Description
Introduction:
Information is crucial in public health surveillance for effectively managing public health issues. The recent pandemic experiences have reiterated the importance of community health workers (CHWs), who played a crucial role in disease surveillance and response efforts. Equipping CHWs with digital technologies is widely used to improve data workflow across surveillance systems. However, developing countries struggle to generate and apply quality health information. Studies emphasize that data construction is not just a technical task but is intricately linked to practices and institutions. It's crucial to expand the inquiry beyond technology, considering institutions and organizational practices shaping data construction and enactment. Limited empirical studies in India explore complex settings' influence on information workflows in disease surveillance. The study aims to examine disease surveillance practices at the community level in India, focusing on the recently revamped Integrated Disease Surveillance Programme (IHIP-IDSP) transitioning from paper-based to real-time case-based systems.
Methodology:
The study adopted the qualitative case study approach. The CHWs involved in surveillance and response activities were included in the study. Data was collected through semi-structured interviews and participant observations. Based on the outbreak trend reported over the last two years, the study was conducted for three diseases in three different districts of Maharashtra, India, having high incidences of cases, i.e., Acute diarrhoeal disease, Measles, and Malaria.
Findings:
CHWs not only collect and record data but also offer basic caregiving outreach, emergency first aid, counseling on lifestyle diseases, water quality checks, and logistics at the sub-center. However, issues were identified at different levels of data processing. In data collection, higher officials rely on geolocation matching of household and patient for data validity, overlooking field-level practices where meetings can occur anywhere. In such scenarios, denying basic caregiving services to the community based solely on a mismatched location would undermine the health system’s commitment to fair and equal access. In response, a CHW union refrained from using the IHIP mobile application for data collection. In data reporting, the software lacks the ability to capture target population changes, such as migration within and outside the area, resulting in an incorrect denominator for the indicator calculation and setting inaccurate performance targets for CHWs. Additionally, the application lacks the functionality to convey reasons for the field-level health staff's inability to conduct surveillance, leaving them only able to report Nil data without explanation. In data integration, the software lacks recording and linking the household water quality checks data with case-based data, which is critical for diseases like Malaria and Dengue. Rapid diagnostic test samples at the community level are given low priority at public health laboratories and also lack linking with case-based data, hindering early case confirmation.
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Conclusion:
In conclusion, a strong disease surveillance system can enhance intervention effectiveness. However, empirical evidence indicates that mismatches between field practices and information workflows hinder local knowledge development, impeding infectious disease management. The study emphasizes the need to scale efforts to higher levels for a comprehensive understanding of information workflows, field practices, and their impact on data generation and utilization
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