Outcomes with long-acting injections (LAIs) are generally better than with oral antipsychotic therapy. However, the use of LAIs does not assure compliance with treatment and in clinical practice patients often miss injections or receive their injections later than intended.

We conducted a case–control study to identify demographic and treatment associations with relapse (cases) on paliperidone 1-monthly injection (PP1M) compared with age-, gender-, and ethnicity-matched controls.

We identified 16 cases and matched 43 controls. Baseline variables did not differ except that cases had received significantly more antipsychotic drugs before initiation with PP1M (3.94 vs. 2.12; p < 0.001). Cases had fewer PP1M injections administered compared with the control group (9.69 vs. 11.37; p < 0.001) and this group had a longer interval between injections than the control group (37 vs. 33 days; p < 0.001).

Relapse on PP1M is associated with reduced frequency of injection and a longer interval between doses.

what did we learn in 2020? Every year several thousand scientific papers on alcohol, drugs, and nicotine are published. The picking of five papers must obviously be arbitrary and subjective. However, the scientific literature of 2020 cannot be regarded without acknowledging the many papers concerning the COVID-19 pandemic. Some studies on alcohol, drug, and nicotine show a small increase, some a small decrease, but many no change. The addiction consequences of the pandemic and the societal lockdowns may thus be less dramatic than feared. This is true even if many papers reported higher mental distress during the pandemic and there is a close relationship between mental distress and substance use, a relationship that has been further confirmed in studies from the past year. Furthermore, a review concerning the addictive potential of cannabis has further alarmed us of the current liberalization also affecting Europe. A new figure of “1 in 3” cannabis users getting hooked may possibly replace the old “1 in 10”. Furthermore, the year has brought even more solid knowledge of the transition from substance-induced psychosis (SIP) to schizophrenia, teaching psychiatrists in acute psychiatry an important lesson on how to view SIP. As many as 1 in 3 patients with SIP will eventually receive a diagnosis of schizophrenia, making SIP the most powerful risk factor for schizophrenia known. Lastly, the lecture will present a very novel and unexpected finding regarding alcohol elimination, that may change how we treat intoxications with different alcohols.

No significant relationships.

Abstract: Biomarkers for diagnosis and treatment of Bipolar Disorder: hope or hype? Professor Allan Young, Centre for Affective Disorders, IoPPN, KCL London, SE5 8AF. [email protected] The use of “biomarkers” (biological markers) in basic and clinical research as well as in clinical practice has become so commonplace in many areas of medicine that their presence as primary endpoints in clinical trials is now widely accepted. In clinical disciplines where specific biomarkers have been well characterized and repeatedly shown to correctly predict relevant clinical outcomes across a variety of treatments and populations, this use is entirely justified and appropriate. However, the validity of biomarkers in most psychiatric disorders continues to be evaluated. This lecture will review the current conceptual status of biomarkers as clinical and diagnostic tools for bipolar disorder and as surrogate endpoints in clinical research in bipolar disorder. The conceptual background in terms of current diagnostic categories and research domain criteria will be discussed and the various approaches with putative value (e.g., brain imaging, genetics, and neuroendocrinology) reviewed (1, 2). The lecture will end with a discussion of approaches to evaluating biomarkers of lithium response (3).

Wise et al, Mol Psychiatry. 2016 May 24. doi: 10.1038/mp.2016.72. [Epub ahead of print]; Young AH. Harv Rev Psychiatry. 2014 Nov-Dec;22(6):331–3 Bellivier F, Young AH, et al, Bipolar Disord. 2020 Oct 23. doi: 10.1111/bdi.13023. Online ahead of print.

Paid lectures and advisory boards for the following companies with drugs used in affective and related disorders: Astrazenaca, Eli Lilly, Lundbeck, Sunovion, Servier, Livanova, Janssen, Allegan, Bionomics, Sumitomo Dainippon Pharma, COMPASS Principal Inve

Patients with depression are more susceptible to cardiovascular illness including vascular surgeries. However, health outcomes after vascular surgery among patients with depression is unknown. This study aimed to investigate associations of depression with post-operative health outcomes for vascular surgical patients.

A retrospective observational study was conducted using data from a large mental healthcare provider and linked national hospitalization data for the same south London geographic catchment. OPCS-4 codes were used to identify vascular procedures. Health outcomes were compared between those with/without depression including length of hospital stay (LOS), inpatient mortality, and 30 day emergency hospital readmissions. Predictors of these health outcomes were also assessed.

Vascular surgery was received by 9,267 patients, including 446 diagnosed with depression. Patients with depression had a higher risk of emergency admission for vascular surgery (odds ratio [OR] 1.28; 1.03, 1.59), longer index LOS (IRR 1.38; 1.33–1.42), and a higher risk of 30-day emergency readmission (OR 1.82; 1.35–2.47). Patients with depression had higher inpatient mortality after adjustment for sociodemographic status (1.51; 1.03, 2.23) but not on full adjustment, and had longer emergency readmission LOS (1.13; 1.04, 1.22) after adjustment for sociodemographic factors and cardiovascular disease. Correlates of vascular surgery hospitalization among patients with depression included admission through emergency route for longer LOS, inpatient mortality, and 30-day hospital readmission.

Patients with depression undergoing vascular surgery have substantially poorer health outcomes. Screening for depression prior to surgery might be indicated to target preventative measures.

No significant relationships.

No significant relationships.

Systemic inflammation has been linked with mood disorder and cognitive impairment. The extent of this relationship remains uncertain, with the effects of serum inflammatory biomarkers compared to genetic predisposition toward inflammation yet to be clearly established.

We investigated the magnitude of associations between C-reactive protein (CRP) measures, lifetime history of bipolar disorder or major depression, and cognitive function (reaction time and visuospatial memory) in 84,268 UK Biobank participants. CRP was measured in serum and a polygenic risk score for CRP was calculated, based on a published genome-wide association study. Multiple regression models adjusted for sociodemographic and clinical confounders.

Increased serum CRP was significantly associated with mood disorder history (Kruskal–Wallis H = 196.06, p < 0.001, η2 = 0.002) but increased polygenic risk for CRP was not (F = 0.668, p = 0.648, η2 < 0.001). Compared to the lowest quintile, the highest serum CRP quintile was significantly associated with both negative and positive differences in cognitive performance (fully adjusted models: reaction time B = −0.030, 95% CI = −0.052, −0.008; visuospatial memory B = 0.066, 95% CI = 0.042, 0.089). More severe mood disorder categories were significantly associated with worse cognitive performance and this was not moderated by serum or genetic CRP level.

In this large cohort study, we found that measured inflammation was associated with mood disorder history, but genetic predisposition to inflammation was not. The association between mood disorder and worse cognitive performance was very small and did not vary by CRP level. The inconsistent relationship between CRP measures and cognitive performance warrants further study.

No significant relationships.

The extent and profiles of heterogeneity in cognitive functioning among participants in clinical trials of antidementia medication are unknown. We aimed to quantify and identify profiles of heterogeneity of cognition in Alzheimer’s disease.

Individual-level participant data were analyzed from five pivotal clinical trials of donepezil for Alzheimer’s disease (N = 2,919). Based on Alzheimer’s Disease Assessment Scale–Cognitive Subscale total scores from baseline up to week 12, a latent class model was used to identify heterogeneous groups. A logistic regression model was used to examine factors associated with group membership. Sensitivity analysis was conducted, restricted to the donepezil, and then the placebo arm.

The latent class model identified three classes labeled as low scorers (i.e., least cognitive impairment; N = 1,666, 76.04%), improvers (N = 27, 1.23%), and high scorers (N = 498, 22.73%). Logistic modeling showed that donepezil compared to placebo was significantly (p < 0.05) positively associated with membership in the improvers class (OR = 6.88, 95% CI = 2.03, 42.95), and negatively with high scorers (OR = 0.79, 95% CI = 0.64, 0.98). Sensitivity analysis restricted to the placebo, then donepezil arms replicated similar heterogeneity patterns.

Our results inform clinicians regarding the extent of heterogeneity in cognitive functioning during treatment and contribute to trial design considerations.

No significant relationships.

The fight against the COVID-19 pandemic seems to encompass a social media debate, possibly resulting in emotional contagion and the need for novel surveillance approaches. In the current study, we aimed to examine the flow and content of tweets, exploring the role of COVID-19 key events on the popular Twitter platform.

Using representative freely available data, we performed a focused, social media-based analysis to capture COVID-19 discussions on Twitter, considering sentiment and longitudinal trends between January 19 and March 3, 2020. Different populations of users were considered. Core discussions were explored measuring tweets’ sentiment, by both computing a polarity compound score with 95% Confidence Interval and using a transformer-based model, pretrained on a large corpus of COVID-19-related Tweets. Context-dependent meaning and emotion-specific features were considered.

We gathered 3,308,476 tweets written in English. Since the first World Health Organization report (January 21), negative sentiment proportion of tweets gradually increased as expected, with amplifications following key events. Sentiment scores were increasingly negative among most active users. Tweets content and flow revealed an ongoing scenario in which the global emergency seems difficult to be emotionally managed, as shown by sentiment trajectories.

Integrating social media like Twitter as essential surveillance tools in the management of the pandemic and its waves might actually represent a novel preventive approach to hinder emotional contagion, disseminating reliable information and nurturing trust. There is the need to monitor and sustain healthy behaviors as well as community supports also via social media-based preventive interventions.

Smart healthcare monitoring allows detecting health conditions using Big Data, namely aggregated data concerning physiological and behavioral parameters. The continuous collection of data from smart-devices performed by the Ecological Momentary Assessment approach represents a promising application of Big Data.

This preliminary study was aimed at developing a research protocol focused on the use of Big Data in evaluating the impact of urban environment, affected by a variety of potentially damaging anthropogenic actions, on illness relapses in Bipolar Disorders (BD).

This pilot study was designed by researchers from Departments of Psychiatry and Engineering (CIRIAF), University of Perugia. Environmental, physiological, and behavioral parameters and smart-devices aimed at collecting Big Data were identified. Subjects aged 18-65, affected by BD in current euthymic state referring to the University/General Hospital of Perugia will be recruited.

Subjects will undergo a baseline visit and three monitoring visits during one year. Wearable devices will be provided for collecting data about environmental and physiological parameters. Behavioral data will be collected through smartphone accelerometers, GPS, and overall smartphone use. Big data will be stored into an online platform that will provide real-time feedback concerning the recorded variables. Clinical information concerning BD relapses will be collected. Machine learning techniques, integrated to deterministic analysis of urban environmental conditions, will be used to create possible predictive models for BD relapses.

The present project could allow the creation of a new operative platform for a better health management system correlating real-time Big Data to specific clinical features of BD.

No significant relationships.

No significant relationships.

The coronavirus disease 2019 (COVID-19) pandemic is a major threat to the public. However, the comprehensive profile of suicidal ideation among the general population has not been systematically investigated in a large sample in the age of COVID-19.

A national online cross-sectional survey was conducted between February 28, 2020 and March 11, 2020 in a representative sample of Chinese adults aged 18 years and older. Suicidal ideation was assessed using item 9 of the Patient Health Questionnaire-9. The prevalence of suicidal ideation and its risk factors was evaluated.

A total of 56,679 participants (27,149 males and 29,530 females) were included. The overall prevalence of suicidal ideation was 16.4%, including 10.9% seldom, 4.1% often, and 1.4% always suicidal ideation. The prevalence of suicidal ideation was higher in males (19.1%) and individuals aged 18–24 years (24.7%) than in females (14.0%) and those aged 45 years and older (11.9%). Suicidal ideation was more prevalent in individuals with suspected or confirmed infection (63.0%), frontline workers (19.2%), and people with pre-existing mental disorders (41.6%). Experience of quarantine, unemployed, and increased psychological stress during the pandemic were associated with an increased risk of suicidal ideation and its severity. However, paying more attention to and gaining a better understanding of COVID-19-related knowledge, especially information about psychological interventions, could reduce the risk.

The estimated prevalence of suicidal ideation among the general population in China during COVID-19 was significant. The findings will be important for improving suicide prevention strategies during COVID-19.

No significant relationships.

Good reading skills are important for appropriate functioning in everyday life, scholastic performance, and acquiring a higher socioeconomic status. We conducted the first systematic review and meta-analysis to quantify possible deficits in specific reading skills in people with a variety of mental illnesses, including personality disorders (PDs).

We performed a systematic search of multiple databases from inception until February 2020 and conducted random-effects meta-analyses.

The search yielded 34 studies with standardized assessments of reading skills in people with one or more mental illnesses. Of these, 19 studies provided data for the meta-analysis. Most studies (k = 27; meta-analysis, k = 17) were in people with schizophrenia and revealed large deficits in phonological processing (Hedge’s g = −0.88, p < 0.00001), comprehension (Hedge’s g = −0.96, p < 0.00001) and reading rate (Hedge’s g = −1.22, p = 0.002), relative to healthy controls; the single-word reading was less affected (Hedge’s g = −0.70, p < 0.00001). A few studies in affective disorders and nonforensic PDs suggested weaker deficits (for all, Hedge’s g < −0.60). In forensic populations with PDs, there was evidence of marked phonological processing (Hedge’s g = −0.85, p < 0.0001) and comprehension deficits (Hedge’s g = −0.95, p = 0.0003).

People with schizophrenia, and possibly forensic PD populations, demonstrate a range of reading skills deficits. Future studies are needed to establish how these deficits directly compare to those seen in developmental or acquired dyslexia and to explore the potential of dyslexia interventions to improve reading skills in these populations.

No significant relationships.

Public health measures to curb SARS-CoV-2 transmission rates may have negative psychosocial consequences in youth. Digital interventions may help to mitigate these effects. We investigated the associations between social isolation, COVID-19-related cognitive preoccupation, worries, and anxiety, objective social risk indicators, and psychological distress, as well as use of, and attitude toward, mobile health (mHealth) interventions in youth.

Data were collected as part of the “Mental Health And Innovation During COVID-19 Survey”—a cross-sectional panel study including a representative sample of individuals aged 16–25 years (N = 666; Mage = 21.3; assessment period: May 5, 2020 to May 16, 2020).

Overall, 38% of youth met criteria for moderate or severe psychological distress. Social isolation worries and anxiety, and objective risk indicators were associated with psychological distress, with evidence of dose–response relationships for some of these associations. For instance, psychological distress was progressively more likely to occur as levels of social isolation increased (reporting “never” as reference group: “occasionally”: adjusted odds ratio [aOR] 9.1, 95% confidence interval [CI] 4.3–19.1, p < 0.001; “often”: aOR 22.2, CI 9.8–50.2, p < 0.001; “very often”: aOR 42.3, CI 14.1–126.8, p < 0.001). There was evidence that psychological distress, worries, and anxiety were associated with a positive attitude toward using mHealth interventions, whereas psychological distress, worries, and anxiety were associated with actual use.

Public health measures during pandemics may be associated with poor mental health outcomes in youth. Evidence-based digital interventions may help mitigate the negative psychosocial impact without risk of viral infection given there is an objective need and subjective demand.