Model variations in predicting incidence of Plasmodium falciparum malaria using 1998-2007 morbidity and meteorological data from south Ethiopia

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

Is staying overnight in a farming hut a risk factor for malaria infection in a setting with insecticide-treated bed nets in rural Laos?

<p>Abstract</p> <p>Background</p> <p>Overnight stays in farming huts are known to pose a risk of malaria infection. However, studies reporting the risk were conducted in the settings of poor net coverage. This study sought to assess whether an overnight stay in a farming hut is associated with an increased risk of malaria infection if insecticide-treated bed nets (ITNs) are properly used.</p> <p>Methods</p> <p>A pair of cross-sectional surveys was carried out in the Lamarm district of Sekong province, Laos, in March (dry season) and August (rainy season) in 2008. Questionnaire-based interviews and blood examinations were conducted with farmers and their household members from three randomly selected villages in March (127 households, 891 people) and August (128 households, 919 people). Logistic regression analysis, adjusted for potential confounding factors, was used to assess the association between malaria infection status and frequency of overnight stays for the two weeks prior to the study in both the seasons.</p> <p>Results</p> <p>In March, 13.7% of participants reported staying overnight in a farming hut at least once in the previous two weeks. The percentage increased to 74.6% in August. Not only adults but also young children stayed overnight as often as adults. The use of an ITN the preceding night was common both in farming huts (66.3% in March, 95.2% in August), and in main residences (85.8% in March, 92.5% in August). Logistic regression analysis showed no statistical association between malaria infection status and frequency of overnight stays in farming huts in either study period. However, people sharing one family type net with five people or more were significantly more likely to have malaria than those sharing a net with up to two people in the dry season.</p> <p>Conclusions</p> <p>This study showed that staying overnight in farming huts was not associated with an increased risk of malaria infection in the setting where ITNs were widely used in farming huts. It suggests that malaria infection during overnight stays in farming huts might be preventable if ITNs are properly used in rural Laos.</p

Genotypes and in vivo resistance of Plasmodium falciparum isolates in an endemic region of Iran.

Mutations in the dihydrofolate reductase (DHFR) and dihydropteroate synthase (DHPS) genes of Plasmodium falciparum have been correlated with and used to detect antifolate treatment failure, such as sulfadoxine-pyrimethamine (SP), in regions endemic for malaria. To determine the association between molecular markers of SP resistance and in vivo drug resistance, a quick and simple technique that detects single nucleotide polymorphisms in the DHFR and DHPS genes, using PCR-ELISA and sequence-specific oligonucleotide probes, was applied to 53 isolates obtained from an in vivo study in Sistan and Baluchistan Province, in southeastern Iran. Overall, 11.3% of these isolates were obtained from patients with SP treatment failure. Four DHFR polymorphisms (codons 51, 59, 108, and 164) and five DHPS polymorphisms (codons 436, 437, 540, 581, and 613) were investigated. Mutations DHFR Asn-108, DHFR Arg-59, and DHPS 436-Ala/Phe were very common (100, 81.1, and 85%, respectively). Plasmodium falciparum was isolated from 96% of patients with at least two DHFR/DHPS mutations. All resistant isolates had at least three mutations. The high prevalence of mutation associated with antifolate resistance may point toward low drug efficacy in the future