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Characterizing local-scale heterogeneity of malaria risk: a case study in Bunkpurugu-Yunyoo district in northern Ghana
- Source :
- Malaria Journal, Vol 18, Iss 1, Pp 1-14 (2019), Malaria Journal
- Publication Year :
- 2019
- Publisher :
- Springer Science and Business Media LLC, 2019.
-
Abstract
- Background Bayesian methods have been used to generate country-level and global maps of malaria prevalence. With increasing availability of detailed malaria surveillance data, these methodologies can also be used to identify fine-scale heterogeneity of malaria parasitaemia for operational prevention and control of malaria. Methods In this article, a Bayesian geostatistical model was applied to six malaria parasitaemia surveys conducted during rainy and dry seasons between November 2010 and 2013 to characterize the micro-scale spatial heterogeneity of malaria risk in northern Ghana. Results The geostatistical model showed substantial spatial heterogeneity, with malaria parasite prevalence varying between 19 and 90%, and revealing a northeast to southwest gradient of predicted risk. The spatial distribution of prevalence was heavily influenced by two modest urban centres, with a substantially lower prevalence in urban centres compared to rural areas. Although strong seasonal variations were observed, spatial malaria prevalence patterns did not change substantially from year to year. Furthermore, independent surveillance data suggested that the model had a relatively good predictive performance when extrapolated to a neighbouring district. Conclusions This high variability in malaria prevalence is striking, given that this small area (approximately 30 km × 40 km) was purportedly homogeneous based on country-level spatial analysis, suggesting that fine-scale parasitaemia data might be critical to guide district-level programmatic efforts to prevent and control malaria. Extrapolations results suggest that fine-scale parasitaemia data can be useful for spatial predictions in neighbouring unsampled districts and does not have to be collected every year to aid district-level operations, helping to alleviate concerns regarding the cost of fine-scale data collection. Electronic supplementary material The online version of this article (10.1186/s12936-019-2703-4) contains supplementary material, which is available to authorized users.
- Subjects :
- Male
medicine.medical_specialty
lcsh:Arctic medicine. Tropical medicine
lcsh:RC955-962
030231 tropical medicine
Spatial distribution
Risk Assessment
Bayesian
Ghana
lcsh:Infectious and parasitic diseases
03 medical and health sciences
0302 clinical medicine
Environmental health
parasitic diseases
Prevalence
medicine
Humans
lcsh:RC109-216
Malaria risk
030212 general & internal medicine
Spatial Analysis
Data collection
Research
Fine-scale
Public health
Infant, Newborn
1. No poverty
Infant
medicine.disease
Malaria
3. Good health
Spatial heterogeneity
Infectious Diseases
Geography
Child, Preschool
Geostatistical
Tropical medicine
Female
Topography, Medical
Parasitology
Rural area
Subjects
Details
- ISSN :
- 14752875
- Volume :
- 18
- Database :
- OpenAIRE
- Journal :
- Malaria Journal
- Accession number :
- edsair.doi.dedup.....5d75fd0e2046a238eb9c7f922fd16865