Evidence map›Paper›PMID 33093622›Full record

ArticleScientific reports2020

Spatiotemporal mapping of malaria prevalence in Madagascar using routine surveillance and health survey data.

Rohan Arambepola, Suzanne H Keddie, Emma L Collins, Katherine A Twohig, Punam Amratia, Amelia Bertozzi-Villa, Elisabeth G Chestnutt, Joseph Harris, Justin Millar, Jennifer Rozier and 10 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
–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

26 citing papers in PubMed.

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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

20 authors.

Rohan ArambepolaBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK. rohan.arambepola@stx.ox.ac.uk.
Suzanne H KeddieTelethon Kids Institute, Perth Children's Hospital, Perth, Australia.
Emma L CollinsBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
Katherine A TwohigBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
Punam AmratiaBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
Amelia Bertozzi-VillaBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
Elisabeth G ChestnuttBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
Joseph HarrisTelethon Kids Institute, Perth Children's Hospital, Perth, Australia.
Justin MillarBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
Jennifer RozierTelethon Kids Institute, Perth Children's Hospital, Perth, Australia.
Susan F RumishaBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
Tasmin L SymonsBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
Camilo Vargas-RuizBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
Mauricette AndriamananjaraProgramme National de Lutte contre le Paludisme, Antananarivo, Madagascar.
Saraha RabeherisoaProgramme National de Lutte contre le Paludisme, Antananarivo, Madagascar.
Arsène C RatsimbasoaProgramme National de Lutte contre le Paludisme, Antananarivo, Madagascar.
Rosalind E HowesBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
Daniel J WeissBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
Peter W GethingBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
Ewan CameronBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.

Funding

Bill & Melinda Gates Foundation OPP1197730
6 · The paper itself

Abstract

Malaria transmission in Madagascar is highly heterogeneous, exhibiting spatial, seasonal and long-term trends. Previous efforts to map malaria risk in Madagascar used prevalence data from Malaria Indicator Surveys. These cross-sectional surveys, conducted during the high transmission season most recently in 2013 and 2016, provide nationally representative prevalence data but cover relatively short time frames. Conversely, monthly case data are collected at health facilities but suffer from biases, including incomplete reporting and low rates of treatment seeking. We combined survey and case data to make monthly maps of prevalence between 2013 and 2016. Health facility catchment populations were estimated to produce incidence rates from the case data. Smoothed incidence surfaces, environmental and socioeconomic covariates, and survey data informed a Bayesian prevalence model, in which a flexible incidence-to-prevalence relationship was learned. Modelled spatial trends were consistent over time, with highest prevalence in the coastal regions and low prevalence in the highlands and desert south. Prevalence was lowest in 2014 and peaked in 2015 and seasonality was widely observed, including in some lower transmission regions. These trends highlight the utility of monthly prevalence estimates over the four year period. By combining survey and case data using this two-step modelling approach, we were able to take advantage of the relative strengths of each metric while accounting for potential bias in the case data. Similar modelling approaches combining large datasets of different malaria metrics may be applicable across sub-Saharan Africa.

Indexed as

Population SurveillanceSpatio-Temporal AnalysisBayes TheoremCross-Sectional StudiesHealth SurveysHumansMadagascarMalaria, FalciparumPlasmodium falciparumPrevalence

Identifiers

PMID33093622
PMCPMC7581764

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.