The global burden of attention deficit hyperactivity disorder in children and adolescents from 1990 to 2021

The global burden of attention deficit hyperactivity disorder in children and adolescents from 1990 to 2021 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The global burden of attention deficit hyperactivity disorder in children and adolescents from 1990 to 2021 Lijuan Fan, Jing Gan, Haorui Liu, Jun Chen, Jia Zhang, Xiaoqian Wang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6655009/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Attention deficit hyperactivity disorder (ADHD) is a widespread neurodevelopmental condition impacting children and adolescents globally. This study analyzed data from the Global Burden of Disease 2021 to evaluate ADHD prevalence, disability-adjusted life years (DALYs), their trends and associated risk factors using advanced statistical approaches. We also explored the relationship between the disease burden of ADHD and the socio-demographic index(SDI), and predicted ADHD prevalence from 2022–2036. The global number of children and adolescents in 2021 with ADHD was 46,890,733, and the age-standardized prevalence rate (ASPR) was 638.674/ 100, 000. From 1990 to 2021, the ASPR of ADHD showed a fluctuating declining trend. Age, period, and cohort effects were found to have a significant impact on ADHD prevalence. The global burden of ADHD was increasing, mainly attributed to population growth and changes in population age structure. The DALYs exhibited a declining trend with increasing SDI levels. Finally, it is predicted that the prevalence rates of ADHD will continue to rise between 2022 and 2036. Despite declining prevalence rates, the disease burden continues to increase due to various factors. Future efforts should focus on screening and early intervention in high-risk populations, while enhancing essential mental health services. Health sciences/Health care/Public health Biological sciences/Psychology attention deficit hyperactivity disorder children and adolescents global burden of disease epidemiology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Attention-Deficit/Hyperactivity Disorder (ADHD) is a common childhood behavioral disorder, estimated to impact 5–7% of children [ 1 ]. The diagnosis of ADHD is based on pervasive, developmentally excessive, and impairing levels of hyperactivity, inattention, and impulsivity. In many cases, however, the condition remains either undiagnosed or untreated [ 2 ]. Identifying trends in the epidemiology and overall burden of ADHD can help inform health priorities and policies, as well as social care services, to identify and treat children with ADHD in a timely and appropriate manner, which represents an opportunity to enhance their long-term outcomes [ 3 ]. The Global Burden of Disease (GBD) Study provides robust epidemiological data on the burden of disease assessed as disability-adjusted life years (DALYs), which represent the sum of years of life lost (YLLs) and years lived with disability (YLDs) [ 4 ]. The global prevalence, incidence, and burden of ADHD have been estimated in the 2010 and 2019 versions of the GBD study. In 1990 and 2019, the estimated age-standardized global prevalence of ADHD was 1.24% and 1.13%, varying by region. However, previous research has primarily focused on describing and interpreting the data, with insufficient analysis of the social, economic, and demographic factors that influence the changing trends in the burden of ADHD [ 5 – 7 ]. Hence, there is an urgent need for detailed and comprehensive investigations to inform targeted policy decision-making. Therefore, in our study, we applied a comprehensive analysis to fill these gaps. We performed joinpoint regression to identify statistically significant shifts in the trends of ADHD [ 8 ]. In addition, the Age-Period-Cohort (APC) analysis suggested the effect of age, historical period and cohort on ADHD patterns [ 9 ]. We assessed the relative contributions of specific factors to the burden of attention-deficit hyperactivity disorder (ADHD) using decomposed analysis [ 10 ] and explored the association between socioeconomic development and the burden of ADHD using frontier analysis [ 11 ]. In addition, health inequality analysis provided insights into the distribution of ADHD burden across different socioeconomic populations [ 12 ]. Lastly, historical data were utilized to forecast disease burden trends with the help of Bayesian APC models [ 13 ]. Together, these multifactorial methods provide a comprehensive framework for understanding, monitoring, and addressing the burden of ADHD. The Global Burden of Disease Study 2021 (GBD 2021) is the latest released version of GDB data, which has estimated the burden of more than 350 diseases and injuries in 204 countries and territories. This study utilizes the most recent data from the GBD 2021. It employs a variety of multifactorial approaches to provide a more comprehensive understanding of the burden of ADHD and its determinants across different regions and periods. This study aimed to: 1) describe the prevalence and disability-adjusted life years (DALYs) of ADHD among children and adolescents and the trends during 1990–2021 across 21 regions; 2) examine the impact of age, period, cohort, demographic characteristics and socioeconomic development on the burden of ADHD; and 3) evaluate health inequalities in ADHD prevalence and predict future trends from 2022 to 2036, to finally provide evidence-based recommendations for global and regional prevention programs. 2. Materials and Methods 2.1 Data Source and Indicator Calculation The data were extracted from the Global Burden of Disease study. Global and regional epidemiological data on ADHD in children and adolescents (0–19 years) from 1990 to 2021 were acquired from the official GBD website (Global Burden of Disease Study 2021 (GBD 2021) Data Resources | GHDx). It consists of indicators like crude prevalence rate, age-standardized prevalence rate(ASPR) and DALY. Demographic data corresponding to the disease data were also downloaded for the calculation of standardized prevalence rates. The main indicators that were calculated are the prevalence of ADHD in children and adolescents, ASPR and age-standardized DALY rate for this age group. 2.2 Statistical and Analysis Methods The descriptive analysis of the disease burden: For each region and year, the analysis calculated the case number, crude prevalence rate, ASPR, number of DALYs, age-standardized DALY rate, and percentage change in these indicators from 1990 to 2021. We also generated a heatmap of the disease burden related to ADHD to visualize the worldwide distribution of the disorder in children and adolescents. Joinpoint Regression Analysis: The Joinpoint regression model was applied to assess trends in ADHD prevalence, age-standardized for the population of children and adolescents, between 1990 and 2021. The standard errors of ASPR were estimated with R software, and the data were imported into the Joinpoint software. The independent variable was time, the dependent variable was the age-standardized rate, and the grouping variable was the region. A maximum of five joinpoints was selected, with a 95% confidence interval for parameter testing. Results were expressed as Annual Percentage Change. Age-Period-Cohort (APC) Analysis: To investigate the temporal trend of ADHD in children and adolescents, we excluded 1990 and 1991 and divided 1992 to 2021 into five-year segments. The data was uploaded onto the APC website to analyze the effects of age, period, and cohort. The impact of each factor on the prevalence of ADHD among children and adolescents was assessed, and relative risk graphs were generated for age, period, and cohort. Additionally, we computed the local drift percentage of the disease risk in each age group in relation to the prior age group. Decomposition Analysis: To assess how demographic, epidemiological and population age structure components of the population contributed to changes in ADHD prevalence, we applied the Das Gupta decomposition method. This analysis was conducted on a global scale, differentiated by gender, and across regions with different SDIs. Frontier analysis: We utilized the Data Envelopment Analysis (DEA) to analyze the association between the disease burden of ADHD and the SDI. The FDH model was used to fit the nonlinear production frontier, while the Locally Weighted Regression Scatterplot Smoothing (LOWESS) method was applied to create a smoothed frontier. Super-efficient points (i.e., points that fall below the frontier boundary) were excluded to avoid the influence of outliers. Analysis of Health Inequality: The slope index of inequality (SII) and concentration index(CI) for ASPR of ADHD were calculated. SII indicates absolute inequality, and CI denotes relative inequality. SII and CI were calculated for both 1990 and 2021, and scatter plots were created to visualize the change in health inequality between the two time points. Prediction of ADHD Prevalence: We use the Bayesian Age-Period-Cohort (APC) model to predict the ASPR of ADHD in children and adolescents from 2022 to 2036. This model predicts the projected future disease prevalence using probabilistic inference from age effects, period effects, and cohort effects. All data analysis and plotting were performed using R 4.2.0, and the Bayesian APC model was implemented using the BAPC package. Statistical significance was determined at P < 0.05. 3. Results 3.1 Temporal Trends in the Disease Burden of ADHD among Children and Adolescents There, we provide the most recent epidemiological data on ADHD in children and adolescents over the period from 1990 to 2021 across 21 GBD regions. In 2021, the global number of children and adolescents diagnosed with ADHD was 46,890,733.218 (95% uncertainty interval [UI]: 31,972,948.781-67,244,702.585). The ASPR was 638.674/100,000 (95% UI: 435.418–916.19). East Asia had the highest number of cases in 2021, with 13,018,253.522 (95% UI: 9,011,606.399–18,567,676.956) among all regions, while the lowest was reported in Oceania, with 108,036.219 (95% UI: 73,720.638–157,816.221). The ASPR was significantly high in Australasia, at 1,904.129/100,000 (95% UI: 1,358.323-2,555.408), while it was significantly low in Central Sub-Saharan Africa, at 298.721/100,000 (95% UI: 198.856-438.951). The overall ASPR of ADHD in children and adolescents showed a declining trend from 1990 to 2021. The region with the highest increase in the ASPR was East Asia (18.405%), whereas the most significant decrease was found in North Africa and the Middle East (8.689%, see Table S1 (in Supplementary Information). In 2021, the global number of DALYs caused by ADHD in children and adolescents was 574,978.909 (95% UI: 294,708.328-986,591.953). East Asia had the highest number of DALYs at 160,362.784 (95% UI: 84,310.136-271,731.038), and Oceania had the lowest at 1,320.196 (95% UI: 654.293-2,295.254). In the year 2021, the age-standardized DALY rate was 7.832 (95% UI: 4.014–13.445)/100,000 for ADHD. Australasia had the highest rate of 23.335 (95% UI: 12.474–37.678), while the lowest rate was in Central Sub-Saharan Africa at 3.648 (95% UI: 1.813–6.351). East Asia experienced the most significant increase in age-standardized DALY rate (18.672%) from 1990 to 2021, while the North Africa and Middle East regions had the most significant decrease (8.479%), see Table S2 (in Supplementary Information). 3.2 Joinpoint Regression Analysis The Joinpoint regression model was used to analyze trends in the global ASPR of ADHD among children and adolescents from 1990 to 2021. The results indicated that the global ASPR of ADHD in children and adolescents generally exhibited a fluctuating downward trend during this period, with four significant change points identified in 1993, 1999, 2013, and 2017 (refer to Fig. 1 ). Between 1990 and 1992, the global ASPR of ADHD in children and adolescents showed an increasing trend, with an annual change of 0.641% (P < 0.05). 1993–1998, 1999–2012, 2013–2016 and 2017–2021 prevalence rates showed a pattern of first decline followed by an increase, and the magnitude of the decline first increased and then decreased. The annual percent changes were − 0.123% (P < 0.05), − 0.722% (P < 0.05), − 0.288% (P < 0.05), and 0.118% (P < 0.05), respectively. The gender-specific analyses implied that time trends of ASPR among males and females aged 0–19 years followed the same pattern, but the prevalence rates in males were relatively higher than those in females. 3.3 Age-Period-Cohort Analysis of ADHD Prevalence in Children and Adolescents Significant age, period, and cohort effects were found for the risk of ADHD in children and adolescents from the result of age-period-cohort analyses. A non-linear developmental trajectory was observed in the age effect (Fig. 2 A), where the risk of ADHD increased with age, most significantly at 12.5 years old (2780.702/100,000), and then gradually decreased. The period effect analysis showed a statistically significant downward trend in ADHD risk among children and adolescents between 1994 and 2019 (Fig. 2 B). Using 2004 as a reference point, the Rate Ratio (RR) declined from 1.027 in 1994 to 0.930 in 2019. The analysis of the cohort effect showed significant differences in the risk of ADHD across different birth cohorts (Fig. 2 C). Using the birth cohort of 1992–1997 as a reference (RR = 1), the relative risk decreased from 1.138 in the cohort of 1972–1977 to 0.976 in the cohort of 1997–2002, followed by an increasing trend in the relative risk to 1.015 in the 2012–2017 cohort. Examination of local drifts, i.e., the percentage change in the risk of ADHD for each age group compared to the preceding age group, revealed significant differences among the age groups. The local drifts were negative for all age groups ranging from 2.5 to 17.5 years, suggesting a consistent decline in the risk of ADHD over these ages. Additionally, older age was associated with increased local drifts, indicating that a greater magnitude reduced the risk of ADHD, and the symptoms improved at a more rapid pace as age increased. Late adolescence may represent a key time for symptom improvement (Fig. 2 D). 3.4 Decomposition Analysis of Changes in ADHD disease burden Using the Das Gupta decomposition method, we further investigated the drivers of changes in ADHD among children and adolescents in 1990–2021. This approach decomposes the changes in prevalence to the contributions of the population effect, epidemiological effect, and the age structure of the population. The decomposition analysis of the changes in ADHD prevalence (among children and adolescents) showed that from 1990 to 2021, the global burden of this disease was on an upward trend. Population growth was the dominant driver of this increase (167.06%), with changes in age structure contributing to 35.57% of the increase. Although epidemiological factors had a negative impact on the disease burden (-102.63%), They couldn't completely overshadow the growth effect caused by population factors. Gender-specific analyses revealed that the changes in disease burden trends for males and females closely matched the general global pattern. The major driving force was population growth, accounting for a contribution of 164.38% (males) and 187.67% (females), followed by changes in population age structure, which accounted for a contribution of 35.13% (males) and 39.89% (females). Epidemiological factors had a negative impact (males: -99.52%, females:-127.56%) (Fig. 3 A). Regionally, prevalence decreased in middle and high-middle SDI regions and increased in low, low-middle, and high-SDI regions. The classification of various SDI regional levels is based on prior studies [ 14 ]. Factors associated with changes in prevalence were highly heterogeneous across SDI regions. Specifically, the decrease in middle SDI regions was primarily attributed to the synergistic effects of epidemiological factors (contribution rate of -186.58%) and population growth (contribution rate of -129.02%), while changes in population age structure had an inhibitory effect. In high-middle SDI regions, the decline was primarily driven by population growth, with a contribution rate of -208.54%, while other factors exerted inhibitory effects. Among the regions with increasing prevalence, high SDI regions displayed a distinct pattern: epidemiological factors had a strong positive effect (contribution rate of 313.79%), changes in population age structure contributed positively (72.59%), whereas population growth had a significant negative impact (contribution rate of -286.38%). In contrast, population growth contributed positively in both low-SDI and low-middle SDI regions and was the main reason for the increase in prevalence, while epidemiological factors had a negative impact (see Fig. 3 B). 3.5 Relationship between Disease Burden of ADHD and Socio-Demographic Development Level The frontier analysis of the SDI and age-standardized DALY rate data of ADHD was conducted using the Data Envelopment Analysis (DEA) method. The DEA frontier line represents the theoretically achievable lowest DALY rate at a given SDI level. The results showed that, overall, the DALY rate had a non-linear downward trend with a geographical increase in SDI level (Fig. 4 A). When the SDI was less than 0.5, the DALY rate declined rather slowly. However, as the SDI approached 0.5, the DALY rate demonstrated a significant inflection point and began to decrease rapidly. Improvement in the DALY rate has slowed down and become relatively stable over the higher SDI interval (greater than 0.6). Further analysis of the 2021 cross-sectional data (Fig. 4 B) indicated that several countries, including Australia, Spain, Grenada, and Cuba, had DALY rates that markedly deviate from the DEA efficiency frontier. These countries are positioned above the frontier line, indicating that they still have considerable room for improvement in managing disease burden given their current level of socioeconomic development. 3.6 Health Inequality Analysis The results of the health inequality analysis showed that the SII value was 38.96 in 1990, increasing to 52.13 in 2021 (Fig. 5 A). The positive SII value suggests that the prevalence rates were higher among countries with high SDI than among countries with low SDI. The absolute increase in the SII indicates that the absolute disparity between countries with different socioeconomic statuses has widened. The SII scatter plot (Fig. 5 B) clearly reveals that the fluctuation trend of the SII of ADHD in 1990–2021 has a unique change, which preliminary began at 38.96 in 1990 before decreasing from 1990 to 1995 and rising after 1995, and the first peak was about 50.55 around 1999–2000. Between 2000 and 2007, this trend fluctuated downward, but after 2007, it started growing again, reaching its peak value of 52.13 a year in 2021. The fitted regression line shows an upward trajectory over time in the slope index, suggesting an overall increase that reflects a growing severity of health inequality. The concentration index, an indicator of relative health inequality, showed a trend consistent with the SII. Analysis showed the increase of CI from 0.13(1990) to 0.19(2021) (Fig. 5 C). This indicates that the diagnosis and reporting of ADHD continue to be more prevalent among populations with higher socio-economic status, and the gap in access to ADHD diagnosis and treatment opportunities for children and adolescents worldwide continues to widen. 3.7 Prediction of ADHD Prevalence in Children and Adolescents Bayesian APC model was used to predict ASPR from 2022 to 2036. It is based on data from 1990 to 2021 and allows for the simultaneous adjustment for age, period, and cohort effects. The results indicate that the global ASPR of ADHD among the child and adolescent population will keep rising in the coming 15 years. Notably, the annual ASPR of ADHD will increase over the years from 638.936/100,000 in 2022 to 677.798/100,000 in 2036, with an average annual increase of approximately 1.5%. These data trends suggest that the burden will continue to rise (see Fig. 6 ). 3.8 Regional Distribution of ADHD Disease Burden in Children and Adolescents The global distribution in 1990 presented considerable geographic variations concerning crude prevalence rates. Australia ranked first worldwide with a crude prevalence rate of 5430.476/100,000, followed by relatively high disease burden regions like Barbados, Saint Vincent and the Grenadines. By contrast, lower crude prevalence rates were reported from the United Arab Emirates, Malaysia and Uganda (Fig. 7 A). Regarding ASPR, Australia, Dominica, and Cuba had many times higher ASPRs than the global mean ASPR in 1990, meaning that children and adolescents in these countries and regions had a higher risk of ADHD. In comparison, countries such as Malaysia, the United Arab Emirates, and Mozambique generally had low levels of ASPR (Fig. 7 C). By 2021, the global distribution pattern of ADHD had shown certain stability despite some changes. Australia remained in first place, with a crude prevalence rate of 5619.559/100000, followed by Puerto Rico and Dominica. The crude prevalence rates were still relatively low for the United Arab Emirates, Malaysia, and Chad (Fig. 7 B). Australia continued to show the highest ASPR, while Spain and Grenada emerged as regions with high ASPRs. In contrast, ASPR remained relatively low in Malaysia, the United Arab Emirates, and Gabon (Fig. 7 D). To better evaluate the association between SDI and the disease burden of ADHD, this study performed a correlation analysis of SDI and ASPR across different regions and countries (Fig. 8 A-B). This analysis showed that with increasing SDI levels, the ASPR of ADHD tended to increase (Fig. 8 A). Countries with an SDI above 0.5 typically reported higher ASPR and demonstrated a consistent upward trajectory in 2021(Fig. 8 B). This finding adds further evidence supporting the close link between socioeconomic development and the burden of disease from ADHD. Countries with higher SDIs may be likely to invest more in medical resources, mental health services and public health interventions, which may lead to a higher prevalence of statistically diagnosed ADHD. 4. Discussion Attention-deficit/hyperactivity disorder (ADHD) is a major neurodevelopmental disorder of childhood and adolescence, with long-lasting consequences for health and functioning throughout the life course, and is among the most prevalent disorders worldwide. Using GBD 2021 data, this study offers the most comprehensive analysis to date on the incidence and prevalence, showing important trends and regional differences. Our research indicates that from 1990 to 2021, the ASPR of ADHD in children and adolescents showed an oscillating downward trend. In spite of this, the worldwide burden of ADHD is increasing, largely due to population increases. The distribution of the burden of ADHD across SDI regions is heterogeneous, with high SDI regions having higher prevalence and low SDI regions having more DALYs. The global ASPR of ADHD in children and adolescents has shown three phases: an increase from 1990 to 1992, a prolonged decline from 1993 to 2016, and a slight increase after 2017. Changes in diagnostic criteria and public perceptions may have played a role in these trends. There was very little awareness of ADHD in the early 1990s, although international standardized tools for diagnostic purposes had not yet been widely adopted [ 15 ]. In 1995, the American Psychiatric Association released the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) , which defined three subtypes of ADHD: predominantly inattentive type, predominantly hyperactive-impulsive type, and combined type [ 16 ]. This classification system significantly advanced the standardization of ADHD diagnosis and treatment worldwide. The release of the DSM-5 in 2013 increased the age of symptom onset for ADHD from 7 years to 12 years and reduced the diagnostic threshold for the disorder [ 17 ]. It also recognized that ADHD could continue into adulthood, adding diagnostic criteria for adult ADHD specifically. In the last 10 years, the European Psychiatric Association [ 18 ] and the American Academy of Pediatrics (AAP) [ 19 ] have revised their guidelines for recognizing and treating adult attention-deficit/hyperactivity disorder. Although the awareness of ADHD in adolescence and adulthood has increased, debates surrounding the potential overdiagnosis of ADHD and the overuse of medication have also intensified [ 20 ]. The analysis of the age effect suggested that the risk of ADHD in children and adolescents peaked at 12.5 years, which was consistent with some previous studies reporting that prevalence and DALY of ADHD had a peak at age 10–14 years [ 6 ]. This observation not only clarifies the correlation between the generation of ADHD and neural development but also suggests that the transition period of neuroendocrine systems during adolescence may play a role in the development of ADHD [ 21 ]. Furthermore, it emphasizes the need for clinicians and researchers to focus on early screening, intervention, and psychosocial support during this sensitive period. In addition to age effect analysis, the period effect analyses showed a robust decreasing risk of ADHD (from 1994 to 2019) among children and adolescents. This 25-year trend might be closely relevant to the unceasing optimization of diagnostic criteria, the refinement of early screening and intervention measures, and the modification of social environments and educational models [ 22 ]. However, the local drift analysis shows a downward drift in the risk of ADHD from age 2.5 to 17.5, which seems to contradict the results of the age effect analysis. This difference reflects two different views of analyses: the age effect analyses show a cumulative effect, i.e. the risk accumulates with the increase of age and reaches a maximum at 12.5 years. Conversely, since the local drift analyses adjacent age groups, the negative rate of the change between these two groups represents a slowdown in growth, which reflects marginal changes. Generally speaking, while the overall risk of ADHD is increasing (the total quantity is increasing), the rate of increase is gradually slowing (the increment is decreasing), ultimately leading to a decline that begins at 12.5 years of age. The prevalence of ADHD among children and adolescents increased from 1990 to 2021, according to the decomposition analysis. The impact of several drivers varies significantly based on the SDI level of the countries. In the middle, high-middle and high SDI regions, population growth had an inhibitory effect on the prevalence rate. In contrast, in the low and low-middle SDI regions, population growth was the main reason for the increase in prevalence. For the high-middle and high SDI regions, the epidemiological factors had a positive contribution to the increasing prevalence, while it was inverse for others. Changes in population age structure were positively associated with the prevalence rate in all regions. These findings highlight the effect of the socioeconomic development stage on changes in disease burden and indicate that particular intervention strategies are needed for different SDI regions. For example, in high SDI regions, the focus should be placed on addressing the challenges posed by the population age structure, i.e., an aging society. In middle and low SDI regions, it is essential to address the increased service demand resulting from population growth. The analysis of health inequality indicates that the diagnosis and reporting of ADHD remain concentrated in populations with higher socio-economic status. On the other hand, our frontier analysis indicates that socioeconomic development decreases the DALY rate of ADHD. Similar findings have been reported in other epidemiological studies [ 23 – 25 ]. The high prevalence of ADHD in regions with high SDI might be the result of better diagnostic infrastructure and increased public awareness. At the same time, the high DALY rates in low SDI regions are probably attributable to the lack of treatment and support services. The disparities emphasize the need for targeted interventions: high SDI regions should focus on optimizing diagnostic systems and managing concerns of overdiagnosis, whereas low SDI regions require investments in primary healthcare and public health education. The Bayesian APC model predicts that the global of ADHD in children and adolescents will persistently increase over the next 15 years, suggesting that the burden of ADHD will gradually grow persistently. With growing societal awareness of mental health issues, more individuals are likely to proactively recognize and seek diagnosis and treatment for ADHD, particularly in populations that have previously gone undiagnosed, such as girls and adolescents [ 26 ]. Developments in neuroimaging, genetic tests, and digital health technologies may improve the diagnostic accuracy and objectivity of ADHD in the near future [ 27 – 29 ]. Additionally, the exacerbation of environmental pollution may contribute to an increase in the incidence of ADHD [ 30 ]. Notably, the COVID-19 pandemic, which lasted from 2019 to 2021, drastically altered the educational and living environments of children and adolescents. Some schools transitioned to teaching online, and the excessive use of digital media, fragmented information processing habits, and lack of structured environments may exacerbate ADHD symptoms [ 31 ]. It should be noted that the prediction results are subject to numerous factors (including changes in diagnosis standard, extension of medical resource access, and changes in social awareness), leading to discrepancies between actual prevalence rates and predicted values. There are some limitations to our study. First, although the GBD 2021 data provides a solid foundation, its use of modeled estimates and the potential for underreporting in low-resource settings may limit the accuracy of our findings [ 32 ]. Second, as a cross-sectional study, this study has limited ability to infer causation. Additionally, longer observation studies are needed to analyze the pathogenesis and progression of ADHD. Moreover, high-quality epidemiological studies and prospective cohort studies are needed to explore aetiologies and risk factors of ADHD. Furthermore, attention should be directed toward emerging risk factors such as environmental pollution and lifestyle changes, with an emphasis on implementing effective prevention and control strategies. By addressing these areas, future studies can enhance the understanding of ADHD and contribute to reducing its global burden. 5. Conclusions ADHD is emerging as a serious public health issue among children and adolescents globally. This study analyses the 2021 GDB data to characterize the spatiotemporal heterogeneity of global ADHD prevalence, which provides a basis for policymaking related to prevention and control strategies. Targeted interventions should be designed from a health equity perspective to promote the mental health development of children and adolescents while reducing the disease burden of ADHD. Declarations Data availability Data are publicly available at the Institute for Health Metrics and Evaluation (IHME) website (https://www.healthdata.org/research-analysis/gbd). Acknowledgements We acknowledge the use of data from the Global Burden of Disease Database, provided by the Institute for Health Metrics and Evaluation Population Health Building/Hans Rosling Center. Author contributions Dr. Yajun Shen is the study supervisor with full access to the data in the study. Lijuan Fan and Jing Gan collected the GDB data and drafted the manuscript. Statistical analysis and visualization were made by Haorui Liu, Jun Chen, Jia Zhang, Xiaoqian Wang and Xueyi Rao. Xin Tong and Xue Gong took part in the study concept and design. Administrative and technical support was given by Rong Luo. All authors read and approved the final manuscript. Funding This work was funded by the National Natural Science Foundation of China (No.82071686) and a Grant from the clinical research fund of West China Second University Hospital(No. KL115). These funding agencies had no involvement in this study. Competing interests The authors have indicated they have no potential conflicts of interest to disclose. Ethical approval Not applicable. Additional information Supporting information Additional Supporting Information may be found in the online version of this article. Correspondence and requests for materials should be addressed to L.F. References Popit, S., Serod, K., Locatelli, I. & Stuhec, M. Prevalence of attention-deficit hyperactivity disorder (ADHD): systematic review and meta-analysis. Eur. Psychiat . 67 (1), e68 (2024). Shaw, M. et al. A systematic review and analysis of long-term outcomes in attention deficit hyperactivity disorder: effects of treatment and non-treatment. BMC Med. 10 , 99 (2012). 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6655009","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":464859682,"identity":"56373965-ad73-4094-8f3c-602d7b911095","order_by":0,"name":"Lijuan Fan","email":"","orcid":"","institution":"West China Second University Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Lijuan","middleName":"","lastName":"Fan","suffix":""},{"id":464859683,"identity":"3f698e77-1f97-48ef-857f-d24e7a5b3d84","order_by":1,"name":"Jing Gan","email":"","orcid":"","institution":"WCSUH-Tianfu·Sichuan Provincial Children’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Gan","suffix":""},{"id":464859684,"identity":"4487d582-651b-45d2-b160-17f4f9abe9ec","order_by":2,"name":"Haorui Liu","email":"","orcid":"","institution":"West China Second University Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Haorui","middleName":"","lastName":"Liu","suffix":""},{"id":464859685,"identity":"68426698-5848-481a-bf45-9ea02d5cd087","order_by":3,"name":"Jun Chen","email":"","orcid":"","institution":"West China Second University Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Chen","suffix":""},{"id":464859686,"identity":"ec5533b9-3314-48a8-a249-371fca158c96","order_by":4,"name":"Jia Zhang","email":"","orcid":"","institution":"West China Second University Hospital of Sichuan 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Shen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBAC+/b+h49//vlX38/eQKQWA54zzMaMDQcYZ/YcIFaLRA6bMEjLhhsJRGoxl8g9xly44w6z5MzHG28w1NhEE9Ri2fMu7fHMM8/Y+KXTii0YjqXlNhDUczzB3ICHjZlHcnaOmQRjw2EitBxIMJMAapEwuHmGSC0GJ3LMpHnbDhsY3OAhUotkz7Fkwxln0hIke4B+SSDGL/zszQcffKiwSeBnP7zxxocaGyL8guxIiQRSlEO0kKpjFIyCUTAKRgYAAIa6ROX97h9LAAAAAElFTkSuQmCC","orcid":"","institution":"West China Second University Hospital of Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Yajun","middleName":"","lastName":"Shen","suffix":""}],"badges":[],"createdAt":"2025-05-13 11:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6655009/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6655009/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83815836,"identity":"b87624d0-246b-45c0-a7d5-a9bdd894f010","added_by":"auto","created_at":"2025-06-03 07:40:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":142231,"visible":true,"origin":"","legend":"\u003cp\u003eJoinpoint regression analysis of the global ASPR of ADHD in children and adolescents from 1990 to 2021.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6655009/v1/2d8de39fe0814cbd905a50de.png"},{"id":83816201,"identity":"8b59e003-d2b2-48d6-a7e7-a6f0ced52bf1","added_by":"auto","created_at":"2025-06-03 07:48:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":153352,"visible":true,"origin":"","legend":"\u003cp\u003eAge-period-cohort analysis of ADHD prevalence in children and adolescents globally from 1990 to 2021. (A) Results of the age effect analysis. The x-axis represents age, while the y-axis indicates the relative risk. (B) Results of the period effect analysis. The x-axis represents the period, and the y-axis represents the relative risk, with 2004 serving as the reference period. (C) Results of the cohort effect analysis. The x-axis represents the birth cohort, and the y-axis represents the relative risk, using the 1992-1997 cohort as the reference. (D) The local drift percentage represents the percentage change in the risk of ADHD in each age group compared to the preceding age group. The x-axis represents the age group, and the y-axis represents the local drift percentage.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6655009/v1/23565874e39b63b98ac49fa8.png"},{"id":83816199,"identity":"658e5f9c-6dc0-4e7c-a6dd-77deeceb85d2","added_by":"auto","created_at":"2025-06-03 07:48:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":109436,"visible":true,"origin":"","legend":"\u003cp\u003eDecomposition analysis of changes in the prevalence of ADHD in children and adolescents from 1990 to 2021.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6655009/v1/ca143883c884bab83e61ae87.png"},{"id":83815840,"identity":"dab5c811-a56c-43c3-a3bc-1c8fe871f0f8","added_by":"auto","created_at":"2025-06-03 07:40:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":142347,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between the socio-demographic index and age-standardized DALY rates of ADHD in children and adolescents from 1990 to 2021. (A) Scatter plot of the socio-demographic index and age-standardized DALY rates of ADHD in children and adolescents in 204 countries from 1990 to 2021. The black curve represents the frontier production function fitting curve, indicating the minimum DALY rate at a given SDI level. Points of different colors correspond to different years. (B) Trends in the age-standardized DALY rates of ADHD in children and adolescents for selected countries in 2021. The solid black line denotes the frontier production function fitting curve, while the colored dotted lines depict the changes in DALY rates for each country. Countries in black font represent the 15 points with the largest distance differences; countries in blue font indicate the 5 countries with the smallest distance differences among low SDI countries; and countries in red font represent the 5 countries with the largest distance differences among high SDI countries.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6655009/v1/602ad11de7fd7719bbe78bfb.png"},{"id":83815841,"identity":"c358129b-0b87-4bad-b49e-e9666a034b0c","added_by":"auto","created_at":"2025-06-03 07:40:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":83250,"visible":true,"origin":"","legend":"\u003cp\u003eHealth inequality analysis of the ASPR of ADHD in children and adolescents from 1990 to 2021. (A) Slope Index of ASPR in children and adolescents for 1990 and 2021. A larger absolute value of the index indicates a higher level of inequality. The dashed line represents the reference line, where perfect equality would result in equal shares of prevalence rates and population shares for all groups. (B) Scatter plot of the slope index for ASPR in children and adolescents from 1990 to 2021. (C) Inequality curve of the ASPR of ADHD in children and adolescents. The concentration index measures the relative level of inequality. The x-axis represents the cumulative share of the population, while the y-axis represents the cumulative share of the prevalence rate. The further the curve deviates from the reference line, the greater the degree of inequality.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6655009/v1/4bf1416d5bd0f4c8aa51e6e2.png"},{"id":83815845,"identity":"58edc36e-e97a-4999-b9b0-0cdcb82fecac","added_by":"auto","created_at":"2025-06-03 07:40:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":109280,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction of the global ASPR of ADHD in children and adolescents from 2022 to 2036 based on the Bayesian APC model.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6655009/v1/e4358d0a054e06f54f68f254.png"},{"id":83816203,"identity":"658e8da0-fffd-492b-bfd2-82720a2e20f8","added_by":"auto","created_at":"2025-06-03 07:48:11","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":403020,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal distribution of crude prevalence rates and ASPR of ADHD in children and adolescents in 1990 and 2021. (A) Distribution map of crude prevalence rates of ADHD in children and adolescents across various countries in 1990. (B) Distribution map of crude prevalence rates of ADHD in children and adolescents across various countries in 2021. (C) Distribution map of ASPR of ADHD in children and adolescents across various countries in 1990. (D) Distribution map of ASPR of ADHD in children and adolescents across various countries in 2021. Darker areas indicate countries with higher ASPRs, while lighter areas represent countries with lower ASPRs.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-6655009/v1/2822d0c346dadc01da48148f.png"},{"id":83815849,"identity":"4e394377-d120-44bb-9268-a37f8f981304","added_by":"auto","created_at":"2025-06-03 07:40:11","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":155048,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in ASPR of ADHD in children and adolescents in different SDI regions. (A) A scatter plot of the relationship between ADHD ASPR and SDI in 21 GBD regions. (B) A trend graph of ADHD ASPR in countries with different SDI levels in 2021.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-6655009/v1/501d2e2ab2ce3d14ca4b6401.png"},{"id":92430408,"identity":"f7c8132e-569c-4a9d-8c1e-f5176cee1405","added_by":"auto","created_at":"2025-09-29 16:00:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2090830,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6655009/v1/a216b892-89c4-4cd6-828f-a748fcfbb5b2.pdf"},{"id":83817487,"identity":"0d1dec0e-680e-4dce-99b0-1cbcf222461d","added_by":"auto","created_at":"2025-06-03 07:56:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":178237,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformationscientificreports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6655009/v1/178c2f1d2d1bc85df8085738.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The global burden of attention deficit hyperactivity disorder in children and adolescents from 1990 to 2021","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAttention-Deficit/Hyperactivity Disorder (ADHD) is a common childhood behavioral disorder, estimated to impact 5\u0026ndash;7%\u0026ensp;of children [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The diagnosis of ADHD is based on pervasive, developmentally excessive, and impairing levels of hyperactivity, inattention, and impulsivity. In many cases, however, the condition remains either undiagnosed or untreated [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Identifying trends in the epidemiology and overall burden of ADHD can help inform health priorities and policies, as well as social care services, to identify and treat children with ADHD in a timely and appropriate manner, which represents an opportunity to enhance their long-term outcomes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Global Burden of Disease (GBD) Study provides robust epidemiological data on the burden of\u0026ensp;disease assessed as disability-adjusted life years (DALYs), which represent the sum of years of life lost (YLLs) and years lived with disability (YLDs) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The global prevalence, incidence, and burden of ADHD have been estimated in the 2010 and 2019 versions of the\u0026ensp;GBD study. In 1990 and 2019, the estimated age-standardized global prevalence of ADHD was 1.24% and 1.13%, varying by region. However, previous research has primarily focused on describing and interpreting the data, with insufficient analysis of the social, economic, and demographic factors that influence the changing trends in the burden of ADHD [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Hence, there is an urgent need for detailed and comprehensive investigations to inform targeted policy decision-making.\u003c/p\u003e \u003cp\u003eTherefore, in our study, we applied a comprehensive analysis to\u0026ensp;fill these gaps. We performed joinpoint regression to identify statistically significant shifts in the trends of ADHD [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In addition, the Age-Period-Cohort (APC) analysis suggested the effect of age, historical period and cohort on ADHD patterns [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. We assessed\u0026ensp;the relative contributions of specific factors to the burden of attention-deficit hyperactivity disorder (ADHD) using decomposed analysis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and explored the association between socioeconomic development and the burden of ADHD using frontier analysis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In addition, health inequality analysis provided insights into the distribution of ADHD burden across different socioeconomic populations [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Lastly, historical data were utilized to\u0026ensp;forecast disease burden trends with the help of Bayesian APC models [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Together, these multifactorial methods provide a comprehensive framework for understanding, monitoring, and addressing the burden of ADHD.\u003c/p\u003e \u003cp\u003eThe Global Burden of Disease Study 2021 (GBD 2021) is the latest released version of GDB data, \u0026ensp;which has estimated the burden of more than 350 diseases and injuries in 204 countries and territories. This study utilizes the most recent data from the GBD 2021. It employs a variety of multifactorial approaches to provide a more comprehensive understanding of the burden of ADHD and its determinants across different regions and periods. This study aimed to: 1) describe the prevalence and disability-adjusted life years (DALYs) of ADHD among children and adolescents and the trends during 1990\u0026ndash;2021 across 21 regions; 2) examine the impact of age, period, cohort, demographic characteristics and socioeconomic development on the burden of ADHD; and 3) evaluate health inequalities in ADHD prevalence and predict future trends from 2022 to 2036, to finally provide evidence-based recommendations for global and regional prevention programs.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Source and Indicator Calculation\u003c/h2\u003e \u003cp\u003eThe data were extracted from\u0026ensp;the Global Burden of Disease study. Global and regional epidemiological data on ADHD in children and adolescents (0\u0026ndash;19 years) from 1990 to 2021 were acquired from the official GBD website (Global Burden of Disease Study 2021 (GBD 2021) Data Resources | GHDx). It consists of indicators like crude prevalence\u0026ensp;rate, age-standardized prevalence rate(ASPR) and DALY. Demographic data\u0026ensp;corresponding to the disease data were also downloaded for the calculation of standardized prevalence rates. The main indicators that were calculated are the prevalence of ADHD in children and adolescents, ASPR and age-standardized DALY\u0026ensp;rate for this age group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Statistical and Analysis Methods\u003c/h2\u003e \u003cp\u003eThe descriptive analysis of the disease burden: For each region and year, the analysis calculated the case number, crude prevalence rate, ASPR, number of DALYs, age-standardized DALY rate, and percentage change in these indicators from 1990 to 2021. We also generated a heatmap of the disease burden related to ADHD to visualize the worldwide distribution of the disorder in children and adolescents.\u003c/p\u003e \u003cp\u003eJoinpoint Regression Analysis: The Joinpoint regression model was applied to assess trends in ADHD prevalence, age-standardized for the population of children and adolescents, between 1990 and 2021. The standard errors of ASPR were estimated with R software, and the data were imported into the Joinpoint software. The independent variable was time, the dependent variable was the age-standardized rate, and the grouping variable was the region. A maximum of five joinpoints was selected, with a 95% confidence interval for parameter testing. Results were expressed as Annual Percentage Change.\u003c/p\u003e \u003cp\u003eAge-Period-Cohort (APC) Analysis: To investigate the temporal trend of ADHD in children and adolescents, we excluded 1990 and\u0026ensp;1991 and divided 1992 to 2021 into five-year segments. The data was uploaded onto the APC website to analyze the effects of age, period, and cohort. The impact of each factor on the prevalence of ADHD among children and adolescents was assessed, and relative risk graphs were generated for age, period, and cohort. Additionally, we computed the local drift percentage of the disease risk\u0026ensp;in each age group in relation to the prior age group.\u003c/p\u003e \u003cp\u003eDecomposition Analysis: To assess\u0026ensp;how demographic, epidemiological and population age structure components of the population contributed to changes in ADHD prevalence, we applied the Das Gupta decomposition method. This analysis was conducted on a global scale, differentiated by gender, and across regions with different SDIs.\u003c/p\u003e \u003cp\u003eFrontier analysis: We utilized the Data Envelopment Analysis (DEA) to analyze the association between the disease burden of ADHD and the SDI. The FDH model was used to fit the nonlinear production frontier, while the Locally Weighted Regression Scatterplot Smoothing (LOWESS) method was applied to create a smoothed frontier. Super-efficient points (i.e., points that fall below the frontier boundary) were excluded to avoid the influence of outliers.\u003c/p\u003e \u003cp\u003eAnalysis of Health Inequality: The slope index of inequality (SII) and concentration index(CI) for ASPR of ADHD were calculated. SII indicates absolute inequality, and CI denotes relative inequality. SII and CI were calculated for both 1990 and 2021, and scatter plots were created to visualize the change in health inequality between the two time points.\u003c/p\u003e \u003cp\u003ePrediction of ADHD Prevalence: We use the Bayesian Age-Period-Cohort (APC) model to predict the ASPR of ADHD in children and adolescents from 2022 to 2036. This model predicts the projected future disease prevalence using probabilistic inference from age effects,\u0026ensp;period effects, and cohort effects.\u003c/p\u003e \u003cp\u003eAll data analysis and plotting were performed using R 4.2.0, and the Bayesian APC model was implemented using the BAPC package. Statistical significance was determined at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Temporal Trends in the Disease Burden of ADHD among Children and Adolescents\u003c/h2\u003e \u003cp\u003eThere, we provide the most recent epidemiological data on ADHD in children and adolescents over the period from 1990 to 2021 across 21 GBD regions. In 2021, the global number of children and adolescents diagnosed with ADHD was 46,890,733.218 (95% uncertainty interval [UI]: 31,972,948.781-67,244,702.585). The ASPR was 638.674/100,000 (95% UI: 435.418\u0026ndash;916.19). East Asia had the highest number of cases in 2021, with 13,018,253.522 (95% UI: 9,011,606.399\u0026ndash;18,567,676.956) among all regions, while the lowest was reported in Oceania, with 108,036.219 (95% UI: 73,720.638\u0026ndash;157,816.221). The ASPR was significantly high in Australasia, at 1,904.129/100,000 (95% UI: 1,358.323-2,555.408), while it was significantly low in Central Sub-Saharan Africa, at 298.721/100,000 (95% UI: 198.856-438.951). The overall ASPR of ADHD in children and adolescents showed a declining trend from 1990 to 2021. The region with the highest increase in the ASPR was East Asia (18.405%), whereas the most significant decrease was found in North Africa and the Middle East (8.689%, see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e(in Supplementary Information).\u003c/p\u003e \u003cp\u003eIn 2021, the global number of DALYs caused by ADHD in children and adolescents was 574,978.909 (95% UI: 294,708.328-986,591.953). East Asia had the highest number of DALYs at 160,362.784 (95% UI:\u0026ensp;84,310.136-271,731.038), and Oceania had the lowest at 1,320.196 (95% UI: 654.293-2,295.254). In the year 2021, the age-standardized DALY rate was 7.832 (95% UI: 4.014\u0026ndash;13.445)/100,000 for ADHD. Australasia had the highest rate of 23.335 (95% UI: 12.474\u0026ndash;37.678), while the lowest rate was in Central Sub-Saharan Africa at 3.648 (95% UI: 1.813\u0026ndash;6.351). East Asia experienced the most significant increase in age-standardized DALY rate (18.672%) from 1990 to 2021, while the North Africa and Middle East regions had the most significant decrease (8.479%), see Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e(in Supplementary Information).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Joinpoint Regression Analysis\u003c/h2\u003e \u003cp\u003eThe Joinpoint regression model was used to analyze trends in the global ASPR of ADHD among children and adolescents from 1990 to 2021. The results indicated that the global ASPR of ADHD in children and adolescents generally exhibited a fluctuating downward trend during this period, with four significant change points identified in 1993, 1999, 2013, and 2017 (refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Between 1990 and 1992, the global ASPR of ADHD in children and adolescents showed an increasing trend, with an annual change of 0.641% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). 1993\u0026ndash;1998, 1999\u0026ndash;2012, 2013\u0026ndash;2016 and 2017\u0026ndash;2021 prevalence rates showed a pattern of first decline followed by an increase, and the magnitude of the decline first increased and then decreased. The annual percent changes were \u0026minus;\u0026thinsp;0.123% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), \u0026minus;\u0026thinsp;0.722% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), \u0026minus;\u0026thinsp;0.288% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and 0.118% (P \u0026lt;\u0026ensp;0.05), respectively. The gender-specific analyses implied that time trends of ASPR among males and females aged 0\u0026ndash;19 years followed the same pattern, but the prevalence rates in males were relatively higher than those in females.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Age-Period-Cohort Analysis of ADHD Prevalence in Children and Adolescents\u003c/h2\u003e \u003cp\u003eSignificant age, period, and cohort effects were found for the risk of ADHD in children and adolescents from the result of age-period-cohort analyses. A non-linear developmental trajectory was observed in the age effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), where the risk of ADHD increased with age, most significantly at 12.5 years old (2780.702/100,000), and then gradually decreased. The period effect analysis showed a statistically significant downward trend in ADHD risk among children and adolescents between 1994 and 2019 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Using 2004 as a reference point, the Rate Ratio (RR) declined from 1.027 in 1994 to 0.930\u0026ensp;in 2019. The analysis of the cohort effect showed significant differences in the risk of ADHD across different birth cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Using the birth cohort of 1992\u0026ndash;1997 as a reference (RR\u0026thinsp;=\u0026thinsp;1), the relative risk decreased from 1.138 in the cohort of 1972\u0026ndash;1977 to 0.976 in the cohort of 1997\u0026ndash;2002, followed by an increasing trend in the relative risk to 1.015 in the 2012\u0026ndash;2017 cohort.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eExamination of local drifts, i.e., the percentage change in the risk of ADHD for each age group compared to the preceding age group, revealed significant differences among the age groups. The local drifts were negative for all age groups ranging from 2.5 to 17.5 years, suggesting a consistent decline in the risk of ADHD over these ages. Additionally, older age was associated with increased local drifts, indicating that a greater magnitude reduced the risk of ADHD, and the symptoms improved at a more rapid pace as age increased. Late adolescence may represent a key time for symptom improvement (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Decomposition Analysis of Changes in ADHD disease burden\u003c/h2\u003e \u003cp\u003eUsing the Das Gupta decomposition method, we further investigated the drivers of changes\u0026ensp;in ADHD among children and adolescents in 1990\u0026ndash;2021. This approach decomposes the changes in prevalence to the contributions of the population effect, epidemiological effect, and the age structure of the population.\u003c/p\u003e \u003cp\u003eThe decomposition analysis of the changes in ADHD prevalence (among children and adolescents) showed that from 1990 to 2021, the global burden of this disease was on an upward trend. Population growth was the dominant driver of this increase (167.06%), with changes in age structure contributing to 35.57% of the increase. Although epidemiological factors had a negative impact on the disease burden (-102.63%), They couldn't completely overshadow the growth effect caused by population factors. Gender-specific analyses revealed that the changes in disease burden trends for males and females closely matched the general global pattern. The major driving force was population growth, accounting for a contribution of 164.38% (males) and 187.67% (females), followed by changes in population age structure, which accounted for a contribution of 35.13% (males) and 39.89% (females). Epidemiological factors had a negative impact (males: -99.52%, females:-127.56%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegionally, prevalence decreased in middle and high-middle SDI regions and increased in low, low-middle, and high-SDI regions. The classification of various SDI regional levels is based on prior studies [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Factors associated with changes in prevalence were highly heterogeneous across SDI regions. Specifically, the decrease in middle SDI regions was primarily attributed to the synergistic effects of epidemiological factors (contribution rate of -186.58%) and population growth (contribution rate of -129.02%), while changes in population age structure had an inhibitory effect. In high-middle SDI regions, the decline was primarily driven by population growth, with a contribution rate of -208.54%, while other factors exerted inhibitory effects. Among the regions with increasing prevalence, high SDI regions displayed a distinct pattern: epidemiological factors had a strong positive effect (contribution rate of 313.79%), changes in population age structure contributed positively (72.59%), whereas population growth had a significant negative impact (contribution rate of -286.38%). In contrast, population growth contributed positively in both low-SDI and low-middle SDI regions and was the main reason for the increase in prevalence, while epidemiological factors had a negative impact (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Relationship between Disease Burden of ADHD and Socio-Demographic Development Level\u003c/h2\u003e \u003cp\u003eThe frontier analysis of the SDI and age-standardized DALY rate data of ADHD was conducted using the Data Envelopment Analysis (DEA) method. The DEA frontier line represents the theoretically achievable lowest DALY rate at a given SDI level. The results showed that, overall, the DALY rate had a non-linear downward trend with a geographical increase in SDI level (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). When the SDI was less than 0.5, the DALY\u0026ensp;rate declined rather slowly. However, as the SDI approached 0.5, the DALY rate demonstrated a significant inflection point and began to decrease rapidly. Improvement in the DALY rate has slowed down and become relatively stable over the higher SDI interval (greater than 0.6).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurther analysis of the 2021 cross-sectional data (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB)\u0026ensp;indicated that several countries, including Australia, Spain, Grenada, and Cuba, had DALY rates that markedly deviate from the DEA efficiency frontier. These countries are positioned above the frontier line, indicating that they still have considerable room for improvement in managing disease burden given their current level of socioeconomic development.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Health Inequality Analysis\u003c/h2\u003e \u003cp\u003eThe results of the health inequality analysis showed that the SII value was 38.96 in 1990, increasing to 52.13 in 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The positive SII value suggests\u0026ensp;that the prevalence rates were higher among countries with high SDI than among countries with low SDI. The absolute increase in the SII indicates that the absolute disparity between countries with different socioeconomic statuses has widened. The SII scatter\u0026ensp;plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) clearly reveals that the fluctuation trend of the SII of ADHD in 1990\u0026ndash;2021 has a unique change, which preliminary began at 38.96 in 1990 before decreasing from 1990 to 1995 and rising after 1995, and the first peak was about 50.55 around 1999\u0026ndash;2000. Between 2000 and 2007, this trend fluctuated downward, but after 2007, it started growing again, reaching its peak\u0026ensp;value of 52.13 a year in 2021. The fitted regression line shows an upward trajectory over time in the slope index, suggesting an overall increase that reflects a growing severity of health inequality.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe concentration index, \u0026ensp;an indicator of relative health inequality, showed a trend consistent with the SII. Analysis showed\u0026ensp;the increase of CI from 0.13(1990) to 0.19(2021) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). This indicates that the diagnosis and reporting of ADHD continue to be more prevalent among populations with higher socio-economic status, and the gap in access to ADHD diagnosis and treatment opportunities for children and adolescents worldwide continues to widen.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Prediction of ADHD Prevalence in Children and Adolescents\u003c/h2\u003e \u003cp\u003eBayesian APC model was used to predict ASPR from 2022 to 2036. It is based on data from 1990 to 2021 and allows for the simultaneous adjustment for age, period, and cohort effects. The results indicate that the global ASPR of ADHD among the child and adolescent population will keep rising in the coming 15 years. Notably, the annual ASPR of ADHD will increase over the years from 638.936/100,000 in 2022 to 677.798/100,000 in 2036, with an average annual increase of approximately 1.5%. These data trends suggest that the burden will continue to rise (see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Regional Distribution of ADHD Disease Burden in Children and Adolescents\u003c/h2\u003e \u003cp\u003eThe global distribution in 1990 presented considerable geographic variations concerning crude prevalence rates. Australia ranked first worldwide with a crude prevalence rate of 5430.476/100,000, followed by relatively high disease burden regions like Barbados, Saint Vincent and the Grenadines. By contrast, \u0026ensp;lower crude prevalence rates were reported from the United Arab Emirates, Malaysia and Uganda (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Regarding ASPR, Australia, Dominica, and Cuba had many times higher ASPRs than the global mean ASPR in 1990, meaning that children and adolescents in these countries and regions had a higher risk of ADHD. In comparison, countries such as Malaysia, the United Arab Emirates, and Mozambique generally had low levels of ASPR (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBy 2021, the global distribution pattern of ADHD had shown certain stability despite some changes. Australia remained in first place, with a crude prevalence rate of 5619.559/100000, followed by Puerto Rico and Dominica. The crude prevalence rates were still relatively low for the United Arab Emirates, Malaysia, and Chad (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Australia continued to show the highest ASPR, while Spain and Grenada emerged as regions with high ASPRs. In contrast, ASPR remained relatively low in Malaysia, the United Arab Emirates, \u0026ensp;and Gabon (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eTo better evaluate the association between SDI and the disease burden of ADHD, this study performed a correlation analysis of SDI and ASPR across different regions and countries (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-B). This analysis showed that with increasing\u0026ensp;SDI levels, the ASPR of ADHD tended to increase (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Countries with an SDI above 0.5 typically reported higher ASPR and demonstrated a consistent upward trajectory in 2021(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). This\u0026ensp;finding adds further evidence supporting the close link between socioeconomic development and the burden of disease from ADHD. Countries with higher SDIs may be likely to invest more\u0026ensp;in medical resources, mental health services and public health interventions, which may lead to a higher prevalence of statistically diagnosed ADHD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAttention-deficit/hyperactivity disorder (ADHD) is a major neurodevelopmental disorder of childhood and adolescence, with long-lasting consequences for health and functioning throughout the life course, and is among the most prevalent disorders worldwide. Using GBD 2021 data, this study offers the most comprehensive analysis to date on the incidence and prevalence, showing important trends and regional differences. Our research indicates that from 1990 to 2021, the ASPR of ADHD in children and adolescents showed an oscillating downward trend. In spite of this, the worldwide burden of ADHD is increasing, largely due to population increases. The distribution of the burden of ADHD across SDI regions is heterogeneous, with high SDI regions having higher prevalence and low SDI regions having more DALYs.\u003c/p\u003e \u003cp\u003eThe global ASPR of ADHD in children and adolescents has shown three phases: an increase from 1990 to 1992, a prolonged decline from 1993 to 2016, and a slight increase after 2017. Changes in diagnostic criteria and public perceptions may have played a role in these trends. There was very little awareness of ADHD in the early 1990s, although international standardized tools for diagnostic purposes had not yet been widely adopted [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In 1995, the American Psychiatric Association released the \u003cem\u003eDiagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV)\u003c/em\u003e, which defined three subtypes of ADHD: predominantly inattentive type, predominantly hyperactive-impulsive type, and combined type [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This classification system significantly advanced the standardization of ADHD diagnosis and treatment worldwide. The release of the DSM-5 in 2013 increased the age of symptom onset for ADHD from 7 years to 12 years and reduced the diagnostic threshold for the disorder [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. It also recognized that ADHD could continue into adulthood, adding diagnostic criteria for adult ADHD specifically. In the last 10 years, the European Psychiatric Association [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and the American Academy of Pediatrics (AAP) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] have revised their guidelines for recognizing and treating adult attention-deficit/hyperactivity disorder. Although the awareness of ADHD in adolescence and adulthood has increased, debates surrounding the potential overdiagnosis of ADHD and the overuse of medication have also intensified [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe analysis of the age effect suggested that the risk of ADHD in children and adolescents peaked at 12.5 years, which was consistent with some previous studies reporting that prevalence and DALY of ADHD had a peak at age 10\u0026ndash;14 years [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This observation not only clarifies the correlation between the generation of ADHD and neural development but also suggests that the transition period of neuroendocrine systems during adolescence may play a role in the development of ADHD [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Furthermore, it emphasizes the need for clinicians and researchers to focus on early screening, intervention, and psychosocial support during this sensitive period.\u003c/p\u003e \u003cp\u003eIn addition to age effect analysis, the period effect analyses showed a robust decreasing\u0026ensp;risk of ADHD (from 1994 to 2019) among children and adolescents. This 25-year trend might be closely relevant to the unceasing optimization of diagnostic criteria, the refinement of early screening and intervention measures, and the modification of social environments and educational models [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, the local drift analysis shows a downward drift in the risk of ADHD from age 2.5 to 17.5, which seems to contradict the results of the age effect analysis. This difference reflects two different views of analyses: \u0026ensp;the age effect analyses show a cumulative effect, i.e. the risk accumulates with the increase of age and reaches a maximum at 12.5 years. Conversely, since the local drift analyses adjacent age groups, the negative rate of the change between these two groups represents a slowdown in growth, which reflects marginal changes. Generally speaking, while the overall risk of ADHD is increasing (the total quantity is increasing), the rate of increase is gradually slowing (the increment is decreasing), ultimately leading to a decline that begins at 12.5 years of age.\u003c/p\u003e \u003cp\u003eThe prevalence of ADHD among children and adolescents increased from 1990 to 2021, according to the decomposition analysis. The impact of several drivers varies significantly based on the SDI level of the countries. In the middle, high-middle and high SDI regions, population growth had an inhibitory effect on the prevalence rate. In contrast, in the low and low-middle SDI regions, population growth was the main reason for the increase in prevalence. For the high-middle and high SDI regions, the epidemiological factors had a positive contribution to the increasing prevalence, while it was inverse for others. Changes in population age structure were positively associated with the prevalence rate in all regions. These findings highlight the effect of the socioeconomic development stage on changes in disease burden and indicate that particular intervention strategies are needed for different SDI regions. For example, in high SDI regions, the focus should be placed on addressing the challenges posed by the population age structure, i.e., an aging society. In middle and low SDI regions, it is essential to address the increased service demand resulting from population growth.\u003c/p\u003e \u003cp\u003eThe analysis of health inequality indicates that the diagnosis and reporting of ADHD remain concentrated in populations with higher socio-economic status. On the other hand, our frontier analysis indicates that socioeconomic development decreases the DALY rate of ADHD. Similar findings have been reported in other epidemiological studies [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The high prevalence of ADHD in regions with high SDI might be the result of better diagnostic infrastructure and increased public awareness. At the same time, the high DALY rates in low SDI regions are probably attributable to the lack of treatment and support services. The disparities emphasize the need for targeted interventions: high SDI regions should focus on optimizing diagnostic systems and managing concerns of overdiagnosis, whereas low SDI regions require investments in primary healthcare and public health education.\u003c/p\u003e \u003cp\u003eThe Bayesian APC model predicts that the global of ADHD in children and adolescents will persistently increase over the next 15 years, suggesting that the burden of ADHD will gradually grow persistently. With growing societal awareness of mental health issues, more individuals are likely to proactively recognize and seek diagnosis and treatment for ADHD, particularly in populations that have previously gone undiagnosed, such as girls and adolescents [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Developments in neuroimaging, genetic tests, and digital health technologies may improve the diagnostic accuracy and objectivity of ADHD in the near future [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additionally, the exacerbation of environmental pollution may contribute to an increase in the incidence of ADHD [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Notably, the COVID-19 pandemic, which lasted from 2019 to 2021, drastically altered the educational and living environments of children and adolescents. Some schools transitioned to teaching online, and the excessive use of digital media, fragmented information processing habits, and lack of structured environments may exacerbate ADHD symptoms [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. It should be noted that the prediction results are subject to numerous factors (including changes in diagnosis standard, extension of medical resource access, and changes in social awareness), leading to discrepancies between actual prevalence rates and predicted values.\u003c/p\u003e \u003cp\u003eThere are some limitations to our\u0026ensp;study. First, although the GBD 2021 data provides a solid foundation, its use of modeled estimates and the potential for underreporting in low-resource settings\u0026ensp;may limit the accuracy of our findings [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Second, as\u0026ensp;a cross-sectional study, this study has limited ability to infer causation. Additionally, longer observation studies are needed to analyze the pathogenesis and progression of ADHD. Moreover, high-quality epidemiological studies and prospective cohort studies are needed to explore aetiologies and\u0026ensp;risk factors of ADHD. Furthermore, attention should be directed toward emerging risk factors such as environmental pollution and lifestyle changes, with an emphasis on implementing effective prevention and control strategies. By addressing these areas, future studies can enhance the understanding of ADHD and contribute to reducing its global burden.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eADHD is emerging as a serious public health issue among children and adolescents globally. This study analyses the 2021 GDB data to characterize the spatiotemporal heterogeneity of global ADHD prevalence, which provides a basis for policymaking related to prevention and control strategies. Targeted interventions should be designed from a health equity perspective to promote the mental health development of children and adolescents while reducing the disease burden of ADHD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are publicly available at the Institute for Health Metrics and Evaluation (IHME) website (https://www.healthdata.org/research-analysis/gbd).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the use of data from the Global Burden of Disease Database, provided by the Institute for Health Metrics and Evaluation Population Health Building/Hans Rosling Center.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDr. Yajun Shen is the study supervisor with full access to the data in the study. Lijuan Fan and Jing Gan collected the GDB data and drafted the manuscript. Statistical analysis and visualization were made\u0026nbsp;by Haorui Liu, Jun Chen, Jia Zhang, Xiaoqian Wang and Xueyi Rao. Xin Tong and Xue Gong took part in the study concept and design. Administrative and technical support was given\u0026nbsp;by Rong Luo. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the National Natural Science Foundation of China (No.82071686) and a Grant from the clinical research fund of West China Second University Hospital(No. KL115). These funding agencies had no involvement in this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have indicated they have no potential conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupporting information\u0026nbsp;\u003c/strong\u003eAdditional Supporting Information may be found in the online version of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrespondence\u003c/strong\u003e and requests for materials should be addressed to L.F.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePopit, S., Serod, K., Locatelli, I. \u0026amp; Stuhec, M. 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ADHD Symptoms Increased During the Covid-19 Pandemic: A Meta-Analysis. \u003cem\u003eJ. Atten. Disord\u003c/em\u003e. \u003cb\u003e27\u003c/b\u003e (8), 800\u0026ndash;811 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWyper, G. M. A. The global burden of disease study and Population Health Metrics. \u003cem\u003ePopul. Health Metr.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e (1), 35 (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"attention deficit hyperactivity disorder, children and adolescents, global burden of disease, epidemiology","lastPublishedDoi":"10.21203/rs.3.rs-6655009/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6655009/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAttention deficit hyperactivity disorder (ADHD) is a widespread neurodevelopmental condition impacting children and adolescents globally. This study analyzed data from the \u003cem\u003eGlobal Burden of Disease\u003c/em\u003e 2021 to evaluate ADHD prevalence, disability-adjusted life years (DALYs), their trends and associated risk factors using advanced statistical approaches. We also explored the relationship between the disease burden of ADHD and the socio-demographic index(SDI), and predicted ADHD prevalence from 2022\u0026ndash;2036. The global number of children and adolescents in 2021 with ADHD was 46,890,733, and the age-standardized prevalence rate (ASPR) was 638.674/ 100, 000. From 1990 to 2021, the ASPR of ADHD showed a fluctuating declining trend. Age, period, and cohort effects were found to have a significant impact on ADHD prevalence. The global burden of ADHD was increasing, mainly attributed to population growth and changes in population age structure. The DALYs exhibited a declining trend with increasing SDI levels. Finally, it is predicted that the prevalence rates of ADHD will continue to rise between 2022 and 2036. Despite declining prevalence rates, the disease burden continues to increase due to various factors. Future efforts should focus on screening and early intervention in high-risk populations, while enhancing essential mental health services.\u003c/p\u003e","manuscriptTitle":"The global burden of attention deficit hyperactivity disorder in children and adolescents from 1990 to 2021","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-03 07:40:06","doi":"10.21203/rs.3.rs-6655009/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"607341e9-d703-4016-9c3a-e7f1aeced496","owner":[],"postedDate":"June 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49351125,"name":"Health sciences/Health care/Public health"},{"id":49351126,"name":"Biological sciences/Psychology"}],"tags":[],"updatedAt":"2025-09-29T15:52:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-03 07:40:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6655009","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6655009","identity":"rs-6655009","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}