Evidence map›Paper›PMID 41955078›Full record

ArticleClinical infectious diseases : an official publication of the Infectious Diseases Society of America2026

Mapping Hospitals That Treat Severe Malaria Cases in the United States: Evidence to Inform Health Systems Strategies for Intravenous Artesunate Supply.

Kruthik Ravishankar, Kimberly E Mace, Satoshi Koiso, Alison D Ridpath, Eren Gulbas, Julie Thwing, Regina C LaRocque, Seymour Williams, Edward T Ryan, Tyler S Brown and 1 more

Abstract read
In one paragraph

Article in Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Kruthik RavishankarBoston University School of Medicine, Boston, Massachusetts, USA.ORCID 0009-0009-9823-6137
Kimberly E MaceDivision of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
Satoshi KoisoMedical Practice Evaluation Center, Massachusetts General Hospital, Boston, Massachusetts, USA.ORCID 0000-0001-7542-1801
Alison D RidpathDivision of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.ORCID 0000-0002-4109-3464
Eren GulbasMedical Practice Evaluation Center, Massachusetts General Hospital, Boston, Massachusetts, USA.
Julie ThwingDivision of Global Migration and Quarantine, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.ORCID 0000-0002-0471-281X
Regina C LaRocqueDivision of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, USA.ORCID 0000-0003-4558-0339
Seymour WilliamsDivision of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.ORCID 0000-0003-2631-2216
Edward T RyanDivision of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, USA.
Tyler S BrownBoston University School of Medicine, Boston, Massachusetts, USA.
Emily P HyleMedical Practice Evaluation Center, Massachusetts General Hospital, Boston, Massachusetts, USA.ORCID 0000-0001-8721-106X

Funding

Geographic origins and dispersal of drug-resistant M. tuberculosis in South Africa: Advancing strategies for early detectionK08AI166125 · NIAID · UNIVERSITY OF COLORADO DENVER · PI BROWN, TYLER STEVEN · 2022 to 2025
$780k
National Institutes of Health National Institute of Allergy and Infectious Diseases K08AI166125NCEZID CDC HHS U01 CK000490NCEZID CDC HHS U01 CK000633NIAID NIH HHS K08 AI166125US Centers for Disease Control and Prevention U01CK000490US Centers for Disease Control and Prevention U01CK000633
6 · The paper itself

Abstract

backgroundSevere malaria among travelers returning to the United States is a rare but life-threatening condition, for which standard of care is urgent treatment with intravenous artesunate (IVAS). Uncertainty about where future hospitalized severe malaria cases will present, the need for urgent treatment delivery, and high IVAS costs pose challenges for health systems planning.

methodsWe mapped locations for hospitalized severe malaria cases diagnosed during 2012-2018 and reported to the US Centers for Disease Control and Prevention (CDC). We estimated driving times between each location and the nearest level 1 trauma center (L1TC), as settings that likely treat individuals with severe malaria and are candidate IVAS distribution points. We performed spatial optimization analysis, identifying sets of L1TC locations that minimized travel distances between hospitalized severe malaria cases and candidate IVAS supply points.

resultsWe successfully adjudicated locations for 1466 of 1791 possible hospitalized severe malaria cases reported to the CDC during 2012-2018. Hospitalized severe malaria cases were reported from 292 of 3142 US counties (9.3%) in 46 US states; these hospitals were concentrated in New York, Maryland, Texas, the District of Columbia, and Pennsylvania with minimal year-to-year variation. Of the 1458 cases in the contiguous US, 95.8% were hospitalized within 2 hours driving time of an L1TC. Spatial optimization results were consistent across years.

conclusionsThe geographic distribution of hospitals that treated severe malaria cases in the US was heterogeneous but relatively consistent across years. Mapping and spatial optimization identified locations that could be higher priority for on-site IVAS supply.

Indexed as

AntimalarialsArtesunateHospitalsMalariaAdministration, IntravenousCenters for Disease Control and Prevention, U.S.HospitalizationHumansTravelUnited StatesAntimalarialsArtesunateartesunatemalariaspatial optimization

Identifiers

PMID41955078
PMCPMC13393105

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Registered trials

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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.