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

Luyendijk, Marianne; Vernooij, Robin W.M.; Blommestein, Hedwig M.; Siesling, Sabine; Groot, Carin A. Uyl–de

doi:10.1016/j.jval.2020.05.008

Cited by 2 publications

(2 citation statements)

References 55 publications

(81 reference statements)

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“…However, retrospective claims data analysis has emerged as an important tool to further elucidate patient characteristics, comorbidities, treatment algorithms, and clinical outcomes [11][12][13]. Furthermore, claims data analysis represents an important tool for estimating the economic burden of specific diseases and assessing the cost-effectiveness of certain treatments [14,15]. Claims data aggregate socioeconomic, epidemiological, and clinical data from different healthcare providers in the inpatient and outpatient setting, allowing a holistic evaluation of treatment courses.…”

Section: Introductionmentioning

confidence: 99%

Implementation and Evaluation of a Breast Cancer Disease Model Using Real-World Claims Data in Germany from 2010 to 2020

Dannehl,

von Au,

Engler

et al. 2024

Cancers

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Breast cancer is the leading cause of cancer-related mortality among women in Germany and worldwide. This retrospective claims data analysis utilizing data from AOK Baden-Wuerttemberg, a major statutory German health insurance provider, aimed to construct and assess a real-world data breast cancer disease model. The study included 27,869 female breast cancer patients and 55,738 age-matched controls, analyzing data from 2010 to 2020. Three distinct breast cancer stages were analyzed: Stage A (early breast cancer without lymph node involvement), Stage B (early breast cancer with lymph node involvement), and Stage C (primary distant metastatic breast cancer). Tumor subtypes were estimated based on the prescription of antihormonal or HER2-targeted therapy. The study established that 77.9% of patients had HR+ breast cancer and 9.8% HER2+; HR+/HER2− was the most common subtype (70.9%). Overall survival (OS) analysis demonstrated significantly lower survival rates for stages B and C than for controls, with 5-year OS rates ranging from 79.3% for stage B to 35.4% for stage C. OS rates were further stratified by tumor subtype and stage, revealing varying prognoses. Distant recurrence-free survival (DRFS) analysis showed higher recurrence rates in stage B than in stage A, with HR−/HER2− displaying the worst DRFS. This study, the first to model breast cancer subtypes, stages, and outcomes using German claims data, provides valuable insights into real-world breast cancer epidemiology and demonstrates that this breast cancer disease model has the potential to be representative of treatment outcomes.

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Section: Introductionmentioning

confidence: 99%

Implementation and Evaluation of a Breast Cancer Disease Model Using Real-World Claims Data in Germany from 2010 to 2020

Dannehl,

von Au,

Engler

et al. 2024

Cancers

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“…Such analyses have the potential to inform decision-making across a health system, including health policy decisions around funding mechanisms through to local health service decisions to improve service delivery for specific patient groups. Despite this, analyses of linked datasets often exclude consideration of cost; for example, a recent systematic review of systemic therapies for breast cancer patients explored in claims data internationally found that only 22% of 106 therapies included a consideration of healthcare costs [7].…”

Section: Introductionmentioning

confidence: 99%

DAta Linkage to Enhance Cancer Care (DaLECC): Protocol of a Large Australian Data Linkage Study

Edney

Roseleur

Bright

et al. 2023

IJERPH

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Cancer is a leading cause of global morbidity and mortality, accounting for 250 Disability-Adjusted Life Years and 10 million deaths in 2019. Minimising unwarranted variation and ensuring appropriate cost-effective treatment across primary and tertiary care to improve health outcomes is a key health priority. There are few studies that have used linked data to explore healthcare utilisation prior to diagnosis in addition to post-diagnosis patterns of care. This protocol outlines the aims of the DaLECC project and key methodological features of the linked dataset. The primary aim of this project is to explore predictors of variations in pre- and post-cancer diagnosis care, and to explore the economic and health impact of any variation. The cohort of patients includes all South Australian residents diagnosed with cancer between 2011 and 2020, who were recorded on the South Australian Cancer Registry. These cancer registry records are being linked with state and national healthcare databases to capture health service utilisation and costs for a minimum of one-year prior to diagnosis and to a maximum of 10 years post-diagnosis. Healthcare utilisation includes state databases for inpatient separations and emergency department presentations and national databases for Medicare services and pharmaceuticals. Our results will identify barriers to timely receipt of care, estimate the impact of variations in the use of health care, and provide evidence to support interventions to improve health outcomes to inform national and local decisions to enhance the access and uptake of health care services.

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

Cited by 2 publications

References 55 publications

Implementation and Evaluation of a Breast Cancer Disease Model Using Real-World Claims Data in Germany from 2010 to 2020

Implementation and Evaluation of a Breast Cancer Disease Model Using Real-World Claims Data in Germany from 2010 to 2020

DAta Linkage to Enhance Cancer Care (DaLECC): Protocol of a Large Australian Data Linkage Study

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