Recent Spatiotemporal Patterns of US Lung Cancer by Histologic Type.

BACKGROUND: After a period of increasing rates, lung cancer incidence is declining in the US for men and women. We investigated lung cancer rate patterns by gender, geographic location, and histologic subtype, and for total lung cancer (TLC), for the entire study period, and for 2000-2011 from 17 surveillance, epidemiology, and end results areas. METHODS: For each gender-histologic type combination, time trend plots and maps of age-adjusted rates are presented. Time trend significance was tested by joinpoint regression analysis. Spatial random effects models were applied to examine effects of sociodemographic factors, health insurance coverage, smoking, and physician density at the county level. Linked micromap plots illustrate patterns for important model predictors. RESULTS: Declining incidence trends occurred for TLC (p < 0.05, entire period). Squamous cell carcinoma trends increased for females only (p < 0.05). Small cell carcinoma trends declined overall, p < 0.05, but recently increased faster for females than males. Adenocarcinoma rates initially declined, but were significantly increasing by 2004, p < 0.05. Counties with higher current smoking and family poverty were strongly associated with higher risk for all gender-histologic types (p < 0.0001, for both variables). County socioeconomic status was associated with higher risk for all lung cancer subtypes for females, p < 0.02. Counties with more diagnostic radiologists were associated with higher TLC rates (p < 0.03); counties with greater primary care physician access were associated with lower TLC rates (p < 0.03). TLC incidence rates were higher in eastern and southern states than western areas. Male rates were higher than female rates along the West Coast. Males and females had similar small cell rate patterns, with higher rates in the Midwest and southeast. Squamous cell carcinoma and adenocarcinoma rate patterns were similar to TLC patterns, except for relatively higher female adenocarcinoma rates in the northeast and northwest. CONCLUSION: Geographic patterns and declining time trends for incident lung cancer are consistent with previous mortality patterns. Male-female time trend and geographic pattern differences occur by histologic type. Time trends remain significant, even after adjustment for significant covariates. Knowledge of the variation of lung cancer incidence by region and histologic type is useful for surveillance and for implementing lung cancer control efforts.

Confidence intervals for rate ratios between geographic units.

BACKGROUND: Ratios of age-adjusted rates between a set of geographic units and the overall area are of interest to the general public and to policy stakeholders. These ratios are correlated due to two reasons-the first being that each region is a component of the overall area and hence there is an overlap between them; and the second is that there is spatial autocorrelation between the regions. Existing methods in calculating the confidence intervals of rate ratios take into account the first source of correlation. This paper incorporates spatial autocorrelation, along with the correlation due to area overlap, into the rate ratio variance and confidence interval calculations. RESULTS: The proposed method divides the rate ratio variances into three components, representing no correlation, overlap correlation, and spatial autocorrelation, respectively. Results applied to simulated and real cancer mortality and incidence data show that with increasing strength and scales in spatial autocorrelation, the proposed method leads to substantial improvements over the existing method. If the data do not show spatial autocorrelation, the proposed method performs as well as the existing method. CONCLUSIONS: The calculations are relatively easy to implement, and we recommend using this new method to calculate rate ratio confidence intervals in all cases.

Urban sprawl, obesity, and cancer mortality in the United States: cross-sectional analysis and methodological challenges.

BACKGROUND: Urban sprawl has the potential to influence cancer mortality via direct and indirect effects on obesity, access to health services, physical activity, transportation choices and other correlates of sprawl and urbanization. METHODS: This paper presents a cross-sectional analysis of associations between urban sprawl and cancer mortality in urban and suburban counties of the United States. This ecological analysis was designed to examine whether urban sprawl is associated with total and obesity-related cancer mortality and to what extent these associations differed in different regions of the US. A major focus of our analyses was to adequately account for spatial heterogeneity in mortality. Therefore, we fit a series of regression models, stratified by gender, successively testing for the presence of spatial heterogeneity. Our resulting models included county level variables related to race, smoking, obesity, access to health services, insurance status, socioeconomic position, and broad geographic region as well as a measure of urban sprawl and several interactions. Our most complex models also included random effects to account for any county-level spatial autocorrelation that remained unexplained by these variables. RESULTS: Total cancer mortality rates were higher in less sprawling areas and contrary to our initial hypothesis; this was also true of obesity related cancers in six of seven U.S. regions (census divisions) where there were statistically significant associations between the sprawl index and mortality. We also found significant interactions (p < 0.05) between region and urban sprawl for total and obesity related cancer mortality in both sexes. Thus, the association between urban sprawl and cancer mortality differs in different regions of the US. CONCLUSIONS: Despite higher levels of obesity in more sprawling counties in the US, mortality from obesity related cancer was not greater in such counties. Identification of disparities in cancer mortality within and between geographic regions is an ongoing public health challenge and an opportunity for further analytical work identifying potential causes of these disparities. Future analyses of urban sprawl and health outcomes should consider exploring regional and international variation in associations between sprawl and health.

Predicting US- and state-level cancer counts for the current calendar year: Part I: evaluation of temporal projection methods for mortality.

BACKGROUND: A study was undertaken to evaluate the temporal projection methods that are applied by the American Cancer Society to predict 4-year-ahead projections. METHODS: Cancer mortality data recorded in each year from 1969 through 2007 for the United States overall and for each state from the National Center for Health Statistics was obtained. Based on the mortality data through 2000, 2001, 2002, and 2003, Projections were made 4 years ahead to estimate the expected number of cancer deaths in 2004, 2005, 2006, 2007, respectively, in the United States and in each state, using 5 projection methods. These predictive estimates were compared to the observed number of deaths that occurred for all cancers combined and 47 cancer sites at the national level, and 21 cancer sites at the state level. RESULTS: Among the models that were compared, the joinpoint regression model with modified Bayesian information criterion selection produced estimates that are closest to the actual number of deaths. Overall, results show the 4-year-ahead projection has larger error than 3-year-ahead projection of death counts when the same method is used. However, 4-year-ahead projection from the new method performed better than the 3-year-ahead projection from the current state-space method. CONCLUSIONS: The Joinpoint method with modified Bayesian information criterion model has the smallest error of all the models considered for 4-year-ahead projection of cancer deaths to the current year for the United States overall and for each state. This method will be used by the American Cancer Society to project the number of cancer deaths starting in 2012.

Predicting US- and state-level cancer counts for the current calendar year: Part II: evaluation of spatiotemporal projection methods for incidence.

BACKGROUND: The current study was undertaken to evaluate the spatiotemporal projection models applied by the American Cancer Society to predict the number of new cancer cases. METHODS: Adaptations of a model that has been used since 2007 were evaluated. Modeling is conducted in 3 steps. In step I, ecologic predictors of spatiotemporal variation are used to estimate age-specific incidence counts for every county in the country, providing an estimate even in those areas that are missing data for specific years. Step II adjusts the step I estimates for reporting delays. In step III, the delay-adjusted predictions are projected 4 years ahead to the current calendar year. Adaptations of the original model include updating covariates and evaluating alternative projection methods. Residual analysis and evaluation of 5 temporal projection methods were conducted. RESULTS: The differences between the spatiotemporal model-estimated case counts and the observed case counts for 2007 were < 1%. After delays in reporting of cases were considered, the difference was 2.5% for women and 3.3% for men. Residual analysis indicated no significant pattern that suggested the need for additional covariates. The vector autoregressive model was identified as the best temporal projection method. CONCLUSIONS: The current spatiotemporal prediction model is adequate to provide reasonable estimates of case counts. To project the estimated case counts ahead 4 years, the vector autoregressive model is recommended to be the best temporal projection method for producing estimates closest to the observed case counts.

Covariate adjusted weighted normal spatial scan statistics with applications to study geographic clustering of obesity and lung cancer mortality in the United States.

In the field of cluster detection, a weighted normal model-based scan statistic was recently developed to analyze regional continuous data and to evaluate the clustering pattern of pre-defined cells (such as state, county, tract, school, hospital) that include many individuals. The continuous measures of interest are, for example, the survival rate, mortality rate, length of physical activity, or the obesity measure, namely, body mass index, at the cell level with an uncertainty measure for each cell. In this paper, we extend the method to search for clusters of the cells after adjusting for single/multiple categorical/continuous covariates. We apply the proposed method to 1999-2003 obesity data in the United States (US) collected by CDC's Behavioral Risk Factor Surveillance System with adjustment for age and race, and to 1999-2003 lung cancer age-adjusted mortality data by gender in the United States from the Surveillance Epidemiology and End Results (SEER Program) with adjustment for smoking and income.

Associations between street connectivity and active transportation.

BACKGROUND: Past studies of associations between measures of the built environment, particularly street connectivity, and active transportation (AT) or leisure walking/bicycling have largely failed to account for spatial autocorrelation of connectivity variables and have seldom examined both the propensity for AT and its duration in a coherent fashion. Such efforts could improve our understanding of the spatial and behavioral aspects of AT. We analyzed spatially identified data from Los Angeles and San Diego Counties collected as part of the 2001 California Health Interview Survey. RESULTS: Principal components analysis indicated that ~85% of the variance in nine measures of street connectivity are accounted for by two components representing buffers with short blocks and dense nodes (PRIN1) or buffers with longer blocks that still maintain a grid like structure (PRIN2). PRIN1 and PRIN2 were positively associated with active transportation (AT) after adjustment for diverse demographic and health related variables. Propensity and duration of AT were correlated in both Los Angeles (r = 0.14) and San Diego (r = 0.49) at the zip code level. Multivariate analysis could account for the correlation between the two outcomes.After controlling for demography, measures of the built environment and other factors, no spatial autocorrelation remained for propensity to report AT (i.e., report of AT appeared to be independent among neighborhood residents). However, very localized correlation was evident in duration of AT, particularly in San Diego, where the variance of duration, after accounting for spatial autocorrelation, was 5% smaller within small neighborhoods (approximately 0.01 square latitude/longitude degrees = 0.6 mile diameter) compared to within larger zip code areas. Thus a finer spatial scale of analysis seems to be more appropriate for explaining variation in connectivity and AT. CONCLUSIONS: Joint analysis of the propensity and duration of AT behavior and an explicitly geographic approach can strengthen studies of the built environment and physical activity (PA), specifically AT. More rigorous analytical work on cross-sectional data, such as in the present study, continues to support the need for experimental and longitudinal study designs including the analysis of natural experiments to evaluate the utility of environmental interventions aimed at increasing PA.

Identifying clusters of active transportation using spatial scan statistics.

BACKGROUND: There is an intense interest in the possibility that neighborhood characteristics influence active transportation such as walking or biking. The purpose of this paper is to illustrate how a spatial cluster identification method can evaluate the geographic variation of active transportation and identify neighborhoods with unusually high/low levels of active transportation. METHODS: Self-reported walking/biking prevalence, demographic characteristics, street connectivity variables, and neighborhood socioeconomic data were collected from respondents to the 2001 California Health Interview Survey (CHIS; N=10,688) in Los Angeles County (LAC) and San Diego County (SDC). Spatial scan statistics were used to identify clusters of high or low prevalence (with and without age-adjustment) and the quantity of time spent walking and biking. The data, a subset from the 2001 CHIS, were analyzed in 2007-2008. RESULTS: Geographic clusters of significantly high or low prevalence of walking and biking were detected in LAC and SDC. Structural variables such as street connectivity and shorter block lengths are consistently associated with higher levels of active transportation, but associations between active transportation and socioeconomic variables at the individual and neighborhood levels are mixed. Only one cluster with less time spent walking and biking among walkers/bikers was detected in LAC, and this was of borderline significance. Age-adjustment affects the clustering pattern of walking/biking prevalence in LAC, but not in SDC. CONCLUSIONS: The use of spatial scan statistics to identify significant clustering of health behaviors such as active transportation adds to the more traditional regression analysis that examines associations between behavior and environmental factors by identifying specific geographic areas with unusual levels of the behavior independent of predefined administrative units.

Access to mammography screening in a large urban population: a multi-level analysis.

OBJECTIVE: To understand area-based sociodemographics, physician and medical practice characteristics, and community indicators associated with mammography use in Los Angeles County. An earlier multi-level analysis by Gumpertz et al. found that distance to the nearest mammography facility helped explain the higher proportion of Latinas diagnosed with late stage breast cancer compared with non-Latina Whites in Los Angeles County. Our study examined whether Latinas also have lower rates of mammography use. METHODS: We used a multi-level spatial modeling approach to examine individual and community level associations with mammography use among a diverse group of women aged 40-84 years in Los Angeles County. To build our multi-level spatial data set, we integrated five data sources: (1) 2001 California Health Interview Survey (CHIS) data, (2) 2001 Food and Drug Administration (FDA) certified mammography facility data, (3) 2003 LA Transit Authority data, (4) 2000 US Decennial Census data, and (5) 2001 Community Tracking Study (CTS) Physician's Survey data. RESULTS: Our study confirmed for Los Angeles County many associations for mammography use found in other locations. An unexpected finding was that women with limited English proficiency (predominantly Latina) were significantly more likely to have had a recent mammogram than English-proficient women. We also found that, after controlling for other factors, mammography use was higher in neighborhoods with a greater density of mammography facilities. CONCLUSION: Women with limited English proficiency were especially likely to report recent mammography in Los Angeles. This unexpected finding suggests that the intensive Spanish-language outreach program conducted by the Every Woman Counts (EWC) Program in low-income Latina communities in Los Angeles has been effective. Our study highlights the success of this targeted community-based outreach conducted between 1999 and 2001. These are the same populations that Gumpertz et al. identified as needing intervention. It would be useful to conduct another study of late-stage diagnosis in Los Angeles County to ascertain whether increased rates of mammography have also led to less late-stage diagnosis among Latinas in the neighborhoods where they are concentrated in Los Angeles.

Evaluating spatial methods for investigating global clustering and cluster detection of cancer cases.

There have been articles on comparing methods for global clustering evaluation and cluster detection in disease surveillance, but power and sample size (SS) requirements have not been explored for spatially correlated data in this area. We are developing such requirements for tests of spatial clustering and cluster detection for regional cancer cases. We compared global clustering methods including Moran's I, Tango's and Besag-Newell's R statistics, and cluster detection methods including circular and elliptic spatial scan statistics (SaTScan), flexibly shaped spatial scan statistics, Turnbull's cluster evaluation permutation procedure, local indicators of spatial association, and upper-level set scan statistics. We identified eight geographic patterns that are representative of patterns of mortality due to various types of cancer in the U.S. from 1998 to 2002. We then evaluated the selected spatial methods based on state- and county-level data simulated from these different spatial patterns in terms of geographic locations and relative risks, and varying SSs using the 2000 population in each county. The comparison provides insight into the performance of the spatial methods when applied to varying cancer count data in terms of power and precision of cluster detection.

A new method to evaluate the completeness of case ascertainment by a cancer registry.

BACKGROUND: Epidemiologic research into cancer and subsequent decision making to reduce the cancer burden in the population are dependent on the quality of available data. The more reliable the data, the more confident we can be that the decisions made would have the desired effect in the population. The North American Association of Central Cancer Registries (NAACCR) certifies population-based cancer registries, ensuring uniformity of data quality. An important assessment of registry quality is provided by the index of completeness of cancer case ascertainment. NAACCR currently computes this index assuming that the ratio of cancer incidence rates to cancer mortality rates is constant across geographic areas within cancer site, gender, and race groups. NAACCR does not incorporate the variability of this index into the certification process. METHODS: We propose an improved method for calculating this index based on a statistical model developed at the National Cancer Institute to predict expected incidence using demographic and lifestyle data. We calculate the variance of our index using statistical approximation. RESULTS: We use the incidence model to predict the number of new incident cases in each registry area, based on all available registry data. Then we adjust the registry-specific expected numbers for reporting delay and data corrections. The proposed completeness index is the ratio of the observed number to the adjusted prediction for each registry. We calculate the variance of the new index and propose a simple method of incorporating this variability into the certification process. CONCLUSIONS: Better modeling reduces the number of registries with unrealistically high completeness indices. We provide a fuller picture of registry performance by incorporating variability into the certification process.

A new method of estimating United States and state-level cancer incidence counts for the current calendar year.

The American Cancer Society (ACS) has published the estimated number of new cancer cases and deaths in the current year for the United States that are commonly used by cancer control planners and the media. The methods used to produce these estimates have changed over the years as data (incidence) and statistical models improved. In this paper we present a new method that uses statistical models of cancer incidence that incorporate potential predictors of spatial and temporal variation of cancer occurrence and that account for delay in case reporting and then projects these estimated numbers of cases ahead 4 years using a piecewise linear (joinpoint) regression method. Based on evidence presented here that the new method produces more accurate estimates of the number of new cancer cases for years and areas for which data are available for comparison, the ACS has elected to use it to estimate the number of new cancer cases in Cancer Facts & Figures 2007 and in Cancer Statistics, 2007.

Detection of spatial clusters: application to cancer survival as a continuous outcome.

In this article, we develop the first detailed illustration of the use of a cluster detection method using a spatial scan statistic based on an exponential survival model. We use this approach to study the spatial patterns of survival of patients with stage III or stage IV colorectal cancer or with stage I/II, stage III, or stage IV lung cancer in the State of California and the County of Los Angeles (LA) diagnosed during 1988 through 2002. We present the location of the detected clusters of short survival or long survival and compute nonparametric estimates of survival inside and outside of those detected clusters confirming the survival pattern detected by the spatial scan statistic in both areas. In LA County, we investigate the possible relationship between the cluster locations and race, sex, and histology using nonparametric methods, and we compare socioeconomic factors such as education, employment, income, and health insurance inside and outside of the detected clusters. Finally, we evaluate the effect of related covariates on statistically significant long and short survival clusters detected in LA County using logistic regression models. This article illustrates a new way to understand survival patterns that may point to health disparities in terms of diagnosis and treatment patterns.

U.S. congressional district cancer death rates.

BACKGROUND: Geographic patterns of cancer death rates in the U.S. have customarily been presented by county or aggregated into state economic or health service areas. Herein, we present the geographic patterns of cancer death rates in the U.S. by congressional district. Many congressional districts do not follow state or county boundaries. However, counties are the smallest geographical units for which death rates are available. Thus, a method based on the hierarchical relationship of census geographic units was developed to estimate age-adjusted death rates for congressional districts using data obtained at county level. These rates may be useful in communicating to legislators and policy makers about the cancer burden and potential impact of cancer control in their jurisdictions. RESULTS: Mortality data were obtained from the National Center for Health Statistics (NCHS) for 1990-2001 for 50 states, the District of Columbia, and all counties. We computed annual average age-adjusted death rates for all cancer sites combined, the four major cancers (lung and bronchus, prostate, female breast, and colorectal cancer) and cervical cancer. Cancer death rates varied widely across congressional districts for all cancer sites combined, for the four major cancers, and for cervical cancer. When examined at the national level, broad patterns of mortality by sex, race and region were generally similar with those previously observed based on county and state economic area. CONCLUSION: We developed a method to generate cancer death rates by congressional district using county-level mortality data. Characterizing the cancer burden by congressional district may be useful in promoting cancer control and prevention programs, and persuading legislators to enact new cancer control programs and/or strengthening existing ones. The method can be applied to state legislative districts and other analyses that involve data aggregation from different geographic units.

Spatial analysis of prostate cancer incidence and race in Virginia, 1990-1999.

BACKGROUND: Racial disparities exist in prostate cancer incidence. An important contributor to these disparities may be socioeconomic status. METHODS: Virginia Cancer Registry data, 1990-1999 (37,373 cases) were geocoded to the Census tract and county level. The annualized, age-adjusted incidence rates for African Americans and whites were calculated, and crude and smoothed maps of these rates were produced. Statistical tests for clustering of cases were conducted. Prostate cancer incidence was statistically modeled as a function of area-based measures of poverty, median household income, education, rural status, ratio of physicians to population in each county, percentage of men in each county obtaining prostate cancer screening with the prostate-specific antigen (PSA) test, and percent of households headed by females. RESULTS: Prostate cancer incidence was elevated in the eastern and central portions of the state. Statistical tests for clustering were highly significant (Tango's test, p<0.008; spatial scan statistic, p<0.001). Poverty and lower education were associated with a decreased incidence among whites but not African Americans. Median household income and urban status were positively associated with incidence for both populations. Among whites, increased percent of female heads of households and ratio of physicians per population were associated with increased incidence. Associations between predictor variables and prostate cancer incidence were seen only in the census tract level analyses. CONCLUSIONS: Overall, the findings support the argument that area measures of poverty and education do not explain the increased incidence of prostate cancer among African Americans. Other factors, such as dietary practices, may help explain racial disparities in prostate cancer incidence. Because of the large differences between tract and county level results, the time and expense of obtaining data geocoded to the tract level seems worthwhile.

Geographic bias related to geocoding in epidemiologic studies.

BACKGROUND: This article describes geographic bias in GIS analyses with unrepresentative data owing to missing geocodes, using as an example a spatial analysis of prostate cancer incidence among whites and African Americans in Virginia, 1990-1999. Statistical tests for clustering were performed and such clusters mapped. The patterns of missing census tract identifiers for the cases were examined by generalized linear regression models. RESULTS: The county of residency for all cases was known, and 26,338 (74%) of these cases were geocoded successfully to census tracts. Cluster maps showed patterns that appeared markedly different, depending upon whether one used all cases or those geocoded to the census tract. Multivariate regression analysis showed that, in the most rural counties (where the missing data were concentrated), the percent of a county's population over age 64 and with less than a high school education were both independently associated with a higher percent of missing geocodes. CONCLUSION: We found statistically significant pattern differences resulting from spatially non-random differences in geocoding completeness across Virginia. Appropriate interpretation of maps, therefore, requires an understanding of this phenomenon, which we call "cartographic confounding."

Annual report to the nation on the status of cancer, 1975-2002, featuring population-based trends in cancer treatment.

BACKGROUND: The American Cancer Society (ACS), the Centers for Disease Control and Prevention (CDC), the National Cancer Institute (NCI), and the North American Association of Central Cancer Registries (NAACCR) collaborate annually to provide information on cancer rates and trends in the United States. This year's report updates statistics on the 15 most common cancers in the five major racial/ethnic populations in the United States for 1992-2002 and features population-based trends in cancer treatment. METHODS: The NCI, the CDC, and the NAACCR provided information on cancer cases, and the CDC provided information on cancer deaths. Reported incidence and death rates were age-adjusted to the 2000 U.S. standard population, annual percent change in rates for fixed intervals was estimated by linear regression, and annual percent change in trends was estimated with joinpoint regression analysis. Population-based treatment data were derived from the Surveillance, Epidemiology, and End Results (SEER) Program registries, SEER-Medicare linked databases, and NCI Patterns of Care/Quality of Care studies. RESULTS: Among men, the incidence rates for all cancer sites combined were stable from 1995 through 2002. Among women, the incidence rates increased by 0.3% annually from 1987 through 2002. Death rates in men and women combined decreased by 1.1% annually from 1993 through 2002 for all cancer sites combined and also for many of the 15 most common cancers. Among women, lung cancer death rates increased from 1995 through 2002, but lung cancer incidence rates stabilized from 1998 through 2002. Although results of cancer treatment studies suggest that much of contemporary cancer treatment for selected cancers is consistent with evidence-based guidelines, they also point to geographic, racial, economic, and age-related disparities in cancer treatment. CONCLUSIONS: Cancer death rates for all cancer sites combined and for many common cancers have declined at the same time as the dissemination of guideline-based treatment into the community has increased, although this progress is not shared equally across all racial and ethnic populations. Data from population-based cancer registries, supplemented by linkage with administrative databases, are an important resource for monitoring the quality of cancer treatment. Use of this cancer surveillance system, along with new developments in medical informatics and electronic medical records, may facilitate monitoring of the translation of basic science and clinical advances to cancer prevention, detection, and uniformly high quality of care in all areas and populations of the United States.

Changing area socioeconomic patterns in U.S. cancer mortality, 1950-1998: Part I--All cancers among men.

BACKGROUND: Area socioeconomic deprivation indices are widely used to monitor health disparities in Europe. However, such indices have not been used in cancer surveillance in the United States. We developed an area socioeconomic index to examine area socioeconomic patterns in all-cancer mortality among U.S. men between 1950 and 1998. METHODS: Principal components analysis on 11 census variables was used to develop an area socioeconomic index that was then used to stratify all U.S. counties into one of five socioeconomic categories. The index was linked to 1950-1998 county mortality data to generate annual mortality rates for each area socioeconomic group. Joinpoint regression analysis was used to model mortality trends, and Poisson regression analysis was used to estimate socioeconomic gradients in mortality over time. RESULTS: Area socioeconomic patterns in U.S. male cancer mortality changed dramatically between 1950 and 1998. Throughout the 1950s and 1960s, there was a positive socioeconomic gradient, with higher cancer mortality rates in high area socioeconomic groups than in low area socioeconomic groups. For example, in 1950-1952, cancer mortality was 49% (95% confidence interval [CI] = 41% to 59%) greater in the highest area socioeconomic group than in the lowest. The positive gradient narrowed in the 1970s, and by the late 1980s, socioeconomic differences in cancer mortality began to reverse and widen. In 1997-1998, cancer mortality was 19% (95% CI = 11% to 28%) higher in the lowest area socioeconomic group than in the highest. Gradients were steeper for men aged 25-64 years than for men aged 65 years or older. CONCLUSIONS: Socioeconomic patterns in male cancer mortality have reversed over time in the United States. Area socioeconomic indices could serve as a powerful surveillance tool for monitoring health disparities in cancer outcomes.