Cervical cancer disparities in stage at presentation for disaggregated Asian Americans, Native Hawaiians, and Pacific Islanders.

BACKGROUND: Over 20 million people in the United States identified as Asian American, Native Hawaiian, or Pacific Islander (AANHPI) in 2022. Despite the diversity of immigration histories, lived experiences, and health needs within the AANHPI community, prior studies in cervical cancer have considered this group in aggregate. OBJECTIVE(S): We sought to analyze disparities in cervical cancer stage at presentation in the United States, focusing on disaggregated AANHPI groups. STUDY DESIGN: Data from the United States National Cancer Database from 2004 to 2020 of 122,926 patients newly diagnosed with cervical cancer was retrospectively analyzed. AANHPI patients were disaggregated by country of origin. Logistic regression, adjusted for clinical and sociodemographic factors, was used to calculate adjusted odds ratios. Higher adjusted odds ratios indicate an increased likelihood of metastatic versus non-metastatic disease at diagnosis. RESULTS: Out of 122,926 patients with cervical cancer, 5,142 (4.2%) identified as AANHPI. Compared to non-Hispanic White (NHW) patients, pooled AANHPI patients presented at lower stages of cancer (NHW: 58.7% diagnosed local/regional, AANHPI: 85.6% at local/regional, χ2 P<0.001). The largest AANHPI subgroups included Filipino Americans (n=1051, 20.4% of AANHPI), Chinese Americans (n=995, 19.4%), Asian Indian/Pakistani Americans (n=711, 13.8%), Vietnamese Americans (n=627, 12.2%), and Korean Americans (n=550, 10.7%) respectively. AANHPI disaggregation revealed that Pacific Islander American patients had higher odds of presenting with metastatic disease (aOR 1.58, 95% CI 1.21-2.06, p = 0.001) relative to non-Hispanic White patients. Conversely, Chinese American (aOR 0.47, 95% CI 0.37-0.59, p < 0.001), Vietnamese American (aOR 0.54, 95% CI 0.41-0.70, p < 0.001), Hmong American (aOR 0.46, 95% CI 0.22-0.97, p = 0.040), and Indian/Pakistani American (aOR 0.76, 95% CI 0.61-0.94, p = 0.013) patients were less likely to present with metastatic disease. Compared to the largest AANHPI group (Chinese American), nine other subgroups were more likely to present with metastatic disease. The largest differences were observed in Pacific Islander American (aOR 3.44, 95% CI 2.41-4.91, p < 0.001), Thai American (aOR 2.79, 95% CI 1.41-5.53, p = 0.003), Kampuchean American (aOR 2.39, 95% CI 1.29-4.42, p = 0.006), Native Hawaiian American (aOR 2.23, 95% CI 1.37-3.63, p = 0.001), and Laotian American (aOR 2.02, 95% CI 1.13-3.61, p = 0.017). In contrast, Vietnamese American (aOR 1.20, 95% CI 0.85-1.71, p = 0.303) and Hmong American (aOR 1.09, 95% CI 0.50-2.37, p = 0.828) patients did not show a statistically significant difference in presenting with metastatic disease compared to Chinese American patients. CONCLUSION(S): Aggregated evaluation of the Asian American, Native Hawaiian, or Pacific Islander monolith masks disparities in outcomes for distinct populations at risk for equity gaps. This disaggregation study shows that marginalized groups within the larger AANHPI population - including Pacific Islander American and Thai American patients - may face different exposures and larger structural barriers to cancer screening and early-stage diagnosis. A future focus on community based disaggregated research and tailored interventions is necessary to close these gaps.

A scientometric analysis of fairness in health AI literature.

Artificial intelligence (AI) and machine learning are central components of today's medical environment. The fairness of AI, i.e. the ability of AI to be free from bias, has repeatedly come into question. This study investigates the diversity of members of academia whose scholarship poses questions about the fairness of AI. The articles that combine the topics of fairness, artificial intelligence, and medicine were selected from Pubmed, Google Scholar, and Embase using keywords. Eligibility and data extraction from the articles were done manually and cross-checked by another author for accuracy. Articles were selected for further analysis, cleaned, and organized in Microsoft Excel; spatial diagrams were generated using Public Tableau. Additional graphs were generated using Matplotlib and Seaborn. Linear and logistic regressions were conducted using Python to measure the relationship between funding status, number of citations, and the gender demographics of the authorship team. We identified 375 eligible publications, including research and review articles concerning AI and fairness in healthcare. Analysis of the bibliographic data revealed that there is an overrepresentation of authors that are white, male, and are from high-income countries, especially in the roles of first and last author. Additionally, analysis showed that papers whose authors are based in higher-income countries were more likely to be cited more often and published in higher impact journals. These findings highlight the lack of diversity among the authors in the AI fairness community whose work gains the largest readership, potentially compromising the very impartiality that the AI fairness community is working towards.

The potential use of non-fungible tokens (NFTs) in healthcare and medical research.

Non-fungible tokens (NFTs) are cryptographic assets recorded on the blockchain that can certify authenticity and ownership, and they can be used to monetize health data, optimize the process of receiving a hematopoietic stem cell transplant, and improve the distribution of solid organs for transplantation. Blockchain technology, including NFTs, provides equitable access to wealth, increases transparency, eliminates personal or institutional biases of intermediaries, reduces inefficiencies, and ensures accountability. Blockchain architecture is ideal for ensuring security and privacy while granting individuals jurisdiction over their own information, making it a unique solution to the current limitations of existing health information systems. NFTs can be used to give patients the option to monetize their health data and provide valuable data to researchers. Wearable technology companies can also give their customers the option to monetize their data while providing data necessary to improve their products. Additionally, the process of receiving a hematopoietic stem cell transplant and the distribution of solid organs for transplantation could benefit from the integration of NFTs into the allocation process. However, there are limitations to the technology, including high energy consumption and the need for regulatory guidance. Further research is necessary to fully understand the potential of NFTs in healthcare and how it can be integrated with existing health information technology. Overall, NFTs have the potential to revolutionize the healthcare sector, providing benefits such as improved access to health information and increased efficiency in the distribution of organs for transplantation.

The impact of commercial health datasets on medical research and health-care algorithms.

As the health-care industry emerges into a new era of digital health driven by cloud data storage, distributed computing, and machine learning, health-care data have become a premium commodity with value for private and public entities. Current frameworks of health data collection and distribution, whether from industry, academia, or government institutions, are imperfect and do not allow researchers to leverage the full potential of downstream analytical efforts. In this Health Policy paper, we review the current landscape of commercial health data vendors, with special emphasis on the sources of their data, challenges associated with data reproducibility and generalisability, and ethical considerations for data vending. We argue for sustainable approaches to curating open-source health data to enable global populations to be included in the biomedical research community. However, to fully implement these approaches, key stakeholders should come together to make health-care datasets increasingly accessible, inclusive, and representative, while balancing the privacy and rights of individuals whose data are being collected.

Disparities in access to cancer diagnostics in ASEAN member countries.

Diagnostics, including laboratory tests, medical and nuclear imaging, and molecular testing, are essential in the diagnosis and management of cancer to optimize clinical outcomes. With the continuous rise in cancer mortality and morbidity in the Association of Southeast Asian Nations (ASEAN), there exists a critical need to evaluate the accessibility of cancer diagnostics in the region so as to direct multifaceted interventions that will address regional inequities and inadequacies in cancer care. This paper identifies existing gaps in service delivery, health workforce, health information systems, leadership and governance, and financing and how these contribute to disparities in access to cancer diagnostics in ASEAN member countries. Intersectoral health policies that will strengthen coordinated laboratory services, upscale infrastructure development, encourage health workforce production, and enable proper appropriation of funding are necessary to effectively reduce the regional cancer burden.