Literature DB >> 26618305

Estimating malaria burden in Nigeria: a geostatistical modelling approach.

Nnadozie Onyiri1.   

Abstract

This study has produced a map of malaria prevalence in Nigeria based on available data from the Mapping Malaria Risk in Africa (MARA) database, including all malaria prevalence surveys in Nigeria that could be geolocated, as well as data collected during fieldwork in Nigeria between March and June 2007. Logistic regression was fitted to malaria prevalence to identify significant demographic (age) and environmental covariates in STATA. The following environmental covariates were included in the spatial model: the normalized difference vegetation index, the enhanced vegetation index, the leaf area index, the land surface temperature for day and night, land use/landcover (LULC), distance to water bodies, and rainfall. The spatial model created suggests that the two main environmental covariates correlating with malaria presence were land surface temperature for day and rainfall. It was also found that malaria prevalence increased with distance to water bodies up to 4 km. The malaria risk map estimated from the spatial model shows that malaria prevalence in Nigeria varies from 20% in certain areas to 70% in others. The highest prevalence rates were found in the Niger Delta states of Rivers and Bayelsa, the areas surrounding the confluence of the rivers Niger and Benue, and also isolated parts of the north-eastern and north-western parts of the country. Isolated patches of low malaria prevalence were found to be scattered around the country with northern Nigeria having more such areas than the rest of the country. Nigeria's belt of middle regions generally has malaria prevalence of 40% and above.

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Year:  2015        PMID: 26618305     DOI: 10.4081/gh.2015.306

Source DB:  PubMed          Journal:  Geospat Health        ISSN: 1827-1987            Impact factor:   1.212


  13 in total

1.  Malaria Endemicity in the Rural Communities of Ebonyi State, Nigeria.

Authors:  David Ekene Nwele; Ikechukwu Oliver Onyali; Milliam Okwudili Iwueze; Michael Okpara Elom; Ogbonna Elom Sabastian Uguru
Journal:  Korean J Parasitol       Date:  2022-06-30       Impact factor: 1.776

2.  Is Nigeria winning the battle against malaria? Prevalence, risk factors and KAP assessment among Hausa communities in Kano State.

Authors:  Salwa Dawaki; Hesham M Al-Mekhlafi; Init Ithoi; Jamaiah Ibrahim; Wahib M Atroosh; Awatif M Abdulsalam; Hany Sady; Fatin Nur Elyana; Ado U Adamu; Saadatu I Yelwa; Abdulhamid Ahmed; Mona A Al-Areeqi; Lahvanya R Subramaniam; Nabil A Nasr; Yee-Ling Lau
Journal:  Malar J       Date:  2016-07-08       Impact factor: 2.979

3.  Extensive diversity in the allelic frequency of Plasmodium falciparum merozoite surface proteins and glutamate-rich protein in rural and urban settings of southwestern Nigeria.

Authors:  Roland I Funwei; Bolaji N Thomas; Catherine O Falade; Olusola Ojurongbe
Journal:  Malar J       Date:  2018-01-02       Impact factor: 2.979

4.  Evaluation of Diagnostic Accuracy of Rapid Diagnostic Test for Malaria Diagnosis among Febrile Children in Calabar, Nigeria.

Authors:  Anthony Achizie Iwuafor; Okokon Ita Ita; Godwin Ibitham Ogban; Ubong A Udoh; Chimereze Anthony Amajor
Journal:  Niger Med J       Date:  2018 Nov-Dec

5.  Using mixed effects logistic regression models for complex survey data on malaria rapid diagnostic test results.

Authors:  Chigozie Louisa J Ugwu; Temesgen T Zewotir
Journal:  Malar J       Date:  2018-12-05       Impact factor: 2.979

6.  A cross-sectional study of the prevalence, density, and risk factors associated with malaria transmission in urban communities of Ibadan, Southwestern Nigeria.

Authors:  Oluwaseun Bunmi Awosolu; Zary Shariman Yahaya; Meor Termizi Farah Haziqah; Iyabo Adepeju Simon-Oke; Comfort Fakunle
Journal:  Heliyon       Date:  2021-01-20

7.  Spatial prediction of malaria prevalence in Papua New Guinea: a comparison of Bayesian decision network and multivariate regression modelling approaches for improved accuracy in prevalence prediction.

Authors:  Eimear Cleary; Manuel W Hetzel; Paul Siba; Colleen L Lau; Archie C A Clements
Journal:  Malar J       Date:  2021-06-13       Impact factor: 2.979

Review 8.  Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa.

Authors:  Osadolor Ebhuoma; Michael Gebreslasie
Journal:  Int J Environ Res Public Health       Date:  2016-06-14       Impact factor: 3.390

9.  Spatial and spatio-temporal methods for mapping malaria risk: a systematic review.

Authors:  Julius Nyerere Odhiambo; Chester Kalinda; Peter M Macharia; Robert W Snow; Benn Sartorius
Journal:  BMJ Glob Health       Date:  2020-10

Review 10.  Geospatial estimation of reproductive, maternal, newborn and child health indicators: a systematic review of methodological aspects of studies based on household surveys.

Authors:  Leonardo Z Ferreira; Cauane Blumenberg; C Edson Utazi; Kristine Nilsen; Fernando P Hartwig; Andrew J Tatem; Aluisio J D Barros
Journal:  Int J Health Geogr       Date:  2020-10-13       Impact factor: 3.918

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