Attention deficit hyperactivity disorder (ADHD) is increasingly diagnosed in adults. People with intellectual disability have higher rates of ADHD yet there is little evidence on the presentation and pharmacological treatment of ADHD in this population or how this differs from the general population.

Retrospective cohort study using data from electronic health records. Adults with intellectual disability newly diagnosed with ADHD between 2007 and 2022 were matched to adults with ADHD without intellectual disability and their clinical features and treatments were compared.

A total of 159 adults with ADHD and intellectual disability and 648 adults with ADHD without intellectual disability formed the dataset. Adults with intellectual disability had higher rates of psychiatric co-morbidity and spent more time under mental health services than those without intellectual disability. They were more likely to have recorded agitation, aggression, hostility, and mood instability, and less likely to have poor concentration recorded in the 12 months prior to the diagnosis of ADHD. Following diagnosis, people with intellectual disability were significantly less likely to be prescribed any medication for ADHD than controls without intellectual disability (adjusted odds ratio 0.60, 95% confidence interval 0.38–0.91), and were less likely to be prescribed stimulants (27.7% v 46.0%, p < 0.001).

The presence of behaviors that challenge in adults with intellectual disability may indicate co-occurring ADHD. Further work to define the safety and efficacy of medication for ADHD in adults with intellectual disability is needed to understand differences in prescription rates and to avoid inequities in care outcomes.

There is currently no universally accepted measure for population-based surveillance of mood and anxiety disorders. As such, the use of multiple linked measures could provide a more accurate estimate of population prevalence. Our primary objective was to apply Bayesian methods to two commonly employed population measures of mood and anxiety disorders to make inferences regarding the population prevalence and measurement properties of a combined measure.

We used data from the 2012 Canadian Community Health Survey – Mental Health linked to health administrative databases in Ontario, Canada. Structured interview diagnoses were obtained from the survey, and health administrative diagnoses were identified using a standardised algorithm. These two prevalence estimates, in addition to data on the concordance between these measures and prior estimates of their psychometric properties, were used to inform our combined estimate. The marginal posterior densities of all parameters were estimated using Hamiltonian Monte Carlo (HMC), a Markov Chain Monte Carlo technique. Summaries of posterior distributions, including the means and 95% equally tailed posterior credible intervals, were used for interpretation of the results.

The combined prevalence mean was 8.6%, with a credible interval of 6.8–10.6%. This combined estimate sits between Bayesian-derived prevalence estimates from administrative data-derived diagnoses (mean = 7.4%) and the survey-derived diagnoses (mean = 13.9%). The results of our sensitivity analysis suggest that varying the specificity of the survey-derived measure has an appreciable impact on the combined posterior prevalence estimate. Our combined posterior prevalence estimate remained stable when varying other prior information. We detected no problematic HMC behaviour, and our posterior predictive checks suggest that our model can reliably recreate our data.

Accurate population-based estimates of disease are the cornerstone of health service planning and resource allocation. As a greater number of linked population data sources become available, so too does the opportunity for researchers to fully capitalise on the data. The true population prevalence of mood and anxiety disorders may reside between estimates obtained from survey data and health administrative data. We have demonstrated how the use of Bayesian approaches may provide a more informed and accurate estimate of mood and anxiety disorders in the population. This work provides a blueprint for future population-based estimates of disease using linked health data.

Epidemiological studies indicate that individuals with one type of mental disorder have an increased risk of subsequently developing other types of mental disorders. This study aimed to undertake a comprehensive analysis of pair-wise lifetime comorbidity across a range of common mental disorders based on a diverse range of population-based surveys.

The WHO World Mental Health (WMH) surveys assessed 145 990 adult respondents from 27 countries. Based on retrospectively-reported age-of-onset for 24 DSM-IV mental disorders, associations were examined between all 548 logically possible temporally-ordered disorder pairs. Overall and time-dependent hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using Cox proportional hazards models. Absolute risks were estimated using the product-limit method. Estimates were generated separately for men and women.

Each prior lifetime mental disorder was associated with an increased risk of subsequent first onset of each other disorder. The median HR was 12.1 (mean = 14.4; range 5.2–110.8, interquartile range = 6.0–19.4). The HRs were most prominent between closely-related mental disorder types and in the first 1–2 years after the onset of the prior disorder. Although HRs declined with time since prior disorder, significantly elevated risk of subsequent comorbidity persisted for at least 15 years. Appreciable absolute risks of secondary disorders were found over time for many pairs.

Survey data from a range of sites confirms that comorbidity between mental disorders is common. Understanding the risks of temporally secondary disorders may help design practical programs for primary prevention of secondary disorders.

To compare the accuracy of Mini-Mental State Examination (MMSE) and of the Montreal Cognitive Assessment (MoCA) in tracking mild cognitive impairment (MCI) and Alzheimer’s Disease (AD).

A Systematic review of the PubMed, Bireme, Science Direct, Cochrane Library, and PsycInfo databases was conducted. Using inclusion and exclusion criteria and staring with 1,629 articles, 34 articles were selected. The quality of the selected research was evaluated through the Quality Assessment of Diagnostic Accuracy Studies 2 tool (QUADAS-2).

More than 80% of the articles showed MoCA to be superior to MMSE in discriminating between individuals with mild cognitive impairment and no cognitive impairment. The area under the curve varied from 0.71 to 0.99 for MoCA, and 0.43 to 0.94 for MMSE, when evaluating the ability to discriminate MCI in the cognitively healthy elderly individuals, and 0.87 to 0.99 and 0.67 to 0.99, respectively, when evaluating the detection of AD. The AUC mean value for MoCA was significantly larger compared to the MMSE in discriminating MCI from control [0.883 (CI 95% 0.855-0.912) vs MMSE 0.780 (CI 95% 0.740-0.820) p < 0.001].

The screening tool MoCA is superior to MMSE in the identification of MCI, and both tests were found to be accurate in the detection of AD.

With increasing cultural diversity and growing elderly immigrant populations in Western European countries, the availability of brief cognitive screening instruments adequate for assessment of dementia in people from diverse backgrounds becomes increasingly important. The aim of the present study was to investigate diagnostic accuracy of the Rowland Universal Dementia Assessment Scale (RUDAS) in a multicultural sample and to calculate normative data as a basis for demographic adjustment of RUDAS scores.

The study was a prospective international cross-sectional multi-center study. Receiver operating characteristic curve analysis was used to examine diagnostic accuracy. Regression analysis was used to assess the impact of demographic variables.

Data was collected from 341 cognitively intact participants and 80 people with dementia with a wide age- and educational range. Of the 421 included participants, 239 (57%) had immigrant background. The RUDAS had high diagnostic accuracy with an area under the curve (AUC) of 0.93. The optimal cut-off score was <25 (sensitivity 0.80, specificity 0.90). Regression analysis revealed that RUDAS scores were mainly affected by education and were unrelated to data collection site and immigrant status. Education-adjusted normative data was calculated as a basis for education adjustment of RUDAS scores. Applying education-adjusted RUDAS scores slightly but significantly improved diagnostic accuracy with an AUC of 0.95.

We found the RUDAS to have excellent diagnostic properties in our multicultural sample. However, we suggest that RUDAS scores should be adjusted for education to increase diagnostic accuracy and that the choice of cut-off score should be considered based on the clinical context and expected base rate of dementia.

Despite of their limited availability and potential for significant variation between and within each modality, this is the first study to prospectively measure the clinical utility of MRI and/or SPECT brain scanning in addition to the routine diagnostic workup of patients presenting to memory clinic.

A single center study was conducted over a convenience of 12-month sampling period. For each patient referred for MRI and/or SPECT scanning, the primary geriatrician or psychogeriatrician was asked to assign an initial diagnosis. The initial diagnosis was then compared with the final consensus diagnosis after any scans or neuropsychology testing had been completed.

During the 12-month study period, 66 patients (26%) were referred for scans out of a total of 253 patients included in the study. There were 16/44 (36%) positive MRI outcomes and 13/35 (37%) positive SPECT outcomes. The diagnosis changed consistent with the MRI scan findings in 11/44 (25%) and changed consistent with the SPECT scan findings in 9/35 (26%). Potentially reversible pathology was identified in a single patient, 1/50 (2%), via an MRI scan that suggested normal pressure hydrocephalus. The number needed to test for one positive outcome was 3.8 (95% CI 2.0–23.3), 6.0 (95% CI NA), and 1.7 (95% CI 1.3–2.5) for MRI only, SPECT only, and MRI and SPECT together, respectively.

The clinical utility of MRI and/or SPECT scanning in this study may be broadly superior to the available international evidence, and further research is needed to identify predictors of positive scan outcomes.