Medical Decision Making

Identifying a Single Optimal Integrated Cervical Cancer Prevention Policy in Norway: A Cost-Effectiveness Analysis

Background Interventions targeting the same disease but at different points along the disease continuum (e.g., screening and vaccination to prevent cervical cancer [CC]) are often evaluated in isolation, which can affect cost-effectiveness profiles and policy conclusions. We evaluated nonavalent human papillomavirus (HPV) vaccine (9vHPV) compared with bivalent HPV vaccine (2vHPV) alongside deintensified screening intervals for a vaccinated birth cohort to inform a single optimal integrated CC prevention policy. Methods Using a multimodeling approach, we evaluated the health and economic impacts of alternative CC screening strategies for a Norwegian birth cohort eligible for HPV vaccination in 2021 assuming they received 1) 2vHPV or 2) 9vHPV. We conducted 1) a restricted analysis that evaluated the optimal HPV vaccine under current screening guidelines; and 2) a comprehensive analysis including alternative screening and vaccination strategy combinations. We calculated incremental cost-effectiveness ratios (ICERs) and evaluated them according to different cost-effectiveness thresholds. Results Assuming a cost-effectiveness threshold of $40,000 per quality-adjusted life year (QALY) gained, we found that, while holding screening intensity fixed, switching the routine vaccination program in Norway from 2vHPV to 9vHPV would not be considered cost-effective (ICER of $132,700 per QALY gained). However, when allowing for varying intensities of CC screening, we found that switching to 9vHPV would be cost-effective compared with 2vHPV under an alternative threshold of $55,000 per QALY gained, if coupled with reductions in the number of lifetime screens. Conclusions Our analysis highlights the importance of evaluating the full potential policy landscape for country-level decision makers considering policy adoption, including nonindependent primary and secondary prevention efforts, to draw appropriate conclusions and avoid sub-optimal outcomes. Highlights Without evaluating the full potential policy landscape, including primary and secondary prevention efforts, country-level decision makers may not be able to draw appropriate policy conclusions, resulting in suboptimal outcomes. An applied example from cervical cancer prevention in Norway compared a restricted analysis of current screening guidelines to a comprehensive analysis including alternative screening and vaccination strategy combinations. We found that a switch from bivalent to nonavalent human papillomavirus vaccine would be considered cost-effective in Norway if coupled with reductions in the number of lifetime screens compared with the current screening strategy. A comprehensive analysis that considers how different types of interventions along the disease continuum affect each other will be critical for decision makers interpreting cost-effectiveness analysis results.

Causal Estimation of Long-term Intervention Cost-effectiveness Using Genetic Instrumental Variables: An Application to Cancer

Background This article demonstrates a means of assessing long-term intervention cost-effectiveness in the absence of data from randomized controlled trials and without recourse to Markov simulation or similar types of cohort simulation. Methods Using a Mendelian randomization study design, we developed causal estimates of the genetically predicted effect of bladder, breast, colorectal, lung, multiple myeloma, ovarian, prostate, and thyroid cancers on health care costs and quality-adjusted life-years (QALYs) using outcome data drawn from the UK Biobank cohort. We then used these estimates in a simulation model to estimate the cost-effectiveness of a hypothetical population-wide preventative intervention based on a repurposed class of antidiabetic drugs known as sodium-glucose cotransporter-2 (SGLT2) inhibitors very recently shown to reduce the odds of incident prostate cancer. Results Genetic liability to prostate cancer and breast cancer had material causal impacts on either or both health care costs and QALYs. Mendelian randomization results for the less common cancers were associated with considerable uncertainty. SGLT2 inhibition was unlikely to be a cost-effective preventative intervention for prostate cancer, although this conclusion depended on the price at which these drugs would be offered for a novel anticancer indication. Implications Our new causal estimates of cancer exposures on health economic outcomes may be used as inputs into decision-analytic models of cancer interventions such as screening programs or simulations of longer-term outcomes associated with therapies investigated in randomized controlled trials with short follow-ups. Our method allowed us to rapidly and efficiently estimate the cost-effectiveness of a hypothetical population-scale anticancer intervention to inform and complement other means of assessing long-term intervention value. Highlights The article demonstrates a novel method of assessing long-term intervention cost-effectiveness without relying on randomized controlled trials or cohort simulations. Mendelian randomization was used to estimate the causal effects of certain cancers on health care costs and quality-adjusted life-years (QALYs) using data from the UK Biobank cohort. Given causal data on the association of different cancer exposures on costs and QALYs, it was possible to simulate the cost-effectiveness of an anticancer intervention. Genetic liability to prostate cancer and breast cancer significantly affected health care costs and QALYs, but the hypothetical intervention using SGLT2 inhibitors for prostate cancer may not be cost-effective, depending on the drug’s price for the new anticancer indication. The methods we propose and implement can be used to efficiently estimate intervention cost-effectiveness and to inform decision making in all manner of preventative and therapeutic contexts.

Optimizing the Harms and Benefits of Cervical Screening in a Partially Vaccinated Population in Ontario, Canada: A Modeling Study

Objectives In Ontario, Canada, the first cohorts who were offered school-based human papillomavirus (HPV) vaccination are now eligible for cervical screening. We determined which screening strategies for these populations would result in optimal harms–benefits ratios of screening. Methods We used the hybrid microsimulation model STDSIM- MISCAN-Cervix to determine the harms and cancers prevented of 309 different primary HPV screening strategies, varying by screening ages and triage methods. In addition, we performed an unstratified (i.e., uniform screening protocols) and stratified (i.e., screening protocols by vaccination status) analysis. Harms induced were quantified as a weighted combination of the number of primary HPV-based screens and colposcopy referrals at 1:10. A harms–benefit acceptability threshold of number of harms induced for each cancer prevented was set at the estimated ratio under current screening recommendations in unvaccinated cohorts in Ontario. Results For the unstratified scenario, 5 lifetime screens with HPV16/18 genotyping was optimal. For the stratified scenario, the optimal scenario was 3 lifetime screens with HPV16/18/31/33/45/52/58 genotyping for vaccinated individuals versus 6 lifetime screens with HPV16/18 genotyping for unvaccinated individuals. Conclusions We determined the optimal cervical screening strategy in Ontario over the next decades. To maintain an optimal harms–benefits balance of screening, the Ontario Cervical Screening Program could adjust screening recommendations in the future to reduce the number of lifetime screens and extend screening intervals to account for vaccinated cohorts. Stratified screening by vaccination status could further improve this balance on an individual level. Highlights People in cohorts who were offered HPV vaccination as part of Ontario’s school-based program may achieve a better harms–benefits balance if cervical screening recommendations are updated to a less intensive protocol in future. This holds for the cohorts as a whole (i.e., unstratified screening) as well as for both vaccinated and unvaccinated individuals in these cohorts. Instead of using a cost-effectiveness threshold, it is possible to determine optimal screening protocols by calculating an acceptability threshold using alternative harms–benefits measures based on existing policy. Using univariate harms measures such as primary HPV screening tests or colposcopies per 1,000 people can yield biases in optimizing cervical screening programs. Alternatively, combining both primary screens and colposcopy referrals could provide a more accurate harms measure and result in optimal strategies with a better balance between harms and benefits.

The Impact of Different Screening Model Structures on Cervical Cancer Incidence and Mortality Predictions: The Maximum Clinical Incidence Reduction (MCLIR) Methodology

Background. To interpret cervical cancer screening model results, we need to understand the influence of model structure and assumptions on cancer incidence and mortality predictions. Cervical cancer cases and deaths following screening can be attributed to 1) (precancerous or cancerous) disease that occurred after screening, 2) disease that was present but not screen detected, or 3) disease that was screen detected but not successfully treated. We examined the relative contributions of each of these using 4 Cancer Intervention and Surveillance Modeling Network (CISNET) models. Methods. The maximum clinical incidence reduction (MCLIR) method compares changes in the number of clinically detected cervical cancers and mortality among 4 scenarios: 1) no screening, 2) one-time perfect screening at age 45 that detects all existing disease and delivers perfect (i.e., 100% effective) treatment of all screen-detected disease, 3) one-time realistic-sensitivity cytological screening and perfect treatment of all screen-detected disease, and 4) one-time realistic-sensitivity cytological screening and realistic-effectiveness treatment of all screen-detected disease. Results. Predicted incidence reductions ranged from 55% to 74%, and mortality reduction ranged from 56% to 62% within 15 years of follow-up for scenario 4 across models. The proportion of deaths due to disease not detected by screening differed across the models (21%–35%), as did the failure of treatment (8%–16%) and disease occurring after screening (from 1%–6%). Conclusions. The MCLIR approach aids in the interpretation of variability across model results. We showed that the reasons why screening failed to prevent cancers and deaths differed between the models. This likely reflects uncertainty about unobservable model inputs and structures; the impact of this uncertainty on policy conclusions should be examined via comparing findings from different well-calibrated and validated model platforms.

Evaluating Risk-Stratified HPV Catch-up Vaccination Strategies: Should We Go beyond Age 26?

Background Human papillomavirus (HPV) is the most common sexually transmitted infection in the United States. HPV can cause genital warts and multiple types of cancers in females. HPV vaccination is recommended to youth age 11 or 12 years before sexual initiation to prevent onset of HPV-related diseases. For females who have not been vaccinated previously, catch-up vaccines are recommended through age 26. The extent to which catch-up vaccines are beneficial in terms of disease prevention and cost-effectiveness is questionable given that some women may have been exposed to HPV before receiving the catch-up vaccination. This study aims to examine whether the cutoff age of catch-up vaccination should be determined based on an individual woman’s risk characteristic instead of a one-size-fits-all age 26. Methods We developed a microsimulation model to evaluate multiple clinical outcomes of HPV vaccination for different women based on a number of personal attributes. We modeled the impact of HPV vaccination at different ages on every woman and tracked her course of life to estimate the clinical outcomes that resulted from receiving vaccines. As the simulation model is risk stratified, we used extreme gradient boosting to build an HPV risk model estimating every woman’s dynamic HPV risk over time for the lifetime simulation model. Results Our study shows that catch-up vaccines still benefit all women after age 26 from the perspective of clinical outcomes. Women facing high risk of HPV infection are expected to gain more health benefits compared with women with low HPV risk. Conclusions From a cancer prevention perspective, this study suggests that the catch-up vaccine after age 26 should be deliberately considered.

Vaccination Strategies against HPV Infection and Cervical Cancer in China: A Transmission Modeling Study

Background Cervical cancer, driven predominantly by persistent high-risk human papillomavirus (HPV) infection, ranks as the fourth most common malignancy in women worldwide. China faces barriers to achieving the World Health Organization (WHO) 2030 elimination targets due to low vaccination rates and complex demographics. Strategic intervention optimization is critical for accelerating elimination. Methods We developed an age-stratified deterministic compartmental model integrating demographic data and HPV transmission dynamics, capturing heterogeneity in age, sex, sexual activity, and intervention efficacy. The model simulated cervical cancer natural history, including HPV infection, progression to precancerous lesions, and invasive cancer and was calibrated using epidemiological data from the Global Burden of Disease. We evaluated multiple vaccination scenarios (varying coverage rates, age groups, and durations) to project incidence trajectories, estimate elimination timelines, and calculate the reproduction number. Sensitivity analyses were conducted to assess parameter effects. Results Without vaccination, HPV infection becomes endemic (R 0 = 1.38), causing 2.92 million cervical cancer cases in China during 2021 to 2070. Maintaining the 2020 vaccination rate would prevent 1.01 million cases in this period. While prioritizing females aged 15 to 26 y maximizes the per-dose impact, expanding vaccination to all females aged ≥15 y is essential for achieving elimination before 2040. Even single-year vaccination would confer >50-y protection. A higher vaccination rate accelerates elimination: annual rates of 0.09, 0.15, and 0.21 among females aged ≥15 y achieve elimination by 2037, 2035, and 2034, respectively, accelerating timelines by 15 to 20 y compared with strategies targeting only 15- to 26-y-olds. Conclusions HPV vaccination is pivotal for reducing cervical cancer burden in China, with prioritizing women aged 15 to 26 y as the optimal strategy. Expanding vaccination to all women aged ≥15 y can accelerate the achievement of WHO elimination targets. Highlights An age-stratified model simulates HPV transmission patterns and assesses cervical cancer interventions. Without intervention, HPV remains endemic (R 0 = 1.38), causing 2.92 million cervical cancer cases in China (2021–2070). Prioritizing 15- to 26-y-olds maximizes the per-dose impact, but expanding to 15+ y cohorts is essential for elimination. Even a single year of vaccination offers >50 y of protection. Females ≥15 y vaccinated annually at rates of 0.09, 0.15, and 0.21 achieve elimination by 2037, 2035, and 2034, respectively.

Changes in Risk Tolerance for Ovarian Cancer Prevention Strategies during the COVID-19 Pandemic: Results of a Discrete Choice Experiment

Background Prior to COVID-19, little was known about how risks associated with such a pandemic would compete with and influence patient decision making regarding cancer risk reducing medical decision making. We investigated how the pandemic affected preferences for medical risk-reducing strategies among women at elevated risk of breast or ovarian cancer. Methods We conducted a discrete choice experiment. Women about to undergo genetic testing and counseling at 2 medical centers participated. Enrollment occurred between 2019 and 2022, allowing us to investigate changes in preferences from before the pandemic to after the pandemic. Women chose from permuted scenarios that specified type of surgery, age of menopause, quality of menopausal symptoms, and risk of ovarian cancer, heart disease, or osteoporosis. Results A total of 355 women, with a median age of 36 y, participated. In 2019, women were less likely to choose prevention scenarios with higher ovarian cancer risk (odds ratio [OR] = 0.42 per 10-point increase in risk, 95% confidence interval [CI] 0.22–0.61). In June 2020, the effect of higher ovarian cancer risk scenarios on choice was attenuated (OR = 0.86, 95% CI 0.68–1.04), with the effect becoming more salient again by July 2021 (OR = 0.59, 95% CI 0.52–0.67) ( P = 0.039 for test of temporal interaction). No other attribute demonstrated a temporal trend. Conclusion The risks associated with the COVID-19 pandemic may have attenuated the impact of risk of ovarian cancer on choice of risk-reducing prevention strategies for ovarian cancer. The maximum attenuation occurred at the beginning of the pandemic when access to risk-reducing surgery was most restricted. Our findings highlight how individuals evaluate competing health risks and adjust their uptake of cancer prevention strategies when faced with a future pandemic or similar global crisis. Highlights In this discrete choice experiment, women were much less likely to choose prevention scenarios that had higher ovarian cancer risk prior to the COVID-19 pandemic than after the pandemic. The attenuation of preferences may have persisted through 2022. COVID-19 may have altered the relative importance of factors that motivate women to undergo risk-reducing surgeries.