ORIGINAL PAPER

Trends in health-related quality of life and health serviceuse associated with comorbid diabetes and major depressionin South Australia, 1998–2008

Evan Atlantis • Robert D. Goldney •

Kerena A. Eckert • Anne W. Taylor •

Patrick Phillips

Received: 17 October 2010 / Accepted: 5 May 2011 / Published online: 18 May 2011

� Springer-Verlag 2011

Abstract

Purpose To investigate the trends in health-related qual-

ity of life and health service use associated with diabetes

and/or major depression in South Australia from 1998 to

2008.

Methods Data analyzed were from 9,059 persons aged

C15 years who participated in representative surveys of the

South Australian population in 1998, 2004 and 2008. Major

depression was determined using the mood module of the

Primary Care Evaluation of Mental Disorders (PRIME-

MD). Diagnosed diabetes and health service use were

determined by self-report. Health-related quality of life was

assessed using the 36-item Short-Form Health Survey (SF-

36) and the 15-item Assessment of Quality of Life (AQoL)

instruments. Socio-demographics (including mental health

literacy), arthritis, asthma, osteoporosis, and obesity

covariates were determined by self-report. Weighted age-

standardized and multiple covariate-adjusted means of

dependent measures were computed.

Results The prevalence of diabetes only, major depres-

sion only, and comorbid diabetes and major depression

increased by 3.0 (74%), 2.6 (36%), and 0.4 (53%) per-

centage points, respectively, from 1998 to 2008. Mean

health-related quality of life scores were 9 to 41% lower

(worse), and health service use was 49% higher for persons

with comorbid diabetes and major depression than for

those with diabetes only (all P values \0.05) independent

of all covariates, consistently over the 10-year period.

Conclusions If past trends continue, our results suggest

that the increased population health and economic burden

of comorbid diabetes and major depression could persist

over the next decade or so. These trends have important

implications for making health policy and resource allo-

cation decisions.

Keywords Depression � Diabetes � Trends � QOL �Service utilization

Introduction

Depression and diabetes mellitus are among the leading

causes of disability burden [1]. Depression is consistently

associated with diabetes mellitus prevalence [2–4] and

incidence [5, 6]. Comorbid depression significantly wors-

ens the health and economic burden of diabetes. Depres-

sion in persons with diabetes is associated with non

adherence to treatment and healthy lifestyle guidelines [7,

8], poor glycemic control [9, 10], low health-related quality

of life scores [11], increased medical complications [12],

and high health service utilization and costs [13, 14].

E. Atlantis (&)

Royal Adelaide Hospital/Institute of Medical and Veterinary

Science, SA Health, Government of South Australia, Adelaide,

SA 5000, Australia

e-mail: evan.atlantis@adelaide.edu.au

E. Atlantis � K. A. Eckert � A. W. Taylor � P. Phillips

School of Medicine, The University of Adelaide, Adelaide,

SA 5005, Australia

R. D. Goldney

Discipline of Psychiatry, The University of Adelaide, Adelaide,

SA 5005, Australia

R. D. Goldney

Hanson Institute, SA Pathology, Royal Adelaide Hospital,

Adelaide, SA 5000, Australia

P. Phillips

Department of Endocrinology, The Queen Elizabeth Hospital,

Woodville South, Adelaide, SA 5011, Australia

123

Soc Psychiatry Psychiatr Epidemiol (2012) 47:871–877

DOI 10.1007/s00127-011-0394-4

Population trends provide important epidemiologic

information for estimating changes in the burden of med-

ical conditions, including health-related quality of life

measures, for health resource allocation decisions [15]. We

recently showed that the prevalence of diabetes and major

depression in the Australian population increased signifi-

cantly over the recent decade [16, 17]. Similar trends have

been reported for the US population [18, 19]. Whether

changes in the burden of diabetes differed according to the

presence or absence of major depression during this period

is not yet determined. To address this knowledge gap, we

report 10-year trends in health-related quality of life and

health service use associated with diabetes and/or major

depression in the South Australian population aged

15 years or older from 1998 to 2008, independent of an

extensive range of potential covariates.

Methods

Survey design and sampling procedure

Data were obtained from the 1998, 2004 and 2008 South

Australian Health Omnibus Surveys (SAHOS) conducted

in the autumn of each year by the Population Research and

Outcome Studies Unit, Department of Health, South Aus-

tralia. Ethics approval for each of the three surveys was

received from the South Australian Department of Health

Human Research Ethics Committee.

The SAHOS is an annual user-pays population-based

survey administered to metropolitan and country South

Australian residents, aged 15 years or older, via a face-to-

face interview. Data collection included questions which

were of direct relevance to information requirements of

payee organizations. It uses a random stratified sampling

technique in metropolitan areas based on the census col-

lectors’ districts used by the Australian Bureau of Statis-

tics. Ten dwellings were chosen in each district by

selecting every fourth household from a random starting

point. The rural sample included country towns with a

population of 1,000 or more. Where more than one person,

aged 15 years or older, resided in the household, the person

with the last birthday was selected for interview. A mini-

mum of six visits were made to each household in an

attempt to interview the selected person and replacement

interviews were not conducted.

Measures

Socio-demographics

Age, sex, country of birth, relationship status, education

attainment, area of residence, living arrangement and

employment status information was collected using stan-

dard questions. Mental health literacy was assessed by

asking participants about their knowledge of and beliefs

about depression, as described previously [17, 20]. The

accuracy of responses was rated (scored from 0 to 200)

according to a previously validated algorithm [21], and

tertile cut-offs were used to group participants into ‘poor or

fair’ (0 to 149), ‘good’ (150 to 199) or ‘excellent’ (200)

mental health literacy categories.

Medical conditions

Lifetime diagnosis of arthritis, asthma, osteoporosis, and

diabetes was determined by self-report. Body mass index

(BMI) was derived from self-report height and weight

values, and computed as weight in kilograms divided by

height in meters squared. Obesity was defined by the BMI

cut-off of C30, according to international criteria. Major

depression was determined using the mood module of the

Primary Care Evaluation of Mental Disorders (PRIME-

MD) procedure, which has demonstrated reliability [22],

and diagnostic accuracy in primary health care settings

[23]. It consists of 16 questions relating to symptoms of

depression experienced nearly every day for the last

2 weeks (categorized as yes/no). Classification of major

depression was according to an algorithm based on the

Diagnostic and Statistical Manual of Mental Disorders, 4th

Edition, (DSM-IV) definition.

Health-related quality of life and health service use

outcomes

Health-related quality of life was assessed using the

36-item Short-Form Health Survey (SF-36) (available

from: http://www.qualitymetric.com) and the 15-item

Assessment of Quality of Life (AQoL) [24] instruments.

Physical and Mental Component Summary scores (PCS

and MCS) of the SF-36 were derived from the aggre-

gation of scores from 36 questions across eight dimen-

sions of health and wellbeing. Because the SF-36

Version 1 was used in the 1998 survey whereas the SF-

36 Version 2 was used in the subsequent cycles, T scores

for PCS and MCS were computed using appropriate

Australian population norms [25] for comparisons across

surveys.

AQoL scores are derived from 15 questions across five

dimensions of health-related quality of life (illness, inde-

pendent living, social relationships, physical senses, and

psychological wellbeing) [24]. Individual items are sum-

med to provide a score for each dimension ranging from

zero, worst AQoL (death) to one, full or normal health

(without handicap, impairment or disability) and an overall

score to compare AQoL profiles.

872 Soc Psychiatry Psychiatr Epidemiol (2012) 47:871–877

123

Health service use in the preceding month was deter-

mined by self-report. First, participants were asked ‘‘In the

last month, which of these health services have you used in

South Australia?’’ The list of health services was extensive

and included an ‘‘other (specify)’’ response option, but

dental health services was not listed in the 1998 survey.

Second, participants were asked ‘‘How many times in the

last month have you visited any health services?’’ The total

number health services used (excluding dental) were

aggregated for analysis.

Statistical analyses

Statistical analyses were completed using SPSS version

17.0 (SPSS Inc. Chicago, US). All analyses accounted for

the complex sample design. Statistical tests were 2-sided

and a P value of \0.05 was considered indicative of sta-

tistical significance. All data were age within sex stan-

dardized to the South Australian population distributions

according to the 2001 Census of Population and Housing.

Trends in weighted age-standardized prevalence and mean

estimates of population characteristics were computed

using logistic regression for categorical data fitted with

survey year as a continuous variable, and general linear

models for continuous data with survey year for orthogonal

polynomial contrasts. Weighted age-standardized and

multiple-adjusted means (mean [95% CI]) of SF-36 PCS

T scores, SF-36 MCS T scores, AQoL scores, and health

service use were computed using general linear models that

included survey by diabetes and/or major depression group

interactions and survey year for orthogonal polynomial

contrasts, with adjustment for all socio-demographics and

medical conditions listed in the Table 1 (except for BMI

due to a large number of missing cases). All models were

subsequently repeated with BMI as a covariate, in a series

of sensitivity analyses.

Results

Response rates for the 1998, 2004, and 2008 surveys were

82, 76, and 73%, respectively; excluding persons who were

out of scope (households were vacant, not contactable after

six visits, or had occupants unable to speak English, or with

mental incapacity). Trends in the weighted age-standard-

ized prevalence and mean estimates of population charac-

teristics across the three surveys appear in the Table 1. The

prevalence of diabetes only, major depression only, and

comorbid diabetes and major depression (both) increased

by 3.0 (74%), 2.6 (36%), and 0.4 (53%) percentage points,

respectively, over the 10-year period. Mean SF-36 MCS

T scores and AQoL scores decreased by 2.9 (6%) and 0.02

(3%) points, SF-36 PCS T scores remained relatively

stable, and health service use increased by 0.2 (21%) units

over the 10-year period. The prevalence of male sex, higher

education attainment, full-time or part-time employment,

retirement, excellent mental health literacy, asthma, oste-

oporosis, and obesity increased across survey cycles. In

contrast, the prevalence of residing in metropolitan regions,

living alone, unemployment, and non workforce partici-

pation (other/homemaker/student status) decreased across

survey cycles. No significant changes were observed for

birth region, relationship status, or arthritis variables across

survey cycles.

Results for health-related quality of life scores and

health service use by diabetes/major depression group

appear in the Fig. 1a–d. Weighted age-standardized and

multiple covariate-adjusted mean SF-36 PCS T scores, SF-

36 MCS T-scores, and AQoL scores were 3.63 (9%), 20.35

(41%), and 0.23 (35%) points lower, respectively; and

health service use was 0.75 (49%) units higher for persons

with comorbid diabetes and major depression than for

those with diabetes only (all P values \0.05). No signifi-

cant interactions for survey by diabetes and/or major

depression group were observed. Results remained

unchanged (all P values) when all models were addition-

ally adjusted for BMI.

Conclusions

These results indicate that the prevalence of diabetes only,

major depression only, and comorbid diabetes and major

depression increased by 74, 36 and 53%, respectively, in

the South Australian population from 1998 to 2008.

Health-related quality of life was worse and health service

use was higher for persons with comorbid diabetes and

major depression than for those with diabetes only inde-

pendent of all covariates, consistently over the 10-year

period. These effect sizes are consistent with those reported

in previous cross-sectional studies for health-related qual-

ity of life outcomes [11] and health service utilization in

the US [13].

Our results have implications for both clinical practice

and economic evaluation. Prospective findings of a ran-

domized trial conducted in patients with diabetes show

that a 10% higher health-related quality of life score is

associated with a 7% reduced risk of vascular events, a

13% reduced risk of diabetes complications, and a 14%

lower rate of all-cause mortality [26]. Of further clinical

importance, having problems with mobility was the only

dimension independently predictive of those events,

which suggests that physical rather than mental health

dimensions of health-related quality of life might provide

the most relevant prognostic information in patients with

diabetes.

Soc Psychiatry Psychiatr Epidemiol (2012) 47:871–877 873

123

Table 1 Trends in prevalence and mean estimates of population characteristics for South Australian Health Omnibus Surveys 1998, 2004, and

2008

Weighted no. of participantsa N = 3,010 (1998) N = 3,015 (2004) N = 3,034 (2008) P valueb

Factors % or mean (95% CI) % or mean (95% CI) % or mean (95% CI)

Socio-demographic

Age (Mean years) 45.2 (44.5–45.9) 45.2 (44.5–45.9) 45.2 (44.5–45.9) 0.973

Sex, male (%) 40.2 (38.4–42.0) 41.7 (39.9–43.5) 43.7 (41.9–45.5) 0.006

Birth region, Australia/UK/Ireland (vs. other) (%) 87.8 (86.6–88.9) 87.8 (86.5–88.9) 87.6 (86.4–88.7) 0.967

Relationship status, partnered (%) 53.2 (51.3–55.0) 53.0 (51.2–54.9) 55.3 (53.5–57.2) 0.097

Residential area, metropolitan (vs. rural) (%) 79.3 (77.8–80.7) 78.4 (76.9–79.9) 76.5 (74.9–78.0) 0.011

Living arrangement, alone (vs. with others) (%) 27.6 (26.1–29.3) 27.0 (25.4–28.6) 24.9 (23.4–26.5) 0.016

Education attainment

Primary/trade (%) 33.9 (32.2–35.6) 29.5 (27.8–31.1) 28.4 (26.8–30.1) \0.001

Secondary (%) 33.8 (32.1–35.5) 32.4 (30.7–34.2) 31.1 (29.4–32.8)

Tertiary (%) 32.3 (30.7–34.1) 38.1 (36.4–39.9) 40.5 (38.7–42.3)

Employment status

Working full-time/part-time (%) 50.4 (48.5–52.2) 53.7 (51.8–55.5) 55.4 (53.5–57.2) \0.001

Unemployed (%) 3.6 (2.9–4.3) 2.4 (1.9–3.1) 2.6 (2.1–3.3)

Retired (%) 16.4 (15.2–17.7) 18.6 (17.4–20.0) 19.1 (17.9–20.5)

Other/homemaker/student (%) 29.6 (28.0–31.3) 25.3 (23.7–27.0) 22.9 (21.3–24.5)

Mental health literacy

Poor/Fair (%) 50.1 (48.3–51.9) 33.0 (31.3–34.8) 31.7 (30.0–33.4) \0.001

Good (%) 19.5 (18.1–21.0) 21.9 (20.4–23.5) 20.0 (18.6–21.6)

Excellent (%) 30.4 (28.8–32.1) 45.1 (43.2–46.9) 48.3 (46.4–50.1)

Medical conditions

Arthritis (%) 24.1 (22.6–25.7) 25.1 (23.6–26.7) 24.6 (23.1–26.1) 0.665

Asthma (%) 19.0 (17.6–20.5) 23.3 (21.8–24.9) 25.4 (23.8–27.0) \0.001

Osteoporosis (%) 4.0 (3.4–4.8) 5.6 (4.9–6.4) 5.7 (5.0–6.6) 0.001

Obesityc (%) 17.4 (14.6–17.4) 20.0 (17.1–20.0) 22.4 (19.3–22.4) \0.001

Diabetes and/or major depression

None (%) 88.0 (86.8–89.1) 83.8 (82.4–85.1) 82.0 (80.5–83.3) \0.001

Diabetes only (%) 4.0 (3.4–4.8) 6.6 (5.8–7.5) 7.0 (6.2–8.0)

Major depression only (%) 7.2 (6.3–8.2) 8.5 (7.5–9.6) 9.8 (8.7–11.0)

Both (%) 0.8 (0.5–1.2) 1.1 (0.8–1.6) 1.2 (0.9–1.6)

Health-related quality of lifed

SF-36 PCS, mean T score 49.1 (48.8–49.5) 49.5 (49.1–49.9) 49.5 (49.1–49.9) 0.189

SF-36 MCS, mean T score 51.4 (51.0–51.8) 49.4 (49.0–49.8) 48.5 (48.1–48.9) \0.001

AQoL, mean score 0.81 (0.80–0.82) 0.80 (0.79–0.81) 0.79 (0.78–0.80) \0.001

Health service use in the preceding month

Mean no. 1.0 (0.9–1.0) 1.1 (1.0–1.1) 1.2 (1.1–1.2) \0.001

No. C2 (vs. 0–1) (%) 23.0 (21.5–24.5) 27.3 (25.7–28.9) 31.3 (29.6–33.0) \0.001

a Age within sex standardized to the South Australian population distribution according to the 2001 Census of Population and Housingb P value for linear trend analysis using logistic regression for categorical data fitted with survey year as a continuous variable, and general linear

model for continuous data with survey year for orthogonal polynomial contrastsc Sample sizes are N = 2,736, N = 2,737, and N = 2,749 for 1998, 2004, and 2008, respectively, due to missing data; obesity defined by body

mass index C30d PCS, physical component summary; MCS, mental component summary; AQoL, assessment of quality of life sample sizes are N = 2,933,

N = 3,015, and N = 3,026 for 1998, 2004, and 2008, respectively, due to missing data

874 Soc Psychiatry Psychiatr Epidemiol (2012) 47:871–877

123

Improving health-related quality of life for risk reduc-

tion in persons with comorbid diabetes and depression

might be achievable with appropriate intervention. Ran-

domized trials have shown that effects of lifestyle (exer-

cise-based) interventions on health-related quality of life

outcomes and clinically significant depressive symptoms

are sufficient in size to reduce the risk of adverse outcomes,

as detailed above [27]. But depression presents significant

barriers to effective and sustainable healthy lifestyle

changes [7, 8] that need to be addressed in order to benefit

persons with comorbid diabetes.

How to address the consistently higher health service

use among persons with major depression compared with

those with diabetes only is less clear. The prevalence of

major depression increased by 3.5 percentage points (from

6.8% to 10.3%) in South Australia over the 10-year period

[17] despite concerted efforts made at government, pro-

fessional, charitable and industry levels to enhance public

knowledge about major depression and the effectiveness

and availability of treatments, including the extension in

2006 of Medicare rebate services to general practitioners

and allied health professionals providing mental health

care, and increases in prescriptions for antidepressant

medications [28]. These initiatives might have even had

unintended consequences, as mental health literacy was

found to be significantly associated with higher prevalence

None Diabetes Major depression Both

None Diabetes Major depression Both None Diabetes Major depression Both

None Diabetes Major depression Both

SF

-36

PC

S T

-sco

re (

wei

gh

ted

mea

n, 9

5% U

CL

)

0

10

20

30

40

50

Ref

Ref

P < 0.001 for all

P = 0.002 P = 0.017

P = 0.158 for survey by diabetes and depression interactionP = 0.065 for orthogonal polynomial contrasts for trends across surveysWeighted N = 9,034; UCL, upper confidence limit

A

SF

-36

MC

S T

-sco

re (

wei

gh

ted

mea

n, 9

5% U

CL

)

0

10

20

30

40

50

60

Ref

Ref

P < 0.001 for both

P < 0.001 P = 0.647

P = 0.390 for survey by diabetes and depression interactionP < 0.001 for orthogonal polynomial contrasts for trends across surveys P < 0.001 for linear trend across survey (coefficient = -2.349)P = 0.363 for quadratic trend across surveys (coefficient = 0.504)Weighted N = 9,034

P = 0.210 B

AQ

oL

(w

eig

hte

d m

ean

, 95%

UC

L)

0.0

0.2

0.4

0.6

0.8

Ref

Ref

P < 0.001 for all

P < 0.001 P = 0.833

PP Weighted N = 8,963

CH

ealt

h s

ervi

ce u

se (

wei

gh

ted

mea

n, 9

5% U

CL

)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Ref

Ref

P < 0.001 for all

P < 0.001 P = 0.163

PPWeighted N = 9047

D

Fig. 1 Weighted age-standardized and multiple-adjusted mean SF-36 PCS T scores (a), SF-36 MCS T scores (b), AQoL scores (c), and health

service use (d) by diabetes and/or major depression group

Soc Psychiatry Psychiatr Epidemiol (2012) 47:871–877 875

123

of major depression [17]. Collectively, this body of evi-

dence suggests that the benefits of enhancing public

knowledge about depression are questionable, and that new

and more efficient management systems for depression

need to be tested in Australia, such as collaborative care

[29] integrated within lifestyle programs.

The strength of our study is that the findings are based

on three large representative population-based surveys that

had used identical methodologies. Information was col-

lected via face-to-face interview, and major depression was

measured accurately and defined according to the DSM-IV

criteria. A principle limitation is that diabetes was mea-

sured by self-report, and thus some persons with unknown

diabetes would likely have been misclassified as not having

diabetes [30]; and specific diabetes (type 1/type 2) infor-

mation was not collected.

Changes in other dimensions of health services such

as integration of care for multiple conditions and inten-

sity of care (duration per consultation) were biases not

accounted for, and so the burden of health service use

assessed by frequency alone might have been under-

estimated. This potential limitation is attenuated some-

what because our main results were independent of a

changes in several medical conditions and obesity over

the 10-year period. Although commonly investigated in

previous studies [11], another limitation is that mental

health components of health-related quality of life mea-

sures overlap with symptoms of major depression.

Indeed, our results show that mean SF-36 MCS T-scores

and AQoL scores were very similar for persons with

major depression only and for those with both condi-

tions, and this raises doubts about the validity of

investigating mental health constructs of health-related

quality of life measures in persons with major depres-

sion. Other limitations include biases due to self-report

measures, respondent compliance, and to misclassifi-

cation errors for covariates.

In summary, health-related quality of life was worse and

health service use was higher in the South Australian

population for persons with comorbid diabetes and major

depression than for those with diabetes only, consistently

from 1998 to 2008. If past trends in the prevalence of

diabetes [16] and major depression [17] continue, in line

with the projected increases worldwide [31, 32], our results

suggest that the increased population health and economic

burden (from health service use) of comorbid diabetes and

major depression could persist over the next decade or so.

This is likely to lead to an increase in vascular events, risk

of diabetes complications and all-cause mortality [26].

Lifestyle interventions might reduce the health and eco-

nomic burden of comorbid diabetes and major depression,

but further research is needed to provide large-scale, long-

term randomized trial data. Furthermore, these trends have

important implications for making health policy and

resource allocation decisions.

Acknowledgments This research has been supported by grants

from Eli Lilly Australia, Janssen-Cilag, Lundbeck Australia, Organon

Australian, Sanofi-Synthelabo, Wyeth Australia, Ramsay Health Care

Australia, The Commonwealth Department of Health and Aging, The

Hanson Institute and Medvet Science, SA Pathology. None of these

organizations participated in the design of the study, analysis of data,

or the writing up of the results.

Conflict of interest None of the authors have any relevant conflict

of interest to disclose.

References

1. Vos T, Mathers C, Herrman H, Harvey C, Gureje O, Bui D,

Watson N, Begg S (2001) The burden of mental disorders in

Victoria, 1996. Soc Psychiatry Psychiatr Epidemiol 36:53–62

2. Goldney RD, Phillips PJ, Fisher LJ, Wilson DH (2004) Diabetes,

depression, and quality of life: a population study. Diabetes Care

27:1066–1070

3. Ali S, Stone MA, Peters JL, Davies MJ, Khunti K (2006) The

prevalence of co-morbid depression in adults with type 2 diabe-

tes: a systematic review and meta-analysis. Diabet Med 23:

1165–1173

4. Rajala U, Keinanen-Kiukaanniemi S, Kivela SL (1997) Non-

insulin-dependent diabetes mellitus and depression in a middle-

aged Finnish population. Soc Psychiatry Psychiatr Epidemiol

32:363–367

5. Atlantis E, Browning C, Sims J, Kendig H (2010) Diabetes

incidence associated with depression and antidepressants in the

Melbourne Longitudinal Studies on Healthy Ageing (MELSHA).

Int J Geriatr Psychiatry 25:688–696

6. Mezuk B, Eaton WW, Albrecht S, Golden SH (2008) Depression

and type 2 diabetes over the lifespan: a meta-analysis. Diabetes

Care 31:2383–2390

7. Gonzalez JS, Peyrot M, McCarl LA, Collins EM, Serpa L,

Mimiaga MJ, Safren SA (2008) Depression and diabetes treat-

ment nonadherence: a meta-analysis. Diabetes Care 31:

2398–2403

8. Lin EH, Katon W, Von Korff M, Rutter C, Simon GE, Oliver M,

Ciechanowski P, Ludman EJ, Bush T, Young B (2004) Rela-

tionship of depression and diabetes self-care, medication adher-

ence, and preventive care. Diabetes Care 27:2154–2160

9. Lustman PJ, Anderson RJ, Freedland KE, de Groot M, Carney

RM, Clouse RE (2000) Depression and poor glycemic control: a

meta-analytic review of the literature. Diabetes Care 23:934–942

10. Chiu C-J, Wray L, Beverly E, Dominic O (2010) The role of

health behaviors in mediating the relationship between depressive

symptoms and glycemic control in type 2 diabetes: a structural

equation modeling approach. Soc Psychiatry Psychiatr Epidemiol

45:67–76

11. Schram MT, Baan CA, Pouwer F (2009) Depression and quality

of life in patients with diabetes: a systematic review from the

European depression in diabetes (EDID) research consortium.

Curr Diabetes Rev 5:112–119

12. Lin EH, Rutter CM, Katon W, Heckbert SR, Ciechanowski P,

Oliver MM, Ludman EJ, Young BA, Williams LH, McCulloch

DK, Von Korff M (2010) Depression and advanced complica-

tions of diabetes: a prospective cohort study. Diabetes Care

33:264–269

876 Soc Psychiatry Psychiatr Epidemiol (2012) 47:871–877

123

13. Egede LE, Zheng D, Simpson K (2002) Comorbid depression is

associated with increased health care use and expenditures in

individuals with diabetes. Diabetes Care 25:464–470

14. Le T, Able S, Lage M (2006) Resource use among patients with

diabetes, diabetic neuropathy, or diabetes with depression. Cost

Eff Resour Alloc 4:18

15. Torrance GW (1986) Measurement of health state utilities for

economic appraisal: a review. J Health Econ 5:1–30

16. Atlantis E, Lange K, Wittert GA (2009) Chronic disease trends

due to excess body weight in Australia. Obes Rev 10:543–553

17. Goldney RD, Eckert KA, Hawthorne G, Taylor AW (2010)

Changes in the prevalence of major depression in an Australian

community sample between 1998 and 2008. Aust N Z J Psy-

chiatry 44:901–910

18. Sclar DA, Robison LM, Skaer TL (2008) Ethnicity/race and the

diagnosis of depression and use of antidepressants by adults in the

United States. Int Clin Psychopharmacol 23:106–109

19. Cowie CC, Rust KF, Ford ES, Eberhardt MS, Byrd-Holt DD, Li

C, Williams DE, Gregg EW, Bainbridge KE, Saydah SH, Geiss

LS, Cowie CC, Rust KF, Ford ES, Eberhardt MS, Byrd-Holt DD,

Li C, Williams DE, Gregg EW, Bainbridge KE, Saydah SH,

Geiss LS (2009) Full accounting of diabetes and pre-diabetes in

the US population in 1988–1994 and 2005–2006. Diabetes Care

32:287–294

20. Jorm AF, Korten AE, Jacomb PA, Christensen H, Rodgers B,

Pollitt P (1997) ‘‘Mental health literacy’’: a survey of the public’s

ability to recognise mental disorders and their beliefs about the

effectiveness of treatment. Med J Aust 166:182–186

21. Dunn KI, Goldney RD, Grande ED, Taylor A (2009) Quantifi-

cation and examination of depression-related mental health lit-

eracy. J Eval Clin Pract 15:650–653

22. Bakker IM, Terluin B, van Marwijk HWJ, van Mechelen W,

Stalman WAB (2009) Test-retest reliability of the PRIME-MD:

limitations in diagnosing mental disorders in primary care. Eur J

Public Health 19:303–307

23. Spitzer RL, Williams JB, Kroenke K, Linzer M, deGruy FV 3rd,

Hahn SR, Brody D, Johnson JG (1994) Utility of a new procedure

for diagnosing mental disorders in primary care. The PRIME-MD

1000 study. JAMA 272:1749–1756

24. Hawthorne G, Richardson J, Osborne R (1999) The Assessment

of Quality of Life (AQoL) instrument: a psychometric measure of

Health-Related Quality of Life. Qual Life Res 8:209–224

25. Hawthorne G, Osborne R, Taylor A, Sansoni J (2007) The SF36

Version 2: critical analyses of population weights, scoring algo-

rithms and population norms. Qual Life Res 16:661–673

26. Clarke PM, Hayes AJ, Glasziou PG, Scott R, Simes J, Keech AC

(2009) Using the EQ-5D index score as a predictor of outcomes

in patients with type 2 diabetes. Med Care 47:61–68

27. Atlantis E, Chow C-M, Kirby A, Fiatarone Singh M (2004) An

effective exercise-based intervention for improving mental health

and quality of life measures: a randomized controlled trial. Prev

Med 39:424–434

28. Mant A, Rendle VA, Hall WD, Mitchell PB, Montgomery WS,

McManus PR, Hickie IB (2004) Making new choices about

antidepressants in Australia: the long view 1975–2002. Med J

Aust 181:S21–S24

29. Gilbody S, Bower P, Fletcher J, Richards D, Sutton AJ (2006)

Collaborative care for depression: a cumulative meta-analysis

and review of longer-term outcomes. Arch Intern Med 166:

2314–2321

30. Dunstan DW, Zimmet PZ, Welborn TA, Sicree RA, Armstrong T,

Atkins R, Cameron AJ, Shaw JE, CSobotAS Committee (2001)

Diabesity & associated disorders in australia—2000. The accel-

erating epidemic. International Diabetes Institute, Melbourne

31. World Health Organization (2008) The global burden of disease:

2004 update WHO Press

32. International Diabetes Federation (2009) IDF Diabetes Atlas 4th

edition. International Diabetes Federation

Soc Psychiatry Psychiatr Epidemiol (2012) 47:871–877 877

123