Laura Horsfall1, Kate Walters, Irene Petersen. 1. Research Department of Primary Care and Population Health, University College London, United Kingdom. laura.horsfall@ucl.ac.uk
Abstract
PURPOSE: To examine the effect of applying different data quality filters on the incidence of disease and prescribing trends over time in a primary care research database and validate a new method for defining periods of adequate computer usage. METHODS: Acceptable computer usage (ACU) was defined as the year in which a general practice was continuously entering on average at least two therapy records, one medical record and one additional health data record per patient per year. The effect of using this date on the incidence of a range of outcomes (antibiotic prescriptions, myocardial infarction, colon cancer, lung cancer) over time was compared with other methods for defining the start of patient follow-up in The Health Improvement Network (THIN) primary care database containing UK patient records. Various combinations of the follow-up start dates were applied to the data to calculate incidence rates: (i) registration date, (ii) practice computerization date, (iii) acceptable mortality recording (AMR) (iv) ACU. RESULTS: On average, the ACU date was 3.3 years after the AMR date. Applying the AMR or ACU dates separately or in combination produced trends in incidence rates more comparable with external data sources than using the year of practice registration or computerization. The estimated incidence rates were highly sensitive to different methods of defining start date in early time periods. CONCLUSIONS: Using the latest of AMR and ACU dates is useful for improving the integrity of THIN data.
PURPOSE: To examine the effect of applying different data quality filters on the incidence of disease and prescribing trends over time in a primary care research database and validate a new method for defining periods of adequate computer usage. METHODS: Acceptable computer usage (ACU) was defined as the year in which a general practice was continuously entering on average at least two therapy records, one medical record and one additional health data record per patient per year. The effect of using this date on the incidence of a range of outcomes (antibiotic prescriptions, myocardial infarction, colon cancer, lung cancer) over time was compared with other methods for defining the start of patient follow-up in The Health Improvement Network (THIN) primary care database containing UK patient records. Various combinations of the follow-up start dates were applied to the data to calculate incidence rates: (i) registration date, (ii) practice computerization date, (iii) acceptable mortality recording (AMR) (iv) ACU. RESULTS: On average, the ACU date was 3.3 years after the AMR date. Applying the AMR or ACU dates separately or in combination produced trends in incidence rates more comparable with external data sources than using the year of practice registration or computerization. The estimated incidence rates were highly sensitive to different methods of defining start date in early time periods. CONCLUSIONS: Using the latest of AMR and ACU dates is useful for improving the integrity of THIN data.
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