Evidence map›Paper›PMID 36683088›Full record

ArticleParasitology research2023

Pupal productivity of larval habitats of Aedes aegypti in Msambweni, Kwale County, Kenya.

Alawih S Mwakutwaa, Harun N Ngugi, Bryson A Ndenga, Amy Krystosik, Moses Ngari, Laila U Abubakar, Shadrack Yonge, Uriel Kitron, A Desiree LaBeaud, Francis M Mutuku

Open access · hybridAbstract read
In one paragraph

Article in Parasitology research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
2.6field-weighted citation impact, top 11% of its field
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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Temporal Variation and Human Host Predominance inPathogens (Basel, Switzerland) · 2025
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
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

10 authors at 5 institutions in 2 countries.

Alawih S MwakutwaaDepartment of Environment and Health Sciences, Technical University of Mombasa, Mombasa, Kenya.
Harun N NgugiDepartment of Biological Sciences, Chuka University, Chuka, Kenya.
Bryson A NdengaCentre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya.
Amy KrystosikDepartment of Pediatrics, Division of Infectious Diseases, Stanford University School of Medicine, Stanford, CA, USA.
Moses NgariKEMRI-Wellcome Trust Research Programme, Kilifi, Kenya.
Laila U AbubakarDepartment of Pure and Applied Sciences, Technical University of Mombasa, Mombasa, Kenya.
Shadrack YongeDepartment of Environment and Health Sciences, Technical University of Mombasa, Mombasa, Kenya.
Uriel KitronDepartment of Environmental Sciences, Emory University, Atlanta, GA, USA.
A Desiree LaBeaudDepartment of Pediatrics, Division of Infectious Diseases, Stanford University School of Medicine, Stanford, CA, USA.
Francis M MutukuDepartment of Environment and Health Sciences, Technical University of Mombasa, Mombasa, Kenya. fmutuku73@gmail.com.
Technical University of Mombasa · KEKenya Medical Research Institute · KEStanford University · USChuka University · KEEmory University · US

Funding

The child health impacts of overlapping vector-borne diseases in KenyaR01AI102918 · NIAID · STANFORD UNIVERSITY · PI Angelle Desiree LaBeaud · 2013 to 2026
$7.7M
NIAID NIH HHS R01 AI102918
6 · The paper itself

Abstract

Aedes aegypti is an important vector of several arboviruses including dengue and chikungunya viruses. Accurate identification of larval habitats of Ae. aegypti is considered an essential step in targeted control. This study determined Ae. aegypti productivity in selected larval habitats in Msambweni, Kwale County, Kenya. Three sequential larval habitat surveys were conducted. The first survey was habitat census (baseline) through which 83 representative larval habitats were identified and selected. The second and third surveys involved estimating daily productivity of the 83 selected larval habitats for 30 consecutive days during a wet and a dry season, respectively. Of 664 larval habitats examined at baseline, 144 larval habitats (21.7%) were found to be infested with Ae. aegypti larvae. At baseline, majority (71%) of the pupae were collected from two (2/6) larval habitat types, tires and pots. Multivariate analysis identified habitat type and the habitat being movable as the predictors for pupal abundance. During the 30-day daily pupal production surveys, only a few of the habitats harbored pupae persistently. Pupae were found in 28% and 12% of the larval habitats during the wet and dry seasons, respectively. In the wet season, drums, tires, and pots were identified as the key habitat types accounting for 85% of all pupae sampled. Three habitats (all drums) accounted for 80% of all the pupae collected in the dry season. Predictors for pupal productivity in the wet season were habitat type, place (whether the habitat is located at the back or front of the house), habitat purpose (use of the water in the habitat), and source of water. Although the multivariate model for habitat type did not converge, habitat type and habitat size were the only significant predictors during the dry season. Drums, pots, and tires were sources of more than 85% of Ae. aegypti pupae, reinforcing the "key container concept." Targeting these three types of habitats makes epidemiological sense, especially during the dry season.

Indexed as

AedesDengueAnimalsEcosystemKenyaLarvaMosquito VectorsPupaSeasonsWaterWaterAedes aegyptiChikungunya virusDengue virusPupal productivityZika virus

Identifiers

PMID36683088
PMCPMC9988718
OpenAlexW4317716325

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.