Do measures of self-reported morbidity bias the estimationof the determinants of health care utilisation?

Matthew Suttona,*, Roy Carr-Hillb, Hugh Gravellea, Nigel Riceb

aNational Primary Care R & D Centre, Centre for Health Economics, University of York, York, YO1 5DD, UKbCentre for Health Economics, University of York, York, YO1 5DD, UK

Abstract

Most national surveys of health care utilisation capture only self-reported measures of morbidity. If self-reportedmorbidity is measured with error, then the results of applied work may be misleading. In this paper we propose amodel of the relationship between morbidity and health service utilisation which allows for reporting errors and

simultaneity. Errors in self-reported morbidity are expressed as a function of person-speci®c reporting thresholdsand recent contact with health services, arising because of better self-evaluation of current health status or a desireto justify consumption of a publicly-provided good. We demonstrate the bias in ignoring the potential problems ofreporting errors and simultaneity for a variety of special cases, but in the general case the biases are of ambiguous

sign. The empirical nature of these biases is investigated using limiting long-standing illness (LLI) and recent contactwith a General Practitioner (GP) in two waves of The UK Health and Lifestyle Survey. Biomedical measures offunctioning are used as objective indicators of health status. We ®nd evidence of substantial and signi®cant

di�erences between individuals in reporting thresholds and some evidence that the reporting of LLI may depend onrecent visits to a GP. Adjustments for these biases signi®cantly increase the estimated e�ect of morbidity onutilisation. # 1999 Elsevier Science Ltd. All rights reserved.

Keywords: Limiting long-term illness; Self-reported health; Demand for health care; General practice utilisation

Introduction

Multivariate analyses of the determinants of health

care utilisation are used in many applications including

the measurement of inequalities in access to medical

services (Van Doorslaer et al., 1993) and the develop-

ment of formulae for appropriate geographical allo-

cations of resources (Carr-Hill et al., 1994; Benzeval

and Judge, 1996). Any such investigation must con-

front the fundamental problem of how to control for

morbidity (Carr-Hill, 1987) and often analysts are

forced to rely on self-reported measures. Errors in the

measurement of these variables can lead to potentially

misleading estimates of the e�ects of morbidity and

other factors on health care utilisation.

The UK Census is fundamental to the planning of

many public services in the UK and, until 1991, the

only morbidity question included related to long term

sickness or disability in those of working age.1

However, a question on limiting long-term illness

(LLI) was introduced into the 1991 Census and is

asked of all individuals. It is established by asking the

following question, `Does the person have any long-

term illness, health problem or handicap which limits

Social Science & Medicine 49 (1999) 867±878

0277-9536/99/$ - see front matter # 1999 Elsevier Science Ltd. All rights reserved.

PII: S0277-9536(99 )00169-0

www.elsevier.com/locate/socscimed

* Corresponding author. Tel.: +44 (0)1904 433660; fax:

+44 (0)1904 433644.

E-mail address: mas6@york.ac.uk (M. Sutton)1 Although o�cial mortaility ®gures could be compiled at

the small area level (Martin et al., 1995).

his/her daily activities or the work he/she can do?

Include problems which are due to old age.'

Respondents could tick one of the two boxes labelled

`Yes, has a health problem which limits activities' or

`Has no such health problem.'2

LLI is fundamentally a measure of functional status

(Bowling, 1991), strongly associated with physical limi-

tations on activity and less related to mental and social

well-being (Cohen et al., 1995). It has since been used

extensively in recent applications (Duncan et al., 1994;

Congdon, 1995; Cohen et al., 1995; Bartley and Owen,

1996; Gould and Jones, 1996). It is also becoming

increasingly important in resource-allocation work

undertaken in government departments. In addition, it

has been adopted frequently to adjust for `need' in stu-

dies of social inequalities in access to medical care

(Pu�er, 1986; O'Donnell and Propper, 1991; Le Grand

and Smaje, 1997).

Relatively few studies have considered the e�ect of

introducing health measures into equations for medi-

cal-care utilisation in a formal framework. The exclu-

sion of health means that the coe�cients on the

included independent variables pick up both their

direct e�ect on utilisation and their indirect e�ect via

health (Van Der Gaag and Wolfe, 1990). Therefore,

much of the attention of applied work has been on

demonstrating the need to use comprehensive health

variables where possible (Manning et al., 1982; Van

Der Gaag and Wolfe, 1990).

However, Manning et al. (1982) also raise questions

about the simultaneous determination of health and

utilisation and concentrate particularly on the pro-

blems of using present health status to predict current

utilisation. They propose that this is a form of postdic-

tion and that, instead, current utilisation should be

predicted using past health status. Unable to sign the

potential bias a priori, they investigate the e�ects

empirically and ®nd that postdiction increases the mag-

nitude of goodness-of-®t statistics and the coe�cient

measuring morbidity e�ects on utilisation.

A further ®nding of Manning et al.'s (1982) study

suggests additional problems of using self-report illness

measures to predict utilisation. They ®nd that frequent

use of outpatient services is associated with self-report

morbidity measures such as worry, physical limitations

and preoccupation with poor health and emphasise that:

``whether this re¯ects exogenous variation in health

status that caused utilisation . . . or whether it rep-

resents an e�ect of use of medical care . . . is an

interesting question that remains for future

research'' (Manning et al., 1982:164±5).

Because they re¯ect individual perceptions of health

and thus measure something di�erent to actual health,

self-report morbidity measures may introduce biases

and systematic reporting errors into any analysis

(Butler et al., 1987). It is well-known that self-reported

morbidity is measured with error. Blaxter (1985), for

example, compared self-reports of any chronic con-

dition and GP records and found only 80% agreement

between them. Moreover, these errors have been found

to be systematically related to variables of interest,

such as socio-economic group (O'Donnell and

Propper, 1991) or geographical area (Senior, 1998).

In this analysis, we allow for these more traditional

types of measurement error, but focus attention on

measurement error which is correlated with the main

variable of interest, medical-care utilisation. This as-

sociation may be caused either directly or via unobser-

vable heterogeneity. Utilisation-dependent

measurement error is a source of bias which has not

been considered formally in the literature and may

have a substantial in¯uence on the appropriateness of

using LLI in studies of GP utilisation.

There are several reasons for expecting measurement

error to depend on GP utilisation. We may expect

di�erences in the relationship between self-reported

and true health due to the provision of information.

Contact with a GP will a�ect the way individuals map

their health status onto reported LLI, either because

they receive more information on their health status,

are able to process this information more accurately or

are advised that their illness should limit their activities.

However, measurement error associated with health-

care utilisation may be expected for other reasons.

Household-based surveys traditionally cover many

issues, such as receipt of state bene®ts, sickness

absence from work, reasons for employment status, as

well as health status and health care utilisation.

Therefore, subtle incentives for strategic reporting of

health status may emerge, because of the developing

relationship between interviewer and interviewee

during the administration of the questionnaire and a

desire to conform to social norms and maintain con-

sistency. Biased reporting may arise if individuals fore-

see the link with resources (Martin et al., 1995) or seek

to justify consumption of a publicly-provided good.3

2 Limiting long-standing illness is established in the General

Household Survey and The Health and Lifestyle Survey using

a two-part question. Details are given in the data section of

this paper. In this form, respondents are requested explicitly

to exclude problems associated with old age. For the purposes

of this analysis, we treat limiting long-term illness and limiting

long-standing illness as analogous concepts and refer to them

both as LLI.3 Such problems can be reduced if the order of questions in

the survey is carefully considered. Nevertheless, a desire to

conform to social norms of behaviour may still encourage

unprompted strategic reporting of health.

M. Sutton et al. / Social Science & Medicine 49 (1999) 867±878868

Thus, whilst health status has been emphasised to be

endogenous in several applications (Manning et al.,1982; Van der Gaag and Wolfe, 1990), there have beenfew attempts to adjust formally for this endogeneity.

An exception is the work of Windmeijer and SantosSilva (1996), who use the second wave of The Healthand Lifestyle Survey (HALS) to consider the endogene-

ity of a self-assessed, overall health measure askingrespondents to rate their health as excellent, good, fair

or poor. Limitations of activities in the last month aretreated as exogenous morbidity variables and arefound to be the most signi®cant predictors of utilis-

ation. Long-standing illness is used as an instrumentfor self-assessed health.

Our work di�ers from that by Windmeijer andSantos Silva (1996) in several ways. By constructing amodel of morbidity and utilisation, we attempt to

decompose formally the reasons for endogeneity ofself-reported health measures in equations for the util-isation of medical care. We make di�erent assumptions

about which HALS health variables are potentially en-dogenous and focus explicitly on the appropriateness

of using LLI as the sole health determinant of utilis-ation. In addition, we use the ®rst-wave HALS data toidentify some possible causes of endogeneity. On the

other hand, Windmeijer and Santos Silva's (1996)paper derives a method for testing for endogeneity incount data models estimated by the Generalised

Method of Moments. In our analysis, we considermorbidity and utilisation as binary variables and esti-

mate our equations using linear probability and probitmodels. This focus on contact probabilities has beentaken elsewhere (Dustmann and Windmeijer, 1996).

Drawing on the Kerkhofs and Lindeboom (1995)model for analysing measurement errors conditionalon labour-market status, we distinguish between classi-

cal and non-classical forms of endogeneity. Classicalendogeneity can be caused either by reverse causation

(Y depends on X and X depends on Y ) or by unobser-vable factors (which in¯uence both X and Y ). Non-classical endogeneity is caused by measurement error

which is either a function of the dependent variable orcorrelated with it via unobservables. Both classical andnon-classical forms of endogeneity can bias coe�cients

estimated by OLS.In the following section, a model is outlined which

expresses the simultaneity between current health andmedical-care utilisation. True health status is treated asunobservable but proxied by two sets of indicators:

`objective' health measures and a self-report healthmeasure. A battery of `objective' health measures are

assumed to generate a `noisy' indicator of health sta-tus. Whilst also `noisy', the self-report morbiditymeasure is assumed to be in¯uenced additionally by

utilisation of medical-care and an unobservable, per-son-speci®c reporting threshold. We use data from the

two waves of HALS to assess empirically the import-ance of biases which may be inherent in the use of LLI

in equations explaining the decision to contact a GP.The two waves of the dataset are exploited to identifydi�erent causes of endogeneity in the self-report health

measure, LLI. Equations for GP utilisation whichinclude separately LLI, LLI adjusted for various typesof endogeneity, and the battery of objective health

measures are compared.

The biases of treating self-reported morbidity as

exogenous in medical-care utilisation

In this section, we analyse the potential problems ofassuming that a subjective health measure is exogenous

in an equation for utilisation, when the true modelcontains simultaneity and measurement error. Thesebiases are derived for a number of special cases basedon a general model of the following form:

Uit � a1hit � a3Xit � eit �1�

hit � d2Uit � d4hi, tÿ1 � vit �2�

Lit � g1hit � g2Uit � mi � sit �3�Eq. (1) is the true utilisation equation which expresses

utilisation, Uit, as a function of health, hit, observablecharacteristics, Xit, and a random error component, eit.Eq. (2) gives an expression for health, which is a func-tion of utilisation, health last period and a stochastic

component, vit. Finally, Eq. (3) proposes that the self-report illness measure, Lit, is a function of health, util-isation, person-speci®c reporting-thresholds, mi, and an

error term sit. In this section (but not in the empiricalwork), all variables are expressed as deviations fromtheir cross-sectional means.

A priori, the expected signs on the coe�cients are asfollows: a1 < 0; d2>0; 0 < d4 < 1; g1 < 0; and g2>0.Thus, improvements in health reduce both utilisation

and reported illness. Utilisation improves health butalso increases the reporting of illness. Health deterio-rates over time but can be replenished by utilisation ofmedical care.

In the absence of an objective measure of health,Eq. (1) is usually estimated by replacing hit with Lit togive:

Uit � a1Lit � a3Xit �4�in which a1 and a3 are OLS estimates from a cross-sec-tional analysis at time t.

Assuming that the error terms in Eqs. (1)±(3) andhi,t-1 are uncorrelated, and that mi=0, we can derive anexpression for the probability limits of a1 and a3 in

M. Sutton et al. / Social Science & Medicine 49 (1999) 867±878 869

terms of the true parameters:

plim a1

� a1�g1 � g2a1�s2v � �g2 � g1d2�s2e�g1 � g2a1�2s2v � �g2 � g1d2�2s2e � �1ÿ a1d2�2s2s

�5�

plim a3

� a3g1�g1 � g2a1�s2v � �1ÿ a1d2�s2s

�g1 ÿ g2a1�2s2v � �g2 � g1d2�2s2e � �1ÿ a1d2�2s2s�6�

We see that, even with the above restrictions, if Eqs.(1)±(3) represent the true model, estimating a utilis-ation equation like Eq. (4) can lead to various pro-blems:

. the estimated coe�cient on health is a biased esti-mate of the e�ect of true health on utilisation and

may not even have the same sign. the estimated coe�cients on other independent vari-

ables are also subject to bias.

The resultant biases in the estimated parameters com-pared to the true parameters, a1 and a3, can be signedfor a number of special cases. We consider three cases:

(i) simultaneous equation bias only; (ii) errors in vari-ables bias only; and (iii) utilisation-dependent reportingerrors. The results are summarised in Table 1.

In special case (i), Lit is a perfect measure of hitexcept for an unimportant change in the units ofmeasurement. If there is no simultaneity (d2=0), the

probability limit for a1 equals the true coe�cient, a1,suitably scaled. If there is simultaneity, the probabilitylimit of a1 is of the correct sign but may be biased in

either direction. Simultaneity does not change the signof the estimated coe�cients on the Xit variables (a3),but biases these coe�cients towards zero. In theerrors-in-variables special case (ii), the coe�cient on

Lit is biased towards zero but does not change sign. Inaddition, there is no e�ect on the Xit coe�cients.However, if utilisation e�ects the reporting of ill-health

(g2>0), as in special case (iii), the Xit coe�cients arebiased towards zero. Moreover, there will be bias in a1although it will have the correct sign.

In empirical applications there are likely to be ad-ditional complications. For example, it may be that mishould enter Eq. (1), that there are measurement di�er-

ences in LLI which are functions of observable charac-teristics (cf. O'Donnell and Propper, 1991), and thattrue health is endogenous in Eq. (1) because of unob-servables (cf. Windmeijer and Santos Silva, 1996). In

these cases, the biases can result in estimated morbiditye�ects of the wrong sign. Therefore, we followManning et al.'s (1982) approach and attempt to esti-

mate their in¯uence empirically. In our empirical workwe do not impose restrictions on the sign of the coe�-cients. In our application we use HALS data which are

described in the next section.

Data

Data from the 1984/5 and 1991/2 Health andLifestyle Surveys (HALS1 and HALS2 respectively)are used in this study. HALS1 is a national, represen-

tative sample of adults (18 years and over) in GreatBritain. The data were collected between Autumn 1984and Summer 1985 during two home visits; a one-hour

Table 1

The direction of bias in a number of special cases

Special cases Bias in a1 Bias in a3

(i) Simultaneous equation bias

g2=0 If d2=0: sign a3=sign a3d2r0 plima1 � a1

g1vplima3v< va3v

s2s=0

Lit=g1 hit If d2>0:

sign� plima1� � sign�plim a1g1�

plima1<>a1g1

(ii) Errors in variables

g2=0 0<plima1<a1g1

plim a3=a3d2=0

s2s>0

g1<0

(iii) Reporting bias

g2>0 sign� plima1� � sign�plim a1g1� sign a3=sign a3

d2=0 plima1<>a1g1

vplim a3v< va3vs2s>0

g1<0

M. Sutton et al. / Social Science & Medicine 49 (1999) 867±878870

interview, and a nurse visit to collect physiological

measurements and apply tests of cognitive function.After the nurse visit, further health questionnaires(including the General Health Questionnaire) were left

with the respondents for self-completion and return bypost. HALS1 comprises 9003 individuals living in pri-vate households. This represents a response rate of

73.5%. When information from the nurse visit andquestionnaire is included, the response rate falls to

53.7%. As HALS1 is a survey of private households, itmay be prone to sample selection bias, as `those indi-viduals with chronic health problems and disabilities

are more likely to be in hospital, or otherwise unavail-able for interview' (Cox et al., 1987).

Cox et al. (1987) compared the survey to the 1981Census of Population, to gauge its representativeness.Among respondents who completed all three stages of

the survey, there is a slight over-representation ofwomen, particularly elderly women, and some under-representation of those individuals with low incomes

and less education. However, overall, the authors con-clude that ``the study appears to o�er a good and

representative sample of the population'' (Cox et al.,1987).The second wave is a follow-up from this data. Of

the original sample, 59% were interviewed in the fol-low-up. The total number of available respondents inHALS2 is 5352. Of the original sample 9% had died

by 1991/2 and 15% could not be traced. Of thosetraced, the vast majority were interviewed (78%).

Refusal was a much more signi®cant contributor toattrition (N=842) than the small number (126) whowere `too ill' to be interviewed. Nevertheless, because

the HALS2 sample is partially selected on health status(particularly mortality), its use may cause selectionbias in some applications.

The utilisation variable used from the HALS data isthe number of visits to or by a general practitioner in

the month before the interview. This variable was notavailable at HALS1. The utilisation variable is recodedinto a binary variable representing recent contact with

a GP when it is used as a dependent variable. In theHALS data LLI represents respondents' answers to a

two-part question. Interviewees were asked: `Do youhave any long-standing illness, disability or in®rmity?'If the answer to this question was `Yes', respondents

were then asked: `Does it limit your activities in anyway compared with other people of your own age?'Respondents answering `Yes' to this question were

coded as having limiting long-standing illness. Thequestions on health status precede those on GP utilis-

ation which may decrease the propensity to strategi-cally report ill-health in this dataset.Measures of health status included in the battery of

`objective' measures were based on the nurse's visit tothe respondent's home. We include height, body mass

index (BMI), arterial pressure and forced expiratory

volume (FEV). Height is measured height in bare or

stockinged feet. BMI (also known as Quetelet's

measure) is obtained by dividing the assessed nude

weight of the respondent in kilograms by their squared

height in metres. Respondents were weighed in indoor

clothing (i.e. with shoes and jackets removed) and an

adjustment was made by the nurse for the nature of

the clothing worn to reach an estimate of nude weight.

Blood pressure measurements were made using an

`Accutorr' automatic measuring instrument. Four

recordings were made at one minute intervals which

was designed to allow the respondent to relax and to

investigate changes occurring during the measurement

period. The lowest mean arterial blood pressure ®gure

is used, as this was assumed to be when the respondent

was least apprehensive and most relaxed (Cox, 1993).

FEV represents forced expiratory volume in one sec-

ond measured using an electronic spirometer.

Respondents were given instructions on the use of the

equipment and were monitored by the attendant nurse.

The use of this measure of expiratory volume is pre-

ferred over other measures, such as Forced Vital

Capacity or Peak Expiratory Flow, ``as this index is

less likely to have been a�ected by the respondent's

inappropriate use of the spirometer'' (Cox et al.,

1987:19).

Measures of the Registrar General's classi®cation of

social class are used to explain health care utilisation.

Reported lifestyles at HALS1 are included to predict

objective health at HALS2. They are measured by a

dummy variable indicating whether an individual is a

current smoker, and a continuous measure of the num-

ber of units of alcohol consumed in the previous week.

Reasons for the rejection of cases from the original

HALS1 sample of 9,003 individuals are summarised in

Table 2. It can be seen that the main reasons for

sample selection relate to failures to follow-up respon-

dents at HALS2 or arrange nurse visits in either wave.

The ®nal sample used represents 3807 individuals. The

proportion of included respondents in non-manual

social classes is 6% higher than in the original HALS1

sample.

For this sample, descriptive statistics for variables

used in the analysis are shown in Table 3. At HALS1

15% of the included respondents reported having LLI.

By HALS2 over 19% of the sample reported having

LLI. However, only 56% of those reporting LLI at

HALS1 also reported LLI at HALS2. The objective

health measures are normalised by their approximate

HALS1 means. The sets of dummy variables represent-

ing individual characteristics have been contrasted

against the largest categories (female, social class III

Manual). We allow for non-monotonic functions of

the continuous variables by including squared terms.

M. Sutton et al. / Social Science & Medicine 49 (1999) 867±878 871

Estimation strategy

The estimation procedure comprised four stages.

Stage 1: Estimating reduced-forms for LLI

As a ®rst-stage we estimated a function for LLI in

terms of the objective health measures (Ho) and ex-ogenous variables which may in¯uence reporting.Equations are estimated for LLI at both HALS1 and

HALS2. In addition, we estimate equations for LLI atHALS2 with various sets of predictors, speci®cally:objective health measures at HALS1; LLI at HALS1;

and objective health measures at HALS2 with LLI atHALS1. Conditional on Ho, other characteristics suchas educational attainment and region of residence werefound to have no signi®cant impact on LLI and were

removed. Therefore, age and gender are the only per-sonal characteristics included in these reduced forms.Generalised-residuals (Inverse Mill's Ratios) fromthese equations are used in Stage 4 to investigate the

empirical e�ects of di�erent forms of endogeneity.

Stage 2: Testing for measurement error in the LLI

reduced-forms

Using the equations estimated in Stage 1, it is poss-

ible to investigate whether measurement error of twoforms is present in the HALS2 LLI measure. The ®rstform which we investigate is that caused by the unob-

servable person-speci®c reporting-threshold e�ects, mi.Based on Eq. (3), the error terms from the reduced-form equation for HALS1 LLI are correlated with thisunobservable e�ect in HALS2. Assuming that utilis-

Table 3

Descriptive statisticsa

Variable name and description HALS1 HALS2

Mean SD Mean SD

Visit (dummy;=1 if any visit to GP in last month) n/a n/a 0.278 ±

Visits (count; number of visits to GP in last month) n/a n/a 0.387 0.815

LLI (dummy;=1 if has limiting long-standing illness) 0.150 ± 0.193 ±

Male (dummy;=1 if male) 0.461 ± 0.461 ±

Age (continuous; years6100) 0.441 0.153 0.511 0.153

Arterial Pressure (continuous; mmHg699) 0.999 0.157 0.988 0.153

FEV (continuous; litres62.8) 1.006 0.316 0.941 0.313

BMI (continuous; kg/m2625) 1.000 0.162 1.046 0.176

Height (continuous; in666) 1.000 0.058 0.999 0.058

Social ClassÐI (dummy) ± ± 0.068 ±

Social ClassÐII (dummy) ± ± 0.268 ±

Social ClassÐIII Non-Manual (dummy) ± ± 0.123 ±

Social ClassÐIV (dummy) ± ± 0.147 ±

Social ClassÐV (dummy) ± ± 0.053 ±

Regsmkr (dummy;=1 if a current regular smoker) 0.313 ± ± ±

Alq (count; units of alcohol consumed in last week) 8.980 14.792 ± ±

a Base group is Social Class III Manual.

Table 2

Reasons for rejection of cases from the initial sample of 9003 individuals

Reason for rejection Additional cases missing Cumulative cases remaining

Not interviewed at follow-up 3651 5352

No data on LLI1 and/or LLI2 7 5345

No data on GP visits 6 5339

HALS1 biomedical data missing 1131 4208

HALS2 biomedical data missing 400 3808

Lifestyle variables missing 1 3807

M. Sutton et al. / Social Science & Medicine 49 (1999) 867±878872

ation in 1984/54 is not correlated with the unobserva-bles in the HALS2 reduced-form equation, introduc-

tion of this error term (in the form of the InverseMill's Ratio from a probit equation) into the reduced-form HALS2 LLI equation provides a test of the sig-

ni®cance of the time-invariant, person-speci®c unobser-vables.The second form of measurement error in which we

are interested is measurement error which is related toutilisation. To test for utilisation-dependent measure-ment error, we introduce measures of utilisation into

the HALS2 LLI equation. The e�ect of utilisation isinvestigated with and without allowing for the person-speci®c unobservables, mi. Utilisation dependency ismodelled in two forms: as a dummy variable represent-

ing any utilisation in the past month; and as a combi-nation of this binary variable and a count variablerepresenting the number of visits. This latter form is a

more stringent test of utilisation-dependent measure-ment error. To test whether the utilisation e�ect isspurious, we also introduce HALS2 utilisation into the

HALS1 LLI equation, both with and without the esti-mated individual-speci®c e�ect from the HALS2 LLIequation. There is no reason to believe that utilisation

at HALS2 should have an e�ect on LLI at HALS1.Identi®cation of utilisation-dependent measurement

error by this method relies on the assumption that theobjective health measures are `noisy' measures of true

health and that, conditional on these objective healthmeasures, LLI has no additional in¯uence on utilis-ation. If utilisation is endogenous in the HALS2 LLI

equation (because LLI a�ects utilisation over andabove the objective health measures), then our estimateof utilisation-dependent measurement error will be

biased.

Stage 3: Testing for simultaneity between health and

utilisation

Eqs. (1) and (2) posit a simultaneous relationship

between true health and utilisation. Using our objec-tive health measures as an unbiased proxy for truehealth, we investigate the simultaneity of this relation-

ship. We compare results from estimated utilisationequations assuming: (i) HALS1 objective healthmeasures are exogenous; (ii) HALS2 objective health

measures are exogenous; and (iii) HALS1 objectivehealth measures can be used as instruments for theHALS2 objective health measures. In (iii) we alsointroduce HALS1 smoking status and alcohol con-

sumption levels as additional instruments. This permitsthe application of an overidenti®cation J-test to assess

the validity of the instruments (Godfrey and Hutton,1993). OLS estimates of (i) and (ii) are compared with

Two-Stage Least Squares (2SLS) estimates for (iii).Because of the binary nature of the dependent vari-able, linear functional forms may be misspeci®ed.

Endogeneity of the HALS2 objective health measurescaused by simultaneity should appear as signi®cantchanges in the 2SLS coe�cients compared to those

estimated by OLS.

Stage 4: The e�ects of adjusting the utilisation equation

when LLI is endogenous

We compare six ways of incorporating LLI into theutilisation equations. These include: (a) assuming

HALS1 LLI is exogenous; (b) assuming HALS2 LLI isexogenous; (c) using HALS1 LLI and HALS2 objec-tive health measures to instrument for HALS2 LLI;

(d) using HALS1 LLI only to instrument for HALS2LLI; (e) using HALS2 objective health measures toinstrument for HALS2 LLI; and (f) using HALS1

objective health measures to instrument for HALS2LLI. Potentially, all models su�er from errors-in-vari-ables bias, although we may expect this to be most sig-

ni®cant when HALS1 LLI is directly substituted in forHALS2 LLI in model (a). Model (b) is prone to allfour potential sources of bias: errors-in-variables; clas-sical endogeneity caused by simultaneity between true

health and utilisation; unobservable heterogeneitycaused by person-speci®c reporting thresholds; andutilisation-dependent measurement error. Model (c)

avoids utilisation-dependent measurement error butincludes measurement error caused by unobservableheterogeneity and may su�er from simultaneity. Model

(d), on the other hand, avoids problems associatedwith utilisation-dependent measurement error and clas-sical endogeneity. Model (e) avoids both utilisation-dependent measurement error and measurement error

introduced by heterogeneity in reporting thresholdsbut still may su�er from classical endogeneity if truehealth is simultaneously determined with utilisation.

Model (f) additionally allows for classical endogeneity.Di�erences between the coe�cients estimated in thevarious formulations of the utilisation equation are

used to infer the empirical nature of di�erent forms ofendogeneity bias.

Results

Stage 1

The estimated coe�cients for ®ve equations for LLI

are shown in Table 4. HALS2 LLI is the dependentvariable in all but the ®rst equation. In all cases, theobjective health measures are signi®cant and have

4 Note that this is not observed, as this question was not

included in the ®rst wave of the study.

M. Sutton et al. / Social Science & Medicine 49 (1999) 867±878 873

expected signs. Age and gender signi®cantly a�ectreported morbidity with males more likely to report

limitations. Surprisingly, use of the HALS1 objectiveheath measures to predict HALS2 LLI does not reducethe explanatory power of the equation substantially

and the coe�cients are similar on age, gender, lungfunctioning (FEV) and height. The results for BMI aremore similar to the way in which the HALS1 objectivemeasures predict HALS1 LLI. HALS1 LLI is a power-

ful predictor of HALS2 LLI suggesting person-speci®cterms may be signi®cant and, because of the change inthe estimated coe�cients, may be correlated with age

and gender.

Stage 2

Results of our direct tests for measurement error areshown in Table 5. Utilisation signi®cantly increasesreporting of LLI in HALS2 and the term re¯ectingdi�erences in reporting behaviour is highly signi®cant.

The utilisation coe�cients change very little when thevariable representing unobservable reporting thresholdsis introduced. The estimated coe�cients are smaller

than in the HALS2 LLI equation and become insigni®-cant when the HALS2 estimates for the reportingthresholds are included. The large change in the utilis-

ation coe�cients when HALS2 measures of the unob-servables are introduced suggests omitted variable biasin the HALS2 utilisation coe�cient initially.

Stage 3

Table 6 gives OLS and 2SLS results for the utilis-ation equations including objective health measuresand excluding LLI. For 2SLS, the HALS2 objective

health measures are predicted using the HALS1 objec-tive health measures and lifestyle variables. The J-testindicates no problems with these instruments.Although the R 2 statistics are low, there are no indi-

cations of misspeci®cation or omitted variables basedon RESET tests. The coe�cients on the demographiccharacteristics are broadly similar between OLS and

2SLS in HALS2. However, some of the coe�cients onthe objective health measures change considerably.These changes are consistent with simultaneity between

current objective health measures and current utilis-ation.

Stage 4

The J-tests in Table 7 indicate no problems with the

instruments in the four ways of incorporating LLI intothe probit utilisation equation. The e�ects of simulta-neity between utilisation and current objective health

can be ascertained by comparing models (c) and (d)and models (e) and (f). In the former comparison thecoe�cient on LLI decreases slightly. In the latter com-

parison, there is a slight increase in the coe�cient.Therefore, the in¯uence of simultaneity is of ambigu-ous sign and is likely to be small.

Table 4

Probit reduced-forms for LLI (t-ratios in brackets)

Year of dependent variable 1984 1991 1991 1991 1991

Year of objective health variables 1984 1991 1984 n/a 1991

Constant ÿ1.050 (ÿ0.953) ÿ0.616 (ÿ0.586) ÿ2.561 (ÿ2.411) ÿ2.129 (ÿ7.327) ÿ0.938 (ÿ0.853)Male 0.181 (2.370) 0.147 (2.044) 0.108 (1.493) 0.061 (1.208) 0.094 (1.229)

Age 3.266 (3.117) 3.497 (3.081) 2.993 (2.697) 2.224 (2.029) 2.537 (2.144)

Age squared ÿ2.738 (ÿ2.546) ÿ2.245 (ÿ2.217) ÿ2.009 (ÿ2.011) ÿ0.655 (ÿ0.664) ÿ1.479 (ÿ1.379)Arterial pressure ÿ0.425 (ÿ0.313) ÿ3.226 (ÿ2.298) 0.599 (0.453) ± ÿ2.909 (ÿ1.988)Arterial pressure squared 0.317 (0.514) 1.392 (2.115) ÿ0.156 (ÿ0.260) ± 1.235 (1.798)

FEV ÿ0.832 (ÿ2.009) ÿ1.994 (ÿ5.301) ÿ1.660 (ÿ4.363) ± ÿ1.755 (ÿ4.422)FEV squared 0.043 (0.207) 0.644 (3.302) 0.443 (2.390) ± 0.628 (3.070)

BMI ÿ2.544 (ÿ2.307) ÿ0.960 (ÿ1.059) ÿ1.743 (ÿ1.655) ± ÿ0.471 (ÿ0.500)BMI squared 1.302 (2.648) 0.657 (1.703) 1.029 (2.189) ± 0.431 (1.075)

Height 1.131 (1.716) 1.785 (2.839) 2.059 (3.286) ± 1.545 (2.341)

LLI (1984) ± ± ± 1.218 (20.075) 1.183 (19.291)

McFadden's R 2a 0.052 0.065 0.064 0.151 0.165

Reset t-statisticb 0.927 0.884 0.731 0.126 0.026

a McFadden's R 2 is given by the change in log-likelihood divided by the log-likelihood for a restricted model which contains

only a constant term.b At the 95% signi®cance level, values exceeding 1.96 indicate misspeci®cation or omitted variables.

M. Sutton et al. / Social Science & Medicine 49 (1999) 867±878874

The e�ects of utilisation-dependent measurement

error can be based on a comparison of models (b) and(c). When utilisation-dependent measurement error isavoided the coe�cient on LLI increases quite consider-

ably. This seems to indicate that utilisation a�ectsreported illness over and above its e�ect on true healthstatus.

The in¯uence of unobservable heterogeneity in

reporting thresholds can be seen in the change frommodels (c) to (e) and models (d) to (f). The e�ect ofcontrolling for unobservable di�erences in reporting

behaviour is an increase in the estimated e�ect of mor-bidity on utilisation of about the same magnitude asthat caused by measurement error associated with util-

Table 5

Tests for measurement errora

Utilisation-dependent Unobservables Unobservables and utilisation-

dependent

HALS2

HALS2 Visit (0,1) 0.466 (9.013) 0.290 (3.386) ± 0.424 (7.846) 0.262 (2.936)

HALS2 Visits (count) ± 0.126 (2.583) ± ± 0.116 (2.288)

IMR84b ± ± 0.638 (18.984) 0.625 (18.475) 0.624 (18.427)

HALS1

HALS2 Visit (0,1) 0.288 (5.249) 0.181 (2.096) 0.149 (2.535) 0.101 (1.148)

HALS2 Visits (count) ± 0.076 (1.607) ± 0.034 (0.725)

IMR91b ± ± 0.624 (18.524) 0.623 (18.463)

a Table cells contain the coe�cients (t-ratios) on utilisation and person-speci®c reporting terms when they are introduced into the

basic reduced-form equations (columns 2 and 3; Table 3). Therefore, all equations additionally include age, gender and objective

health measures from the year contemporaneous with the dependent variable.b IMR84ÐInverse Mill's Ratio from the 1984 LLI equation (column 2; Table 3); IMR91ÐInverse Mill's Ratio from the 1991

LLI equation (column 3; Table 3).v.

Table 6

Testing for simultaneity between true health and HALS2 utilisation using the Ho measuresa

Estimation OLS 2SLS

Year of Ho measures HALS1 HALS2 HALS2

Constant 0.660 (2.046) 0.571 (1.757) 2.079 (2.053)

Male ÿ0.037 (ÿ1.669) ÿ0.034 (ÿ1.558) ÿ0.028 (ÿ1.116)Age 0.568 (1.761) 0.513 (1.562) 0.664 (1.704)

Age squared ÿ0.435 (ÿ1.447) ÿ0.369 (ÿ1.220) ÿ0.635 (ÿ1.707)SCI ÿ0.042 (ÿ1.392) ÿ0.041 (ÿ1.359) ÿ0.047 (ÿ1.529)SCII ÿ0.031 (ÿ1.671) ÿ0.029 (ÿ1.566) ÿ0.026 (ÿ1.386)SCIIINM 0.020 (0.824) 0.019 (0.793) 0.023 (0.943)

SCIV 0.024 (1.064) 0.022 (0.992) 0.025 (1.068)

SCV 0.060 (1.792) 0.059 (1.768) 0.068 (1.869)

Arterial pressure ÿ0.624 (ÿ1.574) ÿ0.456 (ÿ1.032) ÿ3.648 (ÿ1.764)Arterial pressure squared 0.312 (1.696) 0.204 (0.975) 1.796 (1.790)

FEV ÿ0.148 (ÿ1.245) ÿ0.239 (ÿ2.017) ÿ0.303 (ÿ1.728)FEV squared 0.006 (0.111) 0.048 (0.822) 0.049 (0.605)

BMI ÿ0.562 (ÿ1.684) ÿ0.343 (ÿ1.202) ÿ0.567 (ÿ1.524)BMI squared 0.309 (2.044) 0.195 (1.591) 0.301 (1.887)

Height 0.161 (0.851) 0.130 (0.684) 0.355 (1.605)

R2 0.025 0.024 0.006

Reset t-statistic 1.288 0.749 ±

J-test ± ± 2.634+

a Instruments for the HALS2 objective health measures are HALS1 objective health measures, regular smoking and number of

units of alcohol consumed per week at HALS1; the J-test is less than its critical value: w22 (0.05)= 5.99.

M. Sutton et al. / Social Science & Medicine 49 (1999) 867±878 875

Table

7

AcomparisonofmethodsforincludingLLIin

theGPutilisationequation

Model

(a)

(b)

(c)

(d)

(e)

(f)

Method

HALS1LLI

HALS2LLI

Instruments:HALS2H

o

andHALS1LLI

Instruments:

HALS1LLI

Instruments:

HALS2H

o

Instruments:

HALS1H

o

Potentialbiasesa

E,m

E,S,m,

UE,S,m

E,m

E,S

E

Constant

ÿ1.185(ÿ

4.807)

ÿ1.185(ÿ

4.787)

ÿ1.144(ÿ

4.612)

ÿ1.151(ÿ

4.643)

ÿ1.095(ÿ

4.395)

ÿ1.082(ÿ

4.338)

Male

ÿ0.216(ÿ

4.871)

ÿ0.226(ÿ

5.062)

ÿ0.235(ÿ

5.239)

ÿ0.233(ÿ

5.212)

ÿ0.245(ÿ

5.432)

ÿ0.248(ÿ

5.493)

Age

1.796(1.900)

1.711(1.803)

1.476(1.548)

1.522(1.597)

1.171(1.211)

1.099(1.137)

Agesquared

ÿ0.966(ÿ

1.114)

ÿ1.007(ÿ

1.156)

ÿ0.974(ÿ

1.116)

ÿ0.986(ÿ

1.131)

ÿ0.947(ÿ

1.086)

ÿ0.939(ÿ

1.076)

SCI

ÿ0.173(ÿ

1.826)

ÿ0.148(ÿ

1.553)

ÿ0.142(ÿ

1.485)

ÿ0.148(ÿ

1.549)

ÿ0.125(ÿ

1.309)

ÿ0.124(ÿ

1.297)

SCII

ÿ0.118(ÿ

2.064)

ÿ0.099(ÿ

1.717)

ÿ0.095(ÿ

1.658)

ÿ0.099(ÿ

1.721)

ÿ0.084(ÿ

1.460)

ÿ0.087(ÿ

1.515)

SCIIIN

M0.040(0.551)

0.037(0.501)

0.040(0.542)

0.037(0.505)

0.045(0.609)

0.043(0.584)

SCIV

0.070(1.040)

0.083(1.226)

0.078(1.146)

0.080(1.178)

0.077(1.139)

0.078(1.155)

SCV

0.183(1.854)

0.180(1.819)

0.178(1.796)

0.180(1.819)

0.174(1.748)

0.172(1.730)

LLIb

0.360(6.073)

0.517(9.555)

0.888(6.583)

0.830(5.866)

1.412(4.603)

1.528(4.902)

IMR

c±

±ÿ0

.244(ÿ

3.006)

ÿ0.203(ÿ

2.396)

ÿ0.522(ÿ

2.966)

ÿ0.590(ÿ

3.296)

J-testd

±±

8.154

±e

4.312

7.827

aE,errors-in-variables;S,simultaneity;m,

unobservable

reportingthresholds;U,utilisation-dependentmeasurementerror.

bWhereinstruments

are

usedandInverse

Mill'sRatiosincluded,thecoe�

cientisadjusted

forendogeneity

(BlundellandSmith,1993).

cThesigni®cance

oftheerrorterm

s(Inverse

Mill'sRatios)

from

reduced-form

equationsforLLIprovides

atest

forendogeneity

(BlundellandSmith,1993).

dIn

allcasestheJ-teststatistic

isless

thanitscriticalvalue:

w2 6(0.05)=

12.59.

eNotapplicable

because

notoveridenti®ed.

M. Sutton et al. / Social Science & Medicine 49 (1999) 867±878876

isation. This suggests that individuals who are lesslikely to report ill-health for a given level of `objective'

health status also tend to be those who have a higherprobability of visiting their GP.As has been found elsewhere (Carr-Hill et al., 1995),

we ®nd a signi®cant gradient across socio-economicgroups in the probability of contacting a GP. There isa slight reduction in this gradient when corrections are

made for the di�erent forms of endogeneity bias.

Discussion

The focus of this paper has been on whether

measurement errors in self-report health measures areassociated with utilisation and whether health statusand the utilisation of medical care are simultaneously

determined. In a simple model in which health statusand utilisation are simultaneously determined and util-isation a�ects the reporting of illness, we have shown

that estimated morbidity e�ects may be biased andeven of the wrong sign.Traditional approaches to the modelling of GP util-

isation which include self-reported morbidity as an ex-

ogenous e�ect, may result in biased estimates andincorrect inference concerning the role of morbidityand other variables correlated with it. This is because,

in addition to any true e�ect of morbidity on utilis-ation, the estimated parameter picks up the e�ect offour factors: (a) utilisation on true health; (b) individ-

ual-speci®c unobservables in¯uencing both health andutilisation; (c) utilisation on individual reporting beha-viour; (d) individual-speci®c unobservables in¯uencing

both reporting behaviour and utilisation. As a result,indices designed to adjust for the e�ects of morbidityon health-care utilisation may be biased, possibly lead-ing to inappropriate resource-allocation formulae and

incorrect estimates of inequality in the use of primarycare.This paper has presented an econometric assessment

of the suitability of LLI for use in demand equationsfor primary care utilisation as measured by whethersomeone has visited a General Practitioner in the last

month. The results of our empirical analysis suggestthat controlling for various forms of bias leads to athree-fold increase in the estimated coe�cient for mor-bidity. We have attempted to test directly for the

dependence of reporting behaviour on recent contactwith a GP and to infer the e�ect indirectly using aninstrumental variables approach. The direct approach

relies on an assumption that a battery of objectivehealth measures collected during a nurse visit, captureall aspects of morbidity pertinent to primary care. This

approach suggests evidence that respondents may bemore likely to report illness if they have recently vis-ited their GP. Using the objective health measures as a

proxy for true health status, we also ®nd some evi-dence of simultaneity between utilisation and current

health, which is compatible with the hypothesis thatutilisation improves health.Our indirect approach to inferring the empirical

nature of di�erent forms of endogeneity bias throughthe use of alternative sets of instruments for LLI alsosuggests biases may be present. We ®nd ambiguous

results for the e�ect of utilisation on true health status.Unobservable di�erences between individuals in theirillness-reporting behaviour is associated with utilisation

and, when controlled for, increases the estimated e�ectof morbidity on utilisation quite substantially.Moreover, controlling for measurement error depen-dent on recent GP utilisation also increases the esti-

mated e�ect of morbidity on utilisation.Our conceptual work indicates the many potential

biases which may arise if self-reported morbidity vari-

ables are used to `explain' or standardise health careutilisation for health status. Our empirical resultssuggest that the use of LLI leads to a substantial

underestimation of the e�ect of chronic illness on GPutilisation and a minor overestimation of the utilis-ation gradient across socio-economic groups.

Acknowledgements

The authors acknowledge funding from theDepartment of Health. MS and HG are funded

through the National Primary Care Research andDevelopment Centre. RACH and NR are part of theResource-Allocation, Deployment and Skill-Mix

(RADS) group. The opinions expressed are not necess-arily those of the Department of Health. Data fromthe Health and Lifestyle Survey were supplied by the

ESRC Data Archive. We are grateful to RoshniMangalore for some of the data preparation and toTom Buchmueller, Rob Manning, Paul Contoyannis,

Martin Forster, Andrew Jones and Jenny Roberts, forhelpful comments.

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