Most epidemiological studies show a decrease of internalizing disorders at older ages, but it is unclear how the prevalence exactly changes with age, and whether there are different patterns for internalizing symptoms and traits, and for men and women. This study investigates the impact of age and sex on the point prevalence across different mood and anxiety disorders, internalizing symptoms, and neuroticism.

We used cross-sectional data on 146 315 subjects, aged 18–80 years, from the Lifelines Cohort Study, a Dutch general population sample. Between 2012 and 2016, five current internalizing disorders – major depression, dysthymia, generalized anxiety disorder, social phobia, and panic disorder – were assessed according to DSM-IV criteria. Depressive symptoms, anxiety symptoms, neuroticism, and negative affect (NA) were also measured. Generalized additive models were used to identify nonlinear patterns across age, and to investigate sex differences.

The point prevalence of internalizing disorders generally increased between the ages of 18 and 30 years, stabilized between 30 and 50, and decreased after age 50. The patterns of internalizing symptoms and traits were different. NA and neuroticism gradually decreased after age 18. Women reported more internalizing disorders than men, but the relative difference remained stable across age (relative risk ~1.7).

The point prevalence of internalizing disorders was typically highest between age 30 and 50, but there were differences between the disorders, which could indicate differences in etiology. The relative gap between the sexes remained similar across age, suggesting that changes in sex hormones around the menopause do not significantly influence women's risk of internalizing disorders.

The association between depression and metabolic syndrome is becoming more obvious.

We examined the relationship between the number and individual components of metabolic syndrome and late-life depressive symptom clusters.

In 1279 individuals aged 50 through 70 participating in the Nijmegen Biomedical Study (Cross-sectional populationbased survey), we measured all metabolic syndrome components and depressive symptoms using the Beck Depression Inventory (BDI). Principal components analysis of BDI-items yielded two factors, representing a cognitive-affective and a somatic-affective symptom-cluster. Multiple regression analyses adjusted for confounders were conducted with BDI sum score and both depression symptom-clusters as dependent variables, respectively. We explored the differences in this association between men and women.

In fully adjusted models, both presence of the metabolic syndrome as well as number of components was associated with the BDI sumscore(resp. β=0.063;p=0.022 vs. β=0.112;p< 0.001), the latter showing the strongest association. These associations were primarily driven by the somatic-affective symptom-cluster. Testing individual components of the metabolic syndrome, showed that in men waist circumference, triglycerides and HDL cholesterol were significantly associated with depression, whereas in women only the waist circumference.

The specific association somatic-affective symptoms suggest confounding by a (subclinical) somatic condition in stead of a real association with classical depression. The identified sex-differences suggest different pathways between depression and metabolic perturbations in men only. However, as vascular disease develops at higher ages in women and findings were in the same direction but non-significant in women, future research in older women sample should confirm our findings.

There is increasing interest in day-to-day affect fluctuations of patients with depressive and anxiety disorders. Few studies have compared repeated assessments of positive affect (PA) and negative affect (NA) across diagnostic groups, and fluctuation patterns were not uniformly defined. The aim of this study is to compare affect fluctuations in patients with a current episode of depressive or anxiety disorder, in remitted patients and in controls, using affect instability as a core concept but also describing other measures of variability and adjusting for possible confounders.

Ecological momentary assessment (EMA) data were obtained from 365 participants of the Netherlands Study of Depression and Anxiety with current (n = 95), remitted (n = 178) or no (n = 92) DSM-IV defined depression/anxiety disorder. For 2 weeks, five times per day, participants filled-out items on PA and NA. Affect instability was calculated as the root mean square of successive differences (RMSSD). Tests on group differences in RMSSD, within-person variance, and autocorrelation were performed, controlling for mean affect levels.

Current depression/anxiety patients had the highest affect instability in both PA and NA, followed by remitters and then controls. Instability differences between groups remained significant when controlling for mean affect levels, but differences between current and remitted were no longer significant.

Patients with a current disorder have higher instability of NA and PA than remitted patients and controls. Especially with regard to NA, this could be interpreted as patients with a current disorder being more sensitive to internal and external stressors and having suboptimal affect regulation.

Although depression with anxious distress appears to be a clinically relevant subtype of Major Depressive Disorder (MDD), whether it involves specific pathophysiology remains unclear. Inflammation has been implicated, but not comprehensively studied. We examined within a large MDD sample whether anxious distress and related anxiety features are associated with differential basal inflammation and innate cytokine production capacity.

Data are from 1078 MDD patients from the Netherlands study of depression and anxiety. Besides the DSM-5 anxious distress specifier, we studied various dimensional anxiety scales (e.g. Inventory of Depressive Symptomatology anxiety arousal subscale [IDS-AA], Beck Anxiety Inventory [BAI], Mood and Anxiety Symptoms Questionnaire Anxious Arousal scale [MASQ-AA]). Basal inflammatory markers included C-reactive protein, interleukin (IL)-6 and tumor-necrosis factor (TNF)-α. Innate production capacity was assessed by 13 lipopolysaccharide (LPS)-stimulated inflammatory markers. Basal and LPS-stimulated inflammation index scores were created.

Basal inflammation was not associated with anxious distress in MDD patients (anxious distress prevalence 54.3%), except for modest positive associations for IDS-AA and BAI scores. However, anxious distress was associated with higher LPS-stimulated levels (interferon-ɣ, IL-2, IL-6, monocyte chemotactic protein (MCP)-1, macrophage inflammatory protein (MIP)-1α, MIP-1β, matrix metalloproteinase-2, TNF-α, TNF-β, LPS-stimulated index). Oher anxiety indicators (number of specifier items and anxiety diagnoses, IDS-AA, BAI, MASQ-AA) were also associated with increased innate production capacity.

Within a large MDD sample, the anxious distress specifier was associated with increased innate cytokine production capacity but not with basal inflammation. Results from dimensional anxiety indicators largely confirm these results. These findings provide new insight into the pathophysiology of anxious depression.

High rates of psychiatric comorbidity are subject of debate: to what extent do they depend on classification choices such as diagnostic thresholds?

To investigate the influence of different thresholds on rates of comorbidity between major depressive disorder (MDD) and generalized anxiety disorder (GAD).

Point prevalence of comorbidity between MDD and GAD was measured in 74,092 subjects from the general population according to DSM-IV-TR criteria. Comorbidity rates were compared for different thresholds by varying the number of necessary criteria from ≥ 1 to all 9 symptoms for MDD, and from ≥ 1 to all 7 symptoms for GAD.

According to DSM-thresholds, 0.86% had MDD only, 2.96% GAD only and 1.14% both MDD and GAD (Odds Ratio [OR] 42.6). Lower thresholds for MDD led to higher rates of comorbidity (1.44% for ≥ 4 of 9 MDD-symptoms, OR 34.4), whereas lower thresholds for GAD hardly influenced comorbidity (1.16% for ≥ 3 of 7 GAD-symptoms, OR 38.8). Specific patterns in the distribution of symptoms within the population explained this finding: 37.3% of subjects with core criteria of MDD and GAD reported subthreshold MDD symptoms, whereas only 7.6% reported subthreshold GAD symptoms.

Lower thresholds for MDD increased comorbidity with GAD, but not vice versa, owing to specific symptom patterns in the population. Generally, comorbidity rates result from both empirical symptom distributions and classification choices and cannot be reduced to either of these exclusively. This insight invites further research into the formation of disease concepts that allow for reliable predictions and targeted therapeutic interventions.

The authors have not supplied their declaration of competing interest.

The life expectancy of severe mentally ill (SMI) patients is shortened up to 30 years, due to cardiometabolic diseases, partly caused by unhealthy lifestyles behaviors. In residential facilities, adopting a healthy lifestyle is hampered by the obesogenic environment; an obesity promoting environment.

To determine, the effectiveness of a 12 month lifestyle intervention addressing the obesogenic environment to improve cardiometabolic health of SMI residential patients.

The effectiveness of lifestyle interventions in psychiatry (ELIPS) trial is a multi-site, cluster randomized controlled pragmatic trial. Twenty-nine sheltered and long-term clinical care teams serving SMI patients in the Netherlands were randomized into intervention (n = 15) or control (n = 14) arm, including 736 patients (73% psychotic disorder, 63% male, 48 ± 13 years). The intervention aimed to improve the obesogenic environment using a small change approach with a focus on nutrition and physical activity. Primary outcome was waist circumference (WC) after three and twelve month's intervention. Secondary outcomes were BMI and metabolic syndrome.

General linear mixed models adjusted for age, gender, housing facility and antipsychotic medication showed that WC significantly decreased with 1.51 cm (95%CI = −2.99;−0.04, Cohen's d = 0.07) in the intervention group compared to control group after three months and tended to remain lower with 1.28 cm (95%CI = −2.79; 0.23, Cohen's d = 0.06) after twelve months. Metabolic syndrome Z-score decreased after three months with 0.225 SD (95% CI = −0.4038;−0.096, Cohen's d = 0.20), mainly due to lower fasting glucose and WC. No significant effects were found on BMI.

A small change approach targeting the obesogenic environment of SMI residential patients reduces cardiometabolic risk.

The authors have not supplied their declaration of competing interest.

Multidisciplinary guidelines in adolescent mental health care are based on RCTs, while treatment efficacy can be different from effectiveness seen in ‘the real world’. Studies in the real world conducted so far suggest that treatment has a negligible effect on follow-up symptomatology. However, these studies did not incorporate the pre-treatment trajectory of symptoms nor investigated a dose-response relationship.

To test whether future treatment users and non-users differed in emotional and behavioural problem scores, whether specialist mental health treatment (SMHT) was effective in reducing problem levels while controlling for pre-treatment trajectory, and to seek evidence of a dose-response relationship.

Six-year follow up data were used from the Tracking Adolescents’ Individual Lives Survey (TRAILS). We identified adolescents with a clinical level of problem behaviour on the Child Behaviour Checklist or Youth Self Report and first SMHT between the ages 13 and 16. Adolescents with a clinical level of problem behaviour but without SMHT use served as control group. A psychiatric case register provided data on number of treatment contacts. Using regression analysis, we predicted the effect of treatment on post-treatment problem scores.

Treated adolescents more often had a (severe) diagnosis than untreated adolescents. Pre-treatment trajectories barely differed between treated and untreated adolescents. Treatment predicted an increase in follow-up problem scores, regardless of the number of sessions.

The quasi-experimental design calls for modest conclusions. We might however need to take a closer look at real-world service delivery, and invest in developing treatments that can achieve sustainable benefits.

The authors have not supplied their declaration of competing interest.

Studies investigating the link between depressive symptoms and inflammation have yielded inconsistent results, which may be due to two factors. First, studies differed regarding the specific inflammatory markers studied and covariates accounted for. Second, specific depressive symptoms may be differentially related to inflammation. We address both challenges using network psychometrics.

We estimated seven regularized Mixed Graphical Models in the Netherlands Study of Depression and Anxiety (NESDA) data (N = 2321) to explore shared variances among (1) depression severity, modeled via depression sum-score, nine DSM-5 symptoms, or 28 individual depressive symptoms; (2) inflammatory markers C-reactive protein (CRP), interleukin 6 (IL-6), and tumor necrosis factor α (TNF-α); (3) before and after adjusting for sex, age, body mass index (BMI), exercise, smoking, alcohol, and chronic diseases.

The depression sum-score was related to both IL-6 and CRP before, and only to IL-6 after covariate adjustment. When modeling the DSM-5 symptoms and CRP in a conceptual replication of Jokela et al., CRP was associated with ‘sleep problems’, ‘energy level’, and ‘weight/appetite changes’; only the first two links survived covariate adjustment. In a conservative model with all 38 variables, symptoms and markers were unrelated. Following recent psychometric work, we re-estimated the full model without regularization: the depressive symptoms ‘insomnia’, ‘hypersomnia’, and ‘aches and pain’ showed unique positive relations to all inflammatory markers.

We found evidence for differential relations between markers, depressive symptoms, and covariates. Associations between symptoms and markers were attenuated after covariate adjustment; BMI and sex consistently showed strong relations with inflammatory markers.

To effectively shape mental healthcare policy in modern-day China, up-to-date epidemiological data on mental disorders is needed. The objective was to estimate the prevalence, age-of-onset (AOO) and sociodemographic correlates of mental disorders in a representative household sample of the general population (age ⩾ 18) in the Tianjin Municipality in China.

Data came from the Tianjin Mental health Survey (TJMHS), which was conducted between July 2011 and March 2012 using a two-phase design. 11 748 individuals were screened with an expanded version of the General Health Questionnaire and 4438 subjects were selected for a diagnostic interview by a psychiatrist, using the Structured Clinical Interview for the Diagnostic and Statistical Manual – fourth edition (SCID).

The lifetime and 1-month prevalence of any mental disorder were 23.6% and 12.8%, respectively. Mood disorders (lifetime: 9.3%; 1-month: 3.9%), anxiety disorders (lifetime: 4.5% 1-month: 3.1%) and substance-use disorders (lifetime: 8.8%; 1-month: 3.5%) were most prevalent. The median AOO ranged from 25 years [interquartile range (IQR): 23–32] for substance-use disorders to 36 years (IQR: 24–50) for mood disorders. Not being married, non-immigrant status (i.e. local ‘Hukou’), being a farmer, having <6 years of education and male gender were associated with a higher lifetime prevalence of any mental disorder.

Results from the current survey indicate that mental disorders are steadily reported more commonly in rapidly-developing urban China. Several interesting sociodemographic correlates were observed (e.g. male gender and non-immigrant status) that warrant further investigation and could be used to profile persons in need of preventive intervention.

In search of empirical classifications of depression and anxiety, most subtyping studies focus solely on symptoms and do so within a single disorder. This study aimed to identify and validate cross-diagnostic subtypes by simultaneously considering symptoms of depression and anxiety, and disability measures.

A large cohort of adults (Lifelines, n = 73 403) had a full assessment of 16 symptoms of mood and anxiety disorders, and measurement of physical, social and occupational disability. The best-fitting subtyping model was identified by comparing different hybrid mixture models with and without disability covariates on fit criteria in an independent test sample. The best model's classes were compared across a range of external variables.

The best-fitting Mixed Measurement Item Response Theory model with disability covariates identified five classes. Accounting for disability improved differentiation between people reporting isolated non-specific symptoms [‘Somatic’ (13.0%), and ‘Worried’ (14.0%)] and psychopathological symptoms [‘Subclinical’ (8.8%), and ‘Clinical’ (3.3%)]. Classes showed distinct associations with clinically relevant external variables [e.g. somatization: odds ratio (OR) 8.1–12.3, and chronic stress: OR 3.7–4.4]. The Subclinical class reported symptomatology at subthreshold levels while experiencing disability. No pure depression or anxiety, but only mixed classes were found.

An empirical classification model, incorporating both symptoms and disability identified clearly distinct cross-diagnostic subtypes, indicating that diagnostic nets should be cast wider than current phenomenology-based categorical systems.

Major depressive disorder (MDD) and generalized anxiety disorder (GAD) often co-occur with somatic symptomatology. Little is known about the contributions of individual symptoms to this association and more insight into their relationships could help to identify symptoms that are central in the processes behind the co-occurrence. This study explores associations between individual MDD/GAD symptoms and somatic symptoms by using the network approach.

MDD/GAD symptoms were assessed in 2704 participants (mean age 41.7 years, 66.1% female) from the Netherlands Study of Depression and Anxiety using the Inventory of Depressive Symptomatology. Somatic symptoms were assessed with the somatization scale of the Four-Dimensional Symptom Questionnaire. The technique eLasso was used to estimate the network of MDD/GAD and somatic symptoms.

The network structure showed numerous associations between MDD/GAD and somatic symptoms. In general, neurovegetative and cognitive/affective MDD/GAD symptoms showed a similar strength of connections to the somatic domain. However, associations varied substantially across individual symptoms. MDD/GAD symptoms with many and strong associations to the somatic domain included anxiety and fatigue, whereas hypersomnia and insomnia showed no connections to somatic symptoms. Among somatic symptoms, excessive perspiration and pressure/tight feeling in chest were associated with the MDD/GAD domain, while muscle pain and tingling in fingers showed only a few weak associations.

Individual symptoms show differential associations in the co-occurrence of MDD/GAD with somatic symptomatology. Strongly interconnected symptoms are important in furthering our understanding of the interaction between the symptom domains, and may be valuable targets for future research and treatment.

Timely recognition and treatment of mental disorders with an onset in childhood and adolescence is paramount, as these are characterized by greater severity and longer persistence than disorders with an onset in adulthood. Studies examining time-to-treatment, also referred to as treatment delay, duration of untreated illness or latency to treatment, and defined as the time between disorder onset and initial treatment contact, are sparse and all based on adult samples. The aim of this study was to describe time-to-treatment and its correlates for any health care professional (any care) and secondary mental health care (secondary care), for a broad range of mental disorders, in adolescents.

Data from the Dutch community-based cohort study TRacking Adolescents’ Individual Lives Survey (TRAILS; N = 2230) were used. The Composite International Diagnostic Interview (CIDI) was administered to assess DSM-IV disorders, the age of onset, and the age of initial treatment contact with any health care professional in 1584 adolescents of 18–20 years old. In total 43% of the adolescents (n = 675) were diagnosed with a lifetime DSM-IV disorder. The age of initial treatment contact with secondary care was based on administrative records from 321 adolescents without a disorder onset before the age of 10. Descriptive statistics, cumulative lifetime probability plots, and Cox regression analyses were used analyze time-to-treatment.

The proportion of adolescents who reported lifetime treatment contact with any care varied from 15% for alcohol dependence to 82% for dysthymia. Regarding secondary care, proportions of lifetime treatment contact were lower for mood disorders and higher for substance dependence. Time-to-treatment for any care varied considerably between and within diagnostic classes. The probability of lifetime treatment contact for mood disorders was above 90%, whereas for other mental disorders this was substantially lower. An earlier age of onset predicted a longer, and the presence of a co-morbid mood disorder predicted a shorter time-to-treatment in general. Disorder severity predicted a shorter time-to-treatment for any care, but not for secondary care. Time-to-treatment for secondary care was shorter for adolescents from low and middle socioeconomic background than for adolescents from a high socioeconomic background.

Although the time-to-treatment was shorter for adolescents than for adults, it was still substantial, and the overall patterns were remarkably similar to those found in adults. Efforts to reduce time-to-treatment should therefore be aimed at children and adolescents. Future research should address mechanisms underlying time-to-treatment and its consequences for early-onset disorders in particular.

Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful.

We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalised) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments.

Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention v. control) or differential treatment outcomes (i.e., intervention A v. intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalised treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials.

Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists.

Clinical and aetiological heterogeneity have impeded our understanding of depression.

To evaluate differences in psychiatric and somatic course between people with depression subtypes that differed clinically (severity) and aetiologically (melancholic v. atypical).

Data from baseline, 2-, 4- and 6-year follow-up of The Netherlands Study of Depression and Anxiety were used, and included 600 controls and 648 people with major depressive disorder (subtypes: severe melancholic n = 308; severe atypical n = 167; moderate n = 173, established using latent class analysis).

Those with the moderate subtype had a significantly better psychiatric clinical course than the severe melancholic and atypical subtype groups. Suicidal thoughts and anxiety persisted longer in those with the melancholic subtype. The atypical subtype group continued to have the highest body mass index and highest prevalence of metabolic syndrome during follow-up, although differences between groups became less pronounced over time.

Course trajectories of depressive subtypes mostly ran parallel to each other, with baseline severity being the most important differentiator in course between groups.

Although variation in the long-term course of major depressive disorder (MDD) is not strongly predicted by existing symptom subtype distinctions, recent research suggests that prediction can be improved by using machine learning methods. However, it is not known whether these distinctions can be refined by added information about co-morbid conditions. The current report presents results on this question.

Data came from 8261 respondents with lifetime DSM-IV MDD in the World Health Organization (WHO) World Mental Health (WMH) Surveys. Outcomes included four retrospectively reported measures of persistence/severity of course (years in episode; years in chronic episodes; hospitalization for MDD; disability due to MDD). Machine learning methods (regression tree analysis; lasso, ridge and elastic net penalized regression) followed by k-means cluster analysis were used to augment previously detected subtypes with information about prior co-morbidity to predict these outcomes.

Predicted values were strongly correlated across outcomes. Cluster analysis of predicted values found three clusters with consistently high, intermediate or low values. The high-risk cluster (32.4% of cases) accounted for 56.6–72.9% of high persistence, high chronicity, hospitalization and disability. This high-risk cluster had both higher sensitivity and likelihood ratio positive (LR+; relative proportions of cases in the high-risk cluster versus other clusters having the adverse outcomes) than in a parallel analysis that excluded measures of co-morbidity as predictors.

Although the results using the retrospective data reported here suggest that useful MDD subtyping distinctions can be made with machine learning and clustering across multiple indicators of illness persistence/severity, replication with prospective data is needed to confirm this preliminary conclusion.

Telomere length is considered an emerging marker of biological aging. Depression and anxiety are associated with excess mortality risk but the mechanisms remain obscure. Telomere length might be involved because it is associated with psychological distress and mortality. The aim of this study was to test whether anxiety and depressive disorders predict telomere length over time in a large population-based sample.

All analyses were performed in a longitudinal study in a general population cohort of 974 participants. The Composite International Diagnostic Interview (CIDI) was used to measure the presence of anxiety and depressive disorders. Telomere length was measured using monochrome multiplex polymerase chain reaction (PCR) at approximately 2 years of follow-up. We used linear multivariable regression models to evaluate the association between anxiety and depressive disorders and telomere length, adjusting for adverse life events, lifestyle factors, educational level and antidepressant use.

The presence of anxiety disorders predicted shorter telomeres at follow-up (β = –0.073, t = –2.302, p = 0.022). This association was similar after controlling for adverse life events, lifestyle factors, educational level and antidepressant use (β = –0.077, t = –2.144, p = 0.032). No association was found between depressive disorders and shorter telomeres at follow-up (β = 0.010, t = 0.315, p = 0.753).

This study found that anxiety disorders predicted shorter telomere length at follow-up in a general population cohort. The association was not explained by adverse life events, lifestyle factors, educational level and antidepressant use. How anxiety disorders might lead to accelerated telomere shortening and whether this might be a mediator explaining the excess mortality risk associated with anxiety deserve further investigation.

Loneliness has a significant influence on both physical and mental health. Few studies have investigated the possible associations of loneliness with mortality risk, impact on men and women and whether this impact concerns the situation of being alone (social isolation), experiencing loneliness (feeling lonely) or both. The current study investigated whether social isolation and feelings of loneliness in older men and women were associated with increased mortality risk, controlling for depression and other potentially confounding factors.

In our prospective cohort study of 4004 older persons aged 65–84 years with a 10-year follow-up of mortality data a Cox proportional hazard regression analysis was used to test whether social isolation factors and feelings of loneliness predicted an increased risk of mortality, controlling for psychiatric disorders and medical conditions, cognitive functioning, functional status and sociodemographic factors.

At 10 years follow-up, significantly more men than women with feelings of loneliness at baseline had died. After adjustment for explanatory variables including social isolation, the mortality hazard ratio for feelings of loneliness was 1.30 [95% confidence interval (CI) 1.04–1.63] in men and 1.04 (95% CI 0.90–1.24) in women. No higher risk of mortality was found for social isolation.

Feelings of loneliness rather than social isolation factors were found to be a major risk factor for increasing mortality in older men. Developing a better understanding of the nature of this association may help us to improve quality of life and longevity, especially in older men.

Associations between physical health and depression are consistent across cultures among adults up to 65 years of age. In later life, the impact of physical health on depression is much more substantial and may depend on sociocultural factors.

To examine cross-national differences in the association between physical health and depressive symptoms in elderly people across western Europe.

Fourteen community-based studies on depression in later life in nine western European countries contributed to a total study sample of 22 570 respondents aged 65 years and older. Measures were harmonised for depressive symptoms (EURO-D scale), functional limitations and chronic physical conditions.

In the majority of the participating samples, the association of depressive symptoms with functional disability was stronger than with chronic physical conditions. Associations were slightly more pronounced in the UK and Ireland.

The association between physical health and depressive symptoms in later life is consistent across western Europe.

The association between depression and increased mortality risk in older persons may depend on the severity of the depressive disorder and gender.

To investigate the association between major and mild depressive syndromes and excess mortality in community-living elderly men and women.

Depression (Geriatric Mental State AGECAT) was assessed in 4051 older persons, with a 6-year follow-up of community death registers. The mortality risk of neurotic and psychotic depression was calculated after adjustment for demographic variables, physical illness, cognitive decline and functional disabilities.

A total of 75% of men and 41% of women with psychotic depression had died at follow-up. Psychotic depression was associated with significant excess mortality in both men and women. Neurotic depression was associated with a 1.67-fold higher mortality risk in men only.

In the elderly, major depressive syndromes increase the risk of death in both men and women, but mild depression increases the risk of death only in men.