Geographic and Demographic Disparities in Late-stage Breast and Colorectal Cancer Diagnoses Across the US.

PROBLEM: In 2009, breast cancer was the most common cancer in women, and colorectal cancer was the third most common cancer in both men and women. Currently, the majority of colorectal and almost 1/3 of breast cancers are diagnosed at an advanced stage in the US, which results in higher morbidity and mortality than would obtain with earlier detection. The incidence of late-stage cancer diagnoses varies considerably across the US, and few analyses have examined the entire US. PURPOSE: Using the newly available US Cancer Statistics database representing 98% of the US population, we perform multilevel analysis of the incidence of late-stage cancer diagnoses and translate the findings via bivariate mapping, answering questions related to both Why and Where demographic and geographic disparities in these diagnoses are observed. METHODS: To answer questions related to Why disparities are observed, we utilize a three-level, random-intercepts model including person-, local area-, and region- specific levels of influence. To answer questions related to Where disparities are observed, we generate county level robust predictions of late-stage cancer diagnosis rates and map them, contrasting counties ranked in the upper and lower quantiles of all county predicted rates. Bivariate maps are used to spatially translate the geographic variation among US counties in the distribution of both BC and CRC late-stage diagnoses. CONCLUSIONS: Empirical modeling results show demographic disparities, while the spatial translation of empirical results shows geographic disparities that may be quite useful for state cancer control planning. Late stage BC and CRC diagnosis rates are not spatially random, manifesting as place-specific patterns that compare counties in individual states to counties across all states. Providing a relative comparison that enables assessment of how results in one state compare with others, this paper is to be disseminated to all state cancer control and central cancer registry program officials.

Geographic disparities in late-stage cancer diagnosis: multilevel factors and spatial interactions.

In 2009 in the United States, breast cancer was the most common cancer in women, and colorectal cancer was the third most common cancer in both men and women. Currently, over 40% of these cancers are diagnosed at an advanced stage, which results in higher morbidity and mortality than would obtain with optimal cancer screening utilization. To provide information that might improve these cancer outcomes we use spatial analysis to answer questions related to both Why and Where disparities in late-stage cancer diagnoses are observed. In examining Why, we include state level characteristics reflecting characteristics of states' cancer control planning, insurance markets and managed care environments to help model the spatial heterogeneity from place to place. To answer questions related to Where disparities are observed, we generate county level predictions of late-stage cancer rates from a random-intercept multilevel model estimated on the population data from 11 pooled SEER Registries. The findings allow for comparisons across states that reveal logical starting points for a national effort to control cancer.

Mammography facilities are accessible, so why is utilization so low?

OBJECTIVE: This study examines new socio-ecological variables reflecting community context as predictors of mammography use. METHODS: The conceptual model is a hybrid of traditional health-behavioral and socio-ecological constructs with an emphasis on spatial interaction among women and their environments, differentiating between several levels of influence for community context. Multilevel probability models of mammography use are estimated. The study sample includes 70,129 women with traditional Medicare fee-for-service coverage for inpatient and outpatient services, drawn from the SEER-Medicare linked data. The study population lives in heterogeneous California, where mammography facilities are dense but utilization rates are low. RESULTS: Several contextual effects have large significant impacts on the probability of mammography use. Women living in areas with higher proportions of elderly in poverty are 33% less likely to use mammography. However, dually eligible women living in these poor areas are 2% more likely to use mammography than those without extra assistance living in these areas. Living in areas with higher commuter intensity, higher violent crime rates, greater land use mix (urbanicity), or more segregated Hispanic communities exhibit -14%, -1%, -6%, and -3% (lower) probability of use, respectively. Women living in segregated American Indian communities or in communities where more elderly women live alone exhibit 16% and 12% (higher) probability of use, respectively. Minority women living in more segregated communities by their minority are more likely to use mammography, suggesting social support, but this is significant for Native Americans only. Women with disability as their original reason for entitlement are found 40% more likely to use mammography when they reside in communities with high commuter intensity, suggesting greater ease of transportation for them in these environments. CONCLUSIONS: Socio-ecological variables reflecting community context are important predictors of mammography use in insured elderly populations, often with larger magnitudes of effect than personal characteristics such as race or ethnicity (-3% to -7%), age (-2%), recent address change (-7%), disability (-5%) or dual eligibility status (-1%). Better understanding of community factors can enhance cancer control efforts.

Heterogeneity in mammography use across the nation: separating evidence of disparities from the disproportionate effects of geography.

BACKGROUND: Mammography is essential for early detection of breast cancer and both reduced morbidity and increased survival among breast cancer victims. Utilization is lower than national guidelines, and evidence of a recent decline in mammography use has sparked concern. We demonstrate that regression models estimated over pooled samples of heterogeneous states may provide misleading information regarding predictors of health care utilization and that comprehensive cancer control efforts should focus on understanding these differences and underlying causal factors. Our study population includes all women over age 64 with breast cancer in the Surveillance Epidemiology and End Results (SEER) cancer registries, linked to a nationally representative 5% reference sample of Medicare-eligible women located in 11 states that span all census regions and are heterogeneous in racial and ethnic mix. Combining women with and without cancer in the sample allows assessment of previous cancer diagnosis on propensity to use mammography. Our conceptual model recognizes the interplay between individual, social, cultural, and physical environments along the pathways to health care utilization, while delineating local and more distant levels of influence among contextual variables. In regression modeling, we assess individual-level effects, direct effects of contextual factors, and interaction effects between individual and contextual factors. RESULTS: Pooling all women across states leads to quite different conclusions than state-specific models. Commuter intensity, community acculturation, and community elderly impoverishment have significant direct impacts on mammography use which vary across states. Minorities living in isolated enclaves with others of the same race/ethnicity may be either advantaged or disadvantaged, depending upon the place studied. CONCLUSION: Careful analysis of place-specific context is essential for understanding differences across communities stemming from different causal factors. Optimal policy interventions to change behavior (improve screening rates) will be as heterogeneous as local community characteristics, so no "one size fits all" policy can improve population health. Probability modeling with correction for clustering of individuals within multilevel contexts can reveal important differences from place to place and identify key factors to inform targeting of specific communities for further study.

How sensitive are multilevel regression findings to defined area of context?: a case study of mammography use in California.

The authors develop a hybrid model of health care use that blends features of the traditional Aday-Andersen behavioral model with the socioecological modeling perspective. They use the model to conceptualize the various levels of influence expected from socioecological variables in individuals' mammography use decisions, build contextual variables from fine-grained data into four different types of geographic areas, and then use two- and three-level modeling of personal and area-level contextual factors to explain observed behavior. The central focus is on whether differentiating the conceptualized levels of influence seems to materially affect regression findings. The test could conceivably be confounded by the modifiable areal unit problem, but little evidence for this is found. Findings for California women suggest that distinctions do matter in how the levels of influence are defined for local neighborhood contextual factors. Studies using only county-level contextual factors will miss some meaningful associations related to interpersonal/proximate-level factors.