Kim M Iburg Joshua A Salomon Ajay Tandon Christopher JL Murray. Global Programme on Evidence for Health Policy Discussion Paper No.

Transcription

1 Cross-populaton comparablty of self-reported and physcan-assessed moblty levels: Evdence from the Thrd Natonal Health and Nutrton Examnaton Survey Km M Iburg Joshua A Salomon Ajay Tandon Chrstopher JL Murray Global Programme on Evdence for Health Polcy Dscusson Paper No. 14 World Health Organzaton November 2001 (revsed)

2 1 Introducton Assessng levels of health on varous domans s a key component of measurng populaton health, evaluatng the mpact of health nterventons and montorng ndvdual health levels. Meanngful comparsons across countres are useful n settng goals for the mprovement of populaton health and chartng progress towards attanng these goals. Comparsons are also useful wthn countres n order to understand dfferences n health levels across subpopulatons and to measure health nequaltes. Efforts to compare self-reported health status across populaton subgroups often have ndcated major dfferences between males and females, rch and poor, between dfferent ethnc groups, or across varous other demographc and soco-economc varables. One of the challenges n the measurement of ndvdual health, however, has been n nterpretng self-reported health data n a way that allows meanngful comparsons. Emprcal evdence pontng to concerns about the comparablty of self-reported data on health abounds. Substantal evdence shows that dsablty rates have been fallng rapdly over the last two decades whle trends n self-reported health have been more mxed. In some studes, hgher ncome groups report hgher morbdty than lower ncome groups, even though observed dsablty declnes rapdly wth ncome (Murray 1996). The challenge s to ascertan how much of the dfference s determned by real dfferences n health and how much s due to dfferences n the way ndvduals report on ther health relatve to dfferent norms and expectatons. Prevous studes descrbng comparsons of self-reported health have rased concerns about the face valdty of some of these measures, hghlghtng the fundamental challenge of cross-populaton comparablty. A number of dfferent approaches have been taken to mprove comparablty of selfreported data both across and wthn countres. One approach has been to ensure that specfc survey tems have the same meanng across languages and dfferent cultural settngs. Varatons among questons used n health surveys, such as recall perods, defntons of terms, and response categores are documented for 16 surveys conducted n 11 EU countres (Rasmussen et al. 1999) and another 30 surveys from 23 OECD countres (Gudex & Lafortune 2000). The man fndngs were that varatons n queston content are prevalent n nearly all health surveys, wth the excepton of those that use standardzed health status nstruments such as the SF-36 or EuroQol. The best example of attempts to mnmze dfferences n tems n health surveys s probably the adaptaton of the standardsed SF-36 questonnare to more than 40 countres (Ware 2000). But whle ths may help to remove one mportant barrer to comparsons, t does not account for the fact that ndvduals may have dfferent expectatons for health that are unrelated to lngustc dfferences n the phrasng of questons. A key obstacle to cross-populaton comparablty that remans even after relablty and wthn-populaton valdty of nstruments s establshed relates to the fact that survey questonnares most commonly elct categorcal responses, whch do not provde cardnal values for levels of health,.e., dstances between response categores are not equal, and are unknown. Comparsons are complcated by the fact that dfferent ndvduals use the 2

3 categorcal response scales n dfferent ways. We may conceptualse these dfferences n terms of varaton n ndvdual response category cutponts, whch mark the boundares between categores n reference to an unobserved, contnuous latent scale. Attempts to establsh equvalent scale endponts across dfferent questons may offer some benefts n enhancng comparsons, but they cannot account for cutpont shfts. A recent study by the World Health Organzaton (Sadana et al. 2000) descrbed a confrmatory factor analytc approach to fx the endponts of self-reported data n order to mprove the comparablty of estmated health levels from household ntervew surveys n 64 countres. Despte efforts to mprove comparablty of endponts, the study concluded that a vald and meanngful comparson of exstng data on non-fatal health from household ntervew surveys across countres was lmted. Even n cases where cutpont dfferences would seem unlkely, as n bnary questons about clearly defned physcal phenomena, surprsng results have been reported. For example, a study from Ghana (Belcher 1976) showed that mssng body parts very rarely were selfreported. Other studes have found large dfferences between self-reported morbdty and clncal examnatons (Krueger 1957), and cross-cultural dfferences n people's experences of llness severty and norms for when to seek health servces are well-documented (Tsuj et al.1994, Bletzer et al. 1993, Hunt et al. 1981). When are people feelng sck enough to label themselves as sck persons or healthy enough to say that they have an excellent health? Clearly these dfferences may depend not only on cultural nfluences but also on age, sex, race and soco-economc status. In order to gauge these dfferences n scale references, exogenous nformaton s requred n order to translate categorcal responses nto comparable cardnal measures. It s worth notng that problems stemmng from the non-comparablty of categorcal response data apply not only to self-reported nformaton but to any source of data that uses categorcal responses. Thus, whle t may be useful to dstngush self-reported data from physcan assessments, both forms of nformaton are subject to the constrants of the queston format that they employ. In both cases, responses to categorcal questons wll depend crtcally on the ndvdual cutponts for a partcular queston. Murray et al. (2001) have outlned a seres of dfferent strateges for enhancng crosspopulaton comparablty of survey results through the formal analyss of systematc cutpont shfts. One way to address ths problem, whether t arses n self-reported or physcan-assessed data, s by fxng the levels of the unobserved latent varable of nterest n order to solate cutpont dfferences as the source of varaton n assessments of these levels. There are several ways of fxng the scales, ncludng the use of vgnettes or the ncluson of measured tests (Salomon et al. 2001, Tandon et al. 2001b). In combnaton wth new statstcal models descrbed n Tandon et al. (2001a), the ncorporaton of ths exogenous nformaton allows estmaton of varaton n cutponts attrbutable to socodemographc or other factors. In ths paper, we descrbe the applcaton of ths new approach to the publcally-avalable Natonal Health and Nutrton Examnaton Survey dataset. Usng the results of performance tests on the doman of moblty from ths survey, we estmate dfferences n cutponts for varous sub-groups n the Unted States populaton on a range of self-reported 3

4 and physcan-assessed tems relatng to ths doman. The objectve of ths paper s to examne whether sex, race/ethncty and ncome affect self-reports and physcanassessments of moblty through predctable dfferences n the use of categorcal responses. 2 Materals and methods Data Data orgnate from the thrd Natonal Health and Nutrton Examnaton Survey (NHANES III), conducted n the Unted States from 1988 to 1994 (NCHS 1999). NHANES s a perodc survey conducted by the Natonal Center for Health Statstcs, Centers for Dsease Control and Preventon, desgned to obtan health nformaton on the non-nsttutonalsed cvlan populaton through ntervews, physcal examnatons and laboratory tests. The full data set ncludes nformaton on approxmately 40,000 respondents aged 2 months and older, based on a complex stratfed samplng desgn. NHANES III ncludes tems relatng to a large number of dfferent health domans. For ths study, the objectve was to dentfy one doman to llustrate a new approach to estmatng cutpont dfferences n both self-reported descrptons and physcan assessments of health levels. The approach reles on the avalablty of measured tests that can be used to fx the scale of the unobserved latent varable (level of ablty on a partcular doman) n order to understand systematc dfferences n response category cutponts across dfferent sub-populatons. Our choce of doman for ths analyss was guded by a revew of exstng health status nstruments n order to develop an extensve lst of canddate domans, followed by a systematc nventory of the number of self-reported, physcan assessed and measured tems avalable on varous domans. Ths nventory s summarzed n Table 1 usng a lstng of domans from the Health Utltes Index Mark III (Feeny et al. 1995) as a parsmonous catalogue of key health domans, as well as other components of health captured by varous tems n the survey. Across dfferent domans, there are varyng numbers of tems on selfreports, physcan assessments and measured performance tests. The doman n NHANES III wth the largest number of avalable tems across the three assessment types s physcal ablty. 4

5 Table 1. Numbers of tems n NHANES III by doman and mode of assessment Domans Selfreported Physcanassessed Measured tests Key domans Vson 9 3 Hearng Speech 1 1 Gettng around (physcal ablty) Hands and fngers (dexterty) Feelngs (emotonal functon) 12 5 Memory and thnkng (cogntve functon) Pan and dscomfort 22 2 Other components of health General health 5 9 Socal functonng 12 BMI Heght 1 1 Weght Dental status 3 1 Blood pressure 1 1 Pulse 1 Lung functon Allergy Blood 2 1 Urne 1 1 Gall bladder 1 1 Ocular photo 1 1 The choce of the doman of physcal ablty leads to a more narrow focus on those examnees aged 60 years and older, as the physcal performance tests n NHANES III are confned to ths sub-sample. In total, the fnal study populaton ncludes 5,724 respondents who completed the battery of physcal performance measures. Wthn the doman of physcal ablty, we have dentfed the questons of nterest more precsely as those pertanng specfcally to moblty or ambulaton, whch resulted n the selecton of seven self-reported tems, four physcan-assessed tems, and eght measured tests (Table 2). Self-reported tems concern ablty to walk, clmb stars, bend down, carry heavy objects, do household chores, and get n and out of bed. Physcan assessments refer to abltes to walk, run, bend down, and perform heavy housework and exercse. Selected physcal tests relate to shoulder movements, hp and knee flexblty and tmed performance measurements, such as walkng eght feet and rsng repeatedly from an armless char. 5

6 Table 2. Moblty tems and response codes from NHANES III. Item Response codes Self-reported Dffculty walkng for a quarter of a mle (about 2 or 3 blocks) (Note a) Dffculty walkng up 10 steps wthout restng (Note a) Dffculty stoopng, crouchng or kneelng (Note a) Dffculty lftng or carryng somethng as heavy as 10 pounds (lke a sack of potatoes or rce) Dffculty dong chores around the house (lke vacuumng, sweepng, dustng or straghtenng up) (Note a) (Note a) Dffculty walkng from one room to another on the same level (Note a) Dffculty gettng n or out of bed (Note a) Physcan-assessed Estmated level of dffculty: walkng 1/4 mle (Note b) Estmated level of dffculty: runnng 100 yards (Note b) Estmated level of dffculty: stoopng, crouchng, or kneelng (Note b) Estmated level of dffculty: dong heavy housework, gardenng, exercse or play (Note b) Measured tests Rght shoulder external rotaton Left shoulder external rotaton Rght hp and knee flexon Left hp and knee flexon Tme to complete 8-foot walk (mean tme from 2 trals) Tme tandem stand held Tme to complete fve stands (from an armless char) (1) full, (2) partal, (3) unable (1) full, (2) partal, (3) unable (1) full, (2) partal, (3) unable (1) full, (2) partal, (3) unable 2 60 seconds (1) 10 or more seconds, (2) 1 9 sec, (3) not able 2 93 seconds a b Response codes for self-reports were (1) unable to do, (2) much dffculty, (3) some dffculty, and (4) no dffculty. Response codes for physcan assessments were (1) could not be done, (2) moderate dffculty, (3) some dffculty, and (4) no dffculty. 6

7 As ndependent varables, we nclude sex, race/ethncty, and medan famly ncome per capta n the last 12 months, all defned as dchotomous varables. Thus, the combnatons of these three varables delneate eght dfferent populaton sub-groups for examnaton of dfferences n response category cutponts for each of the 11 self-reports and physcanassessments. Statstcal analyss Mssng data. Across the varables ncluded n our analyss, only approxmately 64% of the observatons nclude complete nformaton on all varables. In order to address the problem of mssng data, we adopt a multple mputaton approach as descrbed by Kng et al. (1999), usng the software program Amela (Honaker et al. 1999). Fve dfferent completed data sets are mputed n order to reflect the uncertanty around the mssng values, and all analyses are run separately on each dataset. The results of the fve sets of analyses are then combned usng standard methods (Kng et al. 1999). HOPIT model. The goal of the analyss s to estmate dfferences n cutponts across dfferent populaton sub-groups for ether self-reported or physcan-assessed categorcal questons. The conceptual bass for the statstcal model s llustrated n Fgure 1. Consder as an example the followng queston: How much dffculty do you have n walkng for a quarter of a mle? wth response categores unable to do, much dffculty, some dffculty and no dffculty. If we assume that there s an unobserved latent varable that represents an ndvdual s true moblty level, then each ndvdual s response to ths queston wll depend on hs or her cutponts, whch are the threshold levels on the latent scale at whch an ndvdual wll transton from one category to the next. In Fgure 1, we magne two hypothetcal ndvduals (A and B) who have dfferent response category cutponts for ths queston. At the same true moblty level, ndvdual A may respond that she has no dffculty, whle ndvdual B reports some dffculty n walkng for a quarter of a mle. Wth some knowledge of true moblty levels, t s possble to understand dfferences n responses to a partcular queston n terms of cutpont shfts. In ths study, we use results from measured performance tests as a source of nformaton on the unobserved moblty levels of ndvduals n order to quantfy these cutpont shfts. 7

8 A B N N 3B S M U 3A 2A 1A S M U 2B 1B Cut-ponts Latent moblty scale N = None, S = Some, M = Much, U = Unable Fgure 1. Illustraton of cut-pont dfferences for physcal ablty at dfferent sex, race and ncome combnatons In order to estmate cutpont dfferences, we apply the herarchcal ordered probt model, an extenton of the ordered probt model descrbed n more detal elsewhere (Tandon et al. 2001a). The HOPIT model, lke the standard ordered probt model, assumes that there s * an unobserved latent varable Y (e.g., level of moblty) that s dstrbuted normally wth mean and varance 1, where s an ndcator for the respondent. 1 The mean level of the latent varable s descrbed by a functon of some set of covarates, n ths case a vector of measured test results X, Y * ~ N(,1) X If we defne y as the observed categorcal response of ndvdual to the queston of nterest (ether a self-report or physcan assessment), the HOPIT model stpulates an observaton mechansm such that, for questons wth four response categores: * y 1 f Y * y 2 f Y * y 3 f Y Snce the latent varable s unobserved, the varance of the latent varable condtonal on determnants s arbtrarly set to 1 n the ordered probt model. In addton, n order to dentfy the model, the constant term s set to 0. These conventons produce a scale that s unque up to any postve affne transformaton,.e., the latent scale has so-called nterval propertes. 8

9 y 4 f 3 Y * where, 1 and 2 are the response category cutponts for ndvdual. The key 3 dfference between the HOPIT model and the standard ordered probt model s that these cutponts are allowed to vary as a functon of covarates such as sex, race and ncome: k Z k Maxmum lkelhood methods are used to derve estmates of the and coeffcents, along wth the varance-covarance matrx for these estmators. In ths study, the HOPIT model s run on all seven self-reports and all four physcan-assessed questons smultaneously, n order to fx the scale of the estmates across questons whle allowng cutponts to vary across questons. Uncertanty analyss. After the model s run, numercal smulaton methods are used n order to compute estmated cutponts by queston for the dfferent sub-populatons delneated by sex, race and ncome, as well as ranges around these estmates. Smulaton allows the combnaton of the results from the fve dfferent mputed data sets n a way that reflects the uncertanty of the estmated coeffcents both wthn and across data sets. For each separate data set, ten dfferent draws of the vector of coeffcents are generated by samplng from the jont dstrbuton defned by the maxmum lkelhood estmates of the parameters and ther varance-covarance matrx. For each draw, we calculate the predcted cutponts for the eght dfferent sub-populatons defned by the three covarates ncluded n the model (sex, race and ncome), for each of the self-reported or physcan assessed questons. The fnal dstrbutons for these cutponts are produced by combnng the draws from the fve dfferent analyses (creatng a total of ffty draws), and we report the medan value and the confdence ntervals defned by values at the the 2.5 th percentle and 97.5 th percentle of ths dstrbuton. These estmated cutponts have been rescaled such that 0 corresponds to the pont on the latent scale defned by the worst possble scores on all measured performance tests, and 1 corresponds to the pont on the latent scale defned by the best possble scores on all measured tests. 3 Results Characterstcs of the study populaton appear n Table 3. The study ncludes nearly equal numbers of men and women. The medan reported ncome n the sample s between $16,000 and $17,000. Non-hspanc whtes make up approxmately 59% of the study populaton. For purposes of sub-group analyses of cutpont dfferences, we have defned both race and ncome dchotomously: race as ether whte (non-hspanc) or non-whte, and annual famly ncome as ether above or below $17,000 per year. 9

10 Table 3. Characterstcs of the study populaton (N = 5,724) Number Percent Sex Males 2, Females 2, Race-ethncty Non-hspanc whte 3, Non-hspanc black 1, Mexcan-Amercan 1, Other Famly ncome (last 12 months) Less than $10,000 1, $10,000 to 16,999 1, $17,000 to 29,999 1, $30,000 to 49, $50,000 and over Unknown Table 4 presents a summary of the HOPIT results on cutpont dfferences by sex, race and ncome for both self-reported and physcan-assessed tems. For each queston, the drecton and magntude of shfts are reported for the three response category cutponts: 3 marks the transton from no dffculty to some dffculty ; 2 the transton from some to moderate / much dffculty ; and 1 the transton from moderate / much dffculty to unable to do. Of most nterest n a general health survey lke NHANES III s perhaps because the largest proporton of respondents s often found n the mldest 3 category of many questons a phenomenon charactersed as a celng effect n populaton surveys. 10

11 Table 4. Results from HOPIT analyss of self-reported and physcan-assessed moblty: cutpont dfferences by sex, race and ncome. Sex (male=1) Race (non-whte=1) Income (hgh=1) Item Coef. p-value Coef. p-value Coef. p-value Self-report 3 Walkng 1/4 mle < <0.001 Walk up 10 steps < < <0.001 Stoopng, crouchng, kneelng < < <0.001 Carryng 10 pounds < < <0.001 Chores around the house < <0.001 Walkng room to room Gettng n or out of bed Walkng 1/4 mle < <0.001 Walk up 10 steps < <0.001 Stoopng, crouchng, kneelng < < <0.001 Carryng 10 pounds < < <0.001 Chores around the house Walkng room to room Gettng n or out of bed Walkng 1/4 mle < <0.001 Walk up 10 steps < <0.001 Stoopng, crouchng, kneelng < < <0.001 Carryng 10 pounds < Chores around the house Walkng room to room Gettng n or out of bed Physcan-assessment 3 Walkng 1/4 mle < <0.001 Runnng 100 yards < <0.001 Stoopng, crouchng, kneelng < < <0.001 Heavy housework, exercse, etc < < < Walkng 1/4 mle < <0.001 Runnng 100 yards < < <0.001 Stoopng, crouchng, kneelng < < <0.001 Heavy housework, exercse, etc < < Walkng 1/4 mle < <0.001 Runnng 100 yards < < <0.001 Stoopng, crouchng, kneelng < <0.001 Heavy housework, exercse, etc <

12 The HOPIT regresson results show that there are sgnfcant dfferences by sex, race and ncome n ndvdual cutponts separatng no dffculty from some dffculty ( 3 ) for all physcan-assessed questons. In all cases, the drecton of the effects are the same, wth lower cutponts for males compared to females, for nonwhtes compared to whtes, and for hgh ncome respondents compared to low ncome respondents. A lower cutpont may be nterpreted as a lower standard for defnng excellent moblty levels; n other words, gven the same level of moblty, an ndvdual wth a lower cutpont wll be more lkely to characterse ths level of moblty favourably than an ndvdual wth a hgher cutpont. For self-reported tems, only ncome s a statstcally sgnfcant predctor of dfferences n for all questons. Sex s statstcally sgnfcant for fve out of seven questons and race 3 for four out of seven questons. Where coeffcents are sgnfcant, the drectons of the effects are the same as n the physcan assessments for all cases except for race n the tems relatng to walkng 10 steps and walkng from room to room. Smlar patterns emerge wth respect to systematc dfferences n for both physcan 2 assessments and self-reports, although the overall magntude of the dfferences tends to be slghtly smaller. A notable excepton s the effect of race on physcan assessments, where the sze of the dfferences s greater for 2 than 3 on all questons. There are fewer sgnfcant effects on 1 n both self-reported and physcan-assessed tems. Nevertheless, there s remarkable consstency n the drecton of the effects on nearly all of the sgnfcant results on all cutponts and questons. Based on the results from the HOPIT regresson, we can estmate predcted cutpont values on each queston for dfferent subgroups n the sample, as well as ranges around these estmates (Fgure 2). The pattern for the estmated cut-ponts for populaton groups s qute smlar for all seven self-reported and four physcan-assessed tems. The example n Fgure 2 depcts cutponts on two parallel questons relatng to dffcultes n walkng onequarter mle. The cutponts are located on the latent moblty scale that emerges from HOPIT, after rescalng based on anchors defned by the best and worst results on the range of performance tests, as descrbed above. Whle the 95% confdence ntervals show that there s some overlap between estmated cutponts n dfferent groups, sgnfcant dfferences reman between varous subgroups n the study populaton. Thus, for example, at the same level of health, a whte female wth low ncome wll report some dffcultes n walkng one-quarter mle, whle a non-whte male wth hgh ncome wll report no dffcultes. Ths example hghlghts two key fndngs: (1) that the orderng of cutponts across the eght subgroups s almost dentcal n physcan assessments as compared to self-reports. In other words, the varaton n norms and expectatons assocated wth sex, race and ncome may apply not only to ndvduals assessng ther own health levels, but also to medcal professonals makng these assessments for ther patents. In fact, judgng by the range n cutponts across subgroups, 12

13 dfferences n norms and expectatons may even be larger for physcans than for selfreportng ndvduals. (2) that physcan cutponts tend to be hgher overall for the threshold between no dffculty and some dffculty, but slghtly lower overall for the threshold between much / moderate dffculty and unable to do ; n other words, gven a relatvely hgh level of health, physcans wll be more lkely to characterze ndvduals as havng dffcultes on ths doman than the ndvduals themselves, whereas physcan apprasals of low levels are more generous. Physcan-assessments Self-reports no dffculty no dffculty unable to do unable to do FWL MWL FNL MNL FWH MWH FNH MNH 0 FWL FNL MWL MNL FWH FNH MWH MNH Fgure 2. Estmated cutponts and ranges by populaton subgroup for self-reports and physcan assessments of dffcultes n walkng one-quarter mle. Bars ndcate 95% confdence ntervals. Abbrevatons for populaton groups are as follows: F = female, M = male, W = whte, N = nonwhte, L = low ncome, H = hgh ncome. 4 Dscusson Self-reported health s a complex functon of observed morbdty, health expectatons, contact wth health servces or other sources of health knowledge, and socal and cultural context. A number of studes have llustrated the dffcultes of comparng responses to self-reported health questons across ndvduals who dffer n terms of varous socoeconomc, demographc or cultural factors. A classc example s from Kerala state n Inda, where rates of self-reported morbdty have been found to be hgher than n anywhere else n Inda, despte the fact that Kerala has the lowest mortalty rates n Inda (Murray & Chen 1992). Another noteworthy example comes from a study n Australa (Mathers & Douglas 1998) n whch the Aborgnal populaton descrbe ther health levels much more favourably than the general populaton, even though the opposte would be expected gven ncdence rates of major health problems and other key health ndcators. Survey results such as these ones suggest that there are major problems of comparablty that make meanngful cross-populaton analyses dffcult. 13

14 One mportant strategy to mprove cross-populaton comparablty of health surveys has been to mprove the standardzaton of questonnares across countres. Even f crosscultural standardzaton n questons could be acheved, however, a major challenge to comparablty remans n the relance on categorcal responses, whch wll be subject to ndvdual varaton n the use of the avalable response categores, even for nstruments wth establshed relablty and valdty. An ndvdual s use of categores such as no dffcultes, some dffcultes, etc. wll depend on ther norms and expectatons for health along a partcular doman. Johansson (1992) has ponted to a cultural nflaton of morbdty, n whch expectatons for health rse n countres as they undergo the health transton to lower rates of mortalty, but even wthn populatons, t s easy to magne that expectatons wll vary accordng to age, sex, educaton, ncome, and a host of other varables. It s therefore essental to adjust categorcal questons to a comparable cardnal scale before attemptng comparsons between countres or across populaton groups wthn countres. In ths paper, we descrbe the applcaton of a new method for addressng the problem of comparablty n categorcal responses to health questons, usng measured tests to capture fxed levels on a latent health doman n order to quantfy ndvdual dfferences n response category cutponts. We apply the herarchcal ordered probt (HOPIT) model descrbed n Tandon et al. (2001) to nformaton from the Thrd Natonal Health and Nutrton Examnaton Survey n the Unted States to develop emprcal estmates of cutpont dfferences n populaton sub-groups defned by sex, race and ncome. Ths study focuses on the doman of moblty, usng nformaton from almost sx thousand adults aged 60 years and older, but any data set contanng both self-reports or physcan-assessments and measured performance tests relatng to the same doman of health could be analysed usng the same approach. The results of ths study pont to sgnfcant dfferences n ndvdual response category cutponts as a functon of sex, race and ncome, on several dfferent questons relatng to moblty levels. Across dfferent questons, the nature of the dfferences are largely consstent. There s a strong tendency for males to have lower cutponts on moblty questons than females, whch mples that males are less lkely to report dffcultes gven the same levels of moblty. The frequently observed pattern n many health surveys n whch women report worse health than men may therefore be understood not smply as an ndcator of lower health levels, but of hgher expectatons for health (.e., hgher cutponts). Our fndngs also pont to lower cutponts for non-whte respondents relatve to whte respondents, whch agan may suggest mportant dfferences n expectatons for health that lead to dfferent characterzatons of the same fxed levels on a partcular doman. A somewhat surprsng fndng s that ndvduals wth hgher ncome levels tend to have lower cutponts for moblty than ndvduals wth lower ncome levels. Ths result runs counter to the noton that standards for excellent health ncrease wth rsng ncome, but mght be understood n terms of a wshful thnkng scenaro n whch wealther ndvduals have a belef that they should be n excellent health and therefore use lberal standards for excellence n reportng on ther own health. These fndngs may have 14

15 mportant mplcatons for the measurement of socal nequaltes n health where these measures rely on categorcal self-reported data. If wealther respondents use more lenent standards n reportng on ther own health levels, ths could exaggerate dspartes n health between the rch and poor. It s nterestng to observe that both self-reported and physcan-assessed health measures are subject to the same varatons n cutponts relatng to soco-demographc factors such as sex, race and ncome. In ths study, the dfferences were even more marked for physcan assessments than for ndvdual self-reports. Thus, the way physcans characterze the moblty levels of dfferent ndvduals wth the same performance levels on measured tests wll depend on whether the examnee s a man or woman, whte or nonwhte, and of hgher or lower economc status. The results from our study add to prevous work that has examned potental bases n physcan evaluatons accordng to certan patent characterstcs. For example, a study from Norway found that general practtoners awareness of ther patents psychosocal problems were dependent on the age and sex of both the doctor and the patent, as well as the patent s educatonal level and lvng condtons (Guldbrandsen et al. 1997). Our study also fnds that physcans and patents, when asked the same queston (such as the one relatng to dffcultes n walkng onequarter mle), wll characterze the same fxed moblty levels n dfferent ways. A prevous study has noted that patents wth multple scleross appear less concerned than ther clncans about physcal dsabltes caused by ther llness (Rothwell et al. 1997), and sgnfcant dfferences have also been found between patents and geratrc experts n Europe and Unted States when comparng the mportance of functonal status tems (Kane et al. 1998). Understandng how patent characterstcs nfluence physcan evaluatons, and how these evaluatons dffer from those of the patents themselves, reman mportant topcs for further study. 5 References 1. Belcher DW, Neumann AK, Wurapa FK, Loure IM. Comparson of morbdty ntervews wth a health examnaton survey n rural Afrca. Amercan Journal of Tropcal Medcne and Hygene 1976; 25: Bletzer KV. Perceved severty: Do they experence llness severty as we conceve t? Human Organzaton 1993;52:1: Feeny DH, Furlone W, Boyle MH, Torrance GW. Multple-attrbute health status classfcaton systems: Health Utlty Index. PharmacoEconomcs 1995;7: Gudex C, Lafortune G. An nventory of health and dsablty-related surveys n OECD countres. Drectorate for Educaton, Employment, Labour and Socal Affars. OECD, Pars,

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