518Trajectories of offending in people with psychotic illness and other mental disorders: a population study

Abstract Background Trajectory analysis has been used to study long-term patterns of offending and identify offending groups. Only few studies have explored patterns in people with psychotic illness and these were restricted to adult offenders. This study examines offending trajectories, and identifies risk factors, for people aged 10-26 with psychotic illness (PI) and other mental disorders (OMD) compared to those with no mental disorders (NMD). Methods This is a whole-population record-linkage study of 184,147 people born in Western Australia (WA) 1983-1991 using data from WA psychiatric case register, WA corrective services and other state-wide registers. Group-based trajectory modelling was used to identify offending trajectories. Results Four offender groups were identified for each mental health status (MHS) group: MHS groups had similar offending patterns, however PI had a lower proportion of individuals in the G1 group and later offending onset in the G3 group. Gender, indigenous status, substance use, childhood victimisation and parental offending were risk factors associated with group membership; for PI, childhood victimisation and parental offending were only significant in the G4 group. Conclusions Overall offending patterns and risk factors were similar for all MHS groups, however, some differences were observed for PI. Key messages To reduce offending in this population, interventions need to occur at an early age.

Download Full-text

Developmental Offending Patterns: Female Offending Beyond the Reference Category

Criminal Justice and Behavior ◽

10.1177/0093854820966729 ◽

2020 ◽

pp. 009385482096672

Author(s):

Kelsey Gushue ◽

Evan C. Mccuish ◽

Raymond R. Corrado

Keyword(s):

Risk Factors ◽

Young Women ◽

Theoretical Models ◽

Self Report ◽

Female Adolescents ◽

Offending Trajectories ◽

Individual Family ◽

Female Offending ◽

School Risk Factors ◽

Offending Patterns

Compared with young men, justice-involved young women are often characterized by a greater array of risk factors, yet show a more limited pattern of offending. This paradox may be related to risk factors functioning differently not only for male versus female adolescents but also among female adolescents involved in offending. Data were used on 284 girls from the Incarcerated Serious and Violent Young Offender Study to address whether risk factors varied across different offending trajectories modeled between ages 12 and 23. Risk factors measured from self-report interviews were compared across the three trajectories identified. Individual, family, and school risk factors varied across trajectory groups, but not always in ways anticipated. Female offending does not appear to fit neatly within existing developmental criminology theory. Theoretical models should be adapted, or new models developed, to account for the complexities of female offending patterns.

Download Full-text

Violence-Related Deaths Among People Released From Prison: A Data Linkage Study

Journal of Interpersonal Violence ◽

10.1177/0886260520905546 ◽

2020 ◽

pp. 088626052090554

Author(s):

Melissa Willoughby ◽

Matthew J. Spittal ◽

Rohan Borschmann ◽

Holly Tibble ◽

Stuart A. Kinner

Keyword(s):

Risk Factors ◽

Indigenous People ◽

Cox Regression ◽

Significant Risk ◽

Linkage Study ◽

Community Members ◽

Indigenous Status ◽

Risk Of Death ◽

Standardized Mortality Ratios ◽

Release From Prison

People released from prison are a socially marginalized group and are at high risk of death from preventable causes, including violence. Despite this, little is known about the epidemiology of violence-related death (VRD) after release from prison. This knowledge is essential for developing targeted, evidence-informed violence prevention strategies. We examined VRDs among a representative sample of people released from prisons in Queensland, Australia, by sex and Indigenous status. Correctional records for all people (aged ≥17 years) released from prisons from January 1994 until December 2007 ( N = 41,970) were linked probabilistically with the National Death Index. The primary outcome was VRD following release from prison. We calculated crude mortality rates (CMRs) and standardized mortality ratios (SMRs) standardized by age and sex to the Australian population. We used Cox regression to identify predictors of VRD. Of 2,158 deaths after release from prison, 3% ( n = 68) were violence-related. The SMR for VRD was 10.0 (95% confidence interval (CI): [7.9, 12.7]) and was greatest for women (SMR = 16.3, 95% CI: [8.2, 32.7]). The rate of VRD was 2.5 deaths per 10,000 person-years (95% CI: [2.0, 3.2]) and was highest between 2 and 6 months after release from prison (CMR = 6.3, 95% CI: [3.4, 11.6]). Risk factors for VRD included short sentences (<90 days; for males and non-Indigenous people) and experiencing two or more imprisonments (for non-Indigenous people). No significant risk factors for VRD were identified for women or Indigenous people. People released from prison die from violence at a rate that is greatly elevated compared with the general population, with women experiencing the greatest elevation in risk. Reducing the number of VRDs in this population could improve the health and wellbeing of some of our most marginalized community members.

Download Full-text

The relation between suicidal feelings and mental disorders in the elderly: results from the Berlin Aging Study (BASE)

Psychological Medicine ◽

10.1017/s0033291703008912 ◽

2004 ◽

Vol 34 (4) ◽

pp. 741-746 ◽

Cited By ~ 28

Author(s):

S. BARNOW ◽

M. LINDEN ◽

H.-J. FREYBERGER

Keyword(s):

Risk Factors ◽

Mental Disorders ◽

Physical Health ◽

Female Gender ◽

The Elderly ◽

Subjective Assessment ◽

Population Based ◽

Wish To Die ◽

Death Wishes ◽

Using Data

Background. The purpose of this study was to demonstrate the influence of several risk factors (particularly physical and mental disorders, loneliness and housing conditions) on the wish to die in the elderly.Method. Using data from a population-based sample of 516 senior citizens (70 to 103 years of age) in Berlin (Germany), we compared 54 persons with death wishes with 462 persons without death wishes on several psychosocial risk factors, physical health and psychiatric diagnoses. A logistic regression analysis was also conducted.Results. The data indicate that the wish to die is strongly associated with the presence of a mental disorder, especially major depression, while higher age, female gender, subjective assessment of physical health and negative living conditions were all only moderately related to death wishes.Conclusions. Our results emphasize the need for very careful diagnosis of death wishes in the very old and question the view that it is a normal and understandable phenomenon in older age.

Download Full-text

Risk factors for severity on admission and the disease progression during hospitalisation in a large cohort of patients with COVID-19 in Japan

BMJ Open ◽

10.1136/bmjopen-2020-047007 ◽

2021 ◽

Vol 11 (6) ◽

pp. e047007

Author(s):

Mari Terada ◽

Hiroshi Ohtsu ◽

Sho Saito ◽

Kayoko Hayakawa ◽

Shinya Tsuzuki ◽

...

Keyword(s):

Risk Factors ◽

Mechanical Ventilation ◽

Chronic Respiratory Disease ◽

Major Effect ◽

Secondary Outcome ◽

Healthcare Facilities ◽

Wide Range ◽

Registration Number ◽

Using Data ◽

Obesity Hypertension

ObjectivesTo investigate the risk factors contributing to severity on admission. Additionally, risk factors of worst severity and fatality were studied. Moreover, factors were compared based on three points: early severity, worst severity and fatality.DesignAn observational cohort study using data entered in a Japan nationwide COVID-19 inpatient registry, COVIREGI-JP.SettingAs of 28 September 2020, 10480 cases from 802 facilities have been registered. Participating facilities cover a wide range of hospitals where patients with COVID-19 are admitted in Japan.ParticipantsParticipants who had a positive test result on any applicable SARS-CoV-2 diagnostic tests were admitted to participating healthcare facilities. A total of 3829 cases were identified from 16 January to 31 May 2020, of which 3376 cases were included in this study.Primary and secondary outcome measuresPrimary outcome was severe or nonsevere on admission, determined by the requirement of mechanical ventilation or oxygen therapy, SpO2 or respiratory rate. Secondary outcome was the worst severity during hospitalisation, judged by the requirement of oxygen and/orinvasive mechanical ventilation/extracorporeal membrane oxygenation.ResultsRisk factors for severity on admission were older age, men, cardiovascular disease, chronic respiratory disease, diabetes, obesity and hypertension. Cerebrovascular disease, liver disease, renal disease or dialysis, solid tumour and hyperlipidaemia did not influence severity on admission; however, it influenced worst severity. Fatality rates for obesity, hypertension and hyperlipidaemia were relatively lower.ConclusionsThis study segregated the comorbidities influencing severity and death. It is possible that risk factors for severity on admission, worst severity and fatality are not consistent and may be propelled by different factors. Specifically, while hypertension, hyperlipidaemia and obesity had major effect on worst severity, their impact was mild on fatality in the Japanese population. Some studies contradict our results; therefore, detailed analyses, considering in-hospital treatments, are needed for validation.Trial registration numberUMIN000039873. https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000045453

Download Full-text

Low Birth Weight Prevalence in Children Diagnosed with Neurodevelopmental Disorders in Dubai

Global Pediatric Health ◽

10.1177/2333794x211031782 ◽

2021 ◽

Vol 8 ◽

pp. 2333794X2110317

Author(s):

Faisal A. Nawaz ◽

Meshal A. Sultan

Keyword(s):

Risk Factors ◽

Birth Weight ◽

Mental Disorders ◽

Low Birth Weight ◽

Neurodevelopmental Disorders ◽

Autism Spectrum ◽

High Birth Weight ◽

Perinatal Risk Factors ◽

Perinatal Risk ◽

The Impact

The aim of this study is to evaluate the prevalence of low birth weight and other perinatal risk factors in children diagnosed with neurodevelopmental disorders. This is one of the first studies in the Arabian Gulf region focused on the contribution of these factors toward the development of various disorders such as attention-deficit/hyperactivity disorder, autism spectrum disorder, and other mental disorders. This descriptive study was based on qualitative data analysis. We reviewed retrospective information from the electronic medical records of 692 patients in Dubai, United Arab Emirates. The prevalence of low birth weight in children with mental disorders was significantly higher as compared to the general population (16% vs 6% respectively). Furthermore, other risk factors, including high birth weight and preterm birth were noted to have a significant association with neurodevelopmental disorders. Future research on the impact of perinatal risk factors will contribute to advancement of early intervention guidelines.

Download Full-text

Prevalence of Mental Disorders by Socioeconomic Status in Australia: A Cross-Sectional Epidemiological Study

American Journal of Health Promotion ◽

10.1177/0890117120968656 ◽

2020 ◽

pp. 089011712096865

Author(s):

Rubayyat Hashmi ◽

Khorshed Alam ◽

Jeff Gow ◽

Sonja March

Keyword(s):

Mental Health ◽

Risk Factors ◽

Socioeconomic Status ◽

Mental Disorders ◽

National Health Survey ◽

Cross Sectional ◽

Lower Odds ◽

Socioeconomic Groups ◽

Income Groups ◽

Weighted Prevalence

Purpose: To present the prevalence of 3 broad categories of mental disorder (anxiety-related, affective and other disorders) by socioeconomic status and examine the associated socioeconomic risk factors of mental disorders in Australia. Design: A population-based, cross-sectional national health survey on mental health and its risk factors across Australia. Setting: National Health Survey (NHS), 2017-2018 conducted by the Australian Bureau of Statistics (ABS) Participants: Under aged: 4,945 persons, Adult: 16,370 persons and total: 21,315 persons Measures: Patient-reported mental disorder outcomes Analysis: Weighted prevalence rates by socioeconomic status (equivalised household income, education qualifications, Socio-Economic Index for Areas (SEIFA) scores, labor force status and industry sector where the adult respondent had their main job) were estimated using cross-tabulation. Logistic regression utilizing subsamples of underage and adult age groups were analyzed to test the association between socioeconomic status and mental disorders. Results: Anxiety-related disorders were the most common type of disorders with a weighted prevalence rate of 20.04% (95% CI: 18.49-21.69) for the poorest, 13.85% (95% CI: 12.48-15.35) for the richest and 16.34% (95% CI: 15.7-17) overall. The weighted prevalence rate for mood/affective disorders were 20.19% (95% CI: 18.63-21.84) for the poorest, 9.96% (95% CI: 8.79-11.27) for the richest, and 13.57% (95% CI: 12.99-14.17) overall. Other mental disorders prevalence were for the poorest: 9.07% (95% CI: 7.91-10.39), the richest: 3.83% (95% CI: 3.14-4.66), and overall: 5.93% (95% CI: 5.53-6.36). These patterns are also reflected if all mental disorders were aggregated with the poorest: 30.97% (95% CI: 29.15-32.86), the richest: 19.59% (95% CI: 18.02-21.26), and overall: 23.93% (95% CI: 23.19-24.69). The underage logistic regression model showed significant lower odds for the middle (AOR: 0.75, 95% CI: 0.53 -1.04, p < 0.1), rich (AOR: 0.71, 95% CI: 0.5-0.99, p < 0.05) and richest (AOR: 0.6, 95% CI: 0.41-0.89, p < 0.01) income groups. Similarly, in the adult logistic model, there were significant lower odds for middle (AOR: 0.84, 95% CI: 0.72-0.98, p < 0.05), rich (AOR: 0.73, 95% CI: 0.62-0.86, p < 0.01) and richest (AOR: 0.76, 95% CI: 0.63-0.91, p < 0.01) income groups. Conclusion: The prevalence of mental disorders in Australia varied significantly across socioeconomic groups. Knowledge of different mental health needs in different socioeconomic groups can assist in framing evidence-based health promotion and improve the targeting of health resource allocation strategies.

Download Full-text

Meaning in Life Buffers the Association Between Risk Factors for Suicide and Hopelessness in Participants With Mental Disorders

Journal of Clinical Psychology ◽

10.1002/jclp.22285 ◽

2016 ◽

Vol 72 (7) ◽

pp. 689-700 ◽

Cited By ~ 22

Author(s):

José H. Marco ◽

Sandra Pérez ◽

Joaquín García-Alandete

Keyword(s):

Risk Factors ◽

Mental Disorders ◽

Meaning In Life ◽

Risk Factors For Suicide

Download Full-text

Falls in the general elderly population: A 3- and 6-year follow-up of risk factors using data from the longitudinal population study “good ageing in Skåne”

European Geriatric Medicine ◽

10.1016/j.eurger.2012.07.068 ◽

2012 ◽

Vol 3 ◽

pp. S53 ◽

Cited By ~ 2

Author(s):

M. Stenhagen ◽

H. Ekström ◽

E. Nordell ◽

S. Elmståhl

Keyword(s):

Risk Factors ◽

Population Study ◽

Elderly Population ◽

Using Data ◽

General Elderly Population

Download Full-text

Artificial Intelligence, Social Media and Depression. ‘Patient’ Autonomy Revisited

Studia Universitatis Babeş-Bolyai Bioethica ◽

10.24193/subbbioethica.2021.spiss.87 ◽

2021 ◽

Vol 66 (Special Issue) ◽

pp. 133-133

Author(s):

Regina Mueller ◽

◽

Sebastian Laacke ◽

Georg Schomerus ◽

Sabine Salloch ◽

...

Keyword(s):

Artificial Intelligence ◽

Social Media ◽

Mental Disorders ◽

Patient Autonomy ◽

Ethical Challenges ◽

Traditional Concept ◽

Ethical Implications ◽

Health Related ◽

Twitter Users ◽

Using Data

"Artificial Intelligence (AI) systems are increasingly being developed and various applications are already used in medical practice. This development promises improvements in prediction, diagnostics and treatment decisions. As one example, in the field of psychiatry, AI systems can already successfully detect markers of mental disorders such as depression. By using data from social media (e.g. Instagram or Twitter), users who are at risk of mental disorders can be identified. This potential of AI-based depression detectors (AIDD) opens chances, such as quick and inexpensive diagnoses, but also leads to ethical challenges especially regarding users’ autonomy. The focus of the presentation is on autonomy-related ethical implications of AI systems using social media data to identify users with a high risk of suffering from depression. First, technical examples and potential usage scenarios of AIDD are introduced. Second, it is demonstrated that the traditional concept of patient autonomy according to Beauchamp and Childress does not fully account for the ethical implications associated with AIDD. Third, an extended concept of “Health-Related Digital Autonomy” (HRDA) is presented. Conceptual aspects and normative criteria of HRDA are discussed. As a result, HRDA covers the elusive area between social media users and patients. "

Download Full-text