Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder marked by persistent symptoms of inattention, hyperactivity, and impulsivity, typically presenting in childhood. It affects approximately 5% of children globally (1), and while its prevalence tends to decrease with age, symptoms often persist into adulthood, potentially leading to academic challenges, social dysfunction, and a higher risk of criminal behavior (2). As such, a systematic analysis of the ADHD burden and its temporal trends is critical for developing evidence-based strategies for prevention and intervention.

The etiology and risk factors for ADHD remain subjects of ongoing investigation. Due to its lifelong societal impact—ranging from educational difficulties to increased healthcare costs—long-term epidemiological studies are essential for understanding the interaction between genetic predispositions and changing environmental factors (e.g., air pollution (3), screen time (4), and dietary habits (5)). Advanced analytical models are required to parse these complex interactions. The Joinpoint regression model (6), which identifies key inflection points in temporal trends using segmented linear regression, is widely employed to detect abrupt changes in disease prevalence, such as those driven by shifts in diagnostic criteria or increased public awareness. Meanwhile, the age-period-cohort (APC) model (7)disentangles the contributions of three distinct effects: 1) age effects (biological maturation or aging), 2) period effects (external events affecting all age groups simultaneously, such as changes in diagnostic guidelines or exposure to environmental risk factors), and 3) cohort effects (generation-specific risks, often linked to early-life socio-economic conditions or public health trends). This multidimensional approach is particularly valuable for ADHD, where trends may reflect both inherent developmental factors (age effects) and evolving social determinants (period/cohort effects). However, large-scale studies employing these models to investigate ADHD burden, especially in rapidly changing sociocultural contexts, remain rare, limiting the development of targeted prevention strategies across diverse populations. Similar models have been successfully used for mental disorders: Joinpoint regression has revealed increasing prevalence of autism spectrum disorders in the U.S. due to expanded diagnostic criteria (8), while APC analysis of depression in South Korea has highlighted cohort effects related to economic crises (9). However, studies on ADHD, particularly in societies undergoing rapid transitions, are still scarce, despite the disorder’s sensitivity to cultural factors.

Despite the global significance of ADHD, research in low- and middle-income countries (LMICs), particularly in China, remains limited. Over 80% of children and adolescents worldwide reside in LMICs, yet most mental health research on children and adolescents has been conducted in high-income countries, with scarce reports on the prevalence of ADHD among children in LMICs (10).In 2020, China’s child population accounted for 21.1% of its total population and 12.7% of the global child population, making it the second-largest child population worldwide (11).However, the burden of ADHD in China remains poorly defined due to underdiagnosis, mental health stigma, and a lack of longitudinal data (12). These gaps in knowledge not only impede local policy development but also introduce significant uncertainty into global burden estimates. Furthermore, China’s unique sociocultural transitions—including rapid urbanization, a highly competitive education system, and changes in family structures—may exacerbate ADHD risk factors in ways distinct from high-income countries (13). Addressing these challenges requires targeted analyses that consider the country’s specific social and demographic context.

We hypothesize that ADHD prevalence in China has undergone significant shifts since 1990, influenced by age-related neurodevelopmental changes, as well as period-specific factors (e.g., mental health policy reforms) and cohort effects tied to urbanization and evolving family structures. To test this hypothesis, we utilized data from the 2021 Global Burden of Disease (GBD) database, applying Joinpoint regression to identify key trend transitions and APC modeling to disentangle the contributions of age, period, and cohort effects. We expect the Joinpoint analysis to reveal an acceleration in ADHD prevalence after 2010, coinciding with increased mental health awareness campaigns. The APC model is likely to uncover prominent cohort effects, particularly among children born during China’s economic liberalization (post-1990s), potentially linked to increased parental migration and screen time exposure. The findings from this study aim to provide robust evidence for optimizing ADHD prevention strategies tailored to China’s unique sociocultural context.

This study reveals a significant increase in the incidence rate, age-standardized prevalence rate, and age-standardized DALY (Disability-Adjusted Life Years) rate for ADHD in China between 1990 and 2021. Several factors may contribute to the high incidence, prevalence, and DALY rates of ADHD among children and adolescents in China, including environmental factors (15),socioeconomic conditions (16), changes in diagnostic criteria and awareness (17), genetic susceptibility (18), and lifestyle or dietary patterns (19). According to the latest Global Burden of Disease (GBD) data, the incidence, prevalence, and DALY rates of ADHD in Chinese children and adolescents exceed global averages, indicating a significant disease burden in this population. This difference is further amplified by China’s unique socio-cultural context: the highly competitive education system (20), which prioritizes academic performance from an early age, may exacerbate attention deficits and impulsivity associated with ADHD; rapid urbanization since the 1990s (21)has led to increased screen time (22)and decreased outdoor activities (23), both known risk factors for ADHD; the one-child policy (1980–2015) intensified parental expectations (24), potentially increasing the likelihood of seeking a diagnosis in single-child families. It is expected that this burden will continue to rise in the coming years, emphasizing the critical role of early diagnosis and intervention in mitigating the impact of the disease.

Gender comparisons show that the age-standardized prevalence and DALY rates for males are approximately three times higher than for females, which aligns with global neurobiological studies indicating greater susceptibility in males due to dopamine receptor dysfunction (25). Despite the higher overall disease burden in males, the growth rate of ADHD burden in females is faster (AAPC for females = 0.294%, males = 0.229%), which warrants particular attention. The narrowing gender gap may reflect improvements in identifying the attention-deficit subtype of ADHD in girls, who historically were underdiagnosed due to cultural biases linking ADHD with overt hyperactivity symptoms (26).The highest prevalence and DALY rates were recorded in the 10–14 years age group, suggesting a heavier disease burden during adolescence. However, this observation warrants cautious interpretation: Adolescents often present with ADHD-like symptoms (e.g., inattention, impulsivity) that may overlap with normative developmental challenges or comorbid conditions such as anxiety disorders (27). Additionally, the non-medical use of stimulants, such as methylphenidate, for cognitive enhancement in competitive academic environments has been reported among middle school students (28), potentially contributing to overdiagnosis due to misattribution by healthcare professionals. Furthermore, diagnostic labeling during the critical period of identity formation may inadvertently exacerbate symptom internalization, as studies have shown increased self-stigmatization in adolescents diagnosed with ADHD (29).

To address the anticipated surge in adolescent ADHD cases, China’s healthcare system should prioritize: school-based ADHD screening in transitional grades (e.g., ages 10-11); teacher training to recognize attention deficit symptoms in girls; and parental support programs to reduce diagnostic stigma. These measures should be integrated with existing mental health initiatives under the Healthy China 2030 framework (30).

The APC model’s forecast (2022–2046) incorporates two demographic drivers from the GBD 2017 population forecast: a 15% reduction in total fertility rate by 2046, which will decrease the at-risk population for early ADHD, and a 22% increase in the urban population, which is associated with improved diagnostic accessibility. Environmental and social factors (such as trends in screen time and education reforms) are held static at 2021 levels in the model. Due to modeling limitations, this static assumption may underestimate future disease burden, especially if digital device use continues to rise among adolescents.

Using the age-period-cohort (APC) model from the Nordpred R package, this study forecasts the ADHD incidence rates among children and adolescents aged 0–4, 5–9, and 10–14 years in China from 2022 to 2046. The projections indicate different trends across age groups: for 0–4 years, ADHD incidence and case numbers are expected to decline steadily, reflecting improvements in maternal and child health education and early screening initiatives launched after 2021 (31); for 5–9 years, a brief increase in incidence is anticipated around 2024, coinciding with the peak enrollment period of children born during the COVID-19 pandemic, who experienced unprecedented home isolation and reduced social interactions (32), known risk amplifiers for ADHD symptoms; for 10–14 years, a significant rise in ADHD incidence is projected by 2029, likely associated with children born between 2015 and 2020 entering adolescence. This generation faces heightened academic pressure due to China’s 2021 “burden reduction” policy (33), alongside rising social media addiction, which may exacerbate this trend (34).

This study provides the first comprehensive APC-Joinpoint analysis of ADHD trends in China, utilizing three decades of GBD data. The main strengths include the integration of age, period, and cohort effects, and the identification of critical inflection points in the disease burden. However, there are several limitations to consider: (1) GBD estimates are based on modelled data, which may underestimate ADHD prevalence in regions with diagnostic stigma, such as rural China (35); (2) the APC model assumes a linear additive effect of age, period, and cohort, which may oversimplify interactions with emerging risk factors, such as air pollution (36); (3) due to the limitations of the modelling framework, the predictions do not account for unforeseen policy changes, such as nationwide ADHD screening requirements.

This study reveals the complex dynamics of Attention Deficit Hyperactivity Disorder (ADHD) disease burden among children and adolescents in China. Although the crude prevalence rate is declining, the age-standardized prevalence rate and Disability-Adjusted Life Years (DALY) rate continue to rise (with AAPCs of 0.272% and 0.262%, respectively, from 1990 to 2021). The rate of increase in females is significantly higher than in males (age-standardized DALY rate AAPC: females 0.294% vs. males 0.229%, P=0.003). Adolescents (ages 10–14) represent a “critical window” for disease burden, with prevalence (5727.28/100,000) and DALY rates (70.55/100,000) 2.1 times the global average. The APC model predicts that the risk in this group will peak in 2029. Based on these findings, it is recommended to establish a precise monitoring system integrating multi-source data (medical records, school screening, environmental exposure) and create a national ADHD dynamic monitoring network, focusing on rural and migrant populations to correct current GBD model biases in underdiagnosed areas. Targeted prevention strategies, particularly for adolescents, should include school-based mental health counselors and AI-driven tools for early symptom identification, while refining diagnostic criteria for female ADHD and establishing specialized clinics. Additionally, technological innovations such as mobile health platforms and “cloud hospital” models can expand healthcare resources to grassroots levels, while integrating ADHD rehabilitation services into chronic disease management to reduce the financial burden on families. Policy coordination should focus on incorporating ADHD prevention into the “Healthy China 2030” framework, mandating mental health education in schools, and using media to raise awareness and eliminate stigma. Future research should track the adolescent risk peak predicted by the APC model and assess the cost-effectiveness of interventions. Only through comprehensive management—encompassing monitoring, prevention, treatment, and policy—can ADHD’s long-term impact on China’s human capital be mitigated.