IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i7p3811-d777736.html
   My bibliography  Save this article

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

Author

Listed:
  • Eric Kalunda Panzi

    (Département de la Santé Communautaire, Institut Supérieur des Techniques Médicales de Kinshasa, B.P. 774, Kinshasa XI, Mont Ngafula, Kinshasa, Democratic Republic of the Congo)

  • Léon Ngongo Okenge

    (Département de la Santé Communautaire, Institut Supérieur des Techniques Médicales de Kinshasa, B.P. 774, Kinshasa XI, Mont Ngafula, Kinshasa, Democratic Republic of the Congo)

  • Eugénie Hamuli Kabali

    (Département de la Santé Communautaire, Institut Supérieur des Techniques Médicales de Kinshasa, B.P. 774, Kinshasa XI, Mont Ngafula, Kinshasa, Democratic Republic of the Congo)

  • Félicien Tshimungu

    (Département de la Santé Communautaire, Institut Supérieur des Techniques Médicales de Kinshasa, B.P. 774, Kinshasa XI, Mont Ngafula, Kinshasa, Democratic Republic of the Congo)

  • Angèle Keti Dilu

    (Ministère de la Santé, Secrétariat Général /Cellule Suivi et Evaluation, 36 Avenue de la Justice Gombe, B.P. 3088, Kinshasa, Democratic Republic of the Congo)

  • Felix Mulangu

    (Ministère de la Santé Publique, Direction de la Surveillance Epidémiologique, 36 Avenue de la Justice Gombe, Kinshasa, Democratic Republic of the Congo)

  • Ngianga-Bakwin Kandala

    (Département de la Santé Communautaire, Institut Supérieur des Techniques Médicales de Kinshasa, B.P. 774, Kinshasa XI, Mont Ngafula, Kinshasa, Democratic Republic of the Congo
    Division of Epidemiology and Biostatistics, School of Public Health, University of the Witwatersrand, Johannesburg 2193, South Africa
    Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK)

Abstract

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.

Suggested Citation

  • Eric Kalunda Panzi & Léon Ngongo Okenge & Eugénie Hamuli Kabali & Félicien Tshimungu & Angèle Keti Dilu & Felix Mulangu & Ngianga-Bakwin Kandala, 2022. "Geo-Climatic Factors of Malaria Morbidity in the Democratic Republic of Congo from 2001 to 2019," IJERPH, MDPI, vol. 19(7), pages 1-16, March.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:7:p:3811-:d:777736
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/7/3811/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/7/3811/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zeileis, Achim & Kleiber, Christian & Jackman, Simon, 2008. "Regression Models for Count Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i08).
    2. Abiodun M. Adeola & Joel O. Botai & Hannes Rautenbach & Omolola M. Adisa & Katlego P. Ncongwane & Christina M. Botai & Temitope C. Adebayo-Ojo, 2017. "Climatic Variables and Malaria Morbidity in Mutale Local Municipality, South Africa: A 19-Year Data Analysis," IJERPH, MDPI, vol. 14(11), pages 1-15, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Totterman, Stephen, 2021. "Vehicle-based recreation and compliance for three beaches in northern New South Wales," OSF Preprints ja8h6, Center for Open Science.
    2. Jong-Hyun Kim & Yong-Gil Lee, 2021. "Factors of Collaboration Affecting the Performance of Alternative Energy Patents in South Korea from 2010 to 2017," Sustainability, MDPI, vol. 13(18), pages 1-25, September.
    3. Olga Alipova & Lada Litvinova & Andrey Lovakov & Maria Yudkevich, 2018. "Inbreds And Non-Inbreds Among Russian Academics: Short-Term Similarity And Long-Term Differences In Productivity," HSE Working papers WP BRP 48/EDU/2018, National Research University Higher School of Economics.
    4. Christian Kleiber & Achim Zeileis, 2016. "Visualizing Count Data Regressions Using Rootograms," The American Statistician, Taylor & Francis Journals, vol. 70(3), pages 296-303, July.
    5. Sewando, Ponsian T. & Mdoe, N. Y. S. & Mutabazi, K. D. S, 2011. "Farmers’ preferential choice decisions to alternative cassava value chain strands in Morogoro rural district, Tanzania," MPRA Paper 29797, University Library of Munich, Germany.
    6. Merl, Robert & Palan, Stefan & Schmidt, Dominik & Stöckl, Thomas, 2023. "Insider trading regulation and trader migration," Journal of Financial Markets, Elsevier, vol. 66(C).
    7. Sean J. Blamires & Cheng-Hui Lai & Ren-Chung Cheng & Chen-Pan Liao & Pao-Sheng Shen & I-Min Tso, 2012. "Body spot coloration of a nocturnal sit-and-wait predator visually lures prey," Behavioral Ecology, International Society for Behavioral Ecology, vol. 23(1), pages 69-74.
    8. Abiodun Adeola & Katlego Ncongwane & Gbenga Abiodun & Thabo Makgoale & Hannes Rautenbach & Joel Botai & Omolola Adisa & Christina Botai, 2019. "Rainfall Trends and Malaria Occurrences in Limpopo Province, South Africa," IJERPH, MDPI, vol. 16(24), pages 1-15, December.
    9. Lawrence N Kazembe, 2013. "A Bayesian Two Part Model Applied to Analyze Risk Factors of Adult Mortality with Application to Data from Namibia," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-10, September.
    10. Erich Striessnig & Elke Loichinger, 2015. "Future differential vulnerability to natural disasters by level of education," Vienna Yearbook of Population Research, Vienna Institute of Demography (VID) of the Austrian Academy of Sciences in Vienna, vol. 13(1), pages 221-240.
    11. Ina Falfán & Luis Zambrano, 2023. "Lacustrine Urban Blue Spaces: Low Availability and Inequitable Distribution in the Most Populated Cities in Mexico," Land, MDPI, vol. 12(1), pages 1-18, January.
    12. Gerike, Regine & Gehlert, Tina & Leisch, Friedrich, 2015. "Time use in travel surveys and time use surveys – Two sides of the same coin?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 76(C), pages 4-24.
    13. Guarino, Ernestino de Souza Gomes & Barbosa, Ana Márcia & Waechter, Jorge Luiz, 2012. "Occurrence and abundance models of threatened plant species: Applications to mitigate the impact of hydroelectric power dams," Ecological Modelling, Elsevier, vol. 230(C), pages 22-33.
    14. Evgenii V. Gilenko & Elena A. Mironova, 2017. "Modern claim frequency and claim severity models: An application to the Russian motor own damage insurance market," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1311097-131, January.
    15. Livio Finos & Fortunato Pesarin, 2020. "On zero-inflated permutation testing and some related problems," Statistical Papers, Springer, vol. 61(5), pages 2157-2174, October.
    16. Andre Jungmittag, 2019. "Service trade restrictiveness and internationalisation of retail trade," International Economics and Economic Policy, Springer, vol. 16(2), pages 293-333, April.
    17. Giulio Cainelli & Donato Iacobucci & Alessandra Micozzi, 2015. "Determinants of territorial differences in entrepreneurial rates. An empirical analysis of Italian local systems," Working Papers 1502, c.MET-05 - Centro Interuniversitario di Economia Applicata alle Politiche per L'industria, lo Sviluppo locale e l'Internazionalizzazione, revised Feb 2015.
    18. Erni, Birgit & Bonnevie, Bo T. & Oschadleus, Hans-Dieter & Altwegg, Res & Underhill, Les G., 2013. "moult: An R Package to Analyze Moult in Birds," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i08).
    19. Christophe Dutang, 2012. "The customer, the insurer and the market," Post-Print hal-01616152, HAL.
    20. Wan Jing Low & Paul Wilson & Mike Thelwall, 2016. "Stopped sum models and proposed variants for citation data," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(2), pages 369-384, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:7:p:3811-:d:777736. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.