Age-Specific Temporal Patterns of Drug Poisoning: A Decade-Long Retrospective Analysis for Emergency Triage Optimization

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Understanding its multidimensional temporal distribution across age groups is essential for optimizing emergency department (ED) resource allocation and targeted interventions. This study investigates the temporal epidemiological characteristics of drug poisoning cases in a tertiary hospital ED over a decade, focusing on age‑specific differences, temporal clustering patterns, and risk factors for suicidal intent. Methods This single‑center retrospective study included 884 patients diagnosed with drug poisoning (ICD‑10: T36–T50) at a tertiary hospital in Shanghai between January 2015 and December 2024. Joinpoint regression, age‑stratified analysis, multiple correspondence analysis (MCA), and multivariate logistic regression were used. Results Joinpoint regression identified 2020 as a significant inflection point, after which the annual growth rate increased 4.6‑fold. Poisoning events exhibited seasonal, monthly, and diurnal clustering, with a bimodal diurnal pattern (primary peak: 18:01–24:00; secondary peak: 12:01–18:00). Poisoning agent composition differed significantly across age groups (χ²=266.3, P < 0.001). MCA revealed three distinct “age‑agent‑time” phenotypes: a pediatric phenotype (0–6 years) involving household medications and daytime/autumn presentation; an adolescent/adult phenotype (7–59 years) involving central nervous system (CNS) agents and nighttime presentation; and an elderly phenotype (≥ 75 years) involving cardiovascular agents and morning presentation. CNS agent poisoning was an independent risk factor for suicidal intent (OR = 5.157, 95% CI: 1.675–15.873, P = 0.004), with the highest risk in the 19–35 years age group. Conclusions This study reveals a significant post‑2020 increase in drug poisoning cases, distinct temporal clustering patterns, and three age‑specific poisoning phenotypes. CNS agent poisoning is a strong independent risk factor for suicidal intent, particularly in young adults. These findings support a shift from static, average‑load resource allocation toward dynamic, temporally optimized ED management strategies. Drug poisoning temporal epidemiology age-specific patterns suicidal intent emergency department resource allocation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Drug poisoning is a common acute medical condition worldwide, posing a severe threat to public health and imposing a persistent, heavy burden on emergency medical systems [ 1 ]. In recent years, with increased drug availability and the growing prevalence of mental health disorders, the incidence of drug poisoning has shown an upward trend in many countries [ 2 , 3 ]. Particularly since the COVID-19 pandemic, the mental health crisis has intensified, and drug overdose and related events have become more frequent in emergency departments, emerging as a prominent clinical and public health concern [ 4 , 5 ]. Therefore, an in-depth understanding of the epidemiological characteristics of drug poisoning, especially its distribution patterns across different populations and temporal dimensions, is critical for optimizing clinical resource allocation and implementing targeted interventions in emergency departments. Temporal epidemiology provides a robust analytical framework for uncovering the dynamic temporal patterns of health events. The predictability derived from the periodicity of cyclic variations holds practical value in routine medical practice [ 6 ]. Studies have demonstrated that acute poisoning events do not occur randomly but exhibit significant clustering at seasonal, weekly, and diurnal scales. These patterns are closely associated with climatic variations, social behavioral rhythms, mood fluctuation cycles, and the accessibility of medical services [ 7 , 8 ]. Notably, individuals in different age groups differ substantially in physiological status, behavioral patterns, and drug exposure environments, which inevitably result in distinct age-specific characteristics in the types of agents involved and the timing of poisoning episodes [ 9 ]. For instance, poisoning in children is mostly attributable to accidental ingestion, poisoning in adolescents and young adults is often linked to mood disorders and impulsive behaviors, and poisoning in older adults is primarily related to polypharmacy and inadvertent intake [ 10 – 12 ]. Nevertheless, although age is a key dimension for understanding the heterogeneity of drug poisoning, studies that systematically integrate three-dimensional data on "age, agent, and presentation time" from an emergency clinical perspective to identify high-risk populations, warn of peak periods, and guide flexible staffing and proactive resource deployment remain scarce. Against this background, the present study aimed to conduct a retrospective, multidimensional analysis of drug poisoning cases presenting to a tertiary hospital emergency department over the past decade (2015–2024) within a temporal epidemiological framework. By comprehensively applying temporal trend analysis, age-stratified comparison, and multidimensional correspondence analysis, this study sought to systematically characterize the poisoning agent profiles among different age groups and clarify their distribution patterns and interrelationships across annual, seasonal, and diurnal scales. We anticipate that the findings will not only enrich the academic understanding of drug poisoning but also provide essential data and evidence to help emergency departments identify high-risk presentation periods, implement management strategies based on age and agent type, and optimize the real-time allocation of human resources and specialized services. 2. Methods 2.1. Study Design and Data Source This was a single-center retrospective observational study. Data were obtained from the electronic medical record system of the emergency department of a tertiary hospital in Shanghai, China. The study protocol was approved by the Institutional Review Board of the hospital (approval number: 2026-IIT-011-E01), and the requirement for informed patient consent was waived. 2.2. Study Population and Inclusion and Exclusion Criteria Study participants were all patients who presented to the emergency department between January 1, 2015, and December 31, 2024, with a primary diagnosis of drug poisoning (International Classification of Diseases, 10th Revision [ICD-10] codes: T36-T50). After systematic retrieval and manual verification of medical records, a total of 884 eligible cases were included. Exclusion criteria included: follow-up records for the same poisoning event, ambiguous diagnoses, severely incomplete medical records, and cases in which the toxic agent was confirmed to be non-pharmaceutical (e.g., pesticides, toxic animals/plants, food). A post-hoc power analysis indicated that with α = 0.05 and power (1-β) = 0.8, the sample size was sufficient to detect moderate differences (effect size w ≥ 0.15) in the main variables. 2.3. Study Variables and Definitions Data extraction was conducted independently by two trained emergency medicine researchers using a standardized form. Key variables were defined as follows: Demographic variables: sex, age. Age was categorized into six groups: 0–6, 7–18, 19–35, 36–59, 60–74, and ≥ 75 years. Clinical variables: Type of poisoning agent: based on the Anatomical Therapeutic Chemical (ATC) classification and clinical practice, agents were classified into seven categories: central nervous system (CNS) drugs, antipyretic analgesics, cardiovascular drugs, anti-infective drugs, endocrine system drugs, traditional Chinese medicines, and others (e.g., dermatological topicals, respiratory drugs). Cause of poisoning: based on medical record review, cases were classified as accidental ingestion or suicidal intent. Case disposition: discharge (outpatient) or admission (inpatient). Temporal variables (based on arrival time at the ED): Year (2015–2024), Month, Season (spring: March-May; summer: June-August; autumn: September-November; winter: December-February), Day of the week, Day type (weekday or holiday/weekend), Diurnal period (early morning: 00:01–06:00; morning: 06:01–12:00; afternoon: 12:01–18:00; night: 18:01–24:00). 2.4. Statistical Analysis All analyses were performed using R software (version 4.3.1). A two-sided P-value < 0.05 was considered statistically significant. Descriptive statistics were presented as medians (IQR) for continuous variables and frequencies (percentages) for categorical variables. Associations between categorical variables were analyzed using the chi-square test. Joinpoint regression was used to analyze annual trends (2015–2024) and identify inflection points. Multiple correspondence analysis (MCA) was performed to explore the overall correlational structure among multiple categorical variables (age group, drug type, arrival period, etc.). A multivariate logistic regression model (forward stepwise method) was used to identify independent risk factors for suicidal intent, with results presented as odds ratios (OR) with 95% confidence intervals (CI). Model fit was assessed using the Hosmer-Lemeshow test, and discriminative performance was evaluated using the area under the receiver operating characteristic curve (AUC). Data visualization was performed using the ggplot2 package. 3. Results 3.1. Demographic and Baseline Clinical Characteristics A total of 884 patients with drug poisoning were enrolled. Among them, 623 (70.5%) were female. The largest age group was 19–35 years (278 cases, 31.4%). Suicidal intent was the predominant cause (716 cases, 81.0%). The vast majority involved single-drug poisoning (642 cases, 72.6%) and were discharged from the ED (788 cases, 89.1%). CNS drugs were the most common poisoning agents (569 cases, 64.3%). (Detailed data is presented in Table 1 ). Table 1 Characteristics of poisoning patients (N = 884) Variable n % χ² value P value Gender Male 261 29.5 148.25 < 0.001 Female 623 70.5 Age group (years) 0–6 41 4.6 243.96 < 0.001 7–18 173 19.6 19–35 278 31.4 36–59 194 21.9 60–74 104 11.8 ≥75 94 10.6 Cause of poisoning Accidental 168 19.0 339.71 < 0.001 Intentional 716 81.0 Type of poison Single 642 72.6 181.00 < 0.001 Multiple 242 27.4 Case source Outpatient 788 89.1 541.70 < 0.001 Inpatient 96 10.9 3.2. Distribution Characteristics of Poisoning Events at Multiple Temporal Scales Joinpoint regression analysis identified a significant inflection point in 2020 (p < 0.05). The number of cases showed a modest upward trend from 2015–2020, followed by a marked acceleration in growth from 2020–2024, representing a 4.6-fold increase in the annual growth rate (Fig. 1 ). Seasonal distribution peaked in autumn (September-November, 29.9% of cases), with December being the peak month (11.2%). Diurnal distribution showed a distinct bimodal pattern, with the primary peak at night (18:01–24:00, 32.2%) and a secondary peak in the afternoon (12:01–18:00, 29.8%). Incidence was significantly higher on weekdays than on holidays. A significant change in trend was observed at the joinpoint in 2020 (p < 0.05). The trend from 2015 to 2020 showed a slope of 7.5, corresponding to an annual percent change (APC) of 8.5%. Following 2020, the trend increased markedly, with a slope of 31.9 and an APC of 36.1% from 2020 to 2024. This indicates a substantial acceleration in poisoning cases after 2020. 3.3. Age-Stratified Differences in the Spectrum of Poisoning Drugs The composition of poisoning agents differed significantly among age groups (χ²=266.3, P < 0.001) (Fig. 2 ). CNS agents peaked in the 7–18 years group (74.5%) and remained most common in all adult subgroups. Cardiovascular drug poisoning exhibited a bimodal age distribution, with high proportions in the 0–6 years (9.8%) and ≥ 75 years (20.2%) groups. Poisoning by "other" agents (e.g., household chemicals) was highly concentrated in the 0–6 years group (53.7%). Stacked bar chart showing the proportional distribution of drug poisoning cases across seven categories and six age groups. Central nervous system (CNS) drugs were the predominant category in all age groups except children aged 0–6 years, in whom 'Other agents' (commonly used household medications) accounted for 53.7% of cases. Cardiovascular drug poisoning showed a bimodal distribution, with peaks in the youngest (9.8%) and oldest (20.2%) age groups. A χ² test confirmed a significant association between age group and drug category (χ²=266.3, P < 0.001). 3.4. Temporal Patterns of Hospital Presentation in Different Age Groups Marked differences were observed in peak arrival times. The 0–6 years group mainly presented during daytime (08:01–18:00). All subgroups aged 7–59 years peaked at night (20:01–24:00). The 60–74 years group peaked in the evening (18:01–21:00), while the ≥ 75 years group was concentrated in the morning (09:01–12:00). Weekly visit distributions also showed distinct age-specific patterns (Fig. 3 ). 3.5. Diurnal Temporal Distribution of Different Drug Categories The composition of poisoning agents varied significantly across diurnal periods (χ²=40.12, P = 0.002). CNS agents were predominant in all periods (57.9%-67.5%). Cardiovascular drug poisoning was highest in the morning (9.6%) and lowest in the early morning (2.6%). Poisoning by antipyretic analgesics showed a relatively higher proportion in the early morning (12.3%). No significant differences were observed by weekday/holiday status. 3.6. Multidimensional Structural Associations of Poisoning Characteristics MCA of 17 categorical variables revealed distinct patterns (Fig. 4 ). Dimension 1 (5.66% inertia) primarily separated seasonal patterns and patient types, associating autumn with pediatric cases (0–6 years). Dimension 2 (5.42% inertia) distinguished drug categories and exposure routes, associating CNS drugs with intentional poisoning. Key patterns included: a pediatric profile (0–6 years, autumn, "other" agents), an adult profile (19–59 years, CNS agents, intentional ingestion), and temporal clustering (nighttime presentations with adult populations and psychotropic drug use). 3.7. Independent Risk Factors for Suicidal Intent Multivariate logistic regression revealed that, compared to the 19–35 years group, the 0–6 years group (OR = 0.014, 95% CI: 0.004–0.044, P < 0.001) and the 60–74 years group (OR = 0.337, 95% CI: 0.174–0.649, P = 0.001) had a significantly lower risk of suicidal intent. Poisoning by CNS agents was an independent risk factor for suicidal intent (OR = 5.157, 95% CI: 1.675–15.873, P = 0.004) (Fig. 5 ). No significant associations were found for sex, visit time, or other drug categories. ** Forest plot showing odds ratios (ORs) and 95% confidence intervals from multivariable logistic regression analysis of factors associated with suicide intent (self-ingestion vs accidental poisoning) in patients with drug poisoning (N = 884). Solid circles indicate statistically significant predictors (P < 0.05), while open circles indicate non-significant predictors (P ≥ 0.05). CNS = Central Nervous System. 4. Discussion Within the framework of chronoepidemiology, the present study conducted a multidimensional analysis of drug poisoning cases in the emergency department over the past decade. The results demonstrated a significant increase in poisoning events since 2020, with distinct temporal clustering across seasons, months, days of the week, and diurnal periods. The categories of implicated agents were closely associated with age, and significant differences were observed in temporal presentation patterns among different age groups. Central nervous system (CNS) drug poisoning was identified as an independent risk factor for suicidal intent, whereas age exerted an important protective or moderating effect. These findings provide evidence-based support for optimizing emergency triage procedures and achieving precise and time‑stratified allocation of medical resources. 4.1. Annual Trends and Temporal Clustering: Macro‑dynamics and Circadian Rhythms of Emergency Poisoning Cases Joinpoint regression analysis revealed that 2020 represented a significant inflection point in the annual growth rate of drug poisoning cases, after which the growth rate increased to 4.6‑fold compared with the previous period. This trend is consistent with the “silent pandemic” of mental health disorders and substance abuse reported in multiple international studies following the COVID‑19 pandemic [ 4 , 5 , 13 ]. This shift is not merely a statistical fluctuation but indicates a sustained change in the demand pattern for emergency psychiatric and psychological crisis services. From a health system perspective, emergency departments should incorporate such macro‑social stressors into early warning indicators for visit burden and long‑term service capacity planning. Furthermore, the peak presentation of drug poisoning cases exhibited multi‑scale temporal clustering. The diurnal pattern was characterized by a major peak during nighttime (18:01–12:00) and a secondary peak in the afternoon, which closely overlapped with the circadian rhythm of emotional crises and the routine high‑volume period of emergency departments from evening to late night [ 8 ]. Such overlapping burden suggests that resource allocation strategies based on “average load” are insufficient to meet actual demand, indicating that emergency scheduling and nursing staffing should be flexibly adjusted according to peak presentation periods of different age groups. 4.2. Phenotypic Classification and Precision Intervention: Three Poisoning Patterns Based on the Age‑Drug‑Time Triad By integrating demographic, temporal, and clinical variables using multiple correspondence analysis (MCA), this study identified an age‑drug‑time triad and summarized three well‑characterized poisoning phenotypes: Childhood Phenotype (0–6 Years): Accidental Exposure and Intervention Opportunities This phenotype showed a strong association among children aged 0–6 years, household chemicals (other agents), and daytime/autumn presentation, reflecting a typical pattern of exploratory poisoning due to inadequate child safety precautions. The findings highlight the need to strengthen safety education and physical protective measures such as cabinet locks [ 14 ]. Increased outdoor activity among children in autumn may further elevate exposure risk. During triage, preventive safety inquiries should be routinely performed with guardians of children presenting during daytime, and brief, structured safety instructions (e.g., proper medication storage) should be integrated into the discharge process as a key entry point for public health education. Adolescent and Adult Phenotype (7–59 Years): High‑Risk Association Between Suicidal Intent and CNS Drugs The prominent feature of this phenotype was “CNS drug poisoning plus nighttime presentation”. The nighttime peak may be related to the circadian nadir of emotional regulation [ 8 ], nocturnal social isolation, and impulsive decision‑making. This study found that CNS drug poisoning was an independent risk factor for suicidal intent, consistent with the central role of psychoactive substances in suicidal poisoning documented in global studies [ 2 , 3 , 15 ]. For patients with these age and toxicological profiles, regardless of their initial complaint, the explanation of “accidental ingestion” should not be readily accepted; instead, standardized and mandatory suicide risk assessment should be initiated to establish a safety prevention mechanism at the triage stage. Geriatric Phenotype (≥ 75 Years): Polypharmacy and Morning Risk This phenotype was characterized by “cardiovascular drug poisoning plus morning presentation”, which may be attributed to chronic disease management, morning medication habits, polypharmacy, complex dosing regimens, and age‑related cognitive or visual impairment [ 12 , 16 ]. This suggests that emergency departments should strengthen medication reconciliation for elderly patients during morning hours. The deployment of clinical pharmacists or the introduction of rapid electronic prescription review systems is recommended to identify dosing errors, drug‑drug interactions, or unintentional duplicate medication intake, thereby effectively preventing recurrent poisoning due to accidental ingestion or dosage errors. These three phenotypes were not randomly distributed but resulted from interactions between individual developmental stages, behavioral patterns, and social rhythms, showing predictable circadian and age‑specific presentation rules that provide clear practical guidance for emergency triage and assessment. For example, aligning the schedules of psychiatric nurses or social workers with evening and night shifts (i.e., peak suicide risk periods among young and middle‑aged adults) or concentrating pediatric expertise during daytime hours enables precise matching of resource allocation to demand. Similarly, the stockpiling and placement of antidotes (e.g., naloxone) and specialized treatment equipment can be optimized according to expected patient composition across different time periods and seasons. 4.3. Core Risk Factors and Clinical Alerts: Roles of Age and CNS Drugs in Suicidal Intent Multivariate logistic regression analysis further demonstrated that age was significantly associated with suicidal intent, with individuals aged 19–35 years carrying the highest risk, consistent with previous studies [ 17 ]. CNS drug poisoning was confirmed as an independent risk factor for suicidal intent (OR = 5.157), and its strong association with suicidal intent is consistent with its pharmacological mechanisms [ 18 ]. Clinicians should maintain high vigilance toward young and middle‑aged patients with CNS drug poisoning, prioritize psychological assessment and crisis intervention at triage, and strengthen prescription management and medication counseling for such agents [ 19 ]. Furthermore, the clinical application of artificial intelligence in pharmacovigilance should be promoted to further improve patient safety and medical outcomes [ 20 ]. 4.4. Limitations and Future Directions This study has several limitations. As a single‑center retrospective investigation, caution is required when extrapolating the identified patterns to other settings. Suicidal intent was determined based on medical records rather than standardized psychometric instruments, which may introduce recording bias or underreporting. In addition, important psychosocial confounding factors such as socioeconomic status and detailed psychiatric history were not included. Future prospective, multi‑center studies incorporating these variables, combined with standardized psychological assessment and long‑term follow‑up, are warranted to validate the distribution patterns identified in this study and to construct risk prediction models. 5. Conclusions and Implications Within the chronoepidemiological framework, this study revealed a significant inflection point in the annual trend of emergency department drug poisoning cases in 2020, with a marked increase in the post‑pandemic era. Case presentation peaks displayed multi‑scale temporal clustering. Multiple correspondence analysis identified an age‑drug‑time triad that defined three poisoning phenotypes (accidental exposure in children, suicidal intent in young and middle‑aged adults, and polypharmacy in the elderly), and confirmed that CNS drug poisoning was a strong independent risk factor for suicidal intent, with the highest risk observed in individuals aged 19–35 years. We recommend that emergency departments shift from static resource allocation based on “average load” to dynamic resource scheduling guided by temporal rhythms, and establish a precision response mechanism in triage workflows that is time‑specific, population‑specific, and agent‑specific: embedding preventive safety guidance for pediatric cases presenting during daytime; initiating mandatory suicide risk assessment for young and middle‑aged patients with CNS drug poisoning presenting at night; and strengthening medication reconciliation and pharmaceutical interventions for elderly cases related to cardiovascular drugs presenting in the morning. Meanwhile, macro‑social stressors (e.g., pandemic impacts) should be integrated into visit burden early warning models, and artificial intelligence should be employed to enhance pharmacovigilance and prescription management, thereby achieving a systematic transformation from passive response to proactive prediction and from uniform allocation to precise matching under constrained resources. Declarations Funding: This study was supported by the Key Discipline Construction Project of Pudong New Area Health Commission, Emergency Medicine (PWZxk2025-09). On behalf of all authors, the corresponding author hereby declares that: Conflicts of Interest: No competing interests exist. The authors have no financial or non‑financial competing interests related to this manuscript. Specifically: The authors declare that the funding source (Key Discipline Construction Project of Pudong New Area Health Commission) had no role in the study design, data collection, analysis, decision to publish, or preparation of the manuscript. No author holds any stocks or shares in any organization that could gain or lose financially from the publication of this manuscript. No author has any paid or unpaid positions, consultancies, or advisory roles that could be related to the subject of this manuscript. No author has any patents or patent applications relevant to the content of this manuscript. No author has any personal relationships or affiliations that could have influenced the work reported in this paper. the authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper. the results/data/figures in this manuscript have not been published elsewhere, nor are they under consideration (from you or one of your Contributing Authors) by another publisher. all of the material is owned by the authors and/or no permissions are required. The data that support the findings of this study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate: This study was approved by the Ethics Committee of Shanghai Pudong Hospital (Approval No. 2026-IIT-011). All procedures were performed in accordance with the Declaration of Helsinki. The requirement for informed consent was waived by the Ethics Committee due to the retrospective nature of this study and the use of anonymized data. This declaration is made in accordance with the policies of BMC Emergency Medicine and the recommendations of the International Committee of Medical Journal Editors (ICMJE). Corresponding Author: Xiong Wujun, PhD, Chief Physician. Shanghai Pudong Hospital, Fudan University Pudong Medical Center Email: [email protected] References Vega IL, Griswold MK, Laskey D. Acute Medication Poisoning. Am Fam Physician. 2024;109(2):143–53. PMID: 38393798. Nyman AAT, Bogstrand ST, Clausen T, Edvardsen HME. Toxicological findings in overdose suicides 2016-21. Tidsskr Nor Laegeforen. 2025;145(2). English, Norwegian. 10.4045/tidsskr.23.0836 . PMID: 39932090. Gummin DD, Mowry JB, Beuhler MC et al. 2022 Annual Report of the National Poison Data System (NPDS) from America‘s Poison Centers: 40th Annual Report. Clin Toxicol (Phila). 2023;61(10):717–939. 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Motivations for Prescription Drug Misuse Related to Mental Health Problems in Adults. Subst Use Misuse. 2022;57(2):316–27. 10.1080/10826084.2021.2012687 . Epub 2021 Dec 14. PMID: 34903123; PMCID: PMC8842830. Dsouza VS, Leyens L, Kurian JR, Brand A, Brand H. Artificial intelligence (AI) in pharmacovigilance: A systematic review on predicting adverse drug reactions (ADR) in hospitalized patients. Res Social Adm Pharm. 2025;21(6):453–62. Epub 2025 Feb 12. PMID: 39961738. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 23 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers invited by journal 21 Apr, 2026 Editor invited by journal 03 Apr, 2026 Editor assigned by journal 01 Apr, 2026 Submission checks completed at journal 01 Apr, 2026 First submitted to journal 29 Mar, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9258675","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":628549203,"identity":"ed6595cd-14f3-4210-b87f-5e036a786913","order_by":0,"name":"Xichang Zhao","email":"","orcid":"","institution":"Shanghai Pudong Hospital(Fudan University Pudong Medical Center)","correspondingAuthor":false,"prefix":"","firstName":"Xichang","middleName":"","lastName":"Zhao","suffix":""},{"id":628549204,"identity":"3c25e902-b12e-45aa-9549-2985996823cc","order_by":1,"name":"Yufang Ye","email":"","orcid":"","institution":"Shanghai Pudong Hospital(Fudan University Pudong Medical Center)","correspondingAuthor":false,"prefix":"","firstName":"Yufang","middleName":"","lastName":"Ye","suffix":""},{"id":628549205,"identity":"3593c7cf-1ac7-4cf1-b0dd-78e6c8b20585","order_by":2,"name":"Aihua Mao","email":"","orcid":"","institution":"Shanghai Pudong Hospital(Fudan University Pudong Medical Center)","correspondingAuthor":false,"prefix":"","firstName":"Aihua","middleName":"","lastName":"Mao","suffix":""},{"id":628549206,"identity":"847cc543-a3c5-40f0-afe4-4b9ba547b739","order_by":3,"name":"Zhihua Wang","email":"","orcid":"","institution":"Shanghai Pudong Hospital(Fudan University Pudong Medical Center)","correspondingAuthor":false,"prefix":"","firstName":"Zhihua","middleName":"","lastName":"Wang","suffix":""},{"id":628549207,"identity":"295d2974-e5ae-4518-9c3c-2702e304608d","order_by":4,"name":"Wujun Xiong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYDACZjjFfODAhwrStLAlHpxxhjT7eIwP87YQoc7gOPOxh1/bDrMb3O75cIC3gUGeX+wAfi2SzWzpxrJth5kN7pzdcEByB4PhzNkJ+LXwM/OYSUtuA2q5kbvhgOEZhgSD2wS0sCG05Dw4kNhGhBaQLZIfIVoYDhwkRgvQL2nSjP/SmSVvpBkcbDgjQdgvBucPH5P8ccY6me9G8uPPfyps5PmlCWgBAWYeBoZkKFuCsHIQYPzBwGBHnNJRMApGwSgYkQAABwNFJG2TtSAAAAAASUVORK5CYII=","orcid":"","institution":"Shanghai Pudong Hospital(Fudan University Pudong Medical Center)","correspondingAuthor":true,"prefix":"","firstName":"Wujun","middleName":"","lastName":"Xiong","suffix":""}],"badges":[],"createdAt":"2026-03-29 12:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9258675/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9258675/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108398457,"identity":"e0c49728-3ae6-4e33-8352-9169e1175165","added_by":"auto","created_at":"2026-05-04 08:31:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":186712,"visible":true,"origin":"","legend":"\u003cp\u003eJoinpoint regression analysis of poisoning cases from 2015 to 2024.\u003c/p\u003e\n\u003cp\u003eA significant change in trend was observed at the joinpoint in 2020 (p\u0026lt;0.05). The trend from 2015 to 2020 showed a slope of 7.5, corresponding to an annual percent change (APC) of 8.5%. Following 2020, the trend increased markedly, with a slope of 31.9 and an APC of 36.1% from 2020 to 2024. This indicates a substantial acceleration in poisoning cases after 2020.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9258675/v1/eb3cf00a693521375b1faed8.png"},{"id":108398458,"identity":"05abb123-599f-4ec2-bead-b2b88fc2c947","added_by":"auto","created_at":"2026-05-04 08:31:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":486382,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAge-stratified distribution of drug poisoning categories (N=884).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStacked bar chart showing the proportional distribution of drug poisoning cases across seven categories and six age groups. Central nervous system (CNS) drugs were the predominant category in all age groups except children aged 0-6 years, in whom 'Other agents' (commonly used household medications) accounted for 53.7% of cases. Cardiovascular drug poisoning showed a bimodal distribution, with peaks in the youngest (9.8%) and oldest (20.2%) age groups. A χ²test confirmed a significant association between age group and drug category (χ²=266.3, P\u0026lt;0.001).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9258675/v1/d9bc0976cc7cb278ea60b876.png"},{"id":108804214,"identity":"5251b133-b63d-45a8-b1b7-f35e19e1522d","added_by":"auto","created_at":"2026-05-08 15:18:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":390484,"visible":true,"origin":"","legend":"\u003cp\u003eSpatiotemporal distribution of poisoning cases by age group. (A) Weekly distribution across days of the week. (B) Diurnal distribution across four time periods. Darker shades indicate higher case concentrations. Young adults (19-35 years) showed the highest overall case counts, with peak presentation during evening hours (18:01-24:00). Older adults (≥75 years) presented more frequently during morning hours (06:01-12:00), while pediatric cases (0-6 years) peaked on Sundays.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9258675/v1/d17b996f7f26755099a9e041.png"},{"id":108398460,"identity":"dcf6cbd5-f247-4f83-92c9-07a2fa84e3d7","added_by":"auto","created_at":"2026-05-04 08:31:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":342773,"visible":true,"origin":"","legend":"\u003cp\u003eMultiple Correspondence Analysis (MCA) of drug poisoning\u003cstrong\u003e \u003c/strong\u003echaracteristics.\u003c/p\u003e\n\u003cp\u003eThe biplot displays associations among variables across five categories: demographics (blue circles), time-related factors (green triangles), exposure routes (orange squares), drug types (red diamonds), and other variables (purple stars). Dimension 1 (x-axis, 5.66% of inertia) primarily distinguishes seasonal patterns and patient types, with autumn and pediatric cases (0-6 years) on the positive end. Dimension 2 (y-axis,5.42% of inertia) separates drug categories and exposure routes, with central nervous system drugs and unintentional poisoning on the positive end. Point size represents contribution to the dimensions, and proximity indicates frequent co-occurrence. Only key variables are labeled for clarity.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9258675/v1/32006b30c05f68c7e5a25ecd.png"},{"id":108493049,"identity":"b161106d-9721-4cc6-a84c-a859d127a11a","added_by":"auto","created_at":"2026-05-05 09:59:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":383485,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredictors of Suicide Intent.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e** Forest plot showing odds ratios (ORs) and 95% confidence intervals from multivariable logistic regression analysis of factors associated with suicide intent (self-ingestion vs accidental poisoning) in patients with drug poisoning (N = 884). Solid circles indicate statistically significant predictors (P \u0026lt; 0.05), while open circles indicate non-significant predictors (P ≥ 0.05). CNS = Central Nervous System.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9258675/v1/b1f814292d3ce81e627a9114.png"},{"id":108809035,"identity":"1c42712a-51d7-40a7-9073-0ac15ffe8f35","added_by":"auto","created_at":"2026-05-08 15:48:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1864972,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9258675/v1/fe6789ee-52e2-4ebf-83ae-31af7bced357.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Age-Specific Temporal Patterns of Drug Poisoning: A Decade-Long Retrospective Analysis for Emergency Triage Optimization","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDrug poisoning is a common acute medical condition worldwide, posing a severe threat to public health and imposing a persistent, heavy burden on emergency medical systems [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In recent years, with increased drug availability and the growing prevalence of mental health disorders, the incidence of drug poisoning has shown an upward trend in many countries [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Particularly since the COVID-19 pandemic, the mental health crisis has intensified, and drug overdose and related events have become more frequent in emergency departments, emerging as a prominent clinical and public health concern [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, an in-depth understanding of the epidemiological characteristics of drug poisoning, especially its distribution patterns across different populations and temporal dimensions, is critical for optimizing clinical resource allocation and implementing targeted interventions in emergency departments.\u003c/p\u003e \u003cp\u003eTemporal epidemiology provides a robust analytical framework for uncovering the dynamic temporal patterns of health events. The predictability derived from the periodicity of cyclic variations holds practical value in routine medical practice [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Studies have demonstrated that acute poisoning events do not occur randomly but exhibit significant clustering at seasonal, weekly, and diurnal scales. These patterns are closely associated with climatic variations, social behavioral rhythms, mood fluctuation cycles, and the accessibility of medical services [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Notably, individuals in different age groups differ substantially in physiological status, behavioral patterns, and drug exposure environments, which inevitably result in distinct age-specific characteristics in the types of agents involved and the timing of poisoning episodes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. For instance, poisoning in children is mostly attributable to accidental ingestion, poisoning in adolescents and young adults is often linked to mood disorders and impulsive behaviors, and poisoning in older adults is primarily related to polypharmacy and inadvertent intake [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Nevertheless, although age is a key dimension for understanding the heterogeneity of drug poisoning, studies that systematically integrate three-dimensional data on \"age, agent, and presentation time\" from an emergency clinical perspective to identify high-risk populations, warn of peak periods, and guide flexible staffing and proactive resource deployment remain scarce.\u003c/p\u003e \u003cp\u003eAgainst this background, the present study aimed to conduct a retrospective, multidimensional analysis of drug poisoning cases presenting to a tertiary hospital emergency department over the past decade (2015\u0026ndash;2024) within a temporal epidemiological framework. By comprehensively applying temporal trend analysis, age-stratified comparison, and multidimensional correspondence analysis, this study sought to systematically characterize the poisoning agent profiles among different age groups and clarify their distribution patterns and interrelationships across annual, seasonal, and diurnal scales. We anticipate that the findings will not only enrich the academic understanding of drug poisoning but also provide essential data and evidence to help emergency departments identify high-risk presentation periods, implement management strategies based on age and agent type, and optimize the real-time allocation of human resources and specialized services.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Design and Data Source\u003c/h2\u003e \u003cp\u003eThis was a single-center retrospective observational study. Data were obtained from the electronic medical record system of the emergency department of a tertiary hospital in Shanghai, China. The study protocol was approved by the Institutional Review Board of the hospital (approval number: 2026-IIT-011-E01), and the requirement for informed patient consent was waived.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Study Population and Inclusion and Exclusion Criteria\u003c/h2\u003e \u003cp\u003eStudy participants were all patients who presented to the emergency department between January 1, 2015, and December 31, 2024, with a primary diagnosis of drug poisoning (International Classification of Diseases, 10th Revision [ICD-10] codes: T36-T50). After systematic retrieval and manual verification of medical records, a total of 884 eligible cases were included. Exclusion criteria included: follow-up records for the same poisoning event, ambiguous diagnoses, severely incomplete medical records, and cases in which the toxic agent was confirmed to be non-pharmaceutical (e.g., pesticides, toxic animals/plants, food). A post-hoc power analysis indicated that with α\u0026thinsp;=\u0026thinsp;0.05 and power (1-β)\u0026thinsp;=\u0026thinsp;0.8, the sample size was sufficient to detect moderate differences (effect size w\u0026thinsp;\u0026ge;\u0026thinsp;0.15) in the main variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Study Variables and Definitions\u003c/h2\u003e \u003cp\u003eData extraction was conducted independently by two trained emergency medicine researchers using a standardized form. Key variables were defined as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eDemographic variables: sex, age. Age was categorized into six groups: 0\u0026ndash;6, 7\u0026ndash;18, 19\u0026ndash;35, 36\u0026ndash;59, 60\u0026ndash;74, and \u0026ge;\u0026thinsp;75 years.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e Clinical variables: Type of poisoning agent: based on the Anatomical Therapeutic Chemical (ATC) classification and clinical practice, agents were classified into seven categories: central nervous system (CNS) drugs, antipyretic analgesics, cardiovascular drugs, anti-infective drugs, endocrine system drugs, traditional Chinese medicines, and others (e.g., dermatological topicals, respiratory drugs). Cause of poisoning: based on medical record review, cases were classified as accidental ingestion or suicidal intent. Case disposition: discharge (outpatient) or admission (inpatient).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTemporal variables (based on arrival time at the ED): Year (2015\u0026ndash;2024), Month, Season (spring: March-May; summer: June-August; autumn: September-November; winter: December-February), Day of the week, Day type (weekday or holiday/weekend), Diurnal period (early morning: 00:01\u0026ndash;06:00; morning: 06:01\u0026ndash;12:00; afternoon: 12:01\u0026ndash;18:00; night: 18:01\u0026ndash;24:00).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll analyses were performed using R software (version 4.3.1). A two-sided P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Descriptive statistics were presented as medians (IQR) for continuous variables and frequencies (percentages) for categorical variables. Associations between categorical variables were analyzed using the chi-square test. Joinpoint regression was used to analyze annual trends (2015\u0026ndash;2024) and identify inflection points. Multiple correspondence analysis (MCA) was performed to explore the overall correlational structure among multiple categorical variables (age group, drug type, arrival period, etc.). A multivariate logistic regression model (forward stepwise method) was used to identify independent risk factors for suicidal intent, with results presented as odds ratios (OR) with 95% confidence intervals (CI). Model fit was assessed using the Hosmer-Lemeshow test, and discriminative performance was evaluated using the area under the receiver operating characteristic curve (AUC). Data visualization was performed using the ggplot2 package.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Demographic and Baseline Clinical Characteristics\u003c/h2\u003e \u003cp\u003eA total of 884 patients with drug poisoning were enrolled. Among them, 623 (70.5%) were female. The largest age group was 19\u0026ndash;35 years (278 cases, 31.4%). Suicidal intent was the predominant cause (716 cases, 81.0%). The vast majority involved single-drug poisoning (642 cases, 72.6%) and were discharged from the ED (788 cases, 89.1%). CNS drugs were the most common poisoning agents (569 cases, 64.3%). (Detailed data is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of poisoning patients (N\u0026thinsp;=\u0026thinsp;884)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2; value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e148.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge group (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e243.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u0026ndash;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCause of poisoning\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccidental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e339.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntentional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of poison\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e181.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCase source\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutpatient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e541.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInpatient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Distribution Characteristics of Poisoning Events at Multiple Temporal Scales\u003c/h2\u003e \u003cp\u003eJoinpoint regression analysis identified a significant inflection point in 2020 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The number of cases showed a modest upward trend from 2015\u0026ndash;2020, followed by a marked acceleration in growth from 2020\u0026ndash;2024, representing a 4.6-fold increase in the annual growth rate (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Seasonal distribution peaked in autumn (September-November, 29.9% of cases), with December being the peak month (11.2%). Diurnal distribution showed a distinct bimodal pattern, with the primary peak at night (18:01\u0026ndash;24:00, 32.2%) and a secondary peak in the afternoon (12:01\u0026ndash;18:00, 29.8%). Incidence was significantly higher on weekdays than on holidays.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA significant change in trend was observed at the joinpoint in 2020 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The trend from 2015 to 2020 showed a slope of 7.5, corresponding to an annual percent change (APC) of 8.5%. Following 2020, the trend increased markedly, with a slope of 31.9 and an APC of 36.1% from 2020 to 2024. This indicates a substantial acceleration in poisoning cases after 2020.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Age-Stratified Differences in the Spectrum of Poisoning Drugs\u003c/h2\u003e \u003cp\u003eThe composition of poisoning agents differed significantly among age groups (χ\u0026sup2;=266.3, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). CNS agents peaked in the 7\u0026ndash;18 years group (74.5%) and remained most common in all adult subgroups. Cardiovascular drug poisoning exhibited a bimodal age distribution, with high proportions in the 0\u0026ndash;6 years (9.8%) and \u0026ge;\u0026thinsp;75 years (20.2%) groups. Poisoning by \"other\" agents (e.g., household chemicals) was highly concentrated in the 0\u0026ndash;6 years group (53.7%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStacked bar chart showing the proportional distribution of drug poisoning cases across seven categories and six age groups. Central nervous system (CNS) drugs were the predominant category in all age groups except children aged 0\u0026ndash;6 years, in whom 'Other agents' (commonly used household medications) accounted for 53.7% of cases. Cardiovascular drug poisoning showed a bimodal distribution, with peaks in the youngest (9.8%) and oldest (20.2%) age groups. A χ\u0026sup2; test confirmed a significant association between age group and drug category (χ\u0026sup2;=266.3, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Temporal Patterns of Hospital Presentation in Different Age Groups\u003c/h2\u003e \u003cp\u003eMarked differences were observed in peak arrival times. The 0\u0026ndash;6 years group mainly presented during daytime (08:01\u0026ndash;18:00). All subgroups aged 7\u0026ndash;59 years peaked at night (20:01\u0026ndash;24:00). The 60\u0026ndash;74 years group peaked in the evening (18:01\u0026ndash;21:00), while the \u0026ge;\u0026thinsp;75 years group was concentrated in the morning (09:01\u0026ndash;12:00). Weekly visit distributions also showed distinct age-specific patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Diurnal Temporal Distribution of Different Drug Categories\u003c/h2\u003e \u003cp\u003eThe composition of poisoning agents varied significantly across diurnal periods (χ\u0026sup2;=40.12, P\u0026thinsp;=\u0026thinsp;0.002). CNS agents were predominant in all periods (57.9%-67.5%). Cardiovascular drug poisoning was highest in the morning (9.6%) and lowest in the early morning (2.6%). Poisoning by antipyretic analgesics showed a relatively higher proportion in the early morning (12.3%). No significant differences were observed by weekday/holiday status.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Multidimensional Structural Associations of Poisoning Characteristics\u003c/h2\u003e \u003cp\u003eMCA of 17 categorical variables revealed distinct patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Dimension 1 (5.66% inertia) primarily separated seasonal patterns and patient types, associating autumn with pediatric cases (0\u0026ndash;6 years). Dimension 2 (5.42% inertia) distinguished drug categories and exposure routes, associating CNS drugs with intentional poisoning. Key patterns included: a pediatric profile (0\u0026ndash;6 years, autumn, \"other\" agents), an adult profile (19\u0026ndash;59 years, CNS agents, intentional ingestion), and temporal clustering (nighttime presentations with adult populations and psychotropic drug use).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Independent Risk Factors for Suicidal Intent\u003c/h2\u003e \u003cp\u003eMultivariate logistic regression revealed that, compared to the 19\u0026ndash;35 years group, the 0\u0026ndash;6 years group (OR\u0026thinsp;=\u0026thinsp;0.014, 95% CI: 0.004\u0026ndash;0.044, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the 60\u0026ndash;74 years group (OR\u0026thinsp;=\u0026thinsp;0.337, 95% CI: 0.174\u0026ndash;0.649, P\u0026thinsp;=\u0026thinsp;0.001) had a significantly lower risk of suicidal intent. Poisoning by CNS agents was an independent risk factor for suicidal intent (OR\u0026thinsp;=\u0026thinsp;5.157, 95% CI: 1.675\u0026ndash;15.873, P\u0026thinsp;=\u0026thinsp;0.004) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). No significant associations were found for sex, visit time, or other drug categories.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e** Forest plot showing odds ratios (ORs) and 95% confidence intervals from multivariable logistic regression analysis of factors associated with suicide intent (self-ingestion vs accidental poisoning) in patients with drug poisoning (N\u0026thinsp;=\u0026thinsp;884). Solid circles indicate statistically significant predictors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while open circles indicate non-significant predictors (P\u0026thinsp;\u0026ge;\u0026thinsp;0.05). CNS\u0026thinsp;=\u0026thinsp;Central Nervous System.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWithin the framework of chronoepidemiology, the present study conducted a multidimensional analysis of drug poisoning cases in the emergency department over the past decade. The results demonstrated a significant increase in poisoning events since 2020, with distinct temporal clustering across seasons, months, days of the week, and diurnal periods. The categories of implicated agents were closely associated with age, and significant differences were observed in temporal presentation patterns among different age groups. Central nervous system (CNS) drug poisoning was identified as an independent risk factor for suicidal intent, whereas age exerted an important protective or moderating effect. These findings provide evidence-based support for optimizing emergency triage procedures and achieving precise and time‑stratified allocation of medical resources.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Annual Trends and Temporal Clustering: Macro‑dynamics and Circadian Rhythms of Emergency Poisoning Cases\u003c/h2\u003e \u003cp\u003eJoinpoint regression analysis revealed that 2020 represented a significant inflection point in the annual growth rate of drug poisoning cases, after which the growth rate increased to 4.6‑fold compared with the previous period. This trend is consistent with the \u0026ldquo;silent pandemic\u0026rdquo; of mental health disorders and substance abuse reported in multiple international studies following the COVID‑19 pandemic [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This shift is not merely a statistical fluctuation but indicates a sustained change in the demand pattern for emergency psychiatric and psychological crisis services. From a health system perspective, emergency departments should incorporate such macro‑social stressors into early warning indicators for visit burden and long‑term service capacity planning.\u003c/p\u003e \u003cp\u003eFurthermore, the peak presentation of drug poisoning cases exhibited multi‑scale temporal clustering. The diurnal pattern was characterized by a major peak during nighttime (18:01\u0026ndash;12:00) and a secondary peak in the afternoon, which closely overlapped with the circadian rhythm of emotional crises and the routine high‑volume period of emergency departments from evening to late night [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Such overlapping burden suggests that resource allocation strategies based on \u0026ldquo;average load\u0026rdquo; are insufficient to meet actual demand, indicating that emergency scheduling and nursing staffing should be flexibly adjusted according to peak presentation periods of different age groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Phenotypic Classification and Precision Intervention: Three Poisoning Patterns Based on the Age‑Drug‑Time Triad\u003c/h2\u003e \u003cp\u003eBy integrating demographic, temporal, and clinical variables using multiple correspondence analysis (MCA), this study identified an age‑drug‑time triad and summarized three well‑characterized poisoning phenotypes:\u003c/p\u003e \u003cp\u003e \u003cb\u003eChildhood Phenotype (0\u0026ndash;6 Years): Accidental Exposure and Intervention Opportunities\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis phenotype showed a strong association among children aged 0\u0026ndash;6 years, household chemicals (other agents), and daytime/autumn presentation, reflecting a typical pattern of exploratory poisoning due to inadequate child safety precautions. The findings highlight the need to strengthen safety education and physical protective measures such as cabinet locks [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Increased outdoor activity among children in autumn may further elevate exposure risk. During triage, preventive safety inquiries should be routinely performed with guardians of children presenting during daytime, and brief, structured safety instructions (e.g., proper medication storage) should be integrated into the discharge process as a key entry point for public health education.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAdolescent and Adult Phenotype (7\u0026ndash;59 Years): High‑Risk Association Between Suicidal Intent and CNS Drugs\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe prominent feature of this phenotype was \u0026ldquo;CNS drug poisoning plus nighttime presentation\u0026rdquo;. The nighttime peak may be related to the circadian nadir of emotional regulation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], nocturnal social isolation, and impulsive decision‑making. This study found that CNS drug poisoning was an independent risk factor for suicidal intent, consistent with the central role of psychoactive substances in suicidal poisoning documented in global studies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. For patients with these age and toxicological profiles, regardless of their initial complaint, the explanation of \u0026ldquo;accidental ingestion\u0026rdquo; should not be readily accepted; instead, standardized and mandatory suicide risk assessment should be initiated to establish a safety prevention mechanism at the triage stage.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGeriatric Phenotype (\u0026ge;\u0026thinsp;75 Years): Polypharmacy and Morning Risk\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis phenotype was characterized by \u0026ldquo;cardiovascular drug poisoning plus morning presentation\u0026rdquo;, which may be attributed to chronic disease management, morning medication habits, polypharmacy, complex dosing regimens, and age‑related cognitive or visual impairment [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This suggests that emergency departments should strengthen medication reconciliation for elderly patients during morning hours. The deployment of clinical pharmacists or the introduction of rapid electronic prescription review systems is recommended to identify dosing errors, drug‑drug interactions, or unintentional duplicate medication intake, thereby effectively preventing recurrent poisoning due to accidental ingestion or dosage errors.\u003c/p\u003e \u003cp\u003eThese three phenotypes were not randomly distributed but resulted from interactions between individual developmental stages, behavioral patterns, and social rhythms, showing predictable circadian and age‑specific presentation rules that provide clear practical guidance for emergency triage and assessment. For example, aligning the schedules of psychiatric nurses or social workers with evening and night shifts (i.e., peak suicide risk periods among young and middle‑aged adults) or concentrating pediatric expertise during daytime hours enables precise matching of resource allocation to demand. Similarly, the stockpiling and placement of antidotes (e.g., naloxone) and specialized treatment equipment can be optimized according to expected patient composition across different time periods and seasons.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Core Risk Factors and Clinical Alerts: Roles of Age and CNS Drugs in Suicidal Intent\u003c/h2\u003e \u003cp\u003eMultivariate logistic regression analysis further demonstrated that age was significantly associated with suicidal intent, with individuals aged 19\u0026ndash;35 years carrying the highest risk, consistent with previous studies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. CNS drug poisoning was confirmed as an independent risk factor for suicidal intent (OR\u0026thinsp;=\u0026thinsp;5.157), and its strong association with suicidal intent is consistent with its pharmacological mechanisms [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Clinicians should maintain high vigilance toward young and middle‑aged patients with CNS drug poisoning, prioritize psychological assessment and crisis intervention at triage, and strengthen prescription management and medication counseling for such agents [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Furthermore, the clinical application of artificial intelligence in pharmacovigilance should be promoted to further improve patient safety and medical outcomes [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Limitations and Future Directions\u003c/h2\u003e \u003cp\u003eThis study has several limitations. As a single‑center retrospective investigation, caution is required when extrapolating the identified patterns to other settings. Suicidal intent was determined based on medical records rather than standardized psychometric instruments, which may introduce recording bias or underreporting. In addition, important psychosocial confounding factors such as socioeconomic status and detailed psychiatric history were not included. Future prospective, multi‑center studies incorporating these variables, combined with standardized psychological assessment and long‑term follow‑up, are warranted to validate the distribution patterns identified in this study and to construct risk prediction models.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions and Implications","content":"\u003cp\u003eWithin the chronoepidemiological framework, this study revealed a significant inflection point in the annual trend of emergency department drug poisoning cases in 2020, with a marked increase in the post‑pandemic era. Case presentation peaks displayed multi‑scale temporal clustering. Multiple correspondence analysis identified an age‑drug‑time triad that defined three poisoning phenotypes (accidental exposure in children, suicidal intent in young and middle‑aged adults, and polypharmacy in the elderly), and confirmed that CNS drug poisoning was a strong independent risk factor for suicidal intent, with the highest risk observed in individuals aged 19\u0026ndash;35 years. We recommend that emergency departments shift from static resource allocation based on \u0026ldquo;average load\u0026rdquo; to dynamic resource scheduling guided by temporal rhythms, and establish a precision response mechanism in triage workflows that is time‑specific, population‑specific, and agent‑specific: embedding preventive safety guidance for pediatric cases presenting during daytime; initiating mandatory suicide risk assessment for young and middle‑aged patients with CNS drug poisoning presenting at night; and strengthening medication reconciliation and pharmaceutical interventions for elderly cases related to cardiovascular drugs presenting in the morning. Meanwhile, macro‑social stressors (e.g., pandemic impacts) should be integrated into visit burden early warning models, and artificial intelligence should be employed to enhance pharmacovigilance and prescription management, thereby achieving a systematic transformation from passive response to proactive prediction and from uniform allocation to precise matching under constrained resources.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Key Discipline Construction Project of Pudong New Area Health Commission, Emergency Medicine (PWZxk2025-09).\u003c/p\u003e\n\u003cp\u003eOn behalf of all authors, the corresponding author hereby declares that:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo competing interests exist.\u003c/p\u003e\n\u003cp\u003eThe authors have no financial or non‑financial competing interests related to this manuscript. Specifically:\u003c/p\u003e\n\u003cp\u003eThe authors declare that the funding source (Key Discipline Construction Project of Pudong New Area Health Commission) had no role in the study design, data collection, analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003eNo author holds any stocks or shares in any organization that could gain or lose financially from the publication of this manuscript.\u003c/p\u003e\n\u003cp\u003eNo author has any paid or unpaid positions, consultancies, or advisory roles that could be related to the subject of this manuscript.\u003c/p\u003e\n\u003cp\u003eNo author has any patents or patent applications relevant to the content of this manuscript.\u003c/p\u003e\n\u003cp\u003eNo author has any personal relationships or affiliations that could have influenced the work reported in this paper.\u003c/p\u003e\n\u003cp\u003ethe authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper.\u003c/p\u003e\n\u003cp\u003ethe results/data/figures in this manuscript have not been published elsewhere, nor are they under consideration (from you or one of your Contributing Authors) by another publisher.\u003c/p\u003e\n\u003cp\u003eall of the material is owned by the authors and/or no permissions are required.\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Shanghai Pudong Hospital (Approval No. 2026-IIT-011).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll procedures were performed in accordance with the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe requirement for informed consent was waived by the Ethics Committee due to the retrospective nature of this study and the use of anonymized data.\u003c/p\u003e\n\u003cp\u003eThis declaration is made in accordance with the policies of BMC Emergency Medicine and the recommendations of the International Committee of Medical Journal Editors (ICMJE).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding Author:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXiong Wujun,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ePhD, Chief Physician.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShanghai Pudong Hospital, Fudan University Pudong Medical Center\u003c/p\u003e\n\u003cp\u003eEmail: [email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVega IL, Griswold MK, Laskey D. 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PMID: 39961738.\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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Drug poisoning, temporal epidemiology, age-specific patterns, suicidal intent, emergency department, resource allocation","lastPublishedDoi":"10.21203/rs.3.rs-9258675/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9258675/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDrug poisoning is a common acute condition with increasing global prevalence, particularly following the COVID-19 pandemic. Understanding its multidimensional temporal distribution across age groups is essential for optimizing emergency department (ED) resource allocation and targeted interventions. This study investigates the temporal epidemiological characteristics of drug poisoning cases in a tertiary hospital ED over a decade, focusing on age‑specific differences, temporal clustering patterns, and risk factors for suicidal intent.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis single‑center retrospective study included 884 patients diagnosed with drug poisoning (ICD‑10: T36\u0026ndash;T50) at a tertiary hospital in Shanghai between January 2015 and December 2024. Joinpoint regression, age‑stratified analysis, multiple correspondence analysis (MCA), and multivariate logistic regression were used.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eJoinpoint regression identified 2020 as a significant inflection point, after which the annual growth rate increased 4.6‑fold. Poisoning events exhibited seasonal, monthly, and diurnal clustering, with a bimodal diurnal pattern (primary peak: 18:01\u0026ndash;24:00; secondary peak: 12:01\u0026ndash;18:00). Poisoning agent composition differed significantly across age groups (χ\u0026sup2;=266.3, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). MCA revealed three distinct \u0026ldquo;age‑agent‑time\u0026rdquo; phenotypes: a pediatric phenotype (0\u0026ndash;6 years) involving household medications and daytime/autumn presentation; an adolescent/adult phenotype (7\u0026ndash;59 years) involving central nervous system (CNS) agents and nighttime presentation; and an elderly phenotype (\u0026ge;\u0026thinsp;75 years) involving cardiovascular agents and morning presentation. CNS agent poisoning was an independent risk factor for suicidal intent (OR\u0026thinsp;=\u0026thinsp;5.157, 95% CI: 1.675\u0026ndash;15.873, P\u0026thinsp;=\u0026thinsp;0.004), with the highest risk in the 19\u0026ndash;35 years age group.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study reveals a significant post‑2020 increase in drug poisoning cases, distinct temporal clustering patterns, and three age‑specific poisoning phenotypes. CNS agent poisoning is a strong independent risk factor for suicidal intent, particularly in young adults. These findings support a shift from static, average‑load resource allocation toward dynamic, temporally optimized ED management strategies.\u003c/p\u003e","manuscriptTitle":"Age-Specific Temporal Patterns of Drug Poisoning: A Decade-Long Retrospective Analysis for Emergency Triage Optimization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 08:31:31","doi":"10.21203/rs.3.rs-9258675/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-23T18:30:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"166192627483147935831691943215118766067","date":"2026-04-23T13:59:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-21T20:59:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-03T07:19:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-01T11:25:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-01T11:24:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Emergency Medicine","date":"2026-03-29T12:10:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5e0f3351-dcf4-4e1d-b003-c7107f87bf8e","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T08:31:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 08:31:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9258675","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9258675","identity":"rs-9258675","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}