Prevention Research Centre, Department of Clinical Psychology and Personality, Nijmegen University, The Netherlands
Institute of Psychiatry, London
Department of Primary Care, University of Oxford, UK
Correspondence: Dr Eva Jané-Llopis, Department of Clinical Psychology and Personality, University of Nijmegen, PO Box 9104, 6500HE Nijmegen, The Netherlands. E-mail: llopis{at}psych.kun.nl
Declaration of interest None. Funding detailed in Acknowledgements.
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ABSTRACT |
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Aims To identify potential predictors of effect in prevention programmes.
Method A meta-analysis was made of 69 programmes to reduce depression or depressive symptoms.
Results The weighted mean effect size of 0.22 was effective for different age groups and different levels of risk, and in reducing risk factors and depressive or psychiatric symptoms.Programmes with larger effect sizes were multi-component, included competence techniques, had more than eight sessions, had sessions 6090 6090 min long, had a high quality of research design and were delivered by a health care provider in targeted programmes. Older people benefited from social support, whereas behavioural methods were detrimental.
Conclusions An 11% improvement in depressive symptoms can be achieved through prevention programmes. Single trial evaluations should ensure high quality of the research design and detailed reporting of results and potential predictors.
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INTRODUCTION |
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Can depression be prevented?
Since the 1980s there has been increased development and implementation of
universal, selective and indicated programmes
(Mrazek & Haggerty, 1994) that aim to reduce risk factors for depression, depressive symptoms and
depressive disorders. Universal prevention interventions target a whole
population group that has not been identified on the basis of increased risk;
selective prevention targets subgroups of population whose risk of developing
a mental disorder is significantly higher than average, as evidenced by
biological, psychological or social risk factors; indicated prevention targets
high-risk persons who are identified as having minimal symptoms foreshadowing
mental disorder or biological markers indicating predisposition for mental
disorder but do not meet diagnostic criteria for disorder at that time.
Although there is substantial evidence that depressive symptoms can be reduced
(Muñoz et al,
1993; Gillham et al,
2000), only a few programmes have shown that depression can be
prevented (Clarke et al,
1995,1995,
2001). This meta-analysis aims
to identify some of the population, programme and research design
characteristics that predict the effect of primary prevention programmes
targeting depression.
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METHOD |
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Studies were selected within the definitions of universal, selective and indicated prevention, excluding any pharmacological intervention, if they included the prevention of depression as a primary or secondary goal or outcome, the improvement of protective factors for depression or mental health (e.g. self-esteem) or the reduction of risk factors related to depression (e.g. negative thinking). Selection was restricted to English-language publications between the years 1985 and 2000 that could be retrieved through the library system, had either a randomly allocated control or equivalent comparison group, had prepost measures, had objective outcome measures and had sufficient statistical information to calculate an effect size. From the selected trials, only those that had depressive symptoms or incidence of depression as an outcome measure were included for analysis.
Coding system and procedures
A coding instrument was developed to operationalise programme outcomes and
hypothesised effect predictors. The coding instrument included trial
descriptors, target group characteristics, programme characteristics,
programme development characteristics, implementation characteristics, quality
of the research design, and outcome indicators. The coding system comprised a
code book with codes for each variable, a coding sheet to operationalise the
information, and a coding instructions book with definitions and instructions
(details available from the author upon request).
A trained coder and E.J.-L. undertook the coding process. Measures to minimise bias were taken into account, such as coders training (Cooper & Hedges, 1994). A random sample of one in five trials was double-coded to assess interrater reliability. The kappa coefficient averaged across codes was 0.91, indicating excellent agreement beyond chance between the two coders (Cooper & Hedges, 1994). Data were entered into a Statistical Package for the Social Sciences (Version 10) data file, checked and cleaned to control for data entry and coding errors.
Calculation of effect sizes and weighted effect sizes
An effect size estimate using the standardised mean difference
(Hedges & Olkin, 1985: p.
79, formula 3) was calculated from the published data for every outcome
measure reported, corrected for pre-test measures and small sample sizes
(Lipsey & Wilson, 2001). Positive effect sizes indicate improvement for the intervention group. Each
effect size was weighted based on the inverse of its variance
(Hedges & Olkin, 1985: p.
86, formula 15). For multiple programmes within one study, weights were
calculated according to Gleser & Olkin
(1994: p. 346, formula 22.13).
Weighted mean effect sizes (Hedges &
Olkin, 1985: p. 111, formula 6) and 95% confidence intervals were
calculated (Hedges & Olkin,
1985: p. 86, formula 16).
Unit of analysis and sample heterogeneity
The unit of analysis in this study is at the programme level, where a
programme was defined as an intervention with a preventive goal and a measure
of depressive symptoms. When the efficacy of more than one programme within a
study was compared with a control condition, the different interventions were
treated independently and an effect size was calculated for each
(Gleser & Olkin, 1994: p.
346, formula 22.13). Effect sizes were averaged within each programme across
outcome measures and follow-up times, resulting in one effect size per
programme.
The Q statistic was calculated to test for heterogeneity (Hedges & Olkin, 1985). The sample was heterogeneous (Q=474.72, d.f.=69, P<0.001) and characteristics of the programmes, target groups and research methodology were examined as independent variables to account for this heterogeneity (Lipsey & Wilson, 2001).
Predictors in the study
Gender was coded as a continuous variable, as the percentage of male
participants in the programmes. Initial level of risk was defined as
universal, selective or indicated (Mrazek
& Haggerty, 1994). The duration of programmes was coded in
months, and the length of individual programme sessions in minutes. The
quality of the research design was assessed with the Cochrane nine-item
dichotomous scale (items scores 1 or 0); high-quality programmes were
considered to be those with a score of 8 or above
(Brown et al, 2000).
Intervention methods were classified into one of five groups: behaviour (e.g.
behaviour change, pleasant activities, modelling); cognition (e.g. cognitive
restructuring, counselling, explanatory-style training); competence (e.g.
broad skill training, social resistance skills); education (e.g. direct
instruction, lectures and workshops); and social support (e.g. network
building, fostering socialisation). Programme providers were divided into
healthcare personnel (physical and mental health professionals) and lay
personnel (peers, family members, schoolteachers).
Statistical analyses to test hypotheses
The z-scores were used as significance tests to compare the
weighted mean effect sizes of the different values of the categorical
independent variables. Weighted least squares regression analyses were used to
identify possible relationships and interactions between predictors in
explaining the variation in effect sizes between studies for continuous and
categorical independent variables, recoded to dummy variables. Each unweighted
programme effect size was corrected by its weight as defined above. Regression
coefficients were obtained and the adjusted R2 was used to
measure the proportion of variance accounted for by an independent variable.
The standard errors of the regression coefficients (B) were corrected
according to Hedges & Olkin
(1985: p. 174) and used in a
z test. Separate regression models were built, first testing for main
effects and then for interaction effects of the target group characteristics
gender, age and level of risk, which had been identified in previous research
as possible moderators of effect (Price
et al, 1992,Price
et al, 1992; Gillham
et al, 2000). Scatter plots and plots of residuals found
no evidence for violation of regression model assumptions. All the tests for
statistical significance were based on two-tailed tests.
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RESULTS |
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Participant characteristics
About a quarter of the programmes (16) targeted children, 9 targeted
adolescents, almost a half (32) were aimed at adults and 12 were for older
people (Table 2). There was no
significant difference in effect size between the different age groups. There
was also no significant difference in effect size between universal, selective
and indicated programmes. Of the 63 programmes in which gender distribution
was specified, weighted least squares regression analyses indicated a direct
positive relationship between percentage of male participants and effect size
(Table 3). There was an
interaction between percentage of male participants and level of risk, so that
the relationship between percentage of males in the programme and effect size
was present for universal and selective programmes, but not for indicated
programmes (Table 3).
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Programme characteristics
Time descriptors
Programmes with more than eight sessions were significantly better than
those with eight sessions or fewer. Programmes with session lengths of
6090 min were significantly better than those with sessions lasting
less than 60 min or longer than 90 min. No significant difference was found
for duration of programmes or distribution of sessions
(Table 4).
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Programme providers
There were 102 programme providers reported in 62 programmes
(Table 5). Programmes that used
a combination of health care professionals and lay personnel had the largest
effect sizes. Programmes provided by health care professionals (physical and
mental health personnel) and those provided by both health care and lay
personnel yielded significantly larger effect sizes than programmes provided
by lay personnel alone. Programmes provided by health care professionals had
larger effect sizes than programmes run by lay personnel only for selective
(z=2.04, P=0.045) and indicated populations
(z=2.37, P=0.016), but the difference was not significant
for universal populations (z=1.90, P=0.057).
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Methods and techniques
Programmes that involved a competence enhancement component yielded the
largest effect sizes, whereas programmes including behavioural methods yielded
the lowest effect sizes (Table
6). When analysed by age group, the worse performance of
programmes that included behavioural methods was present for all age groups,
although it was significant only for the older population (with a behavioural
component, the weighted effect size (WES) was 0.10; without, WES=0.95;
z=7.14, P<0.001). Programmes that included competence
enhancement techniques did significantly better than those that did not
include them. Programmes that included social support did generally worse than
those that did not, except for the older group, for which social support
programmes yielded larger weighted effect sizes (with social support,
WES=0.92; without, WES=0.12; z=7.13, P<0.001).
Programmes that included three or more different types of methods were
significantly better than those that included only one or two.
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Research methodological characteristics
The programmes with a high quality of research design were significantly
more effective than those of low quality
(Table 7). Programmes that
reported attrition rates were significantly better than those that did not.
Programmes rated as having a well-defined intervention were better than those
that did not.
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Changes in risk factors and symptoms
The outcome measures of each programme were subsequently divided into an
averaged measure per programme indicating changes in risk factors
(n=49), changes in depressive symptoms (n=69), and changes
in psychiatric symptoms other than depression, such as anxiety
(n=51). Measures for each group were averaged across programmes to
obtain three mean effect sizes, one per group. There was no significant
difference in effect size between depressive symptoms (WES=0.24, 95% CI
0.130.35), risk factors (WES=0.28, 95% CI 0.150.41)
0.150.41) and other psychiatric symptoms (WES=0.18, 95% CI
0.090.27), all three of which had significant and independent positive
outcomes. Weighted mean effect sizes were further subdivided within these
three groups into universal, selective and indicated approaches
(Fig. 1). Comparisons of means
indicated no significant difference between the type of preventive approach
and changes in depressive symptoms, risk factors and changes in other
psychiatric symptoms.
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DISCUSSION |
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Effective prevention and variation in outcome
Consistent with earlier meta-analyses for mental health promotion
(Durlak & Wells, 1997;
Tobler & Stratton, 1997;
Brown et al, 2000), our meta-analysis found a weighted mean effect size of 0.22. This is
equivalent to an 11% improvement in the intervention groups compared with the
control groups. Effect sizes for prevention programmes tend to be smaller than
those of treatment, largely because prevention applies the same strategies to
a population group that might or might not be at risk for a later mental
health problem. However, from a public health perspective the prevention
strategy can be cost-effective, as a small effect size in a large number of
people can lead to a greater population gain than a large effect size in a
small number of people (Rose,
1993).
What leads to increased effects in depression prevention?
There was a large variation in programme outcomes. Subsequent analyses
aimed to identify what might predict this variation.
Gender differences
There was a relationship between the percentage of male participants in
universal and selective programmes and effect size, but not in indicated
programmes. This finding is consistent with some within-trial findings
(Gillham et al, 1995)
but not with others (Seligman et
al, 1999,Seligman et
al, 1999). It is possible that indicated programmes, which
target specific problems with focused techniques, are more tailored to
depressive symptoms and disorder than universal and selective programmes, and
also take into account gender differences in the development of the programme.
The results should be interpreted with caution, because the relationship is
between the proportion of male participants in the programme and the effect
size, not the actual effect size for each gender subgroup, which unfortunately
is rarely reported. The results stress the importance of analysing and
reporting gender differences in single trial evaluations to understand
gender-specific programme effectiveness.
Initial level of risk
No difference was found between universal, selective and indicated
programmes. There has been a marked preference for targeted interventions for
depression prevention, because of evidence in reducing symptoms and incidence
(Clarke et al,
1995,Clarke et al,
1995) and because subgroups identified at increased risk have
seemed to benefit the most (Price et
al, 1992,Price et
al, 1992; Gillham et
al, 1995). However, evidence has also accumulated that
universal preventive interventions can be beneficial for those at risk,
because of lowered stigma and better socialisation
(Kellam et al, 1998;
Reid et al, 1999).
The results of our analysis have supported both these directions and there
seems merit in interventions that combine both universal and targeted
prevention (Conduct Problems Prevention
Research Group, 2000).
Number and length of sessions
Research has focused on testing the efficacy of shortened versions of
existing prevention programmes
(Muñoz et al,
1993). The results of our analysis indicated that programmes with
more than eight sessions and programmes with session lengths of 6090
min yielded the larger effect sizes. The number of sessions is relevant for
participants ability to internalise methods and processes offered by
the interventions; fewer than nine sessions might not be enough. The length of
sessions is important because of the group focus of prevention, where
sufficient time needs to be allocated for interaction and group processes;
less than an hour might not allow participants to feel engaged in a group
process.
The promise of competence enhancement techniques
In addition to cognitive techniques
(Price & Bennett Johnson,
1999; Seligman et al,
1999,Seligman et al,
1999; Gillham et al,
2000; Clarke et al,
2001), competence methods were also found to be effective across
different age groups. Programmes that included behavioural techniques were
detrimental for the elderly and were not superior for the other age groups.
Programmes that combined three or more intervention methods were more
effective than those that did not, suggesting the importance of
multi-component programmes.
Provider qualifications
Lay personnel have been proposed as potential efficient programme providers
for preventing depression (Muñoz
et al, 1993). However, our meta-analysis found that lay
personnel alone were not the best providers for selective and indicated
programmes. The specificity and severity of depression in targeted populations
who are already experiencing risk factors or symptoms may require trained
personnel who are aware of and skilled in dealing with depressive
symptoms.
Quality of the research design
Consistent with earlier findings
(Tobler & Stratton, 1997;
Brown et al, 2000)
high-quality research trials were predictive of better outcomes. Well-defined
intervention aims and accounting for attrition rates were independent
predictors of effect size. Well-defined aims have already been identified as
effect predictors in health promotion (Kok
et al, 1997). Reporting attrition rates might indicate a
deeper analysis of intervention effects, and studies that do so might be more
likely to have accounted for patient withdrawal at the outset, and to have
provided the target group with incentives to continue in the programme.
Changes in depressive outcomes, risk factors and other psychiatric
symptoms
Simultaneous positive changes in risk and protective factors and in related
psychiatric symptoms (e.g. anxiety) were found in addition to the reductions
in depressive symptoms, indicating the multiple outcome potential of
prevention programmes. However, despite the evidence that prevention
programmes can reduce depressive symptoms for both universal and targeted
populations, few have demonstrated that the incidence of depression can be
reduced (Clarke et al,
1995,1995,
2001). There is an urgent need
for further trials of sufficient power to study the impact of preventing the
onset of depression and the role of moderating and mediating variables.
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Clinical Implications and Limitations |
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LIMITATIONS
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ACKNOWLEDGMENTS |
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We express our deep appreciation to Dr Hendricks Brown for his statistical and methodological support and feedback during the different research phases of the project. We also thank Sietske van Haren, the second coder, for her dedicated input during the coding process and Rianne Kassander for her support during the revision of the paper.
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Received for publication November 19, 2002. Revision received April 14, 2003. Accepted for publication May 6, 2003.