Measuring Healthcare Inequities using the Gini Index

Transcription

1 Measurng Healthcare Inequtes usng the n Index Donald J. Berndt, John W. Fsher, and Rama V. Rajendrababu Informaton Systems and Decson Scences College of Busness Admnstraton Unversty of South Florda {dberndt, jfsher}@coba.usf.edu James Studnck Health Polcy and Management College of Publc Health Unversty of South Florda jstudnc@hsc.usf.edu Abstract Questons of healthcare nequtes have been of contnung concern to health researchers, planners, and polcymakers. The answers to such questons can be dffcult to nterpret, but deeply affect polcy formulaton, resource allocaton, and our perceptons of the farness and compasson nherent n the nsttutons of government and socety as a whole. Ths paper descrbes the data warehouse mplementaton of several measures of nequalty drawn from the feld of economcs, ncludng the n ndex. Past research has often focused on usng these economc measures to characterze a small set of health status ndcators, usually at farly hgh levels of aggregaton, wth comparsons between states or natons. In ths research, an ongong healthcare data warehouse project under the auspces of the Center for Health Outcomes Research at the Unversty of South Florda provdes the nfrastructure for explorng hundreds of health status ndcators at the ZIP code level. The goal s to nclude nformaton on healthcare nequtes n the many reports generated for polcymakers throughout Florda. Introducton Questons of healthcare nequtes have been of contnung concern to health researchers, planners, and polcymakers. The answers to such questons can be dffcult to nterpret, but deeply affect polcy formulaton, resource allocaton, and our perceptons of the farness and compasson nherent n the nsttutons of government and socety as a whole. A data warehouse of healthcare ndcators has been mplemented as part of the ongong research on communty health assessment conducted under the auspces of the Center for Health Outcomes Research (chor.hsc.usf.edu) at the Unversty of South Florda [Studnck et al. 997, Berndt et al. 200]. The data warehouse was orgnally desgned to automate the Comprehensve Assessment for Trackng Communty Health (CATCH) methodology and the producton of ndepth communty assessment reports. Ths data warehouse has grown n scope and now provdes an nterestng envronment for the nvestgaton of a varety of healthcare ssues n Florda. Among these research ntatves are efforts to explore questons of socal justce or nequtes n health burdens. The data warehouse affords the opportunty to pursue such nvestgatons at fne-graned levels of analyss. Ths paper descrbes the data warehouse mplementaton of several measures of nequalty drawn from the feld of economcs, ncludng the n ndex. These measures have been used n the healthcare arena, but have often focused on a restrcted set of health status ndcators, usually at coarse-graned levels of aggregaton for natonal or nternatonal comparsons. Whle ths macro-level focus s certanly an mportant perspectve for natonal polcy, ths paper s amed at nvestgatng these measures n the county-level decsonmakng context. The man objectves of ths research are the followng. Survey and adapt approprate economc measures of nequalty for use n a healthcare data warehouse envronment. Calculate the n ndex at the fne-graned ZIP code level and assess the capablty for recognzng healthcare nequaltes. Investgate n ndex values for a selecton of key healthcare ndcators to determne the relatve magntude of exstng nequaltes and that the Lorenz curves are well-behaved at ths level of analyss. Informaton regardng nequtes n health-related burdens s not routnely reported n the CATCH methodology or other wdely used communty assessment reports. The ultmate goal of ths lne of research s to provde quanttatve measures of healthcare nequtes at the local, county, and state levels for comparson.

2 2 Measurng Inequalty Economcs s a natural place to look for measures of nequalty that can be used to quantfy dfferences n health burdens. Of course, the topc of socal justce has occuped phlosophers of all types for centures, wth no end n sght. However, these debates reman of nterest, beng contnually revsted by poltcans and communty actvsts whenever polcy s formulated. One hstorc defnton of an elte by Lasswell captures some aspects of njustce or lack of justce. The nfluental are those who get the most of what there s to get. Avalable values may be classfed as deference, ncome, safety. Those who get the most are elte; the rest are mass [Lasswell 958]. Two mportant ssues are hghlghted by ths defnton. Frst, the advantages that accrue to the elte can be multfaceted, ncludng skll, ntellectual enlghtenment, affecton (famly and frendshp), physcal and mental well-beng, and moral recttude [Alker 965]. Clearly, good health or physcal and mental well-beng s a very desrable value or advantage. The second ssue s the need to quantfy such nequtes to make more precse the noton of those who get the most. Followng Alker, a smple mathematcal formalzaton of nequalty can be developed [Alker 965]. Whle very sophstcated mathematcal models are possble, farly smplstc methods may be qute useful for the somewhat broader perspectves often used n resource allocaton and plannng. Ths paper focuses on smple measures, such as the n ndex and Schutz coeffcent that have been appled n a varety of stuatons from country-level ncome dstrbuton to healthcare burdens. Therefore, let U denote a unverse composed of values (V) and ndvduals (N) or groups () that share (most lkely n an unequal manner) the avalable values. Further, let f denote the frequency or number of members n group g, and that the followng equatons characterze the total group membershp as the number of ndvduals, as well as the share of avalable values (v ) n the unverse. = f = N () f v V = = (2) In order to defne equalty, there must be some expectaton (e ) of how the values should be dstrbuted. Agan, ths queston has occuped phlosophers for some tme. Arstotle debated such norms and assocated notons of njustce. The democratc concepton of justce s the enjoyment of arthmetcal equalty, and not the enjoyment of proportonate equalty on the bass of desert [.e., mert, as Arstotle hmself would have preferred] [Alker 965]. The debate becomes more complex as values are classfed as basc or luxury goods, and certanly good health s a value that wll generate controversy as to the role of mert n any dstrbutons. Therefore, a smple defnton of democratc equalty s used n the current mplementaton, though any notons of proportonate equalty could be easly ncorporated. Democratc equalty requres that each group (or ndvdual) share equally n the desred values, that s have the same average amount as n Equaton 3 and 4. = v = v (3) e = v (4) Measures of nequalty characterze the departures from the expected values or norms or the extent to whch v does not equal e. A smple rato of advantage or standardzed verson (as n Equaton 5) can then be used to compare ndvdual dfferences n advantage or dsadvantage. v v v = (5) v v It s often more mportant to consder cumulatve measures of nequalty rather than ndvdual measures. That s, to characterze the values held by proportons of a populaton. Several such measures are dscussed n the next secton, ncludng the n ndex, proposed by the Italan statstcan and demographer Corrado n ( ). 2. Lorenz Curve Cumulatve measures of nequalty rank order ndvduals or groups accordng to some rato of advantage, allowng the amount of some value held by cumulatve proportons of a populaton to be dentfed. A Lorenz curve s a natural method of graphcally representng such cumulatve measures. Fgure s a Lorenz curve derved from school system data used by Alker [Alker 965]. The data comes from a New Haven, Connectcut study on racal mbalance n schools, wth four junor hgh schools used n the example. The x axs s a rank order of groups or ndvduals, wth the most dsadvantaged on the left-hand sde, that can be used to dentfy the percentage of some populaton. The y axs represents the percentage of some value held by the correspondng cumulatve proporton of the populaton. In ths example, the x axs s the percentage of the total student populaton (ordered by the rato of advantage % whtes / % students) and the y axs s the percentage of whte students. A 45-degree lne represents democratc equalty or the expected (e ) amount of the value, n ths

3 case whte students. The curve represents the departures from the norm of democratc equalty. In ths example, the frst two schools account for approxmately 44% of the total student populaton (x axs), but only 22% of the whte students (y axs). More mportantly, measures that sum the dfferences and/or ratos can be used to characterze the entre dstrbuton. There are several well-known summatve measures of nequalty [Alker and Russett 964]. Frst, one could sum absolute dfferences or devatons (.e. gnorng the sgn) from the mean as n Equaton 6 or normalzed as n Equaton 7. = v v (6) = v v = v (7) A second measure s the Schutz coeffcent that sums ratos of advantage rather than normalzed devatons (based on the equalty n Equaton 5) above or below the equal share pont (at whch v = v ). Equaton 8 defnes the Schutz coeffcent. v v v v v = (8) v v v The n ndex s a thrd measure and s often used to characterze the natonal ncome dspartes around the world. Ths measure has also been used to a lmted extent n characterzng health-related burdens and s the focus of ths paper. Referrng to the Lorenz curve n Fgure, the n ndex uses the area of nequalty between the curve and the lne of democratc equalty to quantfy the noton of nequty. To normalze or obtan a n ndex between 0 and, the area of nequalty s dvded by the maxmum nequalty value, that s the trangle below the equalty lne. Of course, f the x and y axes range from 0 to, the trangle has an area of ½ and the n ndex s twce the area of nequalty. Several approaches for calculatng the area of nequalty are depcted n Fgure. Essentally, for each group the area s calculated usng the wdth along the x axs and the departure from expected equalty along the y axs as defned n Equatons 0 and. x = x x (0) y = e y () Calculatng the area of nequalty requres summng the area of each trapezod defned by (x -, y - ), (x -, e - ), (x, y ), and (x, e ), or equvalently, the area of the rectangle wth the heght at the mdpont of the x nterval. Thus, Equaton 2 provdes a defnton for the n ndex Expected (equal dstrbuton) curve Data pont area ( y2) * x Mdpont Data pont e2 True area ½( y+ y2) * x where y = e-y Current Equl 0.2 e y2 Mdpont area ½[(e2+e) - ½(y+ y2)] * x 0. y x x Fgure : Calculatng the n Index

4 ( e y ) + ( e y ) ( x x ) 2 = 2 (2) Therefore, a Lorenz curve can be used to vsualze the nequaltes descrbed n aggregate by the n ndex. In subsequent sectons, both the example curves and n ndex values from a healthcare data warehouse are dscussed. 3 Healthcare Inequtes There s an extensve nternatonal lterature whch lnks socoeconomc status (SES) to varaton n a wde range of health problems and condtons. Whle the lnk s clear, the mechansms that are responsble for the assocaton and ther potental applcatons have sparked ongong dscussons wthn the academc and polcy communtes [Mechanc 2002]. Socoeconomc status has most often been defned by educaton, ncome, and occupaton. Educaton provdes the knowledge and lfe sklls a person mght use to be more aware of ther health status, to better access mportant healthcare resources, and to behave n ways that can mprove and mantan well beng. Levels of educaton have been assocated wth the lkelhood of certan types of llness such as cardovascular dsease [Wnkely et al. 992], and early educatonal experences have been suggested to confer longer term health advantages than educaton receved at other perods n the lfe cycle [Hertzman 999]. The drect effects of ncome on health have typcally been studed n two dfferent ways: comparsons between countres and studes of the relatonshp between ncome and mortalty wthn countres. enerally, t has been shown that levels of natonal wealth are postvely correlated wth measures of health status such as lfe expectancy, although the effect s strongest at low levels of NP [World Bank 993]. A recent analyss has also suggested that wealther countres or communtes may mprove health status, ndependent of ncome, by nvestng n certan common, publc supports through taxaton and subsdzed nfrastructure and servces [Sen 999]. Studes wthn the Unted States and Canada have found a consstent health gradent wth personal ncome levels nversely related wth mortalty rates [McDonough 997, Wolfson et al. 993]. Occupatonal status s a more complex varable, although, a smple assocaton between employment and health status has been demonstrated [Ross and Mrovsky 995]. Jobs dffer n prestge, pay levels, qualfcatons, and each of these factors has been lnked to mortalty rsk [regova et al. 997]. It has been observed by many nvestgators that SES nequalty may be operatng through a seres of dfferental exposures or pathways that have a more mmedate effect on health [Mcnns and Foege 993]. SES pathways promnently dscussed nclude envronmental exposure, the socal envronment, the healthcare system, behavoral or lfestyle patterns, and chronc stress. Those persons of lower SES class are more lkely to lve n areas exposed to lead, asbestos, carbon doxde, hgher nose exposure, resdental crowdng, and deteroratng housng [Pamuk et al. 998, Evans and Saegent 2000]. Socal envronmental factors such as solaton and lack of engagement n socal networks have been lnked to mortalty rates as much as fve tmes hgher than n those persons wth better socal connectons [Berkman and lass 2000]. Access to, use of, and the qualty of healthcare have all been shown to vary by socoeconomc status. Level of educaton s assocated wth the lkelhood of beng unnsured and the recept of fewer healthcare servces [Monhet and Vstnes 2000]. Lower SES s assocated wth sedentary lfestyles and lower consumpton of fresh fruts and vegetables [Krebs-Smth et al. 995], as well as heavy consumpton of alcohol [Pamuk et al 998]. Lower SES persons are also lkely to lve and work n more stressful envronments caused by economc stran, nsecure employment, and low control at work [Brunner 997]. At the same tme, more affluent persons are more lkely to partcpate n and be successful at formal programs amed at allevatng the harmful effects of uncontrolled stress. 3. The n Index n Healthcare The n ndex has been used to study several aspects of health nequtes. As n the often cted studes of ncome dstrbutons, comparsons can be made between countres. For example, the Pan Amercan Health Organzaton ( publshed a short survey applyng the n ndex to nfant mortalty rates across a group of South Amercan countres [Castllo-Salgado 200]. Other nvestgators have found correlatons between varous socoeconomc factors and self-rated health status [Kennedy et al. 998, Bobak et al. 2000]. The n ndex has also been used to study spatal patterns of care and healthcare access n Canada [Brown 994], wth the goal of understandng the relatonshp between compettve condtons and practtoner locaton. Fnally, multple measures of nequalty, such as the n coeffcent, the decle rato, the Robn Hood ndex, and the Atknson ndex have been shown to be hghly correlated wth each other and strongly assocated wth mortalty [Kawach and Kennedy 997]. 4 A Data Warehouse Implementaton The Center for Health Outcomes Research at the Unversty of South Florda has developed a statewde healthcare data warehouse for Florda, usng t to nvestgate and assess the health status of communtes [Studnck et al. 997]. The Comprehensve Assessment for Trackng Communty Health (CATCH) methodology collects, organzes, analyses, prortzes, and reports data

5 on over 300 health and socal ndcators on a local communty bass. The methodology has been refned n the feld for more than a decade, wth more than twodozen reports prepared for communtes wthn and outsde the State of Florda. Indcator Indcator 2 Indcator n Communty Health Indcators Indcator Indcator 2 Indcator n State Averages Indcator Indcator 2 Indcator n Peer Communty Averages Indcator Indcator 2 Indcator n Peer Favorable Unfavorable State Favorable Unfavorable Fav/Fav Indcators Unfav/Fav Indcators Fav/Unfav Indcators Health Challenges CATCH Mult-Dmensonal Comparson Matrx Flters. Indcator 2. Indcator j Prortzed Lst of Health Challenges Addtonal Health Standards Fgure 2: The CATCH Process The CATCH methodology can be brefly descrbed as shown n Fgure 2. Communty health ndcator data are gathered from a varety of sources. Secondary data sources nclude health care data reported by hosptals, local, state, and federal health agences, and natonal health care groups. Prmary data sources would nvolve data gathered from door-to-door or mal-n surveys. All health care data are normalzed nto common formats and organzed nto a communty health care report card lstng values for each mportant communty ndcator. Each ndcator value s then compared aganst the state average, an average for a peer group of communtes, and other nterestng values (e.g., a natonal goal for that ndcator). The results of these comparsons are organzed nto an n-dmensonal matrx based on favorable or unfavorable comparsons aganst each comparson dmenson. Fgure 2 shows a 2-dmensonal comparson matrx based on state averages and peer averages. Communty ndcators that demonstrate unfavorable comparsons on all dmensons are hghlghted as communty health challenges. The orgnal CATCH reports ncluded data at the county level. However, the data warehouse has enabled analyss and reportng at both the user-defned communty and ndvdual ZIP code level. Ths, more local perspectve, has been enthusastcally receved by the users of the more recent reports. Plannng at the county level can be better supported through the ablty to re-defne communtes and re-aggregate data for specfc purposes. The enhanced data warehouse provdes the necessary nfrastructure for studyng health outcomes at fner levels of detal. One of the unque aspects of ths research s that the measures of nequalty are appled at the ZIP code level throughout Florda. Roughly 875 ZIP codes are rank ordered by per capta ncome. Each health status ndcator s also calculated and age-adjusted at the ZIP code level, allowng comprehensve Lorenz curves and n ndexes to be produced. The goal s to provde such nformaton for selected health status ndcators as part of the CATCH methodology, enablng polcymakers to consder aspects of health nequtes. 4. n Index Calculatons Fgure 3 presents a Lorenz curve and n ndex or coeffcent for total age-adjusted mortalty n Florda. The curve looks remarkably democratc n terms of equalty, lendng some credence to the adage of the nevtablty of death and taxes. Ths curve also provdes a context for nterpretng more detaled health

6 00% Florda AA Death Dstrbuton 90% 80% 70% n Coeffcent:.0383 % AA Deaths 60% 50% 40% AADeaths Equalty 30% 20% 0% 0% 0% 0% 20% 30% 40% 50% 60% 70% 80% 90% 00% % Total Populaton Fgure 3: n Index for Age-Adjusted Mortalty Based on Florda ZIP Codes status ndcators that comprse total mortalty. Whle there s a slght detectable nequty n burden, ths broadly defned ndcator would be dffcult to affect n any meanngful way through polcy ntatves. In contrast, homcde s a cause of death that demonstrates clear nequtes wth respect to socoeconomc status. Ths would be expected from past research and smple ntuton. Fgure 4 shows an obvous burden on the poor, wth the poorest 20% of the populaton bearng roughly 40% of the homcdes. The n ndex of agan provdes a context for nterpretng other health status ndcators. For example, ths could be consdered one of the more extreme n ndex values lkely to be seen n the healthcare data and mght be useful for normalzng other ndcator values. It s also nterestng that the nearly 900 ponts descrbe a curve that s well-behaved and remnscent of the classc curves found n textbook examples. Barata and Rbero also consdered the correlaton between economc status and homcdes n Sao Paulo, Brazl [Barata and Rbero 2000]. One of the nterestng aspects of ther nvestgaton was that ncome nequalty, rather than smple poverty, mght be a more approprate explanaton for ther partcular epdemc of volence. Fgure 5 presents data on years of productve lfe lost (YPLL), based on an assessment of premature death. Ths ndcator agan dsplays farly obvous nequaltes, though less extreme than homcde. Ths s another health status ndcator that one would expect to be affected substantally by wealth. Fgure 6 depcts a much less clear stuaton regardng sucde. In consderng the detaled data, t appears that sucde s less a problem of the poor or rch, but more of a mddle class burden (though the dfferences are small). Ths ndcator also llustrates some dffcultes n usng the n ndex. If all the departures from democratc equalty are n the same drecton (have the same sgn), the n ndex as defned n Equaton 2 s well behaved. However, f the departures cross the lne of equalty the dfferences wll begn to cancel each other out. On the other hand, f an absolute value s used, a curve that repeatedly crossed the lne of equalty could yeld a n ndex more typcal of a classc nequalty. Therefore, t seems prudent to use the Lorenz curve, as well as other measures of nequalty, to determne the approprateness of the n ndex.

7 00% Florda Homcde Dstrbuton 90% 80% 70% n Coeffcent:.264 % AA Homcdes 60% 50% 40% 30% Homcde Equalty 20% 0% 0% 0% 0% 20% 30% 40% 50% 60% 70% 80% 90% 00% % Total Populaton Fgure 4: n Index for Homcde Based on Florda ZIP Codes 00% Florda YPLL Dstrbuton 90% % Total Years Productve Lfe Lost 80% 70% 60% 50% 40% 30% 20% n Coeffcent:.04 YPLL Equalty 0% 0% 0% 0% 20% 30% 40% 50% 60% 70% 80% 90% 00% % Total Populaton Fgure 5: n Index for Years of Productve Lfe Lost (YPLL) Based on Florda ZIP Codes

8 00% Florda Sucde Dstrbuton 90% 80% % Total Sucdes 70% 60% 50% 40% n Coeffcent:.07 Sucde Equalty 30% 20% 0% 0% 0% 0% 20% 30% 40% 50% 60% 70% 80% 90% 00% % Total Populaton Fgure 6: n Index for Sucde Based on Florda ZIP Codes 5 Conclusons Equty n health s of concern to healthcare researchers, practtoners, and polcymakers throughout the world. Drawng on basc measures of nequalty, ths paper reports on the potental use of the n ndex, the Lorenz curve, and other cumulatve measures of nequalty n a data warehouse envronment. In partcular, a data warehouse of health status ndcators for Florda s used to nvestgate these measures at the ZIP code level for the entre state. Roughly 875 ZIP codes are rank ordered by per capta ncome and pared wth some example age-adjusted ndcators of health status. A n ndex and Lorenz curve s presented for each of these ndcators. Overall age-adjusted mortalty s compared wth ndcators, such as homcde and years of productve lfe lost, whch dsplay a classc pattern of nequalty. The goal of the research s to mplement measures of nequalty n the data warehouse context so that a socal justce perspectve can be presented to polcymakers as part of a health status assessment process. The prelmnary results are encouragng, confrmng the utlty of the n ndex and other measures n quantfyng nequtes n health-related burdens. For nstance, some ndcators such as homcde and years of productve lfe lost, are clearly more burdensome n the less affluent ZIP codes. The assocated Lorenz curves have a classc shape, whch provde an effectve vsualzaton of the nequtes. As ths research effort contnues, measures of nequalty wll be computed for possbly hundreds of addtonal ndcators. The ultmate goal s to provde local communty measures n the context of statewde n ndexes as part of the communty health status reports. It s hoped that ths nformaton wll contrbute to the ablty of county planners and other local stakeholders to reduce wealthrelated varatons n the health status of defned populatons. Acknowledgements: The healthcare data warehouse nfrastructure was funded n part by the U.S. Department of Commerce through a Technology Opportuntes Program (TOP) grant, as well as numerous Florda communtes. Frank Ceo, a graduate student n the College of Publc Health, partcpated n the ntal branstormng sessons for the project. Several other graduate students contrbuted to recent data warehouse development actvtes, ncludng Sanjeet Hedge-Desa and Mansh Nevrekar.

9 6 References [Alker and Russett 964] H. R. Alker, Jr. and B. M. Russett, On Measurng Inequalty, Behavoral Scence, 9(3): , July 964. [Alker 965] H. R. Alker, Jr., Mathematcs and Poltcs, Macmllan, New York, 965. [Barata and Rbero 2000] R. B. Barata and M. C. Rbero, Correlaton Between Homcde Rates and Economc Indcators n Sao Paulo, Brazl, 996, Pan Amercan Journal of Publc Health, 7(2): 8-24, February [Berkman and lass 2000] L. F. Berkman and T. lass, Socal Integraton, Socal Networks, Socal Support, and Health, n Socal Epdemology, L. F. Berkman and I. Kawach edtors, Oxford Unversty Press, New York, 37-73, [Berndt et al. 200] D. J. Berndt, J. W. Fsher, A. R. Hevner, and J. Studnck, Healthcdare Data Warehousng and Qualty Assurance, IEEE Computer, 34(2): 33-42, December 200. [Bobak et al. 2000] M. Bobak, H. Pkhart, R. Rose, C. Hertzman, and M. Marmot, Socoeconomc Factors, Materal Inequaltes, and Perceved Control n Self-Rated Health: Cross Sectonal Data from Seven Post- Communst Countres, Socal Scence & Medcne, 5: , [Brown 994] M. Brown, Usng n-style Indces to Evaluate the Spatal Patterns of Health Practtoners: Theoretcal Consderatons and an Applcaton Based on Alberta Data, Socal Scence & Medcne., 38(9): , 994. [Brunner 997] E. Brunner, Stress and the Bology of Inequalty, Brtsh Medcal Journal, , 7 May 997. [Castllo-Salgado et al. 200] C. Castllo-Salgado, C. Schneder, E. Loyola, O. Mujca, A. Roca, and T. Yerg, Measurng Health Inequaltes, n Coeffcent and Concentraton Index, Epdemologcal Bulletn, (22), March 200. [Evans and Saegent 2000]. W. Evans and S. Saegent, Resdental Crowdng n the Context of Inner Cty Poverty, n Theoretcal Perspectves n Envronmental- Behavor Research, S. Wapner et al. edtors, Kluwer Academc/Plenum Publshers, New York, , [regova et al. 997] D. I. regova, S. J. Walsh, and D. Paturzo, The Effect of Occupaton-Based Socal Poston on Mortalty n a Large Amercan Cohort, Amercan Journal of Publc Health, 87(9): , 997. [Hertzman 999] C. Hertzman, Populaton Health and Human Development, Developmental Health and the Wealth of Natons, D. P. Keatng and C. Hertzman edtors, ulford Press, New York, 999. [Kawach and Kennedy 997] I. Kawach and B. P. Kennedy, The Relatonshp of Income Inequalty to Mortalty: Does the Choce of Indcator Matter? Socal Scence & Medcne, 45(7): 2-27, 997. [Kennedy et al. 998] B. P. Kennedy, I. Kawach, R. lass, and D. Prothrow-Stth, Income Dstrbuton, Socoeconomc Status, and Self Rated Health n the Unted States: Multlevel Analyss, Brtsh Medcal Journal (BMJ), 37, 3 October 998. [Krebs-Smth et al. 995] S. M. Krebs-Smth et al., U.S. Adults Frut and Vegetable Intakes, : A Revsed Baselne for Healthy People 2000 Objectve, Amercan Journal of Publc Health, 85(2): , 995. [Lasswell 958] H. D. Lasswell, Poltcs: Who ets What, When, How, World Publshng Company, Cleveland, Oho, 958. [McDonough et al. 997] P. McDonough et al., Income Dynamcs and Adult Mortalty n the Unted States, , Amercan Journal of Publc Health, , September 997. [Mcnns and Foege 993] J. M. Mcnns and W. H. Foege, Actual Causes of Death n the Unted States, Journal of the Amercan Medcal Assocaton, 270(8): 57-62, 993. [Mechanc 2002] D. Mechanc, Dsadvantage, Inequalty, and Socal Polcy, Health Affars, 2(2): 48-59, [Monhet and Vstnes 2000] A. C. Monhet and J. P. Vstnes, Race/Ethncty and Health Insurance Status: 987 and 996, Medcal Care Research and Revew, 57(): -35, [Pamuk et al. 998] E. Pamuk et al., Socoeconomc Status and Health Chartbook, Natonal Center for Health Statstcs, Hyattsvlle, Maryland, 998. [Ross and Mrovsky 995] C. E. Ross and J. Mrovsky, Does Unemployment Affect Health? Journal of Health and Socal Behavor, 36(3): , 995. [Sen 999] A. Sen, Development as Freedom, Alfred A. Knopf, New York, 999. [Studnck et al. 997] J. Studnck, B. Steverson, B. Myers, A. Hevner, and D. Berndt, Comprehensve Assessment for Trackng Communty Health (CATCH), Best Practces and Benchmarkng n Healthcare, 2(5): , September/October, 997.

10 [Wagstaff et al. 99] A. Wagstaff, P. Pac, and E. Van Doorslaer, On the Measurement of Inequaltes n Health, Socal Scence & Medcne, 33(5): , 99. [Wnkely 992] M. A. Wnkely et al., Socoeconomc Status and Health: How Educaton, Income and Occupaton Contrbute to Rsk Factors for Cardovascular Dsease, Amercan Journal of Publc Health, 82(6): , 992. [Wolfson et al. 993] M. Wolfson et al., Career Earnngs and Death: A Longtudnal Analyss of Older Canadan Men, Journal of eronology, 67-79, July 993. [World Bank 993] World Bank, World Development Report 993, Oxford Unversty Press, New York, 993.