Three parallel information systems for malaria elimination in Swaziland, 2010–2015: are the numbers the same?

Zulu, Z; Kunene, Simon; Mkhonta, Nomcebo; Owiti, Philip; Sikhondze, Welile; Mhlanga, M; Simelane, Z; Geoffroy, Elizabeth; Zachariah, Rony

doi:10.5588/pha.17.0058

Cited by 6 publications

(5 citation statements)

References 5 publications

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“…Malaysia also uses entomological data collected during receptivity assessments to guide the choice of appropriate local vector control strategy [ 48 ]. Some countries, such as Eswatini, have used assessments of receptivity to determine where reactive case detection activities need to be focused [ 49 , 50 ].…”

Section: Discussionmentioning

confidence: 99%

Receptivity to malaria: meaning and measurement

Yukich

Lindblade²,

Kolaczinski³

2022

Malar J

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Abstract“Receptivity” to malaria is a construct developed during the Global Malaria Eradication Programme (GMEP) era. It has been defined in varied ways and no consistent, quantitative definition has emerged over the intervening decades. Despite the lack of consistency in defining this construct, the idea that some areas are more likely to sustain malaria transmission than others has remained important in decision-making in malaria control, planning for malaria elimination and guiding activities during the prevention of re-establishment (POR) period. This manuscript examines current advances in methods of measurement. In the context of a decades long decline in global malaria transmission and an increasing number of countries seeking to eliminate malaria, understanding and measuring malaria receptivity has acquired new relevance.

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Section: Discussionmentioning

confidence: 99%

Receptivity to malaria: meaning and measurement

Yukich

Lindblade²,

Kolaczinski³

2022

Malar J

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“…The combined dataset assembled under one data structure with the variables system used, TB cases notified, period and region had 1,792 observations for all the quarters based on the quarterly aggregated data reports submitted (Table 1). This comparative study considered data reported through the MoH DHIS2 as the gold standard based on analysis of data quality assessment (DQA) results for TB case notification to DHIS2 for the urban districts of Kampala, Wakiso, and Mukono which notify 20% of the TB cases in the whole country 9,10 . These showed negligible discrepancies of 1.8%, and 0.9% for the two rounds or assessments conducted by the USAID Defeat TB project 11 .…”

Section: Data Management and Analysismentioning

confidence: 99%

A comparative analysis of two national tuberculosis reporting systems and their impact on tuberculosis case notification in Uganda

Timothy,

Rita,

Nicholas Sebuliba

et al. 2023

Afr H. Sci.

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Background: Before 2018, the use of parallel tuberculosis (TB) reporting systems was resource intensive with duplication of efforts and hence the need to select one that contributed to better TB case notification at the National TB and Leprosy Program (NLTP) in Uganda. We sought to analyse the difference in reporting rates between the two systems in order to improve NTLP TB case notification rates, logistics management, and planning for better health service delivery initiatives. Methods: We conducted a comparative study to assess TB case notification between the web-based DHIS2 and the district TB supervisor-led health management information system between January 2016 to December 2017. We used Poisson regression analysis to assess the statistical differences in reporting rates between the two reporting systems. Results: The association between TB case notification and the type of reporting system was statistically significant (Prob > chi2 = 0.0000). The Incident Rate Ratio (IRR) for the web-enabled DHIS2 system versus the district TB supervisor-led health management information system was 1.106625.Conclusion: The web-based integrated DHIS2 system was more effective in reporting missing TB cases. It presents an opportunity for better planning and allocation of resources for improved service delivery in a low-income setting.

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“…10 A 2010-2015 study in Swaziland recommended embracing electronic medical data systems to reduce the discrepancies occurring between the existing three information systems put in place for malaria elimination. 11 Recently, a similar demand was issued by practitioners working in humanitarian settings for an epidemiology and demography service to develop robust and timely information from multiple sources to establish priorities that address the population's needs. 12 One important use of routine information systems is to monitor a population's coverage with health service, as Global Alliance on Vaccines and Immunisations has done for vaccination rates since the 1990s.…”

Section: Strengths and Limitations Of This Studymentioning

confidence: 99%

Innovative approach to improve information accuracy in a two-district cross-sectional study in Bihar, India

et al. 2022

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ObjectiveCombine Health Management Information Systems (HMIS) and probability survey data using the statistical annealing technique (AT) to produce more accurate health coverage estimates than either source of data and a measure of HMIS data error.SettingThis study is set in Bihar, the fifth poorest state in India, where half the population lives below the poverty line. An important source of data, used by health professionals for programme decision making, is routine health facility or HMIS data. Its quality is sometimes poor or unknown, and has no measure of its uncertainty. Using AT, we combine district-level HMIS and probability survey data (n=475) for the first time for 10 indicators assessing antenatal care, institutional delivery and neonatal care from 11 blocks of Aurangabad and 14 blocks of Gopalganj districts (N=6 253 965) in Bihar state, India.ParticipantsBoth districts are rural. Bihar is 82.7% Hindu and 16.9% Islamic.Primary outcome measuresSurvey prevalence measures for 10 indicators, corresponding prevalences using HMIS data, combined prevalences calculated with AT and SEs for each type of data.ResultsThe combined and survey estimates differ by <0.10. The combined and HMIS estimates differ by up to 84.2%, with the HMIS having 1.4–32.3 times larger error. Of 20 HMIS versus survey coverage estimate comparisons across the two districts only five differed by <0.10. Of 250 subdistrict-level comparisons of HMIS versus combined estimates, only 36.4% of the HMIS estimates are within the 95% CI of the combined estimate.ConclusionsOur statistical innovation increases the accuracy of information available for local health system decision making, allows evaluation of indicator accuracy and increases the accuracy of HMIS estimates. The combined estimates with a measure of error better informs health system professionals about their risks when using HMIS estimates, so they can reduce waste by making better decisions. Our results show that AT is an effective method ready for additional international assessment while also being used to provide affordable information to improve health services.

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Three parallel information systems for malaria elimination in Swaziland, 2010–2015: are the numbers the same?

Cited by 6 publications

References 5 publications

Receptivity to malaria: meaning and measurement

Receptivity to malaria: meaning and measurement

A comparative analysis of two national tuberculosis reporting systems and their impact on tuberculosis case notification in Uganda

Innovative approach to improve information accuracy in a two-district cross-sectional study in Bihar, India

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