| Title |
Sensitivity and Specificity of Suspected Case Definition Used during West Africa Ebola Epidemic - Volume 24, Number 1—January 2018 - Emerging Infectious Diseases journal - CDC
|
|---|---|
| Published in |
Emerging Infectious Diseases, January 2018
|
| DOI | 10.3201/eid2401.161678 |
| Pubmed ID | |
| Authors |
Christopher H. Hsu, Steven W. Champaloux, Sakoba Keïta, Lise Martel, Pepe Bilivogui, Barbara Knust, Andrea M. McCollum |
| Abstract |
Rapid early detection and control of Ebola virus disease (EVD) is contingent on accurate case definitions. Using an epidemic surveillance dataset from Guinea, we analyzed an EVD case definition developed by the World Health Organization (WHO) and used in Guinea. We used the surveillance dataset (March-October 2014; n = 2,847 persons) to identify patients who satisfied or did not satisfy case definition criteria. Laboratory confirmation determined cases from noncases, and we calculated sensitivity, specificity and predictive values. The sensitivity of the defintion was 68.9%, and the specificity of the definition was 49.6%. The presence of epidemiologic risk factors (i.e., recent contact with a known or suspected EVD case-patient) had the highest sensitivity (74.7%), and unexplained deaths had the highest specificity (92.8%). Results for case definition analyses were statistically significant (p<0.05 by χ2 test). Multiple components of the EVD case definition used in Guinea contributed to improved overall sensitivity and specificity. |
X Demographics
The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 7 | 64% |
| United Kingdom | 1 | 9% |
| Australia | 1 | 9% |
| Unknown | 2 | 18% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 8 | 73% |
| Practitioners (doctors, other healthcare professionals) | 3 | 27% |
Mendeley readers
The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 70 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 15 | 21% |
| Researcher | 13 | 19% |
| Student > Ph. D. Student | 5 | 7% |
| Student > Bachelor | 5 | 7% |
| Student > Postgraduate | 3 | 4% |
| Other | 11 | 16% |
| Unknown | 18 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 22 | 31% |
| Nursing and Health Professions | 10 | 14% |
| Agricultural and Biological Sciences | 3 | 4% |
| Biochemistry, Genetics and Molecular Biology | 3 | 4% |
| Social Sciences | 3 | 4% |
| Other | 6 | 9% |
| Unknown | 23 | 33% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 September 2018.
All research outputs
#6,682,996
of 25,218,929 outputs
Outputs from Emerging Infectious Diseases
#4,924
of 9,700 outputs
Outputs of similar age
#124,088
of 455,458 outputs
Outputs of similar age from Emerging Infectious Diseases
#100
of 152 outputs
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