Literature DB >> 8439438

Evaluation of a method for detecting outbreaks of diseases in six states.

M Wharton1, W Price, F Hoesly, D Woolard, K White, C Greene, S McNabb.   

Abstract

A new statistical method, developed for detection of changes in reporting, has proved useful in analysis of provisional data reported by state health departments to the National Notifiable Diseases Surveillance System (NNDSS). In this system, data from the current four-week period can be compared with data from the previous, same, and subsequent four-week periods from each of the preceding five years, and reports exceeding historical limits are highlighted in a horizontal bar graph. To evaluate the usefulness of this method at the state level, we applied it to weekly reports of seven notifiable diseases in six states over a four-month period. Participating state health departments investigated all events exceeding historical limits and reported known outbreaks that were not identified by the method. During the four-month period, the method identified 27 episodes of disease reports exceeding historical limits. Of these, 14 (52%) represented outbreaks. None was detectable by analysis of aggregate national surveillance data. Five outbreaks known to state health department officials were not identified by the method, because of increased disease activity during the baseline period or lack of timely provisional reporting of outbreak-related cases. Methods for detection of increases in reporting at the state level may identify events of public health importance that are obscured in aggregate national data and may supplement other local sources of information available to state health departments in the recognition of significant public health events.

Mesh:

Year:  1993        PMID: 8439438

Source DB:  PubMed          Journal:  Am J Prev Med        ISSN: 0749-3797            Impact factor:   5.043


  4 in total

1.  A deviation bar chart for detecting dengue outbreaks in Puerto Rico.

Authors:  J G Rigau-Pérez; P S Millard; D R Walker; C C Deseda; A Casta-Vélez
Journal:  Am J Public Health       Date:  1999-03       Impact factor: 9.308

Review 2.  How outbreaks of infectious disease are detected: a review of surveillance systems and outbreaks.

Authors:  Virginia Dato; Michael M Wagner; Abi Fapohunda
Journal:  Public Health Rep       Date:  2004 Sep-Oct       Impact factor: 2.792

3.  The Prediction of Hepatitis E through Ensemble Learning.

Authors:  Tu Peng; Xiaoya Chen; Ming Wan; Lizhu Jin; Xiaofeng Wang; Xuejie Du; Hui Ge; Xu Yang
Journal:  Int J Environ Res Public Health       Date:  2020-12-28       Impact factor: 3.390

4.  Refining historical limits method to improve disease cluster detection, New York City, New York, USA.

Authors:  Alison Levin-Rector; Elisha L Wilson; Annie D Fine; Sharon K Greene
Journal:  Emerg Infect Dis       Date:  2015-02       Impact factor: 6.883

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.