Conference abstract
Strengthening data quality through data quality assessments in primary care setting: experience from the scaling up family planning programme in Mainland Tanzania and Zanzibar
Pan African Medical Journal - Conference Proceedings. 2024:21(20).29
Apr 2024.
doi: 10.11604/pamj-cp.2024.21.20.2351
Archived on: 29 Apr 2024
Contact the corresponding author
Keywords: Strengthening, data quality, assessments, primary care
Oral presentation
Strengthening data quality through data quality assessments in primary care setting: experience from the scaling up family planning programme in Mainland Tanzania and Zanzibar
Nasibu Mwanamsangu1,&, Deus Ngerangera1, Michael Tindo1, Lilian Mfugale1, Lilian Lukumai1, Moke Magoma1, Kate O’Connell1, Danielle Garfinkel2
1EngenderHealth, Washington DC, United States
&Corresponding author
Introduction: data quality is a vital element for informing evidence-informed decision making and improving quality of care. We report the results from periodic data quality assessments (DQAs) in selected health facilities from the Scaling up Family Planning programme during the period of January 2021 to December 2023.
Methods: we completed a descriptive analysis on the programme’s DQA scores from quarterly joint DQAs to identify improvements and challenges to inform appropriate remedial measures. Four elements of DQA; system assessment, documentation review, existence of data quality mechanisms, and data verifications were prioritized as demanded by the national DQA guidelines. A total of 301 (188 in 2021 and 113 in 2023) health facilities were visited across the 8 supported regions in Mainland Tanzania and all 5 regions in Zanzibar. Analysis was done using MS Excel.
Results: improvements were observed across all four national DQA domains. The average score for the documentation improved from 55% to 68%, 59% to 66% for system assessment, 29% to 41% for quality mechanisms (DQA, data review and spot checks) and 45% to 54% for data verification. Notably, 11 (9.7%) of the 113 health facilities visited in 2023 did not conduct any internal DQA, spot-checks and data review due to limited skills and knowledge among service providers.
Conclusion: while the score for documentation, system assessment and data verification increased and remained above 50% after SuFP implementation, that of data quality mechanisms was low and requires additional efforts to address the remaining challenges, with additional support from CHMTs is also required.
Strengthening data quality through data quality assessments in primary care setting: experience from the scaling up family planning programme in Mainland Tanzania and Zanzibar
Nasibu Mwanamsangu1,&, Deus Ngerangera1, Michael Tindo1, Lilian Mfugale1, Lilian Lukumai1, Moke Magoma1, Kate O’Connell1, Danielle Garfinkel2
1EngenderHealth, Washington DC, United States
&Corresponding author
Introduction: data quality is a vital element for informing evidence-informed decision making and improving quality of care. We report the results from periodic data quality assessments (DQAs) in selected health facilities from the Scaling up Family Planning programme during the period of January 2021 to December 2023.
Methods: we completed a descriptive analysis on the programme’s DQA scores from quarterly joint DQAs to identify improvements and challenges to inform appropriate remedial measures. Four elements of DQA; system assessment, documentation review, existence of data quality mechanisms, and data verifications were prioritized as demanded by the national DQA guidelines. A total of 301 (188 in 2021 and 113 in 2023) health facilities were visited across the 8 supported regions in Mainland Tanzania and all 5 regions in Zanzibar. Analysis was done using MS Excel.
Results: improvements were observed across all four national DQA domains. The average score for the documentation improved from 55% to 68%, 59% to 66% for system assessment, 29% to 41% for quality mechanisms (DQA, data review and spot checks) and 45% to 54% for data verification. Notably, 11 (9.7%) of the 113 health facilities visited in 2023 did not conduct any internal DQA, spot-checks and data review due to limited skills and knowledge among service providers.
Conclusion: while the score for documentation, system assessment and data verification increased and remained above 50% after SuFP implementation, that of data quality mechanisms was low and requires additional efforts to address the remaining challenges, with additional support from CHMTs is also required.
