Social Psychiatry and Psychiatric Epidemiology最新文献

Purpose: To test the hypotheses that declining cognitive aging trajectories would increase mortality risk and that predictors of mortality would differ between trajectory groups.

Methods: This is a sub-study of the ZARADEMP project studying dementia and depression in older adults aged 55 years or more, conducted in Zaragoza, Spain, including 2403 cognitively healthy individuals who had completed at least three of the four waves in a 12-year follow-up. The three cognitive trajectories previously identified were based on the Mini-Mental State Examination (MMSE). Mortality information obtained from the city's official population registry was registered up to 6 years after the end of the fourth wave of the study. Cox proportional hazard regression analyses for analyzing the risk of death were performed globally and for each cognitive trajectory.

Results: At follow-up, 42.4% of the participants had died. Individuals in class 2-moderate-stable and in class 3-low-and-declining had a 24% and 96%, respectively, higher risk of mortality than those in class 1-high-to-moderate. Those younger and women showed significant lower risks of death in all the classes. Being single, with diabetes, dependency in basic Activities of Daily Living, ex-drinkers, smokers, and ex-smokers increased the risk in class 2. Hypertension showed a higher risk of death in the high-to-moderate group. In the low-and-declining trajectory, anxiety nearly tripled the risk of death.

Conclusion: Trajectories with cognitive decline are associated with higher mortality, with the risk of death showing a gradient. Predictors of mortality differ by cognitive trajectory; the differences being observed even among the cognitively healthier groups.

Objectives: Social determinants of health (SDoH) are increasingly recognized as fundamental determinants of mental and social well-being. However, the extent to which cumulative, domainal, and individual SDoH shape loneliness trajectories in middle and later life remains unclear.

Methods: This study employed longitudinal data from the China Family Panel Studies (2012-2022). Group-based trajectory modeling (GBTM) was used to classify distinct loneliness trajectories. Logistic regression analyses evaluated the associations between SDoH and trajectory groups. To further explore interrelationships, network analysis was applied to identify central nodes of SDoH.

Results: A total of 11,877 participants were analyzed. GBTM revealed two patterns of loneliness: persistently low and persistently high. Participants with greater cumulative exposure to SDoH (OR = 1.26, 95% CI: 1.22-1.30) were significantly more likely to exhibit persistently high loneliness trajectories. Network analysis identified "residence" and "education level" as the most central nodes.

Discussion: This study underscores the importance of addressing cumulative disadvantages across the life course. Efforts to promote healthy aging should prioritize narrowing urban-rural gaps in social infrastructure and ensuring equitable lifelong learning opportunities, which may mitigate the persistently high loneliness trajectory.

Purpose: We examined courses of distress in US veterans before, during, and after the COVID-19 pandemic and the impact of demographic and environmental characteristics on patterns of distress.

Methods: We used survey data (n = 1064; M_age = 38.85, 49.1% female) from the Longitudinal Investigation of Gender, Health, and Trauma (LIGHT) study. Using data from three time points (pre-pandemic, peri-pandemic, and post-pandemic), participants were classified into one of five groups based on whether they met clinical cut-offs for depression, generalized anxiety, and/or PTSD at a given time point. We then examined demographic and environmental correlates of group membership and used relative importance analyses to identify the strongest correlates of group membership.

Results: For female and younger veterans, distress increased from pre-pandemic to peri-pandemic, decreased from peri-pandemic to post-pandemic, and then returned to pre-pandemic levels two years later. For male and older veterans, there was no change in distress from pre-pandemic to peri-pandemic, an increase in distress from peri-pandemic to post-pandemic, then a return to pre-pandemic distress levels. Most participants were classified as Resistant (60.1%), followed by Persistent (20.5%), Remitted (7.6%), Exacerbated (6.4%), and Resilient (5.5%). Income, community danger, neighborhood cohesion, and post-pandemic related distress were the strongest correlates of persistent distress, the most vulnerable group.

Conclusion: Overall, veterans were resilient in response to the pandemic. However, findings suggest that female and younger veterans were disproportionately impacted. The relationships we identified between environmental factors and persistent distress underscore the need for interventions that address both individual and structural factors that impact distress.

Purpose: Improving population mental health will require greater investment in prevention, yet the level of community support for such efforts is unclear. This study aimed to examine the Australian public's attitudes about the prevention of mental health problems (e.g. depression and anxiety conditions).

Methods: A probability-based panel provider recruited 6,142 Australians aged 16-75 + to complete an online survey. Survey items were developed to assess attitudes about prevention of physical and mental health problems, and support for government action on prevention of mental health problems. Data were weighted to be representative of the Australian adult population.

Results: 80.9% of respondents rated mental health as a top Australian government priority to prevent health problems. 14.6% of respondents thought that mental health problems were not preventable, 24.3% were not sure, and 61% agreed they were preventable, with younger adults being more positive. 79.5% of respondents agreed that the Australian government should play a role in preventing mental health problems in the population and 64.7% thought mental health funding should be spent equally on prevention and treatment. The most important age groups to focus on were adolescence and young adulthood and the most important areas were healthy housing, mental health promotion in schools, and reducing socioeconomic inequality.

Conclusion: There was strong support for prevention of mental health problems as a government priority to improve the health of Australians. Findings can inform where prevention literacy efforts should focus and where government investment in prevention should be targeted to align with community expectations.

Introduction: Personal recovery in mental health services, encouraged by the World Health Organization, has gained significance in research and clinical settings. However, measuring personal recovery remains challenging due to the lack of universally accepted instruments. This study assessed Brief INSPIRE-O's ability to map personal recovery by determining cut-off scores and its ability to detect change in the process of personal recovery.

Method: Data was from the internet-based monitoring system (IMS) at the Mental Health Service, Capital Region of Denmark. Between 2018 and 2020, 8,192 patients with baseline data on Brief INSPIRE-O were included to assess its role in measuring personal recovery. Additionally, for analyses focusing on Brief-INSPIRE-O as a measure of change in personal recovery, we included 2,714 patients with pre- and post-treatment data.

Materials: Brief INSPIRE-O was examined along with well-being (WHO-5) and measures of symptom distress (SCL-10), and functioning (SDS-M).

Results: Scores on all measures improved from pre- to post-treatment, except for functional impairment (SDS-M). Convergent validity was established with symptom distress (SCL-10; r = -0.63) and functioning (SDS-M; r = -0.55). A 10-point change in WHO-5 corresponded to an 18.9-point increase in Brief INSPIRE-O. ROC analysis identified an empirical cutoff of 50 for personal recovery and 8 points for clinically relevant change.

Discussion: The Brief INSPIRE-O demonstrated strong validity and sensitivity to change, supporting its use as a reliable tool for assessing personal recovery and treatment quality in clinical practice. It can be considered a relevant brief patient reported outcome measure to be used in international standards of quality and outcome monitoring.

Purpose: To elucidate the role of community socioeconomic conditions in creating opioid-related risk environments, we assessed community-level socioeconomic measures in association with opioid use disorder (OUD) across a diverse geography.

Methods: We conducted a case-control study using medical records (2012-2020) from a Pennsylvania health system to identify cases of OUD (n = 14,674) and controls (n = 58,696; frequency-matched on age, sex, year, medical record duration). Residential addresses were used to assign community-level measures: community socioeconomic deprivation (CSD), high proportional housing costs (HPHC), population in service occupations (PSO), and community credit score (CCS). Logistic regression analyzed associations of community type (city census tracts [CCT], boroughs, townships) and community socioeconomic features (stratified by community type) with OUD, adjusting for demographics and individual-level socioeconomic status.

Results: CCT or borough (versus township) residence was associated with higher OUD odds. CSD, HPHC, and CCS were associated with OUD across community types; PSO was only associated in CCTs. The highest (versus lowest) level of CSD was associated (odds ratio, 95% CI) with higher OUD odds among individuals in townships (1.18 [1.03, 1.36]), boroughs (1.34 [1.09, 1.63]), and CCTs (1.46 [1.13, 1.88]). "Good" (versus "high fair") CCS was associated with lower odds in townships (0.78 [0.71, 0.86]), boroughs (0.56 [0.41, 0.77]), and CCTs (0.73 [0.44, 1.22]).

Conclusion: Findings indicate poor community socioeconomic conditions are related to higher OUD risk, highlight the value of research regarding opioid-related risk environments, and suggest structural and policy interventions, such as vocational training and rent subsidies, as important for addressing the root causes of OUD.

Background: This study aimed to identify groups of risk perception of illicit drug consumption and associated sociodemographic and mental health factors in Singapore.

Methods: A representative sample of 6,509 Singapore citizens and permanent residents aged 15 to 65 years was randomly selected for participation over 14 months. Information on perceived risk related to illicit drug consumption, and correlates were collected via questionnaires. Data were analysed using latent class analysis to identify risk perception profiles. Logistic regression analyses were performed to determine the sociodemographic and mental health correlates across different risk perception profiles.

Results: Four profiles emerged; high risk perception across all substances and frequencies, low risk perception across all substances and frequencies, low risk perception of cannabis, and low risk perception of occasional illicit drug consumption. About 9.4% (n = 557) reported low risk perception of cannabis. Males (vs. females, RR: 2.15, 95% CI: 1.57-2.94) were more likely to have a low risk perception of cannabis. Those aged 35-65 years (35-49 vs. 15-34, RR: 0.37, 95% CI: 0.25-0.54; 50-65 vs. 15-34, RR: 0.19, 95% CI: 0.10-0.37), with secondary school (vs. degree and above, RR: 0.23, 95% CI: 0.13-0.39) or pre-tertiary (vs. degree and above, RR: 0.42, 95% CI: 0.29-0.61) education were less likely to have a low risk perception of cannabis. Ever-smokers (vs. non-smokers, RR: 1.86, 95% CI: 1.31-2.64), and those grouped as "safe drinking" (vs. abstainers, RR: 3.80, 95% CI: 2.50-5.77) or "hazardous drinking" (vs. abstainers, RR: 7.00, 95% CI: 3.69-13.28) were more likely to perceive low risk of cannabis. Individuals with a low risk perception across all substances and profiles were more likely to report symptoms of anxiety (OR: 7.17, 95% CI: 1.31-38.98) when compared to high risk perception across all substances and frequencies individuals.

Conclusions: These findings provide critical insights for tailoring prevention and education initiatives to address substance use behaviours in Singapore.

Purpose: Research on alcohol use disorder has exclusively focused on either its symptom-level mechanisms-the network perspective or sociodemographic determinants-epidemiological research. Moreover, such research failed to stratify analyses for important person-level factors (e.g., sex or ethnicity). Here, we combine network and epidemiological research and stratify analyses by person-level factors.

Method: Using Bayesian inference, we estimated (1) a logistic regression model predicting past-year alcohol consumption from various sociodemographic factors within a large, multiethnic, urban sample in the Netherlands (complete sample: N = 22,164), (2) a cross-sectional network model of alcohol use symptoms and sociodemographic factors among alcohol drinkers of the same sample (drinkers: N = 10,877), and (3) stratified networks at the sex- and ethnic- levels in the same drinkers subsample.

Results: All of our examined sociodemographic factors predicted past-year alcohol consumption (in order of magnitude: religion, sex, education, employment, perceived ethnic discrimination, and age). Our Bayesian analysis of networks revealed three notable patterns. First, religion was uniquely and negatively related to adverse alcohol use problems (such as having an injury due to drinking). Second, socioeconomic proxies (education and employment) were positively related to binge drinking, but negatively related to its adverse effects (such as 'needing a drink in the morning'). Finally, employment and education were particularly negatively related to alcohol use symptoms within male and female networks, respectively.

Conclusion: Our results suggest that alcohol use symptoms are differentially related to sociodemographic factors and that these effects are moderated by sex and ethnicity. Our highlighted network links and Bayesian methodologies could prove useful for future research and prevention and intervention efforts on alcohol use disorders.Word count: 4198 words.

Purpose: Population-based studies of alcohol-related trends typically collapse across sex while examining race and/or age, limiting understanding of shifts in alcohol involvement at the intersection of sex, race, and age. Therefore, this study evaluated population-level trends in alcohol use and alcohol use disorder (AUD) symptoms as disaggregated within Hispanic, Black, and White female and male U.S. early and middle adults.

Methods: Data were from years 2002 to 2019 of the National Survey on Drug Use and Health, Participants were 18 to 64, Hispanic, Black, or White, and consumed any alcohol. Annualized linear change estimates were computed to assess trends in past-month drinking days and AUD symptoms. Between groups analyses were also conducted to examine (a) sex differences within ethnoracial identity and (b) ethnoracial differences within sex. All analyses were further stratified across early (age 18 to 29) and middle adults (age 30 to 64).

Results: Number of drinking days increased only among Black early adult females and Black middle adult females and decreased for all males except for Black middle adults, with the strongest decrease for Black early adult males. AUD symptoms decreased for all early adult males, and most strongly among Black males. Among middle adults, AUD symptoms decreased only among Hispanic males and increased among White males and females.

Conclusions and relevance: National trends in alcohol use and AUD symptoms are distinct across subpopulations at the intersection of sex, race, and age. Continued disaggregated analyses across heterogeneous U.S. subpopulations are needed to better inform clinical care.

Purpose: Being outside of the labor and education system during young adulthood, a status termed not in education, employment, or training (NEET), is a risk factor for later social and health outcomes. This study examined whether parental substance use (PSU) moderates the relationship between personal alcohol consumption and NEET. Such information may inform screening practices.

Methods: Participants included 2,940 respondents (15-25-year-olds) to a 2014 Danish national survey. In this historical cohort study, survey data were linked with register data on respondents' parents and follow-up register data on respondents' educational/employment status (2015-2018). The Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) identified respondents with hazardous drinking. PSU was measured using survey and register data. Our outcome identified those who were NEET during 1 + years follow-up. Analyses included gender stratified multivariable logistic regressions.

Results: Survey-based PSU was associated with higher odds of NEET (OR = 1.67, p <.001). An AUDIT-C + by survey PSU interaction was statistically significant for females (OR = 2.70, p =.007): AUDIT-C + females with survey PSU had higher odds of NEET than AUDIT-C + females without survey PSU. Results were similar using register PSU. For both genders, other parental factors (unemployment and psychiatric problems) were also associated with higher odds of NEET.

Conclusion: Using family history (including unemployment, psychiatric problems, and substance use) may enhance screening to identify emerging adults at risk of adverse social outcomes. Survey- and register-based PSU were similarly associated with NEET, suggesting that asking emerging adults about their parents' substance use may be as informative as more objective measures of family risk.