time and place: putting individual health into context. a multilevel analysis of the british...

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Health & Place 12 (2006) 279–290

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Time and place: putting individual health into context.A multilevel analysis of the British household

panel survey, 1991–2001

Amanda Sackera,�, Richard D. Wigginsb, Mel Bartleya

aDepartment of Epidemiology and Public Health, Royal Free and University College London Medical School,

1-19 Torrington Place, London WC1E 6BT, UKbDepartment of Sociology, City University London, Northampton Square, London EC1V 0HB, UK

Accepted 1 August 2004

Abstract

Examination of the extent to which time and place affect people’s health has been constrained by the resources

available to answer this question. A British longitudinal, nationally representative survey of 8301 adults aged 16 years

and older living in private households was used to consider the influence of household membership, area of residence

and time using multilevel logistic regression. Self-rated health was assessed by general health and limiting illness during

periods characterized by economic decline (1992), economic improvement (1996) and prosperity (2000). There was

modest evidence of clustering of poor general health within areas and stronger support for within household similarities

in general health which increased over time. Individual, household and area level deprivation accounted for almost all

the area-level variability but had little effect on household variance. There was greater evidence of clustering of limiting

illness within areas: deprivation did not account for this to any great extent. Area differences in general health reduced

as the economy improved but time trends in differences in limiting illness lagged behind the timing of economic

recovery. Both time and place are shown to affect self-rated health although the processes may differ depending on the

health outcome.

r 2005 Elsevier Ltd. All rights reserved.

Keywords: Contextual differences; Multilevel models; Self-rated health; Deprivation; BHPS

Introduction

Existing research on the health damaging effects of

living in a deprived area has generally built on the idea

that there are both contextual and compositional

components adversely affecting self reports of health.

e front matter r 2005 Elsevier Ltd. All rights reserve

althplace.2004.08.011

ing author. Tel.: +44 0 207 679 1820; fax:

242.

ess: [email protected]

That is, the health of an individual depends partly on the

social and physical environment of the area in which

they live (a contextual effect) and partly on their own

health related characteristics (a compositional effect)

(Curtis and Jones, 1998). Typically, the contextual effect

size is modest (Pickett and Pearl, 2001). However, much

of the evidence is based upon cross-sectional studies and

consequently lacks the inferential potential inherent in

longitudinal multilevel analyses (Veugelers et al., 2001).

A longitudinal multilevel analysis of contextual and

compositional influences on health has the ability to

d.

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ARTICLE IN PRESSA. Sacker et al. / Health & Place 12 (2006) 279–290280

consider the temporal ordering of effects with a suitable

means of capturing spatial and residential clustering in

the data. For a recent illustration of work on the

relationship between general health and area see

Stafford et al. (2001). Their study was based on a

population of British civil servants and could usefully be

extended to a general population sample. Many other

studies that have reported area effects on general health

perception have either employed multilevel cross-sec-

tional analyses (Humphreys and Carr-Hill, 1991; Pam-

palon et al., 1999; Marsh et al., 2000; Reijneveld et al.,

2000; Jones and Duncan, 1995) or nonhierarchical

longitudinal (Marsh et al., 2000) or cross-section

methods (Robert, 1998). Research on geography and

self-reports of limiting illness has produced mixed

findings. Some multilevel cross-sectional designs found

evidence for area effects (Malmstrom et al., 2001; Shouls

et al., 1996; Barnett et al., 2001) although others did not

(Ecob, 1996; Humphreys and Carr-Hill, 1991). Both the

nonhierarchical longitudinal regression studies reported

area effects (Sloggett and Joshi, 1998; Boyle et al., 2002)

as did the longitudinal multilevel studies of Wiggins and

colleagues (Wiggins et al., 1998, 2002).

There are several discussion papers which have argued

for a more rigorous analysis of contextual and

compositional effects on health (Jones and Duncan,

1995; Rice et al., 1998; Pickett and Pearl, 2001;

O’Campo, 2003; Merlo, 2003; Diez Roux, 2001, 2002;

Blakely and Woodward, 2000). Chandola et al. (2003)

have expressed concerns about the application of

hierarchical models which do not take full account of

the clustering of individuals within households found in

many national surveys like the British Household Panel

Survey (BHPS). They argue that it is possible that

variation in health at the area level may be due to the

clustering of individuals within households. In their

study of the common mental disorders, Weich et al.

(2003) found little evidence for area effects on common

mental disorders in a model which included household

clustering. By contrast, Pampalon et al. (1999) did find

evidence for regional variation in self-reported general

health in a sophisticated four-level analysis. However,

both these studies were cross-sectional in nature. There

is a need to re-appraise these results in the light of

longitudinal analyses. Thus the first step in this analysis

will examine the evidence for geographic clustering of

poor self-rated health after accounting for clustering of

health within households before undertaking a long-

itudinal analysis of the relationship between area

deprivation and health.

Longitudinal analyses not only allow for the direction

of effects between independent variables and health

outcomes to be clarified but also may enable us to

examine context in terms of temporal and residential

change (Blakely and Woodward, 2000). Geographic

mobility may influence findings if deprived areas have

more fluid populations, for example. Boyle et al. (2002)

found that migrants tend to be healthier than non-

migrants and that sicker people are less likely to be

mobile. Migration may therefore affect the relationship

between health and place in an inconsistent way. Studies

should seek to ensure that individuals are exposed to

their local environment for a sufficient amount of time

to avoid area misclassification and include length of

residence as an adjustment factor in statistical models

(O’Campo, 2003).

This study addresses the concerns outlined above. It

examines whether area deprivation has an adverse effect

on self-rated health once individual deprivation is

accounted for. It is based on data from the BHPS and

uses multilevel models which acknowledge the survey

design: individuals are clustered within households.

Thus a multilevel model with three levels of aggregation

is employed, partitioning the variance into area, house-

hold and individual components. Two health outcomes

are examined: general health and self-reported limiting

illness. The measures of deprivation are all assessed

1 year prior to the ratings of health to ensure temporal

sequencing of effects. The measure of area deprivation is

a composite score derived from census data and

matched to the BHPS members’ data, avoiding possible

same-source bias from the use of aggregated individual

level data as measures of area level effects (O’Campo,

2003). Individual measures of deprivation are taken

from the responses to the BHPS surveys and cover the

components of the composite score. This allows for an

unambiguous control for compositional characteristics

of areas when examining area deprivation as a

contextual effect (Diez Roux, 2002; Pickett and Pearl,

2001). Respondents are only included in the analysis if

they have remained at the same address for at least

1 year so that their assessments of health and depriva-

tion are completed while living in the same area. In

addition, the length of time living in the area is entered

into the models to control for area differences in

migration patterns. We repeat the analysis for health

measured on three occasions, in 1992, 1996 and 2000.

Britain experienced an economic recession from 1991 to

1994, followed by a steady growth in prosperity

(Matheson and Summerfield, 2001). The analysis is

therefore replicated for a period of economic decline

(1992), a period just emerging from recession (1996) and

a period of economic stability and prosperity (2000).

Finally, we follow the advice of Merlo (2003) and exploit

the multilevel framework by examining both regression

parameters and residual variation at different levels of

the model.

In summary, the aims of this study are threefold: to

ascertain whether there is evidence of geographic

clustering of poor self-rated health after accounting for

clustering of self-rated health within households; to

examine whether this clustering of individual self-rated

ARTICLE IN PRESSA. Sacker et al. / Health & Place 12 (2006) 279–290 281

health is stable over time, and to determine the extent to

which this can be understood in terms of area

deprivation.

Material and methods

The BHPS is a longitudinal survey of private house-

holds containing approximately 5500 households and

9000 men and women. The initial sample was a two-

stage stratified cluster design, using postcode sectors as

the first-stage units and individual addresses as the

second stage. The first wave of fieldwork took place

between September and December 1991. Households

have been re-visited each year. All eligible household

members (those aged 16 and over at the time of

interview) are surveyed in the selected households at

each wave. Individuals who took part in the survey in

1991, 1995 and 1999 and completed the health questions

outlined below in the years 1992, 1996 and 2000 were

included in this analysis if they also resided at the same

address at the time of the two consecutive surveys of

interest. Thus, at a minimum, the selected respondents

had spent at least 1 year in the area. The BHPS

investigators matched respondents to their electoral

ward of residence in each year without disclosing

information that could identify individuals.

Assessment of health

Poor general health. Respondents were asked ‘‘Please

think back over the last 12months about how your

health has been. Compared to people of your own age,

would you say that your health has on the whole been

excellent, good, fair, poor, very poor, don’t know?’’.

Answers were taken as an indication of general health

and coded 0 (excellent/good) or 1 (fair/poor/very poor).

Limiting illness. The survey members were identified

with a limiting illness if they responded affirmatively to

‘‘Do you have any of the health problems or disabilities

listed on this card? EXCLUDE TEMPORARY CON-

DITIONS’’ and to ‘‘Does your health limit the type of

work or the amount of work you can do?’’.

Contextual risk factors

Townsend index. This is a composite score designed to

measure small area deprivation (Townsend et al., 1988).

It is based on the following four component variables

for each electoral ward: the number of unemployed

residents over 16 as a percentage of all economically

active residents aged over 16; the number of households

with one person per room and over as a percentage of all

households; the number of households with no car as a

percentage of all households; the number of households

not owning their own home as a percentage of all

households. The index uses Z scores to standardize the

four component variables after first log transforming the

unemployment and overcrowding variables. Scores are

rounded to the nearest 0.5 and truncated at �8.0 and

+12.5, with negative values reflecting less deprived

areas and positive values reflecting more deprived areas.

The data for the index are taken from the 1991 census.

At the time of writing, the index has not been updated to

reflect deprivation in 2001, the year of the most recent

census.

Compositional risk factors

Unemployment. Current labour force status is used to

determine if respondents were unemployed or not.

Those in full-time or part-time work and those not in

employment but not seeking work are included in the

reference category.

Car usage. Survey members were asked ‘‘Is there a car

or van normally available for private use by you or any

members of your household?’’. Responses were coded as

0 for Yes and 1 for No.

Housing tenure. Respondents were asked about their

housing ownership status and tenure. From these

questions, housing tenure was categorized as 0. Outright

owner or owner with a mortgage; 1. Any form of rented

accommodation.

Overcrowding. The number of persons living in the

household and the number of rooms in the accommoda-

tion were used to assess overcrowding. A household is

defined as overcrowded if the household comprises one

or more persons per room.

Confounding variables

Age, gender and number of years resident at address

are entered into the models as control variables because

of their strong associations with both health and

deprivation. The measure for age is age in years on 12

December 1991. Respondents were aged between 9 and

97 years in 1991 (although over 16 when surveyed).

Gender is entered in the models as a dummy variable

coded 0 for male and 1 for female. Years living at the

same address ranges from 1 to 95 years with median

9 year.

Statistical analyses

All modelling is carried out using the software

package MLwiN (Rasbash et al., 2000). The preliminary

analyses consist of two multilevel logistic regression

models (Goldstein, 1995) for each of the health out-

comes. The first model is a 2-level logistic regression

model with individuals at level 1 nested within areas at


level 2. The second model is a 3-level model with

individuals at level 1 nested within households at level 2

nested within areas at level 3. The models are ‘‘null’’

models in respect of having no predictor variables except

a constant term. Variance in self-rated health is

partitioned by the levels of the model. Thus we are able

to address one of the criticisms of spatial analyses:

whether there is evidence for clustering of poor self-rated

health by area in a model which accounts for household

clustering.

The substantive analyses comprise a nested series of 3-

level logistic regression models for the two health

outcomes. Model 1, a baseline model, is first estimated

which does not contain any explanatory variables except

potentially confounding variables: age, gender and years

living at the same address. The compositional effects

model (Model 2) also includes fixed effects for un-

employment, car usage, housing tenure and overcrowd-

ing. Model 3, the contextual plus compositional effects

model, adds the Townsend index to the set of predictors.

The hierarchy used in our analysis clusters individuals

within households within electoral wards. Following

Rice et al. (1998) we do not ‘view hypotheses about area

or household effect as contradictory but as effects

located along a spectrum’. Because the household is a

more immediate environment than the area, household

effects may be expected to have a stronger relationship

to individual health than area effects. The Townsend

index constructed by electoral wards is added to the

multilevel analysis at the area level. Adopting wards as

place of residence presents some problems for the

analysis. Geographic mobility of sample members

throughout the decade meant that by 1999 there were

2.5 times as many wards containing BHPS members as

in 1991. Thus we are unable to compare the impact of

area deprivation at different times. However, it is

possible to add a temporal to a spatial perspective in a

comparison of the proportion of variance at the

different levels of the model at the three time points.

To our knowledge, this is the first time that such an

analysis has been undertaken.

The models are estimated using second-order pena-

lized quasi-likelihood estimation with individual sample

weighting and are repeated for the two measures of poor

health (poor self-rated health and limiting illness). Age is

centred at 45 years in all models. Poor health is assessed

in 1992, 1996 and 2000. The predictors of poor health

are all assessed 1 year prior to the assessment of poor

health to minimize confounding of effects between

health and deprivation.

The results of the modelling provide insights on two

different aspects of the analysis. First, on the interpreta-

tion of the fixed effects and their ability to predict poor

health. Odds ratios and 95% confidence intervals are

obtained from the beta coefficients and their standard

errors in the fixed part of the model. Second, on their

ability to explain differences between areas (based on the

magnitude of the variance component at each level of

the model). Because the outcome variables are binary,

the unexplained variation between areas is calculated

using the ‘‘latent variable approach’’ (Snijders and

Bosker, 1999; Goldstein et al., 2002). It is assumed that

there is a continuous latent variable with a standard

logistic distribution underlying each binary health out-

come, with a single threshold determining the values on

the observed health variable. Level 1 variance on such

an underlying unobserved variable is standardized to

3.29 (the standard logistic variance, p2=3). The propor-

tion of the total variation occurring at each level is

estimated using the standardized value for the level 1

variation and the model estimates for the unobserved

variation at level 2 and level 3.

Results

Of the 9374 BHPS members who completed the

survey in 1992, 92 per cent had also participated in the

first wave of the survey. This resulted in a sample of

8301 individuals aged 16–97 years, resident in 4999

households within 653 electoral wards with complete

data on the variables of interest. The respondents

completing the survey in 1995 and 1996 numbered

8333, providing a sample of 7536 people aged 16–96

years living in 4679 households in 1312 wards who

completed the questions of relevance to this study. In

2000, there were 8582 respondents of whom 94 per cent

had also participated in the 1999 survey, resulting in a

sample of 7247 individuals aged 16–99, resident in 4571

households within 1645 wards with complete data for

the health, contextual and compositional variables.

Table 1 shows the prevalence of poor health over time

in the BHPS. Privileged areas (12.5 per cent) are defined

as those with a Townsend score of 1 standard deviation

below zero and deprived areas (25 per cent) as those with

a score 1 standard deviation above zero. Both poor

general health and limiting illness rates increased during

1992–2000. The table also shows that compared with

more privileged areas, those living in deprived areas had

poorer health on average and were more likely to be

socio-economically disadvantaged. The current state of

the British economy clearly affected the prevalence rates

for the four indicators of deprivation, especially the rate

of unemployment.

Partitioning of variance in poor self-rated health

Figs. 1 and 2 show the partitioning of variance at the

different levels of the models for poor general health and

limiting illness, respectively. In the 2-level model for

poor general health in 1992, a small but significant

ARTICLE IN PRESS

Table

1

Percentageofindividualsin

poorhealthin

1992,1996and2000andtheproportionaffectedbythecomponentsoftheTownsendindex

in1991,1995and1999,respectively,bylevel

ofareadeprivation

1992

1996

2000

Deprived

areasa

Privileged

areasb

Allareas

Deprived

areasa

Privileged

areasb

Allareas

Deprived

areasa

Privileged

areasb

Allareas

Poorgeneralhealth

34.22

19.46

27.99

39.46

24.43

31.74

38.68

25.41

33.02

Lim

itingillness

22.45

14.45

18.23

26.77

17.70

21.13

26.19

17.85

21.76

Over

45year

46.33

56.30

48.89

51.62

57.52

53.90

53.13

60.13

56.28

Fem

ale

54.38

52.30

53.55

54.75

51.87

53.68

55.07

51.45

53.82

Unem

ployed

8.07

2.63

5.23

5.94

2.81

4.03

3.53

1.11

2.27

Nocar

42.26

9.83

25.67

40.09

9.68

23.23

36.50

10.87

20.78

Nonhomeowner

48.75

11.30

29.12

44.28

13.16

27.00

44.38

13.69

25.61

Overcrowded

9.97

3.55

6.59

8.19

2.74

5.41

9.37

1.69

4.44

aTownsendindex

X3.

bTownsendindex

p�3.

Fig. 1. Proportion of variance at each level of a 2-level

(individuals within areas) and 3-level (individuals within

households within areas) baseline model of poor general health

in the British Household Panel Study.

Fig. 2. Proportion of variance at each level of a 2-level

(individuals within areas) and 3-level (individuals within

households within areas) baseline model of limiting illness in

the British Household Panel Study.

A. Sacker et al. / Health & Place 12 (2006) 279–290 283

proportion of the variance (6.67 per cent; 95% C.I.

4.72–8.61 per cent) is estimated to be at the area level. In

the three level model, some of this is re-allocated to the

middle layer, the household level. Nevertheless, a

significant area effect is still observed (level-3 variance

4.99 per cent; 95% C.I. 2.81–7.16 per cent). The

partitioning of the variance in poor general health in

2000 shows a further reduction in variation at the area

level (level-3 variance 2.53 per cent; 95% C.I. 0.13–4.93

per cent). T-tests for differences in the estimates of the

proportion of level-3 variance at the three time-points

were all significant at the 5 per cent level.

There is evidence of more clustering of limiting illness

by area than clustering of poor general health by area

(see Fig. 2). The pattern over time differs from that for

poor general health too. For the latter measure of poor

self-rated health, area level clustering reduced over time.

For limiting illness, estimates of area-level variance

increased in 1996 compared with 1992 and then reduced

again in 2000, although still not returning to the 1992

ARTICLE IN PRESS

Fig. 3. Percentage variance (95% C.I. error bars) at the area

level in the prevalence of poor general health and limiting illness

in the British Household Panel Study.

A. Sacker et al. / Health & Place 12 (2006) 279–290284

level. Taking account of possible household level

clustering did not alter the finding of significant area-

level variance in each of the 3 years studied. This ranged

from 5.12 per cent (95% C.I. 2.05 –8.20 per cent) in 1992

to 12.64 per cent (95% C.I. 7.45–17.53 per cent) in 1996.

The area-level variability in 1992 was significantly lower

than at the later time-points. The comparison between

1992 and 1996 yielded a t statistic ¼ 3.77, p ¼ 0:0002;and for 1992 vs. 2000 t ¼ 2:45; p ¼ 0:015:Figs. 1 and 2 show that a 2-level model, which does

not consider household-level variation in poor health,

places too great an emphasis on individual variability in

health. Most of the household variance in the 3-level

models appears to re-apportion the individual level

variance identified in 2-level models to household-level

variance. This is particularly true in the case of limiting

illness. The next stage in the analysis is to investigate

how far the geographic differences are due to differences

in area deprivation.

Area level differences in self-rated health

In the baseline model for general health (Model 1), the

confounding variables did not alter the proportion of

variance at the area level reported above. The area-level

variance left unexplained after controlling for age,

gender and years at address was around 5 per cent of

the total in 1992 reducing to 3 per cent in the year 2000.

Model 2, which includes the individual characteristics

which match the components of the Townsend scale,

reduced the area variance to 3 per cent of the total in

1992 and 1996, but halved the unexplained variance in

2000 to 1.5 per cent. Compositional factors or char-

acteristics of individuals and their households, ac-

counted for some of the clustering of poor general

health by area. In Model 3, the area-level variance is

reduced further to 2.4 and 2.0 per cent of the total in the

years 1992 and 1996, respectively, and to just under one

per cent of the total variance in the year 2000. Although

much of the area variance could be understood in terms

of the contextual and compositional indicators of

deprivation, a small significant amount remained

unexplained in 1992. Fig. 3 shows the changes in the

amount of area-level variance that is not accounted for

in the models over the period 1992–2000 and how the

addition of both the contextual and compositional

variables explains most of this variance in general

health. Neither the contextual nor the compositional

indicators accounted for much of the household-level

variance.

The partitioning of the variance in the baseline models

for limiting illness showed a pattern different from that

for general health. Individual-level variance accounted

for 59% of the total in 1992, dropping to 28 per cent in

2000. This was counterbalanced by an increase in the

proportion of variance at the household level over time,

from 36 to 64 per cent of the total. After controlling for

age, gender and years living at address, area-level

variance for limiting illness ranged from 5 per cent in

1992 and 2000 to 11 per cent of the total in 1996.

Compositional factors reduced the area variance to 3.5

per cent in 1992 and 2000, and to 10 per cent of the total

in the year 1996. The combined effects of contextual and

compositional factors reduced the area variance further.

Nevertheless, significant differences between areas re-

mained in 1992 and 1996, amounting to 3 per cent of the

total variance in 1992 and rising to 9 per cent in 1996.

However, the residual 3 per cent of variance at the area

level in 2000 was not significantly different from zero.

The difference between the area-level variation in

general health and limiting illness is clear from Fig. 3.

Neither individual nor area deprivation is sufficient for

understanding the spatial variation in limiting illness in

1996, in contrast to their ability to account for the area-

level variation in general health in the same year.

Individual and area deprivation accounted for most of

the spatial variability in self-rated health in 1992 and all

of the variation in 2000.

The prediction of individual poor self-rated health

Table 2 shows the results from the nested series of

compositional and contextual models for poor general

health. Although all four compositional variables

predict poor general health 1 year later in univariate

models (results not shown), unemployment had no

independent effect in the multivariate compositional

model for 1992 and 1996. The relationships of home

ownership and overcrowding with poor general health

became stronger over time. The addition of the

contextual variable in Model 3 demonstrates that both

area deprivation and individual characteristics contri-

bute to the prediction of poor general health. The

Townsend index significantly predicts poor general

ARTICLE IN PRESS

Table 2

Estimated fixed effects (odds ratios) and 95% confidence intervals in parentheses for poor general health in 1992, 1996 and 2000

depending on individual, household and area characteristics in 1991, 1995 and 1999, respectively

Model 1: baseline Model 2: +compositional effects Model 3: +compositional/

contextual effects

Estimate 95% CI Estimate 95% CI Estimate 95% CI

1992

Fixed effects

Constant 0.222 (0.186–0.266) 0.177 (0.146–0.215) 0.174 (0.144–0.212)

Level 1 (individual)

Age (10 year

interval)

1.221 (1.174–1.270) 1.185 (1.140–1.233) 1.197 (1.151–1.245)

Female 1.208 (1.087–1.343) 1.143 (1.025–1.276) 1.149 (1.030–1.282)

Years at current

address

0.998 (0.994–1.002) 1.000 (0.994–1.006) 1.000 (0.994–1.006)

Unemployed 1.149 (0.875–1.509) 1.133 (0.863–1.488)

Level 2 (household)

No car 1.670 (1.456–1.916) 1.567 (1.361–1.804)

Nonhome owner 1.530 (1.339–1.748) 1.435 (1.251–1.646)

Overcrowding 1.256 (0.999–1.580) 1.225 (0.976–1.538)

Level 3 (area)

Townsend index 1.055 (1.033–1.078)

Random variation

Level 1

(individual)

1.000 1.000 1.000

Level 2

(household)

0.520 (0.383–0.657) 0.602 (0.465–0.739) 0.616 (0.481–0.751)

Level 3 (area) 0.165 (0.087–0.243) 0.100 (0.027–0.173) 0.079 (0.010–0.148)

1996

Fixed effects

Constant 0.236 (0.193–0.288) 0.187 (0.152–0.232) 0.187 (0.151–0.231)


Age (10 year

interval)

1.363 (1.311–1.418) 1.336 (1.285–1.390) 1.350 (1.298–1.404)

Female 1.224 (1.090–1.374) 1.178 (1.045–1.328) 1.182 (1.049–1.332)

Years at current

address

0.995 (0.989–1.001) 0.997 (0.991–1.003) 0.997 (0.991–1.003)

Unemployed 1.629 (1.127–2.355) 1.613 (1.114–2.336)

Level 2 (household)

No car 1.548 (1.308–1.832) 1.468 (1.238–1.741)

Nonhome owner 1.654 (1.414–1.934) 1.570 (1.339–1.840)

Overcrowding 1.811 (1.363–2.407) 1.721 (1.293–2.291)

Level 3 (area)

Townsend index 1.043 (1.021–1.066)

Random variation

Level 1

(individual)

1.000 1.000 1.000

Level 2

(household)

0.659 (0.508–0.810) 0.740 (0.587–0.893) 0.782 (0.629–0.935)

Level 3 (area) 0.163 (0.079–0.247) 0.102 (0.022–0.182) 0.074 (0.001–0.147)

2000

Fixed effects

Constant 0.224 (0.186–0.269) 0.203 (0.168–0.243) 0.174 (0.143–0.212)


ARTICLE IN PRESS

Table 2 (continued )


contextual effects



Age (10 year

interval)

1.221 (1.174–1.270) 1.234 (1.186–1.283) 1.197 (1.151–1.245)

Female 1.208 (1.085–1.346) 1.206 (1.080–1.345) 1.148 (1.027–1.284)

Years at current

address

0.998 (0.994–1.002) 0.998 (0.994–1.002) 1.000 (0.994–1.006)

Unemployed 1.097 (1.076–1.119) 1.126 (0.853–1.488)

Level 2 (household)

No car 1.097 (1.076–1.119) 1.570 (1.361–1.811)

Nonhome owner 1.097 (1.076–1.119) 1.439 (1.247–1.660)

Overcrowding 1.097 (1.076–1.119) 1.225 (0.976–1.538)

Level 3 (area)

Townsend index 1.057 (1.034–1.080)

Random variation

Level 1

(individual)

1.000 1.000 1.000

Level 2

(household)

0.895 (0.738–1.052) 0.949 (0.857–1.041) 0.982 (0.819–1.145)

Level 3 (area) 0.094 (0.000–0.188) 0.051 (0.000–0.143) 0.032 (0.000–0.120)


health net of compositional effects. The effect of the four

compositional variables is attenuated, but the reduction

in effect size is slight.

The model estimates for limiting illness are shown in

Table 3. Again, although all four compositional vari-

ables predict limiting illness in univariate analyses, not

all have independent effects in the multivariate composi-

tional model (Model 2). Unemployment was not

independently related to limiting illness in 1992. The

effect of car usage, housing tenure and overcrowding

tended to increase in magnitude over time in contrast to

the effect of unemployment. Table 3 also shows that

after adjustment for compositional effects, the contex-

tual deprivation variable, the Townsend index, was

significantly associated with limiting illness in 1992, 1996

and 2000 (Model 3). Those resident in a deprived area

(one standard deviation above the mean) in 1992 or 2000

were around 40% more likely to have a limiting illness

than those living in a privileged area (one standard

deviation below the mean) after taking their individual

circumstances into account. In 1996, the excess risk

amounted to a 50 per cent increase.

Discussion

In this paper we have carried out a rigorous analysis

of contextual effects on self-reported health which has

taken account of the possibility that these effects could

be a compositional artefact (Subramanian et al., 2003).

Acknowledging recommendations by O’Campo (2003)

before attribution of area effects, the study sample was

confined to those resident in the area for at least 1 year,

and the models included a variable measuring the length

of stay at an address. Boyle et al. (2002) found that the

relationship between area deprivation and limiting

illness was not affected by migration despite migrants

being healthier than nonmigrants. Here, the relationship

between the Townsend scale, an index of area depriva-

tion, and self-reported health was unaffected by length

of stay. Moreover, there was no relationship between

health and length of stay beyond the mandatory 1 year

period. Nevertheless, some further sensitivity analyses

were carried out although they are not been reported in

detail here. Based on the health data for 1996 only, they

showed that a 2 year lag on the predictor variables had

no effect on the substantive results. Neither did treating

general health as a continuous measure alter the

findings. Models which defined general health as an

ordinal outcome were also tested. However, 3-level

applications failed to converge. Since the Townsend

index was based on data from the 1991 census, it is

possible that the contextual influences on self-rated

health are less reliably estimated for the two later time

points. However, unemployment rates during the 1990s

do not correlate well with levels of deprivation and it is

ARTICLE IN PRESS

Table 3

Estimated fixed effects (odds ratios) and 95% confidence intervals in parentheses for limiting illness in 1992, 1996 and 2000 depending

on individual, household and area characteristics in 1991, 1995 and 1999, respectively


contextual effects


1992

Fixed effects

Constant 0.082 (0.064–0.107) 0.063 (0.047–0.083) 0.061 (0.046–0.081)


Age (10 year

interval)

1.665 (1.601–1.732) 1.616 (1.524–1.714) 1.632 (1.539–1.731)

Female 1.158 (1.000–1.342) 1.093 (0.933–1.281) 1.100 (0.938–1.289)

Years at current

address

0.992 (0.986–0.998) 0.994 (0.988–1.000) 0.993 (0.987–0.999)

Unemployed 1.385 (0.951–2.018) 1.363 (0.936–1.986)

Level 2 (household)

No car 1.802 (1.505–2.158) 1.690 (1.403–2.036)

Nonhome owner 1.420 (1.175–1.718) 1.331 (1.096–1.616)

Overcrowding 1.448 (1.037–2.020) 1.409 (1.010–1.966)

Level 3 (area)

Townsend index 1.057 (1.026–1.088)

Random variation

Level 1

(individual)

1.000 1.000 1.000

Level 2

(household)

1.070 (0.807–1.333) 1.203 (0.942–1.464) 1.222 (0.963–1.481)

Level 3 (area) 0.267 (0.126–0.408) 0.195 (0.066–0.324) 0.177 (0.054–0.300)

1996

Fixed effects

Constant 0.093 (0.073–0.119) 0.072 (0.055–0.093) 0.069 (0.053–0.090)


Age (10 year

interval)

1.665 (1.570–1.766) 1.632 (1.539–1.731) 1.665 (1.570–1.766)

Female 1.157 (1.005–1.333) 1.094 (0.943–1.270) 1.099 (0.945–1.278)

Years at current

address

0.993 (0.987–0.999) 0.995 (0.989–1.001) 0.995 (0.989–1.001)

Unemployed 1.347 (0.909–1.998) 1.336 (0.896–1.993)

Level 2 (household)

No car 1.567 (1.288–1.906) 1.480 (1.207–1.815)

Nonhome owner 1.647 (1.367–1.984) 1.582 (1.308–1.914)

Overcrowding 1.726 (1.216–2.452) 1.670 (1.169–2.386)

Level 3 (area)

Townsend index 1.057 (1.034–1.080)

Random variation

Level 1

(individual)

1.000 1.000 1.000

Level 2

(household)

0.952 (0.674–1.230) 1.030 (0.754–1.306) 1.102 (0.826–1.378)

Level 3 (area) 0.408 (0.249–0.567) 0.346 (0.193–0.499) 0.309 (0.162–0.456)

2000

Fixed effects

Constant 0.054 (0.040–0.074) 0.040 (0.029–0.056) 0.040 (0.028–0.055)


ARTICLE IN PRESS

Table 3 (continued )


contextual effects



Age (10 year

interval)

1.896 (1.788–2.011) 1.822 (1.685–1.971) 1.840 (1.702–1.991)

Female 1.252 (1.054–1.488) 1.177 (0.977–1.418) 1.181 (0.980–1.422)

Years at current

address

0.996 (0.990–1.002) 1.000 (0.992–1.008) 0.999 (0.991–1.007)

Unemployed 1.301 (0.738–2.292) 1.290 (0.734–2.269)

Level 2 (household)

No car 1.850 (1.479–2.313) 1.756 (1.393–2.213)

Nonhome owner 1.896 (1.517–2.371) 1.804 (1.440–2.260)

Overcrowding 1.840 (1.177–2.877) 1.740 (1.113–2.721)

Level 3 (area)

Townsend index 1.045 (1.011–1.080)

Random variation

Level 1

(individual)

1.000 1.000 1.000

Level 2

(household)

1.796 (1.484–2.108) 2.029 (1.708–2.350) 2.069 (1.748–2.390)

Level 3 (area) 0.259 (0.071–0.447) 0.188 (0.000–0.384) 0.165 (0.000–0.359)


thought better to employ the index as we have done than

use other unvalidated time-varying measures (Dorling,

D. 2004, pers. comm., 20 May). Overall, we are satisfied

that the conclusions reported here are reliable.

We have shown that evidence of spatial clustering of

poor self-rated health is not an artefact due to ignoring

households. Geographic differences remain in the multi-

level models that include households in their hierarchical

structure. Most of the spatial clustering in poor general

health can be understood in terms of deprivation: that

suffered by individuals, other members of their households

and the wider community. The reasons underlying the

clustering of limiting illness are more complex. Deprivation

is only part of the explanation underlying differences

between areas. Areas were more homogeneous during the

economic recession of the early 1990s and showed greatest

heterogeneity during the economic recovery of 1995–1996.

The compositional indicators of deprivation accounted for

a greater proportion of the spatial inequalities in limiting

illness during the periods of economic decline and

prosperity than the period of economic recovery. Where

and when you live also predicts whether you will have poor

self-rated health. Living in a more privileged area in a time

of prosperity is good for health even if one’s own

circumstances are not so good. Conversely, living in a

disadvantaged area is bad for one’s health even if one’s

personal circumstances are adequate.

Boyle and Willms (1999) reported that larger areas

produce less evidence of area effects than smaller areas.

Another study also reported that the choice of area

coverage affects the magnitude of the variance in the

model but added that the choice of area did not affect

the magnitude of the fixed effects (Reijneveld et al.,

2000). The significant effect of area deprivation on self-

reported health after accounting for individual depriva-

tion is likely to be sound. Pickett and Pearl (2001)

suggest that the choice of contextual variables is less

critical than the proper control for compositional

variables. The components of the Townsend scale were

chosen to measure deprivation at the individual level for

precisely this reason. These components describe each

individual sample member whereas the composite

Townsend score captures context as it applies to the

whole population in any area. The concerns of this

analysis were not to produce a regression equation to

predict poor health but to examine the evidence for

contextual effects on health. Therefore, we did not

include other variables in the model which have been

shown to predict poor health in other studies such as

education or marital status. Nevertheless, other work

using the BHPS has shown that neither education nor

marital status adds much to the prediction of who gets a

limiting illness (Bartley et al., 2004).

Although there was evidence of clustering of poor

health within electoral wards, the effect sizes are modest.

While the spatial variation in general health decreased as

the economy improved, there was greater area-level

variation in limiting illness in 1996, a time of growth in

ARTICLE IN PRESSA. Sacker et al. / Health & Place 12 (2006) 279–290 289

the UK economy, than in the recession year of 1992. By

2000, however, there was a suggestion that area effects

on limiting illness were beginning to decline. Reports of

limiting illness reflect not only health status, per se, but

also a response to a limited job market (Lindholm et al.,

2002). Individuals who exit the labour market for health

reasons may not move back into employment again until

there has been a considerable increase in the demand for

labour, changing employers’ attitudes towards the

health of potential employees. Thus, time trends in area

differences in limiting illness will tend to lag behind the

timing of economic recovery.

The ratio of household to area variability in the

baseline models for limiting illness remained fairly

steady over time while the proportion of variance in

general health attributed to the household increased

from 1992 to 2000. This could imply that the processes

generated by time and place (who you live with and

where you live) operate in different ways for the two

health outcomes. It is suggested that characteristics of

the local economy drive variations in limiting illness.

Travel to work areas (ONS Geography, 2003), located

along the continuum between households and electoral

wards (Rice et al., 1998), may capture area effects on

limiting illness more effectively because they best reflect

differences in the availability of employment. By

contrast, variation in general health may be driven more

by who people live with and the times in which they live

than the wider area where they are living. Given that

general health is well known to reflect both physical and

psychological perceptions of health (Fayers and Spran-

gers, 2002), our findings are entirely consistent with the

findings of Weich and Pampalon and their colleagues.

The former found little support for geographic variation

in common mental disorder while the latter reported

geographic variability in poor general health in studies

accounting for household clustering (Weich et al., 2003;

Pampalon et al., 1999). Several studies have found

similarities in health between couples, and these have

favoured mate selection and to a lesser extent common

environmental influences over contagion explanations

(Bookwala and Schulz, 1996; Dufort et al., 1994; Tambs

and Moum, 1992). Nevertheless, Tambs and Moum

(1992) found no support for converging health status

over time among a Norwegian sample. Our finding that

the similarity of general health within households

increased over time adds to the increasing number of

studies that highlight the magnitude of household level

effects on health. More detailed work is required to

explain these findings.

Acknowledgements

This work was funded as part of the Medical Research

Council’s ‘Health of the Public’ initiative. Grant no.

9900586. Data from the BHPS were supplied by the

ESRC Data Archive. Those who carried out the original

collection and analysis of the data bear no responsibility

for its further analysis and interpretation. The authors

gratefully thank Paul Clarke from the Department of

Infectious Disease Epidemiology, Imperial College,

London, for his helpful advice and comments on the

paper and Nick Buck of the Institute for Social and

Economic Research for providing the Townsend scores

for the BHPS data set.

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