A comparative assessment of the community frontline health workers for their knowledge and practices of malaria diagnosis and treatment in three contiguous districts Mandla, Balaghat, and Dindori of Madhya Pradesh, India.

BACKGROUND: Global malaria cases rose by 14 million, and deaths by 69,000, in 2020. In India, a 46% decline has been reported between 2020 and 2019. In 2017, the Malaria Elimination Demonstration Project conducted a needs-assessment of the Accredited Social Health Activists (ASHAs) of Mandla district. This survey revealed the inadequate level of knowledge in malaria diagnosis and treatment. Subsequently, a training programme was launched for enhancing malaria-related knowledge of ASHAs. The present study was conducted in 2021 to evaluate the impact of training on malaria-related knowledge and practices of ASHAs in Mandla. This assessment was also done in two adjoining districts: Balaghat and Dindori. METHODS: A cross-sectional survey using a structured questionnaire was administered to ASHAs to measure their knowledge and practices related to malaria etiology, prevention, diagnosis, and treatment. A comparison of information collected from these three districts was performed using simple descriptive statistics, comparison of means and multivariate logistic regression analysis. RESULTS: Significant improvement was noted amongst ASHAs of district Mandla between 2017 (baseline) and 2021 (endline) in knowledge related to malaria transmission, preventive measures, adherence to the national drug policy, diagnosis using rapid diagnostic tests, and identification of age group-specific, colour-coded artemisinin combination therapy blister packs (p < 0.05). The multivariate logistic regression analysis revealed that odds of Mandla baseline was 0.39, 0.48, 0.34, and 0.07 times lower for malaria-related knowledge on disease etiology, prevention, diagnosis, and treatment, respectively (p < 0.001). Further, participants in districts Balaghat and Dindori showed significantly lower odds for knowledge (p < 0.001) and treatment practices (p < 0.01) compared to Mandla endline. Education, attended training, having a malaria learner's guide, and minimum 10 years' work experience were potential predictors for good treatment practices. CONCLUSION: The findings of the study unequivocally establishes significant improvement in overall malaria-related knowledge and practices of ASHAs in Mandla as a result of periodic training and capacity building efforts. The study suggests that learnings from Mandla district could be helpful in improving level of knowledge and practices among frontline health workers.

Science of malaria elimination: using knowledge of bottlenecks and enablers from the Malaria Elimination Demonstration Project in Central India for eliminating malaria in the Asia Pacific region.

Malaria poses a major public health challenge in the Asia Pacific. Malaria Elimination Demonstration Project was conducted as a public-private partnership initiative in Mandla between State government, ICMR, and FDEC India. The project employed controls for efficient operational and management decisions. IEC campaigns found crucial in schools and communities. Capacity building of local workers emphasized for better diagnosis and treatment. SOCH mobile app launched for complete digitalization. Better supervision for Indoor Residual Sprays and optimized Long Lasting Insecticidal Nets distribution. Significant malaria cases reduction in Mandla. Insights from MEDP crucial for malaria elimination strategies in other endemic regions of the Asia Pacific.

A model for malaria elimination based on learnings from the Malaria Elimination Demonstration Project, Mandla district, Madhya Pradesh.

BACKGROUND: Malaria Elimination Demonstration Project (MEDP) was started as a Public-Private-Partnership between the Indian Council of Medical Research through National Institute of Research in Tribal Health, Govt. of Madhya Pradesh and Foundation of Disease Elimination and Control of India, which is a Corporate Social Responsibility (CSR) initiative of the Sun Pharmaceutical Industries Limited. The project's goal was to demonstrate that malaria can be eliminated from a high malaria endemic district along with prevention of re-establishment of malaria and to develop a model for malaria elimination using the lessons learned and knowledge acquired from the demonstration project. METHODS: The project employed tested protocols of robust surveillance, case management, vector control, and capacity building through continuous evaluation and training.  The model was developed using the learnings from the operational plan, surveillance and case management, monitoring and feedback, entomological investigations and vector control, IEC and capacity building, supply chain management, mobile application (SOCH), and independent reviews of MEDP. RESULTS: The MEDP has been operational since April 2017 with field operations from August 2017, and has observed: (1) reduction in indigenous cases of malaria by about 91 %; (2) need for training and capacity building of field staff for diagnosis and treatment of malaria; (3) need for improvement insecticide spraying and for distribution and usage of bed-nets; (4) need for robust surveillance system that captures and documents information on febrile cases, RDT positive individuals, and treatments provided; (5) need for effective supervision of field staff based on advance tour plan; (6) accountability and controls from the highest level to field workers; and (7) need for context-specific IEC. CONCLUSIONS: Malaria elimination is a high-priority public health goal of the Indian Government with a committed deadline of 2030. In order to achieve this goal, built-in systems of accountability, ownership, effective management, operational, technical, and financial controls will be crucial components for malaria elimination in India. This manuscript presents a model for malaria elimination with district as an operational unit, which may be considered for malaria elimination in India and other countries with similar geography, topography, climate, endemicity, health infrastructure, and socio-economic characteristics.