Disclosure Of Data Research Articles

Methane Emissions from Oil and Natural Gas Operations—30 Percent Reduction by 2030 Possible if Domestic and International Actions “Stay the Course”

When sworn into office in January 2021, the Biden–Harris Administration—through the National Climate Task Force—launched an ambitious, whole‐of‐government initiative to significantly double efforts and reduce methane emissions from the oil and natural gas sector, landfills, agriculture, and other lesser sources. These initiatives were documented in the U.S. Methane Emissions Reduction Action Plan, which proposed to use regulations, catalytic financial incentives, transparency and disclosure of actionable data, together with public and private partnerships, to identify and reduce methane emissions. President Biden's Build Back Better agenda was designed to accelerate, through investments, many of these domestic methane emissions reduction efforts.

IMPLEMENTATION OF CORPORATE ESG PRACTICES: INTERNATIONAL EXPERIENCE

The article discusses the implementation of environmental, social, and governance (ESG) practices in the global agribusiness sector. The aim of the research is to analyze, systematize, and generalize international experience in the implementation of corporate environmental, social, and governance practices and the formation of ESG strategies. The study presents examples of companies successfully applying ESG strategies to enhance their competitiveness and improve long-term business sustainability. It substantiates the importance of integrating ESG principles to reduce environmental impact, improve social working conditions, and ensure transparent governance at all levels. Specific examples are provided of international companies such as Unilever, Nestlé, Cargill, and Danone, which implement sustainable initiatives in reducing emissions, restoring soils, and supporting local communities. The research identifies that the demand for sustainable investments is a key factor driving the development of ESG practices at a global level. It is revealed that international organizations, such as the UN and the European Union, play a crucial role in the dissemination of ESG standards. They develop regulations and recommendations aimed at increasing reporting transparency and consistency of ESG practices globally. The EU, for example, adopted the Sustainable Finance Disclosure Regulation (SFDR), which significantly strengthened the requirements for companies regarding their environmental and social impact data disclosure. The study shows that investors increasingly prefer companies that demonstrate a high level of responsibility regarding ESG issues, as they are perceived as less risky and have more stable financial performance. Regional differences in the implementation of ESG practices were also identified: European companies more frequently adopt environmentally sustainable approaches compared to North American ones. The research on corporate ESG practices has revealed that the active integration of such principles into business strategy positively impacts companies' reputations, their ability to attract investments, and increase public trust. However, challenges, require enhanced reporting transparency and adherence to international standards, which help build trust among investors and the public.

Decoding privacy concerns: the role of perceived risk and benefits in personal health data disclosure

BackgroundPersonal health data is crucial for effective medical care, personalized treatment, and health monitoring. It enables accurate diagnosis, efficient treatment plans, and informed healthcare decisions. Personal health data should be protected to ensure patient privacy, prevent misuse or unauthorized access, and maintain trust in healthcare systems, thereby safeguarding individuals’ sensitive information from potential harm or exploitation. Therefore, this study aimed to investigate whether perceived risk and perceived benefits have mediating roles in the relationships among individuals’ personal health information disclosure behaviour, perceived control, and privacy concerns.MethodThe population of the study consisted of individuals living in the provinces of Izmir, Konya and Adana. The sample of the study consisted of individuals who were reached through a convenience sampling method. The scales for privacy concerns, perceived control, perceived risk, perceived benefits and information disclosure behaviour were used in the study. Cronbach’s alpha and the AVE were calculated, and a confirmatory factor analysis was performed. A path analysis was performed using the structural equation model to test the hypotheses.ResultsThe analysis revealed a significant negative relationship between individuals’ personal health data disclosure behaviour and their privacy concerns. However, perceived risk and perceived benefit did not mediate this relationship. Additionally, a significant positive relationship was found between individuals’ behaviour of disclosing their perceived control and personal health data, with perceived risk and benefits playing a mediating role in this relationship.ConclusionThe study concluded that as individuals’ concerns about sharing personal health data increase, they are less likely to share these data. It was also found that perceived risk and perceived benefit mediate this relationship. Additionally, higher perceived risk intensifies privacy concerns, further discouraging data sharing, while perceived benefits can mitigate these concerns, promoting greater willingness to disclose health information.

Privacy Risks in the Storytelling of Open Government Data: A Study from the Perspective of User Cognitive Reasoning

ABSTRACTIn recent years, there has been an increase in the openness of government data. Data storytelling has emerged as a means to enhance communication by transforming mundane data into easily understandable narratives. However, it is crucial to pay attention to privacy risks. This paper delves into the concept of storytelling government data, integrating cognitive reasoning and the S‐O‐R model, and evaluates privacy risks. Through comparative experiments and interviews, this study examines how users may infer sensitive information. The research reveals that storytelling data has a positive impact on users' cognition. De‐storytelling, on the other hand, can mitigate privacy risks, reduce information relevance, simplify narratives, and strike a balance between user experience and privacy. Building on this research, the paper proposes a strategy to address privacy risks, recommends strengthening privacy protection awareness from the perspectives of both users and platforms, optimizing data presentation methods, reducing the risk of sensitive information leakage, and ensuring the convenience and security of government data disclosure.

“I don't care about cookies!” data disclosure and time-inconsistent users

Time-inconsistent internet users neglect future privacy costs and release too much data to digital firms. We study how regulation that requires user consent for data processing affects firm profits, user surplus, and welfare, depending on the degree of time inconsistency and on firms' business models. If the firm appropriates sufficiently high profits from data, consent mechanisms increase welfare only if their design facilitates consent refusal and time inconsistency is neither too high nor too low. If firms can make it difficult to opt out, it may be better for society to let the former choose the disclosure level. However, consent policies increase user surplus when time inconsistency is high. Voluntary caps on usage can raise profits by making some users disclose more data.

Is there a sufficient supply of clinical academics for UK medical schools? A retrospective cohort study

ObjectivesClinical academics lead research to deliver medical advancements while also teaching in medical schools to maintain high-quality medical services. The objective of this project was to determine if there is...

Trust-Based Detection and Mitigation of Cyber Attacks in Distributed Cooperative Control of Islanded AC Microgrids

In this study, we address the challenge of detecting and mitigating cyber attacks in the distributed cooperative control of islanded AC microgrids, with a particular focus on detecting False Data Injection Attacks (FDIAs), a significant threat to the Smart Grid (SG). The SG integrates traditional power systems with communication networks, creating a complex system with numerous vulnerable links, making it a prime target for cyber attacks. These attacks can lead to the disclosure of private data, control network failures, and even blackouts. Unlike machine learning-based approaches that require extensive datasets and mathematical models dependent on accurate system modeling, our method is free from such dependencies. To enhance the microgrid’s resilience against these threats, we propose a resilient control algorithm by introducing a novel trustworthiness parameter into the traditional cooperative control algorithm. Our method evaluates the trustworthiness of distributed energy resources (DERs) based on their voltage measurements and exchanged information, using Kullback-Leibler (KL) divergence to dynamically adjust control actions. We validated our approach through simulations on both the IEEE-34 bus feeder system with eight DERs and a larger microgrid with twenty-two DERs. The results demonstrated a detection accuracy of around 100%, with millisecond range mitigation time, ensuring rapid system recovery. Additionally, our method improved system stability by up to almost 100% under attack scenarios, showcasing its effectiveness in promptly detecting attacks and maintaining system resilience. These findings highlight the potential of our approach to enhance the security and stability of microgrid systems in the face of cyber threats.

Unveiling the Influence of Big Data Disclosure on Audit Quality: Evidence from Omani Financial Firms

Unveiling the Influence of Big Data Disclosure on Audit Quality: Evidence from Omani Financial Firms

Balancing Privacy and Precision: Evaluating Meta-Analysis for National Health Data Integration in Canada

BackgroundIn Canada, legislative restrictions exist for the disclosure and transfer of patient data across provincial boundaries. Often, meta-analysis techniques are employed to pool province-specific results to enable national results while maintaining compliance with privacy legislation. However, the effectiveness of this technique remains unexamined using electronic data. To evaluate its performance, we compare results obtained through meta-analysis with those from a fully pooled model. MethodsUsing chronic kidney disease as a case study, this retrospective cohort study simulates the meta-analysis method to evaluate its performance using real data. We analyze data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), a nationwide database of electronic medical records. We meta-analyze provincial logistic regression models to predict nephropathy over a 5-year period at a national level. Estimates are compared to a fully pooled national model. ResultsAnalyses are currently ongoing. We expect the meta-analysis technique to produce similar estimates compared to the fully pooled model. However, we expect certain differences as meta-analysis techniques are insensitive to heterogeneity within provinces that impact the precision of estimates. Observed differences will help to inform future work where we will examine a potential new methodology to analyze health information without sharing patient data. ConclusionsBased on our results, we will reveal strengths and limitations of the meta-analysis technique for interprovincial analyses. Limitations of the meta-analysis technique may underscore the need for further exploration into alternative methodologies that can effectively analyze health information without compromising patient privacy to facilitate better healthcare decision-making and policy development in Canada.

Clinical trial transparency and disclosure from the medical writing perspective

It’s been more than 6 years since our last Medical Writing edition dedicated to clinical trial transparency and disclosure. Since then, we have seen the full implementation of the long-awaited EU Clinical Trials Regulation (CTR), the pause and restart of EMA Policy 0070, and of course a global pandemic which resulted in a world-wide surge in freedom of information requests for Covid-19 vaccine clinical trial data. Although many of the regulations and policies governing public disclosure of clinical trial data and documents remain unchanged, we have seen significant changes in the way these are implemented. As those of us who work in this field know all too well, the landscape is ever changing, and it is essential to keep up to date with those changes. Resources such as the Drug Information Association Clinical Trial Disclosure Community, PHUSE Data Transparency Working Group, and Clarity and Openness in Reporting-E3 based (CORE) Reference provide invaluable updates and insights. This issue of Medical Writing touches on a number of different aspects of clinical trial transparency and disclosure, all of which involve medical writers as key stakeholders

Federated Learning of XAI Models in Healthcare: A Case Study on Parkinson’s Disease

AbstractArtificial intelligence (AI) systems are increasingly used in healthcare applications, although some challenges have not been completely overcome to make them fully trustworthy and compliant with modern regulations and societal needs. First of all, sensitive health data, essential to train AI systems, are typically stored and managed in several separate medical centers and cannot be shared due to privacy constraints, thus hindering the use of all available information in learning models. Further, transparency and explainability of such systems are becoming increasingly urgent, especially at a time when “opaque” or “black-box” models are commonly used. Recently, technological and algorithmic solutions to these challenges have been investigated: on the one hand, federated learning (FL) has been proposed as a paradigm for collaborative model training among multiple parties without any disclosure of private raw data; on the other hand, research on eXplainable AI (XAI) aims to enhance the explainability of AI systems, either through interpretable by-design approaches or post-hoc explanation techniques. In this paper, we focus on a healthcare case study, namely predicting the progression of Parkinson’s disease, and assume that raw data originate from different medical centers and data collection for centralized training is precluded due to privacy limitations. We aim to investigate how FL of XAI models can allow achieving a good level of accuracy and trustworthiness. Cognitive and biologically inspired approaches are adopted in our analysis: FL of an interpretable by-design fuzzy rule-based system and FL of a neural network explained using a federated version of the SHAP post-hoc explanation technique. We analyze accuracy, interpretability, and explainability of the two approaches, also varying the degree of heterogeneity across several data distribution scenarios. Although the neural network is generally more accurate, the results show that the fuzzy rule-based system achieves competitive performance in the federated setting and presents desirable properties in terms of interpretability and transparency.

User Mobility Modeling in Crowdsourcing Application to Prevent Inference Attacks

With the rise of the Internet of Things (IoT), mobile crowdsourcing has become a leading application, leveraging the ubiquitous presence of smartphone users to collect and process data. Spatial crowdsourcing, which assigns tasks based on users’ geographic locations, has proven to be particularly innovative. However, this trend raises significant privacy concerns, particularly regarding the precise geographic data required by these crowdsourcing platforms. Traditional methods, such as dummy locations, spatial cloaking, differential privacy, k-anonymity, and encryption, often fail to mitigate the risks associated with the continuous disclosure of location data. An unauthorized entity could access these data and infer personal information about individuals, such as their home address, workplace, religion, or political affiliations, thus constituting a privacy violation. In this paper, we propose a user mobility model designed to enhance location privacy protection by accurately identifying Points of Interest (POIs) and countering inference attacks. Our main contribution here focuses on user mobility modeling and the introduction of an advanced algorithm for precise POI identification. We evaluate our contributions using GPS data collected from 10 volunteers over a period of 3 months. The results show that our mobility model delivers significant performance and that our POI extraction algorithm outperforms existing approaches.

The sufficiency of disclosure of AI inventions

Abstract The complex and data-driven nature of artificial intelligence (AI) raises questions for the sufficient disclosure of patent applications in this field. What are the European patent disclosure requirements for AI inventions? One challenge is that, prior to training, AI systems can be considered generic models. But after training, they transform into specialized AI systems to solve a particular problem. This transformation requires training data, making it an integral part of the AI system’s definition. But to what extent is the disclosure of the training data or training process necessary for patent disclosure? The Boards of Appeal of the European Patent Office (EPO) first dealt with this challenge in case T 0161/18, which involved a medical AI invention to calculate cardiac output. It held that the specialized artificial neural network (ANN) in the patent could not be carried out by a person skilled in the art due to insufficient disclosure of input data suitable for the training of the ANN or at least one data set suitable for solving the technical problem. Furthermore, without specialization, the invention lacked an inventive step. But, is it always necessary to disclose the input data or at least one data set suitable for solving the technical problem? Are there alternative ways for applicants to satisfy the disclosure requirements for AI inventions? And what evidence is there that patent applicants are disclosing specific details of the AI/machine learning (ML) training or specific AI/ML model architecture? In this article, we analyse case T 0161/18 and subsequent sufficiency of disclosure decisions (T 1539/20; T 0606/21; T 1526/20; T 1191/19) and consider these foundational questions for applicants drafting patent applications with claims directed to AI inventions. We also analyse the EPO’s examination guidelines on sufficiency of disclosure for AI inventions, which were updated in early March 2024.

Consumer Privacy Expectations and the Impact on Buying Intentions

Based on past research and attitudinal theories, this study intends to investigate the effect of attitudes about data privacy protection and disclosure and the perception of corporate data privacy performance on future purchase intention. Additionally, it seeks to understand the relationship between purchase behavior intensity, personal data privacy concerns and privacy expectations (attitude). A total of 271 usable surveys were gathered from United States consumers and analyzed by two multiple linear regression models and correlation analysis. The results show that attitude (data privacy protection and disclosure responsibility) and perceptions about corporate data privacy performance predict purchase intention. Furthermore, past purchase behavior intensity and personal data privacy concerns influence corporate data privacy responsibility attitudes. This study extends the current data privacy literature by understanding how data privacy expectations (attitude) and data privacy performance perceptions impact purchase intent and are influenced by personal data privacy concerns and purchase behavior.

Ensuring Confidentiality and Privacy in Criminal Proceedings: A Comparative Legal Analysis of the Russian Federation and the Republic of Kazakhstan Experience

The paper examines the limitations of personal rights in criminal proceedings, as well as the risks associated with the lack of compliance with security measures due to the unlawful disclosure of preliminary investigation data. During the research, scientific methods such as analysis, comparison, and expert assessment were used. The study resulted in the identification of significant problems in the current criminal procedure legislation of the Russian Federation and the Republic of Kazakhstan in the area of observing the personal rights of a person and citizen, as well as ensuring the confidentiality of criminal case materials. Among these are lack of differentiation of principles establishing inviolability in various spheres of life; lack of a clear mechanism and tools for ensuring confidentiality in criminal proceedings; the optional nature of Article 161 of the Criminal Procedure Code of the Russian Federation, which determines the inadmissibility of disclosing preliminary investigation materials, etc. In studying these problems, possible ways and means of solving them were developed, such as: introducing a special chapter in the Criminal Procedure Code regulating issues of ensuring confidentiality in criminal proceedings; giving an imperative nature to Article 161 of the Criminal Procedure Code of the Russian Federation; differentiation of the principles of criminal procedure in accordance with various spheres of life.

Compatibility between the Auditor and the Institutional Investor and its Impact in Evaluating Requirements with International Accounting Disclosure Standards

The study examined the requirements of accounting disclosure standards from the perspectives of institutional investors and external auditors, highlighting the distinctions between their respective perspectives. due to the importance of the data generated in the financial statements that the institutional investor needs to make investment decisions, and the difference in viewpoints regarding the amount of data disclosure between the two parties. It focusses on outlining what needs to be reported in joint-stock firms' financial statements in a way that promotes objectivity in the financial statements and is transparent, appropriate, and adequate. The study had three main components: the literature and theoretical foundations of accounting disclosure and external auditors comprised the second axis, and the overall methodological framework of the study addressed the first.The idea of disclosure and transparency in the financial statement presentation was covered in the first topic, and the auditor's role and responsibilities in disclosure and transparency in financial reports were covered in the second. As for the third axis, it focused on the practical framework of the research, where an analysis of the sample members’ responses to questionnaire forms distributed to (100) external auditors and (100) institutional investors was presented. The questionnaire questions included three axes, the second axis, which dealt with the income statement's duties under accounting disclosure rules, and the first. It is focused on the balance sheet requirements of accounting disclosure rules. The requirements for accounting disclosure rules for contingent liabilities and events that occur after the budget were covered under the third axis. The research came to several findings after analysing the data for the questions using the SPSS statistical tool. The most important of which were :- The external auditor and the institutional investor have largely agreed upon the requirements of accounting disclosure standards for the income statement. The auditor is particularly interested in the paragraphs that discuss granting credit facilities and avoiding reliance on secondary sources of income, while the investor is more interested in the paragraphs that discuss income and expenses associated with the.fundamental income-generating activities When assessing the accounting disclosure requirements for the balance sheet, the institutional investor and the external auditor have a lot in common. The sections of the financial statements that highlight the uses of money and the requirement to disclose them to users, such as the institutional investor who was concentrating on these lines, are of particular importance to the auditor.due to the fact that they bear in It is fraught with dangers. The external auditor and the institutional investor have essentially reached consensus over their respective points of view when evaluating the accounting disclosure requirements for contingent liabilities and subsequent events . While the investor is primarily focused on the accounting disclosure, the auditor is especially interested in the criteria that revolve around the paragraphs referring to the total value of the provision for losses on loans and advances provided .requirements that place an abnormally high priority on risk In order to set disclosure standards and determine the quantity and quality of data that should be displayed in financial reports while maintaining the transparency necessary to achieve objectivity, the research advises considering the perspectives of the entities that stand to gain from the data generated in those reports as well as the regulatory authorities represented by the external auditor. These guidelines must be updated periodically since investor behaviour changes in response to political, social, and economic factors.

Public perceptions of international genetic information sharing for biomedical research in China: a case study of the social media debate on the article “A Pangenome Reference of 36 Chinese Populations” published in Nature

BackgroundThe international disclosure of Chinese human genetic data continues to be a contentious issue in China, generating public debates in both traditional and social media channels. Concerns have intensified after Chinese scientists’ research on pangenome data was published in the prestigious journal Nature.MethodsThis study scrutinized microblogs posted on Weibo, a popular Chinese social media site, in the two months immediately following the publication (June 14, 2023–August 21, 2023). Content analysis was conducted to assess the nature of public responses, justifications for positive or negative attitudes, and the users’ overall knowledge of how Chinese human genetic information is regulated and managed in China.ResultsWeibo users displayed contrasting attitudes towards the article’s public disclose of pangenome research data, with 18% positive, 64% negative, and 18% neutral. Positive attitudes came primarily from verified government and media accounts, which praised the publication. In contrast, negative attitudes originated from individual users who were concerned about national security and health risks and often believed that the researchers have betrayed China. The benefits of data sharing highlighted in the commentaries included advancements in disease research and scientific progress. Approximately 16% of the microblogs indicated that Weibo users had misunderstood existing regulations and laws governing data sharing and stewardship.ConclusionsBased on the predominantly negative public attitudes toward scientific data sharing established by our study, we recommend enhanced outreach by scientists and scientific institutions to increase the public understanding of developments in genetic research, international data sharing, and associated regulations. Additionally, governmental agencies can alleviate public fears and concerns by being more transparent about their security reviews of international collaborative research involving Chinese human genetic data and its cross-border transfer.

Digital Policy, Green Innovation, and Digital-Intelligent Transformation of Companies

In the midst of rigorous market rivalry, enhancing a company’s competitiveness and operational efficiency in an era of rapid IT advancement is a pressing concern for business leaders. The National Big Data Comprehensive Zone (BDCZ) pilot scheme, instituted by the Chinese government, systematically addresses seven core objectives, encompassing data resource management, sharing and disclosure, data center consolidation, application of data resources, and the circulation of data elements. This policy initiative aims to bolster the establishment of information infrastructure through big data applications, facilitate the influx and movement of talent, and propel corporate sustainable growth. Utilizing a quasi-natural experiment approach, we assess the pilot policy’s influence on the digital-intelligent transformation (DIT) of manufacturing companies from a green innovation ecosystem perspective, employing datasets from 2010 to 2022, and methodologies such as Difference-in-Differences (DID), Synthetic Differences-in-Differences (SDID), and Propensity Score Matching-DID (PSM-DID). The findings indicate that the BDCZ initiative significantly fosters DIT in manufacturing companies. The policy’s establishment confers benefits, including access to increased government support and innovation capital, thereby enhancing the sustainability of green innovation efforts. It also strengthens corporate collaboration, engendering synergistic benefits that improve regional economic progression and establish a conducive environment for digital development, ultimately enhancing the regional innovation ecosystem. The pilot policy’s impact varies across entities, with more profound effects observed in developed financial markets compared to underdeveloped ones. Additionally, non-state-owned companies exhibit a greater response to BDCZ policy interventions than their state-owned counterparts. Moreover, manufacturing bussiness with a higher proportion of executive shareholding are more substantially influenced by the BDCZ. This article fills the research gap by using the quasi-natural experiment of BDCZ to test the impact on DIT of companies and provides inspiration for local governments to mobilize the enthusiasm of manufacturing companies for DIT.

A Legal Obligation on UK Employers to Conduct Labour Law Due Diligence: A Substantive Proposal

Abstract The use of non-financial reporting and due diligence legislation to force companies to address specific adverse effects of their operations has become an increasingly common tool for policymakers in recent years. To date, international legislative activity has mainly focussed on potential breaches of human rights and environmental obligations but has also included aspects of labour law. This article proposes a new law that adapts the concept of corporate due diligence to the field of labour law. The new law uses a reflexive model of legislation which is referred to as Labour Law Due Diligence. The under-resourced system of labour market enforcement in the UK and workers’ reluctance to use litigation to defend their rights means that persistent non-compliance by employers can go unpunished. Employers would be obliged to carry out an annual audit to assess whether they comply with key labour law obligations and make the results public. Non-compliant employers would need to remedy any unlawful practices within a year or face the prospect of enforcement action being taken against them. Through an evaluation of UK legislation which has attempted to force companies to address specific legislative goals through public disclosure of data, the article seeks to address the weaknesses of such legislation by involving stakeholders and introducing robust systems of enforcement.

Data quality in Brazilian population-based cancer registries for gastrointestinal cancers

BackgroundPopulation-based cancer registries (PBCRs) are the primary source of information for cancer surveillance and monitoring. Currently, there are 30 active PBCRs in Brazil. The objective of this study was to analyze the data quality of five gastrointestinal cancers (esophagus, stomach, colorectal, liver, and pancreas) according to the criteria of comparability, validity, completeness, and timeliness in Brazilian cancer registries.MethodsThis study included data from Brazilian PBCRs with more than ten years of historical data starting in the year 2000, regardless of the type of defined geographical coverage (state, metropolitan region, or capital), totaling 16 registries. Brazilian PBCRs were evaluated based on four international data quality criteria: comparability, validity (accuracy), completeness, and timeliness. All cancer cases were analyzed, except for nonmelanoma skin cancer cases (C44) and five gastrointestinal tumors (esophageal cancer, stomach cancer, colorectal cancer, liver cancer, and pancreatic cancer) per cancer registry and sex, according to the available period.ResultsThe 16 Brazilian PBCRs represent 17% of the population (36 million inhabitants in 2021) according to data from 2000 to 2018. There was a variation in the incidence in the historical series ranging from 12 to 19 years. The proportion of morphologically verified (MV%) cases varied from 74.3% (Manaus) to 94.8% (Aracaju), and the proportion of incidentally reported death certificate only (DCO%) cases varied from 3.0% (São Paulo) to 23.9% (Espírito Santo). High-lethality malignant neoplasms, such as liver and pancreas, had DCO percentages greater than 30% in most cancer registries. The sixteen registries have more than a 48-month delay in data release compared to the 2022 calendar year.ConclusionThe studied Brazilian cancer registries met international comparability criteria; however, half of the registries showed indices below the expected levels for validity and completeness criteria for high-lethality tumors such as liver and pancreas tumors, in addition to a long delay in data availability and disclosure. Significant efforts are necessary to ensure the operational and stability of the PBCR in Brazil, which continues to be a tool for monitoring cancer incidence and assessing national cancer control policies.