Shrestha H, McCulloch K, Hedtke SM, Grant WN. Geospatial modeling of pre-intervention nodule prevalence of Onchocerca volvulus in Ethiopia as an aid to onchocerciasis elimination.
PLoS Negl Trop Dis 2022;
16:e0010620. [PMID:
35849615 PMCID:
PMC9333447 DOI:
10.1371/journal.pntd.0010620]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 07/28/2022] [Accepted: 06/28/2022] [Indexed: 11/19/2022] Open
Abstract
Background
Onchocerciasis is a neglected tropical filarial disease transmitted by the bites of blackflies, causing blindness and severe skin lesions. The change in focus for onchocerciasis management from control to elimination requires thorough mapping of pre-control endemicity to identify areas requiring interventions and to monitor progress. Onchocerca volvulus nodule prevalence in sub-Saharan Africa is spatially continuous and heterogeneous, and highly endemic areas may contribute to transmission in areas of low endemicity or vice-versa. Ethiopia is one such onchocerciasis-endemic country with heterogeneous O. volvulus nodule prevalence, and many districts are still unmapped despite their potential for onchocerciasis transmission.
Methodology/Principle findings
A Bayesian geostatistical model was fitted for retrospective pre-intervention nodule prevalence data collected from 916 unique sites and 35,077 people across Ethiopia. We used multiple environmental, socio-demographic, and climate variables to estimate the pre-intervention prevalence of O. volvulus nodules across Ethiopia and to explore their relationship with prevalence. Prevalence was high in southern and northwestern Ethiopia and low in Ethiopia’s central and eastern parts. Distance to the nearest river (RR: 0.9850, 95% BCI: 0.9751–0.995), precipitation seasonality (RR: 0.9837, 95% BCI: 0.9681–0.9995), and flow accumulation (RR: 0.9586, 95% BCI: 0.9321–0.9816) were negatively associated with O. volvulus nodule prevalence, while soil moisture (RR: 1.0218, 95% BCI: 1.0135–1.0302) was positively associated. The model estimated the number of pre-intervention cases of O. volvulus nodules in Ethiopia to be around 6.48 million (95% BCI: 3.53–13.04 million).
Conclusions/Significance
Nodule prevalence distribution was correlated with habitat suitability for vector breeding and associated biting behavior. The modeled pre-intervention prevalence can be used as a guide for determining priorities for elimination mapping in regions of Ethiopia that are currently unmapped, most of which have comparatively low infection prevalence.
Areas with unknown onchocerciasis endemicity may pose a threat to eliminating transmission because they may re-introduce onchocerciasis to areas where interventions have been successful. Additionally, because vectors (and thus Onchocerca volvulus transmission) have specific ecological requirements for growth and development, changes in these ecological factors due to human activities (deforestation, modification of river flows by dam construction, climate change) might change patterns of parasite transmission and endemicity. To estimate the impact of environmental changes, we must first identify ecological factors determining prevalence. We have employed Bayesian geostatistical modeling to create a nationwide O. volvulus nodule prevalence map for Ethiopia based on pre-intervention nodule prevalence data and have explored the effect of environmental variables on nodule prevalence. We estimated the number of pre-intervention cases of nodules and associated uncertainty in previously unmapped areas of Ethiopia to identify areas that need additional data to increase the prediction accuracy. Hydrological variables such as distance to the nearest river, precipitation seasonality, soil moisture, and flow accumulation are associated significantly with O. volvulus nodule prevalence. We show that the spatial distribution of nodule prevalence can be estimated based on ecological data and that predicted prevalence can be used as a guide to prioritize pre-intervention mapping.
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