| Literature DB >> 35322782 |
Ben Hoi-Ching Wong1, Mehrak Vaezinejad2, Paul L Plener3, Tauseef Mehdi4, Liana Romaniuk5, Elizabeth Barrett6, Haseena Hussain7, Alexandra Lloyd7, Jovanka Tolmac8, Manish Rao2, Sulagna Chakrabarti2, Sara Carucci9, Omer S Moghraby2, Rachel Elvins10, Farah Rozali11, Ereni Skouta11, Fiona McNicholas12, Benjamin Baig2, Dejan Stevanovic13, Peter Nagy14, Chiara Davico15, Hassan Mirza16, Evren Tufan17, Fatima Youssef18, Ben Meadowcroft11, Dennis Ougrin1.
Abstract
BACKGROUND: Lockdown during the pandemic has had significant impacts on public mental health. Previous studies suggest an increase in self-harm and suicide in children and adolescents. There has been little research on the roles of stringent lockdown. AIMS: To investigate the mediating and predictive roles of lockdown policy stringency measures in self-harm and emergency psychiatric presentations.Entities:
Keywords: COVID-19; Self-harm; adolescent; children; lockdown; lockdown stringency; psychiatric emergency; retrospective study
Year: 2022 PMID: 35322782 PMCID: PMC8963968 DOI: 10.1192/bjo.2022.41
Source DB: PubMed Journal: BJPsych Open ISSN: 2056-4724
Fig. 1Hypothesised mediation pathway.
Estimates of stringency effects on self-harm presentations in lockdown (n = 470)
| Proportion (available sample size) | Effect estimates (per ten-unit increment of stringency index) | |||
|---|---|---|---|---|
| OR | 95% CI for OR | |||
| Sociodemographic characteristics | ||||
| Female | 75% (462) | 0.93 | [0.87, 0.996] | 0.039 |
| Male | 23% (462) | 1.08 | [1.01, 1.16] | 0.026 |
| Dominant ethnicity | 74% (375) | 1.07 | [1.00, 1.16] | 0.061 |
| In EET | 88% (306) | 0.97 | [0.87, 1.08] | 0.56 |
| Looked after children | 13.2% (325) | 1.12 | [1.003, 1.25] | 0.044 |
| Parents live together | 42% (235) | 0.95 | [0.88, 1.03] | 0.247 |
| Self-harm characteristics and history | ||||
| Severe self-harm | 19.4% (469) | 1.07 | [0.99, 1.16] | 0.104 |
| Suicidal intent | 55% (435) | 1.01 | [0.95, 1.08] | 0.67 |
| Violent method of self-harm | 7.6% (461) | 1.12 | [0.99, 1.27] | 0.076 |
| Alcohol involved in self-harm | 10.0% (372) | 0.94 | [0.84, 1.05] | 0.287 |
| Drug involved in self-harm | 7.0% (371) | 1.01 | [0.87, 1.17] | 0.91 |
| Social media used to communicate self-harm | 8.0% (302) | 1.02 | [0.88, 1.18] | 0.81 |
| Self-harm history in community | 81% (341) | 0.99 | [0.90, 1.09] | 0.87 |
| Self-harm presentation in previous year | 47% (324) | 1.05 | [0.98, 1.13] | 0.158 |
| Family history of self-harm | 18.4% (196) | 1.01 | [0.89, 1.15] | 0.86 |
| Clinical diagnosis | ||||
| Emotional disorders | 66% (384) | 1.00 | [0.93, 1.08] | 0.99 |
| Behavioural disorders | 14.3% (384) | 1.10 | [0.99, 1.21] | 0.076 |
| Psychotic disorders | 2.60% (384) | N/A | N/A | N/A |
| Eating disorders | 3.65% (384) | N/A | N/A | N/A |
| Neurological disorders | 15.9% (384) | 1.06 | [0.96, 1.16] | 0.241 |
| Substance use disorders | 6.8% (384) | 1.06 | [0.93, 1.20] | 0.393 |
| Somatoform disorders | 2.08% (384) | N/A | N/A | N/A |
| Personality disorders | 14.1% (384) | 0.99 | [0.90, 1.09] | 0.84 |
| Precipitating factor | ||||
| Row with a family member | 37.7% (308) | 0.99 | [0.92, 1.08] | 0.90 |
| Row with a friend | 11.4% (308) | 0.87 | [0.77, 0.99] | 0.029 |
| Row with a boyfriend or girlfriend | 10.1% (308) | 1.13 | [0.99, 1.28] | 0.072 |
| Social isolation | 16.9% (308) | 1.15 | [1.04, 1.27] | 0.008 |
| School pressure | 13.3% (308) | 0.84 | [0.74, 0.94] | 0.003 |
EET, education, employment or training; N/A, not available.
Excluded from analysis owing to low counts of events.
Fig. 2Stringency effect on predicted probability of self-harm presentations from each deprivation decile (1st decile = most deprived, 10th decile = least deprived).