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Geo-Climatic Factors of Malaria Morbidity in the Democratic Republic of Congo from 2001 to 2019

Background: Environmentally related morbidity and mortality still remain high worldwide, although they have decreased significantly in recent decades. This study aims to forecast malaria epidemics taking into account climatic and spatio-temporal variations and therefore identify geo-climatic factors...

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Autores principales: Panzi, Eric Kalunda, Okenge, Léon Ngongo, Kabali, Eugénie Hamuli, Tshimungu, Félicien, Dilu, Angèle Keti, Mulangu, Felix, Kandala, Ngianga-Bakwin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8998039/
https://www.ncbi.nlm.nih.gov/pubmed/35409494
http://dx.doi.org/10.3390/ijerph19073811
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author Panzi, Eric Kalunda
Okenge, Léon Ngongo
Kabali, Eugénie Hamuli
Tshimungu, Félicien
Dilu, Angèle Keti
Mulangu, Felix
Kandala, Ngianga-Bakwin
author_facet Panzi, Eric Kalunda
Okenge, Léon Ngongo
Kabali, Eugénie Hamuli
Tshimungu, Félicien
Dilu, Angèle Keti
Mulangu, Felix
Kandala, Ngianga-Bakwin
author_sort Panzi, Eric Kalunda
collection PubMed
description Background: Environmentally related morbidity and mortality still remain high worldwide, although they have decreased significantly in recent decades. This study aims to forecast malaria epidemics taking into account climatic and spatio-temporal variations and therefore identify geo-climatic factors predictive of malaria prevalence from 2001 to 2019 in the Democratic Republic of Congo. Methods: This is a retrospective longitudinal ecological study. The database of the Directorate of Epidemiological Surveillance including all malaria cases registered in the surveillance system based on positive blood test results, either by microscopy or by a rapid diagnostic test for malaria was used to estimate malaria morbidity and mortality by province of the DRC from 2001 to 2019. The impact of climatic factors on malaria morbidity was modeled using the Generalized Poisson Regression, a predictive model with the dependent variable Y the count of the number of occurrences of malaria cases during a period of time adjusting for risk factors. Results: Our results show that the average prevalence rate of malaria in the last 19 years is 13,246 (1,178,383–1,417,483) cases per 100,000 people at risk. This prevalence increases significantly during the whole study period (p < 0.0001). The year 2002 was the most morbid with 2,913,799 (120,9451–3,830,456) cases per 100,000 persons at risk. Adjusting for other factors, a one-day in rainfall resulted in a 7% statistically significant increase in malaria cases (p < 0.0001). Malaria morbidity was also significantly associated with geographic location (western, central and northeastern region of the country), total evaporation under shelter, maximum daily temperature at a two-meter altitude and malaria morbidity (p < 0.0001). Conclusions: In this study, we have established the association between malaria morbidity and geo-climatic predictors such as geographical location, total evaporation under shelter and maximum daily temperature at a two-meter altitude. We show that the average number of malaria cases increased positively as a function of the average number of rainy days, the total quantity of rainfall and the average daily temperature. These findings are important building blocks to help the government of DRC to set up a warning system integrating the monitoring of rainfall and temperature trends and the early detection of anomalies in weather patterns in order to forecast potential large malaria morbidity events.
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spelling pubmed-89980392022-04-12 Geo-Climatic Factors of Malaria Morbidity in the Democratic Republic of Congo from 2001 to 2019 Panzi, Eric Kalunda Okenge, Léon Ngongo Kabali, Eugénie Hamuli Tshimungu, Félicien Dilu, Angèle Keti Mulangu, Felix Kandala, Ngianga-Bakwin Int J Environ Res Public Health Article Background: Environmentally related morbidity and mortality still remain high worldwide, although they have decreased significantly in recent decades. This study aims to forecast malaria epidemics taking into account climatic and spatio-temporal variations and therefore identify geo-climatic factors predictive of malaria prevalence from 2001 to 2019 in the Democratic Republic of Congo. Methods: This is a retrospective longitudinal ecological study. The database of the Directorate of Epidemiological Surveillance including all malaria cases registered in the surveillance system based on positive blood test results, either by microscopy or by a rapid diagnostic test for malaria was used to estimate malaria morbidity and mortality by province of the DRC from 2001 to 2019. The impact of climatic factors on malaria morbidity was modeled using the Generalized Poisson Regression, a predictive model with the dependent variable Y the count of the number of occurrences of malaria cases during a period of time adjusting for risk factors. Results: Our results show that the average prevalence rate of malaria in the last 19 years is 13,246 (1,178,383–1,417,483) cases per 100,000 people at risk. This prevalence increases significantly during the whole study period (p < 0.0001). The year 2002 was the most morbid with 2,913,799 (120,9451–3,830,456) cases per 100,000 persons at risk. Adjusting for other factors, a one-day in rainfall resulted in a 7% statistically significant increase in malaria cases (p < 0.0001). Malaria morbidity was also significantly associated with geographic location (western, central and northeastern region of the country), total evaporation under shelter, maximum daily temperature at a two-meter altitude and malaria morbidity (p < 0.0001). Conclusions: In this study, we have established the association between malaria morbidity and geo-climatic predictors such as geographical location, total evaporation under shelter and maximum daily temperature at a two-meter altitude. We show that the average number of malaria cases increased positively as a function of the average number of rainy days, the total quantity of rainfall and the average daily temperature. These findings are important building blocks to help the government of DRC to set up a warning system integrating the monitoring of rainfall and temperature trends and the early detection of anomalies in weather patterns in order to forecast potential large malaria morbidity events. MDPI 2022-03-23 /pmc/articles/PMC8998039/ /pubmed/35409494 http://dx.doi.org/10.3390/ijerph19073811 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Panzi, Eric Kalunda
Okenge, Léon Ngongo
Kabali, Eugénie Hamuli
Tshimungu, Félicien
Dilu, Angèle Keti
Mulangu, Felix
Kandala, Ngianga-Bakwin
Geo-Climatic Factors of Malaria Morbidity in the Democratic Republic of Congo from 2001 to 2019
title Geo-Climatic Factors of Malaria Morbidity in the Democratic Republic of Congo from 2001 to 2019
title_full Geo-Climatic Factors of Malaria Morbidity in the Democratic Republic of Congo from 2001 to 2019
title_fullStr Geo-Climatic Factors of Malaria Morbidity in the Democratic Republic of Congo from 2001 to 2019
title_full_unstemmed Geo-Climatic Factors of Malaria Morbidity in the Democratic Republic of Congo from 2001 to 2019
title_short Geo-Climatic Factors of Malaria Morbidity in the Democratic Republic of Congo from 2001 to 2019
title_sort geo-climatic factors of malaria morbidity in the democratic republic of congo from 2001 to 2019
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8998039/
https://www.ncbi.nlm.nih.gov/pubmed/35409494
http://dx.doi.org/10.3390/ijerph19073811
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