Demographic and Socioeconomic Factors Influencing ADHD Diagnosis in Children and Adolescents: A Cross-Sectional Study

Demographic and Socioeconomic Factors Influencing ADHD Diagnosis in Children and Adolescents: A Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Demographic and Socioeconomic Factors Influencing ADHD Diagnosis in Children and Adolescents: A Cross-Sectional Study Olast Arrizibita, Marta Gutiérrez-Valencia, Luis Carlos Saiz, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6255678/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Mar, 2026 Read the published version in European Child & Adolescent Psychiatry → Version 1 posted 10 You are reading this latest preprint version Abstract Background Attention deficit hyperactivity disorder (ADHD) is among the most commonly diagnosed psychiatric disorders in childhood, However, demographic and socioeconomic factors influencing diagnosis remain insufficiently explored. The aim was to examine associations between demographic factors (gender, month of birth, migration status, number of siblings) or socioeconomic variables (household income, residence type, school funding type) and ADHD diagnosis in children and adolescents. Methods Population-based case-control study nested in a cohort of individuals born between 1991 and 2011 and followed from 2007 to 2019 using linked health and educational records from Navarre, Spain. Incident ADHD cases were age-matched to three controls (1:3). Data were analyzed with multivariate conditional logistic regression Results Lower family income (< €18,000/year) was associated with a 23% increased risk of ADHD diagnosis (OR = 1.23, 95%CI: 1.16–1.31). Urban residence showed higher diagnostic rates compared to rural areas (OR = 0.88, 95%CI: 0.82–0.93 for rural). The month of birth significantly influenced diagnosis likelihood, with those born later in the year showing increased risk, peaking at a 69% higher risk in October-December (OR = 1.69, 95%CI: 1.55–1.83). Having ≥ 3 siblings reduced diagnosis likelihood by 18% compared to only children (OR = 0.82, 95%CI: 0.75–0.89). Migration status and school type interaction analysis revealed that migrant children in public schools had significantly lower ADHD diagnoses compared to non-migrant peers; this difference diminished in charter schools. Conclusion Socio-economic and demographic factors significantly affect ADHD diagnosis, highlighting disparities and potential diagnostic biases. Strategies to address these inequalities are essential for consistent ADHD diagnostic practices and equitable healthcare interventions. Figures Figure 1 1. INTRODUCTION Attention deficit hyperactivity disorder (ADHD) is one of the most frequent psychiatric conditions diagnosed in childhood and adolescence, linked to difficulties in academic performance, social relationships and quality of life. The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) of the American Psychiatric Association defines ADHD in children under 17 years of age as the presence of six or more symptoms in the domains of inattention or hyperactivity and impulsivity, or both [ 1 ]. The aetiology of ADHD is uncertain, and diagnosis is mainly based on clinical signs and symptoms, as there is no strong evidence of specific biochemical alterations. Therefore, even with potentially clear clinical diagnostic criteria, there is a real risk of ultimate misdiagnosis [ 30 ]. Despite its high prevalence and long-term implications, there are still gaps in the understanding of how demographic and socio-economic factors influence the diagnosis of ADHD. Several demographic and socioeconomic factors have been associated with the likelihood of being diagnosed with ADHD. Previous studies suggest that low family income is associated with a higher prevalence of ADHD due to factors such as socioeconomic stress [ 23 ]. In addition, area of residence (urban, semi-urban or rural) might influence prevalence of ADHD diagnosis, with children in urban areas reported to be more frequently diagnosed than those in rural areas [ 5 ]. Migrant status is also considered a relevant factor; children from migrant families face social, cultural and linguistic barriers that may affect diagnosis and treatment but, on other hand, cultural assets, such as diverse perspectives, strong community ties, and unique coping strategies, can also positively influence their care and resilience. Similarly, the type of funding of the school the child attends, whether public or charter, may have a bearing on the diagnosis of ADHD, as these institutions may vary in their tolerance for and approaches to diversity, influencing how cases are identified and referred to physicians [ 28 ]. Month of birth has also been linked to ADHD diagnosis, with studies indicating that the youngest children of the school class are more likely to be diagnosed due to differences in maturity with their peers [ 31 , 18 ]. Gender differences in ADHD diagnosis are well documented, with girls being less likely to be diagnosed [ 13 ]. In childhood, the prevalence of ADHD in boys is 2 to 2.5 times higher than that in girls, although in adulthood this ratio becomes closer to equal, since the hyperactivity component generally mitigates with increasing age [ 11 ]. In addition, several studies indicate that the number of siblings may influence children's behaviour and attention, noting differences in the likelihood of being diagnosed when the child has or does not have siblings [ 4 ]. The aim of the present study is to investigate possible associations between various demographic and socioeconomic variables and ADHD diagnosis. This analysis aims to provide a more defined picture of how these variables interact and affect ADHD diagnosis in children and adolescents. Specifically, the study will assess whether the observed associations of two factors— quarter of birth and migration status—were subject to modification by type of school attended (public vs. private). We will analyze this variable in depth to explore the potential modulation it may have on the effects of relative age and migrant status on the probability of ADHD diagnosis. Understanding these associations is crucial to identifying the variations, diversity, and lack of homogeneity in current patterns of diagnosis. In fact, disparities in diagnosis may reveal inconsistencies in the same construct of the disorder, leading to inappropriate (supra and infra) interventions, exacerbating challenges associated with ADHD, and perpetuating inequities in addressing underlying problems. This study is focused on filling this gap by comprehensively examining possible associations between various demographic and socioeconomic variables and ADHD diagnosis. By providing new insights into these factors, we hope to contribute to the development of strategies based on the best scientific evidence in the highly controversial field of ADHD. 2. MATERIALS AND METHODS 2.1.- Study design and Data Source A population-based case-control study nested in a cohort was carried out. Data were obtained from the databases of the Navarre Department of Education (EDUCA) and the Navarre Health Service (BARDENA) [ 10 ]. The linkage between these sources was performed by the personnel responsible for pseudonymizing health data for secondary use in BARDENA, ensuring that the researchers received fully anonymized data. To resolve possible inconsistencies in the data, decision algorithms and automated probabilistic techniques were implemented to ensure the integrity and accuracy of the combined information. 2.2.- Population and Follow-Up The study included individuals born between 1991 and 2011 who had access to public health assistance by the Navarre Heath Service and who attended at least one year of compulsory education between 2007 and 2017 in public or subsidised schools funded by the Government of Navarre (during this period, there was only one private, non-subsidised school in the region). Students were excluded if they had health insurance other than that of the National Health System, such as that provided by some public mutual insurance companies. Follow-up of participants started from the age of 5 years and continued until completion of compulsory education or loss of availability of health data, whichever occurred last. Follow-up was discontinued at the latest in case of death, when reaching 20 years of age or on 20 November 2019, the closing date of the study. A more detailed description of the cohort is described elsewhere [ 15 ]. 2.3.- ADHD Diagnostic Codes and Study Variables Individuals with a diagnosis of ADHD during the study period were considered cases. ADHD diagnoses recorded both in public Primary Care in Navarre, using the International Classification of Primary Care-2 (ICPC-2), and in public Specialised Care in Navarre, using the 10th Revision of the International Classification of Diseases (ICD-10) were included (Table S1). Two reviewers (LL and LCS) independently validated each ADHD diagnosis by reviewing the literal text associated with the ICPC-2 codes. Cases whose confirmation was uncertain or doubtful were excluded from the analysis. The date of diagnosis was established as the first occurrence of the ADHD record in either Primary or Specialised Care. In the current study, several variables were used to analyze the demographic and socioeconomic factors that could be related to ADHD diagnosis. The considered variables were gender, date of birth, country of origin of both children and parents, family income, place of residence, number of siblings, and type of school the children attended, distinguishing between public and charter schools. These variables provide a broader view to explore possible correlations between demographic and socioeconomic factors and the diagnosis of ADHD in the population. 2.4.- Statistical analysis Categorical variables were described using frequencies and percentages. Quantitative variables were summarized using mean and standard deviation (SD). Differences between groups on categorical variables were assessed using the χ² test. Differences in continuous variables were determined using Student's t-test or the Mann-Whitney test, depending on the normality of the data. For data analysis, a multivariate conditional logistic regression was performed. This statistical technique was used to assess associations between ADHD diagnosis (dependent variable) and various demographic and socio-economic variables (independent variables), such as family income, place of residence, migration status, month of birth, number of siblings and type of school funding. The model allowed adjustment for these variables, providing adjusted odds ratios (ORs) and 95% confidence intervals (95% CIs) for each association. This allowed us to identify which factors were independently associated with ADHD diagnosis in the study population. Independent variables were described as follows: Income data to which each parent, obtained from the pharmaceutical contribution group (annual income < 18,000 € or ≥ 18,000 €). The higher group was adopted when there was discordance between both parents. Housing was classified in urban, semi-urban or rural areas according to population size. Specifically, areas with less than 2,500 inhabitants were categorised as rural; areas with between 2,500 and 10,000 inhabitants were categorised as semi-urban; and areas with more than 10,000 inhabitants were categorised as urban. Migrant status was divided in four groups: a) born in Spain with both parents Spanish; b) born abroad; c) born in Spain to non-Spanish parents; d) country of birth unknown. According to the month of birth, children born in January-March were compared with those born in April-June, July-September and October-December, respectively. The Spanish education system adopts a January 1 cutoff for school entry, so children born in January are the oldest and those born in December are the youngest in their class. According to the number of siblings, single children were taken as reference and compared with a) children with one or two siblings; and b) children with three or more siblings. Schools were classified based on their funding and management structures: those that are entirely funded and operated by the government, known as public schools; and those that receive government funding but are privately managed, referred to as charter schools. We hypothesized that the relationship between quarter of birth and migration status with ADHD diagnosis could be influenced by type of school funding (public vs. charter). To test this, the interaction terms were included in the model. The main analysis compared the risk of ADHD diagnosis among children and adolescents who met certain exposure factors versus those who did not. Cases included in this analysis were those patients with an ADHD diagnosis that were clinically validated. Analyses were carried out with the statistical software R (Rstudio, R version 4.3.3 (2024-02-29)) [ 25 ]. 2.5.- Sample Selection The final sample for statistical analysis was composed following a 1:3 case-control ratio, including one participant with ADHD for every three matched participants without ADHD. This sampling strategy was implemented to maximise statistical power and improve precision in estimating associations between ADHD and the variables studied. The observation period ended with the occurrence of the event of interest (ADHD) or censoring of the participant. The analysis established a common temporal origin for all subjects, which allowed for standardisation of exposure time. For each case, matched controls were selected according to sex and year of birth. 2.6.- Study Approval The study protocol was approved by the Ethics Committee of the Government of Navarre, Spain in 2016 (project 2016/73). All procedures were in accordance with the ethical standards of the institutional committee. 3. RESULTS 3.1.- Description of the study cohort The study cohort consisted of 124,582 children and adolescents (1,167,478 person-years). Of these, 7,056 were patients with a diagnosis of ADHD (cases), resulting in a prevalence of 5.7%, and 21,168 subjects were selected as controls. The characteristics of the analyzed variables can be observed in Table 1 . Table 1 Characteristics of the study cohort. Controls (N = 21,168) Cases (N = 7,056) Gender ‍Female (%) 5844 (27.6) 1948 (27.6) Year of birth ‍Year of birth, mean (SD) 2001.3 (4.6) 2001.3 (4.6) Income ‍Family income, ≤ 18000 euros/year(%) 11953 (56.7) 4111 (58.4) Migration status ‍Born in Spain, Spanish parents (%) 15961 (75.4) 5782 (81.9) Born abroad 2702 (12.8) 486 ( 6.9) ‍Born in Spain, non Spanish parents (%) 1869 (8.8) 573 (8.1) ‍Unknown (%) 636 (3.0) 215 (3.0) Place residence Urban (%) 10373 (52.4) 3742 (56.1) Large Rural areas(%) 6597 (33.3) 1983 (29.7) Small/Medium-sized Rural areas (%) 2843 (14.3) 942 (14.1) Number siblings 3 or more siblings (%) 4004 (18.9) 1166 (16.5) One or two sibling (%) 11054 (52.2) 3681 (52.2) Only child (%) 6110 (28.9) 2209 (31.3) Type of school funding Charter school (%) 6820 (32.2) 2754 (39.0) As expected by the matching technique, the percentage of females was similar (27.6%) in cases and controls. Also, the mean age of the participants, expressed as mean year of birth, was similar in both groups (2001). The analysis of family income showed that 58.4% of the cases belonged to households with an income of less than 18,000 euros per person and year, compared to 56.7% of the controls. The place of residence also showed notable differences between the groups. More than half of the population in both groups resided in urban areas; however, a higher percentage of cases lived in urban environments compared to controls (56.1% vs. 52.4%). Conversely, more controls lived in large rural areas than cases (29.7% vs. 26.5%). In small or medium-sized rural areas, no differences were observed between the groups. In terms of migrant status, the majority of cases (81.9%) were patients born in Spain with both parents also born in Spain, compared to 75.4% of controls. Additionally, a smaller percentage of cases (6.9%) were born abroad, in contrast to 12.8% of controls. Individuals born in Spain but with at least one parent born abroad, represented 8.1% of cases and 8.8% of controls. Unknown status for parental origin accounted for 3% in both groups. Significant differences were observed between groups in the distribution of month of birth. Births in the quarters April to June and October to December were more frequent among cases (26.5% and 29.9%, respectively), while among controls, most births occurred between January and March (24.7%) and April and June (26.1%). Variations in the number of siblings between cases and controls were also observed. A lower percentage of cases had three or more siblings (16.5%) compared to controls (18.9%), while the proportion of only children was slightly higher among cases (31.3%) than among controls (28.9%). Finally, a significantly higher percentage of cases attended charter schools (39.0%) compared to controls (32.2%). 3.2.-Results on the association between socioeconomic and demographic factors and ADHD diagnosis Multivariate logistic regression analysis reveals a number of statistically significant associations between various socio-demographic variables and ADHD diagnosis. The results are shown in Table 2 . Table 2 Results on the association between socioeconomic and demographic factors and ADHD diagnosis. Variable (Reference) Predictors Odds ratios 95%CI p-value Income ( > = 18,000 euro/person/year) < 18,000 euros/person/year 1.23 1.15–1.31 < 0.001 Quarter of birth (January-March) April-June 1.20 1.11–1.32 < 0.001 July-September 1.35 1.24–1.47 < 0.001 October-December 1.69 1.55–1.83 < 0.001 Place residence (Urban) Large rural 0.88 0.82–0.93 < 0.001 Small/medium rural 0.94 0.86–1.02 0.135 Number siblings (only child) 3 or more sibling 0.83 0.76–0.91 < 0.001 One or two sibling 0.92 0.86–0.99 0.014 Migration status (‍Born in Spain, Spanish parents) Born abroad 0.40 0.36–0.46 < 0.001 Born in Spain, non Spanish parents 0.79 0.70–0.89 < 0.001 Unknown 0.90 0.23–3.48 0.819 Type of school funding (Public school) Charter school 1.21 1.13–1.30 < 0.001 Migration status * type of school funding Born abroad & Charter school 1.91 1.51–2.43 < 0.001 Born in Spain to non Spanish parents & Charter 1.32 1.01–1.73 0.018 Unknown & Charter 0.93 0.24–3.64 0.984 Household income was significantly associated with ADHD diagnosis. Children from families with an income of less than < 18,000 euros per person and year are 23% more likely to be diagnosed with ADHD (OR = 1.23, 95% CI: 1.16–1.31) than families with ≥ 18,000 euro per person and year. With respect to month of birth, significant associations with ADHD diagnosis were found. Children born between April and June were 20% more likely to be diagnosed with ADHD compared to those born between January and March (OR = 1.20, 95% CI: 1.11–1.32). For those born between July and September, the risk was even higher, with a 35% increase in the probability of diagnosis (OR = 1.35, 95% CI: 1.24–1.47). The strongest association was observed in those born between October and December, who are 69% more likely to be diagnosed with ADHD (OR = 1.69, 95% CI: 1.55–1.83). Place of residence also showed a significant effect. Children living in large rural areas were 12% less likely to be diagnosed with ADHD compared to those living in urban areas (OR = 0.88, 95% CI: 0.82–0.93). On the other hand, no significant differences were found for those living in small or medium-sized rural areas (OR = 0.94, 95% CI: 0.87–1.03) compared to urban areas. Regarding the number of siblings, having three or more siblings was associated with a 18% reduction in the probability of being diagnosed with ADHD (OR = 0.82, 95% CI: 0.75–0.89), compared to being an only child. Having one or two siblings was also associated with a slightly reduced risk of ADHD by 8% (OR = 0.92, 95% CI: 0.86–0.98). The interaction between quarter of birth and type of school funding was not statistically significant, indicating that the association between quarter of birth and ADHD did not differ by school funding type. Therefore, this interaction term was removed from the final model. A significant interaction was observed between migrant status and school type. On the one hand, within those attending public schools, children born abroad were 60% less likely to be diagnosed with ADHD compared to those born in Spain to Spanish parents (OR = 0.40, 95% CI: 0.36–0.46). Those born in Spain to non-Spanish parents also had a 21% lower risk of ADHD diagnosis than those born in Spain to Spanish parents (OR = 0.79, 95% CI: 0.70–0.89). However, no significant association was found in those with an unknown migration background (OR = 0.90, 95% CI: 0.23–3.48). On the other hand, within those attending charter schools and compared to those born in Spain to Spanish parents, children born abroad were 23% less likely to be diagnosed with ADHD (OR = 0.77, 95% CI: 0.63–0.94), no significant differences were found with children born in Spain to non-Spanish parents (OR = 1.04, 95%CI: 0.81–1.33), and those with an unknown migration background were 16% less likely to be diagnosed with ADHD (OR = 0.84, 95% CI: 0.70–0.99) (Fig. 1 .a). Among children born in Spain to Spanish parents, the likelihood of being diagnosed was 21% higher for those attending charter schools (OR = 1.21, 95% CI: 1.13–1.30). In contrast, among children born abroad, those in charter schools had more than twice the odds of being diagnosed (OR = 2.32, 95% CI: 1.84–2.92). For children born in Spain to non-Spanish parents, this difference was 60% (OR = 1.60, 95% CI: 1.23–2.08). No significant differences were observed among children with an unknown background (OR = 1.13, 95% CI: 0.29–4.40) (Fig. 1 .b). 4. DISCUSSION This study suggests that demographic, socio-economic and educational settings significantly influence the likelihood of receiving an ADHD diagnosis in our analyzed population. In terms of socio-economic status, children from families with an income of less than 18,000 euros per person and year have a higher risk of being diagnosed with ADHD. This is consistent with what has been shown in other studies [3, 29]. This might reflect how economic factors affect family environment and living conditions, which could contribute to increased stress and exacerbation of ADHD symptoms [ 24 ]. In terms of month of birth, the results reinforce the hypothesis that children born in the last months of the year are more likely to receive an ADHD diagnosis. This trend aligns with the 'relative age effect,' refers to the impact of a child's age relative to their peers within the same educational cohort. Children born just before the cutoff date for school entry are the youngest in their class, making them nearly a year younger than some classmates. This age gap can result in noticeable differences in behavior and maturity [ 26 , 18 ]. Younger children in a classroom setting may display behaviors such as inattention, hyperactivity, or impulsivity—traits often associated with ADHD. However, these behaviors might stem from developmental immaturity rather than a neurodevelopmental disorder [ 18 , 14 , 6 , 17 ]. The results suggest that place of residence influences the ADHD diagnosis, being less likely in children living in large rural areas compared to those in urban areas. Other studies have also found that children living in rural areas were less likely to be diagnosed with ADHD [ 8 ]. Factors such as less exposure to environmental pollutants, more active lifestyles and lower levels of stress could explain this difference. A higher exposure to nature has been associated with reduced ADHD diagnoses and symptom severity [ 12 ]. In addition, in urban areas there is greater awareness and resources for diagnosis, which could contribute to higher rates of ADHD labelling. A larger family size has been found to be a protective factor. The results show that children with three or more siblings have a lower risk of being diagnosed with ADHD, suggesting that family dynamics could play a role in the manifestation of ADHD symptoms, the family tolerance towards them or the health care seeking behaviour.. This trend is also observed, albeit more modestly, in those with one or two siblings. Children could benefit from greater social support at home and less attention focused on individual behaviors, which could reduce the identification of ADHD symptoms. Controversial results have been observed on this issue [ 2 , 4 , 22 ]. The analysis of migration background alongside the school’s funding type reveals that children born abroad who attend public schools show a substantially lower likelihood of receiving a diagnosis compared to those born in Spain to Spanish parents. This pattern was also observed, although to a lesser degree, among children born in Spain to non-Spanish parents. However, these differences were reduced or disappeared entirely in charter schools. In the latter, although children born abroad continued to have lower odds of being diagnosed than their Spanish-born counterparts, the magnitude of the difference was smaller, and no statistically significant differences were found for children born in Spain to non-Spanish parents. These results are consistent with other studies, although the relationship remains controversial, and mixed findings have been reported in different contexts [7, 19, 9, 16]. This observation raises questions about the ways in which cultural and social contexts may influence the perception and diagnosis of symptoms. Cultural differences in the recognition and management of childhood behavior, along with potential barriers to accessing mental health services, could explain the lower rates of diagnosis in these populations [ 20 ]. When comparing the probability of diagnosis between public and charter schools, children born abroad and those born in Spain to non-Spanish parents were more likely to receive an ADHD diagnosis in charter schools. Possible explanations include the greater availability of resources in charter schools to evaluate and facilitate medical diagnoses, educational expectations oriented toward strict behavioral standards and academic performance, or even the potential influence of the pharmaceutical industry on pedagogical teams responsible for ADHD screening, leading to higher detection rates [ 21 ]. The type of school is a factor that has been scarcely studied to date; however, as these findings suggest, it may have an important impact on the diagnosis of ADHD [D17, D18]. As stated in the introduction, there is a genuine risk of misdiagnosis due to the uncertainty surrounding ADHD etiology and the absence of specific biological markers. The disparities observed in our study variables may reflect not only differences in the manifestation or intensity of symptoms but also differences in how adults (professionals, family members, teachers, school counsellors) interpret and respond to certain childhood behaviors. In this regard, sociodemographic factors may contribute to both overdiagnosis and underdiagnosis, depending on cultural expectations, access to healthcare, and the specialized training of those evaluating children. Moreover, the presence of these contextual differences prompts a reflection on possible inconsistencies within the ADHD construct itself, as defined in diagnostic manuals. While the DSM-5 provides detailed clinical criteria, the fact that socioeconomic and educational factors can be so influential may pose a challenge to the objectivity of diagnosis, opening the door to diverse interpretations of the same symptoms. Therefore, it is crucial to further investigate the validity and reliability of assessment tools across different social and cultural contexts, as well as to promote professional and educational training aimed at ensuring more accurate and earlier detection of those cases that genuinely require intervention. Taken together, our findings underscore the need to continue examining how economic, cultural, and educational inequalities might shape the clinical course of ADHD. Identifying and addressing these determinants is essential to prevent inappropriate diagnoses, ensure equitable care, and enhance the quality of life for children and adolescents exhibiting attention and hyperactivity difficulties. The study has several significant strengths. The large sample size, including 28,224 participants, provides adequate statistical power to detect significant associations. Adopting a case-control ratio of 1:3 not only strengthens the robustness of the comparative analysis, but also optimises available resources by focusing the study on a sample size that adequately balances statistical precision and practical feasibility of the project [ 27 ]. The use of real world data obtained from education and healthcare system databases provides a realistic and comprehensive view of the population studied from different perspectives. Multivariate analysis, adjusting for multiple socio-demographic variables, allows for a more detailed understanding of the factors associated with ADHD diagnosis. Independent validation of ADHD diagnoses by two raters increases the reliability of the data. In addition, consideration of multiple factors, including income, place of residence, migrant status, month of birth, number of siblings and type of school, allows for a comprehensive analysis. Identifying interactions, such as with migration status and school type, provides more in-depth information on how these combined factors affect ADHD diagnosis. The study has certain limitations that need to be considered. The study has a retrospective and observational design, and thus it is highly dependent on the quality and accuracy of existing records, which may introduce errors or incomplete information affecting the results. Income data used were limited, relying on the pharmaceutical contribution group as a proxy measure, which may not fully reflect the socio-economic situation of families. The effect of time is another factor, as diagnostic criteria and awareness of ADHD may have changed during the study period (2007–2017), affecting diagnosis rates. The results are based on a specific region of Spain (Navarre), so the conclusions may not be applicable to other regions or countries with different socio-demographic characteristics and educational and healthcare systems. Finally, The diagnosis of ADHD may present ambiguities due to variability in the criteria employed, as well as differences in the clinical interpretation of symptoms. The absence of a universally accepted and rigorously established definition may result in the underlying construct not being fully accurately identified, which in turn affects the validity of the conclusions. Consequently, the findings should be interpreted with caution, acknowledging the potential lack of conceptual clarity inherent in the ADHD diagnostic process. Children from families with a lower income, attending charter schools, and those born in the last months of the year are more likely to be diagnosed with ADHD. In contrast, children born abroad, or born in Spain to parents born abroad are less likely to receive a diagnosis. These findings highlight the importance of considering the socioeconomic and cultural context in the assessment and diagnosis of ADHD. Taking these factors into account is crucial for developing effective, safe and equitable interventions in the management of children receiving an ADHD diagnosis. Declarations Author Contributions Conceptualization, L.C.S., J.E. and J.L.; Data curation, O.A. and J.L.; Formal analysis, O.A., A.G. and J.L.; Funding acquisition, J.E. and J.L.; Methodology, O.A., M.G.-V., L.C.S., L.L., J.E. and J.L.; Software, O.A., A.G. and J.L.; Supervision, O.A., M.G.-V., L.C.S., L.L., A.G., J.E. and J.L.; Validation, O.A., M.G.-V., L.C.S., A.G., L.L., J.E. and J.L.; Writing—original draft, O.A. and M.G.-V.; Writing—review & editing, O.A., M.G.-V., L.C.S., A.G., L.L., J.E. and J.L. All authors have read and agreed to the published version of the manuscript. Funding This research was funded by the Government of Navarre (Spain), Resolución 1519/2017, project n°50. Institutional Review Board Statement The study was approved by the Ethics Committee of the Government of Navarre, Spain, in 2016 (project 2016/73). Informed Consent Statement Patient consent was waived. Data Availability Statement Data were obtained from the Navarre Health Service and the Education Department in Navarre. Restrictions apply to the availability of these data. ACKNOWLEDGMENTS Special thanks to Javier Gorricho for providing data from the Navarre Health Service population database (BARDENA), and to the Education Department in Navarre for providing data from EDUCA database. CONFLICT OF INTEREST STATEMENT Olast Arrizibita is an employee of NNBi, carrying out activities not directly related to the work presented. The authors declare no conflict of interest. The funder had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. 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Kooij, J.J.S.; Bijlenga, D.; Salerno, L.; Jaeschke, R.; Bitter, I.; Balázs, J.; Thome, J.; Dom, G.; Kasper, S.; Nunes Filipe, C.; et al. Updated European Consensus Statement on diagnosis and treatment of adult ADHD. Eur. Psychiatry 2019, 56, 14–34. Layton, T. J., Barnett, M. L., Hicks, T. R., & Jena, A. B. (2018). Attention deficit–hyperactivity disorder and month of school enrollment. New England Journal of Medicine, 379(22), 2122-2130. Leache, L., Arrizibita, O., Gutiérrez-Valencia, M., Saiz, L. C., Erviti, J., & Librero, J. (2021). Incidence of attention deficit hyperactivity disorder (ADHD) diagnoses in navarre (Spain) from 2003 to 2019. International Journal of Environmental Research and Public Health, 18(17), 9208. Lehti, V., Chudal, R., Suominen, A., Gissler, M., & Sourander, A. (2016). Association between immigrant background and ADHD: a nationwide population‐based case–control study. Journal of Child Psychology and Psychiatry, 57(8), 967-975. Librero, J., Izquierdo-María, R., García-Gil, M., & Peiró, S. (2015). Children's relative age in class and medication for attention-deficit/hyperactivity disorder. A population-based study in a health department in Spain. Medicina Clínica (English Edition), 145(11), 471-476 Morrow, R.L.; Garland, E.J.; Wright, J.M.; Maclure, M.; Taylor, S.; Dormuth, C.R. Influence of relative age on diagnosis and treatment of attention-deficit/hyperactivity disorder in children. Can. Med. Assoc. J. 2012, 184, 755–762 Osooli, M., Ohlsson, H., Sundquist, J., & Sundquist, K. (2021). Attention deficit hyperactivity disorder in first-and second-generation immigrant children and adolescents: A nationwide cohort study in Sweden. Journal of Psychosomatic Research, 141, 110330. Pham, D., Lin, A., Rosenthal, H., & Milanaik, R. (2024). ADHD Diagnosis in Children of Non-US-Born Parents: A Cross-Sectional Analysis. Journal of Attention Disorders, 28(1), 3-13. Phillips, C. B. (2006). Medicine goes to school: teachers as sickness brokers for ADHD. PLoS medicine, 3(4), e182. Reimelt, C., Wolff, N., Hölling, H., Mogwitz, S., Ehrlich, S., Martini, J., & Roessner, V. (2021). Siblings and birth order—are they important for the occurrence of ADHD?. Journal of Attention Disorders, 25(1), 81-90. Russell, A.E.; Ford, T.; Russell, G. Socioeconomic associations with ADHD: Findings from a mediation analysis. PLoS ONE 2015, 10, e0128248 Russell, A. E., Ford, T., Williams, R., & Russell, G. (2016). The association between socioeconomic disadvantage and attention deficit/hyperactivity disorder (ADHD): a systematic review. Child Psychiatry & Human Development, 47, 440-458. R Core Team. (2024). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/ Sayal, K.; Chudal, R.; Hinkka-Yli-Salomäki, S.; Joelsson, P.; Sourander, A. Relative age within the school year and diagnosis of attention-deficit hyperactivity disorder: A nationwide population-based study. Lancet Psychiatry 2017, 4, 868–875 Schlesselman, J. J. (1982). Case-control studies: design, conduct, analysis (Vol. 2). Oxford university press. Schneider, H., & Eisenberg, D. (2006). Who receives a diagnosis of attention-deficit/hyperactivity disorder in the United States elementary school population?. Pediatrics, 117(4), e601-e609. Tam, L. Y. C., Taechameekietichai, Y., & Allen, J. L. (2024). Individual child factors affecting the diagnosis of attention deficit hyperactivity disorder (ADHD) in children and adolescents: a systematic review. European Child & Adolescent Psychiatry, 1-28. Thapar, A.; Cooper, M. Attention deficit hyperactivity disorder. Lancet 2016, 387, 1240–1250. Whitely, M., Lester, L., Phillimore, J., & Robinson, S. (2017). Influence of birth month on the probability of Western Australian children being treated for ADHD. Medical Journal of Australia, 206(2), 85-90. doi: 10.5694/mja16.00398. Additional Declarations No competing interests reported. Supplementary Files SUPPLEMENTARYTABLE.docx Cite Share Download PDF Status: Published Journal Publication published 16 Mar, 2026 Read the published version in European Child & Adolescent Psychiatry → Version 1 posted Editorial decision: Revision requested 02 Jan, 2026 Reviews received at journal 01 Jan, 2026 Reviewers agreed at journal 09 Dec, 2025 Reviewers agreed at journal 08 Dec, 2025 Reviews received at journal 14 Nov, 2025 Reviewers agreed at journal 27 Oct, 2025 Reviewers invited by journal 25 Sep, 2025 Editor assigned by journal 29 Aug, 2025 Submission checks completed at journal 29 Aug, 2025 First submitted to journal 18 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6255678","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":524940648,"identity":"45785dae-c406-4538-8905-51424d2b2678","order_by":0,"name":"Olast Arrizibita","email":"","orcid":"","institution":"Public University of Navarre","correspondingAuthor":false,"prefix":"","firstName":"Olast","middleName":"","lastName":"Arrizibita","suffix":""},{"id":524940649,"identity":"ef91a87c-3233-4034-83c6-5d05669329e7","order_by":1,"name":"Marta Gutiérrez-Valencia","email":"","orcid":"","institution":"Servicio Navarro de 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1","display":"","copyAsset":false,"role":"figure","size":33615,"visible":true,"origin":"","legend":"\u003cp\u003ea. Adjusted odds ratios (OR) of ADHD diagnosis for migration status \u003cem\u003eby type of school\u003c/em\u003e. The reference group is children born in Spain to Spanish parents.\u003c/p\u003e\n\u003cp\u003eb. Adjusted odds ratios (OR) of ADHD diagnosis for type of school \u003cem\u003eby migration status\u003c/em\u003e. The reference group is public schools.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6255678/v1/f2b8f698771c1fa8f31bb532.png"},{"id":105224667,"identity":"59630cc7-88e2-4fc2-8aec-faae490dfec9","added_by":"auto","created_at":"2026-03-23 16:15:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":695086,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6255678/v1/2b012ddf-4078-483e-bdc4-4d2b3b515f52.pdf"},{"id":93029203,"identity":"71c0c803-939f-4f6f-87fd-f9009d5978b3","added_by":"auto","created_at":"2025-10-08 09:54:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14807,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYTABLE.docx","url":"https://assets-eu.researchsquare.com/files/rs-6255678/v1/cfff69c86985a8f0ac3b803d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Demographic and Socioeconomic Factors Influencing ADHD Diagnosis in Children and Adolescents: A Cross-Sectional Study","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eAttention deficit hyperactivity disorder (ADHD) is one of the most frequent psychiatric conditions diagnosed in childhood and adolescence, linked to difficulties in academic performance, social relationships and quality of life. The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) of the American Psychiatric Association defines ADHD in children under 17 years of age as the presence of six or more symptoms in the domains of inattention or hyperactivity and impulsivity, or both [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The aetiology of ADHD is uncertain, and diagnosis is mainly based on clinical signs and symptoms, as there is no strong evidence of specific biochemical alterations. Therefore, even with potentially clear clinical diagnostic criteria, there is a real risk of ultimate misdiagnosis [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite its high prevalence and long-term implications, there are still gaps in the understanding of how demographic and socio-economic factors influence the diagnosis of ADHD. Several demographic and socioeconomic factors have been associated with the likelihood of being diagnosed with ADHD. Previous studies suggest that low family income is associated with a higher prevalence of ADHD due to factors such as socioeconomic stress [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In addition, area of residence (urban, semi-urban or rural) might influence prevalence of ADHD diagnosis, with children in urban areas reported to be more frequently diagnosed than those in rural areas [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMigrant status is also considered a relevant factor; children from migrant families face social, cultural and linguistic barriers that may affect diagnosis and treatment but, on other hand, cultural assets, such as diverse perspectives, strong community ties, and unique coping strategies, can also positively influence their care and resilience. Similarly, the type of funding of the school the child attends, whether public or charter, may have a bearing on the diagnosis of ADHD, as these institutions may vary in their tolerance for and approaches to diversity, influencing how cases are identified and referred to physicians [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Month of birth has also been linked to ADHD diagnosis, with studies indicating that the youngest children of the school class are more likely to be diagnosed due to differences in maturity with their peers [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGender differences in ADHD diagnosis are well documented, with girls being less likely to be diagnosed [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In childhood, the prevalence of ADHD in boys is 2 to 2.5 times higher than that in girls, although in adulthood this ratio becomes closer to equal, since the hyperactivity component generally mitigates with increasing age [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In addition, several studies indicate that the number of siblings may influence children's behaviour and attention, noting differences in the likelihood of being diagnosed when the child has or does not have siblings [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe aim of the present study is to investigate possible associations between various demographic and socioeconomic variables and ADHD diagnosis. This analysis aims to provide a more defined picture of how these variables interact and affect ADHD diagnosis in children and adolescents. Specifically, the study will assess whether the observed associations of two factors\u0026mdash; quarter of birth and migration status\u0026mdash;were subject to modification by type of school attended (public vs. private). We will analyze this variable in depth to explore the potential modulation it may have on the effects of relative age and migrant status on the probability of ADHD diagnosis. Understanding these associations is crucial to identifying the variations, diversity, and lack of homogeneity in current patterns of diagnosis. In fact, disparities in diagnosis may reveal inconsistencies in the same construct of the disorder, leading to inappropriate (supra and infra) interventions, exacerbating challenges associated with ADHD, and perpetuating inequities in addressing underlying problems.\u003c/p\u003e\u003cp\u003eThis study is focused on filling this gap by comprehensively examining possible associations between various demographic and socioeconomic variables and ADHD diagnosis. By providing new insights into these factors, we hope to contribute to the development of strategies based on the best scientific evidence in the highly controversial field of ADHD.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1.- Study design and Data Source\u003c/h2\u003e\u003cp\u003eA population-based case-control study nested in a cohort was carried out. Data were obtained from the databases of the Navarre Department of Education (EDUCA) and the Navarre Health Service (BARDENA) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The linkage between these sources was performed by the personnel responsible for pseudonymizing health data for secondary use in BARDENA, ensuring that the researchers received fully anonymized data. To resolve possible inconsistencies in the data, decision algorithms and automated probabilistic techniques were implemented to ensure the integrity and accuracy of the combined information.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2.- Population and Follow-Up\u003c/h2\u003e\u003cp\u003eThe study included individuals born between 1991 and 2011 who had access to public health assistance by the Navarre Heath Service and who attended at least one year of compulsory education between 2007 and 2017 in public or subsidised schools funded by the Government of Navarre (during this period, there was only one private, non-subsidised school in the region). Students were excluded if they had health insurance other than that of the National Health System, such as that provided by some public mutual insurance companies. Follow-up of participants started from the age of 5 years and continued until completion of compulsory education or loss of availability of health data, whichever occurred last. Follow-up was discontinued at the latest in case of death, when reaching 20 years of age or on 20 November 2019, the closing date of the study. A more detailed description of the cohort is described elsewhere [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3.- ADHD Diagnostic Codes and Study Variables\u003c/h2\u003e\u003cp\u003eIndividuals with a diagnosis of ADHD during the study period were considered cases. ADHD diagnoses recorded both in public Primary Care in Navarre, using the International Classification of Primary Care-2 (ICPC-2), and in public Specialised Care in Navarre, using the 10th Revision of the International Classification of Diseases (ICD-10) were included (Table S1). Two reviewers (LL and LCS) independently validated each ADHD diagnosis by reviewing the literal text associated with the ICPC-2 codes. Cases whose confirmation was uncertain or doubtful were excluded from the analysis. The date of diagnosis was established as the first occurrence of the ADHD record in either Primary or Specialised Care.\u003c/p\u003e\u003cp\u003eIn the current study, several variables were used to analyze the demographic and socioeconomic factors that could be related to ADHD diagnosis. The considered variables were gender, date of birth, country of origin of both children and parents, family income, place of residence, number of siblings, and type of school the children attended, distinguishing between public and charter schools. These variables provide a broader view to explore possible correlations between demographic and socioeconomic factors and the diagnosis of ADHD in the population.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4.- Statistical analysis\u003c/h2\u003e\u003cp\u003eCategorical variables were described using frequencies and percentages. Quantitative variables were summarized using mean and standard deviation (SD). Differences between groups on categorical variables were assessed using the χ\u0026sup2; test. Differences in continuous variables were determined using Student's t-test or the Mann-Whitney test, depending on the normality of the data.\u003c/p\u003e\u003cp\u003eFor data analysis, a multivariate conditional logistic regression was performed. This statistical technique was used to assess associations between ADHD diagnosis (dependent variable) and various demographic and socio-economic variables (independent variables), such as family income, place of residence, migration status, month of birth, number of siblings and type of school funding. The model allowed adjustment for these variables, providing adjusted odds ratios (ORs) and 95% confidence intervals (95% CIs) for each association. This allowed us to identify which factors were independently associated with ADHD diagnosis in the study population. Independent variables were described as follows:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eIncome data to which each parent, obtained from the pharmaceutical contribution group (annual income\u0026thinsp;\u0026lt;\u0026thinsp;18,000 \u0026euro; or \u0026ge;\u0026thinsp;18,000 \u0026euro;). The higher group was adopted when there was discordance between both parents.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eHousing was classified in urban, semi-urban or rural areas according to population size. Specifically, areas with less than 2,500 inhabitants were categorised as rural; areas with between 2,500 and 10,000 inhabitants were categorised as semi-urban; and areas with more than 10,000 inhabitants were categorised as urban.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eMigrant status was divided in four groups: a) born in Spain with both parents Spanish; b) born abroad; c) born in Spain to non-Spanish parents; d) country of birth unknown.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAccording to the month of birth, children born in January-March were compared with those born in April-June, July-September and October-December, respectively. The Spanish education system adopts a January 1 cutoff for school entry, so children born in January are the oldest and those born in December are the youngest in their class.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e According to the number of siblings, single children were taken as reference and compared with a) children with one or two siblings; and b) children with three or more siblings.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSchools were classified based on their funding and management structures: those that are entirely funded and operated by the government, known as public schools; and those that receive government funding but are privately managed, referred to as charter schools.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eWe hypothesized that the relationship between quarter of birth and migration status with ADHD diagnosis could be influenced by type of school funding (public vs. charter). To test this, the interaction terms were included in the model.\u003c/p\u003e\u003cp\u003eThe main analysis compared the risk of ADHD diagnosis among children and adolescents who met certain exposure factors versus those who did not. Cases included in this analysis were those patients with an ADHD diagnosis that were clinically validated.\u003c/p\u003e\u003cp\u003eAnalyses were carried out with the statistical software R (Rstudio, R version 4.3.3 (2024-02-29)) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5.- Sample Selection\u003c/h2\u003e\u003cp\u003eThe final sample for statistical analysis was composed following a 1:3 case-control ratio, including one participant with ADHD for every three matched participants without ADHD. This sampling strategy was implemented to maximise statistical power and improve precision in estimating associations between ADHD and the variables studied.\u003c/p\u003e\u003cp\u003eThe observation period ended with the occurrence of the event of interest (ADHD) or censoring of the participant. The analysis established a common temporal origin for all subjects, which allowed for standardisation of exposure time.\u003c/p\u003e\u003cp\u003eFor each case, matched controls were selected according to sex and year of birth.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6.- Study Approval\u003c/h2\u003e\u003cp\u003e The study protocol was approved by the Ethics Committee of the Government of Navarre, Spain in 2016 (project 2016/73). All procedures were in accordance with the ethical standards of the institutional committee.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1.- Description of the study cohort\u003c/h2\u003e\n \u003cp\u003eThe study cohort consisted of 124,582 children and adolescents (1,167,478 person-years). Of these, 7,056 were patients with a diagnosis of ADHD (cases), resulting in a prevalence of 5.7%, and 21,168 subjects were selected as controls. The characteristics of the analyzed variables can be observed in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of the study cohort.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControls\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;21,168)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCases\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;7,056)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026zwj;Female (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5844 (27.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1948 (27.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYear of birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026zwj;Year of birth, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2001.3 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2001.3 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026zwj;Family income, \u0026le; 18000 euros/year(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11953 (56.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4111 (58.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eMigration status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026zwj;Born in Spain, Spanish parents (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15961 (75.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5782 (81.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBorn abroad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2702 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e486 ( 6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026zwj;Born in Spain, non Spanish parents (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1869 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e573 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026zwj;Unknown (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e636 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e215 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003ePlace residence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10373 (52.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3742 (56.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge Rural areas(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6597 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1983 (29.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall/Medium-sized Rural areas (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2843 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e942 (14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eNumber siblings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 or more siblings (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4004 (18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1166 (16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOne or two sibling (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11054 (52.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3681 (52.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOnly child (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6110 (28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2209 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eType of school funding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCharter school (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6820 (32.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2754 (39.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eAs expected by the matching technique, the percentage of females was similar (27.6%) in cases and controls. Also, the mean age of the participants, expressed as mean year of birth, was similar in both groups (2001).\u003c/p\u003e\n \u003cp\u003eThe analysis of family income showed that 58.4% of the cases belonged to households with an income of less than 18,000 euros per person and year, compared to 56.7% of the controls.\u003c/p\u003e\n \u003cp\u003eThe place of residence also showed notable differences between the groups. More than half of the population in both groups resided in urban areas; however, a higher percentage of cases lived in urban environments compared to controls (56.1% vs. 52.4%). Conversely, more controls lived in large rural areas than cases (29.7% vs. 26.5%). In small or medium-sized rural areas, no differences were observed between the groups.\u003c/p\u003e\n \u003cp\u003eIn terms of migrant status, the majority of cases (81.9%) were patients born in Spain with both parents also born in Spain, compared to 75.4% of controls. Additionally, a smaller percentage of cases (6.9%) were born abroad, in contrast to 12.8% of controls. Individuals born in Spain but with at least one parent born abroad, represented 8.1% of cases and 8.8% of controls. Unknown status for parental origin accounted for 3% in both groups.\u003c/p\u003e\n \u003cp\u003eSignificant differences were observed between groups in the distribution of month of birth. Births in the quarters April to June and October to December were more frequent among cases (26.5% and 29.9%, respectively), while among controls, most births occurred between January and March (24.7%) and April and June (26.1%).\u003c/p\u003e\n \u003cp\u003eVariations in the number of siblings between cases and controls were also observed. A lower percentage of cases had three or more siblings (16.5%) compared to controls (18.9%), while the proportion of only children was slightly higher among cases (31.3%) than among controls (28.9%).\u003c/p\u003e\n \u003cp\u003eFinally, a significantly higher percentage of cases attended charter schools (39.0%) compared to controls (32.2%).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2.-Results on the association between socioeconomic and demographic factors and ADHD diagnosis\u003c/h2\u003e\n \u003cp\u003eMultivariate logistic regression analysis reveals a number of statistically significant associations between various socio-demographic variables and ADHD diagnosis. The results are shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults on the association between socioeconomic and demographic factors and ADHD diagnosis.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable (Reference)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePredictors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOdds ratios\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncome\u003c/p\u003e\n \u003cp\u003e(\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;18,000 euro/person/year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;18,000 euros/person/year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.15\u0026ndash;1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eQuarter of birth\u003c/p\u003e\n \u003cp\u003e(January-March)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApril-June\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.11\u0026ndash;1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJuly-September\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.24\u0026ndash;1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOctober-December\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.55\u0026ndash;1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePlace residence\u003c/p\u003e\n \u003cp\u003e(Urban)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge rural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.82\u0026ndash;0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall/medium rural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86\u0026ndash;1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNumber siblings\u003c/p\u003e\n \u003cp\u003e(only child)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 or more sibling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u0026ndash;0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOne or two sibling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86\u0026ndash;0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eMigration status\u003c/p\u003e\n \u003cp\u003e(\u0026zwj;Born in Spain, Spanish parents)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBorn abroad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.36\u0026ndash;0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBorn in Spain, non Spanish parents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u0026ndash;0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.23\u0026ndash;3.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eType of school funding\u003c/p\u003e\n \u003cp\u003e(Public school)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCharter school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.13\u0026ndash;1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eMigration status * type of school funding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBorn abroad \u0026amp; Charter school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.51\u0026ndash;2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBorn in Spain to non Spanish parents \u0026amp; Charter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.01\u0026ndash;1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown \u0026amp; Charter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u0026ndash;3.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eHousehold income was significantly associated with ADHD diagnosis. Children from families with an income of less than \u0026lt;\u0026thinsp;18,000 euros per person and year are 23% more likely to be diagnosed with ADHD (OR\u0026thinsp;=\u0026thinsp;1.23, 95% CI: 1.16\u0026ndash;1.31) than families with \u0026ge;\u0026thinsp;18,000 euro per person and year.\u003c/p\u003e\n \u003cp\u003eWith respect to month of birth, significant associations with ADHD diagnosis were found. Children born between April and June were 20% more likely to be diagnosed with ADHD compared to those born between January and March (OR\u0026thinsp;=\u0026thinsp;1.20, 95% CI: 1.11\u0026ndash;1.32). For those born between July and September, the risk was even higher, with a 35% increase in the probability of diagnosis (OR\u0026thinsp;=\u0026thinsp;1.35, 95% CI: 1.24\u0026ndash;1.47). The strongest association was observed in those born between October and December, who are 69% more likely to be diagnosed with ADHD (OR\u0026thinsp;=\u0026thinsp;1.69, 95% CI: 1.55\u0026ndash;1.83).\u003c/p\u003e\n \u003cp\u003ePlace of residence also showed a significant effect. Children living in large rural areas were 12% less likely to be diagnosed with ADHD compared to those living in urban areas (OR\u0026thinsp;=\u0026thinsp;0.88, 95% CI: 0.82\u0026ndash;0.93). On the other hand, no significant differences were found for those living in small or medium-sized rural areas (OR\u0026thinsp;=\u0026thinsp;0.94, 95% CI: 0.87\u0026ndash;1.03) compared to urban areas.\u003c/p\u003e\n \u003cp\u003eRegarding the number of siblings, having three or more siblings was associated with a 18% reduction in the probability of being diagnosed with ADHD (OR\u0026thinsp;=\u0026thinsp;0.82, 95% CI: 0.75\u0026ndash;0.89), compared to being an only child. Having one or two siblings was also associated with a slightly reduced risk of ADHD by 8% (OR\u0026thinsp;=\u0026thinsp;0.92, 95% CI: 0.86\u0026ndash;0.98).\u003c/p\u003e\n \u003cp\u003eThe interaction between quarter of birth and type of school funding was not statistically significant, indicating that the association between quarter of birth and ADHD did not differ by school funding type. Therefore, this interaction term was removed from the final model.\u003c/p\u003e\n \u003cp\u003eA significant interaction was observed between migrant status and school type. On the one hand, within those attending public schools, children born abroad were 60% less likely to be diagnosed with ADHD compared to those born in Spain to Spanish parents (OR\u0026thinsp;=\u0026thinsp;0.40, 95% CI: 0.36\u0026ndash;0.46). Those born in Spain to non-Spanish parents also had a 21% lower risk of ADHD diagnosis than those born in Spain to Spanish parents (OR\u0026thinsp;=\u0026thinsp;0.79, 95% CI: 0.70\u0026ndash;0.89). However, no significant association was found in those with an unknown migration background (OR\u0026thinsp;=\u0026thinsp;0.90, 95% CI: 0.23\u0026ndash;3.48). On the other hand, within those attending charter schools and compared to those born in Spain to Spanish parents, children born abroad were 23% less likely to be diagnosed with ADHD (OR\u0026thinsp;=\u0026thinsp;0.77, 95% CI: 0.63\u0026ndash;0.94), no significant differences were found with children born in Spain to non-Spanish parents (OR\u0026thinsp;=\u0026thinsp;1.04, 95%CI: 0.81\u0026ndash;1.33), and those with an unknown migration background were 16% less likely to be diagnosed with ADHD (OR\u0026thinsp;=\u0026thinsp;0.84, 95% CI: 0.70\u0026ndash;0.99) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.a).\u003c/p\u003e\n \u003cp\u003eAmong children born in Spain to Spanish parents, the likelihood of being diagnosed was 21% higher for those attending charter schools (OR\u0026thinsp;=\u0026thinsp;1.21, 95% CI: 1.13\u0026ndash;1.30). In contrast, among children born abroad, those in charter schools had more than twice the odds of being diagnosed (OR\u0026thinsp;=\u0026thinsp;2.32, 95% CI: 1.84\u0026ndash;2.92). For children born in Spain to non-Spanish parents, this difference was 60% (OR\u0026thinsp;=\u0026thinsp;1.60, 95% CI: 1.23\u0026ndash;2.08). No significant differences were observed among children with an unknown background (OR\u0026thinsp;=\u0026thinsp;1.13, 95% CI: 0.29\u0026ndash;4.40) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.b).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eThis study suggests that demographic, socio-economic and educational settings significantly influence the likelihood of receiving an ADHD diagnosis in our analyzed population.\u003c/p\u003e\u003cp\u003eIn terms of socio-economic status, children from families with an income of less than 18,000 euros per person and year have a higher risk of being diagnosed with ADHD. This is consistent with what has been shown in other studies [3, 29]. This might reflect how economic factors affect family environment and living conditions, which could contribute to increased stress and exacerbation of ADHD symptoms [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn terms of month of birth, the results reinforce the hypothesis that children born in the last months of the year are more likely to receive an ADHD diagnosis. This trend aligns with the 'relative age effect,' refers to the impact of a child's age relative to their peers within the same educational cohort. Children born just before the cutoff date for school entry are the youngest in their class, making them nearly a year younger than some classmates. This age gap can result in noticeable differences in behavior and maturity [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Younger children in a classroom setting may display behaviors such as inattention, hyperactivity, or impulsivity\u0026mdash;traits often associated with ADHD. However, these behaviors might stem from developmental immaturity rather than a neurodevelopmental disorder [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe results suggest that place of residence influences the ADHD diagnosis, being less likely in children living in large rural areas compared to those in urban areas. Other studies have also found that children living in rural areas were less likely to be diagnosed with ADHD [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Factors such as less exposure to environmental pollutants, more active lifestyles and lower levels of stress could explain this difference. A higher exposure to nature has been associated with reduced ADHD diagnoses and symptom severity [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In addition, in urban areas there is greater awareness and resources for diagnosis, which could contribute to higher rates of ADHD labelling.\u003c/p\u003e\u003cp\u003eA larger family size has been found to be a protective factor. The results show that children with three or more siblings have a lower risk of being diagnosed with ADHD, suggesting that family dynamics could play a role in the manifestation of ADHD symptoms, the family tolerance towards them or the health care seeking behaviour.. This trend is also observed, albeit more modestly, in those with one or two siblings. Children could benefit from greater social support at home and less attention focused on individual behaviors, which could reduce the identification of ADHD symptoms. Controversial results have been observed on this issue [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe analysis of migration background alongside the school\u0026rsquo;s funding type reveals that children born abroad who attend public schools show a substantially lower likelihood of receiving a diagnosis compared to those born in Spain to Spanish parents. This pattern was also observed, although to a lesser degree, among children born in Spain to non-Spanish parents. However, these differences were reduced or disappeared entirely in charter schools. In the latter, although children born abroad continued to have lower odds of being diagnosed than their Spanish-born counterparts, the magnitude of the difference was smaller, and no statistically significant differences were found for children born in Spain to non-Spanish parents. These results are consistent with other studies, although the relationship remains controversial, and mixed findings have been reported in different contexts [7, 19, 9, 16]. This observation raises questions about the ways in which cultural and social contexts may influence the perception and diagnosis of symptoms. Cultural differences in the recognition and management of childhood behavior, along with potential barriers to accessing mental health services, could explain the lower rates of diagnosis in these populations [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhen comparing the probability of diagnosis between public and charter schools, children born abroad and those born in Spain to non-Spanish parents were more likely to receive an ADHD diagnosis in charter schools. Possible explanations include the greater availability of resources in charter schools to evaluate and facilitate medical diagnoses, educational expectations oriented toward strict behavioral standards and academic performance, or even the potential influence of the pharmaceutical industry on pedagogical teams responsible for ADHD screening, leading to higher detection rates [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The type of school is a factor that has been scarcely studied to date; however, as these findings suggest, it may have an important impact on the diagnosis of ADHD [D17, D18].\u003c/p\u003e\u003cp\u003eAs stated in the introduction, there is a genuine risk of misdiagnosis due to the uncertainty surrounding ADHD etiology and the absence of specific biological markers. The disparities observed in our study variables may reflect not only differences in the manifestation or intensity of symptoms but also differences in how adults (professionals, family members, teachers, school counsellors) interpret and respond to certain childhood behaviors. In this regard, sociodemographic factors may contribute to both overdiagnosis and underdiagnosis, depending on cultural expectations, access to healthcare, and the specialized training of those evaluating children.\u003c/p\u003e\u003cp\u003eMoreover, the presence of these contextual differences prompts a reflection on possible inconsistencies within the ADHD construct itself, as defined in diagnostic manuals. While the DSM-5 provides detailed clinical criteria, the fact that socioeconomic and educational factors can be so influential may pose a challenge to the objectivity of diagnosis, opening the door to diverse interpretations of the same symptoms. Therefore, it is crucial to further investigate the validity and reliability of assessment tools across different social and cultural contexts, as well as to promote professional and educational training aimed at ensuring more accurate and earlier detection of those cases that genuinely require intervention.\u003c/p\u003e\u003cp\u003eTaken together, our findings underscore the need to continue examining how economic, cultural, and educational inequalities might shape the clinical course of ADHD. Identifying and addressing these determinants is essential to prevent inappropriate diagnoses, ensure equitable care, and enhance the quality of life for children and adolescents exhibiting attention and hyperactivity difficulties.\u003c/p\u003e\u003cp\u003eThe study has several significant strengths. The large sample size, including 28,224 participants, provides adequate statistical power to detect significant associations. Adopting a case-control ratio of 1:3 not only strengthens the robustness of the comparative analysis, but also optimises available resources by focusing the study on a sample size that adequately balances statistical precision and practical feasibility of the project [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The use of real world data obtained from education and healthcare system databases provides a realistic and comprehensive view of the population studied from different perspectives. Multivariate analysis, adjusting for multiple socio-demographic variables, allows for a more detailed understanding of the factors associated with ADHD diagnosis. Independent validation of ADHD diagnoses by two raters increases the reliability of the data. In addition, consideration of multiple factors, including income, place of residence, migrant status, month of birth, number of siblings and type of school, allows for a comprehensive analysis. Identifying interactions, such as with migration status and school type, provides more in-depth information on how these combined factors affect ADHD diagnosis.\u003c/p\u003e\u003cp\u003eThe study has certain limitations that need to be considered. The study has a retrospective and observational design, and thus it is highly dependent on the quality and accuracy of existing records, which may introduce errors or incomplete information affecting the results. Income data used were limited, relying on the pharmaceutical contribution group as a proxy measure, which may not fully reflect the socio-economic situation of families. The effect of time is another factor, as diagnostic criteria and awareness of ADHD may have changed during the study period (2007\u0026ndash;2017), affecting diagnosis rates. The results are based on a specific region of Spain (Navarre), so the conclusions may not be applicable to other regions or countries with different socio-demographic characteristics and educational and healthcare systems.\u003c/p\u003e\u003cp\u003eFinally, The diagnosis of ADHD may present ambiguities due to variability in the criteria employed, as well as differences in the clinical interpretation of symptoms. The absence of a universally accepted and rigorously established definition may result in the underlying construct not being fully accurately identified, which in turn affects the validity of the conclusions. Consequently, the findings should be interpreted with caution, acknowledging the potential lack of conceptual clarity inherent in the ADHD diagnostic process.\u003c/p\u003e\u003cp\u003eChildren from families with a lower income, attending charter schools, and those born in the last months of the year are more likely to be diagnosed with ADHD. In contrast, children born abroad, or born in Spain to parents born abroad are less likely to receive a diagnosis. These findings highlight the importance of considering the socioeconomic and cultural context in the assessment and diagnosis of ADHD. Taking these factors into account is crucial for developing effective, safe and equitable interventions in the management of children receiving an ADHD diagnosis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eConceptualization, L.C.S., J.E. and J.L.; Data curation, O.A. and J.L.; Formal analysis, O.A., A.G. and J.L.; Funding acquisition, J.E. and J.L.; Methodology, O.A., M.G.-V., L.C.S., L.L., J.E. and J.L.; Software, O.A., A.G. and J.L.; Supervision, O.A., M.G.-V., L.C.S., L.L., A.G., J.E. and J.L.; Validation, O.A., M.G.-V., L.C.S., A.G., L.L., J.E. and J.L.; Writing\u0026mdash;original draft, O.A. and M.G.-V.; Writing\u0026mdash;review \u0026amp; editing, O.A., M.G.-V., L.C.S., A.G., L.L., J.E. and J.L. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Government of Navarre (Spain), Resoluci\u0026oacute;n 1519/2017, project n\u0026deg;50.\u003c/p\u003e\n\u003cp\u003eInstitutional Review Board Statement\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee of the Government of Navarre, Spain, in 2016 (project 2016/73).\u003c/p\u003e\n\u003cp\u003eInformed Consent Statement\u003c/p\u003e\n\u003cp\u003ePatient consent was waived.\u003c/p\u003e\n\u003cp\u003eData Availability Statement\u003c/p\u003e\n\u003cp\u003eData were obtained from the Navarre Health Service and the Education Department in Navarre. Restrictions apply to the availability of these data.\u003c/p\u003e\n\u003cp\u003eACKNOWLEDGMENTS\u003c/p\u003e\n\u003cp\u003eSpecial thanks to Javier Gorricho for providing data from the Navarre Health Service population database (BARDENA), and to the Education Department in Navarre for providing data from EDUCA database.\u003c/p\u003e\n\u003cp\u003eCONFLICT OF INTEREST STATEMENT\u003c/p\u003e\n\u003cp\u003eOlast Arrizibita is an employee of NNBi, carrying out activities not directly related to the work presented. The authors declare no conflict of interest. The funder had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmerican Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 5th ed.; American Psychiatric Association: Arlington, VA, USA, 2013.\u003c/li\u003e\n\u003cli\u003eBerger, I., \u0026amp; Felsenthal-Berger, N. (2009). Attention-deficit hyperactivity disorder (ADHD) and birth order. Journal of Child Neurology, 24(6), 692-696.\u003c/li\u003e\n\u003cli\u003eBozinovic, K., McLamb, F., O\u0026rsquo;Connell, K., Olander, N., Feng, Z., Haagensen, S., \u0026amp; Bozinovic, G. (2021). US national, regional, and state-specific socioeconomic factors correlate with child and adolescent ADHD diagnoses pre-COVID-19 pandemic. Scientific reports, 11(1), 22008.\u003c/li\u003e\n\u003cli\u003eCarballo, J. J., Garc\u0026iacute;a-Nieto, R., \u0026Aacute;lvarez-Garc\u0026iacute;a, R., Caro-Ca\u0026ntilde;izares, I., L\u0026oacute;pez-Castrom\u0026aacute;n, J., Mu\u0026ntilde;oz-Lorenzo, L., ... \u0026amp; Baca-Garc\u0026iacute;a, E. (2013). Sibship size, birth order, family structure and childhood mental disorders. Social psychiatry and psychiatric epidemiology, 48, 1327-1333.\u003c/li\u003e\n\u003cli\u003eC\u0026eacute;nat, J.M.; Blais-Rochette, C.; Morse, C.; Vandette, M.P.; Noorishad, P.G.; Kogan, C.; Ndengeyingoma, A.; Labelle, P.R. Prevalence and Risk Factors Associated with Attention-Deficit/Hyperactivity Disorder among US Black Individuals: A Systematic Review and Meta-analysis. JAMA Psychiatry 2021, 78, 21\u0026ndash;28.\u003c/li\u003e\n\u003cli\u003eChen, M.; Huang, K.; Hsu, J.; Tsai, S.; Su, T.; Chen, T.; Bai, Y. Effect of Relative Age on Childhood Mental Health: A Cohort of 9,548,393 Children and Adolescents. Acta Psychiatr. Scand. 2021, 144, 168\u0026ndash;177.\u003c/li\u003e\n\u003cli\u003eCrouch, E., Radcliff, E., Bennett, K. J., Brown, M. J., \u0026amp; Hung, P. (2021). Examining the relationship between adverse childhood experiences and ADHD diagnosis and severity. Academic pediatrics, 21(8), 1388-1394.\u003c/li\u003e\n\u003cli\u003eDonovan, G. H., Michael, Y. L., Gatziolis, D., Mannetje, A. T., \u0026amp; Douwes, J. (2019). Association between exposure to the natural environment, rurality, and attention-deficit hyperactivity disorder in children in New Zealand: a linkage study. The Lancet Planetary Health, 3(5), e226-e234.\u003c/li\u003e\n\u003cli\u003eGao, X., Zhao, Y., Wang, N., \u0026amp; Yang, L. (2022). Migration modulates the prevalence of ASD and ADHD: a systematic review and meta-analysis. BMC psychiatry, 22(1), 395.\u003c/li\u003e\n\u003cli\u003eGorricho, J., Leache, L., Tamayo, I., S\u0026aacute;nchez-S\u0026aacute;ez, F., Almirantearena, M., San Rom\u0026aacute;n, E., ... \u0026amp; Librero, J. (2023). Data Resource Profile: Results Analysis Base of Navarre (BARDENA). International Journal of Epidemiology, 52(6), e301-e307.\u003c/li\u003e\n\u003cli\u003eHinshaw, S.P.; Nguyen, P.T.; O\u0026rsquo;Grady, S.M.; Rosenthal, E.A. Annual Research Review: Attention-deficit/hyperactivity disorder in girls and women: Underrepresentation, longitudinal processes, and key directions. J. Child. Psychol. Psychiatry Allied Discip. 2021\u003c/li\u003e\n\u003cli\u003eHood, M., \u0026amp; Baumann, O. (2024). Could Nature Contribute to the Management of ADHD in Children? A Systematic Review. International Journal of Environmental Research and Public Health, 21(6), 736.\u003c/li\u003e\n\u003cli\u003eKooij, J.J.S.; Bijlenga, D.; Salerno, L.; Jaeschke, R.; Bitter, I.; Bal\u0026aacute;zs, J.; Thome, J.; Dom, G.; Kasper, S.; Nunes Filipe, C.; et al. Updated European Consensus Statement on diagnosis and treatment of adult ADHD. Eur. Psychiatry 2019, 56, 14\u0026ndash;34.\u003c/li\u003e\n\u003cli\u003eLayton, T. J., Barnett, M. L., Hicks, T. R., \u0026amp; Jena, A. B. (2018). Attention deficit\u0026ndash;hyperactivity disorder and month of school enrollment. New England Journal of Medicine, 379(22), 2122-2130.\u003c/li\u003e\n\u003cli\u003eLeache, L., Arrizibita, O., Guti\u0026eacute;rrez-Valencia, M., Saiz, L. C., Erviti, J., \u0026amp; Librero, J. (2021). Incidence of attention deficit hyperactivity disorder (ADHD) diagnoses in navarre (Spain) from 2003 to 2019. International Journal of Environmental Research and Public Health, 18(17), 9208.\u003c/li\u003e\n\u003cli\u003eLehti, V., Chudal, R., Suominen, A., Gissler, M., \u0026amp; Sourander, A. (2016). Association between immigrant background and ADHD: a nationwide population‐based case\u0026ndash;control study. Journal of Child Psychology and Psychiatry, 57(8), 967-975.\u003c/li\u003e\n\u003cli\u003eLibrero, J., Izquierdo-Mar\u0026iacute;a, R., Garc\u0026iacute;a-Gil, M., \u0026amp; Peir\u0026oacute;, S. (2015). Children\u0026apos;s relative age in class and medication for attention-deficit/hyperactivity disorder. A population-based study in a health department in Spain. Medicina Cl\u0026iacute;nica (English Edition), 145(11), 471-476\u003c/li\u003e\n\u003cli\u003eMorrow, R.L.; Garland, E.J.; Wright, J.M.; Maclure, M.; Taylor, S.; Dormuth, C.R. Influence of relative age on diagnosis and treatment of attention-deficit/hyperactivity disorder in children. Can. Med. Assoc. J. 2012, 184, 755\u0026ndash;762\u003c/li\u003e\n\u003cli\u003eOsooli, M., Ohlsson, H., Sundquist, J., \u0026amp; Sundquist, K. (2021). Attention deficit hyperactivity disorder in first-and second-generation immigrant children and adolescents: A nationwide cohort study in Sweden. Journal of Psychosomatic Research, 141, 110330.\u003c/li\u003e\n\u003cli\u003ePham, D., Lin, A., Rosenthal, H., \u0026amp; Milanaik, R. (2024). ADHD Diagnosis in Children of Non-US-Born Parents: A Cross-Sectional Analysis. Journal of Attention Disorders, 28(1), 3-13.\u003c/li\u003e\n\u003cli\u003ePhillips, C. B. (2006). Medicine goes to school: teachers as sickness brokers for ADHD. PLoS medicine, 3(4), e182.\u003c/li\u003e\n\u003cli\u003eReimelt, C., Wolff, N., H\u0026ouml;lling, H., Mogwitz, S., Ehrlich, S., Martini, J., \u0026amp; Roessner, V. (2021). Siblings and birth order\u0026mdash;are they important for the occurrence of ADHD?. Journal of Attention Disorders, 25(1), 81-90.\u003c/li\u003e\n\u003cli\u003eRussell, A.E.; Ford, T.; Russell, G. Socioeconomic associations with ADHD: Findings from a mediation analysis. PLoS ONE 2015, 10, e0128248\u003c/li\u003e\n\u003cli\u003eRussell, A. E., Ford, T., Williams, R., \u0026amp; Russell, G. (2016). The association between socioeconomic disadvantage and attention deficit/hyperactivity disorder (ADHD): a systematic review. Child Psychiatry \u0026amp; Human Development, 47, 440-458.\u003c/li\u003e\n\u003cli\u003eR Core Team. (2024). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/\u003c/li\u003e\n\u003cli\u003eSayal, K.; Chudal, R.; Hinkka-Yli-Salom\u0026auml;ki, S.; Joelsson, P.; Sourander, A. Relative age within the school year and diagnosis of attention-deficit hyperactivity disorder: A nationwide population-based study. Lancet Psychiatry 2017, 4, 868\u0026ndash;875\u003c/li\u003e\n\u003cli\u003eSchlesselman, J. J. (1982). Case-control studies: design, conduct, analysis (Vol. 2). Oxford university press.\u003c/li\u003e\n\u003cli\u003eSchneider, H., \u0026amp; Eisenberg, D. (2006). Who receives a diagnosis of attention-deficit/hyperactivity disorder in the United States elementary school population?. Pediatrics, 117(4), e601-e609.\u003c/li\u003e\n\u003cli\u003eTam, L. Y. C., Taechameekietichai, Y., \u0026amp; Allen, J. L. (2024). Individual child factors affecting the diagnosis of attention deficit hyperactivity disorder (ADHD) in children and adolescents: a systematic review. European Child \u0026amp; Adolescent Psychiatry, 1-28.\u003c/li\u003e\n\u003cli\u003eThapar, A.; Cooper, M. Attention deficit hyperactivity disorder. Lancet 2016, 387, 1240\u0026ndash;1250.\u003c/li\u003e\n\u003cli\u003eWhitely, M., Lester, L., Phillimore, J., \u0026amp; Robinson, S. (2017). Influence of birth month on the probability of Western Australian children being treated for ADHD. Medical Journal of Australia, 206(2), 85-90. doi: 10.5694/mja16.00398.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-child-and-adolescent-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ecap","sideBox":"Learn more about [European Child \u0026 Adolescent Psychiatry](http://link.springer.com/journal/787)","snPcode":"787","submissionUrl":"https://submission.nature.com/new-submission/787/3","title":"European Child \u0026 Adolescent Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6255678/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6255678/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eAttention deficit hyperactivity disorder (ADHD) is among the most commonly diagnosed psychiatric disorders in childhood, However, demographic and socioeconomic factors influencing diagnosis remain insufficiently explored. The aim was to examine associations between demographic factors (gender, month of birth, migration status, number of siblings) or socioeconomic variables (household income, residence type, school funding type) and ADHD diagnosis in children and adolescents.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003ePopulation-based case-control study nested in a cohort of individuals born between 1991 and 2011 and followed from 2007 to 2019 using linked health and educational records from Navarre, Spain. Incident ADHD cases were age-matched to three controls (1:3). Data were analyzed with multivariate conditional logistic regression\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eLower family income (\u0026lt; \u0026euro;18,000/year) was associated with a 23% increased risk of ADHD diagnosis (OR\u0026thinsp;=\u0026thinsp;1.23, 95%CI: 1.16\u0026ndash;1.31). Urban residence showed higher diagnostic rates compared to rural areas (OR\u0026thinsp;=\u0026thinsp;0.88, 95%CI: 0.82\u0026ndash;0.93 for rural). The month of birth significantly influenced diagnosis likelihood, with those born later in the year showing increased risk, peaking at a 69% higher risk in October-December (OR\u0026thinsp;=\u0026thinsp;1.69, 95%CI: 1.55\u0026ndash;1.83). Having\u0026thinsp;\u0026ge;\u0026thinsp;3 siblings reduced diagnosis likelihood by 18% compared to only children (OR\u0026thinsp;=\u0026thinsp;0.82, 95%CI: 0.75\u0026ndash;0.89). Migration status and school type interaction analysis revealed that migrant children in public schools had significantly lower ADHD diagnoses compared to non-migrant peers; this difference diminished in charter schools.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eSocio-economic and demographic factors significantly affect ADHD diagnosis, highlighting disparities and potential diagnostic biases. Strategies to address these inequalities are essential for consistent ADHD diagnostic practices and equitable healthcare interventions.\u003c/p\u003e","manuscriptTitle":"Demographic and Socioeconomic Factors Influencing ADHD Diagnosis in Children and Adolescents: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-08 09:53:44","doi":"10.21203/rs.3.rs-6255678/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-02T09:33:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-01T20:54:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"39159031776607194859829402485533478605","date":"2025-12-09T09:30:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"324442071327278038139062555557244695912","date":"2025-12-08T19:52:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-14T15:58:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157469816822930315726579798271837430402","date":"2025-10-27T11:28:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-25T15:32:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-29T04:11:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-29T04:09:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Child \u0026 Adolescent Psychiatry","date":"2025-03-18T18:26:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-child-and-adolescent-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ecap","sideBox":"Learn more about [European Child \u0026 Adolescent Psychiatry](http://link.springer.com/journal/787)","snPcode":"787","submissionUrl":"https://submission.nature.com/new-submission/787/3","title":"European Child \u0026 Adolescent Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5181ada3-882f-4c18-9648-befd55787651","owner":[],"postedDate":"October 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-23T16:12:51+00:00","versionOfRecord":{"articleIdentity":"rs-6255678","link":"https://doi.org/10.1007/s00787-026-03007-5","journal":{"identity":"european-child-and-adolescent-psychiatry","isVorOnly":false,"title":"European Child \u0026 Adolescent Psychiatry"},"publishedOn":"2026-03-16 15:59:00","publishedOnDateReadable":"March 16th, 2026"},"versionCreatedAt":"2025-10-08 09:53:44","video":"","vorDoi":"10.1007/s00787-026-03007-5","vorDoiUrl":"https://doi.org/10.1007/s00787-026-03007-5","workflowStages":[]},"version":"v1","identity":"rs-6255678","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6255678","identity":"rs-6255678","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}