- Email: [email protected]
Malaria early warning in Kenya and seasonal climate forecasts
CORRESPONDENCE
replacement doses, therefore, mean that endocrinologists should place renewed emphasis on education of patients with adrenal insufficiency and on crisis prevention.
Malaria early warning in Kenya and seasonal climate forecasts
Deana Kenward, *Katherine G White, on behalf of the Addison’s Disease Self-Help Group
Sir—The Abuja Declaration requires 60% of malaria epidemics to be detected within 2 weeks of inception. Malaria early warning systems (MEWS) that incorporate vulnerability assessment (eg, immune status), seasonal climate forecasts, weather monitoring, and case surveillance may help achieve this target.1 Simon Hay and colleagues (May 17, p 1705)2 assess MEWS in a retrospective analysis of malaria epidemics in the Kenyan highlands in the summer of 2002. Encouragingly, they document the value of rainfall estimates in predicting epidemics in this region. Rainfall estimates have been used for malaria early warning elsewhere in Africa since 1999.3 Hay and colleagues conclude that in this instance the seasonal climate forecasts were inaccurate and that this poor skill, combined with their limited temporal and spatial specificity compromises their usefulness to the health sector. However, we suggest that Hay and colleagues’ broad conclusion might be premature. Seasonal climate forecasting is an emerging science with potential to inform policymakers of adverse conditions months before their actual occurrence. Because of the chaotic nature of the climate, forecasts will never be completely accurate. Their accuracy varies according to geographical region, season, and year, and their usefulness is dependent on their value in informing specific sectoral decisions. Forecasts express a tilt in the odds, and it is for this reason that probabilities of the future climate as above, below, or near normal may be given (see http://iri.columbia.edu). When probabilities across these three scenarios are fairly evenly distributed (as in the March–May, 2002, main rains in the Kenyan western highlands), the tilt is minimal. Climate predictability is highly dependent on the extent to which regional climate is determined by patterns of sea surface temperature. This predictability can in part be seen through correlations with sea surface temperature and related indices, such as the Multivariate El Niño Southern Oscillation Index (MENSOI), and in principle most comprehensively with outputs from physically-based climate models of global sea surface temperature effects on regional climates. The strong relation of indices of sea surface temperature and outputs
Addison’s Disease Self-Help Group, Guildford, Surrey GU1 4NP, UK (e-mail: [email protected]) 1
Arlt W, Allolio B. Adrenal insufficiency. Lancet 2003; 361: 1881–93.
Sir— Wiebke Arlt and Bruno Allolio’s wide-ranging and informative Seminar on adrenal insufficiency1 makes no mention of the mild adrenocortical insufficiency now acknowledged to be present in chronic fatigue syndrome.2,3 Adrenocortical insufficiency was suspected in chronic fatigue syndrome largely because of the similarity of its symptoms with Addison’s disease—namely fatigue, weakness, and a tendency to excess pigmentation.2 Small doses of corticosteroids have been given with some success to patients with chronic fatigue syndrome, but such treatment is rarely used because of the fear of steroid complications.4 As is the case in autoimmune adrenalitis, as part of an autoimmune polyendocrine syndrome there is an overactive immune system in chronic fatigue syndrome, and autoantibodies—for example to thyroid and smooth muscle—are present in some 20% of cases.5 I believe that the mild adrenal insufficiency of chronic fatigue syndrome should be included in any discussion of the broader subject. Alex Poteliakhoff Sudbury, 16B Prince Arthur Road, London NW3 6AY, UK 1 2
3
4
5
Arlt W, Allolio B. Adrenal insufficiency. Lancet 2003; 361: 1881–93. Poteliakhoff A. Adrenocortical activity and some clinical findings in acute and chronic fatigue. J Psychosom Res 1981; 25: 91–95. Demitrack MA, Dale JK, Kruisi MJ, et al. Evidence for impaired activity of the hypothalamic-pituitary-adrenal axis in patients with chronic fatigue syndrome. J Clin Endocrinol Metab 1991; 73: 1224–34. McKenzie R, O’Fallon A, Date J, et al. Low-dose hydrocortisone for treatment of chronic fatigue syndrome. JAMA 1998, 280: 1061–66. Hilgers A, Frank J. Chronic fatigue syndrome: immune dysfunction, the role of pathogens and toxic agents and neurological and cardiac alterations. Wien Med Wschr 1994; 144: 399–406.
580
from global climate models of the Kenyan climate during the short rains—October, November, December—is well documented. For example, we analysed a set of global climate model predictions for 1961–99 run by scientists at the International Research Institute for Climate Prediction with the ECHAM4 model (developed at Max-Planck-Institut für Meteorologie in Germany). The correlation between the best estimate Kenya area-average rainfall prediction for October, November, and December and the area-average Kenya observed rainfall was r=0·88, and the correlation with rainfall at the Kericho station was r=0·71, confirming that the climate in Kenya during this season is predictable in view of the observed pattern of sea surface temperature. By contrast, in an earlier study, Hay and colleagues4 concluded that for 1966–98 there was a poor relation between El Niño Southern Oscillation and climate in Kericho. The region of high predictability includes areas such as northeastern Kenya, where a catastrophic malaria epidemic occurred in 1998 after exceptional rainfall in October– December, 1997.5 Seasonal climate forecasts may yet prove to be useful for MEWS, using present and future knowledge. The key question is how, in view of their spatial limitations and probabilistic nature, such forecasts can be made useful to Kenyan malaria control staff in areas where epidemics are related to predictable climate anomalies. Future progress requires development of a closer dialogue and effective collaborative partnership between the malaria control and research community and climate scientists. *Madeleine Thomson, Matayo Indeje, Stephen Connor, Maxx Dilley, Neil Ward International Research Institute for Climate Prediction, The Earth Institute of Columbia University, Palisades, NY 10964, USA (e-mail: [email protected]) 1
2
3
4
5
WHO. Malaria early warning systems, concepts, indicators and partners: a framework for field research in Africa. Geneva: World Health Organization, 2001. Hay SI, Were EC, Renshaw M, et al. Forecasting, warning, and detection of malaria epidemics, a case study. Lancet 2003; 361: 1705–06. WHO. Final report on the 3rd meeting of the RBM Technical Resource Network on Epidemic Prevention and Control. Geneva: World Health Organization, 2002. Hay SI, Myers MF, Burke DS, et al. Etiology of interepidemic periods of mosquito-borne disease. Proc Natl Acad Sci USA 2000; 97: 9335–39. Brown V, Issak MA, Rossi M, Barboza P, Paugam A. Epidemic of malaria in north-eastern Kenya. Lancet 1998: 352: 1356–57.
THE LANCET • Vol 362 • August 16, 2003 • www.thelancet.com
For personal use. Only reproduce with permission from The Lancet.
replacement doses, therefore, mean that endocrinologists should place renewed emphasis on education of patients with adrenal insufficiency and on crisis prevention.
Malaria early warning in Kenya and seasonal climate forecasts
Deana Kenward, *Katherine G White, on behalf of the Addison’s Disease Self-Help Group
Sir—The Abuja Declaration requires 60% of malaria epidemics to be detected within 2 weeks of inception. Malaria early warning systems (MEWS) that incorporate vulnerability assessment (eg, immune status), seasonal climate forecasts, weather monitoring, and case surveillance may help achieve this target.1 Simon Hay and colleagues (May 17, p 1705)2 assess MEWS in a retrospective analysis of malaria epidemics in the Kenyan highlands in the summer of 2002. Encouragingly, they document the value of rainfall estimates in predicting epidemics in this region. Rainfall estimates have been used for malaria early warning elsewhere in Africa since 1999.3 Hay and colleagues conclude that in this instance the seasonal climate forecasts were inaccurate and that this poor skill, combined with their limited temporal and spatial specificity compromises their usefulness to the health sector. However, we suggest that Hay and colleagues’ broad conclusion might be premature. Seasonal climate forecasting is an emerging science with potential to inform policymakers of adverse conditions months before their actual occurrence. Because of the chaotic nature of the climate, forecasts will never be completely accurate. Their accuracy varies according to geographical region, season, and year, and their usefulness is dependent on their value in informing specific sectoral decisions. Forecasts express a tilt in the odds, and it is for this reason that probabilities of the future climate as above, below, or near normal may be given (see http://iri.columbia.edu). When probabilities across these three scenarios are fairly evenly distributed (as in the March–May, 2002, main rains in the Kenyan western highlands), the tilt is minimal. Climate predictability is highly dependent on the extent to which regional climate is determined by patterns of sea surface temperature. This predictability can in part be seen through correlations with sea surface temperature and related indices, such as the Multivariate El Niño Southern Oscillation Index (MENSOI), and in principle most comprehensively with outputs from physically-based climate models of global sea surface temperature effects on regional climates. The strong relation of indices of sea surface temperature and outputs
Addison’s Disease Self-Help Group, Guildford, Surrey GU1 4NP, UK (e-mail: [email protected]) 1
Arlt W, Allolio B. Adrenal insufficiency. Lancet 2003; 361: 1881–93.
Sir— Wiebke Arlt and Bruno Allolio’s wide-ranging and informative Seminar on adrenal insufficiency1 makes no mention of the mild adrenocortical insufficiency now acknowledged to be present in chronic fatigue syndrome.2,3 Adrenocortical insufficiency was suspected in chronic fatigue syndrome largely because of the similarity of its symptoms with Addison’s disease—namely fatigue, weakness, and a tendency to excess pigmentation.2 Small doses of corticosteroids have been given with some success to patients with chronic fatigue syndrome, but such treatment is rarely used because of the fear of steroid complications.4 As is the case in autoimmune adrenalitis, as part of an autoimmune polyendocrine syndrome there is an overactive immune system in chronic fatigue syndrome, and autoantibodies—for example to thyroid and smooth muscle—are present in some 20% of cases.5 I believe that the mild adrenal insufficiency of chronic fatigue syndrome should be included in any discussion of the broader subject. Alex Poteliakhoff Sudbury, 16B Prince Arthur Road, London NW3 6AY, UK 1 2
3
4
5
Arlt W, Allolio B. Adrenal insufficiency. Lancet 2003; 361: 1881–93. Poteliakhoff A. Adrenocortical activity and some clinical findings in acute and chronic fatigue. J Psychosom Res 1981; 25: 91–95. Demitrack MA, Dale JK, Kruisi MJ, et al. Evidence for impaired activity of the hypothalamic-pituitary-adrenal axis in patients with chronic fatigue syndrome. J Clin Endocrinol Metab 1991; 73: 1224–34. McKenzie R, O’Fallon A, Date J, et al. Low-dose hydrocortisone for treatment of chronic fatigue syndrome. JAMA 1998, 280: 1061–66. Hilgers A, Frank J. Chronic fatigue syndrome: immune dysfunction, the role of pathogens and toxic agents and neurological and cardiac alterations. Wien Med Wschr 1994; 144: 399–406.
580
from global climate models of the Kenyan climate during the short rains—October, November, December—is well documented. For example, we analysed a set of global climate model predictions for 1961–99 run by scientists at the International Research Institute for Climate Prediction with the ECHAM4 model (developed at Max-Planck-Institut für Meteorologie in Germany). The correlation between the best estimate Kenya area-average rainfall prediction for October, November, and December and the area-average Kenya observed rainfall was r=0·88, and the correlation with rainfall at the Kericho station was r=0·71, confirming that the climate in Kenya during this season is predictable in view of the observed pattern of sea surface temperature. By contrast, in an earlier study, Hay and colleagues4 concluded that for 1966–98 there was a poor relation between El Niño Southern Oscillation and climate in Kericho. The region of high predictability includes areas such as northeastern Kenya, where a catastrophic malaria epidemic occurred in 1998 after exceptional rainfall in October– December, 1997.5 Seasonal climate forecasts may yet prove to be useful for MEWS, using present and future knowledge. The key question is how, in view of their spatial limitations and probabilistic nature, such forecasts can be made useful to Kenyan malaria control staff in areas where epidemics are related to predictable climate anomalies. Future progress requires development of a closer dialogue and effective collaborative partnership between the malaria control and research community and climate scientists. *Madeleine Thomson, Matayo Indeje, Stephen Connor, Maxx Dilley, Neil Ward International Research Institute for Climate Prediction, The Earth Institute of Columbia University, Palisades, NY 10964, USA (e-mail: [email protected]) 1
2
3
4
5
WHO. Malaria early warning systems, concepts, indicators and partners: a framework for field research in Africa. Geneva: World Health Organization, 2001. Hay SI, Were EC, Renshaw M, et al. Forecasting, warning, and detection of malaria epidemics, a case study. Lancet 2003; 361: 1705–06. WHO. Final report on the 3rd meeting of the RBM Technical Resource Network on Epidemic Prevention and Control. Geneva: World Health Organization, 2002. Hay SI, Myers MF, Burke DS, et al. Etiology of interepidemic periods of mosquito-borne disease. Proc Natl Acad Sci USA 2000; 97: 9335–39. Brown V, Issak MA, Rossi M, Barboza P, Paugam A. Epidemic of malaria in north-eastern Kenya. Lancet 1998: 352: 1356–57.
THE LANCET • Vol 362 • August 16, 2003 • www.thelancet.com
For personal use. Only reproduce with permission from The Lancet.