Considerations for Advancing Parkinson’s Disease Research in Middle East, North Africa, and South Asia

That said, we would like to offer four considerations that may strengthen the long-term impact and scientific utility of this project:

• 1. Selection bias and representativeness: while the inclusion of 20 centers across nine countries is laudable, the study relies on voluntary participation from institutions with the capacity to collect data and secure IRB approval. This may inadvertently favor tertiary centers in urban regions and neglect rural or under-resourced settings where PD diagnosis and care are more fragmented. To improve representativeness, we suggest incorporating community-based or primary healthcare data sources in future phases, or at least comparing participating vs. non-participating regions to assess coverage gaps [2].

• 2. Lack of longitudinal design: the current database design appears to be cross-sectional in nature. However, PD is a progressive, chronic condition where outcomes, including symptom progression, treatment response, and caregiver burden, evolve over time. A longitudinal follow-up structure, even with minimal time points, would add considerable value by enabling trajectory modeling, health economics studies, and survival analyses. We encourage the authors to consider embedding follow-up assessments in the protocol and leveraging electronic health records where feasible [3].

• 3. Patient and caregiver engagement: while the study commendably includes patient-perceived health status, there is limited discussion of how patients or caregivers were involved in the development of the questionnaire or governance of the consortium. Including patient representatives in design and review stages could enhance cultural appropriateness, increase trust and participation, and ensure the priorities of people living with PD are reflected in the research [4].

• 4. Environmental exposure assessment: the planned exploration of exposures like pesticide use is particularly valuable. Yet, pesticide types and usage patterns are likely to vary significantly between rural and urban, and within agricultural versus industrial regions. Incorporating geospatial mapping or granular exposure categories (e.g., type, duration, intensity) would significantly strengthen the ability to link environmental risk to clinical phenotypes [5].