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Background: The COVID‐19 pandemic had a significant impact on cancer screening and treatment, particularly in 2020. However, no single study has comprehensively analyzed its effects on cancer incidence and disparities among groups such as race/ethnicity, socioeconomic status (SES), persistent poverty (PP), and rurality. Methods: Utilizing the recent data from the United States National Cancer Institute's Surveillance, Epidemiology, and End Results Program, we calculated delay‐ and age‐adjusted incidence rates for 13 cancer sites in 2020 and 2015–2019. Percent changes (PCs) of rates in 2020 compared to 2015–2019 were measured and compared across race/ethnic, census tract‐level SES, PP, and rurality groups. Results: Overall, incidence rates decreased from 2015–2019 to 2020, with varying PCs by cancer sites and population groups. Notably, NH Blacks showed significantly larger PCs than NH Whites in female lung, prostate, and colon cancers (e.g., prostate cancer: NH Blacks −7.3, 95% CI: [−9.0, −5.5]; NH Whites: −3.1, 95% CI: [−3.9, −2.2]). Significantly larger PCs were observed for the lowest versus highest SES groups (prostate cancer), PP versus non‐PP groups (prostate and female breast cancer), and all urban versus rural areas (prostate, female breast, female and male lung, colon, cervix, melanoma, liver, bladder, and kidney cancer). Conclusions: The COVID‐19 pandemic coincided with reduction in incidence rates in the U.S. in 2020 and was associated with worsening disparities among groups, including race/ethnicity, SES, rurality, and PP groups, across most cancer sites. Further investigation is needed to understand the specific effects of COVID‐19 on different population groups of interest. [ABSTRACT FROM AUTHOR]

BackgroundThere is no model to estimate absolute invasive breast cancer risk for Hispanic women.MethodsThe San Francisco Bay Area Breast Cancer Study (SFBCS) provided data on Hispanic breast cancer case patients (533 US-born, 553 foreign-born) and control participants (464 US-born, 947 foreign-born). These data yielded estimates of relative risk (RR) and attributable risk (AR) separately for US-born and foreign-born women. Nativity-specific absolute risks were estimated by combining RR and AR information with nativity-specific invasive breast cancer incidence and competing mortality rates from the California Cancer Registry and Surveillance, Epidemiology, and End Results program to develop the Hispanic risk model (HRM). In independent data, we assessed model calibration through observed/expected (O/E) ratios, and we estimated discriminatory accuracy with the area under the receiver operating characteristic curve (AUC) statistic.ResultsThe US-born HRM included age at first full-term pregnancy, biopsy for benign breast disease, and family history of breast cancer; the foreign-born HRM also included age at menarche. The HRM estimated lower risks than the National Cancer Institute's Breast Cancer Risk Assessment Tool (BCRAT) for US-born Hispanic women, but higher risks in foreign-born women. In independent data from the Women's Health Initiative, the HRM was well calibrated for US-born women (observed/expected [O/E] ratio = 1.07, 95% confidence interval [CI] = 0.81 to 1.40), but seemed to overestimate risk in foreign-born women (O/E ratio = 0.66, 95% CI = 0.41 to 1.07). The AUC was 0.564 (95% CI = 0.485 to 0.644) for US-born and 0.625 (95% CI = 0.487 to 0.764) for foreign-born women.ConclusionsThe HRM is the first absolute risk model that is based entirely on data specific to Hispanic women by nativity. Further studies in Hispanic women are warranted to evaluate its validity.

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]

Data synthesis is an effective statistical approach for reducing data disclosure risk. Generating fully synthetic data might minimize such risk, but its modeling and application can be difficult for data from large, complex surveys. This article extended the two-stage imputation to simultaneously impute item missing values and generate fully synthetic data. A new combining rule for making inferences using data generated in this manner was developed. Two semiparametric missing data imputation models were adapted to generate fully synthetic data for skewed continuous variable and sparse binary variable, respectively. The proposed approach was evaluated using simulated data and real longitudinal data from the Health and Retirement Study. The proposed approach was also compared with two existing synthesis approaches: (1) parametric regressions models as implemented in IVEware ; and (2) nonparametric Classification and Regression Trees as implemented in synthpop package for R using real data. The results show that high data utility is maintained for a wide variety of descriptive and model-based statistics using the proposed strategy. The proposed strategy also performs better than existing methods for sophisticated analyses such as factor analysis. [ABSTRACT FROM AUTHOR]

Background: Evidence is building that strength training may reduce complications associated with cancer such as fatigue, muscle wasting, and lymphedema, particularly among breast and prostate cancer survivors. Population estimates are available for rates of aerobic physical activity; however, data on strength training in this population are limited. The objective of this study was to identify rates of meeting public health recommendations for strength training and aerobic activity among cancer survivors and individuals with no cancer history. Methods-. Data from the Health Information National Trends Survey (HINTS). Iteration 4 Cycle I and Cycle 2 were combined to conduct the analyses. Missing data were imputed, and weighted statistical analyses were conducted in SAS. Results: The proportion of individuals meeting both strength training and aerobic guidelines were low for both cancer survivors and those without a history of cancer. The odds of meeting strength training guidelines were significantly lower for women with a history of any cancer except breast, compared with women with no history of cancer (OR: 0.70, 95% Cl: 0.51–0.96). Conclusions: More work needs to be done to understand why women with cancers other than breast, may be less inclined to engage in aerobic physical activity and strength training. [ABSTRACT FROM AUTHOR]

Cancer incidence and mortality are typically presented as age-standardized rates. Inference about these rates becomes complicated when denominators involve sampling errors. We propose a bias-corrected rate estimator as well as its corresponding variance estimator that take into account sampling errors in the denominators. Confidence intervals are derived based on the proposed estimators as well. Performance of the proposed methods is evaluated empirically based on simulation studies. More importantly, advantage of the proposed method is demonstrated and verified in a real-life study of cancer mortality disparity. A web-based, user-friendly computational tool is also being developed at the National Cancer Institute to accompany the new methods with the first application being calculating cancer mortality rates by US-born and foreign-born status. Finally, promise of proposed estimators to account for errors introduced by differential privacy procedures to the 2020 decennial census products is discussed. [ABSTRACT FROM AUTHOR]

We consider the situation of estimating Cox regression in which some covariates are subject to missing, and there exists additional information (including observed event time, censoring indicator and fully observed covariates) which may be predictive of the missing covariates. We propose to use two working regression models: one for predicting the missing covariates and the other for predicting the missing probabilities. For each missing covariate observation, these two working models are used to define a nearest neighbor imputing set. This set is then used to non-parametrically impute covariate values for the missing observation. Upon the completion of imputation, Cox regression is performed on the multiply imputed datasets to estimate the regression coefficients. In a simulation study, we compare the nonparametric multiple imputation approach with the augmented inverse probability weighted (AIPW) method, which directly incorporates the two working models into estimation of Cox regression, and the predictive mean matching imputation (PMM) method. We show that all approaches can reduce bias due to non-ignorable missing mechanism. The proposed nonparametric imputation method is robust to mis-specification of either one of the two working models and robust to mis-specification of the link function of the two working models. In contrast, the PMM method is sensitive to misspecification of the covariates included in imputation. The AIPW method is sensitive to the selection probability. We apply the approaches to a breast cancer dataset from Surveillance, Epidemiology and End Results (SEER) Program. [ABSTRACT FROM AUTHOR]