costing depression and its management: an australian study

The Mood Disorders Unit (MDU) was establishedat Prince Henry Hospital in 1985, with cliniciansmost commonly referring patients with more severe,persistent and treatment-resistant conditions. Con-sultants generally make a series of treatment recom-mendations involving alternative or augmentingpharmacological approaches, non-pharmacologicalstrategies (e.g. cognitive–behaviour therapy) or a

course of electroconvulsive therapy (ECT). Afterinitial assessment or treatment, patients are referredback to their clinician for ongoing management.

By late 1998, the MDU had assessed more than1000 patients. Nuances of service components havebeen reported [1,2] as has service satisfaction [3,4],and we now report on cost issues. Economic analysesgenerally consider both direct and indirect costs. Theformer include costs of medical consultations, hos-pitalisation and investigations, while indirect costsinclude those associated with lost work productivityand any inability to maintain economic roles due toillness and associated disability.

We principally seek to examine how provision ofthe MDU service impacts on the ongoing directcosts of depression, with pre-service and post-service

Costing depression and its management:an Australian study

Gordon Parker, Kay Roy, Philip Mitchell, Kay Wilhelm, Kerrie Eyers

Objective: To examine the cost impact of referral to a Mood Disorders Unit (MDU),by comparing pre-service and post-service costs, and MDU and control samples.Method: We studied tertiary referral MDU patients and a control group of consult-ants’ depressed patients, with the principal comparison intervals being: (i) 12 monthsprior to and (ii) 6 months following baseline assessment, with costs annualised to allow the impact of assessment and treatment recommendation to be determined.In addition, we assessed any ‘personal cost’ of depression.Results: Following baseline assessment, MDU referrals showed a reduction incosts, while controls’ costs increased, largely driven by contrasting directions in hos-pitalisation and social welfare costs. We identify variables associated with high andincreased costs, including features of the earlier stages of the disorder, whethersocial welfare was received, diagnostic subtype and personality dysfunction, withmultivariate analyses refining the variable sets. Self-report data indicated thatpatients judged the ‘personal cost’ of depression to exceed more formal cost para-meters, so that to experience depression is itself depressogenic.Conclusions: This first Australian attempt to cost depression and its managementin the clinical setting more provides a methodology for wider application in serviceevaluation studies rather than delivers an unequivocal answer to whether a special-ist Mood Disorders Unit is cost efficient or not.Key words: cost, cost–benefit, depression.

Australian and New Zealand Journal of Psychiatry 2000: 34:290–299

Gordon Parker, Professor (Correspondence); Kay Roy, ResearchAssistant; Philip Mitchell, Professor; Kay Wilhelm, AssociateProfessor; Kerrie Eyers, Administrator

School of Psychiatry, The University of New South Wales, andMood Disorders Unit, Prince of Wales Hospital, Randwick, NewSouth Wales 2031, Australia. Email: [email protected]

Received 16 June 1999; second revision 20 October 1999; accepted10 November 1999.

comparative analyses. As our consultants also assessprimary referrals from general practitioners andtake responsibility for area patients admitted with adepressive disorder, we also compare formal MDUreferrals with this group which approximates more togeneralist public hospital practice (our ‘controls’).

We collected data on costs in the 2 years precedingMDU assessment, aiming to avoid limiting pre-MDUcost assessment to an interval where high and pro-tracted morbidity might lead to MDU referral andalso artificially inflate costs. To prevent any spuriousdistortion driven by the MDU assessment/treatmentprocess, we ignore the first 6 months followingMDU assessment, and ‘cost’ only the second 6 months(albeit doubled to create an annual cost). Thus, we donot assess the cost implications of a 3-hour outpatientMDU assessment (or of any MDU hospitalisation)on an index episode. Instead, we examine the extentto which MDU assessment and management plan-ning impacts on resulting costs, a more appropriateobjective for a tertiary facility.

Method

A ‘cost questionnaire’ was embedded in an MDUintake assessment protocol previously described inthis Journal [5], with patients recruited who had had a primary major depressive episode for less than2 years. Sample members included formal tertiaryMDU referrals (tertiary referrals or TRs) as well aspatients referred to an MDU consultant’s indepen-dent practice or routine hospitalised area patientsadmitted under the consultant’s care (control refer-rals or CRs), with all patients invited to a 12-monthfollow up. The CRs had no contact with the MDUother than baseline and follow-up assessment.

A pre-assessment form sought self-reported detailson treatment for the mood disorder over the 2 yearspreceding assessment, with each cost parameter (e.g.number of visits and average duration of visits to apractitioner) being listed in a standardised question-naire. While our baseline assessment sought treat-ment details for both of those years, many patientshad had their index episode occur in the 12 precedingmonths, and thus we principally analysed data forcosts incurred only for that interval. Professionalstaff contact and hospitalisation details were limitedto the depressive condition, be it for its direct impactor consequences such as a suicide attempt.

At follow up, patients completed a similar question-naire for the preceding 6 months, as well as a self-esti-mate of the personal impact of the index episode on

them. Specifically, a five-item questionnaire soughttheir judgement of the extent to which their currentepisode incurred (i) direct financial costs; (ii) indirectfinancial costs (e.g. loss of income); (iii) social costs(e.g. loss of friends/contacts); (iv) relationship costs(e.g. stress with intimates); and (v) personal costs (e.g.drop in self-esteem and self-confidence). Pre-coded options were ‘no’, ‘mild’, ‘moderate’, ‘severe’,‘extreme’ and ‘catastrophic’. Clinical progress acrossthe whole of the review year was assessed according todefinitions devised by Frank et al. [6], and whichprovide operational criteria for remission (partial andfull) and recovery, as well as relapse and recurrence.

While the sample was recruited over a 3-year inter-val, costs were calculated on November 1998 data.Costs for general practitioner and psychiatrist con-sultations, diagnostic tests and ECT treatment werebased on fees as listed then in the Medicare BenefitsSchedule book. We sought to derive ‘total societal’costings. For instance, each cost/patient includes,where relevant, Medicare benefits, private healthinsurance rebates, hospital ‘overheads’, pharmaceuti-cal benefits, dispensing fee, healthcare card holderbenefits and any direct or ‘gap’ cost to the patient.Specific details on certain costings are now noted:

General practitioner and psychiatrist costing for‘mood’ visits

Costing calculations were derived from thepatient’s estimate of (i) the average visit length tothese practitioners; and (ii) the number of visits perprescribed period.

Other mental health professional costs

Consultation with hospital administrators and pro-fessional bodies (e.g. Australian PsychologicalSociety) suggested the following costs: hospital-basedclinical psychologist and social worker (base salary +25% overhead), $36/hour; hospital-based psycholo-gist, $26/hour; private psychologist, $150/hour; andtrained nurse (community-based), $25/hour. Suchcosts were calculated for those patients seeing thepractitioners as out-patients or in community-basedprograms. If a patient had been hospitalised, staff costswere absorbed into the generic hospital cost.

Cost of pathology services

Patients were asked the number of blood test occa-sions for their mood state. We derived an ‘occasion’

G. PARKER, K. ROY, P. MITCHELL, K. WILHELM, K. EYERS 291

cost of $78, made up of three representative generaland specialised components (i.e. a full blood count,liver function tests and thyroid function tests).

Cost of diagnostic imaging services

Cost per patient consisted of the scheduled fee plus25% for hospital overheads. For a computerisedtomography (CT) brain scan, the cost was then $252and for a magnetic resonance image (MRI) scan,$594.

Cost of electroconvulsive therapy

Cost per treatment for ECT was calculated asincluding a fee of $53 for the consultant psychiatristin attendance and of $72 for the anaesthetist, plus25% for hospital overheads. The total ECT cost (perdefined period) was calculated from the total individ-ual treatment cost ($157) multiplied by the number oftreatments received.

Cost of antidepressant medication

Since selective serotonin re-uptake inhibitors(SSRIs) are now the commonest antidepressant typeprescribed in Australia, this class was used as thecosting basis, with a prescription cost of $51 (includ-ing government pharmaceutical benefit, healthcarecard benefit and cost to patient). The cost per patientwas calculated from prescription costs for the dura-tion of the patient’s taking the medication prior tobaseline assessment. Regrettably, we did not collectappropriate data for the subsequent year.

Hospital costs

Hospital cost information was obtained from dis-cussion with the New South Wales Department ofHealth, and from public and private hospital admin-istrators. While estimates varied, costs did not appearto differ between private psychiatric units, publicpsychiatric units and general ward or casualty units.The cost of staying in any type of hospital was there-fore derived from a range of estimates as $400/day.

Cost of social welfare

Social welfare costs were derived for those whowere unemployed, on a sickness benefit or a dis-ability pension due to their ‘mood’ state, andobtained from the University of New South Wales

Social Policy Research Centre. Since we did not havedata on what type of benefit patients were receiving,we calculated social welfare costs based on an esti-mate of unemployment or sickness benefit($321/fortnight) and disability pension ($355/fort-night). Our estimate of $170/week is for a singleperson and does not take into account rent assistanceor means testing for other income.

Results

Patients

The total baseline sample consisted of 270 patientswith depression, of whom 204 were TRs and 66 CRs.For the whole sample, 172 (64%) were female, withthe average age being 43.3 (SD = 14.9) years, while101 (37%) were assessed as inpatients. Some 29%were single, while 27% were separated/divorced orwidowed. Twenty-five per cent were in full-timework, 13% were in part-time work, 14% wereinvolved in home duties or were students, 9% hadretired, 12% were unemployed and 27% were onbenefits. Twenty-three (9%) were diagnosed clini-cally as having a psychotic depression (PD), 85 (31%)endogenous depression (ED), 94 (35%) neuroticdepression (ND) and 68 (25%) reactive depression(RD). Complete costing data were available for all270 for the 12 months prior to baseline assessment.

Depression details

For 80 (30%), the current depressive episode wastheir first. Over their lifetimes, sample members hadhad a mean number of 14 depressive episodes and amean duration of 102 weeks of significant mooddisturbance and, for the 44% of the sample who hadtaken time off work, a mean leave time of 41 weeks.Some 65% had been hospitalised.

In the 12 months prior to MDU assessment, 24%had been hospitalised in a public psychiatric hospital(mean duration 6.3 weeks), 14% in a private psychi-atric hospital (mean duration 8.1 weeks) and 14% ina general hospital ward or casualty department (meanduration 6.5 days). Over that period, and for theirmood state, 50% had had to take time off work (for amean duration of 20 weeks), 20% had required socialservices, 70% had visited a psychiatrist (with a meanof 17 visits) and 68% had visited a general practi-tioner, 20% had received a brain CT and 6% an MRI,while 45% had had diagnostic blood testing for theirmood state.

COSTING DEPRESSION292

Sample outcome at 12 months

Of the 182 (67%) who accepted the 12-monthfollow up, the outcome of the index episode was asfollows: 57% had met criteria [6] for a ‘recovery’(an euthymic state lasting more than 2 months) onaverage some 21 weeks after initial assessment, and6% met criteria for a ‘full remission’ (an euthymicstate lasting less than 2 months). Of those recoveringor remitting, some 23% had a ‘relapse’ to a full majordepressive syndrome (on average 23 weeks after base-line) and 10% had a ‘recurrence’or a new major depres-sive episode (on average 34 weeks past baseline).

Differences between those attending and notattending follow-up

Such comparisons of baseline variables identifiedonly a few differences. Thus, those attending followup had had fewer lifetime depressive episodes (11 vs19, p < 0.05), scored as less likely to exhibit dis-ordered personality function (on a range of measures,including fewer DSM-defined Cluster B personalitytraits) and were less likely to have ever used mari-juana (30% vs 49%, p < 0.01), but did not differsignificantly in frequency of other illicit drugs orcigarette use. The groups did not differ by socio-demographic variables, age of first depressiveepisode, severity of baseline episode or diagnosticsubtype, TR or CR group representation (66% vs73%) and, most importantly here, generated strik-ingly similar total costs in the 12 months prior tobaseline assessment (t = 0.3).

Differences between ‘tertiary’ and ‘control’referrals

The TRs and CRs did not differ significantly byage (43.9 vs 41.7 years), sex (63% vs 67% female),marital status (χ2 = 1.1), age at first depressiveepisode (30.9 vs 31.4 years), number of lifetimedepressive episodes (14.6 vs 12.2), baseline depres-sion severity on two measures (one self-report andone clinician-rated), or their chance of having takenany illicit drugs over their lifetime (40% vs 35%) orhaving smoked cigarettes (34% vs 35%). The groupsdid differ in employment status (χ2 = 13.8, df 4,p < 0.01), with the TRs more likely to be in receipt ofsocial services. The diagnostic profile in the TRs andCRs, respectively, was 7.4% versus 12.1% PDs;35.8% versus 18.2% EDs, 38.2% versus 24.2% NDsand 18.6% versus 45.5% RDs, with the RD represen-

tation identifying the greatest diagnostic differenceacross the samples. The TRs and CRs had similarrates of bipolar patients (11% vs 8%).

Theoretically, we would expect the TRs to havemore persistent and treatment resistant conditions,and thus greater disability, engendering higher costs.Analyses established that they were less likely tobe having their first depressive episode (i.e. 28% vs40%, χ2 = 4.2, p < 0.05) and had a longer currentepisode (35 vs 27 weeks, t = 2.0, p < 0.05), buttended to be less likely to be inpatients (i.e. 36%vs 42%, not significant).

Table 1 compares costs per patient for the 12 monthspreceding baseline assessment, important in provid-ing prevalence data of comparative cost sources (e.g.visiting a general practitioner, having a CT scan).The frequency data indicated that the TRs werepredictably more likely to have attended a psychia-trist, been in contact with a psychiatric nurse, havehad diagnostic tests, have received antidepressantdrugs and ECT, be a recipient of social services andhave been admitted to a public psychiatric hospital(but no more likely to have been admitted to aprivate psychiatric hospital or to a general hospitalfacility).

The unit cost per individual (UCPI) data enrich thepicture. The TRs had significantly higher generalpractitioner costs, but a non-significant trend tohigher psychiatrists’ costs. Distinctions were mostevident for hospitalisation costs (mean costs of $22 000 vs $7000 for CR for all hospital groupings),and with a higher percentage using each of theassessed hospital facility types. The UCPI differ-ences were not distinct for diagnostic tests or treat-ment costs, or even social service costs, so that thegreater overall mean UCPIs incurred by the TRs($14 000 vs $3000) were principally generated byhospitalisation costs.

Tables 2 and 3 aggregate principal group costdetails for the 12 months preceding baseline assess-ment and for the post-assessment interval, annualisedto allow for more appropriate comparison. Quite dif-ferent pictures emerged in the two subsamples. Forthe TRs, Table 2 data illustrate that group costs werereduced by 38%, principally due to a substantivereduction (of 53%) in hospitalisation costs, togetherwith reductions in general practitioner (57%) andpsychiatrist (14%) visit costs, and despite increasesin ECT (115%) and social welfare costs (32%). ForCRs, costs increased (by 137%) after baseline assess-ment, driven by substantial increases in hospitalisa-tion, ECT and social welfare costs. As varying rates


in receipt of ECT were evident across the sub-samples, and as ECT has significant associated hos-pitalisation costs, analyses reported in Tables 2 and 3

were repeated after excluding those who receivedECT following baseline assessment, but there wasno change in the contrasting profiles for the


Table 1. Comparison of mean costs for depression incurred (i.e. to patient, Commonwealth, insurancecompany, hospital) by consultant referrals (CR) and tertiary referral (TR) patients in the 12 months prior

to baseline assessment

Referral sourceCR TR(n = 66) (n = 204)

Cost item % Mean $ % Mean $ Significance testHealth professional ‘mood’ costs

Visited a GP 75.8 63.2 χ2 = 2.5Cost of visits to GP 208.75 428 t = –2.3*Visited a psychiatrist 56.1 73.5 c2 = 8.2**Cost of visits to psychiatrist 1437.10 1 939 t = –1.1Visited a psychologist 21.2 32.4 χ2 = 3.5Cost of visits to psychologista 576.65 530 t = 0.2Visit from a trained nurse 1.5 12.3 χ2 = 18.1***Cost of visits by trained nursea 36.35 65 t = –0.3Visit from social worker 13.6 16.7 χ2 = 0.5Cost of visits by social workera 175.75 180 t = 0.5

Any health professional 92.4 92.1 χ2 = 3.5Total cost health professionals 1201.65 1 964 t = –2.2*

HospitalisationStayed in public psychiatric unit 4.5 29.9 χ2 = 18.1***Cost public psychiatric unit 4666.65 18 361 t = –1.4Stayed in private psychiatric unit 9.1 15.2 χ2 = 1.6Cost private psychiatric unit 9333.35 25 200 t = –1.8Stayed in casualty/general ward 12.1 14.2 χ2 = 0.2Cost casualty/general ward 1550.00 2 869 t = –0.8

All hospital stays for ‘mood’ 18.2 44.6 χ2 = 14.8***Total ‘mood’ hospitalisation cost 6866.65 21 807 t = –2.5*

Diagnostic testsAny CT 7.6 24.0 χ2 = 8.5**Cost of CT 251.80 252 t = 0.0Any MRI 4.5 5.9 χ2 = 0.2Cost of MRI 593.75 594 t = 0.0Any blood tests 25.8 49.5 χ2 = 12.0***Cost of overall blood test 412.95 394 t = 0.1

All diagnostic tests 28.8 58.8 χ2 = 18.0***Total cost diagnostic tests 529.50 496 t = 0.2

TreatmentAny antidepressant medication 57.6 84.8 χ2 = 26.3***Cost of antidepressants 323.30 410 t = –2.0Any ECT 1.5 16.2 χ2 = 9.6**Cost of ECT 1723.25 1 548 t = 0.3

Other costsOn social welfare 6.1 25.0 χ2 = 11.0***Cost of social welfare 4972.50 5 770 t = –0.5

All who used one or more services 93.9 98.5 χ2 = 4.2*Average total cost 3220.30 14 174 t = –4.4***

a2-year pre-MDU cost apportioned as one-third for 12–24 months pre-MDU and two-thirds for 0–12 months pre-MDU.*p = 0.05, **p < 0.01; ***p < 0.001.CT, computerised tomography; ECT, electroconvulsive therapy, GP, general practitioner; MRI, magnetic resonance imaging.


subsamples. Specifically, for the TRs, social welfarecosts doubled but hospitalisation costs were morethan halved. Increased costs for the CRs were drivenby a four-fold increase in hospitalisation and socialwelfare costs. Thus, changed trajectories in costswere not a primary reflection of an increased rate ofECT utilisation in the CRs.

Comparison of utilisation data across the subsam-ples indicated that, in the 6 months prior to follow up,the two groups differed significantly on only onestudy variable, in that the TRs were significantlymore likely (21% vs 8%, χ2 = 3.8, p < 0.05) to be inreceipt of social services. The non-significant differ-ences allowed the conclusion that the two subgroups

had approximated following baseline assessment.The TRs and CRs did not differ in the rate of hospi-talisation (14% vs 10%), length of any such hospital-isation (5.9 vs 7.2 weeks), casualty or general wardadmission (4% each, and 2.2 vs 4.0 days), takingtime off work (24% vs 27%) or in its total duration(21 vs 13 weeks), duration of receipt of social ser-vices (24 vs 32 weeks), whether there was out-patientpsychiatrist follow up (78% vs 73%) or number ofvisits (8.5 vs 5.1), whether the patient visited ageneral practitioner for their depression (34% vs27%) or number of visits (6.2 vs 3.5) or whetherthe patient was currently being in contact with a psy-chiatrist (67% vs 58%).

Table 2. Group total costs and user mean cost for depression incurred (i.e. to patient, Commonwealth,insurance company, hospital) over two annualised intervals for tertiary referrals (TRs)

Time period12 months pre-baseline 6–12 months post-baselinea

(n = 134) (n = 134)Amount used User mean Group total Amount used User mean Group total

Cost item (%) ($) ($) (%) ($) ($)GP visits 62.7 497 41 756 33.6 396 17 816Psychiatrist visits 71.6 2 021 193 973 77.6 1 605 166 956Hospital costs 44.8 22 993 1 379 601 14.9 32 080 641 600ECT treatments 18.7 1 529 38 225 16.4 3 731 82 090Social welfare 24.6 5 188 171 191 20.9 8 087 226 440Total cost 13 929 1 824 747 9 458 1 134 902

aCosts over 6-month interval doubled to provide a 12-month estimate.ECT, electroconvulsive therapy; GP, general practitioner.

Table 3. Group total costs and user mean cost for depression incurred (i.e. to patient, Commonwealth,insurance company, hospital) over two annualised intervals for consultant referrals (CRs)

Time period12 months pre-baseline 6–12 months post-baselinea

(n = 48) (n = 48)Amount used User mean Group total Amount used User mean Group total

Cost item (%) ($) ($) (%) ($) ($)GP visits 75.0 139 5 006 27.1 261 3 388Psychiatrist visits 56.3 1608 43 415 72.9 838 29 328Hospital costs 18.8 7 422 66 800 12.5 34 667 208 000ECT treatments 2.1 1 723 1 723 12.5 3 081 18 486Social welfare 4.2 5 270 10 540 8.3 10 880 43 520Total cost 2 897 127 484 7 762 302 723

aCosts over 6-month interval doubled to provide a 12-month estimate.ECT, electroconvulsive therapy; GP, general practitioner.


Self-rated ‘costs’

Table 4 reports data from the self-estimate ques-tionnaire. While comparison of self-judged costsacross quite differing parameters is clearly problem-atic, mean scores suggest that the impact of depres-sion on social and relationship factors (respectivemean scores of 1.52 and 1.81) were more ‘costly’than direct and indirect financial ones (0.96 and1.46). The highest mean score, however, wasreturned for ‘personal costs’ (i.e. 2.34), indicatingthat depression itself substantively undermines theindividual’s self-confidence. While self-rated directand indirect financial costs were the only parametersto correlate with actual patient-generated costs in thepreceding years, all associations were weak.

Impact of diagnostic subtype

The influence of diagnostic subtyping was evidentacross a number of cost parameters for the wholesample. For example, it was a significant discrimina-tor (F = 5.7, p < 0.001) of psychiatrist visit costs inthe year before baseline assessment, with the averagecost for the NDs being $2009, as against $868–923for the other three (PD, ED and RD) groups, and witha similar pattern in the preceding year. In the yearprior to baseline assessment, hospitalisation costswere highest for the PDs at $15 000, as against $9000for the EDs, $5500 for the NDs and $4500 for theRDs. Electroconvulsive therapy costs were highest(F = 4.0, p < 0.01) in the PDs ($496), followed by theEDs ($265), NDs ($115) and RDs ($107). By con-trast, antidepressant costs were highest in the EDs($385), as against $337 in the NDs, $230 in the PDs

and $197 in the RDs (F = 7.4, p < 0.001). The costgradient over the 12-month interval was quite strik-ing, with the UCPI being $17 326 for the PDs,$13 245 for the EDs, $10 950 for the NDs and $8238for the RDs.

For each year prior to baseline assessment, the 28bipolar patients consistently generated higher bloodtest and antidepressant costs than the remainingunipolar patients, and tended to generate higher hos-pitalisation costs ($2300 vs $1978 for the earlier pre-baseline period, and $11 100 vs $6861 for thesecond pre-baseline period). By contrast, after base-line assessment, their total costs were approximatelyhalf that of the unipolar patients, largely due to lowerhospitalisation costs (i.e. $875 vs $2429).

Impact of other study variables

We examined a number of additional study vari-ables as impacting on costs, across all three for-malised intervals. Receipt of social welfare was asignificant predictor of higher total costs across eachinterval. Patients with a history of self-injury gener-ated higher costs over the earliest assessment period($8906 vs $2665, p < 0.01) and post-baseline assess-ment ($7852 vs $3265, p < 0.05). Those with ahistory of illicit drug use or heavy alcohol use, orwho had smoked cigarettes, generated slightly higherpre-MDU assessment costs (i.e. $5583 vs $4238) aswell as post-MDU costs (i.e. $6465 vs $3549), butsuch differences were not significant. A longer life-time duration of depression consistently predictedhigher costs across each interval (r = 0.45, 0.16 and0.32, respectively). Those rated as having personalitydysfunction generated higher costs over the earliest

Table 4. Self-estimate cost questionnaire data and correlation of scores on each parameter withfinancial costs

CostsDirect Indirect Social Relationship Personal

None (%) 43 37 32 29 17Mild (%) 30 15 19 11 16Moderate (%) 16 21 23 24 13Severe (%) 8 19 18 23 31Extreme (%) 3 6 7 12 16Catastrophic (%) 0 2 1 1 7

Mean score 0.96 1.46 1.52 1.81 2.34Correlation with financial costs (r) 0.17* 0.23** 0.10 0.07 0.10

*p < 0.05, **p < 0.01.

assessment period (r = 0.24, p < 0.001) and acrossthe post-baseline interval (r = 0.27, p < 0.001).

Predictors of costs in 2 years prior to baselineassessment

Such univariate analyses suggested a number ofvariables for several multivariate analyses. A mul-tiple regression examined predictors of total costsover the 2 years prior to the baseline assessment. Therefined set of significant variables (overall F = 12.6,p < 0.001) included patients having a longer lifetimeduration of depression (beta weight of 0.22), havinghigher levels of disordered personality function(B = 0.22), being on social welfare in the 12 monthsprior to baseline assessment (B = 0.18), having abipolar disorder (B = 0.14), receiving a clinical diag-nosis of ED (B = 0.14) and not having a diagnosis ofRD (B = -0.20). Repeating that analysis after exclud-ing those patients whose initial depressive episodeoccurred only in the preceding previous year gener-ated two significant predictors in the whole sample(F = 30.4, p < 0.001): being on social welfare and notreceiving an RD diagnosis. The same analyses insubsamples identified differing predictors. Thus, forthe CRs, predictors of higher total costs were: higherpersonality dysfunction scores, using marijuana andnot receiving an RD diagnosis (F = 9.6, p < 0.001),and, for the TRs, predictors were being on socialwelfare, a longer lifetime duration of depression andreceiving an ED diagnosis (F = 10.4, p < 0.01).

Predictors of cost charges after assessment

Equally importantly, we examined (using logisticregression analyses) for predictors of increased ordecreased costs after baseline consultation; although,to prevent confounding, costs in the preceding 2-yearperiod were not included. In the whole sample, theonly predictor of higher costs was a diagnosis of ED(F = 4.5, p < 0.05). In the CRs, increased costs werepredicted by a longer lifetime duration of depression,a higher personality dysfunction score, a diagnosis ofED and lower trait anxiety (F = 7.6, p < 0.001), whilein the TRs, higher scores were predicted by beingmale and not having a diagnosis of RD.

Discussion

Apart from unpublished studies examining compara-tive cost-benefits of antidepressant drugs, there hasbeen no previous attempt to cost depression and its

treatment in Australasia. The Global Burden ofDisease Report [7] has, however, had a majorimpact in creating awareness about the disability, andassociated personal and financial costs, caused bydepression.

While the present report has a number of obviouslimitations (e.g. it examines select samples ofdepressed patients in relatively small numbers, andprimary data relies on patient self-report details), italso has a number of strengths, and a key one is thatwe went to considerable trouble to detail a method-ology for assessing the direct costs of depression,and particularly its management, in the local setting.Regrettably, we did not collect data on antidepressantcosts for the post-baseline period, but their contribu-tion to overall cost is likely to be minor, as they con-tributed less than 3% in the TRs and 10% in the CRsfor the year preceding baseline assessment. Whilesome unit costs will vary across services and time,we believe that we have identified relevant param-eters, and have reported a wide range of analyticstrategies, so that other services (and health planners)can replicate or modify such strategies to cost theirservice, although there is always the risk of quiteerroneous conclusions being drawn from any dataset. Some will be noted in this discussion.

We also proceeded beyond the common focus onfinancial costs by examining personal costs forpatients. When asked to rate the impact of depressionon their lives, the patients rated indirect financialcosts as having more impact than direct ones, ratedsocial and relationship costs even higher, but clearlyput the ‘personal’ costs of depression highest. Thus,to have a depressive disorder is potentially depresso-genic in and of itself. It would be useful if thisfinding were pursued in qualitative studies to deter-mine to what degree such a high personal cost isdriven by the illness itself, associated stigma and anyother factors.

Across the whole sample we identified a range ofindividual factors that had substantial economic costimplications. The list includes obvious components(such as receipt of social welfare, hospitalisationand therapist costs) but also diagnostic type, ‘trackrecord’ (e.g. previous hospitalisation, lifetime dura-tion of depression), personality dysfunction, self-injurious behaviours, illicit drug-taking and cigarettesmoking. Such factors, as well as ones not measured(e.g. compliance with treatment and medication) allhave substantial potential to impact on costs.

The last set of factors is not surprising to the clini-cian but may be misinterpreted in many cost studies.


For example, several studies (e.g. [8]) have quantifiedgreater disability in those with sub-syndromal depres-sion than in those with major depression. Such acounter-intuitive finding may reflect a focus on mea-suring Axis I symptoms in such analyses. Those withsub-syndromal (and the so-called ‘minor’ depres-sions) may be more likely to have personalities andlife styles that drive depressive symptoms and disabil-ity, generating economic costs which might beunfairly costed to ‘depression’ in such studies. Here,there was evidence that a dysfunctional personalitystyle (as measured directly and indirectly from vari-ables such as self-injurious behaviours) does con-tribute to higher costs in those with depression,but the nature of this contribution needs to be con-ceptualised more clearly in cost and disabilitystudies.

Diagnostic type produced particularly interestingfindings about costs. A diagnosis of RD was sig-nificant in many analyses, indicating that those witha precipitant-weighted or adjustment disorder gener-ated comparatively low costs. By contrast, thosereceiving a clinical diagnosis of ND (and where itmight be assumed that anxiety and/or personalitystyle was contributing to a depressive syndrome)generated the highest out-patient psychiatrist costsprior to assessment. In addition, as they had thelowest rates of recovery, they tended to generate thehighest hospitalisation and social welfare costs longi-tudinally. By contrast, those with the more ‘biologi-cal’ depressive subtypes (i.e. PD and ED) generatedthe highest hospitalisation, investigatory and ECTcosts prior to assessment, but comparatively lowercosts when followed longitudinally. This might indi-cate that, despite the greater severity, associated dis-ability and greater need for investigation andhospitalisation for those with these illnesses, activetreatment for those with psychotic and melancholicdepression is distinctly cost beneficial. The samepattern appeared to hold for bipolar patients (whogenerated higher costs than unipolar patients prior toassessment but subsequently lower ones), and againcould indicate the differentially superior benefits ofactive treatment.

While one focus was on our tertiary MDU referralpatients, we included a comparison group. However,as it was a small group and constituted both by out-patients referred to our consultants and by a signifi-cant percentage of hospitalised area patients, itcannot be regarded as representative. Clearly, a com-parison group treated by a different service or set ofpsychiatrists might generate quite different costs.

For these CRs, as for our TRs, we can conclude that:(i) therapist, investigatory and antidepressant med-ication costs are relatively inexpensive in comparisonwith hospitalisation, ECT and social welfare costs;and (ii) unit costs are not of great importance in andof themselves, as total group costs are more influ-enced by the prevalence of the individual compo-nents and their duration. Thus, a course of ECT wasexpensive but received only by one patient prior toand following baseline assessment. By contrast, rarecost sources for brief periods (e.g. hospitalisation) orextending over time (e.g. social welfare), or commoncost sources (e.g. visiting a psychiatrist, receivingan antidepressant medication) contribute strongly togroup costs.

Against expectation, and in comparison with theTRs, annualised costs increased rather thandecreased in the CRs. This is likely to reflect twofactors. First, 42% were having their first depressiveepisode and might be expected to then have lowannual costs prior to baseline assessment. Second,and perhaps of greater salience, treatment generatescosts. The increased costs here did not relate to healthpractitioner costs (with a distinct drop in the percent-age being so treated by a general practitioner, and amodest increase in the percentage being treated by apsychiatrist), but by distinct increases in hospitalisa-tion, ECT and social welfare costs.

Against this backdrop we now focus on our keyinterest: the extent to which the MDU assessmentprocess may have impacted on costs. For anyepisodic condition, it can be relatively easy todemonstrate (albeit spuriously) that a service iscost-effective. Migraine offers such an example,with patients often having episode epochs whichencourage seeking assistance (and thus generatingcosts). Subsequent improvement to a quiescentinterval may reflect the impact of any interventionor, alternatively, may reflect no more than a ‘regres-sion to the mean’ phenomenon or a spontaneousimprovement in illness pattern. The latter willreduce ‘costs’, and the intervention service mightthen claim that the treatment was cost-effective. To reduce that possibility and because we sought tofocus on any reduction in ongoing (and disability-weighted) costs, we collected data for the 2 pre-ceding years and ignored the 6-month intervalfollowing baseline assessment.

Overall group costs in our MDU-assessed TRswere reduced (despite increased costs for ECT andsocial welfare) by nearly 40 per cent, reflecting somereduction in general practitioner and psychiatrist



costs, but most distinctly by a reduction in hospitali-sation costs. Despite increased hospitalisation costsfor those admitted, the percentage hospitaliseddropped from 45% to 15%, resulting in a saving ofmore than $700 000. The extent to which changes inuse of practitioners, facilities and services reflectsdisorder resolution occurring ‘naturally’ or as a directconsequence of the MDU management recommenda-tions and their implementation by referring therapistscannot be answered, revealing a further limitation tosuch costing studies. To show true cost benefit, theMDU service would need to demonstrate that its‘intervention’ generated lower subsequent costs thanno intervention.

Importantly, however, MDU assessment was notassociated with cost increases. We suspect, and havesome data to support, that it may have most distinctlyimproved the outcome trajectory of those with themore ‘biological’ depressive disorders (i.e. bipolar disorder, psychotic and melancholic depression), pre-sumably achieved by review and modification ofpharmacological treatments, recommendations forECT for some, and attention to second-order factorsthrough pointers to treatments such as cognitive–behaviour therapy and strategies such as anxietymanagement.

Effective services can increase, maintain ordecrease costs, as has been reported following theintroduction of case management models. Evaluationclearly should be weighted to the objectives of anyclinical service. However, to the extent that it ispreferable to have a tertiary referral service assess-ment associated with a lowering of the cost trajec-tory, the MDU appears to meet that criterion.

Acknowledgements

The authors wish to acknowledge Dusan Hadzi-Pavlovic, Marie-Paule Austin, Heather Brotchie,Yvonne Foy, Ian Hickie and Christine Taylor for studyassistance, an NH&MRC Program Grant (993208)and an Infrastructure Grant from the New South WalesDepartment of Health for funding support.

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