Background Administrative data are used extensively for research purposes, but there remains limited information on the quality of these data for identifying comorbidities related to stroke. Objective To compare the prevalence of comorbidities of stroke identified using International Classification Diseases, Australian Modification (ICD-10-AM) or Anatomical Therapeutic Chemical codes, with those from (i) self-reported data and (ii) published studies. Method The cohort included patients with stroke or transient ischaemic attack admitted to hospitals (2012–2016; Victoria and Queensland) in the Australian Stroke Clinical Registry (N = 26,111). Data were linked with hospital and pharmaceutical datasets to ascertain comorbidities using published algorithms. The sensitivity, specificity, and positive predictive value of these comorbidities were compared with survey responses from 623 patients (reference standard). An indirect comparison was also performed with clinical data from published stroke studies. Results The sensitivity of hospital ICD-10-AM data was poor for most comorbidities, except for diabetes (93.0%). Specificity was excellent for all comorbidities (87–96%), except for hypertension (70.5%). Compared to published stroke studies (3 clinical trials and 1 incidence study), the prevalence of diabetes and atrial fibrillation in our cohort was similar using ICD-10-AM codes, but lower for dyslipidaemia and anxiety/depression. Whereas in the pharmaceutical dispensing data, the sensitivity was excellent for dyslipidaemia (94%) and modest for anxiety/depression (77%). In the pharmaceutical data, specificity was modest for hypertension (78%) and anxiety or depression (76%), but specificity was poor for dyslipidaemia (19%) and heart disease (46%). Conclusion Variation was observed in the reporting of comorbidities of stroke in administrative data, and consideration of multiple sources of data may be necessary for research. Further work is needed to improve coding and clinical documentation for reporting of comorbidities in administrative data.

Background: Accurate coded diagnostic data are important for epidemiological research of stroke. Objective: To develop, implement and evaluate an online education program for improving clinical coding of stroke. Method: The Australia and New Zealand Stroke Coding Working Group co-developed an education program comprising eight modules: rationale for coding of stroke; understanding stroke; management of stroke; national coding standards; coding trees; good clinical documentation; coding practices; and scenarios. Clinical coders and health information managers participated in the 90-minute education program. Pre- and post-education surveys were administered to assess knowledge of stroke and coding, and to obtain feedback. Descriptive analyses were used for quantitative data, inductive thematic analysis for open-text responses, with all results triangulated. Results: Of 615 participants, 404 (66%) completed both pre- and post-education assessments. Respondents had improved knowledge for 9/12 questions ( p < 0.05), including knowledge of applicable coding standards, coding of intracerebral haemorrhage and the actions to take when coding stroke (all p < 0.001). Majority of respondents agreed that information was pitched at an appropriate level; education materials were well organised; presenters had adequate knowledge; and that they would recommend the session to colleagues. In qualitative evaluations, the education program was beneficial for newly trained clinical coders, or as a knowledge refresher, and respondents valued clinical information from a stroke neurologist. Conclusion: Our education program was associated with increased knowledge for clinical coding of stroke. To continue to address the quality of coded stroke data through improved stroke documentation, the next stage will be to adapt the educational program for clinicians.