Institute of Health Economics

ObjectivesHeat events are a growing public health concern. There is an opportunity to better characterize how heat events affect healthcare system utilization. We evaluated the heat-related healthcare resource use and costs in Alberta, Canada in the summer of 2021 when record-breaking extreme heat events occurred in the province.MethodsWe conducted a population-based cohort study using Alberta administrative health data from May to September 2021 to identify and describe patients who used heat-related healthcare resources over this period. Costs were quantified and reported in Canadian dollars (CA$) using 2023 values.Results4194 patients used heat-related healthcare resources, including 109 hospitalizations, 1020 ambulatory care visits (99.7% were ED visits), 310 ambulance transfers, and 5555 practitioner claims. Total heat-related healthcare costs were CA$3.2 million. Female sex, age, and a history of myocardial infarction, heart failure, dementia, or diabetes were found to be significantly associated with increased use of heat-related healthcare resources. History of cardiovascular disease (27.1%) or diabetes (12%) were more frequent in patients hospitalized or attended ED (30 and 14.2%, respectively) compared to those who only used outpatient or physician services (26.3 and 11.2%, respectively; all p < 0.05).ConclusionHeat-related healthcare resource use and costs during the summer of 2021 in Alberta was substantial. Females, older persons, and people with a history of cardiovascular disease were the most affected. This is likely an underestimation of the overall heat impact. Additional research is needed to quantify the broader impact of extreme heat events on the healthcare system and on society.

ObjectiveEvaluate the quality and cost-effectiveness of economic evaluations of newborn screening (NBS) for Spinal Muscular Atrophy (SMA).MethodsA systematic review was conducted following Cochrane Handbook guidelines and PRISMA-S checklist. From 146 identified papers, 22 were screened for full-text, and 5 were included. Studies were evaluated for quality of reporting and transparency using the CHEERs and QHES checklists. Data was extracted to inform the review.ResultsFour economic evaluations on NBS for SMA with high reporting quality were identified. Each study employed a cost-utility analysis with similar model structures, using a decision tree for screening and a Markov model for treatment outcomes. They each compared NBS with treatment vs clinical diagnosis (no screening) with treatment. Although treatment protocols of each study varied due to differences in the strategies considered and availability of treatment. All studies included a societal perspective in their analysis and considered a lifetime horizon ranging from 30 months to 100 years. Early NBS with treatment was found to be more cost-effective than late treatment in all studies with ICER values ranging from £−117,541 to $714,000 per QALY. The wide range of ICER values are due to assumptions of long-term outcomes which are still largely unknown.ConclusionNBS with treatment was found to be cost-effective by all studies when compared to no NBS and late treatment. Although there is uncertainty around long term outcomes. Future research should focus on collecting long-term efficacy and safety data and evaluating the cost-effectiveness of pre-symptomatic treatment.

ObjectivesEarly oral hygiene and care help prevent early childhood caries (ECC). As remote Indigenous communities in Canada have limited access to dental care, this project explores Indigenous women's oral hygiene practices during and after pregnancy and in regard to children (0–36 months) within three communities in Northwest Territories (NWT), Canada.MethodsA mixed-methods study design was employed. Self-identifying Indigenous women who were pregnant or had given birth within the 3 years preceding data collection (2019) were invited to complete a semi-structured, interviewer-administered questionnaire.Results156 Indigenous women participated. Of participants who gave birth in the last 3 years (n = 145), 78.8% started brushing infants' teeth/gums. Of participants whose infants had teeth (n = 114), 27.9% had taken the infant to a dentist. Factors impacting infant oral hygiene practices included personal experiences, access to supplies, and health literacy. Among pregnant participants (n = 28), 38.5% had visited a dentist during pregnancy. Among participants who had given birth within 3 years (n = 145), 67.9% had since seen a dentist. Factors impacting dental care utilization during pregnancy included service availability and safety concerns. After giving birth, factors included service availability and competing priorities.ConclusionsWithin Indigenous communities in NWT, inaccessible oral health care, information, and supplies made it challenging for mothers to meet oral health recommendations during and after pregnancy and in regard to young children. The findings of this project suggest the need for long-term, collaborative strategies focused on increasing the availability and accessibility of dental care professionals and services within communities and improving oral health literacy via culturally appropriate and collaboratively developed informational initiatives.

Cost-effectiveness research is an essential tool in healthcare decision-making, enabling the comparison of interventions based on their incremental costs and effects. While traditionally conducted alongside Randomized Controlled Trials (RCTs), there is a growing interest in complementary Real-World Data (RWD) analysis for Cost-Effectiveness Analysis (CEA). In order for healthcare decision-makers to make valid reimbursement decisions, it is essential that the results of CEAs are both valid and applicable to a broader context. While RCTs offer high internal validity, their generalizability can be limited. Therefore, RWD is increasingly being recognized as a valuable source of information that can complement RCT data. RWD offers advantages over RCTs, such as better generalizability because of less strict patient selection and reflection of actual treatment patterns, but come with complexities, like confounding and missing observations. Confounding, stemming from the non-random allocation of treatment, presents a significant challenge in RWD analysis and requires careful adjustment to avoid biased estimates. In this thesis, the concepts of time-fixed and time-dependent confounding are distinguished, with the latter being more common in longitudinal studies. Various methods for dealing with confounding have been proposed, but their performance in different scenarios remains unclear. Within the context of CEA, this thesis aims to clarify terminology, review methods for adjusting confounding, and compare the performance of different techniques in CEA studies using RWD. Firstly, this thesis aimed to bridge the gap between epidemiologists and economists by providing a glossary that equips participants in interdisciplinary collaborations with the essential shared vocabulary (Chapter 2). Next, the thesis provides a scoping review of methods available for addressing time-invariant confounding (Chapter 3). Following the identification of a wide range of methods, the aim was to evaluate the most promising ones and compare them with current research practices in the context of CEA (Chapter 4). This exploration expanded the research question by building upon the time-invariant confounding study. It introduced time-dependent confounding to the simulation setup by adding an extra time point (Chapter 5). Finally, in the general discussion, insights and recommendations for improving the validity and reliability of CEA using RWD are provided based on the scoping review and simulation studies included in this thesis (Chapter 6).

A key component to the recent success of reinforcement learning is the introduction of neural networks for representation learning. Doing so allows for solving challenging problems in several domains, one of which is robotics. However, a major criticism of deep reinforcement learning (DRL) algorithms is their lack of explainability and interpretability. This problem is even exacerbated in robotics as they oftentimes cohabitate space with humans, making it imperative to be able to reason about their behavior. In this paper, we propose to analyze the learned representation in a robotic setting by utilizing Graph Networks (GNs). Using the GN and Layer-wise Relevance Propagation (LRP), we represent the observations as an entity-relationship to allow us to interpret the learned policy. We evaluate our approach in two environments in MuJoCo. These two environments were delicately designed to effectively measure the value of knowledge gained by our approach to analyzing learned representations. This approach allows us to analyze not only how different parts of the observation space contribute to the decision-making process but also differentiate between policies and their differences in performance. This difference in performance also allows for reasoning about the agent’s recovery from faults. These insights are key contributions to explainable deep reinforcement learning in robotic settings.

Abstract Background The EQ-5D-Y is a generic preference-weighted measure for children and adolescents which was developed within Europe. Two versions exist, the EQ-5D-Y-3L (Y-3L) and EQ-5D-Y-5L (Y-5L). This study aimed to cross-culturally adapt the Y-3L and Y-5L for use in Singapore and to assess the content validity, specifically, the relevance and comprehensiveness of the EQ-5D-Y descriptive system (EQ-5D-Y DS) in Asia. Methods To culturally adapt the instruments, an expert panel consisting of paediatricians and primary school educators were consulted. Modifications suggested by the expert panel were tested via cognitive debriefing interviews with children aged 8–12 in Singapore. To assess the content validity of the EQ-5D-Y DS, interviews were conducted with both healthy (n = 8) and ill children (n = 6) aged 8–15. In the interviews, children discussed their experience with poor health and commented on the comprehensiveness and relevance of the EQ-5D-Y DS. Results The cross-cultural adaptation process led to minor modifications to the UK English Y-3L and Y-5L versions, including using phrases familiar to the local children and adding examples to facilitate understanding. The five health dimensions in the EQ-5D-Y DS were spontaneously elicited when children discussed their experience with poor health. All health dimensions related to poor health elicited from the interviews fell into three broad categories: physical health (e.g. Appetite, Mobility, and Sleep), mental well-being (e.g. Annoyed/Frustrated and Scared/Worried), and social relationships (e.g. Family and Friends). The EQ-5D-Y DS was generally found to be relevant and comprehensive, although some health dimensions that may be relevant to the local population (Social relationship and Appetite) were not covered. Conclusions The UK English EQ-5D-Y instruments were adapted to produce the Singapore English EQ-5D-Y instrument that were comprehensible to local children as young as 8 years old. The EQ-5D-Y DS was generally relevant and comprehensive to measure poor health of local children. Future studies should ascertain the benefits of adding bolt-on items related to social relationships and appetite to the EQ-5D-Y DS.

Utility scores associated with preference-based health-related quality-of-life instruments such as the EQ-5D-3L are reported as point estimates. In this study, we develop methods for capturing the uncertainty associated with the valuation study of the UK EQ-5D-3L that arises from the variability inherent in the underlying data, which is tacitly ignored by point estimates. We derive a new tariff that properly accounts for this and assigns a specific closed-form distribution to the utility of each of the 243 health states of the EQ-5D-3L. Using the UK EQ-5D-3L valuation study, we used a Bayesian approach to obtain the posterior distributions of the derived utility scores. We constructed a hierarchical model that accounts for model misspecification and the responses of the survey participants to obtain Markov chain Monte Carlo (MCMC) samples from the posteriors. The posterior distributions were approximated by mixtures of normal distributions under the Kullback-Leibler (KL) divergence as the criterion for the assessment of the approximation. We considered the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm to estimate the parameters of the mixture distributions. We derived an MCMC sample of total size 4,000 × 243. No evidence of nonconvergence was found. Our model was robust to changes in priors and starting values. The posterior utility distributions of the EQ-5D-3L states were summarized as 3-component mixtures of normal distributions, and the corresponding KL divergence values were low. Our method accounts for layers of uncertainty in valuation studies, which are otherwise ignored. Our techniques can be applied to other instruments and countries' populations. Guidelines for health technology assessments typically require that uncertainty be accounted for in economic evaluations, but the parameter uncertainty of the regression model used in the valuation study of the health instrument is often tacitly ignored.We consider the UK valuation study of the EQ-5D-3L and construct a Bayesian model that accounts for layers of uncertainty that would otherwise be disregarded, and we derive closed-form utility distributions.The derived tariff can be used by researchers in economic evaluations, as it allows analysts to directly sample a utility value from its corresponding distribution, which reflects the associated uncertainty of the utility score.

This guide aims to help healthcare professionals of all backgrounds make sense of economic evaluations to determine whether interventions represent value for money.