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Effects of meteorological factors on epidemic malaria in Ethiopia: a statistical modelling approach based on theoretical reasoning

Published online by Cambridge University Press:  13 May 2004

T. A. ABEKU ,
S. J. DE VLAS ,
G. J. J. M. BORSBOOM ,
A. TADEGE ,
Y. GEBREYESUS ,
H. GEBREYOHANNES ,
D. ALAMIREW ,
A. SEIFU ,
N. J. D. NAGELKERKE  and
J. D. F. HABBEMA
T. A. ABEKU
Affiliation:
Department of Public Health, Erasmus MC, University Medical Center Rotterdam, The Netherlands Disease Control and Vector Biology Unit, Department of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, UK
S. J. DE VLAS
Affiliation:
Department of Public Health, Erasmus MC, University Medical Center Rotterdam, The Netherlands
G. J. J. M. BORSBOOM
Affiliation:
Department of Public Health, Erasmus MC, University Medical Center Rotterdam, The Netherlands
A. TADEGE
Affiliation:
National Meteorological Services Agency, Addis Ababa, Ethiopia
Y. GEBREYESUS
Affiliation:
National Meteorological Services Agency, Addis Ababa, Ethiopia
H. GEBREYOHANNES
Affiliation:
National Meteorological Services Agency, Addis Ababa, Ethiopia
D. ALAMIREW
Affiliation:
Disease Prevention and Control Department, Ministry of Health, Addis Ababa, Ethiopia
A. SEIFU
Affiliation:
Disease Prevention and Control Department, Ministry of Health, Addis Ababa, Ethiopia
N. J. D. NAGELKERKE
Affiliation:
Department of Public Health, Erasmus MC, University Medical Center Rotterdam, The Netherlands Department of Medical Statistics, Leiden University Medical Center, The Netherlands
J. D. F. HABBEMA
Affiliation:
Department of Public Health, Erasmus MC, University Medical Center Rotterdam, The Netherlands
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Abstract

This study was conducted to quantify the association between meteorological variables and incidence of Plasmodium falciparum in areas with unstable malaria transmission in Ethiopia. We used morbidity data pertaining to microscopically confirmed cases reported from 35 sites throughout Ethiopia over a period of approximately 6–7 years. A model was developed reflecting biological relationships between meteorological and morbidity variables. A model that included rainfall 2 and 3 months earlier, mean minimum temperature of the previous month and P. falciparum case incidence during the previous month was fitted to morbidity data from the various areas. The model produced similar percentages of over-estimation (19·7% of predictions exceeded twice the observed values) and under-estimation (18·6% were less than half the observed values). Inclusion of maximum temperature did not improve the model. The model performed better in areas with relatively high or low incidence (>85% of the total variance explained) than those with moderate incidence (55–85% of the total variance explained). The study indicated that a dynamic immunity mechanism is needed in a prediction model. The potential usefulness and drawbacks of the modelling approach in studying the weather–malaria relationship are discussed, including a need for mechanisms that can adequately handle temporal variations in immunity to malaria.

Keywords

malariaPlasmodium falciparumhighlandepidemicclimateEthiopialinear mixed model
Type
Research Article
Information
Parasitology , Volume 128 , Issue 6 , June 2004 , pp. 585 - 593
Copyright
2004 Cambridge University Press

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References

REFERENCES

ABEKU, T. A., DE VLAS, S. J., BORSBOOM, G., TEKLEHAIMANOT, A., KEBEDE, A., OLANA, D., VAN OORTMARSSEN, G. J. & HABBEMA, J. D. F. ( 2002). Forecasting malaria incidence from historical morbidity patterns in epidemic-prone areas of Ethiopia: a simple seasonal adjustment method performs best. Tropical Medicine and International Health 7, 851–857.CrossRefGoogle Scholar
ABEKU, T. A., VAN OORTMARSSEN, G. J., BORSBOOM, G., DE VLAS, S. J. & HABBEMA, J. D. F. ( 2003). Spatial and temporal variations of malaria epidemic risk in Ethiopia: factors involved and implications. Acta Tropica 87, 331–340.CrossRefGoogle Scholar
BOUMA, M. J. & DYE, C. ( 1997). Cycles of malaria associated with El Niño in Venezuela. Journal of the American Medical Association 278, 1772–1774.CrossRefGoogle Scholar
BOUMA, M. J. & VAN DER KAAY, J. ( 1996). The El Niño Southern Oscillation and the historic malaria epidemics on the Indian subcontinent and Sri Lanka: an early warning system for future epidemics? Tropical Medicine and International Health 1, 86–96.Google Scholar
COX, J., CRAIG, M., LE SUEUR, D. & SHARP, B. ( 1999). Mapping Malaria Risk in the Highlands of Africa. MARA/HIMAL Technical Report.
CRAIG, M. H., SNOW, R. W. & LE SUEUR, D. ( 1999). A climate-based distribution model of malaria transmission in Sub-Saharan Africa. Parasitology Today 15, 105–111.CrossRefGoogle Scholar
FONTAINE, R. E., NAJJAR, A. E. & PRINCE, J. S. ( 1961). The 1958 malaria epidemic in Ethiopia. American Journal of Tropical Medicine and Hygiene 10, 795–803.CrossRefGoogle Scholar
FREEMAN, T. & BRADLEY, M. ( 1996). Temperature is predictive of severe malaria years in Zimbabwe. Transactions of the Royal Society of Tropical Medicine and Hygiene 90, 232.CrossRefGoogle Scholar
HAY, S. I. & LENNON, J. J. ( 1999). Deriving meteorological variables across Africa for the study and control of vector-borne diseases: a comparison of remote sensing and spatial interpolation of climate. Tropical Medicine and International Health 4, 58–71.CrossRefGoogle Scholar
HAY, S. I., OMUMBO, J. A., CRAIG, M. H. & SNOW, R. W. ( 2000). Earth observation, geographic information systems and Plasmodium falciparum malaria in Sub-Saharan Africa. Advances in Parasitology 47, 173–215.CrossRefGoogle Scholar
HAY, S. I., SNOW, R. W. & ROGERS, D. J. ( 1998). Predicting malaria seasons in Kenya using multitemporal satellite sensor data. Transactions of the Royal Society of Tropical Medicine and Hygiene 92, 12–20.CrossRefGoogle Scholar
KILIAN, A. H. D., LANGI, P., TALISUNA, A. & KABAGAMBE, G. ( 1999). Rainfall pattern, El Niño and malaria in Uganda. Transactions of the Royal Society of Tropical Medicine and Hygiene 93, 22–23.CrossRefGoogle Scholar
LINDSAY, S. W. & MARTENS, W. J. M. ( 1998). Malaria in the African highlands: past, present and future. Bulletin of the World Health Organization 76, 33–45.Google Scholar
LOEVINSOHN, M. E. ( 1994). Climate warming and increased malaria incidence in Rwanda. The Lancet 343, 714–718.CrossRefGoogle Scholar
MYERS, M. F., ROGERS, D. J., COX, J., FLAHAULT, A. & HAY, S. ( 2000). Forecasting disease risk for increased epidemic preparedness in public health. Advances in Parasitology 47, 309–330.CrossRefGoogle Scholar
MACDONALD, G. ( 1957). The Epidemiology and Control of Malaria. Oxford: Oxford University Press.
MOLINEAUX, L. ( 1988). Malaria: the epidemiology of human malaria as an explanation of its distribution, including some implications for control. In Principle and Practice of Malariology ( ed. Wernsdorfer, W. H. & McGregor, I. ), pp. 913–998. Churchill Livingstone, Edinburgh.
NÁJERA, J. A., KOUZNETZSOV, R. L. & DELACOLLETTE, C. ( 1998). Malaria Epidemics: Detection and Control, Forecasting and Prevention. World Health Organization document: WHO/MAL/98.1084.
SAS INSTITUTE INC. ( 1999). SAS/STAT® User's Guide, Version 8. Cary, NC: SAS Institute Inc., USA.
SWAROOP, S. ( 1949). Forecasting of epidemic malaria in the Punjab, India. American Journal of Tropical Medicine 29, 1–17.CrossRefGoogle Scholar
THOMSON, M. C. & CONNOR, S. J. ( 2001). The development of malaria early warning systems for Africa. Trends in Parasitology 17, 438–445.CrossRefGoogle Scholar
TULU, A. N. ( 1996). Determinants of malaria transmission in the highlands of Ethiopia: The impact of global warming on morbidity and mortality ascribed to malaria. Ph.D. thesis. London School of Hygiene and Tropical Medicine, University of London.
VERBEKE, G. & MOLENBERGHS, G. ( 2000). Linear Mixed Models for Longitudinal Data. New York, Springer.