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Monitoring of Cryptosporidium spp. Outbreaks Using Statistical Process Control Tools and Quantitative Risk Analysis Based on NORS Long-term Trending



Mostafa Essam Eissa
 
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Mostafa Essam Eissa , 2019. Monitoring of Cryptosporidium spp. Outbreaks Using Statistical Process Control Tools and Quantitative Risk Analysis Based on NORS Long-term Trending. Microbiology Journal, 9: 1-7.

DOI: 10.3923/mj.2019.1.7

URL: https://scialert.net/abstract/?doi=mj.2019.1.7
 
Received: June 10, 2019; Accepted: June 13, 2019; Published: July 20, 2019


Copyright: © 2019. This is an open access article distributed under the terms of the creative commons attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.

INTRODUCTION

Microbial outbreaks constitute a constant threat to human health and even life in extreme cases. In addition, the epidemics and pandemic diseases are devastating for any country economically. Accordingly, developed nations have established rigorous monitoring and control systems to contain outbreak events and their consequences1. As an example, National Outbreak Recording System (NORS) database provides comprehensive records of different types of outbreaks in the USA from which useful patterns and trends could be analyzed for different types of outbreaks2. Data processing and analysis using statistical process control (SPC) tools provide insight into outbreak properties and behavior. In addition, they might deliver a simple yet important mean to assess the hazard from specific outbreaks events quantitatively using control charts3,4. Despite the usefulness of SPC methodologies, they are still underestimated and uncommon in the field of outbreaks studies although they could provide unique view and interpretation for the monitored diseases and epidemics.

One of the microbial outbreaks that have been recorded and traced is Cryptosporidium spp. which causes cryptosporidiosis a gastrointestinal disease with or without respiratory symptoms5. This Eimeriorinan protozoan parasite of family Cryptosporidiidae and order Eucoccidiorida belongs to the phylum Apicomplexa, class Conoidasida and subclass Coccidia under Alveolata group of protists (infrakingdom) from Eukaryota domain. It is characterized from other Apicomplexan pathogens by its unique ability to complete its life cycle in a single host and release oocysts without the need of other intermediate vectors such as insects for Toxoplasma sp. and Plasmodium spp.5,6. The SPC study of Cryptosporidium spp. outbreaks trend using statistical programs would provide useful information through different perspective.

Long-term monitoring of cryptosporidiosis outbreaks using Pareto analysis shows that the estimated distribution of the etiological agents is as the following: Unidentified species or unknown Cryptosporidium spp. (approximately 43.3%), Cryptosporidium hominis (21.3%) or Cryptosporidium parvum (19.7%) either isolated species alone or associated with other microorganisms or other Cryptosporidium spp. (about 15.7%) such as Escherichia coli (Shiga toxin-producer and enteropathogenic), Clostridium spp., Cyclospora cayetanensis, Giardia spp., Sapovirus and Norovirus. On the other hand, approximately one quarter of the outbreak records which are estimated to be 471 have data about its locations. Nevertheless, about 12% of the settings of outbreak incidences have not been identified or unknown. However, available dataset indicates that parks (water, amusement, state, mobile home, community/municipal and/or Others), resorts, private residences (house, vacation rental house, home, condo, apartment), community/municipality, child day cares, Long-term care, nursing home, assisted living facility, hotels, motels, lodges, inns, public outdoor areas, Schools/Colleges/Universities, caterers, grocery stores, camps/cabin settings, farms/agricultural Settings and clubs were primary locations for occurrence of Cryptosporidium spp. outbreaks.

Less than 20% of the states in the USA are in involved in more than three-fifths of cryptosporidiosis outbreak illnesses with the primary mode of transmission is mainly water, followed by food and finally contact with animals (Fig. 1). However, the mean of transfer in some recorded outbreak incidences could not be identified yet. Despite the possibility of occurrence of cryptosporidiosis outbreaks through the year, more than 60% of incidences occur in the summer periods followed by autumn seasons. The general trend line of the illnesses tends to increase with time since more than 84% of the outbreak sickness cases spotted between years 2011-2017. This could be illustrated in Fig. 2 where a three-dimensional plot shows chronological centering of the reported outbreak illnesses.

Table 1 shows statistical analysis of the disease distribution of illness, hospitalization and death cases. Mortality from cryptosporidiosis infections are very rare phenomena and hospitalization events are low in magnitude if compared with the overall affected individuals by the exposure to the pathogen. In the same line, Fig. 3 demonstrates the distribution of the cases of the outbreak which clearly does not follow Gaussian distribution but may be close to (but not necessarily follow) what is called Log-normal or Weibull pattern of distribution which was observed in other outbreaks analysis study in USA7. In order to visualize the pattern, trend and properties of the observed outbreaks, Shewhart charts deliver indispensable analysis for the inspection characteristics in terms of the general tendency, the upper threshold and the out-of-control or abnormal excursions in the illnesses numbers in the trended outbreaks. These parameters could yield data required for the assessment of the health hazard quantitatively from the analyzed outbreaks.

In Fig. 4, testing for data distribution of illnesses per outbreak validity using SPC program was conducted as a diagnostic test which has lead to the construction of Laney attribute chart then variable process-behavior Individual-Moving Range (I-MR) chart was constructed that showed comparable outcome with the previous modified attribute chart corrected for data dispersion (indicated in graph by σZ value).

Fig. 1(a-b):
Pareto diagram showing the (a) Major states and (b) Modes of transmission involved in Cryptosporidium spp. illness outbreaks from 1998-2017
  Graph generated using Minitab version 17 from NORS database

Table 1:
Column statistics showing analysis of Cryptosporidium spp. outbreaks illnesses in USA during 20 years

Results generated using GraphPad Prism version 6.01 from Windows from NORS database. ****p< 0.0001


Fig. 2(a-c):
Chronological distribution of Cryptosporidium spp. outbreaks illnesses (a) Year, (b) Month and (c) Year vs month in USA during 20 years
  Graph generated using Minitab version 17 from NORS database

Fig. 3(a-c):
Histogram distribution of Cryptosporidium spp. outbreaks (a) Illnesses, (b) Hospitalization and (c) Deaths in USA during 20 years
 
Graph generated using Minitab® version 17.1.0 from NORS database

Fig. 4(a-d): Laney-attribute and variable trending charts of Cryptosporidium spp. outbreaks illnesses in USA from 1998-2017.
  Graph generated using Minitab® version 17.1.0 from NORS database

Thus, this suggests that both types of control charts could be used. Similarly, trending charts could be plotted for hospitalization cases to spot outbreaks with unusual incidences for further investigation and study. The SPC implementation in the monitoring and evaluation of the outbreaks could provide a crucial mean for the study of epidemiological microbial diseases that impact public health and the hazard could be assessed quantitatively.

REFERENCES
CDC., 2015. Pathogen & environment: Cryptosporidium parasites CDC. National Center for Emerging and Zoonotic Infectious Diseases (NCEZID), Division of Foodborne, Waterborne and Environmental Diseases (DFWED). https://www.cdc.gov/parasites/crypto/pathogen.html.

CDC., 2018. National outbreak reporting system: NORS Dashboard. National Center for Immunization and Respiratory Diseases, Division of Viral Diseases. https://www.cdc.gov/nors/data/dashboard/index.html.

Eissa, M., 2019. The attribute control charts for outbreak trends of selected states in the USA: A brief report of the insight into the pattern. Int. Med., 1: 11-14.
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Eissa, M.E.A.M., 2019. Long-term monitoring of Giardia as an etiological agent for food-borne outbreaks in USA: A brief report. Open. J. Nutr. Food. Sci., 1: 10-13.
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Eissa, M.E.A.M., 2019. Statistical analysis review and lessons learned from recent outbreak trends of highest population density states in USA: Massachusetts, New Jersey and Rhode Island. J. Food Chem. Nanotechnol., 5: 8-19.
CrossRef  |  Direct Link  |  

Madhav, N., B. Oppenheim, M. Gallivan, P. Mulembakani, E. Rubin and N. Wolfe, 2017. Pandemics: Risks, impacts and mitigation. In: Disease Control Priorities: Improving Health and Reducing Poverty, Jamison, D.T., H. Gelband, S. Horton (Eds.)., The International Bank for Reconstruction and Development/The World Bank, Washington.

Sponseller, J.K., J.K. Griffiths and S. Tzipori, 2014. The evolution of respiratory cryptosporidiosis: Evidence for transmission by inhalation. Clin. Microbiol. Rev., 27: 575-586.
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