Int J Biometeorol (2009) 53:299–304 DOI 10.1007/s00484-009-0216-5
ORIGINAL PAPER
Malaria morbidity and temperature variation in a low risk Kenyan district: a case of overdiagnosis? John Njuguna & James Muita & George Mundia
Received: 21 January 2008 / Revised: 9 February 2009 / Accepted: 11 February 2009 / Published online: 5 March 2009 # ISB 2009
Abstract Diagnosis of malaria using only clinical means leads to overdiagnosis. This has implications due to safety concerns and the recent introduction of more expensive drugs. Temperature is a major climatic factor influencing the transmission dynamics of malaria. This study looked at trends in malaria morbidity in the low risk Kenyan district of Nyandarua, coupled with data on temperature and precipitation for the years 2003–2006. July had the highest number of cases (12.2% of all cases) followed by August (10.2% of all cases). July and August also had the lowest mean maximum temperatures, 20.1 and 20.2 °C respectively. April, July and August had the highest rainfall, with daily means of 4.0, 4.3 and 4.9 mm, respectively. Observation showed that the coldest months experienced the highest number of cases of malaria. Despite the high rainfall, transmission of malaria tends to be limited by low temperatures due to the long duration required for sporogony, with fewer vectors surviving. These cold months also tend to have the highest number of cases of respiratory infections. There is a possibility that some of these were misdiagnosed as malaria based on the fact that only a small proportion of malaria cases were diagnosed using microscopy or rapid diagnostic tests. We conclude that
J. Njuguna (*) Nyandarua District Public Health Office, P.O. Box 86-20300, Nyahururu, Kenya e-mail:
[email protected] J. Muita Nyahururu District Hospital Laboratory, P.O. Box 86-20300, Nyahururu, Kenya G. Mundia Nyandarua District Health Information Office, P.O. Box 86-20300, Nyahururu, Kenya
overdiagnosis may be prevalent in this district and there may be a need to design an intervention to minimise it. Keywords Malaria . Kenya . Temperature . Overdiagnosis
Introduction Malaria is a leading cause of morbidity and mortality in sub-Saharan Africa, with 90% of all global deaths due to malaria occurring in this region (Snow et al. 2003; World Health Organisation 2003). Treatment of malaria has for a long time been characterised by overtreatment at health facilities and undertreatment at the community and household levels. A review by Amexo et al. (2004) found that clinical diagnosis by health professionals overestimates malaria (number of cases with negative microscopy over the number of malaria clinical diagnosis) by an average of 61%, ranging from 28% to 96%. Climatic factors like temperature, rainfall, and relative humidity influence transmission of malaria. Temperature is a major determinant, especially in cool areas such as the highlands. These regions receive abundant rainfall but the relatively low temperature hinders the proliferation of the female anopheline vector. In South Africa, Craig et al. 2004 analysed malaria case data for 30 years in KwaZulu-Natal and identified a significant correlation between mean maximum daily temperature from January to October of the preceding season and the number of malaria cases. A study in Burkina Faso found that, of the meteorological factors associated with clinical malaria, mean temperature had the largest effect. These authors went on to recommend integration of temperature data in routine health information systems for assessment of the malaria transmission link (Yé et al. 2007). The present study looks at cases of
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malaria reported over a period of 4 years, together with temperature and rainfall data over the same period.
Materials and methods Setting Nyandarua district is located in the central region of Kenya and has an estimated population of 576,334. It has a total area of 3,304 km2 and a density of 145 persons per square kilometre. Nyandarua district constitutes 0.6% of the whole country and 26.7% of Central Province. Recently it was split into two districts: Nyandarua North and South. The Fig. 1 Nyandarua district health facilities
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district is divided into six divisions, which are further subdivided into 26 locations and 79 sub-locations. The population comprises the following cohorts: pregnant women: 3.1%; newborn: 3.0%; children under 5: 10%; late childhood: 21.1%; adolescents: 28.7%; adults: 36.5%; and the elderly: 5.5%. The district is relatively well served by health facilities with three of the divisions having a hospital. Two factors that may hinder access to these health facilities are (1) the roads tend to become impassable during rainy seasons; and (2) due to the vastness of the district some residents may have to travel 10 km or more each way to reach the nearest health facility. A map of the health facilities is shown in Fig. 1, although it does not indicate private clinics and new
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health facilities. Note that Kinangop division was split into two, making a total of six divisions. The number of households is estimated to be 104,401. According to the Kenya Integrated Budget Household survey, between 38% and 59% of the residents are poor, i.e. are unable to meet their minimum food requirements. The altitude of the district varies between 3,990 m a.s.l. to the south east and 1,828 m towards the floor of the Great Rift Valley. The temperature is moderate, with the highest being a mean of 21.5°C, and the lowest in July with a mean of 7.1°C; the overall mean temperature is 14.3°C (Government of Kenya 2001). The district also experiences low temperatures with adverse effects. The cold air that is generated during clear nights on the moorlands of the Aberdare ranges flows down the Kinangop Plateau and Ol Kalou Salient causing night frosts nearly every month. Frost temperatures range between 1.2 and 1°C and normally lasts for a few hours before sunrise. The district has an annual average rainfall of 1,050 mm. Areas close to the Aberdare ranges receive high rainfall ranging between 1,000 and 1,400 mm, but this decreases on plateaus, which experience a maximum of 400 mm (Government of Kenya 2001). The district is classified as a low malaria risk area, characterised by low malaria transmission. Despite this, malaria has consistently been ranked as the second leading disease in terms of outpatient morbidity in the district (Government of Kenya 2006). Methodology Malaria outpatient cases for 4 years (2003–2006) were analysed on a monthly basis and the means determined. Each health facility in the district is required to submit a monthly tally sheet of all outpatient cases treated. Malaria cases were extracted from these sheets to compile the monthly malaria burden. Data on mean maximum temper-
ature and mean daily rainfall for each month for the years 2003–2006 were provided by the Nyahururu meteorology department. Analysis was done using MS Excel 2003 and SPSS version 11 (http://www.spss.com/).
Results The mean number of monthly cases for the 4-year period was 8,313. July had the highest mean number of cases (12,151), followed by August, June and April (Table 1). These 4 months account for 40.7% of all reported cases. Across the 4 years, cases have been constantly on the rise during the months of June and July, whereas August and March reported upsurges followed by declines. December, January and April had the fewest cases. The monthly mean maximum temperature for the 4-year period was 21.7 °C. February and March had the highest temperature, both at 24.2 °C. July had the lowest temperature at 20.1 °C, followed by August and June at 20.2 and 21.1 °C, respectively. Malaria cases have generally been on the rise. Between 2003 and 2004, there was a 59.6% increase in malaria cases, from a mean of 5,178 to 8,267, and an increase of 0.09°C in mean maximum temperature. There was another increase of 22.6% in malaria cases from 2004 to 2005 to a mean of 10,138 cases, and an increase of 0.44 °C in mean maximum temperature. There was a slight decline of 0.05% in malaria cases to a mean of 9,673 and a decline in mean maximum temperature by 0.44 °C. As can be seen in the graph in Fig. 2, malaria cases tend to rise from January and peak slightly in March. In April they decline before gradually rising and peaking in July. This is then followed by another decline. In 2004, cases peaked in August instead of July. Temperatures tend to rise from January and peak in February and March, which have the
Table 1 Malaria morbidity, mean maximum temperature and mean daily rainfall 2003–2006 Month
Number of malaria cases
% of all cases
Mean monthly temperature (°C)
Mean daily rainfall (mm)
January February March April May June July August September October November December
6,994 7,388 8,445 7,069 8,160 9,737 12,151 10,200 8,239 7,727 7,347 6,304
6.7 7.4 8.5 7.1 8.2 9.8 12.2 10.2 8.3 7.8 7.4 6.3
22.2 24.2 24.2 22.2 21.7 21.1 20.1 20.2 21.6 21.8 20.4 21.2
1.1 0.7 2.0 4.0 3.2 2.9 4.3 4.9 2.3 1.2 2.6 2.0
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Fig. 2 Mean monthly malaria cases 2003–2006
highest temperatures. Then a gradual decline follows until July. A rise follows in September and October, and a decline in November, followed by another rise in December. Nyandarua district receives abundant rainfall throughout the year. There was only 1 month across the 4 years when it did not rain, namely February 2003. Highest rainfall is recorded in August, July, April and May. Rainfall follows a general pattern of rising from February and peaking in April, followed by a slight decline and a further peak in August. This is followed by a decline and a slight peak.
Discussion The majority of malaria cases occur in June, July and August, which also have the lowest mean temperatures of 21.1°C, 20.1°C, and 20.2 °C, respectively. July and August also have the highest rainfall. The effects of temperature on transmission are many but its specific effect on sporogonic duration (development of the malaria parasite within the mosquito) and mosquito survival is the most important
(Onori and Grab 1980). Below 18°C, transmission is unlikely because few adults(0.28%) survive the 56 days required for sporogony at that temperature, and because mosquito abundance is limited by long larval duration. At 20°C, sporogony takes 28 days, with only 5.6% of vectors surviving after this period as the life span of anopheles gambiae is 21 days. At 22°C, sporogony is completed in less than 3 weeks and mosquito survival is sufficiently high (15%) for the transmission cycle to be completed Rainfall is important as it provides the essential breeding sites for the mosquito vectors. However, the relationship between mosquito abundance is complex and best studied when temperature is not limiting (Snow et al. 1999). In areas of low temperatures such as the highlands, which experience high rainfall, it is the temperature element that hinders the mosquito from breeding proficiently. Like July, April also experiences high rainfall. It has a mean maximum temperature of 22.2°C compared to 20.1°C for July. It can be estimated that 15% of vectors survive in April compared to 5.6% in July. Thus more cases should be reported in April or May due to a lag effect. April accounts for 7.1% of all cases and May 8.2% compared to 12.2% for July. June has relatively low temperatures, meaning that the likelihood of July benefitting from a lag effect is minimal. A study by Yé and colleagues found that the risk of clinical malaria increased with an increase in mean temperature up to 27°C (Yé et al. 2007). In Zimbabwe, it was found that mean monthly temperature (range 28–32°C), maximum temperature (24–28°C) and high rainfall promote seasonal transmission of malaria (Mabaso et al. 2005). A possible explanation for the coldest months having the highest number of malaria cases could be due to overdiagnosis of malaria cases. In Nyandarua district, respiratory tract infections are the leading cause of outpatient morbidity. During these cold rainy months, cases of respiratory infections tend to increase as they are temperature dependent (Fodha et al. 2004). It has been shown that there is an increased risk of contracting respiratory infections in the rainy season among children (Roca et al. 2006). Some of these cases could be misdiagnosed or misclassified as malaria. A study in Mozambique found that malaria was strongly associated with an increased risk of respiratory infections (Roca et al. 2006). A prospective study in a meso-endemic area in Uganda found malaria to be responsible for only 32% of new febrile cases among children. The other febrile cases with negative blood smears were attributed to diseases like upper respiratory infection, common cold and non-specified fever (Meya et al. 2007). It has also been shown that the symptoms of malaria tend to overlap with those of pneumonia (O’Dempsey et al. 1993), and one Ugandan study found 30% of children had symptom overlap necessitating dual treatment (Kallander et al. 2004).
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Microscopy is the gold standard for malaria diagnosis. In Nyandarua district only a few cases are diagnosed using microscopy. The data available indicate that, in 2002, only 32.8% of all reported cases had a blood slide done, of which only 22.8% were positive. The quality of microscopy in the district is yet to be ascertained. A cross-sectional survey to evaluate the accuracy of routine malaria microscopy in 17 health facilities in two districts in Kenya found that positive predictive value was 21.6% (Zurovac et al. 2006). That means that four out of five cases diagnosed as positive were in fact negative. Conversely, Underdiagnosis of malaria cases cannot be ruled out. In low risk areas, the prevalence of malaria tends to be low and this leads to a decline in the positive predictive value of diagnostic methods, whether they are clinical algorithms or laboratory tests. A study in a low risk Ugandan district found that, of patients treated for malaria, only 24.8% had a genuine malarial episode, resulting in more than 75% of the patients receiving anti-malarials drugs unnecessarily (Ndyomugyenyi et al. 2007). Thus underdiagnosis, if it occurs in areas of low transmission, is insignificant. Studies have also shown that overdiagnosis of malaria is common in sub-Saharan Africa (Chandramohan et al. 2002; Reyburn et al. 2004; Zurovac et al. 2006). Overdiagnosis could be due partly to presumptive treatment practiced by clinicians. This was acceptable in the era of cheap, safe and effective drugs. The recent introduction of expensive artemisinin combination therapies means this has to change. Treatment guidelines in Kenya state that presumptive treatment, if prescribed at all, is advocated only for children less than 5 years old. This is because the risk of malaria in this cohort is high due to their low immune status compared to other cohorts. This study has some potential limitations as data collected using the routine health information system is liable to bias due to misreporting or missing data. Nevertheless, the study does utilise data gathered over 4 years and this is complemented by data from the meteorology department. The latter runs only one weather station for the entire district and thus may not capture variations across the district. The weather station is located at an altitude of 2,400 m a.s.l. and is in Ol jororok division, about 20 km from Nyahururu town. Areas close to the Aberdares ranges experience cooler temperatures and higher rainfall. The district hospital is administratively located in the neighbouring district called Laikipia, thus its catchment area covers part of this district. Laikipia district is relatively warmer and may have more malaria cases compared to Nyandarua. This may lead to an overestimate of malaria cases. Overdiagnosis tends to have adverse effects on patients treated for the wrong disease. One study reported a higher case fatality in slide-negative patients compared to slidepositive patients (Reyburn et al. 2004). It is also uneco-
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nomical given the recent introduction of more expensive drugs (Mutabingwa 2005), whose safety margins are not yet fully known (Lang et al. 2006). Overdiagnosis of malaria cases can be minimised. Masika et al. 2006 carried out an intervention that combined rigorous training, strengthening the laboratory system, and use of local epidemiology data. Clinicians were made aware that the incidence of malaria was very low in the district (0.02 cases per child per year). This led to a decline in inpatient and outpatient malaria cases from 34% to 17%. It may be necessary to design such an intervention in the study area. Acknowledgements We thank the staff of Nyahururu meteorology for kindly providing data on temperature and precipitation at no cost, two anonymous reviewers, and Sister April for editing the paper.
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