Automated Application of Case Classification Logic to Improve Reportable Disease Surveillance

Background:
Computerized information systems are core to effective surveillance for reportable diseases.  Florida has implemented technical enhancements to improve the application of reportable disease case definitions, including case classification criteria. Three problem areas occur: 1) inadequate understanding of case definition components; 2) use of additional clinical, epidemiologic and laboratory data outside of that specified in the case definition to classify a case; and 3) application of case definitions without consideration of the event dates (onset, diagnosis, laboratory) in relation to the effective date of the case definition.  Available technology can improve the systematic application of public health surveillance case definitions.
Methods: 
The Florida notifiable disease surveillance system (Merlin) was modified to incorporate case classification algorithms and effective date logic for case definitions.  For each communicable disease under surveillance the individual case definition components – clinical, laboratory and epidemiologic criteria – were deconstructed into multiple simple present/absence statements.  Based on the user provided response set, the system invokes an algorithm to determine what case classification is met for each case – confirmed, probable, or suspect, or not a case.  Additionally, the system was modified to evaluate the case using the case definition in effect on the earliest case event date.
Results:
The Merlin system now utilizes case classification algorithms in order to set the case classification status and utilizes the earliest possible event date associated with the case in order to determine which case definition should be used when evaluating the case. 
Conclusions:
Uniform reporting criteria, systematic data collection, in addition to the simplicity and timeliness of surveillance data, are fundamental to increasing the specificity of reporting.  The intersection of “traditional PH surveillance” methods with new technological capabilities provides the opportunity to ensure accurate data capture and historical data analysis.  In future, machine-readable versions of case classification criteria could be used to support this function.