Robert W. Snow

BACKGROUND: About 90 percent of the deaths from malaria are in African children, but criteria to guide the recognition and management of severe malaria have not been validated in them. METHODS: We conducted a prospective study of all children admitted to the pediatric ward of a Kenyan district hospital with a primary diagnosis of malaria. We calculated the frequency and mortality rate for each of the clinical and laboratory criteria in the current World Health Organization (WHO) definition of severe malaria, and then used logistic-regression analysis to identify the variables with the greatest prognostic value. RESULTS: We studied 1844 children (mean age, 26.4 months) with a primary diagnosis of malaria. Not included were 18 children who died on arrival and 4 who died of other causes. The mortality rate was 3.5 percent (95 percent confidence interval, 2.7 to 4.3 percent), and 84 percent of the deaths occurred within 24 hours of admission. Logistic-regression analysis identified four key prognostic indicators: impaired consciousness (relative risk, 3.3; 95 percent confidence interval, 1.6 to 7.0), respiratory distress (relative risk, 3.9; 95 percent confidence interval, 2.0 to 7.7), hypoglycemia (relative risk, 3.3; 95 percent confidence interval, 1.6 to 6.7), and jaundice (relative risk, 2.6; 95 percent confidence interval, 1.1 to 6.3). Of the 64 children who died, 54 were among those with impaired consciousness (n = 336; case fatality rate, 11.9 percent) or respiratory distress (n = 251; case fatality rate, 13.9 percent), or both. Hence, this simple bedside index identified 84.4 percent of the fatal cases, as compared with the 79.7 percent identified by the current WHO criteria. CONCLUSIONS: In African children with malaria, the presence of impaired consciousness or respiratory distress can identify those at high risk for death.

Mobile Phone “Hot Spots” An obstacle to developing effective national malaria control programs is a lack of understanding of human movements, which are an important component of disease transmission. As mobile phones have become increasingly ubiquitous, it is now possible to collect individual-level, longitudinal data on human movements on a massive scale. Wesolowski et al. (p. 267 ) analyzed mobile phone call data records representing the travel patterns of 15 million mobile phone owners in Kenya over the course of a year. This was combined with a detailed malaria risk map, to estimate malaria parasite movements across the country that could be caused by human movement. This information enabled detailed analysis of parasite sources and sinks between hundreds of local settlements. Estimates were compared with hospital data from Nairobi to show that local pockets of transmission likely occur around the periphery of Nairobi, accounting for locally acquired cases, contrary to the accepted idea that there is no transmission in the capital.