Case Report

A Long-Term Impact Study of Bacterial Outbreak Using Control Chart-Risk Assessment Combination

Mostafa Essam Ahmed Eissa
Independent Ph.D. Researcher and Candidate, Department of Microbiology and Immunology, Cairo University, Cairo, Egypt

Worldwide Medicine 2019; 1(4): 114-119 | DOI: 10.5455/ww.48101      PDF


Outbreaks are a major health problem that requires immediate and rigorous corrective actions based on accurate data gathering. A correct interpretation of a long-term record of an epidemiological disease is crucial in deriving useful information and lessons-to-learn in order to understand the pattern and nature of outbreaks. An internet-based record extraction was conducted for the National Outbreak Reporting System (NORS) website database which is a platform developed by the Centers for Disease Control and Prevention (CDC). Data were filtered and processed using statistical process control (SPC) software that is available commercially. The selected focus group of the current study was bacterial outbreak incidents in the USA during the 20 years period. Data were interpreted using Laney attribute control charts to overcome for over or under-dispersion of data which may lead to false alarm detection. Control charts showed the pattern of the selected cases of the bacterial outbreak trend in the country. These process-behavior charts could define outbreak parameters such as mean values of ill cases per outbreak, upper control limit (UCL), number of outbreaks during a specific fixed time, number of excursions in the number of ill populations per outbreak (out-of- control). A quantitative risk analysis could be derived from the trending charts. Accordingly, The most influential bacterial outbreaks that contributed by about 88 % of the illness cases in the studied group were Clostridium spp,, Shigella spp. and Escherichia coli, Trending charts can be used as a mean to assess and compare the potential risks of outbreaks from different bacteria.

Keywords: NORS, SPC, Laney attribute control charts, UCL, Out-of-control, CDC