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Introduction Unstructured text data (UTD) are increasingly found in many databases that were never intended to be used for research, including electronic medical record (EMR) databases. Data quality can impact the usefulness of UTD for research. UTD are typically prepared for analysis (i.e., preprocessed) and analyzed using natural language processing (NLP) techniques. Different NLP methods are used to preprocess UTD and may affect data quality. Objective Our objective was to systematically document current research and practices about NLP preprocessing methods to describe or improve the quality of UTD, including UTD found in EMR databases. Methods A scoping review was undertaken of peer-reviewed studies published between December 2002 and January 2021. Scopus, Web of Science, ProQuest, and EBSCOhost were searched for literature relevant to the study objective. Information extracted from the studies included article characteristics (i.e., year of publication, journal discipline), data characteristics, types of preprocessing methods, and data quality topics. Study data were presented using a narrative synthesis. Results A total of 41 articles were included in the scoping review; over 50% were published between 2016 and 2021. Almost 20% of the articles were published in health science journals. Common preprocessing methods included removal of extraneous text elements such as stop words, punctuation, and numbers, word tokenization, and parts of speech tagging. Data quality topics for articles about EMR data included misspelled words, security (i.e., de-identification), word variability, sources of noise, quality of annotations, and ambiguity of abbreviations. Conclusions Multiple NLP techniques have been proposed to preprocess UTD, with some differences in techniques applied to EMR data. There are similarities in the data quality dimensions used to characterize structured data and UTD. While a few general-purpose measures of data quality that do not require external data; most of these focus on the measurement of noise.

ObjectivePost-acute COVID-19 (or ‘long COVID’) manifests as a wide range of long-lasting symptoms affecting multiple organ systems. We are developing criteria for identifying long COVID cases using administrative, clinical, survey and other data from Manitoba, Canada, with the ultimate goal of examining long COVID prevalence, risk factors, prognosis and recovery. ApproachGiven the lack of an accepted clinical definition and resulting lack of diagnostic codes, we are adopting several different creative and complementary strategies to identify long COVID cases. We are examining administrative and clinical data sources (laboratory data, physician claims, drug prescriptions, and electronic medical records) for information on positive COVID tests, common symptoms and complaints, and treatment provided. To identify people with long COVID who may not have sought healthcare, we are collecting survey data from a convenience community sample (members of a medical health fitness facility) and mining data on long COVID symptoms from Twitter. ResultsThe combination of approaches we have adopted and the expanding scientific literature on long COVID are contributing to a more comprehensive understanding of the impacts of long COVID in Manitoba. Through preliminary work on the laboratory data (positive COVID tests March 2020-June 2021), we have developed and characterized a COVID-positive cohort (n=47,515). Work is now underway to develop an algorithm for long COVID using symptoms from free text in electronic medical records, ICD-9 codes, and changes in health-seeking behaviour (compared to the pre-positive COVID test period and a matched sample). This population data-driven approach will then allow us to examine how multiple underlying health conditions, COVID illness severity, COVID vaccination status, and various socio-demographic factors are related to risk of long COVID. ConclusionThis research is generating actionable information by identifying risk factors to support clinical diagnosis of long COVID, making it easier for clinicians to recognize this new illness and develop plans to manage it, and will inform healthcare system planning by quantifying the burden of long COVID at the population level.

ObjectivesRed River Métis are Indigenous people hailing from the Canadian Prairies who have historically experienced poor health outcomes due to colonial practices. Researchers from the Manitoba Métis Federation (MMF) partnered with health services researchers to test whether MMF-led COVID initiatives were associated with access to COVID-19 testing and vaccines. ApproachWe linked the Métis Population Data-Base from the MMF (to identify Red River Métis) with whole-population COVID testing and vaccination data and health and social services administrative data (for information on sociodemographics and confounders) to complete this retrospective cohort study. We used restricted mean survival time models to test whether COVID-19 vaccination differed between Métis and all other Manitobans (AOM); models adjusted for demographics, comorbidities, and other characteristics (age, socioeconomic status, urbanicity, and mental health status). Data were stratified by sex and subsequent effect modification analyses tested whether associations differed by sex and physical health comorbidities. ResultsCOVID testing rates were lower during the first year of the pandemic among Métis than among AOM. During the second year of the pandemic, this finding was reversed - Métis accessed tests at higher rates. There was no difference between Métis and AOM in accessing first vaccine doses before implementation of MMF-led initiatives. After initiatives were put in place, Métis received their second COVID vaccine, on average, 1.3 (95% CI 1.9-0.6) days sooner than AOM, after adjusting for confounders. Effect modification analyses showed this relationship was concentrated among females – female Métis received their second vaccine 1.7 (2.6-0.8) days sooner than female AOM; differences were non-significant for males. Métis with 2+ comorbidities received their vaccine second 2.9 (5.3-0.5) days sooner than AOM with 2+ comorbidities. ConclusionPublic health initiatives prioritizing Métis for vaccines improved uptake. Initiatives led by Métis to improve COVID outcomes were critical to supporting Métis during the course of the pandemic. Public health response efforts need to operate from a standpoint that honours Indigenous sovereignty in their design and implementation.

ObjectivesValidation of algorithms to identify health conditions (e.g., diabetes) or service use (e.g., high-cost users) in administrative data is time-consuming and expensive. Many algorithms are only assessed in a single jurisdiction, which may limit generalizability. Our study described the characteristics of multi-jurisdiction algorithms from a Canadian algorithm repository. ApproachWe summarized algorithms captured in the open-access Algorithms Inventory developed by Health Data Research Network (HDRN) Canada. This inventory contains published algorithms identified through a series of systematic reviews of peer-reviewed research. Algorithms included in the inventory were validated or assessed for feasibility of implementation in two or more provinces/territories; they encompass measures of population health, health service use, and determinants of health. Descriptive statistics were used to characterize the study data on such features as year and discipline of the study journal, algorithm topic area, jurisdictions included in the study, validation source data, and algorithm elements (i.e., diagnosis codes). ResultsThe HDRN Canada Algorithms Inventory currently contains 166 algorithms from 63 published articles. The majority of articles were published in 2010 or later (89%) and more than half (56%) of the articles were found in journals with a clinical focus. Feasibility studies (79%) were conducted more often than validation studies (21%). Most algorithms used data from the provinces of British Columbia, Manitoba, Ontario, and Nova Scotia. The majority of algorithms (72%) measured population health concepts, such as chronic physical health conditions (63%; e.g., hypertension) and mental health conditions (14%; e.g., depression). Algorithms about the determinants of health (17%) mostly focused on measures of socioeconomic status (37%) derived from census data. Multi-jurisdiction algorithms about health service use were least common (11%). ConclusionThis synthesis revealed few Canadian multi-jurisdiction validation studies have been conducted and not all provinces/territories are equally represented. New validation studies, particularly about health service use and determinants of health, will increase the consistency and accuracy of Canadian research. Reusing published algorithms from this inventory will facilitate research reproducibility.

ObjectiveA family history of a chronic disease often predicts disease risk, with predictive value determined by heritability, the proportion of variation in risk explained by inherited genetic factors. Our objective was to assess the validity of disease heritability estimates from electronic healthcare records (EHRs) that capture family relationships and disease diagnoses. ApproachA population-based investigation was conducted using healthcare records from Manitoba, Canada for 1970 to 2021. We constructed family relationships for up to four generations using health insurance registration information containing unique family and individual identifiers. Health histories for family members were created using diagnosis codes in hospital and physician visit records. Linear mixed-effects models were used to estimate heritability (h) for 130 chronic health conditions using open-source Clinical Classifications Software that defines clinically-meaningful disease categories. Comparisons between EHR-derived estimates and genetically-derived estimates from published studies were used to assess validity of the methodology. ResultsHealth insurance registration data were used to construct 10,000 families that included 116,879 individuals. Median family size was 9 (interquartile range: 8). Median observation time was 39.6 years (interquartile range: 25.7). Males comprised half (51.0%) of family members. A total of 272,114 familial relationships were identified; slightly more than half (53%) were first degree (i.e., child and parent) relationships. One-third (33.2%) of families were comprised of four generations; only 15.3% were comprised of two generations. Heritability estimates were consistent with published genetically-derived estimates for several conditions, including diabetes (EHR h = 0.29 vs. 0.22), anemia (EHR h = 0.21 vs. 0.20), and asthma (EHR h = 0.34 vs. 0.33). However, inconsistencies were identified for pancreatic disorders, gastrointestinal conditions, some mental health conditions, and heart disease. ConclusionEHRs provide a promising approach to explore heritability of selected health conditions in large, diverse populations. Inconsistencies between EHR-derived and genetically-derived estimates are indicative of the limitations of diagnoses recorded for administrative purposes. Future research will explore sex-specific heritability estimates and effects of change in disease diagnosis coding over time.

ObjectivesMental disorders are a major public health concern. Genetic and environmental factors, both reflected in family health histories, jointly contribute to the onset of mental disorders. We examined the intergenerational transmission of mental disorders using objectively-measured family health histories from three generations. ApproachA population-based cohort study was conducted using administrative healthcare databases from Manitoba, Canada. The cohort included offspring who were 18 years or older between 1977 and 2020 with linkage to 1+ parent and 1+ grandparent. Mental disorders were identified using diagnosis codes from hospitalization and outpatient physician visit records and included mood and anxiety, psychotic, and substance use disorders. Logistic regression models were mutually adjusted for mental disorder history in grandparents, parents and/or siblings in addition to offspring demographics: sex, region, decade of birth and income quintile, and comorbidity. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were estimated. ResultsOut of 125,070 individuals, 59.1% were females and 57.8% were urban residents. 41,552 (33.2%) had a mental disorder during study period and 108,682 (86.9%) had a family member with a mental disorder history. Individuals were more likely to have a mental disorder if they had a family history: mother (OR 1.52, 95% CI 1.48-1.56), father (OR 1.21, 95% CI 1.17-1.25), sibling (OR 1.33, 95% CI 1.28-1.39), grandparent (OR 1.06, 95% CI 1.03-1.09). Compared with other mental disorders, psychotic disorders had the strongest association with family history: mother (OR 2.37, 95% CI 2.00-2.82), father (OR 3.00, 95% CI 2.40-3.76), sibling (OR 2.34, 95% CI 1.79-3.05). However, there was no association between psychotic disorders and grandparent history (OR 1.00, 95% CI 0.90-1.11). ConclusionsWe observed a strong association between mental disorders family history across three generations and the risk of the mental disorders in offspring. This association was observed for all the investigated mental disorders. This work highlights the value of multigenerational data linkage in understanding the intergenerational transfer of mental disorders.

ObjectivesFirst Nations (FN) organizations worked with public health and governments to improve FN access to COVID-19 vaccines by prioritizing FN communities in vaccination initiatives. FN researchers and data scientists partnered to test whether these efforts were associated with increased access to COVID-19 vaccines among FN compared with all other Manitobans. ApproachThis retrospective cohort study linked whole-population administrative data from (i) the First Nations research file, (ii) COVID testing and vaccination data, and (iii) health and social services for sociodemographic data and information on potential confounders. Several public health policies were created to improve access to COVID vaccines among FN; we tested whether FN received their 1st and 2nd vaccines sooner than all other Manitobans (AOM) using restricted mean survival time models. We adjusted for sociodemographic characteristics, comorbidities, and whether FN lived on- or off-reserve. We conducted sex-specific and effect modification analyses to test whether associations differed by sex. ResultsPrioritizing FN to receive vaccines was associated with increased vaccine uptake compared with AOM. After adjusting for various confounders, FN received their first dose 15.5 (95% CI 14.9 – 16.0) days sooner than AOM and their second dose 13.9 (13.3 – 14.5) days sooner than AOM. Sex-stratified and subsequent effect modification analyses using interaction terms, found that differences were greater for males than for females: FN males received their first dose 18.1 (17.3 – 18.8) days sooner than AOM males and FN females received their first dose 12.9 (12.2 – 13.7) days sooner than AOM females. This pattern held for second doses as well. FN with comorbidities also received vaccines sooner than AOM with similar comorbidity levels 20.9 days (23.1 – 18.8) among those with 3+ comorbidities. ConclusionPartnerships between public health entities and FN organizations that respect FN community sovereignty were instrumental in supporting FN health and well-being during COVID-19. Policies and programs that prioritized FN people for vaccines improved uptake saving lives. This partnership-based COVID-19 response can provide a framework for future public health efforts.

ObjectivesPrediction of asthma risk can potentially be improved by including family history of asthma and related diagnoses, which reflect both genetics and shared environments. We tested the improvements in offspring asthma risk prediction using objectively-measured maternal, paternal, and offspring histories of comorbid conditions from administrative healthcare databases. ApproachA population-based cohort study was conducted using data from Manitoba, Canada. Children born from 1974 to 2000 with linkages to at least one parent using family identification numbers were included. Asthma diagnosis and comorbidities were identified from hospital and outpatient physician visit records. Lasso regression models were used to assess performance and identify important predictors. The base model included offspring demographics, diagnosed allergic conditions and respiratory infections, and diagnosed parental asthma. Subsequent models included multiple comorbid chronic health conditions for offspring and parents. ResultsThe cohort included 195,666 offspring; 51% were males, 13.6% had a parental asthma diagnosis, and 17.7% had an asthma diagnosis (median age at diagnosis: 6.0 years; interquartile range 3.0-11.0 years). The base model achieved a modest prediction performance with an area under the receiver operating characteristic curve of 0.60, sensitivity of 0.46 and a specificity of 0.67 using a threshold of 0.20. Sensitivity significantly improved when we included offspring chronic health conditions (sensitivity= 0.69; specificity = 0.66); both measures further improved when we additionally included parents’ chronic health conditions (sensitivity= 0.72; specificity = 0.70). Chronic obstructive pulmonary disease, noninfectious gastroenteritis and otitis media were among the variables that added incremental predictive value of asthma risk with odd ratios of 1.36, 1.25 and 1.18, respectively. ConclusionsIncluding offspring and parents’ chronic health conditions, identified objectively from administrative healthcare databases, improved the performance of asthma risk prediction models in children. Health histories of comorbid conditions provide important factors to improve risk prediction models of chronic health conditions, which will facilitate disease prevention and treatment strategies.

Background In Manitoba, Canada, school-based clinics providing sexual and reproductive health services for adolescents have been implemented to address high rates of sexually transmitted infections (STIs) and pregnancies. Methods The objectives of this population-based study were to compare pregnancy and STI rates between adolescents enrolled in schools with school-based clinics, those in schools without clinics, and those not enrolled in school. Data were from the PATHS Data Resource held in the Population Health Research Data Repository housed at the Manitoba Centre for Health Policy. Adolescents aged 14 to 19 between 2003 and 2009 were included in the study. Annualized rates of pregnancies and positive STI tests were estimated and Poisson regression models were used to test for differences in rates amongst the three groups. Results As a proportion, pregnancies among non-enrolled female adolescents accounted for 55 % of all pregnancies in this age group during the study period. Pregnancy rates were 2–3 times as high among non-enrolled female adolescents. Compared to adolescents enrolled in schools without school-based clinics, age-adjusted STI rates were 3.5 times (p