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Impact of metric and sample size on determining malaria hotspot boundaries

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Impact of metric and sample size on determining malaria hotspot boundaries. / Stresman, Gillian H.; Giorgi, Emanuele; Baidjoe, Amrish; Knight, Phil; Odongo, Wycliffe; Owaga, Chrispin; Shagari, Shehu; Makori, Euniah; Stevenson, Jennifer; Drakeley, Chris; Cox, Jonathan; Bousema, Teun; Diggle, Peter John.

In: Scientific Reports, Vol. 7, 45849, 12.04.2017.

Research output: Contribution to journalJournal article

Harvard

Stresman, GH, Giorgi, E, Baidjoe, A, Knight, P, Odongo, W, Owaga, C, Shagari, S, Makori, E, Stevenson, J, Drakeley, C, Cox, J, Bousema, T & Diggle, PJ 2017, 'Impact of metric and sample size on determining malaria hotspot boundaries', Scientific Reports, vol. 7, 45849. https://doi.org/10.1038/srep45849

APA

Stresman, G. H., Giorgi, E., Baidjoe, A., Knight, P., Odongo, W., Owaga, C., Shagari, S., Makori, E., Stevenson, J., Drakeley, C., Cox, J., Bousema, T., & Diggle, P. J. (2017). Impact of metric and sample size on determining malaria hotspot boundaries. Scientific Reports, 7, [45849]. https://doi.org/10.1038/srep45849

Vancouver

Stresman GH, Giorgi E, Baidjoe A, Knight P, Odongo W, Owaga C et al. Impact of metric and sample size on determining malaria hotspot boundaries. Scientific Reports. 2017 Apr 12;7. 45849. https://doi.org/10.1038/srep45849

Author

Stresman, Gillian H. ; Giorgi, Emanuele ; Baidjoe, Amrish ; Knight, Phil ; Odongo, Wycliffe ; Owaga, Chrispin ; Shagari, Shehu ; Makori, Euniah ; Stevenson, Jennifer ; Drakeley, Chris ; Cox, Jonathan ; Bousema, Teun ; Diggle, Peter John. / Impact of metric and sample size on determining malaria hotspot boundaries. In: Scientific Reports. 2017 ; Vol. 7.

Bibtex

@article{28cb41d751214b40b9394ea72fa84d59,
title = "Impact of metric and sample size on determining malaria hotspot boundaries",
abstract = "The spatial heterogeneity of malaria suggests that interventions may be targeted for maximum impact. It is unclear to what extent different metrics lead to consistent delineation of hotspot boundaries. Using data from a large community-based malaria survey in the western Kenyan highlands, we assessed the agreement between a model-based geostatistical (MBG) approach to detect hotspots using Plasmodium falciparum parasite prevalence and serological evidence for exposure. Malaria transmission was widespread and highly heterogeneous with one third of the total population living in hotspots regardless of metric tested. Moderate agreement (Kappa = 0.424) was observed between hotspots defined based on parasite prevalence by polymerase chain reaction (PCR)- and the prevalence of antibodies to two P. falciparum antigens (MSP-1, AMA-1). While numerous biologically plausible hotspots were identified, their detection strongly relied on the proportion of the population sampled. When only 3% of the population was sampled, no PCR derived hotspots were reliably detected and at least 21% of the population was needed for reliable results. Similar results were observed for hotspots of seroprevalence. Hotspot boundaries are driven by the malaria diagnostic and sample size used to inform the model. These findings warn against the simplistic use of spatial analysis on available data to target malaria interventions in areas where hotspot boundaries are uncertain.",
author = "Stresman, {Gillian H.} and Emanuele Giorgi and Amrish Baidjoe and Phil Knight and Wycliffe Odongo and Chrispin Owaga and Shehu Shagari and Euniah Makori and Jennifer Stevenson and Chris Drakeley and Jonathan Cox and Teun Bousema and Diggle, {Peter John}",
year = "2017",
month = apr,
day = "12",
doi = "10.1038/srep45849",
language = "English",
volume = "7",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",

}

RIS

TY - JOUR

T1 - Impact of metric and sample size on determining malaria hotspot boundaries

AU - Stresman, Gillian H.

AU - Giorgi, Emanuele

AU - Baidjoe, Amrish

AU - Knight, Phil

AU - Odongo, Wycliffe

AU - Owaga, Chrispin

AU - Shagari, Shehu

AU - Makori, Euniah

AU - Stevenson, Jennifer

AU - Drakeley, Chris

AU - Cox, Jonathan

AU - Bousema, Teun

AU - Diggle, Peter John

PY - 2017/4/12

Y1 - 2017/4/12

N2 - The spatial heterogeneity of malaria suggests that interventions may be targeted for maximum impact. It is unclear to what extent different metrics lead to consistent delineation of hotspot boundaries. Using data from a large community-based malaria survey in the western Kenyan highlands, we assessed the agreement between a model-based geostatistical (MBG) approach to detect hotspots using Plasmodium falciparum parasite prevalence and serological evidence for exposure. Malaria transmission was widespread and highly heterogeneous with one third of the total population living in hotspots regardless of metric tested. Moderate agreement (Kappa = 0.424) was observed between hotspots defined based on parasite prevalence by polymerase chain reaction (PCR)- and the prevalence of antibodies to two P. falciparum antigens (MSP-1, AMA-1). While numerous biologically plausible hotspots were identified, their detection strongly relied on the proportion of the population sampled. When only 3% of the population was sampled, no PCR derived hotspots were reliably detected and at least 21% of the population was needed for reliable results. Similar results were observed for hotspots of seroprevalence. Hotspot boundaries are driven by the malaria diagnostic and sample size used to inform the model. These findings warn against the simplistic use of spatial analysis on available data to target malaria interventions in areas where hotspot boundaries are uncertain.

AB - The spatial heterogeneity of malaria suggests that interventions may be targeted for maximum impact. It is unclear to what extent different metrics lead to consistent delineation of hotspot boundaries. Using data from a large community-based malaria survey in the western Kenyan highlands, we assessed the agreement between a model-based geostatistical (MBG) approach to detect hotspots using Plasmodium falciparum parasite prevalence and serological evidence for exposure. Malaria transmission was widespread and highly heterogeneous with one third of the total population living in hotspots regardless of metric tested. Moderate agreement (Kappa = 0.424) was observed between hotspots defined based on parasite prevalence by polymerase chain reaction (PCR)- and the prevalence of antibodies to two P. falciparum antigens (MSP-1, AMA-1). While numerous biologically plausible hotspots were identified, their detection strongly relied on the proportion of the population sampled. When only 3% of the population was sampled, no PCR derived hotspots were reliably detected and at least 21% of the population was needed for reliable results. Similar results were observed for hotspots of seroprevalence. Hotspot boundaries are driven by the malaria diagnostic and sample size used to inform the model. These findings warn against the simplistic use of spatial analysis on available data to target malaria interventions in areas where hotspot boundaries are uncertain.

U2 - 10.1038/srep45849

DO - 10.1038/srep45849

M3 - Journal article

C2 - 28401903

VL - 7

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

M1 - 45849

ER -