Assessing racial/ethnic and nativity disparities in US cancer mortality using a new integrated platform.

BACKGROUND: Foreign-born (FB) populations in the US have significantly 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 (AAMRs) and rate ratios (RRs) for common cancers by sex, age group, race/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: AAMRs were higher among US-born (UB) populations across nearly all cancer types, with the largest UB- FB difference observed in lung cancer among Black females (RR = 3.67, 95%CI = 3.37-4.00). The well-documented White-Black differences in breast cancer mortality existed mainly among UB women. For all cancers combined, descending trends were more accelerated for the UB compared to the FB in all race/ethnicity groups with changes ranging from -2.6% per year in UB Black males to stable (non-significant) among FB Black females. Pancreas and liver cancers were exceptions with increasing, stable, or decreasing trends depending on nativity and race/ethnicity. Notably, FB Black males and FB Hispanic males did not show a favorable decline in colorectal cancer mortality. CONCLUSIONS: While all groups show beneficial cancer mortality trends, those with higher rates in 2006 have experienced sharper declines. Persistent disparities between the UB and the FB, especially among Black people, necessitate further investigation.

Developing Geographic Areas for Cancer Reporting Using Automated Zone Design.

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.

Inference about ratios of age-standardized rates with sampling errors in the population denominators for estimating both rates.

A rate ratio (RR) is an important metric for comparing cancer risks among different subpopulations. Inference for RR becomes complicated when populations used for calculating age-standardized cancer rates involve sampling errors, a situation that arises increasingly often when sample surveys must be used to obtain the population data. We compare a few strategies of estimating the standardized RR and propose bias-corrected ratio estimators as well as the corresponding variance estimators and confidence intervals that simultaneously consider the sampling error in estimating populations and the traditional Poisson error in the occurrence of cancer case or death. Performance of the proposed methods is evaluated empirically based on simulation studies. An application to immigration disparities in cancer mortality among Hispanic Americans is discussed. Our simulation studies show that a bias-corrected RR estimator performs the best in reducing the bias without increasing the coefficient of variation; the proposed variance estimators for the RR estimators and associated confidence intervals are fairly accurate. Finding of our application study are both interesting and consistent with the common sense as well as the results of our simulation studies.

Effects of Buffer Size and Shape on the Association of Neighborhood SES and Adult Fruit and Vegetable Consumption.

Introduction: Neighborhood environment factors are relevant for dietary behaviors, but associations between home neighborhood context and disease prevention behaviors vary depending on the definition of neighborhood. The present study uses a publicly available dataset to examine whether associations between neighborhood socioeconomic status (NSES) and fruit/vegetable (FV) consumption vary when NSES is defined by different neighborhood sizes and shapes. Methods: We analyzed data from 1,736 adults with data in GeoFLASHE, a geospatial extension of the National Cancer Institute's Family Life, Activity, Sun, Health, and Eating Study (FLASHE). We examined correlations of NSES values across neighborhood buffer shapes (circular or street network) and sizes (ranging from 400 to 1,200 m) and ran weighted simple and multivariable regressions modeling frequency of FV consumption by NSES for each neighborhood definition. Regressions were also stratified by gender. Results: NSES measures were highly correlated across various neighborhood buffer definitions. In models adjusted for socio-demographics, circular buffers of all sizes and street buffers 750 m and larger were significantly associated with FV consumption frequency for women only. Conclusion: NSES may be particularly relevant for women's FV consumption, and further research can examine whether these associations are explained by access to food stores, food shopping behavior, and/or psychosocial variables. Although different NSES buffers are highly correlated, researchers should conceptually determine spatial areas a priori.

Leading cancers contributing to educational disparities in cancer mortality in the US, 2017.

PURPOSE: To inform prevention efforts, we sought to determine which cancer types contribute the most to cancer mortality disparities by individual-level education using national death certificate data for 2017. METHODS: Information on all US deaths occurring in 2017 among 25-84-year-olds was ascertained from national death certificate data, which include cause of death and educational attainment. Education was classified as high school or less (≤ 12 years), some college or diploma (13-15 years), and Bachelor's degree or higher (≥ 16 years). Cancer mortality rate differences (RD) were calculated by subtracting age-adjusted mortality rates (AMR) among those with ≥ 16 years of education from AMR among those with ≤ 12 years. RESULTS: The cancer mortality rate difference between those with a Bachelor's degree or more vs. high school or less education was 72 deaths per 100,000 person-years. Lung cancer deaths account for over half (53%) of the RD for cancer mortality by education in the US. CONCLUSION: Efforts to reduce smoking, particularly among persons with less education, would contribute substantially to reducing educational disparities in lung cancer and overall cancer mortality.

Multilevel mediation analysis on time-to-event outcomes: Exploring racial/ethnic disparities in breast cancer survival in California.

Background: Third-variable effect refers to the effect from a third-variable that explains an observed relationship between an exposure and an outcome. Depending on whether there is a causal relationship from the exposure to the third variable, the third-variable is called a mediator or a confounder. The multilevel mediation analysis is used to differentiate third-variable effects from data of hierarchical structures. Data Collection and Analysis: We developed a multilevel mediation analysis method to deal with time-to-event outcomes and implemented the method in the mlma R package. With the method, third-variable effects from different levels of data can be estimated. The method uses multilevel additive models that allow for transformations of variables to take into account potential nonlinear relationships among variables in the mediation analysis. We apply the proposed method to explore the racial/ethnic disparities in survival among patients diagnosed with breast cancer in California between 2006 and 2017, using both individual risk factors and census tract level environmental factors. The individual risk factors are collected by cancer registries and the census tract level factors are collected by the Public Health Alliance of Southern California in partnership with the Virginia Commonwealth University's Center on Society and Health. The National Cancer Institute work group linked variables at the census tract level with each patient and performed the analysis for this study. Results: We found that the racial disparity in survival were mostly explained at the census tract level and partially explained at the individual level. The associations among variables were depicted. Conclusion: The multilevel mediation analysis method can be used to differentiate mediation/confounding effects for factors originated from different levels. The method is implemented in the R package mlma.

Inference about age-standardized rates with sampling errors in the denominators.

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.

Rural-urban differences in health-related quality of life: patterns for cancer survivors compared to other older adults.

PURPOSE: Health-related quality of life (HRQOL) among older cancer survivors can be impaired by factors such as treatment, comorbidities, and social challenges. These HRQOL impairments may be especially pronounced in rural areas, where older adults have higher cancer burden and more comorbidities and risk factors for poor health. This study aimed to assess rural-urban differences in HRQOL for older cancer survivors and controls. METHODS: Data came from Surveillance, Epidemiology, and End Results-Medicare Health Outcomes Survey (SEER-MHOS), which links cancer incidence from 18 U.S. population-based cancer registries to survey data for Medicare Advantage Organization enrollees (1998-2014). HRQOL measures were 8 standardized subscales and 2 global summary measures. We matched (2:1) controls to breast, colorectal, lung, and prostate cancer survivors, creating an analytic dataset of 271,640 participants (ages 65+). HRQOL measures were analyzed with linear regression models including multiplicative interaction terms (rurality by cancer status), controlling for sociodemographics, cohort, and multimorbidities. RESULTS: HRQOL scores were higher in urban than rural areas (e.g., global physical component summary score for breast cancer survivors: urban mean = 38.7, standard error [SE] = 0.08; rural mean = 37.9, SE = 0.32; p < 0.05), and were generally lower among cancer survivors compared to controls. Rural cancer survivors had particularly poor vitality (colorectal: p = 0.05), social functioning (lung: p = 0.05), role limitation-physical (prostate: p < 0.01), role limitation-emotional (prostate: p < 0.01), and global mental component summary (prostate: p = 0.02). CONCLUSION: Supportive interventions are needed to increase physical, social, and emotional HRQOL among older cancer survivors in rural areas. These interventions could target cancer-related stigma (particularly for lung and prostate cancers) and/or access to screening, treatment, and ancillary healthcare resources.

Trends in Racial/Ethnic Disparity of Health-Related Quality of Life in Older Adults with and without Cancer (1998-2012).

BACKGROUND: Non-White cancer survivors often report poorer health compared with Non-Hispanic Whites. Whether those disparities are changing over time is unknown. We examined changes in health-related quality of life (HRQOL) by race/ethnicity from 1998 to 2012 among older adults with and without cancer. METHODS: Data from Medicare Advantage beneficiaries were obtained from the linkage between the Medicare Health Outcomes Survey and Surveillance, Epidemiology, and End Results cancer registry data (SEER-MHOS). HRQOL was assessed with the SF-36/VR-12 Physical and Mental Component Scores (PCS/MCS) and 8 scales (Physical Functioning, Role-Physical, Bodily Pain, General Health, Vitality, Social Functioning, Mental Health, Role-Emotional). Annual average HRQOL scores, adjusting for age at survey, gender, number of comorbidities, education, and SEER registry, were compared over time. Absolute (between-group variance; BGV) and relative (mean log deviation; MLD) indices of disparity were generated using the National Cancer Institute's health disparities calculator (HD*Calc). Joinpoint was used to test for significant changes in the slopes of the linear trend lines. RESULTS: Racial/ethnic disparities in MCS increased in absolute and relative terms over time for those with [BGV = 15.8 (95% confidence interval [CI], 10.2-21.6); MLD = 16.2 (95% CI, 10.5-22.1)] and without [BGV = 19.3 (95% CI, 14.9-23.8); MLD = 19.6 (95% CI, 15.2-24.0)] cancer. PCS disparities over time did not significantly change. Changes in disparities in 5 of 8 HRQOL scales were significant in those with and without cancer. CONCLUSIONS: Older adults with cancer show increasing racial/ethnic disparities in HRQOL, particularly in mental health status. IMPACT: Future research should evaluate trends in HRQOL and explore factors that contribute to health disparities.

Cancer Incidence in Older Adults in the United States: Characteristics, Specificity, and Completeness of the Data.

INTRODUCTION: The number of cancer cases in the United States continues to grow as the number of older adults increases. Accurate, reliable and detailed incidence data are needed to respond effectively to the growing human costs of cancer in an aging population. The purpose of this study was to examine the characteristics of incident cases and evaluate the impact of death-certificate-only (DCO) cases on cancer incidence rates in older adults. METHODS: Using data from 47 cancer registries and detailed population estimates from the Surveillance, Epidemiology and End Results (SEER) Program, we examined reporting sources, methods of diagnosis, tumor characteristics, and calculated age-specific incidence rates with and without DCO cases in adults aged 65 through ≥95 years, diagnosed 2011 through 2015, by sex and race/ethnicity. RESULTS: The percentage of cases (all cancers combined) reported from a hospital decreased from 90.6% (ages 65-69 years) to 69.1% (ages ≥95 years) while the percentage of DCO cases increased from 1.1% to 19.6%. Excluding DCO cases, positive diagnostic confirmation decreased as age increased from 96.8% (ages 65-69 years) to 69.2% (ages ≥95 years). Compared to incidence rates that included DCO cases, rates in adults aged ≥95 years that excluded DCO cases were 41.5% lower in Black men with prostate cancer and 29.2% lower in Hispanic women with lung cancer. DISCUSSION: Loss of reported tumor specificity with age is consistent with fewer hospital reports. However, the majority of cancers diagnosed in older patients, including those aged ≥95 years, were positively confirmed and were reported with known site, histology, and stage information. The high percentage of DCO cases among patients aged ≥85 years suggests the need to explore additional sources of follow-back to help possibly identify an earlier incidence report. Interstate data exchange following National Death Index linkages may help registries identify and remove erroneous DCO cases from their databases.

Breast Cancer Incidence Trends by Estrogen Receptor Status Among Asian American Ethnic Groups, 1990-2014.

BACKGROUND: Westernization and etiologic heterogeneity may play a role in the rising breast cancer incidence in Asian American (AA) women. We report breast cancer incidence in Asian-origin populations. METHODS: Using a specialized Surveillance, Epidemiology, and End Results-9 Plus API Database (1990-2014), we analyzed breast cancer incidence overall, by estrogen receptor (ER) status, and age group among non-Hispanic white (NHW) and AA women. We used age-period-cohort models to assess time trends and quantify heterogeneity by ER status, race and ethnicity, and age. RESULTS: Overall, breast cancer incidence increased for most AA ethnicities (Filipina: estimated annual percentage change [EAPC] = 0.96%/year, 95% confidence interval [CI] = 0.61% to 1.32%; South Asian: EAPC = 1.68%/year, 95% CI = 0.24% to 3.13%; Chinese: EAPC = 0.65%/year, 95% CI = 0.03% to 1.27%; Korean: EAPC = 2.55%/year, 95% CI = 0.13% to 5.02%; and Vietnamese women: EAPC = 0.88%/year, 95% CI = 0.37% to 1.38%); rates did not change for NHW (EAPC = -0.2%/year, 95% CI = -0.73% to 0.33%) or Japanese women (EAPC = 0.22%/year, 95% CI = -1.26% to 1.72%). For most AA ethnicities, ER-positive rates statistically significantly increased, whereas ER-negative rates statistically significantly decreased. Among older women, ER-positive rates were stable for NHW and Japanese women. ER-negative rates decreased fastest in NHW and Japanese women among both age groups. CONCLUSIONS: Increasing ER-positive incidence is driving an increase overall for most AA women despite declining ER-negative incidence. The similar trends in NHW and Japanese women (vs other AA ethnic groups) highlight the need to better understand the influences of westernization and other etiologic factors on breast cancer incidence patterns in AA women. Heterogeneous trends among AA ethnicities underscore the importance of disaggregating AA data and studying how breast cancer differentially affects the growing populations of diverse AA ethnic groups.

Improved Monte Carlo methods for estimating confidence intervals for eleven commonly used health disparity measures.

Health disparities are commonplace and of broad interest to policy makers, but are also challenging to measure and communicate. The Health Disparity Calculator software (HD*Calc, v1.2.4) offers Monte Carlo simulation (MCS)-based confidence interval (CI) estimation of eleven disparity measures. The MCS approach provides accurate CI estimation, except when data are scarce (e.g., rare cancers). To address sparse data challenges to CI estimation, we propose two solutions: 1) employing the gamma distribution in the MCS and 2) utilizing a zero-inflated Poisson estimate for Poisson sampling in simulation experiments. We evaluate each solution through simulation studies using female breast, female brain, lung, and cervical cancer data from the Surveillance, Epidemiology, and End Results (SEER) program. We compare the coverage probabilities (CPs) of eleven health disparity measures based on simulated datasets. The truncated normal distribution implemented in the MCS with the standard Poisson samples (the default setting of HD*Calc) leads to less-than-optimal coverage probabilities (<95%). When both the gamma distribution and the estimated mean from the zero-inflated Poisson are used for the MCS, the coverage probabilities are close to the nominal level of 95%. Simulation studies also demonstrate that collapsing age categories for better CI estimation is not a pragmatic solution.

Providing Higher Resolution Indicators of Rurality in the Surveillance, Epidemiology, and End Results (SEER) Database: Implications for Patient Privacy and Research.

BACKGROUND: The burden of cancer is higher in rural areas than urban areas. The NCI's Surveillance, Epidemiology, and End Results (SEER) database currently provides county-level information on rurality for cancer patients in its catchment area, but more nuanced measures of rurality would improve etiologic and surveillance studies. METHODS: We analyzed disclosure risk and conducted a sample utility analysis of census tract-level measures of rurality, using (1) U.S. Department of Agriculture's Rural Urban Commuting Area (RUCA) codes and (2) U.S. Census data on percentage of the population living in nonurban areas. We evaluated the risk of disclosure by calculating the percentage of census tracts and cancer cases that would be uniquely identified by a combination of these two rurality measures with a census tract-level socioeconomic status (SES) variable. The utility analyses examined SES disparities across levels of rurality for lung and breast cancer incidence and relative survival. RESULTS: Risk of disclosure was quite low: <0.03% of census tracts and <0.03% of cancer cases were uniquely identified. Utility analyses demonstrated an SES gradient in lung and breast cancer incidence and survival, with relatively similar patterns across rurality variables. CONCLUSIONS: The RUCA and Census rurality measures have been added to a specialized SEER 18 database. Interested researchers can request access to this database to perform analyses of urban/rural differences in cancer incidence and survival. IMPACT: Such studies can provide important research support for future interventions to improve cancer prevention and control.

Cox regression analysis with missing covariates via nonparametric multiple imputation.

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.

Statistical inferences of extended concentration indices for directly standardized rates.

The relative concentration index (RCI) and the absolute concentration index (ACI) have been widely used for monitoring health disparities with ranked health determinants. The RCI has been extended to allow value judgments about inequality aversion by Pereira in 1998 and by Wagstaff in 2002. Previous studies of the extended RCI have focused on survey sample data. This paper adapts the extended RCI for use with directly standardized rates (DSRs) calculated from population-based surveillance data. A Taylor series linearization (TL)-based variance estimator is developed and evaluated using simulations. A simulation-based Monte Carlo (MC) variance estimator is also evaluated as a comparison. Following Wagstaff's approach in 1991, we extend the ACI for use with DSRs. In all simulations, both the TL and MC methods produce valid variance estimates. The TL variance estimator has a simple, closed form that is attractive to users without sophisticated programming skills. The TL and MC estimators have been incorporated into a beta version of the National Cancer Institute's Health Disparities Calculator, a free statistical software tool that enables the estimation of 11 commonly used summary measures of health disparities for DSRs.

Statistical Inference on Health Disparity Indices for Complex Surveys.

The National Cancer Institute developed the Health Disparities Calculator (HD*Calc) to facilitate research on health disparities. HD*Calc calculates multiple measures of health disparities using data collected from population-based disease surveillance systems, such as cancer registries. In this paper, we extend the use of HD*Calc to complex survey data by developing plug-in point estimators and Taylor linearization variance estimators that consider complex designs: stratification, multistage clustering, and differential weighting. Our simulation indicates that the plug-in estimators are approximately unbiased and the Taylor linearization variance estimators are accurate. Using 2011-2016 data from the National Health and Nutrition Examination Survey, we demonstrate the use of these estimators in evaluating socioeconomic disparities in the prevalence of child and adolescent (ages 2-18 years) obesity in the United States. Statistical software has been developed for ease of disparity analyses using complex survey data.

Variance Estimation and Confidence Intervals for 11 Commonly Used Health Disparity Measures.

There is increased interest in eliminating health disparities in the United States and worldwide. Broadly defined, health disparities refer to preventable inequalities in health status, such as cancer to ethnicity, socioeconomic status, gender, education, environment, and geographic locations. To make informed health policy decisions, it is essential to precisely measure the magnitude of disparities and assess trends over time. The Health Disparities Calculator (HD*Calc) is free statistical software that calculates 11 commonly used measures of health disparities and provides corresponding 95% CIs for the 11 measures using either an analytic method or a Monte Carlo simulation-based method; however, the derivation of SEs and coverage properties of the CIs have not been formally evaluated. We used simulation studies to assess the coverage properties of these CIs. We have also conducted bias analyses for measures implemented in HD*Calc using age-adjusted cancer incidence rates from national, state, and county level SEER data. The results of these analyses indicate that HD*Calc should be used with caution to construct CIs for some health disparity measures when the proportion of zero event counts is greater than 25%.

An Exploration of Changes in the Measurement of Mammography in the National Health Interview Survey.

Background: Using the National Health Interview Survey (NHIS), we examined the effect of question wording on estimates of past-year mammography among racially/ethnically diverse women ages 40-49 and 50-74 without a history of breast cancer.Methods: Data from one-part ("Have you had a mammogram during the past 12 months?") and two-part ("Have you ever had a mammogram"; "When did you have your most recent mammogram?") mammography history questions administered in the 2008, 2011, and 2013 NHIS were analyzed. χ2 tests provided estimates of changes in mammography when question wording was either the same (two-part question) or differed (two-part question followed by one-part question) in the two survey years compared. Crosstabulations and regression models assessed the type, extent, and correlates of inconsistent responses to the two questions in 2013.Results: Reports of past-year mammography were slightly higher in years when the one-part question was asked than when the two-part question was asked. Nearly 10% of women provided inconsistent responses to the two questions asked in 2013. Black women ages 50 to 74 [adjusted OR (aOR), 1.50; 95% confidence interval (CI), 1.16-1.93] and women ages 40-49 in poor health (aOR, 2.22; 95% CI, 1.09-4.52) had higher odds of inconsistent responses; women without a usual source of care had lower odds (40-49: aOR, 0.42; 95% CI, 0.21-0.85; 50-74: aOR, 0.42; 95% CI, 0.24-0.74).Conclusions: Self-reports of mammography are sensitive to question wording. Researchers should use equivalent questions that have been designed to minimize response biases such as telescoping and social desirability.Impact: Trend analyses relying on differently worded questions may be misleading and conceal disparities. Cancer Epidemiol Biomarkers Prev; 26(11); 1611-8. ©2017 AACR.

Protecting Confidentiality in Cancer Registry Data With Geographic Identifiers.

The National Cancer Institute's Surveillance, Epidemiology, and End Results Program releases research files of cancer registry data. These files include geographic information at the county level, but no finer. Access to finer geography, such as census tract identifiers, would enable richer analyses-for example, examination of health disparities across neighborhoods. To date, tract identifiers have been left off the research files because they could compromise the confidentiality of patients' identities. We present an approach to inclusion of tract identifiers based on multiply imputed, synthetic data. The idea is to build a predictive model of tract locations, given patient and tumor characteristics, and randomly simulate the tract of each patient by sampling from this model. For the predictive model, we use multivariate regression trees fitted to the latitude and longitude of the population centroid of each tract. We implement the approach in the registry data from California. The method results in synthetic data that reproduce a wide range (but not all) of analyses of census tract socioeconomic cancer disparities and have relatively low disclosure risks, which we assess by comparing individual patients' actual and synthetic tract locations. We conclude with a discussion of how synthetic data sets can be used by researchers with cancer registry data.

Assessing disparities in colorectal cancer mortality by socioeconomic status using new tools: health disparities calculator and socioeconomic quintiles.

PURPOSE: Colorectal cancer mortality rates dropped by half in the past three decades, but these gains were accompanied by striking differences in colorectal cancer mortality by socioeconomic status (SES). Our research objective is to examine disparities in colorectal cancer mortality by SES, using a scientifically rigorous and reproducible approach with publicly available online tools, HD*Calc and NCI SES Quintiles. METHODS: All reported colorectal cancer deaths in the United States from 1980 to 2010 were categorized into NCI SES quintiles and assessed at the county level. Joinpoint was used to test for significant changes in trends. Absolute and relative concentration indices (CI) were computed with HD*Calc to graph change in disparity over time. RESULTS: Disparities by SES significantly declined until 1993-1995, and then increased until 2010, due to a mortality drop in populations living in high SES areas that exceeded the mortality drop in lower SES areas. HD*Calc results were consistent for both absolute and relative concentration indices. Inequality aversion parameter weights of 2, 4, 6 and 8 were compared to explore how much colorectal cancer mortality was concentrated in the poorest quintile compared to the richest quintile. Weights larger than 4 did not increase the slope of the disparities trend. CONCLUSIONS: There is consistent evidence for a significant crossover in colorectal cancer disparity from 1980 to 2010. Trends in disparity can be accurately and readily summarized using the HD*Calc tool. The disparity trend, combined with published information on the timing of screening and treatment uptake, is concordant with the idea that introduction of medical screening and treatment leads to lower uptake in lower compared to higher SES populations and that differential uptake yields disparity in population mortality.