Your browser doesn't support javascript.
loading
Model-based spatial-temporal mapping of opisthorchiasis in endemic countries of Southeast Asia.
Zhao, Ting-Ting; Feng, Yi-Jing; Doanh, Pham Ngoc; Sayasone, Somphou; Khieu, Virak; Nithikathkul, Choosak; Qian, Men-Bao; Hao, Yuan-Tao; Lai, Ying-Si.
Afiliación
  • Zhao TT; Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
  • Feng YJ; Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
  • Doanh PN; Department of Parasitology, Institute of Ecology and Biological Resources, Graduate University of Science and Technology, Vietnam Academy of Sciences and Technology, Cau Giay, Hanoi, Viet Nam.
  • Sayasone S; Lao Tropical and Public Health Institute, Ministry of Health, Vientiane, Lao People's Democratic Republic.
  • Khieu V; National Center for Parasitology, Entomology and Malaria Control, Ministry of Health, Phnom Penh, Cambodia.
  • Nithikathkul C; Tropical and Parasitic Diseases Research Unit, Faculty of Medicine, Mahasarakham University, Mahasarakham, Thailand.
  • Qian MB; National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China.
  • Hao YT; WHO Collaborating Centre for Tropical Diseases, Key Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, China.
  • Lai YS; Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
Elife ; 102021 01 12.
Article en En | MEDLINE | ID: mdl-33432926
Opisthorchiasis is an overlooked danger to Southeast Asia. High-resolution disease risk maps are critical but have not been available for Southeast Asia. Georeferenced disease data and potential influencing factor data were collected through a systematic review of literatures and open-access databases, respectively. Bayesian spatial-temporal joint models were developed to analyze both point- and area-level disease data, within a logit regression in combination of potential influencing factors and spatial-temporal random effects. The model-based risk mapping identified areas of low, moderate, and high prevalence across the study region. Even though the overall population-adjusted estimated prevalence presented a trend down, a total of 12.39 million (95% Bayesian credible intervals [BCI]: 10.10-15.06) people were estimated to be infected with O. viverrini in 2018 in four major endemic countries (i.e., Thailand, Laos, Cambodia, and Vietnam), highlighting the public health importance of the disease in the study region. The high-resolution risk maps provide valuable information for spatial targeting of opisthorchiasis control interventions.
Asunto(s)
Palabras clave

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Opistorquiasis / Enfermedades Endémicas Tipo de estudio: Prevalence_studies / Prognostic_studies / Risk_factors_studies País/Región como asunto: Asia Idioma: En Revista: Elife Año: 2021 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Opistorquiasis / Enfermedades Endémicas Tipo de estudio: Prevalence_studies / Prognostic_studies / Risk_factors_studies País/Región como asunto: Asia Idioma: En Revista: Elife Año: 2021 Tipo del documento: Article