Medicaid expansion in California and breast cancer incidence across neighborhoods with varying social vulnerabilities.

PURPOSE: To investigate changes in breast cancer incidence rates associated with Medicaid expansion in California. METHODS: We extracted yearly census tract-level population counts and cases of breast cancer diagnosed among women aged between 20 and 64 years in California during years 2010-2017. Census tracts were classified into low, medium and high groups according to their social vulnerability index (SVI). Using a difference-in-difference (DID) approach with Poisson regression models, we estimated the incidence rate, incidence rate ratio (IRR) during the pre- (2010-2013) and post-expansion periods (2014-2017), and the relative IRR (DID estimates) across three groups of neighborhoods. RESULTS: Prior to the Medicaid expansion, the overall incidence rate was 93.61, 122.03, and 151.12 cases per 100,000 persons among tracts with high, medium, and low-SVI, respectively; and was 96.49, 122.07, and 151.66 cases per 100,000 persons during the post-expansion period, respectively. The IRR between high and low vulnerability neighborhoods was 0.62 and 0.64 in the pre- and post-expansion period, respectively, and the relative IRR was 1.03 (95% CI 1.00 to 1.06, p = 0.026). In addition, significant DID estimate was only found for localized breast cancer (relative IRR = 1.05; 95% CI, 1.01 to 1.09, p = 0.049) between high and low-SVI neighborhoods, not for regional and distant cancer stage. CONCLUSIONS: The Medicaid expansion had differential impact on breast cancer incidence across neighborhoods in California, with the most pronounced increase found for localized cancer stage in high-SVI neighborhoods. Significant pre-post change was only found for localized breast cancer between high and low-SVI neighborhoods.

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.

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.

Comparisons of individual- and area-level socioeconomic status as proxies for individual-level measures: evidence from the Mortality Disparities in American Communities study.

BACKGROUND: Area-level measures are often used to approximate socioeconomic status (SES) when individual-level data are not available. However, no national studies have examined the validity of these measures in approximating individual-level SES. METHODS: Data came from ~ 3,471,000 participants in the Mortality Disparities in American Communities study, which links data from 2008 American Community Survey to National Death Index (through 2015). We calculated correlations, specificity, sensitivity, and odds ratios to summarize the concordance between individual-, census tract-, and county-level SES indicators (e.g., household income, college degree, unemployment). We estimated the association between each SES measure and mortality to illustrate the implications of misclassification for estimates of the SES-mortality association. RESULTS: Participants with high individual-level SES were more likely than other participants to live in high-SES areas. For example, individuals with high household incomes were more likely to live in census tracts (r = 0.232; odds ratio [OR] = 2.284) or counties (r = 0.157; OR = 1.325) whose median household income was above the US median. Across indicators, mortality was higher among low-SES groups (all p < .0001). Compared to county-level, census tract-level measures more closely approximated individual-level associations with mortality. CONCLUSIONS: Moderate agreement emerged among binary indicators of SES across individual, census tract, and county levels, with increased precision for census tract compared to county measures when approximating individual-level values. When area level measures were used as proxies for individual SES, the SES-mortality associations were systematically underestimated. Studies using area-level SES proxies should use caution when selecting, analyzing, and interpreting associations with health outcomes.

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 and Patterns of Testosterone Therapy among U.S. Male Medicare Beneficiaries, 1999 to 2014.

PURPOSE: We explored the Medicare database (1999 to 2014) to provide a comprehensive assessment of testosterone therapy patterns in the older U.S. male population. MATERIALS AND METHODS: We estimated annual age-standardized incidence (new users) and prevalence (existing users) of testosterone therapy according to demographic characteristics, comorbidities and potential indications. RESULTS: There were 392,698 incident testosterone therapy users during 88 million person-years. Testosterone therapy users were predominantly younger, white nonHispanic, and located in South and West U.S. Census regions. On average testosterone therapy use increased dramatically during 2007 to 2014 (average annual percent change 15.5%), despite a decrease in 2014. In 2014 the most common recorded potential indications for any testosterone therapy were hypogonadism (48%), fatigue (18%), erectile dysfunction (15%), depression (4%) and psychosexual dysfunction (1%). Laboratory tests to measure circulating testosterone concentrations for testosterone therapy were infrequent with 35% having had at least 1 testosterone test in the 120 days preceding testosterone therapy, 4% the recommended 2 pre-testosterone therapy tests, and 16% at least 1 pre-testosterone therapy test and at least 1 post-testosterone therapy test. CONCLUSIONS: Testosterone therapy remains common in the older U.S. male population, despite a recent decrease. Although testosterone therapy prescriptions are predominantly for hypogonadism, a substantial proportion appear to be for less specific conditions. Testosterone tests among men prescribed testosterone therapy appear to be infrequent.

Statistical inference of the relative concentration index for complex surveys.

The relative concentration index is a widely used measure for assessing relative differences in health across all socioeconomic population groups. We extend its usage to individual-level data collected through complex surveys by deriving its variance using the Taylor linearization (TL) method. Two existing plug-in variance estimators that only require grouped data are also compared. We discuss sources of uncertainty that each variance estimator considers and present simulation studies to compare the performance of the three estimators under various sampling designs. The proposed TL variance estimator consistently produces valid results; however, it requires the access to individual-level data. Both plug-in variance estimators are biased because of failure to account for certain error sources. However, when only grouped data is available, one of the plug-in estimators can be valid as long as the socioeconomic groups are treated equally sized, a commonly used analytic strategy to emphasize group's instead of individual's burden of disease in health disparity assessment. We illustrate the three variance estimators by applying them to assessing socioeconomic disparities in child and adolescent obesity using complex survey sampled drawn from the National Health and Nutrition Examination Survey.

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.

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.

A nonparametric multiple imputation approach for missing categorical data.

BACKGROUND: Incomplete categorical variables with more than two categories are common in public health data. However, most of the existing missing-data methods do not use the information from nonresponse (missingness) probabilities. METHODS: We propose a nearest-neighbour multiple imputation approach to impute a missing at random categorical outcome and to estimate the proportion of each category. The donor set for imputation is formed by measuring distances between each missing value with other non-missing values. The distance function is calculated based on a predictive score, which is derived from two working models: one fits a multinomial logistic regression for predicting the missing categorical outcome (the outcome model) and the other fits a logistic regression for predicting missingness probabilities (the missingness model). A weighting scheme is used to accommodate contributions from two working models when generating the predictive score. A missing value is imputed by randomly selecting one of the non-missing values with the smallest distances. We conduct a simulation to evaluate the performance of the proposed method and compare it with several alternative methods. A real-data application is also presented. RESULTS: The simulation study suggests that the proposed method performs well when missingness probabilities are not extreme under some misspecifications of the working models. However, the calibration estimator, which is also based on two working models, can be highly unstable when missingness probabilities for some observations are extremely high. In this scenario, the proposed method produces more stable and better estimates. In addition, proper weights need to be chosen to balance the contributions from the two working models and achieve optimal results for the proposed method. CONCLUSIONS: We conclude that the proposed multiple imputation method is a reasonable approach to dealing with missing categorical outcome data with more than two levels for assessing the distribution of the outcome. In terms of the choices for the working models, we suggest a multinomial logistic regression for predicting the missing outcome and a binary logistic regression for predicting the missingness probability.

Receipt of mammography recommendations among White and non-White women before and after the 2009 United States Preventive Services Task Force recommendation change.

PURPOSE: Receipt of a mammography recommendation from a physician is a strong predictor of obtaining a mammogram. In 2009, the United States Preventive Services Task Force (USPSTF) recommended routine biennial mammography for women aged 50-74 but not for women aged 40-49. We examined changes in reports of clinician recommendations for mammography among White and non-White women after these age-specific recommendations were issued. METHODS: Data from women aged 40-49 and 50-74 were drawn from the 2008 and 2013 National Health Interview Surveys. We used linear probability models to determine whether the proportions of women reporting a mammography recommendation changed after the USPSTF recommendation was issued and whether any changes observed differed across White and non-White women. All analyses were stratified by age groups and mammography history. RESULTS: Among women without a recent mammogram, reported clinician recommendations did not change for White women, but they decreased by 13-percentage points (95 % CI -0.22, -0.03) among non-White women aged 40-49 (p = 0.01) and increased by 9-percentage points (95 % CI 0.01, 0.17) among non-White women aged 50-74 (p = 0.04). Among women with a mammogram in the past 2 years, reported mammography recommendation from a clinician did not change for White or non-White women. CONCLUSIONS: Recommendations to reduce screening may be differentially implemented across racial/ethnic groups. Changes in reports of mammography recommendation from a clinician after the USPSTF breast cancer screening recommendation change were observed only among non-White women without a recent history of mammography. It is unclear whether these differences are due to the clinician, the women, or both.

Multiple imputation of completely missing repeated measures data within person from a complex sample: application to accelerometer data in the National Health and Nutrition Examination Survey.

The Physical Activity Monitor component was introduced into the 2003-2004 National Health and Nutrition Examination Survey (NHANES) to collect objective information on physical activity including both movement intensity counts and ambulatory steps. Because of an error in the accelerometer device initialization process, the steps data were missing for all participants in several primary sampling units, typically a single county or group of contiguous counties, who had intensity count data from their accelerometers. To avoid potential bias and loss in efficiency in estimation and inference involving the steps data, we considered methods to accurately impute the missing values for steps collected in the 2003-2004 NHANES. The objective was to come up with an efficient imputation method that minimized model-based assumptions. We adopted a multiple imputation approach based on additive regression, bootstrapping and predictive mean matching methods. This method fits alternative conditional expectation (ace) models, which use an automated procedure to estimate optimal transformations for both the predictor and response variables. This paper describes the approaches used in this imputation and evaluates the methods by comparing the distributions of the original and the imputed data. A simulation study using the observed data is also conducted as part of the model diagnostics. Finally, some real data analyses are performed to compare the before and after imputation results. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Willingness to Exchange Health Information via Mobile Devices: Findings From a Population-Based Survey.

PURPOSE: The rapid proliferation of mobile devices offers unprecedented opportunities for patients and health care professionals to exchange health information electronically, but little is known about patients' willingness to exchange various types of health information using these devices. We examined willingness to exchange different types of health information via mobile devices, and assessed whether sociodemographic characteristics and trust in clinicians were associated with willingness in a nationally representative sample. METHODS: We analyzed data for 3,165 patients captured in the 2013 Health Information National Trends Survey. Multinomial logistic regression analysis was conducted to test differences in willingness. Ordinal logistic regression analysis assessed correlates of willingness to exchange 9 types of information separately. RESULTS: Participants were very willing to exchange appointment reminders (odds ratio [OR] = 6.66; 95% CI, 5.68-7.81), general health tips (OR = 2.03; 95% CI, 1.74-2.38), medication reminders (OR = 2.73; 95% CI, 2.35-3.19), laboratory/test results (OR = 1.76; 95% CI, 1.62-1.92), vital signs (OR = 1.63; 95% CI, 1.48-1.80), lifestyle behaviors (OR = 1.40; 95% CI, 1.24-1.58), and symptoms (OR = 1.62; 95% CI, 1.46-1.79) as compared with diagnostic information. Older adults had lower odds of being more willing to exchange any type of information. Education, income, and trust in health care professional information correlated with willingness to exchange certain types of information. CONCLUSIONS: Respondents were less willing to exchange via mobile devices information that may be considered sensitive or complex. Age, socioeconomic factors, and trust in professional information were associated with willingness to engage in mobile health information exchange. Both information type and demographic group should be considered when developing and tailoring mobile technologies for patient-clinician communication.

Mining Health App Data to Find More and Less Successful Weight Loss Subgroups.

BACKGROUND: More than half of all smartphone app downloads involve weight, diet, and exercise. If successful, these lifestyle apps may have far-reaching effects for disease prevention and health cost-savings, but few researchers have analyzed data from these apps. OBJECTIVE: The purposes of this study were to analyze data from a commercial health app (Lose It!) in order to identify successful weight loss subgroups via exploratory analyses and to verify the stability of the results. METHODS: Cross-sectional, de-identified data from Lose It! were analyzed. This dataset (n=12,427,196) was randomly split into 24 subsamples, and this study used 3 subsamples (combined n=972,687). Classification and regression tree methods were used to explore groupings of weight loss with one subsample, with descriptive analyses to examine other group characteristics. Data mining validation methods were conducted with 2 additional subsamples. RESULTS: In subsample 1, 14.96% of users lost 5% or more of their starting body weight. Classification and regression tree analysis identified 3 distinct subgroups: "the occasional users" had the lowest proportion (4.87%) of individuals who successfully lost weight; "the basic users" had 37.61% weight loss success; and "the power users" achieved the highest percentage of weight loss success at 72.70%. Behavioral factors delineated the subgroups, though app-related behavioral characteristics further distinguished them. Results were replicated in further analyses with separate subsamples. CONCLUSIONS: This study demonstrates that distinct subgroups can be identified in "messy" commercial app data and the identified subgroups can be replicated in independent samples. Behavioral factors and use of custom app features characterized the subgroups. Targeting and tailoring information to particular subgroups could enhance weight loss success. Future studies should replicate data mining analyses to increase methodology rigor.

Using a composite index of socioeconomic status to investigate health disparities while protecting the confidentiality of cancer registry data.

PURPOSE: The lack of individual socioeconomic status (SES) information in cancer registry data necessitates the use of area-based measures to investigate health disparities. Concerns about confidentiality, however, prohibit publishing patients' residential locations at the subcounty level. We developed a census tract-based composite SES index to be released in place of individual census tracts to minimize the risk of disclosure. METHODS: Two SES indices based on the measures identified in the literature were constructed using factor analysis. The analyses were repeated using the data from the 2000 decennial census and 2005-2009 American Community Survey to create the indices at two time points, which were linked to 2000-2009 Surveillance, Epidemiology, and End Results registry data to estimate incidence and survival rates. RESULTS: The two indices performed similarly in stratifying census tracts and detecting socioeconomic gradients in cancer incidence and survival. The gradient in the incidence is positive for breast and prostate, and negative for lung cancers, in all races, although the level varies. The positive gradient in survival is more salient for regional-staged breast, colorectal, and lung cancers. CONCLUSIONS: The census tract-based SES index provides a valuable tool for monitoring the disparities in cancer burdens while avoiding potential identity disclosure. This index, divided into tertiles and quintiles, is now available to the researchers on request.

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

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.

Use of imputed population-based cancer registry data as a method of accounting for missing information: application to estrogen receptor status for breast cancer.

The National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) Program provides a rich source of data stratified according to tumor biomarkers that play an important role in cancer surveillance research. These data are useful for analyzing trends in cancer incidence and survival. These tumor markers, however, are often prone to missing observations. To address the problem of missing data, the authors employed sequential regression multivariate imputation for breast cancer variables, with a particular focus on estrogen receptor status, using data from 13 SEER registries covering the period 1992-2007. In this paper, they present an approach to accounting for missing information through the creation of imputed data sets that can be analyzed using existing software (e.g., SEER*Stat) developed for analyzing cancer registry data. Bias in age-adjusted trends in female breast cancer incidence is shown graphically before and after imputation of estrogen receptor status, stratified by age and race. The imputed data set will be made available in SEER*Stat (http://seer.cancer.gov/analysis/index.html) to facilitate accurate estimation of breast cancer incidence trends. To ensure that the imputed data set is used correctly, the authors provide detailed, step-by-step instructions for conducting analyses. This is the first time that a nationally representative, population-based cancer registry data set has been imputed and made available to researchers for conducting a variety of analyses of breast cancer incidence trends.