Variation in Malaria Transmission in Southern Ethiopia. The impact of prevention strategies and a need for targeted intervention

Background: Malaria transmission is complex and is believed to be associated with local climate changes. However, simple attempts to extrapolate malaria incidence rates from averaged regional meteorological conditions have proven unsuccessful. Therefore, the objective of this study was to determine if variations in specific meteorological factors are able to consistently predict P. falciparum malaria incidence at different locations in south Ethiopia. Methods: Retrospective data from 42 locations were collected including P. falciparum malaria incidence for the period of 1998-2007 and meteorological variables such as monthly rainfall (all locations), temperature (17 locations), and relative humidity (three locations). Thirty-five data sets qualified for the analysis. Ljung-Box Q statistics was used for model diagnosis, and R squared or stationary R squared was taken as goodness of fit measure. Time series modelling was carried out using Transfer Function (TF) models and univariate auto-regressive integrated moving average (ARIMA) when there was no significant predictor meteorological variable. Results: Of 35 models, five were discarded because of the significant value of Ljung-Box Q statistics. Past P. falciparum malaria incidence alone (17 locations) or when coupled with meteorological variables (four locations) was able to predict P. falciparum malaria incidence within statistical significance. All seasonal AIRMA orders were from locations at altitudes above 1742 m. Monthly rainfall, minimum and maximum temperature was able to predict incidence at four, five and two locations, respectively. In contrast, relative humidity was not able to predict P. falciparum malaria incidence. The R squared values for the models ranged from 16% to 97%, with the exception of one model which had a negative value. Models with seasonal ARIMA orders were found to perform better. However, the models for predicting P. falciparum malaria incidence varied from location to location, and among lagged effects, data transformation forms, ARIMA and TF orders. Conclusions: This study describes P. falciparum malaria incidence models linked with meteorological data. Variability in the models was principally attributed to regional differences, and a single model was not found that fits all locations. Past P. falciparum malaria incidence appeared to be a superior predictor than meteorology. Future efforts in malaria modelling may benefit from inclusion of non-meteorological factors. Background Over 100 million people worldwide are affected by malaria and P. falciparum malaria is responsible for approximately one million deaths annually, with many of those deaths occurring in children under the age of five years. Unfortunately, 90% of the global malarial burden is carried by sub-Saharan Africa [1,2]. Malaria transmission is complex and not yet fully understood; the recent focus of developed nations on global warming has spawned the suspicion of a climate-malaria link. The possible association of changes in temperatures to variations in malaria epidemiology is merited by the welldefined biological effects on life-cycle stages of the * Correspondence: eskindir_loha@yahoo.com 1 Department of Public and Environmental Health, Hawassa University, Ethiopia Full list of author information is available at the end of the article Loha and Lindtjørn Malaria Journal 2010, 9:166 http://www.malariajournal. com/content/9/1/166 Page 2 of 8 Anopheles insect transmission vector and the Plasmodium malaria parasite [3]. For example, increasing of the temperature to 31°C results in a shortened sporogonic period of the Plasmodium parasite, an effect which differs among the P. falciparum and P. vivax species. Higher mean daily temperatures are not favourable for vector survival since increased temperatures speed up development of the aquatic stages of the vector's life cycle. Many researchers, therefore, have proposed developing improved tools to forecast malaria epidemics by using variations in regional temperatures. These efforts have resulted in the medical literature using vastly inconsistent terminology to describe malaria risks, and to distinguish between long-term forecasts, early warning and early detection of epidemics. Long-term epidemic forecasting is usually based on climate forecasting, and relies on such datasets as the El Niño Southern Oscillation indices to predict epidemic risk months in advance over large geographical areas. Such a forecast allows time for the population to prepare for a possible epidemic in the upcoming malaria season. Malaria epidemic early warning is based on surveying transmission risks to predict timing of an increase based on abnormal rainfall or temperatures. Often, such risks are influenced by population vulnerability, such as history of low rates of malaria transmission. Such predictions of malaria epidemics can provide lead times of weeks to months. The long-term and early warning approaches should, however, be distinguished from epidemic early detection, which involves noting the beginning of an unusual epidemic. As such, this surveillance approach is limited in that is offers little lead time (days to weeks) for preparation and implementation of preventive measures. When used in an effective manner, it is able to prevent sickness and death. The aim of this study was to examine if the spatio-temporal distribution of surface temperature and rainfall are useful factors to predict changes in malaria incidence, as a malaria epidemic early warning strategy. This evaluation was based on an assumption that the link between climate and occurrence of malaria is constant and similar for different regional settings. Incorporating prediction and forecasting approaches, however, calls for sound understanding of the complex factors involved in malaria transmission. It has been suggested that the major driving force of malaria transmission is climate [4-8]. However, the data has been largely inconsistent as to exactly how climate influences malaria transmission. In some geographic regions, the minimum temperature has been shown as an important contributory factor for malaria transmission [6,7,9], while in others the maximum temperature has been implicated [5,1012]. Onset of malaria epidemics often coincide with the rainy season or significant rainfall [13,14], but this is not always the case [9]. Inconsistent findings also exist in studies focusing on the number of climate lags, both for rainfall and temperature, associated with malaria epidemics [5-7]. Prediction strategies based on climate information have been most accurate when considering colder locations [6]; still, not all studies have been able to confirm the utility of meteorological variables at varying altitudes [7]. The effect of rainfall on transmission rates has also been found to vary between urban and rural areas [6], suggesting the presence of an additional cofounding factor in one of both of these communities. To date, many different models have been developed based on the simple assumption that a defined set of climatic variables influence malaria incidence; however, the models have different statistical or mathematical forms, incorporate different variables and lag combinations, and demanded different forms of data transformation and analysis [5-8]. This might reflect the complexity between climatic variables and malaria transmission [15] while striving to address biological plausibility. Unfortunately, such biological approaches are able to describe malaria transmission but are not powerful enough to yield reliable predictions of incidence [16]. This is also a limitation that affects implementation of climate-based malaria early warning and forecasting [17]. Attempts have been made to improve the models using historical morbidity and climatic variables [6-8,12]. Nonetheless, the impact of climate on malaria transmission has yet to be firmly established. Thus, there exists a need to consider local variations in climates in order to fully understand the relationship between climate and malaria transmission [11,15,16,18-23]. Taking the average of conditions across large geographic areas, or even making similar assumptions stating the effects of climatic variables to be constant across different locations [7], might cause an underestimation of local variations and disable a models accurate ability to predict malaria incidence [16,17]. In addition to the incorporation of climatic causes, some researchers have suggested building models that consider non-climatic factors such as land use, population movement, immunity, topography, parasite genotypes, vector composition, drug resistance, vector control measures and availability of healthcare services [68,18,20,24]. The study presented herein did not include non-meteorological data because of limited data availability; however, local variations were considered in the link between malaria incidence and meteorological factors [10,18,20]. In areas such as the Ethiopian highlands endemic malaria occurs at altitudes above 2100 m [25]. It has been suggested that global warming will drive malaria transmission at higher altitudes, mainly because of corresponding changes in the distribution of the Anopheles Loha and Lindtjørn Malaria Journal 2010, 9:166 http://www.malariajournal. com/content/9/1/166 Page 3 of 8 vector [26]. However, the study period covered less than 10 years and, thus, was too brief to evaluate the potential effects of global warming on vector distribution and on malaria incidence. Therefore, the principal objective of this study was to explore if variations of meteorological factors are able to consistently predict P. falciparum malaria incidence at different locations in south Ethiopia. Methods Data inputs and inclusion criteria A total of 42 locations in the southern region of Ethiopia were examined for data on varying serial length of P. falciparum malaria incidence during 1998-2007; available data from local meteo