Community workers contribution for data quality improvement in routine health information system: Live births number estimation by capture recapture method

Ahanhanzo, Yolaine Glèlè; Kpozéhouen, Alphonse; Paraiso, N; Lévêque, Alain; Makoutodé, Michel; Dramaix-Wilmet, Michèle

doi:10.4236/ojepi.2014.41008

Cited by 1 publication

(1 citation statement)

References 26 publications

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“…DQ output metric (accuracy, completeness, timeliness) was defined in diverse ways in the selected studies. For example, in separate studies Alhanhanzo et al define completeness indistinctly in 2014 as ‘exhaustiveness ’ 22 and then as ‘ no missing data ’ 23 in 2015. Whereas, Kintu et al 24 describe it as ‘ The proportion of health facilities reporting out of the total number of units in the districts’ (Examples of DQ output definitions by included study is available in online supplemental file S1(2.4) ).…”

Section: Resultsmentioning

confidence: 99%

Interventions to improve district-level routine health data in low-income and middle-income countries: a systematic review

et al. 2021

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BackgroundRoutine health information system(s) (RHIS) facilitate the collection of health data at all levels of the health system allowing estimates of disease prevalence, treatment and preventive intervention coverage, and risk factors to guide disease control strategies. This core health system pillar remains underdeveloped in many low-income and middle-income countries. Efforts to improve RHIS data coverage, quality and timeliness were launched over 10 years ago.MethodsA systematic review was performed across 12 databases and literature search engines for both peer-reviewed articles and grey literature reports on RHIS interventions. Studies were analysed in three stages: (1) categorisation of RHIS intervention components and processes; (2) comparison of intervention component effectiveness and (3) whether the post-intervention outcome improved above the WHO integrated disease surveillance response framework data quality standard of 80% or above.Results5294 references were screened, resulting in 56 studies. Three key performance determinants—technical, organisational and behavioural—were proposed as critical to RHIS strengthening. Seventy-seven per cent [77%] of studies identified addressed all three determinants. The most frequently implemented intervention components were ‘providing training’ and ‘using an electronic health management information systems’. Ninety-three per cent [93%] of pre–post or controlled trial studies showed improvements in one or more data quality outputs, but after applying a standard threshold of >80% post-intervention, this number reduced to 68%. There was an observed benefit of multi-component interventions that either conducted data quality training or that addressed improvement across multiple processes and determinants of RHIS.ConclusionHolistic data quality interventions that address multiple determinants should be continuously practised for strengthening RHIS. Studies with clearly defined and pragmatic outcomes are required for future RHIS improvement interventions. These should be accompanied by qualitative studies and cost analyses to understand which investments are needed to sustain high-quality RHIS in low-income and middle-income countries.

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Section: Resultsmentioning

confidence: 99%