Screening for Postnatal Depression in Routine Primary Care: Properties of the Edinburgh Postnatal Depression Scale in an Australian sample

Abstract

Objectives: First, to explore the utility of the Edinburgh Postnatal Depression Scale (EPDS) in routine primary care through a large community screening program. Next, to compare administration of a second EPDS versus the Beck Depression Inventory (BDI) in identifying postnatal depression in the prescreened population.

Method: Screening with the EPDS through Maternal and Child Health Centres at 4 months post-partum. Women scoring ≥12 were assessed against DSM-IV criteria and completed a BDI and a second EPDS. These data were subjected to receiver operating characteristic (ROC) analyses.

Results: Of 4148 screened, 533 (12.8%) scored ≥12. Of these, 344 were assessed against DSM-IV criteria: 193 (56%) – major depressive disorder; 67 (20%) – other diagnoses that incorporated depression. Positive predictive value at screening was therefore 76%. Another 45 (13%) had non-depressive disorders and 39 (11%) were psychiatric non-cases. The BDI was the better diagnostic instrument in the prescreened population, having a significantly higher efficiency as quantified by ROC curve analysis, though the absolute difference in efficiency was small (approximately 6%).

Conclusions: Screening with the EPDS integrated well into routine primary care. Twostep screening offers one way of achieving acceptable balances of operational simplicity and diagnostic accuracy.

Keywords

Edinburgh Postnatal Depression Scale positive predictive value postnatal depression screening

The majority of women with postnatal depression (PND) do not seek help [1]. Even when women do consult a health care professional, half still remain untreated [2]. Postnatal depression therefore goes untreated in most cases, yet the distress it causes is often acute. The adverse long-term consequences of PND are serious and have been reported elsewhere [3]. Pharmacological and psychotherapeutic treatments for PND are available, and there is accumulating evidence of their effectiveness [4], [5]. However, efficient identification is first necessary to enable appropriate intervention. Although the Edinburgh Postnatal Depression Scale (EPDS) is one of the most widely advocated screening instruments [6], debate continues about screening programs for PND in general and about the EPDS as an adequate instrument for use in such programs [7], [8]. Essential to progress in this debate is clarity about:

how to strike the optimal balance of sensitivity (truepositive rate) and specificity (true-negative rate) of the EPDS in routine primary care settings;

whether detection of depression can be improved by using the EPDS in concert with other validated instruments;

whether administration of the EPDS is feasible in routine care and acceptable to its target populations.

Although there are numerous EPDS validation studies, they tend to focus on specific target populations [9–11]. The diagnostic utility of the EPDS has rarely been explored in routine primary care and in sufficiently large, community-wide samples representing a cross-section of demographic groups. Although some very large population screening studies have used the EPDS, they have usually not been concerned with formal validation against clinical diagnostic criteria [12]. Consequently, explicit EPDS validation studies in large samples (n<1000) remain relatively rare [13], [14]. If universal screening with the EPDS is to be advocated, then it should be on the basis of such large, demographically representative studies.

This study reports on the prevalence of PND assessed by the EPDS in a large Australian sample in routine primary care. Using the subset of those who screened positive, we aim to improve our understanding of the utility of the EPDS by evaluating its predictive value as related to psychiatric diagnostic profile and to compare its accuracy to that of a widely used diagnostic instrument, the Beck Depression Inventory (BDI). The advantages of a two-step screening process are discussed, together with issues surrounding successful community-wide screening.

Method

Procedure

The screened population consisted of 4148 newly delivered mothers attending 47 Maternal and Child Health Centres (M&CHCs) in northern metropolitan Melbourne and in rural eastern Victoria, Australia, over a 3-year period. Nurses at the M&CHCs had contact with all newly delivered mothers, were well placed to screen a wide demographic range and used the EPDS at 4-month post-partum consultations. Nurses did not themselves elicit the responses or administer the EPDS, which was self-administered by women, thus requiring no interpretation except summing of scores. Nurses remained blind to subsequent clinical assessment procedures. We screened at 4 months to allow transient mood disturbances (including ‘maternity blues’) to resolve and because M&CHCs schedule longer consultations at this time, attendance at which is generally high. Informed, written consent was obtained from all participants. Those unable to give informed consent or who had difficulty with English were excluded. The EPDS has been validated using various cut-offs [16]. In general, a higher cut-off yields both higher true-positive and higher false-negative rates [25]. At both initial screening and with the repeat EPDS given at clinical assessment, we used the cut-off score of 11.5 used by Gerrard et al. [15], to yield a lower false-negative rate than the more widely used 12.5 cut-off [16]. Those who had EPDS scores ≥12 were offered clinical assessment with a psychologist involving a structured interview and diagnosis followed by completion of a second EPDS and the BDI. Those diagnosed as depressed were offered a range of treatment options. Time between screening and diagnosis averaged 1–2 weeks.

Measures

The Edinburgh Postnatal Depression Scale

Designed by Cox et al. [6], this self-rated 10-item instrument requires women to read 10 statements relating to emotional health and for each statement choose one of four possible responses. This takes approximately 5minutes. The responses are rated from 0 to 3 and summed to yield the score. The EPDS was developed specifically to screen for PND and has been used extensively with antenatal and postnatal women [16]. It is particularly useful in PND being short and not relying on somatic symptoms, which are common in postnatal women irrespective of depression. In an Australian sample the EPDS was found to have 100% sensitivity and 89% specificity at a cut-off score of 12.5 [9].

The Composite International Diagnostic Interview

The Composite International Diagnostic Interview (CIDI) is a validated, standardized, structured interview that yields psychiatric diagnoses according to DSM criteria [17], [18]. We applied Section E of the CIDI, which deals with depression, to classify women according to current diagnosis on seven DSM-IV categories that include some component of depression, namely: major depressive disorder; depressive disorder not otherwise specified; adjustment disorder with depressed mood; mixed anxiety-depressive disorder; bipolar disorder; psychotic disorder and dysthymic disorder. All women exhibiting transient distress not fitting criteria for clinical diagnosis (i.e. psychiatric non-cases) were included in a category that we termed ‘non-pathological mood fluctuation’.

The Beck Depression Inventory

The BDI is a widely used, well-validated, 21-item clinical instrument that measures cognitive, affective and physiological factors to assess severity of depression. The 21 items rate, from 0 to 3, different symptoms and attitudes found in clinical practice to be associated with depression, for example, pessimism, guilt, suicidal ideas, social withdrawal, insomnia, crying, irritability etc. In the last four decades the BDI has been validated on many psychiatric and normative populations [19]. The original form of the instrument [20] was revised with the aim of increasing agreement with DSM criteria, simplifying the response options and removing double-negative statements [21]. This revised BDI used here has been applied extensively in clinical settings and its psychometric properties have been well characterized. Internal consistency estimates (Cronbach's α) range from 0.79 to 0.9. Good content, construct and criterion validity are also reported [19]. More recently, the BDI has been revised once more and is published as the BDI-II [22].

Demographics

At clinical assessment, participants also completed a questionnaire covering depressive history and demographic information.

Statistical analysis

Frequency data were assessed by χ2 tests. Parameter estimates are given with 95% confidence intervals (CIs). Logistic regression models were fitted to data and regression coefficients (i.e. β =log[OR]) expressed in terms of ORs (i.e. exp[β]). Receiver operating characteristic (ROC) curves were generated with BDI and EPDS data and areas under curves (AUCs), then compared statistically using the U-test method of De Long et al. [23]. Calculations were performed in SAS, Analyse-it and SPSS.

Results

Screening

Despite resource constraints on Maternal and Child Health Nurses (MCHNs), compliance with screening was generally high with MCHNs being able to implement the screening procedure with more than 80% of all new mothers (M. Van Gemert, Personal communication). In total, 4148 women were screened. No complaints were received and a short telephone survey confirmed the acceptability of the screening process. Of those screened, 533 had EPDS scores ≥12. The mean EPDS score of these 533 women was 16.6 (CI=16.3–16.9). Thus, as defined by the EPDS score, the overall prevalence of PND at screening was 12.85% (CI=1.8–13.9).

Participant characteristics

Of the 533 participants who scored ≥12 on the first EPDS at screening, 344 had a structured clinical assessment using DSM-IV criteria, then completed both a second EPDS and the BDI. This group had an average EPDS score at screening of 16.97 (CI=16.6–17.3), while the 189 women who declined or were unable to consult clinical assessment had a mean score of 16.1 (CI=15.6–16.7). Although a significance test seems unnecessary, it seems logical that this difference of less than one point (significant or not) is due to lower uptake of clinical assessment among lower-scoring women. Demographic data revealed the following: of those assessed 41.5% were first-time mothers; English was the first language of 97.4%;most (59%) had completed 6 years of secondary schooling; 30% had higher education; 51% of infants were male; mean age of infants was 17.9 weeks (CI=16.8–19);mean age ofmothers was 30.1 years (CI=29.5–30.7); 88% were married or in de facto relationships, 12% solitary and 86% of partners had full-time work. Average income in $A×103 was 41.4 (CI=39.4–43.1). Self-reported history of depression during pregnancy was strongly predictive of current depression (OR=4.52, CI=2.0–10.2).

Positive predictive value of the Edinburgh Postnatal Depression Scale as a communitywide screening tool

We calculated the positive predictive value (PPV) of the initial EPDS, administered by MCHNs, according to the standard formula:

In total, 260/344 of those participants scoring ≥12 on the EPDS subsequently received DSM-IV diagnoses that included some component of depression and 84 did not. We obtained a central estimate of the PPV of the EPDS on the assumption that the frequency of depression among the 189 women scoring ≥12 but who declined clinical assessment was the same as that in the 344 women who were assessed, thus:

We also calculated the maximum and minimum possible values of the PPV by assuming that 100% of the 189 women declining assessment were either: (i) true positives, yielding the maximum PPV; or (ii) false positives, yielding the minimum possible PPV, thus:

Properties of the Edinburgh Postnatal Depression Scale and the Beck Depression Inventory at clinical assessment (post-screening)

Clinical profile

The clinical profile of the 344 diagnosedwomen is outlined in Table 1. Ten of 11 possible DSM-IV diagnoses were represented. A DSM-IV diagnosis of major depressive disorder accounted for 193 (56%) of these women and 67 (20%) received other diagnoses that included some component of depression. A further 45 women (13%) were diagnosed with psychiatric disorders other than depression (e.g. eating disorder, body dysmorphia; personality disorder was never diagnosed). Noncases accounted for 39 women (11%). In line with intuitive expectation, both BDI and EPDS scores at assessment were highest for the more severe disorders and lowest for transient mood fluctuations (psychiatric non-cases).

Table 1.

Diagnostic profile of women scoring ≥ 12 on the Edinburgh Postnatal Depression Scale (EPDS)

DSM-IV diagnosis	n	Initial screening	Psychiatric assessment
		EPDS (95% CI)	EPDS (95% CI)	BDI (95% CI)
Major depressive disorder†	193	17.7 (17.2–18.2)	17.4 (16.8–18.0)	24.4 (23.2–25.6)
Depressive disorder NOS†	29	16.1 (15.2–17.0)	14.2 (12.6–15.9)	18.9 (17.3–20.4)
Adjustment disorder with depression†	16	15.5 (14.2–16.8)	13 (11.6–14.4)	16.4 (14.2–18.6)
Mixed anxiety-depressive disorder†	16	18.6 (17.4–19.9)	17.7 (15–20.4)	21.4 (16.7–26)
Bipolar disorder†	2	18.0	18.0	27.0
Psychotic disorder†	2	19.5	22.5	22.5
Dysthymic disorder†	2	21.0	21.5	33.0
Adjustment disorder without depression	8	16.3 (12.8–19.7)	9.0 (5.8–12.3)	11.5 (6.3–16.7)
Anxiety disorder without depression	7	15.3 (12.8–17.8)	11.4 (9.4–13.5)	11.6 (8.7–14.9)
Other DSM-IV disorders	30	15.6 (14.3–16.9)	10.3 (8.2–12.3)	10.4 (8.3–12.5)
Psychiatric non-cases	39	15.0 (14.1–15.9)	9.5 (8.1–10.8)	9.6 (8–11.2)
Total	344	17.0 (16.6–17.3)	15.2 (14.6–15.7)	20.0 (19–21)

† Coded as ‘all depressions’ for purposes of binary logistic regression, see text. Tabulated values are mean Edinburgh Postnatal Depression Scale and mean Beck Depression Inventory (BDI) scores, cross-tabulated by DSM-IV diagnostic category. NOS, not otherwise specified.

Diagnostic accuracy

To formally compare the diagnostic accuracies of the EPDS and BDI in the prescreened population of 344 women, we collapsed all the categories into a binary variable coded either as ‘all depressions’ combined or as ‘all other cases’ combined. Separate logistic regression models using EPDS scores andBDI scores as the independent variables were fitted to these data. All regression coefficients were significant (p<0.0001 in all cases) and were transformed to ORs (exp[β]). In terms of predicting depression per se, the second EPDS and BDI administered at clinical assessment performed similarly (EPDS: exp[β]=1.41, CI=1.30–1.53; BDI: exp[β]=1.42, CI=1.31–1.54). Thus, for a one-point increase in either independent variable, the odds in favour of a clinical diagnosis that included depression rose by a factor of approximately 1.4.

Receiver operating characteristic curves were generated for both instruments administered at psychiatric assessment (23] provide a method for deriving a Mann–Whitney U-statistic to test the hypothesis that the AUCs of two ROC curves are non-identical. Applying this method we found that the difference in AUCs of 6.5% was statistically significant (p=0.0015). Since realistic numerical values for the relative costs of false-positive and false-negative results are unavailable (and complex to estimate) formal mathematical solutions to the problem of locating the optimal operating point for each instrument are not possible [24], [25]. The optimal ranges of cut-off values for both instruments were therefore found empirically by visual examination of ROC curve coordinates (Table 2). Rather than applying overall diagnostic accuracy (proportion of cases classified correctly) as the optimality criterion, we looked for the best balances of sensitivity and specificity for each tool. In this population, optimal performance (on the assumption that both high sensitivity and high specificity are desirable) appears to occur coincidentally at values of approximately 12.5 points for both the EPDS and the BDI.

Figure 1.

Receiver operating characteristic curves for the Edinburgh Postnatal Depression Scale (EPDS) and the BDI. Numerals indicate positions of optimal cut-off values on the BDI (solid line) and EPDS (broken line).

Table 2.

Range of ‘optimal’ cut-off values for the Beck Depression Inventory (BDI) and the Edinburgh Postnatal Depression Scale (EPDS)

	True positives	True negatives	False positives	False negatives	Diagnostic accuracy (%)
BDI cut-off
11.5	249	54	30	11	88.1
12.5	246	62	22	14	89.5
13.5	240	67	17	20	89.2
EPDS cut-off
11.5	236	50	34	24	83.1
12.5	222	60	24	38	82.0
13.5	205	72	12	55	80.5

Tabulated values are based on scores, recorded at formal clinical assessment, of the 344 women who scored ≥12 on the EPDS at initial screening.

Maximizing diagnostic efficiency

Given that, as a single diagnostic tool, the BDI performed better than the EPDS, we next asked whether the EPDS might be used to complement the BDI. Sequential logistic regression models were fitted, first with the BDI as the sole predictor variable yielding a deviance statistic (−2log L1) of 213.341. The introduction of the EPDS into the model did not result in a significant change in deviance (−2[log L1−log L2]=−1.333, Wald χ2, df=1, NS). Thus, the logistic regression models indicate that the EPDS adds no non-redundant information over and above that provided by the BDI.

To confirm and better characterize this finding we used methods for combining multiple screening instruments to maximize screening efficiency, which have been used to find optimal rules for the combination of other mental health screening instruments [26–28]. The nature of these combinations may be logical compensatory (the ‘Or’ rule), logical conjunctive (the ‘And’ rule) or they may represent a weighted sum of the various instruments, derived by solution of a logistic regression equation; in our case, this weighted sum regression equation is:

For our own data, the areas under the BDI ROC curve and under the weighted sum ROC curve (AUC=0.931) are not significantly different (p=0.745) and so a weighted sum rule would have performed no better than a simple 12.5 BDI cut-off in this population. Similarly, when we apply either compensatory or conjunctive rules to the EPDS and BDI, by holding the spectrum of possible cut-offs constant on one instrument while varying the cut-off value of the other and vice versa, we find no combinatory rule that clearly outperforms a simple 12.5 BDI cut-off value in predicting DSM-IV diagnosis in this population.

Therefore, as a diagnostic tool, the BDI has a higher diagnostic efficiency than the EPDS in the prescreened population and the addition of the EPDS does not provide significantly better diagnostic power.

Are two Edinburgh Postnatal Depression Scale administrations better than one?

Finally, we want to know if repeat-screening is of clinical value. Although our design does not allow a definitive hypothesis test, we can begin to gauge the utility of repeated EPDS administrations by a χ2 analysis among those 344 women who scored ≥12 at screening. Of these, the 270 women who subsequently scored above cut-off for a second time at clinical assessment were significantly more likely to receive a DSM-IV diagnosis of depression than those (74) who scored below cut-off (χ2 =95.1, df=1, p<0.001). Thus, a repeatscreen does add new information regarding the current likelihood of depression.

The percentage of major depressions (67.8%) was higher in this group. These results must to be interpreted cautiously with regard to the following points. The first EPDS was taken at a routine visit to a MCHN and the second in a hospital where women knew a psychologist was actively assessing them for depression. Also, a second score ≥12 may be expected to be more closely associated with a DSM-IV diagnosis of depression if that score is available to the diagnosing clinician. In any case, it seems intuitively obvious that an EPDS score that is taken on the same day the diagnosis is made should be more closely associated with that diagnosis than an EPDS taken some weeks previously.

Discussion

This study did not aim to validate the EPDS, but quantified its utility in facilitating the identification of PND in a routine primary care setting using a large, demographically representative Australian sample. Under these reallife conditions, the conditions in which any universal screening program must operate, our results confirm previous findings that the EPDS is a simple, safe and precise screening test. At a cut-off of 11.5, administered in routine primary care it correctly identifies approximately 76% of women scoring above cut-off as having depressive disorders. However, the study has some limitations. For example, we gathered no information pertaining to the false-negative rate of the EPDS at the first step of our screening process. In terms of absolute numbers, the false-negative rate is far less worrisome than the situation that currently pertains in many places (i.e. at present, much larger numbers of women go undiagnosed due to the complete absence of screening) which surely represents a worse scenario. As long as the EPDS is regarded as a screening instrument, falsenegatives should not indicate that a woman is not depressed but rather that those scoring positive require definite follow-up assessment. Also, we have little data on those 189 women who declined psychiatric assessment. Although our results indicate that non-uptake was not due to more severe depression, it remains possible that those not assessed differed in some important way from those who were assessed. Almost two decades after the development of the EPDS, these quite basic issues remain to be decisively dealt with by a sufficiently large validation study based on the general postnatal population.

We draw the following conclusions. First, universal screening with the EPDS as part of standard care is feasible. Second, the diagnostic value of the EPDS as a screening tool is adequate, such that of those women who continued to have a clinical assessment, three out of four were diagnosed with depression. This provides a useful (and broadly positive) perspective regarding some concerns over the adequacy of the EPDS [7]. It is also worthy of note that in terms of identifying psychiatric caseness per se the EPDS at a cut-off of 11.5 had a PPV of approximately 89% – only 39 of 344 women were not diagnosed with a DSM-IV disorder of some kind. Third, in a representative cross-section of the population the prevalence of PND as measured by EPDS scores ≥12 is 12.8%. This is virtually identical to an average of 13% reported from a meta-analysis of 59 original studies [29]. Fourth, in a prescreened population, the BDI is actually a better instrument than the EPDS for identifying PND. At face value this finding stands in apparent contradiction to the results of some previous studies [30], [31] and may in part reflect the fact that a diagnostic test's PPV, which is itself a function of population prevalence, is expected to be higher in prescreened populations. The purpose of screening is, after all, to better define a target population in which diagnostic tests can perform with increased power. However, approaches that search for points of optimal performance across the whole ROC curve are likely to be more informative than those that compare different instruments at a fixed number of ‘recommended’ cutoff values [32], [33]. This empirical, evidence-based approach to diagnostic test comparison complements the ongoing need for regular recalibration of optimal operating points to ensure the best possible performance by individual instruments in different populations and under different sets of circumstances. It is unrealistic to expect that one particular cut-off value on the EPDS (or any other screening test) can provide a universal diagnostic decision threshold that will remain valid in all populations for all time.

Finally, the EPDS appears to be acceptable to Australian women and to MCHNs as a screening instrument in routine primary care – out of 4148 administrations over 3 years no complaints were reported and screening was integrated into existing practice with relative ease. Although Shakespeare et al. [8] report a less positive experience in a small, purposive sample of British women (n=39) this may be more a reflection of differences in practices surrounding the wider screening process rather than of the EPDS itself. Obviously, this is an important issue and one that can only be resolved by further research.

Facilitating correct diagnosis in a quick and simple manner would be especially useful in primary care settings. As pointed out by Cox and Holden [16], repeated high scores on the EPDS at successive health visits should alert professionals to an increased likelihood of an enduring depressive disorder. Our own results are consistent with this observation and as a rule of thumb it has the dual virtues of being both simple to apply and of making intuitive sense. However, if the aim is to correctly identify the highest proportion of cases as quickly and as simply as possible, then a simple, two-step screening strategy suggested by our own results is:

Administer the EPDS as a screening instrument on a community-wide basis, using a cut-off pre-optimized with reference to the population being screened (in our case a value of 12.5 may have performed better than the 11.5 cut-off we actually used).

Administer the BDI (again using a pre-optimized cutoff, in retrospect approximately 12.5 in our case) to those scoring above EPDS cut-off.

Following identification of positive cases by these procedures, further discussion/support and outlining of possible options for a management plan and/or formal diagnosis by a suitable health professional are now appropriate. In our own data, a BDI score ≥13 at this point would indicate more than a 9 in 10 chance of a DSM-IV depressive disorder of some kind (from Table 1, PPV=92%). It should also be noted that while the EPDS is freely available, the BDI is commercially produced, so that cost is an issue when choosing between the two instruments in practice.

In conclusion, our results suggest that two-step screening can improve efficiency and that given the right combination of resources, tools, training and organization, the integration of communitywide screening into routine primary care through M&CHCs is a viable proposition.

Footnotes

Acknowledgements

This work was funded by Research & Development Grants Advisory Committee/NHMRC Public Health and Austin Hospital Medical Research Foundation. We thank the women and nurses of Melbourne, in particular Marita van Gemert, who made the study possible. Thanks also to the staff of the Department of Clinical and Health Psychology, Austin Health. Melina Ramp, Caroline De Paola and Kate Neilson assisted with statistical analysis and with literature reviews.

References

MacLellan

Wilson

Taylor

. The self-reported prevalence of postnatal depression. Australian and New Zealand Journal of Obstetrics and Gynaecology 1996; 36: 313.

Hearn

Illif

Jones

. Postnatal depression in the community. British Journal of General Practice 1998; 48: 1064–1066.

Milgrom

Martin

Negri

. Treating postnatal depression: a psychological approach for health care practitioners. Wiley, Chichester 1999.

Appleby

Warner

Whitton

Faragher

. A controlled study of fluoxetine cognitive–behavioural counselling in the treatment of postnatal depression. British Medical Journal 1997; 314: 932–936.

Highet

Drummond

. A comparative evaluation of community treatments for post-partum depression: implications for treatment management practices. Australian and New Zealand Journal of Psychiatry 2004; 38: 212–218.

Cox

J L

Holden

J M

Sagovsky

. Detection of postnatal depression: development of the 10 item Edinburgh Postnatal Depression Scale. British Journal of Psychiatry 1987; 150: 782–786.

Shakespeare

. Evaluation of screening for postnatal depression against the NSC handbook criteria. 2002, Working party document, http://www.nelh.nhs.uk/screening/adult_pps/ postnatal_depresion.html.

Shakespeare

Blake

Garcia

. A qualitative study of the acceptability of routine screening of postnatal depression using the Edinburgh Postnatal Depression Scale. British Journal of General Practice 2003; 53: 614–619.

Boyce

P M

Stubbs

J M

Todd

. The Edinburgh Postnatal Depression Scale: validation for an Australian sample. Australian and New Zealand Journal of Psychiatry 1993; 27: 472–476.

10.

Yonkers

K A

Ramin

S M

Rush

J A

. 2001; 158: 1856–1863.

11.

Lee

D TS

Yip

A SK

Chiu

H FK

Leung

T YS

Chung

T KH

. A psychiatric epidemiological study of postpartum Chinese women. American Journal of Psychiatry 2001; 158: 220–226.

12.

Evans

Heron

Francomb

Oke

Golding

. Cohort study of depressed mood during pregnancy and after childbirth. British Medical Journal 2001; 232: 257–260.

13.

Wickberg

Hwang

C P

. Screening for postnatal depression in a population-based Swedish sample. Acta Psychiatrica Scandinavica 1997; 95: 62–66.

14.

Zelkowitz

Milet

T H

. Screening for post-partum depression in a community sample. Canadian Journal of Psychiatry 1995; 40: 80–86.

15.

Gerrard

Holden

J M

Elliott

S A

McKenzie

Cox

J L

. A trainer's perspective of an innovative programme teaching health visitors about the detection, treatment and prevention of postnatal depression. Journal of Advanced Nursing 1994; 18: 1825–1832.

16.

Cox

Holden

. A guide to the Edinburgh Postnatal Depression Scale. Gaskell, London 2003; 125.

17.

World Health Organisation . Composite International Diagnostic Interview 1.1 – interviewer manual. American Psychiatric Press, Washington 1993.

18.

Wittchen

H U

Robbins

L N

Cottler

L B

Sartorius

Burke

J D

Regier

. Cross cultural feasibility, reliability and sources of variance of the Composite International Diagnostic Interview (CIDI). The Multicentre WHO/ADAMHA Field Trials. British Journal of Psychiatry 1991; 159: 645–653.

19.

Beck

A T

Steer

R A

Garbin

M G

. Psychometric properties of the Beck Depression Inventory: twenty-five years of evaluation. Clinical Psychology Review 1988; 8: 77–100.

20.

Beck

A T

Ward

C H

Mendelson

Mock

Erbaugh

. An inventory for measuring depression. Archives of General Psychiatry 1961; 4: 561–571.

21.

Beck

A T

Steer

R A

. Manual for the revised Beck Depression Inventory. The Psychological Corporation, San Antonio 1987.

22.

Beck

A T

Steer

R A

Brown

G K

. BDI-II manual. The Psychological Corporation, San Antonio 1996.

23.

De Long

Clarke-Pearson

. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988; 44: 837–845.

24.

Zweig

M H

Campbell

. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clinical Chemistry 1993; 39: 561–577.

25.

Zhou

X H

Obuchowski

N A

McClish

D K

. Statistical methods in diagnostic medicine. Wiley, New York 2002.

26.

Mackinnon

Mulligan

. Combining cognitive testing and informant report to increase accuracy in screenimg for dementia. American Journal of Psychiatry 1998; 155: 1529–1535.

27.

Mackinnon

Khalilian

Jorm

A F

Korten

A E

Christensen

Mulligan

. Improving screening accuracy for dementia in a community sample by augmenting cognitive testing with informant report. Journal of Clinical Epidemiology 2003; 56: 358–366.

28.

Pepe

M S

Thompson

M L

. Combining diagnostic test results to increase accuracy. Biostatistics 2000; 1: 123–140.

29.

O'Hara

Swain

. Rates and risk of postpartum depression – a meta-analysis. International Review of Psychiatry 1996; 8: 37–54.

30.

Harris

Huckle

Thomas

Johns

Fung

. The use of rating scales to identify post-natal depression. British Journal of Psychiatry 1989; 154: 813–817.

31.

Beck

C T

. Comparative analysis of the performance of the Postpartum Depression Screening Scale with two other depression instruments. Nursing Research 2001; 50: 242–250.

32.

Halpern

E J

Albert

Krieger

A M

Metz

C E

Maidment

A D

. Comparison of receiver operating characteristic curves on the basis of optimal operating points. Academic Radiology 1996; 3: 245–253.

33.

Somoza

Mossman

. Comparing and optimizing diagnostic tests: an information-theoretical approach. Medical Decision Making 1992; 12: 179–188.