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Objectives Timely access to rheumatologists remains a challenge in Alberta, a Canadian province with vast rural areas, whereas rheumatologists are primarily clustered in metro areas. To address the goal of timely and equitable access to rheumatoid arthritis (RA) care, health planners require information at the regional and local level to determine the RA prevalence and the associated health care needs. Methods Using Alberta Health administrative databases, we identified RA-prevalent cases (April 1, 2015-March 31, 2016) on the basis of a validated case definition. Age- and sex-standardized prevalence rates per 1000 population members and the standardized rates ratio (SRR) were calculated. We applied Global Moran's I and Gi* hotspot analysis using three different weight matrices to explore the geospatial pattern of RA prevalence in Alberta. Results Among 38 350 RA cases (68% female; n = 26 236), the prevalence rate was 11.81 cases per 1000 population members (95% confidence interval [CI] 11.80-11.81) after age and sex standardization. Approximately 60% of RA cases resided in metro (Calgary and Edmonton) and moderate metro areas. The highest rate was observed in rural areas (14.46; 95% CI 14.45-14.47; SRR 1.28), compared with the lowest in metro areas (10.69; 95% CI 10.68-10.69; SRR 0.82). The RA prevalence across local geographic areas ranged from 4.7 to 30.6 cases. The Global Moran's I index was 0.15 using three different matrices (z-score 3.96-4.24). We identified 10 hotspots in the south and north rural areas and 18 cold spots in metro and moderate metro Calgary. Conclusion The findings highlight notable rural-urban variation in RA prevalence in Alberta. Our findings can inform strategies aimed at reducing geographic disparities by targeting areas with high health care needs.

ObjectiveThis study examined the association of anxiety alone, depression alone and the presence of both anxiety and depression with preterm birth (PTB) and further examined whether neighbourhood socioeconomic status (SES) modified this association.DesignCohort study using individual-level data from two community-based prospective pregnancy cohort studies (All Our Families; AOF) and Alberta Pregnancy Outcomes and Nutrition (APrON) and neighbourhood SES data from the 2011 Canadian census.SettingCalgary, Alberta, Canada.ParticipantsOverall, 5538 pregnant women who were 15 years old were enrolled in the cohort studies between 2008 and 2012. 3341 women participated in the AOF study and 2187 women participated in the APrON study, with 231 women participated in both studies. Women who participated in both studies were only counted once.Primary and secondary outcome measuresPTB was defined as delivery prior to 37 weeks of gestation. Depression was defined as an Edinburgh Postnatal Depression Scale (EPDS) score of ≥13, anxiety was defined as an EPDS-anxiety subscale score of ≥6, and the presence of both anxiety and depression was defined as meeting both anxiety and depression definitions.ResultsOverall, 7.3% of women delivered preterm infants. The presence of both anxiety and depression, but neither of these conditions alone, was significantly associated with PTB (OR 1.6, 95% CI 1.1 to 2.3) and had significant interaction with neighbourhood deprivation (p=0.004). The predicted probability of PTB for women with both anxiety and depression was 10.0%, which increased to 15.7% if they lived in the most deprived neighbourhoods and decreased to 1.4% if they lived in the least deprived neighbourhoods.ConclusionsEffects of anxiety and depression on risk of PTB differ depending on where women live. This understanding may guide the identification of women at increased risk for PTB and allocation of resources for early identification and management of anxiety and depression.

Introduction The International Methodology Consortium for Coded Health Information (IMeCCHI) is a collaboration of health services researchers who promote methodological advances in coded health information. The IMeCCHI-DATANETWORK initiative focuses on developing a multi-purpose distributed data infrastructure and common data model (CDM) to enable cross-border data sharing and international comparisons. Methods IMeCCHI consortium partners from six different countries – Canada, Denmark, Italy, New Zealand, South Korea, and Switzerland – used a questionnaire to describe their original databases which differ in size, structure, content and coding systems. To standardize these data, they agreed on a CDM and mapped their population-based databases to meet the CDM specifications. At the end of this process, local data had a more homogenous content and structure, which made them syntactically and semantically interoperable. Data transformation was performed using a common data management software called TheMatrix. Results The CDM encompasses four tables of structured data (person characteristics, hospitalizations, outpatient prescription medication and death), linked at the individual level through a person identifier. It can be used to answer research questions across countries using locally converted databases, which facilitates study replication in a distributed fashion. As a proof-of-concept study, an initial research question was addressed using an agreed protocol. Local data were transformed in csv files in the CDM structure and TheMatrix was tested to transform the standardized data from each partner into local analytical datasets. This allowed results to be shared between countries, whilst maintaining local control over each region’s data. Conclusion The IMeCCHI-DATANETWORK, a model of a distributed data network, demonstrated that it is feasible to analyze international data using standardized analytical methods that enable independent analyses by regions, without relocating datasets thereby protecting local confidentiality obligations. The distributed data infrastructure can produce results that can be generalized to several countries, while facilitating cross-border data sharing and international comparisons. Keywords Common data model, international comparison, cross-border data sharing, interoperability, observational data

IntroductionThe Office of the Auditor General report on Chronic Disease Management in 2014 identified that in Alberta there are no processes to identify individuals with chronic diseases who do not have or cannot find a Primary Health Care (PHC) physician, and provide them with ongoing care. Objectives and ApproachLinked provincial administrative data (Practitioner Claims, National Ambulatory Care Reporting System, Discharge Abstract Database, Clinical Risk Groupers) to identify Albertans with chronic disease who do not have any contact or have low relational continuity with primary care physicians, and examine their healthcare utilization (Hospitalizations, Ambulatory Care Sensitive Conditions, 30-day Hospital Readmissions, Emergency Department visits, and Family practice Sensitive conditions). Usual Provider Continuity index was used to measure relational continuity. Used GIS software to combine findings from secondary data analysis and produced an interactive Online Mapping Application. Ongoing spatial and regression analysis will examine relationship between relational continuity and healthcare utilization. ResultsThe majority of individuals in Alberta with no or low primary care visits were men (62.6%), did not have a chronic disease (94.9%), and belonged to the healthy or healthcare non-user status. Albertans were grouped based on patient-physician relational continuity, and it was seen that the likelihood of emergency department visits and 30-day readmissions declined with increase in continuity, however similar results were not seen in case of hospitalizations. To promote utilization of findings an Online Mapping Application was used to present population groups and their characteristics. Univariate and multivariate analysis will be conducted to examine relation between relational continuity and healthcare utilization, especially usage that could be avoided through regular contact with primary care, while controlling for socio-demographic and health related factors. Conclusion/ImplicationsThe results emphasize that the focus should not be on those with no or low number of primary care visits. Strategies focused on linking patients to PHC services and fostering patient-provider relationship are critical to meet the health needs of Albertans and create efficiencies in health system utilization.

IntroductionPooling data from cohort studies can be used to increase sample size. However, individual datasets may contain variables that measure the same construct differently, posing challenges in the usefulness of combined datasets. Variable harmonization (an effort that provides comparable view of data from different studies) may address this issue. Objectives and ApproachThis study harmonized existing datasets from two prospective pregnancy cohort studies in Alberta Canada (All Our Families (n=3,351) and Alberta Pregnancy Outcome and Nutrition (n=2,187)). Given the comparability of the characteristics of the two cohorts and similarities of the core data elements of interest, data harmonization was justifiable. Data harmonization was performed considering multiple factors, such as complete or partial variable matching regarding question asked/responded, the response coded (value level, value definition, data type), the frequency of measurement, the pregnancy time-period of measurement, and missing values. Multiple imputation was used to address missing data resulting from the data harmonization process. ResultsSeveral variables such as ethnicity, income, parity, gestational age, anxiety, and depression were harmonized using different procedures. If the question asked/answered and the response recorded was the same in both datasets, no variable manipulation was done. If the response recorded was different, the response was re-categorized/re-organized to optimize comparability of data from both datasets. Missing values were created for each resulting unmatched variables and were replaced using multiple imputation if the same construct was measured in both datasets but using different ways/scales. A scale that was used in both datasets was identified as a reference standard. If the variables were measured in multiple times and/or different time-periods, variables were synchronized using pregnancy trimesters data. Finally, harmonized datasets were then combined/pooled into a single dataset (n=5,588). Conclusion/ImplicationsVariable harmonization is an important aspect of conducting research using multiple datasets. It provides an opportunity to increase study power through maximizing sample size, permitting more sophisticated statistical analyses, and to answer novel research questions that could not be addressed using a single study.