The Annual Cost of Cancer Screening in the United States.

BACKGROUND: Cancer has substantial health, quality-of-life, and economic impacts. Screening may decrease cancer mortality and treatment costs, but the cost of screening in the United States is unknown. OBJECTIVE: To estimate the annual cost of initial cancer screening (that is, screening without follow-up costs) in the United States in 2021. DESIGN: Model using national health care survey and cost resources data. SETTING: U.S. health care systems and institutions. PARTICIPANTS: People eligible for breast, cervical, colorectal, lung, and prostate cancer screening with available data. MEASUREMENTS: The number of people screened and associated health care system costs by insurance status in 2021 dollars. RESULTS: Total health care system costs for initial cancer screenings in the United States in 2021 were estimated at $43 billion. Approximately 88.3% of costs were attributable to private insurance; 8.5% to Medicare; and 3.2% to Medicaid, other government programs, and uninsured persons. Screening for colorectal cancer represented approximately 64% of the total cost; screening colonoscopy represented about 55% of the total. Facility costs (amounts paid to facilities where testing occurred) were major drivers of the total estimated costs of screening. LIMITATIONS: All data on receipt of cancer screening are based on self-report from national health care surveys. Estimates do not include costs of follow-up for positive or abnormal screening results. Variations in costs based on geography and provider or health care organization are not fully captured. CONCLUSION: The $43 billion estimated annual cost for initial cancer screening in the United States in 2021 is less than the reported annual cost of cancer treatment in the United States in the first 12 months after diagnosis. Identification of cancer screening costs and their drivers is critical to help inform policy and develop programmatic priorities, particularly for enhancing access to recommended cancer screening services. PRIMARY FUNDING SOURCE: None.

Differential associations between television viewing, computer use, and adiposity by age, gender, and race/ethnicity in United States youth: A cross-sectional NHANES analysis.

BACKGROUND: Time spent on screens and adiposity change rapidly from childhood to adolescence, with differences by gender and race/ethnicity. OBJECTIVE: Apply time-varying effect models (TVEMs) to a nationally representative sample of youth to identify the age ranges when the cross-sectional associations between television viewing, computer use, and adiposity are significant. METHODS: Data from 8 to 15-year-olds (n = 3593) from the National Health and Nutrition Examination Survey (2011-2018) were extracted. TVEMs estimated the associations between television viewing, computer use, and fat mass index as dynamic functions of the participants' age, stratified by gender and race/ethnicity. RESULTS: TVEMs revealed age-specific statistically significant associations that differed by gender and race/ethnicity. Notably, computer use was related to higher adiposity in non-Hispanic White females aged 9.3-11.4 years (slope ß-range: 0.1-0.2) and in non-Hispanic Black females older than 14.8 years (ß-range: 0.1-0.5). In males, these age windows were 13.5-15.0 years (non-Hispanic White, ß-range: 0.1-0.2), 11.4-13.0 years (non-Hispanic Black, ß-range: 0.1-0.14), and older than 13.0 years (Hispanic, ß-range: 0.1-0.4). CONCLUSIONS: More research during the specific age ranges in the demographic subgroups identified here could increase our understanding of tailored interventions in youth.

Applying computer text mining algorithms for oversampling tumor mutation status in medical records for the NCI Patterns of Care studies.

BACKGROUNDS: The National Cancer Institute (NCI) conducts Patterns of Care (POC) studies for selected cancer sites under a Congressional Mandate. These studies aim to collect treatment information beyond what is typically collected by the NCI's Surveillance, Epidemiology, and End Results (SEER) Program. The 2019 POC study focused on non-small cell lung cancer (NSCLC) and melanoma cancer sites. For the NSCLC cases, one of the primary sampling objectives was to oversample patients who tested positive for EGFR/ALK mutations, but initial information on mutation test results was unavailable prior to selecting the study sample. METHODS: To address this, text mining algorithms were developed to screen all eligible NSCLC cases from the SEER database. These algorithms were designed to identify the mutation test status, allowing for stratified sampling based on SEER registry, sex, race/ethnicity, and tumor mutation test results. RESULTS: The final NSCLC sample included 2,434 patients aged 20+ with advanced stage (IIIB-IVB) NSCLC diagnosed in 2017 and 2018. Among this sample, 692 cases (13.2%) tested positive for EGFR/ALK mutations. An evaluation of the text mining algorithms performance, based on cases where both algorithm results and known EGFR/ALK status from medical chart abstraction were available, showed good results: sensitivity of 77.6%, specificity of 90.8%, and an overall accuracy 84.8%. CONCLUSIONS: The adaption of text mining algorithm proved effective in oversample patients with uncommon conditions in studies where electronic medical records are accessible. The 2019 POC study provides valuable data for researchers to evaluate cancer therapy details and patient characteristics, particularly among those with EGFR/ALK test positive cases.

Estimating life expectancy adjusted by self-rated health status in the United States: national health interview survey linked to the mortality.

BACKGROUND: Life expectancy is increasingly incorporated in evidence-based screening and treatment guidelines to facilitate patient-centered clinical decision-making. However, life expectancy estimates from standard life tables do not account for health status, an important prognostic factor for premature death. This study aims to address this research gap and develop life tables incorporating the health status of adults in the United States. METHODS: Data from the National Health Interview Survey (1986-2004) linked to mortality follow-up through to 2006 (age ≥ 40, n = 729,531) were used to develop life tables. The impact of self-rated health (excellent, very good, good, fair, poor) on survival was quantified in 5-year age groups, incorporating complex survey design and weights. Life expectancies were estimated by extrapolating the modeled survival probabilities. RESULTS: Life expectancies incorporating health status differed substantially from standard US life tables and by health status. Poor self-rated health more significantly affected the survival of younger compared to older individuals, resulting in substantial decreases in life expectancy. At age 40 years, hazards of dying for white men who reported poor vs. excellent health was 8.5 (95% CI: 7.0,10.3) times greater, resulting in a 23-year difference in life expectancy (poor vs. excellent: 22 vs. 45), while at age 80 years, the hazards ratio was 2.4 (95% CI: 2.1, 2.8) and life expectancy difference was 5 years (5 vs. 10). Relative to the US general population, life expectancies of adults (age < 65) with poor health were approximately 5-15 years shorter. CONCLUSIONS: Considerable shortage in life expectancy due to poor self-rated health existed. The life table developed can be helpful by including a patient perspective on their health and be used in conjunction with other predictive models in clinical decision making, particularly for younger adults in poor health, for whom life tables including comorbid conditions are limited.

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.

Updated Methodology for Projecting U.S.- and State-Level Cancer Counts for the Current Calendar Year: Part II: Evaluation of Incidence and Mortality Projection Methods.

BACKGROUND: The American Cancer Society (ACS) and the NCI collaborate every 5 to 8 years to update the methods for estimating the numbers of new cancer cases and deaths in the current year for the U.S. and individual states. Herein, we compare our current projection methodology with the next generation of statistical models. METHODS: A validation study was conducted comparing current projection methods (vector autoregression for incidence; Joinpoint regression for mortality) with the Bayes state-space method and novel Joinpoint algorithms. Incidence data from 1996-2010 were projected to 2014 using two inputs: modeled data and observed data with modeled where observed were missing. For mortality, observed data from 1995 to 2009, 1996 to 2010, 1997 to 2011, and 1998 to 2012, each projected 3 years forward to 2012 to 2015. Projection methods were evaluated using the average absolute relative deviation (AARD) between observed counts (2014 for incidence, 2012-2015 for mortality) and estimates for 47 cancer sites nationally and 21 sites by state. RESULTS: A novel Joinpoint model provided a good fit for both incidence and mortality, particularly for the most common cancers in the U.S. Notably, the AARD for cancers with cases in 2014 exceeding 49,000 for this model was 3.4%, nearly half that of the current method (6.3%). CONCLUSIONS: A data-driven Joinpoint algorithm had versatile performance at the national and state levels and will replace the ACS's current methods. IMPACT: This methodology provides estimates of cancer data that are not available for the current year, thus continuing to fill an important gap for advocacy, research, and public health planning.

Updated Methodology for Projecting U.S.- and State-Level Cancer Counts for the Current Calendar Year: Part I: Spatio-temporal Modeling for Cancer Incidence.

BACKGROUND: The American Cancer Society (ACS) and the NCI collaborate every 5-8 years to update the methods for estimating numbers of new cancer cases and deaths in the current year in the United States and in every state and the District of Columbia. In this article, we reevaluate the statistical method for estimating unavailable historical incident cases which are needed for projecting the current year counts. METHODS: We compared the current county-level model developed in 2012 (M0) with three new models, including a state-level mixed effect model (M1) and two state-level hierarchical Bayes models with varying random effects (M2 and M3). We used 1996-2014 incidence data for 16 sex-specific cancer sites to fit the models. An average absolute relative deviation (AARD) comparing the observed with the model-specific predicted counts was calculated for each site. Models were also cross-validated for six selected sex-specific cancer sites. RESULTS: For the cross-validation, the AARD ranged from 2.8% to 33.0% for M0, 3.3% to 31.1% for M1, 6.6% to 30.5% for M2, and 10.4% to 393.2% for M3. M1 encountered the least technical issues in terms of model convergence and running time. CONCLUSIONS: The state-level mixed effect model (M1) was overall superior in accuracy and computational efficiency and will be the new model for the ACS current year projection project. IMPACT: In addition to predicting the unavailable state-level historical incidence counts for cancer surveillance, the updated algorithms have broad applicability for disease mapping and other activities of public health planning, advocacy, and research.

Receipt of guideline-concordant care among young adult women with breast cancer.

BACKGROUND: Little is known about the real-world care of young adult (YA) females (aged 20-39 years) with breast cancer. This study describes factors associated with the receipt of guideline-concordant care (GCC) among YAs. METHODS: The authors identified 1259 YA women with invasive breast cancer diagnosed in 2013 in the National Cancer Institute's Patterns of Care study. Hospital records were re-abstracted, and treatment was verified. Using the National Comprehensive Cancer Network's 2013 breast cancer guidelines, the authors assessed the receipt of GCC by cancer subtype among a subset of YAs (n = 952). Associations between sociodemographic and clinical factors and GCC receipt were examined. RESULTS: Most YAs were 35 to 39 years old (51.2%) and partnered (56.4%); half had hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) tumors. GCC was found for 81.7% of YAs. Relationships between sociodemographic and clinical factors and GCC receipt differed by subtype. Stage was the only significant predictor of GCC receipt for all subtypes (stage II vs III: odds ratio [OR] for HR+/HER2+, 0.20; 95% confidence interval [CI], 0.08-0.50; OR for HR-/HER2+, 0.13; 95% CI, 0.07-0.25; OR for HR-/HER2-, 3.86; 95% CI, 1.55-9.62; OR for HR+/HER2-, 2.81; 95% CI, 1.63-5.80). CONCLUSIONS: GCC is high among YAs with breast cancer. The effects of sociodemographic factors and treatment facility size on GCC differ by subtype. Consistent with recommendations, tumor biology, not age, is associated with GCC for all subtypes. Future studies should assess the effect of GCC on survival among YAs.

Small-Area Estimation of Smoke-Free Workplace Policies and Home Rules in US Counties.

INTRODUCTION: The workplace and home are sources of exposure to secondhand smoke, a serious health hazard for nonsmoking adults and children. Smoke-free workplace policies and home rules protect nonsmoking individuals from secondhand smoke and help individuals who smoke to quit smoking. However, estimated population coverages of smoke-free workplace policies and home rules are not typically available at small geographic levels such as counties. Model-based small-area estimation techniques are needed to produce such estimates. METHODS: Self-reported smoke-free workplace policies and home rules data came from the 2014-2015 Tobacco Use Supplement to the Current Population Survey. County-level design-based estimates of the two measures were computed and linked to county-level relevant covariates obtained from external sources. Hierarchical Bayesian models were then built and implemented through Markov Chain Monte Carlo methods. RESULTS: Model-based estimates of smoke-free workplace policies and home rules were produced for 3134 (of 3143) US counties. In 2014-2015, nearly 80% of US adult workers were covered by smoke-free workplace policies, and more than 85% of US adults were covered by smoke-free home rules. We found large variations within and between states in the coverage of smoke-free workplace policies and home rules. CONCLUSIONS: The small-area modeling approach efficiently reduced the variability that was attributable to small sample size in the direct estimates for counties with data and predicted estimates for counties without data by borrowing strength from covariates and other counties with similar profiles. The county-level modeled estimates can serve as a useful resource for tobacco control research and intervention. IMPLICATIONS: Detailed county- and state-level estimates of smoke-free workplace policies and home rules can help identify coverage disparities and differential impact of smoke-free legislation and related social norms. Moreover, this estimation framework can be useful for modeling different tobacco control variables and applied elsewhere, for example, to other behavioral, policy, or health related topics.

Perceptions of cancer as a death sentence: Tracking trends in public perceptions from 2008 to 2017.

OBJECTIVE: There has been steady progress in reducing cancer mortality in the United States; however, this progress hasn't been evenly distributed across regions. This paper assesses trends in cancer mortality salience (CMS), that is, agreeing that getting cancer is a death sentence, over time in the United States and examines correlates of CMS. METHODS: Data from three administrations of the Health Information National Trends Survey (HINTS), gathered in 2008, 2013, and 2017, were merged, resulting in a total sample of 10,063 respondents. Trends in changes in CMS over time were examined as well as maps of the distribution of CMS in the United States. A logistic regression model was conducted, regressing CMS on a set of sociodemographic, psychological, health-related, and environmental predictors. RESULTS: The aggregated percentage of US adults who agreed with the CMS statement changed over time and was modified by age. Geographic distribution of agreement with CMS was inconsistent across the United States. In the adjusted logistic model, perceived health (worse health), cancer prevention, fatalism, and confusion, and cancer status (no cancer) were all significantly associated with CMS. There was also a significant interaction between survey year and age. CONCLUSION: Despite recent information that cancer mortality rates are decreasing, most US adults still see cancer as a death sentence and this is especially an issue in certain subgroups. These findings have ramifications for groups of people who may be at risk for developing cancer given their attitudes and beliefs that there isn't much they can do to prevent or control it.

Small Area Estimation of Cancer Risk Factors and Screening Behaviors in US Counties by Combining Two Large National Health Surveys.

BACKGROUND: National health surveys, such as the National Health Interview Survey (NHIS) and the Behavioral Risk Factor Surveillance System (BRFSS), collect data on cancer screening and smoking-related measures in the US noninstitutionalized population. These surveys are designed to produce reliable estimates at the national and state levels. However, county-level data are often needed for cancer surveillance and related research. METHODS: To use the large sample sizes of BRFSS and the high response rates and better coverage of NHIS, we applied multilevel models that combined information from both surveys. We also used relevant sources such as census and administrative records. By using these methods, we generated estimates for several cancer risk factors and screening behaviors that are more precise than design-based estimates. RESULTS: We produced reliable, modeled estimates for 11 outcomes related to smoking and to screening for female breast cancer, cervical cancer, and colorectal cancer. The estimates were produced for 3,112 counties in the United States for the data period from 2008 through 2010. CONCLUSION: The modeled estimates corrected for potential noncoverage bias and nonresponse bias in the BRFSS and reduced the variability in NHIS estimates that is attributable to small sample size. The small area estimates produced in this study can serve as a useful resource to the cancer surveillance community.

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.

Adolescent Behavioral Cancer Prevention in the United States: Creating a Composite Variable and Ranking States' Performance.

Preventive behaviors established during adolescence can reduce cancer throughout the life span. Understanding the combinations of multiple behaviors, and how these behaviors vary across states, is important for identifying where additional interventions are needed. Using data on 2011-2015 vaccination, energy balance, and substance use from national surveys, we created state-level composite scores for adolescent cancer prevention. Hierarchical Bayesian linear mixed models were used to predict estimates for states with no data on select behaviors. We used a Monte Carlo procedure with 100,000 simulations to generate states' ranks and 95% confidence intervals. Across states, hepatitis B vaccination was 84.3% to 97.1%, and human papillomavirus vaccination was 41.8% to 78.0% for girls and 19.0% to 59.3% for boys. For energy balance, 20.2% to 34.6% of adolescents met guidelines for physical activity, 4.1% to 15.8% for fruit and vegetable consumption, and 66.4% to 82.0% for healthy weight. For substance use, 82.5% to 93.5% reported abstaining from binge alcohol use, 84.3% to 95.4% from cigarette smoking, and 62.9% to 92.8% from marijuana use. (1) Rhode Island, (2) Colorado, (4) Hawaii and New Hampshire (tied), and (5) Vermont performed the best for adolescent cancer prevention, and (47) Missouri, (48) Arkansas, Mississippi, and South Carolina (tied), and (51) Kentucky performed the worst. However, 95% CIs around ranks often overlapped, indicating lack of statistical differences. Adolescent cancer prevention behaviors clustered into a composite index. States varied on their performance on this index, especially for states at the high and low extremes, but most states did not differ statistically. These findings can inform decision makers about where and how to intervene to improve cancer prevention among adolescents.

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.

Smoking and HIV in Sub-Saharan Africa: A 25-Country Analysis of the Demographic Health Surveys.

BACKGROUND: Having HIV/AIDS has been associated with a higher prevalence of smoking. Moreover, evidence suggests that people with HIV/AIDS who smoke have poorer treatment and survival outcomes. The HIV-smoking relationship is understudied in sub-Saharan Africa, where tobacco use patterns and HIV prevalence differ greatly from other world regions. METHODS: Cross-sectional data from the Demographic Health Surveys and AIDS Indicator Surveys, representing 25 sub-Saharan African countries, were pooled for analysis (n = 286850). The association between cigarette smoking and HIV status was analyzed through hierarchical logistic regression models. This study also examined the relationship between smokeless tobacco (SLT) use and HIV status. RESULTS: Smoking prevalence was significantly higher among men who had HIV/AIDS than among men who did not (25.90% vs 16.09%; p < .0001), as was smoking prevalence among women who had HIV/AIDS compared with women who did not (1.15% vs 0.73%; p < .001). Multivariate logistic regression revealed that the odds of smoking among people who had HIV/AIDS was 1.12 times greater than among people who did not when adjusting for socioeconomic, demographic, and sexual risk factors (adjusted OR = 1.12, 95% CI = 1.04% to 1.21%; p < .001). Similarly, multivariate logistic regression revealed that HIV-positive individuals were 34% more likely to use SLT than HIV-negative individuals (adjusted OR = 1.34, 95% CI = 1.17% to 1.53%). CONCLUSION: Having HIV was associated with a greater likelihood of smoking cigarettes as well as with using SLT in sub-Saharan Africa. These tobacco use modalities were also associated with male sex and lower socioeconomic status. IMPLICATIONS: This study shows that in sub-Saharan Africa, as in more studied world regions, having HIV/AIDS is associated with a higher likelihood of smoking cigarettes when adjusting for demographic, socioeconomic, and sexual risk factors. This study also supports the literature stating that cigarette smoking is inversely associated with socioeconomic status, as evidenced by higher smoking prevalence among poorer individuals, less educated individuals, and manual and agricultural laborers.

State Prevalence and Ranks of Adolescent Substance Use: Implications for Cancer Prevention.

INTRODUCTION: This study statistically ranked states' performance on adolescent substance use related to cancer risk (past-month cigarette smoking, binge alcohol drinking, and marijuana use). METHODS: Data came from 69,200 adolescent participants (50 states and the District of Columbia) in the National Survey on Drug Use and Health (NSDUH) and 450,050 adolescent participants (47 states) in the Youth Risk Behavior Surveillance System (YRBSS). Adolescents were aged 14 to 17 years. For 2011-2015, we estimated and ranked states' prevalence of adolescent substance use. We calculated the ranks' 95% confidence intervals (CIs) using a Monte Carlo method with 100,000 simulations. Spearman correlations examined consistency of ranks. RESULTS: Across states, the prevalence of cigarette smoking was 4.5% to 14.3% in NSDUH and 4.7% to 18.5% in YRBSS. Utah had the lowest prevalence (NSDUH: rank = 51 [95% CI, 47-51]; YRBSS: rank = 47 [95% CI, 46-47]), and states' ranks across surveys were correlated (r = 0.66, P < .001). The prevalence of binge alcohol drinking was 5.9% to 14.3% (NSDUH) and 7.1% to 21.7% (YRBSS). Utah had the lowest prevalence (NSDUH: rank = 50 [95% CI, 40-51]; YRBSS: rank = 47 [95% CI, 47-47]), but ranks across surveys were weakly correlated (r = 0.38, P = .01). The prevalence of marijuana use was 6.3% to 18.7% (NSDUH) and 8.2% to 27.1% (YRBSS). Utah had the lowest prevalence of marijuana use (NSDUH: rank = 50 [95% CI = 33-51]; YRBSS: rank= 46 [95% CI, 46-46]), and ranks across surveys were correlated (r = 0.70, P < .001). Wide CIs for states ranked in the middle of each distribution obscured statistical differences among them. CONCLUSION: Variability emerged across adolescent substance use behaviors and surveys (perhaps because of administration differences). Most states showed statistically equivalent performance on adolescent substance use. Adolescents in all states would benefit from efforts to reduce substance use, to prevent against lifelong morbidity.

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

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

Comparing cancer screening estimates: Behavioral Risk Factor Surveillance System and National Health Interview Survey.

Cancer screening prevalence from the Behavioral Risk Factor Surveillance System (BRFSS), designed to provide state-level estimates, and the National Health Interview Survey (NHIS), designed to provide national estimates, are used to measure progress in cancer control. A detailed description of the extent to which recent cancer screening estimates vary by key demographic characteristics has not been previously described. We examined national prevalence estimates for recommended breast, cervical, and colorectal cancer screening using data from the 2012 and 2014 BRFSS and the 2010 and 2013 NHIS. Treating the NHIS estimates as the reference, direct differences (DD) were calculated by subtracting NHIS estimates from BRFSS estimates. Relative differences were computed by dividing the DD by the NHIS estimates. Two-sample t-tests (2-tails), were performed to test for statistically significant differences. BRFSS screening estimates were higher than those from NHIS for breast (78.4% versus 72.5%; DD=5.9%, p<0.0001); colorectal (65.5% versus 57.6%; DD=7.9%, p<0.0001); and cervical (83.4% versus 81.8%; DD=1.6%, p<0.0001) cancers. DDs were generally higher in racial/ethnic minorities than whites, in the least educated than most educated persons, and in uninsured than insured persons. For example, the colorectal cancer screening DD for whites was 7.3% compared to ≥8.9% for blacks and Hispanics. Despite higher prevalence estimates in BRFSS compared to NHIS, each survey has a unique and important role in providing information to track cancer screening utilization among various populations. Awareness of these differences and their potential causes is important when comparing the surveys and determining the best application for each data source.

Changes in Self-Reported Smokefree Workplace Policy Coverage Among Employed Adults-United States, 2003 and 2010-2011.

Introduction: The workplace is a major source of exposure to secondhand smoke from combustible tobacco products. Smokefree workplace policies protect nonsmoking workers from secondhand smoke and help workers who smoke quit. This study examined changes in self-reported smokefree workplace policy coverage among U.S. workers from 2003 to 2010-2011. Methods: Data came from the 2003 (n = 74,728) and 2010-2011 (n = 70,749) waves of the Tobacco Use Supplement to the Current Population Survey. Among employed adults working indoors, a smokefree workplace policy was defined as a self-reported policy at the respondent's workplace that did not allow smoking in work areas and public/common areas. Descriptive statistics were used to assess smokefree workplace policy coverage at two timepoints overall, by occupation, and by state. Results: The proportion of U.S. workers covered by smokefree workplace policies increased from 77.7% in 2003 to 82.8% in 2010-2011 (p < .00001). The proportion of workers reporting smokefree workplace policy coverage increased in 21 states (p < .001) and decreased in two states (p < .001) over this period. In 2010-2011, by occupation, this proportion ranged from 74.3% for blue collar workers to 84.9% for white collar workers; by state, it ranged from 63.3% in Nevada to 92.6% in Montana. Conclusions: From 2003 to 2010-2011, self-reported smokefree workplace policy coverage among indoor adult workers increased nationally, and occupational coverage disparities narrowed. However, coverage remained unchanged in half of states, and disparities persisted across occupations and states. Accelerated efforts are warranted to ensure that all workers are protected by smokefree workplace policies. Implications: This study assessed changes in the proportion of indoor workers reporting being covered by smokefree workplace policies from 2003 to 2010-2011 overall and by occupation and by state, using data from the Tobacco Use Supplement to the Current Population Survey. The findings indicate that smokefree workplace policy coverage among U.S. indoor workers has increased nationally, with occupational coverage disparities narrowing. However, coverage remained unchanged in half of states, and disparities persisted across occupations and states. Accelerated efforts are warranted to ensure that all workers are protected by smokefree workplace policies.