Clustering of Malaria Infections within an Endemic Population: Risk of Malaria Associated with the Type of Housing Construction

Gamage-Mendis, Asoka C.; Carter, R.; Mendis, Chandana; Zoysa, A. P. K. De; Herath, P. R. J.; Mendis, Kamini

doi:10.4269/ajtmh.1991.45.77

Cited by 143 publications

(122 citation statements)

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“…Equally important as genetics in determining disease burden were nongenetic intrinsic factors (acquired immunity) and environmental factors (e.g., housing); these effects are well known and have been discussed widely in the malaria literature (32,33,(54)(55)(56)(57)(58), although rarely with respect to their contribution to overall variation in disease. Both of these influences exhibited different patterns for the two parasite species, indicating that different immune mechanisms and ecology are operating in P. falciparum and P. vivax, as is well established (54,58).…”

Section: Discussionmentioning

confidence: 99%

“…Most people in our study population have about one clinical attack per year, and generally 1-2% of the population are infected (at detectable levels) with either Plasmodium vivax or Plasmodium falciparum at any point in time (29). Epidemics of P. falciparum malaria used to occur at 7-to 10-year intervals (31), but since 1986, P. falciparum has persisted (32,33). Further details of the study site are given elsewhere (29,33).…”

Section: Methodsmentioning

confidence: 99%

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Quantifying genetic and nongenetic contributions to malarial infection in a Sri Lankan population

Mackinnon

Dissanayake

Rajakaruna

et al. 2000

Proc. Natl. Acad. Sci. U.S.A.

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Explaining the causes of variation in the severity of malarial disease remains a major challenge in the treatment and control of malaria. Many factors are known to contribute to this variation, including parasite genetics, host genetics, acquired immunity, and exposure levels. However, the relative importance of each of these to the overall burden of malarial disease in human populations has not been assessed. Here, we have partitioned variation in the incidence of malarial infection and the clinical intensity of malarial disease in a rural population in Sri Lanka into its component causes by pedigree analysis of longitudinal data. We found that human genetics, housing, and predisposing systematic effects (e.g., sex, age, occupation, history of infections, village) each explained approximately 15% of the variation in the frequency of malarial infection. For clinical intensity of illness, 20% of the variation was explained by repeatable differences between patients, about half of which was attributable to host genetics. The other half was attributable to semipermanent differences among patients, most of which could be explained by known predisposing factors. Three percent of variation in clinical intensity was explained by housing, and an additional 7% was explained by current influences relating to infection status (e.g., parasitemia, parasite species). Genetic control of Plasmodium falciparum infections appeared to modulate the frequency and intensity of infections, whereas genetic control of Plasmodium vivax infections appeared to confer absolute susceptibility or refractoriness but not intensity of disease. Overall, the data show consistent, repeatable differences among hosts in their susceptibility to clinical disease, about half of which are attributable to host genes.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Quantifying genetic and nongenetic contributions to malarial infection in a Sri Lankan population

Mackinnon

Dissanayake

Rajakaruna

et al. 2000

Proc. Natl. Acad. Sci. U.S.A.

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“…These apparently large contrasts in host choice by An. gambiae in East and West Africa may result from between-site differences in innate behavioural traits of the vectors (Touré et al, 1994;Lanzaro et al, 1998;COETZEE et al, 2000), spatial relationships of hosts, vectors and larval habitats (Kitron & Spielman, 1989;Kitron, 1998), personal protection (Lindsay et al, 1989 or housing design (Gamage-Mendis et al, 1991;Ghebreyesus et al, 2000). Of these factors, the unusually longstanding and widespread use of bednets in The Gambia deserves particular attention because such domestic personal protection measures can discourage feeding on humans and reduce human blood indices (Garrett-Jones, 1964;Charlwood & Graves, 1987;Lindsay et al, 1989;Githeko et al, 1996a;Bøgh et al, 1998).…”

Section: Discussionmentioning

confidence: 99%

The availability of potential hosts as a determinant of feeding behaviours and malaria transmission by African mosquito populations

Killeen

McKenzie

Foy

et al. 2001

Transactions of the Royal Society of Tropical Medicine and Hygi

106

148

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A simple model for the influence of host availability on vector bloodmeal choice is applied to estimate the relative availabilities of humans, cattle and other host populations to malaria vectors in African communities, using published human blood indices and ratios of cattle to humans. Cattle were bitten <0.01, 0.021 ± 0.11, 1.61 ± 0.16 and 1.61 ± 0.46 times as often as humans by Anopheles funestus, An. gambiae sensu stricto and An. arabiensis in Segera, Tanzania, and An. gambiae sensu lato in The Gambia, respectively. No significant feeding upon host species other than cattle or humans was detected. Even though An. gambiae s.l. in The Gambia were mostly An. gambiae s.s., they were 77 times more likely to choose cattle over humans than An. gambiae s.s. in Tanzania. The model accurately predicted cattle blood indices for the An. arabiensis population in Tanzania (predicted = 0.99 ± 0.21 × observed + 0.00 ± 0.10; r 2 = 0.66). The potential effect of increased cattle abundance upon malaria transmission intensity was simulated using fitted relative availability parameters and assuming vector emergence rate, feeding cycle length and survivorship were unaffected. The model predicted that increased cattle populations would not affect malaria transmission in Tanzania but could drastically reduce transmission in The Gambia or where An. arabiensis is the dominant vector. We define the availability of a host as the rate at which a typical individual host-seeking vector encounters and feeds upon that host in a single feeding cycle. Mathematical models based on this definition also represent promising tools for quantifying the dependence of vector longevity, feeding cycle length and dispersal upon host availability.

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“…La heterogeneidad de la transmisión malárica está comprobada a escala global, regional y en escala fina en, por ejemplo, Mali, Ghana, Etiopía, Kenia y Tanzania [1][2][3][4][5][6][7][8][9] . No conocemos estudios de puntos calientes/guaridas en Colombia pero sí sabemos de datos que indican, con claridad, que esto sucede también en el país.…”

Section: Introductionunclassified

La Región “Urabá Antioqueño-Cuencas altas de los ríos Sinú y San Jorge-Bajo Cauca Antioqueño”: “guarida” del paludismo colombiano

Carmona‐Fonseca

2017

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Clustering of Malaria Infections within an Endemic Population: Risk of Malaria Associated with the Type of Housing Construction

Cited by 143 publications

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Quantifying genetic and nongenetic contributions to malarial infection in a Sri Lankan population

Quantifying genetic and nongenetic contributions to malarial infection in a Sri Lankan population

The availability of potential hosts as a determinant of feeding behaviours and malaria transmission by African mosquito populations

La Región “Urabá Antioqueño-Cuencas altas de los ríos Sinú y San Jorge-Bajo Cauca Antioqueño”: “guarida” del paludismo colombiano

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