Suchergebnisse

IntroductionThe Applied Health Research Question (AHRQ) portfolio is an initiative funded by the Ontario Ministry of Health. Knowledge users submit AHRQ requests to use administrative health data to inform their planning, policy and program development.
Objectives and ApproachA request to estimate prevalence and incidence of blood-borne viral infections (BBVI) among individuals seeking fertility treatment services was approved by the ICES AHRQ team. To determine whether current BBVI testing guidelines are effective for timely identification of BBVI among individuals undergoing fertility treatment, this AHRQ sought to estimate: a) the testing prevalence for BBVI (using viral hepatitis as a proxy for all BBVI including HIV), and b) BBVI incidence over a five-year follow-up period. We used infertility codes billed by gynecologists and urologists to identify individuals seeking fertility treatment between April 1, 2002 and March 31, 2013. We looked forward five years from a first BBVI test billed by a physician and assessed annual and cumulative testing prevalence and incidence of viral hepatitis and HIV using rule-based administrative algorithms.
ResultsIn total, 184,328 individuals (61.6% female) were included. The five-year BBVI testing prevalence was 45.5% and the median number of tests was 2 (interquartile range: 2-3). An estimated 949 new cases (48% diagnosed within 12 months of first BBVI test) of viral hepatitis were identified in the five-year follow-up with a cumulative incidence of 522 (95%CI 489-556) per 100,000 population. There were 57 new HIV cases (68% diagnosed within 12 months of first BBVI test) with a cumulative incidence of 31.4 (95%CI 23.7-40.6) per 100,000. The median times from first BBVI test to hepatitis and HIV diagnoses were 13.0 months (IQR 2.33-32.2) and 3.0 months (IQR 1.34-20.0), respectively.
Conclusion / ImplicationsThese findings can be used to develop evidence-based BBVI testing guidelines for individuals seeking fertility treatment services.

IntroductionCertain subject behaviours and characteristics increase the risk of some cancer types (e.g., obesity, alcohol intake) while others reduce cancer risk (e.g., physical activity). In 2007, the World Cancer Research Fund (WCRF) and American Institute for Cancer Research (AICR) published recommendations to reduce cancer risk related to these behaviours.
Objectives and ApproachThe objective is to examine the association between self-reported behaviour consistent with WCRF/AICR recommendations for body fatness, physical activity, vegetable/fruit consumption, and alcohol intake and the risk of all cancers combined and specific cancer types. The study cohort, comprised of the Canadian Community Health Survey (CCHS) Ontario sample, will be linked with health administrative databases, including the Ontario Cancer Registry to determine cancer outcomes. Individuals will be assessed for behaviours consistent with WCRF/AICR recommendations based on their responses to CCHS questions and the association of these behaviours with cancer risk will be explored using multivariable Cox proportional hazard regression models.
ResultsTo detect a log hazard ratio of 1.10 (where a=0.05, power=0.80, proportion of the sample assigned to the exposure group=0.25 and R2=0.20), a sample size of 4,538 is required. Based on the number of records in the CCHS data frame (159,474) and an assumption that the CCHS sample experiences cancer incidence at a similar rate to the rest of the Ontario population, we expect to have 5,000 cancer cases for these analyses. Upon completion of the analysis, we will report hazard ratios that estimate the difference in cancer risk between individuals reporting behaviour consistent with the WCRF/AICR recommendations and those reporting behaviour not consistent with the recommendations.
Conclusion/ImplicationsWCRF/AICR recommendations were developed as the basis for primary cancer prevention, both for individuals and population-wide policies and programs. The current study will quantify the difference in overall cancer risk between individuals who do and do not adhere to selected WCRF/AICR recommendations for the first time in a Canadian population.

IntroductionA significant amount of valuable information in Electronic Health Records (EHR) such as laboratory test results or echocardiogram interpretations is embedded in lengthy free-text fields. Often patients' personal information is also included in these narratives. Privacy legislation in different jurisdictions requires de-identification of this information prior to making it available for research. This process can be challenging and time-consuming. In particular, rule-based algorithms may lead to over-masking of essential medical terms, conditions, or devices that are named after individuals.
Objectives and ApproachWe aimed to enhance ICES' existing rule-based application to make it contextually-driven by applying Artificial Intelligence (AI). The ICES team collaborated with computer scientists at the University of Manchester who had already published work in this area and Evenset, a Toronto-based software company. Based on the Manchester University de-identification framework for name entity recognition, three machine learning-based algorithms for name entity recognition were implemented: CRF, BiLSTM recurrent neural networks with GLoVe and ELMo word embeddings. The models were trained on three different types of ICES data: Laboratory results, Electronic Medical Record (EMR) and echocardiogram data. Evenset developed the user interface and the masking modules.
ResultsPreliminary tests have generated very promising results. To improve accuracy of the models, additional data annotation to expand the training datasets is currently being undertaken at ICES. The final framework will be available as an open-source tool for public.
Conclusion / ImplicationsA collaborative approach for solving complex problems like de-identification of text-based medical data is highly efficient, especially where there are unique sets of expertise, resources, data and clinical knowledge among stakeholders.