ResearchModelling the impact of modifying lifestyle risk factors ondementia prevalence in Australian population aged 45 years andover, 2006–2051ajag_392 111..116

Binod Nepal, Laurie Brown and Geetha RanmuthugalaNational Centre for Social and Economic Modelling, University ofCanberra, Canberra, ACT, Australia

Aim: To model impact of modifiable risk behaviour ondementia prevalence among the Australian population aged45 years and over.Methods: A group-based computer model was constructedto estimate the impact of modifying risk behaviour ondementia prevalence.Results: Based on population ageing, the number ofpeople aged 45 years and over living with dementia isexpected to triple from 187 000 in 2006 to 650 000 by2051. A drop in proportion ever smokers by 5% every 5years would lower population with dementia by 2% in2051. If obesity rate drops by 5%, dementia prevalencewould be lower by 6%. A decline in physical inactivity rateby 5% would reduce dementia by 11%. Persistence of thegrowing trend in obesity and physical inactivity wouldresult in a larger than expected dementia epidemic.Conclusion: Improving the risk behaviours has potentialto make a substantial reduction in the number of peoplewith dementia.

Key words: Australia, dementia, modelling, prevention,risk factors.

IntroductionPopulation ageing is often considered the most importantfactor determining the occurrence of dementia in the future.Projections of dementia cases are therefore often based solelyon the projected age composition of the population. As theAustralian population is ageing steadily, this approach wouldsuggest that the number of people with dementia will increasein the future from under 200 000 persons in the year 2000 toover 300 000 by the early 2020s, and rising to approximately500 000 by the 2030s [1–3].

A growing body of evidence, however, suggests that besidesageing, a number of factors can determine the prevalence ofdementia. Lifestyle factors such as smoking, level of physicalactivity and body mass index (BMI) can be modified topotentially reduce the risk of developing dementia and/ordelay the onset of dementia, thus reducing the incidence and

prevalence of dementia. Yet there is a lack of studies model-ling the impact of modifying putative risk factors on preva-lence of dementia in Australia [4]. Using a group-basedcomputer model, this paper shows the potential impact ofmodifying lifestyle risk factors on the future number ofpersons likely to be living with dementia in Australia.

Methods and materialsA group-based computer model was developed in MicrosoftExcel to project the number of people with dementia based onvarying lifestyle change scenarios. The modifiable lifestyle riskfactors considered in this modelling exercise were cigarettesmoking, obesity and level of physical activity. These riskfactors were chosen because of the consistency in the evidencesupporting a causal relationship between these risk factorsand dementia, and the availability of risk factor estimates. Asa result of the lack of risk factor estimates for interactiveeffects, the model cannot assess the impact of altering multiplerisk factors simultaneously. Rather, the impact of alteringrisk behaviour for the three risk factors being studied ismodelled independently – that is, one at a time.

The model constituted three dimensions: age groups, sex andrisk factors. The Australian population aged 45 years andover was distributed in a three-dimensional matrix of age, sexand modifiable risk factor. Age was categorised into 5-yeargroups (i.e. 45–49, 50–54, and so on with the highest agegroup being 85 years and over). Age–sex-specific populationdistribution was taken from the Australian Bureau of Statis-tics (ABS) population projections based on 2006 Census;Series B projections were used to provide a middle of therange estimate [5].

Age–sex-specific dementia prevalence rates for people aged45 years and over were obtained from the most recent inter-national literature [6,7]. Overseas data were used because ofthe lack of up-to-date and representative Australian data ondementia. The 2003 ABS Survey of Disability, Ageing andCarers is the most recent and comprehensive source of datafor Australia but this survey was not used to inform preva-lence estimates because of concerns that this survey substan-tially underestimates dementia prevalence [2].

Age–sex-specific prevalence rates of smoking and physicalinactivity were obtained from the 2004–2005 NationalHealth Survey (NHS) Confidential Unit Record File provided

Correspondence to: Dr Binod Nepal, NATSEM, University ofCanberra. Email: binod.nepal@natsem.canberra.edu.au

DOI: 10.1111/j.1741-6612.2010.00392.x

111Australasian Journal on Ageing, Vol 29 No 3 September 2010, 111–116© 2010 The AuthorsJournal compilation © 2010 ACOTA

by ABS [8]. Smoking was dichotomised as smokers andnon-smokers; where non-smokers are those defined in the2004–2005 NHS as ‘never smoked’, that is, those who neverregularly smoked daily, smoked less than 100 cigarettes intheir lifetime or had smoked pipes, cigars, etc. less than 20times [9]. Physical activity was dichotomised into physicallyinactive and active. Physically inactive adults were those whoreported less than 100 minutes of exercise or no exercise inthe 2 weeks prior to the interview [9]. Obesity prevalence wasobtained from the 1999–2000 AusDiab study [10], the mostrecent source of objective measures of BMI. The 2004–2005NHS was not used to inform BMI distribution as self-reported height and weight have been identified to be lessreliable [11]. Obese adults were defined as those having aBMI of 30 or more.

Age-specific prevalence rates of smoking, obesity and physi-cal inactivity are shown in Figure 1. These data are assumedto be applicable in 2006 for the purpose of this modelling.

Relative risks for developing dementia with exposure to theselected modifiable risk factors were taken from recent esti-mates provided by Anstey et al. [12] and Kivipeltoet al. [13]. In selecting relative risk estimates, preferencewas given to meta-analytic studies followed by longitudinalstudies. The relative risk was estimated to be 1.140 for eversmoked compared to never smoked [12], 2.296 for obeseversus non-obese and 1.693 for physically inactive versusactive [13]. Following previous examples that have treatedrelative risks and odds ratios indiscriminately assumingthat dementia incidence is a rare event [12], we alsohave not differentiated between these measures for thepurpose of this modelling. Because of the lack of publisheddata on age–sex-specific relative risks, we assumed thatthese relative risks applied uniformly across all age–sexgroups.

Based on a 2 ¥ 2 contingency table, an equation was devel-oped to decompose dementia prevalence rate in an age group

into the rates for the exposed and unexposed categories. Thisequation can be expressed as follows:

For the exposed category (e.g. obese):

P R N D N R N Nae ae a au ae ae= ∗ ∗( ) + ∗( )[ ]

For the unexposed category (e.g. non-obese):

P R N D N R N Nau au a ae au au= ∗ ∗( ) + ∗( )[ ]

Where,Pae = dementia prevalence rate among exposed people aged aPau = dementia prevalence rate among unexposed peopleaged aR = relative riskNae = number of persons in age a who belong to exposedcategoryNau = number of persons in age a who belong to unexposedcategoryDa = number of people with dementia in age group a

Dementia prevalence rates for exposed and unexposed cat-egories of a risk factor (e.g. obese and non-obese) werecalculated by applying to the age–sex distribution of the 2006Australian population, the relative risk estimates and riskfactor prevalence. Dementia prevalence rates by risk factorstatus thus estimated are presented in Table 1.

A number of scenarios were developed to examine the impactof modifying risk factor on the prevalence of dementia. The‘ageing only’ scenario provided the baseline comparator,where the expected number of dementia cases in the futurewas calculated by applying age–sex-specific dementia preva-lence rates to the projected age–sex-specific populations.

Other scenarios modelled the impact of various trends inprevalence of the risk factors considered. These scenarioswere derived by changing the proportion of persons in eachrisk factor status group as per the assumed change in riskfactor prevalence. Dementia prevalence rates by exposurecategory (Table 1) were then applied to the new profile of thepopulation to assess the impacts. In modelling the scenarios,we assumed that the changes in risk factor prevalence applyacross all age–sex groups. The relative risk estimates wereassumed to be constant into the future. The results areexpressed as a percentage change in the expected number ofpeople with dementia compared to that from the ‘ageing-only’ scenario.

ResultsFigure 2 presents the projected number of men and womenwith dementia based purely on population ageing; theseprojections provide the baseline for assessing the potentialimpact of altered lifestyle risk factors. These projections,

Figure 1: Age-specific prevalence rates of selected riskfactors. Source: Smoking and physical activity prevalenceestimated from the 2004–2005 National Health SurveyCURF [8]; Obesity prevalence was estimated from the1999–2000 AusDiab study [10].

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while obtained using similar methods used previously byothers [1–3], yield slightly different numbers because of thechoice of prevalence rates and demographic data. Yet, inkeeping with previous reports, the baseline projections pre-sented in Figure 2 suggest that the ageing population willresult in an increase in the prevalence of dementia in Austra-lia. According to our ‘ageing-only’ baseline scenario, thenumber of people aged 45 years and over living with demen-tia is expected to triple from about 187 000 in 2006 toalmost 650 000 by 2051 (Figure 2). In this period, prevalenceof dementia in the population aged 45 years and overincreases from 2.4% in 2006 to 4.1%.

Figure 3 presents the impact of reduced prevalence ofsmoking. The three smoking scenarios model the impact ofthe following relative changes in the proportion of eversmokers: (i) decline of 2.5% every 5 years; (ii) decline of 5%every 5 years; and (iii) decline of 10% every 5 years. It can beinferred from NHS [9] that prevalence of smoking in Austra-

lia has been falling but it is difficult to predict the long-termfuture. We assumed that smoking will continue to decline at arate between 5% and 10% every 5 years, which is betweenthe second and third scenarios being modelled. The firstscenario of a 2.5% reduction per 5 years demonstrates thepotential impact of a modest rate of decline in smokingprevalence. A decline in the proportion of people who eversmoked by 5–10% would lower the number of people withdementia by 2–4% by 2051. This equates to 13 000–26 000fewer Australians with dementia in the future.

Figure 4 shows the impact of modifying obesity rates on theprevalence of dementia. The three scenarios model the impactof the following relative changes in obesity prevalence inAustralia: (i) increases by 5% every 5 years; (ii) increases by2.5% every 5 years; and (iii) decreases by 5% every 5 years.The first scenario is considered to reflect the recent rising trendin obesity in Australia. In the absence of definitive predictionsregarding future prevalence of obesity in Australia, we haveassumed that a 5% growth in the rate of obesity prevalenceevery 5 years would be a plausible upper growth rate. This is aconservative estimate given that obesity rates have beenincreasing alarmingly in Australia [14,15].

Applying the first scenario of a 5% increase in the prevalenceof obesity, we would expect by 2051, a 9% increase in thenumber of people with dementia in Australia compared tothe ‘ageing-only’ scenario; this converts to 60 000 morepersons with dementia. The second scenario represents amore modest growth in the obesity prevalence, resulting inabout 4% more people with dementia in 2051. The lastscenario, a 5% drop in the prevalence every 5 years wouldresult in a reversal of trends resulting in a 6% decrease in thenumber of persons with dementia; this equates to 40 000fewer persons with dementia by the year 2051.

Table 1: Estimated dementia prevalence rates by selected lifestyle risk factors

Age group (years) All persons (%)† Ever smoked (%)‡ Never smoked (%)‡ Obese (BMI � 30) (%)‡ BMI < 30 (%)‡ Sedentary (%)‡ Active (%)‡

Male45–49 0.036 0.038 0.033 0.066 0.029 0.049 0.02950–54 0.066 0.069 0.061 0.119 0.052 0.089 0.05255–59 0.200 0.210 0.184 0.346 0.151 0.266 0.15760–64 0.205 0.212 0.186 0.353 0.154 0.275 0.16365–69 1.600 1.660 1.456 2.920 1.272 2.244 1.32570–74 2.900 2.999 2.630 5.292 2.306 3.967 2.34375–79 5.600 5.787 5.076 11.041 4.809 7.136 4.21680–84 11.000 11.408 10.007 21.688 9.445 13.545 8.00085+ 17.100 17.570 15.413 33.713 14.683 20.639 12.192

Female45–49 0.030 0.032 0.028 0.051 0.022 0.041 0.02450–54 0.059 0.063 0.056 0.100 0.044 0.082 0.04855–59 0.103 0.110 0.097 0.165 0.072 0.144 0.08560–64 0.129 0.139 0.122 0.208 0.091 0.179 0.10665–69 1.000 1.079 0.947 1.663 0.724 1.366 0.80770–74 3.100 3.370 2.956 5.154 2.245 3.944 2.33075–79 6.000 6.557 5.752 11.460 4.991 7.587 4.48180–84 12.600 13.772 12.081 24.065 10.481 14.841 8.76785+ 24.900 27.260 23.913 47.555 20.713 27.675 16.348

†Meta-analyses and other data [6,7], ‡Authors' estimates using dementia prevalence, relative risks, risk factor prevalence and 2006 population. BMI, body mass index.

Figure 2: Projected numbers of people living withdementia considering ageing only.

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Figure 5 shows the impact of modifying the prevalence ofphysical inactivity. The three scenarios model the impact ofchanging the prevalence of physical inactivity as follows: (i)increases by 2.5% every 5 years; (ii) decreases by 2.5% every5 years; and (iii) decreases by 5% every 5 years. The firstscenario reflects a conservative view on the growing trend inphysical inactivity in Australia into the future [16]. The othertwo scenarios illustrate what could happen if levels of physi-cal activity were to fall. As expected, an increase in physicalinactivity among the Australian population at the rate of2.5% every 5 years would result in 7% more people withdementia compared to the ‘ageing-only’ scenario, an increaseof about 47 000 persons. Likewise, if the physical inactivityprevalence were to drop by 2.5% and 5% every 5 years, theexpected number of people with dementia would be lower by6% and 11%, respectively, by 2051 (39 000 and 70 000persons, respectively).

DiscussionThis modelling exercise was inspired by the growing evidencethat the risk of dementia can be reduced by modifying riskbehaviour. Focusing specifically on smoking, physical activ-ity, and obesity rates in Australian men and women aged 45years and over, this modelling exercise demonstrates thereductions in dementia prevalence that can be achieved bymodifying the prevalence of lifestyle risk factors. In additionto these three, a number of other risk factors (e.g. high bloodpressure) can be modified through behaviour change but wecould not model them because of resource and data con-straints. The model is generic and it can easily be adapted toassess the impact of any other risk factor for which appropri-ate data become available.

This work extends previous research projecting futuredementia burden [1–3] by incorporating into the model the

Figure 3: Impact of reducing smoking: percentage change in the number of people with dementia by type of scenarioscompared to the ‘ageing only’ scenario.

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Figure 4: Impact of reducing obesity: percentage change in the number of people with dementia by selected scenarioscompared to the ‘ageing only’ scenario.

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impact of changing modifiable risk factors at a populationlevel. Results suggest that changing the prevalence of modifi-able risk behaviours has the potential to alter the projectednumber of dementia cases that has been based purely onpopulation ageing. Current trends of increasing levels ofinactivity and obesity are likely to result in higher numbersof dementia cases in Australia in the future than that pro-jected on the basis of widely adopted ‘ageing-only’ scenario.In contrast, substantial gains can be achieved from increas-ing the level of physical activity and by reducing obesityprevalence.

Smoking has also been established as a risk factor for demen-tia, and continuing current trends of reductions in the preva-lence of smoking will impact favourably on the projectednumber of dementia cases in Australia. Continued support ofanti-smoking campaigns to further this trend will help tocontain future dementia numbers.

In reality, many of the modifiable risk factors are likely toco-exist and likely to have interactive effects on disease occur-rence. A limitation of this model is the inability to assess theinteractive effect of changes in two or more risk factors.However, the model construction permits the addition of newrisk factors and interactive risk factor estimates if and whenthey become available. Until interactive risk factor estimatesbecome available, we work on the assumption that the riskfactors act independent of each other. Yet we recognise thatthe impact of changing two or more risk behaviours may notnecessarily be the sum of the impact of each of the risk factorsindependently.

Another limitation is the assumption, again due to a lack inthe published literature, that the risk estimates for the threemodifiable risk factors are the same across both sexes and allage groups. The risk estimates of smoking came from themeta-analysis of 19 prospective studies in which average ages

of the participants ranged from 39.9 to 84.0 years [12]. InKivipelto et al.’s study [13], the source of risk estimates forobesity and physical inactivity, mean age of the participantswas 50.4 years at the first examination and 71.3 years atfollow-up. The prevalence of the risk factors varied by ageand sex, and data were also available, but the extent to whichexposure confers higher or lower risk at different ages is notknown. Such information would have helped to examinebetter the influence of each of the risk factors. For example,physical inactivity was much more prevalent in older thanyounger age groups. The large impact of physical inactivityreduction on dementia should therefore be interpreted bykeeping this limitation in mind.

This modelling exercise used dementia prevalence datadrawn from European studies. At the time of undertakingthis modelling exercise, Australian data were unavailable.However, it is anticipated that data for Australia will beavailable in the near future through the Dynamic Analyses toOptimise Ageing (DYNOPTA) research project (NHMRC/ARC Ageing Well, Ageing Productively Program, Project ID410215, ‘Learning how to age well from Australian Longitu-dinal Studies of Ageing’). Data from DYNOPTA will help fillthe current gaps in knowledge on dementia in Australiawhich limited the modelling that could be undertaken here.Once available, revised prevalence estimates can be incorpo-rated into the model, improving the precision of the projec-tions to the Australian setting. It is, however, anticipated thatthe overall picture reported will not differ greatly becauseprevious Australian data on dementia prevalence were alsofound to be similar to European [17].

In conclusion, this modelling exercise illustrated the extent towhich the number of people living with dementia in Australiacould be changed by modifying the prevalence of specificlifestyle factors. The continuance in the growing trend in riskfactors such as physical inactivity and obesity will result in

Figure 5: Impact of promoting physical activity: percentage change in the number of people with dementia by type ofscenarios compared to the ‘ageing only’ scenario.

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considerably more people with dementia than that suggestedby population ageing only. This adds further evidence for theneed for primary and secondary prevention strategies to beimplemented now in order to reduce the health burden that islikely to arise from these risk factors in the future.

AcknowledgementsThis research was supported by the Dementia CollaborativeResearch Centre for Prevention, Early Intervention, andRisk Reduction, an Australian Government Initiative and asupplementary internal research grant from NATSEM. Anearlier version of this paper was presented at the AustralianAssociation of Gerontology 41st National Conference,18–21 November 2008, Fremantle, Western Australia. Theviews expressed in this paper are those of the authors and donot necessarily represent the views of the funding agency orNATSEM.

Key Points• Population ageing is likely to enlarge substantially

the dementia epidemic in Australia, with thenumber of people aged 45 years and over livingwith dementia tripling from 187 000 in 2006 to650 000 by 2051.

• Declining smoking rates is expected to have afavourable impact on dementia prevalence.

• The dementia epidemic is likely to be bigger thanexpected if the growing trend in obesity and phy-sical inactivity rates continues.

• Reducing obesity and physical inactivity has thepotential to contain the dementia epidemic.

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