The COVID-19 pandemic and associated lockdowns were predicted to have a major impact on suicidal behaviour, including self-harm. However, current studies have produced contradictory findings with limited trend data.

Nine years of linked individual-level administrative data were utilised to examine changes in hospital-presenting self-harm and ideation (thoughts of self-harm or suicide) before and during the pandemic.

National self-harm registry data were linked to demographic and socioeconomic indicators from healthcare registration records (n = 1 899 437). Monthly presentations of self-harm or ideation were split (pre-COVID-19 restrictions: April 2012 to February 2020; and during restrictions: March to September 2020). Auto-regressive integrated moving average (ARIMA) models were trained in R taking into consideration trends and seasonal effects. Forecast (‘expected’) monthly values were compared with ‘actual’ values, stratified by demographic factors and method of harm.

The number of individuals presenting with self-harm or ideation dropped significantly at the beginning of the pandemic (March–May 2020), before returning mostly to expected trends from June 2020. Stratified analysis showed similar presentation trends across most demographic subgroups except for those aged over 65 years, living alone or in affluent areas, where presentations remained unaffected, and those aged under 16 years, where numbers presenting with self-harm or ideation increased above expected levels.

Although population trends show an overall drop in presentations before a return to ‘normal’ from June 2020, the demographic profile of those presenting with self-harm or ideation varied significantly, with increases in children under the age of 16 years. This highlights important potential target groups who may have been most negatively affected by the pandemic.

The COVID-19 pandemic and associated lockdowns were predicted to have a major impact on mental health, however current studies have produced contradictory findings with limited longitudinal data.

Nine years of linked, individual-level administrative data were used to examine changes in psychotropic medication uptake before and during the pandemic.

Medication data from a population-wide prescribing database were linked to demographic and socioeconomic indicators from healthcare registration records (n = 1 801 860). Monthly prescription uptake was split (pre-restrictions: January 2012 to February 2020 and during restrictions: March to October 2020). Auto regressive integrated moving average (ARIMA) models were trained in R taking into consideration trends and seasonal effects. Forecast (‘expected’) monthly values were compared with ‘actual’ values, stratified by demographic factors.

Over the study period 38.5% of the study population were in receipt of ≥1 psychotropic medication. Uptake of these medications have been following a strong upward trend since January 2012. In March 2020 uptake of all medications increased beyond expected values, returning to expected trends from May 2020 for antidepressants, anxiolytics and antipsychotics. In the 8 months during restrictions uptake of hypnotic medication was 12% higher than expected among those <18 years, and anxiolytic medication higher than expected in those >65 years.

Results suggest an initial ‘stockpiling’ of medications in March 2020 before trends mostly returned to expected levels. The anticipated tsunami of mental ill health is not yet manifest in psychotropic medication uptake. There are indications of increased anxiety and sleep difficulties in some subgroups, although these conditions may resolve as we emerge from the pandemic without need for psychiatric intervention.

Although non-suicidal self-injury (NSSI) is an issue of major concern to colleges worldwide, we lack detailed information about the epidemiology of NSSI among college students. The objectives of this study were to present the first cross-national data on the prevalence of NSSI and NSSI disorder among first-year college students and its association with mental disorders.

Data come from a survey of the entering class in 24 colleges across nine countries participating in the World Mental Health International College Student (WMH-ICS) initiative assessed in web-based self-report surveys (20 842 first-year students). Using retrospective age-of-onset reports, we investigated time-ordered associations between NSSI and Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-IV) mood (major depressive and bipolar disorder), anxiety (generalized anxiety and panic disorder), and substance use disorders (alcohol and drug use disorder).

NSSI lifetime and 12-month prevalence were 17.7% and 8.4%. A positive screen of 12-month DSM-5 NSSI disorder was 2.3%. Of those with lifetime NSSI, 59.6% met the criteria for at least one mental disorder. Temporally primary lifetime mental disorders predicted subsequent onset of NSSI [median odds ratio (OR) 2.4], but these primary lifetime disorders did not consistently predict 12-month NSSI among respondents with lifetime NSSI. Conversely, even after controlling for pre-existing mental disorders, NSSI consistently predicted later onset of mental disorders (median OR 1.8) as well as 12-month persistence of mental disorders among students with a generalized anxiety disorder (OR 1.6) and bipolar disorder (OR 4.6).

NSSI is common among first-year college students and is a behavioral marker of various common mental disorders.

There is a substantial proportion of patients who drop out of treatment before they receive minimally adequate care. They tend to have worse health outcomes than those who complete treatment. Our main goal is to describe the frequency and determinants of dropout from treatment for mental disorders in low-, middle-, and high-income countries.

Respondents from 13 low- or middle-income countries (N = 60 224) and 15 in high-income countries (N = 77 303) were screened for mental and substance use disorders. Cross-tabulations were used to examine the distribution of treatment and dropout rates for those who screened positive. The timing of dropout was examined using Kaplan–Meier curves. Predictors of dropout were examined with survival analysis using a logistic link function.

Dropout rates are high, both in high-income (30%) and low/middle-income (45%) countries. Dropout mostly occurs during the first two visits. It is higher in general medical rather than in specialist settings (nearly 60% v. 20% in lower income settings). It is also higher for mild and moderate than for severe presentations. The lack of financial protection for mental health services is associated with overall increased dropout from care.

Extending financial protection and coverage for mental disorders may reduce dropout. Efficiency can be improved by managing the milder clinical presentations at the entry point to the mental health system, providing adequate training, support and specialist supervision for non-specialists, and streamlining referral to psychiatrists for more severe cases.