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Selecting key variables for depression prevention: results from a population-based prospective epidemiological study
Journal of Affective Disorders 81 (2004) 241 – 249 www.elsevier.com/locate/jad
Research report
Selecting key variables for depression prevention: results from a population-based prospective epidemiological study Filip Smit *, Aartjan Beekman, Pim Cuijpers, Ron de Graaf, Wilma Vollebergh Trimbos Institute, The Netherlands Institute of Mental Health and Addiction, P.O. Box 275, 3500 AS Utrecht, The Netherlands Received 16 January 2003; received in revised form 27 August 2003; accepted 29 August 2003
Abstract Background: Given the existing economic constraints, prevention of depression has to be carefully targeted, and applied where it is likely to yield the highest possible health benefits at the lowest possible cost. Aim: To identify those risk factors of depression that have the greatest use potential from the perspective of prevention. Method: Data were derived from a population-based prospective cohort of 4664 adults who had never experienced a depression. Their health status was reexamined after 1 year. Incidence rate ratios (IRR), population attributable risks (PAR) and numbers needed to be treated (NNT) were calculated to create a hierarchy of risk factors. Results: Selecting high-risk groups with migraine, abdominal and respiratory complaints and markers of vulnerability or childhood trauma appears to be an indicated strategy. Conclusions: This study illustrates how epidemiology can contribute towards setting a Research and Development agenda for primary prevention of depression. D 2003 Elsevier B.V. All rights reserved. Keywords: Depressive disorder; Prevention; Potential health benefits
In public mental health, much effort is devoted to treating depression. The rationale is clear: depression is a serious condition (Murray et al., 1994), it carries a poor prognosis (Cuijpers and Smit, 2002) and affects many people (Bijl et al., 1998b). However, 40% of prevalent cases are in fact new, or incident, cases (see below in this paper). It is therefore important to reduce the influx of new cases. Here, depression prevention has a keyrole to play. Several criteria—such as feasibility, acceptability, availability, cost and effectiveness of preventive interventions—are used to select pre-
* Corresponding author. Tel.: +31-30-2971100; fax: +31-302971111. E-mail address: [email protected] (F. Smit). 0165-0327/$ - see front matter D 2003 Elsevier B.V. All rights reserved. doi:10.1016/j.jad.2003.08.007
ventive interventions that warrant interest from the perspective of public mental health. Testing interventions against these criteria is a necessary, but slow and expensive stage in the cycle of development, research and implementation of preventive interventions. To inform and rationalise this process at its earliest possible stage, we propose using three additional criteria to identify possibly interesting preventive interventions: the candidate intervention is directed at one (or more) amenable risk factors that (1) are strongly associated with the onset of depressive disorder, (2) result in a substantial decrease in the incidence rate if their adverse effect can be successfully prevented, and (3) are associated with a low number-needed-to-be-treated. The benefit of these criteria is that they can be assessed before embarking
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on a costly and time-consuming prevention trial. Data are required from prospective, population-based, epidemiological studies. These studies yield the relevant statistics and help to inform and rationalise the discussion about the most strategic targets for the primary prevention of depression.
psychometric properties (Wittchen et al., 1991). In this study, we restrict our focus on DSM-III-R major depression as the dependent variable. All putative risk factors were measured at t0. Their selection for this study was in part based on the vulnerability-stress theory of Brown and Harris (Harris, 2000) and previous risk factor epidemiological studies in depression (De Graaf et al., 2002a,b).
1. Method 1.1. Subjects and procedures The data were derived from the Netherlands Mental Health Survey and Incidence Study (Nemesis), a nationally representative cohort study based on a random sample of 7076 people at baseline (t0) of whom 5618 were retained after 1 year at first follow-up (t1). Elsewhere the subjects and the procedures are described in detail (Bijl et al., 1998a) as are the effects of loss-to-follow-up between t0 and t1 (De Graaf et al., 2000). To correct for the combined effect of initial nonresponse and dropout, post-stratification weights were calculated. After weighting, the multivariate distribution (over the variables sex, age, marital status and degree of urbanisation) in the sample was exactly the same as in the population as described by the statistics of the central bureau of statistics. From this data set, we selected people who had never experienced a depression before and were therefore at risk of becoming first-ever incident cases of depression (N = 4664). Of these, 143 developed a depressive disorder in the year between t0 and t1. The latter formed the index group of firstever incident cases of depression. The reference group were people from the same cohort who did not meet the diagnostic criteria for depression in that year. 1.2. Measures DSM-III-R axis-I diagnoses (APA, 1987) were assessed with the Composite International Diagnostic Interview, the CIDI, of the World Health Organisation (WHO, 1990). Use was made of the first Dutch version of the CIDI (Smeets and Dingemans, 1993). The CIDI assessments were carried out by CIDI-trained lays in structured, computer-assisted, face-to-face, interviews both at t0 and t1. The CIDI is known to have excellent
Childhood trauma: i.e. emotional neglect, psychological abuse, physical abuse and sexual abuse before the age of sixteen. During the interviews, the abuse types were explained to the respondents and illustrated with examples. Two new variables were made: one indicating exposure to two or more forms of abuse, i.e. ‘multiple abuse’, and another indicating exposure to any form of neglect or abuse, i.e. ‘any abuse’. Parental history concerned depressive problems, anxiety or phobic problems and problem drinking in one or both biological parents. The presence or absence of parental history was assessed by single questions. The reliability of this procedure was evaluated for the question about parental problem drinking and proved to be well in order (kappa = 0.83, cf. Cuijpers and Smit, 2001), but this is not known for the other parental problems. Two new variables were calculated: one that indicated the presence of two or more parental problems, i.e. ‘multiple parental problems’, and another, which indicated the presence of ‘any parental problem’. Vulnerability indicators were: Locus of control measured with the Mastery Scale (Pearlin and Schooler, 1978). We defined low mastery ( = 1, else = 0) as a score below the mean of the baseline sample (M = 3.9; Cronbach’s a = 0.81). Neuroticism was assessed with the Groningse Neuroticism Questionnaire (Ormel, 1980). We used a cutoff at the mean to obtain an indicator for above average levels of neuroticism (M = 2.7; a = 0.80). In a sensitivity analysis, the 66th percentiles of both the Mastery and the Neuroticism scales were also used, because it was thought that at these levels mastery and neuroticism become etiologically more relevant. Other indicators for vulnerability were: having a score equal to or larger than 2 on the 12-item version of General Health Questionnaire (Goldberg and Hillier, 1974), the Dutch version
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(Koeter and Ormel, 1991), and having ever felt depressed for 2 weeks or longer (i.e. presence of this core symptom of depression on a life time basis). Antecedent mental disorders: any DSM-III-R axisI anxiety disorder, and substance misuse or dependence disorder in the 12 months preceding t0. Antecedent somatic illnesses: presence of somatic disorders (migraine, lower back-pain, complaints pointing towards cardiovascular, respiratory, and nonspecific abdominal problems) in the 12 months preceding t0. Demographic variables are: sex, age, living with a partner, ethnic descent (Dutch, other), education (elementary, lower vocational, secondary, higher vocational and academic) and degree of urbanisation (fewer or more than 500 addresses per square kilometre). 1.3. Key statistics: IRR, PAR and NNT The analyses (see below) had to produce statistics that help to evaluate the utility of risk factors as seen from the prevention perspective. Here we describe them. The incidence rate ratio, IRR, is a measure of strength between an exposure and outcome. It tells how much larger the incidence rate of depression is in the exposed group relative to the incidence rate among those not exposed to a risk factor. When the IRR has a value of 1, it indicates absence of any effect, values larger than 1 indicate an increased incidence rate, and values below 1 indicate a reduced incidence rate in the exposed group as compared to the unexposed group. The population attributable risk, PAR, is a measure of potential health gain in a population. It describes the percentage by which the incidence rate of depression in the population can be reduced when the risk factor is completely eliminated, or when its adverse effect is completely contained. Clearly, it is not realistic to expect that preventive interventions will ever be completely successful and the PAR is best interpreted as a statistic that puts an upper limit to the health gain that could be generated in a population. It is also possible to adjust the PAR statistic for partially successful interventions if so required (cf. Morgenstern and Bursic, 1982; Gunning-Schepers, 1988). For the PAR statistic to be valid, it must further be
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assumed that the risk factor has an etiological role in the pathogenesis of depression. The number needed to be treated, NNT, is a measure of the effort. It shows how many people in the population must receive the intervention in order to avoid one new case of depression. In the more realistic scenario that the intervention is successful for, say, 40%, then (1/0.40)NNT=(2.5)NNT people must receive the intervention to avoid one case. The NNT can thus serve as an indication of the (minimum) required effort to produce a health impact in a population. To summarise, IRR gives information on the strength of association between exposure and outcome, PAR describes the maximum achievable health gain that could be generated in a population when a preventive intervention is successful in blocking the adverse effect of an etiological factor, and NNT helps to quantify the minimum amount of effort that is needed to generate that health gain in a population. Together, these statistics of impact and effort can help to inform and rationalise the discussion on depression prevention. 1.4. Analysis Stata (StataCorp, 1999) was used to carry out the analysis. The key statistics were obtained in three steps. In the first step, the incidence rate ratio, IRR, for each risk factor was obtained under a weighted Poisson regression model with the depression status at t1 as the dependent and the exposure status at t0 as the independent variable, while accounting for exposure time and adjusting for demographics (cf. Kleinbaum et al., 1998). Second, the population attributable risk, PAR, was obtained with help of the equation PAR = P(IRR 1)/(1 + P(IRR 1)), where P is the prevalence of the risk factor in the population at risk (cf. Rothman and Greenland, 1998; see also Miettinen, 1974). In the third step, the number needed to be treated, NNT, was computed as the inverse of the absolute risk difference. The latter was obtained using the weighted linear probability model (McCullah and Nelder, 1989; Long, 1997) in which the risk factor was included as one predictor and the demographic variables as others. All key statistics were adjusted for demographics, because it was thought that the secular impacts of
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demographics are beyond the control of preventive interventions. In order to report correct 95% confidence intervals of the key statistics under weighting, so-called ‘robust’ variance-related estimates were obtained, using the first-order Tailor-series linearisation method, as implemented in Stata.
2. Results 2.1. Prevalence and incidence of depression in the Dutch population On an annual basis, 5.8% of the population aged 18– 65 meet the DSM criteria of depressive disorder (Bijl et al., 1998b). Projected to the 10.7 million Dutch people in this age range, there are 620,000 depressed adults each year in the Netherlands alone. It is worth noting that the incidence of depression is high relative to its prevalence. The incidence occurs at a rate of 2.72 new cases per 100 person-years (Bijl et al., 2002), which is equivalent to 246,000 new cases in a given year. In other words, 40% of the prevalent cases are in fact new cases. Therefore it is inappropriate to focus exclusively on the treatment of prevalent cases. Primary prevention has to play a supplementary role by reducing the annual influx of new cases. 2.2. Description of the people at risk of becoming depressed The demographics of the people at risk of becoming first-ever cases of depressive disorder (i.e. all people except those who already experienced a depressive disorder) can be summarised as follows: female 51% (95% CI = 49.4 – 52.2), mean age 41 years (40.8 – 41.5), living with a partner 68.5% (67.0 – 70.0), immigrant 2.2% (1.8 – 2.7), employed 70% (68.3 – 70.9), living in an urban environment 81.9% (80.8 –83.0), education: elementary 6% (5.5 – 6.8), lower vocational 36% (34.9 – 37.7), high-school 28% (26.9 – 29.3), higher vocational and academic 29% (28.2 – 30.8). 2.3. Detailed results for childhood trauma Many people report neglect and childhood abuse (see Table 1). Emotional neglect is reported by 20.8% (PREV). Neglect predicts a risk for depression which
Table 1 Prevalence of risk indicators in the population at risk (PREV). Incidence rate ratio (IRR), population attributable risk (PAR), absolute risk difference (ARD) and numbers needed to be treated (NNT). Weighted analysis, adjusted for demographics (N = 4664) Risk indicator
PREV (%)
IRR
PAR (%)
ARD
Childhood trauma Emotional neglect Psychological abuse Physical abuse Sexual abuse z 2 forms of abuse Any abuse
20.8 10.3 7.5 5.4 10.7 27.9
2.85** 3.20** 3.84** 1.49NS 3.38** 2.67**
27.8 18.4 17.6 2.6 20.2 31.8
0.036** 0.049** 0.062** 0.015ns 0.052** 0.031**
27.5 20.4 16.1 66.6 19.4 31.9
Parental history Depressive Anxious/phobic Alcoholic z 2 problems Any problem
19.7 9.4 7.3 8.0 23.1
1.46ns 1.52ns 1.14ns 1.62ns 1.43NS
8.6 4.6 1.0 4.7 9.0
0.012ns 0.014ns 0.004ns 0.017ns 0.011ns
86.3 71.0 261.4 59.9 92.5
2.59** 4.96**
37.7 57.0
0.028** 0.047**
36.1 21.4
3.60** 4.05**
34.6 51.0
0.047** 0.041**
21.3 24.4
3.95** 2.53**
21.0 11.6
0.066** 0.030**
22.7 33.7
1.96** 1.36NS 2.23** 2.29* 1.63* 2.85** 1.83**
7.2 6.4 7.1 4.0 5.9 7.4 16.3
0.028* 0.008NS 0.031* 0.037NS 0.017NS 0.049** 0.020**
35.5 127.3 31.8 27.3 59.6 20.4 50.4
Vulnerability indicators Low mastery ( < M) 38.1 High neuroticism 33.5 (>M) GHQ z 2 20.3 Felt depressed 34.1 ( z 2 weeks) Antecedent mental disorders Any anxiety disorder 9.0 Any substance use 8.6 disorder Somatic illnesses Migraine Cardiovascular Respiratory Abdominal Lower back pain z 2 illnesses Any complaint
8.1 1.1 6.2 3.2 10.0 4.3 23.7
NNT
NS = not significant at a < 0.05; M = mean. * Significant at a < 0.05. ** Significant at a < 0.01.
is a factor 2.85 larger as compared to unexposed people (IRR). Elimination of its adverse effect would help to reduce the incidence of depression by 27.8% (PAR). Thus, the incidence rate which is now 2.72 per 100 person-years, would then, in the best possible scenario, decrease to 2.72 (1 0.278) = 1.96. This is equivalent to avoiding the onset of depression in close
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to 70,000 people in the Dutch population aged 18– 65: a very substantial health gain at population level. This health gain has also economic ramifications. In the Netherlands, the annual incremental costs of depression, defined as the costs of excess health service use by depressed people, plus the costs of production losses due to absenteeism from work, are typically about o3300 per year per depressed person (see for some other countries Simon et al., 2002). Therefore, avoiding the onset of depression in 70,000 cases may save as much as 70,000 3300 = o231 million per year. We will return to these statistics in a moment, but first we will take another step. The effort to bring about this health gain and its accompanying economic savings is relatively costeffective: in order to avoid one new case of depression 27.5 people have to engage in the intervention (NNT). If we were to spent the potential savings of o231 million on all 2.1 million people who were exposed to neglect, then the intervention may costs as much as o110 per person before reaching a break-even point at which the costs of prevention would exceed the savings. These statistics, of course, represent the best possible scenario which is based on the unrealistic assumption of a completely effective intervention. Thus the real health gain and cost savings are less than indicated, but these ‘maximum’ statistics help to rank risk factors into a hierarchy. By comparison, physical and multiple abuse have even better IRR, PAR and NNT values. This makes them more important targeting points for prevention. Taking childhood neglect and abuse as targeting points for depression prevention is not without drawbacks. The respective groups are rather large and it will be difficult to recognise them. However, our own experience has told us that people are willing to volunteer information on a history of abuse when asked. Also, information on a history of abuse may be available in medical records of a GP and could then be employed for selecting people eligible for a preventive intervention. The example of childhood trauma illustrated a number of points: the incidence of depression depends on exposure to certain risk factors; it is possible to select risk factors that are especially interesting from the prevention perspective, and some tentative ante hoc cost-benefit calculations can be made.
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2.4. Other risk indicators Now we turn to the remainder of Table 1. The general impression is that vulnerability indicators and childhood trauma are associated with relatively high IRR and PAR values and some of them also have relatively low NNT values. These risk factors, and corresponding high-risk groups warrant interest from the viewpoint of depression prevention in adult people. Parental history, by contrast, appears to be less interesting. More detailed inspection reveals that high IRR and PAR values are found for neuroticism, presence of a depressed feeling, GHQ scores equal or higher than 2 and (physical) abuse. Moreover, (physical) abuse, GHQ z 2 scores and high neuroticism are also associated with low NNT values. 2.5. Cumulative effects The low NNT values for multiple exposures are another interesting feature of Table 1 (see z 2 childhood traumas and z 2 somatic illnesses). This suggests a cumulative effect of multiple exposures. Further analysis shows that this is indeed the case, in particular for the presence of somatic illnesses. Fig. 1 shows the cumulative effect of having none, one, two or three somatic conditions on the risk of becoming depressive. In the presence of multiple exposures, the risk of becoming depressed increases sharply. Similar risk curves can be produced for the combination of other risk factors (results not shown), but Fig. 1 depicts one of the more interesting risk curves. It clearly shows how high-risk groups could
Fig. 1. The risk of becoming depressed (in percent) by number of exposures (exp) to somatic complaints (migraine, respiratory and abdominal complaints).
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be selected in primary care. Further, the low NNT value for this group indicates that targeting primary health patients will be associated with practical and financial advantages.
3. Discussion 3.1. Main findings The outcomes of childhood trauma were already discussed in great detail and we shall not repeat that discussion here. Suffice it to recall that neglect and childhood adversities are strongly associated with the later onset of depressive disorder, as is also reported elsewhere (Brown et al., 1999; Kessler et al., 1997; Nelson et al., 2002; Portegijs et al., 1996), and the containment of their adverse effects would greatly help to reduce the incidence of depression in the population. It appears that this could be done in a cost-effective way. It is generally acknowledged that parental history has a predictive value for the later onset of mental disorders in offspring (cf. Mrazek and Haggerty, 1994; Kendler et al., 1997; Merikangas et al., 1998; Cuijpers et al., 1999). However, we found no support for this notion, although it should be noted that the statistics, as we found them, bordered on significance. Perhaps the fact that we modelled these risk factors prospectively and adjusted them for demographics has led to statistically insignificant results. By contrast, a cross-sectional analysis of our baseline data did show significant relationships between parental history and depression (Bijl et al., 2002a,b). Nevertheless, we must conclude that parental history is not a particularly good starting point for depression prevention in adults. It should be emphasised, however, that a different conclusion might be drawn with respect to young children (cf. Beardslee and Podorefsky, 1988; Beardslee et al., 1997) as opposed to the adults that were studied here. In addition, it should be born in mind that parental depression may not only result in depression in their offspring, but also elevates risks for a range of other disorders like anxiety disorders and substance-related disorders (Cuijpers and Smit, 2002). Vulnerability indicators (cf. Harris, 2000) are associated with high IRRs and PARs and low NNTs and
thus seem to offer an advantageous starting point for preventive interventions. Low mastery may be amenable through a psychosocial intervention and be seen as an etiological factor on the causal pathway towards the onset of depression. Since a GHQ score equal to or larger than 2, and the presence of a depressive core symptom are conceptually overlapping with depression they cannot be seen as proper etiological factors—in a sense they are outcomes rather than predictors. Nevertheless, they may be valuable for depression prevention in terms of early detection, for example, through screening. Antecedent disorders, mental or somatic, may also be helpful in targeting selected groups, which is important for practical and financial reasons. It is well known that anxiety disorders and substance-use disorders may precede or accompany depression (Kessler et al., 1999; Preisig et al., 2001; De Graaf et al., 2002b) and the same can be said about somatic conditions like migraine, lower back-pain, cardiovascular disorders and (nonspecific) respiratory and abdominal complaints (Jenkings et al., 1992; Geerlings et al., 2000). We found support for these notions, but found cardiovascular complaints to be unrelated to the onset of depression. However, it should be recalled that we studied adults under the age of 65, and for older people a different risk set may exist (cf. Geerlings et al., 2000). 3.2. Strengths and limitations of this study The main findings and the conclusions (see below) must be placed in the context of the strengths and limitations of this study. The strengths of this study are (i) the representativeness of the sample for the general Dutch population in the age range of 18 –64, (ii) the measurement of mental disorders with a reliable instrument, (iii) the prospective design which enables the study of incidence and facilitates etiological inference, and (iv) the measurement of exposures which is not biased due to post hoc rationalisation on the part of the respondents, because at t0 they could not have any knowledge about their future health state at t1. Furthermore, this study is among the first to show how a statistical technology could be applied to quantify potential health benefits and the required efforts to generate these health benefits in the field of prevention of
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mental disorders. It thus supplies the sort of technology which is of importance for setting a rational Research and Development agenda for depression prevention. The limitations of this study consist in the not very detailed measurement of the exposures. We do not know at what age, for how long and how intensively subjects were exposed. Moreover, the number of studied risk factors is limited in that, for example, genetic and other biological risk factors were not included. We studied only dyads of a single exposure and a single disorder, but it would be interesting to extend this to the study of joint exposures (to more than one risk factor) and to see how that would impact on more than a single disorder. Fig. 1 shows that this could be very interesting.
4. Conclusions This study was set out to see whether, and how, epidemiology can make contributions to the formulation of a Research and Development agenda for depression prevention. Our main conclusion is that epidemiology can be helpful in this respect. Statistics such as the incidence rate ratio, IRR, population attributable risk, PAR, and the number needed to be treated, NNT, are helpful in selecting risk factors that have potential for prevention. These statistics can also be combined with information from cost-ofillness studies and then used for ex ante cost-benefit analysis. This claim is made on the understanding that, ultimately, the selection of risk factors and corresponding high-risk groups cannot be decided by statistics alone. Other considerations are also important, such as the availability of preventive techniques, knowledge of their (assumed) success rates, and ethical and economic considerations. Once the Research and Development agenda for depression prevention is set, preventive interventions should be designed and tested in prevention trials to empirically ascertain their (cost) effectiveness. Finally, projects with a proven effectiveness should be disseminated. Therefore, it is also understood that the risk-factor epidemiological contributions are only one element in a larger cycle of development and research. Nonetheless, the epidemiological contributions are crucial
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because they help at a very early stage to inform and rationalise the Research and Development agenda for depression prevention. At the substantive level, the results suggest that depression prevention could be effective in primary care settings (compare Jenkings et al., 1992). Patients with one or more somatic complaints will present themselves there and could be screened for the presence of a core symptom of depression, a GHQ score above cutoff, or high levels of neuroticism. When these patients also report a history of childhood abuse or an antecedent anxiety disorder, then selective prevention appears to be indicated. The exact format of this intervention is, as yet, not clear, but (cost-) effective interventions like bibliotherapy (Cuijpers, 1997) or counselling (Bower et al., 2003) are likely candidates. In future research, thought must be given to the quantification of success rates of (future) prevention interventions, the study of joint exposures of several risk-factors, optimisation of cutoff points in risk factors that are on a continuous measurement scale (like mastery and neuroticism), the quantification of impacts on the incidence not only of major depressive disorder, but on the whole array of anxiety and mood disorders, and the accommodation of cost-benefit considerations in this sort of analysis. It is recommended that in the future these technologies will be further developed and that they will play a role in targeting prevention there where it is likely to be most effective.
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F. Smit et al. / Journal of Affective Disorders 81 (2004) 241–249 cance and comorbidity of subthreshold depression and anxiety in the community. Acta Psychiatr. Scand. 104, 96 – 103. Rothman, K.J., Greenland, S., 1998. Modern Epidemiology, 2nd ed. Lippincott-Raven, Philadelphia, PA. Simon, G.E., Chisholm, D., Treglia, M., Bushnell, D.the LIDO Group, 2002. Course of depression, health services costs, and work productivity in an international primary care study. Gen. Hosp. Psych. 24, 328 – 335. Smeets, R.M.W., Dingemans, P.M.A.J., 1993. Composite International Diagnostic Interview (CIDI), Versie 1.1. World Health Organization, Amsterdam.
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Research report
Selecting key variables for depression prevention: results from a population-based prospective epidemiological study Filip Smit *, Aartjan Beekman, Pim Cuijpers, Ron de Graaf, Wilma Vollebergh Trimbos Institute, The Netherlands Institute of Mental Health and Addiction, P.O. Box 275, 3500 AS Utrecht, The Netherlands Received 16 January 2003; received in revised form 27 August 2003; accepted 29 August 2003
Abstract Background: Given the existing economic constraints, prevention of depression has to be carefully targeted, and applied where it is likely to yield the highest possible health benefits at the lowest possible cost. Aim: To identify those risk factors of depression that have the greatest use potential from the perspective of prevention. Method: Data were derived from a population-based prospective cohort of 4664 adults who had never experienced a depression. Their health status was reexamined after 1 year. Incidence rate ratios (IRR), population attributable risks (PAR) and numbers needed to be treated (NNT) were calculated to create a hierarchy of risk factors. Results: Selecting high-risk groups with migraine, abdominal and respiratory complaints and markers of vulnerability or childhood trauma appears to be an indicated strategy. Conclusions: This study illustrates how epidemiology can contribute towards setting a Research and Development agenda for primary prevention of depression. D 2003 Elsevier B.V. All rights reserved. Keywords: Depressive disorder; Prevention; Potential health benefits
In public mental health, much effort is devoted to treating depression. The rationale is clear: depression is a serious condition (Murray et al., 1994), it carries a poor prognosis (Cuijpers and Smit, 2002) and affects many people (Bijl et al., 1998b). However, 40% of prevalent cases are in fact new, or incident, cases (see below in this paper). It is therefore important to reduce the influx of new cases. Here, depression prevention has a keyrole to play. Several criteria—such as feasibility, acceptability, availability, cost and effectiveness of preventive interventions—are used to select pre-
* Corresponding author. Tel.: +31-30-2971100; fax: +31-302971111. E-mail address: [email protected] (F. Smit). 0165-0327/$ - see front matter D 2003 Elsevier B.V. All rights reserved. doi:10.1016/j.jad.2003.08.007
ventive interventions that warrant interest from the perspective of public mental health. Testing interventions against these criteria is a necessary, but slow and expensive stage in the cycle of development, research and implementation of preventive interventions. To inform and rationalise this process at its earliest possible stage, we propose using three additional criteria to identify possibly interesting preventive interventions: the candidate intervention is directed at one (or more) amenable risk factors that (1) are strongly associated with the onset of depressive disorder, (2) result in a substantial decrease in the incidence rate if their adverse effect can be successfully prevented, and (3) are associated with a low number-needed-to-be-treated. The benefit of these criteria is that they can be assessed before embarking
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on a costly and time-consuming prevention trial. Data are required from prospective, population-based, epidemiological studies. These studies yield the relevant statistics and help to inform and rationalise the discussion about the most strategic targets for the primary prevention of depression.
psychometric properties (Wittchen et al., 1991). In this study, we restrict our focus on DSM-III-R major depression as the dependent variable. All putative risk factors were measured at t0. Their selection for this study was in part based on the vulnerability-stress theory of Brown and Harris (Harris, 2000) and previous risk factor epidemiological studies in depression (De Graaf et al., 2002a,b).
1. Method 1.1. Subjects and procedures The data were derived from the Netherlands Mental Health Survey and Incidence Study (Nemesis), a nationally representative cohort study based on a random sample of 7076 people at baseline (t0) of whom 5618 were retained after 1 year at first follow-up (t1). Elsewhere the subjects and the procedures are described in detail (Bijl et al., 1998a) as are the effects of loss-to-follow-up between t0 and t1 (De Graaf et al., 2000). To correct for the combined effect of initial nonresponse and dropout, post-stratification weights were calculated. After weighting, the multivariate distribution (over the variables sex, age, marital status and degree of urbanisation) in the sample was exactly the same as in the population as described by the statistics of the central bureau of statistics. From this data set, we selected people who had never experienced a depression before and were therefore at risk of becoming first-ever incident cases of depression (N = 4664). Of these, 143 developed a depressive disorder in the year between t0 and t1. The latter formed the index group of firstever incident cases of depression. The reference group were people from the same cohort who did not meet the diagnostic criteria for depression in that year. 1.2. Measures DSM-III-R axis-I diagnoses (APA, 1987) were assessed with the Composite International Diagnostic Interview, the CIDI, of the World Health Organisation (WHO, 1990). Use was made of the first Dutch version of the CIDI (Smeets and Dingemans, 1993). The CIDI assessments were carried out by CIDI-trained lays in structured, computer-assisted, face-to-face, interviews both at t0 and t1. The CIDI is known to have excellent
Childhood trauma: i.e. emotional neglect, psychological abuse, physical abuse and sexual abuse before the age of sixteen. During the interviews, the abuse types were explained to the respondents and illustrated with examples. Two new variables were made: one indicating exposure to two or more forms of abuse, i.e. ‘multiple abuse’, and another indicating exposure to any form of neglect or abuse, i.e. ‘any abuse’. Parental history concerned depressive problems, anxiety or phobic problems and problem drinking in one or both biological parents. The presence or absence of parental history was assessed by single questions. The reliability of this procedure was evaluated for the question about parental problem drinking and proved to be well in order (kappa = 0.83, cf. Cuijpers and Smit, 2001), but this is not known for the other parental problems. Two new variables were calculated: one that indicated the presence of two or more parental problems, i.e. ‘multiple parental problems’, and another, which indicated the presence of ‘any parental problem’. Vulnerability indicators were: Locus of control measured with the Mastery Scale (Pearlin and Schooler, 1978). We defined low mastery ( = 1, else = 0) as a score below the mean of the baseline sample (M = 3.9; Cronbach’s a = 0.81). Neuroticism was assessed with the Groningse Neuroticism Questionnaire (Ormel, 1980). We used a cutoff at the mean to obtain an indicator for above average levels of neuroticism (M = 2.7; a = 0.80). In a sensitivity analysis, the 66th percentiles of both the Mastery and the Neuroticism scales were also used, because it was thought that at these levels mastery and neuroticism become etiologically more relevant. Other indicators for vulnerability were: having a score equal to or larger than 2 on the 12-item version of General Health Questionnaire (Goldberg and Hillier, 1974), the Dutch version
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(Koeter and Ormel, 1991), and having ever felt depressed for 2 weeks or longer (i.e. presence of this core symptom of depression on a life time basis). Antecedent mental disorders: any DSM-III-R axisI anxiety disorder, and substance misuse or dependence disorder in the 12 months preceding t0. Antecedent somatic illnesses: presence of somatic disorders (migraine, lower back-pain, complaints pointing towards cardiovascular, respiratory, and nonspecific abdominal problems) in the 12 months preceding t0. Demographic variables are: sex, age, living with a partner, ethnic descent (Dutch, other), education (elementary, lower vocational, secondary, higher vocational and academic) and degree of urbanisation (fewer or more than 500 addresses per square kilometre). 1.3. Key statistics: IRR, PAR and NNT The analyses (see below) had to produce statistics that help to evaluate the utility of risk factors as seen from the prevention perspective. Here we describe them. The incidence rate ratio, IRR, is a measure of strength between an exposure and outcome. It tells how much larger the incidence rate of depression is in the exposed group relative to the incidence rate among those not exposed to a risk factor. When the IRR has a value of 1, it indicates absence of any effect, values larger than 1 indicate an increased incidence rate, and values below 1 indicate a reduced incidence rate in the exposed group as compared to the unexposed group. The population attributable risk, PAR, is a measure of potential health gain in a population. It describes the percentage by which the incidence rate of depression in the population can be reduced when the risk factor is completely eliminated, or when its adverse effect is completely contained. Clearly, it is not realistic to expect that preventive interventions will ever be completely successful and the PAR is best interpreted as a statistic that puts an upper limit to the health gain that could be generated in a population. It is also possible to adjust the PAR statistic for partially successful interventions if so required (cf. Morgenstern and Bursic, 1982; Gunning-Schepers, 1988). For the PAR statistic to be valid, it must further be
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assumed that the risk factor has an etiological role in the pathogenesis of depression. The number needed to be treated, NNT, is a measure of the effort. It shows how many people in the population must receive the intervention in order to avoid one new case of depression. In the more realistic scenario that the intervention is successful for, say, 40%, then (1/0.40)NNT=(2.5)NNT people must receive the intervention to avoid one case. The NNT can thus serve as an indication of the (minimum) required effort to produce a health impact in a population. To summarise, IRR gives information on the strength of association between exposure and outcome, PAR describes the maximum achievable health gain that could be generated in a population when a preventive intervention is successful in blocking the adverse effect of an etiological factor, and NNT helps to quantify the minimum amount of effort that is needed to generate that health gain in a population. Together, these statistics of impact and effort can help to inform and rationalise the discussion on depression prevention. 1.4. Analysis Stata (StataCorp, 1999) was used to carry out the analysis. The key statistics were obtained in three steps. In the first step, the incidence rate ratio, IRR, for each risk factor was obtained under a weighted Poisson regression model with the depression status at t1 as the dependent and the exposure status at t0 as the independent variable, while accounting for exposure time and adjusting for demographics (cf. Kleinbaum et al., 1998). Second, the population attributable risk, PAR, was obtained with help of the equation PAR = P(IRR 1)/(1 + P(IRR 1)), where P is the prevalence of the risk factor in the population at risk (cf. Rothman and Greenland, 1998; see also Miettinen, 1974). In the third step, the number needed to be treated, NNT, was computed as the inverse of the absolute risk difference. The latter was obtained using the weighted linear probability model (McCullah and Nelder, 1989; Long, 1997) in which the risk factor was included as one predictor and the demographic variables as others. All key statistics were adjusted for demographics, because it was thought that the secular impacts of
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demographics are beyond the control of preventive interventions. In order to report correct 95% confidence intervals of the key statistics under weighting, so-called ‘robust’ variance-related estimates were obtained, using the first-order Tailor-series linearisation method, as implemented in Stata.
2. Results 2.1. Prevalence and incidence of depression in the Dutch population On an annual basis, 5.8% of the population aged 18– 65 meet the DSM criteria of depressive disorder (Bijl et al., 1998b). Projected to the 10.7 million Dutch people in this age range, there are 620,000 depressed adults each year in the Netherlands alone. It is worth noting that the incidence of depression is high relative to its prevalence. The incidence occurs at a rate of 2.72 new cases per 100 person-years (Bijl et al., 2002), which is equivalent to 246,000 new cases in a given year. In other words, 40% of the prevalent cases are in fact new cases. Therefore it is inappropriate to focus exclusively on the treatment of prevalent cases. Primary prevention has to play a supplementary role by reducing the annual influx of new cases. 2.2. Description of the people at risk of becoming depressed The demographics of the people at risk of becoming first-ever cases of depressive disorder (i.e. all people except those who already experienced a depressive disorder) can be summarised as follows: female 51% (95% CI = 49.4 – 52.2), mean age 41 years (40.8 – 41.5), living with a partner 68.5% (67.0 – 70.0), immigrant 2.2% (1.8 – 2.7), employed 70% (68.3 – 70.9), living in an urban environment 81.9% (80.8 –83.0), education: elementary 6% (5.5 – 6.8), lower vocational 36% (34.9 – 37.7), high-school 28% (26.9 – 29.3), higher vocational and academic 29% (28.2 – 30.8). 2.3. Detailed results for childhood trauma Many people report neglect and childhood abuse (see Table 1). Emotional neglect is reported by 20.8% (PREV). Neglect predicts a risk for depression which
Table 1 Prevalence of risk indicators in the population at risk (PREV). Incidence rate ratio (IRR), population attributable risk (PAR), absolute risk difference (ARD) and numbers needed to be treated (NNT). Weighted analysis, adjusted for demographics (N = 4664) Risk indicator
PREV (%)
IRR
PAR (%)
ARD
Childhood trauma Emotional neglect Psychological abuse Physical abuse Sexual abuse z 2 forms of abuse Any abuse
20.8 10.3 7.5 5.4 10.7 27.9
2.85** 3.20** 3.84** 1.49NS 3.38** 2.67**
27.8 18.4 17.6 2.6 20.2 31.8
0.036** 0.049** 0.062** 0.015ns 0.052** 0.031**
27.5 20.4 16.1 66.6 19.4 31.9
Parental history Depressive Anxious/phobic Alcoholic z 2 problems Any problem
19.7 9.4 7.3 8.0 23.1
1.46ns 1.52ns 1.14ns 1.62ns 1.43NS
8.6 4.6 1.0 4.7 9.0
0.012ns 0.014ns 0.004ns 0.017ns 0.011ns
86.3 71.0 261.4 59.9 92.5
2.59** 4.96**
37.7 57.0
0.028** 0.047**
36.1 21.4
3.60** 4.05**
34.6 51.0
0.047** 0.041**
21.3 24.4
3.95** 2.53**
21.0 11.6
0.066** 0.030**
22.7 33.7
1.96** 1.36NS 2.23** 2.29* 1.63* 2.85** 1.83**
7.2 6.4 7.1 4.0 5.9 7.4 16.3
0.028* 0.008NS 0.031* 0.037NS 0.017NS 0.049** 0.020**
35.5 127.3 31.8 27.3 59.6 20.4 50.4
Vulnerability indicators Low mastery ( < M) 38.1 High neuroticism 33.5 (>M) GHQ z 2 20.3 Felt depressed 34.1 ( z 2 weeks) Antecedent mental disorders Any anxiety disorder 9.0 Any substance use 8.6 disorder Somatic illnesses Migraine Cardiovascular Respiratory Abdominal Lower back pain z 2 illnesses Any complaint
8.1 1.1 6.2 3.2 10.0 4.3 23.7
NNT
NS = not significant at a < 0.05; M = mean. * Significant at a < 0.05. ** Significant at a < 0.01.
is a factor 2.85 larger as compared to unexposed people (IRR). Elimination of its adverse effect would help to reduce the incidence of depression by 27.8% (PAR). Thus, the incidence rate which is now 2.72 per 100 person-years, would then, in the best possible scenario, decrease to 2.72 (1 0.278) = 1.96. This is equivalent to avoiding the onset of depression in close
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to 70,000 people in the Dutch population aged 18– 65: a very substantial health gain at population level. This health gain has also economic ramifications. In the Netherlands, the annual incremental costs of depression, defined as the costs of excess health service use by depressed people, plus the costs of production losses due to absenteeism from work, are typically about o3300 per year per depressed person (see for some other countries Simon et al., 2002). Therefore, avoiding the onset of depression in 70,000 cases may save as much as 70,000 3300 = o231 million per year. We will return to these statistics in a moment, but first we will take another step. The effort to bring about this health gain and its accompanying economic savings is relatively costeffective: in order to avoid one new case of depression 27.5 people have to engage in the intervention (NNT). If we were to spent the potential savings of o231 million on all 2.1 million people who were exposed to neglect, then the intervention may costs as much as o110 per person before reaching a break-even point at which the costs of prevention would exceed the savings. These statistics, of course, represent the best possible scenario which is based on the unrealistic assumption of a completely effective intervention. Thus the real health gain and cost savings are less than indicated, but these ‘maximum’ statistics help to rank risk factors into a hierarchy. By comparison, physical and multiple abuse have even better IRR, PAR and NNT values. This makes them more important targeting points for prevention. Taking childhood neglect and abuse as targeting points for depression prevention is not without drawbacks. The respective groups are rather large and it will be difficult to recognise them. However, our own experience has told us that people are willing to volunteer information on a history of abuse when asked. Also, information on a history of abuse may be available in medical records of a GP and could then be employed for selecting people eligible for a preventive intervention. The example of childhood trauma illustrated a number of points: the incidence of depression depends on exposure to certain risk factors; it is possible to select risk factors that are especially interesting from the prevention perspective, and some tentative ante hoc cost-benefit calculations can be made.
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2.4. Other risk indicators Now we turn to the remainder of Table 1. The general impression is that vulnerability indicators and childhood trauma are associated with relatively high IRR and PAR values and some of them also have relatively low NNT values. These risk factors, and corresponding high-risk groups warrant interest from the viewpoint of depression prevention in adult people. Parental history, by contrast, appears to be less interesting. More detailed inspection reveals that high IRR and PAR values are found for neuroticism, presence of a depressed feeling, GHQ scores equal or higher than 2 and (physical) abuse. Moreover, (physical) abuse, GHQ z 2 scores and high neuroticism are also associated with low NNT values. 2.5. Cumulative effects The low NNT values for multiple exposures are another interesting feature of Table 1 (see z 2 childhood traumas and z 2 somatic illnesses). This suggests a cumulative effect of multiple exposures. Further analysis shows that this is indeed the case, in particular for the presence of somatic illnesses. Fig. 1 shows the cumulative effect of having none, one, two or three somatic conditions on the risk of becoming depressive. In the presence of multiple exposures, the risk of becoming depressed increases sharply. Similar risk curves can be produced for the combination of other risk factors (results not shown), but Fig. 1 depicts one of the more interesting risk curves. It clearly shows how high-risk groups could
Fig. 1. The risk of becoming depressed (in percent) by number of exposures (exp) to somatic complaints (migraine, respiratory and abdominal complaints).
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be selected in primary care. Further, the low NNT value for this group indicates that targeting primary health patients will be associated with practical and financial advantages.
3. Discussion 3.1. Main findings The outcomes of childhood trauma were already discussed in great detail and we shall not repeat that discussion here. Suffice it to recall that neglect and childhood adversities are strongly associated with the later onset of depressive disorder, as is also reported elsewhere (Brown et al., 1999; Kessler et al., 1997; Nelson et al., 2002; Portegijs et al., 1996), and the containment of their adverse effects would greatly help to reduce the incidence of depression in the population. It appears that this could be done in a cost-effective way. It is generally acknowledged that parental history has a predictive value for the later onset of mental disorders in offspring (cf. Mrazek and Haggerty, 1994; Kendler et al., 1997; Merikangas et al., 1998; Cuijpers et al., 1999). However, we found no support for this notion, although it should be noted that the statistics, as we found them, bordered on significance. Perhaps the fact that we modelled these risk factors prospectively and adjusted them for demographics has led to statistically insignificant results. By contrast, a cross-sectional analysis of our baseline data did show significant relationships between parental history and depression (Bijl et al., 2002a,b). Nevertheless, we must conclude that parental history is not a particularly good starting point for depression prevention in adults. It should be emphasised, however, that a different conclusion might be drawn with respect to young children (cf. Beardslee and Podorefsky, 1988; Beardslee et al., 1997) as opposed to the adults that were studied here. In addition, it should be born in mind that parental depression may not only result in depression in their offspring, but also elevates risks for a range of other disorders like anxiety disorders and substance-related disorders (Cuijpers and Smit, 2002). Vulnerability indicators (cf. Harris, 2000) are associated with high IRRs and PARs and low NNTs and
thus seem to offer an advantageous starting point for preventive interventions. Low mastery may be amenable through a psychosocial intervention and be seen as an etiological factor on the causal pathway towards the onset of depression. Since a GHQ score equal to or larger than 2, and the presence of a depressive core symptom are conceptually overlapping with depression they cannot be seen as proper etiological factors—in a sense they are outcomes rather than predictors. Nevertheless, they may be valuable for depression prevention in terms of early detection, for example, through screening. Antecedent disorders, mental or somatic, may also be helpful in targeting selected groups, which is important for practical and financial reasons. It is well known that anxiety disorders and substance-use disorders may precede or accompany depression (Kessler et al., 1999; Preisig et al., 2001; De Graaf et al., 2002b) and the same can be said about somatic conditions like migraine, lower back-pain, cardiovascular disorders and (nonspecific) respiratory and abdominal complaints (Jenkings et al., 1992; Geerlings et al., 2000). We found support for these notions, but found cardiovascular complaints to be unrelated to the onset of depression. However, it should be recalled that we studied adults under the age of 65, and for older people a different risk set may exist (cf. Geerlings et al., 2000). 3.2. Strengths and limitations of this study The main findings and the conclusions (see below) must be placed in the context of the strengths and limitations of this study. The strengths of this study are (i) the representativeness of the sample for the general Dutch population in the age range of 18 –64, (ii) the measurement of mental disorders with a reliable instrument, (iii) the prospective design which enables the study of incidence and facilitates etiological inference, and (iv) the measurement of exposures which is not biased due to post hoc rationalisation on the part of the respondents, because at t0 they could not have any knowledge about their future health state at t1. Furthermore, this study is among the first to show how a statistical technology could be applied to quantify potential health benefits and the required efforts to generate these health benefits in the field of prevention of
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mental disorders. It thus supplies the sort of technology which is of importance for setting a rational Research and Development agenda for depression prevention. The limitations of this study consist in the not very detailed measurement of the exposures. We do not know at what age, for how long and how intensively subjects were exposed. Moreover, the number of studied risk factors is limited in that, for example, genetic and other biological risk factors were not included. We studied only dyads of a single exposure and a single disorder, but it would be interesting to extend this to the study of joint exposures (to more than one risk factor) and to see how that would impact on more than a single disorder. Fig. 1 shows that this could be very interesting.
4. Conclusions This study was set out to see whether, and how, epidemiology can make contributions to the formulation of a Research and Development agenda for depression prevention. Our main conclusion is that epidemiology can be helpful in this respect. Statistics such as the incidence rate ratio, IRR, population attributable risk, PAR, and the number needed to be treated, NNT, are helpful in selecting risk factors that have potential for prevention. These statistics can also be combined with information from cost-ofillness studies and then used for ex ante cost-benefit analysis. This claim is made on the understanding that, ultimately, the selection of risk factors and corresponding high-risk groups cannot be decided by statistics alone. Other considerations are also important, such as the availability of preventive techniques, knowledge of their (assumed) success rates, and ethical and economic considerations. Once the Research and Development agenda for depression prevention is set, preventive interventions should be designed and tested in prevention trials to empirically ascertain their (cost) effectiveness. Finally, projects with a proven effectiveness should be disseminated. Therefore, it is also understood that the risk-factor epidemiological contributions are only one element in a larger cycle of development and research. Nonetheless, the epidemiological contributions are crucial
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because they help at a very early stage to inform and rationalise the Research and Development agenda for depression prevention. At the substantive level, the results suggest that depression prevention could be effective in primary care settings (compare Jenkings et al., 1992). Patients with one or more somatic complaints will present themselves there and could be screened for the presence of a core symptom of depression, a GHQ score above cutoff, or high levels of neuroticism. When these patients also report a history of childhood abuse or an antecedent anxiety disorder, then selective prevention appears to be indicated. The exact format of this intervention is, as yet, not clear, but (cost-) effective interventions like bibliotherapy (Cuijpers, 1997) or counselling (Bower et al., 2003) are likely candidates. In future research, thought must be given to the quantification of success rates of (future) prevention interventions, the study of joint exposures of several risk-factors, optimisation of cutoff points in risk factors that are on a continuous measurement scale (like mastery and neuroticism), the quantification of impacts on the incidence not only of major depressive disorder, but on the whole array of anxiety and mood disorders, and the accommodation of cost-benefit considerations in this sort of analysis. It is recommended that in the future these technologies will be further developed and that they will play a role in targeting prevention there where it is likely to be most effective.
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