Evidence map›Paper›PMID 25887561›Full record

ArticleHealth policy and planning2016

Using routine health information systems for well-designed health evaluations in low- and middle-income countries.

Bradley H Wagenaar, Kenneth Sherr, Quinhas Fernandes, Alexander C Wagenaar

Abstract read
In one paragraph

Article in Health policy and planning, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 100 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
100citing papers in PubMed, 5 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

100 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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  3. Routine Health Information Systems in the European Context: A Systematic Review of Systematic Reviews.International journal of environmental research and public health · 2021
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40 more citing papers are in PubMed but not listed here.

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

4 authors.

Bradley H WagenaarDepartment of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA, Health Alliance International, Seattle, WA, USA, wagenaarb@gmail.com.
Kenneth SherrDepartment of Global Health, School of Public Health, University of Washington, Seattle, WA, USA, Health Alliance International, Seattle, WA, USA.
Quinhas FernandesDepartment of Monitoring and Evaluation, National Directorate of Planning and Cooperation, Ministry of Health, Maputo, Mozambique and.
Alexander C WagenaarDepartment of Health Outcomes and Policy, College of Medicine, University of Florida, Gainesville, FL, USA.

Funding

HEALTH SYSTEMS STRENGTHENING TO IMPROVE HEALTH OUTCOMES: APPLYING IMPLEMENTATIONK02TW009207 · FIC · UNIVERSITY OF WASHINGTON · PI SHERR, KENNETH · 2011 to 2015
$403k
FIC NIH HHS K02 TW009207
6 · The paper itself

Abstract

Routine health information systems (RHISs) are in place in nearly every country and provide routinely collected full-coverage records on all levels of health system service delivery. However, these rich sources of data are regularly overlooked for evaluating causal effects of health programmes due to concerns regarding completeness, timeliness, representativeness and accuracy. Using Mozambique's national RHIS (Módulo Básico) as an illustrative example, we urge renewed attention to the use of RHIS data for health evaluations. Interventions to improve data quality exist and have been tested in low-and middle-income countries (LMICs). Intrinsic features of RHIS data (numerous repeated observations over extended periods of time, full coverage of health facilities, and numerous real-time indicators of service coverage and utilization) provide for very robust quasi-experimental designs, such as controlled interrupted time-series (cITS), which are not possible with intermittent community sample surveys. In addition, cITS analyses are well suited for continuously evolving development contexts in LMICs by: (1) allowing for measurement and controlling for trends and other patterns before, during and after intervention implementation; (2) facilitating the use of numerous simultaneous control groups and non-equivalent dependent variables at multiple nested levels to increase validity and strength of causal inference; and (3) allowing the integration of continuous 'effective dose received' implementation measures. With expanded use of RHIS data for the evaluation of health programmes, investments in data systems, health worker interest in and utilization of RHIS data, as well as data quality will further increase over time. Because RHIS data are ministry-owned and operated, relying upon these data will contribute to sustainable national capacity over time.

Indexed as

Data AccuracyHealth Information SystemsPopulation SurveillancePovertyDelivery of Health CareDeveloping CountriesHealth PolicyEpidemiologyevaluationhealth information systemshealth policyhealth systems researchimpactimplementationmethodsresearch methodssurvey methods

Identifiers

PMID25887561
PMCPMC4751224

What OpenQuestion holds

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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 OpenQuestion graph.