Abstract Aims Workforce counts and early-readmission indicators often appear side by side in health-system dashboards, encouraging the assumption that more staff should accompany fewer returns to hospital. We examined whether annual variation in previous-year medical-nursing headcount density was associated with Italy’s official provider-side indicators of unplanned readmission within 30 days after eligible ordinary psychiatric discharges from public psychiatric wards and, secondarily, within 7 days. Methods Using national mental-health, hospital-discharge and population data, we assembled a retrospective 2015–2024 panel of 21 Italian territorial units (210 unit-years). Exposure was medical and nursing headcount at the end of the preceding year per 100,000 adults. We modelled 30-day readmission counts using Poisson pseudo-maximum likelihood (PPML) with an eligible-discharge offset and territorial-unit and year fixed effects. Primary inference used a restricted wild cluster score bootstrap with 9,999 Rademacher draws and 21 clusters. Secondary and sensitivity analyses were prespecified, source blanks were not imputed, and analytical materials and scripts are openly archived. Results Outcome counts and eligible-discharge denominators were complete for all 210 observations; exposure was available for 205. The incidence rate ratio (IRR) per 10 additional medical-nursing staff per 100,000 adults was 1.006 (95% CI 0.934–1.034; p = 0.8095), compatible with relative changes from a 6.6% reduction to a 3.4% increase. Across linear PPML sensitivity analyses, IRRs ranged from 0.995 to 1.042, with all confidence intervals including 1. Across 31 leave-one-unit or leave-one-year-out re-estimates, IRRs ranged from 0.974 to 1.044, and every confidence interval included 1. The nonlinearity test gave p = 0.8572. The 7-day IRR was 1.032 (95% CI 0.929–1.075; p = 0.6743), while the weighted linear model estimated +0.215 percentage points (95% CI −1.159 to +0.704; p = 0.7203). Conclusions The data did not provide evidence of a detectable longitudinal ecological association between year-to-year variation in medical-nursing headcount density and the official 30-day readmission indicator. This does not imply that workforce is unimportant: headcount does not capture full-time-equivalent availability, deployment or transition processes, and the provider-side design cannot support patient-level or causal inference. Territorial policy should combine adequate resources with a whole-system view of their deployment, post-discharge organisation, and the social, economic and health context in which care is delivered.
Abstract Aims Post-traumatic stress symptoms (PTSS) are a substantial yet underrecognised mental health concern in oncology. Large-scale epidemiological data across diverse cancer types are scarce. This study aimed to estimate the prevalence of post-traumatic stress disorder (PTSD) and to identify socio-demographic and psychosocial correlates in a large, unselected outpatient cohort. Methods In this cross-sectional study, 4,103 adult outpatients with solid tumours or haematologic malignancies were screened during routine care using the PTSS-8-Cancer scale. Socio-demographic, clinical and psychosocial variables were collected concurrently. Associations between screening results and patient characteristics were examined using correlational and chi-square analyses. Results Overall, 32.5% screened positive for probable PTSD. Prevalence was highest in breast cancer (39.7%), female genital cancers (37.2%) and head and neck cancers (36.1%). A positive screening for PTSD was more common among women, younger patients, those with lower income, without a partner and with a history of psychiatric illness. While these associations were statistically significant, effect sizes were generally small. Conclusions One in three cancer outpatients experience substantial PTSS, with elevated prevalence in certain diagnostic groups. Small effect sizes for socio-demographic predictors underscore that psychological distress is broadly distributed. Findings support universal psychosocial screening and integration of trauma-informed care into oncology services to address a significant, population-level mental health burden.
Abstract Aims Sleep disturbances are linked to psychiatric disorders, and obesity is associated with a higher risk of several psychiatric disorders. However, evidence is lacking on how a composite sleep score relates to psychiatric disorder risk in preclinical obesity and on the proteomic pathways that mediate this relationship. Our study aimed to investigate the association of composite sleep score and the risk of multiple specific psychiatric disorders in participants with preclinical obesity. Methods The prospective cohort study from the UK Biobank included 91,380 participants with preclinical obesity. Six groups were categorized according to sleep scores. Hazard ratios (HRs) for the incidence risk of psychiatric disorders were estimated using multiple Cox proportional hazards regression analysis. Mediation analysis of plasma proteomics in this association was conducted in a subset of participants (n = 9,486). Results After a mean follow-up period of 12.9 years, a total of 8,992 incident cases of psychiatric disorders were observed. Using Group 1 as the reference, the risk of psychiatric disorders decreased significantly across Groups 2–6 with increasing sleep score, reaching the lowest in Group 6 (fully adjusted HR = 0.47, 95% CI: 0.33–0.67). Analyses of specific psychiatric disorders revealed that the inverse associations remained significant after full adjustment, with the most notable effect observed for attention deficit hyperactivity disorder (HR = 0.24, 95% CI: 0.08–0.72). Mediation analysis further identified 16 plasma proteins that exerted significant partial mediating effects in this association. Conclusions Our findings underscore that better sleep quality is associated with a lower risk of psychiatric disorders among individuals with preclinical obesity. The identification of proteomic mediators may provide further insight into the underlying biological mechanisms, highlighting the clinical significance of integrating sleep-related assessment and intervention.
Abstract Aims Mental and physical disorders are leading contributors to global disability. However, estimates of their health-related burden often overlook the pervasive comorbidity among conditions. This study aimed to quantify the independent and combined effects of these disorders on self-rated health in nationally representative Chinese adults, explicitly accounting for comorbidity. Methods Data were drawn from the China Mental Health Survey (CMHS), a cross-sectional nationally representative survey. Mental disorders were assessed using the Composite International Diagnostic Interview 3.0, and physical disorders were measured through structured self-report. Health-related burden was assessed using a 0–100 visual analogue scale (VAS) of self-rated health over the past 30 days. Nested regression models examined disorder-specific and multimorbidity effects, followed by interaction-based counterfactual simulations to estimate disorder-specific burden net of comorbidity at individual and population levels. Results Among 27,505 respondents included in multivariate analyses, comorbidity was highly prevalent. In crude models, all 20 disorders were associated with lower VAS scores, with the largest decrements observed for mental disorders such as generalized anxiety disorder (GAD), bipolar disorder and major depressive disorder (MDD). After accounting for disorder type and comorbidity structure, many associations were attenuated and the independent effect of disorder count became non-significant. Counterfactual simulations showed the largest individual-level burdens for GAD (–14.71), cancer (–9.02), bipolar disorder (–8.59) and MDD (–8.53). At the population level, insomnia symptoms (–1.56), arthritis (–0.81) and cardiovascular disorders (–0.64) contributed the greatest burden. Conclusions Accounting for comorbidity substantially alters estimates of disorder-specific health burden. Mental disorders impose the greatest impairment at the individual level, whereas prevalent physical conditions dominate population burden.
