Data Matching to Support Analysis of Cancer Epidemiology Among Veterans Compared With Non-Veteran Populations—An Exemplar in Brain Tumors

Woo, Christine; Cioffi, Gino; Bej, Taissa; Wilson, Brigid; Briggs, Janet; Markt, Sarah C.; Schumacher, Fredrick R.; Kruchko, Carol; Waite, Kristin; Nabors, L. Burt; Nock, Charles J.; Jump, Robin L.P.; Barnholtz‐Sloan, Jill S.

doi:10.1200/cci.21.00052

Cited by 3 publications

(2 citation statements)

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“…Using a Naïve Bayesian network-based contribution analysis of biologic and clinical factors to cancer disparities, Luo, et al, found that nearly 50% of racial differences in stage at diagnosis for patients with breast cancer can be attributed to the timing and use of biopsy and screening mammography -modifiable and therefore actionable factors [37]. Additionally, a data matching algorithm was able to detect meaningful differences in the distribution of brain tumor histology between Veterans and non-Veterans populations, an approach that could be adapted to other sociodemographic factors [38,39].…”

Section: Big Data and Real-world Datamentioning

confidence: 99%

Clinical Informatics Approaches to Understand and Address Cancer Disparities

2022

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Objectives: Disparities in cancer incidence and outcomes across race, ethnicity, gender, socioeconomic status, and geography are well-documented, but their etiologies are often poorly understood and multifactorial. Clinical informatics can provide tools to better understand and address these disparities by enabling high-throughput analysis of multiple types of data. Here, we review recent efforts in clinical informatics to study and measure disparities in cancer. Methods: We carried out a narrative review of clinical informatics studies related to cancer disparities and bias published from 2018-2021, with a focus on domains such as real-world data (RWD) analysis, natural language processing (NLP), radiomics, genomics, proteomics, metabolomics, and metagenomics. Results: Clinical informatics studies that investigated cancer disparities across race, ethnicity, gender, and age were identified. Most cancer disparities work within clinical informatics used RWD analysis, NLP, radiomics, and genomics. Emerging applications of clinical informatics to understand cancer disparities, including proteomics, metabolomics, and metagenomics, were less well represented in the literature but are promising future research avenues. Algorithmic bias was identified as an important consideration when developing and implementing cancer clinical informatics techniques, and efforts to address this bias were reviewed. Conclusions: In recent years, clinical informatics has been used to probe a range of data sources to understand cancer disparities across different populations. As informatics tools become integrated into clinical decision-making, attention will need to be paid to ensure that algorithmic bias does not amplify existing disparities. In our increasingly interconnected medical systems, clinical informatics is poised to untap the full potential of multi-platform health data to address cancer disparities.

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Section: Big Data and Real-world Datamentioning

confidence: 99%

Clinical Informatics Approaches to Understand and Address Cancer Disparities

2022

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“…2 Additional studies highlight gaps in cancer surveillance data for veterans. An analysis by Woo et al 3 found 25% of Ohio veterans with a primary brain tumor were omitted from the state cancer registry. Direct data matching revealed more glioblastomas in veterans vs nonveterans, independently of sex, exemplifying how veteran-specific cancer patterns may diverge from population-level figures.…”

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confidence: 99%

Long-Term Health Outcomes of Traumatic Brain Injury in Veterans

Massaad,

Kiapour

2024

JAMA Netw Open

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Brain tumors in United States military veterans

Bihn,

Cioffi,

Waite

et al. 2023

Neuro-Oncology

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BACKGROUND Comprehensive analysis of brain tumor incidence and survival in the Veteran population has been lacking. METHODS Veteran data was obtained from the Veterans Health Administration (VHA) Medical Centers via VHA Corporate Data Warehouse. Brain tumor statistics on the overall US population were generated from the Central Brain Tumor Registry of the US data. Cases were individuals (≥18 years) with a primary brain tumor, diagnosed between 2004-2018. The average annual age-adjusted incidence rates (AAIR) and 95% confidence intervals were estimated per 100,000 population and Kaplan-Meier survival curves evaluated overall survival outcomes among Veterans. RESULTS The Veteran population was primarily white (78%), male (93%), and between 60-64 years old (18%). Individuals with a primary brain tumor in the general US population were mainly female (59%) and between 18-49 years old (28%). The overall AAIR of primary brain tumors from 2004-2018 within the VA cancer registry was 11.6. Non-malignant tumors were more common than malignant tumors (AAIR:7.19 vs 4.42). The most diagnosed tumors in Veterans were non-malignant pituitary tumors (AAIR:2.96), non-malignant meningioma (AAIR:2.62), and glioblastoma (AAIR:1.96). In the Veteran population, survival outcomes became worse with age and were lowest among individuals diagnosed with glioblastoma. CONCLUSIONS Differences between Veteran and US populations can be broadly attributed to demographic composition differences of these groups. Prior to this, there have been no reports on national level incidence rates and survival outcomes for Veterans. These data provide vital information that can drive efforts to understand disease burden and improve outcomes for individuals with primary brain tumors.

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Data Matching to Support Analysis of Cancer Epidemiology Among Veterans Compared With Non-Veteran Populations—An Exemplar in Brain Tumors

Cited by 3 publications

References 10 publications

Clinical Informatics Approaches to Understand and Address Cancer Disparities

Clinical Informatics Approaches to Understand and Address Cancer Disparities

Long-Term Health Outcomes of Traumatic Brain Injury in Veterans

Brain tumors in United States military veterans

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