Improving public health information: a data quality intervention in KwaZulu-Natal, South Africa

Mphatswe, Wendy; Mate, Kedar; Bennett, Brandon; Ngidi, H; Reddy, Jennifer; Barker, P.; Rollins, Nigel

doi:10.2471/blt.11.092759

Cited by 158 publications

(152 citation statements)

References 15 publications

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“…[1,2] Data quality in resourceconstrained setting is often compromised by incomplete data and untimely reporting, however, and local health information systems may be the only data sources available for the continuous, routine monitoring. [3,4] Few studies have assessed data quality and completeness for maternal and child health. A study of routine primary care data in South Africa showed 26% of data in prevention of mother to child transmission of HIV (PMTCT) records was complete and only 12.8% of those data recorded was accurate.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a cloud-based electronic medical record for maternal and child health in rural Kenya

Haskew¹,

Rø

Saito

et al. 2015

International Journal of Medical Informatics

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Section: Introductionmentioning

confidence: 99%

Implementation of a cloud-based electronic medical record for maternal and child health in rural Kenya

Haskew¹,

Rø

Saito

et al. 2015

International Journal of Medical Informatics

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“…DHIS2 has been used previously to capture malaria-related information and has helped improve countries’ understanding of the links between malaria disease burden, use of rapid diagnostic tests, and administration of anti-malarial drugs administered [36,37]. However, similar evaluations of the coverage and completeness of data captured in national DHIS 2.0 systems are few [11–13,38,39]. There have also been several attempts to use routine data in model-based geostatistics using data from Namibia [40], Afghanistan [41] and in Madagascar [42] and these have the potential to form part of new strategies for malaria risk mapping in future [41,43].…”

Section: Discussionmentioning

confidence: 99%

Coverage of routine reporting on malaria parasitological testing in Kenya, 2015–2016

Maina

Macharia

Ouma

et al. 2017

Global Health Action

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Background: Following the launch of District Health Information System 2 across facilities in Kenya, more health facilities are now capable of carrying out malaria parasitological testing and reporting data as part of routine health information systems, improving the potential value of routine data for accurate and timely tracking of rapidly changing disease epidemiology at fine spatial resolutions. Objectives: This study evaluates the current coverage and completeness of reported malaria parasitological testing data in DHIS2 specifically looking at patterns in geographic coverage of public health facilities in Kenya. Methods: Monthly facility level data on malaria parasitological testing were extracted from Kenya DHIS2 between November 2015 and October 2016. DHIS2 public facilities were matched to a geo-coded master facility list to obtain coordinates. Coverage was defined as the geographic distribution of facilities reporting any data by region. Completeness of reporting was defined as the percentage of facilities reporting any data for the whole 12-month period or for 3, 6 and 9 months. Results: Public health facilities were 5,933 (59%) of 10,090 extracted. Fifty-nine per Cent of the public facilities did not report any data while 36, 29 and 22% facilities had data reported at least 3, 6 and 9 months, respectively. Only 8% of public facilities had data reported for every month. There were proportionately more hospitals (86%) than health centres (76%) and dispensaries/clinics (30%) reporting. There were significant geographic variations in reporting rates. Counties along the malaria endemic coast had the lowest reporting rate with only 1% of facilities reporting consistently for 12 months. Conclusion: Current coverage and completeness of reporting of malaria parasitological diagnosis across Kenya’s public health system remains poor. The usefulness of routine data to improve our understanding of sub-national heterogeneity across Kenya would require significant improvements to the consistency and coverage of data captured by DHIS2.

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“…The quality improvement intervention in KwaZulu Natal in 2012 reported the discrepancy rate of 37% between the reports and the registers at the beginning of the project [28]. The lesson is that with training and data quality mentorship, the data reporting accuracy improved by two-thirds [28].…”

Section: Discussionmentioning

confidence: 99%

Assessment of four common underfive children illnesses Routine Health Management Information System data for decision making at Ilemela Municipal Council, Northwest Tanzania: A case series analysis

Kabakama

Ngallaba

Musto

et al. 2016

International Journal of Medical Informatics

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Improving public health information: a data quality intervention in KwaZulu-Natal, South Africa

Cited by 158 publications

References 15 publications

Implementation of a cloud-based electronic medical record for maternal and child health in rural Kenya

Implementation of a cloud-based electronic medical record for maternal and child health in rural Kenya

Coverage of routine reporting on malaria parasitological testing in Kenya, 2015–2016

Assessment of four common underfive children illnesses Routine Health Management Information System data for decision making at Ilemela Municipal Council, Northwest Tanzania: A case series analysis

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