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Source publication
Objective:
This study examines the association between community-level marginalization and emergency room (ER) wait time in Ontario.
Methods:
Data sources included ER wait time data and Ontario Marginalization Index scores. Linear regression models were used to quantify the association.
Results:
A positive association between total marginaliza...
Contexts in source publication
Context 1
... ER wait time data were available at the hospital level and marginalization data were available at the CSD level, our analysis had to account for the geographical misalignment of these data. To do so, we first geocoded the street addresses of the 166 Ontario ERs ( Figure 1a) to the geographical coordinates (latitudes and longitudes). Second, we created a set of Thiessen polygons (Kopec 1963) to define the service area for each of the ERs (Figure 1b). ...
Context 2
... do so, we first geocoded the street addresses of the 166 Ontario ERs ( Figure 1a) to the geographical coordinates (latitudes and longitudes). Second, we created a set of Thiessen polygons (Kopec 1963) to define the service area for each of the ERs (Figure 1b). Thiessen polygons take a set of input points (i.e., hospital locations) and construct one polygon around each input point such that any location within the polygon is closest to only its input point and not any other input point in the data set (Brassel and Reif 1979). ...
Citations
... Few health-system issues command as much public attention as Emergency Department (ED) crowding, a highly visible source of prolonged suffering and risk to patients (McDonald et al., 2020). The problem is particularly acute in Canada, which persistently shows the highest ED utilization rates and longest waits among similar countries (Canadian Institute for Health Information, 2021). ...
Background: Satire shows significant potential to engage audiences and effectively translate knowledge on politically-charged issues, yet applications to health-policy research are rare.
Methods: We present the qualitative component of a mixed-methods study evaluating the reception of a research-based digital musical satirizing health-system dysfunction. Following five university-based screenings of Larry Saves the Canadian Healthcare System, 16 students participated in semi-structured interviews to discuss their perceptions and interpretations.
Results: Four themes were identified via thematic analysis: Larry as a health-system primer, engagement through aesthetic appreciation, resonance through connections to personal experience, and hope for the future. Participants overwhelmingly appreciated the satirical approach, and described how it buttressed the show’s educational aims. Some reported learning new information, many indicated that the show reinforced existing knowledge, and most recommended it as a knowledge-translation tool for the public.
Conclusions: Findings open the door to more widespread use of satire as a genre of arts-based knowledge translation.
... Hospital admission Next, we test PMC alongside other methods in application to prediction of inpatient hospital admission for patients visiting the emergency department (ED). The burden of overcrowding and long wait times in EDs is significantly higher among non-white, non-Hispanic patients and socio-economically marginalized patients [32,42]. Recent work has demonstrated risk prediction models that can expedite patient visits by predicting patient admission at an early stage of a visit with a high degree of certainty (AUC ≥ 0.9 across three large care centers) [4,3,5,6]. ...
Multicalibration is a desirable fairness criteria that constrains calibration error among flexibly-defined groups in the data while maintaining overall calibration. However, when outcome probabilities are correlated with group membership, multicalibrated models can exhibit a higher percent calibration error among groups with lower base rates than groups with higher base rates. As a result, it remains possible for a decision-maker to learn to trust or distrust model predictions for specific groups. To alleviate this, we propose proportional multicalibration, a criteria that constrains the percent calibration error among groups and within prediction bins. We prove that satisfying proportional multicalibration bounds a model's multicalibration as well its differential calibration, a stronger fairness criteria inspired by the fairness notion of sufficiency. We provide an efficient algorithm for post-processing risk prediction models for proportional multicalibration and evaluate it empirically. We conduct simulation studies and investigate a real-world application of PMC-postprocessing to prediction of emergency department patient admissions. We observe that proportional multicalibration is a promising criteria for controlling simultenous measures of calibration fairness of a model over intersectional groups with virtually no cost in terms of classification performance.
The aim of this study was to describe the impact of marginalization on DLBCL overall survival (OS) within the Canadian setting. We conducted a population-based retrospective cohort study of adult patients with newly diagnosed DLBCL in Ontario between 1 January 2005 and 31 December 2017 receiving a rituximab-containing chemotherapy regimen with curative intent followed until 1 March 2020. Our primary exposure of interest was the Ontario Marginalization Index (ON-Marg). The primary outcome was 2-year OS, accounting for patient age, sex, cancer characteristics, comorbidity burden, and rural dwelling status. While two-year overall survival was inferior for individuals in the most deprived marginalization quintile (70.4% Q5 vs. 76.0% Q1), after adjustment for relevant covariates neither the composite ON-Marg nor any of its dimensions had a significant effect. Within the Canadian context, among patients who receive chemotherapy, marginalization may not have a significant association with overall survival when accounting for key patient covariates, lending support for preserved outcomes.
To determine the association between neighborhood marginalization and rates of pediatric ED visits in Ottawa, Ontario. Secondary objectives investigated if the association between neighborhood marginalization and rates varied by year, acuity, and distance to hospital.
We calculated rates of pediatric ED visits per 1000 person-years for census dissemination areas within 100 km of the Children’s Hospital of Eastern Ontario for patients < 18 years old from January 2018 through December 2020. The 2016 Ontario Marginalization Index categorized neighborhoods along quintiles of residential instability, material deprivation, ethnic concentration, and dependency. Generalized mixed-effects models determined the incidence rate ratios of pediatric ED visits for each quintile of marginalization; multivariate models were used to control for year of presentation and distance to hospital. Analysis was repeated for low versus high acuity ED visits.
There were 154,146 ED visits from patients in 2055 census dissemination areas within 100 km of CHEO from 2018 to 2020. After controlling for year and distance from hospital in multivariate analyses, there were higher rates of pediatric ED visits for dissemination areas with high residential instability, high material deprivation, and low ethnic concentration. These findings did not change according to visit acuity.
Neighborhood residential instability and material deprivation should be considered when locating alternatives to emergency care.
Fair calibration is a widely desirable fairness criteria in risk prediction contexts. One way to measure and achieve fair calibration is with multicalibration. Multicalibration constrains calibration error among flexibly-defined subpopulations while maintaining overall calibration. However, multicalibrated models can exhibit a higher percent calibration error among groups with lower base rates than groups with higher base rates. As a result, it is possible for a decision-maker to learn to trust or distrust model predictions for specific groups. To alleviate this, we propose proportional multicalibration, a criteria that constrains the percent calibration error among groups and within prediction bins. We prove that satisfying proportional multicalibration bounds a model's multicalibration as well its differential calibration, a fairness criteria that directly measures how closely a model approximates sufficiency. Therefore, proportionally calibrated models limit the ability of decision makers to distinguish between model performance on different patient groups, which may make the models more trustworthy in practice. We provide an efficient algorithm for post-processing risk prediction models for proportional multicalibration and evaluate it empirically. We conduct simulation studies and investigate a real-world application of PMC-postprocessing to prediction of emergency department patient admissions. We observe that proportional multicalibration is a promising criteria for controlling simultaneous measures of calibration fairness of a model over intersectional groups with virtually no cost in terms of classification performance.
In its conception, Healthcare Policy was a partnership between the canadian Institutes of Health Research's Institute of Health Services and Policy Research (IHSPR), the Canadian Association of Health Services and Policy Research and Longwoods Publishing. With the support of IHSPR's scientific director at the time, Dr. Morris Barer, the objective of the journal was to "stimulate communication and cross-fertilization between researchers and healthcare decision makers" (Government of Canada 2006). With a strong focus on knowledge translation and interdisciplinary research, the journal links policy makers with researchers, thus carrying its founding objective forward as a guiding principle for Healthcare Policy.
