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Official URL: http://doi.org/10.1016/S2214-109X(20)30286-2
Related URL: http://dx.doi.org/10.1016/S2214-109X(20)30286-2
Abstract
Background: Lymphatic filariasis is a neglected tropical disease that can cause permanent disability through disruption of the lymphatic system. This disease is caused by parasitic filarial worms that are transmitted by mosquitos. Mass drug administration (MDA) of antihelmintics is recommended by WHO to eliminate lymphatic filariasis as a public health problem. This study aims to produce the first geospatial estimates of the global prevalence of lymphatic filariasis infection over time, to quantify progress towards elimination, and to identify geographical variation in distribution of infection. Methods: A global dataset of georeferenced surveyed locations was used to model annual 2000–18 lymphatic filariasis prevalence for 73 current or previously endemic countries. We applied Bayesian model-based geostatistics and time series methods to generate spatially continuous estimates of global all-age 2000–18 prevalence of lymphatic filariasis infection mapped at a resolution of 5 km2 and aggregated to estimate total number of individuals infected. Findings: We used 14 927 datapoints to fit the geospatial models. An estimated 199 million total individuals (95% uncertainty interval 174–234 million) worldwide were infected with lymphatic filariasis in 2000, with totals for WHO regions ranging from 3·1 million (1·6–5·7 million) in the region of the Americas to 107 million (91–134 million) in the South-East Asia region. By 2018, an estimated 51 million individuals (43–63 million) were infected. Broad declines in prevalence are observed globally, but focal areas in Africa and southeast Asia remain less likely to have attained infection prevalence thresholds proposed to achieve local elimination. Interpretation: Although the prevalence of lymphatic filariasis infection has declined since 2000, MDA is still necessary across large populations in Africa and Asia. Our mapped estimates can be used to identify areas where the probability of meeting infection thresholds is low, and when coupled with large uncertainty in the predictions, indicate additional data collection
| Item Type: | Article |
|---|---|
| Source: | Copyright of this article belongs to Elsevier Ltd. |
| ID Code: | 131783 |
| Deposited On: | 08 Dec 2022 07:48 |
| Last Modified: | 08 Dec 2022 07:48 |
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