Evaluating the Effects of Food Deserts and Food Swamps in an Urban Burn Patient Population.

Poverty is a known risk factor for burn injury and is associated with residency in food deserts and food swamps. Our aim was to determine the prevalence of residency in food deserts and food swamps and to investigate the relationship between food environment, comorbidities, and wound healing in burn patients. We performed a retrospective chart review of all burn patients age ≥ 18 seen in the emergency department or admitted to the burn service at an ABA-verified urban academic center between January 2016 and January 2022. Patient GeoIDs were used to classify residency in food deserts and food swamps and comorbidities and demographics were recorded. A subset of patients with less than 20% total body surface area burns who underwent single-operation split-thickness skin grafting was identified for wound healing analysis. A total of 3,063 patients were included, with 206 in the heal-time analysis. 2,490 (81.3%) lived in food swamps and 96 (3.1%) lived in food deserts. Diabetes, hypertension, and tobacco smoking were more prevalent in food swamps than food deserts or good access areas. While there was no significant effect of food environment on wound healing, diabetes was associated with longer heal times. Most burn patients reside in food swamps, which are associated with a higher prevalence of hypertension, diabetes, and smoking. Food environment was not significantly associated with wound healing. Not having diabetes was associated with a shorter time to wound healing.

Factors contributing to differences in cervical cancer screening in rural and urban community health centers.

INTRODUCTION: Community health centers (CHCs) provide historically marginalized populations with primary care, including cancer screening. Previous studies have reported that women living in rural areas are less likely to be up to date with cervical cancer screening than women living in urban areas. However, little is known about rural-urban differences in cervical cancer screening in CHCs and the contributing factors, and whether such differences changed during the COVID-19 pandemic. METHODS: Using 8-year pooled Uniform Data System (2014-2021) data and Oaxaca-Blinder decomposition, the extent to which CHC- and catchment area-level characteristics explained rural-urban differences in up-to-date cervical cancer screening was estimated. RESULTS: Up-to-date cervical cancer screening was lower in rural CHCs than urban CHCs (38.2% vs 43.0% during 2014-2019), and this difference increased during the pandemic (43.5% vs 49.0%). The rural-urban difference in cervical cancer screening in 2014-2019 was mostly explained by differences in CHC-level proportions of patients with limited English proficiency (55.9%) or income below the poverty level (12.3%) and females aged 21 to 64 years (9.8%), and catchment area-level's unemployment (3.4%) and primary care physician density (3.2%). However, Medicaid (-48.5%) or no insurance (-19.6%) counterbalanced the differences between rural-urban CHCs. The contribution of these factors to rural-urban differences in cervical cancer screening generally increased in 2020-2021. CONCLUSIONS: Rural-urban differences in cervical cancer screening were mostly explained by multiple CHC-level and catchment area-level characteristics. The findings call for tailored interventions, such as providing resources and language services, to improve cancer screening utilization among uninsured, Medicaid, and patients with limited English proficiency in rural CHCs.

American Cancer Society's report on the status of cancer disparities in the United States, 2023.

In 2021, the American Cancer Society published its first biennial report on the status of cancer disparities in the United States. In this second report, the authors provide updated data on racial, ethnic, socioeconomic (educational attainment as a marker), and geographic (metropolitan status) disparities in cancer occurrence and outcomes and contributing factors to these disparities in the country. The authors also review programs that have reduced cancer disparities and provide policy recommendations to further mitigate these inequalities. There are substantial variations in risk factors, stage at diagnosis, receipt of care, survival, and mortality for many cancers by race/ethnicity, educational attainment, and metropolitan status. During 2016 through 2020, Black and American Indian/Alaska Native people continued to bear a disproportionately higher burden of cancer deaths, both overall and from major cancers. By educational attainment, overall cancer mortality rates were about 1.6-2.8 times higher in individuals with ≤12 years of education than in those with ≥16 years of education among Black and White men and women. These disparities by educational attainment within each race were considerably larger than the Black-White disparities in overall cancer mortality within each educational attainment, ranging from 1.03 to 1.5 times higher among Black people, suggesting a major role for socioeconomic status disparities in racial disparities in cancer mortality given the disproportionally larger representation of Black people in lower socioeconomic status groups. Of note, the largest Black-White disparities in overall cancer mortality were among those who had ≥16 years of education. By area of residence, mortality from all cancer and from leading causes of cancer death were substantially higher in nonmetropolitan areas than in large metropolitan areas. For colorectal cancer, for example, mortality rates in nonmetropolitan areas versus large metropolitan areas were 23% higher among males and 21% higher among females. By age group, the racial and geographic disparities in cancer mortality were greater among individuals younger than 65 years than among those aged 65 years and older. Many of the observed racial, socioeconomic, and geographic disparities in cancer mortality align with disparities in exposure to risk factors and access to cancer prevention, early detection, and treatment, which are largely rooted in fundamental inequities in social determinants of health. Equitable policies at all levels of government, broad interdisciplinary engagement to address these inequities, and equitable implementation of evidence-based interventions, such as increasing health insurance coverage, are needed to reduce cancer disparities.

Defining aggressive prostate cancer: a geospatial perspective.

BACKGROUND: Spatial analysis can identify communities where men are at risk for aggressive prostate cancer (PCan) and need intervention. However, there are several definitions for aggressive PCan. In this study, we evaluate geospatial patterns of 3 different aggressive PCan definitions in relation to PCan-specific mortality and provide methodologic and practical insights into how each definition may affect intervention targets. METHODS: Using the Pennsylvania State Cancer Registry data (2005-2015), we used 3 definitions to assign "aggressive" status to patients diagnosed with PCan. Definition one (D1, recently recommended as the primary definition, given high correlation with PCan death) was based on staging criteria T4/N1/M1 or Gleason score ≥ 8. Definition two (D2, most frequently-used definition in geospatial studies) included distant SEER summary stage. Definition three (D3) included Gleason score ≥ 7 only. Using Bayesian spatial models, we identified geographic clusters of elevated odds ratios for aggressive PCan (binomial model) for each definition and compared overlap between those clusters to clusters of elevated hazard ratios for PCan-specific mortality (Cox regression). RESULTS: The number of "aggressive" PCan cases varied by definition, and influenced quantity, location, and extent/size of geographic clusters in binomial models. While spatial patterns overlapped across all three definitions, using D2 in binomial models provided results most akin to PCan-specific mortality clusters as identified through Cox regression. This approach resulted in fewer clusters for targeted intervention and less sensitive to missing data compared to definitions that rely on clinical TNM staging. CONCLUSIONS: Using D2, based on distant SEER summary stage, in future research may facilitate consistency and allow for standardized comparison across geospatial studies.

Ethnic enclaves and colon cancer stage at diagnosis among New Jersey Hispanics.

Ethnic enclaves are neighborhoods with high concentrations of individuals of the same ethnic origin. Researchers have hypothesized that residence in ethnic enclaves may contribute to cancer outcomes through detrimental or protective pathways. A limitation of previous work, however, is their cross-sectional approach whereby an individual's residence at the time of diagnosis was used to capture residence in an ethnic enclave at a single point in time. This study addresses this limitation by adopting a longitudinal approach to investigating the association between the duration of residence in an ethnic enclave and the colon cancer (CC) stage at diagnosis. Colon cancer incidence cases diagnosed between 2006 and 2014, for Hispanics aged 18 years and older from the New Jersey State Cancer Registry (NJSCR) were linked to residential histories obtained from a commercial database LexisNexis, Inc. We examined associations between residence in an enclave and stage at diagnosis using binary and multinomial logistic regression, adjusted for age, sex, primary payer, and marital status. Among the 1076 Hispanics diagnosed with invasive colon cancer in New Jersey from 2006 to 2014, 48.4% lived in a Hispanic enclave at the time of diagnosis. Over the ten years preceding CC diagnosis, 32.6% lived in an enclave for the entire period. We found that Hispanics living in an ethnic enclave at diagnosis had significantly lower odds of distant-stage CC than Hispanics not living in an enclave at the time of diagnosis. Additionally, we found a significant association between living in an enclave for an extended period (e.g., over ten years) and lower odds of being diagnosed with distant stage CC. Integrating residential histories opens research possibilities to examine how minorities' residential mobility and residence in enclaves affect cancer diagnosis over time.

Progress in reducing cancer mortality in the United States by congressional district, 1996-2003 to 2012-2020.

BACKGROUND: United States cancer death rates have been steadily declining since the early 1990s, but information on disparities in progress against cancer mortality across congressional districts is lacking. This study examined trends in cancer death rates, overall and for lung, colorectal, female breast, and prostate cancer by congressional district. METHODS: County level cancer death counts and population data from the National Center for Health Statistics were used to estimate relative change in age-standardized cancer death rates from 1996-2003 to 2012-2020 by sex and congressional district. RESULTS: From 1996-2003 to 2012-2020, overall cancer death rates declined in every congressional district, with most congressional districts showing a 20%-45% decline among males and a 10%-40% decline among females. In general, the smallest percent of relative declines were found in the Midwest and Appalachia, whereas the largest declines were found in the South along the East Coast and the southern border. As a result, the highest cancer death rates generally shifted from congressional districts across the South in 1996-2003 to districts in the Midwest and central divisions of the South (including Appalachia) in 2012-2020. Death rates for lung, colorectal, female breast, and prostate cancers also declined in almost all congressional districts, although with some variation in relative changes and geographical patterns. CONCLUSIONS: Progress in reducing cancer death rates during the past 25 years considerably vary by congressional district, underscoring the need for strengthening existing and implementing new public health policies for broad and equitable application of proven interventions such as raising tax on tobacco and Medicaid expansion.

State Variation in Racial and Ethnic Disparities in Incidence of Triple-Negative Breast Cancer Among US Women.

Importance: There are few data on state variation in racial and ethnic disparities in incidence of triple-negative breast cancer (TNBC) in the US, limiting the ability to inform state-level health policy developments toward breast cancer equity. Objective: To quantify between and within racial and ethnic disparities in TNBC incidence rates (IRs) among US women across states. Design, Setting, and Participants: This cohort study using population-based cancer registry data included data for all women with TNBC diagnosed from January 1, 2015, to December 31, 2019, identified in the US Cancer Statistics Public Use Research Database. Data were analyzed from July through November 2022. Exposures: State and race and ethnicity (Hispanic, non-Hispanic American Indian or Alaska Native, non-Hispanic Asian or Pacific Islander, non-Hispanic Black, or non-Hispanic White) abstracted from medical records. Main Outcomes and Measures: The main outcomes were diagnosis of TNBC, age-standardized IR per 100 000 women, state-specific incidence rate ratios (IRRs) using the rate among White women in each state as a reference for between-population disparities, and state-specific IRRs using the race and ethnicity-specific national rate as a reference for within-population disparities. Results: The study included data for 133 579 women; 768 (0.6%) were American Indian or Alaska Native; 4969 (3.7%), Asian or Pacific Islander; 28 710 (21.5%), Black; 12 937 (9.7%), Hispanic; and 86 195 (64.5%), White. The TNBC IR was highest among Black women (25.2 per 100 000 women), followed by White (12.9 per 100 000 women), American Indian or Alaska Native (11.2 per 100 000 women), Hispanic (11.1 per 100 000 women), and Asian or Pacific Islander (9.0 per 100 000 women) women. Racial and ethnic group-specific and state-specific rates substantially varied, ranging from less than 7 per 100 000 women among Asian or Pacific Islander women in Oregon and Pennsylvania to greater than 29 per 100 000 women among Black women in Delaware, Missouri, Louisiana, and Mississippi. Compared with White women, IRRs were statistically significantly higher in 38 of 38 states among Black women, ranging from 1.38 (95% CI, 1.10-1.70; IR, 17.4 per 100 000 women) in Colorado to 2.32 (95% CI, 1.90-2.81; IR, 32.0 per 100 000 women) in Delaware; lower in 22 of 22 states among Asian or Pacific Islander women, varying from 0.50 (95% CI, 0.34-0.70; IR, 5.7 per 100 000 women) in Oregon to 0.82 (95% CI, 0.75-0.90; IR, 10.5 per 100 000 women) in New York; and did not differ among Hispanic and American Indian or Alaska Native women in 22 of 35 states and 5 of 8 states, respectively. State variations within each racial and ethnic population were smaller but still substantial. For example, among White women, compared with the national rate, IRRs varied from 0.72 (95% CI, 0.66-0.78; IR, 9.2 per 100 000 women) in Utah to 1.18 (95% CI, 1.11-1.25; IR, 15.2 per 100 000 women) in Iowa, 1.15 (95% CI, 1.07-1.24; IR, 14.8 per 100 000 women) in Mississippi, and 1.15 (95% CI, 1.07-1.24; IR, 14.8 per 100 000 women) in West Virginia. Conclusions and Relevance: In this cohort study, there were substantial state variations in racial and ethnic disparities in TNBC incidence, with Black women in Delaware, Missouri, Louisiana, and Mississippi having the highest rates among all states and racial and ethnic populations. The findings suggest that more research is needed to identify factors contributing to the substantial geographic variations in racial and ethnic disparities in TNBC incidence to develop effective preventive measures and that social determinants of health contribute to the geographic disparities in TNBC risk.

Geographic diffusion of digital mammography in the United States.

BACKGROUND: Examining temporal and spatial diffusion of a new technology, such as digital mammography, can provide important insights into potential disparities associated with access to new medical technologies and how quickly these technologies are adopted. Although digital mammography is currently a standard technology in the United States for breast cancer screening, its adoption and geographic diffusion, as medical facilities transitioned from film to digital units, has not been explored well. METHODS: This study evaluated the geographic diffusion of digital mammography facilities from 2001 to 2014 in the contiguous United States (excluding Alaska and Hawaii) and estimated the geographic accessibility to this new technology for women aged ≥45 years at the census tract level within a 20-minute drivetime by population density, rural/urban residence, and race/ethnicity. The number of mammography units by technology type (film or digital) and density per 10,000 women were also summarized. RESULTS: The adoption of digital mammography advanced first in densely populated regions and last in remote rural areas. Overall, proportion of digital mammography units increased from 1.4% in 2001 to 94.6% in 2014, but since 2008, there was a decline in density of units from 2.31 per 10,000 women aged ≥45 years to 1.97 in 2014. In 2014, approximately 87% of women aged ≥45 years in the contiguous United States had accessibility to digital mammography, but this proportion was substantially lower for Native American women (67%) and rural residents (32%). CONCLUSION: Understanding the diffusion of and accessibility to digital mammography may help predict future medical technology diffusion and assess its role in geographic differences in cancer diagnosis and treatment.

Disparities in Cutaneous T-Cell Lymphoma Incidence by Race/Ethnicity and Area-Based Socioeconomic Status.

Cutaneous T-cell lymphoma (CTCL) is a rare type of extranodal non-Hodgkin lymphoma (NHL). This study uses population-based data from the New Jersey (NJ) State Cancer Registry to examine geographic variation in CTCL incidence and evaluates whether CTCL risk varies by race/ethnicity and census tract socioeconomic status (SES). The study included 1163 cases diagnosed in NJ between 2006 and 2014. Geographic variation and possible clustering of high CTCL rates were assessed using Bayesian geo-additive models. The associations between CTCL risk and race/ethnicity and census tract SES, measured as median household income, were examined using Poisson regression. CTCL incidence varied across NJ, but there were no statistically significant geographic clusters. After adjustment for age, sex, and race/ethnicity, the relative risk (RR) of CTCL was significantly higher (RR = 1.47, 95% confidence interval: 1.22-1.78) in the highest income quartile than in the lowest. The interactions between race/ethnicity and SES indicated that the income gradients by RR were evident in all groups. Compared to non-Hispanic White individuals in low-income tracts, CTCL risk was higher among non-Hispanic White individuals in high-income tracts and among non-Hispanic Black individuals in tracts of all income levels. Our findings suggest racial disparities and a strong socioeconomic gradient with higher CTCL risk among cases living in census tracts with higher income compared to those living in lower-income tracts.

Mortality by Education Before and During the COVID-19 Pandemic, U.S., 2017-2020.

INTRODUCTION: Mortality disparities by SES, including education, have steadily increased in the U.S. over the past decades. This study examined whether these disparities overall and for 7 major causes of death were exacerbated in 2020, coincident with the emergence of the COVID-19 pandemic. METHODS: Using data on 7,123,254 U.S. deaths from 2017 to 2020, age-standardized death rates and mortality rate differences per 100,000 population and rate ratios comparing least with most educated were calculated by sex and race/ethnicity. RESULTS: All-cause death rates were approximately 2 times higher among adults with least than among those with most education. Disparities in all-cause mortality by educational attainment slightly increased from 2017 (rate ratio=1.97; 95% CI=1.95, 1.98; rate difference=739.9) to 2019 (rate ratio=2.04; 95% CI=2.03, 2.06; rate difference=761.3) and then greatly increased in 2020 overall (rate ratio=2.32; 95% CI=2.30, 2.33; rate difference=1,042.9) and when excluding COVID-19 deaths (rate ratio=2.27; 95% CI=2.25, 2.28; rate difference=912.3). Similar patterns occurred across race/ethnicity and sex, although Hispanic individuals had the greatest relative increase in disparities for all-cause mortality from 2019 (rate ratio=1.47; 95% CI=1.43, 1.51; rate difference=282.4) to 2020 overall (rate ratio=2.00; 95% CI=1.94, 2.06; rate difference=652.3) and when excluding COVID-19 deaths (rate ratio=1.84; 95% CI=1.79, 1.90; rate difference=458.7). Disparities in cause-specific mortality by education were generally stable from 2017 to 2019, followed by a considerable increase from 2019 to 2020 for heart disease, cancer, cerebrovascular disease, and unintentional injury. Among these causes of death, the relative increase in rate ratio from 2019 to 2020 was greatest for unintentional injury (24.8%; from 3.41 [95% CI=3.23, 3.60] to 4.26 [95% CI=3.99, 4.53]). CONCLUSIONS: Mortality disparities by education widened in the U.S. in 2020, during the COVID-19 pandemic. Further research is warranted to understand the reasons for these widened disparities.

Changes in geographic accessibility to mammography by state and rural-urban status, United States, 2006-2022.

Although mammography is widely available in the United States, differences in accessibility in rural and urban areas and changes over time are not well described. In this study, we estimated the number and proportion of women aged 45-84 years who had limited travel time-based geographic accessibility to mammography by urban-rural status and state in the contiguous United States in 2006 and 2022. The proportion of women aged 45-84 years with limited accessibility to mammography did not substantially change from 2006 (12.7%; representing 7.5 million women) to 2022 (12.2%; 8.2 million women). Accessibility to mammography varied by state, but in 10 states, more than 26% of the statewide female population aged 45-84 years had limited accessibility in 2022. This proportion was substantially higher in rural areas (50.6% overall; ≥50% in 28 states in 2022) than in urban areas (3.0% overall). Increasing accessibility to mammography could increase utilization of breast cancer screening and, potentially, improve breast cancer survival.

Disparities and Trends in Genitourinary Cancer Incidence and Mortality in the USA.

BACKGROUND: Previous studies have reported on incidence and mortality patterns for individual genitourinary cancers in the USA. However, these studies addressed individual cancer types rather than genitourinary cancers overall. OBJECTIVE: To comprehensively examine disparities and trends in the incidence and mortality for the four major genitourinary cancers (bladder, kidney, prostate, and testis) in the USA. DESIGN, SETTING, AND PARTICIPANTS: We obtained incidence data from the National Cancer Institute 22-registry Surveillance, Epidemiology and End Results (SEER) database and the US Cancer Statistics database (Centers for Disease Control and Prevention) and mortality data from the National Center for Health Statistics to examine cross-sectional and temporal trends in incidence and death rates stratified by sex, race/ethnicity, and county. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Age-adjusted incidence and death rates were calculated using SEER*Stat software. Temporal trends were analyzed using Joinpoint regression for a two-sided significance level of p < 0.05. RESULTS AND LIMITATIONS: Incidence and mortality rates for bladder and kidney cancers were two to four times higher for men than for women. Among non-Hispanic White individuals, the highest incidence rates were found in the Northeast for bladder cancer and in Appalachia for kidney cancer, whereas the highest death rates for prostate cancer were found in the West. Incidence rates increased for cancers of the kidney and testis and for advanced-stage prostate cancer in almost all racial/ethnic populations and for bladder cancer in the American Indian/Alaska Native population. Death rates increased for testicular cancer in the Hispanic population and stabilized for prostate cancer among White and Asian American/Pacific Islander men after a steady decline since the early 1990s. Study limitations include misclassification of race/ethnicity on medical records and death certificates. CONCLUSIONS: We found persistent sociodemographic disparities and unfavorable trends in incidence or mortality for all four major genitourinary cancers. Future studies should elucidate the reasons for these patterns. PATIENT SUMMARY: In the USA, rates of cancer cases are increasing for kidney, testis, and advanced-stage prostate cancers in the overall population, and for bladder cancer in the American Indian/Alaska Native population. Differences in the rates by sex and race/ethnicity remain.

Person-years of life lost and lost earnings from cigarette smoking-attributable cancer deaths, United States, 2019.

State-specific information on lost earnings due to smoking-attributable cancer deaths to inform and advocate for tobacco control policies is lacking. We estimated person-years of life lost (PYLL) and lost earnings due to cigarette smoking-attributable cancer deaths in the United States nationally and by state. Proportions and numbers of cigarette smoking-attributable cancer deaths and associated PYLL among individuals aged 25 to 79 years in 2019 were calculated and combined with annual median earnings to estimate lost earnings attributable to cigarette smoking. In 2019, estimated total PYLL and lost earnings associated with cigarette smoking-attributable cancer deaths in ages 25 to 79 years in the United States were 2 188 195 (95% CI, 2 148 707-2 231 538) PYLL and $20.9 billion ($20.0 billion-$21.7 billion), respectively. States with the highest overall age-standardized PYLL and lost earning rates generally were in the South and Midwest. The estimated rate per 100 000 population ranged from 352 (339-366) in Utah to 1337 (1310-1367) in West Virginia for PYLL and from $4.3 million ($3.5 million-$5.2 million) in Idaho to $14.8 million ($10.6 million-$20.7 million) in Missouri for lost earnings. If age-specific PYLL and lost earning rates in Utah had been achieved by all states, 58.2% (57.0%-59.5%) of the estimated total PYLL (1 274 178; 1 242 218-1 306 685 PYLL) and 50.5% (34.2%-62.4%) of lost earnings ($10.5 billion; $7.1 billion-$13.1 billion) in 2019 nationally would have been avoided. Lost earnings due to smoking-attributable cancer deaths are substantial in the United States and are highest in states with weaker tobacco control policies.

Systematic review of neighborhood socioeconomic indices studied across the cancer control continuum.

BACKGROUND: There is extensive interest in understanding how neighborhood socioeconomic status (nSES) may affect cancer incidence or survival. However, variability regarding items included and approaches used to form a composite nSES index presents challenges in summarizing overall associations with cancer. Given recent calls for standardized measures of neighborhood sociodemographic effects in cancer disparity research, the objective of this systematic review was to identify and compare existing nSES indices studied across the cancer continuum (incidence, screening, diagnosis, treatment, survival/mortality) and summarize associations by race/ethnicity and cancer site to inform future cancer disparity studies. METHODS: Using PRISMA guidelines, peer-reviewed articles published between 2010 and 2019 containing keywords related to nSES and cancer were identified in PubMed. RESULTS: Twenty-four nSES indices were identified from 75 studies. In general, findings indicated a significant association between nSES and cancer outcomes (n = 64/75 studies; 85.33%), with 42/64 (65.63%) adjusting for highly-correlated individual SES factors (e.g., education). However, the direction of association differed by cancer site, race/ethnicity, and nSES index. CONCLUSIONS: This review highlights several methodologic and conceptual issues surrounding nSES measurement and potential associations with cancer disparities. Recommendations pertaining to the selection of nSES measures are provided, which may help inform disparity-related disease processes and improve the identification of vulnerable populations in need of intervention.

Examining socio-spatial mobility patterns among colon cancer patients after diagnosis.

Given the growing number of cancer survivors, it is important to better understand socio-spatial mobility patterns of cancer patients after diagnosis that could have public health implications regarding post-diagnostic access to care for treatment and follow-up surveillance. In this exploratory study, residential histories from LexisNexis were linked to New Jersey colon cancer cases diagnosed from 2006 to 2011 to examine differences in socio-spatial mobility patterns after diagnosis by stage at cancer diagnosis, sex, and race/ethnicity. For the colon cancer cases, we summarized and compared the number of residences and changes in the residential census tract and neighborhood poverty after the diagnosis. We found only minor changes in neighborhood poverty among the cases during the follow-up period after diagnosis. During the follow-up period of up to 10 years after diagnosis, 67% of the patients did not move to a different residential census tract, and 10.8% moved from New Jersey to another state. Cases that moved to a different census tract changed after diagnosis were generally less wealthy than non-movers, but the destination of relocation varied by race/ethnicity and socioeconomic status. We also found a significant association between residential mobility and stage at diagnosis, whereby patients diagnosed with colon cancer at an early stage were more likely to be movers. This study contributes to understanding of the socio-spatial mobility patterns in colon cancer patients and may help to inform cancer research by summarizing the extent to which colon cancer patients move after diagnosis.

Geographic clustering of cutaneous T-cell lymphoma in New Jersey: an exploratory analysis using residential histories.

PURPOSE: Cutaneous T-cell lymphoma (CTCL) is a rare type of non-Hodgkin lymphoma. Previous studies have reported geographic clustering of CTCL based on the residence at the time of diagnosis. We explore geographic clustering of CTCL using both the residence at the time of diagnosis and past residences using data from the New Jersey State Cancer Registry. METHODS: CTCL cases (n = 1,163) diagnosed between 2006-2014 were matched to colon cancer controls (n = 17,049) on sex, age, race/ethnicity, and birth year. Jacquez's Q-Statistic was used to identify temporal clustering of cases compared to controls. Geographic clustering was assessed using the Bernoulli-based scan-statistic to compare cases to controls, and the Poisson-based scan-statisic to compare the observed number of cases to the number expected based on the general population. Significant clusters (p < 0.05) were mapped, and standard incidence ratios (SIR) reported. We adjusted for diagnosis year, sex, and age. RESULTS: The Q-statistic identified significant temporal clustering of cases based on past residences in the study area from 1992 to 2002. A cluster was detected in 1992 in Bergen County in northern New Jersey based on the Bernoulli (1992 SIR 1.84) and Poisson (1992 SIR 1.86) scan-statistics. Using the Poisson scan-statistic with the diagnosis location, we found evidence of an elevated risk in this same area, but the results were not statistically significant. CONCLUSION: There is evidence of geographic clustering of CTCL cases in New Jersey based on past residences. Additional studies are necessary to understand the possible reasons for the excess of CTCL cases living in this specific area some 8-14 years prior to diagnosis.

Measuring Neighborhood Landscapes: Associations between a Neighborhood's Landscape Characteristics and Colon Cancer Survival.

Landscape characteristics have been shown to influence health outcomes, but few studies have examined their relationship with cancer survival. We used data from the National Land Cover Database to examine associations between regional-stage colon cancer survival and 27 different landscape metrics. The study population included all adult New Jersey residents diagnosed between 2006 and 2011. Cases were followed until 31 December 2016 (N = 3949). Patient data were derived from the New Jersey State Cancer Registry and were linked to LexisNexis to obtain residential histories. Cox proportional hazard regression was used to estimate hazard ratios (HR) and 95% confidence intervals (CI95) for the different landscape metrics. An increasing proportion of high-intensity developed lands with 80-100% impervious surfaces per cell/pixel was significantly associated with the risk of colon cancer death (HR = 1.006; CI95 = 1.002-1.01) after controlling for neighborhood poverty and other individual-level factors. In contrast, an increase in the aggregation and connectivity of vegetation-dominated low-intensity developed lands with 20-<40% impervious surfaces per cell/pixel was significantly associated with the decrease in risk of death from colon cancer (HR = 0.996; CI95 = 0.992-0.999). Reducing impervious surfaces in residential areas may increase the aesthetic value and provide conditions more advantageous to a healthy lifestyle, such as walking. Further research is needed to understand how these landscape characteristics impact survival.

Liver Cancer Incidence and Area-Level Geographic Disparities in Pennsylvania-A Geo-Additive Approach.

Many neighborhood socioeconomic index measures (nSES) that capture neighborhood deprivation exist but the impact of measure selection on liver cancer (LC) geographic disparities remains unclear. We introduce a Bayesian geoadditive modeling approach to identify clusters in Pennsylvania (PA) with higher than expected LC incidence rates, adjusted for individual-level factors (age, sex, race, diagnosis year) and compared them to models with 7 different nSES index measures to elucidate the impact of nSES and measure selection on LC geospatial variation. LC cases diagnosed from 2007-2014 were obtained from the PA Cancer Registry and linked to nSES measures from U.S. census at the Census Tract (CT) level. Relative Risks (RR) were estimated for each CT, adjusted for individual-level factors (baseline model). Each nSES measure was added to the baseline model and changes in model fit, geographic disparity and state-wide RR ranges were compared. All 7 nSES measures were strongly associated with high risk clusters. Tract-level RR ranges and geographic disparity from the baseline model were attenuated after adjustment for nSES measures. Depending on the nSES measure selected, up to 60% of the LC burden could be explained, suggesting methodologic evaluations of multiple nSES measures may be warranted in future studies to inform LC prevention efforts.

Towards precision public health: Geospatial analytics and sensitivity/specificity assessments to inform liver cancer prevention.

OBJECTIVES: Liver cancer (LC) continues to rise, partially due to limited resources for prevention. To test the precision public health (PPH) hypothesis that fewer areas in need of LC prevention could be identified by combining existing surveillance data, we compared the sensitivity/specificity of standard recommendations to target geographic areas using U.S. Census demographic data only (percent (%) Hispanic, Black, and those born 1950-1959) to an alternative approach that couples additional geospatial data, including neighborhood socioeconomic status (nSES), with LC disease statistics. METHODS: Pennsylvania Cancer Registry data from 2007-2014 were linked to 2010 U.S. Census data at the Census tract (CT) level. CTs in the top 80th percentile for 3 standard demographic variables, %Hispanic, %Black, %born 1950-1959, were identified. Spatial scan statistics (SatScan) identified CTs with significantly elevated incident LC rates (p-value<0.05), adjusting for age, gender, diagnosis year. Sensitivity, specificity, and positive predictive value (PPV) of a CT being located in an elevated risk cluster and/or testing positive/negative for at least one standard variable were calculated. nSES variables (deprivation, stability, segregation) significantly associated with LC in regression models (p < 0.05) were systematically evaluated for improvements in sensitivity/specificity. RESULTS: 9,460 LC cases were diagnosed across 3,217 CTs. 1,596 CTs were positive for at least one of 3 standard variables. 5 significant elevated risk clusters (CTs = 402) were identified. 324 CTs were positive for a high risk cluster AND standard variable (sensitivity = 92%; specificity = 37%; PPV = 17.4%). Incorporation of 3 new nSES variables with one standard variable (%Black) further improved sensitivity (93%), specificity (62.9%), and PPV (26.3%). CONCLUSIONS: We introduce a quantitative assessment of PPH by applying established sensitivity/specificity assessments to geospatial data. Coupling existing disease cluster and nSES data can more precisely identify intervention targets with a liver cancer burden than standard demographic variables. Thus, this approach may inform prioritization of limited resources for liver cancer prevention.

Residential Mobility and Geospatial Disparities in Colon Cancer Survival.

BACKGROUND: Identifying geospatial cancer survival disparities is critical to focus interventions and prioritize efforts with limited resources. Incorporating residential mobility into spatial models may result in different geographic patterns of survival compared with the standard approach using a single location based on the patient's residence at the time of diagnosis. METHODS: Data on 3,949 regional-stage colon cancer cases diagnosed from 2006 to 2011 and followed until December 31, 2016, were obtained from the New Jersey State Cancer Registry. Geographic disparity based on the spatial variance and effect sizes from a Bayesian spatial model using residence at diagnosis was compared with a time-varying spatial model using residential histories [adjusted for sex, gender, substage, race/ethnicity, and census tract (CT) poverty]. Geographic estimates of risk of colon cancer death were mapped. RESULTS: Most patients (65%) remained at the same residence, 22% changed CT, and 12% moved out of state. The time-varying model produced a wider range of adjusted risk of colon cancer death (0.85-1.20 vs. 0.94-1.11) and resulted in greater geographic disparity statewide after adjustment (25.5% vs. 14.2%) compared with the model with only the residence at diagnosis. CONCLUSIONS: Including residential mobility may allow for more precise estimates of spatial risk of death. Results based on the traditional approach using only residence at diagnosis were not substantially different for regional stage colon cancer in New Jersey. IMPACT: Including residential histories opens up new avenues of inquiry to better understand the complex relationships between people and places, and the effect of residential mobility on cancer outcomes.See related commentary by Williams, p. 2107.