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Use of earth observation-derived hydrometeorological variables to model and predict rotavirus infection (MAL-ED): a multisite cohort study.
Colston, Josh M; Zaitchik, Benjamin; Kang, Gagandeep; Peñataro Yori, Pablo; Ahmed, Tahmeed; Lima, Aldo; Turab, Ali; Mduma, Esto; Sunder Shrestha, Prakash; Bessong, Pascal; Peng, Roger D; Black, Robert E; Moulton, Lawrence H; Kosek, Margaret N.
Afiliação
  • Colston JM; Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
  • Zaitchik B; Department of Earth and Planetary Sciences, Johns Hopkins Krieger School of Arts and Sciences, Baltimore, MD, USA.
  • Kang G; Christian Medical College, Vellore, India.
  • Peñataro Yori P; Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
  • Ahmed T; Nutrition & Clinical Services Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh.
  • Lima A; Federal University of Ceará, Fortaleza, Brazil.
  • Turab A; Interactive Research and Development, Maternal and Child Health (MCH) Program, Karachi, Pakistan.
  • Mduma E; Haydom Global Health Institute, Haydom, Tanzania.
  • Sunder Shrestha P; Department of Child Health, Institute of Medicine of Tribhuvan University, Kathmandu, Nepal.
  • Bessong P; University of Venda, Thohoyandou, South Africa.
  • Peng RD; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
  • Black RE; Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
  • Moulton LH; Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
  • Kosek MN; Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. Electronic address: mkosek@jhu.edu.
Lancet Planet Health ; 3(6): e248-e258, 2019 06.
Article em En | MEDLINE | ID: mdl-31229000
BACKGROUND: Climate change threatens to undermine recent progress in reducing global deaths from diarrhoeal disease in children. However, the scarcity of evidence about how individual environmental factors affect transmission of specific pathogens makes prediction of trends under different climate scenarios challenging. We aimed to model associations between daily estimates of a suite of hydrometeorological variables and rotavirus infection status ascertained through community-based surveillance. METHODS: For this analysis of multisite cohort data, rotavirus infection status was ascertained through community-based surveillance of infants in the eight-site MAL-ED cohort study, and matched by date with earth observation estimates of nine hydrometeorological variables from the Global Land Data Assimilation System: daily total precipitation volume (mm), daily total surface runoff (mm), surface pressure (mbar), wind speed (m/s), relative humidity (%), soil moisture (%), solar radiation (W/m2), specific humidity (kg/kg), and average daily temperatures (°C). Lag relationships, independent effects, and interactions were characterised by use of modified Poisson models and compared with and without adjustment for seasonality and between-site variation. Final models were created with stepwise selection of main effects and interactions and their validity assessed by excluding each site in turn and calculating Tjur's Coefficients of Determination. FINDINGS: All nine hydrometeorological variables were significantly associated with rotavirus infection after adjusting for seasonality and between-site variation over multiple consecutive or non-consecutive lags, showing complex, often non-linear associations that differed by symptom status and showed considerable mutual interaction. The final models explained 5·9% to 6·2% of the variability in rotavirus infection in the pooled data and their predictions explained between 0·0% and 14·1% of the variability at individual study sites. INTERPRETATION: These results suggest that the effect of climate on rotavirus transmission was mediated by four independent mechanisms: waterborne dispersal, airborne dispersal, virus survival on soil and surfaces, and host factors. Earth observation data products available at a global scale and at subdaily resolution can be combined with longitudinal surveillance data to test hypotheses about routes and drivers of transmission but showed little potential for making predictions in this setting. FUNDING: Bill & Melinda Gates Foundation; Foundation for the National Institutes of Health, National Institutes of Health, Fogarty International Center; Sherrilyn and Ken Fisher Center for Environmental Infectious Diseases, Johns Hopkins School of Medicine; and NASA's Group on Earth Observations Work Programme.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Infecções por Rotavirus / Tempo (Meteorologia) / Clima Tipo de estudo: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans / Infant / Newborn País/Região como assunto: Africa / America do sul / Asia / Brasil / Peru Idioma: En Revista: Lancet Planet Health Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Holanda

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Infecções por Rotavirus / Tempo (Meteorologia) / Clima Tipo de estudo: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans / Infant / Newborn País/Região como assunto: Africa / America do sul / Asia / Brasil / Peru Idioma: En Revista: Lancet Planet Health Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Holanda