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Research
Meteorologic Influences on
Plasmodium falciparum Malaria in the Highland Tea Estates of Kericho,
Western Kenya
G. Dennis Shanks,* Simon I. Hay,†‡ David I. Stern,§ Kimutai Biomndo,¶1
Robert W. Snow†‡
*U.S. Army Medical Research Unit–Kenya, Nairobi, Kenya; †University of Oxford,
Oxford, U.K.; ‡Kenya Medical Research Institute/Wellcome Trust Collaborative
Programme, Nairobi, Kenya; §Rensselaer Polytechnic Institute, Troy, New
York, USA; and ¶Brooke Bond Central Hospital, Kericho, Kenya
Suggested citation for this article: Shanks GD,
Hay SI, Stern DI,Biomndo K, Snow RW, et al.. Meteorologic Influences
on Plasmodium falciparum Malaria in the Highland Tea Estates of Kericho,
Western Kenya. Emerg Infect Dis [serial online] 2002 Dec [date cited];8.
Available from: URL: http://www.cdc.gov/ncidod/EID/vol8no12/02-0077.htm
Recent epidemics
of Plasmodium falciparum malaria have been observed in high-altitude
areas of East Africa. Increased malaria incidence in these areas of
unstable malaria transmission has been attributed to a variety of changes
including global warming. To determine whether the reemergence of malaria
in western Kenya could be attributed to changes in meteorologic conditions,
we tested for trends in a continuous 30-year monthly malaria incidence
dataset (1966–1995) obtained from complete hospital registers at a Kenyan
tea plantation. Contemporary monthly meteorologic data (1966–1995) that
originated from the tea estate meteorologic station and from global
climatology records were also tested for trends. We found that total
hospital admissions (malaria and nonmalaria) remained unchanged
while malaria admissions increased significantly during the period.
We also found that all meteorologic variables showed no trends
for significance, even when combined into a monthly suitability
index for malaria transmission. We conclude that climate changes have
not caused the highland malaria resurgence in western Kenya.
Highland malaria has returned to the tea estates of western Kenya after
an absence of nearly 30 years (1-3). Altitude and weather
influence malaria epidemiology in highland areas because of the slowing
of parasite development within the anopheline vectors at lower temperatures
(4). Increased malaria incidence in unstable transmission
areas has been variously attributed to changes in land-use patterns (5);
population migration (6,7); changes in mosquito vector
populations (8); breakdown in provision of health services
(9), especially insecticide spraying (10,11);
drug resistance (12-16); and meteorologic
changes (17,18), particularly global
warming (19-25).
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 (26-28).
Our previous studies have also examined various aspects of the epidemiology
of malaria in the Kenyan highlands (29,30).
Methods
Meteorologic Data
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 km2]
at the equator) gridded dataset of monthly terrestrial surface climate
for the 1966–1995 period (33,34) (available from: URL: http://www.cru.uea.ac.uk/link).
The dataset was used to ensure that results from the single meteorologic
station were in agreement with data from a wider geographic area; this
procedure also allowed a wider range of climate variables, including temperature
extremes, to be tested. Primary variables of precipitation (mm), mean
temperature (°C), and diurnal temperature range (°C) were available and
interpolated from extensive meteorologic station data by using angular
distance-weighted averaging of anomaly fields. The secondary variable
of vapor pressure was also provided, interpolated where available, and
calculated from primary variables, when the coverage of meteorologic stations
was insufficient. Minimum and maximum monthly temperature estimates were
created by subtracting or adding, respectively, half the diurnal temperature
range from mean monthly temperature. Time series were derived by using
an extraction routine developed in ENVI (Research Systems Inc., Boulder,
CO) with georeferencing information for Kericho (0.33°S, 35.37°E), obtained
from Encarta (Microsoft, Seattle, WA).
To investigate whether a combination of meteorologic conditions was changing
and thus facilitating the resurgence of malaria, we also categorized months
as suitable for Plasmodium falciparum transmission if they had
a mean monthly temperature exceeding 15°C (since temperatures experienced
by the indoor resting Anopheles gambiae vectors are likely
to be 3°C–5°C higher) and monthly rainfall totals exceeding 152 mm (1,4)
by using the gridded climatology data. The numbers of suitable months
for transmission were summed, totaled for each year, and tested for the
1966–1995 period.
Statistical Analyses
To test for trends in the climate and malaria suitability time series,
we estimated the following regression equation:
Dyt = +
t + yt-1
+
di Dyt-1
+
mj dj + et
(1)
where y is the variable of interest; ,
, ,
and µj's are regression parameters; et is
a normally distributed error term with mean zero; and t is a deterministic
time trend. The centered dummy variables dj model the monthly
seasonal variations in climate. The coefficients µj sum
to zero. D 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
in (1). If the series is a random walk, however, or a
more complex stochastically trending process, the critical levels for
the distribution of the t score in this regression are much greater than
usual (35), and alternative tests should be employed.
Since many climate time series contain a stochastically trending component
(36), the nature of the series must be explored before
testing for climate change. This methodology issue complicates the evaluation
of the significance of trends established with standard regression procedures
often used in such studies.
If =0 (a unit root
in the autoregressive process) and =0,
then y is a random walk. The random walk may also have a deterministic
drift term ( not equal
to 0). In either case, however, the series is nonstationary, and classical
regression inference does not apply. The nonstandard distributions of
, ,
and have been tabulated
by Dickey and Fuller (37,38). We first
tested for the presence of a unit root by evaluating the t statistic for
against its nonstandard
distribution. The critical value for this so-called Augmented Dickey-Fuller
at the 5% level is -3.45. Values of the t statistic for
more negative than this critical value indicate that the series is not
a random walk and vice versa. If the null hypothesis is rejected, then
the t statistics associated with
and are normally
distributed. If the unit root hypothesis is accepted, then these statistics
also have nonstandard distributions. The correct test for a trend is then
the t test on
in (1) with the omission of the linear trend. The
test’s critical value at the 5% significance level is 2.54. The results
of these tests are presented in the Table.
We also regressed temperature and rainfall data from the meteorologic
station at Kericho on the same variables from the interpolated climatology
(33,34) by using a variety of formulations including
levels, logarithms, and a regression adjusted for heteroscedasticity.
We then tested whether the slope coefficients were significantly different
from unity, which should not be the case if the gridded dataset is a good
proxy for the climate at Kericho.
Results
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 (Table
and Figure 1a,b). Measurements of mean monthly
temperature and total monthly rainfall also showed no significant changes
(Table and Figure 1c,d).
Similar results were shown by the climatology data interpolated from
a wider area. Mean, maximum, and minimum monthly temperatures; precipitation;
and vapor pressure all demonstrated no significant trends (Table;
Figure 2a,b,c). Moreover, the interpolated climatology
data, when transformed into month of malaria transmission suitability
(1,4), again showed no significant changes (Table;
and Figure 2d).
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 (33,34). The coefficient for the regression of
the meteorologic station rainfall data on the interpolated climatology
precipitation data is in every case not significantly different from unity.
Significance levels are 10% for the model in levels, 18% for the heteroscedasticity-adjusted
model, and 96% for the logarithmic model. In the regression of the two
temperature series, however, the coefficient is significantly different
from unity in every case, as is a joint test statistic for the two slope
coefficients.
Discussion
The resurgence of P. falciparum malaria in the East African highlands
(3,8,18,26,
40-44) has led several researchers
to speculate that climate change is a predominant cause (23,45-50).
On the basis of these studies, which have been disputed by experts in
vector-borne disease biology (10,27-29,
51,52), and some biological modeling,
which has been robustly criticized (53), the International
Panel on Climate Change has recently concluded with “medium-to-high confidence”
that there will be a net increase in the range and incidence of malaria
(49); the results of our work do not support these conclusions.
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 (32). Travel to and from the Lake
Victoria region by a minority of the tea estate workers also exerts an
upward influence on malaria transmission in Kericho since such travel
increases the numbers of workers asymptomatically carrying gametocytes,
which infect mosquitoes for further human infection. This complex topic
is the subject of a future publication.
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 (26) and lend support to the
appropriateness of interpolated climate data for use in these investigations.
We also think that, when examining trends in meteorologic phenomena, epidemiologists
should use more robust statistical techniques for the reasons outlined
in the methods. The results of this detailed examination of coincident
empirical data do not support the widespread, recent speculation regarding
malaria resurgences in response to climate change. No aspect of climate
has changed significantly—neither the temperature extremes (maximum and
minimum) nor the periods when meteorologic data were transformed into
months when malaria transmission is possible. Further study has also shown
that variability in these meteorologic variables, independent of any longer
term trends, has decreased (54). We must therefore look
elsewhere for the causes of these resurgences (27,28,32).
These factors are likely to vary. In Kericho, however, increased chloroquine
resistance has been strongly argued to be the cause, since all other relevant
environmental and sociologic factors are unchanged (32).
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 (13,14)
to Madagascar (10). Where such malaria increases have
been examined in detail, however, alternative explanations such as discontinuation
of anti-vector measures in Madagascar (10) or chloroquine
resistance in Senegal appear to be more likely causes (13,14).
Malaria epidemiology is greatly influenced by a range of local factors,
making a consistent continent-wide explanation seem unlikely (28,52).
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.
Acknowledgments
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 Wellcome
Trust (#056642). RWS is a senior Wellcome Trust fellow (#033340).
Col. 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.
References
- Garnham PCC. Malaria epidemics at exceptionally high
altitudes in Kenya. BMJ 1945;11:45–7.
- Strangeways-Dixon D. Paludrine (proguanil) as a malarial prophylactic
amongst African labour in Kenya. East Afr Med J 1950;27:127–30.
- Malakooti MA, Biomndo K, Shanks GD. Reemergence
of epidemic malaria in the highlands of western Kenya. Emerg Infect
Dis 1998;4:671–6.
- Garnham PCC. The incidence of malaria at high altitudes. Journal of
the National Malaria Society 1948;7:275–84.
- Lindblade KA, Walker ED, Onapa AW, Katungu J, Wilson ML. Land
use change alters malaria transmission parameters by modifying temperature
in a highland area of Uganda. Trop Med Int Health 2000;5:263–74.
- Van der Stuyft P, Manirankunda L, Delacollette C. L'approche de risque
dans le diagnostic du paludisme-maladie en regions d'altitude. Annales
de la Société Belge de Médecine Tropicale 1993;73:81–9.
- Bashford G, Richens J. Travel to the coast by highlanders and its
implications for malaria control. Papua New Guinea Medical Journal 1992;35:306–7.
- Lindblade KA, Walker ED, Onapa AW, Katungu J, Wilson ML.
Highland malaria in Uganda: prospective analysis of an epidemic associated
with El Niño. Trans R Soc Trop Med Hyg 1999;93:480–7.
- Pitt S, Pearcy BE, Stevens RH, Sharipov A, Satarov K, Banatvala N.
War
in Tajikistan and re-emergence of Plasmodium falciparum.
Lancet 1998;352:1279.
- Mouchet J, Manguin S, Sircoulon J, Laventure S, Faye O, Onapa AW,
et al. Evolution
of malaria in Africa for the past 40 years: impact of climatic and human
factors. J Am Mosq Control Assoc 1998;14:121–30.
- Mouchet J. L'origine
des épidémies de paludisme sur les Plateaux de Madagascar et les montagnes
d'Afrique de L'est et du Sud. Bull Soc Pathol Exot 1998;91:64–6.
- Warsame M, Wernsdorfer WH, Huldt G, Björkman A. An
epidemic of Plasmodium falciparum malaria in Balcad, Somalia,
and its causation. Trans R Soc Trop Med Hyg 1995;89:142–5.
- Trape JF. Impact of chloroquine resistance on malaria mortality. Comptes
Rendus de l'Academie des Sciences, Paris 1998;321:689–97.
- Trape JF. The
public health impact of chloroquine resistance in Africa. Am J Trop
Med Hyg 2001;64:12–7.
- Bødker R, Kisinza W, Malima R, Msangeni H, Lindsay S. Resurgence of
malaria in the Usambara mountains, Tanzania, an epidemic of drug-resistant
parasites. Global Change and Human Health 2000;1:134–53.
- Etchegorry MG, Matthys F, Galinski M, White NJ, Nosten F.
Malaria epidemic in Burundi. Lancet 2001;357:1046–7.
- Brown V, Issak MA, Rossi M, Barboza P, Paugam A. Epidemic
of malaria in north-eastern Kenya. Lancet 1998;352:1356–7.
- van der Hoek W, Konradsen F, Perera D, Amerasinghe PH, Amerasinghe
FP. Correlation
between rainfall and malaria in the dry zone of Sri Lanka. Ann Trop
Med Parasitol 1997;91:945–9.
- Loevinsohn ME. Climatic
warming and increased malaria incidence in Rwanda. Lancet 1994;343:714–8.
- Bouma MJ, Dye C, Van der Kaay HJ. Falciparum
malaria and climate change in the northwest Frontier province of Pakistan.
Am J Trop Med Hyg 1996;55:131–7.
- Lindsay SW, Birley MH. Climate
change and malaria transmission. Ann Trop Med Parasitol 1996;90:573–88.
- Lindsay SW, Martens WJM. Malaria
in the African highlands: past, present and future. Bull World Health
Organ 1998;76:33–45.
- McMichael AJ, Haines A, Sloof R, Kovats S. Climate change and human
health. Geneva:World Health Organization; 1996.
- Martens P, Kovats RS, Nijhof S, de Vries P, Livermore MTJ, Bradley
DJ, et al. Climate change and future populations at risk of malaria.
Global Environmental Change 1999;9:89–107.
- National Research Council. Under the weather: climate, ecosystems,
and infectious disease. Washington: The Council; 2001.
- Hay SI, Cox J, Rogers DJ, Randolph SE, Stern DI, Shanks GD, et al.
Climate
change and the resurgence of malaria in the East African highlands.
Nature 2002;415:905–9.
- 2Hay SI, Cox J, Rogers DJ, Randolph SE, Stern DI, Shanks GD, et al.
East African highland malaria resurgence independent of climate change.
Directions in Science 2002;1:82–5.
- Hay SI, Rogers DJ, Randolph SE, Stern DI, Cox J, Shanks GD, et al.
Hot topic or hot air? Climate change and malaria resurgence in African
highlands. Trends Parasitol 2002;18:530-4.
- Hay SI, Noor AM, Simba M, Busolo M, Guyatt HL, Ochola SA, et al. The
clinical epidemiology of malaria in the highlands of Western Kenya.
Emerg Infect Dis 2002;8:543–8.
- Hay SI, Simba M, Busolo M, Noor AM, Guyatt HL, Ochola SA, et al. Defining
and detecting malaria epidemics in the highlands of western Kenya.
Emerg Infect Dis 2002;8:555–62.
- 3Hay SI, Myers MF, Burke DS, Vaughn DW, Endy T, Ananda
N, et al. Etiology
of interepidemic periods of mosquito-borne disease. Proc Natl Acad
Sci U S A 2000;97:9335–9.
- Shanks GD, Biomndo K, Hay SI, Snow RW. Changing
patterns of clinical malaria since 1965 among a tea estate population
located in the Kenyan highlands. Trans R Soc Trop Med Hyg 2000;94:253–5.
- New M, Hulme M, Jones P. Representing twentieth-century space-time
climate variability. Part I: development of a 1961-90 mean monthly terrestrial
climatology. Journal of Climatology 1999;12:829–57.
- New M, Hulme M, Jones P. Representing twentieth-century space-time
climate variability. Part II: development of 1901-1996 monthly grids
of terrestrial surface climate. Journal of Climatology 2000;13:2217–38.
- Granger CWJ, Newbold P. Spurious regressions in econometrics. Journal
of Econometrics 1974;2:111–20.
- Stern DI, Kaufmann RK. Detecting a global warming signal in hemispheric
temperature series: a structural time series analysis. Climatic Change
2000;47:411–38.
- Dickey DA, Fuller WA. Distribution of the estimators for autoregressive
time series with a unit root. Journal of the American Statistical Association
1979;74:427–31.
- Dickey DA, Fuller WA. Likelihood ratio statistics for autoregressive
processes. Econometrica 1981;49:1057–72.
- Box G, Pierce D. Distribution of autocorrelations in autoregressive
moving average time series models. Journal of the American Statistical
Association 1970;65:1509–26.
- Matola YG, White GB, Magayuka SA. The
changed pattern of malaria endemicity and transmission at Amani in the
eastern Usambara Mountains, north-eastern Tanzania. J Trop Med Hyg
1987;90:127–34.
- Marimbu J, Ndayiragije A, Le Bras M, Chaperon J.
Environment
and malaria in Burundi: apropos of a malaria epidemic in a non-endemic
mountainous region. Bull Soc Pathol Exot 1993;86:399–401.
- Some E. Effects
and control of highland malaria epidemic in Uasin Gishu District, Kenya.
East Afr Med J 1994;71:2–8.
- Tulu AN. Determinants of malaria transmission in the highlands of
Ethiopia: the impact of global warming on mortality and morbidity ascribed
to malaria. In: London School of Hygiene and Tropical Medicine. London:University
of London; 1996.
- Kilian AHD, Langi P, Talisuna A, Kabagambe G. Rainfall
pattern, El Niño and malaria in Uganda. Trans R Soc Trop Med Hyg
1999;93:22–3.
- Epstein PR, Diaz HF, Elias S, Grabherr G, Graham NE, Martens WJM,
et al. Biological and physical signs of climate change: focus on mosquito-borne
diseases. Bulletin of the American Meteorological Society 1998;79:409–17.
- Martens P. How will climate change affect human health? American Scientist
1999;87:534–41.
- Patz JA, Lindsay SW. New
challenges, new tools: the impact of climate change on infectious diseases.
Curr Opin Microbiol 1999;2:445–51.
- Bonora S, De Rosa FG, Boffito M, Di Perri G, Rossati A.
Rising temperature and the malaria epidemic in Burundi. Trends Parasitol
2001;17:572–3.
- McCarthy JJ, Canziani OF, Leary NA, Dokken DJ, White KS. Climate change
2001: impacts, adaptation, and vulnerability—contribution of Working
Group II to the Third Assessment Report of the Intergovernmental Panel
on Climate Change. Cambridge: Cambridge Univ. Press; 2001.
- Patz JA, Reisen WK.
Immunology, climate change and vector-borne diseases. Trends Immunol
2001;22:171–2.
- Reiter P. Global-warming
and vector-borne disease in temperate regions and at high altitude.
Lancet 1998;351:839.
- Reiter P. Climate
change and mosquito-borne disease. Environ Health Perspect 2001;109:141–61.
- Rogers DJ, Randolph SE. The
global spread of malaria in a future, warmer world. Science 2000;289:1763–6.
- Rogers DJ, Randolph SE, Snow RW, Hay SI. Satellite
imagery in the study and forecast of malaria. Nature 2002;415:710–5.
Table. Trend of malaria,
climate, and malaria suitability variables, Kericho tea estates,
1966–1995a,b
|
|
Variable
|
p
|
ADFc
|
b
|
t
|
p valuec
|
ta
|
Q
|
Sig. Q
|
|
Malaria incidence
|
5
|
-4.00
|
0.0238
|
2.49
|
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. (°C)
|
8
|
-3.41
|
0.0004
|
1.76
|
0.0799
|
-0.0211
|
40.8630
|
0.2653
|
Rain met. stat. (mm)
|
1
|
-11.91
|
-0.0202
|
-0.52
|
0.6066
|
-0.0074
|
43.3753
|
0.1858
|
Tmean clim. (°C)
|
1
|
-7.51
|
0.0035
|
1.60
|
0.1103
|
-0.0980
|
46.6888
|
0.1094
|
Tmax clim. (°C)
|
24
|
-4.66
|
0.0070
|
1.68
|
0.0935
|
0.0592
|
22.6634
|
0.9592
|
Tmin clim. (°C)
|
1
|
-8.36
|
0.0038
|
1.55
|
0.1233
|
-0.1944
|
45.1424
|
0.1412
|
Precipitation clim. (mm)
|
1
|
-11.70
|
-0.0098
|
-0.36
|
0.7205
|
-0.0745
|
34.2984
|
0.5497
|
Vapor pressure clim. (hPa)
|
1
|
-8.37
|
0.0038
|
1.66
|
0.0974
|
-0.1829
|
45.5674
|
0.1318
|
Garnham suitability (mo)d
|
4
|
-4.21
|
-0.0380
|
-0.89
|
0.3850
|
-0.4488
|
5.6658
|
0.7729
|
|
aTmean,
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 (33,34).
bFigures in bold denote significance at the 5% level. p
is the number of lagged differenced dependent variables selected.
cADF, the Augmented Dickey-Fuller t-test for =0.
The 5% critical value is -3.45. Exact p values are not available for
ADF and ta statistics. The distribution of the t statistic for the
slope parameter b has the standard t distribution under the assumption
that <0. ta
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 =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 (39).
dGarnham suitability (1,4) refers to the
number of months with a mean monthly temperature exceeding 15°C and
monthly rainfall totals exceeding 152 mm (when the gridded climatology
data are used). These data are therefore annual data, whereas all
other time-series are monthly observations. |
1Dr. Biomndo is deceased.
|