Estimating the spatial distribution of acute undifferentiated fever (AUF) and associated risk factors using emergency call data in India. A symptom-based approach for public health surveillance
| Autor: | Boris Kauhl, Ramana Rao, Jürgen Schweikart, Thomas Krafft, Oliver Gruebner, Eva Pilot |
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| Přispěvatelé: | International Health, Metamedica, RS: CAPHRI School for Public Health and Primary Care, RS: CAPHRI - R4 - Health Inequities and Societal Participation |
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
Male
CASTE Health (social science) INFORMATION Geography Planning and Development Pilot Projects Disease DETERMINANTS MALARIA RISK Disease Outbreaks Dengue fever Public health surveillance Risk Factors Epidemiology Public Health Surveillance Child Spatial regression education.field_of_study Transmission (medicine) Incidence Acute undifferentiated fever (AUF) BANGLADESH GIS COVERAGE COMMUNITY Child Preschool Preparedness Infectious diseases Female Medical emergency Adult medicine.medical_specialty DENGUE Adolescent TRANSMISSION Population India Fever of Unknown Origin Geographic information systems (GIS) medicine Humans education business.industry Infant Newborn Public Health Environmental and Occupational Health Infant medicine.disease Geographic Information Systems Rural area business |
| Zdroj: | Health & Place, 31, 111-119. ELSEVIER SCI LTD |
| ISSN: | 1353-8292 |
| DOI: | 10.1016/j.healthplace.2014.11.002 |
| Popis: | The System for Early-warning based on Emergency Data (SEED) is a pilot project to evaluate the use of emergency call data with the main complaint acute undifferentiated fever (AUF) for syndromic surveillance in India. While spatio-temporal methods provide signals to detect potential disease outbreaks, additional information about socio-ecological exposure factors and the main population at risk is necessary for evidence-based public health interventions and future preparedness strategies. The goal of this study is to investigate whether a spatial epidemiological analysis at the ecological level provides information on urban-rural inequalities, socio-ecological exposure factors and the main population at risk for AUF. Our results displayed higher risks in rural areas with strong local variation. Household industries and proximity to forests were the main socio-ecological exposure factors and scheduled tribes were the main population at risk for AUF. These results provide additional information for syndromic surveillance and could be used for evidence-based public health interventions and future preparedness strategies. |
| Databáze: | OpenAIRE |
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