ResearchCities, environmental stressors, ageing and chronic diseaseajag_552 1..5

Deborah Black, Kate O'Loughlin, Hal Kendig and Leigh WilsonAgeing, Work and Health Unit, Faculty of Heath Sciences, Universityof Sydney, Sydney, New South Wales, Australia

Aim: This study attempts to identify any associationbetween length of exposure to urban environmental riskand non-infectious chronic disease in a group of Australianresidents who lived in the same location for at least 20years.Methods: Using Australian survey data, we selected 1256participants who were 45 years and older and had lived inthe same location for at least 20 years. We used logisticregression and survival analysis to identify factorsassociated with the presence of non-infectious chronicdisease at baseline and after 7 years.Results: Results show that increasing age, living in anurban area and living in an area with a lowersocioeconomic status increase the odds of having along-term health condition.Conclusion: This study provides empirical evidence thatolder long-term Australian urban residents are more likelyto have a non-infectious chronic disease, which may beassociated with environmental exposure, than those livingin rural locations.

Key words: ageing, city, environmental stressor,non-infectious chronic disease, urban area.

IntroductionGlobally, populations are ageing and countries are becomingmore urbanised. The Australian Bureau of Statistics (ABS)predict that by 2026, 22% of the Australian population willbe aged 65 or older [1]. Increasingly, the global populationwill be living in urban areas and proportionally more of thepopulation will be aged over 60 years, contributing to anincreased burden of disease [2]. The link between urbanisa-tion and population health is well described [3–5], but thereis limited literature available on the health effects of urbanliving on an increasingly ageing population. The lack ofresearch in this area is partly due to the difficulties of con-ducting research on environmental exposure levels in a studypopulation who is geographically mobile, has varying levelsof exposure to environmental stressors and has individualfactors which moderate geographic and disease risk.

Evidence suggests that in developed countries the ageingpopulation have greater levels of social interaction in an

urban setting, leading to positive effects on both physical andmental health [6,7]. As the body ages, its ability to defendagainst environmental assault diminishes and exposure to arange of environmental stressors can accelerate the ageingprocess and trigger, or exacerbate, disease. In 2003, Hoodstated about ageing populations, ‘[D]ecreased efficiency inthe blood-brain barrier and the cardiovascular, pulmonary,immune, musculoskeletal, hepatic, renal, and gastrointestinalsystems can alter response to environmental agents, leadingto heightened susceptibility to the toxic effects of air pollu-tion, pesticides, and other exogenous threats to health.’ [8](Page A756) [9]. There is a large body of literature detailingthe impact of urban environmental stressors on public health[4,9–11]. Urban exposure to noise, air pollution and pesti-cides are known to increase the likelihood of specific chronicdiseases [9–14].

There is growing evidence that environmental stressors havea significant impact on long-term health conditions. Litt andcolleagues outline the lack of information on the role ofenvironmental exposures in relation to the incidence of non-infectious chronic diseases and other conditions includingasthma, neurological disorders, diabetes and developmentaldisabilities [15]. This is particularly relevant for ageing popu-lations because they have longer exposure to these pollutantsin the environment [7].

Cities are significant ‘places’ to analyse urban environmentalstressors and the associated public health effects. The wayhuman settlement patterns are organised, deployed and uti-lised by people going about their daily activities impact onthe health of the population. It is the complexity of emergingpublic health problems that present major new challenges forsustainable development, well-being and quality of life forthe ageing population who live in cities. It is difficult to definethe beginning and ending of a ‘city’ or urban area and thecommencement of a rural one; however, in all areas of Aus-tralia the ABS defines these ‘Sections of State’ (SOS) bypopulation numbers; cities are defined as ‘major urban’ witha population of 100 000 and over, ‘other urban’ are placeswith populations between 1000 and 99 999, and a boundedlocality (rural and remote) has populations of <999 [16].

Socioeconomic status has long been associated with poorerhealth outcomes [17,18]. Those of lower socioeconomicstatus are more likely to live in ‘other urban’ areas wherehousing is cheaper, generally located nearer to industrialareas, airports or busy roads.

Environmental stressors including aircraft, road and railnoise, fine particulate matter and poor air quality, high

Correspondence to: Professor Deborah Black, Faculty of HealthSciences, University of Sydney.Email: deborah.black@sydney.edu.au

DOI: 10.1111/j.1741-6612.2011.00552.x

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density housing, lack of adequate transport, poor or inad-equate road and urban design and lack of green space clearlyhave both dimensions of location and time (duration by timeof day, seasonality and trends over time). It is extremelydifficult to account for a person’s lifetime exposure to envi-ronmental stressors, and factors such as genetics, lifestylefactors and socioeconomic factors all have a result on theoutcome [19]. As a result, there are very few studies thatinvestigate the effects of environmental exposure throughoutthe life-course of an individual.

This study aimed to investigate the relationship betweenlong-time exposure to urban environmental stressors and thelikelihood of developing a non-infectious chronic illness. Asstated, this research is difficult based on the variability of thepopulation under study. With this in mind, and in a prelimi-nary attempt to conduct these empirical analyses, this studyused geographical location of residence as a proxy for expo-sure level, ensuring the participants had lived at their currentlocation for at least 20 years. Consideration of the followingexplanatory variables were factored into the analysis:number of high rise buildings able to be seen from partici-pants residence, level of noise from airport, trains or industry,level of traffic noise, amount of rubbish lying around resi-dence, degree of access to available services and distance topublic transport.

MethodThe Household Income and Labour Dynamics in Australia(HILDA) survey is a comprehensive and nationally represen-tative panel dataset that surveys both individuals and house-holds. We selected all participants who were interviewed aspart of their participation in the HILDA survey. Commencingin 2001, HILDA data are collected annually in ‘waves’, andanalyses of all available waves (from 2001 to 2007: Wave 1 toWave 7) were undertaken. Participants aged 45 years or olderat the Wave 1 baseline were included. To ensure participantshad a lengthy exposure to the local environment, we selectedonly participants who had lived at their current urban,regional, rural or remote location for 20 or more years.

Location was defined using the Section of State (SOS)definition as: Major Urban, other urban and boundedlocality (rural and remote) [1].

On entry to the HILDA Study each participant was assessedfor the presence of an existing long-term health condition.Conditions included: arthritis, asthma, cancer (any type),chronic bronchitis or emphysema, Type 2 diabetes (adultonset), depression/anxiety, heart/coronary disease, hyperten-sion or other circulatory problems. Although the pathways tothese conditions vary considerably with genetic factors, eachof these conditions has also been associated with increasedlevels of environmental stress.

Using Statistical Package for the Social Sciences (spss) Soft-ware, version 16.0, logistic regression was undertaken with

the presence of at least one long-term health condition as theoutcome variable. Explanatory variables were included in themodel if there was an association with the outcome at a =0.25 [20]. This method for inclusion of explanatory variablesin logistic is used by Hosmer and Lemeshow as the level ofType 1 error for association between explanatory variablesand outcome [20]. Explanatory variables were consideredsignificant in the logistic regression model at a = 0.05 and95% confidence intervals were used for odds ratios.

Survival was defined as ‘not reporting a long-term healthcondition occurring for the first time’ and was measured ateach wave of the HILDA data collection. Life Tables wereused to determine predictors of survival. spss Version 16.0was used for the survival analyses with a = 0.05.

ResultsAt baseline, there were 1256 HILDA interviewees aged 45years and older who had lived at their current address for atleast 20 years and continued through to Wave 7 of the study.The majority (92%) spoke only English at home. Character-istics of participants are outlined in Table 1.

One third of participants (418) had an existing long-termhealth condition at baseline. Of these, women (57%) weremore affected than men (43%). The majority of participantslived in either metropolitan (58%) or regional (40%) areas.Only 23 participants (2%) lived in remote areas. Based on thesmall number of participants living in remote locations,analysis used the ABS SOS categories for geographical analy-sis: major urban, other urban and rural.

At Wave 7 the variables provided by the HILDA datasetand entered into the regression model included: age, sex,

Table 1: Characteristics of HILDA Wave one participantswho had lived in one place for at least 20 years

Age (years)45–49 2%50–54 8%55–59 10%60–64 14%65+ 66%

Male 44%Socioeconomic status

SEIFA Quintile 1 24%SEIFA Quintile 2 21%SEIFA Quintile 3 18%SEIFA Quintile 4 18%SEIFA Quintile 5 19%

Section of stateUrban 58%Outer urban 40%Rural and remote 2%

LanguageEnglish speaking at home 92%

Long-term condition at baselineMale 43%Female 57%

HILDA, the Household Income and Labour Dynamics in Australia; SEIFA, Socio-EconomicIndicator for Area.

B l a c k D , O ’ L o u g h l i n K , K e n d i g H e t a l .

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geographic area, and socioeconomic status (as defined bySocio-Economic Indicator for Area (SEIFA)) [21]. SEIFAareas were aggregated into quintiles for logistic regressionanalyses.

The logistic regression results show that increasing age, livingin an urban area and living in an area with a lower socioeco-nomic status increase the odds of having a long-term healthcondition.

A 1-year increase in age increases the odds of having along-term health condition by 5% (95% CI 1.044–1.072,P < 0.001). Living in the lowest socioeconomic areaalone increased the odds of having a long-term healthcondition by 1.89 (95% CI 1.24–2.89, P = 0.003). Analysisof the interaction between socioeconomic status andarea of residence showed that those who were living in anouter urban area and in SEIFA Quintile 2 had significantodds of 12 of having a long-term health condition com-pared with those living in a rural area with the most advan-taged SEIFA Quintile (Table 2). Although not significant,Table 2 shows the decreasing odds of having a long-termhealth condition as the SEIFA Quintile level of advantageincreases.

Survival analyses of the seven waves of HILDA (2001–2007)was undertaken using Life Tables with the time to develop-ment of a long-term health condition measured at discreteintervals (yearly at each wave), restricted to those who hadnot moved residence between Wave 1 and Wave 7. Thesurvival analyses showed that survival without a long-termhealth condition was not associated with living in major citiesor other urban areas compared with rural localities forpeople aged 45 years or older at baseline. Nor was there anysignificant difference in development of a long-term healthcondition based on SEIFA Quintile, although there is a trendfor those living in the most advantaged SEIFA Quintile (5)

to take longer to develop a long-term health condition.(Figure 1) Using this method shows that only one factorexplains greater survival and that is that older age at baselineincreases one’s long-term health condition free survival time(Table 3).

DiscussionThis study highlights that there is a possible link betweenlong-term exposure to environmental stressors in outer urbanareas and developing long-term health conditions. The base-line analysis showed that urban dwellers were more likely tohave one or more long-term health conditions. The odds ofdeveloping a long-term health condition in a cohort of ageingAustralians was however, not significantly higher in moredensely populated areas compared with rural locations. Oneof the difficulties associated with conducting research of thistype is the number of variables, which have an impact on thelikelihood of an individual developing a chronic illness.Genetic and lifestyle factors are known to significantly influ-ence the development of conditions such as asthma, cancer,diabetes, heart disease, depression and arthritis. Althoughthere is a large body of evidence linking the effects of envi-ronmental stressors with these conditions it is difficult to‘tease out’ the factors associated with causality. It is for this

Table 2: Predictors of having a long-term health conditionat HILDA baseline (2001)

Predictor OR 95% CI P-value

Age 1.054 1.044–1.072 <0.001Living in an outer urban centre 1.45 1.020–2.076 P = 0.039†Living in SEIFA Quintile 1 (most

disadvantaged)1.899 1.248–2.891 P = 0.003‡

Living in SEIFA Quintile 2(second highest level ofdisadvantage)

1.464 0.963–2.225 P = 0.074

Living in SEIFA Quintile 3 (midlevel of advantage/disadvantage)

1.344 0.880–2.052 P = 0.172

Living in SEIFA Quintile 4(second highest level ofadvantage)

1.180 0.792–1.303 P = 0.916

Living in an outer urban areaand in SEIFA Quintile 2

12.125 1.053–139.623 P = 0.045§

†Compared with rural area. ‡Compared with most advantaged SEIFA Quintile. §Comparedwith rural area with most advantaged SEIFA Quintile. 95% CI, 95% confidence interval; HILDA,the Household Income and Labour Dynamics in Australia; OR, odds ratio; SEIFA,Socio-Economic Indicator for Area.

Figure 1: Length of time to the development of a long-termhealth condition by Socio-Economic Indicator for Area(SEIFA) Quintile Waves 1–7.

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Time

SEIFA Quintiles

Table 3: Predictors of long-term health condition freesurvival for HILDA cohort aged 45 years or older at baseline

Predictor OR 95% CI P-value

Age 0.988 0.979–997 0.008Male 0.941 0.783–1.131 0.518Living in inner regional area† 0.988 0.719–1.35 0.862Living in outer regional area‡ 1.026 0.767–1.373 0.941

†Compared with women. ‡Compared with major city. 95% CI, 95% confidence interval;HILDA, the Household Income and Labour Dynamics in Australia; OR, odds ratio.

C i t i e s , s t r e s s o r s , a g e i n g a n d c h r o n i c d i s e a s e

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reason that this study investigated geographic location ofresidence as a proxy for ‘exposure’ to a range of levels ofenvironmental stressors. Although we included exposure totransport noise, traffic noise and whether participants couldsee high rise buildings (as a proxy for housing density) in theanalysis, there was no association with the development of along-term health condition in major urban dwellers in thissample. This suggests that other factors in outer urban areasinfluence the likelihood of developing a long-term healthcondition.

There are major limitations in using geographic location as aproxy for dosage or levels exposure to environmental stres-sors such as noise and pollution. People living in the samelocation may have varying levels of exposure depending ontheir occupation, lifestyle activities and type of dwelling[22,23]. Measuring the response to environmental stressorexposure also comes with limitations. First, the long-termhealth conditions were self-reported and may be subject toerror. Second, healthy lifestyle choices, such as undertakingexercise are known to be affected by the built environmentand this can have an impact on the occurrence of long-termconditions [13,24]. Third, response to environmental stres-sors varies considerably between individuals. For example,while some people may dislike high density living and find itstressful, others enjoy the company of many people living inclose proximity and live a less stressful life as a result.Research suggests that older people, who live in the highdensity metropolitan fringe and outer urban areas whereaccommodation is more affordable, are disadvantaged interms of health and social inclusion [25]. These areas aregenerally less well served by health services and access topublic transport is often limited, increasing the reliance onmotor vehicles [25,26].

Lastly, many older residents may have moved to urban loca-tions at retirement based on pre-existing health conditionsand the need for access to tertiary-level health-care services.Conversely, many of the ‘well elderly’ make the retirementchoice to leave the city (after long periods of high levels ofenvironmental exposure), for calmer and less stressful ruraland remote locations. Although not significant in this study,the trend for those in major urban areas to have a longersurvival time without a long-term health condition maysuggest better access to tertiary health services.

Based on these factors and limitations, future research needsto be specifically designed to accurately measure a range ofvariables known to be associated with an increase in chronicillness (including ambient air quality and noise), and corre-late these with the life-course of an ageing population cohort.A study of this type should also include the systematic docu-mentation of any chronic illness diagnosis, lifestyle factors(including exercise levels, dietary habits, smoking status),which impact either positively or negatively on health statusand genetic predisposition to, or family history of, chronichealth conditions.

Notwithstanding the limitations of using a longitudinal data-base that was not specifically designed to measure environ-mental exposure, this study has shown that ageingAustralians who live in urban areas are more likely to have anon-infectious chronic disease than those living in moreremote locations. Given that the Australian population isageing and increasingly living in urban areas with high levelsof environmental pollutants, this new and important area ofresearch provides evidence as to the urgency of addressing theimpact of environmental exposures in the ageing.

ConclusionThis study provides empirical evidence that older Australianswho live in urban areas for at least 20 years are more likely tohave a non-infectious chronic disease, which may be associ-ated with environmental exposure, than those living in moreremote locations. The paucity of literature into the effects ofenvironmental exposure on the life-course of an individualhighlights the difficulties associated with the conduct of thistype of research. As urban dwelling populations’ age andurban environmental exposures increase there is the likeli-hood that the burden of disease will increase as a result of thecombination of these factors.

In line with the current evidence on environmental factorsand their effect on population health, this study providesadditional evidence on which to base future health promo-tion initiatives and interventions in an ageing population,particularly those who live in an urban setting.

AcknowledgementsThis study was conducted at the University of Sydney andutilised data from the Australian Household Income andLabour Dynamics in Australia (HILDA) survey. DB con-ducted statistical analysis, DB, KOL and HK were respon-sible for concept development, and DB, KOL, HK and LWprovided intellectual input and document preparation.

Key Points• Older Australians who live in low socioeconomic

urban areas are more likely to have a non-infectious chronic disease than those in high socio-economic or rural areas.

• Low socioeconomic, urban areas are known to beat a locational disadvantage in terms of servicesand public transport.

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