Peter Butterworth’s research while affiliated with Deakin University and other places

Importance Test accuracy studies often use small datasets to simultaneously select an optimal cutoff score that maximizes test accuracy and generate accuracy estimates. Objective To evaluate the degree to which using data-driven methods to simultaneously select an optimal Patient Health Questionnaire-9 (PHQ-9) cutoff score and estimate accuracy yields (1) optimal cutoff scores that differ from the population-level optimal cutoff score and (2) biased accuracy estimates. Design, Setting, and Participants This study used cross-sectional data from an existing individual participant data meta-analysis (IPDMA) database on PHQ-9 screening accuracy to represent a hypothetical population. Studies in the IPDMA database compared participant PHQ-9 scores with a major depression classification. From the IPDMA population, 1000 studies of 100, 200, 500, and 1000 participants each were resampled. Main Outcomes and Measures For the full IPDMA population and each simulated study, an optimal cutoff score was selected by maximizing the Youden index. Accuracy estimates for optimal cutoff scores in simulated studies were compared with accuracy in the full population. Results The IPDMA database included 100 primary studies with 44 503 participants (4541 [10%] cases of major depression). The population-level optimal cutoff score was 8 or higher. Optimal cutoff scores in simulated studies ranged from 2 or higher to 21 or higher in samples of 100 participants and 5 or higher to 11 or higher in samples of 1000 participants. The percentage of simulated studies that identified the true optimal cutoff score of 8 or higher was 17% for samples of 100 participants and 33% for samples of 1000 participants. Compared with estimates for a cutoff score of 8 or higher in the population, sensitivity was overestimated by 6.4 (95% CI, 5.7-7.1) percentage points in samples of 100 participants, 4.9 (95% CI, 4.3-5.5) percentage points in samples of 200 participants, 2.2 (95% CI, 1.8-2.6) percentage points in samples of 500 participants, and 1.8 (95% CI, 1.5-2.1) percentage points in samples of 1000 participants. Specificity was within 1 percentage point across sample sizes. Conclusions and Relevance This study of cross-sectional data found that optimal cutoff scores and accuracy estimates differed substantially from population values when data-driven methods were used to simultaneously identify an optimal cutoff score and estimate accuracy. Users of diagnostic accuracy evidence should evaluate studies of accuracy with caution and ensure that cutoff score recommendations are based on adequately powered research or well-conducted meta-analyses.

Introduction Much has been made of the decline in population mental health over COVID but most studies show this just exacerabted a loing term trend This has predominnatly been attributed to changes in adolescent mental health over the past decade but there ahs been little evalaution of whether this post Millenium cohort was the first to demonstrate such a decline Objectives This study investigates to what extent mental differs in people born in different decades – i.e., possible birth cohort differences in the mental health of the popualtion over the past two decades To remove the linear dependency and identify any differences in trends between cohorts, we model mental health for each cohort as a nonlinear smooth function of age in an age-cohort model. Methods This analysis draws on 20 annual waves of the Household Income and Labour Dynamic in Australia (HILDA) survey.,is a nationally representative household panel that commenced in 2001 with 13,969 participants. The birth cohort of each person was defined by the decade of birth year(1940s, 1950s, etc). Mental ill health was assessed with the MHI5 from the SF36, in each wave and K10 from alternate waves. We estimate and compare penalized smooth trends in mental health for each cohort using restricted maximum likelihood (REML) using generalized additive mixed modelling (GAMM). Cohort effects are captured by directly estimating the differences between the smooth age trends of adjacent cohorts. Results Later cohorts were more likely to have poorer mental health, higher distress, more likely to be single and unemployed, and less likely to be chronically ill or disabled. Mental health was worse for younger age-groups in each survey year, and this discrepancy is much greater in more recent surveys - consistent with a birth cohort effect. Millennials (those born in the early 1990s) had a lower score at the same age as earlier generations, and the later cohorts do not show the age-related improvement seen in other earlier cohorts as they aged. At age 30 the average MHI-5 score of those born in the 1990s was 67, compared to 72.5 and 74 for people born in the 1980s and 1970s. Conclusions The deterioration in mental health over time which has been reported in large cross-sectional surveys, likely reflects cohort-specific effects related to the experiences of young people born in the Millennial generation and, to a lesser extent, those from the immediately prior cohort born in the1980s. We need to understand whether later cohorts are less resilient to similar risk factors experienced by earlier cohorts or whether they experience more and/or a greater severity of risks for mental ill-health. Such evidence is critical if the deteriorating pattern of mental health is to be arrested. Disclosure of Interest None Declared

Background Mental disorders are a significant contributor to disease burden. However, there is a large treatment gap for common mental disorders worldwide. This systematic review summarizes the factors associated with mental health service use. Methods PubMed, Scopus, and the Web of Science were searched for articles describing the predictors of and barriers to mental health service use among people with mental disorders from January 2012 to August 2023. The initial search yielded 3230 articles, 2366 remained after removing duplicates, and 237 studies remained after the title and abstract screening. In total, 40 studies met the inclusion and exclusion criteria. Results Middle-aged participants, females, Caucasian ethnicity, and higher household income were more likely to access mental health services. The use of services was also associated with the severity of mental symptoms. The association between employment, marital status, and mental health services was inconclusive due to limited studies. High financial costs, lack of transportation, and scarcity of mental health services were structural factors found to be associated with lower rates of mental health service use. Attitudinal barriers, mental health stigma, and cultural beliefs also contributed to the lower rates of mental health service use. Conclusion This systematic review found that several socio-demographic characteristics were strongly associated with using mental health services. Policymakers and those providing mental health services can use this information to better understand and respond to inequalities in mental health service use and improve access to mental health treatment.

Background Outcome monitoring can support the delivery of quality service that meets the needs of clients, clinicians and services. However, few studies have examined client or staff perspectives on the design and implementation of outcomes monitoring within a service. Implementation of outcomes monitoring requires understanding the preferences and expectations of relevant stakeholders. Objective Informed by the Consolidated Framework for Implementation Research, this study aimed to investigate the preferences and priorities of relevant staff, clients and family members to inform the design and implementation of an effective outcomes monitoring system in the context of a mental health service for military veterans and their families. Method Twenty‐nine staff participated across five online focus groups, including clinical staff, peer workers, policy staff and supervisors. Ten clients participated in online or telephone semi‐structured interviews. Thematic analysis was used to develop themes from the data. Results Clients and staff agreed that outcomes monitoring should cover more than symptoms, particularly by incorporating functional outcomes. Assessing mental health over time was considered a valuable tool for supporting treatment processes and providing actionable information. Challenges identified by clients and staff included the need for efficient processes, ensuring measures are relevant and acceptable, and maintaining client privacy. The ability to personalise data collection and have a streamlined, responsive system were key attributes of a quality outcomes monitoring framework. Conclusions Findings suggest that an effective outcome monitoring framework should be client‐led, tailored to the individual's needs, and provide feedback on progress. Outcomes monitoring should also be efficient, accessible and allow for safe information sharing.

Self-report measures are useful in psychological research and practice, but scores may be impacted by administration methods. This study investigated whether changing the recall period (from 30 to 7 days) and response option order (from ascending to descending) alters the score distribution of the Kessler Psychological Distress Scale (K10). Participants were presented with the K10 with either different recall periods or different response option orders. There was weak evidence of lower mean K10 scores when using a 7-day recall period than when using the 30-day recall period (B = 1.96, 95% CI [0.04–3.90]) but no evidence of a change in the estimated prevalence of very high psychological distress. Presenting the response options in ascending order did not affect mean scores, but there was weak evidence of reduced prevalence of very high distress relative to the descending order (incidence rate ratio [IRR] = 0.60, 95% CI [0.36–0.98]). These findings suggest that varying the administration method may result in minor differences in population estimates of very high psychological distress when using the K10.

Administration mode of patient-reported outcome measures (PROMs) may influence responses. We assessed if Patient Health Questionnaire-9 (PHQ-9), Edinburgh Postnatal Depression Scale (EPDS) and Hospital Anxiety and Depression Scale – Depression subscale (HADS-D) item responses and scores were associated with administration mode. We compared (1) self-administration versus interview-administration; within self-administration (2) research or medical setting versus private; and (3) pen-and-paper versus electronic; and within interview-administration (4) in-person versus phone. We analysed individual participant data meta-analysis datasets with item-level data for the PHQ-9 (N = 34,529), EPDS (N = 16,813), and HADS-D (N = 16,768). We used multiple indicator multiple cause models to assess differential item functioning (DIF) by administration mode. We found statistically significant DIF for most items on all measures due to large samples, but influence on total scores was negligible. In 10 comparisons conducted across the PHQ-9, EPDS, and HADS-D, Pearson's correlations and intraclass correlation coefficients between latent depression symptom scores from models that did or did not account for DIF were between 0.995 and 1.000. Total PHQ-9, EPDS, and HADS-D scores did not differ materially across administration modes. Researcher and clinicians who evaluate depression symptoms with these questionnaires can select administration methods based on patient preferences, feasibility, or cost.

Purpose Difficulties managing work and family demands are common and have been found to be associated with stress and poor mental health. However, very few studies have examined Work Family Conflict (WFC) in connection with diagnosable anxiety disorders (and none with Australian representative data). The current study investigated whether high WFC was significantly associated with a diagnosis of Generalised Anxiety Disorder (GAD) after controlling for a broad range of socio-demographic contextual factors, related psychosocial job, family and individual characteristics, and prior anxiety symptom history. Methods Data was analysed from an Australian population-based community cohort - the Personality and Total Health (PATH) Through Life project. Eligible participants (N = 1159) were employed full-time or part-time, with data collected by both online questionnaire and face-to-face interview. Presence of Generalised Anxiety Disorder (GAD) in the past 12-months was diagnosed by the GAD module in the Composite International Diagnostic Interview (CIDI) (based on DSM-IV criteria) and severe anxiety symptoms were measured using the Patient Health Questionnaire (PHQ) 7-item ‘other anxiety’ model. Results The findings consistently showed that those experiencing high WFC had higher odds of a GAD diagnosis (final adjusted model: CIDI: OR: 2.55, CI: 1.38–4.70) as well as clinical levels of anxiety symptoms (PHQ: OR:2.61, CI:1.44,4.72). This was the case after controlling for an extensive range of covariates. Conclusions This is one of the first studies to show that WFC is associated with greater likelihood of GAD. The challenge of juggling both work and family can have far-reaching impacts - not just increasing distress broadly, but also potentially increasing the likelihood of clinically diagnosable anxiety.