Your browser doesn't support javascript.
loading
Evaluating Hospital-Based Surveillance for Outbreak Detection in Bangladesh: Analysis of Healthcare Utilization Data.
Nikolay, Birgit; Salje, Henrik; Sturm-Ramirez, Katharine; Azziz-Baumgartner, Eduardo; Homaira, Nusrat; Ahmed, Makhdum; Iuliano, A Danielle; Paul, Repon C; Rahman, Mahmudur; Hossain, M Jahangir; Luby, Stephen P; Cauchemez, Simon; Gurley, Emily S.
  • Nikolay B; Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Paris, France.
  • Salje H; Centre National de la Recherche Scientifique, URA3012, Paris, France.
  • Sturm-Ramirez K; Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris, France.
  • Azziz-Baumgartner E; Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Paris, France.
  • Homaira N; Centre National de la Recherche Scientifique, URA3012, Paris, France.
  • Ahmed M; Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris, France.
  • Iuliano AD; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
  • Paul RC; Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.
  • Rahman M; Infectious Diseases Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh.
  • Hossain MJ; Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.
  • Luby SP; Infectious Diseases Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh.
  • Cauchemez S; Discipline of Paediatrics, School of Women's and Children's Health, University of New South Wales, Sydney, New South Wales, Australia.
  • Gurley ES; School of Public Health, University of Texas Health Science Center, Houston, Texas, United States of America.
PLoS Med ; 14(1): e1002218, 2017 Jan.
Article en En | MEDLINE | ID: mdl-28095468
ABSTRACT

BACKGROUND:

The International Health Regulations outline core requirements to ensure the detection of public health threats of international concern. Assessing the capacity of surveillance systems to detect these threats is crucial for evaluating a country's ability to meet these requirements. METHODS AND

FINDINGS:

We propose a framework to evaluate the sensitivity and representativeness of hospital-based surveillance and apply it to severe neurological infectious diseases and fatal respiratory infectious diseases in Bangladesh. We identified cases in selected communities within surveillance hospital catchment areas using key informant and house-to-house surveys and ascertained where cases had sought care. We estimated the probability of surveillance detecting different sized outbreaks by distance from the surveillance hospital and compared characteristics of cases identified in the community and cases attending surveillance hospitals. We estimated that surveillance detected 26% (95% CI 18%-33%) of severe neurological disease cases and 18% (95% CI 16%-21%) of fatal respiratory disease cases residing at 10 km distance from a surveillance hospital. Detection probabilities decreased markedly with distance. The probability of detecting small outbreaks (three cases) dropped below 50% at distances greater than 26 km for severe neurological disease and at distances greater than 7 km for fatal respiratory disease. Characteristics of cases attending surveillance hospitals were largely representative of all cases; however, neurological disease cases aged <5 y or from the lowest socioeconomic group and fatal respiratory disease cases aged ≥60 y were underrepresented. Our estimates of outbreak detection rely on suspected cases that attend a surveillance hospital receiving laboratory confirmation of disease and being reported to the surveillance system. The extent to which this occurs will depend on disease characteristics (e.g., severity and symptom specificity) and surveillance resources.

CONCLUSION:

We present a new approach to evaluating the sensitivity and representativeness of hospital-based surveillance, making it possible to predict its ability to detect emerging threats.
Asunto(s)

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Vigilancia de la Población / Brotes de Enfermedades / Hospitales Tipo de estudio: Diagnostic_studies / Prognostic_studies / Screening_studies Límite: Humans País como asunto: Asia Idioma: En Año: 2017 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Vigilancia de la Población / Brotes de Enfermedades / Hospitales Tipo de estudio: Diagnostic_studies / Prognostic_studies / Screening_studies Límite: Humans País como asunto: Asia Idioma: En Año: 2017 Tipo del documento: Article