Updating Data Quality Filters in the New Year
Time gets away from us. Then, surprise! Your data look odd. This can happen if the query calculation for your filter or application programming interface (API) isn’t current or accounting for the new year.
At a minimum, check the time coverage of the data quality filters in your API URLs. You might need to modify the filters to account for rolling into a new year.
Example: NSSP analysts want to automate a routine product that generates a time series figure of weekly emergency department (ED) encounters identified by a chief complaint/discharge diagnosis (CC/DD) category. The R code to generate this figure uses an ESSENCE API URL. The API pulls data from January 2018 to date and has CoV and DDI data quality filter parameters to limit data to those facilities with consistent reporting and high completeness of discharge diagnoses. Because the R code was developed in mid-2022, the 4-year-to-date filters were used to generate the API URL. In January 2023, the code was not modified and began producing figures with weekly counts higher than expected.
What happened? Because the current year was now 2023, the 4-year-to-date filters only covered January 2019 to date—plus, the number of facilities from which data were included had increased. To constrain facilities based on data from 2018 to date, the data quality filter parameters in the API URL must be updated to 5-year-to-date parameters. After this modification, the weekly trends will be consistent with results generated in 2022.