Review highlights multimodal treatment options for ADHD in adults, suggesting improved care infrastructure in India.
Purpose of Review: Attention-Deficit/Hyperactivity Disorder (ADHD) is increasingly recognised as a lifelong neurodevelopmental condition that persists into adulthood, often associated with significant functional impairment and high psychiatric comorbidity. Despite global advances, services in India remain severely underdeveloped. This paper reviews international guidelines—including those from NICE, the Canadian ADHD Resource Alliance (CADDRA), the British Association for Psychopharmacology (BAP), and the Indian Psychiatric Society (IPS)—to identify best practices for the assessment and multimodal treatment of adult ADHD. Collection and Analysis of Data: The paper reviews international guidelines on adult ADHD care, highlighting convergent recommendations and their applicability to the Indian context. It analyses systemic gaps in India’s service delivery, including the shortage of trained mental health professionals, low public and professional awareness, lack of culturally adapted tools and materials, and limited access to pharmacological and psychosocial interventions. As an applied example, the paper presents Amaha’s adult ADHD care model—developed in 2022—as a scalable, evidence-based approach tailored to the Indian ecosystem. The model integrates psychiatrist-led assessments using the DIVA-5 tool, structured psychoeducation, cautious pharmacotherapy, and adjunctive psychotherapy within a digitally enabled care framework prioritising, continuity of care, family engagement, and community support. Conclusions: This review proposes recommendations to strengthen ADHD care infrastructure in India. These include workforce training, public awareness initiatives, development of India-centric resources, telepsychiatry reforms, and integration of ADHD into public policy and insurance schemes. Addressing adult ADHD in India is both a clinical and public health imperative, requiring scalable, multidisciplinary, and context-sensitive solutions.
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Divya Ganesh Nallur (2025) studied this question.
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