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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.

Background Knowledge of geospatial pattern in comorbidities prevalence is critical to an understanding of the local health needs among people with osteoarthritis (OA). It provides valuable information for targeting optimal OA treatment and management at the local level. However, there is, at present, limited evidence about the geospatial pattern of comorbidity prevalence in Alberta, Canada. Methods Five administrative health datasets were linked to identify OA cases and comorbidities using validated case definitions. We explored the geospatial pattern in comorbidity prevalence at two standard geographic areas levels defined by the Alberta Health Services: descriptive analysis at rural-urban continuum level; spatial analysis (global Moran’s I, hot spot analysis, cluster and outlier analysis) at the local geographic area (LGA) level. We compared area-level indicators in comorbidities hotspots to those in the rest of Alberta (non-hotspots). Results Among 359,638 OA cases in 2013, approximately 60% of people resided in Metro and Urban areas, compared to 2% in Rural Remote areas. All comorbidity groups exhibited statistically significant spatial autocorrelation (hypertension: Moran’s I index 0.24, z score 4.61). Comorbidity hotspots, except depression, were located primarily in Rural and Rural Remote areas. Depression was more prevalent in Metro (Edmonton-Abbottsfield: 194 cases per 1000 population, 95%CI 192–195) and Urban LGAs (Lethbridge-North: 169, 95%CI 168–171) compared to Rural areas (Fox Creek: 65, 95%CI 63–68). Comorbidities hotspots included a higher percentage of First Nations or Inuit people. People with OA living in hotspots had lower socioeconomic status and less access to care compared to non-hotspots. Conclusions The findings highlight notable rural-urban disparities in comorbidities prevalence among people with OA in Alberta, Canada. Our study provides valuable evidence for policy and decision makers to design programs that ensure patients with OA receive optimal health management tailored to their local needs and a reduction in current OA health disparities.

The utilization of non-local primary care physicians (PCP) is a key primary care indicator identified by Alberta Health to support evidence-based healthcare planning. This study aims to identify area-level factors that are significantly associated with non-local PCP utilization and to examine if these associations vary between rural and urban areas. We examined rural–urban differences in the associations between non-local PCP utilization and area-level factors using multivariate linear regression and geographically weighted regression (GWR) models. Global Moran’s I and Gi* hot spot analyses were applied to identify spatial autocorrelation and hot spots/cold spots of non-local PCP utilization. We observed significant rural–urban differences in the non-local PCP utilization. Both GWR and multivariate linear regression model identified two significant factors (median travel time and percentage of low-income families) with non-local PCP utilization in both rural and urban areas. Discontinuity of care was significantly associated with non-local PCP in the southwest, while the percentage of people having university degree was significant in the north of Alberta. This research will help identify gaps in the utilization of local primary care and provide evidence for health care planning by targeting policies at associated factors to reduce gaps in OA primary care provision.

Data pooling from pre-existing multiple datasets can be useful to increase study sample size and statistical power to answer a research question. However, individual datasets may contain variables that measure the same construct differently, posing challenges for data pooling. Variable harmonization, an approach that can generate comparable datasets from heterogeneous sources, can address this issue in some circumstances. As an illustrative example, this paper describes the data harmonization strategies that helped generate comparable datasets across two Canadian pregnancy cohort studies– the All Our Families and the Alberta Pregnancy Outcomes and Nutrition. Variables were harmonized considering multiple features across the datasets: the construct measured; question asked/response options; the measurement scale used; the frequency of measurement; timing of measurement, and the data structure. Completely matching, partially matching, and completely un-matching variables across the datasets were determined based on these features. Variables that were an exact match were pooled as is. Partially matching variables were synchronized across the datasets considering the frequency of measurement, the timing of measurement, and response options. Variables that were completely unmatching could not be harmonized into a single variable. The variable harmonization strategies that were used to generate comparable cohort datasets for data pooling are applicable to other data sources. Future studies may employ or evaluate these strategies. Variable harmonization and pooling provide an opportunity to increase study power and the utility of existing data, permitting researchers to answer novel research questions in a statistically efficient, timely, and cost-efficient manner that could not be achieved using a single data source.

There is limited evidence on the geographic distribution of osteoarthritis (OA) in Alberta to inform planning of equitable access to health care services. This research aimed to explore the geographic variation in age-sex standardized OA prevalence rates by geographic areas across the rural-urban continuum, and by six-digit postal codes using global Moran's I and hot spot analysis. The results demonstrated a substantially higher OA prevalence rate in Rural Remote (134.7 cases per 1000 population) and Rural (128.5), compared to Metro (107.4) and Urban areas (107.3). Metro-Edmonton had a substantially higher OA prevalence rate (124.4) compared to Metro-Calgary (94.4). OA hot spots were identified in north rural communities and Metro-Edmonton. These variations should be considered when planning programs for health promotion and prevention of osteoarthritis and access to associated health care services. Further research is needed to identify the underlying factors contributing to this geographic variation.

Rural Canadians have high health care needs due to high prevalence of osteoarthritis (OA) but lack access to care. Examining realized access to three types of providers (general practitioners (GPs), orthopedic surgeons (Ortho), and physiotherapists (PTs)) simultaneously helps identify gaps in access to needed OA care, inform accessibility assessment, and support health care resource allocation. Travel time from a patient’s postal code to the physician’s postal code was calculated using origin–destination network analysis. We applied descriptive statistics to summarize differences in travel time, hotspot analysis to explore geospatial patterns, and distance decay function to examine the travel pattern of health care utilization by urbanicity. The median travel time in Alberta was 11.6 min (IQR = 4.3–25.7) to GPs, 28.9 (IQR = 14.8–65.0) to Ortho, and 33.7 (IQR = 23.1–47.3) to PTs. We observed significant rural–urban disparities in realized access to GPs (2.9 and IQR = 0.0–92.1 in rural remote areas vs. 12.6 and IQR = 6.4–21.0 in metropolitan areas), Ortho (233.3 and IQR = 171.3–363.7 in rural remote areas vs. 21.3 and IQR = 14.0–29.3 in metropolitan areas), and PTs (62.4 and IQR = 0.0–232.1 in rural remote areas vs. 32.1 and IQR = 25.2–39.9 in metropolitan areas). We identified hotspots of realized access to all three types of providers in rural remote areas, where patients with OA tend to travel longer for health care. This study may provide insight on the choice of catchment size and the distance decay pattern of health care utilization for further studies on spatial accessibility.

In Alberta, no standardized processes exist to identify patients with chronic diseases (CDs) who do not have a family physician. This study examined the association between relational continuity (continuity of care) and healthcare utilization patterns in this population. Relational continuity was assessed using health administrative data to calculate a Usual Provider Continuity (UPC) Index. The majority of patients with no or a low UPC Index were male, did not have CD and were healthy or non-users of healthcare. When grouped by UPC Index, regression modelling revealed that emergency department visits and unplanned hospitalization declined with increased continuity of care. Advanced state of disease(s) and location of residence increased the likelihood of utilization of these services in the low- and moderate-continuity groups.

This study examined the performance of multiple anxiety scales in measuring anxiety during pregnancy, an important issue due to the possible effect of pregnancy-related symptoms on the measurement of anxiety.Secondary data on anxiety, measured by the State-Trait Anxiety Inventory-State (STAI-S) 20-item and six-item scales, the Edinburgh Postnatal Depression Scale-Anxiety Subscale (EPDS-3A) and the Symptoms Checklist-90-Anxiety Subscale (SCL-90), were obtained from two pregnancy cohort studies. Both cohorts completed the EPDS-3A, while 3341 women completed the STAI-S and 2187 women completed the SCL-90, with 231 women participating in both cohorts. Data were analyzed using confirmatory factor analysis and Spearman correlation.The STAI-6 had adequate model fit, while the STAI-20 and the SCL-90 had inadequate model fit. Model fitness for the EPDS-3A could not be assessed due to its low number of items. The correlation between the STAI-20 and STAI-6 was excellent (Findings indicate that these scales do not measure anxiety as a single dimension and that these scales are incomparable and may conceptualize anxiety differently.

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

BACKGROUND The existing inconsistent association between the caesarean rate and maternal socio-economic status (SES) may be the result of a failure to examine the association across indications for caesarean. This study examined the variation in caesarean rates by maternal SES across diverse obstetric-indications. METHODS Data on demographics, education, insurance status, medical-conditions, and obstetric characteristics needed to classify deliveries according to Robson's 10 obstetric-groups were extracted from the 2015 US birth certificate data (n = 3 988 733). Multivariable log-binomial regression was used to analyse the data adjusting for confounders. RESULTS The caesarean rate was 34.1% for women with high SES and 26.8% for those with low SES. After adjustment for confounders, the rate was similar between women with graduate degrees and those who did not complete high school (relative risk (RR) 1.0, 95% confidence interval (CI) 0.9, 1.1). However, different rates of caesareans across SES were observed for particular obstetric-indications. Notably, women with graduate education compared to those who did not complete high school were more likely to have a caesarean (RR 3.0, 95% CI 2.9, 3.1) for a low-risk condition (group 1: nulliparous women with single, cephalic, ≥37 gestational weeks, and spontaneous labour). Women with private insurance were more likely to have a caesarean in almost all obstetric groups, compared to those without private insurance or Medicaid. CONCLUSION Examining the overall caesarean rate obscures the relationship between SES and the use of caesarean for particular obstetric-indications. The unequal utilisation of caesareans across SES highlights overuse and potential underuse of the caesareans among American women.

Introduction In ischaemic stroke care, fast reperfusion is essential for disability free survival. It is unknown if bypassing thrombolysis centres in favour of endovascular thrombectomy (mothership) outweighs transport to the nearest thrombolysis centre for alteplase and then transfer for endovascular thrombectomy (drip-and-ship). We use conditional probability modelling to determine the impact of treatment times on transport decision-making for acute ischaemic stroke. Materials and methods Probability of good outcome was modelled using a previously published framework, data from the Irish National Stroke Register, and an endovascular thrombectomy registry at a tertiary referral centre in Ireland. Ireland was divided into 139 regions, transport times between each region and hospital were estimated using Google’s Distance Matrix Application Program Interface. Results were mapped using ArcGIS 10.3. Results Using current treatment times, drip-and-ship rarely predicts best outcomes. However, if door to needle times are reduced to 30 min, drip-and-ship becomes more favourable; even more so if turnaround time (time from thrombolysis to departure for the endovascular thrombectomy centre) is also reduced. Reducing door to groin puncture times predicts better outcomes with the mothership model. Discussion This is the first case study modelling pre-hospital transport for ischaemic stroke utilising real treatment times in a defined geographic area. A moderate improvement in treatment times results in significant predicted changes to the optimisation of a national acute stroke patient transport strategy. Conclusions Modelling patient transport for system-level planning is sensitive to treatment times at both thrombolysis and thrombectomy centres and has important implications for the future planning of thrombectomy services.

BACKGROUND Multiple studies indicate a significant association between area-level socio-economic status (SES) and adverse maternal health outcomes; however, the impact of area-level SES on maternal co-morbidities and obstetric interventions has not been examined. OBJECTIVE To examine the variation in maternal co-morbidities and obstetric interventions across area-level SES. METHODS This study used data from the Discharge Abstract Database that comprised birth data in Alberta between 2005-2007 (n = 120 285). Co-morbidities and obstetric interventions were identified using validated case-definitions. Material deprivation index was obtained for each dissemination area through linkage of hospitalisation and census data. Multilevel logistic regression was used to analyse the data adjusting for potential confounding variables. RESULTS The prevalence of any co-morbidity varied across area-level SES. Drug abuse odds ratio (OR) 2.5 (95% confidence interval (CI) 1.8, 3.5), pre-existing diabetes OR 1.7 (95% CI 1.1, 2.6), and prolonged hospital stay OR 1.5 (95% CI 1.4, 1.6) were significantly more likely to occur in the most deprived areas compared to the least deprived areas. In contrast, caesarean delivery OR 0.9 (95% CI 0.8, 0.9) was less likely to occur in the most deprived areas compared to the least deprived areas. Area-level deprivation explained area-level variance of drug abuse, HIV, and other mental diseases only. CONCLUSION Many co-morbidities and obstetric interventions vary at the area-level, but only some are associated with area-level SES, and few of them vary due to the area-level SES. This indicates that other area-level factors, in addition to area-level SES, need to be considered when investigating maternal health and use of health interventions.

To assess the geographic distribution of acute complications in patients with type 1 diabetes in a large urban centre; and to assess the association between acute complications and community-level sociodemographic factors.Adults (aged ≥18 years old) with type 1 diabetes and acute complications were identified between 2004 and 2008 by using a diabetes centre clinical database or discharge abstracts for acute complications (diabetic ketoacidosis or hypoglycemia). Using a geographic information system, hot-spot analysis was used to identify spatial clusters of acute complications in a large urban centre. The association between acute complications and community-level sociodemographic factors were assessed by Spearman rank correlation.We identified 1779 patients with type 1 diabetes, of whom 456 had been hospitalized for acute complications. The mean age of patients was 40.9±16.0 years, and men were more likely to have acute complications (59.2% vs. 52.3%; p0.01). Spatial clusters of high values and low values were identified. Higher median family income (r=-0.36; p0.0001) and higher education levels (r=-0.30; p0.0001) were associated with lower rates of acute complications.This study demonstrated geographic clusters of hospitalizations for acute complications and important community sociodemographic factors. Prevention strategies and interventions targeting these geographic and sociodemographic disparities need to be explored as a means of minimizing hospitalizations for acute complications.

ObjectiveThis study developed and internally validated a predictive model for preterm birth (PTB) to examine the ability of neighbourhood socioeconomic status (SES) to predict PTB.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.ParticipantsPregnant women who were 15 years old were enrolled in the cohort studies between 2008 and 2012. Overall, 5297 women participated in at least one of these cohorts: 3341 women participated in the AOF study, 2187 women participated in the APrON study and 231 women participated in both studies. Women who participated in both studies were only counted once.Primary and secondary outcome measuresPTB (delivery prior to 37 weeks of gestation).ResultsThe rates of PTB in the least and most deprived neighbourhoods were 7.54% and 10.64%, respectively. Neighbourhood variation in PTB was 0.20, with an intra-class correlation of 5.72%. Neighbourhood SES, combined with individual-level predictors, predicted PTB with an area under the receiver-operating characteristic curve (AUC) of 0.75. The sensitivity was 91.80% at a low-risk threshold, with a high false-positive rate (71.50%), and the sensitivity was 5.70% at a highest risk threshold, with a low false-positive rate (0.90%). An agreement between the predicted and observed PTB demonstrated modest model calibration. Individual-level predictors alone predicted PTB with an AUC of 0.60.ConclusionAlthough neighbourhood SES combined with individual-level predictors improved the overall prediction of PTB compared with individual-level predictors alone, the detection rate was insufficient for application in clinical or public health practice. A prediction model with better predictive ability is required to effectively find women at high risk of preterm delivery.

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.

Background Alberta is considering capping daily fee-for-service physician billings, but little is known about high-volume practice in the province and its impact on patient health outcomes. In this initial study, we conducted a descriptive analysis of general practitioners' patient volumes and billing practices in relation to associated practitioner demographic characteristics. Methods We conducted a retrospective descriptive analysis of the associations of practitioner characteristics, including full-time versus non-full-time practice, provider sex, years in practice, geographic location and international medical graduate status, with high-volume (> 50 visits/d) practice using general practice billing data from 2011 to 2016. Use of general practitioner service codes was described and compared by general practitioner volume status, with adjustment for physician demographic characteristics and geographic parameters. Results We included 3465 general practitioners practising fee-for-service in Alberta between 2011 and 2016, of whom 233 (6.7%) were identified as high-volume providers. Physicians who had been in practice longer (odds ratio [OR] 1.04 per year, 95% confidence interval [CI] 1.02-1.05) and international medical graduates (OR 1.89, 95% CI 1.40-2.54) were more likely to exceed 50 patient visits/day. Female physicians were less likely to exceed 50 patient visits/day (OR 0.14, 95% CI 0.07-0.28). Rural practice location was negatively associated with high-volume practice (OR 0.87, 95% CI 0.79-0.95) when we controlled for zone within the province. Zone 5 (North) was associated with high-volume practice (OR 1.95, 95% CI 1.06-3.58). Less than full-time practice was prevalent (1836 providers [53.0%]). High-volume general practitioners billed fewer service codes requiring longer visits, except for the most highly remunerated code (patients with complex health issues). Interpretation These results can inform policy-makers when considering payment system changes. Our next step is to examine the association of high-volume practice with outcomes important to patients, such as evidence of treatment failure (emergency department visits and hospital admissions) for conditions sensitive to primary care management.

Background: Health care administrative databases are useful for assessing the population-level burden of disease and examining issues related to access, costs and quality of care. In these databases, the diagnoses and procedures are coded with the use of the World Health Organization International Classification of Diseases (ICD). We examined the validity of 2 ICD-10 coding definitions for categorizing patients with acute myocardial infarction (MI) as having ST-elevation MI (STEMI) or non–ST-elevation MI (non-STEMI). Methods: Charts of patients with acute MI discharged between April and June 2007 from 3 hospitals in Edmonton, were reviewed to define the acute MI subtype (i.e., STEMI v. non-STEMI). The agreement between clinician chart review and STEMI/non-STEMI classification based on the standard (ICD-10 I21.x) and the supplementary electrocardiogram (ECG) codes (R94.3x) was determined. We assessed the effect of these alternative definitions on in-hospital mortality estimates by applying them to the data for all patients with acute MI admitted to hospital in the province from April 2007 to March 2010. Results: Of the 297 patients, 49.2% were identified as having STEMI based on chart review, 44.4% using the standard definition, and 44.1% using the ECG definition. Both the standard and ECG definitions provided high agreement (92% for STEMI and 100% for non-STEMI) with the chart review classification. In the larger population-level cohort (n = 15 148), use of the standard definition or the ECG definition did not affect in-hospital mortality estimates for patients with STEMI and those with non-STEMI. Interpretation: The standard definition appears equivalent to the definition using supplementary ECG codes to subcategorize patients with acute MI as having STEMI or non-STEMI. These findings may be relevant for the development of later versions of ICD codes.

ObjectivesThe International Methodology Consortium for Coded Health Information (IMECCHI), an international collaboration of health services researchers, launched the IMECCHI-DATANETWORK initiative in October 2015. Its main objective is to enable replication of observational studies across countries through a distributed data infrastructure. ApproachIn a distributed data infrastructure, individual raw data are not shared. Instead, data are converted locally using a common data model (CDM) and loaded into a common software for data processing and analysis. Whenever a study protocol is agreed upon and ethically approved, an ad hoc procedure is programmed - using that software - including the data processing steps needed to create the analytical dataset from the CDM: record linkage, case selection, sampling, matching, etc. The procedure is then shared and locally run by each partner to generate an analytical dataset of integrated data. Analytical datasets may then be shared and pooled for statistical analyses. ResultsSix partners of the IMECCHI collaboration, located in countries across 4 continents (Canada, Denmark, Italy, New Zealand, South Korea, and Switzerland), currently participate in the initiative. They first conducted a survey to describe the origin, content, completeness and main attributes of each table in their original databases. Based on the results of the survey, a CDM was created, encompassing 4 tables of coded or structured data to be linked at the individual level using a common personal identifier: (1) characteristics of the subjects with dates of birth and death; (2) hospital discharge summaries with diagnosis and procedure codes, and admission, discharge and procedure dates; (3) drug dispensing information with date of dispensing, drug name, duration of the amount of active principle according to the Defined Daily Dose of the World Health Organization; (4) causes of death. In each table, additional attributes describe the coding systems in which the other attributes are coded. Using such specific attributes facilitates interoperability across multiple coding systems. The open source Java-based software, TheMatrix, which operates on flat csv files using a domain-specific programming language, was chosen to embed the ad hoc procedure. ConclusionWithin the IMECCHI-DATANETWORK initiative, databases from various countries will be locally converted in a CDM which will facilitate study replication in a distributed fashion while granting interoperability across coding systems. Through such international data networks, data are empowered for creating results which are generalizable to multiple countries. Cross-border data sharing and international comparisons are also facilitated.

Background Although the nature and frequency of postoperative complications after intestinal resection in patients with inflammatory bowel disease have been previously described, short-term readmission has not been characterized in population-based studies. We therefore assessed the risk of postoperative complications and emergent readmissions after discharge from an intestinal resection. Methods We used a Canadian provincial-wide inpatient hospitalization database to identify 2638 Crohn's disease (CD) and 559 ulcerative colitis (UC) admissions with intestinal resection from 2002 to 2011. We identified the cumulative risk of in-hospital complication and emergent readmission within 90 days after discharge along with predictors for both outcomes using a Poisson regression for binary outcomes. Results The cumulative risks of in-hospital postoperative complications and 90-day emergent readmission were 23.8% and 12.6%, respectively in CD and 33.3% and 11.1%, respectively in UC. The predictors for in-hospital postoperative complications for CD and UC included older age, comorbidities, and open laparatomy for CD, additional predictors included emergent admission, stoma surgery, and concurrent resection of both small and large bowel. The predictors for 90-day readmission for CD included a postoperative complication (risk ratio, 1.61; 95% confidence interval, 1.30-2.01), emergent admission (risk ratio, 1.39; 95% confidence interval, 1.12-1.73), and stoma formation (risk ratio, 1.49; 95% confidence interval, 1.15-1.93) at the hospitalization requiring surgery. Conclusions Readmission and postoperative complications are common after intestinal resection in CD and UC. Clinicians should closely monitor surgical patients who required emergent admission, undergo surgery with stoma formation, or develop in-hospital postoperative complications to anticipate need for readmission or interventions to prevent readmission.

Abstract: Evaluation designs that can capture the complexity of health promotion (HP) interventions are needed. Our objective was to assess if such evaluations use a Complex Adaptive Systems (CAS) perspective, by using a scoping review of evaluations of HP interventions concerning alcohol and tobacco use in the peer-reviewed (PR) and grey literature (GL). We developed indicator questions to assess CAS aspects. Our study revealed that none of the 45 PR and 9 GL evaluations that we reviewed explicitly used a CAS perspective; however, most indirectly assessed complexity aspects. Our indicator questions are a step toward addressing the challenges of the practical application of a CAS perspective.

Walking to school is a potentially healthy activity, yet it may expose school children to potentially unhealthy behaviors. The presence of fast-food restaurants within school walksheds may promote unhealthy eating behaviors. Children who walk to school may be more exposed to this risk than those who commute in a vehicle (school bus or family vehicle). Fast-food restaurants provide ample opportunities for adults as well as for children to consume less expensive but unhealthy and energy dense food. Easy access to fast-food restaurants may be one of the many factors determining how walkable a neighborhood is but the location of fast food restaurants in the walkshed of schools may contribute to the rise in obesity in children. The Calgary Board of Education (CBE) has designated walk zones (1.2 km for elementary and 2.4 km for junior high age group students) where school transportation service is not provided. We focus on elementary students only. School addresses were obtained from the City of Calgary’s Open Data. After geocoding the addresses, we perform spatial analysis to define 1.2 km walkshed area around elementary schools and determine the location and number of fast-food restaurants within and outside of this walkable distance. Out of 495 fast-food places, 423 are located within 1.2 km from school. We compared the children (4.5 to 7 years old) overweight and obesity rates living inside and outside of the walk zone for each residential community of the City of Calgary and found the spatial variation across the city. Further investigation is needed to quantify the impact of fast-food outlet/restaurants inside the walk zone of elementary schools in Calgary.

Objective The purpose of this study was to assess the relationship between diabetic ketoacidosis (DKA) hospitalization and driving distance from home to outpatient diabetes care in adults with type 1 diabetes mellitus. Methods We identified adults with type 1 diabetes using clinical and administrative databases living in Calgary, Alberta. The International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, codes were used to identify DKA hospitalizations, and geographic information systems were used to obtain road distance. Multivariate logistic regression was used to assess the association between driving distance (exposure) to diabetes care sites and the outcome of DKA hospitalization. Results We identified 1467 patients (151 patients with DKA) with type 1 diabetes. Patients with DKA hospitalizations were younger (35.6 vs. 41.0 years), had shorter duration of diabetes (13.6 vs. 18.7 years) and higher glycated hemoglobin (9.2% vs. 8.4%). Driving distance from home to diabetes centre 1 (adjusted odds ratio 1.02 per 1 km; 95% confidence interval, 0.96 to 1.07), diabetes centre 2 (adjusted odds ratio 1.01; 95% confidence interval, 0.99 to 1.04) or closest general practitioner (adjusted odds ratio 0.9; 95% confidence interval, 0.63 to 1.25) was not associated with DKA hospitalization. Driving distance was also not associated with glycemic control. Conclusions Within a large urban city, driving distance to diabetes centres does not appear to be protective of DKA hospitalization. However, this work does not preclude the role of local travel distance and diabetes outcomes. More research is required to explore the role of other individual, neighbourhood and community factors that influence DKA hospitalization.

Background There are few studies on the relationship between the severity of bicycle-related injury in children and characteristics of the built environment. Methods Emergency Medical Services (EMS) 9–1–1 calls completed between 2010–2013 in Calgary and Edmonton, Alberta, Canada for bicycle injury events in children Results Among all paediatric EMS calls that were linked to a health care database, 575 involved bicycle injuries. To date, we have completed 157 location audits using the PEDS instrument with Google Maps. Analysis of audits completed to date indicates 83% of the subjects were male. Almost half (45%) of all injuries occurred on road segments with intersections. Two-thirds of injuries occurred at locations with a lack of street crossing aids. Conclusions We have made progress toward profiling the individual and environmental characteristics of bicycle injuries. Next steps involve completing this process for the remaining injury locations. Injuries requiring hospital admission (i.e., severe injuries) will be compared with the ones that did not (i.e., non-severe injuries) for personal (e.g., age, sex) and location (e.g., presence of intersection) variables.

The connection between public health and geography can be traced back to Hippocrates (c. 400 BC) who deduced that spatially varying factors such as climate, elevation, environmental toxins, ethnicity and race contributed to the spatial patterns of illness (Parchman et al., 2002). The observations of Hippocrates still hold true today and these relationships between geography and disease have allowed geospatial methods to become valuable within the field of public health. Maps have long been a useful tool for visualizing patterns in health care. One of the earliest and most commonly cited examples is from the mid 1800s when Dr. John Snow deduced the source of a cholera outbreak in London based on a simple visualization of the incidents of cholera in relation to water pumps (Johnson, 2007). Although visualizing data geographically is still very valuable for uncovering patterns and associations over space, geospatial analysis has become more sophisticated over time.

Background In order to reduce the delays encountered through patient transfer, regional care models have been developed that directly transport subsets of acute myocardial infarction (AMI) patients to hospitals with percutaneous coronary intervention (PCI) facilities. Calgary is a Canadian city that implemented this type of model in 2004. Methods The study population included 9768 AMI patients admitted to Calgary hospitals between 1997 and 2007. Administrative data were used to define patients who were directly admitted to the PCI hospital and those transferred there after initial admission to a hospital without specialized cardiac care. The differences in clinical characteristics and mortality trends of patients grouped by hospital delivery site and transfer practice are described. Results The proportion of patients directly admitted to a PCI hospital has increased with the implementation of a regional care model. Among patients admitted to non-PCI facilities, the patients who are transferred are younger, more likely to be male, have a shorter length of stay, and have lower proportions of several comorbid conditions. The risk-adjusted in-hospital mortality odds ratio for patients who received care at the PCI hospital postmodel relative to those treated at non-PCI hospitals premodel was 0.38 (95% confidence interval, 0.31-0.47). The corresponding adjusted odds ratio was 0.60 (0.47-0.76). Conclusions Our results suggest changing care over time and trends toward improved outcomes. Patients' clinical characteristics appear to play a major role in the decision to transfer. Avoidance of the risk treatment paradox through refinement of regional transfer protocols ought to be a priority.

Background Primary percutaneous coronary intervention (PCI) is preferred over fibrinolysis for the treatment of ST-segment elevation myocardial infarction (STEMI). In the United States, nearly 80% of people aged 18 years and older have access to a PCI facility within 60 minutes. We conducted this study to evaluate the areas in Canada and the proportion of the population aged 40 years and older with access to a PCI facility within 60, 90 and 120 minutes. Methods We used geographic information systems to estimate travel times by ground transport to PCI facilities across Canada. Time to dispatch, time to patient and time at the scene were considered in the overall access times. Using 2006 Canadian census data, we extracted the number of adults aged 40 years and older who lived in areas with access to a PCI facility within 60, 90 and 120 minutes. We also examined the effect on these estimates of the hypothetical addition of new PCI facilities in underserved areas. Results Only a small proportion of the country’s geographic area was within 60 minutes of a PCI facility. Despite this, 63.9% of Canadians aged 40 and older had such access. This proportion varied widely across provinces, from a low of 15.8% in New Brunswick to a high of 72.6% in Ontario. The hypothetical addition of a single facility to each of 4 selected provinces could increase the proportion by 3.2% to 4.3%, depending on the province. About 470 000 adults would gain access in such a scenario of new facilities. Interpretation We found that nearly two-thirds of Canada’s population aged 40 years and older had timely access to PCI facilities. The proportion varied widely across the country. Such information can inform the development of regionalized STEMI care models.