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Assessment of immunization data management practices in Cameroon: unveiling potential barriers to immunization data
Yauba Saidu1,2, Jessica Gu3, Budzi Michael Ngenge4
1Clinton Health Access Initiative Inc, PO Box 2664, Yaounde, Cameroon. ysaidu@clintonhealthaccess.org.
Frontline health workers in Cameroon face challenges with immunization data collection due to overburdened schedules and limited resources. Improving data management practices is crucial for enhancing immunization coverage in the region.
Area of Science:
- Public Health
- Health Informatics
- Global Health
Background:
- Paucity of timely, high-quality data hinders universal immunization coverage in Sub-Saharan Africa.
- Frontline immunization staff are often overburdened with data responsibilities, impacting clinical tasks and data collection.
- This study investigates data management practices and barriers to data quality in Cameroon's immunization program.
Purpose of the Study:
- To assess the data management practices of immunization staff in Cameroon.
- To identify potential barriers affecting immunization data quality at the facility level.
Main Methods:
- A descriptive cross-sectional study was conducted across all 10 regions of Cameroon.
- Multi-stage sampling was used to select health districts and facilities.
- Data were collected using structured questionnaires and observation checklists from Expanded Program of Immunization (EPI) staff.
Main Results:
- Limited availability of essential data recording tools such as vaccination cards (43%) and stock cards (57%).
- Incomplete filling of core data collection tools was prevalent (37% for registers, 81% for tally sheets).
- Significant non-adherence to recommended data recording procedures and deficiencies in collecting data on vaccine administration, stock levels, storage temperatures, and wastage.
Conclusions:
- Gaps in facility-level data collection practices negatively impact Cameroon's immunization data quality.
- Urgent need for capacity building in data management for frontline staff.
- Standardizing processes and ensuring tool availability are critical for improving data quality.
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