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Assessment of data quality. Dr Venanzio Vella , Epidemiologist Key Expert CEEN. Overview of the analysis. Individual data were available from all the regions of the RS , BD, 6 cantons in the Federation (4 provided weekly data) for 2008-11;
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Assessment of data quality Dr Venanzio Vella, Epidemiologist Key Expert CEEN
Overview of the analysis • Individual data were available from all the regions of the RS, BD, 6 cantons in the Federation (4 provided weekly data) for 2008-11; • The analysis provides an example of what can be done when individual records are available in electronic format; • This includes: data quality, seasonality, outliers.
Differential Diagnoses (e.g. Borelliosis) Only 2 cases of borelliosis in FBiH
Timeliness • Time between diagnosis and reporting; • It should be within the first week from the diagnosis;
Seasonality • It is important to take into account weekly and monthly variation to identify cases exceeding the expected seasonal values (outliers); • Two objectives in identifyng outliers: • Check the quality of data • If the outliers are confirmed investigate potential epidemics
Other Gastrointestinal Infect., Monthly Seasonality, Sarajevo Canton
Evaluation of declining trends Central Bosnia Canton
Conclusions • Individual data set is now available for 2008-11 for all BiH except 4 cantons (weekly data); • Data are not in such bad shape and seasonal values can be estimated for key disease; • This will allow to identify outliers to be corrected (in case of poor quality of data) or to be confirmed and thus decide an investigation
Next steps • Recuperate the individual records from the remaining four cantons of BiH and check if the data before 2008 could be added for RS, FBiH and BD; • Use the past seasonality to identify outliers for the new EWS to systematize the routine data quality check and the identification of epidemics; • Pilot test the database for standardized data entry; • Validate the use of threshold against future epidemics; • Analyse the reasons behind declining or increasing trends (e.g. Brucellosis, TB); • Disaggregate the analysis to the municipality level;