ABSTRACT Purpose This study quantified temporal trends in antidepressant dispensing among children and adolescents and described patterns by antidepressant class, drug, and demographic subgroups. Methods This population‐based cohort study used administrative health data from Manitoba, Canada and included individuals aged 6–18 years between 2000 and 2024. Antidepressant dispensing was identified in prescription dispensing records using the Anatomical Therapeutic Chemical code N06A. Annual prevalence was defined as the proportion of individuals dispensed antidepressants in a given year; annual incidence rate was defined as the rate of new antidepressant dispensing following a 2‐year washout period. Analyses were stratified by antidepressant class, drug, age group (6‐12, 13‐18), sex and neighborhood income quintiles. Results The cohort included 699 526 individuals; 66 895 (9.6%) had an antidepressant dispensing, of which most were dispensed selective serotonin reuptake inhibitors ( n = 49 219, 73.6%). The prevalence of antidepressant dispensing increased from 14.9 (95% confidence interval [CI] 14.5–15.5) to 48.7 (95% CI 47.9–49.6) per 1000 individuals in 2000 and 2024, respectively. The incidence increased from 9.2 (95% CI 8.8–9.6) to 17.4 (95% CI 16.8–17.9) per 1000 person‐years. A decline in dispensing between 2003 and 2005, and a sharp rise beginning in 2020, were observed. Subgroup analyses showed a temporal increase in antidepressant dispensing across all subgroups, particularly among females and adolescents aged 13–18 years. Conclusions This study found a marked increase in antidepressant dispensing in children and adolescents over time, particularly in females and adolescents. Future studies are needed to evaluate appropriateness, clinical outcomes and long‐term safety of antidepressant use in this population.

PURPOSE:This study quantified temporal trends in antidepressant dispensing among children and adolescents and described patterns by antidepressant class, drug, and demographic subgroups. METHODS:This population-based cohort study used administrative health data from Manitoba, Canada and included individuals aged 6-18 years between 2000 and 2024. Antidepressant dispensing was identified in prescription dispensing records using the Anatomical Therapeutic Chemical code N06A. Annual prevalence was defined as the proportion of individuals dispensed antidepressants in a given year; annual incidence rate was defined as the rate of new antidepressant dispensing following a 2-year washout period. Analyses were stratified by antidepressant class, drug, age group (6-12, 13-18), sex and neighborhood income quintiles. RESULTS:The cohort included 699 526 individuals; 66 895 (9.6%) had an antidepressant dispensing, of which most were dispensed selective serotonin reuptake inhibitors (n = 49 219, 73.6%). The prevalence of antidepressant dispensing increased from 14.9 (95% confidence interval [CI] 14.5-15.5) to 48.7 (95% CI 47.9-49.6) per 1000 individuals in 2000 and 2024, respectively. The incidence increased from 9.2 (95% CI 8.8-9.6) to 17.4 (95% CI 16.8-17.9) per 1000 person-years. A decline in dispensing between 2003 and 2005, and a sharp rise beginning in 2020, were observed. Subgroup analyses showed a temporal increase in antidepressant dispensing across all subgroups, particularly among females and adolescents aged 13-18 years. CONCLUSIONS:This study found a marked increase in antidepressant dispensing in children and adolescents over time, particularly in females and adolescents. Future studies are needed to evaluate appropriateness, clinical outcomes and long-term safety of antidepressant use in this population.

Introduction The International Classification of Diseases (ICD) is used to report medical diagnoses and procedures in most electronic health databases. Periodic revisions and region/country-specific adaptations reflect advances in medical knowledge and local needs. Therefore, health databases often include multiple ICD versions. Comprehensive and accurate mapping between different ICD versions and adaptations is necessary to facilitate research over time and across regions and populations. Objective This scoping review describes published literature about methods to map diagnosis codes between different versions or adaptations of the ICD system. Methods A systematic search across MEDLINE, EMBASE, Scopus and Web of Science Core Collection from inception until June 21, 2024, was performed. Primary research and review articles describing the mapping methods were included. Information was extracted on article characteristics, data characteristics (e.g., versions of ICD codes mapped), mapping methods, and measures to assess mapping quality. Study data were descriptively analysed using frequencies and percentages. Results Among 1359 articles identified from the search, 25 were included in the review; 13 (52%) articles were published between 2020 and 2023. Two-thirds of the articles used US electronic health databases. Mappings were most frequently (48%) created between ICD-9-CM (i.e., 9th revision, clinical modification) and ICD-10-CM (i.e., 10th revision, clinical modification). Mapping methods were mostly (88%) manual, relying primarily on expert review and existing resources to generate mappings and assess their quality. Only 36% of the articles implemented at least two or more strategies to mitigate loss of information due to mapping. Conclusion We identified several methods to map different ICD version, all methods relied on expert review to assess accuracy of the mappings. The application of automated approaches through the utilization of AI tools could represent an opportunity for future research.

The International Classification of Diseases (ICD) is a medical coding system used for healthcare system administration, public health surveillance, and research. Crosswalk tables, which map diagnosis codes across ICD versions, are time-consuming and costly to produce but are essential to reduce medical coding errors when healthcare systems periodically adopt ICD updates. Automated crosswalk development could better support clinical staff during transition periods and facilitate research spanning multiple ICD versions. Our aim was to evaluate the ability of a pre-trained large language model (LLM) to generate ICD crosswalks. We evaluated the accuracy of the fourth-generation OpenAI Generative Pre-trained Transformer (GPT-4) model to translate chronic disease diagnoses across the 9th and 10th revisions of U.S. and Canadian ICD systems. Nine prompting strategies were developed. The three most-accurate prompts were combined to form composite prompts for Canadian and U.S. contexts. Accuracy was evaluated against crosswalks developed by Canadian and U.S. health services organizations. Each prompt was executed 10 times to assess variability, with mean accuracy ± standard deviation (SD) reported across replications. Across the nine prompting strategies evaluated for translating Canadian ICD codes, accuracy ranged from 32.5

IntroductionLinked administrative data integrating health and non-health information can support population-based research about biological and contextual environmental factors that influence child health. Database linkage studies leverage existing data to provide more comprehensive information than would be available from any single source. However, it is unknown the extent by which child health studies capitalise on linked multi-domain Canadian administrative data. ObjectiveThis scoping review aims to describe Canadian population-based child health studies that used linked multi-domain (i.e., health and non-health) administrative data. MethodsA systematic search was conducted of MEDLINE, Embase, Scopus and Global Health from inception until March 12, 2025. Articles were included if they focused on children (birth to 18 years), used Canadian administrative data, and linked health with non-health data. Two reviewers independently screened titles/abstracts and full texts; a pilot test ensured consistency. Article characteristics, province/territory, parental linkage, and non-health variables, were collected using an extraction form. ResultsThe search yielded 4,437 articles, of which 42 met inclusion criteria. Most articles were conducted in Manitoba (45%) and Ontario (36%). Maternal linkage was common, whereas paternal linkage was limited to Manitoba and British Columbia. Immigration status was the most common non-health variable. Health service use, particularly preventive care, such as screening and vaccination coverage, was a common research theme. No multi-jurisdictional studies were identified. ConclusionsMulti-domain administrative data linkage studies remain concentrated in a few provinces. Expanding parental linkage, integrating non-health variables, and strengthening multi-jurisdictional studies are crucial for improving population-based understanding of child health influences across Canada

Mental disorders are highly prevalent, and comorbidities between physical and mental health conditions are common. Physical comorbidities and family health histories may improve the accuracy of mental disorder risk prediction. We developed prediction models for mental disorder risk using comprehensive individual and family mental and physical health histories. We conducted a population-based cohort study using administrative Health data in Manitoba, Canada, and included adults between 1977 and 2020 with linkages to at least one parent and one grandparent. Mental disorders (mood and anxiety, substance use and psychotic disorders) for individuals, parents and grandparents were identified in inpatient and outpatient Health records. Predictors included demographics, family history of mental disorders and 130 health conditions in individuals, parents and grandparents. We used the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression to build prediction models that sequentially included health conditions in individuals, parents and grandparents. Predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value and Brier score. Of 125 070 individuals identified, 109 359 had no preexisting mental disorder. 52.9

ObjectiveTo explore the prevalence of adverse drug events (ADE) leading to drug changes using Artificial Intelligence (AI) based free text analysis of primary care encounter notes . ApproachWe used electronic medical record l encounter notes linked to population-based drug dispensation records. Dispensation records were used to identify drug switching to another agent in the same therapeutic class, which could suggest an undesirable side-effect requiring discontinuation of the drug. We annotated clinical notes by identifying ADEs after retrieving the last encounter note written for that patient on or before the identified date. We ran different AI models, including BERT and Large Language Models (LLMs), to identify ADEs. ResultsWe annotated 1085 notes with 362 ADEs. These were used to train BERT models (precision or PPV: 0.584 ) and prompt-based LLM (0.563) but with improved sensitivity (0.725) and specificity (0.739) . ConclusionsIdentifying ADEs requires deep medical knowledge and simple Natural Language Processing models trying to identify ADEs only on the basis of language of encounter notes was not successful based on the BERT model performance. Prompt based models have deep knowledge but were trained with the objective of producing grammatically correct, coherent sentences, not with the medical knowledge required. LLMs also require significant computational power. ImplicationsWhile AI does have significant potential to advance our capacity for complex analytic tasks the current LLMs are not yet able to adequately identify ADEs in a corpus of electronic clinical notes.

ObjectiveAdministrative data often lacks complete family linkages, particularly to fathers, hindering familial health research. We compared methods to address missing paternal linkages in research investigating the familial transfer of mental disorders. ApproachA population-based cohort study of Manitoba (Canada) born adults, +18 years old between 1977 and 2017 with maternal linkages. Three methods were used to address missing paternal linkages: indicator category, complete case, and multiple imputation, which identified three candidate fathers for each individual within -4 to +7 years of mother’s age with same postal code. For each method, the association of maternal and paternal history with mental disorder risk during follow-up was tested using multivariable logistic regression models adjusted for demographics and comorbidities. ResultsThe cohort included 142,549 individuals; 22.6% lacked paternal linkages. Using indicator category, maternal and paternal histories were associated with mental disorder risk, with odds ratio (OR) 1.49, 95% confidence interval (CI): 1.45-1.52 and OR 1.34, 95% CI: 1.30-1.37, respectively. Similar results were obtained in the complete case analysis (OR 1.49, 95% CI: 1.45-1.53 and OR 1.33, 95% CI: 1.30-1.37, for maternal and paternal history, respectively). With multiple imputation, maternal history’s association with mental disorder risk remained consistent with the other methods (pooled OR 1.52, 95% CI: 1.49-1.56); paternal history was associated with a smaller risk (pooled OR 1.25 95% CI: 1.22-1.29). Conclusions and ImplicationsThe three methods for addressing missing paternal linkages produced similar findings about the familial transfer of mental disorders. Future familial studies could incorporate multiple methods to demonstrate robustness of findings.

BackgroundThe International Classification of Diseases (ICD) is revised over time and there are region-specific versions, including ICD-10-CA (Canada) and ICD-9-CM (USA). Studies spanning multiple ICD versions require crosswalks to translate diagnosis codes across versions, but manual crosswalk development is costly and requires clinical expertise. ObjectiveTo evaluate the accuracy of a pre-trained large language model (LLM) to automatically translate chronic disease diagnosis codes from ICD-10-CA to ICD-9-CM. ApproachEight prompts were developed to instruct the OpenAI Generative Pre-trained Transformer 4 (GPT-4) LLM to translate 1,272 ICD-10-CA codes for the Elixhauser Comorbidity Index to ICD-9-CM. Prompt accuracy (%) was measured against a crosswalk developed by the Canadian Institute of Health Information. Variability was assessed by replicating each prompt three times. Mean accuracy ± standard deviation was reported for each prompt across replications, for both five-digit and truncated three-digit codes. ResultsThe highest prompt performance was observed when assigning a persona of a medical coding specialist (40.8% ± 0.9%), requesting justification for the selected code (41.4% ± 1.1%), and providing diagnosis code labels (47.5% ± 0.7%). For truncated three-digit codes, these prompts achieved accuracy of 82.0% ± 0.5%, 80.8% ± 0.9%, and 82.7% ± 0.1%, respectively. Combining these three prompting techniques marginally improved accuracy to 48.6% ± 0.7% for five-digit codes and 84.3% ± 0.2% for truncated three-digit codes. ConclusionGeneral-purpose LLMs are currently not sufficiently accurate at automating ICD code translation for chronic diseases. ImplicationsAdditional experiments with fine-tuning, task-specific training, and prompt engineering are needed to improve accuracy and reduce variability.

OBJECTIVES:The aetiology of mental disorders involves genetic and environmental factors, both reflected in family health history. We examined the intergenerational transmission of multiple mental disorders from parents and grandparents using population-based, objectively measured family histories. METHODS:This population-based retrospective cohort study used administrative healthcare databases in Manitoba, Canada and included adults living in Manitoba from 1977 to 2020 with linkages to at least one parent and one grandparent. Index date was when individuals turned 18 or 1 April 1977, whichever occurred later. Mental disorder diagnoses (mood and anxiety, substance use and psychotic disorders) were identified in individuals, parents and grandparents from hospitalization and outpatient records. Cox proportional hazards regression models included sociodemographic characteristics, individual's comorbidity and mental disorder history in a grandparent, mother and father. RESULTS:Of 109,359 individuals with no mental disorder prior to index date, 47.1% were female, 36.3% had a mental disorder during follow-up, and 90.9% had a parent or grandparent with a history of a mental disorder prior to the index date. Both paternal and maternal history of a mental disorder increased the risk of the disorder in individuals. Psychotic disorders had the strongest association with parental history and were mostly influenced by paternal (hazards ratio [HR] 3.73, 95% confidence interval [CI] 2.99 to 4.64) compared to maternal history (HR 2.23, 95% CI, 1.89 to 2.64). Grandparent history was independently associated with the risk of all mental disorders but had the strongest influence on substance use disorders (HR 1.42, 95% CI, 1.34 to 1.50). CONCLUSIONS:Parental history of mental disorders was associated with an increased risk of all mental disorders. Grandparent history of mental disorders was associated with a small risk increase of the disorders above and beyond parental history influence. This three-generation study further highlights the need for family-based interventional programs in families affected by mental disorders. PLAIN LANGUAGE SUMMARY TITLE:The Intergenerational Transfer of Mental Illnesses.

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 AND AIMS:Family history of substance use disorder (SUD) affects a child's risk of the disorder through both genetic and shared environmental factors. We aimed to estimate the association between parental or older sibling SUD history with the risk of adolescent SUD diagnosis.DESIGN, SETTING AND PARTICIPANTS:We conducted a population-based cohort study using administrative health-care databases in the Province of Manitoba, Canada, which has a universal and publicly funded health-care system. We included all children born from 1984 to 2000 who have linkages to both parents and were followed until age 18 years. We used generalized estimating equation models to produce unadjusted and adjusted relative risk (RR) estimates of adolescent SUD risk. The study cohort included 134 389 children and 31 307 full sibling pairs; 51.3% were male and 35.4% first-born.MEASUREMENTS:The exposure was SUD diagnosis in a mother or father in either hospitalization or outpatient physician visit records before the children's age of 13 years. The secondary exposure was an adolescent SUD diagnosis in an older full sibling. The outcome was SUD diagnosis during adolescence (13 and 18 years of age) identified in either hospitalization or physician visit records. Children demographics and characteristics associated with SUD diagnosis were included in the models.FINDINGS:Of the 134 389 children, 9.5% had a mother with a history of SUD, 11.3% had a father and 1.3% had an older sibling with a history of SUD diagnosis; 2566 (1.9%) had an adolescent SUD diagnosis. An increased risk of adolescent SUD was observed with SUD history in mothers [adjusted RR (aRR) = 2.50; 95% confidence interval (CI) = 2.26, 2.79], fathers (aRR = 2.15; 95% CI = 1.95, 2.37), both parents (aRR = 3.74; 95% CI = 3.24, 4.31) and older sibling (aRR = 3.85; 95% CI = 2.53, 5.87).CONCLUSIONS:A family history of substance use disorder in parents or older siblings appears to be associated with increased SUD risk in adolescents.

Background Diagnosis codes in administrative health data are routinely used to monitor trends in disease prevalence and incidence. The International Classification of Diseases (ICD), which is used to record these diagnoses, have been updated multiple times to reflect advances in health and medical research. Our objective was to examine the impact of transitions between ICD versions on the prevalence of chronic health conditions estimated from administrative health data. Methods Study data (i.e., physician billing claims, hospital records) were from the province of Manitoba, Canada, which has a universal healthcare system. ICDA-8 (with adaptations), ICD-9-CM (clinical modification), and ICD-10-CA (Canadian adaptation; hospital records only) codes are captured in the data. Annual study cohorts included all individuals 18 + years of age for 45 years from 1974 to 2018. Negative binomial regression was used to estimate annual age- and sex-adjusted prevalence and model parameters (i.e., slopes and intercepts) for 16 chronic health conditions. Statistical control charts were used to assess the impact of changes in ICD version on model parameter estimates. Hotelling’s T 2 statistic was used to combine the parameter estimates and provide an out-of-control signal when its value was above a pre-specified control limit. Results The annual cohort sizes ranged from 360,341 to 824,816. Hypertension and skin cancer were among the most and least diagnosed health conditions, respectively; their prevalence per 1,000 population increased from 40.5 to 223.6 and from 0.3 to 2.1, respectively, within the study period. The average annual rate of change in prevalence ranged from -1.6% (95% confidence interval [CI]: -1.8, -1.4) for acute myocardial infarction to 14.6% (95% CI: 13.9, 15.2) for hypertension. The control chart indicated out-of-control observations when transitioning from ICDA-8 to ICD-9-CM for 75% of the investigated chronic health conditions but no out-of-control observations when transitioning from ICD-9-CM to ICD-10-CA. Conclusions The prevalence of most of the investigated chronic health conditions changed significantly in the transition from ICDA-8 to ICD-9-CM. These results point to the importance of considering changes in ICD coding as a factor that may influence the interpretation of trend estimates for chronic health conditions derived from administrative health data.

Objective A 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. Approach A 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. Results Health 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. Conclusion EHRs 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.

Families are important components of society; given their shared genetic and social environments, studying families can provide critical insight into health and social outcomes within family members and across generations. Few places in the world have high-quality linkages within families at a population level; one such location is Manitoba, Canada. The Manitoba Multigenerational Cohort (MMC) has been developed to facilitate health and social research using family-based designs. The MMC is derived from the Manitoba Health Insurance Registry (‘the Registry’)—a population-based registry of all individuals registered with Manitoba Health and Seniors Care (MHSC).1 The Registry is updated at the Manitoba Centre for Health Policy (MCHP) twice a year and is integrated with historical registry data to create a longitudinal population-based registry. Over 80 administrative and survey-based data sets are linkable at the individual level; these data sets make up the Manitoba Population Research Data Repository (‘ the Repository’) and are housed in a secure environment at the MCHP.

Background and ObjectivesTo highlight the potential of multiple file record linkage. Linkage increases the value of existing information by supplying missing data or correcting errors in existing data, through generating important covariates, and by using family information to control for unmeasured variables and expand research opportunities.MethodsRecent Manitoba papers highlight the use of linkage to produce better studies. Specific ways in which linkage helps deal with different substantive issues are described.ResultsWide data files—files containing considerable amounts of information on each individual—generated by linkage improve research by facilitating better design. Nonexperimental work in particular benefits from such linkages. Population registries are especially valuable in supplying family data to facilitate work across different substantive fields.ConclusionSeveral examples show how record linkage magnifies the value of information from individual projects. The results of observational studies become more defensible through the better designs facilitated by such linkage.

Family health history is a well-established risk factor for many health conditions but the systematic collection of health histories, particularly for multiple generations and multiple family members, can be challenging. Routinely-collected electronic databases in a select number of sites worldwide offer a powerful tool to conduct multigenerational health research for entire populations. At these sites, administrative and healthcare records are used to construct familial relationships and objectively-measured health histories. We review and synthesize published literature to compare the attributes of routinely-collected, linked databases for three European sites (Denmark, Norway, Sweden) and three non-European sites (Canadian province of Manitoba, Taiwan, Australian state of Western Australia) with the capability to conduct population-based multigenerational health research. Our review found that European sites primarily identified family structures using population registries, whereas non-European sites used health insurance registries (Manitoba and Taiwan) or linked data from multiple sources (Western Australia). Information on familial status was reported to be available as early as 1947 (Sweden); Taiwan had the fewest years of data available (1995 onwards). All centres reported near complete coverage of familial relationships for their population catchment regions. Challenges in working with these data include differentiating biological and legal relationships, establishing accurate familial linkages over time, and accurately identifying health conditions. This review provides important insights about the benefits and challenges of using routinely-collected, population-based linked databases for conducting population-based multigenerational health research, and identifies opportunities for future research within and across the data-intensive environments at these six sites.

OBJECTIVES:Previous research suggests an intergenerational influence of diabetes on bone health. We examined the association between parental diabetes and major osteoporotic fracture (MOF) risk in offspring. METHODS:This population-based cohort study used de-identified administrative health data from Manitoba, Canada, which capture population-level records of hospitalizations, physician visits and drug dispensations. The cohort included individuals ≥40 years of age with at least 1 parent identified in the data between 1997 and 2015. The exposure was parental diagnosis of diabetes since 1970; the outcome was offspring incident MOF diagnosis of the hip, forearm, spine or humerus. Both measures were identified from hospital and physician visit records using validated case definitions. Multivariable Cox proportional hazards regression models tested the association of parental diabetes and offspring MOF risk. RESULTS:The cohort included 279,085 offspring; 48.5% were females and 86.8% were ≤44 years of age. Both parents were identified for 89.4% of the cohort; 36.7% had a parental diabetes diagnosis. During a median follow up of 12.0 (interquartile range, 6.0 to 18.0) years, 8,762 offspring had an MOF diagnosis. After adjusting for fracture risk factors, parental diabetes diagnosis was not associated with MOF risk, whether diagnosed in fathers (adjusted hazard ratio [aHR], 1.02; 95% confidence interval [CI], 0.97 to 1.08), mothers (aHR, 1.02; 95% CI, 0.97 to 1.07) or both parents (aHR, 1.01; 95% CI, 0.93 to 1.11). The results remained consistent in a stratified analysis by offspring sex, secondary analysis based on MOF site and sensitivity analyses. CONCLUSIONS:The results indicate parental diabetes is not associated with offspring MOF risk.

Introduction Administrative health data capture diagnoses using the International Classification of Diseases (ICD), which has multiple versions over time. To facilitate longitudinal investigations using these data, we aimed to map diagnoses identified in three ICD versions – ICD-8 with adaptations (ICDA-8), ICD-9 with clinical modifications (ICD-9-CM), and ICD-10 with Canadian adaptations (ICD-10-CA) – to mutually exclusive chronic health condition categories adapted from the open source Clinical Classifications Software (CCS). Methods We adapted the CCS crosswalk to 3-digit ICD-9-CM codes for chronic conditions and resolved the one-to-many mappings in ICD-9-CM codes. Using this adapted CCS crosswalk as the reference and referring to existing crosswalks between ICD versions, we extended the mapping to ICDA-8 and ICD-10-CA. Each mapping step was conducted independently by two reviewers and discrepancies were resolved by consensus through deliberation and reference to prior research. We report the frequencies, agreement percentages and 95% confidence intervals (CI) from each step. Results We identified 354 3-digit ICD-9-CM codes for chronic conditions. Of those, 77 (22%) codes had one-to-many mappings; 36 (10%) codes were mapped to a single CCS category and 41 (12%) codes were mapped to combined CCS categories. In total, the codes were mapped to 130 adapted CCS categories with an agreement percentage of 92% (95% CI: 86%–98%). Then, 321 3-digit ICDA-8 codes were mapped to CCS categories with an agreement percentage of 92% (95% CI: 89%–95%). Finally, 3583 ICD-10-CA codes were mapped to CCS categories; 111 (3%) had a fair or poor mapping quality; these were reviewed to keep or move to another category (agreement percentage=77% [95% CI: 69%–85%]). Conclusions We developed crosswalks for three ICD versions (ICDA-8, ICD-9-CM, and ICD-10-CA) to 130 clinically meaningful categories of chronic health conditions by adapting the CCS classification. These crosswalks will benefit chronic disease studies spanning multiple decades of administrative health data.

Introduction Major osteoporotic fractures (MOF) are associated with significant morbidity and healthcare system burden. Objectives and Approach We aimed to determine whether sibling fracture history is associated with MOF risk amongst individuals from a population-based cohort using objectively-ascertained measures of fracture history. This retrospective cohort study used administrative databases from the province of Manitoba, Canada, which has a universal healthcare system. The cohort included individuals aged 40 years and older between 1997 and 2015 with linkage to at least one sibling. The exposure was MOF diagnosis occurring at age 40 years or older in a randomly selected sibling. The outcome was incident clinically-diagnosed MOF (hip, wrist, humerus or spine) identified in hospital and physician records using established case definitions. A multivariable Cox proportional hazards regression was used to test the association of sibling fracture history with the risk of MOF in individuals after adjustment for known fracture risk factors. Results The cohort included 217,519 individuals; 92% were linked to full siblings (i.e., same mother/father) and 49% were females. During a median follow-up of 11 years (IQR 5 -15), 7274 (3.3%) incident MOF cases were identified. Sibling MOF history was associated with increased risk of MOF (HR 1.71, 95% CI 1.48–1.97). The risk was elevated in both men (HR 1.63, 95% CI 1.29-2.06) and women (HR 1.78, 95% CI 1.48-2.13) but was higher among sisters (HR 2.08, 95% CI 1.65-2.61) compared to brothers (HR 1.67, 95% CI 1.20-2.32). In a secondary analysis of sibling fracture site, the highest risk was observed with diagnosis of wrist followed by spine fractures (HR 1.86, 95% CI 1.57-2.21 and HR 1.46, 95% CI 1.08-1.98, respectively). Conclusion Sibling fracture history is associated with increased MOF risk in individuals and should be considered as a candidate risk factor for improving fracture risk prediction.