This research demonstrates the feasibility of using big data to conduct case-control analyses to identify associations between possible risk factors and cancers and demonstrates the limitations of the traditional epidemiological approach by requiring less effort, time, and cost.
Abstract
Background/Context: Cancer, a very prevalent and deadly disease, is expected to increase significantly to approximately 35 million new cancer cases and 17 million deaths by 2050. There has been progress in cancer research; however, this progress applies to relatively few cancer types, and current approaches to epidemiological research are limiting. Problem Statement/Objective: Ninety to ninety-five percent of cancers are due to external exposures, but only 30–50 percent of all cancer cases can be prevented by reducing exposure to risk factors. Therefore, there is a lot to learn about the causal risk factors of cancers. Methods: Using secondary EHR data, we identified twelve cancer groupings that we consider poorly understood (5,950 cases, and 17,850 controls) and conducted case-control analyses to identify associations with selected clinical and environmental exposures. Results: Radiation exposure, hormone therapy, breast implants, orthopedic implants, and formaldehyde exposure were associated with increased odds in the incidence of poorly understood cancers. Conclusion/Implications: This research demonstrates the feasibility of using big data to conduct case-control analyses to identify associations between possible risk factors and cancers. This methodology is the first step to identify causal risk factors between cancers and exposures. It can quickly provide guidance on which factors seem to have an association, and which do not. This can increase the rate at which the causal risk factors of cancers are identified and lead to an equitable reduction in the number of expected cancer cases and deaths. This methodology can be replicated with varying cancer types and exposures and enables research on rare conditions that was not possible due to small numbers. This methodology also overcomes the limitations of the traditional epidemiological approach by requiring less effort, time, and cost. The use of secondary data for research can produce results at a faster rate while incurring less cost and effort.
Evaluating the occurrence of new-onset CVD in women with breast cancer undergoing chemotherapy and identifying factors associated with increased risk of cardiovascular disease highlights the need for sustained cardiovascular surveillance in breast cancer survivors.
V. Dvorovy, L. Kováčová, M. Selvek et al.· European Heart Journal, Supp...· 0 citations
Cancer remains the second most prevalent cause of death in the United States, claiming 605,213 lives in 2021, surpassing COVID-19 deaths. The cancer mortality rate continued to decline between 2019 and 2020, dropping by 1.5%, marking a significant 33% decrease since 1991. This ongoing improvement primarily mirrors advances in treatment, allowing patients to achieve clinical remission and recovery. Now, a cancer patient is simultaneously exposed to the risk of primary cancer as well as other risks, such as other cancer(s) or other diseases, leading to a competing risks scenario. Analysis of survival data under competing risks and the presence of cured patients have been extensively studied individually, but there is limited work in the current literature that models the possibility of cure from one risk in the presence of competing risks. Moreover, such a model should allow for the possibility of cure from the cause-specific risk of the primary cancer; however, the overall survival probability should eventually approach zero, thereby incorporating the prevalent belief of eventual failure with certainty. We propose a novel unified competing risks cure model, based on the cause-specific hazard approach, that satisfies the aforementioned desired properties. The conditions required to establish model identifiability are studied in detail. To find the maximum likelihood estimates of the model parameters, a computationally efficient expectation maximization algorithm is developed. An extensive simulation study is carried out to demonstrate the performance of the proposed model and estimation method under different parameter settings and in the presence of multiple competing risks. Finally, an application is illustrated using breast cancer data from the SEER cancer database.
S. Pal, S. Rathmann, Qi Jiang et al.· Journal of the Royal Statist...· 0 citations
Objective: Breast cancer is the most frequently diagnosed cancer among women worldwide and is a leading cause of cancer-related deaths. Comparative assessments of breast cancer lifetime risks across populations are limited. This study estimated the global, regional, and national lifetime risks, temporal trends, and socioeconomic inequalities in the burden of breast cancer. Methods: Using incidence and mortality data from GLOBOCAN 2022 (185 countries) and United Nations population and all-cause death data, lifetime risks were calculated using the adjusted for multiple primaries (AMP) method, which could adjust for multiple primary cancers, competing risks of other causes death, and life expectancy. Longitudinal data of breast cancer incidence from 2003–2017 were retrieved from the Cancer Incidence in Five Continents (CI5) Plus database. The temporal trends for breast cancer deaths were abstracted from the WHO Mortality Database. The lifetime risk of developing and dying from breast cancer were analyzed by socioeconomic characteristics, 20 predefined geographic regions and menopausal status. Results: The overall worldwide lifetime risk of developing and dying from breast cancer was 5.51% (95% CI: 5.50%–5.52%) and 1.82% (95% CI: 1.82%–1.83%) in 2022, respectively. The estimated lifetime risks of developing breast cancer had a positive relationship with Human Development Index (HDI) levels and corresponding risks of 10.37%, 4.42%, 2.96%, and 2.91% in very high, high, middle, and low HDI regions, respectively. Very high HDI regions presented the highest lifetime risk of breast cancer death (2.70%), followed by low HDI (1.70%), medium HDI (1.54%), and high (1.38%) HDI regions. A significant correlation was identified between lifetime risks and health economics capacity. The lifetime risk of developing and dying from breast cancer primarily involved individuals ≥ 55 years of age with remaining risks of 3.77% (developing) and 1.43% (dying) from 55 years to death. The proportion of lifetime risks among individuals 0–44 years of age was higher in Africa regions compared to other regions. In surveillance data from 36 countries, a significant increasing trend in the average annual percentage change (AAPC) was noted in 32 countries, which ranged from 0.17% in the United States to 5.84% in the Republic of Korea. Conclusions: Globally, an estimated 1 in 18 individuals were diagnosed with breast cancer during their lifetime and approximately 1 in 55 died from the disease in 2022. Lifetime risk of breast cancer disparities reveal the socioeconomic inequalities of breast cancer. Therefore, country-tailored intervention plans for breast cancer require prioritization within precision prevention to mitigate global breast cancer inequities and burden.
Weijia Kong, Hengna Lin, Jie Li et al.· Cancer Biology and Medicine· 0 citations
This review identifies barriers to effective disease management and discusses the current and future cancer burden across the African continent for non-specialist readers and those seeking to understand oncological care in Africa.
C. Kimwele, Catherine Kaluwa, A. Nyongesa et al.· Cancer Plus· 0 citations
Patients with cardiovascular disease (CVD) have an increased risk of cancer, partly due to shared risk factors, chronic inflammation and systemic mediators. However, most previous studies have focused on individuals with pre-existing CVD. Whether patients with a new diagnosis of CVD are at particularly high risk of incident cancer, and whether the spectrum of cancer types differs from that of the general population, remains insufficiently explored.
To describe the five-year incidence of cancer in patients with a new diagnosis of CVD in our healthcare area and to compare the distribution of cancer types with that reported in the general population.
We conducted a population-based cohort study using clinical databases from our healthcare area. All adults (>18 years) without a previous history of cancer who were newly diagnosed with CVD between 2010 and 2022 were included. Incident cancers diagnosed between 3 months and 5 years after the index CVD diagnosis were recorded. The incidence and distribution of cancer types were analysed and compared with data from the general population.
A total of 9,900 patients with newly diagnosed CVD and no prior cancer history were identified. The mean age of the cohort was 64 years, 43% were women, and comorbidities were frequent. During follow-up, 1,922 patients (19.4%) developed incident cancer within five years. Patients who developed cancer were more often male, older, and had a higher burden of comorbidities than those who did not. Among CVD subtypes, chronic heart failure was associated with the highest risk of incident cancer. The most frequently diagnosed cancers were gastrointestinal (27%), lung (14.5%), genitourinary (13.5%) and male reproductive system cancers (13.4%).
In our healthcare area, the five-year incidence of cancer following a new diagnosis of CVD was markedly high. Moreover, the distribution of cancer types in this population differed from that of the general population, with a higher proportion of genitourinary cancers and a lower proportion of breast cancer. These findings highlight the importance of cancer awareness and surveillance strategies in patients with newly diagnosed CVD.Table.Basal characteristics. Figure.Types of incident cancer.
P. Moliner, H. Morillas, A. Ricarte-Marin et al.· European Heart Journal, Supp...· 0 citations