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Abstract Objectives This study aimed to investigate the impact of pregnancy and pre-existing comorbidities on COVID-19 infections and associated complications of hospitalisation and mortality in women of reproductive age (WRA). The study also compared the risk of severe COVID-19 complications between pregnant women (PW) and non-pregnant women (NPW) with and without pre-existing comorbidities. Special focus was placed on some understudied comorbidities of immunosuppression, chronic renal disease and chronic obstructive pulmonary disease (COPD). Methods The study utilized anonymized patient-related information for a population of 7,342,869 WRA from the Mexican Ministry of Health data repository on COVID-19. Descriptive variables were characterized using frequencies, percentages, means, and standard deviations. Adjusted odds ratios (aORs) were used to assess the associations between risk factors and outcomes of hospitalisation and mortality. The study covered the entire COVID-19 pandemic period from January 30, 2020, to May 5, 2023. Results The findings revealed that PW were not more likely to get COVID-19 infections than NPW. PW with COVID-19 infections were more likely to require hospital admission, intubation treatments, and ICU admission compared to NPW with COVID-19. PW with immunosuppression had an increased odds ratio (aOR) of getting COVID-19 infections compared to NPW (PW: aOR = 1.0396; NPW: aOR = 0.8373). NPW with immunosuppression had higher risk of mortality (all-cause death: aOR = 1.7084; COVID-19-associated death: aOR = 1.4079) and hospitalisation (all-cause hospitalisation: aOR = 4.1328; COVID-19-associated hospitalisation: aOR = 3.0451) than NPW without immunosuppression. Renal disease was identified as a concerning pre-existing condition that increased the risks of COVID-19 associated mortality/hospitalizations and all-cause mortality/hospitalizations for both PW and NPW. NPW with renal disease had much higher odds ratio (aOR) of either COVID-19-associated-hospitalisations (NPW: aOR = 8.639; PW: aOR = 1.7603) or all-cause hospitalisations (NPW: aOR = 8.8594; PW: aOR = 1.786) than PW with renal disease. Conclusions This study provides valuable insights into the impact of pregnancy and pre-existing comorbidities on COVID-19 outcomes in WRA. The findings underscore the importance of considering demographic factors and pre-existing comorbidities in the management of PW with COVID-19. The study also highlights the need for further research to understand the unique impacts of different comorbidities, particularly immunosuppression and renal disease, on COVID-19 outcomes in WRA.

Introduction and Objectives: The burden of chronic liver disease and cirrhosis continues to increase in North America. We sought to estimate the incidence and prevalence of cirrhosis in Manitoba, Canada over time and assess changes in trends between 2010-2019. Material and Methods: We performed a population-based study using Manitoba administrative health care data, and two validated case-finding algorithms. Annual incidence and prevalence rates were estimated using a generalized linear model with generalized estimating equations, adjusting for age and sex. Changes in estimates were tested using linear trend regression models. Results: Two algorithms estimated the number of prevalent cirrhosis to be 16,140 and 29,943 respectively. The age- and sex-adjusted incidence rates increased over the study (from 149 to 264 cases per 100,000 population in 2010, to 177 to 388 cases per 100,000 population in 2019). Cirrhosis incidence increased annually by 2-6 %, with the largest increase (6-8 % 95 % CI 7-9 %, p

ContextThere are limited real-world data evidence assessing the clinical characteristics of hospitalized osteoporotic fractures in China.ObjectiveTo investigate the clinical characteristics of hospitalized major osteoporotic fractures in Northeast China.MethodsWe identified hospitalized fracture patients aged 50 and over from the First Affiliated Hospital of Jinzhou Medical University between January 1, 2018, and December 31, 2022. Major osteoporotic fractures including hip, vertebral, forearm and wrist, and humerus were diagnosed based on x-ray reports extracted from the electronic medical records (EMR). The cause of fracture, comorbidities, surgical methods, and anti-osteoporotic medications (AM) use were extracted from EMR.ResultsThe study population included 1823 fracture patients, 510 males and 1313 females. Over half of fracture patients were aged over 70 years. Hip fractures accounted for 68.4% of all fractures in males and 57.9% in females. For those with hip fractures, the most common sites were the femoral neck (62.9%) and intertrochanteric (35.3%). Most fractures were due to falls (81.0% in males and 80.2% in females). The two most common comorbidities for males and females were hyperlipemia (45.9% vs. 47.1%) and hypertension (38.2% vs. 41.6%). Only 4.7% males and 8.6% females were treated with AM.ConclusionsHip fractures, especially femoral neck fractures, accounted for the majority of osteoporotic fractures in a tertiary public hospital in Northeast China. Common comorbidities in these fracture patients were hyperlipemia and hypertension. There was a very low rate of AM use among these patients.

Abstract Background We estimated the short-term effects of an educational workshop and 10-week outdoor walk group (OWG) compared to the workshop and 10 weekly reminders (WR) on increasing outdoor walking (primary outcome) and walking capacity, health-promoting behavior, and successful aging defined by engagement in meaningful activities and well-being (secondary outcomes) in older adults with difficulty walking outdoors. Methods In a 4-site, parallel-group randomized controlled trial, two cohorts of community-living older adults (≥ 65 years) reporting difficulty walking outdoors participated. Following a 1-day workshop, participants were stratified and randomized to a 10-week OWG in parks or 10 telephone WR reinforcing workshop content. Masked evaluations occurred at 0, 3, and 5.5 months. We modeled minutes walked outdoors (derived from accelerometry and global positioning system data) using zero-inflated negative binomial regression with log link function, imputing for missing observations. We modeled non-imputed composite measures of walking capacity, health-promoting behavior, and successful aging using generalized linear models with general estimating equations based on a normal distribution and an unstructured correlation matrix. Analyses were adjusted for site, participation on own or with a partner, and cohort. Results We randomized 190 people to the OWG (n = 98) and WR interventions (n = 92). At 0, 3, and 5.5 months, median outdoor walking minutes was 22.56, 13.04, and 0 in the OWG, and 24.00, 26.07, and 0 in the WR group, respectively. There was no difference between groups in change from baseline in minutes walked outdoors based on incidence rate ratio (IRR) and 95% confidence interval (CI) at 3 months (IRR = 0.74, 95% CI 0.47, 1.14) and 5.5 months (IRR = 0.77, 95% CI 0.44, 1.34). Greater 0 to 3-month change in walking capacity was observed in the OWG compared to the WR group (βz-scored difference = 0.14, 95% CI 0.02, 0.26) driven by significant improvement in walking self-efficacy; other comparisons were not significant. Conclusions A group, park-based OWG was not superior to WR in increasing outdoor walking activity, health-promoting behavior or successful aging in older adults with difficulty walking outdoors; however, the OWG was superior to telephone WR in improving walking capacity through an increase in walking self-efficacy. Community implementation of the OWG is discussed. Trial registration ClinicalTrials.gov NCT03292510 Date of registration: September 25, 2017.

Objective To examine the association between preterm delivery and parental separation and identify associated risk factors.Methods All opposite sex, married or common-law parents whose relationship status was available at index delivery and for the next 5 years were eligible in this retrospective population-based cohort study in Manitoba, Canada. Parents of children born preterm were matched 1:5 to parents of children born full-term. Parental separation within 5 years of childbirth was the primary outcome. Secondary outcome was the time to separation. Multivariable Poisson regression model was created to estimate the 5-year incidence rate ratios (IRR) of the primary outcome.Results Parents of 6367 children born preterm (n=164 for

Objectives We developed a machine-learning model-based algorithm (MBA) for smoking in Administrative Health Data (AHD). The validity of this MBA was compared to a rule-based algorithm (RBA). Approaches The study included adults (≥18 years) from a clinical registry containing self-reported current smoking from 2017 to 2020 in Manitoba, Canada. Clinical data were linked to up to five years of hospitalization, physician billing claims, and prescription medication records. The RBA was based on diagnosis codes for tobacco use and nicotine dependence medication. MBAs, constructed using random forest (RF) models, included these indicators in addition to comorbid condition diagnoses and sociodemographic factors. Sensitivity, specificity, positive and negative predictive values (PPV, NPV), and 95% confidence intervals (CIs) were estimated. Results The cohort comprised 24,718 individuals (88.6% female); prevalence of current smokers was 10.0%. The RBA had sensitivity of 27.3% (95% CI: 24.2-30.7), specificity of 96.6% (95% CI: 96.1-97.0), and PPV of 47.2% (95% CI: 42.9-51.5). The MBA had sensitivity of 68.6% (95% CI: 65.1-71.9), specificity of 76.3% (95% CI: 75.2-77.3), and PPV of 24.3% (95% CI: 23.2-25.6). NPV was high irrespective of algorithms. Stratified analyses revealed similar estimates for males and females, and the number of years of AHD did not affect the MBA results. Conclusions An RF-based MBA for smoking ascertainment in linked AHD sources improved sensitivity compared to the RBA. However, the RBA excelled in specificity and PPV. Implication Balancing accurate smoker identification with the risk of false positives is crucial when choosing an algorithm to ascertain current smokers using AHD.

Objective Multi-site studies that use routinely-collected, linked health data often rely on validated algorithms to harmonize definitions of health conditions or other population characteristics across sites. If algorithm validation is not practicable, feasibility studies that assess whether an algorithm can be consistently and accurately implemented across sites, are recommended. We investigated the transparency of reporting on multi-site algorithm feasibility studies found in Health Data Research Network (HDRN) Canada’s Algorithms Inventory. Approach Published, multi-site Canadian feasibility studies about chronic or infectious disease algorithms were assessed using an adaptation of the RECORD (REporting of studies Conducted using Observational Routinely collected health Data) and STROBE (STrengthening the Reporting of OBservational studies in Epidemiology) guidelines. Reviewers were trained on a subset of studies. The remainder were independently assessed by two reviewers and quality checks were conducted. Results were descriptively summarized. Results Thirty published multi-site algorithm feasibility studies were reviewed; the majority were conducted in four or more sites, and 83% assessed chronic disease algorithms. Almost one-fifth (17%) of the studies did not justify the methods used to assess algorithm feasibility. However, the majority (>95%) addressed potential sources of bias and discussed algorithm generalizability. Conclusions We observed accurate and complete reporting of most elements of multi-site algorithm feasibility studies conducted in Canada. These studies provided important information about availability of data elements, generalizability, and potential algorithm uses. Implications Transparent reporting of algorithm feasibility studies facilitates algorithm reuse, enhances the credibility and reproducibility of study findings, and promotes collaboration within the data linkage community.

Abstract Background Smoking is a risk factor for many chronic diseases. Multiple smoking status ascertainment algorithms have been developed for population-based electronic health databases such as administrative databases and electronic medical records (EMRs). Evidence syntheses of algorithm validation studies have often focused on chronic diseases rather than risk factors. We conducted a systematic review and meta-analysis of smoking status ascertainment algorithms to describe the characteristics and validity of these algorithms. Methods The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. We searched articles published from 1990 to 2022 in EMBASE, MEDLINE, Scopus, and Web of Science with key terms such as validity, administrative data, electronic health records, smoking, and tobacco use. The extracted information, including article characteristics, algorithm characteristics, and validity measures, was descriptively analyzed. Sources of heterogeneity in validity measures were estimated using a meta-regression model. Risk of bias (ROB) in the reviewed articles was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Results The initial search yielded 2086 articles; 57 were selected for review and 116 algorithms were identified. Almost three-quarters (71.6%) of algorithms were based on EMR data. The algorithms were primarily constructed using diagnosis codes for smoking-related conditions, although prescription medication codes for smoking treatments were also adopted. About half of the algorithms were developed using machine-learning models. The pooled estimates of positive predictive value, sensitivity, and specificity were 0.843, 0.672, and 0.918 respectively. Algorithm sensitivity and specificity were highly variable and ranged from 3 to 100% and 36 to 100%, respectively. Model-based algorithms had significantly greater sensitivity (p = 0.006) than rule-based algorithms. Algorithms for EMR data had higher sensitivity than algorithms for administrative data (p = 0.001). The ROB was low in most of the articles (76.3%) that underwent the assessment. Conclusions Multiple algorithms using different data sources and methods have been proposed to ascertain smoking status in electronic health data. Many algorithms had low sensitivity and positive predictive value, but the data source influenced their validity. Algorithms based on machine-learning models for multiple linked data sources have improved validity.

ObjectiveThe Royal Canadian Mounted Police (RCMP) Study includes longitudinal multimodal assessments of RCMP cadets from pre-training (i.e., starting the Cadet Training Program [CTP]) to post-deployment and for five years thereafter. The data allow for investigating the multidimensionality of volitional participation in digital health data collection frameworks within serial data collection platforms and the impact of participation inequalities by classifying cadets using the 90–9-1 rule. By classifying cadets as Lurkers, Contributors, and Superusers formally described by the 90–9-1 rule, where 90% of actors do not participate, 9% seldom contribute, and 1% contribute substantially allows for the assessing of relationships between participation inequalities in self-monitoring behaviors as well as whether mental health disorder symptoms at pre-training (i.e., starting the CTP) were associated with subsequent participation.MethodsParticipants were asked to complete a Full Assessment prior to their training at CTP, as well as short daily surveys throughout their training. Participation frequency was described using a process where participants were rank ordered by the number of daily surveys completed and classified into one of three categories. Full assessment surveys completed prior to their training at CTP included screening tools for generalized anxiety disorder (GAD), major depressive disorder (MDD), posttraumatic stress disorder (PTSD), alcohol use disorder (AUD), and panic disorder (PD). The Kruskal-Wallis H test was used to assess differences in participation rates between mental health disorder symptom screening groups for each measure at pre-training, and Spearman’s Rho was used to test for associations amongst self-reported Full Assessment screening tool responses and the number of daily surveys completed during CTP.ResultsThere were 18557 daily survey records collected from 772 participants. The rank-ordering of cadets by the number of daily surveys completed produced three categories in line with the 90–9-1 rule: Superusers who were the top 1% of cadets (n=8) and produced 6.4% of all recordings; Contributors who were the next 9% of cadets (n=68) and produced 49.2% of the recordings; and Lurkers who were the next 90% of cadets (n=695) and produced 44.4% of daily survey recordings. Lurkers had the largest proportion of positive screens for self-reported mental health disorders at pre-training.ConclusionThe creation of highly individualized, population-based mental health injury programs has been limited by an incomplete understanding of the causal relationships between protective factors and mental health. Disproportionate rates of disengagement from persons who screen positive for mental health disorders further compounds the difficulty in understanding the relationships between training programs and mental health. The current results suggest persons with mental health challenges may be less likely to engage in some forms of proactive mental health training. The current results also provide useful information about participation, adherence, and engagement that can be used to inform evidence-based paradigm shifts in health-related data collection in occupational populations.

ContextOsteoporotic fracture is a major public health issue globally. Human research on the association between amino acids (AAs) and fracture is still lacking.ObjectiveTo examine the association between AAs and recent osteoporotic fractures.MethodsThis age and sex matched incident case-control study identified 44 recent x-ray confirmed fracture cases in the Second Hospital of Jilin University and 88 community-based healthy controls aged 50+ years. Plasma AAs were measured by high performance liquid chromatography coupled with mass spectrometry. After adjusting for covariates (i.e., body mass index, milk intake >1 time/week, falls and physical activity), we conducted conditional logistical regression models to test the association between AAs and fracture.ResultsAmong cases there were 23 (52.3%) hip fractures and 21 (47.7%) non-hip fractures. Total, essential, and non-essential AAs were significantly lower in cases than in controls. In the multivariable conditional logistic regression models, after adjusting for covariates, each standard deviation increase in the total (odds ratio [OR]: 0.304; 95% confidence interval [CI]: 0.117–0.794), essential (OR: 0.408; 95% CI: 0.181–0.923) and non-essential AAs (OR: 0.290; 95%CI: 0.107–0.782) was negatively associated with recent fracture. These inverse associations were mainly found for hip fracture, rather than non-hip fractures. Among these AAs, lysine, alanine, arginine, glutamine, histidine and piperamide showed the significantly negative associations with fracture.ConclusionThere was a negative relationship between AAs and recent osteoporotic fracture; such relationship appeared to be more obvious for hip fracture.

IntroductionImmune-mediated inflammatory diseases (IMID), such as multiple sclerosis (MS), inflammatory bowel disease (IBD) or rheumatoid arthritis (RA) have high rates of elevated anxiety symptoms. This can may worsen functioning and increase IMID disease burden. The rate of and factors associated with elevated anxiety symptoms may differ between males and females, which, in turn can affect diagnosis and disease management. We evaluated whether the frequency and factors associated with comorbid elevated anxiety symptoms in those with an IMID differed by sex.MethodsParticipants with an IMID (MS, IBD or RA) completed two anxiety measures (HADS, GAD-7). We used logistic regression to investigate whether sex differences exist in the presence of comorbid elevated anxiety symptoms or in the endorsement of individual anxiety items in those with an IMID.ResultsOf 656 participants, females with an IMID were more likely to have elevated anxiety symptoms compared to males (adjusted odds ratio [aOR] 2.05; 95%CI: 1.2, 3.6). Younger age, higher depressive symptoms and income were also associated with elevated anxiety symptoms in IMID. Lower income in males with an IMID, but not females, was associated with elevated anxiety symptoms (aOR: 4.8; 95%CI: 1.5, 15.6). No other factors demonstrated a sex difference. Males had nearly twice the odds of endorsing restlessness on the GAD-7 (OR = 1.8, 95%CI: 1.07, 3.15) compared to females.DiscussionWe found evidence for sex differences in the factors associated with experiencing elevated anxiety symptoms in those with an IMID. These findings could be helpful to sensitize clinicians to monitor for comorbid anxiety symptoms in males with an IMID.

Introduction Administrative health records (AHRs) are used to conduct population-based post-market drug safety and comparative effectiveness studies to inform healthcare decision making. However, the cost of data extraction, and the challenges associated with privacy and securing approvals can make it challenging for researchers to conduct methodological research in a timely manner using real data. Generating synthetic AHRs that reasonably represent the real-world data are beneficial for developing analytic methods and training analysts to rapidly implement study protocols. We generated synthetic AHRs using two methods and compared these synthetic AHRs to real-world AHRs. We described the challenges associated with using synthetic AHRs for real-world study. Methods The real-world AHRs comprised prescription drug records for individuals with healthcare insurance coverage in the Population Research Data Repository (PRDR) from Manitoba, Canada for the 10-year period from 2008 to 2017. Synthetic data were generated using the Observational Medical Dataset Simulator II (OSIM2) and a modification (ModOSIM). Synthetic and real-world data were described using frequencies and percentages. Agreement of prescription drug use measures in PRDR, OSIM2 and ModOSIM was estimated with the concordance coefficient. Results The PRDR cohort included 169,586,633 drug records and 1,395 drug types for 1,604,734 individuals. Synthetic data for 1,000,000 individuals were generated using OSIM2 and ModOSIM. Sex and age group distributions were similar in the real-world and synthetic AHRs. However, there were significant differences in the number of drug records and number of unique drugs per person for OSIM2 and ModOSIM when compared with PRDR. For the average number of days of drug use, concordance with the PRDR was 16% (95% confidence interval [CI]: 12%-19%) for OSIM2 and 88% (95% CI: 87%-90%) for ModOSIM. Conclusions ModOSIM data were more similar to PRDR than OSIM2 data on many measures. Synthetic AHRs consistent with those found in real-world settings can be generated using ModOSIM. Synthetic data will benefit rapid implementation of methodological studies and data analyst training.

Public significance statements/highlights Research on how to mitigate posttraumatic stress injuries (PTSIs) among public safety personnel (PSP) who are exposed to potentially psychologically traumatic events is limited. The PSP PTSI Study has been designed to develop, implement, and assess the impact of tools and skills designed to proactively mitigate PTSIs. PSP recruited into the study to receive the augmented training are assessed before and after training, and again 1 year later. The PSP PTSI Study results are expected to benefit the mental health of all participants and other PSP by informing strategies to mitigate PTSI.

ObjectiveThe current study examined variations in cardioautonomic lability during the Royal Canadian Mounted Police (RCMP) Cadet Training Program (CTP) between cadets starting their training who did or did not screen positive for one or more mental health disorders (i.e., posttraumatic stress disorder [PTSD], major depressive disorder [MDD], social anxiety disorder [SAD], generalized anxiety disorder [GAD], panic disorder [PD], alcohol use disorder [AUD]).MethodsElectrocardiogram (ECG) signals integrated into Hexoskin garments were used to record ECG and heart rate Over the 26-week CTP. There were 31 heart rate variability (HRV) parameters calculated using Kubios Premium HRV analysis software. Mann–Whitney U-tests were used to perform groupwise comparisons of participant raw values and HRV during the CTP.ResultsA total of 157 cadets (79% male) were screened for any mental disorder using self-report surveys and then grouped by positive and negative screening. Analyses indicated a statistically significant (p

IntroductionRoyal Canadian Mounted Police (RCMP) officers self-report high levels of mental health disorder symptoms, such as alcohol use disorder, generalized anxiety disorder, major depressive disorder, panic disorder, and posttraumatic stress disorder. Participation in regular mental health monitoring has been associated with improved mental health disorder symptom reporting and may provide an accessible tool to support RCMP mental health. The current study assessed relationships between self-reported mental health disorder symptoms and the completion of daily surveys (i.e., daily mental health disorder symptom monitoring) by RCMP cadets during the Cadet Training Program (CTP).MethodsParticipants were RCMP cadets (n = 394; 76.1% men) in the Standard Training Program who completed the 26-week CTP and daily self-monitoring surveys, as well as full mental health assessments at pre-training (i.e., starting the CTP) and pre-deployment (i.e., ~2 weeks prior to deployment to the field). Symptoms of alcohol use disorder, generalized anxiety disorder, major depressive disorder, panic disorder, and posttraumatic stress disorder were assessed. Changes in mental health disorder symptom reporting from pre-training to pre-deployment were calculated. Spearman’s rank correlations were estimated for number of daily surveys completed and change in mental health disorder symptom scores between pre-training and pre-deployment.ResultsThere were statistically significant inverse relationships between number of daily surveys completed and number of mental health disorder symptoms reported; specifically, cadets who completed more daily surveys during CTP reported fewer symptoms of alcohol use disorder, generalized anxiety disorder, major depressive disorder, panic disorder, and posttraumatic stress disorder.ConclusionAn inverse correlation between number of daily surveys completed and mental health disorder symptom scores indicated that participation in daily mental health monitoring was associated with improvements in self-reported mental health disorder symptoms between pre-training and pre-deployment. Regular self-monitoring of mental health disorder symptoms may help to mitigate mental health challenges among RCMP cadets and officers.

Abstract Background Characteristics of patients using newer 2nd and 3rd line antidiabetic drugs in a real-world setting are poorly understood. We described the characteristics of new users of sodium-glucose co-transporter-2 inhibitors (SGLT-2i), dipeptidyl peptidase-4 inhibitors (DPP-4i), and glucagon-like peptide-1 receptor agonists (GLP-1 RA) in Canada and the United Kingdom (UK) between 2016 and 2018. Methods We conducted a multi-database cohort study using administrative health databases from 7 Canadian provinces and the UK Clinical Practice Research Datalink. We assembled a base cohort of antidiabetic drug users between 2006 and 2018, from which we constructed 3 cohorts of new users of SGLT-2i, DPP-4i, and GLP-1 RA between 2016 and 2018. Results Our cohorts included 194,070 new users of DPP-4i, 166,722 new users of SGLT-2i, and 27,719 new users of GLP-1 RA. New users of GLP-1 RA were more likely to be younger (mean ± SD: 56.7 ± 12.2 years) than new users of DPP-4i (67.8 ± 12.3 years) or SGLT-2i (64.4 ± 11.1 years). In Canada, new users of DPP-4i were more likely to have a history of coronary artery disease (22%) than new users of SGLT-2i (20%) or GLP-1 RA (15%). Conclusion Although SGLT-2i, DPP-4i, and GLP-1 RAs are recommended as 2nd or 3rd line therapy for type 2 diabetes, important differences exist in the characteristics of users of these drugs. Contrary to existing guidelines, new users of DPP-4i had a higher prevalence of cardiovascular disease at baseline than new users of SGLT2i or GLP-1RA.

Les membres de la Gendarmerie royale du Canada (GRC), comme l’ensemble du personnel de la sécurité publique, sont fréquemment exposés à des événements potentiellement traumatiques sur le plan psychologique qui contribuent à la survenue de blessures de stress post­traumatique. Le peu de travaux de recherche disponibles sur le sujet limite l’étude de ces blessures. Le présent article décrit l'étude de la GRC, qui fait partie des efforts concertés que la GRC a déployés pour réduire les blessures de stress posttraumatique en améliorant l'accès aux évaluations, aux traitements et aux formations fondés sur des données probantes, le recrutement des participants, ainsi que les développements de l'étude de la GRC à ce jour. L’étude de la GRC a été conçue pour : 1) élaborer et mettre en place un système d’évaluations annuelles, mensuelles et quotidiennes fondées sur des données probantes et en évaluer l’effet; 2) évaluer les associations entre les variables démographiques et les blessures de stress post­traumatique; 3) évaluer de manière longitudinale les différences individuelles associées aux blessures de stress post­traumatique; 4) intégrer au Programme de formation des cadets de la GRC des compétences permettant d’atténuer de façon proactive les blessures de stress post­traumatique et 5) évaluer l’effet de laformation renforcée (FR) par rapport à la formation standard (FS). Les participants à la FS (n = 480) et à la FR (n = 480) sont évalués avant et après la formation, ainsi que chaque année pendant cinq ans à compter de la date de leur déploiement; ils répondent également à de courts sondages mensuels et quotidiens. Les résultats de l'étude de la GRC devraient être bénéfiques pour la santé mentale de tous les participants, pour la GRC et pour le personnel de la sécurité publique, car ils contribueront à réduire les blessures de stress post­traumatique de tous ceux qui servent la population.

The Royal Canadian Mounted Police (RCMP), like all public safety personnel (PSP), are frequently exposed to potentially psychologically traumatic events that contribute to posttraumatic stress injuries (PTSI). Addressing PTSI is impeded by the limited available research. In this protocol paper, we describe the RCMP Study, part of the concerted efforts by the RCMP to reduce PTSI by improving access to evidence-based assessments, treatments and training as well as participant recruitment and RCMP Study developments to date. The RCMP Study has been designed to (1) develop, deploy and assess the impact of a system for ongoing annual, monthly and daily evidence-based assessments; (2) evaluate associations between demographic variables and PTSI; (3) longitudinally assess individual differences associated with PTSI; (4) augment the RCMP Cadet Training Program with skills to proactively mitigate PTSI; and (5) assess the impact of the augmented training condition (ATC) versus the standard training condition (STC). Participants in the STC (n = 480) and ATC (n = 480) are assessed before and after training and annually for 5 years on their deployment date; they also complete brief monthly and daily surveys. The RCMP Study results are expected to benefit the mental health of all participants, RCMP and PSP by reducing PTSI among all who serve.

Abstract Background Network analysis, a technique for describing relationships, can provide insights into patterns of co-occurring chronic health conditions. The effect that co-occurrence measurement has on disease network structure and resulting inferences has not been well studied. The purpose of the study was to compare structural differences among multimorbidity networks constructed using different co-occurrence measures. Methods A retrospective cohort study was conducted using four fiscal years of administrative health data (2015/16 – 2018/19) from the province of Manitoba, Canada (population 1.5 million). Chronic conditions were identified using diagnosis codes from electronic records of physician visits, surgeries, and inpatient hospitalizations, and grouped into categories using the Johns Hopkins Adjusted Clinical Group (ACG) System. Pairwise disease networks were separately constructed using each of seven co-occurrence measures: lift, relative risk, phi, Jaccard, cosine, Kulczynski, and joint prevalence. Centrality analysis was limited to the top 20 central nodes, with degree centrality used to identify potentially influential chronic conditions. Community detection was used to identify disease clusters. Similarities in community structure between networks was measured using the adjusted Rand index (ARI). Network edges were described using disease prevalence categorized as low (

Abstract 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 T2 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.

BackgroundEvidence for a relationship between oxidative stress and osteoporotic fractures in humans is limited. Fluorescent oxidation products (FlOPs, excitation/emission wavelengths 320/420nm denoted FlOP_320; 360/420nm [FlOP_360]; and 400/475nm [FlOP_400]) are global biomarkers of oxidative stress, and reflect oxidative damage to proteins, phospholipids, and nucleic acids. We investigated the association between FlOPs and a recent osteoporotic fracture.MethodsWe conducted a case-control study in a Chinese population aged 50 years or older. A recent osteoporotic fracture in the cases was confirmed by x-ray. Cases were matched with community-based non-fracture controls (1:2 ratio) for age (± 4 years) and sex. In addition, we conducted a sensitivity unmatched case-control study which included all fracture cases and all eligible non-fracture controls prior to matching. Plasma FlOPs were measured with a fluorescent microplate reader. We used unconditional logistic regression to analyze the association between FlOPs (per 1-SD increase in logarithmic scale) and fracture; odds ratios (OR) and 95% confidence intervals (95% CI) were reported.ResultsForty-four cases and 88 matched controls (mean age: 68.2 years) were included. After covariate adjustment (i.e., body mass index, physical activity, and smoking), higher FlOP_360 (OR = 1.85; 95% CI = 1.03 – 3.34) and FlOP_400 (OR = 13.29; 95% CI = 3.48 – 50.69) levels, but not FlOP_320 (OR = 0.56; 95% CI = 0.27 – 1.15), were associated with increased fracture risk. Subgroup analyses by fracture site and unmatched case-control study found comparable associations of FlOP_360 and FlOP_400 with hip and non-hip fractures.ConclusionsHigher FlOP_360 and FlOP_400 levels were associated with increased risk of fracture, and this association was comparable for hip and non-hip fractures. Prospective studies are warranted to confirm this finding.

Objectives Among people with immune-mediated inflammatory disease (IMID), including multiple sclerosis (MS), inflammatory bowel disease (IBD) and rheumatoid arthritis (RA) most research has focused on mental illness rather than on mental health. We assessed dimensions of mental health among persons with IMID and compared them across IMID. We also evaluated demographic and clinical characteristics associated with flourishing mental health.Design Participants: Adults with an IMID (MS, 239; IBD, 225; RA 134; total 598) who were participating in a cohort study.Setting Tertiary care centre in Manitoba, Canada.Primary outcome measure Participants completed the Mental Health Continuum Short-Form (MHC-SF), which measures emotional, psychological and social well-being, and identifies flourishing mental health. This outcome was added midway through the study on the advice of the patient advisory group. Depression, anxiety, pain, fatigue and physical function were also assessed.Results Total MHC-SF and subscale scores were similar across IMID groups. Nearly 60% of participants were considered to have flourishing mental health, with similar proportions across disease types (MS 56.5%; IBD 58.7%; RA 59%, p=0.95). Older age was associated with a 2% increased odds of flourishing mental health per year of age (OR 1.02; 95% CI: 1.01 to 1.04). Clinically meaningful elevations in anxiety (OR 0.25; 95% CI: 0.12 to 0.51) and depressive symptoms (OR 0.074; 95% CI: 0.009 to 0.61) were associated with lower odds. Higher levels of pain, anxiety and depressive symptoms were associated with lower total Mental Health Continuum scores at the 50th quantile.Conclusions Over half of people with MS, IBD and RA reported flourishing mental health, with levels similar across the disease groups. Interventions targeting symptoms of depression and anxiety, and upper limb impairments, as well as resilience training may help a higher proportion of the IMID population achieve flourishing mental health.

ObjectiveRoyal Canadian Mounted Police report experiencing extremely frequent potentially psychologically traumatic events (PPTE). In a recent study, approximately half of participating RCMP screened positive for one or more mental disorders, which is approximately five times the diagnostic proportion for the general Canadian population. Increased reporting of mental health symptoms been linked to PPTE exposures. Programs promoting physical activity may be useful interventions to supplement or pair with mental health interventions, providing anxiolytic, antidepressant, and stress-buffering effects. The current study was designed to assess the relationship between physical activity behaviors and reported mental health disorder symptoms of cadets during the Royal Mounted Canadian Police (RCMP) Cadet Training Program (CTP). The current study also examined the relationship between exercise and mental health disorder symptoms of cadets during the CTP.MethodsThe study included data from 394 cadets (76.1% male). An analysis of variance (ANOVA) and a series of t-tests were used to assess several differences across sociodemographic groups. Bivariate Spearman’s Rank correlations were performed between the average number of active calories burned per day, as recorded by Apple Watches, and changes in self-reported mental health disorder symptoms (i.e., Generalized Anxiety Disorder [GAD], Major Depressive Disorder [MDD], Posttraumatic Stress Disorder [PTSD], Social Anxiety Disorder [SAD]. Alcohol Use Disorders [AUD], Panic Disorder [PD]) from pre-training (starting the CTP) to pre-deployment (completing the CTP) 26 weeks later.ResultsThere were statistically significant correlations between physical activity and self-reported mental health disorder symptom scores during CTP. Cadets who performed more physical activity from pre-training to pre-deployment had statistically significantly greater decreases in symptoms of GAD (ρ = −0.472, p 0.05). There were also no statistically significant relationships between pre-CTP mental health disorder symptom scores and the volume of physical activity performed during CTP.ConclusionThere was evidence of a significant relationship between reductions in mental health disorder symptom scores and physical activity during the 26-week CTP. The results highlight the role that exercise can play as an important tool for reducing mental health disorder symptoms, considering there was no relationship between pre-CTP baseline mental health scores and physical activity performed during CTP. Further research is needed to understand differences in physical activity behaviours among cadets and serving RCMP.

Abstract Background Algorithms used to identify disease cases in administrative health data may be sensitive to changes in the data over time. Control charts can be used to assess how variations in administrative health data impact the stability of estimated trends in incidence and prevalence for administrative data algorithms. We compared the stability of incidence and prevalence trends for multiple juvenile diabetes algorithms using observed-expected control charts. Methods Eighteen validated algorithms for juvenile diabetes were applied to administrative health data from Manitoba, Canada between 1975 and 2018. Trends in disease incidence and prevalence for each algorithm were modelled using negative binomial regression and generalized estimating equations; model-predicted case counts were plotted against observed counts. Control limits were set as predicted case count ±0.8*standard deviation. Differences in the frequency of out-of-control observations for each algorithm were assessed using McNemar’s test with Holm-Bonferroni adjustment. Results The proportion of out-of-control observations for incidence and prevalence ranged from 0.57 to 0.76 and 0.45 to 0.83, respectively. McNemar’s test revealed no difference in the frequency of out-of-control observations across algorithms. A sensitivity analysis with relaxed control limits (2*standard deviation) detected fewer out-of-control years (incidence 0.19 to 0.33; prevalence 0.07 to 0.52), but differences in stability across some algorithms for prevalence. Conclusions Our study using control charts to compare stability of trends in incidence and prevalence for juvenile diabetes algorithms found no differences for disease incidence. Differences were observed between select algorithms for disease prevalence when using wider control limits.

Abstract Background We tested for differences in direct health care costs among long-term care (LTC) residents age 65 and older with clinically significant pain (CSP) and with no pain or non-daily mild pain (NP/NDMP). We are not aware of any other large scale investigation that examined the cost of pain in LTC environments. Methods Population-based administrative health data from Saskatchewan, Canada for 2004 to 2015 were used to compare direct health care costs for CSP and NP/NDMP groups up to one year after admission to LTC. Total accumulated costs for hospitalization, physician services, LTC, and prescription drugs were calculated in 2015 Canadian dollars. Group differences were tested using generalized linear models with generalized estimating equations. Results Amongst 24,870 LTC residents, 8289 (33.3%) were censored due to death or discharge in the 365-day study observation period. Of the 16,581 (66.7%) observed residents, 5683 (34.3%) had CSP at admission. Residents (66.3% female) had a mean age of 85 years (SD = 7.4). The mean annual total direct health care cost per resident was higher among the CSP group (CAD $8063) than the NP/NDMP group (CAD $6455). This difference was found even after including LTC costs, and for each cost component (i.e., CSP residents had higher hospitalization, physician, and prescription drug costs). Similar results were obtained after controlling for demographics, comorbidities, physical and cognitive impairment, prior health care costs, and facility characteristics. Conclusion The higher costs incurred by CSP residents compared to NP/NDMP residents are likely underestimated because pain problems are often missed in residents with dementia, who comprise a large portion of the LTC population. Improved pain care can reduce such costs and improve quality of life.

IntroductionLes registres d’assurance-maladie, qui recueillent des renseignements sur la couverture et certaines caractéristiques des individus à l’échelle de larges populations, sont précieux pour la surveillance de la santé de la population et de la recherche fondée sur l’utilisation de données administratives. Le fait que les données des registres des provinces et des territoires du Canada ne soient pas normalisées est susceptible de nuire à la comparabilité des mesures de surveillance. Nous avons évalué le contenu des registres d’assurance-maladie au Canada afin de décrire les populations couvertes et de relever les similitudes et les différences entre les registres. MéthodologieL’équipe de l’étude et des représentants de l’Agence de la santé publique du Canada ont élaboré une enquête sur les caractéristiques individuelles et les données sur la population recueillies dans les registres d’assurance-maladie. Les réponses des informateurs clés des provinces et territoires qui ont répondu à l’enquête ont été analysées de façon descriptive. RésultatsToutes les provinces ont répondu à l’enquête et les Territoires du Nord-Ouest ont fourni des réponses partielles. Certaines caractéristiques individuelles figurant dans les registres d’assurance-maladie (l’adresse principale, la date de naissance et le sexe) sont saisies dans toutes les provinces et tous les territoires. Les données disponibles sur les liens de parenté, l’origine ethnique et le statut socioéconomique sont variables selon les provinces et territoires, tout comme les dates de début et de fin de couverture et la fréquence de mise à jour du registre. L’information sur des populations particulières, comme les membres des Premières Nations, est saisie dans certaines provinces et certains territoires seulement. ConclusionLes registres d’assurance-maladie constituent une riche source d’information sur les populations assurées des provinces et des territoires. Cependant, l’hétérogénéité des données peut avoir un impact sur les personnes incluses et exclues dans les estimations de surveillance de la population produites à partir de données administratives sur la santé. L’élaboration d’un cadre de données harmonisé favoriserait l’obtention de résultats comparables et à jour en matière de recherche et de surveillance de la santé de la population dans le cadre d’études intergouvernementales.

IntroductionHealth insurance registries, which capture insurance coverage and demographic information for entire populations, are a critical component of population health surveillance and research when using administrative data. Lack of standardization of registry information across Canada’s provinces and territories could affect the comparability of surveillance measures. We assessed the contents of health insurance registries across Canada to describe the populations covered and document registry similarities and differences. MethodsA survey about the data and population identifiers in health insurance registries was developed by the study team and representatives from the Public Health Agency of Canada. The survey was completed by key informants from most provinces and territories and then descriptively analyzed. ResultsResponses were received from all provinces; partial responses were received from the Northwest Territories. Demographic information in health insurance registries, such as primary address, date of birth and sex, were captured in all jurisdictions. Data captured on familial relationships, ethnicity and socioeconomic status varied among jurisdictions, as did start and end dates of coverage and frequency of registry updates. Identifiers for specific populations, such as First Nations individuals, were captured in some, but not all jurisdictions. ConclusionHealth insurance registries are a rich source of information about the insured populations of the provinces and territories. However, data heterogeneity may affect who is included and excluded in population surveillance estimates produced using administrative health data. Development of a harmonized data framework could support timely and comparable population health research and surveillance results from multi-jurisdiction studies.

Abstract Background Cardiovascular death is a common outcome in population-based studies about new healthcare interventions or treatments, such as new prescription medications. Vital statistics registration systems are often the preferred source of information about cause-specific mortality because they capture verified information about the deceased, but they may not always be accessible for linkage with other sources of population-based data. We assessed the validity of an algorithm applied to administrative health records for identifying cardiovascular deaths in population-based data. Methods Administrative health records were from an existing multi-database cohort study about sodium-glucose cotransporter-2 (SGLT2) inhibitors, a new class of antidiabetic medications. Data were from 2013 to 2018 for five Canadian provinces (Alberta, British Columbia, Manitoba, Ontario, Quebec) and the United Kingdom (UK) Clinical Practice Research Datalink (CPRD). The cardiovascular mortality algorithm was based on in-hospital cardiovascular deaths identified from diagnosis codes and select out-of-hospital deaths. Sensitivity, specificity, and positive and negative predictive values (PPV, NPV) were calculated for the cardiovascular mortality algorithm using vital statistics registrations as the reference standard. Overall and stratified estimates and 95% confidence intervals (CIs) were computed; the latter were produced by site, location of death, sex, and age. Results The cohort included 20,607 individuals (58.3% male; 77.2% ≥70 years). When compared to vital statistics registrations, the cardiovascular mortality algorithm had overall sensitivity of 64.8% (95% CI 63.6, 66.0); site-specific estimates ranged from 54.8 to 87.3%. Overall specificity was 74.9% (95% CI 74.1, 75.6) and overall PPV was 54.5% (95% CI 53.7, 55.3), while site-specific PPV ranged from 33.9 to 72.8%. The cardiovascular mortality algorithm had sensitivity of 57.1% (95% CI 55.4, 58.8) for in-hospital deaths and 72.3% (95% CI 70.8, 73.9) for out-of-hospital deaths; specificity was 88.8% (95% CI 88.1, 89.5) for in-hospital deaths and 58.5% (95% CI 57.3, 59.7) for out-of-hospital deaths. Conclusions A cardiovascular mortality algorithm applied to administrative health records had moderate validity when compared to vital statistics data. Substantial variation existed across study sites representing different geographic locations and two healthcare systems. These variations may reflect different diagnostic coding practices and healthcare utilization patterns.

Abstract Background The objective of this study was to determine primary prescription adherence for obstructive lung diseases (e.g., asthma, COPD) in an adult primary care patient population over a 3-year period. Methods A retrospective analysis of electronic medical record and administrative data was performed to determine primary adherence, defined as dispensation of a new prescription within 90 days of the date the prescription was written. Multivariable logistic regression models were used to test predictors of prescription primary adherence. Results Of 13,220 prescriptions for obstructive airway disease, 75.9% (N = 10,038) were filled. In multivariate analysis, depression, certain age groups (18–44 years), higher income quartile were associated with reduced prescription adherence. However, 1–2 ER visits in the previous year (compared to no ER visits), number of ambulatory visits in the previous year, and number of hospitalizations in the previous year, did not increase the likelihood of prescription adherence. Interpretation This study provides important insights about factors associated with prescription nonadherence and is the first study examining primary medication adherence with medications for obstructive lung disease in adults, providing indications of prescription nonadherence patterns among a broad population.

Abstract Background Previous research has shown that chronic disease case definitions constructed using population-based administrative health data may have low accuracy for ascertaining cases of episodic diseases such as rheumatoid arthritis, which are characterized by periods of good health followed by periods of illness. No studies have considered a dynamic approach that uses statistical (i.e., probability) models for repeated measures data to classify individuals into disease, non-disease, and indeterminate categories as an alternative to deterministic (i.e., non-probability) methods that use summary data for case ascertainment. The research objectives were to validate a model-based dynamic classification approach for ascertaining cases of juvenile arthritis (JA) from administrative data, and compare its performance with a deterministic approach for case ascertainment. Methods The study cohort was comprised of JA cases and non-JA controls 16 years or younger identified from a pediatric clinical registry in the Canadian province of Manitoba and born between 1980 and 2002. Registry data were linked to hospital records and physician billing claims up to 2018. Longitudinal discriminant analysis (LoDA) models and dynamic classification were applied to annual healthcare utilization measures. The deterministic case definition was based on JA diagnoses in healthcare use data anytime between birth and age 16 years; it required one hospitalization ever or two physician visits. Case definitions based on model-based dynamic classification and deterministic approaches were assessed on sensitivity, specificity, and positive and negative predictive values (PPV, NPV). Mean time to classification was also measured for the former. Results The cohort included 797 individuals; 386 (48.4 %) were JA cases. A model-based dynamic classification approach using an annual measure of any JA-related healthcare contact had sensitivity = 0.70 and PPV = 0.82. Mean classification time was 9.21 years. The deterministic case definition had sensitivity = 0.91 and PPV = 0.92. Conclusions A model-based dynamic classification approach had lower accuracy for ascertaining JA cases than a deterministic approach. However, the dynamic approach required a shorter duration of time to produce a case definition with acceptable PPV. The choice of methods to construct case definitions and their performance may depend on the characteristics of the chronic disease under investigation.

The objective of this project is to implement a harmonized artificial intelligence (AI)-based de-identification of free-text medical data across multiple Canadian jurisdictions. This federated learning approach will allow these jurisdictions to leverage each other’s data and resources while no individual-level data leaves the jurisdiction. Federated Learning enables health data centers in different jurisdictions to collaborate in training machine learning models without sharing individual-level data. This approach will significantly reduce privacy and cybersecurity risks and barriers that are involved in sharing and moving data across different jurisdictions. In a federated learning environment, machine learning models are trained on multiple data sources available in local data centers; local data are not shared to a central computing/analysis environment. Instead, parameters (such as model weights) are shared between these local data centers to generate a global model that will be shared and used by all participating data centers. In this case study, four health research data centers in different Canadian provinces will take part in deployment of an AI-based application for de-identification of free-text data. The data centers are members of Health Data Research Network (HDRN) Canada. The deployment will include: • harmonized annotation and labeling of local data, • local training of entity recognition algorithms, • integrating model weights from each data centers to create a global model • development of license agreements between the participating data centers to allow sharing model weights This is an ongoing project. The talk will demonstrate learning experiences, advantages, and challenges in a federated learning environment and explore the feasibility of transporting this approach to other multi-jurisdiction research networks.

ObjectiveUse of benzodiazepines and Z-drugs (non-benzodiazepine sedative hypnotics) is controversial due to adverse health outcomes in the general population. However, little is known about their use in people with multiple sclerosis (MS). We estimated the incidence and prevalence of benzodiazepine and Z-drug use (jointly BZD) in the MS population as compared to an age-, sex- and geographically-matched population without MS, and examined the association of mood/anxiety disorders with the use of BZD over a twenty-year period.MethodsUsing administrative data from Manitoba, Canada, we identified 2,985 persons with incident MS and 14,891 persons without MS matched 5:1 on sex, birth year and region. We applied validated case definitions to identify persons with any mood/anxiety disorder. Dispensations of BZD were identified. To assess the association between MS, mood/anxiety disorders and BZD use we constructed generalized linear models adjusting for age, sex, index year, socioeconomic status, urban/rural residence, physical comorbidities, and health care use. We also examined patterns of BZD use.ResultsIn 2016, the crude incidence of benzodiazepine use in the MS cohort was 2.10% (95%CI: 1.43–2.98%), 1.49-fold higher than in the non-MS cohort (1.41%; 95%CI: 1.18–1.67%). The crude incidence of Z-drug use in the MS cohort was 1.77% (95%CI: 1.20–2.51%), 1.78-fold higher than in the non-MS cohort (0.99%; 95%CI: 0.81–1.21%). After adjusting for covariates, among individuals without an active mood/anxiety disorder, the MS cohort had a 39% increased incidence rate of benzodiazepine use and a 72% increased incidence rate of Z-drug use as compared to the non-MS cohort. Among individuals with an active mood/anxiety disorder, the incidence of BZD use did not differ between the MS and non-MS cohorts. A higher proportion of people with MS used BZD for ≥6 months than people without MS.ConclusionUse of BZD is more common in people with MS than in general population controls, and use of these agents is in persons with MS is often chronic.

Background The overall impact of physician prescribers on population-level adherence rates are unknown. We aimed to quantify the influence of general practitioner (GP) physician prescribers on the outcome of optimal statin medication adherence. Methods We conducted a retrospective cohort study using health administrative databases from Saskatchewan, Canada. Participants included physician prescribers and their patients beginning a new statin medication between January 1, 2012 and December 31, 2017. We grouped prescribers based on the prevalence of optimal adherence (i.e., proportion of days covered ≥ 80%) within their patient group. Also, we constructed multivariable logistic regression analyses on optimal statin adherence using two-level non-linear mixed-effects models containing patient and prescriber-level characteristics. An intraclass correlation coefficient was used to estimate the physician effect. Results We identified 1,562 GPs prescribing to 51,874 new statin users. The median percentage of optimal statin adherence across GPs was 52.4% (inter-quartile range: 35.7% to 65.5%). GP prescribers with the highest patient adherence (versus the lowest) had patients who were older (median age 61.0 vs 55.0, pInterpretation The overall impact of GP prescribers on statin adherence appears to be very limited. Even “high-performing” physicians face significant levels of sub-optimal adherence among their patients.

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.

Abstract Objective The investigation and management of celiac disease places a high burden on the health care system. Accurate methods to ascertain cases of celiac disease (CD) in population-based administrative data can facilitate epidemiologic and health services research to guide disease management. The study aim was to develop and validate administrative data case definitions for CD to facilitate further studies about the effect of CD on osteoporosis and fracture risk. Results Population-based data from the Manitoba Bone Mineral Density (BMD) Program registry, which contains medical information on all individuals in the province of Manitoba, Canada who have received BMD testing, was used to define the study cohort. Linked hospital discharge abstracts and physician billing claims were used to ascertain diagnoses of celiac disease in administrative data. A population-based CD serologic registry was used as the validation database. One diagnosis code in hospital discharge abstracts or two or more diagnosis codes in physician billing claims optimized the detection of positive celiac serology with sensitivity of 84% (95% CI 80–88%), specificity of 97% (95% CI 80–88%), PPV of 80% (95% CI 80–88%), and NPV of 97% (95% CI 80–88%). Our administrative data case definition for celiac disease demonstrates good sensitivity and specificity for detecting positive celiac serology.

Abstract Background The study evaluates Performance Measures (PMs) for Juvenile Idiopathic Arthritis (JIA): The percentage of patients with new onset JIA with at least one visit to a pediatric rheumatologist in the first year of diagnosis (PM1); and the percentage of patients with JIA under rheumatology care seen in follow-up at least once per year (PM2). Methods Validated JIA case ascertainment algorithms were used to identify cases from provincial health administrative databases in Manitoba, Canada in patients 12 and > 14 months between consecutive visits were also calculated. Results One hundred ninety-four incident JIA cases were diagnosed between 01/04/2008 and 03/31/2015. The median age at diagnosis was 9.1 years and 71% were female. PM1: Across the years, 51–81% of JIA cases saw a pediatric rheumatologist within 1 year. PM2: Between 58 and 78% of patients were seen in yearly follow-up. Gaps > 12, and > 14, months were observed once during follow-up in 52, and 34%, of cases, and ≥ twice in 11, and 5%, respectively. Conclusions Suboptimal access to pediatric rheumatologist care was observed which could lead to diagnostic and treatment delays and lack of consistent follow-up, potentially negatively impacting patient outcomes.

Abstract Background Administrative health records (AHRs) and electronic medical records (EMRs) are two key sources of population-based data for disease surveillance, but misclassification errors in the data can bias disease estimates. Methods that combine information from error-prone data sources can build on the strengths of AHRs and EMRs. We compared bias and error for four data-combining methods and applied them to estimate hypertension prevalence. Methods Our study included rule-based OR and AND methods that identify disease cases from either or both data sources, respectively, rule-based sensitivity-specificity adjusted (RSSA) method that corrects for inaccuracies using a deterministic rule, and probabilistic-based sensitivity-specificity adjusted (PSSA) method that corrects for error using a statistical model. Computer simulation was used to estimate relative bias (RB) and mean square error (MSE) under varying conditions of population disease prevalence, correlation amongst data sources, and amount of misclassification error. AHRs and EMRs for Manitoba, Canada were used to estimate hypertension prevalence using validated case definitions and multiple disease markers. Results The OR method had the lowest RB and MSE when population disease prevalence was 10%, and the RSSA method had the lowest RB and MSE when population prevalence increased to 20%. As the correlation between data sources increased, the OR method resulted in the lowest RB and MSE. Estimates of hypertension prevalence for AHRs and EMRs alone were 30.9% (95% CI: 30.6–31.2) and 24.9% (95% CI: 24.6–25.2), respectively. The estimates were 21.4% (95% CI: 21.1–21.7), for the AND method, 34.4% (95% CI: 34.1–34.8) for the OR method, 32.2% (95% CI: 31.8–32.6) for the RSSA method, and ranged from 34.3% (95% CI: 34.1–34.5) to 35.9% (95% CI, 35.7–36.1) for the PSSA method, depending on the statistical model. Conclusions The OR and AND methods are influenced by correlation amongst the data sources, while the RSSA method is dependent on the accuracy of prior sensitivity and specificity estimates. The PSSA method performed well when population prevalence was high and average correlations amongst disease markers was low. This study will guide researchers to select a data-combining method that best suits their data characteristics.

Abstract Background Joint replacement, an increasingly common procedure amongst older adults, can substantially improve health-related quality of life (HRQoL). However, differential item functioning (DIF) may affect the accurate interpretation of differences in HRQoL amongst patients with different demographic and health status characteristics but the same underlying (i.e., latent) level of the investigated construct. This study tested for DIF in pre-operative SF-12 physical health (PH) and mental health (MH) sub-scale items amongst patients undergoing total hip arthroplasty (THA) and total knee arthroplasty (TKA). Methods Data were from a population-based joint replacement registry from the Canadian province of Manitoba. TKA and THA patients who had surgery between 2009 and 2015 and completed a pre-operative assessment were included. DIF was tested using the multiple indicators multiple causes (MIMIC) method with sex, age group, body weight status, and presence of multiple comorbid conditions (i.e., multimorbidity) as covariates. Analyses were stratified by joint type. Results The study cohort included 8820 patients; 42.1% underwent THA, 57.3% were female, 32.7% were 70+ years, and 52.8% were obese. For each sub-scale, four of the six items exhibited DIF in both THA and TKA groups. Differences in the covariate effect estimates for DIF and No-DIF models on the MH latent variable were largest for age and body weight status for the THA group, and for sex and multimorbidity for the TKA group. All of the differences were small for PH. Multimorbidity had the strongest association with PH and age and sex had the strongest association with MH in the DIF models. Conclusions Demographic and health status characteristics influenced SF-12 PH and MH item responses in joint replacement populations, although the size of the effects were not large for PH. We recommend testing and adjusting for DIF effects to ensure comparability of HRQoL measures in joint replacement populations.

Abstract Background Clinical registries, which capture information about the health and healthcare use of patients with a health condition or treatment, often contain patient-reported outcomes (PROs) that provide insights about the patient’s perspectives on their health. Missing data can affect the value of PRO data for healthcare decision-making. We compared the precision and bias of several missing data methods when estimating longitudinal change in PRO scores. Methods This research conducted analyses of clinical registry data and simulated data. Registry data were from a population-based regional joint replacement registry for Manitoba, Canada; the study cohort consisted of 5631 patients having total knee arthroplasty between 2009 and 2015. PROs were measured using the 12-item Short Form Survey, version 2 (SF-12v2) at pre- and post-operative occasions. The simulation cohort was a subset of 3000 patients from the study cohort with complete PRO information at both pre- and post-operative occasions. Linear mixed-effects models based on complete case analysis (CCA), maximum likelihood (ML) and multiple imputation (MI) without and with an auxiliary variable (MI-Aux) were used to estimate longitudinal change in PRO scores. In the simulated data, bias, root mean squared error (RMSE), and 95% confidence interval (CI) coverage and width were estimated under varying amounts and types of missing data. Results Three thousand two hundred thirty (57.4%) patients in the study cohort had complete data on the SF-12v2 at both occasions. In this cohort, mixed-effects models based on CCA resulted in substantially wider 95% CIs than models based on ML and MI methods. The latter two methods produced similar estimates and 95% CI widths. In the simulation cohort, when 50% of the data were missing, the MI-Aux method, in which a single hypothetical auxiliary variable was strongly correlated (i.e., 0.8) with the outcome, reduced the 95% CI width by up to 14% and bias and RMSE by up to 50 and 45%, respectively, when compared with the MI method. Conclusions Missing data can substantially affect the precision of estimated change in PRO scores from clinical registry data. Inclusion of auxiliary information in MI models can increase precision and reduce bias, but identifying the optimal auxiliary variable(s) may be challenging.

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.

We aimed to examine rates of breast and cervical cancer screening in women with immune-mediated inflammatory diseases (IMID), including inflammatory bowel disease (IBD), multiple sclerosis (MS) and rheumatoid arthritis (RA) versus a matched cohort with IMID; and examine the association of psychiatric comorbidity with screening in these populations. We conducted a retrospective cohort study in Manitoba, Canada using administrative data. We identified women with IBD, MS and RA, and controls without these IMID matched on age and region. Annually, we identified individuals with any active mood/anxiety disorder. Using physician claims, we determined the proportion of each cohort who had cervical cancer screening within three-year intervals, and mammography screening within two-year intervals. We modeled the difference in the proportion of the IMID and matched cohorts who underwent mammography; and pap tests using log-binomial regression with generalized estimating equations, adjusting for sociodemographics, comorbidity and immune therapy use. We tested for additive interactions between cohort and mood/anxiety disorder status. During 2006–2016, we identified 17,230 women with IMID (4,623 with IBD, 3,399 with MS, and 9,458 with RA) and 85,349 matched controls. Having an IMID was associated with lower (-1%) use of mammography; however, this reflected a mixture of more mammography in the IBD cohort (+2.9%) and less mammography in the MS (-4.8 to -5.2%) and RA (-1.5%) cohorts. Within the IBD, MS and RA cohorts, having an active mood/anxiety disorder was associated with more mammography use than having an inactive mood/anxiety disorder. The MS and RA cohorts were less likely to undergo Pap testing than their matched cohorts. In the absence of an active mood/anxiety disorder, the IBD cohort was more likely to undergo Pap testing than its matched cohort; the opposite was true when an active mood/anxiety disorder was present. Among women with an IMID, mood/anxiety disorder influence participation in cancer screening.

Summary: Objective: To estimate the 1) accuracy of algorithms for identifying osteoarthritis (OA) using health administrative data; and 2) population-level OA prevalence and incidence over time in Ontario, Canada. Method: We performed a retrospective chart abstraction study to identify OA patients in a random sample of 7500 primary care patients from electronic medical records. The validation sample was linked with several administrative data sources. Accuracy of administrative data algorithms for identifying OA was tested against two reference standard definitions by estimating the sensitivity, specificity and predictive values. The validated algorithms were then applied to the Ontario population to estimate and compare population-level prevalence and incidence from 2000 to 2017. Results: OA prevalence within the validation sample ranged from 10% to 23% across the two reference standards. Algorithms varied in accuracy depending on the reference standard, with the sensitivity highest (77%) for patients with OA documented in medical problem lists. Using the top performing administrative data algorithms, the crude population-level OA prevalence ranged from 11% to 25% and standardized prevalence ranged from 9 to 22% in 2017. Over time, prevalence increased whereas incidence remained stable (~1% annually). Conclusion: Health administrative data have limited sensitivity in adequately identifying all OA patients and appear to be more sensitive at detecting OA patients for whom their physician formally documented their diagnosis in medical problem lists than individuals who have their diagnosis documented outside of problem lists. Irrespective of the algorithm used, OA prevalence has increased over the past decade while annual incidence has been stable.

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.

Introduction The SPOR (Strategy for Patient-Oriented Research) Canadian Data Platform aims to facilitate multi-jurisdictional research through a variety of activities, including the development of standardized algorithms for health conditions, health service use, and the determinants of health. An initial step towards standardization was to identify existing health measures that have been validated or assessed for feasibility of implementation in multi-jurisdictional research, document features of these measures, and describe the methods used to validate or assess feasibility. Objectives and Approach We constructed an inventory of published algorithms to measure population health, health services use, and the determinants of health. A systematic review of published literature identified algorithms from validation or feasibility studies in two or more Canadian provinces/territories. The search strategy was applied to Medline, Embase, and Scopus. The Algorithms and Harmonized Data Working Group of the Canadian Data Platform identified relevant fields for data extraction, including study type, population characteristics, data source, jurisdictions, and algorithm details. A searchable online resource was created to maintain and share the algorithms. Results Of the 2758 articles retrieved, 1998 articles underwent title and abstract review and 60 articles were selected for full review. A total of 8 validation and 26 feasibility studies were assessed; they contributed over 140 algorithms. Chronic physical health conditions, such as diabetes, depression, hypertension and dementia, were most often represented in the algorithms. British Columbia and Manitoba were the jurisdictions most frequently represented in the studies. Methods to facilitate automated searching of the on-line resource are under development. Conclusion / Implications Our inventory of algorithms provides valuable information for researchers interested in conducting multi-jurisdictional studies, and reveals gaps where further algorithm development could be undertaken. This comprehensive collection of existing algorithms will support future studies aimed at improving population health and monitoring health service use in Canada.

Introduction Capture of obesity using administrative health data is poor, with many cases being under coded within the data. Linking multiple health data sources may improve case ascertainment and facilitate the use of administrative health data for obesity research and surveillance. Objectives and Approach This research aims to determine if using individual-level linked data from multiple sources can improve case ascertainment for obesity in administrative health data. Data from between April 1, 2001 and March 31, 2015 were obtained from the Manitoba Population Data Repository. Eighteen obesity case definitions were developed with different observation times and combinations of diagnosis, procedure, and prescription codes from physician billing claims, hospitalization abstracts, and prescription drug records. Body mass index (BMI) records from primary care data and the Bone Mineral Density (BMD) registry were used for validation. Sensitivity, specificity, and Cohen’s kappa were calculated. Results Individuals with a higher BMI class had more physician visits and were more likely to have comorbidities and obese codes in the administrative health data. A higher BMI class was associated with being in a lower income quintile and the age group 40-59. Overall, the case definitions for obesity had high specificity (0.98-0.99) and low sensitivity (0.005-0.19) when validated using primary care data. Case definitions with obesity codes from multiple databases 3 year prior to and including the index date had the highest sensitivity (0.06-0.19) and kappa (0.04-0.23). Results with the BMD data were similar (specificity: 0.97-0.99; sensitivity: 0.007-0.21). Stratified analyses found agreement measures improved slightly for females, those who had chronic conditions and a later index year, and the age group 40-59. Conclusion / Implications When using multiple databases to build a case definition for obesity, sensitivity improves but remains low. Individuals with other chronic conditions and a higher BMI class were more likely to be accurately classified as an obese case.

Introduction. Le Système canadien de surveillance des maladies chroniques (SCSMC) de l’Agence de la santé publique du Canada (ASPC) produit des estimations de la prévalence et de l’incidence des maladies chroniques au sein de la population à l’aide de données administratives sur la santé. Notre projet visait à évaluer les tendances des taux d’incidence au fil du temps, car elles jouent un rôle essentiel dans la compréhension de l’évolution des risques pour la population et dans l’orientation des politiques. Méthodologie. Nous avons obtenu les cas incidents et diagnostiqués d’asthme, de maladie pulmonaire obstructive chronique (MPOC), de diabète, d’hypertension, de cardiopathie ischémique et d’accident vasculaire cérébral (AVC) auprès de l’infobase en ligne du SCSMC pour les années 1999 à 2012. Nous avons analysé les tendances dans les estimations de l’incidence à l’échelle nationale et régionale à l’aide d’un modèle de régression binomiale négative utilisant l’année comme prédicteur linéaire. Nous avons ensuite utilisé des modèles utilisant l’année comme spline cubique restreinte pour vérifier les écarts de linéarité au moyen du test du rapport de vraisemblance. L’âge et le sexe ont été intégrés comme covariables dans tous les modèles. Résultats. Dans les modèles où l’année a été utilisée comme prédicteur linéaire, les taux d’incidence nationaux diminuaient dans le temps pour toutes les maladies sauf le diabète et les taux d’incidence régionaux diminuaient également pour la plupart des maladies et dans la plupart des régions. Toutefois, les tests du rapport de vraisemblance ont révélé des écarts statistiquement significatifs par rapport à l’effet linéaire de l’année pour de nombreuses maladies et dans de nombreuses régions, en particulier pour l’hypertension. Conclusion. Les estimations de l’incidence des maladies chroniques fondées sur les données du SCSMC font état d’une diminution dans le temps, mais à un taux non constant. D’autres recherches sont nécessaires pour déterminer si cette diminution est associée à des changements dans l’état de santé de la population, dans la qualité des données ou dans les pratiques médicales. De la même manière, les caractéristiques de la population susceptibles d’avoir une influence sur l’évolution des tendances de l’incidence requièrent un examen approfondi.

Introduction: The Public Health Agency of Canada’s Canadian Chronic Disease Surveillance System (CCDSS) produces population-based estimates of chronic disease prevalence and incidence using administrative health data. Our aim was to assess trends in incidence rates over time, trends are essential to understand changes in population risk and to inform policy development. Methods: Incident cases of diagnosed asthma, chronic obstructive pulmonary disease (COPD), diabetes, hypertension, ischemic heart disease (IHD), and stroke were obtained from the CCDSS online infobase for 1999 to 2012. Trends in national and regional incidence estimates were tested using a negative binomial regression model with year as a linear predictor. Subsequently, models with year as a restricted cubic spline were used to test for departures from linearity using the likelihood ratio test. Age and sex were covariates in all models. Results: Based on the models with year as a linear predictor, national incidence rates were estimated to have decreased over time for all diseases, except diabetes; regional incidence rates for most diseases and regions were also estimated to have decreased. However, likelihood ratio tests revealed statistically significant departures from a linear year effect for many diseases and regions, particularly for hypertension. Conclusion: Chronic disease incidence estimates based on CCDSS data are decreasing over time, but not at a constant rate. Further investigations are needed to assess if this decrease is associated with changes in health status, data quality, or physician practices. As well, population characteristics that may influence changing incidence trends also require exploration.

Abstract Background Limited outdoor walking is a marker of frailty and a risk factor for decline in mobility and self-care functioning, social isolation, and reduced health-related quality of life (HRQL). Objectives were to evaluate the safety, feasibility, and preliminary effect of a supervised outdoor walking group and interactive workshop compared to the workshop alone in increasing outdoor walking activity and identify an optimal method for estimating outdoor walking activity among older adults who infrequently walk outdoors. Methods A pilot 2-parallel-group randomized controlled trial was conducted. Adults aged ≥ 65 years who reported walking ≤ 20 min/week outdoors were randomized in a 2:1 ratio to receive the GO-OUT program (1-day workshop and 9-week outdoor walking group), or the workshop alone. An external site conducted the randomization after workshop completion. The eight workshop activity stations aimed to build knowledge and skills to safely walk outdoors. The group-based outdoor walking program consisted of repetitive practice of mobility tasks at local parks. The primary outcome of outdoor walking activity used an activity monitor and GPS; secondary outcomes included aerobic, balance, and walking capacity; physical activity; participation; mood; and HRQL. Blinded outcome assessors evaluated participants at 0, 3, and 6 months. Qualitative interviews occurred after 3 months; data were analyzed with qualitative description. Quantitative data were summarized using descriptive statistics. Results Forty-eight individuals were screened; 9 were eligible and randomized to the GO-OUT (n = 6) or workshop (n = 3) group. Data from 9 participants were analyzed. Mean age was 77 and 74 years in the GO-OUT and workshop groups, respectively. No falls occurred during the workshop and outdoor walking program. Average attendance of the walking group was 61%. All participants attended the evaluations and workshop. An analysis method combining data from activity monitors and GPS was developed to estimate outdoor walking. Themes from the qualitative analysis included the barriers to outdoor walking, impact of the workshop and GO-OUT walking group, and feasibility and acceptance of the assessment and intervention strategies. Conclusions The trial protocol was deemed safe and feasible. Results were used to inform changes to the protocol to conduct a full-scale study. Trial registration Clinical Trials.gov: NCT02339467.

Abstract Background Residence in public housing, a subsidized and managed government program, may affect health and healthcare utilization. We compared healthcare use in the year before individuals moved into public housing with usage during their first year of tenancy. We also described trends in use. Methods We used linked population-based administrative data housed in the Population Research Data Repository at the Manitoba Centre for Health Policy. The cohort consisted of individuals who moved into public housing in 2009 and 2010. We counted the number of hospitalizations, general practitioner (GP) visits, specialist visits, emergency department visits, and prescriptions drugs dispensed in the twelve 30-day intervals (i.e., months) immediately preceding and following the public housing move-in date. Generalized linear models with generalized estimating equations tested for a period (pre/post-move-in) by month interaction. Odds ratios (ORs), incident rate ratios (IRRs), and means are reported along with 95% confidence intervals (95% CIs). Results The cohort included 1942 individuals; the majority were female (73.4%) who lived in low income areas and received government assistance (68.1%). On average, the cohort had more than four health conditions. Over the 24 30-day intervals, the percentage of the cohort that visited a GP, specialist, and an emergency department ranged between 37.0% and 43.0%, 10.0% and 14.0%, and 6.0% and 10.0%, respectively, while the percentage of the cohort hospitalized ranged from 1.0% to 5.0%. Generally, these percentages were highest in the few months before the move-in date and lowest in the few months after the move-in date. The period by month interaction was statistically significant for hospitalizations, GP visits, and prescription drug use. The average change in the odds, rate, or mean was smaller in the post-move-in period than in the pre-move-in period. Conclusions Use of some healthcare services declined after people moved into public housing; however, the decrease was only observed in the first few months and utilization rebounded. Knowledge of healthcare trends before individuals move in are informative for ensuring the appropriate supports are available to new public housing residents. Further study is needed to determine if decreased healthcare utilization following a move is attributable to decreased access.

Abstract Background To improve care, planners require accurate information about nursing home (NH) residents and their healthcare use. We evaluated how accurately measures of resident user status and healthcare use were captured in the Minimum Data Set (MDS) versus administrative data. Methods This retrospective observational cohort study was conducted on all NH residents (N = 8832) from Winnipeg, Manitoba, Canada, between April 1, 2011 and March 31, 2013. Six study measures exist. NH user status (newly admitted NH residents, those who transferred from one NH to another, and those who died) was measured using both MDS and administrative data. Rates of in-patient hospitalizations, emergency department (ED) visits without subsequent hospitalization, and physician examinations were also measured in each data source. We calculated the sensitivity, specificity, positive and negative predictive values (PPV, NPV), and overall agreement (kappa, κ) of each measure as captured by MDS using administrative data as the reference source. Also for each measure, logistic regression tested if the level of disagreement between data systems was associated with resident age and sex plus NH owner-operator status. Results MDS accurately identified newly admitted residents (κ = 0.97), those who transferred between NHs (κ = 0.90), and those who died (κ = 0.95). Measures of healthcare use were captured less accurately by MDS, with high levels of both under-reporting and false positives (e.g., for in-patient hospitalizations sensitivity = 0.58, PPV = 0.45), and moderate overall agreement levels (e.g., κ = 0.39 for ED visits). Disagreement was sometimes greater for younger males, and for residents living in for-profit NHs. Conclusions MDS can be used as a stand-alone tool to accurately capture basic measures of NH use (admission, transfer, and death), and by proxy NH length of stay. As compared to administrative data, MDS does not accurately capture NH resident healthcare use. Research investigating these and other healthcare transitions by NH residents requires a combination of the MDS and administrative data systems.