Geographical Variation In Medication and Health Resource Use In Multiple Sclerosis.

BACKGROUND: Understanding disease-modifying therapy (DMT) use and healthcare resource utilization by different geographical areas among people living with multiple sclerosis (pwMS) may identify care gaps that can be used to inform policies and practice to ensure equitable care. METHODS: Administrative data was used to identify pwMS on April 1, 2017 (index date) in Alberta. DMT use and healthcare resource utilization were compared between those who resided in various geographical areas over a 2-year post-index period; simple logistic regression was applied. RESULTS: Among the cohort (n = 12,338), a higher proportion of pwMS who resided in urban areas (versus rural) received ≥ 1 DMT dispensation (32.3% versus 27.4%), had a neurologist (67.7% versus 63.9%), non-neurologist specialist (88.3% versus 82.9%), ambulatory care visit (87.4% versus 85.3%), and MS tertiary clinic visit (59.2% versus 51.7%), and a lower proportion had an emergency department (ED) visit (46.3% versus 62.4%), and hospitalization (20.4% versus 23.0%). Across the provincial health zones, there were variations in DMT selection, and a higher proportion of pwMS who resided in the Calgary health zone, where care is managed by MS tertiary clinic neurologists, had an outpatient visit to a neurologist or MS tertiary clinic versus those who resided in other zones where delivery of MS-related care is more varied. CONCLUSIONS: Urban/rural inequalities in DMT use and healthcare resource utilization appear to exist among pwMS in Alberta. Findings suggest the exploration of barriers with consequent strategies to increase access to DMTs and provide timely outpatient MS care management, particularly for those pwMS residing in rural areas.

Food marketing to teenagers: Examining the power and platforms of food and beverage marketing in Canada.

Marketing pressure on teenagers when it comes to promoting unhealthy foods and food brands is a significant public health concern. Teenagers are aggressively targeted by food marketing messages, yet a research gap exists when it comes to the engagement by teens with this marketing in real world settings, and specific techniques (or power) used to capture their attention. This exploratory study engages in participatory research to explore the persuasive power and platforms of exposure of teen-targeted food marketing. Using an innovative smartphone app called "GrabFM!" ("Grab Food Marketing!"), teens ages 13-17 (n = 62) identified and tagged examples (n = 339) of targeted food marketing (from mainstream and digital media, and the built environment) over a 7-day period, providing information on the food brand, product, platform, and indicators (i.e., persuasive techniques). Results revealed the top brand (FritoLay, 8.3%), food product category (candy/chocolate, 23.3%), platform of exposure (Instagram, 76.4%), and indicator (visual style, 52.5%) identified by teens. Insights were also gained into the intersection of gender and platform, gender, age and indicators (older teens 15+ more likely to report multiple indicators per ad), and co-occurrence of indicators (majority of ads tagged with one indicator only). The results of this study provide guidance on the power, platforms and brands that teens felt uniquely spoke to them. When it comes to monitoring efforts, it is useful to know that Instagram commands teenagers' attention and that marketing power resides in particular indicators (visual style, special offer, theme), which teenagers appear to readily and consistently identify.

Strategies for working across Canadian practice-based research and learning networks (PBRLNs) in primary care: focus on frailty.

BACKGROUND: Practice based research and learning networks (PBRLNs) are groups of learning communities that focus on improving delivery and quality of care. Accurate data from primary care electronic medical records (EMRs) is crucial in forming the backbone for PBRLNs. The purpose of this work is to: (1) report on descriptive findings from recent frailty work, (2) describe strategies for working across PBRLNs in primary care, and (3) provide lessons learned for engaging PBRLNs. METHODS: We carried out a participatory based descriptive study that engaged five different PBRLNs. We collected Clinical Frailty Scale scores from a sample of participating physicians within each PBRLN. Descriptive statistics were used to analyze frailty scores and patients' associated risk factors and demographics. We used the Consolidated Framework for Implementation Research to inform thematic analysis of qualitative data (meeting minutes, notes, and conversations with co-investigators of each network) in recognizing challenges of working across networks. RESULTS: One hundred nine physicians participated in collecting CFS scores across the five provinces (n = 5466). Percentages of frail (11-17%) and not frail (82-91%) patients were similar in all networks, except Ontario who had a higher percentage of frail patients (25%). The majority of frail patients were female (65%) and had a significantly higher prevalence of hypertension, dementia, and depression. Frail patients had more prescribed medications and numbers of healthcare encounters. There were several noteworthy challenges experienced throughout the research process related to differences across provinces in the areas of: numbers of stakeholders/staff involved and thus levels of burden, recruitment strategies, data collection strategies, enhancing engagement, and timelines. DISCUSSION: Lessons learned throughout this multi-jurisdictional work included: the need for continuity in ethics, regular team meetings, enhancing levels of engagement with stakeholders, the need for structural support and recognizing differences in data sharing across provinces. CONCLUSION: The differences noted across CPCSSN networks in our frailty study highlight the challenges of multi-jurisdictional work across provinces and the need for consistent and collaborative healthcare planning efforts.

Disease-modifying therapy use and health resource utilisation associated with multiple sclerosis over time: A retrospective cohort study from Alberta, Canada.

BACKGROUND: Estimating multiple sclerosis (MS) prevalence and incidence, and assessing the utilisation of disease-modifying therapies (DMTs) and healthcare resources over time is critical to understanding the evolution of disease burden and impacts of therapies upon the healthcare system. METHODS: A retrospective population-based study was used to determine MS prevalence and incidence (2003-2019), and describe utilisation of DMTs (2009-2019) and healthcare resources (1998-2019) among people living with MS (pwMS) using administrative data in Alberta. RESULTS: Prevalence increased from 259 (95% confidence interval [CI]: 253-265) to 310 (95% CI: 304, 315) cases per 100,000 population, and incidence decreased from 21.2 (95% CI: 19.6-22.8) to 12.7 (95% CI: 11.7-13.8) cases per 100,000 population. The proportion of pwMS who received ≥1 DMT dispensation increased (24% to 31% annually); use of older platform injection therapies decreased, and newer oral-based, induction, and highly-effective therapies increased. The proportion of pwMS who had at least one MS-related physician, ambulatory, or tertiary clinic visits increased, and emergency department visits and hospitalizations decreased. CONCLUSIONS: Alberta has one of the highest rates of MS globally. The proportion of pwMS who received DMTs and had outpatient visits increased, while acute care visits decreased over time. The landscape of MS care appears to be rapidly evolving in response to changes in disease burden and new highly-effective therapies.

Machine learning for identification of frailty in Canadian primary care practices.

INTRODUCTION: Frailty is a medical syndrome, commonly affecting people aged 65 years and over and is characterized by a greater risk of adverse outcomes following illness or injury. Electronic medical records contain a large amount of longitudinal data that can be used for primary care research. Machine learning can fully utilize this wide breadth of data for the detection of diseases and syndromes. The creation of a frailty case definition using machine learning may facilitate early intervention, inform advanced screening tests, and allow for surveillance. OBJECTIVES: The objective of this study was to develop a validated case definition of frailty for the primary care context, using machine learning. METHODS: Physicians participating in the Canadian Primary Care Sentinel Surveillance Network across Canada were asked to retrospectively identify the level of frailty present in a sample of their own patients (total n = 5,466), collected from 2015-2019. Frailty levels were dichotomized using a cut-off of 5. Extracted features included previously prescribed medications, billing codes, and other routinely collected primary care data. We used eight supervised machine learning algorithms, with performance assessed using a hold-out test set. A balanced training dataset was also created by oversampling. Sensitivity analyses considered two alternative dichotomization cut-offs. Model performance was evaluated using area under the receiver-operating characteristic curve, F1, accuracy, sensitivity, specificity, negative predictive value and positive predictive value. RESULTS: The prevalence of frailty within our sample was 18.4%. Of the eight models developed to identify frail patients, an XGBoost model achieved the highest sensitivity (78.14%) and specificity (74.41%). The balanced training dataset did not improve classification performance. Sensitivity analyses did not show improved performance for cut-offs other than 5. CONCLUSION: Supervised machine learning was able to create well performing classification models for frailty. Future research is needed to assess frailty inter-rater reliability, and link multiple data sources for frailty identification.

Developing and Validating a Primary Care EMR-based Frailty Definition using Machine Learning.

INTRODUCTION: Individuals who have been identified as frail have an increased state of vulnerability, often leading to adverse health events, increased health spending, and potentially detrimental outcomes. OBJECTIVE: The objective of this work is to develop and validate a case definition for frailty that can be used in a primary care electronic medical record database. METHODS: This is a cross-sectional validation study using data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) in Southern Alberta. 52 CPCSSN sentinels assessed a random sample of their own patients using the Rockwood Clinical Frailty scale, resulting in a total of 875 patients to be used as reference standard. Patients must be over the age of 65 and have had a clinic visit within the last 24 months. The case definition for frailty was developed using machine learning methods using CPCSSN records for the 875 patients. RESULTS: Of the 875 patients, 155 (17.7%) were frail and 720 (84.2%) were not frail. Validation metrics of the case definition were: sensitivity and specificity of 0.28, 95% CI (0.21 to 0.36) and 0.94, 95% CI (0.93 to 0.96), respectively; PPV and NPV of 0.53, 95% CI (0.42 to 0.64) and 0.86, 95% CI (0.83 to 0.88), respectively. CONCLUSIONS: The low sensitivity and specificity results could be because frailty as a construct remains under-developed and relatively poorly understood due to its complex nature. These results contribute to the literature by demonstrating that case definitions for frailty require expert consensus and potentially more sophisticated algorithms to be successful.