High-value breast cancer care within resource limitations.

Breast cancer care is a costly global health issue where effective management depends on early detection and treatment. A breast cancer diagnosis can result in financial catastrophe especially in low- and middle-income countries (LMIC). Large inequities in breast cancer care are observed and represent a global challenge to caregivers and patients. Strategies to improve early diagnosis include awareness and clinical breast examination in LMIC, and screening in high-income countries (HIC). The use of clinical guidelines for the management of breast cancer is needed. Adapted guidelines from HIC can address disparities in populations with limited resources. Locally developed strategies still provide effective guidance in improving survival. Integrated practice units (IPU) with timely multidisciplinary breast care conferences and patient navigators are required to achieve high-value, personalized breast cancer management in HIC as well as LMIC. Breast cancer patient care should include a quality of life evaluation using ideally patient-reported outcomes (PROM) and experience measurements (PREM). Evaluation of breast cancer outcomes must include the financial cost of delivered care. The resulting value perspective should guide resource allocation and program priorities. The value of care must be improved by translating the findings of social and economic research into practice and resolving systemic inequity in clinical breast cancer research. Cancer survivorship programs must be put in place everywhere. The treatment of patients with metastatic breast cancer must require more attention in the future, especially in LMIC.

The effectiveness of personalised surveillance and aftercare in breast cancer follow-up: a systematic review.

PURPOSE: Breast cancer follow-up (surveillance and aftercare) varies from one-size-fits-all to more personalised approaches. A systematic review was performed to get insight in existing evidence on (cost-)effectiveness of personalised follow-up. METHODS: PubMed, Scopus and Cochrane were searched between 01-01-2010 and 10-10-2022 (review registered in PROSPERO:CRD42022375770). The inclusion population comprised nonmetastatic breast cancer patients ≥ 18 years, after completing curative treatment. All intervention-control studies studying personalised surveillance and/or aftercare designed for use during the entire follow-up period were included. All review processes including risk of bias assessment were performed by two reviewers. Characteristics of included studies were described. RESULTS: Overall, 3708 publications were identified, 64 full-text publications were read and 16 were included for data extraction. One study evaluated personalised surveillance. Various personalised aftercare interventions and outcomes were studied. Most common elements included in personalised aftercare plans were treatment summaries (75%), follow-up guidelines (56%), lists of available supportive care resources (38%) and PROs (25%). Control conditions mostly comprised usual care. Four out of seven (57%) studies reported improvements in quality of life following personalisation. Six studies (38%) found no personalisation effect, for multiple outcomes assessed (e.g. distress, satisfaction). One (6.3%) study was judged as low, four (25%) as high risk of bias and 11 (68.8%) as with concerns. CONCLUSION: The included studies varied in interventions, measurement instruments and outcomes, making it impossible to draw conclusions on the effectiveness of personalised follow-up. There is a need for a definition of both personalised surveillance and aftercare, whereafter outcomes can be measured according to uniform standards.

European quality indicators developed by the European Commission Initiative on Breast Cancer: a first nationwide assessment for the Dutch setting.

PURPOSE: This observational study aims to assess the feasibility of calculating indicators developed by the European Commission Initiative on Breast Cancer (ECIBC) for the Dutch breast cancer population. METHODS: Patients diagnosed with invasive or in situ breast cancer between 2012 and 2018 were selected from the Netherlands Cancer Registry (NCR). Outcomes of the quality indicators (QI) were presented as mean scores and were compared to a stated norm. Variation between hospitals was assessed by standard deviations and funnel plots and trends over time were evaluated. The quality indicator calculator (QIC) was validated by comparing these outcomes with the outcomes of constructed algorithms in Stata. RESULTS: In total, 133,527 patients were included. Data for 24 out of 26 QIs were available in the NCR. For 67% and 67% of the QIs, a mean score above the norm and low or medium hospital variation was observed, respectively. The proportion of patients undergoing a breast reconstruction or neoadjuvant systemic therapy increased over time. The proportion treated within 4 weeks from diagnosis, having >10 lymph nodes removed or estrogen negative breast cancer who underwent adjuvant chemotherapy decreased. The outcomes of the constructed algorithms in this study and the QIC showed 100% similarity. CONCLUSION: Data from the NCR could be used for the calculation of more than 92% of the ECIBC indicators. The quality of breast cancer care in the Netherlands is high, as more than half of the QIs already score above the norm and medium hospital variation was observed. The QIC can be easy and reliably applied.

External validation and clinical utility assessment of PREDICT breast cancer prognostic model in young, systemic treatment-naïve women with node-negative breast cancer.

BACKGROUND: The validity of the PREDICT breast cancer prognostic model is unclear for young patients without adjuvant systemic treatment. This study aimed to validate PREDICT and assess its clinical utility in young women with node-negative breast cancer who did not receive systemic treatment. METHODS: We selected all women from the Netherlands Cancer Registry who were diagnosed with node-negative breast cancer under age 40 between 1989 and 2000, a period when adjuvant systemic treatment was not standard practice for women with node-negative disease. We evaluated the calibration and discrimination of PREDICT using the observed/expected (O/E) mortality ratio, and the area under the receiver operating characteristic curve (AUC), respectively. Additionally, we compared the potential clinical utility of PREDICT for selectively administering chemotherapy to the chemotherapy-to-all strategy using decision curve analysis at predefined thresholds. RESULTS: A total of 2264 women with a median age at diagnosis of 36 years were included. Of them, 71.2% had estrogen receptor (ER)-positive tumors and 44.0% had grade 3 tumors. Median tumor size was 16 mm. PREDICT v2.2 underestimated 10-year all-cause mortality by 33% in all women (O/E ratio:1.33, 95%CI:1.22-1.43). Model discrimination was moderate overall (AUC10-year:0.65, 95%CI:0.62-0.68), and poor for women with ER-negative tumors (AUC10-year:0.56, 95%CI:0.51-0.62). Compared to the chemotherapy-to-all strategy, PREDICT only showed a slightly higher net benefit in women with ER-positive tumors, but not in women with ER-negative tumors. CONCLUSIONS: PREDICT yields unreliable predictions for young women with node-negative breast cancer. Further model updates are needed before PREDICT can be routinely used in this patient subset.

Cost-Effectiveness Analysis of MammaPrint® to Guide the Use of Endocrine Therapy in Patients with Early-Stage Breast Cancer.

BACKGROUND: Gene expression profiling tests can predict the risk of disease recurrence and select patients who are expected to benefit from therapy, while allowing other patients to forgo therapy. For breast cancers, these tests were initially designed to tailor chemotherapy decisions, but recent evidence suggests that they may also guide the use of endocrine therapy. This study evaluated the cost effectiveness of a prognostic test, MammaPrint®, to guide the use of adjuvant endocrine therapy in patients eligible according to Dutch treatment guidelines. METHODS: We constructed a Markov decision model to calculate the lifetime costs (in 2020 Euros) and effects (survival and quality-adjusted life-years) of MammaPrint® testing versus usual care (endocrine therapy for all patients) in a simulated cohort of patients. The population of interest includes patients for whom MammaPrint® testing is currently not indicated, but for whom it may be possible to safely omit endocrine therapy. We applied both a health care perspective and a societal perspective and discounted costs (4%) and effects (1.5%). Model inputs were obtained from published research (including randomized controlled trials), nationwide cancer registry data, cohort data and publicly available data sources. Scenario and sensitivity analyses were conducted to explore the impact of uncertainty around input parameters. Additionally, threshold analyses were performed to identify under which circumstances MammaPrint® testing would be cost effective. RESULTS: Adjuvant endocrine therapy guided by MammaPrint® resulted in fewer side effects, more (quality-adjusted) life-years (0.10 and 0.07 incremental QALYS and LYs, respectively) and higher costs (€18,323 incremental costs) compared with the usual care strategy in which all patients receive endocrine therapy. While costs for hospital visits, medication costs and productivity costs were somewhat higher in the usual care strategy, these did not outweigh costs of testing in the MammaPrint® strategy. The incremental cost-effectiveness ratio was €185,644 per QALY gained from a healthcare perspective and €180,617 from a societal perspective. Sensitivity and scenario analyses showed that the conclusions remained the same under changed input parameters and assumptions. Our results show that MammaPrint® can become a cost-effective strategy when either the price of the test is reduced (> 50%), or the proportion of patients for which treatment is altered (i.e. those with ultra-low risk) increases to > 26%. CONCLUSION: Standard MammaPrint® testing to guide the use of endocrine therapy in our simulated patient population appears not to be a cost-effective strategy compared with usual care. The cost effectiveness of the test can be improved by reducing the price or preselecting a population more likely to benefit from the test.

Inequalities in the omission of axillary dissection in sentinel lymph node positive patients in the Netherlands: Innovative hospitals are early adopters of a de-escalating approach.

During the last decade completion axillary lymph node dissection (cALND) was gradually omitted in sentinel lymph node positive (SLN+) breast cancer patients. However, adoption varies among hospitals. We analyzed factors associated with the omission of cALND in all Dutch SLN+ patients. As one of the focus hospital-related factors we defined "innovative" as the percentage of gene-expression profile (GEP) deployment within the indicated group of patients per hospital as a proxy for early adoption of innovations. cT1-2N0M0 SLN+ patients treated between 2011 and 2018 were selected from the Netherlands Cancer Registry. Hospitals were defined to be innovative based on their GEP use. Multivariable logistic regression (MLR) was performed to assess the relationship between innovative capacity, patient-, treatment- and hospital-related characteristics and cALND performance. 14 317 patients were included. Treatment in a hospital with high innovative capacity was associated with a lower probability of receiving cALND (OR 0.69, OR 0.46 and OR 0.35 in modestly, fairly and very innovative, respectively). Other factors associated with a lower probability of receiving a cALND were age 70 and 79 years and ≥79 years (ORs 0.59 [95% CI: 0.50-0.68] and 0.21 [95% CI: 0.17-0.26]) and treatment in an academic hospital (OR 0.41 [95% CI: 0.33-0.51]). Factors associated with an increased probability of undergoing cALND were HR-/HER2- tumors (OR 1.46 [95% CI: 1.19-1.80]), macrometastatic lymph node involvement (OR 6.37 [95% CI: 5.70-7.13]) and mastectomy (OR 4.57 [95% CI: 4.09-5.10]). Patients treated in a hospital that early adopted innovations were less likely to receive cALND. Our findings endorse the need for studies on barriers and facilitators of implementing innovations.

Systematic Review of Health Economic Evaluations Focused on Artificial Intelligence in Healthcare: The Tortoise and the Cheetah.

OBJECTIVES: This study aimed to systematically review recent health economic evaluations (HEEs) of artificial intelligence (AI) applications in healthcare. The aim was to discuss pertinent methods, reporting quality and challenges for future implementation of AI in healthcare, and additionally advise future HEEs. METHODS: A systematic literature review was conducted in 2 databases (PubMed and Scopus) for articles published in the last 5 years. Two reviewers performed independent screening, full-text inclusion, data extraction, and appraisal. The Consolidated Health Economic Evaluation Reporting Standards and Philips checklist were used for the quality assessment of included studies. RESULTS: A total of 884 unique studies were identified; 20 were included for full-text review, covering a wide range of medical specialties and care pathway phases. The most commonly evaluated type of AI was automated medical image analysis models (n = 9, 45%). The prevailing health economic analysis was cost minimization (n = 8, 40%) with the costs saved per case as preferred outcome measure. A total of 9 studies (45%) reported model-based HEEs, 4 of which applied a time horizon >1 year. The evidence supporting the chosen analytical methods, assessment of uncertainty, and model structures was underreported. The reporting quality of the articles was moderate as on average studies reported on 66% of Consolidated Health Economic Evaluation Reporting Standards items. CONCLUSIONS: HEEs of AI in healthcare are limited and often focus on costs rather than health impact. Surprisingly, model-based long-term evaluations are just as uncommon as model-based short-term evaluations. Consequently, insight into the actual benefits offered by AI is lagging behind current technological developments.

Assessment of Studies Evaluating Incremental Costs, Effectiveness, or Cost-Effectiveness of Systemic Therapies in Breast Cancer Based on Claims Data: A Systematic Review.

OBJECTIVES: Large secondary databases, such as those containing insurance claims data, are increasingly being used to compare the effects and costs of treatments in routine clinical practice. Despite their appeal, however, caution must be exercised when using these data. In this study, we aimed to identify and assess the methodological quality of studies that used claims data to compare the effectiveness, costs, or cost-effectiveness of systemic therapies for breast cancer. METHODS: We searched Embase, the Cochrane Library, Medline, Web of Science, and Google Scholar for English-language publications and assessed the methodological quality using the Good Research for Comparative Effectiveness principles. This study was registered with the International Prospective Register of Systematic Reviews (PROSPERO) under number CRD42018103992. RESULTS: We identified 1251 articles, of which 106 met the inclusion criteria. Most studies were conducted in the United States (74%) and Taiwan (9%) and were based on claims data sets (35%) or claims data linked to cancer registries (58%). Furthermore, most included large samples (mean 17 130 patients) and elderly patients, and they covered various outcomes (eg, survival, adverse events, resource use, and costs). Key methodological shortcomings were the lack of information on relevant confounders, the risk of immortal time bias, and the lack of information on the validity of outcomes. Only a few studies performed sensitivity analyses. CONCLUSIONS: Many comparative studies of cost, effectiveness, and cost-effectiveness have been published in recent decades based on claims data, and the number of publications has increased over time. Despite the availability of guidelines to improve quality, methodological issues persist and are often inappropriately addressed or reported.

Applying Risk-Based Follow-Up Strategies on the Dutch Breast Cancer Population: Consequences for Care and Costs.

OBJECTIVES: An important aim of follow-up after primary breast cancer treatment is early detection of locoregional recurrences (LRR). This study compares 2 personalized follow-up scheme simulations based on LRR risk predictions provided by a time-dependent prognostic model for breast cancer LRR and quantifies their possible follow-up efficiency. METHODS: Surgically treated early patients with breast cancer between 2003 and 2008 were selected from the Netherlands Cancer Registry. The INFLUENCE nomogram was used to estimate the 5-year annual LRR. Applying 2 thresholds, they were defined according to Youden's J-statistic and a predefined follow-up sensitivity of 95%, respectively. These patient's risk estimations served as the basis for scheduling follow-up visits; 2 personalized follow-up schemes were simulated. The number of potentially saved follow-up visits and corresponding cost savings for each follow-up scheme were compared with the current Dutch breast cancer guideline recommendation and the observed utilization of follow-up on a training and testing cohort. RESULTS: Using LRR risk-predictions for 30 379 Dutch patients with breast cancer from 2003 to 2006 (training cohort), 2 thresholds were calculated. The threshold according to Youden's approach yielded a follow-up sensitivity of 62.5% and a potential saving of 62.1% of follow-up visits and €24.8 million in 5 years. When the threshold corresponding to 95% follow-up sensitivity was used, 17% of follow-up visits and €7 million were saved compared with the guidelines. Similar results were obtained by applying these thresholds to the testing cohort of 11 462 patients from 2007 to 2008. Compared with the observed utilization of follow-up, the potential cost-savings decline moderately. CONCLUSIONS: Personalized follow-up schemes based on the INFLUENCE nomogram's individual risk estimations for breast cancer LRR could decrease the number of follow-up visits if one accepts a limited risk of delayed LRR detection.

Breast Cancer: global quality care optimizing care delivery with existing financial and personnel resources.

Our vision about breast cancer quality care within a global health framework was recently published by Oxford University Press. The aim of our work was to reflect on the potential to achieve a world-wide improvement in quality care, assessing value for money. The population-based survival estimates from the CONCORD programme and the Breast Health Global Initiative (BHGI) are valuable tools for this global effort. Because cancer care delivery is becoming unsustainable in many countries assessing healthcare value for the cost is becoming increasingly important. Recommendations are made for better global quality care for patients with breast cancer.

Clinical decision trees support systematic evaluation of multidisciplinary team recommendations.

PURPOSE: EUSOMA's recommendation that "each patient has to be fully informed about each step in the diagnostic and therapeutic pathway" could be supported by guideline-based clinical decision trees (CDTs). The Dutch breast cancer guideline has been modeled into CDTs ( www.oncoguide.nl ). Prerequisites for adequate CDT usage are availability of necessary patient data at the time of decision-making and to consider all possible treatment alternatives provided in the CDT. METHODS: This retrospective single-center study evaluated 394 randomly selected female patients with non-metastatic breast cancer between 2012 and 2015. Four pivotal CDTs were selected. Two researchers analyzed patient records to determine to which degree patient data required per CDT were available at the time of multidisciplinary team (MDT) meeting and how often multiple alternatives were actually reported. RESULTS: The four selected CDTs were indication for magnetic resonance imaging (MRI) scan, preoperative and adjuvant systemic treatment, and immediate breast reconstruction. For 70%, 13%, 97% and 13% of patients, respectively, all necessary data were available. The two most frequent underreported data-items were "clinical M-stage" (87%) and "assessable mammography" (28%). Treatment alternatives were reported by MDTs in 32% of patients regarding primary treatment and in 28% regarding breast reconstruction. CONCLUSION: Both the availability of data in patient records essential for guideline-based recommendations and the reporting of possible treatment alternatives of the investigated CDTs were low. To meet EUSOMA's requirements, information that is supposed to be implicitly known must be explicated by MDTs. Moreover, MDTs have to adhere to clear definitions of data-items in their reporting.

Transformation of the National Breast Cancer Guideline Into Data-Driven Clinical Decision Trees.

PURPOSE: The essence of guideline recommendations often is intertwined in large texts. This impedes clinical implementation and evaluation and delays timely modular revisions needed to deal with an ever-growing amount of knowledge and application of personalized medicine. The aim of this project was to model guideline recommendations as data-driven clinical decision trees (CDTs) that are clinically interpretable and suitable for implementation in decision support systems. METHODS: All recommendations of the Dutch national breast cancer guideline for nonmetastatic breast cancer were translated into CDTs. CDTs were constructed by nodes, branches, and leaves that represent data items (patient and tumor characteristics [eg, T stage]), data item values (eg, T2 or less), and recommendations (eg, chemotherapy), respectively. For all data items, source of origin was identified (eg, pathology), and where applicable, data item values were defined on the basis of existing classification and coding systems (eg, TNM, Breast Imaging Reporting and Data System, Systematized Nomenclature of Medicine). All unique routes through all CDTs were counted to measure the degree of data-based personalization of recommendations. RESULTS: In total, 60 CDTs were necessary to cover the whole guideline and were driven by 114 data items. Data items originated from pathology (49%), radiology (27%), clinical (12%), and multidisciplinary team (12%) reports. Of all data items, 101 (89%) could be classified by existing classification and coding systems. All 60 CDTs could be integrated in an interactive decision support app that contained 376 unique patient subpopulations. CONCLUSION: By defining data items unambiguously and unequivocally and coding them to an international coding system, it was possible to present a complex guideline as systematically constructed modular data-driven CDTs that are clinically interpretable and accessible in a decision support app.

A conditional model predicting the 10-year annual extra mortality risk compared to the general population: a large population-based study in Dutch breast cancer patients.

OBJECTIVE: Many cancer survivors are facing difficulties in getting a life insurance; raised premiums and declinatures are common. We generated a prediction model estimating the conditional extra mortality risk of breast cancer patients in the Netherlands. This model can be used by life insurers to accurately estimate the additional risk of an individual patient, conditional on the years survived. METHODOLOGY: All women diagnosed with stage I-III breast cancer in 2005-2006, treated with surgery, were selected from the Netherlands Cancer Registry. For all stages separately, multivariable logistic regression was used to estimate annual mortality risks, conditional on the years survived, until 10 years after diagnosis, resulting in 30 models. The conditional extra mortality risk was calculated by subtracting mortality rates of the general Dutch population from the patient mortality rates, matched by age, gender and year. The final model was internally and externally validated, and tested by life insurers. RESULTS: We included 23,234 patients: 10,101 stage I, 9,868 stage II and 3,265 stage III. The final models included age, tumor stage, nodal stage, lateralization, location within the breast, grade, multifocality, hormonal receptor status, HER2 status, type of surgery, axillary lymph node dissection, radiotherapy, (neo)adjuvant systemic therapy and targeted therapy. All models showed good calibration and discrimination. Testing of the model by life insurers showed that insurability using the newly-developed model increased with 13%, ranging from 0%-24% among subgroups. CONCLUSION: The final model provides accurate conditional extra mortality risks of breast cancer patients, which can be used by life insurers to make more reliable calculations. The model is expected to increase breast cancer patients' insurability and transparency among life insurers.

Risk-based breast cancer follow-up stratified by age.

Although personalization of cancer care is recommended, current follow-up after the curative treatment of breast cancer is consensus-based and not differentiated for base-line risk. Every patient receives annual follow-up for 5 years without taking into account the individual risk of recurrence. The aim of this study was to introduce personalized follow-up schemes by stratifying for age. Using data from the Netherlands Cancer Registry of 37 230 patients with early breast cancer between 2003 and 2006, the risk of recurrence was determined for four age groups (<50, 50-59, 60-69, >70). Follow-up was modeled with a discrete-time partially observable Markov decision process. The decision to test for recurrences was made two times per year. Recurrences could be detected by mammography as well as by self-detection. For all age groups, it was optimal to have more intensive follow-up around the peak in recurrence risk in the second year after diagnosis. For the first age group (<50) with the highest risk, a slightly more intensive follow-up with one extra visit was proposed compared to the current guideline recommendation. The other age groups were recommended less visits: four for ages 50-59, three for 60-69, and three for ≥70. With this model for risk-based follow-up, clinicians can make informed decisions and focus resources on patients with higher risk, while avoiding unnecessary and potentially harmful follow-up visits for women with very low risks. The model can easily be extended to take into account more risk factors and provide even more personalized follow-up schedules.

Effects of oncological care pathways in primary and secondary care on patient, professional, and health systems outcomes: protocol for a systematic review and meta-analysis.

BACKGROUND: The high impact of a cancer diagnosis on patients and their families and the increasing costs of cancer treatment call for optimal and efficient oncological care. To improve the quality of care and to minimize healthcare costs and its economic burden, many healthcare organizations introduce care pathways to improve efficiency across the continuum of cancer care. However, there is limited research on the effects of cancer care pathways in different settings. METHODS: The aim of this systematic review and meta-analysis described in this protocol is to synthesize existing literature on the effects of oncological care pathways. We will conduct a systematic search strategy to identify all relevant literature in several biomedical databases, including Cochrane library, MEDLINE, Embase, and CINAHL. We will follow the methodology of Cochrane Effective Practice and Organisation of Care (EPOC), and we will include randomized trials, non-randomized trials, controlled before-after studies, and interrupted time series studies. In addition, we will include full economic evaluations (cost-effectiveness analyses, cost-utility analyses, and cost-benefit analyses), cost analyses, and comparative resource utilization studies, if available. Two reviewers will independently screen all studies and evaluate those included for risk of bias. From these studies, we will extract data regarding patient, professional, and health systems outcomes. Our systematic review will follow the PRISMA set of items for reporting in systematic reviews and meta-analyses. DISCUSSION: Following the protocol outlined in this article, we aim to identify, assess, and synthesize all available evidence in order to provide an evidence base on the effects of oncological care pathways as reported in the literature. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42017057592 .

Shifting breast cancer surveillance from current hospital setting to a community based setting: a cost-effectiveness study.

BACKGROUND: This study explores the effectiveness and cost-effectiveness of surveillance after breast cancer treatment provided in a hospital-setting versus surveillance embedded in the community-based National Breast Cancer Screening Program (NBCSP). METHODS: Using a decision tree, strategies were compared on effectiveness and costs from a healthcare perspective over a 5-year time horizon. Women aged 50-75 without distant metastases that underwent breast conserving surgery in 2003-2006 were selected from the Netherlands Cancer Registry (n = 14,093). Key input parameters were mammography sensitivity and specificity, risk of loco regional recurrence (LRR), and direct healthcare costs. Primary outcome measure was the proportion true test results (TTR), expressed as the positive and negative predictive value (PPV, NPV). The incremental cost-effectiveness ratio (ICER) is defined as incremental costs per TTR forgone. RESULTS: For the NBCSP-strategy, 13,534 TTR (8 positive; 13,526 negative), and 12,923 TTR (387 positive; 12,536 negative) were found for low and high risks respectively. For the hospital-based strategy, 26,663 TTR (13 positive; 26,650 negative) and 24,883 TTR (440 positive; 24,443 negative) were found for low and high risks respectively. For low risks, the PPV and NPV for the NBCSP-based strategy were 3.31% and 99.88%, and 2.74% and 99.95% for the hospital strategy respectively. For high risks, the PPV and NPV for the NBCSP-based strategy were 64.10% and 98.87%, and 50.98% and 99.71% for the hospital-based strategy respectively. Total expected costs of the NBCSP-based strategy were lower than for the hospital-based strategy (low risk: €1,271,666 NBCSP vs €2,698,302 hospital; high risk: €6,939,813 NBCSP vs €7,450,150 hospital), rendering ICERs that indicate cost savings of €109 (95%CI €95-€127) (low risk) and €43 (95%CI €39-€56) (high risk) per TTR forgone. CONCLUSION: Despite expected cost-savings of over 50% in the NBCSP-based strategy, it is nearly 50% lower accurate than the hospital-based strategy, compromising the goal of early detection of LRR to an extent that is unlikely to be acceptable.

Resource utilization in lung cancer diagnostic procedures: Current use and budget consequences.

OBJECTIVES: The main objective of this study is to determine the current use of lung cancer diagnostic procedures in two large hospitals in the Netherlands, to explore deviations in guideline adherence between the hospitals, and to estimate the budget impact of the diagnostic work-up as well as the over- and underutilization. MATERIALS & METHODS: A state transition model for the diagnostic pathway for lung cancer patients was developed using existing clinical practice guidelines (CPG) combined with a systematic literature. In addition to the CPGs depicting current practice, diagnostic utilization was gathered in two large hospitals representing an academic tertiary care hospital and a large regional teaching hospital for patients, who were selected from the Netherlands cancer registry. RESULTS: The total population consisted of 376 patients with lung cancer. Not in all cases the guideline was followed, for instance in the usage of MR brain with stage III lung cancer patients (n = 70). The state-transition model predicts an average budget impact for the diagnostic pathway per patient estimated of € 2496 in the academic tertiary care hospital and € 2191 in the large regional teaching hospital. CONCLUSION: The adherence to the CPG's differed between hospitals, which questions the adherence to CPG's in general. Adherence to CPG's could lead to less costs in the diagnostic pathway for lung cancer patients.

Disparities in survival of stomach cancer among different socioeconomic groups in North-East Netherlands.

BACKGROUND: Survival differences in stomach cancer are depended on patient, tumour and treatment factors. Some populations are more prone to develop stomach cancer, such as people with low socioeconomic status (SES). The aim of this population based study was to assess whether differences in socioeconomic status (SES) alone, after adjusting for confounding factors, also influence survival. METHODS: From 1989 to 2007 all patients with stomach cancer were selected from the cancer registry of the Comprehensive Cancer Centre North-East. Postal code at diagnosis was used to determine SES, dividing patients in three groups; low, intermediate and high SES. Associations between age, localization, grade, stage, and treatment were determined using Chi-square analysis. Relative survival analysis was used to estimate relative excess risk (RER) of dying according to SES. RESULTS: In low SES neighbourhoods diagnosis was established at older age. More distal tumours were detected in patients with low SES, whereas pathology showed more poorly differentiated tumours in patients with high SES. Overall, more resections were performed in, and more chemotherapy was administrated to patients in high SES neighbourhoods. After adjusting for confounding factors, the risk of dying was lower for patients with high SES (RER 0.89, 95% Confidence Interval 0.81-0.98) compared to patients with low SES. CONCLUSION: SES proved to be an independent prognostic factor for survival in patients with stomach cancer.