epidemiology psychiatric morbidity: multilevel approach ... · regionalvariations inpsychiatric...

290

Psychiatric morbidity: a multilevel approach to

regional variations in the UK

Craig Duncan, Kelvyn Jones, Graham Moon

Journal of Epidemiology and Community Health 1995;49:290-295

AbstractStudy objective - To establish whetherregional variations in psychiatric mor-bidity in Britain constitute a distinctivegeography of mental health arising fromfactors that are context-specific at arealevel or whether these variations are anartifact generated by sampling fluc-tuations and differing population com-positions in areas.Design - Multilevel modelling techniqueswere applied to data from the 1984-85health and lifestyle survey. The outcomewas the prevalence of psychiatric mor-bidity as recorded by the application ofthe general health questionnaire in thissurvey.Setting -The analysis was undertaken sim-ultaneously at the individual level, elect-oral ward level, and regional level forEngland, Wales, and Scotland.Participants - A total of 6572 adults wereselected from the electoral register.Main results - Regional variations weredetected in crude aggregate general healthquestionnaire scores but these were foundto be the result of sampling fluctuationsand varying regional population com-positions rather than higher level con-textual effects. There was certainly noevidence ofa clear north-south distinctionin psychiatric morbidity as was suggestedby earlier work. In addition, the localneighbourhood did not seem to have anyimportance beyond the type ofpeople wholived there. A number of individual char-acteristics was shown to be associatedwith mental wellbeing but a large degreeof individual variation remained un-explained.Conclusions - In terms of low level psy-chiatric disturbance it seems that thecharacteristics ofindividuals have greaterimportance than the characteristics ofareas, although the latter may still operateas important mediating factors. Multilevelmodelling represents a robust statisticalmethod of exang area variations inhealth outcomes and further work needs tobe conducted, particularly onmore seriouspsychiatric conditions.

(y Epidemiol Community Health 1995;49:290-295)

While research into the associations betweenarea ofresidence and health has a long traditionin Britain much of the work that has been

conducted has been problematic in both con-ceptual and technical terms. As is now re-cognised, many studies'3 used area level dataonly, because information about individualswas not available.4 Furthermore, since suchstudies were primarily concerned with eval-uating the association between material dep-rivation and health, any significant areavariations found were often "explained" interms of the social class composition of theareas5 - a tendency likely to have been en-couraged by considerable fear among epi-demiologists of committing the ecologicalfallacy.6 Some recent area studies, however,have become more sophisticated and suggestthat there may be area effects independent ofpopulation composition.7-9 These show thatinterest has moved on to assessing the relativeimportance of the characteristics of areas com-pared to the characteristics of the people foundwithin those areas, and they suggest that thereis an area, or contextual, effect over and abovean effect that arises from compositional factors.Consequently, it seems that not only do areasdiffer but that area itself makes a difference.Health outcomes depend on both individualand area characteristics.Most of the work on the effects of area on

health outcomes has been conducted in relationto physical health. When mental health hasbeen considered, attention has focussed almostexclusively at the subregional level'0: most workexamines intra-urban variations" and thedifferences between urban and rural areas. 2 Aspsychiatric morbidity is widespread within thepopulation at large - a recent Department ofHealth report'3 has suggested that it is as com-mon as heart disease and three times as com-mon as lung cancer - the tendency for mentalhealth research to have ignored larger spatialscales, in particular a regional dimension, isboth surprising and disconcerting. Regionallocation could be expected to provide one im-portant context, shaping and structuring someof the opportunities and constraints of dailyliving which have importance for individualpsychological wellbeing.Two substantive recent studies have, how-

ever, considered the regional dimension tomental health. Both have used data from the1984-85 health and lifestyle survey.'4 Blaxterhas considered regional variations in "psycho-social health" using a composite index formedfrom the responses to two questions in thehealth and lifestyle survey.7 Controlling for age,sex, and social class this work showed higherlevels of psychiatric morbidity in the north ofthe country, except in women of non-manualclass where the west and the Midlands were

Department ofGeography,University ofPortsmouth,Buckingham Building,Lion Terrace,Portsmouth PO1 3HEC DuncanK Jones

School of Social andHistorical Studies,University ofPortsmouth,PortsmouthG Moon

Correspondence to:Mr C Duncan.Accepted for publicationDecember 1994

on January 17, 2021 by guest. Protected by copyright.

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Regional variations in psychiatric morbidity

highest. Blaxter also considered variations atsmaller geographical scales and suggested thatthe local neighbourhood is more significantthan the larger region in determining psy-chosocial health. Another recent study by Lewisand Booth,"5 also based on information fromthe health and lifestyle survey, used the re-sponses given when the general health ques-tionnaire (GHQ)'6 was administered as part ofthe survey. Again, a greater concentration ofpsychiatric morbidity was identified in thenorth of the country than the south but it wassuggested that this was probably because ofcomposition rather than area factors. The studyconcluded tentatively that the nature of theregional variations differed according to thesocial class position and the immediate, ex-

ternal living environment of individuals. Inparticular, the north-south difference did notseem to exist for those in social classes I andII and was less marked for those living withinurban areas without access to open spaces.While they provide a useful and conceptually

sophisticated introduction to a neglected area

of research both of the above studies havetechnical problems. In Blaxter's work, stand-ardised ratios were used, and to avoid smallnumber problems coarse aggregate groupingswere applied. Social class was reduced to twoclasses and the regional geography of GreatBritain was collapsed into just three areas. Noother compositional variables were includedother than age and gender. A more soph-isticated logistic modelling strategy was usedin Lewis and Booth's analysis but again a verycoarse regionalisation was adopted (the countrywas divided into five regions) and, most im-portantly, a traditional, single level, aggregatemodelling approach was applied. Such an ap-proach is inherently problematic for the pur-

poses of this present paper given that its centralconcern is with assessing the relative importanceof effects at different levels: the individual, andsimultaneously, the area level. As is becomingincreasingly recognised,'7 the study of the sizeand nature ofarea variations in health outcomes

Table 1 Data structure applied in the analysis

Level No Meaning1 6572 Individuals2 396 Electoral wards3 22 Regions

Region Electoral Individualswards

Strathclyde 18 283East Central Scotland 9 149Rural Scotland 11 217Rural North 15 258Industrial North East 20 371Merseyside 7 116Greater Manchester 20 328Rest of the North West 18 284West Yorkshire 12 200South Yorkshire 14 224Rural Wales 10 164Industrial South Wales 10 185West Midlands Conurbation 17 257Rest of West Midlands 19 314East Midlands 28 519East Anglia 14 270Devon & Comwall 6 115Wessex 26 415Inner London 16 185Outer London 32 454Outer Metropolitan area 43 738Outer South East 31 526

is better approached using newly developedmultilevel modellingtechniques that are capableof working at more than one level sim-ultaneously.'8 While such techniques have beenapplied in the study ofarea variations in physicalhealth and health-related behaviours,'9-2'they have not been used in studies of mentalhealth outcomes. The present study, therefore,applies a multilevel modelling approach to theGHQ data from the health and lifestyle surveyto provide a more sensitive analysis of areavariations in mental health in Britain. Whilethe main focus is on regional variations, con-sideration is also given to the importance ofthe local neighbourhood.

MethodsThe 1984-85 health and lifestyle survey wasbased on a multistage sampling design in whichindividuals, within electoral wards, withinelectoral constituencies, and within regionswere randomly selected. In a multilevel analysisthis hierarchical data structure can be explicitlyrecognised and maintained. Thus, it is possibleto consider individuals, who can be describedin terms of a wide range of personal socio-economic characteristics, within a highly re-fined quantitative representation of thecontextual settings in which they reside. As theanalysis works at both individual and area levelssimultaneously, the ecological fallacy is avoidedand more accurate distinctions can be madebetween variations caused by contextual effectsand those that are an artefact generated fromvariations in the characteristics of people con-tained within specific areas. In addition, work-ing at more than one level enables relationshipsto vary according to context. Recognising themulti-layered nature of social life also brings anumber of technical advantages. Thus, multi-level models are not affected by the seriousmis-estimation problems associated withautocorrelation in traditional, single level re-gression114 and ameliorate problems arisingfrom small numbers and sampling fluc-

25tuations.The fundamentals of multilevel modelling as

it applies to health research have been coveredcomprehensively elsewhere.26 27 The presentstudy focusses on a three level analysis of thehealth and lifestyle survey data with individualsat level 1 nested within electoral wards at level2, nested in regions at level 3. Unlike previousstudies, therefore, which considered one geo-graphical scale at a time, this analysis exploresthe geography of mental health at two differentarea levels simultaneously, one local and oneregional, thus helping ensure that any areaeffects are apportioned to the appropriate level.The present study is also able to use a moresophisticated regional classification than thatemployed by Blaxter and Lewis and Booth -The Economist classification which defines 22regions and essentially represents a subdivisionof the standard regions into metropolitan andnon-metropolitan areas. The actual data struc-ture used is shown in table 1.The analysis models each individual's overall

GHQ score (0-30) as a continuous variable.

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Duncan, _'ones, Moon

This approach is adopted in an initial report

detailing the survey's findings'4 and is justifiedby research which has shown that if the GHQis simply dichotomised on the basis of thosescoring 5 or more, many of those exceedingthis threshold turn out to be "non-cases".28Three types ofmultilevel model are applied andall were calibrated using the software packageML3.29 The first represents a simple, "null",three level, random-intercepts model in whichthe variation in the response variable is "ex-plained" by a single fixed intercept term, thenational average GHQ score, and three randomterms which apportion the degree ofremainingvariation in the intercept to each level of themodel.A second model is formed by including a

number of fixed explanatory variables whichreflect a range ofindividual level characteristics.In this paper nine, level one explanatory vari-ables were used: age, sex, social class (definedaccording to the Registrar General's 1980 clas-sification), marital status, employment status,limiting long term illness, immediate externalliving environment, housing tenure, and eth-nicity. This list corresponds to and extends thatused by Lewis and Booth. Apart from age,these variables were specified as a set of 17dummy, indicator variables, coded so that theintercept term in the model would representthe stereotypical respondent to the GHQ inthe health and lifestyle survey: an employedwoman in social group III-manual who is mar-ried, living in an owned house in an urban area

with adjacent open space or garden, has no

limiting long term illness, and is not from an

ethnic minority group. Age was treated as a

continuous variable and was centred aroundthe sample mean value of 45 years, and so thestereotypical respondent is also of average age.In this model, the two higher level randomterms now assess the ward and regional differ-ences after "controlling" for variations in thecomposition ofareas on the basis ofthe selectedindividual characteristics. Ifthese terms are notsignificant, then area variations are simply an

Table 2 Aggregate average general health questionnaire(GHQ) scores based on the 1984-85 health and lifestylesurvey

Region Average GHQ score(rank) *

Strathclyde 4-36 (18)East Central Scotland 3-55 (5)Rural Scotland 3-46 (4)Rural North 4 04 (12)Industrial North East 4-51 (19)Merseyside 5-25 (22)Greater Manchester 4-95 (21)Rest of the North West 3-91 (10)West Yorkshire 3-76 (7)South Yorkshire 4-33 (16)Rural Wales 3-15 (1)Industrial South Wales 3-92 (11)West Midlands Conurbation 4-34 (17)Rest of West Midlands 4 30 (15)East Midlands 3-43 (3)East Anglia 3-80 (9)Devon & Cornwall 3-42 (2)

Wessex 4-08 (13)Inner London 4-12 (14)Outer London 4-74 (20)Outer Metropolitan area 3-66 (6)Outer South East 3-79 (8)Total sample 4 03

Rank position, 1 =lowest GHQ score; 22 =highest GHQ score.

artefact of place composition and there is not

a geography ofmental health, in terms of singleoverall ward or regional differences affectingall types of people, which remains after com-

positional control and results from higher levelcontextual effects.Area variations in mental health could, how-

ever, be more complex. This is the situationproposed by Lewis and Booth when they sug-

gested that there was no regional variability inGHQ scores for people in social classes I andII and that it was reduced for those living inurban areas with no access to open spaces.

Thus, they suggested that there are differentplace effects for different types ofpeople. Theirconclusions in this respect should be treatedwith caution, however, as their models assess

this variation without controlling for the com-

positional effects of any other individual levelcharacteristics. This problem can be avoidedby using a third type of multilevel model, thecomplex variation model. In these models, itis possible to allow the parameter estimatesrelating to any particular explanatory variableto vary from area to area while at the same

time controlling for compositional effects.

ResultsThe crude average GHQ scores as recorded inthe health and lifestyle survey according to The

Economist regionalisation scheme are shown intable 2. The regional averages range from a

high of 5-25 in Merseyside to a low of 3-15 inrural Wales. As these are simple aggregate rates,the variation displayed in table 2 may be an

artefact of either population composition or ofsampling fluctuations.

Table 3 Multi-level estimates

(A) (B)Null model Random-intercepts

model

Fixed effectsLevel 1 (individual)Intercept 4 05 3-63Age -0-01 (-2 75)Sex - male -0 90 (-7 09)Social class:

I&II -0-31 (-1-93)IV&V 0-11 (0-61)III non-manual -0-16 (-0 79)Other 0 40 (0 82)

Marital status:Single -0 04 (-0- 19)Widowed 0-96 (3 67)Divorced/separated 1-50 (5 53)

Employment status -unemployed 1-59 (5-24)Illness - limiting longterm 2-92 (17-15)Living environment:Urban without space 0-25 (1-67)Country -0-48 (-2 80)Other -0-23 (-0 42)

Housing status:Local authority renter 0-51 (3 24)Other renter 0-32 (1-33)

Ethnicity:Non-white 1 21 (2 92)Unknown 0-81 (0 93)

Random effects varianceLevel 3 (region)

Intercept 0-12 (1-70) 0-04 (1-03)Level 2 (ward)

Intercept 0-27 (1-97) 0 07 (0 60)Level 1 (individual)

Intercept 26-82 25

Note: estimates represent GHQ score points; figures in par-entheses represent ratio of estimates to standard error.

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Table 4 Multi-level estimates of complex variationmodels

Model (A) Model (B)

Fixed effectsLevel 1 (individual):

Intercept 3-63 3-63Age -0-01 (-2-83) -0-01 (-2-71)Sex- male -0-91 (-7-10) -0-90 (-7-10)

Social class:I&II -0-30 (-1-88) -0-30 (-1-89)IV&V 0-04 (0-21) 0-10 (0-59)m non-manual -0-16 (-0-77) -0-16 (-0-79)Other 0-40 (0-82) 0-40 (0-82)

Marital status:Single -0-03 (-0-17) -0-04 (-0-22)Widowed 0-96 (3-67) 0-93 (3-58)Divorced/separated 1-52 (5-59) 1-51 (5-55)

Employment status -unemployed 1-60 (5-26) 1-60 (5-26)Illness - limiting longterm 2-93 (17-17) 2-92 (17-13)Living environment:Urban without space 0-24 (1-64) 0-24 (1-54)Country -0-48 (-2-80) -0-49 (-2-78)Other -0-23 (-0-42) -0-19 (-0-34)

Housing status:Local authorityrenter 0-53 (3-33) 0-53 (3-32)Other renter 0-32 (1-32) 0-34 (1-40)

Ethnicity:Non-white 1-20 (2-91) 1-19 (2-88)Unknown 0-78 (0-90) 0-81 (0-93)

Random effects varianceLevel 3 (region):

Intercept 0-04 (0-83) 0-05 (1-12)IV&V 0-29 (1-20)

Level 2 (ward):Intercept 0-06 (0-50) 0-21 (1-27)Urban without space 1-17 (1-97)

Level 1 (individual):Intercept 24-96 24-81

Random effects covarianceLevel 3 (region):

Intercept/IV&V -0-01 (-0-19)Level 2 (ward):

Intercept/Urbanwithout space -0-51 (-1-78)

Estimates represent GHQ score points; figures in parenthesesrepresent ratio of estimates to standard error.As these models contain more than one random term, randomeffects covariance terms are also estimated.

To examine this possibility a simple threelevel "null" model was fitted and the resultsare summarised in table 3, column A. Theintercept gives the nationwide average GHQscore and is estimated at 4 05. This value doesnot remain constant across either regions orwards and the random effects variance at thebottom of table 3 (column A) show the levelto which the remaining variability can be ap-portioned. These values show that most vari-ation (98-6%) occurs at level 1 (betweenindividuals), and very small amounts occurat the higher levels - 1-01% at the ward leveland 0-44% at the regional level (these valuesare obtained by dividing the variation at eachlevel by the total variation at all three levels).These higher level terms can be tested forsignificance by calculating the ratio ofestimatesto their standard error. If the ratio is more than+ 2, the estimate is judged to be significantlydifferent from zero at the 0-05 level. As shownin table 3 (column A) neither of the higherlevel random terms meets this criteria, althoughthe ward level variation approaches sig-nificance. Model A includes no individual levelexplanatory variables. Consequently, it doesnot provide evidence that the crude averagesin table 2 are an artefact of varying regionalcompositions; instead it suggests that the vari-ations result from sampling fluctuations.

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The extent of any compositional artefacteffect can be assessed by including individuallevel explanatory variables (table 3, columnB). The intercept term now represents thestereotypical respondent to the GHQ in thehealth and lifestyle survey. The estimates showthat GHQ scores are significantly higher if therespondent is widowed, divorced, or separated;unemployed; incapacitated by long term illness;renting local authority housing; or from anethnic minority. In contrast, GHQ scores aresignificantly lower if the respondent is older,male, or living in the country. Interestingly,there are no large differences in score on thebasis of social class, although there is somesuggestion that social groups I and II haveslightly lower scores.The inclusion of the compositional ex-

planatory terms reduces the variance of therandom intercepts at both of the two higherlevels and this is especially noticeable at theward level. The variance that remains at theselevels quantitatively measures contextualeffects on GHQ scores assuming single overallarea differentials and controlling for individualcompositional factors. As can be seen, neitherward nor regional contextual effects are nearsignificance and the variance that can be at-tributed to these two levels has been reducedto 026% and 0-15% respectively. The regionalgeography of GHQ scores before and aftercontrolling for individual compositional factorsis shown in the figure, which plots the regionaldifferences compared to the national averageobtained from each of these two initial models.While there is no evidence of single overall

area differences, it may be that the scores ofparticular types of individuals vary across re-gions and wards. The suggestions of Lewis andBooth relating to social class and external livingenvironment to this end were examined. Interms of social class non-zero, but non-stat-istically significant, variation was found at theregional level for the base category (III manual)and social groups IV and V (table 4, columnA). As can be seen none of the new randomterms reach significance when the estimates arecompared to their standard errors. It shouldbe noted, however, that this form ofsignificancetesting is less reliable for random parametersthan fixed parameters. A more reliable testingprocedure is to calculate a likelihood ratio stat-istic before and after the extra random termshave been included as the difference of thelikelihoods follows a x2 distribution with de-grees of freedom equal to the number of newparameters. This test confirmed the non-sig-nificance of the new terms (difference in like-lihood ratio=3-3, df=2, p=0 19). It seems,therefore, that there is no significant regionalvariation in GHQ scores for any of the socialclass categories used here. At the ward levelthe only variation detected was a very smalland non-significant amount for social groupsIV and V (results not presented).

In terms of the immediate living en-vironment, no significant differing variabilitywas found at the regional level for any of thecategories (results not presented). However,there was some suggestion of differing vari-


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Low score High score shows that its residual goes from positive toStrathclyde - negative when the explanatory variables are

E Cent Scotland - included showing that this urban area actuallyRural Scotland has a lower GHQ score than its population

Industral NEo composition would suggest.Merseyside - The most striking impression from the figure,

Gtr Manchester - ,------however, is that all the regional effects areRest of North West - minimal both before and after controlling for

West Yorks - composition. Before controlling for com-

Rural Wales-

- position, the effects are all within the range ofIndustrial S Wales ±+05 points of the national average GHQ

West Midlands Con - score; afterwards they are all within + 0-25Rest of West Mids -

fi , , points of the national average GHQ score. ItEast Midlands-East Anglia - M del A seems, therefore, that in terms of low level

Devon & Cornwall - Model B psychiatric disturbance it is the characteristicsWessex - of individuals and not the characteristics of

Inner London - areas that are important. This study has shownOuter London - that mental wellbeing is associated with an

Outer South EastnI I_ individual's position in terms of gender, marital-01.6 -0.4 -0.2 0 0.2 0.4 0.6 status, employment status, physical health, im-

Residual mediate living environment, and ethnicity. Itshould also be noted, however, that a large

Regional differences in the general health questionnaire scores. degree of individual variation remains "un-explained", as the variation between individualsis reduced by a small amount only when the

ability at ward level for those living in urban explanatory variables are included (see table 3,areas without an adjacent open space (table 4, columns A & B). Interestingly, the analysis alsocolumn B) (difference in likelihood =5-10, df= showed no large social class differences, and2, p = 0 08); it seems that being in a particular therefore supports the recent call for the healthward may have some differential effect for inequalities debate to be broadened by con-people with this type of immediate, external sidering a range of social locations and notliving environment. just those deriving from economic activity.30 In

terms of psychiatric disturbance, measures ofsocial isolation may be especially important

Discussion and their omission in this analysis may accountThis study has shown only small variations in for the "unexplained" individual variation.the prevalence of psychiatric morbidity at both While this study has suggested the primacyelectoral ward and regional levels. These are of individual characteristics in underpinningthe result of sampling fluctuations and varying psychiatric morbidity, it should not be con-regional population compositions rather than cluded that geographical locales have nohigher level contextual effects. Certainly, there importance for mental health. As a recent paperis no evidence for a clear north-south dis- suggests,4 studies which attempt to classify areatinction in psychiatric wellbeing, as was sug- variations into compositional and contextualgested by Blaxter and Lewis and Booth, and effects carry an implicit assumption that thethe study also finds no evidence for Lewis type of area a person lives in is independent ofand Booth's suggestion that there are regional a person's social class position. This as-differences in the effects of individual social sumption is unrealistic, and while the char-class and living environment. Although this acteristics of areas may not have a directstudy uses a different response variable, Lewis independent role they may still operate as im-and Booth themselves recognise there is no portant mediating factors between social classreason why this in itselfwould produce different and health. Consequently, the findings pre-results. It is likely that these new results arise sented here should not be interpreted as sug-from using a technically more robust statistical gesting that public health interventions targetedmethod together with a more sophisticated at mental health issues should exclude strat-regionalisation. egies that focus on improving the social andThe local neighbourhood does not seem to physical environment. Furthermore, although

have any special importance beyond the type this study is based on selfreported assessments,of people that reside there, and this study and individual level associations should not bedoes not therefore support Blaxter's conclusion distorted by differences in diagnostic pro-about the relative importance of area effects at cedures, they will be susceptible to variationsdifferent spatial scales. The study does, how- in the willingness to report emotional dif-ever, produce some support, in terms of a ficulties and this is likely to be particularlyregional effect, for the hypothesis that urban important in relation to the gender differenceliving impairs mental health.'2 The most highly identified here.3'urbanised regions of Strathclyde, the industrial Psychiatric morbidity represents a majornorth east, Merseyside and Greater Man- challenge for public health and does, un-

chester have higher GHQ scores, while more doubtedly, warrant as much attention as phys-rural regions (rural Scotland, rural Wales, and ical illness. As the information base isDevon and Cornwall) have low scores. Inner expanding, and robust statistical multilevelLondon is an interesting exception; the figure methods are now available, further work needs

294 Duncan, _76nes, Moon


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295Regional variations in psychiatric morbidity

to be conducted on area variations in mentalhealth, especially at the regional level and par-ticularly with regard to more serious psychiatricconditions.

We thank the ESRC Data Archive at the University of Essexfor providing the health and lifestyle survey data.32

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