The effectiveness of mental health interventions involving non-specialists and digital technology in low-and middle-income countries - a systematic review.

BACKGROUND: Combining non-specialists and digital technologies in mental health interventions could decrease the mental healthcare gap in resource scarce countries. This systematic review examined different combinations of non-specialists and digital technologies in mental health interventions and their effectiveness in reducing the mental healthcare gap in low-and middle-income countries. METHODS: Literature searches were conducted in four databases (September 2023), three trial registries (January-February 2022), and using forward and backward citation searches (May-June 2022). The review included primary studies on mental health interventions combining non-specialists and digital technologies in low-and middle-income countries. The outcomes were: (1) the mental health of intervention receivers and (2) the competencies of non-specialists to deliver mental health interventions. Data were expressed as standardised effect sizes (Cohen's d) and narratively synthesised. Risk of bias assessment was conducted using the Cochrane risk-of-bias tools for individual and cluster randomised and non-randomised controlled trials. RESULTS: Of the 28 included studies (n = 32 interventions), digital technology was mainly used in non-specialist primary-delivery treatment models for common mental disorders or subthreshold symptoms. The competencies of non-specialists were improved with digital training (d ≤ 0.8 in 4/7 outcomes, n = 4 studies, 398 participants). The mental health of receivers improved through non-specialist-delivered interventions, in which digital technologies were used to support the delivery of the intervention (d > 0.8 in 24/40 outcomes, n = 11, 2469) or to supervise the non-specialists' work (d = 0.2-0.8 in 10/17 outcomes, n = 3, 3096). Additionally, the mental health of service receivers improved through digitally delivered mental health services with non-specialist involvement (d = 0.2-0.8 in 12/27 outcomes, n = 8, 2335). However, the overall certainty of the evidence was poor. CONCLUSION: Incorporating digital technologies into non-specialist mental health interventions tended to enhance non-specialists' competencies and knowledge in intervention delivery, and had a positive influence on the severity of mental health problems, mental healthcare utilization, and psychosocial functioning outcomes of service recipients, primarily within primary-deliverer care models. More robust evidence is needed to compare the magnitude of effectiveness and identify the clinical relevance of specific digital functions. Future studies should also explore long-term and potential adverse effects and interventions targeting men and marginalised communities.

Muva physical activity intervention to improve social functioning in people with a severe mental illness: study protocol of a pragmatic stepped wedge cluster randomized trial.

BACKGROUND: People with severe mental illness (SMI) often suffer from long-lasting symptoms that negatively influence their social functioning, their ability to live a meaningful life, and participation in society. Interventions aimed at increasing physical activity can improve social functioning, but people with SMI experience multiple barriers to becoming physically active. Besides, the implementation of physical activity interventions in day-to-day practice is difficult. In this study, we aim to evaluate the effectiveness and implementation of a physical activity intervention to improve social functioning, mental and physical health. METHODS: In this pragmatic stepped wedge cluster randomized controlled trial we aim to include 100 people with SMI and their mental health workers from a supported housing organization. The intervention focuses on increasing physical activity by implementing group sports activities, active guidance meetings, and a serious game to set physical activity goals. We aim to decrease barriers to physical activity through active involvement of the mental health workers, lifestyle courses, and a medication review. Participating locations will be divided into four clusters and randomization will decide the start of the intervention. The primary outcome is social functioning. Secondary outcomes are quality of life, symptom severity, physical activity, cardiometabolic risk factors, cardiorespiratory fitness, and movement disturbances with specific attention to postural adjustment and movement sequencing in gait. In addition, we will assess the implementation by conducting semi-structured interviews with location managers and mental health workers and analyze them by direct content analysis. DISCUSSION: This trial is innovative since it aims to improve social functioning in people with SMI through a physical activity intervention which aims to lower barriers to becoming physically active in a real-life setting. The strength of this trial is that we will also evaluate the implementation of the intervention. Limitations of this study are the risk of poor implementation of the intervention, and bias due to the inclusion of a medication review in the intervention that might impact outcomes. TRIAL REGISTRATION: This trial was registered prospectively in The Netherlands Trial Register (NTR) as NTR NL9163 on December 20, 2020. As the The Netherlands Trial Register is no longer available, the trial can now be found in the International Clinical Trial Registry Platform via: https://trialsearch.who.int/Trial2.aspx?TrialID=NL9163 .

More than cost-effectiveness? Applying a second-stage filter to improve policy decision making.

BACKGROUND: Apart from cost-effectiveness, considerations like equity and acceptability may affect health-care priority setting. Preferably, priority setting combines evidence evaluation with an appraisal procedure, to elicit and weigh these considerations. OBJECTIVE: To demonstrate a structured approach for eliciting and evaluating a broad range of assessment criteria, including key stakeholders' values, aiming to support decision makers in priority setting. METHODS: For a set of cost-effective substitute interventions for depression care, the appraisal criteria were adopted from the Australian Assessing Cost-Effectiveness initiative. All substitute interventions were assessed in an appraisal, using focus group discussions and semi-structured interviews conducted among key stakeholders. RESULTS: Appraisal of the substitute cost-effective interventions yielded an overview of considerations and an overall recommendation for decision makers. Two out of the thirteen pairs were deemed acceptable and realistic, that is investment in therapist-guided and Internet-based cognitive behavioural therapy instead of cognitive behavioural therapy in mild depression, and investment in combination therapy rather than individual psychotherapy in severe depression. In the remaining substitution pairs, substantive issues affected acceptability. The key issues identified were as follows: workforce capacity, lack of stakeholder support and the need for change in clinicians' attitude. CONCLUSIONS: Systematic identification of stakeholders' considerations allows decision makers to prioritize among cost-effective policy options. Moreover, this approach entails an explicit and transparent priority-setting procedure and provides insights into the intended and unintended consequences of using a certain health technology. PATIENT CONTRIBUTION: Patients were involved in the conduct of the study for instance, by sharing their values regarding considerations relevant for priority setting.

Multimodal lifestyle intervention using a web-based tool to improve cardiometabolic health in patients with serious mental illness: results of a cluster randomized controlled trial (LION).

BACKGROUND: Unhealthy lifestyle behaviours contribute to alarming cardiometabolic risk in patients with serious mental illness (SMI). Evidence-based practical lifestyle tools supporting patients and staff in improving patient lifestyle are lacking. METHODS: This multi-site randomized controlled pragmatic trial determined the effectiveness of a twelve-month multimodal lifestyle approach, including a web-based tool to improve patients' cardiometabolic health, versus care-as-usual. Using the web tool, nurses (trained in motivational interviewing) assisted patients in assessing their lifestyle behaviours, creating a risk profile and constructing lifestyle goals, which were discussed during fortnightly regular care visits. Twenty-seven community-care and sheltered-living teams were randomized into intervention (N = 17) or control (N = 10) groups, including 244 patients (140 intervention/104 control, 49.2% male, 46.1 ± 10.8 years) with increased waist circumference (WC), BMI or fasting glucose. The primary outcomes concerned differences in WC after six and twelve months intervention, while BMI and metabolic syndrome Z-score were secondary outcome measures. RESULTS: General multilevel linear mixed models adjusted for antipsychotic medication showed that differences in WC change between intervention and control were - 0.15 cm (95%CI: - 2.49; 2.19) after six and - 1.03 cm (95%CI: - 3.42; 1.35) after twelve months intervention; however, the differences were not statistically significant. No intervention effects were found for secondary outcome measures. The intervention increased patients' readiness to change dietary behaviour. CONCLUSION: A multimodal web-based intervention facilitating nurses to address lifestyle changes in SMI patients did not improve patient cardiometabolic health. Web-tool use was lower than expected and nurses need more lifestyle coaching knowledge and skills. The type of intervention and delivery mode need optimization to realize effective lifestyle care for SMI patients. TRIAL REGISTRATION: Dutch Trial Registry, www.trialregister.nl , NTR3765, 21 December 2012.

Changing the obesogenic environment to improve cardiometabolic health in residential patients with a severe mental illness: cluster randomised controlled trial.

BackgroundFor patients with severe mental illness (SMI) in residential facilities, adopting a healthy lifestyle is hampered by the obesity promoting (obesogenic) environment.AimsTo determine the effectiveness of a 12-month lifestyle intervention addressing the obesogenic environment with respect to diet and physical activity to improve waist circumference and cardiometabolic risk factors v. care as usual (Dutch Trial Registry: NTR2720).MethodIn a multisite cluster randomised controlled pragmatic trial, 29 care teams were randomised into 15 intervention (365 patients) and 14 control teams (371 patients). Intervention staff were trained to improve the obesogenic environment.ResultsWaist circumference decreased 1.51 cm (95% CI -2.99 to -0.04) in the intervention v. control group after 3 months and metabolic syndrome z-score decreased 0.22 s.d. (95% CI -0.38 to -0.06). After 12 months, the decrease in waist circumference was no longer statistically significantly different (-1.28 cm, 95% CI -2.79 to 0.23, P=0.097).ConclusionsTargeting the obesogenic environment of residential patients with SMI has the potential to facilitate reduction of abdominal adiposity and cardiometabolic risk, but maintaining initial reductions over the longer term remains challenging.

Design of the Lifestyle Interventions for severe mentally ill Outpatients in the Netherlands (LION) trial; a cluster randomised controlled study of a multidimensional web tool intervention to improve cardiometabolic health in patients with severe mental illness.

BACKGROUND: The cardiometabolic health of persons with a severe mental illness (SMI) is alarming with obesity rates of 45-55% and diabetes type 2 rates of 10-15%. Unhealthy lifestyle behaviours play a large role in this. Despite the multidisciplinary guideline for SMI patients recommending to monitor and address patients' lifestyle, most mental health care professionals have limited lifestyle-related knowledge and skills, and (lifestyle) treatment protocols are lacking. Evidence-based practical lifestyle tools may support both patients and staff in improving patients' lifestyle. This paper describes the Lifestyle Interventions for severe mentally ill Outpatients in the Netherlands (LION) trial, to investigate whether a multidimensional lifestyle intervention using a web tool can be effective in improving cardiometabolic health in SMI patients. METHODS/DESIGN: The LION study is a 12-month pragmatic single-blind multi-site cluster randomised controlled trial. 21 Flexible Assertive Community Treatment (ACT) teams and eight sheltered living teams of five mental health organizations in the Netherlands are invited to participate. Per team, nurses are trained in motivational interviewing and use of the multidimensional web tool, covering lifestyle behaviour awareness, lifestyle knowledge, motivation and goal setting. Nurses coach patients to change their lifestyle using the web tool, motivational interviewing and stages-of-change techniques during biweekly sessions in a) assessing current lifestyle behaviour using the traffic light method (healthy behaviours colour green, unhealthy behaviours colour red), b) creating a lifestyle plan with maximum three attainable lifestyle goals and c) discussing the lifestyle plan regularly. The study population is SMI patients and statistical inference is on patient level using multilevel analyses. Primary outcome is waist circumference and other cardiometabolic risk factors after six and twelve months intervention, which are measured as part of routine outcome monitoring using standard protocols. Secondary outcomes include depressive and negative symptoms, cost-effectiveness, and barriers and facilitators in intervention implementation. DISCUSSION: Adequate health care should target both mental health and lifestyle behaviours in SMI patients. This trial contributes by studying a 12-month multidimensional lifestyle intervention as a potential evidence based (nursing) tool for targeting multiple lifestyle behaviours in SMI patients. TRIAL REGISTRATION: Nederlands Trialregister NTR3765 (trialregister.nl; registered 21 December 2012).

Mental health care use in adolescents with and without mental disorders.

The aim of the study was to estimate the proportion of adolescents with and without a psychiatric diagnosis receiving specialist mental health care and investigate their problem levels as well as utilization of other types of mental health care to detect possible over- and undertreatment. Care utilization data were linked to psychiatric diagnostic data of 2230 adolescents participating in the TRAILS cohort study, who were assessed biannually starting at age 11. Psychiatric diagnoses were established at the fourth wave by the Composite International Diagnostic Interview. Self-, parent- and teacher-reported emotional and behavioral problems and self-reported mental health care use were assessed at all four waves. Of all diagnosed adolescents, 35.3 % received specialist mental health care. This rate increased to 54.5 % when three or more disorders were diagnosed. Almost a third (28.5 %) of specialist care users had no psychiatric diagnosis; teachers gave them relatively high ratings on attention and impulsivity subscales. Diagnosed adolescents without specialist mental health care also reported low rates of other care use. We found no indication of overtreatment. Half of the adolescents with three or more disorders do not receive specialist mental health care nor any other type of care, which might indicate unmet needs.

Changing the obesogenic environment of severe mentally ill residential patients: ELIPS, a cluster randomised study design.

BACKGROUND: Severe mentally ill (SMI) patients have a reduced life expectancy of 13-30 years compared to the general population, largely due to an increased risk of cardiovascular mortality. Unhealthy lifestyle behaviours in SMI patients contribute to this increased risk. The obesogenic living environment of patients in residential facilities may even pose an extra risk. Although several studies have shown positive effects of lifestyle interventions on SMI patients' weight status, studies including residential patients and their obesogenic environment are scarce. This paper describes the Effectiveness of Lifestyle Interventions in PSychiatry trial (ELIPS). The goal of this trial is to improve cardiometabolic health in severe mentally ill residential patients by addressing the obesogenic environment. METHODS/DESIGN: The ELIPS study is a multi-site cluster randomised controlled trial (RCT) based on the principles of a pragmatic RCT. All residential and long-term clinical care teams of two large mental health care organisations in the North of the Netherlands serving SMI patients are invited to participate. The intervention is aimed at team level. Lifestyle coaches first develop a team specific lifestyle plan that tailors the ELIPS goals and protocol and then train teams on how to create a healthy environment and stimulate healthy behaviours in patients. After three months, teams take over the intervention after they have set out goals to achieve in the following nine months. In this phase, adherence to the lifestyle plan and pre-set goals is monitored. Patients in the control arm receive care as usual. Primary outcome measure is waist circumference at three and 12 months after baseline. DISCUSSION: ELIPS is different from previously published lifestyle intervention studies in three ways. First, it follows the principles of a pragmatic design, which enables the examination of effects in everyday practice. Second, by implementing the intervention at team level, we expect lifestyle activities to be maintained when interventionists leave. Last, by targeting the obesogenic environment we create a prerequisite for any sustainable health improvement, as patients can only make healthy choices in a healthy living environment. TRIAL REGISTRATION: Nederlands Trialregister NTR2720 (Dutch Trial Register, www.trialregister.nl). Registered 27 January 2011.

Explanatory and modifying factors of the association between sex and depression onset during adolescence: An exploratory study.

BACKGROUND: The prevalence of Major Depressive Disorder (MDD) is twice as high in women as in men and this difference already emerges during adolescence. Because the mechanisms underlying this sex-difference remain poorly understood, we took a bottom-up approach to identify factors explaining the sex-MDD relationship. METHODS: Data came from the TRacking Adolescents' Individual Lives Survey (TRAILS), a population study investigating youths' development from age 11 into adulthood. We assessed multiple baseline covariates (e.g., demographic, social and psychological) at ages 11-13 years and MDD onset at ages 19 and 25 years. In regression analyses, each covariate's role in the sex-MDD association as an effect modifier or confounder/explanatory variable was investigated. Replicability was evaluated in an independent sample. RESULTS: The analyses identified no effect-modifiers. Baseline internalizing problems, behavioral inhibition, dizziness, comfort in classroom, physical complaints, attention problems, cooperation, self/effortful control, interpersonal life events and computer use partially explained the association between sex and MDD at age 19. The association between sex and MDD at age 25 was explained by largely the same variables, but also by shyness, acne, antisocial behavior, aggression, affection from peers and time spent shopping. The explanatory roles of internalizing problems, behavioral inhibition, negative events involving gossip/rumors and leisure-time spending (computer-use/shopping) were replicated. LIMITATIONS: Potentially important baseline variables were not included or had low response rates. Gender roles or identification were not considered. Baseline MDD was not adjusted for. CONCLUSION: The sex-MDD association is partially explained by sex differences in symptoms and vulnerability factors already present in early adolescence.

Early symptom change contributes to the outcome prediction of cognitive behavioral therapy for depression patients: A machine learning approach.

BACKGROUND: Limited evidence exists regarding the association between early symptom change and later outcomes of cognitive behavioral therapy (CBT). This study aimed to apply machine learning algorithms to predict continuous treatment outcomes based on pre-treatment predictors and early symptom changes and to uncover whether additional variance could be explained compared to regression methods. Additionally, the study examined early subscale symptom changes to determine the most significant predictors of treatment outcome. METHODS: We investigated CBT outcomes in a large naturalistic dataset (N = 1975 depression patients). The sociodemographic profile, pre-treatment predictors, and early symptom change, including total and subscale scores were used to predict the Symptom Questionnaire (SQ)48 score at the 10th session as a continuous outcome. Different machine learners were compared to linear regression. RESULTS: Early symptom change and baseline symptom score were the only significant predictors. Models with early symptom change explained 22.0 % to 23.3 % more variance than those without early symptom change. Specifically, the baseline total symptom score, and the early symptom score changes of the subscales pertaining to depression and anxiety were the top three predictors of treatment outcome. LIMITATION: Excluded patients with missing treatment outcomes had slightly higher symptom scores at baseline, indicating possible selection bias. CONCLUSION: Early symptom change improved the prediction of treatment outcomes. The prediction performance achieved is far from clinical relevance: the best learner could only explain 51.2 % of the variance in outcomes. Compared to linear regression, more sophisticated preprocessing and learning methods did not substantially improve performance.

Comparative analysis of algorithm-guided treatment and predefined duration treatment programmes for depression: exploring cost-effectiveness using routine care data.

BACKGROUND: More knowledge on the cost-effectiveness of various depression treatment programmes can promote efficient treatment allocation and improve the quality of depression care. OBJECTIVE: This study aims to compare the real-world cost-effectiveness of an algorithm-guided programme focused on remission to a predefined duration, patient preference-centred treatment programme focused on response using routine care data. METHODS: A naturalistic study (n=6295 in the raw dataset) was used to compare the costs and outcomes of two programmes in terms of quality-adjusted life years (QALY) and depression-free days (DFD). Analyses were performed from a healthcare system perspective over a 2-year time horizon. Incremental cost-effectiveness ratios were calculated, and the uncertainty of results was assessed using bootstrapping and sensitivity analysis. FINDINGS: The algorithm-guided treatment programme per client yielded more DFDs (12) and more QALYs (0.013) at a higher cost (€3070) than the predefined duration treatment programme. The incremental cost-effectiveness ratios (ICERs) were around €256/DFD and €236 154/QALY for the algorithm guided compared with the predefined duration treatment programme. At a threshold value of €50 000/QALY gained, the programme had a probability of <10% of being considered cost-effective. Sensitivity analyses confirmed the robustness of these findings. CONCLUSIONS: The algorithm-guided programme led to larger health gains than the predefined duration treatment programme, but it was considerably more expensive, and hence not cost-effective at current Dutch thresholds. Depending on the preferences and budgets available, each programme has its own benefits. CLINICAL IMPLICATION: This study provides valuable information to decision-makers for optimising treatment allocation and enhancing quality of care cost-effectively.

The impact of COVID-19-pandemic-related adversity on mental health: longitudinal study in Dutch populations with and without mental health disorders.

BACKGROUND: Despite growing concerns about mental health during the COVID-19 pandemic, particularly in people with pre-existing mental health disorders, research has shown that symptoms of depression and anxiety were generally quite stable, with modest changes in certain subgroups. However, individual differences in cumulative exposure to COVID-19 stressors have not been yet considered. AIMS: We aimed to quantify and investigate the impact of individual-level cumulative exposure to COVID-19-pandemic-related adversity on changes in depressive and anxiety symptoms and loneliness. In addition, we examined whether the impact differed among individuals with various levels of pre-pandemic chronicity of mental health disorders. METHOD: Between April 2020 and July 2021, 15 successive online questionnaires were distributed among three psychiatric case-control cohorts that started in the 2000s (N = 1377). Outcomes included depressive and anxiety symptoms and loneliness. We developed a COVID-19 Adversity Index (CAI) summarising up to 15 repeated measures of COVID-19-pandemic-related exposures (e.g. exposure to COVID-19 infection, negative economic impact and quarantine). We used linear mixed linear models to estimate the effects of COVID-19-related adversity on mental health and its interaction with pre-pandemic chronicity of mental health disorders and CAI. RESULTS: Higher CAI scores were positively associated with higher increases in depressive symptoms, anxiety symptoms and loneliness. Associations were not statistically significantly different between groups with and without (chronic) pre-pandemic mental health disorders. CONCLUSIONS: Individual differences in cumulative exposure to COVID-19-pandemic-related adversity are important predictors of mental health, but we found no evidence for higher vulnerability among people with (chronic) pre-pandemic mental health disorders.

Benefits of extensive recruitment effort persist during follow-ups and are consistent across age group and survey method. The TRAILS study.

BACKGROUND: Extensive recruitment effort at baseline increases representativeness of study populations by decreasing non-response and associated bias. First, it is not known to what extent increased attrition occurs during subsequent measurement waves among subjects who were hard-to-recruit at baseline and what characteristics the hard-to-recruit dropouts have compared to the hard-to-recruit retainers. Second, it is unknown whether characteristics of hard-to-recruit responders in a prospective population based cohort study are similar across age group and survey method. METHODS: First, we compared first wave (T1) easy-to-recruit with hard-to-recruit responders of the TRacking Adolescents' Individual Lives Survey (TRAILS), a prospective population based cohort study of Dutch (pre)adolescents (at first wave: n = 2230, mean age = 11.09 (SD 0.56), 50.8% girls), with regard to response rates at subsequent measurement waves. Second, easy-to-recruit and hard-to-recruit participants at the fourth TRAILS measurement wave (n = 1881, mean age = 19.1 (SD 0.60), 52.3% girls) were compared with fourth wave non-responders and earlier stage drop-outs on family composition, socioeconomic position (SEP), intelligence (IQ), education, sociometric status, substance use, and psychopathology. RESULTS: First, over 60% of the hard-to-recruit responders at the first wave were retained in the sample eight years later at the fourth measurement wave. Hard-to-recruit dropouts did not differ from hard-to-recruit retainers. Second, extensive recruitment efforts for the web based survey convinced a population of nineteen year olds with similar characteristics as the hard-to-recruit eleven year olds that were persuaded to participate in a school-based survey. Some characteristics associated with being hard-to-recruit (as compared to being easy-to-recruit) were more pronounced among non-responders, resembling the baseline situation (De Winter et al.2005). CONCLUSIONS: First, extensive recruitment effort at the first assessment wave of a prospective population based cohort study has long lasting positive effects. Second, characteristics of hard-to-recruit responders are largely consistent across age groups and survey methods.

A Promising Approach to Optimizing Sequential Treatment Decisions for Depression: Markov Decision Process.

The most appropriate next step in depression treatment after the initial treatment fails is unclear. This study explores the suitability of the Markov decision process for optimizing sequential treatment decisions for depression. We conducted a formal comparison of a Markov decision process approach and mainstream state-transition models as used in health economic decision analysis to clarify differences in the model structure. We performed two reviews: the first to identify existing applications of the Markov decision process in the field of healthcare and the second to identify existing health economic models for depression. We then illustrated the application of a Markov decision process by reformulating an existing health economic model. This provided input for discussing the suitability of a Markov decision process for solving sequential treatment decisions in depression. The Markov decision process and state-transition models differed in terms of flexibility in modeling actions and rewards. In all, 23 applications of a Markov decision process within the context of somatic disease were included, 16 of which concerned sequential treatment decisions. Most existing health economic models relating to depression have a state-transition structure. The example application replicated the health economic model and enabled additional capacity to make dynamic comparisons of more interventions over time than was possible with traditional state-transition models. Markov decision processes have been successfully applied to address sequential treatment-decision problems, although the results have been published mostly in economics journals that are not related to healthcare. One advantage of a Markov decision process compared with state-transition models is that it allows extended action space: the possibility of making dynamic comparisons of different treatments over time. Within the context of depression, although existing state-transition models are too basic to evaluate sequential treatment decisions, the assumptions of a Markov decision process could be satisfied. The Markov decision process could therefore serve as a powerful model for optimizing sequential treatment in depression. This would require a sufficiently elaborate state-transition model at the cohort or patient level.

The effects of a computerized clinical decision aid on clinical decision-making in psychosis care.

OBJECTIVE: Clinicians in mental healthcare have few objective tools to identify and analyze their patient's care needs. Clinical decision aids are tools that support this process. This study examines whether 1) clinicians working with a clinical decision aid (TREAT) discuss more of their patient's care needs compared to usual treatment, and 2) agree on more evidence-based treatment decisions. METHODS: Clinicians participated in consultations (n = 166) with patients diagnosed with psychotic disorders from four Dutch mental healthcare institutions (research registration number 201700763). Primary outcomes were measured with the modified Clinical Decision-making in Routine Care questionnaire and combined with psychiatric, physical and social wellbeing related care needs. A multilevel analysis compared discussed care needs and evidence-based treatment decisions between treatment as usual (TAU) before, TAU after and the TREAT condition. RESULTS: First, a significant increase in discussed care needs for TREAT compared to both TAU conditions (ß = 20.2, SE = 5.2, p = 0.00 and ß = 15.8, SE = 5.4, p = 0.01) was found. Next, a significant increase in evidence-based treatments decisions for care needs was observed for TREAT compared to both TAU conditions (ß = 16.7, SE = 4.8, p = 0.00 and ß = 16.0, SE = 5.1, p = 0.01). CONCLUSION: TREAT improved the discussion about physical health issues and social wellbeing related topics. It also increased evidence-based treatment decisions for care needs which are sometimes overlooked and difficult to treat. Our findings suggest that TREAT makes sense of routine outcome monitoring data and improves guideline-informed care.

Mental health and perceived impact during the first Covid-19 pandemic year: A longitudinal study in Dutch case-control cohorts of persons with and without depressive, anxiety, and obsessive-compulsive disorders.

BACKGROUND: Little is known about the longer-term impact of the Covid-19 pandemic beyond the first months of 2020, particularly for people with pre-existing mental health disorders. Studies including pre-pandemic data from large psychiatric cohorts are scarce. METHODS: Between April 2020 and February 2021, twelve successive online questionnaires were distributed among participants of the Netherlands Study of Depression and Anxiety, Netherlands Study of Depression in Older Persons, and Netherlands Obsessive Compulsive Disorder Association Study (N = 1714, response rate 62%). Outcomes were depressive symptoms, anxiety, worry, loneliness, perceived mental health impact of the pandemic, fear of Covid-19, positive coping, and happiness. Using linear mixed models we compared trajectories between subgroups with different pre-pandemic chronicity of disorders and healthy controls. RESULTS: Depressive, anxiety and worry symptoms were stable since April-May 2020 whereas happiness slightly decreased. Furthermore, positive coping steadily decreased and loneliness increased - exceeding pre-Covid and April-May 2020 levels. Perceived mental health impact and fear of Covid-19 fluctuated in accordance with national Covid-19 mortality rate changes. Absolute levels of all outcomes were poorer with higher chronicity of disorders, yet trajectories did not differ among subgroups. LIMITATIONS: The most vulnerable psychiatric groups may have been underrepresented and results may not be generalizable to lower income countries. CONCLUSIONS: After a year, levels of depressive and worry symptoms remained higher than before the pandemic in healthy control groups, yet not in psychiatric groups. Nevertheless, persistent high symptoms in psychiatric groups and increasing loneliness in all groups are specific points of concern for mental health care professionals.

Identifying mismatch and match between clinical needs and mental healthcare use trajectories in people with anxiety and depression: Results of a longitudinal study.

BACKGROUND: Mismatch between need and mental healthcare (MHC) use (under-and overuse) has mainly been studied with cross-sectional designs, not accurately capturing patterns of persistence or change in clinical burden and MHC-use among persons with depressive and/or anxiety disorders. AIMS: Determining and describing [mis]match of longitudinal trajectories of clinical burden and MHC-use. METHODS: Six-year longitudinal burden and MHC-use data came from the Netherlands Study of Depression and Anxiety (n=2981). The sample was split into four subgroups: I) no clinical burden but constant MHC use, II) constant clinical burden but no MHC-use, III) changing clinical burden and MHC-use, and IV) healthy non-users. Within subgroups I)-III), specific clinical burden and MHC trajectories were identified (growth mixture modeling). The resulting classes' associations with predisposing, enabling, and need factors were investigated (regression analysis). RESULTS: Subgroups I-III revealed different trajectories. I) increasing MHC without burden (4.1%). II) slightly increasing (1.9%), strongly increasing (2.4%), and decreasing (9.5%) burden without MHC. III) increasing (41.4%) or decreasing (19.4%) burden and concurrently increasing MHC use (first underuse, then matched care), thus revealing delayed MHC-use. Only having suicidal ideation (p<.001, Cohen's d= .6-1.5) was a significant determinant of being in latter classes compared to underusers (strongly increasing burden without MHC-use). LIMITATIONS: More explanatory factors are needed to explain [mis]match. CONCLUSION: Mismatch occurred as constant underuse or as delayed MHC-use in a high-income country (Netherlands). Additionally, no meaningful class revealed constantly matched care on average. Presence of suicidal ideation could influence the probability of symptomatic individuals receiving matched MHC or not.

Predictors of mental health deterioration from pre- to post-COVID-19 outbreak.

BACKGROUND: Mental health was only modestly affected in adults during the early months of the COVID-19 pandemic on the group level, but interpersonal variation was large. AIMS: We aim to investigate potential predictors of the differences in changes in mental health. METHOD: Data were aggregated from three Dutch ongoing prospective cohorts with similar methodology for data collection. We included participants with pre-pandemic data gathered during 2006-2016, and who completed online questionnaires at least once during lockdown in The Netherlands between 1 April and 15 May 2020. Sociodemographic, clinical (number of mental health disorders and personality factors) and COVID-19-related variables were analysed as predictors of relative changes in four mental health outcomes (depressive symptoms, anxiety and worry symptoms, and loneliness), using multivariate linear regression analyses. RESULTS: We included 1517 participants with (n = 1181) and without (n = 336) mental health disorders. Mean age was 56.1 years (s.d. 13.2), and 64.3% were women. Higher neuroticism predicted increases in all four mental health outcomes, especially for worry (ß = 0.172, P = 0.003). Living alone and female gender predicted increases in depressive symptoms and loneliness (ß = 0.05-0.08), whereas quarantine and strict adherence with COVID-19 restrictions predicted increases in anxiety and worry symptoms (ß = 0.07-0.11).Teleworking predicted a decrease in anxiety symptoms (ß = -0.07) and higher age predicted a decrease in anxiety (ß = -0.08) and worry symptoms (ß = -0.10). CONCLUSIONS: Our study showed neuroticism as a robust predictor of adverse changes in mental health, and identified additional sociodemographic and COVID-19-related predictors that explain longitudinal variability in mental health during the COVID-19 pandemic.

Real-World Treatment Costs and Care Utilization in Patients with Major Depressive Disorder With and Without Psychiatric Comorbidities in Specialist Mental Healthcare.

BACKGROUND: The majority of patients with major depressive disorder (MDD) have comorbid mental conditions. OBJECTIVES: Since most cost-of-illness studies correct for comorbidity, this study focuses on mental healthcare utilization and treatment costs in patients with MDD including psychiatric comorbidities in specialist mental healthcare, particularly patients with a comorbid personality disorder (PD). METHODS: The Psychiatric Case Register North Netherlands contains administrative data of specialist mental healthcare providers. Treatment episodes were identified from uninterrupted healthcare use. Costs were calculated by multiplying care utilization with unit prices (price level year: 2018). Using generalized linear models, cost drivers were investigated for the entire cohort. RESULTS: A total of 34,713 patients had MDD as a primary diagnosis over the period 2000-2012. The number of patients with psychiatric comorbidities was 24,888 (71.7%), including 13,798 with PD. Costs were highly skewed, with an average ± standard deviation cost per treatment episode of €21,186 ± 74,192 (median €2320). Major cost drivers were inpatient days and daycare days (50 and 28% of total costs), occurring in 12.7 and 12.5% of episodes, respectively. Compared with patients with MDD only (€11,612), costs of patients with additional PD and with or without other comorbidities were, respectively, 2.71 (p < .001) and 2.06 (p < .001) times higher and were 1.36 (p < .001) times higher in patients with MDD and comorbidities other than PD. Other cost drivers were age, calendar year, and first episodes. CONCLUSIONS: Psychiatric comorbidities (especially PD) in addition to age and first episodes drive costs in patients with MDD. Knowledge of cost drivers may help in the development of future stratified disease management programs.

Satisfaction with social connectedness as a predictor for positive and negative symptoms of psychosis: A PHAMOUS study.

PURPOSE: This study examines satisfaction with social connectedness (SSC) as predictor of positive and negative symptoms in people with a psychotic disorder. METHODS: Data from the Pharmacotherapy Monitoring and Outcome Survey (PHAMOUS) was used from patients assessed between 2014 and 2019, diagnosed with a psychotic disorder (N = 2109). Items about social connectedness of the Manchester short assessment of Quality of Life (ManSA) were used to measure SSC. Linear mixed models were used to estimate the association of SSC with the Positive and Negative Syndrome Scale (PANSS) after one and two years against α = 0.01. Analyses were adjusted for symptoms, time since onset, gender and age. Additionally, fluctuation of positive and negative symptom scores over time was estimated. RESULTS: The mean duration of illness of the sample was 18.8 years (SD 10.7) with >65% showing only small variation in positive and negative symptoms over a two to five-year time period. After adjustment for covariates, SSC showed to be negatively associated with positive symptoms after one year (ß = -0.47, p < 0.001, 95% CI = -0.70, -025) and two years (ß = -0.59, p < 0.001, 95% CI = -0.88, -0.30), and for negative symptoms after one year (ß = -0.52, p < 0.001, 95% CI = -0.77, -0.27). The prediction of negative symptoms was not significant at two years. CONCLUSION: This research indicates that interventions on SSC might positively impact mental health for people with psychosis. SSC is a small and robust predictor of future levels of positive symptoms. Negative symptoms could be predicted by SSC at one year.