November 2023Original Research: Impact of a Publicly-Funded Pharmacare Program on Prescription Stimulant use

Impact of a Publicly-Funded Pharmacare Program on Prescription Stimulant use Among Children and Youth: A Population-Based Observational Natural Experiment
Effet d’un programme d’assurance-médicaments financé par l’État sur l’utilisation de stimulants sur ordonnance chez les enfants et les jeunes : une expérience naturelle par observation dans la populationTony Antoniou, PhD^1,2,3,4

, Daniel McCormack, MSc², Sophie Kitchen, MSc², Kathleen Pajer, MD, MPH^5,6, William Gardner, PhD^2,7

, Yona Lunsky, PhD^2,8,9

, Melanie Penner, MD, MSc^10,11

, Mina Tadrous, PharmD, PhD^2,12, Muhammad Mamdani, PharmD, MPH^{1,2,12,13,14,15}, David N. Juurlink, MD, PhD^2,11,16 and Tara Gomes, PhD^1,2,12,15

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The Canadian Journal of Psychiatry / La Revue Canadienne de Psychiatrie
2023, Vol. 68(11) 826‐837
© The Author(s) 2023
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Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/07067437231166836
TheCJP.ca | LaRCP.caimg2

Abstract:Objetive: Stimulants are first-line pharmacotherapy for individuals with attention-deficit hyperactivity disorder. However, disparities in drug coverage may contribute to inequitable treatment access. In January 2018, the government of Ontario, Canada, implemented a publicly-funded program (OHIP+) providing universal access to medications at no cost to children and youth between the ages of 0 and 24. In April 2019, the program was amended to cover only children and youth without private insurance. We studied whether these policy changes were associated with changes in prescription stimulant dispensing to Ontario children and youth.Methods: We conducted a population-based observational natural experiment study of stimulant dispensing to children and youth in Ontario between January 2013 and March 2020. We used interventional autoregressive integrated moving average models to estimate the association between OHIP+ and its subsequent modification with stimulant dispensing trends.Results: The implementation of OHIP+ was associated with a significant immediate increase in the monthly rate of stimulant dispensing of 53.6 individuals per 100,000 population (95% confidence interval [CI], 36.8 to 70.5 per 100,000) and a 14.2% (95% CI, 12.8% to 15.6%) relative percent increase in stimulant dispensing rates between December 2017 and March 2019 (1198.6 vs. 1368.7 per 100,000 population). The April 2019 OHIP+ program amendment was associated with an increase in monthly stimulant dispensing trends of 10.2 individuals per 100,000 population (95% CI, 5.0 to 15.5), with rates increasing 7.5% (95% CI, 6.2% to 8.7%) between March 2019 and March 2020 (1368.7 vs. 1470.8 per 100,000 population). These associations were most pronounced among males, children and youth living in the highest income neighbourhoods and individuals aged 20 to 24.Conclusion: A publicly-funded pharmacare program was associated with more children and youth being dispensed stimulants.AbrégéObjectif: Les stimulants sont une pharmacothérapie de première intention pour les personnes souffrant du trouble de déficit d’attention avec hyperactivité. Cependant, des disparités dans la couverture des médicaments peuvent contribuer à un accès inéquitable au traitement.. En janvier 2018, le gouvernement de l’Ontario, Canada, a mis en œuvre un programme financé par l’État (OHIP+) offrant un accès universel aux médicaments sans frais aux enfants et aux adolescents âgés de 0 à 24 ans. En avril 2019, le programme a été modifié pour ne couvrir que les enfants et adolescents sans assurance privée. Nous avons étudié si ces changements de politique étaient associés à des changements dans la distribution de stimulants sur ordonnance pour les enfants et adolescents ontariens.Méthodes : Nous avons mené une étude expérimentale par observation dans la population de la distribution des stimulants aux enfants et aux adolescents en Ontario entre janvier 2013 et mars 2020. Nous avons utilisé des modèles de moyennes mobiles intégrés autorégressifs interventionnels pour estimer l’association entre OHIP+ et sa modification subséquente avec les tendances de la distribution de stimulants.Résultats : La mise en œuvre d’OHIP+ était associée à une augmentation significative immédiate du taux mensuel de distribution de stimulants à 53,6 personnes par 100 000 de population (intervalle de confiance [IC] à 95%; 36,8 à 70,5 par 100 000) et une augmentation relative de 14,2% (IC à 95% 12,8% à 15,6%) des taux du pourcentage de distribution des stimulants entre décembre 2017 et mars 2019 (1198,6 contre 1368,7 par 100 000 de population). La modification du programme OHIP+ en avril 2019 était associée à une hausse des tendances de la distribution mensuelle de stimulants de 10,2 personnes par 100 000 de population (IC à 95% 5,0 à 15,5), avec des taux augmentant à 7,5% (IC à 95% 6,2% à 8,7%) entre mars 2019 et mars 2020 (1368,7 contre 1470,8 par 100 000 de population). Ces associations étaient les plus prononcées chez les garçons et les jeunes de sexe masculin habitant les quartiers au revenu élevé, âgés de 20 à 24 ans.Conclusion : Un programme d’assurance-médicaments financé par l’État était associé à la distribution de stimulants à un plus grand nombre d’enfants et de jeunes.Keywords
child, adolescent, central nervous system stimulants, prescriptions/statistics & numerical data, policyIntroductionAttention-deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder of childhood, with a worldwide prevalence of 2.2% to 7.2%.^1–4 Stimulant medications are first-line pharmacotherapy for ADHD, with increasing worldwide use.^5–9 Factors associated with increased stimulant treatment include male sex, age and ethnicity.^10–13Prescription drug coverage is a less well-studied determinant of medication access among individuals with ADHD. In one US study, children without insurance were less likely to be prescribed stimulants than those with private or public insurance.¹² Similarly, a separate US study found that most stimulant prescriptions were paid through commercial insurance, with copayments required for nearly two-thirds of prescriptions dispensed.¹⁴ Therefore, disparities in insurance status may be an important source of inequity in treatment for children and youth with ADHD, favouring individuals with private insurance and the financial resources to cover out-of-pocket costs. This assertion is supported by findings from research associating higher income with a greater like-lihood of stimulant treatment¹⁵ and lower treatment rates in children with ADHD from low-income families despite being at least as likely to meet the diagnostic criteria for this condition as high-income children.¹⁶Universal publicly-funded pharmacare programs represent one mechanism for reducing prescription drug access inequity.¹⁷ In January 2018, the Ontario Ministry of Health and Long-Term Care implemented a publicly-funded pharmacare program providing all children and youth aged 24 and under access to prescription medications listed on the Ontario Drug Benefit formulary at no cost.¹⁸ Coverage was automatic, with no deductibles or copayments. The program, referred to as OHIP+, was subsequently modified in April 2019 to cover medications only for children and youth without private insurance.¹⁹ These successive policy changes provide a natural experiment for studying the implications of changes in prescription coverage on stimulant use among children and youth. Accordingly, we studied the association of OHIP+ and its modification with changes in prescription stimulant use among the entire population of individuals aged 0 to 24 in Ontario, home to approximately 40% of Canadian children and youth.²⁰MethodsSettingWe conducted an exploratory population-based observational time-series study of all Ontario residents aged 0 to 24 years between 1 January 2013, and 31 March 2020. We selected this period to allow a sufficiently long observation period prior to the implementation of OHIP+ and to avoid the potential confounding effects of COVID on patterns of health service use. The reporting of this study aligns with STROBE guidelines for observational studies (Supplemental Appendix).Data SourcesWe used Ontario’s administrative health databases. These datasets were linked using unique encoded identifiers and analyzed at ICES. We identified stimulant prescriptions using the Narcotics Monitoring System database, which contains comprehensive records of all prescriptions for controlled substances, including stimulants, dispensed from community pharmacies in Ontario, regardless of payer. Most (99.2%) stimulant preparations dispensed during the study period were included on the Ontario Drug Benefit formulary and covered by OHIP+. We used the ICES Corporate Provider Database to determine prescriber specialty and the Registered Persons Database to ascertain demographic characteristics for all children and youth dispensed stimulants over the study period. The use of data in this project was authorized under section 45 of Ontario’s Personal Health Information Protection Act, which does not require review by a Research Ethics Board.Study Population and OutcomesFor each month of the study period, we defined our study population as all Ontario residents aged 0 to 24 who were alive on the first day of the month. Our primary outcome was the monthly rate of stimulant use per 100,000 children and youth, defined as the number of individuals dispensed a stimulant (i.e., amphetamine, dextroamphetamine, lisdexamfetamine, methylphenidate) in a given month divided by the population of children and youth aged 0 to 24 for that period.Statistical analysisWe determined relative percent changes in stimulant dispensing rates following the implementation and modification of OHIP+, and estimated 95% confidence intervals (CIs) using the Poisson distribution. Because the relative percent change does not account for prior trends, temporal correlation and seasonality, we used interventional autoregressive integrated moving average (ARIMA) models to estimate the association between the implementation and modification of OHIP+ on stimulant dispensing rates among children and youth, accounting for background trend and seasonality.^21,22 We used the Dickey-Fuller test to determine the stationarity of the time series and applied first-order and seasonal differencing to arrive at a stationary series.^22,23 We used the autocorrelation function and partial autocorrelation function to identify autoregressive and/or moving average components and correct for autocorrelation remaining after differencing and selected the best models using goodness-of-fit tests.^21,22 We used residual plots and the Portmanteau statistic to confirm that residuals from specified ARIMA models were a white noise process.^21,22,24 Finally, once the ARIMA models were specified, we obtained predicted stimulant dispensing rates for the 12-months following OHIP+ implementation (1 January 2018 to 31 December 2018) from the ARIMA models, and compared these values with the observed rates. Next, we used a step intervention function to test for a change in the rate of stimulant dispensing during the period in which OHIP+ provided universal coverage of prescription medication to Ontario children and youth (1 January 2018, to 31 March 2019). We also used a ramp-intervention function to determine if stimulant dispensing rate trends changed following amendments to the program covering only children and youth without private-insurance. We stratified our interventional analyses by sex, age category (0 to 9 years, 10 to 14 years, 15 to 19 years, 20 to 24 years), neighbourhood income quintile and urban versus rural residence, defined on the first day of the month of interest. We defined sex as biological attributes associated with physical and physiological features.²⁵ Our databases did not include information on patient gender. We defined rural residence using the Statistics Canada definition of communities with a population of less than 10,000 people by linking 2016 Census data to individual residential postal codes. We derived neighbourhood income quintile using postal codes linked to census dissemination areas. We did not adjust for multiple comparisons because of the exploratory nature of the study. We used standardized differences to compare demographic characteristics between individuals receiving a stimulant during the pre-OHIP+ (January 2013 to December 2017), OHIP+ (January 2018 to March 2019) and modified OHIP+ (April 2019 to March 2020) periods, with differences greater than 0.1 considered meaningful.²⁶ All analyses were completed using SAS Enterprise Guide, version 7.1 (SAS Institute Inc., Cary, NC, USA) and Stata version 17.0 (StataCorp LLC, College Station, TX, USA).ResultsDuring our 7-year study period, 241,794 individuals 24 years of age or younger were dispensed a stimulant. Most stimulant-treated children and youth were male (n = 163,117; 67.5%), and the median age was 13 years (interquartile range: 8 to 18 years) (Table 1). The monthly number of individuals dispensed a stimulant ranged from 34,989 to 60,192. A slight socioeconomic gradient in stimulant dispensing was evident, with a higher proportion of treated individuals residing in the highest income (n = 57,271; 23.7%) relative to the lowest income (n = 45,949; 19.0%) neighbourhoods (Table 1). Overall, the demographic characteristics of children and youth receiving a stimulant did not change appreciably across the pre-OHIP+, OHIP+ and modified OHIP+ periods (Table 1). Most stimulant prescriptions were for methylphenidate- rather than amphetamine-based formulations, with the former representing 63.3%, 59.2% and 58.1% of stimulants dispensed in the pre-OHIP+, OHIP+ and modified OHIP+ periods, respectively. In addition, almost all stimulant prescriptions were for long-acting relative to short-acting formulations, accounting for 93.3%, 94.7%, and 95.2% of stimulants dispensed in the pre-OHIP+, OHIP+ and modified OHIP+ periods, respectivelyComparison of Actual and Predicted Stimulant Dispensing Rates Following Implementation of OHIP+Stimulant dispensing rates in the first 12 months of OHIP+ were higher than those predicted by the ARIMA model in the absence of this program (Table 2). The largest relative increase was in January 2018, with predicted and observed dispensing rates of 1265.6 and 1348.7 per 100,000 population, respectively (relative percent increase: 6.6%, 95% CI, 5.3% to 7.8%). This increase corresponded to an additional 3,413 children and youth being dispensed a stimulant for January 2018 (Table 2).Table 1. Demographic and Clinical Characteristics of Individuals Aged 0 to 24 Dispensed a Stimulant^a.Tab1Change in Stimulant Dispensing Rates Following Implementation of OHIP+We observed a modest relative percent increase in stimulant dispensing to children and youth following the implementation of OHIP+, with rates increasing 14.2% (95% CI, 12.8% to 15.6%) between December 2017 and March 2019 (1198.6 vs. 1368.7 per 100,000 population, respectively) (Table 3). The increase was more pronounced in females (704.1 vs. 852.6 individuals per 100,000 population) than males (1668.4 vs. 1858.7 individuals per 100,000 population), with relative percent increases of 21.1% (95% CI, 18.4% to 23.8%) and 11.4% (95% CI, 9.8% to 13.0%), respectively. In analyses stratified by age, individuals aged 20 to 24 (818.4 vs. 1132.4 individuals per 100,000 population) had the greatest increase in stimulant dispensing following the implementation of OHIP+, with a relative percent increase of 38.4% (95% CI, 34.4% to 42.5%). In terms of socioeconomic status, the effect of OHIP+ on stimulant dispensing was greatest in children and youth living in the highest income neighbourhoods, with rates increasing 17.5% (95% CI, 14.6% to 20.5%) (1288.4 vs. 1514.1 individuals per 100,000 population). Following ARIMA modelling, implementation of OHIP+ was associated with a significant immediate increase in the overall monthly rate of stimulant dispensing of 53.6 per 100,000 population (95% CI, 36.8 to 70.5) (Table 3, Figure 1). In stratified analyses, the largest increases were observed among males (69.2 per 100,000; 95% CI, 44.5 to 94.0), individuals aged 20 to 24 (82.3 per 100,000; 95% CI, 67.2 to 97.3) and those living in the highest income neighbourhoods (73.3 per 100,000; 95% CI, 49.6 to 96.9).Change in Stimulant Dispensing Rates Following Modification of OHIP+The April 2019 change in the OHIP+ program maintaining universal coverage only for uninsured children and youth was associated with a 7.5% (95% CI, 6.2% to 8.7%) relative percent increase in stimulant dispensing between March 2019 and March 2020 (1368.7 vs. 1470.8 individuals per 100,000 population). In stratified analyses, results were similar to those obtained following the implementation of OHIP+ in January 2018, with relative percent increases in stimulant dispensing of 11.1% (95% CI, 8.8% to 13.4%) among females (852.6 vs. 946.9 individuals per 100,000), 9.9% (95% CI, 7.0% to 12.8%) among individuals aged 20 to 24 (1132.4 vs. 1244.0 individuals per 100,000), and 10.0% (95% CI, 7.4% to 12.6%) among children in the highest income neighbourhoods (1514.1 vs. 1665.5 individuals per 100,000) (Table 4). ARIMA models estimated that monthly stimulant dispensing trends increased by 10.2 individuals per 100,000 (95% CI, 5.0 to 15.5) in April 2019, with the largest changes in trend observed among males (13.3 individuals per 100,000; 95% CI, 5.6 to 21.1), individuals aged 10 to 14 (22.1 individuals per 100,000; 95% CI, 10.7 to 33.4) and children and youth living in the highest income neighbourhoods (14.0 individuals per 100,000; 95% CI, 6.5 to 21.4).DiscussionIn our population-based study, we observed increases in stimulant dispensing among children and youth following theimplementation of a publicly-funded pharmacare program covering prescription medication costs for the entire Ontario population between the ages of 0 and 24. This finding likely represents increased use among previously uninsured individuals and those with partial drug insurance previously encumbered by copayments and/or caps on coverage. Rates continued to increase following the modification of the program maintaining universal coverage only for those children and youth without private insurance. This finding may reflect greater demand for stimulants among older youth. Further, because there was no mechanism for dispensing pharmacists to confirm OHIP+ eligibility after April 2019, privately-insured individuals with copayments and coverage caps may have continued to initiate stimulants through OHIP+ once maintenance of the program was announced. This could have been especially true for older youth at post-secondary institutions, who could access stimulants without parental involvement, and individuals in high-income neighbourhoods with partial drug coverage.Table 2. Projected and Actual Stimulant Dispensing in Children and Youth, January 2018 to December 2018.Tab2Table 3. Changes in Stimulant Dispensing to Children and Youth Following the Introduction of the OHIP+ Pharmacare Program in January 2018.Tab3Our findings add to earlier research. Although studies in several countries have found increased stimulant use among children and youth over time,^7,8 our study is unique as a natural experiment examining changes in stimulant use associated with the implementation and partial rescindment of a universal public pharmacare program. Moreover, we were also able to study the association between these policy changes and stimulant dispensing among subpopulations of children and youth that past research has identified as being potentially underdiagnosed and undertreated for ADHD, such as females and low-income children.^16,27,28 Finally, while nearly one-third of stimulant prescriptions in the United States were associated with out-of-pocket costs exceeding $25 in 2019,¹⁴ representing an essential barrier to treatment access, this would not apply in Ontario between January 2018 and April 2019, during which medications were provided at no cost to all children and youth.¹⁸ Our work therefore extends the study of pediatric stimulant utilization trends to a setting with universal health insurance and a period during which all financial barriers to drug therapy were eliminated.Fig1Figure 1. Impact of OHIP+ implementation (January 2018) and modification (April 2019) on monthly rates of stimulant dispensing among Ontario residents between the ages of 0 and 24, January 2013 to March 2020.Our study has implications for practice and policy. Most notably, we observed disparities in stimulant dispensing according to socioeconomic status, with greater use among children in the highest relative to the lowest-income neighbourhoods. In addition, this disparity remained and was made more pronounced by OHIP+, with a greater immediate increase and increase in trend among children and youth in higher-income neighbourhoods following implementation of the program and its subsequent modification to cover only children and youth without private insurance. Consequently, removing drug coverage as a barrier to stimulant access did not reduce disparities in access related to socioeconomic status. This finding is especially salient given past research associating childhood ADHD with socioeconomic disadvantage and higher treatment rates in children and youth from high-income families.^16,29–32 Although we cannot infer treatment indication from our data, these findings suggest that universal drug coverage alone does not overcome other social or structural barriers to accessing treatment for low-income children with ADHD. While the financial burden associated with transportation costs and time away from work is an important material barrier to accessing care for low-income families of children with ADHD,³³ studies suggest that less overt social processes may also produce and reproduce socioeconomic disparities in stimulant treatment. Specifically, prevailing notions of academic normalcy and the “ideal student” among high-income families, the potential for differential impairment thresholds based on socioeconomic status, more knowledge about ADHD among high-income parents, and stronger treatment endorsement by teachers working in high- relative to low-income neighbourhoods may promote greater treatment uptake among children and youth from high-income families.^34,35 In addition, research has found that the social networks of high-income families of children with ADHD are larger and include a higher proportion of healthcare professionals than those of lower socioeconomic status families.³⁶ It is therefore possible that higher income families were more aware of changes in drug coverage for children and youth through their social networks. Stigma and perceptions of ADHD treatment among parents and children and youth are also important determinants of accessing care,^37,38 with one study demonstrating that perceived stigma among adolescents with ADHD reduced the odds of engagement with mental health services five-fold.³⁹ Furthermore, past research has found that race and ethnicity contribute to ADHD treatment and diagnostic disparities among low-income families, with non-White and/or low-income children being less likely to be diagnosed with and treated for ADHD than White children.⁴⁰ Reasons for these disparities include systemic biases that act as barriers to diagnosis and treatment for non-White children and youth, including less access to and use of mental health services following diagnosis, racial and ethnic discrimination within schools, school environments that are less responsive to the needs of African American children, and White families being more likely than Black families to receive information about ADHD from physicians.^41–45 These data, taken together, suggest that various social processes intersect to create socioeconomic gradients facilitating access to ADHD treatment through mechanisms not impacted by changes in drug coverage.⁴⁶ Therefore, removing socioeconomic inequities in treatment access may require complementary interventions which address barriers unrelated to insurance status or medication cost, such as financial support to families to attend appointments and programs for parents and teachers to enhance knowledge about ADHD and its treatment. In addition, training primary care providers to provide specialist-level care to children and youth with ADHD may mitigate the effects of barriers related to family social networks and specialist access.⁴⁷Table 4. Changes in Stimulant Dispensing to Children and Youth Following the April 2019 Modification of the OHIP+ Pharmacare Program Restricting Coverage Only to Individuals Without Private Insurance.Tab4Our finding of a greater relative percent increase in stimulant dispensing to females relative to males is consistent with international research showing that the male-to-female ratio in ADHD treatment may be decreasing with time.^48–51 However, approximately two-thirds of stimulant recipients in our study were male, a result consistent with international research demonstrating that males remain far more likely to be diagnosed with and treated for ADHD than females.^52–56 Moreover, the association of OHIP+ with stimulant dispensing was more pronounced among males than females, with a greater immediate increase observed in males following program implementation and a larger increase in monthly dispensing trends following modification of the program to cover only those children and youth without private insurance. Although reasons for sex- and gender-based differences in the prevalence of ADHD and its treatment are areas of ongoing research, previous studies suggest that parents and teachers conceptualize the benefits of treatment as being greater for males than females, with the symptoms of females with ADHD being more amenable to classroom intervention.^57–61 Sex-based differences in ADHD presentation may also contribute to disparities in diagnosis and treatment. Females are more likely to present with the inattentive phenotype of ADHD, while males more often present with hyperactivity and impulsivity that can be disruptive.^58,62,63 Furthermore, a study demonstrating that hyperactivity, impulsivity and conduct disorder symptoms are stronger predictors of diagnosis in females than males suggests that females may be less likely to be diagnosed with ADHD in the absence of externalizing problems.²⁸ Prior research also demonstrates that teachers are more likely to refer males for assessment than females, even with equal levels of impairment.^57,61 Consequently, a referral bias may exist, wherein teachers or parents are more likely to seek help for males. However, such biases may also contribute to concerns of overdiagnosis and overtreatment of males with stimulants.^64,65 Given these factors, universal drug coverage alone would likely be insufficient to remediate sex-based inequity in stimulant access. Instead, interventions providing education to parents, teachers and primary care providers regarding sex-specific manifestations of ADHD and the benefits of pharmacological treatment are needed to counter sex- and gender-based disparities in stimulant use.Although stimulant dispensing rates remained highest among younger children and youth following the implementation of OHIP+, we observed a larger increase in stimulant dispensing following OHIP+ among individuals between the ages of 20 and 24 relative to younger individuals. This may reflect improved access to drug therapy for young adults with ADHD enrolled at post-secondary institutions, as school insurance plans typically cover only a portion of drug costs or place caps on annual prescription drug benefits. Similarly, young adults without post-secondary or employment drug benefits would have also been able to avail themselves of coverage through OHIP+ until the age of 24. Young adults accessing stimulants without parental involvement may also explain some of the increased use among individuals over 20 years of age. Hence, OHIP+ may have achieved the intended effect of promoting equity in drug treatment access for this population. However, a possible unintended consequence of greater stimulant access in young adults is the misuse of these drugs in individuals without ADHD.^66–68 Whether this occurred following the implementation of OHIP+ is unknown and requires additional research.Strengths of our study include complete stimulant claims data for all children and youth in Ontario, regardless of insurance status. However, our study has some limitations. First, we could not ascertain the appropriateness of stimulant use. Second, our databases do not include race or ethnicity. Third, we could not estimate whether the implementation or modification of OHIP+ impacted adherence to stimulants. Fourth, the period during which OHIP+ covered medication for all children and youth comprised 15 months. This period may have been too brief to ascertain the impact of publiclyfunded pharmacare on stimulant dispensing, and treatment access might have further evolved had the program remained in its original form. Similarly, longer follow-up may be necessary to fully understand the impact of modifications to the program. Fifth, our databases do not include individual income and prescription drug insurance coverage. Sixth, we could not account for the role of social media in individuals seeking treatment for ADHD during the study period.⁶⁹ Finally, our study was conducted in a single Canadian province, potentially limiting the generalizability of our findings.In summary, we found that implementing a universal publicly-funded pharmacare program and its subsequent modification covering only individuals without private insurance was associated with increased stimulant dispensing among children and youth. However, the program may have had the unintended consequence of increasing disparities in treatment access, suggesting that universal drug coverage alone is insufficient for promoting the equitable treatment of ADHD in children and youth. Further research exploring the causal mechanisms of these disparities is needed to complement and fully realize the role of pharmacare in ensuring equitable access to drug and non-drug therapies and improving the health of children and youth with ADHD.Data AccessThe dataset from this study is held securely in coded form at ICES. While legal data sharing agreements between ICES and data providers (e.g., healthcare organizations and government) prohibit ICES from making the dataset publicly available, access may be granted to those who meet prespecified criteria for confidential access, available at www.ices.on.ca/DAS.AcknowledgmentsThis study was supported by ICES, which is funded by an annual grant from the Ontario Ministry of Health (MOH) and the Ministry of Long-Term Care (MLTC). Parts of this material are based on data and/or information compiled and provided by CIHI and the Ontario Ministry of Health. The analyses, conclusions, opinions and statements expressed herein are solely those of the authors and do not reflect those of the funding or data sources; no endorsement is intended or should be inferred. This document used data adapted from the Statistics Canada Postal Code^OM Conversion File, which is based on data licensed from Canada Post Corporation, and/or data adapted from the Ontario Ministry of Health Postal Code Conversion File, which contains data copied under license from ©Canada Post Corporation and Statistics Canada. We thank IQVIA Solutions Canada Inc. for use of their Drug Information File.Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Melanie Penner has received consulting fees for unrelated work from Addis & Associates/Roche and from the Government of Nova Scotia. Mina Tadrous has received consulting fees for unrelated work from Green Shield Canada and the Canadian Agency for Drugs and Technologies in Health. Tara Gomes has received funding from the Ontario MOH for unrelated work.FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by the Canadian Institutes of Health Research (funding reference number 166149).ORCID iDsTony Antoniou

https://orcid.org/0000-0002-6984-8953William Gardner

https://orcid.org/0000-0003-1918-3540Yona Lunsky

https://orcid.org/0000-0002-1866-9728Melanie Penner

https://orcid.org/0000-0002-8376-9768Tara Gomes

https://orcid.org/0000-0002-1468-1965Supplemental MaterialThe supplemental material for this article is available online.References

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¹ Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, Ontario, Canada² ICES, Toronto, Ontario, Canada³ Department of Family and Community Medicine, University of Toronto, Toronto, Ontario, Canada⁴ Department of Family and Community Medicine, St. Michael’s Hospital, Toronto, Ontario, Canada⁵ Children’s Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada⁶ Department of Psychiatry, University of Ottawa, Ottawa, Ontario, Canada⁷ School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada⁸ Azrieli Adult Neurodevelopmental Centre, Centre for Addiction and Mental Health, Toronto, Canada⁹ Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada¹⁰ Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Canada¹¹ Department of Pediatrics, University of Toronto, Toronto, Ontario, Canada¹² Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada¹³ Li Ka Shing Centre for Healthcare Analytics Research & Training, Unity Health Toronto, Ontario, Canada¹⁴ Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada¹⁵ Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Ontario, Canada¹⁶ Department of Medicine, University of Toronto, Toronto, Ontario, CanadaCorresponding Author:
Tony Antoniou, PhD, St. Michael’s Hospital, 250 Yonge Street - 6th Floor, Toronto ON M5B 227, Canada.
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