Do expenditures on public health reduce preventable mortality in the long run? Evidence from the Canadian provinces.

BACKGROUND: Investments in public health - prevention of illnesses, and promotion, surveillance, and protection of population health - may improve population health, however, effects may only be observed over a long period of time. OBJECTIVE: To investigate the potential long-run relationship between expenditures on public health and avoidable mortality from preventable causes. METHODS: We focused on the country spending the most on public health in the OECD, Canada. We constructed a longitudinal dataset on mortality, health care expenditures and socio-demographic information covering years 1979-2017 for the ten Canadian provinces. We estimated error correction models for panel data to disentangle short-from long-run relationships between expenditures on public health and avoidable mortality from preventable causes. We further explored some specific causes of mortality to understand potential drivers. For comparison, we also estimated the short-run relationship between curative expenditures and avoidable mortality from treatable causes. RESULTS: We find evidence of a long-run relationship between expenditures on public health and preventable mortality, and no consistent short-run associations between these two variables. Findings suggest that a 1% increase in expenditures on public health could lead to 0.22% decrease in preventable mortality. Reductions in preventable mortality are greater for males (-0.29%) compared to females (-0.09%). These results are robust to different specifications. Reductions in some cancer and cardiovascular deaths are among the probable drivers of this overall decrease. By contrast, we do not find evidence of a consistent short-run relationship between curative expenditures and treatable mortality, except for males. CONCLUSION: This study supports the argument that expenditures on public health reap health benefits primarily in the long run, which, in this case, represents a reduction in avoidable mortality from preventable causes. Reducing public health expenditures on the premise that they have no immediate measurable benefits might thus harm population health outcomes in the long run.

Risk assessment of diabetic retinopathy with machine and deep learning models with PPG signals and PWV.

Retinopathy is one of the most common micro vascular impairments in diabetic subjects. Elevated blood glucose leads to capillary occlusion, provoking the uncontrolled increase in local growth of new vessels in the retina. When left untreated, it can lead to blindness. Traditional approaches for retinopathy detection require expensive devices and high specialized personnel. Being a microvascular complication, the retinopathy could be detected using the photoplethysmography (PPG) technology. In this paper we investigate the predictive value of the pulse wave velocity and PPG signal analysis with machine and deep learning approaches to detect retinopathy in diabetic subjects. PPG signals and pulse wave velocity (PWV) showed promising results in assessing the diabetic retinopathy. The best performances were scored by a LightGBM based model trained over a subset of the available dataset obtaining 80% of specificity and sensitivity.Clinical relevance- PPG based retinopathy detection could make the retinopathy detection more accessible since it does not need neither expensive devices for signal acquisition nor highly specialized personnel to be interpreted.

CNN-Based Self-Attention Weight Extraction for Fall Event Prediction Using Balance Test Score.

Injury, hospitalization, and even death are common consequences of falling for elderly people. Therefore, early and robust identification of people at risk of recurrent falling is crucial from a preventive point of view. This study aims to evaluate the effectiveness of an interpretable semi-supervised approach in identifying individuals at risk of falls by using the data provided by ankle-mounted IMU sensors. Our method benefits from the cause-effect link between a fall event and balance ability to pinpoint the moments with the highest fall probability. This framework also has the advantage of training on unlabeled data, and one can exploit its interpretation capacities to detect the target while only using patient metadata, especially those in relation to balance characteristics. This study shows that a visual-based self-attention model is able to infer the relationship between a fall event and loss of balance by attributing high values of weight to moments where the vertical acceleration component of the IMU sensors exceeds 5 m/s² during an especially short period. This semi-supervised approach uses interpretable features to highlight the moments of the recording that may explain the score of balance, thus revealing the moments with the highest risk of falling. Our model allows for the detection of 71% of the possible falling risk events in a window of 1 s (500 ms before and after the target) when compared with threshold-based approaches. This type of framework plays a paramount role in reducing the costs of annotation in the case of fall prevention when using wearable devices. Overall, this adaptive tool can provide valuable data to healthcare professionals, and it can assist them in enhancing fall prevention efforts on a larger scale with lower costs.

Clustered photoplethysmogram pulse wave shapes and their associations with clinical data.

Photopletysmography (PPG) is a non-invasive and well known technology that enables the recording of the digital volume pulse (DVP). Although PPG is largely employed in research, several aspects remain unknown. One of these is represented by the lack of information about how many waveform classes best express the variability in shape. In the literature, it is common to classify DVPs into four classes based on the dicrotic notch position. However, when working with real data, labelling waveforms with one of these four classes is no longer straightforward and may be challenging. The correct identification of the DVP shape could enhance the precision and the reliability of the extracted bio markers. In this work we proposed unsupervised machine learning and deep learning approaches to overcome the data labelling limitations. Concretely we performed a K-medoids based clustering that takes as input 1) DVP handcrafted features, 2) similarity matrix computed with the Derivative Dynamic Time Warping and 3) DVP features extracted from a CNN AutoEncoder. All the cited methods have been tested first by imposing four medoids representative of the Dawber classes, and after by automatically searching four clusters. We then searched the optimal number of clusters for each method using silhouette score, the prediction strength and inertia. To validate the proposed approaches we analyse the dissimilarities in the clinical data related to obtained clusters.

Does doctors' personality differ from those of patients, the highly educated and other caring professions? An observational study using two nationally representative Australian surveys.

OBJECTIVES: Personality differences between doctors and patients can affect treatment outcomes. We examine these trait disparities, as well as differences across medical specialities. DESIGN: Retrospective, observational statistical analysis of secondary data. SETTING: Data from two data sets that are nationally representative of doctors and the general population in Australia. PARTICIPANTS: We include 23 358 individuals from a representative survey of the general Australian population (with subgroups of 18 705 patients, 1261 highly educated individuals and 5814 working in caring professions) as well as 19 351 doctors from a representative survey of doctors in Australia (with subgroups of 5844 general practitioners, 1776 person-oriented specialists and 3245 technique-oriented specialists). MAIN OUTCOME MEASURES: Big Five personality traits and locus of control. Measures are standardised by gender, age and being born overseas and weighted to be representative of their population. RESULTS: Doctors are significantly more agreeable (a: standardised score -0.12, 95% CIs -0.18 to -0.06), conscientious (c: -0.27 to -0.33 to -0.20), extroverted (e: 0.11, 0.04 to 0.17) and neurotic (n: 0.14, CI 0.08 to 0.20) than the general population (a: -0.38 to -0.42 to -0.34, c: -0.96 to -1.00 to -0.91, e: -0.22 to -0.26 to -0.19, n: -1.01 to -1.03 to -0.98) or patients (a: -0.77 to -0.85 to -0.69, c: -1.27 to -1.36 to -1.19, e: -0.24 to -0.31 to -0.18, n: -0.71 to -0.76 to -0.66). Patients (-0.03 to -0.10 to 0.05) are more open than doctors (-0.30 to -0.36 to -0.23). Doctors have a significantly more external locus of control (0.06, 0.00 to 0.13) than the general population (-0.10 to -0.13 to -0.06) but do not differ from patients (-0.04 to -0.11 to 0.03). There are minor differences in personality traits among doctors with different specialities. CONCLUSIONS: Several personality traits differ between doctors, the population and patients. Awareness about differences can improve doctor-patient communication and allow patients to understand and comply with treatment recommendations.

Prioritization of public health financing, organization, and workforce transformation: a Delphi study in Canada.

BACKGROUND: The increased scrutiny on public health brought upon by the ongoing COVID-19 pandemic provides a strong impetus for a renewal of public health systems. This paper seeks to understand priorities of public health decision-makers for reforms to public health financing, organization, interventions, and workforce. METHODS: We used an online 3-round real-time Delphi method of reaching consensus on priorities for public health systems reform. Participants were recruited among individuals holding senior roles in Canadian public health institutions, ministries of health and regional health authorities. In Round 1, participants were asked to rate 9 propositions related to public health financing, organization, workforce, and interventions. Participants were also asked to contribute up to three further ideas in relation to these topics in open-ended format. In Rounds 2 and 3, participants re-appraised their ratings in the view of the group's ratings in the previous round. RESULTS: Eighty-six public health senior decision-makers from various public health organizations across Canada were invited to participate. Of these, 25/86 completed Round 1 (29% response rate), 19/25 completed Round 2 (76% retention rate) and 18/19 completed Round 3 (95% retention rate). Consensus (defined as more than 70% of importance rating) was achieved for 6 out of 9 propositions at the end of the third round. In only one case, the consensus was that the proposition was not important. Proposition rated consensually important relate to targeted public health budget, time frame for spending this budget, and the specialization of public health structures. Both interventions related and not related to the COVID-19 pandemic were judged important. Open-ended comments further highlighted priorities for renewal in public health governance and public health information management systems. CONCLUSION: Consensus emerged rapidly among Canadian public health decision-makers on prioritizing public health budget and time frame for spending. Ensuring that public health services beyond COVID-19 and communicable disease are maintained and enhanced is also of central importance. Future research shall explore potential trade-offs between these priorities.

The political and fiscal determinants of public health and curative care expenditures: evidence from the Canadian provinces, 1980-2018.

OBJECTIVES: Public health systems have been centre stage during the COVID-19 pandemic, but governments invest relatively little in public health as compared to curative care. Previous research has shown that public health expenditures are under pressure during recessions and could be politically determined, but very few studies analyze quantitatively their determinants. This study investigates the political and fiscal determinants of public health and curative care expenditures. METHODS: After constructing a dataset building on disaggregated health expenditures in the Canadian provinces from 1975 to 2018, we use error correction models to study the short-run and long-run influence of fiscal and political determinants on public health expenditures and on curative expenditures. Fiscal determinants include measures of public debt charges and federal transfers. Political determinants include government partisanship and election cycles. We also explore whether curative expenditures crowd out public health expenditures. RESULTS: We find no difference between left and right governments in curative care expenditures but show that left governments spend more on public health if we control for past spending decisions in favour of curative care. Fiscal austerity reduces both public health and curative expenditures, and provincial governments use additional intergovernmental transfers to increase their curative care budgets. A growth in the proportion of curative care relative to total health budgets is associated with a decline in public health expenditures. CONCLUSION: Even though they have low political salience, public health expenditures remain driven by partisanship and electoral concerns. Despite their widely acknowledged importance, public health programs develop in the shadow of curative care priorities.

RéSUMé: OBJECTIFS: Bien que les systèmes de santé publique aient occupé le devant de la scène pendant la pandémie de COVID-19, les gouvernements investissent relativement peu dans la santé publique par rapport aux soins de santé curatifs. Des recherches antérieures ont montré que les dépenses de santé publique sont vulnérables aux récessions économiques et pourraient être influencées par la politique, mais très peu d'études analysent quantitativement les déterminants des dépenses de santé publique. Cette étude examine les déterminants politiques et fiscaux des dépenses de santé publique et de soins curatifs. MéTHODES: Nous avons assemblé une base de données regroupant les dépenses de santé désagrégées dans les provinces canadiennes de 1975 à 2018. Nous utilisons des modèles de correction d'erreurs pour étudier l'influence à court et long terme des déterminants fiscaux et politiques des dépenses de santé publique et des dépenses de santé curatives. Les déterminants fiscaux comprennent des mesures des intérêts sur la dette publique et des transferts fédéraux. Les déterminants politiques comprennent l'idéologie du gouvernement et les cycles électoraux. Nous examinons également si la croissance des dépenses curatives entraîne un effet d'éviction sur les dépenses de santé publique. RéSULTATS: Nous ne trouvons aucune différence entre les dépenses en soins curatifs effectuées par les gouvernements de gauche et de droite, mais nous montrons que les gouvernements de gauche dépensent plus en santé publique si nous contrôlons pour les décisions passées en faveur des soins curatifs. L'austérité fiscale réduit à la fois les dépenses de santé publique et les dépenses en soins curatifs, et les gouvernements provinciaux utilisent les transferts intergouvernementaux supplémentaires pour augmenter leurs budgets de soins curatifs. Une augmentation de la proportion des budgets de santé alloués aux soins curatifs est associée à une baisse des dépenses de santé publique. CONCLUSION: Même si elles ont une faible saillance politique, les dépenses de santé publique restent guidées par la partisanerie et les préoccupations électorales. Malgré leur importance largement reconnue, les programmes de santé publique se développent à l'ombre de la priorité donnée aux soins curatifs.

How do respondents of primary care surveys compare to typical users of primary care? A comparison of two surveys.

BACKGROUND: Primary care surveys are a key source of evaluative data; understanding how survey respondents compare to the intended population is important to understand results in context. The objective of this study was to examine the physician and patient representativeness of two primary care surveys (TRANSFORMATION and QUALICOPC) that each used different sampling and recruitment techniques. METHODS: We linked the physician and patient participants of the two surveys to health administrative databases. Patients were compared to other patients visiting the practice on the same day and other randomly selected dates using sociodemographic data, chronic disease diagnosis, and health system utilization. Physicians were compared to other physicians in the same practice, and other physicians in the intended geographic area using sociodemographic and practice characteristics. RESULTS: Physician respondents of the TRANSFORMATION survey included more males compared to their practice groups, but not to other physicians in the area. TRANSFORMATION physicians cared for a larger roster of patients than other physicians in the area. Patient respondents of the QUALICOPC survey did not have meaningful differences from other patients who visit the practice. Patient respondents of the TRANSFORMATION survey resided in more rural areas, had less chronic disease, and had lower use of health services than other patients visiting the practice. CONCLUSION: Differences in survey recruitment methods at the physician and patient level may help to explain some of the differences in representativeness. When conducting primary care surveys, investigators should consider diverse methods of ensuring representativeness to limit the potential for nonresponse bias.

"We cobble together a storyline of system performance using a diversity of things": a qualitative study of perspectives on public health performance measurement in Canada.

BACKGROUND: There have been longstanding calls for public health systems transformations in many countries, including Canada. Core to these calls has been strengthening performance measurement. While advancements have been made in performance measurement for certain sectors of the health care system (primarily focused on acute and primary health care), effective use of indicators for measuring public health systems performance are lacking. This study describes the current state, anticipated challenges, and future directions in the development and implementation of a public health performance measurement system for Canada. METHODS: We conducted a qualitative study using semi-structured interviews with public health leaders (n = 9) between July and August 2021. Public health leaders included researchers, government staff, and former medical officers of health who were purposively selected due to their expertise and experience with performance measurement with relevance to public health systems in Canada. Thematic analysis included both a deductive approach for themes consistent with the conceptual framework and an inductive approach to allow new themes to emerge from the data. RESULTS: Conceptual, methodological, contextual, and infrastructure challenges were highlighted by participants in designing a performance measurement system for public health. Specifically, six major themes evolved that encompass 1) the mission and purpose of public health systems, including challenges inherent in measuring the functions and services of public health; 2) the macro context, including the impacts of chronic underinvestment and one-time funding injections on the ability to sustain a measurement system; 3) the organizational structure/governance of public health systems including multiple forms across Canada and underdevelopment of information technology systems; 4) accountability approaches to performance measurement and management; and 5) timing and unobservability in public health indicators. These challenges require dedicated investment, strong leadership, and political will from the federal and provincial/territorial governments. CONCLUSION: Unprecedented attention on public health due to the coronavirus disease 2019 pandemic has highlighted opportunities for system improvements, such as addressing the lack of a performance measurement system. This study provides actionable knowledge on conceptual, methodological, contextual, and infrastructure challenges needed to design and build a pan-Canadian performance measurement system for public health.

Diabetes Detection and Management through Photoplethysmographic and Electrocardiographic Signals Analysis: A Systematic Review.

(1) Background: Diabetes mellitus (DM) is a chronic, metabolic disease characterized by elevated levels of blood glucose. Recently, some studies approached the diabetes care domain through the analysis of the modifications of cardiovascular system parameters. In fact, cardiovascular diseases are the first leading cause of death in diabetic subjects. Thanks to their cost effectiveness and their ease of use, electrocardiographic (ECG) and photoplethysmographic (PPG) signals have recently been used in diabetes detection, blood glucose estimation and diabetes-related complication detection. This review's aim is to provide a detailed overview of all the published methods, from the traditional (non machine learning) to the deep learning approaches, to detect and manage diabetes using PPG and ECG signals. This review will allow researchers to compare and understand the differences, in terms of results, amount of data and complexity that each type of approach provides and requires. (2) Method: We performed a systematic review based on articles that focus on the use of ECG and PPG signals in diabetes care. The search was focused on keywords related to the topic, such as "Diabetes", "ECG", "PPG", "Machine Learning", etc. This was performed using databases, such as PubMed, Google Scholar, Semantic Scholar and IEEE Xplore. This review's aim is to provide a detailed overview of all the published methods, from the traditional (non machine learning) to the deep learning approaches, to detect and manage diabetes using PPG and ECG signals. This review will allow researchers to compare and understand the differences, in terms of results, amount of data and complexity that each type of approach provides and requires. (3) Results: A total of 78 studies were included. The majority of the selected studies focused on blood glucose estimation (41) and diabetes detection (31). Only 7 studies focused on diabetes complications detection. We present these studies by approach: traditional, machine learning and deep learning approaches. (4) Conclusions: ECG and PPG analysis in diabetes care showed to be very promising. Clinical validation and data processing standardization need to be improved in order to employ these techniques in a clinical environment.

Comprehensive compartmental model and calibration algorithm for the study of clinical implications of the population-level spread of COVID-19: a study protocol.

INTRODUCTION: The complex dynamics of the coronavirus disease 2019 (COVID-19) pandemic has made obtaining reliable long-term forecasts of the disease progression difficult. Simple mechanistic models with deterministic parameters are useful for short-term predictions but have ultimately been unsuccessful in extrapolating the trajectory of the pandemic because of unmodelled dynamics and the unrealistic level of certainty that is assumed in the predictions. METHODS AND ANALYSIS: We propose a 22-compartment epidemiological model that includes compartments not previously considered concurrently, to account for the effects of vaccination, asymptomatic individuals, inadequate access to hospital care, post-acute COVID-19 and recovery with long-term health complications. Additionally, new connections between compartments introduce new dynamics to the system and provide a framework to study the sensitivity of model outputs to several concurrent effects, including temporary immunity, vaccination rate and vaccine effectiveness. Subject to data availability for a given region, we discuss a means by which population demographics (age, comorbidity, socioeconomic status, sex and geographical location) and clinically relevant information (different variants, different vaccines) can be incorporated within the 22-compartment framework. Considering a probabilistic interpretation of the parameters allows the model's predictions to reflect the current state of uncertainty about the model parameters and model states. We propose the use of a sparse Bayesian learning algorithm for parameter calibration and model selection. This methodology considers a combination of prescribed parameter prior distributions for parameters that are known to be essential to the modelled dynamics and automatic relevance determination priors for parameters whose relevance is questionable. This is useful as it helps prevent overfitting the available epidemiological data when calibrating the parameters of the proposed model. Population-level administrative health data will serve as partial observations of the model states. ETHICS AND DISSEMINATION: Approved by Carleton University's Research Ethics Board-B (clearance ID: 114596). Results will be made available through future publication.

Evidence for an internal model of friction when controlling kinetic energy at impact to slide an object along a surface toward a target.

Although the role of an internal model of gravity for the predictive control of the upper limbs is quite well established, evidence is lacking regarding an internal model of friction. In this study, 33 male and female human participants performed a striking movement (with the index finger) to slide a plastic cube-like object to a given target distance. The surface material (aluminum or balsa wood) on which the object slides, the surface slope (-10°, 0, or +10°) and the target distance (25 cm or 50 cm) varied across conditions, with ten successive trials in each condition. Analysis of the object speed at impact and spatial error suggests that: 1) the participants chose to impart a similar speed to the object in the first trial regardless of the surface material to facilitate the estimation of the coefficient of friction; 2) the movement is parameterized across repetitions to reduce spatial error; 3) an internal model of friction can be generalized when the slope changes. Biomechanical analysis showed interindividual variability in the recruitment of the upper limb segments and in the adjustment of finger speed at impact in order to transmit the kinetic energy required to slide the object to the target distance. In short, we provide evidence that the brain builds an internal model of friction that makes it possible to parametrically control a striking movement in order to regulate the amount of kinetic energy required to impart the appropriate initial speed to the object.

GLYFE: review and benchmark of personalized glucose predictive models in type 1 diabetes.

Due to the sensitive nature of diabetes-related data, preventing them from being easily shared between studies, and the wide discrepancies in their data processing pipeline, progress in the field of glucose prediction is hard to assess. To address this issue, we introduce GLYFE (GLYcemia Forecasting Evaluation), a benchmark of machine learning-based glucose predictive models. We present the accuracy and clinical acceptability of nine different models coming from the literature, from standard autoregressive to more complex neural network-based models. These results are obtained on two different datasets, namely UVA/Padova Type 1 Diabetes Metabolic Simulator (T1DMS) and Ohio Type-1 Diabetes Mellitus (OhioT1DM), featuring artificial and real type 1 diabetic patients respectively. By providing extensive details about the data flow as well as by providing the whole source code of the benchmarking process, we ensure the reproducibility of the results and the usability of the benchmark by the community. Those results serve as a basis of comparison for future studies. In a field where data are hard to obtain, and where the comparison of results from different studies is often irrelevant, GLYFE gives the opportunity of gathering researchers around a standardized common environment.

Comparing the Attainment of the Patient's Medical Home Model across Regions in Three Canadian Provinces: A Cross-Sectional Study.

BACKGROUND: The aim of this work was to show the feasibility of providing a comprehensive portrait of regional primary care performance. METHODS: The TRANSFORMATION study used a mixed-methods concurrent study design where we analyzed survey data and case studies. Data were collected in British Columbia, Ontario and Nova Scotia. Patient's Medical Home (PMH) pillar scores were created by calculating mean clinic-level scores across regions. Scores and qualitative themes were compared. RESULTS: Participation included 86 practices (n = 1,929 patients; n = 117 clinicians). Regions had differential attainment towards PMH orientation with respect to infrastructure; community adaptiveness and accountability; and patient and family partnered care. The lowest PMH attainment for all regions were observed in connected care; accessible care; measurement, continuous quality improvement and research; and training, education and continuing professional development. CONCLUSIONS: Comprehensive performance reporting that draws on multiple data sources in primary care is possible. Regional portraits highlighting many of the key pillars of a PMH approach to primary care show that despite differences in policy contexts, achieving a PMH remains elusive.

Transfer learning of CNN-based signal quality assessment from clinical to non-clinical PPG signals.

Photoplethysmography (PPG) is a non-invasive and cost-efficient optical technique used to assess blood volume variation inside the micro-circulation. PPG technology is widely used in a variety of clinical and non-clinical devices in order to investigate the cardiovascular system. One example of clinical PPG device is the pulse oxymeter, while non-clinical PPG devices include smartphones and smartwatches. Such a wide diffusion of PPG devices generates plenty of different PPG signals that differ from each other. In fact, intrinsic device characteristics strongly influence PPG waveform. In this paper we investigate transfer learning approaches on a Covolutional Neural Network based quality assessment method in order to generalize our model across different PPG devices. Our results show that our model is able to classify accurately signal quality over different PPG datasets while requiring a small amount of data for fine-tuning.Clinical relevance- A precise detection and extraction of high quality PPG segments could enhance significantly the reliability of the medical analysis based on the signal.

Interpreting forty-three-year trends of expenditures on public health in Canada: Long-run trends, temporal periods, and data differences.

The COVID-19 pandemic has raised concerns around public health (PH) investments. Among OECD countries, Canada devotes one of the largest shares of total health expenditures to PH. Examining retrospectively PH spending growth over a very long period may hold lessons on how to reach this high share. Further, different historical periods can be used to understand how macroeconomic conditions affect PH spending growth. Using forty-three years of data, we examine real PH spending growth per capita, comparatively between thirteen Canadian jurisdictions and with other key publicly funded healthcare sectors (physicians, hospitals, and pharmaceuticals), as well as by four periods defined by macroeconomic conditions. We find a five-fold increase on average in PH spending since 1975, a growth above physicians and hospitals, but below pharmaceuticals. However, there is substantial variation in PH growth between periods and across the country. Because concerns have been raised over PH spending data in other OECD countries, we explore differences between spending estimates reported by the national agency and ten provincial budgetary estimates, and find the former is larger. The magnitude of the difference varies between jurisdictions but not much over time. Although these differences do not challenge the presence of growth in PH spending, they show that the growth may be below that of hospitals. A better categorization of PH financing data is warranted.

Adversarial multi-source transfer learning in healthcare: Application to glucose prediction for diabetic people.

BACKGROUND AND OBJECTIVES: Deep learning has yet to revolutionize general practices in healthcare, despite promising results for some specific tasks. This is partly due to data being in insufficient quantities hurting the training of the models. To address this issue, data from multiple health actors or patients could be combined by capitalizing on their heterogeneity through the use of transfer learning. METHODS: To improve the quality of the transfer between multiple sources of data, we propose a multi-source adversarial transfer learning framework that enables the learning of a feature representation that is similar across the sources, and thus more general and more easily transferable. We apply this idea to glucose forecasting for diabetic people using a fully convolutional neural network. The evaluation is done by exploring various transfer scenarios with three datasets characterized by their high inter and intra variability. RESULTS: While transferring knowledge is beneficial in general, we show that the statistical and clinical accuracies can be further improved by using of the adversarial training methodology, surpassing the current state-of-the-art results. In particular, it shines when using data from different datasets, or when there is too little data in an intra-dataset situation. To understand the behavior of the models, we analyze the learnt feature representations and propose a new metric in this regard. Contrary to a standard transfer, the adversarial transfer does not discriminate the patients and datasets, helping the learning of a more general feature representation. CONCLUSION: The adversarial training framework improves the learning of a general feature representation in a multi-source environment, enhancing the knowledge transfer to an unseen target. The proposed method can help improve the efficiency of data shared by different health actors in the training of deep models.

Evolution of the determinants of unmet health care needs in a universal health care system: Canada, 2001-2014.

While ensuring adequate access to care is a central concern in countries with universal health care coverage, unmet health care needs remain prevalent. However, subjective unmet health care needs (SUN) can arise from features of a health care system (system reasons) or from health care users' choices or constraints (personal reasons). Furthermore, investigating the evolution of SUN within a health care system has rarely been carried out. We investigate whether health needs, predisposing factors and enabling factors differentially affect SUN for system reasons and SUN for personal reasons, and whether these influences are stable over time, using representative data from the Canadian Community Health Surveys from 2001 to 2014. While SUN slightly decreased overall during our period of observation, the share of SUN for system reasons increased. Some key determinants appear to consistently increase SUN reporting over all our observation periods, in particular being a woman, younger, in poorer health or not having a regular doctor. The distinction between personal and system reasons is important to better understand individual experiences. Notably, women report more SUN for system reasons and less for personal reasons, and reporting system reasons increases with age. Given this stability over time, our results may inform health policymakers on which subpopulations to target to ensure access to health care is universal.

An analysis of the policy responses to the COVID-19 pandemic in France, Belgium, and Canada.

OBJECTIVES: This paper presents an overview and comparative analysis of the epidemiological situation and the policy responses in France, Belgium, and Canada during the early stages of the 2020 Covid-19 pandemic (Feb.-Aug. 2020). These three countries are compared because they represent a spectrum of different governance structures while also being OECD nations that are similar in many other respects. METHODS: A rapid review of primary data from the three countries was conducted. Data was collected from official government documents whenever possible, supplemented by information from international databases and local media reports. The data was then analysed to identify common patterns as well as significant divergences across the three countries, especially in the areas of health policy and technology use. RESULTS: France, Belgium and Canada faced differing epidemiological situations during the Covid-19 pandemic, and the wide variety of policy actions taken appears to be linked to existing governance and healthcare structures. The varying degrees of federalism and regional autonomy across the three countries highlight the different constraints faced by national policy-makers within different governance models. CONCLUSIONS: The actions taken by all three countries appear to have been largely dictated by existing health system capacity, with increasing federalism associated with more fragmented strategies and less coordination across jurisdictions. However, the implications of certain policies related to economic resilience and health system capacity cannot yet be fully evaluated and may even prove to have net negative impacts into the future.

Explaining primary care physicians' decision to quit patient-centered medical homes: Evidence from Quebec, Canada.

OBJECTIVE: To examine the factors explaining primary care physicians' (PCPs) decision to leave patient-centered medical homes (PCMHs). DATA SOURCES: Five-year longitudinal data on all the 906 PCPs who joined a PCMH in the Canadian province of Quebec, known there as a Family Medicine Group. STUDY DESIGN: We use fixed-effects and random-effects logit models, with a variety of regression specifications and various subsamples. In addition to these models, we examine the robustness of our results using survival analysis, one lag in the regressions and focusing on a matched sample of quitters and stayers. DATA COLLECTION/EXTRACTION METHODS: We extract information from Quebec's universal health insurer billing data on all the PCPs who joined a PCMH between 2003 and 2005, supplemented by information on their elderly and chronically ill patients. PRINCIPAL FINDINGS: About 17 percent of PCPs leave PCMHs within 5 years of follow-up. Physicians' demographics have little influence. However, those with more complex patients and higher revenues are less likely to leave the medical homes. These findings are robust across a variety of specifications. CONCLUSION: As expected, higher revenue favors retention. Importantly, our results suggest that PCMH may provide appropriate support to physicians dealing with complex patients.