Healthcare use and costs in the last six months of life by level of care and cause of death.

BACKGROUND: Existing knowledge on healthcare use and costs in the last months of life is often limited to one patient group (i.e., cancer patients) and one level of healthcare (i.e., secondary care). Consequently, decision-makers lack knowledge in order to make informed decisions about the allocation of healthcare resources for all patients. Our aim is to elaborate the understanding of resource use and costs in the last six months of life by describing healthcare use and costs for all causes of death and by all levels of formal care. METHOD: Using five national registers, we gained access to patient-level data for all individuals who died in Norway between 2009 and 2013. We described healthcare use and costs for all levels of formal care-namely primary, secondary, and home- and community-based care -in the last six months of life, both in total and differentiated across three time periods (6-4 months, 3-2 months, and 1-month before death). Our analysis covers all causes of death categorized in ten ICD-10 categories. RESULTS: During their last six months of life, individuals used an average of healthcare resources equivalent to €46,000, ranging from €32,000 (Injuries) to €64,000 (Diseases of the nervous system and sense organs). In terms of care level, 63% of healthcare resources were used in home- and community-based care (i.e., in-home nursing, practical assistance, or nursing home care), 35% in secondary care (mostly hospital care), and 2% in primary care (i.e., general practitioners). The amount and level of care varied by cause of death and by time to death. The proportion of home- and community-based care which individuals received during their last six months of life varied from 38% for cancer patients to 92% for individuals dying with mental diseases. The shorter the time to death, the more resources were needed: nearly 40% of all end-of-life healthcare costs were expended in the last month of life across all causes of death. The composition of care also differed depending on age. Individuals aged 80 years and older used more home- and community-based care (77%) than individuals dying at younger ages (40%) and less secondary care (old: 21% versus young: 57%). CONCLUSIONS: Our analysis provides valuable evidence on how much healthcare individuals receive in their last six months of life and the associated costs, broken down by level of care and cause of death. Healthcare use and costs varied considerably by cause of death, but were generally higher the closer a person was to death. Our findings enable decision-makers to make more informed resource-allocation decisions and healthcare planners to better anticipate future healthcare needs.

Place of living at end-of-life according to cause of death: a comparative analysis of all decedents 70 years or older in 2009-2013 in Finland and Norway.

BACKGROUND: Time at home at end-of-life is perceived as valuable to individuals. Increasing home care is therefore often a political goal. Yet, little is known about where individuals live towards their end-of-life. Our aim was to describe where individuals reside their last 6 months of life in Finland and Norway, and how this differed by cause of death, sex, age, marital status, and income. METHODS: We used individual-leveled national registry data on all decedents aged >70 years in 2009-2013 to describe the number of days individuals spent at home, in hospital, in long-term care (LTC) and short-term care (STC) facilities. We described the place of residence for all and by causes of death: cancer, diseases of the circulatory system, disease in the respiratory system, and mental and behavioral disorders (primarily dementia). We analyzed how age, marital status (indicating informal care), and income associated with place of residence. Analyses were stratified by sex and country. RESULTS: During the last 6 months of life, decedents in Finland (n=186,017) and Norway (n=159,756) spent similar amounts of days in hospital (8 and 11 days) and in STC facilities (15 and 13 days). Finnish decedents spent more days at home (96 vs. 84 days) and fewer days in LTC facilities (64 vs. 80 days). Living arrangement differed similarly by cause of death in the two countries, e.g., decedents from cancer and mental and behavioral disorders spent 123 [113] vs. 29 [21] days at home in Finland (Norway). In both countries, for all causes of death, lower age and marital status were associated with more days at home, for both males and females. While those with higher income spent more days at home in Norway, the opposite was found in Finland. CONCLUSIONS: Older individual's living arrangements in the last 6 months of life were similar in Finland and Norway but differed by cause of death. Younger individuals and those with access to informal care spent more days at home, compared to their counterparts. With aging populations, more individuals will likely need LTC at their end of life. Policies should align with these needs when developing future health care services.

Is Risk-Stratifying Patients with Colorectal Cancer Using a Deep Learning-Based Prognostic Biomarker Cost-Effective?

OBJECTIVES: Accurate risk stratification of patients with stage II and III colorectal cancer (CRC) prior to treatment selection enables limited health resources to be efficiently allocated to patients who are likely to benefit from adjuvant chemotherapy. We aimed to investigate the cost-effectiveness of a recently developed deep learning-based prognostic method, Histotyping, from the perspective of the Norwegian healthcare system. METHODS: Two partitioned survival models were developed to assess the cost-effectiveness of Histotyping for two treatment cohorts: patients with CRC stage II and III. For each of the two cohorts, Histotyping was used for risk stratification to assign adjuvant chemotherapy and was compared with the standard of care (SOC) (adjuvant chemotherapy to all patients). Health outcomes measured in the model were quality-adjusted life years (QALYs) and life years (LYs) gained. Deterministic and probabilistic sensitivity analyses were performed to determine the impact of uncertainty. Scenario analyses were performed to assess the impact of the parameters with the greatest uncertainty. RESULTS: Risk-stratifying patients with CRC stage II and III using Histotyping was dominant (less costly and more effective) compared to SOC. In patients with CRC stage II, the net monetary benefit of Histotyping was 270,934 Norwegian kroners (NOK) (year of valuation is 2021), and the net health benefit of Histotyping was 0.99. In stage III, the net monetary benefit of Histotyping was 195,419 NOK, and the net health benefit of Histotyping was 0.71. CONCLUSIONS:  Risk-stratifying patients with CRC using Histotyping prior to the administration of adjuvant chemotherapy is likely to be a cost-effective strategy in Norway.

Impacts of a health literacy-informed intervention in people with chronic obstructive pulmonary disease (COPD) on hospitalization, health literacy, self-management, quality of life, and health costs - A randomized controlled trial.

OBJECTIVE: To compare the effect of motivational interviewing (MI) and tailored health literacy (HL) follow-up with usual care on hospitalization, costs, HL, self-management, Quality of life (QOL), and psychological stress in people with chronic obstructive pulmonary disease (COPD). METHODS: A RCT was undertaken in Norway between March 2018-December 2020 (n = 127). The control group (CG, n = 63) received usual care. The intervention group (IG, n = 64) received tailored HL follow-up from MI-trained COPD nurses with home visits for eight weeks and phone calls for four months after hospitalization. Primary outcomes were hospitalization at eight weeks, six months, and one year from baseline. The trial was registered with ClinicalTrials.gov (NCT03216603) and analysed per protocol. RESULTS: Compared with the IG, the CG had 2.8 higher odds (95% CI [1.3 to 5.8]) of hospitalization and higher hospital health costs (MD=€ -6230, 95% CI [-6510 to -5951]) and lower QALYs (MD=0.1, 95% CI [0.10 to 0.11]) that gives an ICER = - 62,300. The IG reported higher QOL, self-management, and HL (p = 0.02- to <0.01). CONCLUSION: MI-trained COPD nurses using tailored HL follow-up is cost-effective, reduces hospitalization, and increases QOL, HL, and self-care in COPD. PRACTICE IMPLICATION: Tailored HL follow-up is beneficial for individuals with COPD and the healthcare system.

The socioeconomic impact of cancer on patients and their relatives: Organisation of European Cancer Institutes task force consensus recommendations on conceptual framework, taxonomy, and research directions.

Loss of income and out-of-pocket expenditures are important causes of financial hardship in many patients with cancer, even in high-income countries. The far-reaching consequences extend beyond the patients themselves to their relatives, including caregivers and dependents. European research to date has been limited and is hampered by the absence of a coherent theoretical framework and by heterogeneous methods and terminology. To address these shortages, a task force initiated by the Organisation of European Cancer Institutes (OECI) produced 25 recommendations, including a comprehensive definition of socioeconomic impact from the perspective of patients and their relatives, a conceptual framework, and a consistent taxonomy linked to the framework. The OECI task force consensus statement highlights directions for future research with a view towards policy relevance. Beyond descriptive studies into the dimension of the problem, individual severity and predictors of vulnerability should be explored. It is anticipated that the consensus recommendations will facilitate and enhance future research efforts into the socioeconomic impact of cancer and cancer care, providing a crucial reference point for the development and validation of patient-reported outcome instruments aimed at measuring its broader effects.

Healthcare utilization and costs associated with autologous haematopoietic stem cell transplantation in Norwegian patients with relapsing remitting multiple sclerosis.

Multiple sclerosis (MS) patients experience long-term deterioration of neurological function, reduced quality of life, long-lasting treatment cycles, and an increased risk of early workability loss imposing an economic burden to society. Autologous haematopoietic stem cell transplantation (AHSCT) has shown promising treatment effects for relapsing remitting MS (RRMS). This study employs a micro-costing approach to estimate healthcare utilization and costs associated with AHSCT in Norwegian RRMS patients. Patient-level data were extracted from medical journals of 30 RRMS patients receiving AHSCT treatment at Haukeland University Hospital in the period from January 2015 to January 2018. The time horizon for the analysis was from the pretransplant screening until one year after AHSCT. A correlation was found between patient body weight and total healthcare cost. The average total healthcare cost of AHSCT for RRMS patients was estimated to EUR 66 304 (95% CI: EUR 63 598 - EUR 69 010) including costs associated with the pre-AHSCT period, AHSCT treatment phases and one-year follow-up. The majority of the costs, EUR 64 329, occurred during the treatment phase and within the first 100 days after AHSCT. The results indicate that long-term healthcare cost savings may be achieved using AHSCT in selected patients with aggressive RRMS. This is due to the high costs of most used disease modifying treatments. Further research including long-term clinical data is needed to determine the cost-effectiveness of this treatment.

Implementation of advance care planning in the routine care for acutely admitted patients in geriatric units: protocol for a cluster randomized controlled trial.

BACKGROUND: Acutely ill and frail older adults and their next of kin are often poorly involved in treatment and care decisions. This may lead to either over- or undertreatment and unnecessary burdens. The aim of this project is to improve user involvement and health services for frail older adults living at home, and their relatives, by implementing advance care planning (ACP) in selected hospital wards, and to evaluate the clinical and the implementation interventions. METHODS: This is a cluster randomized trial with 12 hospital units. The intervention arm receives implementation support for 18 months; control units receive the same support afterwards. The ACP intervention consists of 1. Clinical intervention: ACP; 2. Implementation interventions: Implementation team, ACP coordinator, network meetings, training and supervision for health care personnel, documentation tools and other resources, and fidelity measurements with tailored feedback; 3. Implementation strategies: leadership commitment, whole ward approach and responsive evaluation. Fidelity will be measured three times in the intervention arm and twice in the control arm. Here, the primary outcome is the difference in fidelity changes between the arms. We will also include 420 geriatric patients with one close relative and an attending clinician in a triadic sub-study. Here, the primary outcomes are quality of communication and decision-making when approaching the end of life as perceived by patients and next of kin, and congruence between the patient's preferences for information and involvement and the clinician's perceptions of the same. For patients we will also collect clinical data and health register data. Additionally, all clinical staff in both arms will be invited to answer a questionnaire before and during the implementation period. To explore barriers and facilitators and further explore the significance of ACP, qualitative interviews will be performed in the intervention units with patients, next of kin, health care personnel and implementation teams, and with other stakeholders up to national level. Lastly, we will evaluate resource utilization, costs and health outcomes in a cost-effectiveness analysis. DISCUSSION: The project may contribute to improved implementation of ACP as well as valuable knowledge and methodological developments in the scientific fields of ACP, health service research and implementation science. TRIAL REGISTRATION: ClinicalTrials.gov Identifier NCT05681585. Registered 03.01.23.

A Guide to an Iterative Approach to Model-Based Decision Making in Health and Medicine: An Iterative Decision-Making Framework.

Decision makers frequently face decisions about optimal resource allocation. A model-based economic evaluation can be used to guide decision makers in their choices by systematically evaluating the magnitude of expected health effects and costs of decision options and by making trade-offs explicit. We provide a guide to an iterative approach to the medical decision-making process by following a coherent framework, and outline the overarching iterative steps of model-based decision making. We systematized the framework by performing three steps. First, we compiled the existing guidelines provided by the ISPOR-SMDM Modeling Good Research Practices Task Force, and the ISPOR Value of Information Task Force. Second, we identified other previous work related to frameworks and guidelines for model-based decision analyses through a literature search in PubMed. Third, we assessed the role of the evidence and iterative process in decision making and formalized key steps in a model-based decision-making framework. We provide guidance on an iterative approach to medical decision making by applying the compiled iterative model-based decision-making framework. The framework formally combines the decision problem conceptualization (Part I), the model conceptualization and development (Part II), and the process of model-based decision analysis (Part III). Following the overarching steps of the framework ensures compliance to the principles of evidence-based medicine and regular updates of the evidence, given that value of information analysis represents an essential component of model-based decision analysis in the framework. Following the provided guide and the steps outlined in the framework can help inform various health care decisions, and therefore it has the potential to improve decision making.