The prevalence and demographicdistribution of treated epilepsy:a community-based study in Tasmania,Australia

Introduction

Studying the distribution and determinants ofepilepsy in population-based samples has thepotential to improve our understanding of thiscommon disorder (1). Such comparative studiesare important as they provide information on theburden of the condition, both at the populationlevel and in specific subpopulations, and mayalso indicate etiological factors acting at theselevels.The International League Against Epilepsy

divides the world into six regions comprising:Africa, Asia-Oceania, Eastern Mediterranean,European, Latin America, and North America.The Asia–Oceania region, which includes Australia,

extends from India, in the west, to the PacificIslands in the east. Despite its great geographic andcultural diversity, the lifetime prevalence in thisregion is between 3.4 and 7.5 per 1000 (2, 3), asimilar range to that seen in the developed world,with treated epilepsy having a prevalence of about4–5 per 1000 (2, 4). However, with few exceptions,most prevalence studies have had insufficient powerto include analyses by age, gender, region (5, 6), orsocioeconomic status (7, 8).In Australia, the national prescription database

appears representative of community-treated epi-lepsy and can provide an effective and efficientmethod for large-scale patient recruitment forepidemiological research (9). Utilizing this data-base, we estimated the prevalence of treated

Acta Neurol Scand 2012: 125: 96–104 DOI: 10.1111/j.1600-0404.2011.01499.x � 2011 John Wiley & Sons A ⁄ SACTA NEUROLOGICA

SCANDINAVICA

D�Souza WJ, Quinn SJ, Fryer JL, Taylor BV, Ficker DM, O�Brien TJ,Pearce N, Cook MJ. The prevalence and demographic distribution oftreated epilepsy: a community-based study in Tasmania, Australia.Acta Neurol Scand: 2012: 125: 96–104.� 2011 John Wiley & Sons A ⁄S.

Objectives – To estimate the prevalence and demographic distributionof treated epilepsy in a community-based population. Materials &methods – We surveyed all residents in Tasmania, Australia, who weresupplied at least one antiepileptic drug prescription between July 1,2001 and June 30, 2002, recorded on the national prescriptiondatabase. We adjusted for the effect of disease-related non-responsebias by imputation methods. Results – After three mail contacts,54.0% (4072 ⁄ 7541) responded, with 1774 (43.6%) indicating treatmentfor epilepsy, representing 86.0% of the estimated total possible cases inTasmania. The adjusted treated epilepsy prevalence was 4.36 per 1000(95% CI 4.34, 4.39); lower in women (prevalence ratio 0.92 (95% CI0.84, 1.00)); greater with increasing age (P < 0.001); similar in thethree main geographic regions; and similar with socioeconomic statusof postcode of residence. Conclusions – Although our estimates arelikely to be affected by access to health services, overall treated epilepsyprevalence of 4.4 per 1000 is similar to previous studies. Our finding ofhigh elderly prevalence has been reported in a few recent studies indeveloped countries and has important clinical and public healthimplications in populations with similar aging demographics.

W. J. D�Souza1,2, S. J. Quinn1,3,J. L. Fryer1, B. V. Taylor1,D. M. Ficker4, T. J. O�Brien5,N. Pearce6,7, M. J. Cook2

1The Menzies Research Institute, The University ofTasmania, Hobart, Tasmania; 2The Department ofMedicine, St Vincents Hospital, The University ofMelbourne, Melbourne, Vic., Australia; 3Flinders ClinicalEffectiveness, Flinders University Adelaide, SA,Australia; 4The University of Cincinnati Academic HealthCentre, Department of Neurology, Cincinnati, OH, USA;5The Department of Medicine, The Royal MelbourneHospital, The University of Melbourne, Melbourne, Vic.,Australia; 6Centre for Public Health Research, MasseyUniversity Wellington Campus, Wellington, NewZealand; 7Department of Medical Statistics, LondonSchool of Hygiene and Tropical Medicine, London, UK

Key words: antiepileptic drugs; epidemiology;epidemiological methods; epilepsy; prevalence

W. D�Souza, The Department of Neurology & Neuro-logical Research, 5th Floor, The Daly Wing, St Vincent�sHospital, The University of Melbourne, Melbourne, POBox 2900, Fitzroy 3065, Melbourne, Vic., AustraliaTel.: 613 9288 3341Fax: 613 92883350e-mail: dsouzaw@iprimus.com.au

Accepted for publication January 20, 2011

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epilepsy in Tasmania and its distribution by age-group, gender, region, and socioeconomic status.

Materials and methods

The design and methods of patient recruitmenthave been described previously in detail (9) andwill only be outlined briefly here.

Geography and population

Tasmania is an island state off the southeasterncoast of Australia comprising an area of 68,000square km, with a population of 472,672 in 2002(10). The state is divided into three main geo-graphic, administrative, and postcode regions:southern (231,662), northern (134,701), and north-western (106,309).

Pharmaceutical benefits scheme and the Australian nationalprescription database

In Australia, consultative and investigationalhealth services are significantly funded by thefederal government, allowing universal mean-inde-pendent access to health services. National pre-scription data records are generated when thegovernment contributes to the cost of a productdispensed under the Australian PharmaceuticalBenefits Scheme (PBS) and Repatriation Pharma-ceutical Benefits Scheme (RPBS). The PBS ⁄RPBSis a subsidization program monitored by theHealth Insurance Commission (HIC). Patients areclassified into one of two categories, determiningthe amount the patient contributes and the amountof subsidy paid by the government. Generalbeneficiaries make a maximum patient contribu-tion (A $22.40 in 2002) per prescription item;concessional beneficiaries (primarily social securityrecipients) or veteran affairs (returned servicemenand women) categories purchase drugs at a con-cession rate (A $3.60 in 2002). Additional �safety

net� arrangements limit the total annual contribu-tion that a family can make toward prescriptioncosts for each of these categories of patient. Oncethese limits are reached, any PBS ⁄RPBS prescrip-tions dispensed are either free or with a muchreduced co-payment for the remainder of the safetynet period (11). These arrangements have implica-tions on the PBS ⁄RPBS data set. When a patientpays the entire cost of the medication, there is noHIC record of the prescription. Prescriptionrecords for drugs costing less than the generalpatient co-payment will not be complete (onlyrecorded for concessional beneficiaries and thosewho have reached safety net entitlements). Therewill be complete capture for more expensive drugs,as the government would have made a contributionin every case. Only five of these anticonvulsants(phenobarbitone, phenytoin suspension, carba-mazepine liquid, carbamazepine 50 mg, and lamo-trigine 5 mg) cost <$22.40, and so had limitedcapture only for �General� beneficiaries. However,barbiturates are infrequently prescribed in Tasma-nia (2.3% of all antiepileptic drugs� (AED) pre-scriptions – data not shown), liquid preparationsare prescribed for patients with swallowing diffi-culties and children typically <8 years of age, andlow-dose tablet preparations are usually used indose titration (rather than dose end-point). Withless �general� patients in Tasmania than otherAustralian states, the HIC sample frame isexpected to be a comprehensive AED treatmentdatabase for all �concession� card holders and, withthese few exceptions, the majority of �general�patients (Table 1). Benzodiazepines and acetazol-amide prescriptions were excluded from selectionbecause they were more likely to be prescribed assingle agents for non-epilepsy conditions.

Mail invitations

An invitation letter asked potential participants toagree to participate in a longitudinal disease cohort

Table 1 Response to the health insurance commission mail invitations

Mailout one Mailout two Mailout three Total

(n = 7541) % (n = 5448) % (n = 3871) % n %

Responders 1957 26.00 1418 26.00 697 18.01 4072 54.00Have blank spells, seizures, or epilepsy 990 13.10 517 9.50 267 6.90 1774 23.52Do not have blank spells, seizures, epilepsy 796 10.60 704 12.90 338 8.73 1838 24.37Do not know whether have blank spells, seizures, or epilepsy 49 0.60 41 0.80 19 0.49 109 1.45Did not specify whether have blank spells, seizures, or epilepsy 122 1.60 156 2.90 73 1.89 351 4.65

Ineligible 136 1.80 159 2.90 16 0.41 311 4.12Deceased 21 0.30 27 0.50 10 0.26 58 0.77Not at address 115 1.50 132 2.40 5 0.13 252 3.34

Non-responders 5448 72.20 3871 71.10 3159 81.61 3159 41.89

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– The Tasmanian Epilepsy Register (TER) andasked �Are you taking antiepileptic medications forblank spells, seizures or epilepsy?�(9). Mailout oneoccurred on November 20, 2002. A second mailinvitation was sent on February 19, 2003 to thoseinvited patients who had failed to respond, exceptthose where a �return to sender� notification hadbeen received or we had received information thatthey were deceased. A third anonymous HIC mailcontact was sent to AED prescribers of non-responders who did not respond to the secondmailout, in February 2004. This letter requestedthe treating doctor to provide the indication forAED treatment and to advocate participation ontothe TER.

Socioeconomic index for area (SEIFA)

As an indicator of socioeconomic status, eachpatient�s postcode of residence was scored into oneof five ordered categories according to the Socio-economic Index for Area (SEIFA) constructed bythe Australian Bureau of Statistics (using thesubcategory for Relative Advantage /Disadvan-tage) (12).

Ethical approvals

Approval was obtained from the southern Tasma-nia Health and Medical Human Research EthicsCommittee, the HIC, and the Department of Vet-erans� Affairs Human Research Ethics Committee.

Statistical analysis

Representativeness of sample – To assess the repre-sentativeness of our final participant cohort, wecompared this cohort�s demographic features fordifferences with that of the Tasmanian population,mail responders, and non-responders with a chi-square test for differences between proportions. Atest for trend in the proportion responding acrosslevels of the demographic characteristic [age, socio-economic status (SES)] was also conducted byfitting a univariable log binomial model and fittingthe characteristic as a linear predictor (9).

Period prevalence of treated epilepsy – Prevalentcases of epilepsy were identified from individualssupplied at least one prescription for an AED inTasmania above the �reportable� retail price thresh-old during the twelve-month period from July 1,2001 to June 30, 2002: Carbamazepine, Ethosuxi-mide, Gabapentin, Lamotrigine, Methylphenobar-bitone, Phenobarbitone, Phenytoin, Primidone,Sodium Valproate, Sulthiame, Tiagibine, Topira-

mate, and Vigabatrin. During the study period,Levetiracetam, Oxcarbazepine, Pregabalin,Rufinamide, Locosamide, and Zonisamide werenot available in Australia. Patients had to be aliveand have a postcode listed in Tasmania at some-time during this time frame and to indicate thatAEDs were prescribed for epilepsy. Respondentswere classified as having epilepsy when they self-affirmed to �have blank spells seizures or epilepsy�or when their treating doctor affirmed they �hadbeen prescribed anticonvulsant medications forepilepsy�. Mail respondents who answered �don�tknow� or �didn�t specify� were classified as diseasenon-responders for imputation correction.To correct observed prevalence estimates for

disease non-response bias, the method proposed byDrane was used to determine imputed prevalence(13). By assuming an exponential decay in diseaseresponse rate with each subsequent contact, thetrue prevalence can be estimated by fitting aregression model to the observed disease responserate at each mail contact and summing over allextrapolated mail contacts (Fig. 1). Confidenceintervals for each imputed Tasmanian demo-graphic prevalence estimate (age-group, gender,region, and socioeconomic status) were obtainedby converting the corresponding confidence inter-val endpoints of each imputed sample estimate intoprevalence estimates over Tasmania.

Validation study – Validation of the question �haveblank spells seizures or epilepsy� to confirm theoverall (but not strata specific) false positive rate of

020

040

060

080

010

00C

ases

Mailout1 2 3 4 5 .. ... n ....

CapturedImputed

1774 captured cases; 289 imputed cases

Figure 1. Captured and imputed cases used in estimating epi-lepsy prevalence in Tasmania. *For the purposes of diseaseresponse, mail respondents were classified as epilepsy caseswhen they affirmed to �have blank spells seizures or epilepsy�and classified as not being epilepsy case when they affirmed to�don�t have blank spells, seizures or epilepsy�. Epilepsy preva-lence was imputed from total disease non-respondents, whichcomprise mail respondents who �didn�t know�, �didn�t specify,�and all mail non-respondents.

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this question against a neurologist diagnosis ofepilepsy was undertaken in a random sample of293 mail respondents using a validated modifieddiagnostic questionnaire administered by a final-year trainee neurologist with the responses inter-preted by an epilepsy specialist using standardizeddiagnostic guidelines (14). For these 293 respon-dents: none reported they were taking AEDs forindications other than epilepsy; epilepsy statusremained uncertain after interview in 4 (1.4%); afalse positive diagnosis of epilepsy was reached in23 (7.8%) (one single seizure, three migraine, ninepsychogenic, and 10 syncope); and a true positivediagnosis was confirmed in 266 (90.8%). Includingthe �uncertain� category as false positives gave apositive predictive value (PPV, the number of casesfound correctly to have the disease by the goldstandard) of 0.91, and PPV was 0.92 when thesefour uncertain cases were excluded. It was notpossible to estimate the false negative and truenegative rate of self-identification of epilepsy forthis question (i.e., respondents who answered �no�,�don�t know,� or �didn�t specify�) for privacyreasons.

Associations between demographic characteristics andprevalence of treated epilepsy – Assuming an expo-nential decay in response rate with response timewithin each demographic subgroup, the distribu-tion of treated epilepsy by 5-year age-group,gender, region, and socioeconomic status bySEIFA quintiles were estimated. We calculatedunivariate prevalence ratios to compare differenceswithin variable categories by fitting a log binomialmodel to test for trend in prevalence for age andSIEFA, and whether prevalence is associated withregion and gender, after adjusting for the increasedconfidence intervals obtained using Drane�s impu-tation method. Only a univariate analysis wasconducted as our final estimates for total preva-lence and its distribution by age, gender, region,and SES yielded imputed rather than actual casesnot allowing a multivariate approach. All analyseswere performed using Stata Version 9.

Antiepileptic drug polytherapy – For patientsresponding to our survey and indicating treatmentfor epilepsy, the percentage and estimated preva-lence of patients prescribed concurrent AED wasrepresented.

Results

A total of 4072 persons responded to the survey,giving an overall response rate of 54.0%. Patientstaking anticonvulsant were more likely to respond

with increasing age (trend P < 0.001), or whenfrom a higher socioeconomic quintile (linear trendP < 0.001) with over-representation if femalealmost reaching significance (P = 0.053). In addi-tion, patients taking anticonvulsants were morelikely to respond to our invitation if their prescrip-tion was obtained from a neurologist and less likelyto respond if their prescription was obtained froma psychiatrist (P = 0.007).Table 1 shows the breakdown of response to the

three HIC mail invitations. There was evidence of adiminishing response rate with response time. The1774 indicating treatment for epilepsy (=capturedcases) and 289 (=imputed cases) giving an overallestimated prevalence of 2063 cases in Tasmania(see Fig. 1).Table 2 shows the estimated prevalence of

treated epilepsy in Tasmania by twenty-year age-group, gender, region, and SEIFA. The crudeprevalence of epilepsy was estimated to be 4.36 per1000 (2063 ⁄472,672 · 1000) persons in the Tasma-nian population with 95% confidence interval (CI)4.34–4.39. The Tasmanian figures are shown, asage–sex standardization to the World StandardPopulation or Australian Standard Population didnot alter our results appreciably. Adjusted treatedepilepsy prevalence was 8% lower (95% CI 6–38%) in women than men, similar in the threegeographic regions and not associated with SEIFA(P = 0.50). We examined the association betweenage and prevalence of epilepsy using fractionalpolynomials and found that the data were bestfitted to a log binomial model by transforming ageto 1 ⁄ �age. Fig. 2 displays the nonlinear relation-ship between prevalence of epilepsy and increasingage.Finally, Table 3 shows the observed percentage

and imputed prevalence of patients prescribedconcurrent AED among those responding to ourmail invitation indicating treatment for �blankspells, seizures, or epilepsy�. More than two con-current AED for epilepsy were taken by 142 (8.0%)of patients.

Discussion

There are limited studies describing epilepsy prev-alence patterns by gender, age-group, region, orsocioeconomic status. Although our estimates areinfluenced by factors that affect a person acquiringa diagnostic label for treatment purposes, caseascertainment by AED prescription provides anefficient recruitment method for epidemiologicalstudies in communities characterized by highaccess to health services (2). The estimate of 4.4per 1000 for overall treated prevalence is consistent

Prevalence & distribution of treated epilepsy in Tasmania

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with similar studies, while our observation of highelderly prevalence has been suggested by a fewrecent reports, and has important clinical and

public health implications in countries with similaraging demographics.Lack of community-based sampling has been a

major criticism of most case ascertainment meth-ods in previous epidemiological research intoepilepsy (15). Previous studies have suggestedthat the medical practitioner writing AED pre-scriptions is also most likely to be responsible fordisease supervision and follow-up (4, 16). If this istrue, with 70.9% of patients with epilepsy receivingtheir AED prescriptions exclusively from theirgeneral practitioner and 19.1% receiving them inpart from a medical specialist in the precedingtwelve months, it suggests that our cohort repre-sents community-treated disease (9).

Table 2 Estimated prevalence* of treated epilepsy in Tasmania by age-group, gender, region, and SEIFA�

Tasmanianpopulation�

Observedcases

Estimatedcases*

Estimatedprevalence §

Prevalence ratioEstimate (95% CI) P-value

Age-group (years)1–19 131,813 170 215 1.63 1.0020–39 120,883 329 586 4.85 2.97 (2.54, 3.47)40–59 131,215 601 757 5.77 3.54 (3.04, 4.11)60+ 88,761 506 623 7.02 4.30 (3.69, 5.02) P < 0.001–

GenderMale 232,768 869 1059 4.55 1.00Female 239,904 905 1003 4.18 0.92 (0.84, 1.00) P = 0.05

RegionNorthwestern 106,309 341 478 4.50 1.00Northern 134,701 438 550 4.08 0.91 (0.80, 1.03) P = 0.141Southern 231,662 728 1035 4.47 0.99 (0.89, 1.11) P = 0.908

SEIFA1 – low 104,859 331 452 4.31 1.002 – low ⁄ moderate 92,058 301 446 4.84 1.12 (0.99, 1.28)3 – moderate 91,397 282 354 3.87 0.90 (0.78, 1.03)4 – moderate ⁄ high 90,750 323 429 4.73 1.10 (0.96, 1.25)5 – high 93,608 261 378 4.04 0.94 (0.82, 1.07) P = 0.99–

*Imputed prevalence reported. Age–sex standardization to the World Standard or Australian Standard Population did not alter my results appreciably and so the Tasmanianfigures are represented. For the purposes of disease response, mail respondents were classified as having epilepsy when they affirmed to �have blank spells seizures orepilepsy� and classified as not having epilepsy when they affirmed to �don�t have blank spells, seizures or epilepsy�. Disease prevalence was imputed from total disease non-respondents, which comprise mail respondents who �didn�t know�, �didn�t specify,� and all mail non-respondents. �As an indicator of socioeconomic status, each patient�spostcode of residence was scored into one of five ordered categories according to the Socioeconomic Index of Relative Advantage ⁄ Disadvantage (SEIFA) constructed by theAustralian Bureau of Statistics. �472, 672 (38). §Per 1000. –Test for linear trend using a log binomial model.

02

46

8

Pre

vale

nce

(per

100

0)

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85Age (years)

Figure 2. Prevalence* of treated epilepsy in Tasmania by 5-year age-groups. Age-group 0–4 years is excluded because ofsmall numbers. *Imputed prevalence reported. For the pur-poses of disease response, mail respondents were classified ashaving epilepsy when they affirmed to �have blank spells sei-zures or epilepsy� and classified as not having epilepsy whenthey affirmed to �don�t have blank spells, seizures or epilepsy�.Disease prevalence was imputed from total disease non-respondents, which comprise mail respondents who �didn�tknow�, �didn�t specify,� and all mail non-respondents. Imputedprevalence reported with smoother and 95% confidence inter-vals was also shown. The smoother and 95% confidenceinterval was obtained from a log binomial regression modelwith age modeled as 1 ⁄ �age.

Table 3 Observed percentage and estimated prevalence of epilepsy treated withconcurrent antiepileptic drug medications in Tasmania between July 1, 2001 andJune 30, 2002

Number ofconcurrent AEDs

Observedcases

Observedpercentage

Estimatedcases*

Estimatedprevalence�� (95% CI)

One AED 1139 64.2 1368 2.90 (2.80–2.99)Two AEDs 432 24.4 519 1.10 (1.04–1.16)More than

two AEDs142 8.0 161 0.34 (0.26–0.42)

Missing data 61 3.4 68 0.14 (0.09–0.20)TOTAL 1774 100.00 2116 4.48 (4.20–4.76)

*Imputed prevalence reported; �472, 672 (38); �Per 1000. AED, antiepileptic drugs.

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However, to be an ideal recruitment approachfor prevalence estimation, AED prescription pen-etration for epilepsy treatment should have uni-versality and validity (17). We acknowledge thatdisease non-disclosure and concealment maylower prevalence estimates. Our inclusion of�don�t know� or �didn�t specify� responders asdisease non-responders for imputation is anattempt to at least correct for this possibilityamong survey non-responders. We are also awarethat AED initiation and withdrawal practicesamong local physicians and patients may affectprevalence estimates from prescription data (18).In Australia, the typical practice is to commenceAED treatment after the second unprovokedseizure, and this is reflected in the low numberof patients (0.34%) found to have been treatedafter a single seizure in our validation study, asimilar figure found in a Swedish study (19).Therefore, patient recruitment by AED prescrip-tion appears to have yielded few false positivecases because of �early� treatment.A potential limitation of the estimation of

prevalence from studies using AEDs prescriptionsis that it may overestimate the number of epilepsycases because these medications are sometimesprescribed for misdiagnosed epilepsy (9, 19) or forother diseases such as a migraine, depression,mood stabilization, and chronic pain conditions(20). Therefore, we used the response to thequestion �Are you taking antiepileptic medicationsfor blank spells, seizures or epilepsy?� to confirmepilepsy status among mail respondents. Althoughthis is unlikely to be a sensitive screening tool forepilepsy in household surveys, our aim was toobtain a high PPV (0.92) for capturing patientswith epilepsy. We did not adjust our overallprevalence estimate using this measured falsepositive rate (7.8%) as it was similar to otherstudies presenting unadjusted estimates (21). Wetherefore expect our AED estimate to be a usefulmeasure of clinically active epilepsy prevalence(22).Underestimating prevalence is the more likely

consequence of prevalence measures based solelyon AED utilization, with the lowest reported ratesusually arising from developing countries (23) orindigenous groups (24) where access to healthservices result in few patients on regular treatment.Therefore, although demonstrated to be an effi-cient mechanism for generating community-treatedcases, our ascertainment method utilizing the HICprescription database almost certainly underesti-mates the true prevalence of epilepsy as it fails tosample undiagnosed and untreated disease. Wealso acknowledge that disease concealment will

further act to lower our prevalence estimates (25).Although it is uncertain whether this form ofmeasurement bias would be overcome by completeparticipation, a higher response rate would haveallowed us more confidence in the precision andaccuracy of our prevalence estimates (26).Nevertheless, in a number of communities,

AEDs have been demonstrated to have widespreaduse and penetration in treating epilepsy, makingthem potentially a good target for identifying casesfor clinical epidemiological research (27). Everyperson identified as having epilepsy in a door-to-door community survey in Australia had beenprescribed AEDs at some time in their lifetime,with virtually all of the one-third �off medicationon survey day� seizure free in the preceding year(2). Although this Australian study suggests nolifetime AED treatment gap for epilepsy, it wasbased on �doctor-diagnosed epilepsy� making itlikely to also be an underestimate. It is difficult todetermine the exact magnitude of our underesti-mate. We suspect that the overall situation inAustralia is more likely to resemble countries withsimilar universal primary and tertiary health careaccess such as Sweden or the UK, where AEDunderascertainment of 8–25% has been observed(19, 28).Non-response is the rule rather than the excep-

tion in epidemiological surveys that generallyworldwide has been increasing (29). Imputation isone mechanism to correct for unmeasured diseasein survey non-responders. The theoretical basis forthis methodology does not require independentpopulation sampling and comes from Little andRubin (26) and Hansen et al. (30), which allowsimputation correction from two or three mailoutsfrom a diminishing cohort of non-responders fromthe same initial population sample, i.e., eachsubsequent strata is dependent on the previous(31). The benefits of non-response imputation are,first it improves the accuracy and precision ofprevalence estimates (26). Secondly, it takes advan-tage of typical human behavior previously shownto be present in conditions other than epilepsy, e.g.,asthma (32) where disease respondents tend toreply earlier allowing for a functional connectionbetween mail contact and changing disease preva-lence over time. Thirdly, it is relatively easy toperform simply by designing surveys with either atwo or three mail contact at regular intervals.Finally, although derived from published theory(13, 33), it is also consistent with others and ourcurrent study observations confirming an exponen-tial fall in epilepsy prevalence among non-respond-ers with resistance to mail response primarily frompatients receiving AEDs for other conditions. Its

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main criticism is that it is methodologically vul-nerable where there is evidence of �hard core�disease non-responders (34) as it is based on theassumption that disease responders are moresensitive to replying earlier rather than resistingreply because of disease concealment. In the case ofpeople with epilepsy, although we know diseaseconcealment occurs (25, 35), it is largely unknownwhether this is a temporary or permanent state,i.e., affected by time to response. Reassuringly, ourobservations appear not to support this.We acknowledge our third mail invitation to

AED prescribers of all non-responders to oursecond mail invitation yielded a relatively poor18.0% (697 ⁄3871) response; however, it allowed usto confirm a number of important assumptions.First, the poor physician response although disap-pointing was not unexpected and justified ourdesign to initially bypass physicians for recruitmentand confirmed the universal difficulties in engagingbusy primary physicians in non-core researchactivities (36). Secondly, it also confirmed resis-tance to mail response was occurring dispropor-tionately among those taking AED for otherindications (48.5%, i.e., 338 ⁄697) rather thanepilepsy (38.3%, i.e., 267 ⁄698). Finally, mostimportantly, it confirmed a further observeddecline in disease prevalence with time, allowingprediction of the unobserved disease in non-responders using the methods of Drane (13, 33).Given the high PPV from our validity study,negligible differential misclassification biasbetween self-reported and doctor-reported epilepsystatus was assumed (37), allowing us to combineclassification sources in a three contact exponentialdecay rather than two contact linear prevalenceimputation approach (13).Useful insights into epilepsy treatment in Tasma-

nia are obtained through theHICAEDprescriptiondata. At 8.0%, the percentage of patients receivingmore than two concurrent AEDs for epilepsyresponding to our mail survey is at the high end ofpolytherapy seen in most other developed countries(19, 38). Although not always possible to avoid,AED polytherapy is currently only recommendedfor inadequately controlled epilepsy when patientsfail serial monotherapy and dual therapy (39).Previous studies have suggested that significantlymore patients prescribedAEDs, or, when diagnosedand managed by doctors other than neurologists orpediatricians, are on multiple AEDs compared withthose treated by private specialists or hospitaldoctors (4, 40). Therefore, the relatively highpercentage of patients on multiple AED may eitherreflect increased prevalence of severe epilepsy (27) orinadequate epilepsy management in Tasmania.

Our final cohort demonstrated similar trendswith older age, female, and higher socioeconomicgroups that have been previously observed frommail surveys in other disease groups (41). Althoughthese responder differences have the potential toreduce our prevalence estimates in the lowersocioeconomic groups and inflate our prevalenceestimates in the higher age-groups, they areunlikely to be the major explanation for thedifferences observed in this study. Our estimatedtreated epilepsy prevalence of 4.36 per 1000 isconsistent with a number of previous studies thathave derived prevalence from AED prescription(22), primary care (28), or household surveysources (2). Prevalence was 8% lower in womenthan men. While a few studies have found higherepilepsy prevalence in women than men (42), andsome have found no gender differences (6), themajority are consistent with our findings, withgreater prevalence in men compared to women(43), at all age-groups (24). The explanations forthese gender differences still remain largelyunknown.At present, our knowledge of socioeconomic

differences in epilepsy prevalence is limited andconflicting (7, 8). The SEIFA index utilized in thisstudy is comprised of variables relating to income,education, occupation, wealth, and living condi-tions with the highest weighting being given to thefirst three variables (12). As SEIFA reflects thesocioeconomic well-being of an area, rather than ofan individual, it is possible for a relatively advan-taged person to be resident in an area of low indexscore. SEIFA also has a varying impact on healthservice uptake, and potentially AED prescriptiontreatment, with SEIFA 1 having higher generalpractice uptake in metropolitan and lower in ruralAustralian settings (44). Therefore, one explana-tion of our finding of no association in treatedepilepsy prevalence and SEIFA is that it is real anddoes not reflect access to general practice services.Alternatively, it may be as a result of misclassifi-cation of individual socioeconomic status or con-founding by area of residence.In our study, no differences in treated epilepsy

prevalence were found between the three maingeographic regions of Tasmania. Higher urbanprevalence has been noted in two previous studies(27, 45), but more commonly, studies have foundhigher prevalence in rural compared to urbansettings (5, 46, 47). Although it has been speculatedthat these contrary findings may be attributed togreater access to health services in the urban settingat the treatment (27) or primary prevention level(47), no clear etiological reasons for these differ-ences have been identified.

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Epilepsy is often considered a disease of youngerage-groups, and the highest prevalence rates occurin children in a number of developing countries(46). In earlier studies, this higher childhoodpattern was also seen in developed countries (48).However, recent studies have reported a reversedpattern with prevalence rates being lowest inchildren <14 years (22, 43) and highest in elderlypeople over 65 years of age (22, 28) in developedcountries. Consistent with these latter studies, wealso found the lowest prevalence rates in childrenand highest in the elderly. Although lower sam-pling of liquid AED preparations in children<8 years probably partly explains the lower prev-alence rates seen in this age, they persist beyond theage that these issues would continue to have animpact. Our results suggest that incidence may belower in the younger age-group, or that the illnesstreatment duration is more short-lived, comparedto adults and the elderly (49). If true, withdemographers predicting a dramatically greaterelderly population in the future (50), these findingshave important implications for health serviceplanning in developed countries.

Acknowledgements

Wendyl D�Souza was supported from a Pfizer Alfred & StVincent�s Hospital Electrophysiology Fellowship, St Vincent�sHospital Neuro-epidemiology Fellowship and FRACP GSKFellowship in Neurology. This project was generously sup-ported by grants from The Booth Estate Launceston, RoyalHobart Hospital Research Foundation, GSK Neurology,Clifford Craig Medical Research Trust – North West Tasmaniaand Menzies Research Institute NHMRC Capacity BuildingGrant. The Centre for Public Health Research is supported bya programme Grant from the Health Research Council of NewZealand. We thank the Tasmanian Regional Divisions ofGeneral Practice, Specialist Physicians of Tasmania, TheEpilepsy Association of Tasmania, the Pharmaceutical Guildof Tasmania, the Tasmanian Branch of the Pharmacist Societyof Australia, and the Society of Hospital Pharmacists. We alsothank our research assistants Nicola Mulcahy, Leanne Barnes,and Charlotte McKercher from the Menzies Research Institutefor their considerable efforts in participant liaison, dataprocessing, and management.

Disclosure

The authors report no conflicts of interest.

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