COPD With Lung Cancer Among Older United States Adults: Prevalence, Diagnostic Timeliness, and Association With Earlier Stage Tumors.

Rationale: Chronic obstructive pulmonary disease (COPD) is a common comorbidity among patients with lung cancer, and an important determinant of their outcomes, however, it is commonly underdiagnosed. Objective: Our objective was to estimate the prevalence of COPD among a cohort of U.S. lung cancer patients, the timing of a COPD diagnosis relative to their lung cancer diagnosis, and the association between an earlier diagnosis of COPD and stage of lung cancer, with consideration of patient sociodemographic modifying factors. Methods: We conducted an analysis of the Medicare-linked Surveillance, Epidemiology, and End Results database including patients aged 68+ years who were diagnosed with lung cancer between 2008 to 2017. Exposure: Prevalence of COPD was identified using claims and subclassified based on the timing of its diagnosis relative to the lung cancer diagnostic episode-"preexisting" if diagnosed > 3 months before lung cancer, and "concurrent" if diagnosed around the same time as the lung cancer (+/-3 months). Outcome: The stage of cancer at diagnosis (early versus late) was the outcome. Results: Among 159,542 patients with lung cancer, 73.5% had COPD. Among those with COPD, 34.4% were diagnosed within 3 months of their lung cancer diagnosis and considered to have "concurrent COPD." We observed a positive association between preexisting COPD diagnosis and early-stage lung cancer (prevalence ratio= 1.27; 95% confidence interval= 1.23-1.30), in adjusted models which were stronger for male, non-Hispanic Black, and Hispanic patients. Conclusions: Seven out of 10 patients with lung cancer have COPD, however, many do not receive their COPD diagnosis until around the time of their lung cancer diagnosis. Among these patients, an early COPD diagnosis may improve early detection of lung cancer.

Emergency department involvement in the diagnosis of cancer among older adults: a SEER-Medicare study.

BACKGROUND: Internationally, 20% to 50% of cancer is diagnosed through emergency presentation, which is associated with lower survival, poor patient experience, and socioeconomic disparities, but population-based evidence about emergency diagnosis in the United States is limited. We estimated emergency department (ED) involvement in the diagnosis of cancer in a nationally representative population of older US adults, and its association with sociodemographic, clinical, and tumor characteristics. METHODS: We analyzed Surveillance, Epidemiology, and End Results Program-Medicare data for Medicare beneficiaries (≥66 years old) with a diagnosis of female breast, colorectal, lung, and prostate cancers (2008-2017), defining their earliest cancer-related claim as their index date, and patients who visited the ED 0 to 30 days before their index date to have "ED involvement" in their diagnosis, with stratification as 0 to 7 or 8 to 30 days. We estimated covariate-adjusted associations of patient age, sex, race and ethnicity, marital status, comorbidity score, tumor stage, year of diagnosis, rurality, and census-tract poverty with ED involvement using modified Poisson regression. RESULTS: Among 614 748 patients, 23% had ED involvement, with 18% visiting the ED in the 0 to 7 days before their index date. This rate varied greatly by tumor site, with breast cancer at 8%, colorectal cancer at 39%, lung cancer at 40%, and prostate cancer at 7%. In adjusted models, older age, female sex, non-Hispanic Black and Native Hawaiian or Other Pacific Islander race, being unmarried, recent year of diagnosis, later-stage disease, comorbidities, and poverty were associated with ED involvement. CONCLUSIONS: The ED may be involved in the initial identification of cancer for 1 in 5 patients. Earlier, system-level identification of cancer in non-ED settings should be prioritized, especially among underserved populations.

Understanding mechanisms of racial disparities in breast cancer: an assessment of screening and regular care in the Carolina Breast Cancer Study.

PURPOSE: Screening history influences stage at detection, but regular preventive care may also influence breast tumor diagnostic characteristics. Few studies have evaluated healthcare utilization (both screening and primary care) in racially diverse screening-eligible populations. METHODS: This analysis included 2,058 women age 45-74 (49% Black) from the Carolina Breast Cancer Study, a population-based cohort of women diagnosed with invasive breast cancer between 2008 and 2013. Screening history (threshold 0.5 mammograms per year) and pre-diagnostic healthcare utilization (i.e. regular care, based on responses to "During the past ten years, who did you usually see when you were sick or needed advice about your health?") were assessed as binary exposures. The relationship between healthcare utilization and tumor characteristics were evaluated overall and race-stratified. RESULTS: Among those lacking screening, Black participants had larger tumors (5 + cm) (frequency 19.6% vs 11.5%, relative frequency difference (RFD) = 8.1%, 95% CI 2.8-13.5), but race differences were attenuated among screening-adherent participants (10.2% vs 7.0%, RFD = 3.2%, 0.2-6.2). Similar trends were observed for tumor stage and mode of detection (mammogram vs lump). Among all participants, those lacking both screening and regular care had larger tumors (21% vs 8%, RR = 2.51, 1.76-3.56) and advanced (3B +) stage (19% vs 6%, RR = 3.15, 2.15-4.63) compared to the referent category (screening-adherent and regular care). Under-use of regular care and screening was more prevalent in socioeconomically disadvantaged areas of North Carolina. CONCLUSIONS: Access to regular care is an important safeguard for earlier detection. Our data suggest that health equity interventions should prioritize both primary care and screening.

Immortal Time Bias With Time-Varying Exposures in Environmental Epidemiology: A Case Study in Lung Cancer Survival.

Immortal time bias is a well-recognized bias in clinical epidemiology but is rarely discussed in environmental epidemiology. Under the target trial framework, this bias is formally conceptualized as a misalignment between the start of study follow-up (time 0) and treatment assignment. This misalignment can occur when attained duration of follow-up is encoded into treatment assignment using minimums, maximums, or averages. The bias can be exacerbated in the presence of time trends commonly found in environmental exposures. Using lung cancer cases from the California Cancer Registry (2000-2010) linked with estimated concentrations of particulate matter less than or equal to 2.5 µm in aerodynamic diameter (PM2.5), we replicated previous studies that averaged PM2.5 exposure over follow-up in a time-to-event model. We compared this approach with one that ensures alignment between time 0 and treatment assignment, a discrete-time approach. In the former approach, the estimated overall hazard ratio for a 5-µg/m3 increase in PM2.5 was 1.38 (95% confidence interval: 1.36, 1.40). Under the discrete-time approach, the estimated pooled odds ratio was 0.99 (95% confidence interval: 0.98, 1.00). We conclude that the strong estimated effect in the former approach was likely driven by immortal time bias, due to misalignment at time 0. Our findings highlight the importance of appropriately conceptualizing a time-varying environmental exposure under the target trial framework to avoid introducing preventable systematic errors.

Symptoms and signs of lung cancer prior to diagnosis: case-control study using electronic health records from ambulatory care within a large US-based tertiary care centre.

OBJECTIVE: Lung cancer is the most common cause of cancer-related death in the USA. While most patients are diagnosed following symptomatic presentation, no studies have compared symptoms and physical examination signs at or prior to diagnosis from electronic health records (EHRs) in the USA. We aimed to identify symptoms and signs in patients prior to diagnosis in EHR data. DESIGN: Case-control study. SETTING: Ambulatory care clinics at a large tertiary care academic health centre in the USA. PARTICIPANTS, OUTCOMES: We studied 698 primary lung cancer cases in adults diagnosed between 1 January 2012 and 31 December 2019, and 6841 controls matched by age, sex, smoking status and type of clinic. Coded and free-text data from the EHR were extracted from 2 years prior to diagnosis date for cases and index date for controls. Univariate and multivariable conditional logistic regression were used to identify symptoms and signs associated with lung cancer at time of diagnosis, and 1, 3, 6 and 12 months before the diagnosis/index dates. RESULTS: Eleven symptoms and signs recorded during the study period were associated with a significantly higher chance of being a lung cancer case in multivariable analyses. Of these, seven were significantly associated with lung cancer 6 months prior to diagnosis: haemoptysis (OR 3.2, 95% CI 1.9 to 5.3), cough (OR 3.1, 95% CI 2.4 to 4.0), chest crackles or wheeze (OR 3.1, 95% CI 2.3 to 4.1), bone pain (OR 2.7, 95% CI 2.1 to 3.6), back pain (OR 2.5, 95% CI 1.9 to 3.2), weight loss (OR 2.1, 95% CI 1.5 to 2.8) and fatigue (OR 1.6, 95% CI 1.3 to 2.1). CONCLUSIONS: Patients diagnosed with lung cancer appear to have symptoms and signs recorded in the EHR that distinguish them from similar matched patients in ambulatory care, often 6 months or more before diagnosis. These findings suggest opportunities to improve the diagnostic process for lung cancer.

Healthcare utilization is a collider: an introduction to collider bias in EHR data reuse.

OBJECTIVES: Collider bias is a common threat to internal validity in clinical research but is rarely mentioned in informatics education or literature. Conditioning on a collider, which is a variable that is the shared causal descendant of an exposure and outcome, may result in spurious associations between the exposure and outcome. Our objective is to introduce readers to collider bias and its corollaries in the retrospective analysis of electronic health record (EHR) data. TARGET AUDIENCE: Collider bias is likely to arise in the reuse of EHR data, due to data-generating mechanisms and the nature of healthcare access and utilization in the United States. Therefore, this tutorial is aimed at informaticians and other EHR data consumers without a background in epidemiological methods or causal inference. SCOPE: We focus specifically on problems that may arise from conditioning on forms of healthcare utilization, a common collider that is an implicit selection criterion when one reuses EHR data. Directed acyclic graphs (DAGs) are introduced as a tool for identifying potential sources of bias during study design and planning. References for additional resources on causal inference and DAG construction are provided.

How Timely Is Diagnosis of Lung Cancer? Cohort Study of Individuals with Lung Cancer Presenting in Ambulatory Care in the United States.

The diagnosis of lung cancer in ambulatory settings is often challenging due to non-specific clinical presentation, but there are currently no clinical quality measures (CQMs) in the United States used to identify areas for practice improvement in diagnosis. We describe the pre-diagnostic time intervals among a retrospective cohort of 711 patients identified with primary lung cancer from 2012-2019 from ambulatory care clinics in Seattle, Washington USA. Electronic health record data were extracted for two years prior to diagnosis, and Natural Language Processing (NLP) applied to identify symptoms/signs from free text clinical fields. Time points were defined for initial symptomatic presentation, chest imaging, specialist consultation, diagnostic confirmation, and treatment initiation. Median and interquartile ranges (IQR) were calculated for intervals spanning these time points. The mean age of the cohort was 67.3 years, 54.1% had Stage III or IV disease and the majority were diagnosed after clinical presentation (94.5%) rather than screening (5.5%). Median intervals from first recorded symptoms/signs to diagnosis was 570 days (IQR 273-691), from chest CT or chest X-ray imaging to diagnosis 43 days (IQR 11-240), specialist consultation to diagnosis 72 days (IQR 13-456), and from diagnosis to treatment initiation 7 days (IQR 0-36). Symptoms/signs associated with lung cancer can be identified over a year prior to diagnosis using NLP, highlighting the need for CQMs to improve timeliness of diagnosis.

In-Home Cannabis Smoking Among a Cannabis-Using Convenience Sample from the Global Drug Survey: With Weighted Estimates for U.S. Respondents.

Introduction: Secondhand and thirdhand tobacco smoke exposure most often occur at home, but little is known about occurrences of in-home cannabis smoking. We ascertained in-home cannabis smoking reported by all cannabis-using (i.e., used in the last 12 months) respondents to the Global Drug Survey (GDS; international-GDS sample), and among U.S. cannabis-using respondents (US-GDS sample). Materials and Methods: We used data collected November 2019-January 2020 for the 2020 GDS, an annual anonymous, cross-sectional survey; respondents were 16+ years old, from 191 countries. We estimated any and daily in-home cannabis smoking in the last 30 days among international-GDS respondents (n=63,797), using mixed effects logistic regression. US-GDS respondents (n=6,580) were weighted to the covariate distribution of the nationally representative 2018 National Survey on Drug Use and Health cannabis-using sample, using inverse odds probability weighting, to make estimates more generalizable to the U.S. cannabis-using population. Results: For the international-GDS cannabis-using respondents, any in-home cannabis smoking was reported by 63.9% of men, 61.9% of women, and 68.6% of nonbinary people; and by age (<25 years old=62.7%, 25-34 years old=65.0%, and 35+ years old=62.8%). Daily in-home cannabis smoking was highest among nonbinary (28.7%) and respondents 35+ years of age (28.0%). For the weighted US-GDS cannabis-using respondents, any in-home cannabis smoking was reported by 49.8% of males and 61.2% of females; and by age (<25 years old=62.6%, 25-34 years old=41.8%, 35+ years old=57.9%). Weighted daily in-home smoking was 23.2% among males and 37.1% among females; by age (<25 years old=34.8%, 25-34 years old=27.8%, and 35+ years old=21.6%). Conclusions: There was high daily cannabis smoking in homes of international-GDS and US-GDS respondents who used cannabis in the last 12 months. In part, due to cannabis legalization, the number of users worldwide has increased over the past decade. Criminal stigma historically associated with cannabis continues to drive those users indoors. In this context, our findings support further investigation of cannabis use behavior to understand how often people are exposed to secondhand and thirdhand cannabis smoke and the consequences of that exposure.

The impact of patient travel time on disparities in treatment for early stage lung cancer in California.

BACKGROUND: Travel time to treatment facilities may impede the receipt of guideline-concordant treatment (GCT) among patients diagnosed with early-stage non-small cell lung cancer (ES-NSCLC). We investigated the relative contribution of travel time in the receipt of GCT among ES-NSCLC patients. METHODS: We included 22,821 ES-NSCLC patients diagnosed in California from 2006-2015. GCT was defined using the 2016 National Comprehensive Cancer Network guidelines, and delayed treatment was defined as treatment initiation >6 versus ≤6 weeks after diagnosis. Mean-centered driving and public transit times were calculated from patients' residential block group centroid to the treatment facilities. We used logistic regression to estimate risk ratios and 95% confidence intervals (CIs) for the associations between patients' travel time and receipt of GCT and timely treatment, overall and by race/ethnicity and neighborhood socioeconomic status (nSES). RESULTS: Overall, a 15-minute increase in travel time was associated with a decreased risk of undertreatment and delayed treatment. Compared to Whites, among Blacks, a 15-minute increase in driving time was associated with a 24% (95%CI = 8%-42%) increased risk of undertreatment, and among Filipinos, a 15-minute increase in public transit time was associated with a 27% (95%CI = 13%-42%) increased risk of delayed treatment. Compared to the highest nSES, among the lowest nSES, 15-minute increases in driving and public transit times were associated with 33% (95%CI = 16%-52%) and 27% (95%CI = 16%-39%) increases in the risk of undertreatment and delayed treatment, respectively. CONCLUSION: The benefit of GCT observed with increased travel times may be a 'Travel Time Paradox,' and may vary across racial/ethnic and socioeconomic groups.

Bias Reduction through Analysis of Competing Events (BRACE) Correction to Address Cancer Treatment Selection Bias in Observational Data.

PURPOSE: Cancer treatments can paradoxically appear to reduce the risk of noncancer mortality in observational studies, due to residual confounding. Here we introduce a method, Bias Reduction through Analysis of Competing Events (BRACE), to reduce bias in the presence of residual confounding. EXPERIMENTAL DESIGN: BRACE is a novel method for adjusting for bias from residual confounding in proportional hazards models. Using standard simulation methods, we compared BRACE with Cox proportional hazards regression in the presence of an unmeasured confounder. We examined estimator distributions, bias, mean squared error (MSE), and coverage probability. We then estimated treatment effects of high versus low intensity treatments in 36,630 prostate cancer, 4,069 lung cancer, and 7,117 head/neck cancer patients, using the Veterans Affairs database. We analyzed treatment effects on cancer-specific mortality (CSM), noncancer mortality (NCM), and overall survival (OS), using conventional multivariable Cox and propensity score (adjusted using inverse probability weighting) models, versus BRACE-adjusted estimates. RESULTS: In simulations with residual confounding, BRACE uniformly reduced both bias and MSE. In the absence of bias, BRACE introduced bias toward the null, albeit with lower MSE. BRACE markedly improved coverage probability, but with a tendency toward overcorrection for effective but nontoxic treatments. For each clinical cohort, more intensive treatments were associated with significantly reduced hazards for CSM, NCM, and OS. BRACE attenuated OS estimates, yielding results more consistent with findings from randomized trials and meta-analyses. CONCLUSIONS: BRACE reduces bias and MSE when residual confounding is present and represents a novel approach to improve treatment effect estimation in nonrandomized studies.

Validation of NRG Oncology's prognostic nomograms for oropharyngeal cancer in the Veterans Affairs database.

BACKGROUND: To test whether nomograms developed by NRG Oncology for oropharyngeal squamous cell carcinoma (OPSCC) patients could be validated in an independent population-based sample. METHODS: The authors tested nomograms for estimating progression-free survival (PFS) and overall survival (OS) in patients from the Veterans Health Administration with previously untreated locoregionally advanced OPSCC, diagnosed between 2008 and 2017, managed with definitive radiotherapy with or without adjuvant systemic therapy. Covariates were age, performance status, p16 status, T/N category, smoking history, education history, weight loss, marital status, and anemia. We used multiple imputation to handle missing data and performed sensitivity analyses on complete cases. Validation was assessed via Cox proportional hazards models, log-rank tests, and c-indexes. RESULTS: A total of 4007 patients met inclusion criteria (658 patients had complete data). Median follow-up time was 3.20 years, with 967 progression events and 471 noncancer deaths. Each risk score was associated with poorer outcomes per unit increase (PFS score, hazard ratio [HR], 1.42 [1.37-1.47]; OS score, HR, 1.40 [1.34-1.45]). By risk score quartile, 2-year PFS estimates were 89.2%, 78.5%, 65.8%, and 48.3%; OS estimates were 92.6%, 83.6%, 73.9%, and 51.3%, respectively (P < .01 for all comparisons). C-indices for models of PFS and OS were 0.65 and 0.67, for all patients, respectively (0.69 and 0.73 for complete cases). The nomograms slightly overestimated PFS and OS in the overall cohort but exhibited high agreement in complete cases. CONCLUSIONS: NRG nomograms were effective for predicting PFS and OS for patients with OPSCC, supporting their broader applicability in the OPSCC population undergoing definitive radiotherapy.

Asian American/Pacific Islander and Hispanic Ethnic Enclaves, Neighborhood Socioeconomic Status, and Hepatocellular Carcinoma Incidence in California: An Update.

BACKGROUND: Using more recent cancer registry data, we analyzed disparities in hepatocellular carcinoma (HCC) incidence by ethnic enclave and neighborhood socioeconomic status (nSES) among Asian American/Pacific Islander (AAPI) and Hispanic populations in California. METHODS: Primary, invasive HCC cases were identified from the California Cancer Registry during 1988-1992, 1998-2002, and 2008-2012. Age-adjusted incidence rates (per 100,000 population), incidence rate ratios, and corresponding 95% confidence intervals were calculated for AAPI or Hispanic enclave, nSES, and the joint effects of ethnic enclave and nSES by time period (and the combination of the three periods), sex, and race/ethnicity. RESULTS: In the combined time period, HCC risk increased 25% for highest versus lowest quintile of AAPI enclave among AAPI males. HCC risk increased 22% and 56% for lowest versus highest quintile of nSES among AAPI females and males, respectively. In joint analysis, AAPI males living in low nSES areas irrespective of enclave status were at 17% to 43% increased HCC risk compared with AAPI males living in areas of nonenclave/high nSES. HCC risk increased by 22% for Hispanic females living in areas of low nSES irrespective of enclave status and by 19% for Hispanic males living in areas of nonenclave/low nSES compared with their counterparts living in areas of nonenclave/high nSES. CONCLUSIONS: We found significant variation in HCC incidence by ethnic enclave and nSES among AAPI and Hispanic populations in California by sex and time period. IMPACT: Future studies should explore how specific attributes of enclaves and nSES impact HCC risk for AAPI and Hispanic populations.

Cancer Mortality in U.S.-Born versus Foreign-Born Asian American Groups (2008-2017).

BACKGROUND: Asian Americans (AA) are the fastest growing ethnic group in the United States with high proportions of immigrants. Nativity is important as cancer risk factors vary by country. We sought to understand differences in cancer mortality among AAs by nativity (foreign-born vs. U.S.-born). METHODS: Ninety-eight thousand eight hundred and twenty-six AA (Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese) decedents with cancer-related deaths from U.S. death certificates (2008-2017) were analyzed. Thirteen cancers that contribute significantly to Asian-American cancer mortality were selected and categorized by etiology: tobacco-related, screen-detected, diet-/obesity-related, and infection-related. Ten-year age-adjusted mortality rates [AAMR; 95% confidence interval (CI); per 100,00] and standardized mortality ratios (SMR; 95% CI) using foreign-born as the reference group were calculated. RESULTS: Overall, foreign-born AAs had higher mortality rates than U.S.-born. Japanese U.S.-born males had the highest tobacco-related mortality rates [foreign-born AAMR: 43.02 (38.72, 47.31); U.S.-born AAMR: 55.38 (53.05, 57.72)]. Screen-detected death rates were higher for foreign-born than U.S.-born, except for among Japanese males [SMR 1.28 (1.21-1.35)]. Diet-/obesity-related AAMRs were higher among females than males and highest among foreign-born females. Foreign-born males and females had higher infection-related AAMRs than U.S.-born; the highest rates were foreign-born males-Korean [AAMR 41.54 (39.54, 43.53)] and Vietnamese [AAMR 41.39 (39.68, 43.09)]. CONCLUSIONS: We observed substantial heterogeneity in mortality rates across AA groups and by nativity. Contrary to the Healthy Immigrant Effect, most foreign-born Asians were dying at higher rates than U.S.-born AAs. IMPACT: Disaggregated analysis of AA cancers, targeted and culturally tailored cancer screening, and treatments for infections among foreign-born Asians is critical for cancer prevention efforts.

Incidence of Lung Cancer Among Never-Smoking Asian American, Native Hawaiian, and Pacific Islander Females.

BACKGROUND: Although lung cancer incidence rates according to smoking status, sex, and detailed race/ethnicity have not been available, it is estimated that more than half of Asian American, Native Hawaiian, and Pacific Islander (AANHPI) females with lung cancer have never smoked. METHODS: We calculated age-adjusted incidence rates for lung cancer according to smoking status and detailed race/ethnicity among females, focusing on AANHPI ethnic groups, and assessed relative incidence across racial/ethnic groups. We used a large-scale dataset that integrates data from electronic health records from 2 large health-care systems-Sutter Health in Northern California and Kaiser Permanente Hawai'i-linked to state cancer registries for incident lung cancer diagnoses between 2000 and 2013. The study population included 1 222 694 females (n = 244 147 AANHPI), 3297 of which were diagnosed with lung cancer (n = 535 AANHPI). RESULTS: Incidence of lung cancer among never-smoking AANHPI as an aggregate group was 17.1 per 100 000 (95% confidence interval [CI] = 14.9 to 19.4) but varied widely across ethnic groups. Never-smoking Chinese American females had the highest rate (22.8 per 100 000, 95% CI = 17.3 to 29.1). Except for Japanese American females, incidence among every never-smoking AANHPI female ethnic group was higher than that of never-smoking non-Hispanic White females, from 66% greater among Native Hawaiian females (incidence rate ratio = 1.66, 95% CI = 1.03 to 2.56) to more than 100% greater among Chinese American females (incidence rate ratio = 2.26, 95% CI = 1.67 to 3.02). CONCLUSIONS: Our study revealed high rates of lung cancer among most never-smoking AANHPI female ethnic groups. Our approach illustrates the use of innovative data integration to dispel the myth that AANHPI females are at overall reduced risk of lung cancer and demonstrates the need to disaggregate this highly diverse population.

Comparing Age at Cancer Diagnosis between Hispanics and Non-Hispanic Whites in the United States.

BACKGROUND: Population age structure may confound the comparison of age at cancer diagnosis across racial/ethnic groups. We compared age at cancer diagnosis for U.S. Hispanics, a population that is younger on average, and non-Hispanic whites (NHW), before and after adjustment for the age structure of the source population. METHODS: We used Surveillance, Epidemiology, and End Results data from 18 U.S. regions in 2015 for 34 cancer sites to calculate crude and adjusted (using age- and sex-specific weights) mean ages at diagnosis. Differences in age at diagnosis comparing Hispanics to NHWs (δ) were assessed using independent sample t tests. RESULTS: Crude mean ages at diagnosis were lower among Hispanic males and females for all sites combined and for most cancer sites. After age-adjustment, Hispanic (vs. NHW) males remained younger on average at diagnosis of chronic myeloid leukemia [δ = -6.1; 95% confidence interval (CI), -8.1 to -4.1 years], testicular cancer (δ =-4.7; 95% CI, -5.4 to -4.0), Kaposi sarcoma (δ =-3.6; 95% CI,-6.3 to -0.8), mesothelioma (δ =-3.0; 95% CI,-4.3 to -1.7), and anal cancer (δ =-2.4; 95% CI, -3.9 to -0.8), and older at diagnosis of gallbladder cancer (δ = +3.8; 95% CI, 1.8 to 5.7) and Hodgkin's lymphoma (δ = +7.5; 95% CI, 5.7 to 9.4), and Hispanic (vs. NHW) females remained younger at diagnosis of mesothelioma (δ = -3.7; 95% CI, -6.7 to -0.7) and gallbladder cancer (δ = -3.0; 95% CI, -4.3 to -1.7) and older at diagnosis of skin cancer (δ = +3.8; 95% CI, 3.1 to 4.5), cervical cancer (δ = +4.1; 95% CI, 3.3 to 4.8), and Hodgkin's lymphoma (δ = +7.0; 95% CI, 5.0 to 9.1). CONCLUSIONS: On average, Hispanics are diagnosed with cancer at younger ages than NHWs; however, for many cancers these differences reflect the younger age structure in Hispanics. IMPACT: Population age structure should be considered when comparing age at cancer diagnosis across racial/ethnic groups.