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Social Science & Medicine 60 (2005) 2755–2771

www.elsevier.com/locate/socscimed

Selective migration, health and deprivation:a longitudinal analysis

Paul Normana,�, Paul Boyleb, Philip Reesc

aCathie Marsh Centre for Census and Survey Research, University of Manchester, Manchester, M13 9PL, UKbSchool of Geography and Geosciences, University of St Andrews, St Andrews, KY16 9ST, UK

cSchool of Geography, University of Leeds, Leeds, LS2 9JT, UK

Available online 22 December 2004

Abstract

Population migration is a major determinant of an area’s age-sex structure and socio-economic characteristics. The

suggestion that migration can contribute to an increase or decrease in place-specific rates of illness is not new. However,

differences in health status between small geographical locations that may be affected by the inter-relationships between

health, area-based deprivation and migration are under-researched. Using the Office for National Statistics (ONS)

England and Wales Longitudinal Study (LS) 1971–1991, this research tracks individuals to identify any systematic

sorting of people that has contributed to the area-level relationships between health (limiting long-term illness and

mortality) and deprivation (Carstairs quintiles). The results demonstrate that among the young, migrants are generally

healthier than non-migrants. Migrants who move from more to less deprived locations are healthier than migrants who

move from less to more deprived locations. Within less deprived areas migrants are healthier than non-migrants but

within deprived areas migrants are less healthy than non-migrants. Over the 20 year period, the largest absolute flow is

by relatively healthy migrants moving away from more deprived areas towards less deprived areas. The effect is to raise

ill-health and mortality rates in the origins and lower them in the destinations. This is reinforced by a significant group

of people in poor health who move from less to more deprived locations. In contrast, a small group of unhealthy people

moved away from more deprived into less deprived areas. These countercurrents of less healthy people have a slight

ameliorating effect on the health–deprivation relationship. Whilst health–deprivation relationships are more marked for

migrants there are also health (dis-) benefits for non-migrants if their location becomes relatively more or less deprived

over time.

Overall we found that between 1971 and 1991, inequalities in health increased between the least and most deprived

areas, compared with the health–deprivation relationship which would have existed if peoples’ locations and

deprivation patterns had stayed geographically constant. Migration, rather than changes in the deprivation of the area

that non-migrants live in, accounts for the large majority of change.

r 2004 Elsevier Ltd. All rights reserved.

Keywords: Health selective migration; Deprivation; Limiting long-term illness; All-cause mortality; England and Wales longitudinal

study, UK

e front matter r 2004 Elsevier Ltd. All rights reserve

cscimed.2004.11.008

ing author. Tel.: +44161 275 7766.

ess: paul.norman@manchester.ac.uk

Introduction

Together with fertility and mortality, migration is a

major determinant of an area’s population structure. In

d.

ARTICLE IN PRESSP. Norman et al. / Social Science & Medicine 60 (2005) 2755–27712756

many parts of the UK, at both regional and small area

levels, change in population composition due to internal

and international migration is greater than the differ-

ences resulting from natural change (Champion, Fother-

ingham, Rees, Boyle, & Stillwell, 1998; Scott & Kilbey,

1999). Moreover, since migrants also differ from each

other and from non-migrants, migration events will have

different effects on the socio-economic and demographic

characteristics of the populations in both the origins and

destinations. The suggestion that population migration

can contribute to an increase or decrease in place-

specific rates of illness is not new (Farr, 1864; Welton,

1872) but, as Verheij, Dike van de Mheen, de Bakker,

Groenewegen, and Mackenbach (1998) point out, there

is relatively little empirical evidence with respect to the

effects of selective migration on geographical variations

in health outcomes. In particular, we are interested here

in the role that migration plays in influencing the

relationship between health and area-based deprivation.

Deprivation is usually taken to be a state of

disadvantage relative to the local community or the

wider society or nation to which an individual, family or

group belongs (Townsend, 1987). People can be

deprived of adequate education, housing of good

quality, rewarding employment, sufficient income, good

health and opportunities for enjoyment. However, a

frequent approach has been to calculate an area’s

deprivation using census-based indicator variables com-

bined into a single deprivation score for each area.

Commonly used UK deprivation indexes include the

Carstairs Index (Morris & Carstairs, 1991), Townsend

Index (Townsend, Phillimore, & Beattie, 1988) and

Jarman Underprivileged Area (UPA) Index (Jarman,

1983)1 and in the analysis below we use the Carstairs

Index.

After the Black Report (Black, Smith, & Townsend,

1982) revived interest in place effects on health inequal-

ities, numerous research projects have demonstrated

that area-based measures of deprivation are related to

mortality (Drever & Whitehead, 1995; Eames, Ben-

Shlomo, & Marmot, 1993; McLoone & Boddy, 1994;

Senior, Williams, & Higgs, 2000; Sloggett & Joshi,

1994), life expectancy (Raleigh & Kiri, 1997), morbidity

(Boyle, Duke-Williams, & Gatrell, 1999) and specific

health outcomes (Crombie, Kenicer, Smith, & Tunstall-

Pedoe, 1989). However, the apparent links between

deprivation and health are based on comparing the

measured deprivation in a person’s residential area at

the time of diagnosis or death; the assumption being that

the deprivation score of the current area of residence is

an adequate measure of exposure to possible risk

(Gatrell, 2002). This ignores the fact that people move

so that, for many, the circumstances that had most

1For a full review of indexes and a critique of their

construction and usage see Senior (2002).

influence on their health may not have been the same as

when their health deteriorated or when they died.

Bentham (1988) believes that most studies of associa-

tions between diseases and hypothesised causes have

paid insufficient attention to the implications of

residential migration. Sometimes real environmental

causes of disease may be obscured, in other circum-

stances spurious associations may be produced. While

the problems of associating residence at the time of

diagnosis with exposure to environmental conditions

have been researched (e.g. Mancuso & Sterling, 1974),

studies that account for migration are rare (though see,

for example, Giggs, Bourke, & Katschinski, 1988;

Kliewer, 1992; Riise et al., 1991; Rogerson & Han,

2002; Sabel, Gatrell, Loytonen, Maasilata, & Jokelai-

nen, 2000). Ignoring population mobility is a problem

because migration is selective.

At any one time the characteristics of migrants in the

UK are different from the population as a whole and the

migration process is therefore selective. Migrants differ

from the rest of the population in terms of their age, life

stage, housing tenure, socio-economic position and

educational achievement (Boyle, Halfacree & Robinson,

1998; Buck, Gershuny, Rose, & Scott, 1994; Champion

et al., 1998). Importantly, migration is also selective by

health status and we know that young adult migrants

tend to be healthier than non-migrants, while older

migrants tend to be less healthy than their sedentary

counterparts (Bentham, 1988; Findlay, 1988; Boyle,

Norman, & Rees, 2002; Rogerson & Han, 2002; Boyle &

Duke-Williams, forthcoming, 2004).

Of course, migrants are also selective in terms of their

choice of destination. Place characteristics have long

been acknowledged as important determinants of

migration (Walters, 2000) and factors that potentially

‘push’ or ‘pull’ migrants between different places vary

with age and stage in the life course (Boyle et al., 1998;

Champion et al., 1998). Young adulthood is the peak

age for migration when people tend to move into urban

areas for education and employment opportunities. In

mid-life, moves will usually correspond to changes in

employment or housing needs and in later life retire-

ment-related migration may involve moves into care, to

be nearer family and friends, or to coastal or semi-rural

‘retirement areas’ (Law & Warnes, 1976; Rogers, 1992).

On the whole it seems reasonable to expect that, in terms

of aspirations, people will want to move away from

more deprived locations and towards less deprived

locations.

Bentham (1988) distinguished between three types of

health selective migration. First, if sick migrants are able

to move away from an area perceived as harmful to

health (a deprived location, for example), the effect

would be to reduce observed mortality and morbidity

rates in migrant origins and to raise rates in the

destinations. This would diminish the strength of

ARTICLE IN PRESS

2A Scottish LS sample was drawn in 1971 with the original

England and Wales study but was completely abandoned by

1991. A project is currently underway to establish the Scottish

Longitudinal Study (SLS) which will be a 5.5% sample of the

Scottish population linked between the 1991 and 2001

Censuses.

P. Norman et al. / Social Science & Medicine 60 (2005) 2755–2771 2757

relationship between health and area deprivation. Even

if migration is not selective of individuals by their health

status, it still has implications for geographical studies of

associations between health and area characteristics. If

people have only recently migrated into an area, they

may not have been exposed to local conditions long

enough for any adverse effect on their health and this

will artificially reduce any excess in mortality or

morbidity rates in the area. Second, people who are ill

may move to be better placed for either formal medical

care or informal care provided by their families.

Locations with care institutions will attract populations

selected for their poor health, even though they may be

situated in relatively wealthy places. Theoretically, this

will elevate mortality and morbidity rates in areas with

institutions whilst migrant origins without care institu-

tions will show apparently favourable levels of health.

Third, while some types of migration may be selective of

people in poor health, migration in general will be

dominated by individuals, particularly young adults

(who are the most mobile group), whose health is better

than average. The depletion of a relatively healthy

proportion of the population would result in higher

morbidity and mortality rates at their origins. The

converse will be found for areas that are the destinations

of relatively healthy migrants.

Dorling, Shaw, and Brimblecombe (2000) ask whether

health selective migration can explain geographical

inequalities in health and to date there is conflicting

evidence regarding the scale over which these effects may

operate. As long ago as 1864 Farr noted that migrants

from urban to rural areas differed in their health from

migrants moving from rural to urban areas. In 1872

Welton pointed out that selective migration could have a

profound effect on local mortality rates. Welton showed

that younger migrants leaving rural areas to live in cities

were healthier than those who remained behind so that

origin area mortality rates were raised. At the same time,

in-migration to cities of this selectively healthy subgroup

had the effect of reducing mortality rates.

Fox and Goldblatt (1982) found that health-related

differential migration in England contributed to widen-

ing regional differences in health inequalities since

people moving away from high mortality areas had

lower mortality rates than those moving in and persons

moving between ‘healthy’ regions had lower mortality

rates than both of these groups. On the other hand,

Brimblecombe, Dorling, and Shaw (1999) found that

migration made little difference to inter-regional differ-

ences in mortality, but at local authority district level the

observed differences in mortality were accounted for

entirely by migration. Since the majority of migration is

short distance and there are wider mortality gaps

between small areas within regions than there are

between the regions, it would be surprising if migration

did not in part contribute to inequalities between areas.

We should also be aware that people can experience

changing levels of deprivation through time without

moving. Focusing on non-migrants in non-deprived

households, Boyle, Norman, and Rees (2004) show that

when the places they are living in become more deprived

over time, their health suffers. Thus a line of enquiry is

whether, over and above this effect on non-migrants,

there is an additional effect on ill health and mortality

rates that is due to migrants changing their location.

The selective nature of migration is particularly

problematic when cross-sectional data are used. In this

research an extract from the Office for National

Statistics (ONS) England and Wales Longitudinal Study

(LS) for the period 1971–1991 has been reaggregated at

different time points to create population samples

stratified by ward deprivation categories. This approach

allows aggregate effects of the changes in individual

circumstances to be explored. Specifically, we examine

whether healthier persons tend to migrate into less

deprived locations and less healthy persons into more

deprived locations, thereby exaggerating the apparent

relationship between deprivation and health. We do this

using both limiting long-term illness (LLTI) in 1991 and

all-cause mortality between 1991 and 1999 as the

outcome measures. Essentially, this paper seeks to

determine if, over the 20-year period from 1971 to

1991, there has been any systematic sorting of healthy

and unhealthy people along the gradient of deprivation.

The ONS longitudinal study data for England and Wales

The ONS LS contains linked decennial census and

vital events data (e.g. births and deaths) and cancer

registrations for a one percent sample of the population

of England and Wales2 (Hattersley & Creeser, 1995). It

was initiated following the UKs 1971 Census when the

original LS sample was drawn using individuals born on

one of four birth dates. This provided an initial sample

of approximately 500,000 LS members. New members

are added to the LS if either newly born children or

immigrants have the correct birthday. People can leave

the LS through death or emigration, although their

records, including cause of death, are retained in the

database. The study was designed as a continuous,

multi-cohort study with subsequent samples drawn at

the 1981 and 1991 Censuses using the same selection

criteria. In due course linkages will be made to the 2001

Census. Information is included about the other

ARTICLE IN PRESSP. Norman et al. / Social Science & Medicine 60 (2005) 2755–27712758

residents in households that include a member of the LS,

although the records for these individuals are not linked

through time.

In addition to the individual-level microdata, the LS

contains some variables from the censuses which capture

the characteristics of the small areas in which each

sample member lived.

The sample

The data extracted for this analysis involved a closed

sample population present at the 1971, 1981 and 1991

Censuses. International migrants and those who re-

ported being permanently sick or disabled in 1971 or

1981 were excluded from the sample. This is in line with

other LS studies (Sloggett & Joshi, 1998; Harding, 2003)

and is because the particular focus here is on internal

UK migrants since these form the vast majority of

moves. We also have no detailed information about the

origins of international migrants. Persons identified at a

census as being resident in a communal establishment

are also excluded since locations with such institutions

will receive exaggerated numbers of ill in-migrants

(Bentham, 1988). The resulting dataset comprised

315,684 individuals. The LS extract is a relatively

healthy sample since these persons are presumed to be

healthy at the start of the period and all survive until

1991; the implications of this are discussed below. The

categorical variables used in this analysis are described

in Table 1.

The age of our sample members was aggregated into

10-year age groups (up to 80+) allowing the calculation

of Standardised Illness Ratios (SIRs), based on LLTI

data. LLTI was captured in the 1991 Census for the first

time, so we are only able to identify those with LLTI at

one time point. Of course, there are problems with any

self-reported illness variable, such as LLTI, as people’s

perceptions of a ‘limiting long term’ illness will vary.

Even so, LLTI data have formed the basis of many

geographical studies since 1991 (e.g. Boyle et al., 1999;

Boyle, Duke-Williams, & Gatrell, 2001; Boyle et al.,

2002; Congdon, Shouls, & Curtis, 1997; Gould & Jones,

1996; Senior, 1998) and the variable has also been used

widely in studies using the LS (e.g. Bartley & Plewis,

Table 1

ONS Longitudinal Study variables used in the analysis

Variable Description

Age Ten year birth cohort to whi

Limiting long-term illness (LLTI) Reported in the 1991 Census

All-cause mortality Died after the 1991 Census (

Deprivation Carstairs quintile of the pers

deprived) to Quintile 5 (most

Residential migration Migrated between the 1971 a

2002; Ecob & Jones, 1998; Harding, 2003; Sloggett &

Joshi, 1998; Wiggins et al., 2002). Results for LLTI also

appear to correlate well with data from General

Practitioner (GP) consultations and in- and out-patient

visits to hospital (Dale, 1993) with responses to this type

of question found to be most strongly associated with

general health perceptions and physical limitations

rather than psychological health (Cohen, Forbes, &

Garraway, 1995). We also calculated Standardised

Mortality Ratios (SMRs), identifying those sample

members present at the 1971–1991 Censuses who

subsequently died between 1991 and 1999. There have

been numerous studies of mortality using the LS

(e.g. Harding & Balarajan, 2001; Ecob & Jones, 1998;

Jessop, 1999).

We used the Carstairs Index, which is calculated from

four census variables (Table 2), to identify the depriva-

tion of the ward in which each sample member resided at

the time of the 1971 and 1991 censuses (Morris &

Carstairs, 1991). To protect the confidentiality of

respondents, individual scores were aggregated into

quintiles. Carstairs quintiles for 1991 were already

included in the LS database, but the 1971 scores were

calculated as part of this study. As can be seen in Table

2, there are some small differences in the 1971 and 1991

scores as precise matches for the 1971 variables were not

available in 1991. However, the inconsistencies are

minimal. The scores ranged from less to more deprived

between �5.02 and +19.35 in 1971 and �5.64 and

+24.69 in 1991. These variables are relative as the score

for each ward allows a comparison of its score with

other wards in the same census year. If 1971 and 1991

wards have the same Carstairs scores it would not

necessarily mean that the ward was identically deprived

throughout the period. However, the interest here is in

the ward deprivation circumstances in which the LS

members were living in 1971 and 1991, relative to other

wards in England and Wales in those years.

The LS database includes ten-year migration indica-

tors to denote whether a person’s residential address

changed between 1971 and 1981 and between 1981 and

1991. While it is not possible to identify the particular

small area locations in which individuals resided due to

confidentiality measures, it is feasible to track through

ch the individual belongs

(yes/no)

and up to 1999) (yes/no)

on’s ward of residence (1971 and 1991). Quintile 1 (least

deprived)

nd 1991 Censuses (yes/no)

ARTICLE IN PRESS

Table 2

Census-derived variables used to calculate the ward-level Carstairs Index for 1971 and 1991

Variable Descriptions for 1971 and 1991

Unemployment 1971: Male residents over 15 seeking work as a proportion of all economically active male residents aged

over 15

1991: Unemployed male residents over 16 as a proportion of all economically active male residents aged

over 16

Overcrowding 1971: Households with 1 or more persons per room as a proportion of all households

1991: Persons in households with 1 and more persons per room as a proportion of all residents in

households

Non car ownership 1971: Households with no car as a proportion of all households

1991: Residents in households with no car as a proportion of all residents in households

Low social class 1971: Economically active head of household in social class IV or V (estimated by socio-economic groups

(SEGs) 7, 10, 11 and 15) as a proportion of all economically active persons (all SEGs)

1991: Residents in households with an economically active head of household in social class IV or V as a

proportion of all residents in households

P. Norman et al. / Social Science & Medicine 60 (2005) 2755–2771 2759

time the journeys of LS members from their origins to

destinations across ward-level deprivation space. We are

therefore able to identify those who have remained in

the same location between 1971 and 1991 (the depriva-

tion quintile of the ward they have lived in may or may

not have changed during this period) and those who

have moved address (again, they may or may not have

moved to a ward in a different deprivation quintile).

Methods

We calculated indirectly standardised illness and

mortality ratios (SIRs and SMRs, respectively) for

different combinations of deprivation categories as

recorded in 1971 and 1991. Expected morbidity and

mortality were calculated using all the LS members

present in 1971–1991 as the standard population and the

95% confidence intervals were calculated as:

SIR or SMR � 1:96� 100 �

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiObserved

p

Expected:

We also calculated age-specific illness rates (ASIRs)

and mortality rates (ASMRs) and the 95% confidence

intervals for these were calculated as:

ASIR or ASMR � 1:96 � 100 �

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiRateð1� RateÞ

Population

s:

Population subgroups under consideration

Four groups of people can be imagined: (1) non-

migrants who did not change address between 1971 and

1991 where the ward they resided in was classified in the

same deprivation quintile in both censuses; (2) non-

migrants who did not change address between 1971 and

1991, but the deprivation quintile of the place they lived

in changed during the period (this group were focused

on by Boyle et al., 2004); (3) migrants who did change

address between 1971 and 1991, but the wards they lived

in were classified in the same deprivation quintile in both

censuses; (4) migrants who did change address between

1971 and 1991 and the deprivation quintiles of the places

were also different. In particular, we are interested in

how movements up or down the deprivation hierarchy,

either through migration or because the place itself has

changed, impacts upon the illness and mortality rates in

differently deprived areas.

Area classification types

Results are initially given below by deprivation

quintile in 1971 and 1991, but to identify the most

important changes within the deprivation hierarchy

between 1971 and 1991, SIRs and SMRs were also

calculated for seven different combinations of depriva-

tion quintiles for 1971 and 1991 (Table 3). All areas have

been classified to allow a focus on locations which:

(a)

Remained at the same relative level of deprivation in

both 1971 and 1991: whether least (quintile 1);

middle (quintiles 2–4); or most (quintile 5) deprived,

(b)

Became less deprived by being in: one of quintiles

2–4 in 1971 and in quintile 1 in 1991; or in quintile 5

in 1971 and in one of quintiles 1–4 in 1991,

(c)

Became more deprived by being in: quintile 1 in 1971

and in one of quintiles 2–4 in 1991; or in quintiles

1–4 in 1971 and in quintile 5 in 1991.

ARTICLE IN PRESS

Table 3

Categories of areas defined by combinations of deprivation quintiles for 1971 and 1991

1971 deprivation quintile 1991 deprivation quintile

Least deprived Middle deprived Most deprived

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5

Least deprived Quintile 1 Q1 1971 Q1 1971 Q1–4 1971

Q1 1991 Q2–4 1991 Q5 1991

Middle deprived Quintile 2 Q2–4 1971 Q2–4 1971

Quintile 3 Q1 1991 Q2–4 1991

Quintile 4

Most deprived Quintile 5 Q5 1971 Q5 1971

Q1–4 1991 Q5 1991

P. Norman et al. / Social Science & Medicine 60 (2005) 2755–27712760

These seven categories are far more manageable than

presenting results for all 25 possible 1971–1991 combi-

nations of quintiles and avoid potential small number

problems.

Results

According to the Census Local Base Statistics 100%

data for England and Wales, 12.2% of people reported

LLTI in 1991, while 16% of our sample reported LLTI.

This is because the 0–9 and 10–19 cohorts are not

included in 1991 and rates of LLTI are considerably

lower in the younger age groups. Of the LS members in

the extract 12% died between 1991 and 1999 and, of

these persons, 49% had reported LLTI in the 1991

Census. In 1971 26% of the sample lived in the most

deprived wards (Carstairs quintile 5), and 10% lived in

the least deprived wards (Carstairs quintile 1). These

figures were 25% and 14% in 1991, respectively.

Morbidity and mortality by deprivation quintiles 1971 and

1991

The SIRs for LLTI reported in 1991 (Fig. 1) and the

SMRs for mortality during 1991–1999 (Fig. 2) were both

calculated for the 1971 and 1991 Carstairs quintiles.

Thus for 1971, while the LLTI and mortality events

relate to a later time point, the LS members have been

returned to the relative deprivation in which they were

living 20 years previously. Because this is a closed

sample, we are dealing with the same group of people in

both 1971 and 1991.

The expected relationship between deprivation and

health is found with better health in the least deprived

areas. However, particularly for LLTI, the gradient is

steeper in 1991 than it might have been, had people

remained in the same deprivation circumstances they were

experiencing at the 1971 Census. Note that the SIRs for

1991 were significantly different from the 1971 SIRs in all

quintiles except the fourth (significant differences are those

where the 95% error bars are not overlapping). It appears

that the least deprived areas have become relatively

healthier and the more deprived areas less healthy over

the 20-year period. For mortality, the overall pattern was

similar, but the difference was only significant for quintile

5. These initial results for both LLTI and mortality suggest

that redistributive processes have occurred that result in a

strengthening in the relationship between deprivation and

health between 1971 and 1991. Those who end up in good

health seem to become more likely to reside in less

deprived areas, while those who end up in poor health

become more likely to reside in more deprived areas and

this selection process is at the heart of this investigation.

Various processes may simultaneously be operating

over time, each of which might influence changes in the

distribution of morbidity and mortality across the

geography of deprivation. First, there can be different

re-distributions through migration of people who end the

study period either healthy or unhealthy, or alive or dead.

Second, changes can occur in the distributions of persons

with attributes relevant to the calculation of the Carstairs

Index. These changes can be by migrants who change

their location and/or by non-migrants whose attributes

change. Third, there may be an age dimension to these

processes since people of different ages may have differing

migration patterns with respect to their health and/or to

area deprivation and because we are dealing with a closed

sample, it gets progressively older. Finally, it must be

recognised that some small redistributions between

censuses may be due to ward boundary changes (see

Norman, Rees, & Boyle, 2003) and slight changes in the

definitions of Carstairs-related variables (Table 2).

Changes in counts of LS members potentially affecting

illness and mortality rates in differently deprived areas

We suggested above that re-distributions of morbidity

and mortality in relation to deprivation may have

ARTICLE IN PRESS

Fig. 2. Standardised Mortality Ratios by deprivation quintile of residence in 1971 and 1991.

Fig. 1. Standardised Illness Ratios by deprivation quintile of residence in 1971 and 1991.

P. Norman et al. / Social Science & Medicine 60 (2005) 2755–2771 2761

occurred. Fig. 3a shows counts of LS members who did

not report LLTI by the quintile of deprivation in which

they were living in 1971 and in 1991. This shows that

over the 20 year period there has been a substantial

accumulation of healthy people in Carstairs quintile 1,

but in all other quintiles there has been a reduction. Fig.

3b shows the equivalent for LS members who did report

LLTI in 1991. The largest increase in numbers of ill

persons occurred in quintile 5 but there has also been an

increase in persons who reported LLTI in quintile 1.

This is somewhat surprising given the reduction in SIR

illustrated in Fig. 1. Similarly, Fig. 4a shows counts of

LS members who survived the 1991–1999 period. In

quintiles 2–5 there was a reduction in the number of

survivors but in quintile 1 there was an increase. An

accumulation of persons who died following the 1991

Census occurred in quintiles 4 and 5 and, as with LLTI,

also in quintile 1 (Fig. 4b).

So is it changes in location by migrants that

contributes most to these differences in counts by area

type or is it changes that occur when areas change their

deprivation characteristics whilst non-migrants remain

in situ? Due to the large number of combinations of

changes between area types, we focus on changes

affecting counts in electoral wards classified as the least

deprived (quintile 1) and the most deprived (quintile 5).

Table 4 shows the LS members present in 1991 in

quintiles 1 and 5 by whether they remained in that

quintile for the 20-year period, or whether they were

a migrant into a more or less deprived ward, or a

ARTICLE IN PRESS

Fig. 3. (a and b). Counts of LS members with and without LLTI by deprivation quintile 1971 and 1991.

P. Norman et al. / Social Science & Medicine 60 (2005) 2755–27712762

non-migrant living in a ward that had itself become

more or less deprived between 1971 and 1991. Since the

largest percentages of LS members in both these

categories, but particularly in quintile 1, migrated from

a differently deprived ward it is likely that the migration

process had a considerable impact on the deprivation–-

health relationship.

Figs. 5 and 6 illustrate the net effects on quintiles 1

and 5 of migrants moving between differently deprived

locations and of non-migrants whose areas have become

more or less deprived.3 In Fig. 5, the largest change in

quintile 1 is due to a net gain of migrants who did not

report LLTI in 1991; this means that more people

without LLTI moved into wards categorised as quintile

3The ‘net effect’ of migrants is the difference between the

numbers of persons moving into and out of differently deprived

wards. The ‘net effect’ of non-migrants is the difference in

counts of persons by deprivation quintile that occurs when

wards become more or less deprived but people remain in situ.

1 in 1991 than moved away. However, there was a small

net gain through migration of unhealthy persons. In

quintile 5 the largest net loss is of healthy migrants

moving away from the most deprived wards. While a

small net gain of unhealthy migrants occurred, there was

a larger gain of unhealthy persons who were non-

migrants in areas that became the most deprived (this

refers to non-migrants who lived in areas which changed

deprivation category between 1971 and 1991). A similar

situation is shown for mortality (Fig. 6) whereby a

relatively large accumulation of survivors occurred

through migration to the least deprived areas (quintile

1) with a net loss of migrant survivors from the most

deprived locations (quintile 5). For those who died in the

1991–1999 period, although the net effects were small,

more people had migrated into quintile 1 and subse-

quently died than people who were non-migrants but

whose areas improved. In quintile 5 there was a small

net loss of migrants who died but a ‘net gain’ of non-

migrants whose areas became more deprived.

ARTICLE IN PRESS

Table 4

Counts of LS members in Carstairs quintiles 1 (least deprived) and 5 (most deprived) by migrant and non-migrant sub-groups,

1971–1991 (number and row percentage)

Migrants (person changes

area)

Non-migrants (area changes

deprivation)

Existing persons (migrants

and non-migrants within the

same quintile)

Total persons

To quintile 1 from a more

deprived quintile

To quintile 1 from a more

deprived quintile

Remained in quintile 1

1971–1991

In quintile 1 1991

29,335 (65%) 6472 (14%) 9659 (21%) 45,466 (100%)

To quintile 5 from a less

deprived quintile

To quintile 5 from a less

deprived quintile

Remained in quintile 5

1971–1991

In quintile 5 1991

33,871 (42%) 13,770 (17%) 32,362 (40%) 80,003 (100%)

Source: ONS Longitudinal Study.

Fig. 4. (a and b). Counts of LS members survived/died (1991–1999) by deprivation quintile 1971 and 1991.

P. Norman et al. / Social Science & Medicine 60 (2005) 2755–2771 2763

These findings go some way towards identifying how

the differences in health along the gradient of depriva-

tion between 1971 and 1991 occurred. However, there

are no controls for age and a lack of clarity about

differences in health rates when migrants change their

area or when non-migrants live in areas that change

ARTICLE IN PRESS

Fig. 5. Net effects on least and most deprived areas by migrants and non-migrants with/without LLTI in 1991.

Fig. 6. Net effects on least and most deprived areas by migrants and non-migrants surviving/dying 1991–1999.

P. Norman et al. / Social Science & Medicine 60 (2005) 2755–27712764

their characteristics. We therefore examine LLTI and

mortality for the seven types of area described above

which had different combinations of deprivation in 1971

and 1991, presenting results for both migrants and non-

migrants. The expectations are that areas which

remained the most deprived places between 1971 and

1991 will exhibit the worst health and mortality

outcomes (the highest SIRs and SMRs) and those

locations which remained the least deprived places will

have the best outcomes. However, for areas that were

more deprived in 1991 than in 1971, health will have

become worse in aggregate while the converse will be

true for areas which became less deprived during the 20

years. These changes will occur if relatively healthy

people migrated from more to less deprived areas (and

vice versa) and/or if non-migrants received some (dis-)

benefit when their location changed its deprivation

characteristics around them.

Standardised illness ratios

Fig. 7 provides SIRs by the seven area categories. For

both migrants and non-migrants SIRs were significantly

higher for those living in areas in quintile 5 (the most

deprived quintile) in both 1971 and 1991 than those

living in areas that were in quintiles 2–4 in 1971 and

1991, and the rates in both these types of areas were

significantly higher than for those living in areas in

quintile 1 (the least deprived quintile) in both 1971 and

1991. The intervening SIRs are also in line with

expectations and a strong gradient in health is revealed.

People in areas that became less deprived between 1971

and 1991 had lower SIRs than those in areas which

became more deprived. Particularly striking though, is

that people who lived in the most deprived areas

(quintile 5) in 1971 but were in quintiles 1–4 by 1991

(their area’s deprivation status had improved) had

ARTICLE IN PRESS

Fig. 7. Standardised Illness Ratios (1991) for migrants and non-migrants in 7 types of area.

P. Norman et al. / Social Science & Medicine 60 (2005) 2755–2771 2765

significantly better health than either those who

remained in quintile 5 in both 1971 and 1991, or those

who were in quintiles 1–4 in 1971 but were in quintile 5

by 1991.

Comparing migrants and non-migrants across the area

categories we find some interesting differences. Migrants

were generally healthier than non-migrants in the less

deprived areas (although the differences were not sig-

nificant) and this will maintain the relatively advantaged

health status of these areas. Conversely, and with a much

stronger effect, those who migrated into the most deprived

areas were significantly less healthy than the non-migrants

in these areas; thereby exaggerating the disadvantaged

health status of these areas. The highest SIRs of all were

for migrants who moved within the most deprived areas

(quintile 5), while the lowest were for migrants moving

within the least deprived areas (quintile 1).

Standardised mortality ratios

The results for mortality were very similar to those for

LLTI, but the gradient across the seven deprivation

combination areas was less steep (Fig. 8). Thus,

migrants and non-migrants who lived in the most

deprived areas over the 20-year period had the highest

SMRs, while those who lived in the least deprived areas

had the lowest SMRs; these differences are significant.

The SMRs for those remaining in quintiles 2–4

throughout the period fell in the middle of the range

and were significantly different to the SMRs for areas

that were consistently the most and least deprived. The

ranking of SMRs for those areas which changed their

relative deprivation status between 1971 and 1991 were

consistent with those for LLTI. Areas that became less

deprived had lower SMRs than those areas which

became more deprived.

The migrants in the less deprived areas had lower

SMRs than non-migrants, while migrants in the most

deprived locations were less healthy than non-migrants.

However, unlike the results for LLTI, none of the

differences between migrants and non-migrants in the

seven area categories were significant. Thus, while these

differential movements of healthy and unhealthy mi-

grants will support the relative area-based health

statuses along the deprivation gradient, they will not

exaggerate it to a significant degree.

Age-specific illness rates

These SIRs and SMRs provide a summary measure of

the relative health or mortality in different areas of the

(non-) migrant subgroups. It is also possible to examine

the percentage of different age-groups reporting LLTI

(ASIRs) or dying (ASMRs) and this provides an age-

specific insight into which migrant and non-migrant age-

groups are differentially affecting health rates.

Figs. 9a–d provides the ASIRs by 20-year cohorts (age

as in 1991). Note that since rates vary considerably with

age, the y-axes are different for each cohort. Considering

migrants and non-migrants separately for the 20–39 cohort

(Fig. 9a), ASIRs were very low but still retained the

expected gradient with deprivation. Although the differ-

ences were not significant, migrants were healthier than

non-migrants across all area types, consistent with the

findings of previous studies reviewed above. The healthiest

group were those migrating from more deprived areas in

1971 (quintiles 2–4) into the least deprived areas in 1991

(quintile 1). A similar pattern is revealed for the cohort

aged 40–59 in 1991 (Fig. 9b), except that the ASIRs were

higher and the gradient across the deprivation categories

was steeper. Migrants in all areas were healthier than non-

migrants with the differences significant between areas that

ARTICLE IN PRESS

Fig. 8. Standardised Mortality Ratios (1991–1999) for migrants and non-migrants in 7 types of area.

Fig. 9. (a–d). Age-Specific Illness Rates for LLTI (1991) by 20 year age-group for migrants and non-migrants in 7 types of area.

P. Norman et al. / Social Science & Medicine 60 (2005) 2755–27712766

change deprivation categories. People migrating into the

least deprived areas were healthier than those migrating

away; people migrating away from the most deprived areas

were healthier than those persons migrating into wards

classified as quintile 5 in 1991. Areas gaining migrants aged

20–39 or 40–59 will be receiving a subset of people

healthier than the non-migrants they join, but the

differences are not always significant. Migrants were

generally healthier than non-migrants in the less deprived

areas, which is consistent with the results in Fig. 9, but

migrants were also healthier than non-migrants in more

deprived areas, which is not consistent with Fig. 7.

ARTICLE IN PRESS

Fig. 10. (a–d). Age-Specific Mortality Rates for those dying (1991–1999) by 20 year age-group for migrants and non-migrants in 7

types of area.

P. Norman et al. / Social Science & Medicine 60 (2005) 2755–2771 2767

The ASIRs for the 60–79 cohort (Fig. 9c) were

considerably higher than for ages 40–59 but with a similar

gradient with deprivation for both migrants and non-

migrants. Here though, migrants were generally less

healthy than non-migrants (again, these findings for older

people are consistent with findings from previous studies),

and the differences were significant for those who remained

in, or moved into, the most deprived quintile. This is

consistent with the results in Fig. 7, demonstrating that it is

this age group which accounts for most of the difference

between migrants and non-migrants in the most deprived

areas. It is not surprising that the 60–79 cohort have a

particularly high impact on LLTI rates as 51% of the

LLTI events were reported by these persons. The most

elderly people aged 80+ (Fig. 5d) had the highest rates of

illness but with no significant differences between the

deprivation categories or between migrants and non-

migrants. There was, however, a slight tendency for

migrants to be less healthy than non-migrants in deprived

areas but more healthy in the less deprived locations.

Age-specific mortality rates

Figs. 10a–d provides the ASMRs by 20-year cohorts

(age as in 1991). The y-axes are consistent for each

cohort with the illness rates in Fig. 9. The ASMRs for

the 20–39 cohort (Fig. 10a) were extremely low with no

apparent differences by area type or migrant status.

Although the ASMRs for persons aged 40–59 were still

relatively low (Fig. 10b), a slight gradient with depriva-

tion exists and migrants were significantly healthier than

non-migrants in most area types. All persons whose

areas became less deprived were significantly healthier

than those persons in areas which became less deprived.

These differences were greater for migrants.

ASMRs for the 60–79 cohort (Fig. 10c) were

considerably higher than for those aged 40–59 and, as

with LLTI, mortality events were most numerous for

this cohort comprising 59% of deaths in this LS extract.

Whilst the differences between the least, middle and

most deprived areas were significantly different, and the

expected gradient occurred across all categories, there

were overlaps in the confidence intervals for the areas

changing deprivation categories. Moreover, there were

no significant differences in ASMRs between migrants

and non-migrants in the same area types. As would be

expected, the ASMRs for the 80+cohort (Fig. 10d) were

very high. Similar to the ASIRs, though, there were no

significant differences across the deprivation categories

and between migrants and non-migrants.

ARTICLE IN PRESSP. Norman et al. / Social Science & Medicine 60 (2005) 2755–27712768

Conclusion

Strong relationships between deprivation and health

have regularly been demonstrated but few epidemiological

or geographical studies account for the effects of selective

migration; this is particularly the case in cross-sectional

analyses. If healthier individuals are more likely to migrate

away from deprived areas and less healthy individuals

more likely to migrate into deprived areas then the

aggregate relationship between illness and deprivation

and the health inequalities between small geographical

areas will be maintained or even exaggerated by the effects

of migration. To investigate this, a closed sample of the

population present between the 1971 and 1991 UK

Censuses has been extracted from the LS. For each LS

member variables have been obtained that indicate the

person’s age and whether they reported limiting long-term

illness in the 1991 Census, died in the period 1991–1999,

and whether they migrated between census time points

(1971 and 1991). The Carstairs score of the small area in

which the individuals lived at each census was used to

indicate the relative deprivation of these places.

Differences in SIRs and SMRs for 1971 and 1991

across the Carstairs deprivation quintiles suggest that

over the 20 year period in England and Wales the

inequality increased between the least and most

deprived, compared with the health–deprivation rela-

tionship that would have existed if peoples’ locations

and deprivation patterns had stayed geographically

constant. Net counts of LS members changing between

differently deprived areas reveal a large accumulation of

healthy and surviving people in least deprived areas with

a net loss from the most deprived areas. Migration,

rather than changes in the deprivation of the area that

non-migrants live in, accounts for the large majority of

these changes. It is reasonable to suggest that migration

may also be shaping other sociodemographic geogra-

phies since, for example, migrants may be moving away

from deprived areas because they have found work

elsewhere. This may result in raised unemployment rates

at their origins and reduced rates at their destinations.

The SIRs and SMRs for non-migrants in areas which

became either more or less deprived in relation to other

areas support the expectations that reductions in

deprivation are associated with improvements in health

and vice versa (see also Boyle et al., 2004). Moreover, as

a group, migrants are generally more healthy than non-

migrants when either young and/or moving from more

to less deprived locations and when compared to

migrants who are older or moving from less to more

deprived locations. Within deprived areas migrants are

less healthy than non-migrants and migrants who move

from less to more deprived areas have higher LLTI and

mortality rates than non-migrants living in areas which

became more deprived during the period. Conversely,

migrants who move from more to less deprived areas

have lower LLTI and mortality rates than non-migrants

living in areas which became less deprived between 1971

and 1991. In comparison with their respective national

rates, since the results were stronger for LLTI than

mortality, it appears that changes in deprivation (either

because a person moves to a different type of place, or

because the place a non-migrant lives in changes around

them) are more related to morbidity than mortality.

Whilst there is a strong relationship between LLTI and

mortality, LLTI is self-rated health that tends to relate

to physical conditions and may be reported differently

by people in differently deprived areas. All-cause

mortality is readily defined but being a rarer event than

LLTI may not produce such clear results.

In terms of the health selectivity of the migration

process, our findings are broadly in line with various

health selective migration scenarios identified by Ben-

tham (1988). The dominant flow is by relatively healthy

migrants, particularly those aged 20–59 in 1991, moving

away from the more deprived areas and towards the less

deprived areas. The effect of this is to raise ill-health rates

in the origin wards and lower them in the destinations.

On the other hand, migration from less to more deprived

locations was selective of people in poor health. This

reinforces the expected health–deprivation relationship

and is consistent with findings for Scotland whereby

migrants into and between the most deprived wards have

the highest probabilities of illness (Boyle et al., 2002). In

contrast, a much smaller but important group of

unhealthy people have moved away from more deprived

into less deprived areas. Although the 1991 Census does

not give reasons for changes of address, it can be assumed

these persons are moving to improve their circumstances,

perhaps to be cared for by family. The net gain of

migrants in the least deprived wards who reported LLTI

in 1991, or died in the 1991–1999 period, comprise

counter-currents of people who marginally reduce

observed morbidity and mortality rates in migrant origins

and raise rates in their destinations. Less healthy people

moving away from more deprived areas have an

ameliorating effect so that associations between depriva-

tion and health are reduced a little in strength.

The findings of this research have important implica-

tions for aggregate-level studies of associations between

mortality or morbidity and deprivation. Areas with poor

socio-economic conditions are likely to be selectively

losing healthy migrants who will tend to be attracted to

economically favourable, less deprived locations. Popu-

lation health status in the area of origin will worsen,

whereas in the area of destination it will improve. A

positive association between good health and favourable

socio-economic conditions can be exaggerated by

selective migration of this nature and this is reinforced

by migration from less to more deprived locations by

people in poor health. Conversely, when sick people

move away from the more deprived areas, perhaps

ARTICLE IN PRESSP. Norman et al. / Social Science & Medicine 60 (2005) 2755–2771 2769

because of their health, the health of the population

remaining at their origin would apparently improve

whilst at their destination it would reduce. As a result,

the association between the geographical distribution of

health and deprivation would weaken. Since this last

scenario only involves relatively small numbers of

persons, we can conclude that on balance migration

does exaggerate the relationship between deprivation

and health over and above the health (dis-) benefits that

exist for non-migrants who live in areas where the

relative deprivation score changes over the 20 year

period (for more details on this aspect using a sub-

sample of the same LS extract, see Boyle et al., 2004).

There are limitations to this study. First, because the

sample is a closed population it is selective of survivors

and mortality events are under-recorded in this extract.

Those who died between 1971 and 1991 were excluded

and it is probable that proportionally more deaths

occurred in more deprived areas than less deprived

areas. Thus, the survivors in these areas may be a

selected group of healthier people. Despite this, we still

find a strong gradient of health and mortality outcomes

across the deprivation spectrum but because we may

have under-counted deaths in deprived areas the

relationship for mortality will be somewhat weakened.

Second, the health and mortality outcomes were

measured for the end of the period and, in the case of

LLTI, for example, we do not know whether those who

moved were suffering from LLTI prior to the move, or

whether they became ill following the move. Given that

the definition of this self-reported variable emphasises

‘long-term’ illness, we might speculate that there is a

reasonable chance that the illness existed at the time of

migration, but because the migration definition was

based on movement any time between 1971 and 1991,

this may not be the case for many migrants who moved

in the early 1970s. Even so, our results suggest that

either ill migrants, or migrants who are more vulnerable

to illness, were more likely to move into deprived areas,

as they were significantly more likely to report illness in

1991 than non-migrants.

Acknowledgements

The Census data are Crown Copyright. The Long-

itudinal Study (LS) data extract has been provided by

permission of the Office for National Statistics (ONS)

through the LS academic user support group at the

Centre for Longitudinal Study (CLS) at the Institute of

Education, University of London with assistance from

Sarah Jones and Kevin Lynch. Angela Donkin (ONS)

and Rosemary Creeser (CLS) provided advice in the

early stages of the project. Latterly, LS academic user

support has been by ‘CeLSIUS’ at the London School of

Hygiene and Tropical medicine.

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