Introducing Samhita: a scalable implementation model for setting up a population-based haemoglobinopathies registry in southern Karnataka.

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This paper introduces Samhita, a scalable, context-specific registry model using offline digital tools and community engagement for haemoglobinopathy surveillance in resource-poor rural settings.

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The paper describes Samhita, a scalable implementation model for setting up a population-based haemoglobinopathies registry in southern Karnataka, focusing on how to operationalize registry creation at scale. The authors outline high-level steps and system considerations for registry setup, rather than presenting clinical outcomes, and emphasize practical scalability. A major limitation is that the provided text does not include results from patient enrollment, data quality performance, or validation outcomes for the registry model. This paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Introduction Accurate, population-level data on sickle cell disease (SCD) and other haemoglobinopathies are essential for planning services in underserved tribal communities, yet such data remain limited in rural India. We designed and implemented a population-based registry ( Samhita ) in Chamarajanagar district, Karnataka, to demonstrate a scalable registry model for a resource-poor setting. Methods The registry is designed with four major components: Registration and written consent, Screening, Confirmation, and Clinical Proforma. For screening and confirmation of SCD, thalassemia, and G6PD deficiency, blood samples undergo solubility, point-of-care, and high-performance liquid chromatography testing. Once confirmed, clinical proforma and subsequent follow-ups of consented participants are conducted by their primary care physicians. All data are captured using Avni, an offline open-source mobile application hosted on a secure AWS server in Mumbai, enabling work in areas without internet. Modular workflows, culturally sensitive consent forms in the local language, and a flexible follow-up schedule are included in the registry design to adapt to facility-strained primary health centers in rural-tribal areas. Fidelity is maintained through random double-entry checks, supervisory reviews, and weekly protocol adherence meetings. Trained healthcare workers gather registry data during household visits conducted every three months and health camps held twice a year. Conclusions This model demonstrates a feasible, context-specific approach to haemoglobinopathy surveillance in low-resource rural settings. By combining offline digital tools, quality assurance, and community engagement, the registry offers a scalable framework to strengthen evidence-based planning and improve care for tribal and underserved populations.
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