Long-term trends in age at psychiatric admission (2006–2022): influence of sex, diagnosis, and admission type in a single Italian catchment

Long-term trends in age at psychiatric admission (2006–2022): influence of sex, diagnosis, and admission type in a single Italian catchment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Long-term trends in age at psychiatric admission (2006–2022): influence of sex, diagnosis, and admission type in a single Italian catchment Tommaso Barlattani, Federico Salfi, Valentina Socci, Giulio Renzi, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8809812/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Aims Following Italy’s psychiatric reform, national inpatient numbers declined, but how the age at admission has changed across sex, diagnostic, and admission-type subgroups remains unclear. This study examined 17 consecutive years (2006–2022) of psychiatric admissions to the sole ward serving L’Aquila, assessing temporal trends in age at admission by diagnosis, sex, and admission status (voluntary vs compulsory). Methods All adult admissions (≥ 18 years) were extracted from hospital discharge records (Schede di Dimissione Ospedaliera, SDO). Analyses were conducted at the admission-episode level. Primary diagnoses were grouped into four ICD-9 categories: schizophrenia spectrum, major depressive disorder, bipolar disorder, and alcohol/substance use disorder. Multiple linear models tested time-related changes in mean age at admission, including interactions for time × diagnosis × sex × admission type. Results Across 5,207 admission episodes, the Trimester × Diagnosis interaction showed a marked decline in age at admission for major depression ( B = − 0.20 per trimester, p < 0.001) and bipolar disorder ( B = − 0.09, p = 0.03), equivalent to approximately − 0.80 and − 0.36 years per year, yielding total reductions of ~ 13.6 and ~ 6.1 years over 2006–2022. The Trimester × Diagnosis × Sex interaction indicated that the decline in depression was driven by men ( B = − 0.32, p < 0.001; ≈ −1.28 years/year; −21.76 over 17 years), while in bipolar disorder it was driven by women ( B = − 0.14, p = 0.03; ≈ −0.56 years/year; −9.52 over 17 years). The Trimester × Admission type interaction showed the reduction was specific to voluntary admissions ( B = − 0.10, p < 0.001; ≈ −0.40 years/year), while compulsory admissions were stable ( B = − 0.01, p = 0.87). No significant age change occurred in schizophrenia spectrum disorders or in alcohol/substance use disorders. Conclusions Between 2006 and 2022, age at admission declined steadily among voluntary affective-disorder cases—especially men with depression and women with bipolar disorder—while remaining stable among compulsory admissions, in schizophrenia spectrum and alcohol/substance use disorder admissions. Findings are compatible with a progressive rejuvenation of voluntary affective-disorder inpatient admissions, possibly reflecting earlier self-initiated help-seeking and/or evolving referral pathways, while underscoring the need to balance youth-focused outreach with adequate capacity for chronic psychosis and substance use disorder. Age at admission Temporal trends Psychiatric epidemiology Early intervention Figures Figure 1 Figure 2 Figure 3 Introduction The Italian psychiatric reform (Law 180) closed asylums and relocated acute psychiatric beds to general hospital wards, establishing a community-oriented model that reduced institutionalization while preserving an inpatient safety net for clinical crises (de Girolamo & Cozza, 2000 ; de Girolamo et al., 2007 ; Thompson et al., 2004 ; Amaddeo et al., 2012 ; Barbui et al., 2018 ). While national hospitalization rates have largely stabilized since the mid-2000s, far less is known about how age at admission has evolved within local catchments. International and Italian evidence suggests that reorganization of services and early-intervention models may shift the demographic composition of inpatient cohorts toward younger, help-seeking populations, particularly for affective disorders (Allison et al., 2014 ; Birchwood et al., 2013 ; Kirkbride et al., 2017 ; Pellegrino et al., 2025 ; McGorry & Mei, 2018 ; Davey & McGorry, 2019 ; Vieta et al., 2018 ). In Italy, early post-reform investigations of general-hospital psychiatric wards already described age-at-first-admission patterns and sex differences, indicating that women tend to be older than men at first hospital contact and that local system configuration shapes the profiles reaching inpatient care (de Girolamo et al., 1988a ; de Girolamo et al., 1988b ). Multicenter surveys then provided concrete age benchmarks for acute wards: the PERSEO study across 62 SPDCs reported a mean age at admission in the mid-forties with women older than men, and complementary national work on first-ever inpatients found a median admission age around 40 years, consistent with a younger profile at incident admission compared with the prevalent inpatient population (Ballerini et al., 2007b ; Guzzetta et al., 2010 ). Globally, most mental disorders begin before age 25, as shown by large-scale epidemiological studies (Kessler et al., 2007 ). This makes earlier detection particularly plausible for conditions characterized by preserved insight, such as major depression and bipolar disorder. Conversely, disorders marked by impaired insight—such as psychotic disorders—tend to present in late adolescence or early adulthood; meta-analytic data indicate their median age at first hospitalization is around 24–25 years, reflecting relatively stable age-at-admission distributions across cohorts (Solmi et al., 2022 ). Substance use disorder–related hospital contacts may show heterogeneous, age-band–specific changes rather than a uniform linear shift in mean age; for example, an analysis of hospital discharge registries from the Abruzzo region (2006–2015) reported differential temporal patterns across age ranges in drug-dependence hospitalizations (Di Giovanni et al., 2020). Recent observational evidence—albeit in younger settings—likewise reports no age shift for schizophrenia but declining age for affective disorders, consistent with diagnosis-specific trajectories in help-seeking and referral (Zheng et al., 2023 ). In the present study, we analyzed seventeen consecutive years (2006–2022) of admissions to the sole 15-bed psychiatric ward serving L’Aquila, focusing exclusively on temporal trends in age at admission by diagnosis, sex, and admission status (voluntary vs compulsory), to provide high-resolution local evidence for service planning. Methods Participants and data extraction We retrieved all discharge summaries (Scheda di Dimissione Ospedaliera, SDO) from the psychiatric ward of the San Salvatore Hospital in L’Aquila (Italy) covering the period 2006–2022. The data extraction, quality checks, and analytic workflow followed the approach described in our previous report on admission patterns in the same ward (Barlattani et al., 2024 ). Hospital discharge records included sociodemographic information, admission and discharge dates, and the primary diagnosis at discharge, coded according to the International Classification of Diseases, Ninth Revision (ICD-9). ICD-9 codes used to define each diagnostic category are provided in Supplementary Table S2 . ICD-9 was employed because the regional health information system of Abruzzo is still based on this classification, which is used across the electronic medical record systems, archives, and discharge documentation. To preserve privacy, all patient identifiers were anonymized prior to analysis. Because discharge records were anonymized and did not include a stable unique patient identifier for longitudinal linkage, the unit of analysis was the admission episode. Repeated admissions from the same individual could not be identified and were therefore analysed as separate episodes. Sex was extracted from administrative hospital discharge records as recorded at admission (male/female). After verifying data integrity, completeness, and internal consistency, all variables were checked for missing or inconsistent values. Episodes with missing data on key variables (age, trimester, primary diagnosis, or admission type) were excluded via complete-case analysis (n = 503), yielding a final analytic sample of 5,207 admission episodes. Participants were grouped into four diagnostic categories, based on the ICD-9 discharge diagnosis: schizophrenia spectrum disorders, major depressive disorder, bipolar disorder, and alcohol/substance use disorder. Statistical analysis All analyses were performed in R (version 4.1.2, R Foundation for Statistical Computing, Vienna, Austria). The date of admission was used to assign each hospitalization to its corresponding trimester, resulting in 68 consecutive quarters covering the entire 17-year observation window (2006–2022). The quarter variable was mean-centred to facilitate interpretation of interaction terms and reduce multicollinearity; the centring constant was retained to reconstruct the original time scale for graphical representation. The resulting variable was then mean-centered to facilitate interpretation of interaction terms and reduce multicollinearity; the centering constant was retained to reconstruct the original temporal scale for graphical representation. Descriptive statistics were computed to summarize demographic and clinical characteristics across diagnostic groups. Continuous variables (e.g., age) are reported as mean ± standard deviation (SD), while categorical variables (sex: male/female, admission type: voluntary/compulsory) are reported as absolute and relative frequencies (N, %). To assess whether age at admission varied as a function of time, diagnosis, sex, and admission type, we fitted a multiple linear model including all possible interactions up to the four-way level (time × diagnosis × sex × admission type). Sum-to-zero contrasts were applied to categorical factors, and Type-III ANOVA tables were computed using the car package. In the presence of significant main effects or interactions, simple slope analyses were conducted ( emmeans , jtools ) to clarify the source of effects. All statistical tests were two-tailed, with the significance threshold set at α = 0.05. Model assumptions and diagnostics were formally evaluated. Residual autocorrelation was not detected (Durbin–Watson = 1.97, p = 0.16). A Breusch–Pagan test indicated heteroskedasticity (BP = 63.51, p < 0.001). The residual distribution deviated from normality on a large-N Shapiro–Wilk test (W = 0.99, p 5,000 observations due to the asymptotic normality of estimators. Influence diagnostics (Cook’s distance and leverage–residual plots) did not reveal influential observations of concern. Multicollinearity was modest: for all predictors and interactions, GVIF¹ᐟ²ᴰᶠ ≤ 2.07, well below conventional alert thresholds. To address the detected heteroskedasticity, all tests were re-estimated with heteroskedasticity-consistent standard errors (HC3). The pattern of statistical significance remained unchanged across main effects and interactions. Given the stability of results using robust estimators, the absence of autocorrelation, the lack of influential outliers, and the need for full compatibility with the subsequent simple-effects decomposition, we report OLS estimates with conventional standard errors in the main text, while providing robust (HC3) results as a supplementary robustness check (see Supplementary Table S1 ). Results A total of 5,710 discharge records were retrieved; 503 episodes were excluded due to missing data on age, trimester, diagnosis, or admission type, resulting in 5,207 admission episodes included in the analyses. Demographic and admission characteristics of the overall sample are reported in Table 1 , while model results are summarized in Table 2 . Table 1. Demographic and admission characteristics of psychiatric admission episodes recorded between 2006 and 2022, stratified by discharge diagnosis. Sex N (%) Admission type N (%) Diagnosis Age mean ± SD Male Female Compulsory Voluntary Schizophrenia spectrum 44.2 ± 14.47 1649 (57.4) 1224 (42.6) 493 (17.16) 2380 (82.84) Major depression 49.57 ± 16.29 408 (44.88) 501 (55.12) 116 (12.76) 793 (87.24) Bipolar disorder 47.23 ± 15.08 468 (49.63) 475 (50.37) 145 (15.38) 798 (84.62) Alcohol/substance use disorder 43.72 ± 14 324 (67.22) 158 (32.78) 60 (12.45) 422 (87.55) Total 45.64 ± 15.02 2849 (54.71) 2358 (45.29) 814 (15.63) 4393 (84.37) Table 2. Results for the linear model predicting age at admission as a function of trimester, diagnosis, sex, and admission type, including all possible interactions. Effect F -value p -value Trimester 5.35 0.02 Diagnosis 14.75 < 0.001 Sex 7.74 0.005 Admission type 6.66 0.01 Trimester × Diagnosis 7.16 < 0.001 Trimester × Sex 0.30 0.59 Diagnosis × Sex 1.59 0.19 Trimester × Admission type 4.01 0.04 Diagnosis × Admission type 0.55 0.65 Sex × Admission type 1.45 0.23 Trimester × Diagnosis × Sex 2.77 0.04 Trimester × Diagnosis × Admission type 0.96 0.41 Trimester × Sex × Admission type 2.06 0.15 Diagnosis × Sex × Admission type 0.56 0.64 Trimester × Diagnosis × Sex × Admission type 0.72 0.54 Notes: Significant main effects and interactions are highlighted in bold. The model revealed significant main effects of Trimester, Diagnosis, Sex, and Admission type. Age at admission therefore varied across diagnostic groups, sexes, and admission types, and showed a temporal reduction over the 17‑year period. Among two‑way interactions, Trimester × Diagnosis and Trimester × Admission type was significant, indicating that temporal variations in admission age differed across diagnostic and admission typology subgroups. Moreover, a significant three‑way interaction (Trimester × Diagnosis × Sex) indicated that the differential temporal trends in admission age across diagnostic categories were further modulated by patients’ sex. No higher‑order interactions reached significance ( p ≥ 0.15). Simple slope analyses on Trimester × Diagnosis interaction ( Figure 1 ) showed that mean age at admission significantly decreased across years for inpatient admissions with major depression ( B = −0.20, p < 0.001) and bipolar disorder ( B = −0.09, p = 0.03), while remaining stable for schizophrenia spectrum ( B = 0.02, p = 0.26) and alcohol/substance use disorders ( B = 0.06, p = 0.33). These results indicate a progressive reduction in the average age of affective‑disorder admissions, corresponding to an estimated decrease of approximately 0.80 years per year for depression and 0.36 years per year for bipolar disorder. Over the entire observation window (2006–2022), this translates into a total reduction of about 13.6 years in admission age for depression and 6.1 years for bipolar disorder. Figure 1. Predicted admission age trajectories by diagnosis. Notes: Lines represent model-estimated marginal means, and shaded areas indicate the standard error of the mean. Asterisks denote significant temporal effects for the corresponding color-coded group (* p < 0.05, *** p < 0.001) The Trimester × Diagnosis × Sex interaction ( Figure 2 ) clarified that these temporal effects were sex‑specific: in major depression, the decline in admission age was mainly driven by male patients ( B = −0.32, p < 0.001), whereas in bipolar disorder the decreasing trend was primarily explained by female admissions ( B = −0.14, p = 0.03). These slopes correspond to a mean annual decrease of 1.28 years per year for men with depression and 0.56 years per year for women with bipolar disorder, amounting to a total reduction of 21.76 years and 9.52 years, respectively, over the 17‑year observation period. No significant changes over time were observed in the opposite sex within each diagnostic group, nor for schizophrenia or alcohol/substance use disorders (all p ≥ 0.10). Figure 2. Predicted admission age trajectories by diagnosis and sex. Notes: Lines represent model-estimated marginal means, and shaded areas indicate the standard error of the mean. Asterisks denote significant temporal effects for the corresponding color-coded group (* p < 0.05, *** p < 0.001) Finally, the Trimester × Admission type interaction ( Figure 3 ) revealed that the overall reduction in age at admission was specific to voluntary hospitalizations ( B = −0.10, p < 0.001), whereas compulsory admissions remained stable across the observation period ( B = −0.01, p = 0.87). This corresponds to a mean annual decrease of 0.40 years per year among voluntary admissions, totaling about 6.80 years over 2006–2022. Taken together, these results indicate a gradual younger age profile of voluntary affective-disorder admissions, especially among men with major depression and women with bipolar disorder, whereas schizophrenia spectrum and alcohol/substance use disorder admissions remained stable over time. Figure 3. Predicted admission age trajectories by admission type. Notes: Lines represent model-estimated marginal means, and shaded areas indicate the standard error of the mean. Asterisks denote significant temporal effects for the corresponding color-coded group (* p < 0.05, *** p < 0.001) Discussion This seventeen-year single-catchment analysis demonstrates a clear temporal shift in age at psychiatric admission. Age at admission declined steadily between 2006 and 2022, entirely driven by voluntary admissions and most marked among men with major depression and women with bipolar disorder, whereas compulsory, psychotic-disorder and alcohol/substance use disorder admissions remained stable. Set against Italian post-reform benchmarks, these patterns are expected. In the national PERSEO survey across 62 SPDCs, the mean age at admission was in the mid-forties and women were older than men, offering a pragmatic reference for sex differences at entry to inpatient care; national data on first-ever inpatients reported a median admission age around 40 years, consistent with a younger profile at incident admission compared with the prevalent inpatient population (Ballerini et al., 2007a ; Guzzetta et al., 2010 ). Early post-reform ward studies had already documented age-at-first-admission patterns and sex differences in neighboring catchments with different service configurations (de Girolamo et al., 1988a ; de Girolamo et al., 1988b ). Within this context, the decline in age for affective disorders likely reflects earlier recognition and help seeking among younger adults, consistent with rising mental health literacy and reduced stigma (Jorm, 2000 ; Clement et al., 2015 ), and the diffusion of community and early intervention pathways (McGorry & Mei, 2018 ; Davey & McGorry, 2019 ; Birchwood et al., 2013 ; Kirkbride et al., 2017 ; Ruggeri et al., 2015 ). The fact that the reduction is confined to voluntary admissions possibly indicates a process driven by awareness and autonomous engagement, rather than by coercive thresholds. (Gulliver et al., 2010 ; Bindman et al., 2005 ). By contrast, the stability of age at admission for schizophrenia accords with impaired insight and crisis-driven presentations that more often culminate in compulsory admission (Beaglehole et al., 2021 ; Ferracuti et al., 2021 ); in this scenario, stable age patterns are expected, as also suggested by cohort evidence reporting no age shift for schizophrenia while affective disorders show decreasing age (Zheng et al., 2023 ). Italian studies on coercion and psychosis further support a persistent, crisis-driven pathway for schizophrenia (Oliva et al., 2019 ; Donisi et al., 2016; Montemagni et al., 2011 ). Sex-specific effects strengthen this reading. Among men with depression, the steep decline suggests an erosion of traditional barriers to seeking help; among women with bipolar disorder, a moderate but significant reduction points to improved recognition and timelier intervention (Vieta et al., 2018 )—both consistent with known gender differences in help-seeking (Addis & Mahalik, 2003 ; Pattyn et al., 2015 ; Vigod et al., 2016 ) and with the PERSEO observation that women present older at admission on average (Ballerini et al., 2007a ). Importantly, age at admission for alcohol/substance use disorders remained stable across the observation window. This net stability may reflect the coexistence of different cohort dynamics—such as an ageing segment of long-term substance users alongside increasing visibility of alcohol- and drug-related problems in younger groups—that can offset each other when admissions are analyzed in aggregate. Evidence on age- and cohort-specific trends in substance-use admissions and on changing alcohol-attributable morbidity supports the plausibility of these counterbalancing processes (Chhatre et al., 2017 ; Kraus et al., 2024 ; Green et al., 2007 ; Kelly & Daley, 2013 ). Consistent with this interpretation, a discharge-registry study from the Abruzzo region documented age-range–specific temporal changes in drug-dependence hospitalizations, supporting the possibility that aggregate stability in mean age may coexist with divergent trends within age strata (Di Giovanni et al., 2020). In addition, cross-national data suggest progressive convergence of drinking behaviors between genders during adolescence, which may contribute to a more stable overall age distribution over time when both sexes are considered (Kuntsche et al., 2011 ; Green et al., 2007 ; Kelly & Daley, 2013 ) Moreover, the absence of a temporal shift in mean admission age for alcohol/substance use disorders should not be equated with demographic stagnation. This broad category likely aggregates heterogeneous pathways (e.g., alcohol vs other substances; primary SUD vs dual diagnosis), in which opposite cohort dynamics may offset each other at the level of the mean. In addition, within the Italian service configuration, a substantial proportion of SUD care is delivered through addiction services, and psychiatric ward admissions may selectively capture crisis-driven or comorbidity-heavy presentations, which could sustain a relatively stable age profile over time. Future work should disentangle alcohol- vs drug-related admissions and examine distributional changes (e.g., age quantiles or age-band-specific admission rates) to test whether stability in the mean masks divergent trends at the extremes. Finally, the 2006–2022 window encompassed major exogenous shocks for this catchment, the 2009 L’Aquila earthquake and the COVID-19 pandemic. While our aim was to characterise long-term trends, we modelled time as a linear effect across quarters and did not formally test for structural breaks or interrupted time-series effects; therefore, estimated slopes should be interpreted as average trends over the whole period rather than event-specific changes. Strengths include the long horizon, complete catchment coverage, and a modelling strategy that tested higher-order interactions alongside comprehensive diagnostics and robust (HC3) checks. Limitations include reliance on the primary ICD-9-CM discharge diagnosis, which did not capture comorbidities or illness severity. Second, analyses were conducted at the admission-episode level; the anonymized discharge data did not allow linkage of repeated admissions for the same individual, which may have influenced observed trends. In addition, sex was recorded administratively as a binary variable and does not capture gender identity or non-binary classifications.68Nonetheless, the consistency of results and diagnostics supports the reliability of the observed temporal trends. Taken together, these findings argue for consolidating youth-focused, early intervention outreach for affective disorders—particularly engaging younger men, who remain less likely to seek help and show higher rates of untreated illness (Rice et al., 2018 ; Seidler et al., 2016 )—while strengthening psychoeducation and low-threshold voluntary pathways that enable timely care before crises emerge (Rickwood et al., 2023 ). At the same time, planners should preserve high-dependency capacity and assertive follow-up for chronic psychosis with poor insight and alcohol/substance use disorder. In practical terms, inpatient psychiatry admissions appear to be bifurcating into a preventive, voluntary, younger stream and a more stable, crisis-driven psychosis stream and alcohol/substance use disorder, and systems will need to balance these complementary demands within a coherent continuum of care (Stain et al., 2019 ). Conclusion From 2006 to 2022, the age at psychiatric admission declined among voluntary affective-disorder inpatient admissions, most notably men with major depression and women with bipolar disorder, while remaining stable in schizophrenia spectrum and alcohol/substance use disorders and among compulsory admissions. This pattern is consistent with increased awareness and earlier, self-initiated help-seeking for conditions with better insight, alongside a persistent, crisis-driven pathway for schizophrenia spectrum and alcohol/substance use disorder. Service planning should strengthen youth- and gender-sensitive early-intervention and psychoeducation to support timely voluntary care, while maintaining high-dependency capacity and assertive follow-up for chronic psychosis. Ongoing, high-resolution local surveillance of age-at-admission trajectories will be essential to monitor these demographic shifts and guide balanced resource allocation. Declarations Acknowledgements None. Ethics approval and consent to participate All procedures performed in studies involving human participants were conducted ethically, adhering to the institutional guidelines and the 1964 Declaration of Helsinki, including subsequent updates. The Ethics Committee obtained ethical approval from the Internal Review Board of the University of L’ Aquila on 18/10/2016, Sequence No: 05/2016. The participant consent requirement was waived because of the retrospective design, based on data from the records of the San Salvatore Hospital. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Author contributions TB, FP, FR and FS conceived and designed the project idea. TB, GR, FS, and AR acquired and analysed the data. TB, AB, FP, FS, ST, GR and VS drafted the manuscript. ET, FS, ST, AB and AR drew figures and tables. All authors edited the draft and approved the final manuscript. Funding None. Data availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request Generative AI statement The author(s) declare that Generative AI was used in the creation of this manuscript. The authors declare that they used ChatGPT (OpenAI) for language editing and proofreading. The authors confirm that they have verified the accuracy of all content, including citations, references, data, and accept full responsibility for the accuracy and integrity of the results presented. References Addis ME, Mahalik JR. Men, masculinity, and the contexts of help seeking. Am Psychol. 2003;58:5–14. https://doi.org/10.1037/0003-066X.58.1.5 . Allison S, Bastiampillai T, Goldney R. Acute versus sub-acute care beds: should Australia invest in community beds at the expense of hospital beds? Aust N Z J Psychiatry. 2014;48(10):952–4. https://doi.org/10.1177/0004867414538106 . Amaddeo F, Barbui C, Tansella M. 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Am J Psychiatry. 2017;174(2):143–53. https://doi.org/10.1176/appi.ajp.2016.16010103 . Kraus L, Möckl J, Manthey J, Rovira P, Olderbak S, Rehm J. Trends in alcohol-attributable morbidity and mortality in Germany 2000–2021: a modelling study. Drug Alcohol Rev. 2024;43:1662–75. https://doi.org/10.1111/dar.13928 . Kuntsche E, Kuntsche S, Knibbe R, et al. Cultural and gender convergence in adolescent drunkenness: evidence from 23 European and North American countries. Arch Pediatr Adolesc Med. 2011;165:152–8. https://doi.org/10.1001/archpediatrics.2010.191 . McGorry PD, Mei C. Early intervention in youth mental health: progress and future directions. Evid Based Ment Health. 2018;21(4):182–4. 10.1136/ebmental-2018-3000 . Montemagni C, Badà A, Castagna F, et al. Predictors of compulsory admission in schizophrenia spectrum patients: excitement, insight, emotion perception. Prog Neuro-psychopharmacol Biol Psychiatry. 2011;35:137–45. https://doi.org/10.1016/j.pnpbp.2010.10.005 . Oliva F, Ostacoli L, Versino E, Portigliatti Pomeri A, Furlan PM, Carletto S, Picci RL. Compulsory psychiatric admissions in an Italian urban setting: are they compliant with ‘need for treatment’ criteria or arranged for dangerous non-clinical condition? Front Psychiatry. 2019;9:740. https://doi.org/10.3389/fpsyt.2018.00740 . Pattyn E, Verhaeghe M, Bracke P. The gender gap in mental health service use. Soc Psychiatry Psychiatr Epidemiol. 2015;50:1089–95. https://doi.org/10.1007/s00127-015-1038-x . Pellegrino R, Bonetto C, Isaia P, Barcella M. Early Intervention Programme for Young Adults in Northern Italy: a 10-year analysis of socio-demographic and clinical characteristics. Early Interv Psychiat. 2025;19:e13632. https://doi.org/10.1111/eip.13632 . Rice SM, Purcell R, McGorry PD, Adolescent, and Young Adult Male Mental Health. : Transforming System Failures Into Proactive Models of Engagement. J Adolesc Health. 2018;62(3S):S9-S17. 10.1016/j.jadohealth.2017.07.024 . PMID: 29455724. Rickwood D, McEachran J, Saw A, Telford N, Trethowan J, McGorry P. (2023). Sixteen years of innovation in youth mental healthcare: Outcomes for young people attending Australia’s headspace centre services. PLoS ONE, 18(6), e0282040. Ruggeri M, Bonetto C, Lasalvia A, GET UP Group, et al. Feasibility and effectiveness of a multi-element psychosocial intervention for first-episode psychosis: cluster-randomized controlled GET UP PIANO trial. Schizophr Bull. 2015;41:1192–203. https://doi.org/10.1093/schbul/sbv058 . Seidler ZE, Dawes AJ, Rice SM, Oliffe JL, Dhillon HM. The role of masculinity in men's help-seeking for depression: A systematic review. Clin Psychol Rev. 2016;49:106–18. 10.1016/j.cpr.2016.09.002 . Epub 2016 Sep 10. PMID: 27664823. Solmi M, Radua J, Olivola M, de Pablo GS, et al. Age at onset of mental disorders worldwide: meta-analysis of 192 epidemiological studies. Mol Psychiatry. 2022;27:281–95. https://doi.org/10.1038/s41380-021-01161-7 . Stain HJ, Mawn L, Common S, Pilton M, Thompson A. Research and practice for ultra-high risk for psychosis: A national survey of early intervention in psychosis services in England. Early Interv Psychiat. 2019;13(1):47–52. https://doi.org/10.1111/eip.12443 . Thompson A, Shaw M, Harrison G, et al. Patterns of hospital admission for adult psychiatric illness in England: analysis of Hospital Episode Statistics. Br J Psychiatry. 2004;185:334–41. https://doi.org/10.1192/bjp.185.4.334 . Vieta E, Salagre E, Grande I, et al. Early Intervention in Bipolar Disorder. Am J Psychiatry. 2018;175(5):411–26. 10.1176/appi.ajp.2017.17090972 . Vigod SN, Kurdyak PA, Dennis CL, et al. Psychiatric hospitalizations: comparison by gender, clinical profile and post-discharge outcomes. Psychiatric Serv. 2016;67:1376–9. https://doi.org/10.1176/appi.ps.201500547 . Zheng H, Jiang X, Yang R, Wang S, Zhong H. Changes in major psychiatric disorders in children and adolescents from 2001 to 2020: A retrospective single-center study. Front Psychiatry. 2023;13:1079456. 10.3389/fpsyt.2022.1079456 . PMID: 36699486; PMCID: PMC9868601. Additional Declarations No competing interests reported. Supplementary Files TableS1longterm.docx TableS2longterm.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 27 Mar, 2026 Reviews received at journal 06 Mar, 2026 Reviewers agreed at journal 06 Mar, 2026 Reviewers agreed at journal 05 Mar, 2026 Reviewers invited by journal 05 Mar, 2026 Editor assigned by journal 10 Feb, 2026 Submission checks completed at journal 10 Feb, 2026 First submitted to journal 06 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8809812","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":602573379,"identity":"c1eee572-59fa-47ee-b754-167af741d6d0","order_by":0,"name":"Tommaso 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17:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8809812/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8809812/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104470752,"identity":"91f2dc77-914e-41d5-8514-328525b06aca","added_by":"auto","created_at":"2026-03-12 07:23:15","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":17250,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredicted admission age trajectories by diagnosis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNotes: \u003c/em\u003eLines represent model-estimated marginal means, and shaded areas indicate the standard error of the mean. Asterisks denote significant temporal effects for the corresponding color-coded group (*\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001)\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8809812/v1/6f880e7f2482a6701d66b56a.jpg"},{"id":104470747,"identity":"d87324c7-d1b0-4856-9995-d7e1f28b2848","added_by":"auto","created_at":"2026-03-12 07:23:11","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47850,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredicted admission age trajectories by diagnosis and sex.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNotes: \u003c/em\u003eLines represent model-estimated marginal means, and shaded areas indicate the standard error of the mean. Asterisks denote significant temporal effects for the corresponding color-coded group (*\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001)\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8809812/v1/ac628932c2acdea101fc5670.jpg"},{"id":104471148,"identity":"47796156-c742-404b-8c1a-1ef5965e6ac9","added_by":"auto","created_at":"2026-03-12 07:25:30","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":14656,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredicted admission age trajectories by admission type.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNotes: \u003c/em\u003eLines represent model-estimated marginal means, and shaded areas indicate the standard error of the mean. Asterisks denote significant temporal effects for the corresponding color-coded group (*\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001)\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8809812/v1/bc155511579a1cb8f0cb7430.jpg"},{"id":104780807,"identity":"a0ae8eb8-7157-4812-9193-da80074e6e63","added_by":"auto","created_at":"2026-03-17 07:54:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":981680,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8809812/v1/90db3682-deea-4522-9419-40b9b4294b11.pdf"},{"id":104470749,"identity":"5a9d2519-c588-456d-b0e8-770e063dc31f","added_by":"auto","created_at":"2026-03-12 07:23:11","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15614,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1longterm.docx","url":"https://assets-eu.researchsquare.com/files/rs-8809812/v1/73ab90445148cb79db5399b5.docx"},{"id":104470755,"identity":"15042925-10ad-4496-a7a8-03e55709d358","added_by":"auto","created_at":"2026-03-12 07:23:17","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":14963,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2longterm.docx","url":"https://assets-eu.researchsquare.com/files/rs-8809812/v1/07d0f3c86583cdb20d9a4e2b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Long-term trends in age at psychiatric admission (2006–2022): influence of sex, diagnosis, and admission type in a single Italian catchment","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Italian psychiatric reform (Law 180) closed asylums and relocated acute psychiatric beds to general hospital wards, establishing a community-oriented model that reduced institutionalization while preserving an inpatient safety net for clinical crises (de Girolamo \u0026amp; Cozza, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; de Girolamo et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Thompson et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Amaddeo et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Barbui et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile national hospitalization rates have largely stabilized since the mid-2000s, far less is known about how age at admission has evolved within local catchments. International and Italian evidence suggests that reorganization of services and early-intervention models may shift the demographic composition of inpatient cohorts toward younger, help-seeking populations, particularly for affective disorders (Allison et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Birchwood et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kirkbride et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pellegrino et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; McGorry \u0026amp; Mei, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Davey \u0026amp; McGorry, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Vieta et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In Italy, early post-reform investigations of general-hospital psychiatric wards already described age-at-first-admission patterns and sex differences, indicating that women tend to be older than men at first hospital contact and that local system configuration shapes the profiles reaching inpatient care (de Girolamo et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1988a\u003c/span\u003e; de Girolamo et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1988b\u003c/span\u003e). Multicenter surveys then provided concrete age benchmarks for acute wards: the PERSEO study across 62 SPDCs reported a mean age at admission in the mid-forties with women older than men, and complementary national work on first-ever inpatients found a median admission age around 40 years, consistent with a younger profile at incident admission compared with the prevalent inpatient population (Ballerini et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007b\u003c/span\u003e; Guzzetta et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGlobally, most mental disorders begin before age 25, as shown by large-scale epidemiological studies (Kessler et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). This makes earlier detection particularly plausible for conditions characterized by preserved insight, such as major depression and bipolar disorder. Conversely, disorders marked by impaired insight\u0026mdash;such as psychotic disorders\u0026mdash;tend to present in late adolescence or early adulthood; meta-analytic data indicate their median age at first hospitalization is around 24\u0026ndash;25 years, reflecting relatively stable age-at-admission distributions across cohorts (Solmi et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Substance use disorder\u0026ndash;related hospital contacts may show heterogeneous, age-band\u0026ndash;specific changes rather than a uniform linear shift in mean age; for example, an analysis of hospital discharge registries from the Abruzzo region (2006\u0026ndash;2015) reported differential temporal patterns across age ranges in drug-dependence hospitalizations (Di Giovanni et al., 2020).\u003c/p\u003e \u003cp\u003eRecent observational evidence\u0026mdash;albeit in younger settings\u0026mdash;likewise reports no age shift for schizophrenia but declining age for affective disorders, consistent with diagnosis-specific trajectories in help-seeking and referral (Zheng et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the present study, we analyzed seventeen consecutive years (2006\u0026ndash;2022) of admissions to the sole 15-bed psychiatric ward serving L\u0026rsquo;Aquila, focusing exclusively on temporal trends in age at admission by diagnosis, sex, and admission status (voluntary vs compulsory), to provide high-resolution local evidence for service planning.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and data extraction\u003c/h2\u003e \u003cp\u003eWe retrieved all discharge summaries (Scheda di Dimissione Ospedaliera, SDO) from the psychiatric ward of the San Salvatore Hospital in L\u0026rsquo;Aquila (Italy) covering the period 2006\u0026ndash;2022.\u003c/p\u003e \u003cp\u003eThe data extraction, quality checks, and analytic workflow followed the approach described in our previous report on admission patterns in the same ward (Barlattani et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHospital discharge records included sociodemographic information, admission and discharge dates, and the primary diagnosis at discharge, coded according to the International Classification of Diseases, Ninth Revision (ICD-9). ICD-9 codes used to define each diagnostic category are provided in \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eICD-9 was employed because the regional health information system of Abruzzo is still based on this classification, which is used across the electronic medical record systems, archives, and discharge documentation. To preserve privacy, all patient identifiers were anonymized prior to analysis. Because discharge records were anonymized and did not include a stable unique patient identifier for longitudinal linkage, the unit of analysis was the admission episode. Repeated admissions from the same individual could not be identified and were therefore analysed as separate episodes. Sex was extracted from administrative hospital discharge records as recorded at admission (male/female).\u003c/p\u003e \u003cp\u003eAfter verifying data integrity, completeness, and internal consistency, all variables were checked for missing or inconsistent values. Episodes with missing data on key variables (age, trimester, primary diagnosis, or admission type) were excluded via complete-case analysis (n\u0026thinsp;=\u0026thinsp;503), yielding a final analytic sample of 5,207 admission episodes. Participants were grouped into four diagnostic categories, based on the ICD-9 discharge diagnosis: schizophrenia spectrum disorders, major depressive disorder, bipolar disorder, and alcohol/substance use disorder.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll analyses were performed in R (version 4.1.2, R Foundation for Statistical Computing, Vienna, Austria). The date of admission was used to assign each hospitalization to its corresponding trimester, resulting in 68 consecutive quarters covering the entire 17-year observation window (2006\u0026ndash;2022). The quarter variable was mean-centred to facilitate interpretation of interaction terms and reduce multicollinearity; the centring constant was retained to reconstruct the original time scale for graphical representation.\u003c/p\u003e \u003cp\u003eThe resulting variable was then mean-centered to facilitate interpretation of interaction terms and reduce multicollinearity; the centering constant was retained to reconstruct the original temporal scale for graphical representation.\u003c/p\u003e \u003cp\u003eDescriptive statistics were computed to summarize demographic and clinical characteristics across diagnostic groups. Continuous variables (e.g., age) are reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), while categorical variables (sex: male/female, admission type: voluntary/compulsory) are reported as absolute and relative frequencies (N, %).\u003c/p\u003e \u003cp\u003eTo assess whether age at admission varied as a function of time, diagnosis, sex, and admission type, we fitted a multiple linear model including all possible interactions up to the four-way level (time \u0026times; diagnosis \u0026times; sex \u0026times; admission type). Sum-to-zero contrasts were applied to categorical factors, and Type-III ANOVA tables were computed using the \u003cem\u003ecar\u003c/em\u003e package. In the presence of significant main effects or interactions, simple slope analyses were conducted (\u003cem\u003eemmeans\u003c/em\u003e, \u003cem\u003ejtools\u003c/em\u003e) to clarify the source of effects. All statistical tests were two-tailed, with the significance threshold set at α\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eModel assumptions and diagnostics were formally evaluated. Residual autocorrelation was not detected (Durbin\u0026ndash;Watson\u0026thinsp;=\u0026thinsp;1.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.16). A Breusch\u0026ndash;Pagan test indicated heteroskedasticity (BP\u0026thinsp;=\u0026thinsp;63.51, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The residual distribution deviated from normality on a large-N Shapiro\u0026ndash;Wilk test (W\u0026thinsp;=\u0026thinsp;0.99, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), a common and negligible issue in ordinary least squares (OLS) models with \u0026gt;\u0026thinsp;5,000 observations due to the asymptotic normality of estimators. Influence diagnostics (Cook\u0026rsquo;s distance and leverage\u0026ndash;residual plots) did not reveal influential observations of concern. Multicollinearity was modest: for all predictors and interactions, GVIF\u0026sup1;ᐟ\u0026sup2;ᴰᶠ \u0026le; 2.07, well below conventional alert thresholds. To address the detected heteroskedasticity, all tests were re-estimated with heteroskedasticity-consistent standard errors (HC3). The pattern of statistical significance remained unchanged across main effects and interactions. Given the stability of results using robust estimators, the absence of autocorrelation, the lack of influential outliers, and the need for full compatibility with the subsequent simple-effects decomposition, we report OLS estimates with conventional standard errors in the main text, while providing robust (HC3) results as a supplementary robustness check (see \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 5,710 discharge records were retrieved; 503 episodes were excluded due to missing data on age, trimester, diagnosis, or admission type, resulting in 5,207 admission episodes included in the analyses.\u003c/p\u003e\n\u003cp\u003eDemographic and admission characteristics of the overall sample are reported in \u003cstrong\u003e\u003cem\u003eTable 1\u003c/em\u003e\u003c/strong\u003e, while model results are summarized in \u003cstrong\u003e\u003cem\u003eTable 2\u003c/em\u003e\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e\u0026nbsp; Demographic and admission characteristics of psychiatric admission episodes recorded between 2006 and 2022, stratified by discharge diagnosis.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdmission type\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDiagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003emean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCompulsory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVoluntary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSchizophrenia spectrum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e44.2 \u0026plusmn; 14.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1649 (57.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1224 (42.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e493 (17.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2380 (82.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMajor depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49.57 \u0026plusmn; 16.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e408 (44.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e501 (55.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e116 (12.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e793 (87.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBipolar disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47.23 \u0026plusmn; 15.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e468 (49.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e475 (50.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e145 (15.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e798 (84.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAlcohol/substance use disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43.72 \u0026plusmn; 14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e324 (67.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e158 (32.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60 (12.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e422 (87.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45.64 \u0026plusmn; 15.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2849 (54.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2358 (45.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e814 (15.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4393 (84.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Results for the linear model predicting age at admission as a function of trimester, diagnosis, sex, and admission type, including all possible interactions.\u0026nbsp;\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"460\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eEffect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eF\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTrimester\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eDiagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAdmission type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTrimester \u0026times; Diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTrimester \u0026times; Sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eDiagnosis \u0026times; Sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTrimester \u0026times; Admission type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eDiagnosis \u0026times; Admission type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSex \u0026times; Admission type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTrimester \u0026times; Diagnosis \u0026times; Sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTrimester \u0026times; Diagnosis \u0026times; Admission type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTrimester \u0026times; Sex \u0026times; Admission type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eDiagnosis \u0026times; Sex \u0026times; Admission type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTrimester \u0026times; Diagnosis \u0026times; Sex \u0026times; Admission type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eNotes:\u0026nbsp;\u003c/em\u003eSignificant main effects and interactions are highlighted in bold.\u003c/p\u003e\n\u003cp\u003eThe model revealed significant main effects of Trimester, Diagnosis, Sex, and Admission type. Age at admission therefore varied across diagnostic groups, sexes, and admission types, and showed a temporal reduction over the 17‑year period.\u003c/p\u003e\n\u003cp\u003eAmong two‑way interactions, Trimester \u0026times; Diagnosis and Trimester \u0026times; Admission type was significant, indicating that temporal variations in admission age differed across diagnostic and admission typology subgroups. Moreover, a significant three‑way interaction (Trimester \u0026times; Diagnosis \u0026times; Sex) indicated that the differential temporal trends in admission age across diagnostic categories were further modulated by patients\u0026rsquo; sex. No higher‑order interactions reached significance (\u003cem\u003ep\u003c/em\u003e \u0026ge; 0.15).\u003c/p\u003e\n\u003cp\u003eSimple slope analyses on Trimester \u0026times; Diagnosis interaction (\u003cstrong\u003e\u003cem\u003eFigure 1\u003c/em\u003e\u003c/strong\u003e) showed that mean age at admission significantly decreased across years for inpatient admissions with major depression (\u003cem\u003eB\u003c/em\u003e = \u0026minus;0.20, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and bipolar disorder (\u003cem\u003eB\u003c/em\u003e = \u0026minus;0.09, \u003cem\u003ep\u003c/em\u003e = 0.03), while remaining stable for schizophrenia spectrum (\u003cem\u003eB\u003c/em\u003e = 0.02, \u003cem\u003ep\u003c/em\u003e = 0.26) and alcohol/substance use disorders (\u003cem\u003eB\u003c/em\u003e = 0.06, \u003cem\u003ep\u003c/em\u003e = 0.33). These results indicate a progressive reduction in the average age of affective‑disorder admissions, corresponding to an estimated decrease of approximately 0.80 years per year for depression and 0.36 years per year for bipolar disorder. Over the entire observation window (2006\u0026ndash;2022), this translates into a total reduction of about 13.6 years in admission age for depression and 6.1 years for bipolar disorder.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1. Predicted admission age trajectories by diagnosis.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNotes:\u0026nbsp;\u003c/em\u003eLines represent model-estimated marginal means, and shaded areas indicate the standard error of the mean. Asterisks denote significant temporal effects for the corresponding color-coded group (*\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001)\u003c/p\u003e\n\u003cp\u003eThe Trimester \u0026times; Diagnosis \u0026times; Sex interaction (\u003cstrong\u003e\u003cem\u003eFigure 2\u003c/em\u003e\u003c/strong\u003e) clarified that these temporal effects were sex‑specific: in major depression, the decline in admission age was mainly driven by male patients (\u003cem\u003eB\u003c/em\u003e = \u0026minus;0.32, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), whereas in bipolar disorder the decreasing trend was primarily explained by female admissions (\u003cem\u003eB\u003c/em\u003e = \u0026minus;0.14, \u003cem\u003ep\u003c/em\u003e = 0.03). These slopes correspond to a mean annual decrease of 1.28 years per year for men with depression and 0.56 years per year for women with bipolar disorder, amounting to a total reduction of 21.76 years and 9.52 years, respectively, over the 17‑year observation period. No significant changes over time were observed in the opposite sex within each diagnostic group, nor for schizophrenia or alcohol/substance use disorders (all \u003cem\u003ep\u003c/em\u003e \u0026ge; 0.10).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2. Predicted admission age trajectories by diagnosis and sex.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNotes:\u0026nbsp;\u003c/em\u003eLines represent model-estimated marginal means, and shaded areas indicate the standard error of the mean. Asterisks denote significant temporal effects for the corresponding color-coded group (*\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001)\u003c/p\u003e\n\u003cp\u003eFinally, the Trimester \u0026times; Admission type interaction (\u003cstrong\u003e\u003cem\u003eFigure 3\u003c/em\u003e\u003c/strong\u003e) revealed that the overall reduction in age at admission was specific to voluntary hospitalizations (\u003cem\u003eB\u003c/em\u003e = \u0026minus;0.10, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), whereas compulsory admissions remained stable across the observation period (\u003cem\u003eB\u003c/em\u003e = \u0026minus;0.01, \u003cem\u003ep\u003c/em\u003e = 0.87). This corresponds to a mean annual decrease of 0.40 years per year among voluntary admissions, totaling about 6.80 years over 2006\u0026ndash;2022.\u003c/p\u003e\n\u003cp\u003eTaken together, these results indicate a gradual younger age profile of voluntary affective-disorder admissions, especially among men with major depression and women with bipolar disorder, whereas schizophrenia spectrum and alcohol/substance use disorder admissions remained stable over time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3. Predicted admission age trajectories by admission type.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNotes:\u0026nbsp;\u003c/em\u003eLines represent model-estimated marginal means, and shaded areas indicate the standard error of the mean. Asterisks denote significant temporal effects for the corresponding color-coded group (*\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001)\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis seventeen-year single-catchment analysis demonstrates a clear temporal shift in age at psychiatric admission. Age at admission declined steadily between 2006 and 2022, entirely driven by voluntary admissions and most marked among men with major depression and women with bipolar disorder, whereas compulsory, psychotic-disorder and alcohol/substance use disorder admissions remained stable.\u003c/p\u003e \u003cp\u003eSet against Italian post-reform benchmarks, these patterns are expected. In the national PERSEO survey across 62 SPDCs, the mean age at admission was in the mid-forties and women were older than men, offering a pragmatic reference for sex differences at entry to inpatient care; national data on first-ever inpatients reported a median admission age around 40 years, consistent with a younger profile at incident admission compared with the prevalent inpatient population (Ballerini et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007a\u003c/span\u003e; Guzzetta et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Early post-reform ward studies had already documented age-at-first-admission patterns and sex differences in neighboring catchments with different service configurations (de Girolamo et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1988a\u003c/span\u003e; de Girolamo et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1988b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWithin this context, the decline in age for affective disorders likely reflects earlier recognition and help seeking among younger adults, consistent with rising mental health literacy and reduced stigma (Jorm, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Clement et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and the diffusion of community and early intervention pathways (McGorry \u0026amp; Mei, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Davey \u0026amp; McGorry, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Birchwood et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kirkbride et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ruggeri et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe fact that the reduction is confined to voluntary admissions possibly indicates a process driven by awareness and autonomous engagement, rather than by coercive thresholds. (Gulliver et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Bindman et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBy contrast, the stability of age at admission for schizophrenia accords with impaired insight and crisis-driven presentations that more often culminate in compulsory admission (Beaglehole et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ferracuti et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); in this scenario, stable age patterns are expected, as also suggested by cohort evidence reporting no age shift for schizophrenia while affective disorders show decreasing age (Zheng et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Italian studies on coercion and psychosis further support a persistent, crisis-driven pathway for schizophrenia (Oliva et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Donisi et al., 2016; Montemagni et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSex-specific effects strengthen this reading. Among men with depression, the steep decline suggests an erosion of traditional barriers to seeking help; among women with bipolar disorder, a moderate but significant reduction points to improved recognition and timelier intervention (Vieta et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u0026mdash;both consistent with known gender differences in help-seeking (Addis \u0026amp; Mahalik, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Pattyn et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Vigod et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and with the PERSEO observation that women present older at admission on average (Ballerini et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007a\u003c/span\u003e). Importantly, age at admission for alcohol/substance use disorders remained stable across the observation window. This net stability may reflect the coexistence of different cohort dynamics\u0026mdash;such as an ageing segment of long-term substance users alongside increasing visibility of alcohol- and drug-related problems in younger groups\u0026mdash;that can offset each other when admissions are analyzed in aggregate. Evidence on age- and cohort-specific trends in substance-use admissions and on changing alcohol-attributable morbidity supports the plausibility of these counterbalancing processes (Chhatre et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kraus et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Green et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Kelly \u0026amp; Daley, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Consistent with this interpretation, a discharge-registry study from the Abruzzo region documented age-range\u0026ndash;specific temporal changes in drug-dependence hospitalizations, supporting the possibility that aggregate stability in mean age may coexist with divergent trends within age strata (Di Giovanni et al., 2020). In addition, cross-national data suggest progressive convergence of drinking behaviors between genders during adolescence, which may contribute to a more stable overall age distribution over time when both sexes are considered (Kuntsche et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Green et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Kelly \u0026amp; Daley, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) Moreover, the absence of a temporal shift in mean admission age for alcohol/substance use disorders should not be equated with demographic stagnation. This broad category likely aggregates heterogeneous pathways (e.g., alcohol vs other substances; primary SUD vs dual diagnosis), in which opposite cohort dynamics may offset each other at the level of the mean. In addition, within the Italian service configuration, a substantial proportion of SUD care is delivered through addiction services, and psychiatric ward admissions may selectively capture crisis-driven or comorbidity-heavy presentations, which could sustain a relatively stable age profile over time. Future work should disentangle alcohol- vs drug-related admissions and examine distributional changes (e.g., age quantiles or age-band-specific admission rates) to test whether stability in the mean masks divergent trends at the extremes. Finally, the 2006\u0026ndash;2022 window encompassed major exogenous shocks for this catchment, the 2009 L\u0026rsquo;Aquila earthquake and the COVID-19 pandemic. While our aim was to characterise long-term trends, we modelled time as a linear effect across quarters and did not formally test for structural breaks or interrupted time-series effects; therefore, estimated slopes should be interpreted as average trends over the whole period rather than event-specific changes.\u003c/p\u003e \u003cp\u003eStrengths include the long horizon, complete catchment coverage, and a modelling strategy that tested higher-order interactions alongside comprehensive diagnostics and robust (HC3) checks. Limitations include reliance on the primary ICD-9-CM discharge diagnosis, which did not capture comorbidities or illness severity. Second, analyses were conducted at the admission-episode level; the anonymized discharge data did not allow linkage of repeated admissions for the same individual, which may have influenced observed trends. In addition, sex was recorded administratively as a binary variable and does not capture gender identity or non-binary classifications.68Nonetheless, the consistency of results and diagnostics supports the reliability of the observed temporal trends.\u003c/p\u003e \u003cp\u003eTaken together, these findings argue for consolidating youth-focused, early intervention outreach for affective disorders\u0026mdash;particularly engaging younger men, who remain less likely to seek help and show higher rates of untreated illness (Rice et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Seidler et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u0026mdash;while strengthening psychoeducation and low-threshold voluntary pathways that enable timely care before crises emerge (Rickwood et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt the same time, planners should preserve high-dependency capacity and assertive follow-up for chronic psychosis with poor insight and alcohol/substance use disorder. In practical terms, inpatient psychiatry admissions appear to be bifurcating into a preventive, voluntary, younger stream and a more stable, crisis-driven psychosis stream and alcohol/substance use disorder, and systems will need to balance these complementary demands within a coherent continuum of care (Stain et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFrom 2006 to 2022, the age at psychiatric admission declined among voluntary affective-disorder inpatient admissions, most notably men with major depression and women with bipolar disorder, while remaining stable in schizophrenia spectrum and alcohol/substance use disorders and among compulsory admissions. This pattern is consistent with increased awareness and earlier, self-initiated help-seeking for conditions with better insight, alongside a persistent, crisis-driven pathway for schizophrenia spectrum and alcohol/substance use disorder. Service planning should strengthen youth- and gender-sensitive early-intervention and psychoeducation to support timely voluntary care, while maintaining high-dependency capacity and assertive follow-up for chronic psychosis. Ongoing, high-resolution local surveillance of age-at-admission trajectories will be essential to monitor these demographic shifts and guide balanced resource allocation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures performed in studies involving human participants were conducted ethically, adhering to the institutional guidelines and the 1964 Declaration of Helsinki, including subsequent updates. The Ethics Committee obtained ethical approval from the Internal Review Board of the University of L\u0026rsquo; Aquila on 18/10/2016, Sequence No: 05/2016. The participant consent requirement was waived because of the retrospective design, based on data from the records of the San Salvatore Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTB, FP, FR and FS conceived and designed the project idea. TB, GR, FS, and AR\u003c/p\u003e\n\u003cp\u003eacquired and analysed the data. TB, AB, FP, FS, ST, GR and VS drafted the manuscript. ET,\u003c/p\u003e\n\u003cp\u003eFS, ST, AB and AR drew figures and tables. All authors edited the draft and approved\u003c/p\u003e\n\u003cp\u003ethe final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from\u003c/p\u003e\n\u003cp\u003ethe corresponding author on reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenerative AI statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declare that Generative AI was used in the creation of this manuscript. The authors declare that they used ChatGPT (OpenAI) for language editing and proofreading. The authors confirm that they have verified the accuracy of all content, including citations, references, data, and accept full responsibility for the accuracy and integrity of the results presented. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAddis ME, Mahalik JR. Men, masculinity, and the contexts of help seeking. Am Psychol. 2003;58:5\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/0003-066X.58.1.5\u003c/span\u003e\u003cspan address=\"10.1037/0003-066X.58.1.5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllison S, Bastiampillai T, Goldney R. Acute versus sub-acute care beds: should Australia invest in community beds at the expense of hospital beds? 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PMID: 36699486; PMCID: PMC9868601.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"annals-of-general-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agps","sideBox":"Learn more about [Annals of General Psychiatry](http://annals-general-psychiatry.biomedcentral.com/)","snPcode":"12991","submissionUrl":"https://submission.nature.com/new-submission/12991/3","title":"Annals of General Psychiatry","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Age at admission, Temporal trends, Psychiatric epidemiology, Early intervention","lastPublishedDoi":"10.21203/rs.3.rs-8809812/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8809812/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAims\u003c/h2\u003e \u003cp\u003eFollowing Italy\u0026rsquo;s psychiatric reform, national inpatient numbers declined, but how the age at admission has changed across sex, diagnostic, and admission-type subgroups remains unclear. This study examined 17 consecutive years (2006\u0026ndash;2022) of psychiatric admissions to the sole ward serving L\u0026rsquo;Aquila, assessing temporal trends in age at admission by diagnosis, sex, and admission status (voluntary vs compulsory).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAll adult admissions (\u0026ge;\u0026thinsp;18 years) were extracted from hospital discharge records (Schede di Dimissione Ospedaliera, SDO). Analyses were conducted at the admission-episode level. Primary diagnoses were grouped into four ICD-9 categories: schizophrenia spectrum, major depressive disorder, bipolar disorder, and alcohol/substance use disorder. Multiple linear models tested time-related changes in mean age at admission, including interactions for time \u0026times; diagnosis \u0026times; sex \u0026times; admission type.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAcross 5,207 admission episodes, the Trimester \u0026times; Diagnosis interaction showed a marked decline in age at admission for major depression (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.20 per trimester, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and bipolar disorder (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.09, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03), equivalent to approximately\u0026thinsp;\u0026minus;\u0026thinsp;0.80 and \u0026minus;\u0026thinsp;0.36 years per year, yielding total reductions of ~\u0026thinsp;13.6 and ~\u0026thinsp;6.1 years over 2006\u0026ndash;2022. The Trimester \u0026times; Diagnosis \u0026times; Sex interaction indicated that the decline in depression was driven by men (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.32, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u0026asymp; \u0026minus;1.28 years/year; \u0026minus;21.76 over 17 years), while in bipolar disorder it was driven by women (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03; \u0026asymp; \u0026minus;0.56 years/year; \u0026minus;9.52 over 17 years). The Trimester \u0026times; Admission type interaction showed the reduction was specific to voluntary admissions (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.10, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u0026asymp; \u0026minus;0.40 years/year), while compulsory admissions were stable (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.87). No significant age change occurred in schizophrenia spectrum disorders or in alcohol/substance use disorders.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eBetween 2006 and 2022, age at admission declined steadily among voluntary affective-disorder cases\u0026mdash;especially men with depression and women with bipolar disorder\u0026mdash;while remaining stable among compulsory admissions, in schizophrenia spectrum and alcohol/substance use disorder admissions. Findings are compatible with a progressive rejuvenation of voluntary affective-disorder inpatient admissions, possibly reflecting earlier self-initiated help-seeking and/or evolving referral pathways, while underscoring the need to balance youth-focused outreach with adequate capacity for chronic psychosis and substance use disorder.\u003c/p\u003e","manuscriptTitle":"Long-term trends in age at psychiatric admission (2006–2022): influence of sex, diagnosis, and admission type in a single Italian catchment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-12 07:23:04","doi":"10.21203/rs.3.rs-8809812/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-27T12:44:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-06T13:26:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"24131796508133305037124908415200136092","date":"2026-03-06T12:38:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176825140958285032682110385296648322115","date":"2026-03-05T19:28:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-05T17:22:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-10T09:38:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-10T09:36:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Annals of General Psychiatry","date":"2026-02-06T17:36:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"annals-of-general-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agps","sideBox":"Learn more about [Annals of General Psychiatry](http://annals-general-psychiatry.biomedcentral.com/)","snPcode":"12991","submissionUrl":"https://submission.nature.com/new-submission/12991/3","title":"Annals of General Psychiatry","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b164cb31-4059-43d3-960a-4d79cce375c5","owner":[],"postedDate":"March 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T15:39:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-12 07:23:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8809812","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8809812","identity":"rs-8809812","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}