Risk of self-harm after the diagnosis of psychiatric disorders in Hong Kong, 2000–10: a nested case-control study

Abstract

BackgroundPsychiatric disorders are established risk factors for self-harm. However, variation in risk of self-harm by specific psychiatric disorder, stratified by gender and age, is rarely examined using population representative samples. This study aims to investigate the risk of self-harm following the diagnosis of different psychiatric disorders based on inpatient records retrieved from the Hong Kong Clinical Data Analysis and Reporting System (CDARS). MethodA cohort of 86,353 people with a first-recorded diagnosis of depression, alcohol abuse/dependence, personality disorders, bipolar disorders, anxiety disorders, schizophrenia, or substance abuse/dependence, along with 134,857 matched controls, were followed between 2000 and 2010. For each diagnostic category, a Cox proportional hazard regression model was fitted to estimate the adjusted hazard ratio (aHR) (95% confidence intervals) of associated selfharm, adjusting for gender, age, admission time, district of residence, and comorbidities. OutcomesThe personality disorders and substance abuse/dependence groups had the highest self-harm incidences of 3,174 and 3,018 per 100,000 patient-years, respectively. The highest risk of selfharm was found in the substance abuse/dependence group (aHR, 9•6; 95% CI, 8•4-11•0), followed by the groups with personality disorders (3•7; 2•8-4•9) and alcohol abuse/dependence (3•2; 2•9-3•7). When stratified by gender and age, the highest risk was found in substance abuse/dependence group for both genders (female: aHR,

Prevalence and risk factors for repetition of non-fatal self-harm in Hong Kong, 2002–2016: A population-based cohort study

“…All CDARS records are anonymised and thus there is no risk of patient identification. Previous studies have validated the coding accuracy of case ascertainment in the CDARS and reported high positive predictive values (PPV) of clinical diagnoses, including hip fractures (100%), gastrointestinal bleeding (100%), atrial fibrillation (95%) and stroke (91%) [15] , [16] , [17] Data from the CDARS have been used in several earlier epidemiological studies and have proved to be reliable [13 , 18] .…”

“…This was extracted from either inpatient diagnostic information or from poisoning nature code and traumatic type code in A&E data when applicable. According to previous evidence concerning risk factors for index and repeated self-harm, [3 , 18 , 20] psychiatric disorders and physical illnesses of interest were depression and bipolar disorders (ICD-9-CM code: 296 and 311), alcohol misuse (291 and 303), personality disorders (301), anxiety and neurotic disorders (300, 308, and 309), schizophrenia (295), substance misuse (292, 304, and 305), asthma (493), diabetes (250), migraine (346), epilepsy (345), cancer (140–239), dermatitis and eczema (690–693, and 698), and psoriasis (696).…”

Predicting dementia diagnosis from cognitive footprints in electronic health records: a case–control study protocol

“…Data from the CDARS have been used in several earlier epidemiological studies on either the relationship between exposure and health outcomes or disease/medication trends and have proven to be reliable. [40][41][42][43] This case-control study will be nested within the CDARS data from 2001 to 2018.…”

“…The risk factors in this study will be divided into two general groups: established risk factors and exploratory risk factors. The established factors include diabetes mellitus (ICD-9-CM 250), 49 midlife hypertension (401), 49 midlife obesity (278), depression (296.2, 296.3, 300.4 and 311), 43 head injuries (800-804, 850-854 and 959.01) 50 and low education. In CDARS, educational level is recorded in five categories: less than primary, primary, secondary, tertiary education or above, and unknown.…”

Problematic shopping and self-injurious behaviors in adolescents

“…Nixon et al found that hospitalized adolescents with repetitive SIB experienced urges and tension relieved only by SIB, preoccupation with SIB, and difficulty cutting back on SIB despite negative consequences, which are characteristic of addictions (Nixon, Cloutier, & Aggarwal, 2002). SIB has also been found to co-occur with substance use disorders in hospitalized patients, as well as with behavioral addictions like gambling disorder in community samples (Chai et al, 2020;Farhat et al, 2020). In addition to having potentially addictive features related to repetitive engagement, SIB has been recognized as a way to modify negative emotions, cope with negativity, and provide feelings of excitement and exhilaration (Edmondson, Brennan, & House, 2016).…”