Evidence map›Paper›PMID 31288840›Full record

ArticleMalaria journal2019

Spatio-temporal prevalence of malaria and anaemia in relation to agro-ecosystems in Mvomero district, Tanzania.

Susan F Rumisha, Elizabeth H Shayo, Leonard E G Mboera

Open access · goldAbstract read
In one paragraph

Article in Malaria journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 2 pooled it
4.9field-weighted citation impact, top 5% 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

18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 44 citations in OpenAlex.

  1. Pooled it
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  12. Impact of Agricultural Irrigation on Anemia in Western Kenya.The American journal of tropical medicine and hygiene · 2022
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  17. Geo-Epidemiology of Malaria at the Health Area Level, Dire Health District, Mali, 2013-2017.International journal of environmental research and public health · 2020
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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

3 authors at 2 institutions in 1 country.

Susan F RumishaNational Institute for Medical Research, 3 Barack Obama Drive, P.O. Box 9653, Dar es Salaam, Tanzania. siaeli@gmail.com.ORCID http://orcid.org/0000-0001-7058-488X
Elizabeth H ShayoNational Institute for Medical Research, 3 Barack Obama Drive, P.O. Box 9653, Dar es Salaam, Tanzania.
Leonard E G MboeraNational Institute for Medical Research, 3 Barack Obama Drive, P.O. Box 9653, Dar es Salaam, Tanzania.
National Institute for Medical Research · TZSouthern African Centre for Infectious Disease Surveillance · TZ

Funding

International Water Management Institute through the Systemwide Initiative in Malaria and Agriculture Received by the Ministry of Health
6 · The paper itself

Abstract

backgroundAgro-ecological systems have been associated with increased malaria intensity. This study determined association between different agro-ecological systems, prevalence of malaria parasitaemia and anaemia in Mvomero district, Tanzania.

methodsThe study was carried out in three agro-ecosystems namely, savannah, rice-irrigation, and sugarcane. Malaria and anaemia prevalence were measured in four seasons of a year. Villages were categorized according to environmental characteristics, proportion of water-shaded areas and agro-ecosystems. Mixed-effects logistic regression analysis was used to determine factors associated with malaria infection.

resultsA total of 7888 individuals were involved with the overall malaria prevalence of 34.4%. Plasmodium falciparum was the dominant (99.52%) malaria species. Malaria prevalence was highest (42.9%) in children of 10-15 years of age, and significantly low during dry and hot season. Of the infected individuals, 78.1% were from rice-irrigation, 18.7% savannah and 3.2% sugarcane ecosystem. Individuals living in villages with high levels of water-shaded areas had highest malaria risk. Over three-quarters (78.9%) of the individuals slept under a mosquito net, with the highest (88.5%) coverage among individuals in sugarcane ecosystem. On average 47.1% of the children were anaemic. Anaemia was more prevalent (60.5%) among individuals in the savannah than in the rice-irrigation (48.2%) or sugarcane communities (23%). Analysis indicated that ecosystems and levels of water-shaded area were highly correlated, and altered levels of malaria infection. Gender, age, mosquito net-use, and season were other significant determinants of P. falciparum infection. Males had higher odds than females (OR = 1.16, 95% CI 1.05, 1.29). The risk for children 6-9 years and older children (10-15 years) was over 50% and 24%, respectively, higher compared to young ones (0-5 years). Use of mosquito net reduced malaria risk by 26%. The risk of infection was higher during dry and cool season (OR = 1.92, 95 %CI 1.66, 2.23) compared to other seasons. Living in villages with high level of water-shaded areas increased the chances of getting malaria up to 15 times than living in drier areas. Similarly, infection odds increased when living in savannah and rice-irrigation ecosystems than in the sugarcane ecosystem.

conclusionsFindings show significant variations in malaria prevalence between communities living in different agro-ecosystems within the same district. Local malaria control strategies should consider these variations and liaise with agricultural experts while designing interventions to maximize effectiveness.

Indexed as

AgricultureAdolescentAdultAgedAged, 80 and overAnemiaChildChild, PreschoolCross-Sectional StudiesEcosystemFemaleHumansInfantMalaria, FalciparumMaleMiddle AgedAgro-ecosystemsAnaemiaMalariaParasitaemiaTanzania

Identifiers

PMID31288840
PMCPMC6617584
OpenAlexW2960280466

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.