Evidence map›Paper›PMID 28095468›Full record

ArticlePLoS medicine2017

Evaluating Hospital-Based Surveillance for Outbreak Detection in Bangladesh: Analysis of Healthcare Utilization Data.

Birgit Nikolay, Henrik Salje, Katharine Sturm-Ramirez, Eduardo Azziz-Baumgartner, Nusrat Homaira, Makhdum Ahmed, A Danielle Iuliano, Repon C Paul, Mahmudur Rahman, M Jahangir Hossain and 3 more

Abstract read
In one paragraph

Article in PLoS medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  4. Review
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  12. Comparing insights from clinic-based versus community-based outbreak investigations: a case study of chikungunya in Bangladesh.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2020
    Article
  13. Article
  14. Spatiotemporal Patterns of Cholera Hospitalization in Vellore, India.International journal of environmental research and public health · 2019
    Article
  15. Hospital-based zoonotic disease surveillance in Bangladesh: design, field data and difficulties.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019
    Article
  16. Article
  17. Article
  18. Estimating undetected Ebola spillovers.PLoS neglected tropical diseases · 2019
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Birgit NikolayMathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Paris, France.
Henrik SaljeMathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Paris, France.
Katharine Sturm-RamirezInfluenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.
Eduardo Azziz-BaumgartnerInfluenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.
Nusrat HomairaInfectious Diseases Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh.
Makhdum AhmedSchool of Public Health, University of Texas Health Science Center, Houston, Texas, United States of America.ORCID http://orcid.org/0000-0002-0772-8710
A Danielle IulianoInfluenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.
Repon C PaulInfectious Diseases Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh.
Mahmudur RahmanInstitute of Epidemiology, Disease Control and Research, Dhaka, Bangladesh.ORCID http://orcid.org/0000-0002-3698-4852
M Jahangir HossainMedical Research Council Unit The Gambia, Banjul, The Gambia.
Stephen P LubyInfectious Diseases Division, Stanford University, Stanford, California, United States of America.ORCID http://orcid.org/0000-0001-5385-899X
Simon CauchemezMathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Paris, France.
Emily S GurleyInfectious Diseases Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh.

Funding

Models for synthesising molecular, clinical and epidemiological data, and translaU01GM110721 · NIGMS · IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE · PI CAUCHEMEZ, SIMON, FERGUSON, NEIL MORRIS · 2014 to 2018
$1.9M
NIGMS NIH HHS U01 GM110721
6 · The paper itself

Abstract

backgroundThe 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

findingsWe 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.

conclusionWe 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.

Indexed as

Disease OutbreaksBangladeshCentral Nervous System InfectionsHospitalizationHospitalsHumansPopulation SurveillanceRespiratory Tract InfectionsSensitivity and Specificity

Identifiers

PMID28095468
PMCPMC5240927

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.