Rainfall pattern. El Niño and malaria in Uganda

Kilian, Albert; Langi, Peter; Talisuna, AO; Kabagambe, G.

doi:10.1016/s0035-9203(99)90165-7

Cited by 123 publications

(93 citation statements)

References 4 publications

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“…Bouma & colleagues have shown that many epidemics of malaria are associated with El Niño-driven climate extremes (17-21). El Niño resulted in torrential rain in parts of East Africa and a malaria epidemic in the southwestern Ugandan highlands (93,105). However, El Niño is certainly not responsible for all epidemics; the extreme Ethiopian epidemics in 1953 and 1958, which resulted in thousands of deaths, did not occur in El Niño years.…”

Section: Interannualmentioning

confidence: 99%

The Effects of Changing Weather on Public Health

Patz

Engelberg²

2000

Annu. Rev. Public Health

178

111

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Key Words climate change, global warming, heat waves, waterborne disease, air pollution, vectorborne disease s Abstract Many diseases are influenced by weather conditions or display strong seasonality, suggestive of a possible climatic contribution. Projections of future climate change have, therefore, compelled health scientists to re-examine weather/disease relationships. There are three projected physical consequences of climate change: temperature rise, sea level rise, and extremes in the hydrologic cycle. This century, the Earth has warmed by about 0.5 degrees centigrade, and the mid-range estimates of future temperature change and sea level rise are 2.0 degrees centigrade and 49 centimeters, respectively, by the year 2100. Extreme weather variability associated with climate change may especially add an important new stress to developing nations that are already vulnerable as a result of environmental degradation, resource depletion, overpopulation, or location (e.g. low-lying coastal deltas). The regional impacts of climate change will vary widely depending on existing population vulnerability. Health outcomes of climate change can be grouped into those of: (a) direct physical consequences, e.g. heat mortality or drowning; (b) physical/chemical sequelae, e.g. atmospheric transport and formation of air pollutants; (c) physical/biological consequences, e.g. response of vector-and waterborne diseases, and food production; and (d) sociodemographic impacts, e.g. climate or environmentally induced migration or population dislocation. Better understanding of the linkages between climate variability as a determinant of disease will be important, among other key factors, in constructing predictive models to guide public health prevention.

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Section: Interannualmentioning

confidence: 99%

The Effects of Changing Weather on Public Health

Patz

Engelberg²

2000

Annu. Rev. Public Health

178

111

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“…5 In highland African cities, there is a need for timely deployment of spatially targeted malaria interventions to curtail malaria epidemics. Although studies have identified important climatic, seasonal, and demographic factors underlying large-scale spatial and temporal patterns of malaria epidemics in African countries, [6][7][8][9][10][11][12][13] thus far, attempts to develop predictive models of malaria epidemics, which are accurate on the local scale, have not met with success. 14,15 At present, there are few small-scale temporal-spatial studies of malaria incidence, particularly in urban highland area of Africa.…”

Section: Introductionmentioning

confidence: 99%

A Temporal-Spatial Analysis of Malaria Transmission in Adama, Ethiopia

Peterson¹,

Borrell²,

El‐Sadr³

et al. 2009

The American Journal of Tropical Medicine and Hygiene

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Abstract. Urban malaria is a growing problem in Africa. Small-scale spatial studies are useful in identifying foci of malaria transmission in urban communities. A population-based cohort study comprising 8,088 individuals was conducted in Adama, Ethiopia. During a single malaria season, the Kulldorff scan statistic identified one temporally stable spatial malaria cluster within 350 m of a major Anopheles breeding site. Factors associated with malaria incidence were residential proximity to vector breeding site, poor house condition (incidence rate ratio [IRR] = 2.0, 95% confidence interval [CI] = 1.4, 2.9), and a high level of vegetation (IRR = 1.8, 95% CI = 1.0, 3.3). Maximum (IRR = 1.4, 95% CI = 1.1, 1.9) and minimum daily temperatures (°C; IRR = 1.3, 95% CI = 1.2, 1.5) were positively associated with malaria incidence after a 1-month delay. Rainfall was positively associated with malaria incidence after a 10-day delay. Findings support the use of small scale mapping and targeted vector control in urban malaria control programs in Africa.

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“…During the last century, malaria has encroached into these highlands as a result of wide-scale population settlement (Mboera et al, 2002;Matson, 1957;Campbell, 1929). The reasons for the increase in malaria in the highlands are many, and may include frequent human population movements from the malaria endemic lowlands (Matola et al, 1987), climate change (Kilian et al, 1999;Odongo-Aginya et al, 2005), increased land use for agricultural extension and antimalarial drug resistance (Bødker et al, 2000). The unstable and seasonal malaria transmission in such highland areas in Tanzania has resulted into frequent malaria epidemics (Mboera & Kitua, 2001;Mboera, 2004).…”

Section: Discussionmentioning

confidence: 99%

Malaria in Bulambya, Ileje district, south-west Tanzania

Maegga¹,

Kalinga²,

Chacha³

et al. 2006

Tanzania J Hlth Res

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This study was carried out to determine malaria situation in Bulambya Division, Ileje district in southwest Tanzania. Outpatient attendance records from 1999 to 2002 were examined for malaria cases in eleven health facilities. A cross-sectional study to determine malaria prevalence and its potential vectors was done in May 2004. Blood samples were collected from 502 schoolchildren from 16 primary schools and examined microscopically for malaria parasites. Indoor resting mosquitoes were collected in ten houses in each of the ten villages covered. Health facility data for 1999 showed the highest (60.7%) and lowest (11.5%) annual malaria morbidity rates were recorded at Itumba and at Ibaba, respectively. Malaria parasitaemia rate among schoolchildren ranged from 0-36.7%; the lowest prevalence was observed in the high altitude mountainous range and the highest in the lower altitude plateaux. Of 1,504 mosquitoes caught, 205 (13.6%) were female Anopheles gambiae s.l., 215 (14.3%) An. funestus and 68.9% were culicines. Data from both health facility records and school surveys indicate that there is high malaria morbidity in the lower plateaux (1100 to =1350m), around Itumba and Isongole, where indoor anopheles mosquito densities were relatively higher than in the higher altitudes (>1,350 m) around Ibaba. In the lower altitude range, there was less malaria on steep slopes than on flat terrain. Further studies are recommended to closely monitor malaria situation in these epidemic prone highlands of southwest Tanzania.

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Rainfall pattern. El Niño and malaria in Uganda

Cited by 123 publications

References 4 publications

The Effects of Changing Weather on Public Health

The Effects of Changing Weather on Public Health

A Temporal-Spatial Analysis of Malaria Transmission in Adama, Ethiopia

Malaria in Bulambya, Ileje district, south-west Tanzania

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