SOME FACTORS THAT DETERMINE EXPOSURE TO MALARIA VECTORS SPECIES IN RUSINGA ISLAND, KENYA.
NGEIYWA M. M*. and MUKOYA S. O.
University of Eldoret
P. O. Box 1125 – 30100
* Corresponding author
+254 718013149
ABSTRACT
Different factors which determine exposure to malaria vectors were assessed. Mosquitoes were sampled using MMX trap in 160 houses. The houses were divided in groups of 10 and every 10 houses sampled on a single night. Catches from the houses were identified, counted and recorded in excel spreadsheet. The factors that were analyzed were; presence of animals sleeping outdoors, cooking indoors, and number of people sleeping in a house. 538 mosquitoes were collected in the 160 houses, of which 283 (1.77) were Anophelines. There were 156 (0.98) Anopheles funestus and 127(0.79) Anopheles gambiae. A total of 108 (0.96) Anopheles gambiae and 138 (1.22) Anopheles funestus were caught in houses where there were no animals sleeping outdoors. On the other hand, a total of 19 (0.40) Anopheles gambiae and 18 (0.38) Anopheles funestus were caught in houses where animals slept outdoors. This association was statistically significant (p-value = 0.002). A total of 102 (0.96) Anopheles gambiae and 129 (1.22) Anopheles funestus were caught in houses where there was no cooking indoors. On the other hand, a total of 25 (0.46) Anopheles gambiae and 27 (0.50) Anopheles funestus were caught in houses where cooking took place outdoors. There was negative linear relationship observed thus there is reduction in mean mosquitoes resting indoors where cooking in the house is evident. A total of 88 (0.17) Anopheles gambiae and 112 (0.90) Anopheles funestus were caught in houses which had 1 to 5 people sleeping inside. On the other hand, a total of 39 (1.08) Anopheles gambiae and 44 (1.22) Anopheles funestus were caught in houses which had 6 to 10 people sleeping inside. However there was no association between the mosquito population and the number of people sleeping indoors. This study demonstrates that the risk of malaria transmission is greatest in compounds where animals are absent and in houses where cooking does not take place indoors. These factors need to be considered in the design and analysis of intervention studies designed to reduce malaria transmission in Rusinga Island and other parts of the world.
INTRODUCTION
The greatest impact of malaria in terms of morbidity and mortality is in sub-Saharan African, where Plasmodium falciparum is predominant, accounting for more than 25% of childhood mortality outside the neonatal period (Winstanley, et al., 2004). Recent reports on disease burden of malaria have revealed that about one million deaths occur yearly in Africa from the direct effects of malaria. Of these, more than 75% occurs in children below five years (Snow, et al., 1999).
In Kenya, there are an estimated 6.7 million new clinical cases of malaria and 4,000 deaths each year, and those living in Western Kenya have an especially high risk of malaria. (Opiyo, et al., 2007). The house is a common site of human-vector contact and this is reflected in the high degree of endophily and endophagy exhibited by important vector species, such as the malaria-transmitting Anopheles gambiae sensu stricto Giles and An. funestus Giles mosquitoes over much of Africa (Gillies, et al., 1968).
The prevention of malaria in vulnerable populations is one of the priorities for African leaders and international agencies (WHO, 2000). It is therefore, important to understand the factors that expose people to malaria vectors so that a national strategic plan for epidemic prevention and control can be developed in all regions. The current study was designed to evaluate some of the factors that contributed to exposure to malaria vectors in Rusinga Island.
Malaria control remains a major problem in most parts of Africa and Rusinga Island is not an exception. The extent of malaria control has not been fully achieved due to lack of knowledge on the factors that expose people to malaria vectors. The aim of the current project was to investigate some of the factors which influence exposure to malaria vectors. Malaria control has contributed to the reduction of malaria morbidity and mortality by using ITNs, IRS and effective anti-malarial therapies. The three strategies were estimated to reduce global incidence of malaria by 17% since 2000 (WHO, 2004).The burden is largely attributed to potential threat from continued emergence and spread of parasite resistance to antimalarial drugs and mosquito resistance to insecticides (WHO, 2004). Because of the insecticide resistance in mosquitoes and the concern about environmental pollution when using pesticides, there has been an increased emphasis on the development of alternative mosquito control technologies (Kline, et al., 1994). For the control technologies to be effective, we should have a better understanding of the factors which contribute to expose people to malaria vectors. The outcome of such understanding will guide the selection and the design of control strategies to achieve a reduction in malaria vectors thus reducing malaria morbidity.
This research evaluated some of the risk factors which expose people to malaria vectors in Rusinga Island in western Kenya.
The specific objectives were to determine the effect of the presence of livestock sleeping outdoors on the abundance of mosquitoes caught indoors, assess the effect of cooking indoors on the abundance of mosquitoes resting indoors and determine the effect of number of people sleeping in a house on indoor mosquito resting populations.
LITERATURE REVIEW
Malaria is re-emerging in most of the African highlands exposing the non-immune population to deadly epidemics. (Mouchet, et al., 1998). A better understanding of the factors impacting transmission is crucial to improve well targeted malaria control strategies. Former reviews published in 1998, have already shown the complexity of factors influencing malaria. (Lindsay, et al., 1998). Based on a literature review, different risk factors for malaria in African were identified and used to build a conceptual model. The main source of information was peer-reviewed scientific papers obtained through PubMed using the keywords ‘‘malaria’’. Both English and French papers, describing malaria potential risk factors, were used. The reported risk factors were classified according to their impact on vectors or on malaria. To determine the hierarchical importance of different risk factors identified in the conceptual model the Classification and Regression Trees (CART) were used on malaria data collected in the Burundi highlands. (Thang, et al., 2008)
Half of the world’s populations rely on biomass or coal as their primary household fuel source. (WHO, 2006). Anecdotal evidence exists for smoke having a repellent effect on mosquitoes. (Ziba et al, 1994). This raises the possibility that interventions to reduce indoor air pollution might increase the risk of malaria and other insect-borne diseases. Eighty percent of the burden of insect-borne diseases and ninety percent of the resulting deaths are due to malaria and the report therefore focuses on this disease. (Gordon et al 2004) A literature review was performed to assess the extent of evidence for smoke providing protection from malaria or mosquitoes. Although there is evidence that the smoke from certain plant products contains active compounds that have a repellent effect, no experimental evidence was found for a repellent effect attributable to smoke from domestic biomass fuels. (WHO, 2008). The evidence base was, however, extremely limited, with only one published experimental study found. (Albalak, et al., 1999). Seven early (pre-1940) observational studies were identified relating to the effects of smoke from domestic fuel use. (WHO, 2008). Three of these (none from Africa) suggested that smoke from domestic fires can deter mosquitoes from resting or hibernating in houses. By contrast three African studies reported no effect of cooking smoke on mosquito numbers observed in houses. A fourth African study found no difference in numbers of mosquitoes caught between houses with a separate kitchen and those without (the latter presumed to be less smoky) (WHO, 2008)
Screening doors, windows and eaves of houses should reduce house entry by eusynanthropic insects, including the common African house mosquito Culex pipiens quinquefasciatus and other culicines. In the pre-intervention year of a randomized controlled trial investigating the protective effects of house screening against mosquito house entry, a multi-factorial risk factor analysis study was used to identify factors influencing house entry by culicines of nuisance biting and medical importance. These factors were house location, architecture, human occupancy and their mosquito control activities, and the number and type of domestic animals within the compound. (Kirby M, et al., 2008).
MATERIALS AND METHODS
Field studies were carried on Rusinga Island (0°35'–0°44' South; 34°11'–34°22' East; altitude 1,100 m) from June 2014 to July 2014. Rusinga Island is the second largest island in Lake Victoria with an area of 42 km2. Due to its close proximity to the mainland, a 200 m long causeway was constructed in 1983 to link the island with Mbita Township, the major trading and the administrative Centre of the district. The terrain is extensively deforested and it is generally rocky and hilly with limited vegetation cover. There are a number of seasonal rivers which contain water only during the rainy season and the lake provides the main water source for the population. Two rainy seasons are typical for the area, the 'long rains' between March and June and the 'short rains' between October and November, but these rainy seasons are unreliable with some years being characterized by prolonged dry periods. Malaria transmission fluctuates seasonally but is sustained all year round by the three primary malaria vectors: An.gambiae,Anopheles arabiensisand, to a lesser extent,Anopheles funestus (Fillinger, et al., 2004). The predominant language spoken is Dholuo. People living on Rusinga face a multitude of problems. The island has suffered enormous environmental degradation, soil erosion and extended drought conditions in recent years leaving little productive land and few opportunities to make money other than through fishing. Furthermore, construction activities, deforestation, vegetation clearance and poorly planned infrastructure development has led to an increased abundance of mosquito larval habitats (Fillinger, et al., 2004), notably those suitable for malaria transmission. Two government health Centre’s serve Rusinga's population; one in the north-eastern part of the Island and another one in Mbita Township. Additionally, there are three registered and many unregistered private health facilities on the island. Due to the bad condition of the roads public transport is rare especially during the rainy season and it is difficult to reach the health facilities.
The study was based on an observational randomized block design. One hundred and sixty (160) houses were sampled out of 7335 households both in Rusinga East and West. All these houses were captured during the baseline and given unique codes for identification in the database. Samsung Galaxy tab fixed with Wi-Fi and GPS coordinates was used to give the direction of the houses to be sampled. The selected houses were divided in groups of 10 for easy sampling. Every 10 houses were sampled on a single night.
Sampling was conducted by setting MMX-trap (Figure 1) in selected houses 15cm from the ground. The traps were connected to 12v battery and a carbon dioxide producing container. The trap also had baits which was prepared from five different compounds. The compounds were Lactic acid, Ammonia, Tetradecanoic acid, 1 Butyl amine and 3- methyl-1-butanol prepared from different concentrations. The blend was called MB5 and it was prepared at icipe Mbita point. The trap consisted of two fans in which one propelled the odor from the bait to the environment which attracted the mosquitoes. The second fan acted to suck mosquito into the trap. The trap was hanged 15 centimeters above the ground and as mosquitoes fly around the trap they were sucked up into the trap. The traps were set in the evening and collected the following morning for mosquito identification and counting at icipe-Mbita station.
The mosquitoes caught in each trap per night were killed by freezing at -25◦C for two hours and identified using morphological keys published by Gillies and Coetzee (1987), counted and recorded on Excel spreadsheet.
Figure 1: MMX trap placed indoors.
The traps were also connected to a carbon dioxide producing container prepared from a mixture of 17.5 g of yeast ( Angel yeast), 250 ml of molasses purchased from Mumias Sugar Company and 2 liters of water . The carbon dioxide produced acted as mosquito attractant. The trap also had baits which was prepared from five different compounds. The blend was called MB5 and it was prepared at Icipe-Mbita point station. The nylon strips obtained from ladies stockings were soaked in these compounds. The strips were cut into small pieces of 26.5 cm in length by 1cm width. Each strip had its own unique compound. The compounds were Lactic acid, Ammonia, Tetradecanoic acid, 1 Butyl amine and 3- methyl-1-butanol prepared from different concentrations.
The parameters that were recorded were presence of animals sleeping outdoors, number of people sleeping in the trapping houses and whether the occupants cook indoors or not.
Mosquito collections were removed from each trap and their species and numbers recorded on Excel spreadsheet. The association of each risk factor with mosquito catch size was individually analyzed by chi-square. Analyses were conducted using SPSS version 15.0 (SPSS Inc., Chicago, IL).
RESULTS
A total of 538 mosquitoes were collected in 160 houses during the study period, of which 283 (1.77) were Anophelines. There was 156 (0.98) Anopheles funestus and 127(0.79) Anopheles gambiae. Other mosquito species caught were Culex, Mansonia and Aedes species.
A total of 108 (0.96) Anopheles gambiae and 138 (1.22) Anopheles funestus were caught in houses where there were no animals sleeping outdoors. On the other hand, a total of 19 (0.40) Anopheles gambiae and 18 (0.38) Anopheles funestus were caught in houses where animals slept outdoors (Table 1).
Table 1: Mosquito species distribution in households with and without domestic animals sleeping outdoors
Domestic animalspresence / An gambiae / An funestus / Culex / Mansonia / Aedes
No Sum
Mean
Yes Sum
Mean
Total Sum
Mean / 108
0.96
19
0.40
127
0.79 / 138
1.22
18
0.38
156
0.98 / 197
1.74
21
0.45
218
1.36 / 18
0.16
2
0.04
20
0.13 / 16
0.14
1
0.02
17
0.11
The mean number of mosquitoes in houses where there were no animals outside was significantly higher (p = 0.02) compared to where there were animals. Thus the presence of animals sleeping outdoors reduces abundance of indoor resting mosquitoes.