The effect of screening on treatment cost: The case of colorectal cancer

This paper presents a medical cost function developed for a screening programme. The medical cost function is a function of advancement both directly and indirectly through survival. We discuss how the medical cost function is affected by screening through a shift in the distribution of cancers according to advancement. We show that screening reduces the treatment cost for cancers diagnosed at the screening, even though the medical cost function not unambiguously increases with stage of advancement. This is the first step in a cost-effectiveness analysis, and even though the results are favourable to the introduction of screening for colorectal cancer as a preventive health measure, total screening costs and health benefits must be evaluated to arrive at a recommendation.treatment cost; stage of advancement; screening; probabilistic sensitivity analysis; bootstrap method

Pecuniary compensation increases the participation rate in screening for colorectal cancer

Typically, the participation rate is below 100 per cent. In this paper pecuniary compensation is used to increase the participation rate. In a postal questionnaire to 5,000 people invited to screening for colorectal cancer, those not participating were asked "would you participate if you were given NOK X in compensation?" The results show that compensation increases participation and that the participation probability systematically varies with travel expenses, income, age, county, native country, marital status, use of health care services, genetic predisposition, expected benefit from the screening, subjective health status, and education. The estimated costs per additional screening are increasingparticipation; willingness-to pay; compensation; costs; binary probit

Cost-effectiveness of screening for colorectal cancer with once-only flexible sigmoidoscopy and faecal occult blood test

On the basis of a randomized controlled trial we estimate the cost per life-year gained for six different strategies for colorectal cancer screening. Individuals in the age group 50 to 64 years were randomly selected for either flexible sigmoidoscopy or a combination of flexible sigmoidoscopy and a faecal occult blood test. A comprehensive dataset was collected from the trial to estimate costs and gained life-years. There are some indications that screening for colorectal cancer can be cost-effective, but the results are not statistically significant after this short follow-up period.Keywords – screening; cost-effectiveness analysis; colorectal cancer; multinomial logit; probabilistic sensitivity analysis

The Role of Health Status and Social Capital in Cancer Mortality: Insights from Matched Register and Survey Data

Cancer mortality has been shown to be associated with social- and human capital. Several channels have been suggested, such as early detection, better compliance to treatment and better health prior to diagnosis. In this paper we study how health status and social capital jointly affect cancer mortality and cancer severity at the time of diagnosis. The analyses are based on study sample of individuals with cancer diagnosis. Our merged dataset contain information on cancer diagnosis and death from the Cancer Registry of Norway and health status, social capital and other individual level data from several national health surveys measured before the time of diagnosis. Health status and social capital are treated as unobserved latent variables, and we apply generalized structural equation modelling framework to estimate conditional statistical associations of social capital and individual health on cancer severity and mortality. We find that health has negative, and statistically significant effect, on cancer mortality, while we cannot conclude on the association between health and cancer severity (metastasis yes/no). We cannot conclude that cancer mortality and the probability of cancer metastasis is associated nor disassociated with social capital. Our results add nuance to prior studies, which frequently report a significant association between social capital and cancer mortality.The Role of Health Status and Social Capital in Cancer Mortality: Insights from Matched Register and Survey DatapublishedVersio

Bruk av paneldatametoder til å belyse allmennlegers henvisningsmønster

Fordi paneldata gir oss muligheter til å kontrollere for individspesifikk og/eller tidsspesifikk heterogenitet er det mange fordeler ved å benytte denne typen data i økonometriske undersøkelser (Biørn, 2000). Hensikten med denne rapporten er ikke resultater per se, det sentrale er snarere å gjennomgå og å diskutere de empiriske spesifikasjonene som ligger til grunn når vi estimerer på et paneldatasett. Å illustrere disse metodene med utgangspunkt i en faktisk problemstilling og et faktisk datasett bringer frem nye elementer og gir dermed en bedre innsikt og forståelse for de økonometriske metodene som er hovedtemaet for rapporten. Vi har valgt å ta utgangspunkt i et paneldatasett innsamlet og tilrettelagt i forbindelse med evalueringen av fastlegeforsøket. Bakgrunnen for denne datainnsamlingen var et ønske om å belyse hva som faktisk skjer med antallet henvisninger når dagens allmennlegetjeneste blir erstattet av en ny organisering, og en ny avlønningsordning for allmennlegene. Resultater fra undersøkelsen, samt politikkimplikasjoner av funnene er tidligere publisert (Iversen og Lurås 2000). For å kunne se sammenhenger mellom hypoteser, data, empiriske spesifikasjoner og analyser vil vi likevel gjennomgå hypotesene som ligger til grunn for datainnsamlingen.Paneldata; legers henvisningsmønster;

A Generic Model for Follicular Lymphoma: Predicting Cost, Life Expectancy, and Quality-Adjusted-Life-Year Using UK Population–Based Observational Data

Objectives To use real-world data to develop a flexible generic decision model to predict cost, life expectancy, and quality-adjusted life-years (QALYs) for follicular lymphoma (FL) in the general patient population. Methods All patients newly diagnosed with FL in the UK’s population-based Haematological Malignancy Research Network (www.hmrn.org) between 2004 and 2011 were followed until 2015 (N = 740). Treatment pathways, QALYs, and costs were incorporated into a discrete event simulation to reflect patient heterogeneity, including age and disease management. Two scenario analyses, based on the latest National Institute for Health and Clinical Excellence (NICE) guidelines (rituximab induction therapy for newly diagnosed asymptomatic patients and rituximab maintenance therapy for patients between treatments), were conducted and their economic impacts were compared to current practice. Results Incidence-based analysis revealed expected average lifetime costs ranging from £6,165 [US$7,709] to £63,864 [US$79,862] per patient, and average life expectancy from 75 days to 17.56 years. Prevalence-based analysis estimated average annual treatment costs of £60–65 million [US$75-80 million], accounting for approximately 10$0.75-2.75 million]. Conclusions Costs, survival, and QALYs associated with FL vary markedly with patient characteristics and disease management. Allowing the production of more realistic outcomes across the patient population as a whole, our model addresses this heterogeneity and is a useful tool with which to evaluate new technologies/treatments to support healthcare decision makers

Care pathways at end-of-life for cancer decedents : registry based analyses of the living situation, healthcare utilization and costs for all cancer decedents in Norway in 2009-2013 during their last 6 months of life

Background: Research on end-of-life care is often fragmented, focusing on one level of healthcare or on a particular patient subgroup. Our aim was to describe the complete care pathways of all cancer decedents in Norway during the last six months of life. Methods: We used six national registries linked at patient level and including all cancer decedents in Norway between 2009-2013 to describe patient use of secondary, primary-, and home- and community-based care. We described patient’s car pathway, including patients living situation, healthcare utilization, and costs. We then estimated how cancer type, individual and sociodemographic characteristics, and access to informal care influenced the care pathways. Regression models were used depending on the outcome, i.e., negative binomial (for healthcare utilization) and generalized linear models (for healthcare costs). Results: In total, 52,926 patients were included who died of lung (16%), colorectal (12%), prostate (9%), breast (6%), cervical (1%) or other (56%) cancers. On average, patients spent 123 days at home, 24 days in hospital, 16 days in short-term care and 24 days in long-term care during their last 6 months of life. Healthcare utilization increased towards end-of-life. Total costs were high (on average, NOK 379,801). 60% of the total costs were in the secondary care setting, 3% in the primary care setting, and 37% in the home- and community-based care setting. Age (total cost-range NOK 361,363-418,618) and marital status (total cost-range NOK354,100-411,047) were stronger determining factors of care pathway than cancer type (total cost-range NOK341,318- 392,655). When patients died of cancer types requiring higher amounts of secondary care (e.g., cervical cancer), there was a corresponding lower utilization of primary, and home- and community-based care, and vice versa. Conclusion: Cancer patient’s care pathways at end-of-life are more strongly associated with age and access to informal care than underlying type of cancer. More care in one care setting (e.g., the secondary care) is associated with less care in other settings (primary- and home- and community based care setting) as demonstrated by the substitution between the different levels of care in this study. Care at end-of-life should therefore not be evaluated in one healthcare level alone since this might bias results and lead to suboptimal priorities.publishedVersionPeer reviewe

Simultaneous Resection of Primary Colorectal Cancer and Synchronous Liver Metastases: Contemporary Practice, Evidence and Knowledge Gaps

The timing of surgical resection of synchronous liver metastases from colorectal cancer has been debated for decades. Several strategies have been proposed, but high-level evidence remains scarce. Simultaneous resection of the primary tumour and liver metastases has been described in numerous retrospective audits and meta-analyses. The potential benefits of simultaneous resections are the eradication of the tumour burden in one procedure, overall shorter procedure time, reduced hospital stay with the likely benefits on quality of life and an expected reduction in the use of health care services compared to staged procedures. However, concerns about accumulating complications and oncological outcomes remain and the optimal selection criteria for whom simultaneous resections are beneficial remains undetermined. Based on the current level of evidence, simultaneous resection should be restricted to patients with a limited liver tumour burden. More high-level evidence studies are needed to evaluate the quality of life, complication burden, oncological outcomes, as well as overall health care implications for simultaneous resections

An Efficient Method for Computing Expected Value of Sample Information for Survival Data from an Ongoing Trial

BACKGROUND: Decisions about new health technologies are increasingly being made while trials are still in an early stage, which may result in substantial uncertainty around key decision drivers such as estimates of life expectancy and time to disease progression. Additional data collection can reduce uncertainty, and its value can be quantified by computing the expected value of sample information (EVSI), which has typically been described in the context of designing a future trial. In this article, we develop new methods for computing the EVSI of extending an existing trial's follow-up, first for an assumed survival model and then extending to capture uncertainty about the true survival model. METHODS: We developed a nested Markov Chain Monte Carlo procedure and a nonparametric regression-based method. We compared the methods by computing single-model and model-averaged EVSI for collecting additional follow-up data in 2 synthetic case studies. RESULTS: There was good agreement between the 2 methods. The regression-based method was fast and straightforward to implement, and scales easily included any number of candidate survival models in the model uncertainty case. The nested Monte Carlo procedure, on the other hand, was extremely computationally demanding when we included model uncertainty. CONCLUSIONS: We present a straightforward regression-based method for computing the EVSI of extending an existing trial's follow-up, both where a single known survival model is assumed and where we are uncertain about the true survival model. EVSI for ongoing trials can help decision makers determine whether early patient access to a new technology can be justified on the basis of the current evidence or whether more mature evidence is needed. HIGHLIGHTS: Decisions about new health technologies are increasingly being made while trials are still in an early stage, which may result in substantial uncertainty around key decision drivers such as estimates of life-expectancy and time to disease progression. Additional data collection can reduce uncertainty, and its value can be quantified by computing the expected value of sample information (EVSI), which has typically been described in the context of designing a future trial.In this article, we have developed new methods for computing the EVSI of extending a trial's follow-up, both where a single known survival model is assumed and where we are uncertain about the true survival model. We extend a previously described nonparametric regression-based method for computing EVSI, which we demonstrate in synthetic case studies is fast, straightforward to implement, and scales easily to include any number of candidate survival models in the EVSI calculations.The EVSI methods that we present in this article can quantify the need for collecting additional follow-up data before making an adoption decision given any decision-making context