Risk factors for Plasmodium falciparum infection in the Kenyan Highlands: a cohort study.

BACKGROUND: Malaria transmission in African highland areas can be prone to epidemics, with minor fluctuations in temperature or altitude resulting in highly heterogeneous transmission. In the Kenyan Highlands, where malaria prevalence has been increasing, characterising malaria incidence and identifying risk factors for infection is complicated by asymptomatic infection. METHODS: This all-age cohort study, one element of the Malaria Transmission Consortium, involved monthly follow-up of 3155 residents of the Kisii and Rachuonyo South districts during June 2009-June 2010. Participants were tested for malaria using rapid diagnostic testing at every visit, regardless of symptoms. RESULTS: The incidence of Plasmodium falciparum infection was 0.2 cases per person, although infections were clustered within individuals and over time, with the majority of infections detected in the last month of the cohort study. Overall, incidence was higher in the Rachuonyo district and infections were detected most frequently in 5-10-year-olds. The majority of infections were asymptomatic (58%). Travel away from the study area was a notable risk factor for infection. CONCLUSIONS: Identifying risk factors for malaria infection can help to guide targeting of interventions to populations most likely to be exposed to malaria.

Use of different transmission metrics to describe malaria epidemiology in the highlands of western Kenya.

BACKGROUND: Monitoring and evaluation of malaria programmes may require a combination of approaches to detect any effects of control. This is particularly true at lower transmission levels where detecting both infection and exposure to infection will provide additional evidence of any change. This paper describes use of three transmission metrics to explore the malaria epidemiology in the highlands of western Kenya. METHODS: A malariometric survey was conducted in June 2009 in two highland districts, Kisii and Rachuonyo South, Nyanza Province, Kenya using a cluster design. Enumeration areas were used to sample 46 clusters from which 12 compounds were randomly sampled. Individuals provided a finger-blood sample to assess malaria infection (rapid diagnostic test, PCR) and exposure (anti-Plasmodium falciparum MSP-1 antibodies) and a questionnaire was administered to record household factors and assess use of vector control interventions. RESULTS: Malaria prevalence infection rates were 3.0 % (95 % CI 2.2-4.2 %) by rapid diagnostic test (RDT) and 8.5 % (95 % CI 7.0-10.4 %) by PCR and these ranged from 0-13.1 to 0-14.8 % between clusters for RDT and PCR, respectively. Seroprevalence was 36.8 % (95 % CI 33.9-39.8) ranging from 18.6 to 65.8 %. Both RDT and PCR prevalences were highest in children aged 5-10 years but the proportion of infections that were sub-patent was highest in those between 15 and 20 years of age (78.1 %, 95 % CI 63.0-93.3 %) and those greater than 20 years (73.3 %, 95 % CI 64.5-81.9 %). Those reporting both indoor residual spraying (IRS) in their home and use of bed nets had lower exposure to malaria compared to those who reported using IRS or bed nets alone. CONCLUSIONS: In this highland site in western Kenya malaria transmission was low, but highly heterogeneous. To accurately characterize the true extent of malaria transmission, more sensitive and complementary metrics such as PCR or serology are required in addition to the standard microscopy and/or RDTs that are routinely used. This is likely to be the case in other low endemicity settings.

High levels of asymptomatic and subpatent Plasmodium falciparum parasite carriage at health facilities in an area of heterogeneous malaria transmission intensity in the Kenyan highlands.

In endemic settings, health facility surveys provide a convenient approach to estimating malaria transmission intensity. Typically, testing for malaria at facilities is performed on symptomatic attendees, but asymptomatic infections comprise a considerable proportion of the parasite reservoir. We sampled individuals attending five health facilities in the western Kenyan highlands. Malaria prevalence by rapid diagnostic test (RDT) was 8.6-32.9% in the health facilities. Of all polymerase chain reaction-positive participants, 46.4% (95% confidence interval [95% CI] = 42.6-50.2%) of participants had infections that were RDT-negative and asymptomatic, and 55.9% of those infections consisted of multiple parasite clones as assessed by merozoite surface protein-2 genotyping. Subpatent infections were more common in individuals reporting the use of non-artemisinin-based antimalarials in the 2 weeks preceding the survey (odds ratio = 2.49, 95% CI = 1.04-5.92) compared with individuals not reporting previous use of antimalarials. We observed a large and genetically complex pool of subpatent parasitemia in the Kenya highlands that must be considered in malaria interventions.

Quantifying travel behavior for infectious disease research: a comparison of data from surveys and mobile phones.

Human travel impacts the spread of infectious diseases across spatial and temporal scales, with broad implications for the biological and social sciences. Individual data on travel patterns have been difficult to obtain, particularly in low-income countries. Travel survey data provide detailed demographic information, but sample sizes are often small and travel histories are hard to validate. Mobile phone records can provide vast quantities of spatio-temporal travel data but vary in spatial resolution and explicitly do not include individual information in order to protect the privacy of subscribers. Here we compare and contrast both sources of data over the same time period in a rural area of Kenya. Although both data sets are able to quantify broad travel patterns and distinguish regional differences in travel, each provides different insights that can be combined to form a more detailed picture of travel in low-income settings to understand the spread of infectious diseases.

Reliability of school surveys in estimating geographic variation in malaria transmission in the western Kenyan highlands.

BACKGROUND: School surveys provide an operational approach to assess malaria transmission through parasite prevalence. There is limited evidence on the comparability of prevalence estimates obtained from school and community surveys carried out at the same locality. METHODS: Concurrent school and community cross-sectional surveys were conducted in 46 school/community clusters in the western Kenyan highlands and households of school children were geolocated. Malaria was assessed by rapid diagnostic test (RDT) and combined seroprevalence of antibodies to bloodstage Plasmodium falciparum antigens. RESULTS: RDT prevalence in school and community populations was 25.7% (95% CI: 24.4-26.8) and 15.5% (95% CI: 14.4-16.7), respectively. Seroprevalence in the school and community populations was 51.9% (95% CI: 50.5-53.3) and 51.5% (95% CI: 49.5-52.9), respectively. RDT prevalence in schools could differentiate between low (<7%, 95% CI: 0-19%) and high (>39%, 95% CI: 25-49%) transmission areas in the community and, after a simple adjustment, were concordant with the community estimates. CONCLUSIONS: Estimates of malaria prevalence from school surveys were consistently higher than those from community surveys and were strongly correlated. School-based estimates can be used as a reliable indicator of malaria transmission intensity in the wider community and may provide a basis for identifying priority areas for malaria control.

Simulation of malaria epidemiology and control in the highlands of Western Kenya.

BACKGROUND: Models of Plasmodium falciparum malaria epidemiology that provide realistic quantitative predictions of likely epidemiological outcomes of existing vector control strategies have the potential to assist in planning for the control and elimination of malaria. This work investigates the applicability of mathematical modelling of malaria transmission dynamics in Rachuonyo South, a district with low, unstable transmission in the highlands of western Kenya. METHODS: Individual-based stochastic simulation models of malaria in humans and a deterministic model of malaria in mosquitoes as part of the OpenMalaria platform were parameterized to create a scenario for the study area based on data from ongoing field studies and available literature. The scenario was simulated for a period of two years with a population of 10,000 individuals and validated against malaria survey data from Rachuonyo South. Simulations were repeated with multiple random seeds and an ensemble of 14 model variants to address stochasticity and model uncertainty. A one-dimensional sensitivity analysis was conducted to address parameter uncertainty. RESULTS: The scenario was able to reproduce the seasonal pattern of the entomological inoculation rate (EIR) and patent infections observed in an all-age cohort of individuals sampled monthly for one year. Using an EIR estimated from serology to parameterize the scenario resulted in a closer fit to parasite prevalence than an EIR estimated using entomological methods. The scenario parameterization was most sensitive to changes in the timing and effectiveness of indoor residual spraying (IRS) and the method used to detect P. falciparum in humans. It was less sensitive than expected to changes in vector biting behaviour and climatic patterns. CONCLUSIONS: The OpenMalaria model of P. falciparum transmission can be used to simulate the impact of different combinations of current and potential control interventions to help plan malaria control in this low transmission setting. In this setting and for these scenarios, results were highly sensitive to transmission, vector exophagy, exophily and susceptibility to IRS, and the detection method used for surveillance. The level of accuracy of the results will thus depend upon the precision of estimates for each. New methods for analysing and evaluating uncertainty in simulation results will enhance the usefulness of simulations for malaria control decision-making. Improved measurement tools and increased primary data collection will enhance model parameterization and epidemiological monitoring. Further research is needed on the relationship between malaria indices to identify the best way to quantify transmission in low transmission settings. Measuring EIR through mosquito collection may not be the optimal way to estimate transmission intensity in areas with low, unstable transmission.