scholarly journals Identifying Incidents of Public Health Significance Using the National Poison Data System, 2013–2018

2020 ◽  
Vol 110 (10) ◽  
pp. 1528-1531
Author(s):  
Joseph E. Carpenter ◽  
Arthur S. Chang ◽  
Alvin C. Bronstein ◽  
Richard G. Thomas ◽  
Royal K. Law

Data System. The American Association of Poison Control Centers (AAPCC) and the Centers for Disease Control and Prevention (CDC) jointly monitor the National Poison Data System (NPDS) for incidents of public health significance (IPHSs). Data Collection/Processing. NPDS is the data repository for US poison centers, which together cover all 50 states, the District of Columbia, and multiple territories. Information from calls to poison centers is uploaded to NPDS in near real time and continuously monitored for specific exposures and anomalies relative to historic data. Data Analysis/Dissemination. AAPCC and CDC toxicologists analyze NPDS-generated anomalies for evidence of public health significance. Presumptive results are confirmed with the receiving poison center to correctly identify IPHSs. Once verified, CDC notifies the state public health department. Implications. During 2013 to 2018, 3.7% of all NPDS-generated anomalies represented IPHSs. NPDS surveillance findings may be the first alert to state epidemiologists of IPHSs. Data are used locally and nationally to enhance situational awareness during a suspected or known public health threat. NPDS improves CDC’s national surveillance capacity by identifying early markers of IPHSs.

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Royal K. Law ◽  
Howard Burkom ◽  
Alvin Bronstein ◽  
Josh Schier

This presentation compares surveillance algorithms used in the National Poison Data System to identify incidents of public health significance with recently expanded filtering capabilities and with methods beyond the NPDS generalized historical limits model. Collected data series from 55 poison centers over 7 years include hourly counts of general call volumes and of substance-specific (e.g. CO exposure) calls. By applying current, modified, and novel methods to known and simulated clusters among these data, the authors will present the most efficient algorithms for identifying incidents of public health significance.


2010 ◽  
Vol 48 (10) ◽  
pp. 979-1178 ◽  
Author(s):  
Alvin C. Bronstein ◽  
Daniel A. Spyker ◽  
Louis R. Cantilena ◽  
Jody L. Green ◽  
Barry H. Rumack ◽  
...  

2014 ◽  
Vol 52 (10) ◽  
pp. 1032-1283 ◽  
Author(s):  
James B. Mowry ◽  
Daniel A. Spyker ◽  
Louis R. Cantilena ◽  
Naya McMillan ◽  
Marsha Ford

2015 ◽  
Vol 53 (10) ◽  
pp. 962-1147 ◽  
Author(s):  
James B. Mowry ◽  
Daniel A. Spyker ◽  
Daniel E. Brooks ◽  
Naya McMillan ◽  
Jay L. Schauben

2016 ◽  
Vol 54 (10) ◽  
pp. 924-1109 ◽  
Author(s):  
James B. Mowry ◽  
Daniel A. Spyker ◽  
Daniel E. Brooks ◽  
Ashlea Zimmerman ◽  
Jay L. Schauben

2012 ◽  
Vol 50 (10) ◽  
pp. 911-1164 ◽  
Author(s):  
Alvin C. Bronstein ◽  
Daniel A. Spyker ◽  
Louis R. Cantilena ◽  
Barry H. Rumack ◽  
Richard C. Dart

2013 ◽  
Vol 51 (10) ◽  
pp. 949-1229 ◽  
Author(s):  
James B. Mowry ◽  
Daniel A. Spyker ◽  
Louis R. Cantilena ◽  
J. Elise Bailey ◽  
Marsha Ford

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