The exposure fallacy: migration, mobility and ecological

analysis of health status in Ireland

Frank Houghton National Institute of Regional and Spatial Analysis (NIRSA), NUI, Maynooth & the

Department of Public Health, MWHB

Kevin KelleherDepartment of Public Health, MWHB

ABSTRACT

The problem of differential rates of exposure within areas resulting from spa-tially inconsistent rates of migration and mobility is largely ignored in ecologi-cal health analysis. This error may be termed the ‘exposure fallacy’. This paperquantifies population mobility and migration in Ireland using a variety of cen-sus measures. The results indicate that there is not only substantial residentialmobility in Ireland, but that the nature and extent of this mobility differs signif-icantly across areas. For example in 1996 more 250,000 people were not livingin the same residence they had occupied twelve months previously. The viabil-ity of ecological analysis is therefore questioned. As anticipated the mostmobile groups were young adults aged 20-35 years, and most residential moveswere of a local nature. The error associated with the exposure fallacy may bepartially reduced if analysis is conducted at county level and if young adults areexcluded. The development of an area based mobility index for Ireland is alsoproposed for use in weighted least squares regression analysis. However signif-icant problems remain despite these precautions. The adoption of a Finnish stylecentralised population and housing register is proposed, as is the introduction ofa unique identifier for all individuals within the State.

Key index words: Exposure, ecological analysis, mobility, migration.

Introduction

Health/Medical Geographers and their detractors are familiar with the issue of the‘ecological fallacy’ wherein assumptions are made on the basis of aggregate data about therelationship between two or more variables without confirmation of an individual level link(Robinson, 1950; Selvin, 1958). Robinson (1950) demonstrated this using historical (1930)data from the USA including individual level and divisional and State level data relating towhether a person was foreign or native born and whether they were literate or illiterate. Usingindividual level data Robinson noted a positive correlation of .118, which confirmed hisexpectations of higher levels of illiteracy in the foreign-born population. However, using anecological approach to repeat the experiment at divisional and State level, Robinson recordedcorrelations of -.526 and -.619 respectively. Another routinely cited example of thisphenomenon is Durkheim’s classic work on suicide in Prussia (English, 1996; Poppel and Day,1996). Durkheim (1897) noted that rates of suicides in an area were positively associated withthe proportion of the population that was Protestant. Durkheim developed his theory on suicidein part on the assumption that the suicides noted were largely those of Protestants. Howevergiven that the aggregate data from Prussia did not identify the religious persuasion of thoseindividuals committing suicide, it is possible to suggest that the high suicide rates noted in thepredominantly Protestant areas were in fact those of the minority Catholic population.

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48 Houghton and Kelleher

A related concern for ecological analysis is that of evaluating exposure, and may betermed the ‘exposure fallacy’. For example in evaluating the relationship between specificallyarea level variables such as income inequality or social capital and health, although thepopulation of a particular area may be known, it is often unclear how long this population hasactually been resident there. This is a key issue as English (1996:6) notes: ‘the validity of theecological approach depends on how well the surrogate indicator of exposure (place ofresidence) for an individual who develops the disease of interest measures the actualexposure’. The erroneous assumption in most ecological analysis is that there is a uniformlevel of exposure among resident populations, or that this phenomenon occurs at a low andconsistent rate and can be discounted. The crux of this problem is that population movementsoccur in a spatially inconsistent manner and therefore this factor has the potential seriously toconfound results. There are two elements to this spatial inconsistency. Firstly there is thenature of the people involved in such migration patterns. For example some inner-city urbanareas may experience a gentrification over time. Other areas, such as local authority estatesmay experience a process of residualisation as working families relocate to more affluentareas. It should be noted that in a case such as that of low demand public sector housingestates, the age and social characteristics of out-migrants may be broadly similar to that of in-migrants. In these circumstances the effects of migration are likely to be lessened. Howeverin scenarios such as that of gentrification, in-migrants are likely to display significantdifferences in age, education, income and class. The second element of this spatialinconsistency refers to the differential levels of migration and mobility that occurs betweenareas. For reasons of data access, this topic will be the primary focus of this analysis.

When discussing the exposure fallacy in the context of epidemiology, it is also importantto note the potential confounding factor of the age structure of the population. Older peoplefor example will have been subjected to a longer exposure to adverse environmental factors,and it is unclear if age standardisation provides a complete solution to this problem.

In most ecological analysis attempts are routinely made to incorporate the effects ofmigration, birth and deaths on incidence / prevalence calculations. Obvious difficulties canemerge with the denominator, for example, if an area experiences a rapid population influx orexodus. Even fairly basic analysis will attempt to control for this rather obvious difficulty.However equally obvious difficulties exist in relation to the effects of population migrationand mobility on exposure, that are all too often not incorporated or discussed in ecologicalanalysis. Few writers have touched on this topic. Exceptions include the work conducted byWarner et al. who attempted to create more realistic models of lung cancer risk based on thepropensity of population mobility to reduce radon exposure (Warner et al., 1996; Warner et

al., 1995), and Pringle et al. (1995) who attempted to examine the effect of migration onschizophrenia prevalence in east county Cavan. In measuring short-term exposure, someresearchers have started to use passive samplers attached to outer garments, rather than morecrude measures of location-based exposure assessment (Mukala et al., 1996). Otherresearchers concerned with daily mobility have produced intricate time/space models of day-to-day movement to demonstrate variations in exposure (Schaerstrom, 1996; Mead andEarickson, 2000). However to date these models, although interesting are of little practicaluse in assisting in an assessment of population exposure.

Further developments are required to incorporate population mobility (Briggs, 1996) intoecological analysis if this avenue of investigation is to remain viable. This paper seeks toexplore this issue of the ‘exposure fallacy’ and utilises data from the Irish Central StatisticsOffice in an effort to quantify the impact and nature of population mobility.

Data sources

The main publications used here are volumes 4 and 6 from the 1996 Census produced bythe Central Statistics Office. These detail Usual Residence and Migration, Birthplaces andTravel to Work, School and College respectively (CSO, 1997; CSO, 1998). Additionalinformation was obtained from the CSO’s Report on Vital Statistics 1996 (CSO, 2000).

Residential mobility

Swerdlow (1996: 59) notes that ‘in general, the greater the degree of population mobility… the less useful will be analysis based on address at incidence’. The simplest indicator ofmigration in Ireland is undoubtedly the census question that asks respondents (aged over one)whether they were resident at the same address one year previously. In Ireland in 1996 92.5percent of those aged one year and over and usually resident (3,548,001) in the State, wereresident at the same address one year previously. For convenience hereafter, unless otherwisestated, reference to populations will assume that they are ‘aged one year and over and usuallyresident in the State’. This means that over a quarter of a million people in Ireland aged overone had moved address in the twelve months prior to the 1996 Census (265,073). This is asignificant population movement in so short a time period and raises serious concerns overthe level of accuracy in ecological analysis in Ireland. This factor is particularly pertinent asthere was substantial variation in these rates between areas. Figure 1 details residentialmobility over a twelve-month period at county and county borough level. Galway CountyBorough had the lowest percentage of people resident at the same address one year earlier at81.0 percent. County Cavan however had the highest percentage of people in this category at96.11 percent. It should be noted that this crude census measure may exaggerate the impactof mobility upon ecological analysis because of the local; nature of many moves. This is afactor that will be discussed below.

Those respondents not living at the same address one year earlier have been divided intothree groups: (1) those living at a different address one year previously but in the samecounty; (2) those living at a different address one year previously but in a different county ofthe Republic of Ireland; and (3) those that were living abroad one year previously.

Examining the first of these three groups indicates that 3.9 percent lived at a differentaddress one year previously but in the same county. This figure was at its highest in DublinCounty Borough at 6.1 percent, and at its lowest in county Leitrim (1.7 percent). Analysis ofthe second group reveals that 2.4 percent of people lived in a different county of the Republicof Ireland one year previously. This figure was lowest for county Monaghan (0.8 percent) andhighest for Limerick County Borough (4.3 percent). Investigation of the third group indicatesthat 1.1 percent of people were living abroad one year earlier. This figure was lowest incounty Louth at 0.7 percent and highest in Galway County Borough at 2.9 percent.Approximately half of this group were born in the Republic of Ireland, with the remainderhaving been born abroad.

Additional detail is provided at the sub-county level and further variation can be seen inthe data relating to residence status one year earlier in towns with a population of 1500 andmore. In its analysis of towns, the Census details not just whether people have moved sinceone year earlier, and whether they are still living in the same county, but also whether theyare still living in the same town. Table 1 demonstrates the considerable variation that existsbetween highest and lowest proportions in each town size category.

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Table 1: The percentage of residential moves (as a proportion of all moves) that were of alocal nature in the twelve months prior to the 1996 Census.

Population Population Population Population County1500-2999 3000-4999 5000-9999 10,000+ Boroughs*

Highest Muinebeag Portarlington Carrick-on-Suir Dundalk Cork CB72.0% 61.7% 68.7% 62.5% 58.6%

Lowest Tower Clane Portmarnock Leixlip Waterford CB2.4% 10.5% 20.4% 29.5% 1.1%

* Dublin is included within the Greater Dublin Area (66.6%).

Figure 1: Percentage of the population aged one year and over and usually resident in Ireland who

resided at the same address as one year previously (1996).

International migration

As well as exploring residence one year earlier, the Census also asks respondents if theyhave ever lived abroad for more than a year. In the 1996 Census 11.2 percent of personsusually resident in the State stated that they had have lived outside the state for one year ormore. This represents a considerable number of people (403,428). Figure 2 details thepercentage of people that had lived outside of Ireland for one year or more at county andcounty borough level. County Donegal (16.8%), Galway County Borough (16.3%) and DunLaoghaire-Rathdown (16.2%) had the highest percentages of people in this category, whilecounty Kilkenny at 7.9 percent had the least. It is clear from Figure 2 that the westernseaboard has generally higher rates of prior overseas residence, while the midlands had lowerrates.

Although no information is collected in the census on the length of time spent abroad,information is collected on the country of former residence and the date of return again to theRepublic of Ireland. Table 2 details the date of taking up residence in the State. Significantcounty differences exist in this distribution as in the others mentioned above.

Table 2: Persons usually resident in the State who lived outside the State for one year ormore classified by year of taking up residence in the Republic of Ireland.*

Before 1951 1961- 1971- 1981- 1991- Not Total1951 1960 1970 1980 1990 1996 stated

12,647 14,241 46,112 97,340 92,695 133,830 6,563 403,428

* Including those brought to live in the Republic of Ireland before their first birthday

Further spatial differences can be observed in the data relating to where respondents thathave lived abroad for more than one year were based (listed as the UK, Other EU countries,the USA, and Other Countries). In addition variation also exists on the basis of age andmarital status in different areas as can be seen in the breakdowns of the residence status oneyear earlier.

Births

A further indicator of migration can be seen in residence and birth data in the Republicof Ireland. The1996 Census revealed that 20.4 percent of people born in the State and usuallyresident here were living outside of the county of their birth. This represents almost 700,000people. Once again there was substantial variation between counties on this measure with 7.9percent of the population of county Donegal born in Ireland were born in a county other thanDonegal. However 50.1 percent of the population of county Kildare born in Ireland were bornin a different county. Figure 3 details the percentage of the usually resident population thatare not living in their county of birth. The most notable feature of Figure 3 is the high ratesin a broad semi-circle around Dublin.

Over a quarter of a million people (251,624) usually resident in the Republic of Irelandwere born abroad. The vast majority of these were born in the UK; 139,330 were born inEngland or Wales, with 39,957 having being born in Northern Ireland, and 11,751 inScotland. The next most common birthplace was the USA, where 15,619 were born. Theproportion of those born outside Ireland varies from county to county. For example, among

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those born in England or Wales, but now resident in Ireland, it may be noted that theseconstitute just 2.8 percent of the population of county Monaghan, but 5.7 percent of thepopulation on county Mayo.

Marriages

Another potential indicator of residential mobility rates that is updated annually may liein the annual Reports on Vital Statistics (CSO, 2000). These annual reports detail residenceof both the bride and groom before each marriage at county and county Borough level. Thisindicator is though probably of decreasing importance in an era of increasing cohabitation. Inaddition its potential as a migration indicator is dependent upon the assumption that themajority of people getting married live with each other after their wedding. Obviously this isnot always the case and further research is required to determine the value of this potentialindicator.

Figure 2: Percentage of the population who had lived outside Ireland for more than 1 year (1996).

Age

It is important to note the importance of age in migration patterns. International researchhas demonstrated that the majority of people moving are between 20-35 years of age (Long,1992). An examination of Irish data from the 1996 Census supports this conclusion.Individuals in the 20-24 year old age group were least likely to still be in the same addressthat they were living in twelve months previously (79.8 percent were). Some 80.4 percent of25-29 year olds were in the same location as they were twelve months earlier, whereas for 30-34 year olds this figure was 87.8 percent. These figures are significantly lower than thepercentages noted for those aged 55-64 years and 65-74 years (97.9 percent and 98.1 percentrespectively).

Mobility

In addition to the issue of population migration another confounding variable is that ofpopulation mobility. This is obviously a key factor when conducting epidemiological studiesin areas with high seasonal variation in the population. Examples include tourist areas, whichmay experience a doubling or trebling of their population at peak holiday periods. The fullimpact of such population movements will rarely be caught in a census routinely conductedin March or April. However putting aside the issue of summer holidays, in the 1996 Census91,395 persons were not enumerated at their usual residence out of a total of 3,596,543persons (representing 2.5 percent of the total resident population). On a related issue, itshould also be noted a proportion of the population at any time in a given area are personswhose usual residence is outside the State. In Ireland in 1996 29,544 persons out of a totalof 3,626,087 persons enumerated were usually resident outside of the State (representing 0.81percent of those enumerated).

Travel to work, school or college

Routine travel patterns may also significantly affect population exposure. For example,according to the 1996 Census, 28.0 percent of working people aged 15 and over, a total of260,068 individuals, travel ten miles (16km) or more to work daily. As one might expect longdistance commuter rates are higher in the ‘commuter’ counties around the Capital, such asMeath (45.7%), Wicklow (41.9%) and Kildare (39.2%). Such rates are also high aroundprovincial cities such as Galway, where 44.2 percent of commuters travel this distance.However such commuting is not purely a phenomenon occurring in commuter belts aroundDublin and the provincial cities. The highest rate of workers travelling ten or more miles towork was recorded in county Leitrim at 45.9 percent. The lowest rates were generally found,as might be expected, in the county boroughs. In Cork County Borough 17 percent of workerstravelled ten miles or more to work, with the figures for the County Boroughs of Dublin,Waterford, Galway and Limerick being 16.9 percent, 14.1 percent, 16.3 percent and 22percent respectively. The lowest rate of this type of long distance commuting was noted inDun Laoghaire-Rathdown, where just 12.6 percent commute ten or more miles to work. The1996 Census also notes that 6472 children aged between five and twelve years commuted tenor more miles to school in 1996 (1.6%). The figure was 36,576 (11.1%) for students agedbetween thirteen and eighteen years and among students aged nineteen and over it was 19,248(21.5%).

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Under-estimating mobility and Census 2002

It should be noted that the real figures depicting mobility are higher than those mentionedhere because children aged less than twelve months are excluded from many of the censusquestions discussed above. It should also be noted that it is generally acknowledged that theCensus will under-enumerate the total population. Groups that are particularly vulnerable tothis undercount are highly mobile, transient and socially excluded groups such as Travellers,the homeless and refugees/ asylum seekers. A certain degree of mobility will also beunderestimated because of the nature of the one-year residence question in the Irish Census.For example a number of people will no doubt currently be living in the same residence theywere living twelve months ago. However in the mean time they may have moved elsewherefor an extended period of up to, but less than, a year. The data examined here are from the1996 Census. It is highly probable however that the rate of migration into Ireland from abroadhas increased significantly, particularly among asylum seekers and among nationals of otherEU States. In addition it also seems likely that the number of Irish migrants that have returned

Figure 3: Percentage of the usually resident population not resident in their county of birth.

from working abroad will have increased. It is also probable that the ‘Celtic Tiger’ has alsoled to increased internal migration as employment opportunities expanded in industrial areas.In addition widespread housing shortages in urban areas have forced many working people tocommute increasing distances. The forthcoming 2002 Census will clarify the validity, orotherwise, of these expectations.

Discussion

In a wide-ranging review of the utility of ecological studies English states that despitetheir limitations such studies ‘do have a continued role’ (1996:9), particularly where there islittle information on individual level exposure and where ‘exposure among individuals maybe so homogenous as to be considered to occur only at a group level’. Ecological analysis hasa strong and successful history within medical geography. Decisions about the future ofecological health analysis therefore must weigh this history carefully in any decision.However the confounding effects of high and variable rates of migration and mobilityoccurring in the population in a spatially inconsistent manner raise serious questions over theviability of ecological analysis in Ireland. The Census 96 finding that over a quarter of amillion people usually resident in the State had moved residence since one year previouslycannot be ignored. It is important to note that although the issue of significant populationmobility appears to be a serious threat to ecological health analysis in Ireland, research byLong (1991) indicates that by international standards Ireland has a relatively low level ofresidential turnover. Age standardised rates of residential moves over a one-year period(using data from c.1981), indicate that whereas Ireland may have a rate of only 6.4 percentand Great Britain a rate of 10.6 percent, countries such as New Zealand and the USA havesignificantly higher rates (19.6% and 17.5% respectively). The importance of the exposurefallacy for other countries would therefore appear to be even more significant internationally.

As noted in this analysis and in research elsewhere (Long, 1992; Dieleman et al., 2000),high mobility rates are a particular feature of the young adult population aged between 20 and35. Older age groups exhibit a considerably higher level of residential stability than youngadults. By 35 years of age many ‘triggering’ events (Clarke et al., 1994) such as leavinghome, starting work, changing jobs, getting married, and family formation become lessfrequent. Some improvement in reliability may be achieved by excluding this populationfrom ecological health analysis. Obviously the exclusion of such populations is not feasiblewhen investigating conditions that exhibit a relatively high incidence among young adults,such as Hodgkin’s disease. Even if young adults are excluded from analysis, caution will stillbe needed when examining disease incidence among those higher age groups in relation toconditions with a long lead-in period. However excluding young adults from ecologicalhealth analysis will be at best a minor correction in most ecological health analysis.Understanding of disease causation is still in its infancy and therefore there is uncertaintyabout the causation timeframe, which may extend back to the in utero stage (Barker et al.,2001).

In line with other research (Clark, 1986), this analysis has also highlighted the fact thatthe majority of residential moves are over short distances. For example although a quarter ofa million people aged one year and over and usually resident in Ireland were no longer livingat the same address where they had been domiciled twelve months prior to the censusadministration, approximately half of this number had been living in the same county.Therefore restricting ecological health analysis to county level would appear to increase the

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reliability of exposure data. However county level analysis still cannot overcome theconfounding factor of spatial variation in residential mobility rates. In addition county levelanalysis represents a crude level of analysis that encompasses socially heterogeneouspopulations with differing levels of healthcare access and provision.

Further research is required to investigate population mobility. The aggregate censusinformation examined in this paper does not reveal differences within different segments ofthe population. It is probable that there are distinct groups within the general population thatmay move repeatedly, while others may remain relatively static. Longitudinal individual levelresearch on this issue is required to explore this topic in depth.

An alternative strategy to the proposals for future ecological analysis to be conducted atcounty level and / or to involve just older age groups may involve weighting area results inregression analysis. Further research needs to be directed into the development of an index ofmobility for census areas (deds, urban/rural districts and counties/county boroughs) in theRepublic of Ireland. The creation of such an index using some of the census items discussedabove could then be incorporated into weighted least squares regression analysis. In thisanalysis the weighting given to each area unit would be based upon the level of mobilityexperienced within the area.

A common tactic is simply to ignore the possible influence of migration in the majorityof ecological analysis. This tactic is strongly supported by the reality that the most health dataavailable in Ireland makes no allowance for this factor. Stated bluntly most health analystswould no doubt respond with the argument that ‘the best that we have got’ does not includedetails of migration. Some commentators might also suggest that increased accuracy is notessential, as although the findings from ecological analysis can be extremely useful, they areonly ever taken judged as the lowest level of evidence acceptable in meta-reviews. Howeverthis approach appears to embrace inaccuracy, belittles health/medical geography andcondemns ecological analysis to the sidelines in investigative analysis.

It is not sufficient to end discussion of inaccuracies in ecological analysis resulting fromthe exposure fallacy at this point. Population health surveillance and protection necessitatesimproved information systems covering the life span of individuals. Such systems need notbe associated with ‘big brother’ connotations of totalitarian regimes. With the exception ofIreland and the UK, central and local population registers are a feature of most Europeanstates (Redfern, 1989; Diamond, 1996). In some countries local population registers date backto the seventeenth century. Improving the capacity to conduct ecological analysis is essentialin order to conduct population health surveillance. It is imperative therefore that the potentialof leading health information systems internationally, such as that of Finland, should beexplored. Finland has population registers at both the central and local level. In addition everyperson in Finland has a unique identifier that facilitates record linkage to a host of datasetsincluding mortality data and the Finnish Cancer Register. A key feature of the capacity ofFinish Central Population Register is the ability to link person and building data files.Building data files include GPS point location data. This system maintains retrospectiveaddresses facilitating the calculation of exposure over time. Using the capacity of a GIS toidentify ‘at risk’ housing units, these datasets therefore have been used in investigations ofsuch topics as cancer risk from overhead power lines (Valjus et al., 1995; Verkasalo, 1996;Verkasalo et al., 1996) and the risks of depression from residential proximity to overheadpower lines (Verkasalo et al., 1997).

The advantage of campaigning for the introduction of ‘Finnish style’ central populationregisters, unique identifiers, and geo-coded household data is that if introduced it wouldsignificantly aid the more routine introduction analysis of health data using multi-levelmodelling techniques. Such techniques facilitate the simultaneous evaluation of the effects ofindividual and area level attributes. Although multi-level modelling requires individual leveldata, it may become the yardstick against which other forms of analysis will be evaluated inthe years to come.

Acknowledgements

Special thanks are due to Dr Dennis Pringle for his insightful direction at the inceptionof this research paper. We would also like to thank the Health Services Research Group fortheir comments on a seminar presentation of a draft version of this paper. Particular thanksare given to Prof. Colin Bradley, Dr Colin Thunhurst, Mr Brendan McElroy, and Prof. IvanPerry.

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