First do no harm: extending the debate on the provision of preventive tamoxifen

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

The POpulation HEalth Model (POHEM) is a health microsimulation model that was developed at Statistics Canada in the early 1990s. POHEM draws together rich multivariate data from a wide range of sources to simulate the lifecycle of the Canadian population, specifically focusing on aspects of health. The model dynamically simulates individuals’ disease states, risk factors, and health determinants, in order to describe and project health outcomes, including disease incidence, prevalence, life expectancy, health-adjusted life expectancy, quality of life, and healthcare costs. Additionally, POHEM was conceptualized and built with the ability to assess the impact of policy and program interventions, not limited to those taking place in the healthcare system, on the health status of Canadians. Internationally, POHEM and other microsimulation models have been used to inform clinical guidelines and health policies in relation to complex health and health system problems. This paper provides a high-level overview of the rationale, methodology, and applications of POHEM. Applications of POHEM to cardiovascular disease, physical activity, cancer, osteoarthritis, and neurological diseases are highlighted.

Canadian Cancer Risk Management Model: evaluation of cancer control

OBJECTIVES

The aim of this study was to develop a decision support tool to assess the potential benefits and costs of new healthcare interventions.

METHODS

The Canadian Partnership Against Cancer (CPAC) commissioned the development of a Cancer Risk Management Model (CRMM)--a computer microsimulation model that simulates individual lives one at a time, from birth to death, taking account of Canadian demographic and labor force characteristics, risk factor exposures, and health histories. Information from all the simulated lives is combined to produce aggregate measures of health outcomes for the population or for particular subpopulations.

RESULTS

The CRMM can project the population health and economic impacts of cancer control programs in Canada and the impacts of major risk factors, cancer prevention, and screening programs and new cancer treatments on population health and costs to the healthcare system. It estimates both the direct costs of medical care, as well as lost earnings and impacts on tax revenues. The lung and colorectal modules are available through the CPAC Web site (www.cancerview.ca/cancerrriskmanagement) to registered users where structured scenarios can be explored for their projected impacts. Advanced users will be able to specify new scenarios or change existing modules by varying input parameters or by accessing open source code. Model development is now being extended to cervical and breast cancers.

Uncertainty Analysis in Population-Based Disease Microsimulation Models

Objective. Uncertainty analysis (UA) is an important part of simulation model validation. However, literature is imprecise as to how UA should be performed in the context of population-based microsimulation (PMS) models. In this expository paper, we discuss a practical approach to UA for such models. Methods. By adapting common concepts from published UA guidelines, we developed a comprehensive, step-by-step approach to UA in PMS models, including sample size calculation to reduce the computational time. As an illustration, we performed UA for POHEM-OA, a microsimulation model of osteoarthritis (OA) in Canada. Results. The resulting sample size of the simulated population was 500,000 and the number of Monte Carlo (MC) runs was 785 for 12-hour computational time. The estimated 95% uncertainty intervals for the prevalence of OA in Canada in 2021 were 0.09 to 0.18 for men and 0.15 to 0.23 for women. The uncertainty surrounding the sex-specific prevalence of OA increased over time. Conclusion. The proposed approach to UA considers the challenges specific to PMS models, such as selection of parameters and calculation of MC runs and population size to reduce computational burden. Our example of UA shows that the proposed approach is feasible. Estimation of uncertainty intervals should become a standard practice in the reporting of results from PMS models.

The evaluation of cancer control interventions in lung cancer using the Canadian Cancer Risk Management Model

SUMMARY Background: Rising healthcare costs will increasingly require policy-makers to make difficult decisions based on the potential benefits and costs of new healthcare interventions. The Canadian Partnership Against Cancer commissioned the development of the Cancer Risk Management Model as a tool to aid such decisions. This computer microsimulation model projects future population health and economic impacts of cancer control programs in Canada. Lung cancer was the first simulation module to be developed and was selected because of the magnitude of lung cancer burden in Canada and recent screening and treatment interventions that require policy decisions. Methods: The model simulates one individual life at a time, from birth to death, taking account of Canadian demographic and labor force characteristics, risk factor exposures and health histories, and then combines this information from all the simulated lives to produce aggregate measures of health outcomes for the Canadian population as a whole or for particular subpopulations. The direct costs of medical care can be estimated, as well as lost earnings and impacts on tax revenues. Results: The lung module is available through the Canadian Partnership Against Cancer website to registered users where structured scenarios can be explored for their projected impacts. Conclusion: The Cancer Risk Management Model for lung cancer is now available via the internet to assist healthcare policy analysts, researchers and decision-makers in their work.

Validation of population-based disease simulation models: a review of concepts and methods

BACKGROUND

Computer simulation models are used increasingly to support public health research and policy, but questions about their quality persist. The purpose of this article is to review the principles and methods for validation of population-based disease simulation models.

METHODS

We developed a comprehensive framework for validating population-based chronic disease simulation models and used this framework in a review of published model validation guidelines. Based on the review, we formulated a set of recommendations for gathering evidence of model credibility.

RESULTS

Evidence of model credibility derives from examining: 1) the process of model development, 2) the performance of a model, and 3) the quality of decisions based on the model. Many important issues in model validation are insufficiently addressed by current guidelines. These issues include a detailed evaluation of different data sources, graphical representation of models, computer programming, model calibration, between-model comparisons, sensitivity analysis, and predictive validity. The role of external data in model validation depends on the purpose of the model (e.g., decision analysis versus prediction). More research is needed on the methods of comparing the quality of decisions based on different models.

CONCLUSION

As the role of simulation modeling in population health is increasing and models are becoming more complex, there is a need for further improvements in model validation methodology and common standards for evaluating model credibility.

Development of a population-based microsimulation model of osteoarthritis in Canada

OBJECTIVES

The purpose of the study was to develop a population-based simulation model of osteoarthritis (OA) in Canada that can be used to quantify the future health and economic burden of OA under a range of scenarios for changes in the OA risk factors and treatments. In this article we describe the overall structure of the model, sources of data, derivation of key input parameters for the epidemiological component of the model, and preliminary validation studies.

DESIGN

We used the Population Health Model (POHEM) platform to develop a stochastic continuous-time microsimulation model of physician-diagnosed OA. Incidence rates were calibrated to agree with administrative data for the province of British Columbia, Canada. The effect of obesity on OA incidence and the impact of OA on health-related quality of life (HRQL) were modeled using Canadian national surveys.

RESULTS

Incidence rates of OA in the model increase approximately linearly with age in both sexes between the ages of 50 and 80 and plateau in the very old. In those aged 50+, the rates are substantially higher in women. At baseline, the prevalence of OA is 11.5%, 13.6% in women and 9.3% in men. The OA hazard ratios for obesity are 2.0 in women and 1.7 in men. The effect of OA diagnosis on HRQL, as measured by the Health Utilities Index Mark 3 (HUI3), is to reduce it by 0.10 in women and 0.14 in men.

CONCLUSIONS

We describe the development of the first population-based microsimulation model of OA. Strengths of this model include the use of large population databases to derive the key parameters and the application of modern microsimulation technology. Limitations of the model reflect the limitations of administrative and survey data and gaps in the epidemiological and HRQL literature.

Breast cancer prevention

We have developed a new approach for breast cancer prevention, capitalizing in the preventive effect of early first full-term pregnancy, hormonally induced differentiation and our ability to identify specific genomic signatures that allow us to predict risk reduction. Early pregnancy imprints in the breast permanent genomic changes or a 'signature' that reduces the susceptibility of this organ to cancer. At cellular level, what we have achieved is the shifting of the Stem Cell 1 population, highly susceptible to cancer, to a population of Stem Cell 2 that is refractory to carcinogenesis. In a case-control study, we have compared the gene expression profile in normal breast tissue from nulliparous and parous postmenopausal women with (case) and without (control) breast cancer. We have determined that early first full-term pregnancy induces a specific genomic signature in the postmenopausal breast that is the biomarker for the Stem cell 2. The Stem cell 2 contains specific genes controlling transcription, RNA processing, immune response, apoptosis and DNA repair. We have further detected in the plasma, using an ELISA assay, the proteins coded by the gene signature. We are developing clinical trials to demonstrate the proof of the principle that r-hCG can induce in the human breast a genomic signature of the Stem cell 2. This is a concept that challenges the currently available chemopreventive agents that need to be given for extended periods for maintaining the suppression of a specific metabolic pathway or the abrogation of the function of an organ.

Ocular toxicity during adjuvant chemoendocrine therapy for early breast cancer

BACKGROUND

Others have reported ocular toxicity after adjuvant chemoendocrine therapy, but this study looked at ocular toxicity in similarly treated patients from large randomized clinical trials.

METHODS

Information was retrieved on incidence and timing of ocular toxicity from the International Breast Cancer Study Group (IBCSG) database of 4948 eligible patients randomized to receive tamoxifen or toremifene alone or in combination with chemotherapy (either concurrently or sequentially). Case reports of patients with ocular toxicity were evaluated to determine whether ocular toxicity occurred during chemotherapy and/or hormonal therapy. Additional information was obtained from participating institutions for patients in whom ocular toxicity occurred after chemotherapy but during administration of tamoxifen or toremifene.

RESULTS

Ocular toxicity was reported in 538 of 4948 (10.9%) patients during adjuvant treatment, mainly during chemotherapy. Forty-five of 4948 (0.9%) patients had ocular toxicity during hormone therapy alone, but only 30 (0.6%) patients had ocular toxicity reported either without receiving any chemotherapy or beyond 3 months after completing chemotherapy and, thus, possibly related to tamoxifen or toremifene. In 3 cases, retinal alterations, without typical aspects of tamoxifen toxicity, were reported; 4 patients had cataract (2 bilateral), 12 impaired visual acuity, 10 ocular irritation, 1 optical neuritis, and the rest had other symptoms.

CONCLUSION

Ocular toxicity during adjuvant therapy is a common side effect mainly represented by irritative symptoms due to chemotherapy. By contrast, ocular toxicity during hormonal therapy is rare and does not appear to justify a regular program of ocular examination. However, patients should be informed of this rare side effect so that they may seek prompt ophthalmic evaluation for ocular complaints.

Preferences of Women Evaluating Risks of Tamoxifen (POWER) study of preferences for tamoxifen for breast cancer risk reduction

BACKGROUND

The objective of this study was to understand the attitudes and preferences of risk-eligible women regarding use of tamoxifen for breast cancer risk reduction.

METHODS

A cross-sectional, mixed-methods interview study was conducted at a university medical center and at community sites. Participants were women who had an estimated 5-year breast cancer risk > or = 1.7% and no prior breast cancer. Interviews were conducted in English or Spanish. The interview included a 15-minute, standardized educational session on the potential benefits and harms of tamoxifen followed by close-ended and open-ended questions about participants' inclinations to take tamoxifen and factors important to their decision. A demographic questionnaire, a test on knowledge of potential benefits and harms of tamoxifen, and an interview evaluation were included.

RESULTS

Two hundred fifty-five women completed interviews. Their estimated mean 5-year breast cancer risk was 2.8%; and their mean self-perceived 5-year risk was 32.7%. After the educational intervention, 45 women (17.6%) were inclined to take tamoxifen. Very high risk women (> 3.5%) were no more inclined to take it than women with lower risk (1.7-3.5%). In a multivariable analysis, lower income, confidence in the effectiveness of tamoxifen, and concern about fractures were associated with being inclined to take it; concern about pulmonary embolism, dyspareunia, cataracts, and low self-perceived breast cancer risk were associated negatively with taking tamoxifen. Participants expressed concerns about adverse effects.

CONCLUSIONS

Less than 20% of women were interested in tamoxifen after education about potential benefits and harms, despite a very high self-perceived breast cancer risk. Candidate chemoprevention agents must have few potential adverse effects to achieve widespread acceptance.

American Society of Clinical Oncology technology assessment of pharmacologic interventions for breast cancer risk reduction including tamoxifen, raloxifene, and aromatase inhibition

OBJECTIVE

To update an evidence-based technology assessment of chemoprevention strategies for breast cancer risk reduction. POTENTIAL INTERVENTIONS: Tamoxifen, raloxifene, aromatase inhibition, and fenretinide.

OUTCOMES

Outcomes of interest include breast cancer incidence, breast cancer-specific survival, overall survival, and net health benefit.

EVIDENCE

A comprehensive, formal literature review was conducted for relevant topics. Testimony was collected from invited experts and interested parties. The American Society of Clinical Oncology (ASCO) prescribed technology assessment procedure was followed.

VALUES

More weight was given to published randomized trials. BENEFITS/HARMS: A woman's decision regarding breast cancer risk reduction strategies is complex and will depend on the importance and weight attributed to information regarding both cancer- and noncancer-related risks and benefits.

CONCLUSIONS

For women with a defined 5-year projected breast cancer risk of > or= 1.66%, tamoxifen (at 20 mg/d for 5 years) may be offered to reduce their risk. Risk/benefit models suggest that greatest clinical benefit with least side effects is derived from use of tamoxifen in younger (premenopausal) women (who are less likely to have thromboembolic sequelae and uterine cancer), women without a uterus, and women at higher breast cancer risk. Data do not as yet suggest that tamoxifen provides an overall health benefit or increases survival. In all circumstances, tamoxifen use should be discussed as part of an informed decision-making process with careful consideration of individually calculated risks and benefits. Use of tamoxifen combined with hormone replacement therapy or use of raloxifene, any aromatase inhibitor or inactivator, or fenretinide to lower the risk of developing breast cancer is not recommended outside of a clinical trial setting. This technology assessment represents an ongoing process and recommendations will be updated in a timely matter.

VALIDATION

The conclusions were endorsed by the ASCO Health Services Research Committee and the ASCO Board of Directors.