- Email: [email protected]
The Epidemiological Investigation and Intelligent Analytical System for foodborne disease
Food Control 21 (2010) 1466–1471
Contents lists available at ScienceDirect
Food Control journal homepage: www.elsevier.com/locate/foodcont
The Epidemiological Investigation and Intelligent Analytical System for foodborne disease Ligui Wang a,1, Yuanyong Xu a,1, Yong Wang a,1, Shicun Dong b,1, Zhidong Cao c,1, Wen Zhou a, Hailong Sun a, Donghui Huo a, Hui Zhang a, Yansong Sun a, Liuyu Huang a, Zhengquan Yuan a,***, Dajun Zeng c,**, Hongbin Song a,* a b c
PLA Institute of Disease Control and Prevention, Beijing 100071, China Qinghai Center for Disease Control and Prevention, Qinghai 810007, China Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
a r t i c l e
i n f o
Article history: Received 27 December 2009 Received in revised form 27 March 2010 Accepted 6 April 2010
Keywords: Foodborne disease Epidemiology investigation System analysis Feedback
a b s t r a c t The objective of this research was to design and develop the Epidemiological Investigation and Intelligent Analytical System for foodborne disease to improve the ability to control foodborne disease events in field epidemiological investigations. The system is composed of standard electronic investigation questionnaires for collecting data from field investigations, a statistical analysis module integrated with statistically programmed methods for calculating odds and risk ratios for risk factors, an intelligent auxiliary diagnosis module featuring a feedback function able to enhance diagnostic accuracy, and a knowledge database for acquiring information in areas related to foodborne disease. The system has been provided to the People’s Liberation Army Emergency Response Team for public health events. In field experiments, the system was very effective and improved work efficiency in epidemiological investigations of foodborne disease. Ó 2010 Published by Elsevier Ltd.
1. Introduction Foodborne diseases represent one of the most important public health emergencies in the world. In a widely cited US estimate by Mead et al. (1999), derived from information gathered by the Centers for Disease Control and Prevention (CDC), it has been reported that 76 million foodborne illnesses occur annually in the United States, resulting in 325,000 hospitalizations and 5200 deaths (Buzby & Roberts, 2009). In 2008, the network direct reporting system of the national CDC reported that there were 13,095 people diagnosed with foodborne diseases and 154 deaths in China. However, rapid and effective methods to deal with foodborne disease events for CDC staff are scarce. At present, traditional epidemiological investigation methods for foodborne diseases still utilize paper questionnaires in many countries, which are not suited for the demand for rapid and effective responses to emerging public health events. In addition, investigators are often dependent on their experience when judging symptoms of foodborne dis-
* Corresponding author. Tel./fax: +86 10 66948475. ** Corresponding author. *** Corresponding author. E-mail addresses: [email protected] (Z. Yuan), [email protected] (D. Zeng), [email protected] (H. Song). 1 These authors contributed equally to the work. 0956-7135/$ - see front matter Ó 2010 Published by Elsevier Ltd. doi:10.1016/j.foodcont.2010.04.015
eases, given the lack of auxiliary diagnosis tools available during field investigations. Moreover, to analyze data from epidemiology investigation questionnaires, much precious time is spent entering large amounts of questionnaire data into computers. Under these circumstances, investigators constantly lose optimal chances to take effective and corrective measures to control the spread of foodborne disease. To solve the issues mentioned above, we established an auxiliary investigation and diagnosis tool including standard electronic investigation questionnaires to improve speed of collecting, sharing, and exchanging data. We also constructed a statistical analysis module to identify risk factors, calculate the incubation period of foodborne disease, and describe epidemiological distribution features. In addition, we developed an intelligent diagnosis module to judge causes of foodborne disease in the field. To provide decision support for investigators working in the field, we built knowledge databases which not only include information about pathogens, epidemiological information, symptoms, laboratory tests, and treatments for all kinds of foodborne diseases, but also cover laws and rules related to foodborne disease. With the development of modern information technology, rapid advances in hardware and software have motivated researchers to focus on new techniques to improve efficiency in dealing with foodborne disease events. In this article, we integrated the modules above and developed the Epidemiological Investigation and
L. Wang et al. / Food Control 21 (2010) 1466–1471
Intelligent Analytical System for foodborne disease. The system has a user-friendly interface and has been provided to the People’s Liberation Army Emergency Response Team for use at public health events.
2. Materials and methods 2.1. Standard electronic investigation questionnaires To develop standard electronic investigation questionnaires, we collected and arranged various kinds of questionnaires. A total of 51 investigation questionnaires were collected, including infectious disease case questionnaires, non-causal disease case questionnaires, and foodborne disease case questionnaires. Fig. 1 presents the process of establishing the standard electronic investigation questionnaires.
2.2. Analysis of diagnosis methods There were many statistical methods used in our system, including calculation of relative numbers, medians, odds ratios, risk ratios, and chi square statistics. To describe the epidemical distribution features of foodborne diseases, a statistical chart was added to the statistical analysis module.
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2.4. Knowledge base Our knowledge database implemented in this research has three principal parts. First, there is a listing of 68 types of foodborne diseases in the system, comprising 29 kinds of microorganisms and parasites, 10 kinds of biotoxins, five kinds of chemical poisoning, nine kinds of plant-derived foodborne diseases, and 15 kinds of animal foodborne diseases. The important features of the 68 diseases are written in the database, including, for example, causes of foodborne diseases, epidemiological information, symptoms, and laboratory tests. Second, the treatment database is composed of a set of all drugs used to treat foodborne diseases, including their dosages and interactions with other drugs. Third, the related information database includes a series of laws, rules, and emergency measures pertaining to foodborne diseases. 2.5. Interface development The Epidemiological Investigation and Intelligent Analytical System for foodborne disease was developed based on Visual Basic, and Microsoft Access 2003 was used to construct the database. The system was installed and tested on both Windows XP and Vista operating systems. We developed a very user-friendly interface, allowing users to utilize the system through the interface without having to read the help documentation. Fig. 3 is the login interface of the system.
2.3. Intelligent auxiliary diagnosis module
3. Results
Bayesian algorithms, widely used in science and engineering fields, are mathematical formulas used for calculating conditional probabilities. In this module, a Bayesian algorithm was used to diagnose causes of foodborne disease. Fig. 2 shows the diagnostic process and calculation steps. The most important function of this system is the automatic feedback device. If diagnostic accuracy is low, the intelligent feedback function will automatically search the database and sort the probability of patients with all kinds of different symptoms by descending sequence, excluding features that have been given. Hence, it can remind the investigator of the more important features of the disease automatically.
3.1. Establishment of standard electronic investigation questionnaires A total of 51 investigation questionnaires were collected, including infectious disease case questionnaires, non-causal disease case questionnaires, and foodborne disease case questionnaires. Subsequently, all questionnaires were categorized into seven basic investigation questionnaires (Fig. 4). Items included in the questionnaires are as follows: 39 basic patient information variables (e.g., name, sex, and age; Fig. 5), 94 symptom information variables (e.g., fever, cough, and headache), 14 patient contact history variables (e.g., name, contact frequency, and methods of con-
Fig. 1. The process of establishing standard electronic investigation questionnaires.
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L. Wang et al. / Food Control 21 (2010) 1466–1471
Fig. 2. The process and calculation steps for constructing the diagnosis module.
Fig. 3. The login interface of the system.
L. Wang et al. / Food Control 21 (2010) 1466–1471
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3.2. Constructed statistical analysis module The aim of an epidemiological investigation is to find the causes of diseases, risk factors, and epidemical distribution features, all of which need to be subjected to statistical analysis methods. There are many software packages which can be used for statistical analysis, such as SAS, SPSS, and STATA. However, these software packages are not easily operated in the field by staff whom are not familiar with statistics. Thus, a simple statistical module was developed in this system (Fig. 6). Any staff member can use it easily and get useful information from statistical results, even individuals unfamiliar with statistics. These statistical modules not only calculate the value of relative numbers, medians, odds ratios, risk ratios, and chi square statistics, but can also form statistical charts. All of these statistics are very useful in preventing and controlling diseases spread in the field during an epidemiological investigation. For instance, staff investigators can easily determine the incubation period of foodborne diseases through the calculation of medians and identify risk factors by calculating risk ratios and chi square statistics. 3.3. Constructed intelligent diagnosis module Fig. 4. The conceptual model sketch map of electric investigation questionnaires.
tact), eight animal contact history variables (e.g., animal name, amount of contact, and animal quantity), seven activity history variables (e.g., activity time, activity place, and type of activity), six food history variables (e.g., meal time, meal place, and food name), 13 close contact variables (e.g., name, age, amount of contact) and five laboratory data variables (e.g., sample name, sample time, and analysis results). Finally, we established the seven electronic investigation questionnaires using an access database. These questionnaires contain 181 variables covering the content of recent epidemiological investigations.
The intelligent diagnosis module (Fig. 7) was designed to generate a ranked differential diagnosis based on signs, symptoms, laboratory tests, incubation period, and nature exposure according to Bayesian arithmetic. An intelligent feedback function, never used in other diagnosis systems, was first proposed and applied to our intelligent diagnosis module. The intelligent feedback function can remind staff to get important information from patients, thereby improving the accuracy of the diagnosis. For example, in an investigation, if we identify patients reporting vomiting and diarrhea, we enter those symptoms into the intelligent diagnosis module subsequence. However, the diagnosis results indicate that the probability of Salmonella infection is 48.9%, Shigella infection is 12.1%, and Staphylococcus aureus infection is 11.6%. The 48.9% probability of Salmo-
Fig. 5. Part of the electronic investigation questionnaires.
1470
L. Wang et al. / Food Control 21 (2010) 1466–1471
Fig. 6. Example of the statistical analysis module.
Fig. 7. The intelligent diagnosis module interface.
nella infection is not high enough to be in line with our diagnosis requirement. Thus, other complementary patient data are needed to improve diagnostic accuracy using the intelligent feedback function. The further complement of patient features (e.g., patient is a child, has abdominal pain, and has eaten eggs) are collected and entered into the system. Finally, the results show that the probability of the foodborne disease induced by Salmonella is up to 85.7%.
3.4. Constructed knowledge database First, we constructed a disease database, including, for example, the causes of all kinds of foodborne diseases, epidemiological information, symptoms, and laboratory tests. The disease database was not only used to compare symptoms in the database with symptoms of field foodborne disease to confirm possible disease, but
L. Wang et al. / Food Control 21 (2010) 1466–1471
also was used for intelligent diagnosis, as mentioned above. Second, we constructed a treatment database that includes all drugs used for foodborne diseases with dosages for adults and children to help staffs provide correct treatment measures to patients quickly. Finally, we constructed a policy database that includes 43 laws related to foodborne diseases and other infectious diseases as well as 35 emergence measures and six technical standard rules to deal with diseases. For example, China’s health quarantine regulations, infectious disease prevention and control regulations, and school sanitation regulations are written in the database. This knowledge database can provide decision support for investigators in a timely manner, and it is easy to query whenever needed. 4. Discussion Many diagnostic systems for infectious diseases have been reported (Bravata et al., 2004). For example, a computer program used for diagnosing and teaching geographic medicine (Berger & Blackman, 1995) is used to provide a differential diagnosis of infectious diseases matched to 22 clinical parameters for a patient. The DXplain system (Hammersley & Cooney, 1988) is used to provide a differential diagnosis based on clinician-entered signs and symptoms. The GIDEON system (Berger, 2005; Edberg, 2005) is used to provide differential diagnoses for patients with diseases of infectious etiology. Although the systems mentioned above are very useful for the staff of disease prevention and control centers, these systems cannot be used in field epidemiological investigation due to lack of electronic investigation questionnaires. Therefore, investigators in the field cannot identify the reasons why diseases occur, risk factors, or epidemical distribution due to the lack of a statistic analysis module. More importantly, they cannot get feedback on important missing data in real time. In China, two systems to investigate foodborne disease have been reported in recent years. One is a foodborne disease management system (He, 2003), which only has data collection and statistical analysis functions. Another is an evidence-based analysis of foodborne disease system (Sun, Sun, Duan, & Zhang, 2009), which lacks standard electronic investigation questionnaires and statistical analysis functions. As far as the two systems are concerned, they both have some shortcomings in dealing with field foodborne disease aspects, and currently little is reported regarding practical applications in field investigations. Based on the shortcomings of the two systems mentioned above, we established the Epidemiological Investigation and Intelligent Analytical System for foodborne disease, which has much more comprehensive functions and can be easily used in field investigations. If a mass foodborne disease event happens, investigators can use this system to deal with foodborne disease rapidly and effectively. They can use standard electronic investigation questionnaires to collect epidemiological data rapidly, then calculate the incubation period of foodborne disease, risk factors, and epidemical distribution through the statistical analysis module. Then, the causes of foodborne disease can be diagnosed through the intelligent diagnosis module. More importantly, field investigators are able to supplement important missing data in real time through the feedback function. Investigators can also provide necessary treatment advice for patients through the treatment data-
1471
base with the knowledge query module and take correct measures to stop the spread of foodborne disease. Compared with other systems, this intelligent analytical system for foodborne disease can be used not only to collect foodborne disease data, but can also be used to collect other infectious disease data through electronic investigation questionnaires. Moreover, electronic investigation questionnaires apply an automatic error detection function to avoid data error. Another highlight of the system is the intelligent feedback function applied in the intelligent diagnosis module. This function integrated into our intelligent diagnosis module plays an important role in foodborne disease investigations, guiding investigators to extensively search for important factors and avoid spending time on testing aimlessly. At present, the Epidemiological Investigation and Intelligent Analytical System for foodborne disease has been supplied to the People’s Liberation Army Emergency Response Team for public health events. Through the application in the field experiment, the system has demonstrated that it is a very powerful tool which can improve work efficiency in field epidemiological investigations of foodborne disease. With the development of computer technology and statistical methods, more functions will be added to this system. Hence, even more sophisticated systems will be utilized and more widely applied for foodborne disease control in future public health emergencies. Conflict of interest The authors have declared that no conflict of interest exists. Acknowledgments This work was supported by grants from Mega-projects of Science and Technology Research (No. 2009ZX10004-315 and No. 2008ZX10004-008) and the PLA ‘‘11th Five” Science and Technology Project (No. 09MA028). References Berger, S. A. (2005). GIDEON: A comprehensive web-based resource for geographic medicine. International Journal of Health Geographics, 4, 10–21. Berger, S. A., & Blackman, U. (1995). Computer program for diagnosing and teaching geographic medicine. Journal of Travel Medicine, 2, 199–203. Bravata, D. M., Sundaram, V. S., McDonald, K. M., Smith, W. M., Szeto, H., Schleinitz, M. D., et al. (2004). Evaluating detection and diagnostic decision support systems for bioterrorism response. Emerging Infectious Diseases, 10(1), 100–108. Buzby, J. C., & Roberts, T. (2009). The economics of enteric infections: Human foodborne disease costs. Gastroenterology, 136, 1851–1862. Edberg, S. C. (2005). Global infectious diseases and epidemiology network (GIDEON): A world wide web-based program for diagnosis and informatics in infectious diseases. Clinical Infectious Diseases, 40, 123–126. Hammersley, J. R., & Cooney, K. (1988). Evaluating the utility of available differential diagnosis systems. In Proceedings of the annual symposium on computer applications in medical care (pp. 229–231), November 9. He, H. (2003). The development and application of a food poisoning management system. Occupation and Health, 19(8), 64–65 (Chinese). Mead, P. S., Slutsker, L., Dietz, V., McCaig, L. F., Bresee, J. S., Shapiro, C., et al. (1999). Food-related illness and death in the United States. Emerging Infectious Diseases, 5, 607–625. Sun, Y., Sun, T., Duan, D., & Zhang, X. (2009). Evidence-based analysis on food poisoning epidemiological investigations. Journal of Prognostics and Health Management, 25(4), 369–370 (Chinese).
Contents lists available at ScienceDirect
Food Control journal homepage: www.elsevier.com/locate/foodcont
The Epidemiological Investigation and Intelligent Analytical System for foodborne disease Ligui Wang a,1, Yuanyong Xu a,1, Yong Wang a,1, Shicun Dong b,1, Zhidong Cao c,1, Wen Zhou a, Hailong Sun a, Donghui Huo a, Hui Zhang a, Yansong Sun a, Liuyu Huang a, Zhengquan Yuan a,***, Dajun Zeng c,**, Hongbin Song a,* a b c
PLA Institute of Disease Control and Prevention, Beijing 100071, China Qinghai Center for Disease Control and Prevention, Qinghai 810007, China Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
a r t i c l e
i n f o
Article history: Received 27 December 2009 Received in revised form 27 March 2010 Accepted 6 April 2010
Keywords: Foodborne disease Epidemiology investigation System analysis Feedback
a b s t r a c t The objective of this research was to design and develop the Epidemiological Investigation and Intelligent Analytical System for foodborne disease to improve the ability to control foodborne disease events in field epidemiological investigations. The system is composed of standard electronic investigation questionnaires for collecting data from field investigations, a statistical analysis module integrated with statistically programmed methods for calculating odds and risk ratios for risk factors, an intelligent auxiliary diagnosis module featuring a feedback function able to enhance diagnostic accuracy, and a knowledge database for acquiring information in areas related to foodborne disease. The system has been provided to the People’s Liberation Army Emergency Response Team for public health events. In field experiments, the system was very effective and improved work efficiency in epidemiological investigations of foodborne disease. Ó 2010 Published by Elsevier Ltd.
1. Introduction Foodborne diseases represent one of the most important public health emergencies in the world. In a widely cited US estimate by Mead et al. (1999), derived from information gathered by the Centers for Disease Control and Prevention (CDC), it has been reported that 76 million foodborne illnesses occur annually in the United States, resulting in 325,000 hospitalizations and 5200 deaths (Buzby & Roberts, 2009). In 2008, the network direct reporting system of the national CDC reported that there were 13,095 people diagnosed with foodborne diseases and 154 deaths in China. However, rapid and effective methods to deal with foodborne disease events for CDC staff are scarce. At present, traditional epidemiological investigation methods for foodborne diseases still utilize paper questionnaires in many countries, which are not suited for the demand for rapid and effective responses to emerging public health events. In addition, investigators are often dependent on their experience when judging symptoms of foodborne dis-
* Corresponding author. Tel./fax: +86 10 66948475. ** Corresponding author. *** Corresponding author. E-mail addresses: [email protected] (Z. Yuan), [email protected] (D. Zeng), [email protected] (H. Song). 1 These authors contributed equally to the work. 0956-7135/$ - see front matter Ó 2010 Published by Elsevier Ltd. doi:10.1016/j.foodcont.2010.04.015
eases, given the lack of auxiliary diagnosis tools available during field investigations. Moreover, to analyze data from epidemiology investigation questionnaires, much precious time is spent entering large amounts of questionnaire data into computers. Under these circumstances, investigators constantly lose optimal chances to take effective and corrective measures to control the spread of foodborne disease. To solve the issues mentioned above, we established an auxiliary investigation and diagnosis tool including standard electronic investigation questionnaires to improve speed of collecting, sharing, and exchanging data. We also constructed a statistical analysis module to identify risk factors, calculate the incubation period of foodborne disease, and describe epidemiological distribution features. In addition, we developed an intelligent diagnosis module to judge causes of foodborne disease in the field. To provide decision support for investigators working in the field, we built knowledge databases which not only include information about pathogens, epidemiological information, symptoms, laboratory tests, and treatments for all kinds of foodborne diseases, but also cover laws and rules related to foodborne disease. With the development of modern information technology, rapid advances in hardware and software have motivated researchers to focus on new techniques to improve efficiency in dealing with foodborne disease events. In this article, we integrated the modules above and developed the Epidemiological Investigation and
L. Wang et al. / Food Control 21 (2010) 1466–1471
Intelligent Analytical System for foodborne disease. The system has a user-friendly interface and has been provided to the People’s Liberation Army Emergency Response Team for use at public health events.
2. Materials and methods 2.1. Standard electronic investigation questionnaires To develop standard electronic investigation questionnaires, we collected and arranged various kinds of questionnaires. A total of 51 investigation questionnaires were collected, including infectious disease case questionnaires, non-causal disease case questionnaires, and foodborne disease case questionnaires. Fig. 1 presents the process of establishing the standard electronic investigation questionnaires.
2.2. Analysis of diagnosis methods There were many statistical methods used in our system, including calculation of relative numbers, medians, odds ratios, risk ratios, and chi square statistics. To describe the epidemical distribution features of foodborne diseases, a statistical chart was added to the statistical analysis module.
1467
2.4. Knowledge base Our knowledge database implemented in this research has three principal parts. First, there is a listing of 68 types of foodborne diseases in the system, comprising 29 kinds of microorganisms and parasites, 10 kinds of biotoxins, five kinds of chemical poisoning, nine kinds of plant-derived foodborne diseases, and 15 kinds of animal foodborne diseases. The important features of the 68 diseases are written in the database, including, for example, causes of foodborne diseases, epidemiological information, symptoms, and laboratory tests. Second, the treatment database is composed of a set of all drugs used to treat foodborne diseases, including their dosages and interactions with other drugs. Third, the related information database includes a series of laws, rules, and emergency measures pertaining to foodborne diseases. 2.5. Interface development The Epidemiological Investigation and Intelligent Analytical System for foodborne disease was developed based on Visual Basic, and Microsoft Access 2003 was used to construct the database. The system was installed and tested on both Windows XP and Vista operating systems. We developed a very user-friendly interface, allowing users to utilize the system through the interface without having to read the help documentation. Fig. 3 is the login interface of the system.
2.3. Intelligent auxiliary diagnosis module
3. Results
Bayesian algorithms, widely used in science and engineering fields, are mathematical formulas used for calculating conditional probabilities. In this module, a Bayesian algorithm was used to diagnose causes of foodborne disease. Fig. 2 shows the diagnostic process and calculation steps. The most important function of this system is the automatic feedback device. If diagnostic accuracy is low, the intelligent feedback function will automatically search the database and sort the probability of patients with all kinds of different symptoms by descending sequence, excluding features that have been given. Hence, it can remind the investigator of the more important features of the disease automatically.
3.1. Establishment of standard electronic investigation questionnaires A total of 51 investigation questionnaires were collected, including infectious disease case questionnaires, non-causal disease case questionnaires, and foodborne disease case questionnaires. Subsequently, all questionnaires were categorized into seven basic investigation questionnaires (Fig. 4). Items included in the questionnaires are as follows: 39 basic patient information variables (e.g., name, sex, and age; Fig. 5), 94 symptom information variables (e.g., fever, cough, and headache), 14 patient contact history variables (e.g., name, contact frequency, and methods of con-
Fig. 1. The process of establishing standard electronic investigation questionnaires.
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L. Wang et al. / Food Control 21 (2010) 1466–1471
Fig. 2. The process and calculation steps for constructing the diagnosis module.
Fig. 3. The login interface of the system.
L. Wang et al. / Food Control 21 (2010) 1466–1471
1469
3.2. Constructed statistical analysis module The aim of an epidemiological investigation is to find the causes of diseases, risk factors, and epidemical distribution features, all of which need to be subjected to statistical analysis methods. There are many software packages which can be used for statistical analysis, such as SAS, SPSS, and STATA. However, these software packages are not easily operated in the field by staff whom are not familiar with statistics. Thus, a simple statistical module was developed in this system (Fig. 6). Any staff member can use it easily and get useful information from statistical results, even individuals unfamiliar with statistics. These statistical modules not only calculate the value of relative numbers, medians, odds ratios, risk ratios, and chi square statistics, but can also form statistical charts. All of these statistics are very useful in preventing and controlling diseases spread in the field during an epidemiological investigation. For instance, staff investigators can easily determine the incubation period of foodborne diseases through the calculation of medians and identify risk factors by calculating risk ratios and chi square statistics. 3.3. Constructed intelligent diagnosis module Fig. 4. The conceptual model sketch map of electric investigation questionnaires.
tact), eight animal contact history variables (e.g., animal name, amount of contact, and animal quantity), seven activity history variables (e.g., activity time, activity place, and type of activity), six food history variables (e.g., meal time, meal place, and food name), 13 close contact variables (e.g., name, age, amount of contact) and five laboratory data variables (e.g., sample name, sample time, and analysis results). Finally, we established the seven electronic investigation questionnaires using an access database. These questionnaires contain 181 variables covering the content of recent epidemiological investigations.
The intelligent diagnosis module (Fig. 7) was designed to generate a ranked differential diagnosis based on signs, symptoms, laboratory tests, incubation period, and nature exposure according to Bayesian arithmetic. An intelligent feedback function, never used in other diagnosis systems, was first proposed and applied to our intelligent diagnosis module. The intelligent feedback function can remind staff to get important information from patients, thereby improving the accuracy of the diagnosis. For example, in an investigation, if we identify patients reporting vomiting and diarrhea, we enter those symptoms into the intelligent diagnosis module subsequence. However, the diagnosis results indicate that the probability of Salmonella infection is 48.9%, Shigella infection is 12.1%, and Staphylococcus aureus infection is 11.6%. The 48.9% probability of Salmo-
Fig. 5. Part of the electronic investigation questionnaires.
1470
L. Wang et al. / Food Control 21 (2010) 1466–1471
Fig. 6. Example of the statistical analysis module.
Fig. 7. The intelligent diagnosis module interface.
nella infection is not high enough to be in line with our diagnosis requirement. Thus, other complementary patient data are needed to improve diagnostic accuracy using the intelligent feedback function. The further complement of patient features (e.g., patient is a child, has abdominal pain, and has eaten eggs) are collected and entered into the system. Finally, the results show that the probability of the foodborne disease induced by Salmonella is up to 85.7%.
3.4. Constructed knowledge database First, we constructed a disease database, including, for example, the causes of all kinds of foodborne diseases, epidemiological information, symptoms, and laboratory tests. The disease database was not only used to compare symptoms in the database with symptoms of field foodborne disease to confirm possible disease, but
L. Wang et al. / Food Control 21 (2010) 1466–1471
also was used for intelligent diagnosis, as mentioned above. Second, we constructed a treatment database that includes all drugs used for foodborne diseases with dosages for adults and children to help staffs provide correct treatment measures to patients quickly. Finally, we constructed a policy database that includes 43 laws related to foodborne diseases and other infectious diseases as well as 35 emergence measures and six technical standard rules to deal with diseases. For example, China’s health quarantine regulations, infectious disease prevention and control regulations, and school sanitation regulations are written in the database. This knowledge database can provide decision support for investigators in a timely manner, and it is easy to query whenever needed. 4. Discussion Many diagnostic systems for infectious diseases have been reported (Bravata et al., 2004). For example, a computer program used for diagnosing and teaching geographic medicine (Berger & Blackman, 1995) is used to provide a differential diagnosis of infectious diseases matched to 22 clinical parameters for a patient. The DXplain system (Hammersley & Cooney, 1988) is used to provide a differential diagnosis based on clinician-entered signs and symptoms. The GIDEON system (Berger, 2005; Edberg, 2005) is used to provide differential diagnoses for patients with diseases of infectious etiology. Although the systems mentioned above are very useful for the staff of disease prevention and control centers, these systems cannot be used in field epidemiological investigation due to lack of electronic investigation questionnaires. Therefore, investigators in the field cannot identify the reasons why diseases occur, risk factors, or epidemical distribution due to the lack of a statistic analysis module. More importantly, they cannot get feedback on important missing data in real time. In China, two systems to investigate foodborne disease have been reported in recent years. One is a foodborne disease management system (He, 2003), which only has data collection and statistical analysis functions. Another is an evidence-based analysis of foodborne disease system (Sun, Sun, Duan, & Zhang, 2009), which lacks standard electronic investigation questionnaires and statistical analysis functions. As far as the two systems are concerned, they both have some shortcomings in dealing with field foodborne disease aspects, and currently little is reported regarding practical applications in field investigations. Based on the shortcomings of the two systems mentioned above, we established the Epidemiological Investigation and Intelligent Analytical System for foodborne disease, which has much more comprehensive functions and can be easily used in field investigations. If a mass foodborne disease event happens, investigators can use this system to deal with foodborne disease rapidly and effectively. They can use standard electronic investigation questionnaires to collect epidemiological data rapidly, then calculate the incubation period of foodborne disease, risk factors, and epidemical distribution through the statistical analysis module. Then, the causes of foodborne disease can be diagnosed through the intelligent diagnosis module. More importantly, field investigators are able to supplement important missing data in real time through the feedback function. Investigators can also provide necessary treatment advice for patients through the treatment data-
1471
base with the knowledge query module and take correct measures to stop the spread of foodborne disease. Compared with other systems, this intelligent analytical system for foodborne disease can be used not only to collect foodborne disease data, but can also be used to collect other infectious disease data through electronic investigation questionnaires. Moreover, electronic investigation questionnaires apply an automatic error detection function to avoid data error. Another highlight of the system is the intelligent feedback function applied in the intelligent diagnosis module. This function integrated into our intelligent diagnosis module plays an important role in foodborne disease investigations, guiding investigators to extensively search for important factors and avoid spending time on testing aimlessly. At present, the Epidemiological Investigation and Intelligent Analytical System for foodborne disease has been supplied to the People’s Liberation Army Emergency Response Team for public health events. Through the application in the field experiment, the system has demonstrated that it is a very powerful tool which can improve work efficiency in field epidemiological investigations of foodborne disease. With the development of computer technology and statistical methods, more functions will be added to this system. Hence, even more sophisticated systems will be utilized and more widely applied for foodborne disease control in future public health emergencies. Conflict of interest The authors have declared that no conflict of interest exists. Acknowledgments This work was supported by grants from Mega-projects of Science and Technology Research (No. 2009ZX10004-315 and No. 2008ZX10004-008) and the PLA ‘‘11th Five” Science and Technology Project (No. 09MA028). References Berger, S. A. (2005). GIDEON: A comprehensive web-based resource for geographic medicine. International Journal of Health Geographics, 4, 10–21. Berger, S. A., & Blackman, U. (1995). Computer program for diagnosing and teaching geographic medicine. Journal of Travel Medicine, 2, 199–203. Bravata, D. M., Sundaram, V. S., McDonald, K. M., Smith, W. M., Szeto, H., Schleinitz, M. D., et al. (2004). Evaluating detection and diagnostic decision support systems for bioterrorism response. Emerging Infectious Diseases, 10(1), 100–108. Buzby, J. C., & Roberts, T. (2009). The economics of enteric infections: Human foodborne disease costs. Gastroenterology, 136, 1851–1862. Edberg, S. C. (2005). Global infectious diseases and epidemiology network (GIDEON): A world wide web-based program for diagnosis and informatics in infectious diseases. Clinical Infectious Diseases, 40, 123–126. Hammersley, J. R., & Cooney, K. (1988). Evaluating the utility of available differential diagnosis systems. In Proceedings of the annual symposium on computer applications in medical care (pp. 229–231), November 9. He, H. (2003). The development and application of a food poisoning management system. Occupation and Health, 19(8), 64–65 (Chinese). Mead, P. S., Slutsker, L., Dietz, V., McCaig, L. F., Bresee, J. S., Shapiro, C., et al. (1999). Food-related illness and death in the United States. Emerging Infectious Diseases, 5, 607–625. Sun, Y., Sun, T., Duan, D., & Zhang, X. (2009). Evidence-based analysis on food poisoning epidemiological investigations. Journal of Prognostics and Health Management, 25(4), 369–370 (Chinese).