The Population Health Model (POHEM): an overview of rationale, methods and applications

Projections of functional dependence among the late middle-aged and older population from 2018-2048 in China: a dynamic microsimulation

BACKGROUND

The population of China is aging rapidly. However, the long-term trajectories of functionally dependent late middle-aged and older Chinese people are currently absent. The present study aimed to estimate the population size and proportion of late middle-aged and older adults with difficulties and dependence on activities of daily living (ADL) and instrumental activities of daily living (IADL) in China from 2018 to 2048.

METHODS

We constructed a dynamic microsimulation model to project the population size and proportions of late middle-aged and older Chinese people who have difficulty and dependence in ADL and IADL from 2018-2048. The model was populated with a representative sample of the target population and allowed individual-level interaction between risk factors, diseases, and health outcomes. Analyses by socioeconomic subgroups were also conducted.

RESULTS

Almost 25% and 38% of late middle-aged and older people in China will become ADL- and IADL-dependent by 2048, respectively. Also, 17% of the target population will be severely ADL-disabled by 2048. The inequity in functional status across subgroups by sex, educational level, and urban/rural residency will become substantial.

CONCLUSIONS

The numbers and percentages of China's functionally difficult and dependent late middle-aged and older population will increase by magnitudes as of the mid-21st century, the pressure of which is compounded by its disproportionate distribution across subgroups. To alleviate the overwhelming challenge, efforts to improve the functional status of the underserved subpopulation should also be iterated.

Budget impact analysis of adopting primary care-based case detection of chronic obstructive pulmonary disease in the Canadian general population

BACKGROUND

An estimated 70% of Canadians with chronic obstructive pulmonary disease (COPD) have not received a diagnosis, creating a barrier to early intervention, and there is growing interest in the value of primary care-based opportunistic case detection for COPD. We sought to build on a previous cost-effectiveness analysis by evaluating the budget impact of adopting COPD case detection in the Canadian general population.

METHODS

We used a validated discrete-event microsimulation model of COPD in the Canadian general population aged 40 years and older to assess the costs of implementing 8 primary care-based case detection strategies over 5 years (2022-2026) from the health care payer perspective. Strategies varied in eligibility criteria (based on age, symptoms or smoking history) and testing technology (COPD Diagnostic Questionnaire [CDQ] or screening spirometry). Costs were determined from Canadian studies and converted to 2021 Canadian dollars. Key parameters were varied in one-way sensitivity analysis.

RESULTS

All strategies resulted in higher total costs compared with routine diagnosis. The most cost-effective scenario (the CDQ for all patients) had an associated total budget expansion of $423 million, with administering case detection and subsequent diagnostic spirometry accounting for 86% of costs. This strategy increased the proportion of individuals diagnosed with COPD from 30.4% to 37.8%, and resulted in 4.6 million referrals to diagnostic spirometry. Results were most sensitive to uptake in primary care.

INTERPRETATION

Adopting a national COPD case detection program would be an effective method for increasing diagnosis of COPD, dependent on successful uptake. However, it will require prioritisation by budget holders and substantial additional investment to improve access to diagnostic spirometry.

Culture intelligent workflow, structure, and steps

Introduction

Technologies abstract intelligence and provide predictor and precision insight in workflows that manage disorders, similar to cardiology and hematological disease. Positive perceptions of Artificial Intelligence (AI) that support Machine Learning (ML) and Deep Learning (DL) manage transformations with a safe system that improves wellbeing. In sections, workflow introduces an eXamination (X = AI) as an end-to-end structure to culture workstreams in a step-by-step design to manage populace health in a governed system.

Method

To better healthcare outcomes, communities and personnel benefit from an explanation and an interpretive that elucidates workflow for citizens or practitioners to comprehend personalized platforms. Therefore, the author undertook structure and practice reviews and appraised perspectives that impact the management of AI in public health and medicine.

Results

Figures for the management of AI workflow illustrate and inform on the model, structure, culture, assurance, process steps, values, and governance required for abstract insights in public health and medicine. The papers' end-to-end structure with explanans in a work culture interprets the step-by-step designs that manage the success of AI. Personalized care graphics offer an explanandum in the management of biological analytic value.

Discussion

Healthcare leadership collaboratives plan population health with an upstream, workplace and workstream format. Secure workflow and safety wellbeing system requirements prove that genomics and AI improve medicine. Therefore, the paper discusses group understanding of current practice, ethics, policy, and legality.

Conclusion

"Culture, intelligent workflow, structure, and steps" improve wellbeing with personalized care and align a percept for national opportunities, regional control, and local needs. Personalized practice cultures support analytic systems to describe, predict, precision, and prescript medicine in population health management eXaminations.

On the role of data, statistics and decisions in a pandemic

A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which countermeasures are appropriate at a particular stage of the pandemic? How can the severity of the pandemic be measured? What is the effect of vaccination in the population and which groups should be vaccinated first? The process of decision-making starts with data collection and modeling and continues to the dissemination of results and the subsequent decisions taken. The goal of this paper is to give an overview of this process and to provide recommendations for the different steps from a statistical perspective. In particular, we discuss a range of modeling techniques including mathematical, statistical and decision-analytic models along with their applications in the COVID-19 context. With this overview, we aim to foster the understanding of the goals of these modeling approaches and the specific data requirements that are essential for the interpretation of results and for successful interdisciplinary collaborations. A special focus is on the role played by data in these different models, and we incorporate into the discussion the importance of statistical literacy and of effective dissemination and communication of findings.

The OncoSim-Breast Cancer Microsimulation Model

Background: OncoSim-Breast is a Canadian breast cancer simulation model to evaluate breast cancer interventions. This paper aims to describe the OncoSim-Breast model and how well it reproduces observed breast cancer trends. Methods: The OncoSim-Breast model simulates the onset, growth, and spread of invasive and ductal carcinoma in situ tumours. It combines Canadian cancer incidence, mortality, screening program, and cost data to project population-level outcomes. Users can change the model input to answer specific questions. Here, we compared its projections with observed data. First, we compared the model’s projected breast cancer trends with the observed data in the Canadian Cancer Registry and from Vital Statistics. Next, we replicated a screening trial to compare the model’s projections with the trial’s observed screening effects. Results: OncoSim-Breast’s projected incidence, mortality, and stage distribution of breast cancer were close to the observed data in the Canadian Cancer Registry and from Vital Statistics. OncoSim-Breast also reproduced the breast cancer screening effects observed in the UK Age trial. Conclusions: OncoSim-Breast’s ability to reproduce the observed population-level breast cancer trends and the screening effects in a randomized trial increases the confidence of using its results to inform policy decisions related to early detection of breast cancer.

A comparison of three strategies to reduce the burden of osteoarthritis: A population-based microsimulation study

Objectives

The purpose of this study was to compare three strategies for reducing population health burden of osteoarthritis (OA): improved pharmacological treatment of OA-related pain, improved access to joint replacement surgery, and prevention of OA by reducing obesity and overweight.

Methods

We applied a validated computer microsimulation model of OA in Canada. The model simulated a Canadian-representative open population aged 20 years and older. Variables in the model included demographics, body mass index, OA diagnosis, OA treatment, mortality, and health-related quality of life. Model parameters were derived from analyses of national surveys, population-based administrative data, a hospital-based cohort study, and the literature. We compared 8 what-if intervention scenarios in terms of disability-adjusted life years (DALYs) relative to base-case, over a wide range of time horizons.

Results

Reductions in DALYs depended on the type of intervention, magnitude of the intervention, and the time horizon. Medical interventions (a targeted increase in the use of painkillers) tended to produce effects quickly and were, therefore, most effective over a short time horizon (a decade). Surgical interventions (increased access to joint replacement) were most effective over a medium time horizon (two decades or longer). Preventive interventions required a substantial change in BMI to generate a significant impact, but produced more reduction in DALYs than treatment strategies over a very long time horizon (several decades).

Conclusions

In this population-based modeling study we assessed the potential impact of three different burden reduction strategies in OA. Data generated by our model may help inform the implementation of strategies to reduce the burden of OA in Canada and elsewhere.

Population norms for quality adjusted life years for the United States of America, China, the United Kingdom and Australia

Health economics uses quality adjusted life years (QALYs) to help healthcare decision makers. However, unlike life expectancy for which age- and sex-dependent national life tables are available, no general population norms exist to use as a benchmark against which to compare observed or modeled projections of QALYs in sub-populations or patients. We developed a 2-state Markov model to generate QALY population norms for the USA, UK, China and Australia. Annual age- and sex-specific probabilities of all-cause mortality were taken from life tables combined with general population country-specific age- and sex-specific health state utilities for the EQ-5D-3L (all countries); and SF-6D (Australia) multi-attribute utility instruments (MAUI). To validate our QALY benchmark model we found that the model closely predicted population life expectancies. Using EQ-5D-3L, undiscounted QALYs for males/females aged 18 years ranged 54.62/58.90 (USA), 55.55/60.21 (China), 57.11/60.16 (Australia), and 58.01/61.43 (UK) years. SF-6D benchmark QALYs for Australia were consistently lower than those generated from the EQ-5D-3L. The gap in undiscounted QALYs between the UK (highest) and the USA (lowest) was 2.53 QALYs in women and 3.39 QALYs in men aged 18 years. Our model's QALY population norms can be used for internal validation of future health economic models for the country-specific value sets for the instruments that we adopted, and when quantifying burden of disease in terms of QALYs lost due to illness compared to the general population. We have created a publicly available repository to continuously include QALY benchmarks that use country-specific value sets for other MAUIs and life expectancies.

Simulation modeling to assess performance of integrated healthcare systems: Literature review to characterize the field and visual aid to guide model selection

Background

The guiding principle of many health care reforms is to overcome fragmentation of service delivery and work towards integrated healthcare systems. Even though the value of integration is well recognized, capturing its drivers and its impact as part of health system performance assessment is challenging. The main reason is that current assessment tools only insufficiently capture the complexity of integrated systems, resulting in poor impact estimations of the actions taken towards the ‘Triple Aim’. We describe the unique nature of simulation modeling to consider key health reform aspects: system complexity, optimization of actions, and long-term assessments.

Research question

How can the use and uptake of simulation models be characterized in the field of performance assessment of integrated healthcare systems?

Methods

A systematic search was conducted between 2000 and 2018, in 5 academic databases (ACM D. Library, CINAHL, IEEE Xplore, PubMed, Web of Science) complemented with grey literature from Google Scholar. Studies using simulation models with system thinking to assess system performance in topics relevant to integrated healthcare were selected for revision.

Results

After screening 2274 articles, 30 were selected for analysis. Five modeling techniques were characterized, across four application areas in healthcare. Complexity was defined in nine aspects, embedded distinctively in each modeling technique. ‘What if?’ & ‘How to?’ scenarios were identified as methods for system optimization. The mean time frame for performance assessments was 18 years.

Conclusions

Simulation models can evaluate system performance emphasizing the complex relations between components, understanding the system’s adaptability to change in short or long-term assessments. These advantages position them as a useful tool for complementing performance assessment of integrated healthcare systems in their pursuit of the ‘Triple Aim’. Besides literacy in modeling techniques, accurate model selection is facilitated after identification and prioritization of the complexities that rule system performance. For this purpose, a tool for selecting the most appropriate simulation modeling techniques was developed.

Importance of Geospatial Heterogeneity in Chronic Disease Burden for Policy Planning in an Urban Setting Using a Case Study of Singapore

Chronic disease burdens continue to rise in highly dense urban environments where clustering of type II diabetes mellitus, acute myocardial infarction, stroke, or any combination of these three conditions is occurring. Many individuals suffering from these conditions will require longer-term care and access to clinics which specialize in managing their illness. With Singapore as a case study, we utilized census data in an agent-modeling approach at an individual level to estimate prevalence in 2020 and found high-risk clusters with >14,000 type II diabetes mellitus cases and 2000-2500 estimated stroke cases. For comorbidities, 10% of those with type II diabetes mellitus had a past acute myocardial infarction episode, while 6% had a past stroke. The western region of Singapore had the highest number of high-risk individuals at 173,000 with at least one chronic condition, followed by the east at 169,000 and the north with the least at 137,000. Such estimates can assist in healthcare resource planning, which requires these spatial distributions for evidence-based policymaking and to investigate why such heterogeneities exist. The methodologies presented can be utilized within any urban setting where census data exists.

Projecting health-ageing trajectories in Europe using a dynamic microsimulation model

The extent of the challenges and opportunities that population ageing presents depends heavily on the population's health. Hence, for the development of appropriate strategies that enable countries to adopt the emerging demographic and epidemiological realities, information on future health trajectories of elderly population is a natural requirement. This study presents an innovative methodological framework for projecting the health of individuals using a dynamic microsimulation model that considers interactions between sociodemographic characteristics, health, mortality, bio-medical and behavioral risk factors. The model developed, called ATHLOS-Mic, is used to project the health of cohorts born before 1960 for the period 2015-2060 for selected European Countries using SHARE data to illustrate the possible effects of some selected risk factors and education on future health trajectories. Results show that, driven by a better educational attainment, each generation will be healthier than the previous one at same age. Also, we see that, on average, an individual of our base population will live about 18 more years since the start of the projection period, but only 5 years in good health. Finally, we find that a scenario that removes the effect of having a low level of education on individual health has the largest impact on the projected average health, the average number of years lived per person, and the average number of years lived in good health.

Projecting the prevalence of obesity in South Korea through 2040: a microsimulation modelling approach

OBJECTIVE

To project the prevalence of obesity in 2040 among individuals 19 years and older in South Korea.

DESIGN, SETTING, AND PARTICIPANTS

Using the 'Population Health Model-body mass index' (BMI) microsimulation model, the prevalence of obesity in Korean adults 19 years and older was projected until 2040. The model integrated individual survey data from the Korea Health Panel Survey of 2011 and 2012, population statistics based on resident registration, population projections and complete life tables categorised by sex and age. Birth rate, life expectancy and international migration were based on a medium growth scenario. The base population of Korean adults in 2012, devised through data aggregation, was 39 842 730. The prediction equations were formulated using BMI as the dependent variable; the individual's sex, age, smoking status, physical activity and preceding year's BMI were used as predictive factors.

OUTCOME MEASURE

BMI categorised by sex.

RESULTS

The median BMI for Korean adults in 2040 was expected to be 23.55 kg/m² (23.97 and 23.17 kg/m² for men and women, respectively). According to the Korean BMI classification, 70.05% of all adults were expected to be 'preobese' (ie, have BMIs 23-24.9 kg/m²) by 2040 (81.23% of men and 59.07% of women) and 24.88% to be 'normal'.

CONCLUSIONS

We explored the possibility of applying and expanding on the concept of microsimulation in the field of healthcare by combining data sources available in Korea and found that more than half of the adults in this study population will be preobese, and the proportions of 'obesity' and 'normal' will decrease compared with those in 2012. The results of our study will aid in devising healthy strategies and spreading public awareness for preventing this condition.

Development of a Synthetic Population Model for Assessing Excess Risk for Cardiovascular Disease Death

Importance

Evaluating the association of social determinants of health with chronic diseases at the population level requires access to individual-level factors associated with disease, which are rarely available for large populations. Synthetic populations are a possible alternative for this purpose.

Objective

To construct and validate a synthetic population that statistically mimics the characteristics and spatial disease distribution of a real population, using real and synthetic data.

Design, Setting, and Participants

This population-based decision analytical model used data for Allegheny County, Pennsylvania, collected from January 2015 to December 2016, to build a semisynthetic population based on the synthetic population used by the modeling and simulation platform FRED (A Framework for Reconstructing Epidemiological Dynamics). Disease status was assigned to this population using health insurer claims data from the 3 major insurance providers in the county or from the National Health and Nutrition Examination Survey. Biological, social, and other variables were also obtained from the National Health Interview Survey, Allegheny County, and public databases. Data analysis was performed from November 2016 to February 2020.

Exposures

Risk of cardiovascular disease (CVD) death.

Main Outcomes and Measures

Difference between expected and observed CVD death risk. A validated risk equation was used to estimate CVD death risk.

Results

The synthetic population comprised 1 188 112 individuals with demographic characteristics similar to those of the 2010 census population in the same county. In the synthetic population, the mean (SD) age was 40.6 (23.3) years, and 622 997 were female individuals (52.4%). Mean (SD) observed 4-year rate of excess CVD death risk at the census tract level was -40 (523) per 100 000 persons. The correlation of social determinant data with difference between expected and observed CVD death risk indicated that income- and education-based social determinants were associated with risk. Estimating improved social determinants of health and biological factors associated with disease did not entirely remove the excess in CVD death rates. That is, a 20% improvement in the most significant determinants still resulted in 105 census tracts with excess CVD death risk, which represented 24% of the county population.

Conclusions and Relevance

The results of this study suggest that creating a geographically explicit synthetic population from real and synthetic data is feasible and that synthetic populations are useful for modeling disease in large populations and for estimating the outcome of interventions.

Cerebral palsy in Canada, 2011-2031: results of a microsimulation modelling study of epidemiological and cost impacts

INTRODUCTION

The objective of our study was to present model-based estimates and projections on current and future health and economic impacts of cerebral palsy in Canada over a 20-year time horizon (2011-2031).

METHODS

We used Statistics Canada's Population Health Model (POHEM)-Neurological to simulate individuals' disease states, risk factors and health determinants and to describe and project health outcomes, including disease incidence, prevalence, life expectancy, health-adjusted life expectancy, health-related quality of life and health care costs over the life cycle of Canadians. Cerebral palsy cases were identified from British Columbia's health administrative data sources. A population-based cohort was then used to generate the incidence and mortality rates, enabling the projection of future incidence and mortality rates. A utility-based measure (Health Utilities Index Mark 3) was also included in the model to reflect various states of functional health to allow projections of health-related quality of life. Finally, we estimated caregiving parameters and health care costs from Canadian national surveys and health administrative data and included them as model parameters to assess the health and economic impact of cerebral palsy.

RESULTS

Although the overall crude incidence rate of cerebral palsy is projected to remain stable, newly diagnosed cases of cerebral palsy will rise from approximately 1800 in 2011 to nearly 2200 in 2031. In addition, the number of people with the condition is expected to increase from more than 75 000 in 2011 to more than 94 000 in 2031. Direct health care costs in constant 2010 Canadian dollars were about $11 700 for children with cerebral palsy aged 1-4 years versus about $600 for those without the condition. In addition, people with cerebral palsy tend to have longer periods in poorer health-related quality of life.

CONCLUSION

Individuals with cerebral palsy will continue to face challenges related to an ongoing need for specialized medical care and a rising need for supportive services. Our study offers important insights into future costs and impacts associated with cerebral palsy and provides valuable information that could be used to develop targeted health programs and strategies for Canadians living with this condition.

Transparency in Decision Modelling: What, Why, Who and How?

Transparency in decision modelling is an evolving concept. Recently, discussion has moved from reporting standards to open-source implementation of decision analytic models. However, in the debate about the supposed advantages and disadvantages of greater transparency, there is a lack of definition. The purpose of this article is not to present a case for or against transparency, but rather to provide a more nuanced understanding of what transparency means in the context of decision modelling and how it could be addressed. To this end, we review and summarise the discourse to date, drawing on our collective experience. We outline a taxonomy of the different manifestations of transparency, including reporting standards, reference models, collaboration, model registration, peer review and open-source modelling. Further, we map out the role and incentives for the various stakeholders, including industry, research organisations, publishers and decision makers. We outline the anticipated advantages and disadvantages of greater transparency with respect to each manifestation, as well as the perceived barriers and facilitators to greater transparency. These are considered with respect to the different stakeholders and with reference to issues including intellectual property, legality, standards, quality assurance, code integrity, health technology assessment processes, incentives, funding, software, access and deployment options, data protection and stakeholder engagement. For each manifestation of transparency, we discuss the 'what', 'why', 'who' and 'how'. Specifically, their meaning, why the community might (or might not) wish to embrace them, whose engagement as stakeholders is required and how relevant objectives might be realised. We identify current initiatives aimed to improve transparency to exemplify efforts in current practice and for the future.

Microsimulation Modeling in Oncology

PURPOSE

Microsimulation is a modeling technique that uses a sample size of individual units (microunits), each with a unique set of attributes, and allows for the simulation of downstream events on the basis of predefined states and transition probabilities between those states over time. In this article, we describe the history of the role of microsimulation in medicine and its potential applications in oncology as useful tools for population risk stratification and treatment strategy design for precision medicine.

METHODS

We conducted a comprehensive and methodical search of the literature using electronic databases-Medline, Embase, and Cochrane-for works published between 1985 and 2016. A medical subject heading search strategy was constructed for Medline searches by using a combination of relevant search terms, such as "microsimulation model medicine," "multistate modeling cancer," and "oncology."

RESULTS

Microsimulation modeling is particularly useful for the study of optimal intervention strategies when randomized control trials may not be feasible, ethical, or practical. Microsimulation models can retain memory of prior behaviors and states. As such, it allows an explicit representation and understanding of how various processes propagate over time and affect the final outcomes for an individual or in a population.

CONCLUSION

A well-calibrated microsimulation model can be used to predict the outcome of the event of interest for a new individual or subpopulations, assess the effectiveness and cost effectiveness of alternative interventions, and project the future disease burden of oncologic diseases. In the growing field of oncology research, a microsimulation model can serve as a valuable tool among the various facets of methodology available.

Use of Real-World Data Sources for Canadian Drug Pricing and Reimbursement Decisions: Stakeholder Views and Lessons for Other Countries

Background

Canada has a long history of the use of clinical evidence to support healthcare decision making. Given improvements in data holdings and analytic capacity in Canada and stakeholder interest, the purpose of this study is to reflect on perceptions of the value of real-world evidence in pricing and reimbursement decisions, barriers to its optimal use in pricing and reimbursement, current initiatives that may lead to its increased use, and what role the pharmaceutical industry may play in this.

Methods/Results

To capture stakeholder perceptions, ninety-one participants identified as key stakeholders were identified according to background roles and geography and invited to participate in four round table discussions conducted under Chatham House rule. Important themes emerging from these discussions included: (i) the need to understand what “real world” evidence means; (ii) barriers to using real world evidence from differences in access, governance, inter-operability, system structures, expertise, and quality across Canadian health systems; (iii) differing views on industry's role.

Conclusions

The use of real-world data in Canada to inform pricing and reimbursement decisions is far from routine but nascent and slowly increasing. Barriers, including interoperability concerns, may also apply to other federated health systems that need to focus on the networking of healthcare administrative data across provincial jurisdictional boundaries. There also appears to be a desire to see better use of pragmatic trials linked to these administrative data sets. Emerging initiatives are under way to use real world evidence more broadly, and include identification of common data elements and approaches to networking data.

Microsimulation Modeling for Health Decision Sciences Using R: A Tutorial

Microsimulation models are becoming increasingly common in the field of decision modeling for health. Because microsimulation models are computationally more demanding than traditional Markov cohort models, the use of computer programming languages in their development has become more common. R is a programming language that has gained recognition within the field of decision modeling. It has the capacity to perform microsimulation models more efficiently than software commonly used for decision modeling, incorporate statistical analyses within decision models, and produce more transparent models and reproducible results. However, no clear guidance for the implementation of microsimulation models in R exists. In this tutorial, we provide a step-by-step guide to build microsimulation models in R and illustrate the use of this guide on a simple, but transferable, hypothetical decision problem. We guide the reader through the necessary steps and provide generic R code that is flexible and can be adapted for other models. We also show how this code can be extended to address more complex model structures and provide an efficient microsimulation approach that relies on vectorization solutions.

Development and Validation of the Evaluation Platform in COPD (EPIC): A Population-Based Outcomes Model of COPD for Canada

BACKGROUND

We report the development, validation, and implementation of an open-source population-based outcomes model of chronic obstructive pulmonary disease (COPD) for Canada.

METHODS

Evaluation Platform in COPD (EPIC) is a discrete-event simulation model of Canadians 40 years of age or older. Three core features of EPIC are its open-population design (incorporating projections of future population growth, aging, and smoking trends), its incorporation of heterogeneity in lung function decline and burden of exacerbations, and its modeling of the natural history of COPD from inception. Multiple original data analyses, as well as values reported in the literature, were used to populate the model. Extensive face validity and internal and external validity evaluations were performed.

RESULTS

The model was internally validated on demographic projections, mortality rates, lung function trajectories, COPD exacerbations, costs and health state utility values, and stability of COPD prevalence over time within strata of risk factors. In external validation, it moderately overestimated the rate of overall exacerbations in 2 independent trials but generated consistent estimates of rate of severe exacerbations and mortality.

LIMITATIONS

In its current version, EPIC does not consider uncertainty in the evidence. Several components such as additional (e.g., environmental and occupational) risk factors, treatment, symptoms, and comorbidity will have to be added in future iterations. Predictive validity of EPIC needs to be examined prospectively against future empirical studies.

CONCLUSIONS

EPIC is the first multipurpose, open-source, outcome- and policy-focused model of COPD for Canada. Platforms of this type have the capacity to be iteratively updated to incorporate the latest evidence and to project the outcomes of many different scenarios within a consistent framework.

Internal consistency of a synthetic population construction method for chronic disease micro-simulation models

BACKGROUND

Micro-simulation models of risk-factors and chronic diseases are built increasingly often, and each model starts with an initial population. Constructing such populations when no survey data covering all variables are available is no trivial task, often requiring complex methods based on several (untested) assumptions. In this paper, we propose a method for evaluating the merits of construction methods, and apply this to one specific method: the construction method used in the DYNAMO-HIA model.

METHODS

The initial population constructed using the DYNAMO-HIA method is compared to another population constructed by starting a simulation with only newborns and simulating the course taken by one risk-factor and several diseases. In this simulation, the age- and sex-specific prevalence of the risk-factor is kept constant over time.

RESULTS

Our simulations show that, in general, the DYNAMO-HIA method clearly outperforms a method that assumes independence of the risk-factor and the prevalence of diseases and independence between all diseases. In many situations the DYNAMO-HIA method performs reasonably well, but in some the proportion with the risk-factor for those with a disease is under- or overestimated by as much as 10 percentage points. For determining comorbidity between diseases linked by a common causal disease or a common risk-factor it also performs reasonably well. However, the current method performs poorly for determining the comorbidity between one disease caused by the other.

CONCLUSION

The DYNAMO-HIA methods perform reasonably well; they outperform a baseline assumption of independence between the risk-factor and diseases in the initial population. The method for determining the comorbidity between diseases that are causally linked needs improvement. Given the existing discrepancies for situations with high relative risks, however, developing more elaborate methods based on running simulation models to generate an initial population would be worthwhile.

Simulation modeling to enhance population health intervention research for chronic disease prevention

Population Health Intervention Research (PHIR) is an expanding field that explores the health effects of population-level interventions conducted within and outside of the health sector. Simulation modeling-the use of mathematical models to predict health outcomes in populations given a set of specified inputs-is a useful, yet underutilized tool for PHIR. It can be employed at several phases of the research process: (1) planning and designing PHIR studies; (2) implementation; and (3) knowledge translation of findings across settings and populations. Using the example of community-wide, built environment interventions for the prevention of type 2 diabetes, we demonstrate how simulation models can be a powerful technique for chronic disease prevention research within PHIR. With increasingly available data on chronic disease risk factors and outcomes, the use of simulation modeling in PHIR for chronic disease prevention is anticipated to grow. There is a continued need to ensure models are appropriately validated and researchers should be cautious in their interpretation of model outputs given the uncertainties that are inherent with simulation modeling approaches. However, given the complexity of disease pathways and methodological challenges of PHIR studies, simulation models can be a valuable tool for researchers studying population interventions that hold the potential to improve health and reduce health inequities.

Physician extenders on surgical services: the need for a systems perspective

SUMMARY

Adding physician extenders (PEs) to surgical teams has the potential to affect care delivery in multiple ways. To develop evidence-based recommendations on integrating PEs into surgical teams, we must recognize that patient care is a complex, adaptive system and requires a health systems perspective to understand how changes will affect outcomes. It is the best method of assessing the system adaptations and trade-offs of adding PEs prior to implementation. Such work would help to optimize research and management of limited health care resources.

Reducing the costs of chronic kidney disease while delivering quality health care: a call to action

The treatment of chronic kidney disease (CKD) and of end-stage renal disease (ESRD) imposes substantial societal costs. Expenditure is highest for renal replacement therapy (RRT), especially in-hospital haemodialysis. Redirection towards less expensive forms of RRT (peritoneal dialysis, home haemodialysis) or kidney transplantation should decrease financial pressure. However, costs for CKD are not limited to RRT, but also include nonrenal health-care costs, costs not related to health care, and costs for patients with CKD who are not yet receiving RRT. Even if patients with CKD or ESRD could be given the least expensive therapies, costs would decrease only marginally. We therefore propose a consistent and sustainable approach focusing on prevention. Before a preventive strategy is favoured, however, authorities should carefully analyse the cost to benefit ratio of each strategy. Primary prevention of CKD is more important than secondary prevention, as many other related chronic diseases, such as diabetes mellitus, hypertension, cardiovascular disease, liver disease, cancer, and pulmonary disorders could also be prevented. Primary prevention largely consists of lifestyle changes that will reduce global societal costs and, more importantly, result in a healthy, active, and long-lived population. Nephrologists need to collaborate closely with other sectors and governments, to reach these aims.

A Practical ANOVA Approach for Uncertainty Analysis in Population-Based Disease Microsimulation Models

OBJECTIVES

To provide a practical approach for calculating uncertainty intervals and variance components associated with initial-condition and dynamic-equation parameters in computationally expensive population-based disease microsimulation models.

METHODS

In the proposed uncertainty analysis approach, we calculated the required computational time and the number of runs given a user-defined error bound on the variance of the grand mean. The equations for optimal sample sizes were derived by minimizing the variance of the grand mean using initial estimates for variance components. Finally, analysis of variance estimators were used to calculate unbiased variance estimates.

RESULTS

To illustrate the proposed approach, we performed uncertainty analysis to estimate the uncertainty associated with total direct cost of osteoarthritis in Canada from 2010 to 2031 according to a previously published population health microsimulation model of osteoarthritis. We first calculated crude estimates for initial-population sampling and dynamic-equation parameters uncertainty by performing a small number of runs. We then calculated the optimal sample sizes and finally derived 95% uncertainty intervals of the total cost and unbiased estimates for variance components. According to our results, the contribution of dynamic-equation parameter uncertainty to the overall variance was higher than that of initial parameter sampling uncertainty throughout the study period.

CONCLUSIONS

The proposed analysis of variance approach provides the uncertainty intervals for the mean outcome in addition to unbiased estimates for each source of uncertainty. The contributions of each source of uncertainty can then be compared with each other for validation purposes so as to improve the model accuracy.

Multiple sclerosis in Canada 2011 to 2031: results of a microsimulation modelling study of epidemiological and economic impacts

INTRODUCTION

The objective of our study was to present model-based estimates and projections on current and future health and economic impacts of multiple sclerosis (MS) in Canada over a 20-year time horizon (2011-2031).

METHODS

Using Statistics Canada's Population Health Microsimulation Model (POHEM) framework, specifically the population-based longitudinal, microsimulation model named POHEM-Neurological, we identified people with MS from health administrative data sources and derived incidence and mortality rate parameters from a British Columbia population-based cohort for future MS incidence and mortality projections. We also included a utility-based measure (Health Utilities Index Mark 3) reflecting states of functional health to allow projections of health-related quality of life. Finally, we estimated caregiving parameters and health care costs from Canadian national surveys and health administrative data and included them as model parameters to assess the health and economic impact of the neurological conditions.

RESULTS

The number of incident MS cases is expected to rise slightly from 4051 cases in 2011 to 4794 cases per 100 000 population in 2031, and the number of Canadians affected by MS will increase from 98 385 in 2011 to 133 635 in 2031. The total per capita health care cost (excluding out-of-pocket expenses) for adults aged 20 and older in 2011 was about $16 800 for individuals with MS, and approximately $2500 for individuals without a neurological condition. Thus, after accounting for additional expenditures due to MS (excluding out-of-pocket expenses), total annual health sector costs for MS are expected to reach $2.0 billion by 2031. As well, the average out-of-pocket expenditure for people with MS was around $1300 annually throughout the projection period.

CONCLUSION

MS is associated with a significant economic burden on society, since it usually affects young adults during prime career- and family-building years. Canada has a particularly high prevalence of MS, so research such as the present study is essential to provide a better understanding of the current and future negative impacts of MS on the Canadian population, so that health care system policymakers can best plan how to meet the needs of patients who are affected by MS. These findings also suggest that identifying strategies to prevent MS and more effectively treat the disease are needed to mitigate these future impacts.

Alzheimer's and other dementias in Canada, 2011 to 2031: a microsimulation Population Health Modeling (POHEM) study of projected prevalence, health burden, health services, and caregiving use

BACKGROUND

Worldwide, there is concern that increases in the prevalence of dementia will result in large demands for caregivers and supportive services that will be challenging to address. Previous dementia projections have either been simple extrapolations of prevalence or macrosimulations based on dementia incidence.

METHODS

A population-based microsimulation model of Alzheimer's and related dementias (POHEM:Neurological) was created using Canadian demographic data, estimates of dementia incidence, health status (health-related quality of life and mortality risk), health care costs and informal caregiving use. Dementia prevalence and 12 other measures were projected to 2031.

RESULTS

Between 2011 and 2031, there was a projected two-fold increase in the number of people living with dementia in Canada (1.6-fold increase in prevalence rate). By 2031, the projected informal (unpaid) caregiving for dementia in Canada was two billion hours per year, or 100 h per year per Canadian of working age.

CONCLUSIONS

The projected increase in dementia prevalence was largely related to the expected increase in older Canadians, with projections sensitive to changes in the age of dementia onset.