Completeness of malaria indicator data reporting via the District Health Information Software 2 in Kenya, 2011–2015

Githinji, Sophie; Oyando, Robinson; Malinga, Josephine; Ejersa, Waqo; Soti, David; Rono, Josea; Snow, Robert W.; Buff, Ann M.; Noor, Abdisalan M.

doi:10.1186/s12936-017-1973-y

Cited by 64 publications

(101 citation statements)

References 18 publications

(16 reference statements)

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“…However, the detail, completeness and coverage of these data necessary to provide reliable epidemiological data for malaria programmes remains poor [37]. In Kenya, not all fevers are tested [20], the numbers of people tested is not always recorded, data are aggregated over districts, age groups, and time losing the granularity of information on age, seasonality, location and pregnancy status [38]. …”

Section: Discussionmentioning

confidence: 99%

A national health facility survey of malaria infection among febrile patients in Kenya, 2014

Githinji

Noor

Malinga

et al. 2016

Malar J

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BackgroundThe use of malaria infection prevalence among febrile patients at clinics has a potential to be a valuable epidemiological surveillance tool. However, routine data are incomplete and not all fevers are tested. This study was designed to screen all fevers for malaria infection in Kenya to explore the epidemiology of fever test positivity rates.MethodsRandom sampling was used within five malaria epidemiological zones of Kenya (i.e., high lake endemic, moderate coast endemic, highland epidemic, seasonal low transmission and low risk zones). The selected sample was representative of the number of hospitals, health centres and dispensaries within each zone. Fifty patients with fever presenting to each sampled health facility during the short rainy season were screened for malaria infection using a rapid diagnostic test (RDT). Details of age, pregnancy status and basic demographics were recorded for each patient screened.Results10,557 febrile patients presenting to out-patient clinics at 234 health facilities were screened for malaria infection. 1633 (15.5%) of the patients surveyed were RDT positive for malaria at 124 (53.0%) facilities. Infection prevalence among non-pregnant patients varied between malaria risk zones, ranging from 0.6% in the low risk zone to 41.6% in the high lake endemic zone. Test positivity rates (TPR) by age group reflected the differences in the intensity of transmission between epidemiological zones. In the lake endemic zone, 6% of all infections were among children aged less than 1 year, compared to 3% in the coast endemic, 1% in the highland epidemic zone, less than 1% in the seasonal low transmission zone and 0% in the low risk zone. Test positivity rate was 31% among febrile pregnant women in the high lake endemic zone compared to 9% in the coast endemic and highland epidemic zones, 3.2% in the seasonal low transmission zone and zero in the low risk zone.ConclusionMalaria infection rates among febrile patients, with supporting data on age and pregnancy status presenting to clinics in Kenya can provide invaluable epidemiological data on spatial heterogeneity of malaria and serve as replacements to more expensive community-based infection rates to plan and monitor malaria control.

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Section: Discussionmentioning

confidence: 99%

A national health facility survey of malaria infection among febrile patients in Kenya, 2014

Githinji

Noor

Malinga

et al. 2016

Malar J

Self Cite

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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%

“…Additionally, activities in these facilities typically do not rely on government or donor-funding hence the need for and use of standardized data collection tools necessary for monitoring and evaluation characteristic of increased demand for data seen in much of the public sector is non-existent. Hence the private sector remains characterized by poorer reporting rates [11,44]. …”

Section: Discussionmentioning

confidence: 99%

“…The private sector has grown significantly over the last two decades, however, enumerating and regulating this sector has remained a challenge [18,19]. Unlike the public sector, investments in the capacity of facilities in the private sector to provide any routine data and or better-quality routine data has been lacking [11]. …”

Section: Methodsmentioning

confidence: 99%

“…Kenya has a long history of routine health information systems that have allowed for the investigation of the malaria burden [11,12]. In 2011, DHIS2 was launched in Kenya [12,13], the first African country to adopt the online version of the system.…”

Section: Introductionmentioning

confidence: 99%

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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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Geography of Disease Burden: Case Studies in Namibia and Eritrea

Alegana

Atkinson

2021

Practicing Health Geography

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Africa continues to experience the highest infectious disease burden despite an increase in investments. These include investments in malaria, HIV/AIDS, tuberculosis, as well as in communicable diseases. The global targets are to reduce the burden of these diseases through improved surveillance, prevention of outbreaks, effective case management, elimination and eventually, eradication. Achieving these targets, however, is limited by the poor geographic descriptions of the disease burden. Of the big five infectious disease burdens, malaria is the most advanced in terms of mapping its distribution. Malaria cartography has since formed the evidence-base for the design of many national malaria control programmes. This chapter focuses on malaria as an example, demonstrating its geographical descriptions. The availability of georeferenced malaria case data whether based on prevalence or incidence indicators has been used extensively in the mapping of geographical extents at national and sub-national scales. However, routine surveillance data is emerging as a valuable methodology of tracking burden in sub-Saharan Africa. A particular focus of this chapter is the use of routine national health systems surveillance data to describe, at a fine-scale, the distribution of malaria. However, routine data can be applied to the cartographic description of other diseases beyond malaria. The methodological aspects of burden estimation from routine surveillance platforms and cartography are highlighted.

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Completeness of malaria indicator data reporting via the District Health Information Software 2 in Kenya, 2011–2015

Cited by 64 publications

References 18 publications

A national health facility survey of malaria infection among febrile patients in Kenya, 2014

A national health facility survey of malaria infection among febrile patients in Kenya, 2014

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

Geography of Disease Burden: Case Studies in Namibia and Eritrea

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