Current recommendations on the estimation of transition probabilities in Markov cohort models for use in health care decision-making: a targeted literature review

A health decision analytical model to evaluate the cost-effectiveness of female genital schistosomiasis screening strategies: The female genital schistosomiasis SCREEN framework

Female genital schistosomiasis is a chronic gynaecological disease caused by the waterborne parasite Schistosoma (S.) haematobium. It affects an estimated 30-56 million girls and women globally, mostly in sub-Saharan Africa where it is endemic, and negatively impacts their sexual and reproductive life. Recent studies found evidence of an association between female genital schistosomiasis and increased prevalence of HIV and cervical precancer lesions. Despite the large population at risk, the burden and impact of female genital schistosomiasis are scarcely documented, resulting in neglect and insufficient resource allocation. There is currently no standardised method for individual or population-based female genital schistosomiasis screening and diagnosis which hinders accurate assessment of disease burden in endemic countries. To optimise financial allocations for female genital schistosomiasis screening, it is necessary to explore the cost-effectiveness of different strategies by combining cost and impact estimates. Yet, no economic evaluation has explored the value for money of alternative screening methods. This paper describes a novel application of health decision analytical modelling to evaluate the cost-effectiveness of different female genital schistosomiasis screening strategies across endemic settings. The model combines a decision tree for female genital schistosomiasis screening strategies, and a Markov model for the natural history of cervical cancer to estimate the cost per disability-adjusted life-years averted for different screening strategies, stratified by HIV status. It is a starting point for discussion and for supporting priority setting in a data-sparse environment.

Estimating Transition Probabilities for Modeling Major Depression in Adolescents by Sex and Race or Ethnicity Combinations in the USA

OBJECTIVE

About one-fifth of US adolescents experienced major depressive symptoms, but few studies have examined longitudinal trends of adolescents developing depression or recovering by demographic factors. We estimated new transition probability inputs, and then used them in a simulation model to project the epidemiologic burden and trajectory of depression of diverse adolescents by sex and race or ethnicity combinations.

METHODS

Transition probabilities were first derived using parametric survival analysis of data from the National Longitudinal Study of Adolescent to Adult Health and then calibrated to cross-sectional data from the National Survey on Drug Use and Health. We developed a cohort state-transition model to simulate age-specific depression outcomes of US adolescents. A hypothetical adolescent cohort was modeled from 12-22 years with annual transitions. Model outcomes included proportions of youth experiencing depression, recovery, or depression-free cases and were reported for a US adolescent population by sex, race or ethnicity, and sex and race or ethnicity combinations.

RESULTS

At 22 years of age, approximately 16% of adolescents had depression, 12% were in recovery, and 72% had never developed depression. Depression prevalence peaked around 16-17 years-old. Adolescents of multiracial or other race or ethnicity, White, American Indian or Alaska Native, and Hispanic, Latino, or Spanish descent were more likely to experience depression than other racial or ethnic groups. Depression trajectories generated by the model matched well with historical observational studies by sex and race or ethnicity, except for individuals from American Indian or Alaska Native and multiracial or other race or ethnicity backgrounds.

CONCLUSIONS

This study validated new transition probabilities for future use in decision models evaluating adolescent depression policies or interventions. Different sets of transition parameters by demographic factors (sex and race or ethnicity combinations) were generated to support future health equity research, including distributional cost-effectiveness analysis. Further data disaggregated with respect to race, ethnicity, religion, income, geography, gender identity, sexual orientation, and disability would be helpful to project accurate estimates for historically minoritized communities.

State transitions across the Strep A disease spectrum: scoping review and evidence gaps

The spectrum of diseases caused by Streptococcus pyogenes (Strep A) ranges from superficial to serious life-threatening invasive infections. We conducted a scoping review of published articles between 1980 and 2021 to synthesize evidence of state transitions across the Strep A disease spectrum. We identified 175 articles reporting 262 distinct observations of Strep A disease state transitions. Among the included articles, the transition from an invasive or toxin-mediated disease state to another disease state (i.e., to recurrent ARF, RHD or death) was described 115 times (43.9% of all included transition pairs) while the transition to and from locally invasive category was the lowest (n = 7; 0.02%). Transitions from well to any other state was most frequently reported (49%) whereas a relatively higher number of studies (n = 71) reported transition from invasive disease to death. Transitions from any disease state to locally invasive, Strep A pharyngitis to invasive disease, and chronic kidney disease to death were lacking. Transitions related to severe invasive diseases were more frequently reported than superficial ones. Most evidence originated from high-income countries and there is a critical need for new studies in low- and middle-income countries to infer the state transitions across the Strep A disease spectrum in these high-burden settings.

Modeling the multi-state natural history of rare diseases with heterogeneous individual patient data: A simulation study

Multi-state survival models are used to represent the natural history of a disease, forming the basis of a health technology assessment comparing a novel treatment to current practice. Constructing such models for rare diseases is problematic, since evidence sources are typically much sparser and more heterogeneous. This simulation study investigated different one-stage and two-stage approaches to meta-analyzing individual patient data (IPD) in a multi-state survival setting when the number and size of studies being meta-analyzed are small. The objective was to assess methods of different complexity to see when they are accurate, when they are inaccurate and when they struggle to converge due to the sparsity of data. Biologically plausible multi-state IPD were simulated from study- and transition-specific hazard functions. One-stage frailty and two-stage stratified models were estimated, and compared to a base case model that did not account for study heterogeneity. Convergence and the bias/coverage of population-level transition probabilities to, and lengths of stay in, each state were used to assess model performance. A real-world application to Duchenne Muscular Dystrophy, a neuromuscular rare disease, was conducted, and a software demonstration is provided. Models not accounting for study heterogeneity were consistently out-performed by two-stage models. Frailty models struggled to converge, particularly in scenarios of low heterogeneity, and predictions from models that did converge were also subject to bias. Stratified models may be better suited to meta-analyzing disparate sources of IPD in rare disease natural history/economic modeling, as they converge more consistently and produce less biased predictions of lengths of stay.

Cost-effectiveness analysis of adding durvalumab to chemotherapy as first-line treatment for advanced biliary tract cancer based on the TOPAZ-1 trial

BACKGROUND

Durvalumab plus gemcitabine and cisplatin has a significant clinical benefit for advanced biliary tract cancer (BTC). However, the high price of durvalumab warrants an exploration of the economics.

OBJECTIVE

To investigate the cost-effectiveness of adding durvalumab to gemcitabine and cisplatin compared with gemcitabine and cisplatin in first-line therapy of advanced BTC from the perspective of the Chinese healthcare system.

METHODS

According to the TOPAZ-1 trial, a three-state Markov model was built by the TreeAge Pro 2022 software. The total costs and quality-adjusted life years (QALYs) were estimated, and the incremental cost-effectiveness ratio (ICER) was used as the evaluation index. The triple 2021 Chinese per capita gross domestic product (GDP) of $37,663.26/QALY was used as the willingness-to-pay (WTP) threshold. Outputs were analyzed for two scenarios with and without a durvalumab drug charity assistance policy. In the scenario analysis, the base-case model was run multiple times with different prices of durvalumab to determine the effect on the ICER. Moreover, the robustness of the model was tested through sensitivity analyses.

RESULTS

Compared with chemotherapy alone, durvalumab plus chemotherapy resulted in an additional 0.12 QALY and an incremental cost of $18,555.19, the ICER was $159,644.70/QALY under the situation of charity assistance, and the ICER was $696,571.11/QALY without charity assistance, both exceeding the WTP threshold in China. The scenario analysis demonstrated that when the price of durvalumab fell by more than 94.2% to less than $0.33/mg, durvalumab plus chemotherapy will be more economical compared with chemotherapy alone under the situation of no charity assistance. One-way sensitivity analyses suggested that the cost of durvalumab had the greatest influence on the ICERs, and the probabilistic sensitivity analyses demonstrated that durvalumab plus chemotherapy was impossible to be cost-effective at the WTP threshold whether the charity assistance was available or not.

CONCLUSIONS

Adding durvalumab to gemcitabine and cisplatin was not cost-effective for advanced BTC regardless of receiving and not receiving charitable assistance.

Cost-Utility Analysis of Camrelizumab Plus Chemotherapy Versus Chemotherapy Alone as a First-Line Treatment for Advanced Nonsquamous Non-Small Cell Lung Cancer in China

Purpose

To evaluate the cost utility of camrelizumab plus standard chemotherapy versus standard chemotherapy alone as a first-line treatment for advanced nonsquamous non-small cell lung cancer (NSCLC) from the perspective of the Chinese health care system and to provide a reference for health decision-making.

Methods

A Markov model consisting of three health states was designed to evaluate the cost utility of these two treatment regimens for NSCLC patients with the incremental cost-effectiveness ratio (ICER) as the primary output indicator. Clinical data were derived from a published phase III clinical trial (CameL; ClinicalTrials.gov; NCT03134872). One-way sensitivity analysis and probabilistic sensitivity analysis were performed to assess the model uncertainty.

Results

Base case analysis showed that the ICER of camrelizumab plus chemotherapy compared with chemotherapy alone was $43,275.43 per QALY. It was higher than the willingness-to-pay (WTP) threshold of $31,510.57 per QALY in China, which has a standard of three times the GDP per capita recommended by the WHO. One-way sensitivity analysis showed that the utility value of PFS had the greatest influence on the results, and the other sensitive parameters were the cost of subsequent second-line therapy in the two group, the pemetrexed price, the cost of adverse event management and the utility value of PD. The probability sensitivity analysis showed that the probabilities of the cost-effectiveness of camrelizumab plus standard chemotherapy were 27.1%, 66.7% and 88.0% when the WTP values were $40,000, $50,000 and $60,000 per QALY, respectively.

Conclusions

Taking three times the GDP per capita in China as the WTP threshold, the camrelizumab plus standard chemotherapy regimen does not have a cost-effectiveness advantage compared with the standard chemotherapy regimen alone as a first-line treatment for advanced NSCLC.

Cost Utility of Bronchial Thermoplasty for Severe Asthma: Implications for Future Cost-Effectiveness Analyses Based on Phenotypic Heterogeneity

Background

Asthma is a disease with tremendous phenotypic heterogeneity, and the patients who are most severely impacted by the disease are high utilizers of the United States healthcare system. In the past decade, there has been many advances in asthma therapy for those with severe disease, including the use of a procedure called bronchial thermoplasty (BT) and the use of biologic therapy for certain phenotypes, but questions remain regarding the long-term durability and cost effectiveness of these therapies. The purpose of this analysis was (1) to assess the cost utility of BT relative to usual care (base case) and (2) to assess the cost utility of BT relative to usual care plus biologic therapy (omalizumab) (scenario analysis) based on updated 10-year clinical trial outcomes.

Methods

A Markov cohort model was developed and used to estimate the cost utility of BT to estimate the costs and quality-of-life impact of BT versus the comparisons over a 10-year time frame using a limited societal perspective, which included both direct health utilization costs and indirect costs associated with missed days of work, among those with severe persistent asthma.

Results

In the base case and the scenario analysis, BT was the dominant treatment strategy compared to usual care alone and usual care plus biologic therapy. The net monetary benefit for BT was $483,555.49 over a 10-year time horizon.

Conclusion

Cost-utility models are central to policy decisions dictating coverage, and can be extended to inform the patient and provider, during clinical decision-making, of the relative trade-offs of therapy, assessing long-term clinical and cost outcomes. Phenotypic classification of severe asthma is central to patient management and should also be integrated into economic analysis frameworks, particularly as new biologic agents are developed that are specific to a phenotype. Despite a larger upfront cost of BT therapy, there is a durable clinical and economic benefit over time for those with severe asthma.

Impact of a Digitally Enhanced Diabetes Self-Management Program on Glycemia and Medical Costs

OBJECTIVE

To analyze economic savings and health impacts associated with a virtual digitally enhanced diabetes self-management education and support (DSMES) program.

RESEARCH DESIGN AND METHODS

Participants (n = 1,494) were nonpregnant adults with diagnosed type 2 diabetes and baseline body mass index (BMI) of 25 kg/m² (23 kg/m² if of Asian descent) or higher who enrolled in virtual DSMES between February 2019 and April 2020 for at least 4 months. Participants' changes in glycated hemoglobin (A1C) and body weight were calculated as the difference between program start and last recorded values between months 4 and 6. Outcomes for all participants were analyzed; subanalyses were done on 628 participants with starting A1C >7% (53 mmol/mol), who could benefit most from DSMES. Markov-based microsimulation approach was used to model the potential reductions in diabetes sequalae and medical expenditures if observed improvements in A1C and BMI were maintained.

RESULTS

DSMES participants with starting A1C >7% experienced average reductions of 0.9% A1C and 2.1 kg of body weight (-1.7% of BMI) within 6 months. If these improvements were maintained, simulated outcomes include reduced 5-year onset of ischemic heart disease by 9.2%, myocardial infarction by 10.6%, stroke by 12.1%, chronic kidney disease by 16.5%, and reduced onset of other sequelae. Simulated cumulative reduction in medical expenditures is $1160 after 1 year, $4150 after 3 years, $7790 after 5 years, and $18 020 after 10 years.

CONCLUSIONS

Participation in virtual DSMES improves A1C and body weight, with the potential to slow onset of diabetes sequelae and reduce medical expenditures.

When History and Heterogeneity Matter: A Tutorial on the Impact of Markov Model Specifications in the Context of Colorectal Cancer Screening

BACKGROUND

Markov models are used in health research to simulate health care utilization and disease states over time. Health phenomena, however, are complex, and the memoryless assumption of Markov models may not appropriately represent reality. This tutorial provides guidance on the use of Markov models of different orders and stratification levels in health decision-analytic modeling. Colorectal cancer (CRC) screening is used as a case example to examine the impact of using different Markov modeling approaches on CRC outcomes.

METHODS

This study used insurance claims data from commercially insured individuals in Oregon to estimate transition probabilities between CRC screening states (no screen, colonoscopy, fecal immunochemical test or fecal occult blood test). First-order, first-order stratified by sex and geography, and third-order Markov models were compared. Screening trajectories produced from the different Markov models were incorporated into a microsimulation model that simulated the natural history of CRC disease progression. Simulation outcomes (e.g., future screening choices, CRC incidence, deaths due to CRC) were compared across models.

RESULTS

Simulated CRC screening trajectories and resulting CRC outcomes varied depending on the Markov modeling approach used. For example, when using the first-order, first-order stratified, and third-order Markov models, 30%, 31%, and 44% of individuals used colonoscopy as their only screening modality, respectively. Screening trajectories based on the third-order Markov model predicted that a higher percentage of individuals were up-to-date with CRC screening as compared with the other Markov models.

LIMITATIONS

The study was limited to insurance claims data spanning 5 y. It was not possible to validate which Markov model better predicts long-term screening behavior and outcomes.

CONCLUSIONS

Findings demonstrate the impact that different order and stratification assumptions can have in decision-analytic models.

HIGHLIGHTS

This tutorial uses colorectal cancer screening as a case example to provide guidance on the use of Markov models of different orders and stratification levels in health decision-analytic models.Colorectal cancer screening trajectories and projected health outcomes were sensitive to the use of alternate Markov model specifications.Although data limitations precluded the assessment of model accuracy beyond a 5-y period, within the 5-y period, the third-order Markov model was slightly more accurate in predicting the fifth colorectal cancer screening action than the first-order Markov model.Findings from this tutorial demonstrate the importance of examining the memoryless assumption of the first-order Markov model when simulating health care utilization over time.

Cost-effectiveness of a Digital Health Intervention for Acute Myocardial Infarction Recovery

BACKGROUND

Acute myocardial infarction (AMI) is a common cause of hospital admissions, readmissions, and mortality worldwide. Digital health interventions (DHIs) that promote self-management, adherence to guideline-directed therapy, and cardiovascular risk reduction may improve health outcomes in this population. The "Corrie" DHI consists of a smartphone application, smartwatch, and wireless blood pressure monitor to support medication tracking, education, vital signs monitoring, and care coordination. We aimed to assess the cost-effectiveness of this DHI plus standard of care in reducing 30-day readmissions among AMI patients in comparison to standard of care alone.

METHODS

A Markov model was used to explore cost-effectiveness from the hospital perspective. The time horizon of the analysis was 1 year, with 30-day cycles, using inflation-adjusted cost data with no discount rate. Currencies were quantified in US dollars, and effectiveness was measured in quality-adjusted life-years (QALYs). The results were interpreted as an incremental cost-effectiveness ratio at a threshold of $100,000 per QALY. Univariate sensitivity and multivariate probabilistic sensitivity analyses tested model uncertainty.

RESULTS

The DHI reduced costs and increased QALYs on average, dominating standard of care in 99.7% of simulations in the probabilistic analysis. Based on the assumption that the DHI costs $2750 per patient, use of the DHI leads to a cost-savings of $7274 per patient compared with standard of care alone.

CONCLUSIONS

Our results demonstrate that this DHI is cost-saving through the reduction of risk for all-cause readmission following AMI. DHIs that promote improved adherence with guideline-based health care can reduce hospital readmissions and associated costs.

Estimation of Transition Probabilities for State-Transition Models: A Review of NICE Appraisals

State transition models are used to inform health technology reimbursement decisions. Within state transition models, the movement of patients between the model health states over discrete time intervals is determined by transition probabilities (TPs). Estimating TPs presents numerous issues, including missing data for specific transitions, data incongruence and uncertainty around extrapolation. Inappropriately estimated TPs could result in biased models. There is limited guidance on how to address common issues associated with TP estimation. To assess current methods for estimating TPs and to identify issues that may introduce bias, we reviewed National Institute for Health and Care Excellence Technology Appraisals published from 1 January, 2019 to 27 May, 2020. Twenty-eight models (from 26 Technology Appraisals) were included in the review. Several methods for estimating TPs were identified: survival analysis (n = 11); count method (n = 9); multi-state modelling (n = 7); logistic regression (n = 2); negative binomial regression (n = 2); Poisson regression (n = 1); and calibration (n = 1). Evidence Review Groups identified several issues relating to TP estimation within these models, including important transitions being excluded (n = 5); potential selection bias when estimating TPs for post-randomisation health states (n = 2); issues concerning the use of multiple data sources (n = 4); potential biases resulting from the use of data from different populations (n = 2), and inappropriate assumptions around extrapolation (n = 3). These issues remained unresolved in almost every instance. Failing to address these issues may bias model results and lead to sub-optimal decision making. Further research is recommended to address these methodological problems.

Pharmacoeconomic Evaluation of Erlotinib for the Treatment of Pancreatic Cancer

PURPOSE

To evaluate the cost-effectiveness of gemcitabine and gemcitabine plus erlotinib as first-line treatments for advanced pancreatic cancer.

METHODS

On the basis of the Gemcitabine With/Out Erlotinib in Unresectable Locally Advanced/Metastatic Pancreatic Cancer (PA.3) trial, the Markov model was constructed to simulate the development of advanced pancreatic cancer. Cost-effectiveness analysis was used to determine the economic level of the treatments, according to the willingness-to-pay (WTP) threshold. The sensitivity analysis was conducted for cost-effectiveness and other indexes.

FINDINGS

The results of the cost-effectiveness analysis revealed that the cost-effectiveness ratios for the first-line treatment of advanced pancreatic cancer were ¥60,492.78 (US$8892.44/€7568.88) per 6.34 quality-adjusted life-months (QALMs) for gemcitabine and ¥99,595.39 (US$14,640.52/€12,461.42) per 7.02 QALMs for gemcitabine plus erlotinib. The incremental cost-effectiveness of the 2 regimens was ¥57,503.84 ($8453.06/€7194.90) per QALM, which was higher than the WTP set in this study (¥16,161 [$2375.66/€2022.07] per QALM). The results of the sensitivity analysis indicate that the analysis results were stable. Gemcitabine was more cost-effective than gemcitabine plus erlotinib.

IMPLICATIONS

Compared with gemcitabine, gemcitabine plus erlotinib was not cost-effective at the level of the WTP. Gemcitabine plus erlotinib therapy has no economic significance as a first-line medical treatment for pancreatic cancer.

Applied Methods for Estimating Transition Probabilities from Electronic Health Record Data

BACKGROUND

Electronic health record (EHR) data contain longitudinal patient information and standardized diagnostic codes. EHR data may be useful for estimating transition probabilities for state-transition models, but no guidelines exist on appropriate methods. We applied 3 potential methods to estimate transition probabilities from EHR data, using pediatric eating disorders (EDs) as a case study.

METHODS

We obtained EHR data from PEDsnet, which includes 8 US children's hospitals. Data included inpatient, outpatient, and emergency department visits for all patients with an ED. We mapped diagnoses to 3 ED health states: anorexia nervosa, bulimia nervosa, and other specified feeding or eating disorder. We estimated 1-y transition probabilities for males and females using 3 approaches: simple first-last proportions, a multistate Markov (MSM) model, and independent survival models.

RESULTS

Transition probability estimates varied widely between approaches. The first-last proportion approach estimated higher probabilities of remaining in the same health state, while the MSM and independent survival approaches estimated higher probabilities of transitioning to a different health state. All estimates differed substantially from published literature.

LIMITATIONS

As a source of health state information, EHR data are incomplete and sometimes inaccurate. EHR data were especially challenging for EDs, limiting the estimation and interpretation of transition probabilities.

CONCLUSIONS

The 3 approaches produced very different transition probability estimates. Estimates varied considerably from published literature and were rescaled and calibrated for use in a microsimulation model. Estimation of transition probabilities from EHR data may be more promising for diseases that are well documented in the EHR. Furthermore, clinicians and health systems should work to improve documentation of ED in the EHR. Further research is needed on methods for using EHR data to inform transition probabilities.

Using informative prior based on expert opinion in Bayesian estimation of the transition probability matrix in Markov modelling-an example from the cost-effectiveness analysis of the treatment of patients with predominantly negative symptoms of schizophrenia with cariprazine

Background

When patient health state transition evidence is missing from clinical literature, analysts are inclined to make simple assumptions to complete the transition matrices within a health economic model. Our aim was to provide a solution for estimating transition matrices by the Bayesian statistical method within a health economic model when empirical evidence is lacking.

Methods

We used a previously published cost-effectiveness analysis of the use of cariprazine compared to that of risperidone in patients with predominantly negative symptoms of schizophrenia. We generated the treatment-specific state transition probability matrices in three different ways: (1) based only on the observed clinical trial data; (2) based on Bayesian estimation where prior transition probabilities came from experts’ opinions; and (3) based on Bayesian estimation with vague prior transition probabilities (i.e., assigning equal prior probabilities to the missing transitions from one state to the others). For the second approach, we elicited Dirichlet prior distributions by three clinical experts. We compared the transition probability matrices and the incremental quality-adjusted life years (QALYs) across the three approaches.

Results

The estimates of the prior transition probabilities from the experts were feasible to obtain and showed considerable consistency with the clinical trial data. As expected, the estimated health benefit of the treatments was different when only the clinical trial data were considered (QALY difference 0.0260), its combination with the experts’ beliefs were used in the economic model (QALY difference 0.0253), and when vague prior distributions were used (QALY difference 0.0243).

Conclusions

Imputing zeros to missing transition probabilities in Markov models might be untenable from the clinical perspective and may result in inappropriate estimates. Bayesian statistics provides an appropriate framework for imputing missing values without making overly simple assumptions. Informative priors based on expert opinions might be more appropriate than vague priors.

Modelling the economic and clinical burden of non-alcoholic steatohepatitis in East Asia: Data from Hong Kong

AIM

Non-alcoholic steatohepatitis (NASH) is the progressive form of non-alcoholic fatty liver disease (NAFLD) and prevalence is rising in Asia due to increasing rates of urbanization, sedentary lifestyles, and poor nutrition.

METHODS

We built a Markov model with 20-year horizon to estimate the burden of NASH in Hong Kong. Cohort size was determined by population size, prevalence of NAFLD, and incidence of NASH in 2017. Health states include hepatic steatosis, fibrosis stages 0-3, compensated and decompensated cirrhosis, hepatocellular carcinoma, post-liver transplant, and liver-related, cardiovascular, and background mortality. Transition probabilities were estimated from published reports and we converted 2017 Gazette price from the Hospital Authority of Hong Kong to US dollars. We discounted costs by 3% annually. Health utilities were assumed to be the same as in the USA.

RESULTS

Non-alcoholic steatohepatitis will cost $1.32 billion and 124 liver transplants over 20 years, with average cost per person-year of $257. Sensitivity analyses show our model is robust in predicting costs for the prevalent population but likely overestimates costs for the incident population.

CONCLUSIONS

Non-alcoholic steatohepatitis will contribute to a significant clinical and economic burden in Hong Kong over the next two decades. Due to the limited number of donors and small number of liver transplants undertaken annually, patients with advanced liver disease due to NASH in Hong Kong are more likely to die from NASH than their counterparts in North America and Europe. Thus, rising prevalence of metabolic syndrome in elderly adults in Hong Kong make NASH an important consideration for clinicians and policy makers.

Analysis of the effectiveness of two noninvasive fecal tests used to screen for colorectal cancer in average-risk adults

OBJECTIVES

Colorectal cancer (CRC) is the second leading cause of cancer-related death in the United States. Although a significant proportion of CRC cases and deaths are preventable by screening, the morbidity and mortality from CRC remains high and is attributed to suboptimal screening rates. Low levels of population CRC screening uptake may be due to reluctance toward invasiveness of some screening tests, embarrassment, exposure to anesthesia, and grueling preparation, especially for the invasive screening tests. Noninvasive tests overcome many of these barriers because they are more convenient and potentially more attractive to patients compared to invasive tests. This study uses Markov cohort simulation model developed with the help of TreeAge pro software to compare two noninvasive fecal CRC screens, fecal immunohistochemical test (FIT) and multitarget stool DNA test (Mt-sDNA) with no screening in order to identify the more effective noninvasive fecal test to screen for colorectal cancer in average-risk adults.

STUDY DESIGN

Simulation study developed with Markov model using TreeAge pro software, which included a hypothetical cohort at the average risk of developing colorectal cancer.

METHODS

Markov model was used to compare population-level CRC-related cases and deaths averted, life-years gained (LYG), and colonoscopies required for two noninvasive CRC screening strategies compared with no screening: annual fecal immunohistochemical testing (FIT) and 3-yearly multitarget stool DNA testing (Mt-sDNA). The model simulated the natural history of the adenoma-carcinoma sequence in average-risk persons starting at age 50 years, and natural history parameters were estimated from the literature and via verification to data on precancerous lesions (i.e. adenomas) and CRC incidence. Screening strategies were then superimposed on the natural history component of the model, allowing for precancerous lesions to be detected and removed, or CRC to be detected and treated at a potentially earlier stage. The sensitivity and specificity for each screen for precancerous lesions and CRC were the performance parameters used to estimate the effectiveness.

RESULTS

Annual FIT was more effective than three yearly Mt-sDNA in reducing CRC cases, averting CRC-related deaths, and increasing the LYG compared to no screening. On average, annual FIT resulted in 3.5 fewer CRC cases, and 2.9 fewer CRC deaths per 1000 persons screened compared to 3-yearly Mt-sDNA. Annual FIT usage resulted in a 0.18 LYG compared to Mt-sDNA, which allowed 0.16 LYG, and an annual FIT screening led to a total of 203 more colonoscopies performed compared to Mt-sDNA. One-way sensitivity analysis conducted over the sensitivity rates of each screen by type of lesion showed that FIT remained the more effective strategy for all ranges of sensitivity. Threshold analysis results identified the lowest FIT sensitivity value at which Mt-sDNA performed better for conventional high-risk adenomas and CRC detection to be 0.16 and 0.052, respectively.

CONCLUSION

Both the noninvasive screens were effective compared to no screening. Additionally, annual FIT as a first step noninvasive screening test for CRC appears to be more effective compared to three-yearly Mt-sDNA.

Annual transition probabilities of overweight and obesity in older adults: Evidence from World Health Organization Study on global AGEing and adult health

Overweight/obesity is becoming increasingly prevalent in sub-Saharan Africa including Ghana. However, transition probabilities, an essential component to develop cost-effective measures for weight management is lacking in this population. We estimated annual transition probabilities between three body mass index (BMI) categories: normal weight (BMI ≥18.5 and <25.0 kg/m2), overweight (BMI ≥25.0 and <30.0 kg/m2), and obesity (BMI ≥30.0 kg/m2), among older adults aged ≥50 years in Ghana. Data were used from a nationally representative, multistage sample of 1496 (44.3% females) older adults in both Waves 1 (2007/8) and 2 (2014/15) of the Ghana WHO SAGE. A multistage Markov model was used to estimate annual transition probabilities. We further examined the impact of specific socio-economic factors on the transition probabilities. At baseline, 22.8% were overweight and 11.1% were obese. The annual transition probability was 4.0% (95% CI: 3.4%, 4.8%) from normal weight to overweight, 11.1% (95% CI: 9.5%, 13.0%) from overweight to normal weight and 4.9% (95% CI: 3.8%, 6.2%) from overweight to obesity. For obese individuals, the probability of remaining obese, transitioning to overweight and completely reverting to normal weight was 90.2% (95% CI: 87.7%, 92.3%), 9.2% (95% CI: 7.2%, 11.6%) and 0.6% (95% CI: 0.4%, 0.8%) respectively. Being female, aged 50-65 years, urban residence, having high education and high wealth were associated with increased probability of transitioning into the overweight or obese categories. Our findings highlight the difficulty in transitioning away from obesity, especially among females. The estimated transition probabilities will be essential in health economic simulation models to determine sustainable weight management interventions.

Burden of Illness and Economic Model for Patients With Nonalcoholic Steatohepatitis in the United States

Nonalcoholic steatohepatitis (NASH) is the progressive form of nonalcoholic fatty liver disease. Our aim was to estimate the total economic burden of NASH and advanced NASH in the United States. We constructed lifetime Markov models for all stages of NASH and a separate model to specifically identify the increased burden of advanced NASH (fibrosis stage >3). The models comprised patients aged 18+, who moved through seven different health states. We used a lifetime horizon with 1-year cycles for each transition. Cohort size was estimated using US population data, and prevalence and incidence rates were obtained from the literature. Transition probabilities between states were derived from meta-analyses. Costs included inpatient, outpatient, professional services, emergency department, and drug costs, which were obtained from the Center for Medicare and Medicaid Services Fee Schedule 2017 and published data. All future costs were discounted at an annual rate of 3%. Our models estimated that there are 6.65 million adults (18+ years old) with NASH in the United States and that there were 232,000 incident cases in 2017. Lifetime costs of all NASH patients in the United States in 2017 will be $222.6 billion, and the cost of the advanced NASH population will be $95.4 billion. Conclusion: NASH, especially advanced NASH, is associated with high lifetime economic burden; in the absence of treatment, the total direct costs of illness for these patients will continue to grow, and these costs would be even greater if the societal costs are included.

Simple but not simpler: a systematic review of Markov models for economic evaluation of cervical cancer screening

The aim of this study was to critically evaluate the quality of the models used in economic evaluations of screening strategies for cervical cancer prevention. We systematically searched multiple databases, selecting model-based full economic evaluations (cost-effectiveness analyses, cost-utility analyses, and cost-benefit analyses) of cervical cancer screening strategies. Two independent reviewers screened articles for relevance and performed data extraction. Methodological assessment of the quality of the models utilized formal checklists, and a qualitative narrative synthesis was performed. Thirty-eight articles were reviewed. The majority of the studies were conducted in high-income countries (82%, n=31). The Pap test was the most used screening strategy investigated, which was present in 86% (n=33) of the studies. Half of the studies (n=19) used a previously published Markov model. The deterministic sensitivity analysis was performed in 92% (n=35) of the studies. The mean number of properly reported checklist items was 9 out of the maximum possible 18. Items that were better reported included the statement of decision problem, the description of the strategies/comparators, the statement of time horizon, and information regarding the disease states. Compliance with some items of the checklist was poor. The Markov models for economic evaluation of screening strategies for cervical cancer varied in quality. The following points require improvement: 1) assessment of methodological, structural, heterogeneity, and parameter uncertainties; 2) model type and cycle length justification; 3) methods to account for heterogeneity; and 4) report of consistency evaluation (through calibration and validation methods).