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Background Foreign-born populations in the United States have markedly increased, yet cancer trends remain unexplored. Survey-based Population-Adjusted Rate Calculator (SPARC) is a new tool for evaluating nativity differences in cancer mortality. Methods Using SPARC, we calculated 3-year (2016-2018) age-adjusted mortality rates and rate ratios for common cancers by sex, age group, race and ethnicity, and nativity. Trends by nativity were examined for the first time for 2006-2018. Traditional cancer statistics draw populations from decennial censuses. However, nativity-stratified populations are from the American Community Surveys, thus involve sampling errors. To rectify this, SPARC employed bias-corrected estimators. Death counts came from the National Vital Statistics System. Results Age-adjusted mortality rates were higher among US-born populations across nearly all cancer types, with the largest US-born, foreign-born difference observed in lung cancer among Black women (rate ratio = 3.67, 95% confidence interval [CI] = 3.37 to 4.00). The well-documented White–Black differences in breast cancer mortality existed mainly among US-born women. For all cancers combined, descending trends were more accelerated for US-born compared with foreign-born individuals in all race and ethnicity groups with changes ranging from –2.6% per year in US-born Black men to stable (statistically nonsignificant) among foreign-born Black women. Pancreas and liver cancers were exceptions with increasing, stable, or decreasing trends depending on nativity and race and ethnicity. Notably, foreign-born Black men and foreign-born Hispanic men did not show a favorable decline in colorectal cancer mortality. Conclusions Although all groups show beneficial cancer mortality trends, those with higher rates in 2006 have experienced sharper declines. Persistent disparities between US-born and foreign-born individuals, especially among Black people, necessitate further investigation. [ABSTRACT FROM AUTHOR]

Genetic simulation programs are used to model data under specified assumptions to facilitate the understanding and study of complex genetic systems. Standardized data sets generated using genetic simulation are essential for the development and application of novel analytical tools in genetic epidemiology studies. With continuing advances in high-throughput genomic technologies and generation and analysis of larger, more complex data sets, there is a need for updating current approaches in genetic simulation modeling. To provide a forum to address current and emerging challenges in this area, the National Cancer Institute (NCI) sponsored a workshop, entitled "Genetic Simulation Tools for Post-Genome Wide Association Studies of Complex Diseases" at the National Institutes of Health (NIH) in Bethesda, Maryland on March 11-12, 2014. The goals of the workshop were to (1) identify opportunities, challenges, and resource needs for the development and application of genetic simulation models; (2) improve the integration of tools for modeling and analysis of simulated data; and (3) foster collaborations to facilitate development and applications of genetic simulation. During the course of the meeting, the group identified challenges and opportunities for the science of simulation, software and methods development, and collaboration. This paper summarizes key discussions at the meeting, and highlights important challenges and opportunities to advance the field of genetic simulation.

Joinpoint regression can model trends in time-specific estimates from aggregated data. These methods have been developed mainly for nonsurvey data such as cancer registry data assuming that the time-specific estimates are uncorrelated from time point to time point. This independence assumption can be violated for trends in time-specific estimates from complex survey samples due to using the same primary sampling units across time and, therefore, the full variance–covariance matrix of the time-specific estimates should be incorporated into the regression model fitting. This article extends these joinpoint methods for analyzing complex survey data within the National Cancer Institute's Joinpoint software and empirically compares the extended method to existing methods for analyses of time trends in three surveys. [ABSTRACT FROM AUTHOR]

Selecting the number of change points in segmented line regression is an important problem in trend analysis, and there have been various approaches proposed in the literature. We first study the empirical properties of several model selection procedures and propose a new method based on two Schwarz type criteria, a classical Bayes Information Criterion (BIC) and the one with a harsher penalty than BIC ( BIC 3 ). The proposed rule is designed to use the former when effect sizes are small and the latter when the effect sizes are large and employs the partial R 2 to determine the weight between BIC and BIC 3 . The proposed method is computationally much more efficient than the permutation test procedure that has been the default method of Joinpoint software developed for cancer trend analysis, and its satisfactory performance is observed in our simulation study. Simulations indicate that the proposed method performs well in keeping the probability of correct selection at least as large as that of BIC 3 , whose performance is comparable to that of the permutation test procedure, and improves BIC 3 when it performs worse than BIC. The proposed method is applied to the U.S. prostate cancer incidence and mortality rates. [ABSTRACT FROM AUTHOR]

In its 2021 update of lung cancer screening recommendations, the U.S. Preventive Services Task Force (USPSTF) evaluated strategies that selected people on the basis of risk and emphasized the need for further research on similar studies. This article describes such a study and compares the USPSTF recommendation with an alternative that includes optimal risk thresholds. Visual Abstract. A Cost-Effectiveness Analysis of Risk Model–Based Lung Cancer Screening.: In its 2021 update of lung cancer screening recommendations, the U.S. Preventive Services Task Force (USPSTF) evaluated strategies that selected people on the basis of risk and emphasized the need for further research on similar studies. This article describes such a study and compares the USPSTF recommendation with an alternative that includes optimal risk thresholds. Background: In their 2021 lung cancer screening recommendation update, the U.S. Preventive Services Task Force (USPSTF) evaluated strategies that select people based on their personal lung cancer risk (risk model–based strategies), highlighting the need for further research on the benefits and harms of risk model–based screening. Objective: To evaluate and compare the cost-effectiveness of risk model–based lung cancer screening strategies versus the USPSTF recommendation and to explore optimal risk thresholds. Design: Comparative modeling analysis. Data Sources: National Lung Screening Trial; Surveillance, Epidemiology, and End Results program; U.S. Smoking History Generator. Target Population: 1960 U.S. birth cohort. Time Horizon: 45 years. Perspective: U.S. health care sector. Intervention: Annual low-dose computed tomography in risk model–based strategies that start screening at age 50 or 55 years, stop screening at age 80 years, with 6-year risk thresholds between 0.5% and 2.2% using the PLCOm2012 model. Outcome Measures: Incremental cost-effectiveness ratio (ICER) and cost-effectiveness efficiency frontier connecting strategies with the highest health benefit at a given cost. Results of Base-Case Analysis: Risk model–based screening strategies were more cost-effective than the USPSTF recommendation and exclusively comprised the cost-effectiveness efficiency frontier. Among the strategies on the efficiency frontier, those with a 6-year risk threshold of 1.2% or greater were cost-effective with an ICER less than $100 000 per quality-adjusted life-year (QALY). Specifically, the strategy with a 1.2% risk threshold had an ICER of $94 659 (model range, $72 639 to $156 774), yielding more QALYs for less cost than the USPSTF recommendation, while having a similar level of screening coverage (person ever-screened 21.7% vs. USPSTF's 22.6%). Results of Sensitivity Analyses: Risk model–based strategies were robustly more cost-effective than the 2021 USPSTF recommendation under varying modeling assumptions. Limitation: Risk models were restricted to age, sex, and smoking-related risk predictors. Conclusion: Risk model–based screening is more cost-effective than the USPSTF recommendation, thus warranting further consideration. Primary Funding Source: National Cancer Institute (NCI). [ABSTRACT FROM AUTHOR]

The considerable deficit in cancer diagnoses in 2020 due to COVID-19 pandemic disruptions in health care can pose challenges in the estimation and interpretation of long-term cancer trends. Using Surveillance, Epidemiology, and End Results (SEER) (2000-2020) data, we demonstrate that inclusion of the 2020 incidence rates in joinpoint models to estimate trends can result in a poorer fit to the data and less accurate or less precise trend estimates, providing challenges in the interpretation of the estimates as a cancer control measure. To measure the decline in 2020 relative to 2019 cancer incidence rates, we used the percent change of rates in 2020 compared with 2019. Overall, SEER cancer incidence rates dropped approximately 10% in 2020, but for thyroid cancer the decrease was as large as 18% after adjusting for reporting delay. The 2020 SEER incidence data are available in all SEER released products, except for joinpoint estimates of trends and lifetime risk of developing cancer. [ABSTRACT FROM AUTHOR]

The reporting and analysis of population-based cancer statistics in the United States has traditionally been done for counties. However, counties are not ideal for analysis of cancer rates, due to wide variation in population size, with larger counties having considerable sociodemographic variation within their borders and sparsely populated counties having less reliable estimates of cancer rates that are often suppressed due to confidentiality concerns. There is a need and an opportunity to utilize zone design procedures in the context of cancer surveillance to generate coherent, statistically stable geographic units that are more optimal for cancer reporting and analysis than counties. To achieve this goal, we sought to create areas within each US state that are: 1) similar in population size and large enough to minimize rate suppression; 2) sociodemographically homogeneous; 3) compact; and 4) custom crafted to represent areas that are meaningful to cancer registries and stakeholders. The resulting geographic units reveal the heterogeneity of rates that are hidden when reported at the county-level while substantially reducing the need to suppress data. We believe this effort will facilitate more meaningful comparative analysis of cancer rates for small geographic areas and will advance the understanding of cancer burden in the United States. [ABSTRACT FROM AUTHOR]