Overview
Chad’s high malaria burden and sparse infrastructure made accurate disease monitoring a significant challenge. In 2020, HISP Rwanda collaborated with Chad’s Ministry of Public Health to configure a national DHIS2 instance, develop a specialized malaria tracker, and train provincial teams using a cascade model. This post explores the technical customization, capacity building efforts, and measurable impact of the Chad malaria surveillance project.
Objectives
- Customize DHIS2 for Chad’s Needs: Adapt DHIS2 modules to national malaria, maternal, HIV, and immunization indicators.
- Develop Malaria Tracker App: Create a weekly reporting tool with built-in validation to improve data accuracy at health posts.
- Implement Cascade Training: Train 12 master trainers who would train health staff across Chad’s 23 provinces.
- Enhance Data Use: Conduct workshops to enable district health teams to convert data insights into operational plans.
Pre-Implementation Assessment
- Indicator Alignment: Convened a working group with the National Health Information System Directorate to finalize malaria-specific data elements, including case confirmation criteria and age stratification.
- Infrastructure Mapping: Identification of provincial connectivity—only 8 provinces had stable internet. Tablets and solar chargers were earmarked for rural facilities.
- Human Resource Survey: Determined that over 70% of data clerks lacked DHIS2 experience, necessitating intensive, hands-on training.
Implementation Strategy
1. National DHIS2 Configuration
- Data Element Mapping: Translated Ministry guidelines into DHIS2 data elements: suspected malaria, confirmed malaria (by RDT or microscopy), severe malaria admissions, and antimalarial stock levels.
- User Role Definitions: Defined roles from national administrators down to facility data clerks, each with tailored data entry or analysis permissions.
- French & Arabic Localization: All DHIS2 interfaces were provided in French and Arabic to accommodate Chad’s bilingual staff.
2. Malaria Tracker Customization
- Tracker App Design: Developed a dedicated DHIS2 Malaria Tracker App focusing on weekly case reporting at health posts. Added validation rules: requiring fever + positive RDT/microscopy for confirmed cases.
- Automated SMS Reminders: Integrated with an SMS gateway to send weekly reminders to district supervisors for missing reports.
- Dashboards & Analytics: Created real-time dashboards displaying malaria incidence by district, age group, and severity. This allowed program managers to identify hotspots, such as high-incidence districts in the Sahel region.
3. Cascade Training Model
- Master Trainer Workshops: In N’Djamena, HISP Rwanda trained 12 master trainers on DHIS2 configuration, tracker use, and dashboard interpretation. Trainers received a comprehensive manual and flash drives containing tutorial videos.
- Provincial Workshops: Over six months (July–December 2020), each master trainer led workshops in two provinces. Over 600 data clerks, nurses, and district supervisors gained hands-on experience in data entry, report generation, and basic analytics.
- Training Materials: Developed French-language job aids, step-by-step cheat sheets, and interactive PDF guides distributed to all trainees.
4. Data Use Workshops
- District-Level Sessions: In late 2020, held week-long “Data-to-Action” workshops in Moundou and Abeche. District teams learned to interpret malaria incidence maps, calculate case fatality rates, and adjust commodity orders (e.g., ACTs, bed nets).
- Operational Planning: District teams created micro-plans—e.g., in Batha region, a targeted indoor residual spraying plan based on hotspot analysis reduced cases by 15% during peak transmission season.
5. Community Feedback Integration
- Mobile Survey Tool: Collaborated with CRS (Catholic Relief Services) to pilot a KoboToolbox survey capturing community malaria risk factors—bed net usage, larval habitats, and health-seeking behaviors. Data aggregated directly into DHIS2’s survey module for comprehensive analysis.
Impact
- Reporting Rates: Nationwide routine reporting jumped to 88% from 52% within eight months, even during seasonal flooding.
- Malaria Case Management: Batha Province saw a 15% decrease in severe malaria admissions in 2021 due to timely interventions guided by tracker data.
- Resource Allocation: Data-driven decisions led to reallocation of 20,000 additional bed nets to Dohone district, which previously reported the highest incidence.
- Community Engagement: Data from the mobile survey informed community mobilization activities, boosting bed net usage from 60% to 75% within three months.
Partners
- Ministry of Public Health, Chad: Led indicator definition, policy guidance, and workshop coordination.
- Global Fund: Financed malaria tracker customization, bed net distribution analytics, and training materials.
- UNICEF Chad: Supported tablet procurement, solar charger distribution, and funding for data use workshops.
- Catholic Relief Services (CRS): Collaborated on mobile survey integration and to coordinate community-based data collection.
Challenges and Mitigation
- Connectivity Issues: Seasonal flooding disrupted internet in southern provinces. Offline data entry on tablets with solar chargers mitigated this issue.
- Language Diversity: Some district staff spoke local Arabic dialects. Supplemented French manuals with pictogram-based job aids to enhance comprehension.
- Data Sensitivity: Community health data raised privacy concerns. Implemented strict user-level access controls and encrypted mobile data at rest.
Lessons Learned & Future Directions
- Local Ownership is Key: Empowering local master trainers ensured continuity when external consultants departed.
- Offline Readiness: Offline capabilities coupled with solar power remain essential for rainy-season resilience.
- Community Data Value: Integrating community feedback with DHIS2 enhanced malaria prevention strategies.
Future Plans: By late 2025, HISP Rwanda aims to expand the DHIS2 malaria tracker to include SMC (seasonal malaria chemoprevention) campaign monitoring. Additionally, we will pilot a GIS heatmap layer to visualize community risk factors in real time.