Does socio-economic status explain the differentials in malaria parasite prevalence? Evidence from The Gambia.

BACKGROUND: Malaria is commonly associated with poverty. Macro-level estimates show strong links between malaria and poverty, and increasing evidence suggests that the causal link between malaria and poverty runs in both directions. However, micro-level (household and population) analyses on the linkages between malaria and poverty have often produced mixed results. METHODS: The Gambia Malaria Indicator Survey (MIS) 2010/11 was carried out between November 2010 and January 2011. Laboratory-confirmed malaria and wealth quintiles were used to assess the association of socio-economic status and malaria infection in children and the general population. Simple and multiple logistic regressions and survey data analysis procedures, including linearized standard errors to account for cluster sampling and unequal selection probabilities were applied. RESULTS: Children (six to 59 months) from the second, third, fourth and richest quintiles were significantly less likely to have malaria compared to children from the poorest quintiles. Children (five to 14 years) from the fourth and richest quintiles were also significantly less likely to have malaria compared to those from the poorest quintiles. The malaria burden has shifted from the under-five children (six to 59 months) to children aged five to 14 years. Malaria prevalence was significantly higher in the Central River Region compared to the Upper River Region; and males bear the malaria brunt more than females. Children (six to 59 months) and children (five to 14 years) living in houses with poor walls, floors, roofs and windows were significant associated with higher prevalence of malaria. However, in the general population, only poor wall housing materials were associated with higher prevalence of malaria. CONCLUSIONS: Investments in strategies that address socio-economic disparities and improvements in the quality of housing could, in the long term, significantly reduce the malaria burden in the poorest communities.

Malaria stratification, climate, and epidemic early warning in Eritrea.

Eritrea has a successful malaria control program, but it is still susceptible to devastating malaria epidemics. Monthly data on clinical malaria cases from 242 health facilities in 58 subzobas (districts) of Eritrea from 1996 to 2003 were used in a novel stratification process using principal component analysis and nonhierarchical clustering to define five areas with distinct malaria intensity and seasonality patterns, to guide future interventions and development of an epidemic early warning system. Relationships between monthly clinical malaria incidence by subzoba and monthly climate data from several sources, and with seasonal climate forecasts, were investigated. Remotely sensed climate data were averaged over the same subzoba geographic administrative units as the malaria cases. Although correlation was good between malaria anomalies and actual rainfall from ground stations (lagged by 2 months), the stations did not have sufficiently even coverage to be widely useful. Satellite derived rainfall from the Climate Prediction Center Merged Analysis of Precipitation was correlated with malaria incidence anomalies, with a lead time of 2-3 months. NDVI anomalies were highly correlated with malaria incidence anomalies, particularly in the semi-arid north of the country and along the northern Red Sea coast, which is a highly epidemic-prone area. Eritrea has 2 distinct rainy seasons in different parts of the country. The seasonal forecasting skill from Global Circulation Models for the June/July/August season was low except for the Eastern border. For the coastal October/November/December season, forecasting skill was good only during the 1997-1998 El Niño event. For epidemic control, shorter-range warning based on remotely sensed rainfall estimates and an enhanced epidemic early-detection system based on data derived for this study are needed.