journa l h om epa ge: www.elsev ier .com/ locate /sosc i j
egional cultures and health outcomes: Implications forerformance measurement, public health and policy
ackson Williams ∗
ialysis Patient Citizens, Washington, DC, USA
a r t i c l e i n f o
rticle history:eceived 31 August 2011eceived in revised form6 September 2013ccepted 16 September 2013vailable online 17 October 2013
a b s t r a c t
The U.S. Medicare program now ties payment to health care providers based on theirpatients’ outcomes. This change comes as compilations of data on geographic variationsin health outcomes and quality of care indicate patterns that appear to be deeply ingrained.This study explores whether cultural characteristics correlate with health outcomes suchthat quality indicators may be measuring something other than quality of care, and whetherregional subcultures have a significant impact on public health. It concludes that twocultural dimensions, social capital and traditional/rational-secularism, which explain a suf-
ficient proportion of outcome variations to cast doubt as to whether outcome measurescapture provider quality. Correlations are explored between American regional subcul-tures identified by Joel Lieske and the variation in health outcomes. In a multidimensionalanalysis of Lieske’s typology, results indicate that certain U.S. subpopulations have culturaladvantages or disadvantages relating to health.
In recent years, health services researchers have docu-ented wide variations in health outcomes across regions
British Heart Foundation, 2009a, 2009b; Commonwealthund, 2009; Ezzati, Friedman, Kulkarni, & Murray, 2008;romhout, 1999; Morris et al., 2001; Murray et al., 2006;helton, 2009; University of Wisconsin Population Healthnstitute, 2010). Meanwhile, new policies adjust pay-
ents to health care providers based on patient outcomes.otably, the U.S. Medicare program is reducing pay-ents to those hospitals whose patients, with regard to
ationwide norms, experience high mortality or excessive
eadmissions (Medicare Payment Advisory Commission,012). Little consideration has been given to the degreeo which regional health variations may be attributable
∗ Correspondence address: 122 C Street NW #510, Washington DC0001, USA. Tel.: +1 202 441 6998.
to regional cultural differences over which providers exertrelatively little influence. Knowledge of such linkages couldimprove the formulation of pay-for-performance policiesand reporting of so-called rankings or scorecards, and assistpublic health officials in addressing health disparities.
The purpose of this study is to explore whether culturalcharacteristics (1) correlate with health outcomes such thathealth care quality indicators are measuring somethingother than quality of care, and (2) have a significant impacton public health. The hypothesis is that quantifiable char-acteristics of regional cultures predict health behaviors andhealth outcomes.
2. Culture and health
To the extent that culture impacts health outcomes,it does so by affecting individual behavior. Edberg
(2007) defines culture as shared and learned elementsof behavior—a pattern of living acquired at the collectivelevel—as opposed to those elements unique to an individ-ual. The literature on individual decision-making related
to health offers various frameworks for understandinghow socially transmitted meanings and values could affectbehavior.
Most models for understanding consumer healthbehaviors distinguish between cognitive and noncog-nitive elements. Cognitive elements include seekinghealth information and processing health messages, whilenoncognitive elements encompass feelings and instincts(Keselman, Logan, Smith, Leroy, & Zeng-Treitler, 2008).For instance, Moorman and Matulich (1993) propose onemodel of consumer preventive health behavior, positingthat it is a function of health ability, including resources,skills and proficiencies; and health motivation, the degreeto which consumers make it a personal goal to engage inpreventive health behaviors.
Heckman (2007, pp. 13, 250) identifies several noncog-nitive factors that may underlie motivation—perseverance,risk aversion and time preference—and argues thatimproved health comes “with greater self-control and con-scientiousness”. Time-preference refers to the hypothesisthat individuals with an orientation toward the future willseek more education and make more substantial invest-ments in their long-term health (Grossman, 2003). The factthat time preference also affects schooling can confoundour ability to distinguish between cognitive and noncogni-tive causes.
Health habits and health beliefs (Rosenstock, 1966)are factors beyond motivation that could be shaped byculture. Habitual diet and health patterns are part of astyle of behavior that may initially be shaped by envi-ronmental factors favoring particular agricultural productsor physical activity requirements which are then passeddown through generations to connect to a shared socialstructure. Health belief-based diets and patterns involveinterplay between cognitive and noncognitive influenceson choice, where different meanings and values favored indifferent cultures result in different outcomes. For exam-ple, an individual may understand that eating fruits andvegetables may extend his or her life, but motivationto consume them may be influenced by a culture thatplaces greater value on the gratification gained by eatinga steak.
The premise here is that these noncognitive character-istics of individuals will vary as if distributed along a bellcurve, with, in Hofstede’s (2006, p. 3) evocative phrasing,“the variation between cultures [constituting] the shift ofthe bell curve when one moves from one society to theother.”
Two main research approaches are available for look-ing at the impact of culture on health: unidimensionaland multidimensional (Lieske, 2012). Unidimensional anal-yses apply parsimonious models centering on specificexplanatory characteristics of regional cultures that can bequantified on a numeric scale (Hofstede, 2006). Multidi-mensional analyses view cultures as discrete, free-standingunits, e.g., Japanese, American, Mediterranean, in whichunique cultural characteristics or effects can be identified at
the regional, national, or ethnic level. Lieske (1993) devel-oped classifications of regional subcultures in the U.S., butthese have not been applied to analyses of health out-comes.
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3. Unidimensional analyses
The first aim of this paper is to identify characteristicsof regional cultures that may correlate with health beliefs,and it begins with social capital. At its heart, social capitalrefers to social bonds that give rise to norms of reciprocity(Putnam, 2001), but it has several dimensions includingone encompassing achievement and conscientiousness. Assuch, social capital is correlated with educational per-formance (Putnam, 2001). Kawachi, Kennedy, Lochner,and Prothrow-Stith (1997) document correlations betweensocial capital indicators and mortality. Folland (2006) andBrown, Scheffler, Seo, and Reed (2006) associate socialcapital with such healthy and risk-avoiding behaviors asexercise, eating fruits and vegetables, and abstention fromharmful substances.
Putnam (2001) documents a persistent pattern amongU.S. states of higher levels of social capital in northern statesand lower levels in southern states, with many state-levelsocial and economic outcomes correlating with social cap-ital.
A second dimension of interest is Inglehart’s (n.d.)traditional/secular-rational values dimension, derivedfrom the World Values Survey that compares attitudes indifferent societies. The traditional/secular-rational valuesdimension, one of two in the Inglehart model, differen-tiates societies based on the importance they place onreligion.
3.1. Method
The analysis begins with several sets of cross-sectionalstate-level, and one set of nation-level, statistics on healthbehaviors and outcomes. We regress these indicators onproxy independent variables for social capital and tradi-tional/secular culture.
3.2. Data sources
Data for this part of the study include a hospitalcomposite quality index derived from 2007 Centers forMedicare and Medicaid Services (CMS) Hospital Comparedata (Commonwealth Fund, 2009); Medicare readmis-sions in 2006 and 2007 (Commonwealth Fund, 2009);and Percent Optimal Medication Possession Ratio (MPR),a measure of medication adherence reported by CVS Care-mark. For each of these indicators the independent variableis Putnam’s Comprehensive Social Capital Index, a com-bination of survey data sources that track interpersonaltrust and civic participation. Other data sources for thissection are aggregated responses from the World ValuesSurvey and the American Religious Identification Survey;medication nonadherence data collected by Hirth, Greer,Albert, Young, and Piette (2008) in their international
study on out-of-pocket costs and medication adherenceamong kidney failure patients; and hospital admissionsfor ambulatory care sensitive conditions (CommonwealthFund, 2009).
J. Williams / The Social Science Journal 50 (2013) 461–470 463
Fig. 1. Social capital and hospital quality.Sources are: for comprehensive social capital index, www.bowlingalone.com; for hospital quality composite measure, Commonwealth Fund compilationo
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.3. Social capital—results
Fig. 1 displays the hospital composite quality index plot-ed against Putnam’s Comprehensive Social Capital Index.he graph reveals a strong positive association betweenocial capital and hospital quality.
Fig. 2 displays Medicare readmissions in 2006 and 2007lotted against the Putnam index. The same pattern is visi-le except that readmissions have a negative correlation.ote that R2 for both regressions is a robust .41. Both
eadmissions and elements of the quality composite areutcomes for which hospitals are now being financiallyenalized by the Medicare program.
Fig. 3 illustrates a possible mechanism driving theoorer outcomes. A measure developed by a CVS Caremarkesearch team, Percent Optimal MPR, reports the percent ofnsurance plan members covered by Caremark’s pharma-eutical benefit management program with a medicationossession ratio of .80 or higher. Medication possessionatio is calculated using pharmacy claim data to deter-ine whether patients fill and refill prescriptions (Thier
t al., 2008). The correlation with social capital is strong:2 = .439.
The correlation between social capital and medication
dherence highlights how cultural differences in patientngagement may contribute to outcomes. In Vermont,atients had all their drugs on hand an average 82% of theime and 75% of patients had at least 80% of their drugs
on hand. Contrast this with Mississippi, where only 57% ofpatients had 80% of their drugs on hand.
3.4. Social capital—discussion
Skinner and Staiger (2005) display a plot very similar toFig. 1 using quality measures from 2000, and only marginalchanges in the Putnam distribution are evident seven yearslater. Putnam notes a “general pattern [in which] socialcapital drives out other possible competing variables inregression analysis.” Thus, when we observe regional dif-ferences in health outcomes and accompanying qualitymeasurements, it is difficult to be certain whether the indi-cators measure variation in the quality of care traceableto management and staff and practitioners in a specifichospital or variations in the population of its catchmentarea.
3.5. Traditional/secular-rational—results
Fig. 4 plots international medication nonadherencedata collected by Hirth et al. (2008) in their internationalstudy on out-of-pocket costs and medication adherenceamong kidney failure patients against World Values Sur-
vey responses to a question on belief in an afterlife, a proxyvariable for the traditional/secular dimension. This dimen-sion appears to account for nearly 40% of the variation inmedication adherence—the same as the out-of-pocket cost
464 J. Williams / The Social Science Journal 50 (2013) 461–470
and hoom; for
Fig. 2. Social capitalSources are for comprehensive social capital index, www.bowlingalone.cmonwealth Fund compilation of Medicare data.
of the medications which was the focus of the Hirth et al.(2008) article.
Fig. 5 plots U.S. data on ambulatory care sensitive hospi-tal admissions against a traditional/secular proxy variable.In this case it is the percent of state residents reporting noreligion from the City University of New York’s 2001 Amer-ican Religious Identification Survey. Again, the correlationis negative and significant.
3.6. Traditional/secular-rational—discussion
The multiple regression model in Table 1 showsthat social capital and the traditional/secular cultural
Table 1Predictors of ambulatory care sensitive hospital admissions by state.
Variable Ambulatory care sensitivehospital admissions
Coefficient S.E.
Constant 1,070.92 107.61Social capital −106.21* 21.67Pct reporting no religion −17.47* 3.80Income −.012 .016
R2 .594
Sources: Comprehensive Social Capital Index: www.bowlingalone.com.Percent reporting no religion: American Religious Identification Survey.Ambulatory Care-Sensitive Hospital Admissions: Commonwealth FundState Scorecard on Health System Performance, 2009.Note: n = 48.
* p < .01.
spital readmissions. Medicare 30-day hospital readmissions as percent of admissions, Com-
dimension together explain 59% of the variation inambulatory care sensitive hospital admissions. The tra-ditional/secular dimension, in addition to explaining thevarious cultural tendencies identified by Inglehart, alsosuggests a possible fatalism that might subtly disincen-tivize healthy behavior among more religious populations.
4. Multidimensional analysis
We next examine how particular regional subculturesand associated traits might predispose some subpopula-tions to better health outcomes than others. For instance,Ancel Keys’ Seven Countries Study demonstrates howdifferent cultures and associated diets particularly theso-called Mediterranean diet, result in different healthoutcomes (Kromhout, 1999). Marmot and Syme (1976)demonstrate how the benefits of a relatively healthfulculture can be lost when migrants are transplanted andacculturated to a less healthful culture: Japanese immi-grants to the U.S. have a higher prevalence of coronaryheart disease than Japanese in Japan. Marmot and Syme dis-tinguish between wholly dietary effects and other culturalfeatures such as societal cohesion that may be protectiveagainst social stress associated with coronary heart disease.
Within the U.S., geographic unit-level analyses of
heath differences has documented the existence of aStroke Belt—southeastern states whose residents sufferhigh stroke mortality even after controlling for race andepidemiological factors thought to contribute to stroke
J. Williams / The Social Science Journal 50 (2013) 461–470 465
Fig. 3. Social capital and medication adherence.Sources are for comprehensive social capital index, www.bowlingalone.com; for percent optimal medication possession ratio, CVS Caremark MedicalClaims Research Database. Percent Not Believing in Afterlife Medication Non-Adherence.
Med
icat
ion
Non
-Adh
eren
ce
Percent No t B elieving in Afterl ife
Fig. 4. Traditional/secular-rational dimension and medication adherence.Sources are for percent not believing in afterlife; World Values Survey; for medication nonadherence Hirth et al., 2008.
466 J. Williams / The Social Science Journal 50 (2013) 461–470
and amation Su
Fig. 5. Traditional/secular-rational dimensionSources are for percent reporting no religion, American Religious IdentificFund State Scorecard on Health System Performance, 2009.
(Richardson, Liao, & Tucker, 2005). Other health issues aretracked on a geographic basis as well, including obesity,which the U.S. CDC (2010) has described as a national epi-demic. CDC notes that the rate of obesity varies from a lowof 18.6% in Colorado to a high of 34.4% in Mississippi.
Transnational differences in health behaviors and out-comes are relevant to American regional differencesbecause America is a multicultural society, and a significantbody of literature has traced identifiable American regionalsubcultures to mother-country origins. Rice and Feldman(1997) document correlations between attitudes of Amer-icans with ethnic ties to European nations and attitudescommon in those nations, despite the passage of time anddistance, and Putnam (2001) notes the association of a highdegree of social capital with Scandinavian origins. Lieske(1993) developed classifications based on the sources ofpotential cultural differences, such as ethnic ancestry andreligious affiliation, which could then be explored for vari-ations in behavior.
The Lieske typology identified groupings of U.S. coun-ties whose residents share common cultural traits in termsof race, ethnicity, and religion, delineating ten regional
clusters. Four of these subcultures roughly correspond tofour waves of emigration from the British Isles identifiedby Fischer (1989): an Anglo-French2 subculture, with
2 This journal does not follow convention of using quotation marks tointroduce invented or coined expressions per APA Publication Manual
bulatory care sensitive hospital admissions.rvey; for ambulatory care-sensitive hospital admissions: Commonwealth
Puritan roots, prominent in northern New England; aBlackbelt subculture originating with the emigration ofCavaliers, including their servants and slaves, stretchingthrough the South; a Heartland subculture that originatedin England’s North Midlands that extends through Amer-ica’s midsection to Kansas; and a Border subculture inwhich Scots-Irish immigrants are prominent, stretchingthrough America’s Border South region.
Other subcultures Lieske identifies include the Nordicand Germanic, located primarily in the upper Midwest;the Ethnic, centered in rust-belt metropolitan areas; andthe Hispanic, located in areas adjacent to Mexico. Of someimportance here is what Lieske termed the Rurban sub-culture, which is composed of residents of “rural-urbanhabitats [having] high levels of education, professionaland managerial occupations, working women, populationmobility, and younger populations. . . generally found inpastoral academic settings” and the West. (This usage of theterm rurban is distinct from its connotation in geographyor land use planning.)
Comparison of Lieske’s map of U.S. regional subculturesside-by-side with the life expectancy maps produced by
Murray et al. (2006) indicates that the patterns on themaps overlay one another; for example, Lieske’s Border andBlackbelt counties correspond with swaths of red on the
Section 4.07. The reader should not interpret these category names asbeing in use or understood outside of the studies for which they werecoined.
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Table 2Predictors of white male life expectancy by state.
Sources: Regional Subcultures: Lieske (2012). Life Expectancy by State,1979–1991: U.S. Census Bureau, Statistical Abstract of the United States.
J. Williams / The Social Sc
urray maps that indicate low life expectancy, and Lieske’sermanic and Nordic counties correspond with swaths oflue on the Murray maps that indicate high life expectancy.
ndeed, while not referencing ethnicity within white popu-ations nor any literature on regional culture, Murrayt al. (2006) delineates “eight Americas” with distinctatterns of health outcomes, including three white geo-raphic/population groups that roughly correspond withieske’s Nordic and Germanic subcultures (America 2)1;eartland subculture (America 3); and Border subculture
America 4).3 Also notable is the correspondence of Lieske’surban subculture with high life expectancy, particularlyisible in the Pacific region and Colorado, which, as notedarlier, has the lowest rate of obesity among U.S. states.he pattern can also be seen in Fig. 5, wherein the America
states are clustered in the upper left quadrant, and themerica 2 states are clustered in the lower left quadrant.urban states are located at the extreme lower right.
.1. Method
The foregoing suffices to pass the “interocular trauma”est—a pattern that hits the observer between the eyesPutnam, 2001). A county-by-county quantitative anal-sis of the association between subcultures and healthutcomes would be the preferred research method. Unfor-unately, there is no dataset available that documentsounty-level subcultures.
.2. Data sources
In 2010, Lieske published a revised cultural typology.mong other changes, the update substituted a Globalubculture for the Ethnic, added an 11th category, andeclassified many counties to Rurban and Germanic. The993 map, with its more nuanced portrayal of regional her-
tage and more granular depiction of cultural influence onhe health of age cohorts who lived most of their lives inhe second half of the last century, would be a better dataource. But the 2010 Lieske typology can be used for quan-itative analysis, as he has released a dataset containing
percentage distribution of regional subculture by stateLieske, 2012). That dataset is used for the independentariables. The dependent variable is race- and sex-adjusted
3 Notably, Colin Woodard (2011) produced another similar typology:e refers to the Border subculture as “Greater Appalachia;” divides Anglo-rench between “New France” and a “Yankeedom” that also includes theermanic and Nordic regions; and designates the “Heartland” subcul-
ure as “Midland.” Although Woodard’s names are different, the Woodard,ieske, and Ezzati-Murray maps all overlay each other nearly identically.he primary exception is that Lieske’s use of American (northern) Bap-ist as an indicator for assignment into the Heartland cluster, and leadso the anomaly of West Virginia not being classified as part of the Bor-er region. Ezzati and Murray place West Virginia in America 4; Woodardlaces it in Greater Appalachia; and the Ezzati/Murray maps demonstrate
ife expectancy in West Virginia to be as poor as in other Border subculturereas. The US Census ethnicity map also provides an interesting over-ay. In the counties/states in the Border subculture, census respondents
ere most likely to describe their ethnic origin as “American.” This phe-omenon among Scots-Irish is well known and was the basis for Patrickriffin dubbing the Scots-Irish “The People With No Name” in his book of
hat title.
Note: n = 51.* p < .01.
** p < .05.
life expectancy by state for 1989–1991 obtained from U.S.National Center for Health Statistics, National Vital Statis-tics Reports.
4.3. Results
Table 2 displays white male life expectancy by stateregressed on ten vector independent variables represent-ing Lieske’s cultural categories. Germanic is the referencecategory. The constant of 73.2 years is higher than theactual national average of 72.7, so the table tends to accen-tuate regional subcultures that are below the Germanicaverage. States with large percentages of Border, Black-belt, and Heartland regional subcultures have significantlylower life expectancies. In states such as Alabama, Missis-sippi, West Virginia, Kentucky, Tennessee, and Arkansas,where these three subcultures represent 70% or more ofresidents, life expectancy is as much as 2–3 years lowerthan the Germanic benchmark. States with significantNordic and Mormon presences are a year or two higher.The Anglo-French, Rurban, and Global subcultures appearto confer about the same relative health advantage as theGermanic subculture.
5. Discussion
A theory attributing geographic variation in health out-comes to regional cultures implies a much smaller rolefor socio-economic status (SES) as an explanatory factor.The socio-economic view, prevalent in the U.K., holds thathealth status is associated with the socioeconomic charac-teristics of both individuals and their communities (Curtis,Southall, Congdon, & Dodgeon, 2004). In this view, the mosteconomically disadvantaged have the poorest health, andcommunity-level deprivation is associated with furtherhealth disadvantages. This school of thought acknowledges
a possible effect of social capital and cultural factors as wellas the residual health disadvantage, after allowing for SES,experienced in northern England and Scotland (Curtis et al.,2004).
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468 J. Williams / The Social Sc
With regard to the American experience, Murray et al.(2006) reach a different conclusion about the impor-tance of SES. They find that considerable variation inhealth outcomes is unexplained by differences in averageincome, noting that low-income white rural populationsin Minnesota, the Dakotas, Iowa, Montana, and Nebraska(America 2) are much healthier than the rest of white Amer-ica despite lower incomes, while low-income whites inAppalachia and the Mississippi Valley (America 4) have alower life expectancy.
These opposing views can be bridged by a focus onregional culture as an explanatory factor underlying vari-ations in both health and SES. Lieske (1993, 2010) finds anassociation between regional subcultures and certain socialpathologies, including poverty.
It appears that, rather than a bare analysis of SES, thereis better correspondence of health indicators among U.S.states associated with the same regional subcultures, andbetween the U.S. regional subcultures and their “mother”cultures abroad, paralleling the Rice and Feldman (1997)findings; it seems that, like civic attitudes, health habits andbeliefs endure. Germany, Austria, Sweden, and Norway dobetter than the U.K. and the OECD average in obesity, lifeexpectancy, and diabetes complications; (National Health,2009; OECD, 2009) their affinity states in the U.S., such asMinnesota and Wisconsin, fare very well relative to sisterstates. Lieske’s Border subculture originates in Scotland,Northern Ireland, and the Scottish/English Border region.Scotland is the least healthy part of Western Europe interms of coronary heart disease death rates, and the Bordercounties of England are among the worst in England (BritishHeart Foundation, 2009a, 2009b; OECD, 2009). Similarly,counties in Border region states such as Kentucky, Ten-nessee, Alabama, Arkansas, and Oklahoma fare the mostpoorly in health outcomes in the U.S. These similarities areexplored further in Part B of the Online Supplement.
The evidence reviewed here suggests face validity forthe theory that individual regional subcultures are asso-ciated with health habits and health beliefs, along withgradations in outcomes. Further, unidimensional analysisindicates there are plausible, articulable differences amonggeographic units, independent of health, that may manifestthemselves in cultural influences on health beliefs and giverise to different health outcomes.
6. Conclusion
The analysis presented here suggests that regional sub-cultures contribute to health outcomes. The mechanismscould be health habits, such as a cultural predisposition tophysical activity or eating healthy foods, or health beliefs.With regard to the latter, if responses to health belief sur-vey questions vary systematically by region, it might helpto inform segmentation in public health social marketingcampaigns so that typologies of lifestyle clusters reflectcultural and geographic differences, and to tease out thecontribution of patient behavior to outcomes so that qual-
ity measures can be adjusted appropriately.
If American regional subcultures place different val-ues on preventative care and adherence to medication,physicians who practice in communities with lower time
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preference for the future could be disadvantaged by sharedsavings regimes or localized global budgets pegged tonational benchmarks, and discouraged from voluntarilyparticipating in the pilot projects upon which Congress haspinned its hopes for improving the quality and efficiencyof health care. Indeed, it appears from the locations of par-ticipants in U.S. Medicare’s Physician Group Practice andPioneer Accountable Care Organization demonstrationsthat physicians already intuitively sense which populationsare likely to yield bonuses and which are not—no grouppractices located in high-mortality regions have steppedup to participate in these programs that put doctors at riskfor not achieving improved health outcomes. Of a total of38 volunteering organizations, nary a one is located in areasencompassed by Lieske’s Border or Blackbelt subcultures,and only two sites are located in Heartland regions.
In August 2013, Medicare officials announced the read-missions penalties that hospitals will bear during the 2014fiscal year Rau (2013). Eighteen hospitals will receive themaximum 2% penalty. Only 2 are north of the Mason–Dixonline. Eleven are located in counties classified as Border inthe Lieske typology; two each are in Blackbelt and Heart-land counties, and none are in Rurban counties or America2. This suggests that current risk adjustment methods failto capture the full range of health disadvantages prevailingin these regional cultures.
Another question provoked by the data presented hereis whether the phenomenon known among U.K. publichealth researchers as the Scottish effect (Shelton, 2009) hasa counterpart in the United States. Areas where the Bor-der regional subculture is dominant are relatively low insocial capital and secular values and seem to follow the lowfruit and vegetable consumption patterns in their Britishcounterpart regions. This last is particularly disturbing asit appears that healthy dietary habits have overcome oth-erwise unfavorable cultural patterns in producing goodhealth outcomes in many OECD nations. Webb (2004)notes that the Scots-Irish subculture has been shapedby the deprivations wrought by centuries of conflict onthe Scottish/English border; even after migrating moreor less voluntarily to the U.S., this population has beenexploited by dominant groups. Fischer (1989, pp. 697–699)regards the Border subculture as one where “nescient fatal-ism” prevails due to the history of violence and disease,resulting in beliefs that “life [is] precarious” and death is“inevitable” and “capricious”. Coyne, Demian-Popescu, andFriend (2006) reject Appalachian fatalism as a stereotype,but find a tendency to delay seeking health care in theregion, as well as a view of disease and accidents as a normalpart of life.
This study suggests there is a U.S. equivalent to the Scot-tish effect constituting a likely Scots-Irish health crisis. Thisis probably intensified by what Fischer calls the “culturalhegemony” that the Scots-Irish have developed in the com-munities where they historically reside; that is, influence“even greater than their proportion in the population”—anassertion that Webb (2004) echoes. Such communities are
scattered across the U.S., even beyond greater Appalachia. Itappears that a 28 percentage-point increase in this culturesubtracts one year from average white male life expectancyin a state. Further study is needed to understand this
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henomenon, to develop new and well-tailored publicealth strategies to address it, and to refine providerayment reform strategies to ensure that they do notxacerbate it.
It appears that U.S. officials may have too quicklymbraced the use of quality measures originally developedor self-critical analysis to inform pay-for-performance for-
ulas based upon nationwide comparisons. It is clear thatome regions are disadvantaged by such a competition. Byontrast, merit pay for school teachers has started withhe assumption that students’ backgrounds are the primaryeterminant of outcomes, and has measured teacher per-ormance as the residual “value added” after accounting forhe expected level of achievement of students in a localizedohort.
Further, care must be taken in publishing rankingsnd scorecards that might stigmatize providers and pub-ic health officials for culturally influenced behaviors over
hich they have little control. More research is neededo determine the degree to which outcome measures arenfluenced by regional variations in medication adher-nce; if so, there needs to be analysis of whether tendencyoward nonadherence is a near-immutable cultural trait,r something over which providers can have real influ-nce (Gawande, 2004). More thought needs to be giveno whether these rankings also stigmatize populations forehaviors that arise from deeply engrained cultural pat-erns or values that may have been shaped centuries ago,nd in the case of some American communities, shapedutside the American continent.
Finally, public health practitioners need to considerxpanding their conception of health disparities beyondraditional notions of race and language to understand andddress the growing problem of poor health in America’sorder communities.
ppendix A. Supplementary data
Supplementary data associated with this article cane found, in the online version, at doi:10.1016/j.soscij.013.09.007.
eferences
ritish Heart Foundation. (2009a). CHD mortality in Scotland.http://www.heartstats.org/datapage.asp?id=3668
rown, T., Scheffler, R., Seo, S., & Reed, M. (2006). The empirical relation-ship between community social capital and the demand for cigarettes.Health Economics, 15, 1159–1172.
enters for Disease Control and Prevention. (2010). CDC Vital Signs: AdultObesity. CDC.
ommonwealth Fund. (2009). Aiming higher: Results from a state score-card on health. System performance. New York, NY: CommonwealthFund.
oyne, C. A., Demian-Popescu, C., & Friend, D. (2006). Social andcultural factors influencing health in southern West Virginia: Aqualitative study. Preventing Chronic Disease, 3, A124. Availablefrom:. http://www.cdc.gov/pcd/issues/2006/oct/06 0030.htm (serial
online).
urtis, S. E., Southall, H., Congdon, P., & Dodgeon, B. (2004). Area effectson health variation over the life-course: Analysis of the longitudi-nal study sample in England using new data on area of residence inchildhood. Social Science and Medicine, 58, 57–74.
rnal 50 (2013) 461–470 469
Edberg, M. (2007). Essentials of health behavior: Social and behavioral theoryin public health. Boston: Jones & Bartlett.
Ezzati, M., Friedman, A. B., Kulkarni, S. C., & Murray, C. (2008). The reversalof fortunes: Trends in mortality and mortality disparities in the U.S.counties. PLoS Medicine, 5(4), e66.
Fischer, D. H. (1989). Albion’s seed. New York, NY: Oxford University Press.Folland, S. (2006). Value of life and behavior toward health risks:
An interpretation of social capital. Health Economics, 15, 159–171.
Gawande, A. (2004, December 6). The bell curve: What happens whenpatients find out how good their doctors really are? The New Yorker.http://www.newyorker.com/archive/2004/12/06/041206fa fact#ixzz167jkCeCs
Grossman, M. (2003). Household production and health. Review of Eco-nomics of the Household, 1, 331–342.
Heckman, J. (2007). The economics, technology, and neuroscience ofhuman capability formation. Proceedings of the National Academy ofSciences, 104(33), 13250–13255.
Hirth, R., Greer, S., Albert, J., Young, E., & Piette, J. D. (2008).Out-of-pocket spending and medication adherence among dial-ysis patients in twelve countries. Health Affairs, 27, 89–102.http://dx.doi.org/10.1377/hlthaff.27.1.89
Hofstede, G. (2006). W. J. Lonner, D. L. Dinnel, S. A. Hayes, &D. N. Sattler (Eds.). Dimensionalizing cultures: The Hofstedemodel in context. Online Readings in Psychology and Culture,http://www.ac.wwu.edu/∼culture/hofstede.htm
Inglehart, R. (n.d.). In Inglehart-Welzel cultural map of the world.http://www.worldvaluessurvey.org/wvs/articles/folder published/base 54
Kawachi, I., Kennedy, B., Lochner, K., & Prothrow-Stith, D. (1997). Socialcapital, income inequality and mortality. American Journal of PublicHealth, 87, 1491–1498.
Keselman, A., Logan, R., Smith, C., Leroy, G., & Zeng-Treitler, Q. (2008).Developing informatics tools and strategies for consumer-centeredhealth communication. Journal of the American Medical InformaticsAssociation, 15(4), 473–483.
Kromhout, D. (1999). On the waves of the Seven Countries Study: Apublic health perspective on cholesterol. European Heart Journal, 20,796–802.
Lieske, J. (1993). Regional subcultures of the United States. Journal of Pol-itics, 55, 888–913.
Lieske, J. (2010). The changing regional subcultures of the American states.Political Research Quarterly, 63(3), 538–552.
Lieske, J. (2012). American state cultures: Testing a new measure andtheory. Publius, 42(1), 108–133.
Marmot, M. G., & Syme, S. L. (1976). Acculturation and coronary heartdisease in Japanese-Americans. American Journal of Epidemiology, 104,225–247.
Moorman, C., & Matulich, E. (1993). A model of consumers’ preventivehealth behaviors: The role of health motivation and health ability.Journal of Consumer Research, 20, 208–228.
Morris, R., Whincup, P., Lampe, F., Walker, M., Wannamethee, S., & Shaper,A. (2001). Geographic variation in incidence of coronary heart diseasein Britain: The contribution of established risk factors. Heart, 86(3),277–283. http://dx.doi.org/10.1136/heart.86.3.277
Murray, C., Kulkarni, S., Michaud, C., Tomijima, N., Bulzacchelli, M. T.,Iandiorio, T. J., et al. (2006). Eight Americas: Investigating causes ofmortality disparities across races, counties and race-counties. PLoSMedicine, 3(9), e260.
National Health Service, National Obesity Observatory. (2009). Interna-tional Comparisons of Obesity Prevalence.
OECD. (2009). Health at a glance 2009: OECD indicators.http://www.oecd.org/health/healthataglance
Putnam, R. (2001). Social capital: Measurement and consequences. Isuma:Canadian Journal of Policy Research, 2, 41–51.
Rau, J. (2013, August 2). Armed with bigger fines, medicare topunish 2,225 hospitals for excess readmissions. Kaiser HealthNews, http://www.kaiserhealthnews.org/Stories/2013/August/02/readmission-penaltiesmedicare-hospitals-year-two.aspx
Rice, T., & Feldman, J. (1997). Civic culture and democracy from Europe toAmerica. Journal of Politics, 59, 1143–1172.
Richardson, A., Liao, Y., & Tucker, P. (2005). Regional and racial differencesin prevalence of stroke—23 states and District of Columbia. Morbidityand Mortality Weekly Report, 54(19), 481–484.
Rosenstock, I. M. (1966). Why people use health services. Milbank Memo-rial Fund Quarterly, 44, 94–124.
Care, 17(2), 48–57.University of Wisconsin Population Health Institute. (2010). County Health
470 J. Williams / The Social Sc
Shelton, N. J. (2009). Regional risk factors for health inequalities in Scot-land and England and the “Scottish effect”. Social Science and Medicine,69(5), 761–767.
Skinner, J., & Staiger, D. (2005). Technology Adoption from Hybrid Cornto Beta Blockers NBER Working Paper No. W11251. Cambridge, MA:National Bureau of Economic Research.
Thier, S., Yu-Isenberg, K., Leas, B., Cantrell, C., DeBussey, S., Goldfarb, N.,et al. (2008). In chronic disease, nationwide data show poor adherence
rnal 50 (2013) 461–470
by patients to medication and by physicians to guidelines. Managed
Rankings. http://www.countyhealthrankings.org/Webb, J. (2004). Born fighting, how the Scots-Irish shaped America. New
