Evidence map›Paper›PMID 37790419›Full record

ArticlemedRxiv : the preprint server for health sciences2023

Targeted malaria elimination interventions reduce

Jade Benjamin-Chung, Haodong Li, Anna Nguyen, Gabriella Barratt Heitmann, Adam Bennett, Henry Ntuku, Lisa M Prach, Munyaradzi Tambo, Lindsey Wu, Chris Drakeley and 7 more

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed, 4 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors at 9 institutions in 4 countries.

Jade Benjamin-ChungDepartment of Epidemiology and Population Health, Stanford University, Stanford, United States.ORCID 0000-0003-3631-3132
Haodong LiDivision of Biostatistics, University of California, Berkeley.
Anna NguyenDepartment of Epidemiology and Population Health, Stanford University, Stanford, United States.ORCID 0000-0002-2211-9040
Gabriella Barratt HeitmannDepartment of Epidemiology and Population Health, Stanford University, Stanford, United States.
Adam BennettMalaria Elimination Initiative, Global Health Group, University of California, San Francisco (UCSF) , San Francisco, United States.
Henry NtukuMalaria Elimination Initiative, Global Health Group, University of California, San Francisco (UCSF) , San Francisco, United States.
Lisa M PrachMalaria Elimination Initiative, Global Health Group, University of California, San Francisco (UCSF) , San Francisco, United States.
Munyaradzi TamboMultidisciplinary Research Centre, University of Namibia, Windhoek, Namibia.
Lindsey WuDepartment of Infection Biology, London School of Hygiene and Tropical Medicine, London, UK.
Chris DrakeleyDepartment of Infection Biology, London School of Hygiene and Tropical Medicine, London, UK.
Roly GoslingMalaria Elimination Initiative, Global Health Group, University of California, San Francisco (UCSF) , San Francisco, United States.
Davis MumbengegwiUniversity of Namibia, Windhoek, Namibia.
Immo KleinschmidtMRC International Statistics and Epidemiology Group, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
Jennifer L SmithMalaria Elimination Initiative, Global Health Group, University of California, San Francisco (UCSF) , San Francisco, United States.
Alan HubbardDivision of Biostatistics, University of California, Berkeley.ORCID 0000-0002-3769-0127
Mark van der LaanDivision of Biostatistics, University of California, Berkeley.
Michelle S HsiangChan Zuckerberg Biohub, San Francisco, United States.
University of California, San Francisco · USUniversity of California, Berkeley · USLondon School of Hygiene & Tropical Medicine · GBStanford University · USUniversity of Namibia · NAChan Zuckerberg Initiative (United States) · USGlobal Brain Health Institute · USProgram for Appropriate Technology in Health · USSouth African Medical Research Council · ZA

Funding

Stanford BSSR Pre-Doctoral Training Program at the Intersection of Data Sciences with Behavioral, Social, and Population Health ResearchT32HL151323 · NHLBI · STANFORD UNIVERSITY · PI Michelle Christina Odden, David H Rehkopf · 2020 to 2026
$1.6M
Measuring spillover effects of reactive, focal malaria elimination interventionsK01AI141616 · NIAID · UNIVERSITY OF CALIFORNIA BERKELEY · PI BENJAMIN-CHUNG, JADE · 2019 to 2022
$601k
NHLBI NIH HHS T32 HL151323NIAID NIH HHS K01 AI141616
6 · The paper itself

Abstract

Malaria elimination interventions in low-transmission settings aim to extinguish hot spots and prevent transmission to nearby areas. In malaria elimination settings, the World Health Organization recommends reactive, focal interventions targeted to the area near malaria cases shortly after they are detected. A key question is whether these interventions reduce transmission to nearby uninfected or asymptomatic individuals who did not receive interventions. Here, we measured direct effects (among intervention recipients) and spillover effects (among non-recipients) of reactive, focal interventions delivered within 500m of confirmed malaria index cases in a cluster-randomized trial in Namibia. The trial delivered malaria chemoprevention (artemether lumefantrine) and vector control (indoor residual spraying with Actellic) separately and in combination using a factorial design. We compared incidence, infection prevalence, and seroprevalence between study arms among intervention recipients (direct effects) and non-recipients (spillover effects) up to 3 km away from index cases. We calculated incremental cost-effectiveness ratios accounting for spillover effects. The combined chemoprevention and vector control intervention produced direct effects and spillover effects. In the primary analysis among non-recipients within 1 km from index cases, the combined intervention reduced malaria incidence by 43% (95% CI 20%, 59%). In secondary analyses among non-recipients 500m-3 km from interventions, the combined intervention reduced infection by 79% (6%, 95%) and seroprevalence 34% (20%, 45%). Accounting for spillover effects increased the cost-effectiveness of the combined intervention by 37%. Our findings provide the first evidence that targeting hot spots with combined chemoprevention and vector control interventions can indirectly benefit non-recipients up to 3 km away.

Identifiers

PMID37790419
PMCPMC10543053
OpenAlexW4386879272

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.