Evidence map›Paper›PMID 34117009›Full record

SynthesisBMJ global health2021

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

Jieun Lee, Caroline A Lynch, Lauren Oliveira Hashiguchi, Robert W Snow, Naomi D Herz, Jayne Webster, Justin Parkhurst, Ngozi A Erondu

Abstract readSystematic Review
In one paragraph

Synthesis in BMJ global health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

31 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Jieun LeeDepartment of Disease Control, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK.
Caroline A LynchDepartment of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.
Lauren Oliveira HashiguchiDepartment of Disease Control, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK.
Robert W SnowPopulation and Health Unit, KEMRI - Wellcome Trust Research Programme, Nairobi, Kenya.
Naomi D HerzMedical and Healthcare Innovation, British Heart Foundation, London, UK.
Jayne WebsterDepartment of Disease Control, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK.
Justin ParkhurstDepartment of Health Policy, London School of Economics and Political Science, London, UK.ORCID 0000-0003-0831-6213
Ngozi A EronduDepartment of Disease Control, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK ngozierondu@gmail.com.ORCID 0000-0003-0268-6838

Funding

Wellcome TrustWellcome Trust 103602Wellcome Trust 203077Wellcome Trust 212176
6 · The paper itself

Abstract

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.

Indexed as

Developing CountriesHealth Information SystemsDelivery of Health CareHumansIncomePovertyepidemiologyhealth systemspublic healthsystematic review

Identifiers

PMID34117009
PMCPMC8202107

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.