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ABSTRACT |
 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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In community-wide intervention trials, bed nets and insecticide have been provided without cost to ensure high coverage in intervention groups. In operational programs, coverage may be lower. In areas of sub-Saharan Africa where ITNs are available, the cost of nets and insecticide is often cited as a major barrier to the uptake and maintenance of insecticide treated nets.13–15 In The Gambia16 and coastal Kenya,17 retreatment rates decreased considerably when people had to pay for insecticide. Social and cultural factors may also affect the level of ITN use in a community. Therefore, in practice, coverage of ITNs may be incomplete.
Incomplete coverage of ITNs could have several effects on mosquito distribution and behavior. Mosquitoes may be diverted from houses with ITNs to those without them, thus increasing malaria transmission and disease risk among people without ITNs.18 It has been suggested that this may have occurred in trials of untreated bed nets and that observed differences in malaria morbidity were due to increased infections in unprotected persons rather than a decrease in morbidity among bed net users.19 Alternatively, the use of ITNs may act to depress the population of vector mosquitoes in large areas. If large enough, this community effect would provide some protection to everyone living in a given region including those who do not possess ITNs. Evidence of a community effect in mosquito populations has been observed on the Kenyan coast where compounds located within 400 meters of an intervention village had significantly fewer mosquitoes than compounds further away.20 Studies in Tanzania and Burkina Faso suggested that ITNs or curtains reduced vector survival and that unprotected persons sleeping indoors or outdoors within intervention villages experienced fewer mosquito bites than persons sleeping in control villages.6,8 In two studies, these community effects had a measurable impact on child morbidity21 and mortality.22 However, several studies in The Gambia indicated no evidence for a community effect.23–25
In this report, we used a geographic information system to test the hypothesis that widespread ITN use affects the distribution and abundance of mosquitoes in adjacent areas without ITNs. Specifically, we tested whether control compounds that neighbor intervention villages experience a change, whether an increase or decrease, in the abundance of blood fed malaria vectors found inside houses. Through repeated measures of the vector population and precise measurements of household locations, we determined the degree to which abundance of malaria vectors changed as a function of distance from houses with ITNs.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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The study population consisted of approximately 17,000 inhabitants, most of whom are members of the Luo ethnic group. The population of the study area is dispersed among approximately 2,500 family compounds distributed throughout the study area. Compounds are defined as one or more houses (usually 3–5 houses) and surrounding farmland. Most houses are constructed with stick and mud walls and a thatched roof. Smaller numbers are constructed with brick walls and/or corrugated tin roofs. Most inhabitants practice subsistence farming with maize the staple crop. Family compounds in the study area were grouped into 19 villages ranging in size from 46 to 242 compounds and 225 to 1,714 inhabitants. In July 1995, this area was enrolled as part of a large-scale trial of ITNs and, in December 1996, enough ITNs (Siamdutch Mosquito Netting Co., Bangkok, Thailand) were distributed to cover all sleeping places (beds or mats) in 9 of 19 villages. Villages receiving ITNs (intervention villages) were assigned by lottery; the 10 villages that did not receive ITNs in December 1996 (control villages) were given nets in early 1999. Bed nets were pre-treated with 0.5 g of permethrin/m2 of netting and were re-treated with that target dose approximately every six months.
Geographic information system.
The mapping of the study area using a differential global positioning system (GPS) has been described in detail elsewhere.30 Briefly, all family compounds, market areas, schools, health facilities, and potential mosquito breeding sites (dams and burrow pits) were mapped using carrier phase GPS processing with differential correction. All roads and streams were mapped using mobile point GPS processing. Mapping was done with Magellan (Magellan System Corp., San Dimas, CA) Pro Mark X GPS units. A base station was constructed at the Kenya Medical Research Institute field station in Kisian, 10 km west of Kisumu. The position of the base station was estimated by taking the average of several readings. The base station took readings every second throughout the day and saved them into a control file. At the end of each day, control files and remote files were downloaded to a personal computer for post-processing. Post-processing of point and mobile data was done using Magellan software to calibrate positions of the remote files. Points from mobile files then were replaced with smoothed lines using AutoCAD (Autodesk, Inc., San Raphael, CA). Resulting files were imported into AtlasGIS (Environmental Systems Research Institute, Redlands, CA) to create a base map of the study area that incorporated the location (latitude, longitude, and elevation) of each compound sampled (Figure 1
). Compound locations are accurate to within a few meters.
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Data analysis.
Minimum distances from compounds in control villages to compounds in intervention villages were calculated using SAS statistical software (SAS Institute, Inc., Cary, NC). Minimum distances also were calculated from compounds in control villages to potential mosquito breeding sites: dams, burrow pits, the lakeshore, and streambeds. Compounds were categorized by distance to lakeshore as 0–999 meters, 1,000–1,999 meters, 2,000–2,999 meters, or 
3,000 meters. For the other distance measures, compounds were assigned to one of four categories: 0–299 meters, 300–599 meters, 600–899 meters, and 900 meters. Continuous variables (rainfall and temperature) were centered around the mean. Statistical significance of the effect of distance to nearest intervention compound upon the abundance of An. gambiae and An. funestus was tested by repeated measures Poisson regression using the GENMOD procedure in SAS. The statistical test used an autocorrelative structure with the house as the repeated subject. This analysis incorporates an assumption that the correlation between measurements on the same house decreased as the time between measurements increased. Interaction terms between time (pre-intervention versus intervention periods) and distance from intervention compounds were included in all models. This allowed for estimation of the effect of distance to the nearest compound in an intervention village in 1997 while adjusting for pre-intervention abundance in 1996. Additional factors controlled for in the model of An. gambiae abundance were the distance to the lakeshore, rainfall 1–4 weeks before collection and average daily maximum temperature 1–4 weeks before collection. The model also included a significant interaction between rainfall 1–4 weeks before collection and year. The model for An. funestus was similar but included rainfall 5–8 weeks before collection because this was a stronger predictor of An. funestus abundance than rainfall 1–4 weeks before collection. The An. funestus model did not include the interaction between rainfall and year because this interaction was not significant. Distances to the nearest river, dam, or burrow pit were not significant and, therefore, were not included in the final models. Each model was run twice. First, dummy variables for distance from intervention villages were created using abundance at distances 900 meters from an intervention village as the baseline. This allowed for comparisons of the effect of distance at each distance category. Second, models were run treating the distance from intervention village as a continuous categorical variable to test for trends. The sporozoite rates for An. gambiae and An. funestus were modeled with repeated measures logistic regression using the GENMOD procedure in SAS. Only distance to the nearest compounds in an intervention village and time were included in these models.
Ethical clearance. The bed net trial and the longitudinal cohort study were reviewed and approved by the institutional review boards of the Kenya Medical Research Institute (Nairobi, Kenya) and the Centers for Disease Control and Prevention (Atlanta, GA). Informed consent was obtained from all caregivers after the study was explained in the local language.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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. Of 277 An. gambiae s.l. identified by PCR during the two-year period, 94.2% were An. gambiae s.s. In the intervention year, average numbers were lower compared with the pre-intervention year for An. gambiae (P = 0.001) but not for An. funestus (P = 0.157). In trend tests (Table 2), there was a significant trend for decreasing An. gambiae abundance with decreasing distance from intervention villages in both the pre-intervention year (P = 0.027) and the intervention year (P = 0.002). Comparison between years indicated a significantly stronger trend in the intervention year (P = 0.05). No trend for An. funestus abundance with distance from an intervention village was observed in the pre-intervention year (P = 0.373). In the intervention year, a significant trend for decreasing abundance with decreasing distance from an intervention village was detected (P = 0.027). Comparison between years indicated a significant difference in the trends (P = 0.014).
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. For each species and year, the risk ratio indicates the difference in mosquito abundance relative to the reference group (houses 900 meters from the nearest intervention village). Risk ratios less than 1 indicate lower numbers relative to the reference group and differences are statistically significant when the confidence intervals do not overlap one. One minus the risk ratio is an estimate of the relative difference in abundance compared with houses that were 900 meters from an intervention village. The P value listed in Table 3 is the probability level for the interaction term. A significant interaction term indicates that the risk ratios for the pre-intervention year and the intervention year are significantly different. In the pre-intervention year, the average number of An. gambiae was 21% lower in houses 600–899 meters from an intervention village compared with houses 900 meters from an intervention village (P = 0.089). The average number of An. gambiae was 29% lower in houses 300–599 meters from an intervention village (P = 0.017) and 26% lower in houses 0–299 meters from an intervention village (P = 0.103). The difference was statistically significant at 300–599 meters. During the intervention year, An. gambiae abundance was 32% lower 600–899 meters from the intervention villages (P = 0.118), 56% lower at 300–599 meters (P = 0.002), and 52% lower at 0–299 meters (P = 0.005). The differences were statistically significant at 0–299 meters and 300–599 meters when compared with mosquito numbers 900 meters from the intervention village. Comparing between years, the risk ratios were all lower in the intervention year. The difference in risk ratios was statistically significant at 300–599 meters (P = 0.045). The difference in risk ratios 0–299 meters from an intervention village was not statistically significant (P = 0.102), but difference was similar to that observed in the 300–599 meter distance category, and the higher P value may reflect a smaller sample size in the 0–299 meter distance category.
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900 meters from an intervention village (P = 0.910). The average number of An. funestus was 15% higher in houses 300–599 meters from an intervention village (P = 0.428) and 11% higher in houses 0–299 meters from an intervention village (P = 0.587). In the intervention year, An. funestus numbers increased with increasing distance from intervention villages. Relative to control houses that were 900 meters from intervention villages, An. funestus numbers were 10% higher in houses 600–899 meters from an intervention village (P = 0.739), 38% lower in houses 300–599 meters (P = 0.181) and 58% lower in houses 0–299 meters (P = 0.013). The difference was statistically significant at 0–299 meters. Comparing between pre-intervention and intervention years, the risk ratios were significantly lower in the 0–299 meters distance category in the intervention year (P = 0.010). There was a marked decrease in sporozoite rates in both An. gambiae and An. funestus following the introduction of the ITNs in 1997. Overall, pre-intervention infection rates were 5.81% for An. gambiae and 5.09% for An. funestus. Infection rates were 0.56% and 0.41%, respectively, during the intervention period. The effect of year on the probability of infection was statistically significant for both An. gambiae (P < 0.001) and An. funestus (P < 0.001). However, logistic regression analysis showed no significant association between the distance from intervention villages and the risk of infection in the vectors in either the pre-intervention or the intervention years.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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900 meters from an intervention village was lower in the intervention year compared with the pre-intervention year. The evidence supports the hypothesis that a community effect reduces the overall vector population, and that persons lacking ITNs who live near the compounds of those using ITNs are afforded some protection from vector mosquitoes. No evidence was found for divergence of mosquitoes from intervention compounds to control compounds. Further evidence of a community effect was observed in health outcomes in children. Children residing in control villages but within 300 meters of an intervention village had a lower risk of malaria parasitemia, high-density parasitemia, anemia, and death relative to children who lived near the center of a control village.35 The introduction of ITNs appeared to have a stronger impact upon the distribution of An. funestus compared with An. gambiae. The stronger impact upon An. funestus was not entirely unexpected. Estimates of the direct impact of ITNs in the current trial indicated that An. funestus was significantly reduced in all intervention homes, even when residents did not use their nets.9 This species has proven highly susceptible to chemical control measures and slow to recolonize areas from which it has been eliminated.36 These results suggest that the degree of community effect may depend partly upon the species of malaria vectors present in a given area.
Data from the current and previous trials of ITNs indicate that the community effect upon mosquito abundance also results in improved health outcomes for children residing near intervention villages. In Ghana, child mortality increased by 6.7% for every increase of 100 meters from an intervention compound,22 and studies in coastal Kenya indicated that for a child not sleeping under a bed net, the risk of malaria decreased with increasing use of bed nets in the area surrounding that child.21 In the current trial, rates of parasitemia, high-density parasitemia, anemia, and mortality were lower in children who did not use nets but lived within 300 meters of an intervention village.35 For these health outcomes, the protective effect of living near an intervention village was nearly as strong as that for children living within the intervention villages. This was somewhat surprising given that the reduction in mosquito abundance in control houses located near intervention villages was not nearly as strong as that observed in houses with ITNs.9 This difference may simply reflect the difficulty in modeling mosquito abundance over a large geographic area for an extended time, as well as the uncertainty in the exact relationship between transmission intensity and health outcomes.
The degree of protection conferred to persons not sleeping under ITNs is likely related to the proportion of persons sleeping under nets within a given area. Studies in Burkina Faso, which indicated a stronger community effect than observed in the current study, were based upon collections made near the geographic center of intervention villages and up to 4 km from the nearest control compound.8 In The Gambia, evidence for community effect was observed only in one village which was geographically isolated from other villages and had a high level of net usage.24 Hawley and others35 showed that the community effect appeared to follow a dose response with increasing coverage, with little or no community effect observed when coverage was less than 25%. The community effects observed in these trials suggest that estimates of the protective efficacy of bed nets may have been underestimated because some control houses may indirectly benefit from their proximity to intervention villages.
Although there was evidence for a community effect rather than divergence of mosquitoes, it should be emphasized that the design of community-wide intervention trials will not reflect the true picture of ITN use under operational settings. In intervention trials, ITNs usually are allocated to entire villages and steps are taken to ensure high compliance. In reality, ITN use may be sparse within villages. In this situation, ITN coverage may not be high enough to achieve a community effect. It is possible that very low coverage of ITNs with a random distribution within villages may have different effects, causing diversion of mosquitoes to nearby compounds and increasing malaria risk among the residents of those compounds. Continued monitoring of malaria control programs that implement ITNs as part of their control strategy is necessary to estimate the degree of coverage required to obtain a community effect and to monitor whether lower levels of coverage actually cause divergence of vectors to unprotected persons. Lastly, the type of insecticide used and the dose at which it is applied to nets may affect the strength of any community effect. The insecticide used in the current study was permethrin at a target dose of 500 mg/m2 of netting. In the future, other insecticides such as alphacypermethrin, lambdacyhalothrin, or deltamethrin likely will be used with increasing frequency. These insecticides have higher killing activity and lower repellency compared with permethrin.37,38 Thus, the probability of divergence of mosquitoes due to these chemicals may be lower and any community effect may be higher than that observed in the current study.
Acknowledgments: We thank Maurice Ombok for coordinating the GPS mapping of the study area and George Olang for organizing the mosquito collections. We also thank Joseph Nduati and George Ojwang for conducting the sporozoite ELISA and for PCR identification of An. gambiae s.l. We also are grateful the residents of Asembo Bay for their support and cooperation throughout the study. This paper has been published with the permission of the director of the Kenya Medical Research Institute.
Financial support: The ITN project was funded by the United States Agency for International Development.
Disclaimer: The opinions or assertions contained in this manuscript are the private ones of the authors and are not to be construed as official or reflecting the views of the U.S. Public Health Service or Department of Health and Human Services. Use of trade names is for identification only and does not imply endorsement by the U.S. Public Health Service or Department of Health and Human Services.
Authors’ addresses: John E. Gimnig, Margarette S. Kolczak, Allen W. Hightower, Penelope A. Phillips-Howard, Feiko O. ter Kuile, and William A. Hawley, Division of Parasitic Diseases, National Center for Infectious Diseases, Centers for Disease Control and Prevention, Mailstop F-22, 4770 Buford Highway, Atlanta, GA 30341. John M. Vulule and Erik Schoute, Centre for Vector Biology and Control Research, Kenya Medical Research Institute, PO Box 1578, Kisumu, Kenya. Luna Kamau, Centre for Biotechnology Research and Development, Kenya Medical Research Institute, PO Box 54840, Nairobi, Kenya. Bernard L. Nahlen, Roll Back Malaria, World Health Organization, Avenue Appia 20, 1211 Geneva 27, Switzerland World Health Organization, CH-1211 Geneva 27, Switzerland.
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REFERENCES |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
