Evidence map›Paper›PMID 36578997›Full record

ArticleJournal of arthropod-borne diseases2021

Predicting the Potential Distribution of Major Malaria Vectors Based on Climate Changes in Sistan and Baluchistan Province, Southeastern Iran.

Jalil Nejati, Hassan Vatandoost, Mehdi Zanganeh Baygi, Ahmad Ali Hanafi-Bojd

Abstract read
In one paragraph

Article in Journal of arthropod-borne diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Epidemiology of malaria in saravan city and its suburbs from 2018 to 2023, Southeast Iran.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2024
    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

4 authors.

Jalil NejatiHealth Promotion Research Center, Zahedan University of Medical Sciences, Zahedan, Iran.
Hassan VatandoostDepartment of Medical Entomology and Vector Control, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Mehdi Zanganeh BaygiHealth Promotion Research Center, Zahedan University of Medical Sciences, Zahedan, Iran.
Ahmad Ali Hanafi-BojdDepartment of Medical Entomology and Vector Control, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Given the significance of climate change and its substantial effects on mosquitoes' habitats, this study was aimed to model the spatial distribution of the main malaria vectors in the south east of Iran. Methods: Several scientific databases between 1980 and 2019 were reviewed to find mosquito species and their spatial information in this area. The archived folders in the center for diseases control and prevention were used to exploit essential data on malaria cases and foci. Three representative concentration pathways (RCP2.6, RCP4.5 and RCP8.5) were chosen to stand for three possible climate scenarios. Finally the potential species distribution of Results: So far, a total of 39 mosquito species belonging to the family Culicidae have been reported from the study area. In 2019, the total malaria cases have increased by 91% compared to 2015, as well as a sharp rise than 2018 (249%). In that year, 91% of cases were imported from other countries, which caused 40% increase in the new potential foci than in 2018. The Jackknife test demonstrated the annual mean temperature and precipitation of the coldest quarter with the greatest impact on the environmental suitability of the mentioned two species. Conclusion: The effect of climate change on the appearance and recurrence of mosquito-borne diseases has been demonstrated in various studies. Collecting further data and conducting investigation on this issue will improve control management, especially for the malaria vectors.

Indexed as

AnophelesIranMalariaMaximum entropy model

Identifiers

PMID36578997
PMCPMC9759442

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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.