A micro-epidemiological analysis of febrile malaria in Coastal Kenya showing hotspots within hotspots
- Kilifi KEMRI-Wellcome Trust Collaborative Research Programme, Kenya
- Kilifi KEMRI-Wellcome Trust Collaborative Research Programme, United Kingdom
- University of Oxford, United Kingdom
- University of California, San Francisco, United States
- London School of Hygiene and Tropical Medicine, United Kingdom
- John Hopkins Malaria Research Institute, United States
Abstract
Malaria transmission is spatially heterogeneous. This reduces the efficacy of control strategies, but focusing control strategies on clusters or 'hotspots' of transmission may be highly effective. Among 1,500 homesteads in coastal Kenya we calculated a) the fraction of febrile children with positive malaria smears per homestead, and b) the mean age of children with malaria per homestead. These two measures were inversely correlated, indicating that children in homesteads at higher transmission acquire immunity more rapidly. This inverse correlation increased gradually with increasing spatial scale of analysis, and hotspots of febrile malaria were identified at every scale. We found hotspots within hotspots, down to the level of an individual homestead. Febrile malaria hotspots were temporally unstable, but 4km radius hotspots could be targeted for one month following one month periods of surveillance.
Article and author information
Author details
Reviewing Editor
- Mercedes Pascual, University of Michigan, United States
Ethics
Human subjects: Informed consent for participation was obtained, and specific ethical approval was obtained from the KEMRI Ethical Review Committee (SSC Protocol No. 2413: Spatial Epidemiology of Malaria Cases in the Kilifi District Demographic Surveillance Area). The KEMRI ethical review committee required that participants consent for participation in research and for their data to be stored, but does not require a further explicit statement consenting to publication. Our institutional guidelines would require this only in the event that individuals were identifiable in the publication.
Version history
- Received: December 19, 2013
- Accepted: April 1, 2014
- Accepted Manuscript published: April 24, 2014 (version 1)
- Version of Record published: April 29, 2014 (version 2)
Copyright
© 2014, Bejon et al.
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 2,418
- views
-
- 295
- downloads
-
- 113
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
A micro-epidemiological analysis of febrile malaria in Coastal Kenya showing hotspots within hotspots
eLife 3:e02130.
https://doi.org/10.7554/eLife.02130
Further reading
-
- Epidemiology and Global Health
- Microbiology and Infectious Disease
eLife has published papers on many tropical diseases, including malaria, Ebola, leishmaniases, Dengue and African sleeping sickness.
-
- Cancer Biology
- Epidemiology and Global Health
Background:
Age is the most important risk factor for cancer, but aging rates are heterogeneous across individuals. We explored a new measure of aging-Phenotypic Age (PhenoAge)-in the risk prediction of site-specific and overall cancer.
Methods:
Using Cox regression models, we examined the association of Phenotypic Age Acceleration (PhenoAgeAccel) with cancer incidence by genetic risk group among 374,463 participants from the UK Biobank. We generated PhenoAge using chronological age and nine biomarkers, PhenoAgeAccel after subtracting the effect of chronological age by regression residual, and an incidence-weighted overall cancer polygenic risk score (CPRS) based on 20 cancer site-specific polygenic risk scores (PRSs).
Results:
Compared with biologically younger participants, those older had a significantly higher risk of overall cancer, with hazard ratios (HRs) of 1.22 (95% confidence interval, 1.18–1.27) in men, and 1.26 (1.22–1.31) in women, respectively. A joint effect of genetic risk and PhenoAgeAccel was observed on overall cancer risk, with HRs of 2.29 (2.10–2.51) for men and 1.94 (1.78–2.11) for women with high genetic risk and older PhenoAge compared with those with low genetic risk and younger PhenoAge. PhenoAgeAccel was negatively associated with the number of healthy lifestyle factors (Beta = –1.01 in men, p<0.001; Beta = –0.98 in women, p<0.001).
Conclusions:
Within and across genetic risk groups, older PhenoAge was consistently related to an increased risk of incident cancer with adjustment for chronological age and the aging process could be retarded by adherence to a healthy lifestyle.
Funding:
This work was supported by the National Natural Science Foundation of China (82230110, 82125033, 82388102 to GJ; 82273714 to MZ); and the Excellent Youth Foundation of Jiangsu Province (BK20220100 to MZ).
