The Indian Ocean Dipole and malaria risk in the highlands of western Kenya

Abstract

Epidemics of malaria in the East African highlands in the last 2 decades have often been associated with climate variability, particularly the El Niño-Southern Oscillation (ENSO). However, there are other factors associated with malaria risk and there is increased interest in the influences of the Indian Ocean Dipole (IOD), a climate mode of coupled ocean-atmosphere variability, on East African rainfall. This study explores the relationship between IOD and the number of malaria patients in 7 hospitals from 2 districts in the western Kenyan highlands, controlling for the effects of ENSO. We examined temporal patterns (1982-2001) in the number of malaria cases in relation to the dipole mode index (DMI), defined as the difference in sea surface temperature anomaly between the western (10 degrees S-10 degrees N, 50 degrees-70 degrees E) and eastern (10 degrees S-0 degrees, 90 degrees-110 degrees E) tropical Indian Ocean. We used Poisson regression models, adjusted for ENSO index Niño 3 region (NINO3), seasonal and interannual variations. The number of malaria patients per month increased by 3.4%-17.9% for each 0.1 increase above a DMI threshold (3-4 months lag). Malaria cases increased by 1.4%-10.7% per month, for each 10 mm increase in monthly rainfall (2-3 months lag). In 6 of 7 places, there was no evidence of an association between NINO3 and the number of malaria cases after adjusting for the effect of DMI. This study suggests that the number of malaria cases in the western Kenyan highlands increases with high DMI in the months preceding hospital visits.

Fig. 1.

Time series for the number of malaria inpatients each month in the Kapsabet district hospital, the rainfall in Kapsabet, the dipole mode index (DMI), and the NINO3 SST anomaly (NINO3), 1982–1999. The number of malaria inpatients each month and rainfall are represented by a standardized anomaly relative to the 1982–1999 mean for each variable (The standardized anomaly was calculated only for descriptive analysis and raw data were used for regression analysis).

Fig. 2.

Relationships between the relative risk (RR) of malaria scaled to the mean monthly number of inpatients in Kapsabet (A) and Kisii (B) district hospitals and the dipole mode index (DMI) (lag 3–4 months), and the rainfall (lag 2–3 months). The middle line in each graph shows the estimated spline curve, and the top and bottom lines represent 95% confidence limits.

Fig. 3.

Relationships between rainfall scaled as relative risk to the mean monthly rainfall in Kapsabet (1982–1999) (A) and Kisii (1986–2000) (B) and the dipole mode index (DMI) (lag 1 month). The center line in each graph shows the estimated spline curve, and the Upper and Lower lines represent 95% confidence limits.

Fig. 4.

Locations of the 7 study hospitals in the highlands of western Kenya.