The COVID-19 pandemic and associated declines in cancer incidence by race/ethnicity and census-tract level SES, rurality, and persistent poverty status.

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.

Description of census-tract-level social determinants of health in cancer surveillance data.

BACKGROUND: Disparities in cancer incidence, stage at diagnosis, and mortality persist by race, ethnicity, and many other social determinants, such as census-tract-level socioeconomic status (SES), poverty, and rurality. Census-tract-level measures of these determinants are useful for analyzing trends in cancer disparities. METHODS: The purpose of this paper was to demonstrate the availability of the Surveillance, Epidemiology, and End Results Program's specialized census-tract-level dataset and provide basic descriptive cancer incidence, stage at diagnosis, and survival for 8 cancer sites, which can be screened regularly or associated with infectious agents. We present these analyses according to several census-tract-level measures, including the newly available persistent poverty as well as SES quintile, rurality, and race and ethnicity. RESULTS: Census tracts with persistent poverty and low SES had higher cancer incidence rates (except for breast and prostate cancer), higher percentages of cases diagnosed with regional or distant-stage disease, and lower survival than non-persistent-poverty and higher-SES tracts. Outcomes varied by cancer site when analyzing based on rurality as well as race and ethnicity. Analyses stratified by multiple determinants showed unique patterns of outcomes, which bear further investigation. CONCLUSIONS: This article introduces the Surveillance, Epidemiology, and End Results specialized dataset, which contains census-tract-level social determinants measures, including persistent poverty, rurality, SES quintile, and race and ethnicity. We demonstrate the capacity of these variables for use in producing trends and analyses focusing on cancer health disparities. Analyses may inform interventions and policy changes that improve cancer outcomes among populations living in disadvantaged areas, such as persistent-poverty tracts.

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.

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.

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.

A SEMIPARAMETRIC MULTIPLE IMPUTATION APPROACH TO FULLY SYNTHETIC DATA FOR COMPLEX SURVEYS.

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.

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.

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.

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.

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.

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.

Indicators of Socioeconomic Status for Individuals, Census Tracts, and Counties: How Well Do Measures Align for Demographic Subgroups?

OBJECTIVES: Researchers often approximate individual-level socioeconomic status (SES) from census tract and county data. However, area-level variables do not serve as accurate proxies for individual-level SES, particularly among some demographic subgroups. The present study aimed to analyze the potential bias introduced by this practice. METHODS: Data included (1) individual-level SES from the Mortality Disparities in American Communities study (n ≈ 3,471,000 collected in 2008), and (2) census tract- and county-level SES from the 2006-2010 American Community Survey. Analyses included correlations among SES indicators (eg, median household income, having a high school degree, unemployment) across individual versus census tract and county levels, stratified by sex, age, race/ethnicity, and urbanicity. Finally, generalized estimating equations evaluated demographic differences in whether area-level SES matched or underestimated individual-level SES. RESULTS: Low correlations were observed between individual- and area-level SES (census tract: Spearman's r range = 0.048 for unemployment to 0.232 for median household income; county: r range = 0.028 for unemployment to 0.157 for median household income; all P < .0001). SES indicators were more likely to match for males, older participants, and urban groups. Area-level SES indicators were more likely to underestimate individual-level SES for older participants and rural groups, indicating that individuals who are part of these groups may live in systematically lower-SES communities than their own SES might connote. CONCLUSIONS: In this population-based study of 3.5 million participants, area-level indicators were poor proxies for individual-level SES, particularly for participants living in rural areas.

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.

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.

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.

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.

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.

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.