Faculty Bibliography

Nine hundred and seventy million individuals across the globe are estimated to carry the burden of a mental disorder. Limited progress has been achieved in alleviating this burden over decades of effort, compared to progress achieved for many other medical disorders. Progress on outcome improvement for all medical disorders, including mental disorders, requires research capable of discovering causality at sufficient scale and speed, and a diagnostic nosology capable of encoding the causal knowledge that is discovered. Accordingly, the field's guiding paradigm limits progress by maintaining: (a) a diagnostic nosology (DSM-5) with a profound lack of causality; (b) a misalignment between mental health etiologic research and nosology; (c) an over-reliance on clinical trials beyond their capabilities; and (d) a limited adoption of newer methods capable of discovering the complex etiology of mental disorders. We detail feasible directions forward, to achieve greater levels of progress on improving outcomes for mental disorders, by: (a) the discovery of knowledge on the complex etiology of mental disorders with application of Causal Data Science methods; and (b) the encoding of the etiological knowledge that is discovered within a causal diagnostic system for mental disorders.

Despite the speedy development of vaccines for COVID-19, their rollout has posed a major public health challenge, as vaccine hesitancy (VH) and refusal are high. Addressing vaccine hesitancy is a multifactorial and context-dependent challenge. This perspective focuses on VH in the world region of Latin America and the Caribbean (LAC) and includes people suffering from severe mental illness, therefore covering populations and subpopulations often neglected in scientific literature. We present an overview of VH in LAC countries, discussing its global and historical context. Vaccine uptake has shown to widely vary across different subregions of LAC. Current data points to a possible correlation between societal polarization and vaccination, especially in countries going through political crises such as Brazil, Colombia, and Venezuela. Poor accessibility remains an additional important factor decreasing vaccination rollout in LAC countries and even further, in the whole Global South. Regarding patients with severe mental illness in LAC, and worldwide, it is paramount to include them in priority groups for immunization and monitor their vaccination coverage through public health indicators.

Individualized treatment rules (ITRs) recommend treatments that are tailored specifically according to each patient's own characteristics. It can be challenging to estimate optimal ITRs when there are many features, especially when these features have arisen from multiple data domains (e.g., demographics, clinical measurements, neuroimaging modalities). Considering data from complementary domains and using multiple similarity measures to capture the potential complex relationship between features and treatment can potentially improve the accuracy of assigning treatments. Outcome weighted learning (OWL) methods that are based on support vector machines using a predetermined single kernel function have previously been developed to estimate optimal ITRs. In this paper, we propose an approach to estimate optimal ITRs by exploiting multiple kernel functions to describe the similarity of features between subjects both within and across data domains within the OWL framework, as opposed to preselecting a single kernel function to be used for all features for all domains. Our method takes into account the heterogeneity of each data domain and combines multiple data domains optimally. Our learning process estimates optimal ITRs and also identifies the data domains that are most important for determining ITRs. This approach can thus be used to prioritize the collection of data from multiple domains, potentially reducing cost without sacrificing accuracy. The comparative advantage of our method is demonstrated by simulation studies and by an application to a randomized clinical trial for major depressive disorder that collected features from multiple data domains. Supplemental materials for this article are available online.

Attention-Deficit Hyperactivity Disorder (ADHD) is a complex and heterogeneous neurodevelopmental condition for which curative treatments are lacking. Whilst pharmacological treatments are generally effective and safe, there is considerable inter-individual variability among patients regarding treatment response, required dose, and tolerability. Many of the non-pharmacological treatments, which are preferred to drug-treatment by some patients, either lack efficacy for core symptoms or are associated with small effect sizes. No evidence-based decision tools are currently available to allocate pharmacological or psychosocial treatments based on the patient's clinical, environmental, cognitive, genetic, or biological characteristics. We systematically reviewed potential biomarkers that may help in diagnosing ADHD and/or stratifying ADHD into more homogeneous subgroups and/or predict clinical course, treatment response, and long-term outcome across the lifespan. Most work involved exploratory studies with cognitive, actigraphic and EEG diagnostic markers to predict ADHD, along with relatively few studies exploring markers to subtype ADHD and predict response to treatment. There is a critical need for multisite prospective carefully designed experimentally controlled or observational studies to identify biomarkers that index inter-individual variability and/or predict treatment response.

Attention-Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental condition associated with impaired function and increased risk of poor outcomes in children, young people and adults with the condition. Currently approved pharmacological treatments for ADHD include a range of stimulant (methylphenidate, amphetamine) and nonstimulant (atomoxetine, guanfacine, clonidine) medications. All have been shown to be effective in treating the symptoms of ADHD and improving other functional outcomes including quality of life, academic performance, rates of accidents and injuries, and do not appear to be associated with significant adverse outcomes or side effects. In this chapter, we review medications for ADHD by summarising the mechanisms of action of each of the two main classes of compounds (stimulants and nonstimulants), the formulations of the most commonly prescribed medications within each class, their efficacy in treating ADHD symptoms and other outcomes, and other factors that influence treatment decisions including side effects and tolerability, comorbidities and medical history. We conclude with a summary of the treatment decisions made by clinicians and suggest some next steps for research. Further research is needed to understand the mechanisms of action of these medications and how exactly they improve symptoms, and to examine their effects on commonly occurring comorbidities.

Background: Significant racial and ethnic disparities exist in the community in underprescribing analgesics for pain and overprescribing antipsychotics for behavioral symptoms in persons with dementia. In hospice these drugs are commonly used to provide comfort, but little is known about prescription patterns in minoritized populations. We aimed to identify prescribing patterns in minoritized racial and ethnic groups among persons with living advanced dementia in home hospice.
Method(s): A cross-sectional study of 6,874 participants with advanced dementia from eight hospices across the United States. Demographics, antipsychotic (typical, atypical) and analgesic (opioid, non-opioid) prescriptions at admission, days of prescription use in hospice and length of stay were collected from electronic records. Descriptive statistics were calculated and hurdle regression models estimated to examine the association between race/ethnicity and prescription rates for each drug (days of drug use per 100 person-days).
Result(s): Participants were 10.7% Black, 34.8% Hispanic, 51.1% white, and 3.3% from other racial and ethnic groups. On admission, Hispanics and Blacks had similar rates of antipsychotic prescription that were lower than whites (11.9% & 12.3% vs 16.8%) and Hispanics had substantially lower non-opioid analgesic prescription vs Blacks and whites (23.3% vs 36.0% & 37.3%); During the hospice stay, Hispanics were prescribed antipsychotics (atypical RR =1.03, 95 % CI: 1.02-1.04; typical RR:1.04, 95% CI: 1.01-1.07) and analgesics (opioid RR =1.03, 95% CI: 1.02-1.04; non-opioid RR = 1.03, 95% CI = 1.02-1.03) for more days than whites. Blacks were prescribed analgesics (opioid RR =1.09, 95% CI 1.08-1.11; non-opioid RR = 1.01, 95% CI: 1-1.02) for more days than whites.
Conclusion(s): Disparities in analgesic and antipsychotic use on admission amongst Blacks and Hispanics were found, yet hospice narrowed this gap significantly. While less likely to be prescribed opioids, Blacks and Hispanics had more person days on analgesics overall. However, there was divergence in antipsychotic use over time between groups that requires further investigation given the controversial role of antipsychotics in management of dementia symptoms