^{1}

Recent epidemics of

Highland malaria has returned to the tea estates of western Kenya after an absence of nearly 30 years (

We investigated whether climate changes could be implicated in the reemergence of malaria in a unique 30-year malaria and meteorologic time series, collected from the health-care system on a tea plantation in the western highlands of Kenya. Our detailed substudy included site-specific meteorologic and malariometric data from a larger analysis of trends in meteorologic conditions across East Africa from 1911 to 1995 (

Long-term malaria illness and total hospital admissions data (January 1966–December 1995) exist from a large tea plantation in Kericho, Kenya, which is operated by Brooke Bond Kenya Ltd. (^{2} and ranges from 1,780 to 2,225 m above mean sea level. Epidemic malaria was first recorded on the Kericho tea estates during World War II and was eventually controlled by a combination of mass administration of proguanil and residual insecticide spraying during the late 1940s (

Two meteorologic datasets were compiled. Point locality measurements of mean monthly temperature (°C) and monthly total rainfall (mm) were obtained from the Tea Research Foundation meteorologic station on the Kericho tea estates for the 1966–1995 period. Climate data were also obtained from a global 0.5 x 0.5° (approximately 55 x 55 km [3,025 km^{2}] at the equator) gridded dataset of monthly terrestrial surface climate for the 1966–1995 period (33,34) (available from: URL:

To investigate whether a combination of meteorologic conditions was changing and thus facilitating the resurgence of malaria, we also categorized months as suitable for

To test for trends in the climate and malaria suitability time series, we estimated the following regression equation:_{t} = α + β t + γ y_{t-1} + _{i} Δy_{t-1} + _{j} d_{j} + ε_{t}_{j} are regression parameters; ε_{t} is a normally distributed error term with mean zero; and t is a deterministic time trend. The centered dummy variables d_{j} model the monthly seasonal variations in climate. The coefficients μ_{j} sum to zero. Δ is the first difference operator. The lagged values of the dependent variable model the serial correlation in the dependent variable. We chose the number of lags, p, using the adjusted R-square statistic. The maximal number of lags p considered was 24.

If the time series y can be characterized as the sum of a stationary stochastic process and a linear time trend, then the appropriate test for the trend is a t test on β in (

If γ =0 (a unit root in the autoregressive process) and β =0, then y is a random walk. The random walk may also have a deterministic drift term (α≠0). In either case, however, the series is nonstationary, and classical regression inference does not apply. The nonstandard distributions of α, β, and γ have been tabulated by Dickey and Fuller (

Variable | p | ADF^{c} | β | t | p value^{c} | τα | Q | Sig. Q |
---|---|---|---|---|---|---|---|---|

Malaria incidence | 5 | 0.0238 | 0.0133 | 0.1801 | 58.7394 | 0.0097 | ||

Total admissions | 6 | -2.76 | -0.0069 | -0.28 | 0.7820 | -0.4151 | 30.9302 | 0.7083 |

Tmean met. stat. (^{o}C) | 8 | -3.41 | 0.0004 | 1.76 | 0.0799 | -0.0211 | 40.8630 | 0.2653 |

Rain met. stat. (mm) | 1 | -0.0202 | -0.52 | 0.6066 | -0.0074 | 43.3753 | 0.1858 | |

Tmean clim. (^{o}C) | 1 | 0.0035 | 1.60 | 0.1103 | -0.0980 | 46.6888 | 0.1094 | |

Tmax clim. (^{o}C) | 24 | 0.0070 | 1.68 | 0.0935 | 0.0592 | 22.6634 | 0.9592 | |

Tmin clim. (^{o}C) | 1 | 0.0038 | 1.55 | 0.1233 | -0.1944 | 45.1424 | 0.1412 | |

Precipitation clim. (mm) | 1 | -0.0098 | -0.36 | 0.7205 | -0.0745 | 34.2984 | 0.5497 | |

Vapor pressure clim. (hPa) | 1 | 0.0038 | 1.66 | 0.0974 | -0.1829 | 45.5674 | 0.1318 | |

Garnham suitability (mo)^{d} | 4 | -0.0380 | - | 0.3850 | -0.4488 | 5.6658 | 0.7729 |

^{a}Tmean, the mean monthly temperature; Tmax, the mean of maximum monthly temperatures; Tmin, the mean of minimum monthly temperatures; met. stat., meteorologic station data from the Kericho tea estate; clim., data derived from the global gridded climatology dataset (^{b}Figures in bold denote significance at the 5% level. p is the number of lagged differenced dependent variables selected.
^{c}ADF, the Augmented Dicke-Fuller t-test for γ=0. The 5% critical value is -3.45. Exact p values are not available for ADF and τα statistics. The distribution of the t statistic for the slope parameter β has the standard t distribution under the assumption that γ<0. τα is the t statistic for the intercept term in the autoregression without a linear time trend. This test is the appropriate one for a trend if γ=0. Its 5% critical value is 2.54. The Q statistic is a portmanteau test for general serial correlation and is distributed as chi square (^{d}Garnham suitability (

We also regressed temperature and rainfall data from the meteorologic station at Kericho on the same variables from the interpolated climatology (

During the period 1966–1995, malaria incidence increased significantly (p=0.0133) while total (i.e., malarial and other) admissions to the tea estate hospital showed no significant change (

Malaria, hospital admissions, and meteorologic station data, Kericho tea estate, 1966–1995. Malaria incidence (a) total hospital admissions (b) mean monthly temperature (c) and total monthly rainfall (d) are all plotted with a 25-point (month) moving average (bold) to show the overall movement in the data. The significance of these movements is presented in

Climate and malaria suitability data for the Kericho area from the global gridded climatology data, including meteorologic and malaria suitability time series. Minimum (bottom), mean (middle) and maximum (top) monthly temperature (a) total monthly precipitation (b) and mean vapor pressure (c) are all plotted with a 25-point (month) moving average (bold) to show the overall movement in the data. The number of months per year suitable for malaria transmission (d) are also plotted. Suitability was determined if rainfall exceeded 152 mm and temperature exceeded 15°C in any month (

Results were very similar, though significance levels varied, between the three formulations of the regression model that compared the local meteorologic station data and those from the interpolated climatology data (

The resurgence of

Malaria incidence increased significantly (p=0.0133) during the 1966–1995 period, while total admissions remained unchanged. Besides an increase in local malaria transmission, two other factors may have influenced the increase in malaria hospitalizations. An increase in malaria severity indicated by an increased case-fatality rate (from 1.3% in the 1960s to 6% in the 1990s) is most likely linked to chloroquine resistance, which we believe to be the probable cause of much of the overall increase in malaria transmission (

All climate variables, whether from the Kericho tea estate meteorologic station or the pixel covering Kericho in the global climatology dataset showed no significant trends, despite the fact that equivalence tests showed some significant differences between the temperature time series—findings that are in agreement with a broader geographic analysis of East African data from 1911 to 1995 (

The attraction of the global warming hypothesis as an explanation of highland malaria is the existence of a continental trend toward global warming coincident with a trend toward increasing malaria incidence in several parts of Africa, ranging from Senegal (

We do not argue that meteorologic conditions have no immediate impact on the seasonal dynamics and incidence of malaria or that climate change is probably not an important future concern in public health. Rather we urge some caution in the interpretation of synonymous changes in climate over wider areas and local changes in malaria incidence.

The authors acknowledge the management and staff of Brooke Bond Kenya Ltd. and its Central Hospital in Kericho, whose outstanding medical system made this study possible, and the support of the Kenya Medical Research Institute in Nairobi, Kenya. We also thank Wilson K. Ngetich for supplying the local meteorologic data.

SIH is currently supported as an advanced training fellow by the Welcome Trust (#056642). RWS is a senior Wellcome Trust fellow (#033340).

1 Dr. Biomndo is deceased.

Col. G. Dennis Shanks is the former director of the U.S. Army Component of the Armed Forces Institute of Medical Research in Bangkok, Thailand, which is a part of the Walter Reed Army Institute of Research. He is a physician trained in pediatrics and tropical medicine whose main professional interests are malaria chemotherapy, malaria epidemiology, and clinical trials in developing countries.