Epi Info™ ─ Mesh4x Synchronization Prototype

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An example scenario where this technology would be useful is in an outbreak investigation. Many epidemiologists are familiar with the food borne outbreak in Oswego, New York, U.S.A. on April 18th, 1940. In this outbreak, 75 of the 80 people known to have been present at the pot-luck church supper were interviewed. A survey was created and interviews were conducted with participants to determine the source of the contamination. While the Oswego study focused on a single region, the significant value of data synchronization can be seen by expanding this scenario to where interviews and data entry are conducted in different localities. Therefore, imagine that the Oswego church supper was attended by residents of the Oswego county and four other neighboring counties: Jefferson, Lewis, Oneida, and Wayne. Imagine two epidemiologists are investigating this outbreak; an Epidemic Intelligence Service (or EIS) officer investigating the outbreak in Oneida county and another officer at the state health department investigating the other counties. After data synchronization, both investigators will have a clear picture of the spread of the illness over space and time and the actual cause of the outbreak.

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  • Epi Info™ ─ Mesh4x Synchronization Prototype

    1. 1. EPI INFO™ ─ MESH4X SYNCHRONIZATION PROTOTYPE Taha A. Kass-Hout, MD, MS Director, Health Informatics and Biosurveillance InSTEDD David A. Nitschke Project Manager - Epi Info™ Development Project US CDC December 18 th , 2008—Atlanta, GA
    2. 2. The Team <ul><li>David Nitschke (Lead) </li></ul><ul><li>Roger Mir </li></ul><ul><li>Mark Berndt </li></ul><ul><li>Enrique Nieves, MS </li></ul><ul><li>Taha Kass-Hout, MD, MS (Lead) </li></ul><ul><li>Eduardo (Ed) Jezierski </li></ul><ul><li>Juan Marcelo Tondato </li></ul><ul><li>Daniel Cazzulino </li></ul><ul><li>Pablo M. Cibraro </li></ul>
    3. 3. EPI INFO™ ─ MESH4X SYNCHRONIZATION PROTOTYPE <ul><li>The Scenario… </li></ul>
    4. 4. OSWEGO—AN OUTBREAK OF GASTROINTESTINAL ILLNESS FOLLOWING A CHURCH SUPPER <ul><li>Oswego County, New York: 1940 </li></ul><ul><ul><li>80 people attended a church supper on 4/18/1940 </li></ul></ul><ul><ul><ul><li>75 people (ill and non-ill) interviewed </li></ul></ul></ul><ul><ul><ul><ul><li>46 people who attended the supper suffered from gastrointestinal illness beginning 4/18/1940 and ending 4/19/1940 </li></ul></ul></ul></ul><ul><ul><li>Investigation focus: church supper as source of infection </li></ul></ul>Original Scenario
    5. 5. <ul><li>Point-Source Outbreak </li></ul><ul><li>Staphylococcus aureus suspected pathogen based on 4.3 hr average incubation period </li></ul><ul><li>Vanilla ice cream suspected source of infection (highest food-specific Attack Rate (AR) of 80%) </li></ul>OSWEGO—AN OUTBREAK OF GASTROINTESTINAL ILLNESS FOLLOWING A CHURCH SUPPER Original Scenario
    6. 6. OSWEGO—AN OUTBREAK OF GASTROINTESTINAL ILLNESS FOLLOWING A CHURCH SUPPER <ul><li>Provided mockup Geo-referenced information </li></ul><ul><ul><li>Added addresses for all 75 interviewees </li></ul></ul><ul><ul><li>Expanded the scenario to include 4 counties (in addition to Oswego) </li></ul></ul>Modified Scenario DISCLAIMER: All places used in the modified (imaginary) scenario are fictitious. Any similarity to real places is a coincidence.
    7. 7. OUTBREAK INVESTIGATION FORM
    8. 8. THE PROTOTYPE: ARCHITECTURE
    9. 9. EPI INFO™ ─ MESH4X SYNCHRONIZATION PROTOTYPE <ul><li>The Demonstration… </li></ul>
    10. 10. OUTBREAK INVESTIGATION IN ONEIDA COUNTY <ul><li>Oneida Medical Officer (Dr. Roger Mir) receives a call from local doctors about patients reporting to clinics with gastrointestinal illness. Dr. Mir launches an investigation and begins interviews. </li></ul>
    11. 11. ANALYSIS OF OUTBREAK DATA <ul><li>Dr. Mir has returned to the office from the interviews and he wants to perform some preliminary analysis on the data. He launches the Epi Info™ Menu and opens the Analysis module to read and analyze the data </li></ul><ul><ul><ul><li>Epi Info™ Command: READ epiinfo.mdb: viewOswego </li></ul></ul></ul>
    12. 12. ANALYSIS OF OUTBREAK DATA <ul><li>In the output window, Dr. Mir finds a Record Count of 28. </li></ul><ul><ul><li>Frequencies County and ILL </li></ul></ul><ul><ul><ul><li>Epi Info™ Command: FREQ COUNTY ILL </li></ul></ul></ul><ul><li>The results show that all 28 cases are in Oneida County (12 interviewees that were ILL and 16 that were NOT ILL) </li></ul>
    13. 13. ANALYSIS OF OUTBREAK DATA <ul><li>First, Dr. Mir will create a 2 by 2 table of BAKED HAM by ILL. </li></ul><ul><ul><ul><li>Epi Info™ Command: TABLES BAKEDHAM ILL </li></ul></ul></ul><ul><li>The results show that BAKED HAM is significant (at P = 0.05 level). </li></ul>
    14. 14. ANALYSIS OF OUTBREAK DATA <ul><li>Dr. Mir also creates a 2 by 2 table for VANILLA by ILL. </li></ul><ul><ul><ul><ul><ul><li>Epi Info™ Command: TABLES VANILLA ILL </li></ul></ul></ul></ul></ul><ul><li>This results show VANILLA is also significant. </li></ul>
    15. 15. ANALYSIS OF OUTBREAK DATA <ul><li>Dr. Mir, having analyzed the data, he knows that there are at least two potential sources of the illness—BAKED HAM and VANILLA ICE CREAM. </li></ul>
    16. 16. GENERATE GOOGLE EARTH MAP <ul><li>In order to gain a perspective of the cases, Dr. Mir will create a KML file to display in Google Earth (map layers). To create the KML file, he launches the Epi Info™ Mesh4x tool and generates a local map as follows: </li></ul><ul><ul><li>Click the “Map Exchange” tab </li></ul></ul><ul><ul><li>Specify the data source (EpiInfo.mdb) and the Oswego table </li></ul></ul><ul><ul><li>Click the “Create Map” button and this will generate the KML file </li></ul></ul>
    17. 17. GENERATE GOOGLE EARTH MAP <ul><li>It takes Dr. Mir about 30 seconds to make the KML file. To show the map, Dr. Mir opens the Google Earth file generated by the Epi Info™ Mesh4x tool. </li></ul><ul><li>The results map has 28 cases in Oneida county: </li></ul><ul><ul><ul><li>12 patients reported with GI illness ( Red pins ) between April 18 th and 19 th </li></ul></ul></ul><ul><ul><ul><li>16 interviewers did not show any illness ( Yellow pins ) </li></ul></ul></ul>
    18. 18. <ul><li>Dr. Mir logged into Epi-X and discovered a discussion about an outbreak in his neighboring county of Oswego. He initiates a call to Dr. Nitschke (with the State Health Department) to further discuss the situation with him… </li></ul>NEXT STEP…
    19. 19. OUTBREAK INVESTIGATION IN THE OTHER COUNTIES <ul><li>The NY State Epidemiologist requests an EIS officer (Dr. David Nitschke) to help investigate a GI outbreak that appears in the counties of Oswego, Lewis, Jefferson, and Wayne. </li></ul>
    20. 20. ANALYSIS OF OUTBREAK DATA <ul><li>Dr. Nitschke has returned to the office from the interviews in 4 counties and he wants to perform some preliminary analysis on the data. He launches the Epi Info™ Menu and opens the Analysis module to read and analyze the data </li></ul><ul><ul><ul><li>Epi Info™ Command: READ epiinfo.mdb: viewOswego </li></ul></ul></ul>
    21. 21. ANALYSIS OF OUTBREAK DATA <ul><li>In the Output window, Dr. Nitschke finds a Record Count of 47 across the 4 counties. </li></ul><ul><ul><li>Frequencies County and ILL </li></ul></ul><ul><ul><ul><li>Epi Info™ Command: FREQ COUNTY ILL </li></ul></ul></ul>
    22. 22. ANALYSIS OF OUTBREAK DATA <ul><li>Dr. Nitschke creates a 2 by 2 table for VANILLA by ILL. </li></ul><ul><ul><ul><li>Epi Info™ Command: TABLES VANILLA ILL </li></ul></ul></ul><ul><li>This results show VANILLA is significant. </li></ul>
    23. 23. GENERATE GOOGLE EARTH MAP <ul><li>In order to gain a perspective of the cases, Dr. Nitschke will create a KML file to display in Google Earth (map layers). To create the KML file, he launches the Epi Info™ Mesh4x tool and generates a local map as follows: </li></ul><ul><ul><li>Click the “Map Exchange” tab </li></ul></ul><ul><ul><li>Specify the data source (EpiInfo.mdb) and the Oswego table </li></ul></ul><ul><ul><li>Click the “Create Map” button and this will generate the KML file </li></ul></ul>
    24. 24. GENERATE GOOGLE EARTH MAP <ul><li>It takes Dr. Nitschke about 30 seconds to make the KML file. To show the map, Dr. Nitschke opens the Google Earth file generated by the Epi Info™ Mesh4x tool. </li></ul><ul><li>The results map has 47 records in the 4 counties: </li></ul><ul><ul><ul><li>33 patients reported with GI illness ( Red pins ) between April 18 th and 19 th </li></ul></ul></ul><ul><ul><ul><li>14 interviewers did not show any illness ( Yellow pins ) </li></ul></ul></ul>
    25. 25. NEXT STEP… <ul><li>Dr. Nitschke just received a call from Dr. Mir and learned that Oneida county has 28 cases, but Dr. Mir is unable to conclude the source of the outbreak based on the information he has. So, they agree to share information. </li></ul>
    26. 26. DATA SYNCHRONIZATION BETWEEN ONEIDA COUNTY AND THE OTHER NEIGHBORING COUNTIES <ul><li>Dr. Mir, Oneida County MO, received an Epi-X alert about reported illnesses in nearby counties. Dr. Mir calls Dr. Nitschke requesting data from the nearby counties. Dr. Mir is now working with Dr. Nitschke on synchronizing his information with the State. </li></ul>
    27. 27. SYNCHRONIZING ONEIDA COUNTY DATA WITH THE OTHER COUNTIES <ul><li>Dr. Nitschke launches the Epi Info™ Mesh4x tool and Sync data over the Amazon EC2/S3 cloud (State’s available online data) </li></ul><ul><ul><li>Select the Data Exchange tab and click Synchronize </li></ul></ul>
    28. 28. SYNCHRONIZING ONEIDA COUNTY DATA WITH THE OTHER COUNTIES <ul><li>Dr. Nitschke evaluates the data in the cloud (State’s Data ready to be shared) </li></ul>Before Synchronization: No data in the cloud (State’s Data ready to be shared) After Synchronization: Dr. Nitschke verifies that the State’s data is in the cloud (Total of 47 Records)
    29. 29. SYNCHRONIZING ONEIDA COUNTY DATA WITH THE OTHER COUNTIES <ul><li>Dr. Mir launches the Epi Info™ Mesh4x tool and Sync data over the Amazon EC2/S3 cloud (State’s available online data) </li></ul><ul><ul><li>Select the Data Exchange tab and click Synchronize </li></ul></ul>
    30. 30. SYNCHRONIZING ONEIDA COUNTY DATA WITH THE OTHER COUNTIES <ul><li>Dr. Mir evaluates the data in the cloud (State’s Data ready to be shared) </li></ul>Before Synchronization: State’s available online Data (47 Records) After Synchronization: Dr. Mir has data for all other counties (Total of 75 Records)
    31. 31. ANALYSIS OF OUTBREAK DATA: ALL COUNTIES <ul><li>Dr. Mir now has the data from the other counties. The State’s Database in the cloud will also have all the data from Oneida County. </li></ul><ul><li>We see that Dr. Mir now has 75 cases across the 5 counties (46 ILL and 29 NOT ILL) </li></ul><ul><ul><ul><li>Epi Info™ Command: READ ‘C:epiinfodataepiinfo.mdb’: viewOswego </li></ul></ul></ul><ul><ul><ul><li>Epi Info™ Command: FREQ COUNTY ILL </li></ul></ul></ul>
    32. 32. ANALYSIS OF OUTBREAK DATA: ALL COUNTIES <ul><li>Dr. Mir runs the analysis again </li></ul><ul><ul><ul><li>Epi Info™ Command: TABLES VANILLA ILL </li></ul></ul></ul><ul><li>The results show that BAKEDHAM is no longer significant and that VANILLA is clearly the potential source of the outbreak. </li></ul>
    33. 33. GENERATE GOOGLE EARTH MAP <ul><li>The results map shows 75 cases across ALL counties </li></ul><ul><ul><li>46 patients reported with GI illness ( Red pins ) between April 18 th and 19 th </li></ul></ul><ul><ul><li>29 interviewers did not show any illness ( Yellow pins ). </li></ul></ul><ul><li>Drs. Mir and Nitschke each launches Epi Info™ Mesh4x tool on their laptops and generate a local map for their respective counties (as previously described) </li></ul>
    34. 34. UPDATING RECORDS <ul><li>Dr. Nitschke receives a call about a new incident case from Lewis County. Upon further investigation, the previously not ill person is now ill because he ate Vanilla Ice Cream that he took home from the church supper. </li></ul>
    35. 35. OUTBREAK INVESTIGATION FORM
    36. 36. OUTBREAK INVESTIGATION FORM Data updated in Epi Info™
    37. 37. SYNCHRONIZING NEW CASE <ul><li>Dr. Nitschke launches Epi Info™ Mesh4x tool </li></ul><ul><ul><li>Sync data over the Amazon EC2/S3 cloud (State’s online available data) </li></ul></ul><ul><ul><li>Dr. Nitschke now also has data from the Oneida county </li></ul></ul><ul><li>Dr. Mir also launches Epi Info™ Mesh4x tool to get the update record </li></ul><ul><ul><li>Sync data over the Amazon EC2/S3 cloud (State’s online available data) </li></ul></ul><ul><ul><li>Dr. Mir now has the most up-to-date information </li></ul></ul>Data updated in the cloud (State’s available online data)
    38. 38. GENERATE GOOGLE EARTH MAP <ul><li>The results map shows 75 cases across ALL counties </li></ul><ul><ul><li>46 patients reported with GI illness ( Red pins ) between April 18th and 19 th </li></ul></ul><ul><ul><li>29 interviewers did not show any illness ( Yellow pins ) </li></ul></ul><ul><ul><ul><li>Patient19 status has changed to ill ( Red pin ) </li></ul></ul></ul><ul><li>Drs. Nitschke and Mir each launches Epi Info™ Mesh4x tool on their laptops and generate a local map for their respective counties (as previously described) </li></ul>
    39. 39. SHARING DATA WITH CDC <ul><li>Dr. Mark Berndt, an epidemiologist at the CDC foodborne branch, is now involved in the investigation and wants to access State data </li></ul>
    40. 40. SHARING DATA WITH CDC <ul><li>Dr. Berndt launches the Epi Info™ Mesh4x tool and receives NY state’s data </li></ul><ul><ul><li>Select the Data Exchange tab and click Synchronize </li></ul></ul>
    41. 41. GENERATE GOOGLE EARTH MAP <ul><li>The results map shows 75 cases in NY state across 5 counties </li></ul><ul><ul><li>47 patients reported with GI illness ( Red pins ) between April 18 th and 19 th </li></ul></ul><ul><ul><li>28 interviewers did not show any illness ( Yellow pins ). </li></ul></ul><ul><li>Dr. Berndt launches Epi Info™ Mesh4x tool on his desktop and generates a map for the state of NY (as previously described) </li></ul>
    42. 42. The Tools <ul><li>Epi Info™ </li></ul><ul><li>http://cdc.gov/epiinfo </li></ul><ul><li>Epi Info™ - Community Edition </li></ul><ul><li>http://www.codeplex.com/EpiInfo </li></ul><ul><li>Mesh4x Project </li></ul><ul><li>http://code.google.com/p/mesh4x </li></ul><ul><li>Discussion Group </li></ul><ul><li>http://groups.google.com/group/mesh4x </li></ul>
    43. 43. Q&A SESSION
    44. 44. THANK YOU! <ul><li>David A. Nitschke </li></ul><ul><li>Project Manager - Epi Info™ Development Project </li></ul><ul><li>Centers for Disease Control and Prevention (CDC) </li></ul><ul><li>404.498.6272 </li></ul><ul><li>[email_address] </li></ul><ul><li>http://www.CDC.gov/EpiInfo </li></ul><ul><li>Taha A. Kass-Hout, MD, MS </li></ul><ul><li>Director, Health Informatics and Biosurveillance </li></ul><ul><li>InSTEDD </li></ul><ul><li>[email_address] </li></ul><ul><li>http://www.instedd.org </li></ul><ul><li>http://taha.instedd.org </li></ul>
    45. 45. BACKUP SLIDERS
    46. 46. CREATE A MESH Go to: http://sync.staging.instedd.org:8080/mesh4x Example: http://sync.staging.instedd.org:8080/mesh4x/Epiinfo
    47. 47. CREATE A DATA FEED Go to: http://sync.staging.instedd.org:8080/mesh4x Example: http://sync.staging.instedd.org:8080/mesh4x
    48. 48. CLEAR DATA FROM A DATA FEED Go to: http://sync.staging.instedd.org:8080/mesh4x Example: http://sync.staging.instedd.org:8080/mesh4x/Epiinfo/test Note: Case Sensitive!

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