Field Name Definition

Rationale

Variable Format

Case ID

ID assigned by organization/agency investigating the outbreak

Case ID is used for identification of cases

Alphanumeric

Provincial ID

P/T public health case ID supplied by P/T (e.g. iPHIS number)

P/T case ID allows provincial colleagues to accurately identify the individual within their database for data verification and supports accurate linkage with laboratory identifiers and national IDs

Alphanumeric

Laboratory ID

ID assigned by the laboratory, e.g. provincial laboratory, National Microbiology Lab (NML)

Alphanumeric

Case Initials

If relevant, the initials of the case can be included

Text

P/T

Province/Territory where case usually resides. This field is typically the field that is used to assign a case to a P/T for case updates (follow-up)

P/T of usual residence is used in geographic analysis and for data management purposes

Drop down menu: lists each P/T

HU

Health Unit (HU) where case usually resides

Health Unit is used in geographic analysis and for data management purposes

Text

City

City where case usually resides

City is used in geographic analysis and for data management purposes.

Text

Age

Age of case at time of illness onset

Age allows for identification of age-related trends at varying degrees of granularity, from specific years to compiled age-groups appropriate for the particular analysis

The age of the person, expressed in years (and months for children under 1 year of age), at time of illness

Sex

Legal sex of case

Sex allows for the identification and analysis of differential impacts and outcomes experienced by different sexes, and better identification of at-risk populations

Drop down menu: Male, Female, <leave blank>

Case Status

Classification of case’s status in the investigation specific to each investigation.

Case definitions will be defined at the time of OICC investigation and will vary by circumstance. The category list can be modified with different case naming conventions if needed. Laboratory confirmed and Probable are most often used.

Classification Status supports the appropriate categorization of a case for reporting requirements and analysis

Drop down menu: Confirmed, Probably, Unknown, <leave blank>

Report Date to Health Unit

Date case was reported to the local health unit.

Date case reported allows for measurement of timeliness of case notification during an investigation.

yyyy-mm-dd

Report Date to ________

Date case was reported to an organization/agency of choice.

Date case reported allows for measurement of timeliness of case notification during an investigation.

yyyy-mm-dd

Illness Onset Date

The date when the case began showing symptoms.

Symptom onset date type allows for data management and supports appropriate reporting and analysis.

yyyy-mm-dd

Specimen Collection Date

The date when the specimen was collected.

Allows for data management and supports appropriate reporting and analysis.

yyyy-mm-dd

Isolation Date

The date when the specimen was isolated.

Allows for data management and supports appropriate reporting and analysis.

yyyy-mm-dd

MLVA Report Date

The date when MLVA results were uploaded by NML.

Allows for data management and supports appropriate reporting and analysis.

yyyy-mm-dd

Other Date

Any other date of interest can be entered.

Allows for data management and supports appropriate reporting and analysis.

yyyy-mm-dd

Other Date

Any other date of interest can be entered.

Allows for data management and supports appropriate reporting and analysis.

yyyy-mm-dd

Asymptomatic

Case did not show any symptoms.

Drop down menu: Yes, No, Don’t Know (DK), N/A, <leave blank>

Diarrhea

Three of more loose stools within 24 hours.

Drop down menu: Yes, No, Don’t Know (DK), N/A, <leave blank>

Bloody Diarrhea (HUS)

Hemolytic Uremic Syndrome (HUS). This field is only to be filled out if the case has E. coli

HUS is used as a measure of severity and burden of illness.

Drop down menu: Yes, No, Don’t Know (DK), N/A, <leave blank>

Vomiting

Drop down menu: Yes, No, Don’t Know (DK), N/A, <leave blank>

Headache

Drop down menu: Yes, No, Don’t Know (DK), N/A, <leave blank>

Chills

Drop down menu: Yes, No, Don’t Know (DK), N/A, <leave blank>

Nausea

Drop down menu: Yes, No, Don’t Know (DK), N/A, <leave blank>

Muscle Aches

Drop down menu: Yes, No, Don’t Know (DK), N/A, <leave blank>

Fever

Drop down menu: Yes, No, Don’t Know (DK), N/A, <leave blank>

Abdominal Cramps

Drop down menu: Yes, No, Don’t Know (DK), N/A, <leave blank>

Other

Drop down menu: Yes, No, Don’t Know (DK), N/A, <leave blank>

Other

Drop down menu: Yes, No, Don’t Know (DK), N/A, <leave blank>

Hospitalized

Whether the person was admitted to hospital in relation to the reported episode of the disease/condition.  Individuals who visit an emergency room or outpatient clinic are not included.

Admitted to hospital is used as a measure of severity and burden of illness.

Drop down menu: Yes, No, Don’t Know (DK), N/A, <leave blank>

Death

The vital status of the person.

Died is used to calculate case fatality and mortality rates and to estimate severity and burden of illness.

Drop down menu: Yes, No, Don’t Know (DK), N/A, <leave blank>

Underlying Illness

List any pre-existing medical conditions that the case currently has or is experiencing, that may result in a compromised immune system, thus placing the case at higher risk for more severe disease.

It is acknowledged that this variable is not systematically collected across jurisdictions. Variable is optional but available to capture information if it is collected by some jurisdictions. This variable could be very helpful in identifying pathogens with increased virulence. For example, if you were seeing increased rates of hospitalisations, HUS or deaths in young people, it would be important to understand if these individuals were healthy, or considered higher risk given underlying conditions. 

Underlying illnesses is used in the analysis of risk. Individuals with underlying illness are known to be at higher risk for more severe disease.

Drop down menu: Yes, No, Don’t Know (DK), N/A, <leave blank>

Pregnant

Whether the person is pregnant. To be entered for Listeria outbreak investigations only. If the case is a male, enter “not applicable.”

Pregnancy is used to describe the case and population at risk; also to help hypothesize on the source of illness.

Drop down menu: Yes, No, Don’t Know (DK), N/A, <leave blank>

Other Complications

Report if any other complications reported.

Other complications is used in the analysis of risk.

Drop down menu: Yes, No, Don’t Know (DK), N/A, <leave blank>

Specimen Collection Done

Whether any specimen testing was done for this case.

Drop down menu: Yes, No, Don’t Know (DK), N/A, <leave blank>

Type of Specimen Collected

The type of specimen collected for each case: food, stool, blood, environmental.

Drop down menu: Food, Stool, blood, Environmental, N/A

Genus Species (Pathogen)

The pathogen isolated for each case.

Text

Serotype/Serovar

Serotype information for the isolated pathogen.

Alphanumeric

WGS Cluster Code

Cluster code assigned by PulseNet Canada.

Alphanumeric

MLVA

MLVA information for the case.

Alphanumeric

Notes

Notes related to clinical specimen testing or diagnostics

Text

Link to Other Cases

List the National ID of the case(s) that  are linked to this case

Cases linked to other cases is used to document epidemiologic links between cases where common exposures may have occurred.

(Abbreviated P/T characters-#)

Questionnaire Received

Whether questionnaires were received for each case.

Drop down menu: Yes, No, Don’t Know (DK), N/A, <leave blank>

Questionnaire Type Used

Track the type/name of the questionnaire on file for the case.

Text

Notes

Other Notes can be captured here.

Text

Exposures/Risk Factors (Exposures #1-71)

Exposure(s) of interest at the initial stage of the investigation (including travel/animal exposures); specific exposures are reported as separate variables. Preliminary exposure information includes travel, food consumption, animal contact, and social events.

Preliminary exposure information can provide initial hypotheses regarding source, as well as initial inclusion/exclusion criteria for cases (e.g. exclusion of travel-related cases). Additional (more detailed) exposure information, such as that from hypothesis-generating questionnaires, should be captured in a separate investigation-specific database.

Text