exclusive

(February 13, 2002), pp. W96W114; and

Elliott Fisher, More Care Is Not Better

Care, Expert Voices, Issue 7 (National Insti-

tute for Health Care Management, January

2005)] that nearly 30% of Medicares costs

could be saved without negatively affecting

health outcomes if spending in high- and

medium-cost areas could be reduced to the

level in low-cost areasand those estimates

could probably be extrapolated to the health

care system as a whole [reference]. With

health care spending currently representing

16 percent of GDP, that estimate would

suggest that nearly 5 percent of GDPor

roughly $700 billion each yeargoes to

health care spending that cannot be shown to

improve health outcomes.

McAllen, Texas, the highest-cost HRRin the Dartmouth Atlas analysis, became the

symbol for the problem. In a widely influent

article inThe New Yorker, Gawande (15) wro

When you look across the spectrum from

Grand Junction to McAllenand the almost

threefold difference in the costs of careyou

come to realize that we are witnessing a bat-

tle for the soul of American medicine. Some-

where in the United States at this moment, a

patient with chest pain, or a tumor, or a cough

is seeing a doctor. And the damning question

we have to ask is whether the doctor is set up

to meet the needs of the patient, first and fore-

most, or to maximize revenue. . . . Physicians

in places like McAllen behave differently from

others. The $2.4-trillion question is why.

Over many years few had questioned eith

the methodology of the Dartmouth Atlas or conclusions. Most commentaries were strong

supportive (16). The argument about waste waccepted because we have seen use of expensi

technology that proved to have no scientimerit, and everyone has seen physicians wh

order a full battery of tests on every patienpossibly without regard to indications,

physicians who admit patients for extend

hospital stays, perhaps without medical ncessity. That these physicians might sort o

geographically was at least plausible. Indee

the state of affairs was aptly summarized Reinhardt in 2005 (17): It is remarkable however, that no one in the medical communi

or elsewhere has been able to invalidate thefindings convincingly with similarly caref

research. If only by default, then, the researreported by Wennberg and colleagues stan

as a hitherto unchallenged Jaccuse.

The mere assertion of geographic variatioand its presumed causes probably would n

have been sufficient provocation to bring abo

a scientific debate over the methodologies geographic variation. What changed was thlegislators in the upper midwest looked at t

map (Figure 1); saw that they representapparently efficient practice regions, especia

compared to the major metropolitan areas the northeast and west; and concluded th

496 Rosenthal


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Figure 1

Total Medicare spending during the last two years of life for patients with at least one of nine chronic conditions. Deaths occurrduring 20012006. Numbers are the ratio of state to the U.S. average of Medicare spending per decedent during the last two yealife. Source: Map redrawn from data in Reference 13.

their doctors and hospitals should be rewardedfor their efficiency, while those geographic

areas that were identified in the work asinefficient (notably most major metropolitanareas) should be disincentivized for their

inefficiency. They decided to effect this changeby redistributing Medicare payment from the

latter to the former, and they pushed to havethis encoded into the health reform law (18).

This would have been worth tens of billions ofdollars. Finally thescientific basis of geographic

variation and possible flaws in its methodologyand conclusions were interesting to a lot of

people. Science had become political science.

QUESTIONS ABOUTMETHODOLOGY

The first published analyses of the Dartmouthwork identified several conceptual problems

(19, 20). Each highlighted the fallacy of us-ing patients who died as a surrogate for all pa-

tients to avoid the problem of possible differ-ences in outcome. Both observed that differentcategories of patients utilize different resources

even in dying, and that the point at which a pa-tient is determined to be dying (and therefore

further attempts at curative therapy are futile)is different for every patient. Even if it were

possible to precisely choose a cohort of dyingpatients, there are often legitimate reasons for

physicians to continue care even if the outcomeis still death. For example, even when there

is no chance of recovery, some patients fam-ilies insist on desperation care such as dial-ysis, chemotherapy, and even ventricular assist

devices.Many people have a mental map that as-

sumes health outcomes are fairly equal acrossthe United States. In fact, the age-adjusted



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mortality rate varies by 50% across states, so

outcomes must be considered (21). If outcomeshave to be considered, then adequacy of risk

adjustments for the populations must be ad-dressed. The methods used in the geographic

variation studies utilized the Iezonni classifica-tion of chronic diseases, which put patients in

one of 13 categories based on diagnosis-relatedgroups (22). Risk adjustment in some studies

was simply 1 versus >1 chronic disease code;in other studies it consisted of stratification by

number of chronic diseases. In contrast, there

are >25 covariants used in calculating trans-plant survival rates, and these are obtained both

from administrative data and by chart review(23). More than 40 covariants are used in risk

adjusting coronary artery bypassgraft rates(24).These numbers suggest that the methodology

used in the geographic variation work may be

good enough to make directionally correct con-clusions but is insufficiently sensitive to makefine comparisons between providers in differ-

ent areas. Reschovsky, studying Medicare ben-eficiaries at the individual level, has shown that

health status of the individual patient is the pre-

dominant predictor of costs, whereas physician,practice, and provider supply are minimally

correlated with cost (25).An additional potential confounder is that

patients may not receive all their care in one

HRR. The original definition of the HRR wasan area where at least 51% of the care wasdelivered. The Dartmouth Atlas assumes that

there is equal stickiness of patients across all306 HRRs, therefore obviating the need to ac-

count for movement of patients. It is not true atthestatelevel,wheretherearesignificantdiffer-

ences in flows of patients who receive care in a

state outside their residence (26). This may notinvalidate the general observations about geo-

graphic variation, but it is an additional variable

that calls into question the specificity of com-parison between one area and another. Furthercomplicating the picture, HRRs can differ dra-

matically from each other. If one is comparingthe Los Angeles HRR with its 10 million eth-

nically diverse urban population to the Great

Falls, Montana HRR with its 150,000 rur

mostly white population, its unlikely that eery relevant factor to population health and

the health delivery system can be captured uing Medicare claims data.

If Medicare payments are to correspond utilization of health care services, then pa

ments not related to utilization, such as paments for graduate medical education (GME

should be removed from the counts, and pricshould be adjusted so that wage differences a

equalized across areas. These adjustments we

not made in the 2006 Atlas work (12), whichpuzzling because their necessity was identifi

in the 1996 Atlas and the potential significachangebylocalitywaswellknown(11).MedP

(27), The American Hospital Association (2and the Greater New York Hospital Associ

tion (29) ran the analysis of Medicare spendin

by HRR accounting for prices and revalidatthe importance of price adjustment.

The result of these critiques was the dro

ping of provisionsto changeMedicare spendi

by region based on the Dartmouth Atlas enof-life studies. The compromise provision w

the requirement for the Secretary of Health aHuman Services to commission the Institu

of Medicine (IOM) to do its own study of gegraphic variation in Medicare spending, taki

into consideration all relevant factors (30). T

results of the IOM study were reported in Ap2011 (31). In addition to factoring in prices antaking out GME payments, the IOM study u

lizes the more sensitive Medicare hierarchiccoexisting conditions methodology (32). T

differences between the Dartmouth Atl

analysis (without price adjustments and wionly a modest risk adjustment) and the IO

analysis (with price adjustments and a mosophisticated risk adjustment) are strikin

Variance is reduced by 40%. Specific are

deemed to be paragons of efficiency turn onot to be, and major metropolitan areas suas New York, Boston, Philadelphia, Detro

and Los Angeles, implicated by the DartmouAtlas data to be the most costly, turn out to ha

Medicare spending at or below the nation

498 Rosenthal


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average. The upper midwest generally turns

out to be within one standard deviation of themean spending. The two areas that are lower

Medicare spending regions are the west coastand rural northeast. The area of the country

that has the highest Medicare spending is theDeep South. Sacramento had been touted as

the benchmark efficient region in California(33), and although it is still on the low-cost end,

the lowest-cost metropolitan area in Californiain the IOM analysis is San Francisco.

It could be argued that the original conclu-

sions are still correctthat even with the vari-ous adjustments to risk and prices, there is sub-

stantial geographic variation, it is evidence ofwaste, and it can be corrected easily. Another

view of the IOM study is that 96% of the U.S.population lives in areas within two standard

deviations of the mean of Medicare spending,and 76% live within one standard deviation of

the mean. Statistically, this is close to a nor-mal curve of variation. Considering that what

is being measured is all Medicare spending forall medical services, it could be concluded that

there is a remarkably small amount of variation

in Medicare spending (Figure 2).Drawing conclusions about local practices

based on aggregated regional data is a majorpurpose of geographic variation studies. The

possibility of drawing erroneous conclusions

is a known risk of this type of study and iscalled the ecologic fallacy. In a classic 1950paper, Robinson (34) describes the relationship

between statistical analysis of correlation atan individual level and statistical analysis at an

ecologic level, and he concludes that an eco-logical correlation is almost certainly not equal

to its corresponding individual correlation.

The ecologic fallacy is likely to occur whenthere are complex, nonlinear, confounding

variables (35). Although there are statistical

methods to try to obviate the problem (36),the more significant issue is when importantvariables are missed entirely (37). The ecologic

fallacy does not invalidate all conclusionsdrawn from aggregated regional data; if it

did, there would be no field of epidemiology.

Ecologic research can provide insights about

individual behaviors, if its findings are validatedat the individual level (2).

Prices were an important missing variable.Are there possibly other missing variables?

Cooper (38) suggests that the variation workfails to adjust for socioeconomic status and that

population differences in income can explaina significant amount of the remaining varia-

tion. There is abundant evidence of health caredisparities based on socioeconomic status (39).

Although there is controversy on whether in-

equality itself is a factor (40, 41), it is clear thatabsolute poverty levels negatively impact many

measures of health, including mortality (4244). It might be intuitive to conclude that worse

health outcomes among the poor are caused byless access to and utilization of health care ser-

vices. In fact, evidence points to higher healthcare utilization, with the rise being especially

steep at the lowest income levels, suggestingthat the causes of poorer health relate to higher

disease burdens among the poor and other not-

well-understood consequences of living in con-centrated areas of poverty, which results in

higher health care costs. Low-income Medi-care beneficiaries have fewer visits to physi-

cians for ambulatory care and fewer preven-tive services such as mammograms, but they are

hospitalized more often (42). Wier et al. (45)

showed that rates of hospitalizations were 22%higher for rural poor than rural wealthy and27% higher for urban poor than urban wealthy.

Pappas et al. (46) showed a 2.5-fold higher rateof hospitalizations for avoidable hospital condi-

tions among people who live in low-income zip

codes than high-income zip codes. Jiang et al.(47) showed a two- to fourfold higher rate of

hospitalizations for the preventable conditionsof diabetes, heart failure, and asthma when they

compared the highest quartile of income zip

codes by population with the lowest quartileof income across the United States. Chen &Escarce (48) showed 50% higher total health

care spending for adults below 125% of the fed-eral poverty level than those with higherincome

levels.



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1,000

1,500

2,000

2,500

All other LA

LA poverty core

LA County

Me

dicarehospitaldays

per1,

000Medicareenrollees

MinnesotaMinnesota

Pacific Ocean

LA County

a

b

LA Core

Population 10,200,000 1,800,000

Per capita income $20,700 $11,500

Black and Latino 55% 80%


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in the rest of L.A. (50) and the U.S. aver-

age of 2.6 beds/1,000 (51). Thus, poverty is amore likely explanation for the differences than

supply-driven demand. The effects of povertyand class are independent of the impact of race,

so that adjusting for race will not simultane-ously adjust for factors related to income (52).

If high Medicare spending and utilizationreflect high-intensity wasteful practice styles

of physicians, then Medicare spending shouldtightly correlate with overall health spending.

Data at the state level show little correlation

between Medicare spending and total healthspending (26, 55). Some states are high in both,

some are low in both, some (like Texas andLouisiana) are high in Medicare and low in total

health spending, and some (like Minnesota andWisconsin) are low in Medicare spending and

high in total health spending (Figure 4). Dataare not available for total health care spending

by HRR; in a recent study of commercial in-surance, Gabel et al. found that when the Dart-

mouth Atlas reimbursement index was addedto their geographic commercial practice cost

index, it was statistically significant, but with a

negative coefficient (56). There are no good hy-pothesesto explain this relationship. Onepossi-

ble explanation is an interrelationship betweencommercial and government prices at the local

level. Another possible explanation is the vari-

ation in end-of-life spending for Medicare pa-tients, which affects Medicare patients but doesnot affect commercial populations to the same

degree. Regardless, Medicare spending is nota good proxy for overall health spending or

utilization.

THE WAY FORWARD

Geographic variation is a useful tool in un-

derstanding health care delivery. It became

controversial during the debate over healthcare reform and was subject to scientificquestioning in a way that had not occurred

previously. The following facts are not con-troversial: There is geographic variation in

health care utilization and spending. Some of

the variation is explainable by illness burden,

price differentials, levels of insurance coverage,and community wealth and poverty. Some

variation remains unexplained. What remainscontroversial is the interpretation. Does the

magnitude of variation in Medicare spendingindicate significant differences in health care

utilization caused by supply-driven demand,which then differentiates efficient and ineffi-

cient practitioners? Does all spending abovethat of the lowest-spending region indicate

waste? I do not believe so, but others disagree.

Should Medicare change its payments tocities and states based on geographic variation,

rewarding the efficient and taking away pay-ments from inefficient regions? This is up to the

IOM to recommend to the Secretary of Healthand Human Services, who will decide. How-

ever, it is difficult to see how shifting Medicaredollars from rural Louisiana to San Francisco

will improve health care delivery, or positivelychange the incentives, whatever they are, in ei-

ther place.What should be done moving forward?

More effort can be expended to refine the use

of the Medicare claims data to more accuratelyreflect local reality. Even if this can be done, it

is not clear that it will provide the kind of datanecessary to change local practices, and much

effort will be expended arguing arcane aspects

of statistical analysis. That more tests and con-sultations and application of expensive technol-ogy and procedures do not necessarily improve

health in individual patients is incontrovertible.Aaron (57) states the problem well:

To be meaningful, however, a definition of

waste must rest on an ex ante perspective:

What is the expected value of outcomes for

definable classes of patients? And, most of

the care that analysts label as waste is not

uniformly useless but produces average ben-

efits that are judged to be small relative to

costand typically that cost is widely diffused

among payers other than the patient. Even

those interventions deemed excessively costly

actually help some patients. So it is easy to



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a

b

>1 SD

1 SD

1 SD

1 SD


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understand whyapart from self-interest

physicians may provide their patients with

wasteful care. Thus, the very definition of

waste is unclear, and the term is fraught with

ethical ambiguity.

Put another way, one persons waste is another

persons medical necessity.There is an imperative to address the unsus-

tainability of health care inflation (58). How-ever, belief in the need for health care reform

does not rest on the pillar of geographic varia-

tion. Describing geographic variation does notnecessarily provide solutions. Many more stud-

ies are needed at finer levels of granularity, eventhough they are expensive and time consuming.

One example is the work of Barnato et al. (59)on end-of-life care; they use structured simula-

tions to understand communication practices ofphysicians making treatment decisions for crit-

ically ill patients. Ong et al. (60) compare bothresource use and outcomes in heart failure pa-

tients at six California academic medical cen-ters, providing a baseline for interventions that

can be utilized to reduce variation, lower cost,

and improve quality. Rettig et al. (61) have con-tributed a comprehensive and masterful analysis

of the rise and fall of bone marrow transplan-tation for advanced breast cancer, describing

the interconnected stories of patient demand,

conflicting roles of physicians, and scientific,clinical, legal, economic, and political factors.Their analysis demonstrates how difficult the

task is to understand and change health careutilization.

Eventually control of health care inflationis going to require physician integration into

larger practice units, an explicit societal con-

sensus regarding technology, and creation ofa norm of efficiency in physician professional

culture. In the meantime, what is universallyagreed on is the need to understand the delivery

of health care at a local level, particularly thefactors causing variation in health care delivery

and cost. A close partnership between clinicalleadership and health outcomes researchers is

necessary to achieve this. An effort to system-atically understand the quality and outcome

side began 10 years ago with the publication of

the IOM reports (62). The cost of measuringthe Medicare core quality measures has been

estimated at $350 million for the UnitedStates (63), a number approximately equal to

the entire Agency for Healthcare Research andQuality annual budget in 2008. Development

of a robust understanding of factors causingvariation in health delivery and cost will not

occur overnight and will require a significantincrease in funding for research and devel-

opment of metrics. The establishment and

funding of the Patient-Centered OutcomesResearch Institute and the $1.1 billion pro-

vided for clinical effectiveness research in theAmerican Recovery and Reinvestment Act are

good beginnings.The work over 30 years on geographic vari-

ation has consistently shined a light on the

problems of health care cost and has consis-tentlydemandedtheadditionalworkthatwouldlead to understanding and improvement in the

scientific basis of medical practice. If the re-cent debate on geographic variation results in

that effort, it will have made an enormous

contribution.

Figure 4

Per capita spending by state, unadjusted for prices, on (a) Medicare patients and (b) all health care. Red represents states with pespending >1 standard deviation (SD) from the mean; pink, 1 SD; white,


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DISCLOSURE STATEMENT

The author is not aware of any affiliations, memberships, funding, or financial holdings that migbe perceived as affecting the objectivity of this review.

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61. Required reading

for anyone who wants to

understand the forces at

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http://www.nap.edu/openbook.php?record_id=9728&page=1

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RELATED RESOURCES

1. Paul-Shaheen P, Clark JD, Williams D. 1987. Small area analysis: a review and analysis of t

North American literature. J. Health Politics, Policy and Law 12(4):741809. Although datethis is a comprehensive review of small-area variation with a thorough literature review to da

Its analysis of methodology and recommendations are still applicable.

2. Williams DR. 2005. The health of U.S. racial and ethnic populations.J. Gerontol. 60B(SpeIss. II):5362. This is a good general review of health disparities, one of several papers on thsubject in this special issue.

3. Abelson R, Harris G. 2010. Critics question study cited in health debate. NY Times Ju2, http://www.nytimes.com/2010/06/03/business/03dartmouth.html.This is a rich an

comprehensive scientific debate on geographic variation, its methodology, and its use in thealth care reform debate. It is evocative of the time that the discussion is in a newspap

508 Rosenthal
http://www.hcup-us.ahrq.gov/reports/statbriefs/sb72.jsphttp://www.oshpd.ca.gov/hid/products/preventable_hospitalizations/SearchCharts.aspxhttp://www.oshpd.ca.gov/hid/products/preventable_hospitalizations/SearchCharts.aspxhttp://statehealthfacts.org/comparemaptable.jsp?ind=384&cat=8http://statehealthfacts.org/comparemaptable.jsp?ind=384&cat=8http://statehealthfacts.org/comparemaptable.jsp?ind=384&cat=8http://statehealthfacts.org/comparemaptable.jsp?ind=384&cat=8http://statehealthfacts.org/comparemaptable.jsp?ind=384&cat=8http://www.unitedwayla.org/getinformed/rr/socialreports/Pages/2007zipcode.aspxhttp://www.unitedwayla.org/getinformed/rr/socialreports/Pages/2007zipcode.aspxhttp://www.nap.edu/openbook.php?record_id=9728&page=1http://www.nap.edu/openbook.php?record_id=9728&page=1http://www.nap.edu/openbook.php?record_id=9728&page=1http://www.nap.edu/openbook.php?record_id=9728&page=1http://www.nap.edu/openbook.php?record_id=9728&page=1http://www.hospitalqualityalliance.org/hospitalqualityalliance/files/BAH1206.pdfhttp://www.nytimes.com/2010/06/03/business/03dartmouth.htmlhttp://www.nytimes.com/2010/06/03/business/03dartmouth.htmlhttp://www.nytimes.com/2010/06/03/business/03dartmouth.htmlhttp://www.hospitalqualityalliance.org/hospitalqualityalliance/files/BAH1206.pdfhttp://www.nap.edu/openbook.php?record_id=9728&page=1http://www.unitedwayla.org/getinformed/rr/socialreports/Pages/2007zipcode.aspxhttp://www.unitedwayla.org/getinformed/rr/socialreports/Pages/2007zipcode.aspxhttp://statehealthfacts.org/comparemaptable.jsp?ind=384&cat=8http://www.oshpd.ca.gov/hid/products/preventable_hospitalizations/SearchCharts.aspxhttp://www.oshpd.ca.gov/hid/products/preventable_hospitalizations/SearchCharts.aspxhttp://www.hcup-us.ahrq.gov/reports/statbriefs/sb72.jsp


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and not a scientific journal. The links to the rebuttal by Drs. Fisher and Skinner are pro-

vided on the web, and the rebuttal to the rebuttal is athttp://www.nytimes.com/2010/

06/19/business/19dartmouth.html.

4. Robinson JC. 2001. The end of managed care.JAMA 285(20):262228. This paper reviews theattempts the health system made in the 1990s to control health care costs through managed

care. It describes the forces that led to its failure. The need to manage care has reappeared,and the social forces are unchanged, making this an excellent guide to the challenges ahead.

http://www.nytimes.com/2010/06/19/business/19dartmouth.htmlhttp://www.nytimes.com/2010/06/19/business/19dartmouth.htmlhttp://www.nytimes.com/2010/06/19/business/19dartmouth.htmlhttp://www.nytimes.com/2010/06/19/business/19dartmouth.htmlhttp://www.nytimes.com/2010/06/19/business/19dartmouth.html


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Annual Review

Medicine

Volume 63, 201Contents

Huntingtons Disease: Advocacy Driving Science

Nancy S. Wexler 1

Direct-to-Consumer Genetic Testing: Perceptions, Problems,

and Policy Responses

Timothy Caulfield and Amy L. McGuire 23

Human Genome Sequencing in Health and Disease

Claudia Gonzaga-Jauregui, James R. Lupski, and Richard A. Gibbs

35The Genetic Architecture of Schizophrenia: New Mutations

and Emerging Paradigms

Laura Rodriguez-Murillo, Joseph A. Gogos, and Maria Karayiorgou 63

CCR5 Antagonism in HIV Infection: Current Concepts

and Future Opportunities

Timothy J. Wilkin and Roy M. Gulick 81

New Paradigms for HIV/AIDS Vaccine Development

Louis J. Picker, Scott G. Hansen, and Jeffrey D. Lifson 95

Emerging Concepts on the Role of Innate Immunity in the Preventionand Control of HIV Infection

Margaret E. Ackerman, Anne-Sophie Dugast, and Galit Alter 113

Immunogenetics of Spontaneous Control of HIV

Mary Carrington and Bruce D. Walker 131

Recent Progress in HIV-Associated Nephropathy

Christina M. Wyatt, Kristin Meliambro, and Paul E. Klotman 147

Screening for Prostate Cancer: Early Detection or Overdetection?

Andrew J. Vickers, Monique J. Roobol, and Hans Lilja 161

Targeting Metastatic Melanoma

Keith T. Flaherty 171

Nanoparticle Delivery of Cancer Drugs

Andrew Z. Wang, Robert Langer, and Omid C. Farokhzad 185

v


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Circulating Tumor Cells and Circulating Tumor DNA

Catherine Alix-Panabieres, Heidi Schwarzenbach, and Klaus Pantel 1

Translation of Near-Infrared Fluorescence Imaging Technologies:

Emerging Clinical Applications

E.M. Sevick-Muraca 2

Familial and Acquired Hemophagocytic Lymphohistiocytosis

G.E. Janka

2

The Management of Gastrointestinal Stromal Tumors: A Model

for Targeted and Multidisciplinary Therapy of Malignancy

Heikki Joensuu and Ronald P. DeMatteo 24

Carotid Stenting Versus Endarterectomy

David Doig and Martin M. Brown 2

Mitral Valve Prolapse

T. Sloane Guy and Arthur C. Hill 2

Telomeres, Atherosclerosis, and the Hemothelium: The Longer ViewAbraham Aviv and Daniel Levy 2

Aquaporins in Clinical Medicine

A.S. Verkman 3

Role of Endoplasmic Reticulum Stress in Metabolic Disease

and Other Disorders

Lale Ozcan and Ira Tabas 3

Role of Fructose-Containing Sugars in the Epidemics of Obesity

and Metabolic Syndrome

Kimber L. Stanhope

3Vaccines for Malaria: How Close Are We?

Mahamadou A. Thera and Christopher V. Plowe 34

Crisis in Hospital-Acquired, Healthcare-Associated Infections

David P. Calfee 3

Novel Therapies for Hepatitis C: Insights from the Structure

of the Virus

Dahlene N. Fusco and Raymond T. Chung 3

Multiple Sclerosis: New Insights in Pathogenesis and Novel

Therapeutics

Daniel Ontaneda, Megan Hyland, and Jeffrey A. Cohen 3

Traumatic Brain Injury and Its Neuropsychiatric Sequelae

in War Veterans

Nina A. Sayer 4

vi C on te nt s


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Eosinophilic Esophagitis: Rapidly Advancing Insights

J. Pablo Abonia and Marc E. Rothenberg 421

Physician Workforce Projections in an Era of Health Care Reform

Darrell G. Kirch, Mackenzie K. Henderson, and Michael J. Dill 435

Reducing Medical Errors and Adverse Events

Julius Cuong Pham, Monica S. Aswani, Michael Rosen, HeeWon Lee,

Matthew Huddle, Kristina Weeks, and Peter J. Pronovost

447

Relationships Between Medicine and Industry: Approaches to the

Problem of Conflicts of Interest

Raymond Raad and Paul S. Appelbaum 465

Wireless Technology in Disease Management and Medicine

Gari D. Clifford and David Clifton 479

Geographic Variation in Health Care

Tom Rosenthal 493

Deep Brain Stimulation for Intractable Psychiatric DisordersWayne K. Goodman and Ron L. Alterman 511

Contemporary Management of Male Infertility

Peter J. Stahl, Doron S. Stember, and Marc Goldstein 525

Indexes

Cumulative Index of Contributing Authors, Volumes 5963 541

Cumulative Index of Chapter Titles, Volumes 5963 545

Errata

An online log of corrections toAnnual Review of Medicinearticles may be found at

http://med.annualreviews.org/errata.shtml

geographic var.pdf

Documents