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Breast cancer is the most commonly diagnosed cancer among US women across all racial/ethnic groups. Stage at diagnosis is one of the major factors determining breast cancer prognosis. The 5-year relative survival for breast cancer ranges from 99% for localized stage at diagnosis, to 84% for regional stage, and to 27% for distant (metastatic) stage breast cancer. The proportion of women diagnosed with breast cancer at later stages (regional and distant) in the US are higher among women with lower socio-economic status and among non-Hispanic Black women. Historic mortgage security redlining, implemented by the Home Owners’ Loan Corporation (HOLC) in the 1930s across numerous US cities, continues to have a negative influence on breast cancer stage at diagnosis, largely due to continued social and economic isolation and poor living environments resulting in many adverse consequences, including lower education, limited job opportunities, no/limited health insurance coverage, and suboptimal access to care, including cancer screening services. Additionally, studies have reported that living in areas with greater contemporary mortgage lending bias towards the non-Hispanic Black population (measured as higher odds of mortgage denial) is associated with late-stage breast cancer diagnosis in several US metropolitan areas. In this study, we aim to examine the association between the historic HOLC-based “redlining” and contemporary mortgage lending bias and stage of breast cancer at diagnosis among women aged 18 years and older in New Jersey diagnosed with first primary invasive breast cancer in 2010-2015 (N= 32,939). The study population was derived from the New Jersey State Cancer Registry. Historic “redlining” data based on 1930s neighborhood boundaries and transformed to corresponding 2010 census tracts borders were obtained from Inter-university Consortium for Political and Social Research. Mortgage lending bias score for the study period was calculated at the census tract level following methodology developed by Beyers and colleagues. Associations between census tract-level historic “redlining”, contemporary mortgage lending bias and breast cancer stage at diagnosis were evaluated using multinomial logistic regression models after adjusting for age alone, and then for age, race/ethnicity, marital status, and health insurance status. The study included 21,038 local, 9,765 regional, and 2,136 distant stage breast cancer cases. After adjusting for age, race/ethnicity, marital status, and health insurance, women living in historically redlined census tracts were more likely to be diagnosed with regional (OR=1.23; 95% CI 1.03-1.48) and distant (OR=1.55; 95% CI 1.09-2.22) stage breast cancer compared to women living in other census tracts. Odds for regional (OR=1.14; 95% CI 1.06-1.23) and distant (OR=1.33; 95% CI 1.16-1.53) stage breast cancer were also significantly higher for women living in areas with highest mortgage lending bias score. Stratifying by age (< 65 and >=65 years) showed similar patterns (data not shown). Both historic “redlining” and contemporary mortgage lending bias were associated with being diagnosed with breast cancer at later stages, notably distant stage. Targeting the legacy of systematic racism and addressing any contemporary discriminatory policies may help reduce breast cancer disparities in the diagnosis stage and thus mortality. Association between the historic redlining and contemporary mortgage lending bias and stage of breast cancer at diagnosis among women aged ≥18 years in New Jersey, 2010-2015 Note: For contemporary mortgage lending bias, highest score is indicative of highest bias (highest proportion of mortgage denial) towards Black people. *Fully adjusted model includes covariates: age, race/ethnicity, marital status, health insurance status ^Areas designated ”Best” were individual housing markets with sufficient levels of financing and were preserved exclusively for White and wealthy population. Areas defined as “Hazardous” were those lacking financial resources and were designated for Black and poor population. Citation Format: Daniel Wiese, Antoinette M. Stroup, Ahmedin Jemal, Kevin A. Henry, Farhad Islami. Associations of historic and contemporary “redlining” with breast cancer stage at diagnosis [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P1-06-01.

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.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.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]).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.

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.

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.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.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.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.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.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.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.

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.

Introduction Poverty is a known risk factor for burn injury and is associated with residency in food deserts and food swamps. Food deserts are areas with low or no access to nutritious, healthy foods while food swamps are areas with much higher access to high-calorie, unhealthy foods than to foods with good nutritious value. The purpose of our investigation was to determine the prevalence of residency in food deserts and food swamps in burn patients evaluated at an urban academic center and to determine the relationship between residency in these areas and the presence of patient comorbidities. Methods We performed a retrospective chart review of patients with burn injuries seen at an ABA verified urban academic center between January 2016 and January 2022. All patients over age 18 seen in the emergency department or admitted to the burn service were included in the study. Patient residential locations were used in conjunction with the Modified Retail Food Environment Index (mREFI) data to classify residency in food deserts and food swamps. Patient demographics and comorbidities were also recorded. Chi square and ANOVA analyses were completed to determine differences between the food desert, food swamp, and good access groups. Results A total of 3064 subjects were included in the study, of these 1370 (44.7%) were female and 1694 (55.3%) were male. In terms of access, 96 (3.1%) individuals lived in food deserts, 477 (15.6%) lived in good access areas, and 2490 (81.3%) resided in food swamps. This difference was found to be statistically significant (p < 0.0001). Age, sex, and BMI did not differ significantly between the three groups. A significantly higher percentage of food swamp residents had a diagnosis of diabetes (15.0%) as compared to subjects living in good food access areas (11.7%) and food deserts (8.3%), (p < 0.043). Hypertension was also found to be more prevalent in food swamps (24.8%) than food deserts (20.8%) and good access areas (19.3%), (p < 0.028). Finally, smoking was more common in food swamps (45.3%) than good access areas (40.4%) and food deserts (27.9%), (p < 0.0028). Conclusions A majority of burn patients reside in food swamps and residency in these areas is associated with a higher prevalence of hypertension, diabetes, and tobacco smoking. This is the first study to investigate the epidemiology of food access in an urban burn population. Applicability of Research to Practice Nutrition is known to be important to overall health and to wound healing in the setting of an acute injury. Our data contributes to the current literature on the role of nutrition in burns.

Introduction Many victims of burn injury live in food deserts or food swamps. Food deserts are low-income areas with minimal or no access to nutritious foods while food swamps are areas with much greater access to unhealthy, high-calorie foods. We examined the relationship between residency in a food swamp or food desert by census tract and wound healing. Methods We performed a retrospective review of burn patients who underwent split-thickness skin grafting at an ABA verified urban academic burn center between September 2018 and August 2022. Inclusion criteria were burn area less than 20% total body surface area (TBSA), age ≥ 18, and single operation for split-thickness skin grafting. Patient residential locations were used in conjunction with Modified Retail Food Environment Index (mREFI) data to classify residency in food deserts and food swamps. The primary outcome was time to donor site healing. Time-to-event Cox proportional regression analysis was performed to evaluate risk factors for poor wound healing. Results A total of 238 patients were included in the study, of whom 110 (46.2%) were female and 128 (53.8%) were male. Mean age was 48, BMI was 28.5, TBSA 3.7, and average days to donor site healing was 26. In categorizing residency by mREFI data, 45 (18.9%) individuals had good access, 11 (4.6%) resided in food deserts, and 182 (76.5%) resided in food swamps. Although time to healing was longer on average in the food desert (25.8 days) and food swamp (26.4 days) groups compared to the group with good access to food within ½ mile (22.9 days), this difference was not statistically significant. Not having diabetes was found to be associated with shorter time to wound healing (HR=1.68, 95% CI [1.15, 2.47], p=0.007). Living in a good access area was associated with faster wound healing (compared to swamp: HR=1.23, 95% CI [0.86, 1.75], compare to desert: HR=1.06, 95% CI [0.51, 2.20]), however this association was not statistically significant. Conclusions Residency in a food swamp or food desert does not significantly influence time to healing in burn injured patients. However, not having diabetes was associated with a shorter time to wound healing. This is the first study to compare residency in food deserts or food swamps by census tract level data with time to wound healing in an urban burn population. Applicability of Research to Practice Wound healing is a complex and important process known to be affected by malnutrition. Our data contributes to the current body of literature on nutrition and wound healing.

ImportanceThere 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.ObjectiveTo quantify between and within racial and ethnic disparities in TNBC incidence rates (IRs) among US women across states.Design, Setting, and ParticipantsThis 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.ExposuresState 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 MeasuresThe 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.ResultsThe 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 RelevanceIn 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.

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. 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

Background: Triple-negative breast cancer (TNBC) (i.e., estrogen receptor-negative, progesterone receptor-negative, and human epidermal growth factor receptor type 2-negative) is an aggressive subtype of breast cancer, more frequently diagnosed among non-Hispanic Black women than other racial/ethnic groups in the United States. TNBC risk also varies by location, but limited data exist for state-level variation in the racial/ethnic disparities in TNBC risk. Methods: Data for TNBC diagnosed at all ages during 2015-2019 were obtained from the National Program of Cancer Registries and Surveillance, Epidemiology & End Results for racial/ethnic groups classified as non-Hispanic American Indian/Alaska Native (AIAN), non-Hispanic Asian or Pacific Islander (API), non-Hispanic Black (Black), Hispanic, or non-Hispanic White (White). State-specific incidence rates were available for 8, 23, and 36 states for AIAN, API, and Hispanic women, respectively, and 38 states and Washington, D.C. for Black women and 50 states and Washington, D.C. (hereafter referred to collectively as “states”) for White women. State-specific rates were compared within and between racial/ethnic groups using incidence rate ratios (IRR). Results: Nationally, the age-standardized incidence rate of TNBC was the highest among Black women (25.1 per 100,000), followed by White (12.9 per 100,000), AIAN (11.1 per 100,000), Hispanic (11.1 per 100,000), and API (9.0 per 100,000) women. The highest state-specific rates were found among Black women in Delaware, Missouri, and Louisiana (>30 per 100,000) and the lowest rates were among API women in Oregon and Pennsylvania (< 7 per 100,000). State variations within a population were relatively larger among AIAN (2.8-fold) and Hispanic (2.3-fold) women, and smaller among White women (1.65-fold). Compared with White women, Black women had a higher rate of TNBC in all 39/39 states with the greatest IRRs in Delaware (2.31, 95%CI=1.90-2.81), Missouri (2.28, 95%CI=2.08-2.50), and Louisiana (2.20, 95%CI=2.02-2.38) and the lowest IRRs in Minnesota (1.38, 95%CI=1.12-1.68) and Colorado (1.38, 95%CI=1.12-1.68). In contrast, API women had a lower rate than White women in 22/23 states, except for Nevada, with the lowest IRR in Oregon (0.50, 95%CI=0.34-0.70) and the highest IRR in New York (0.82, 95%CI=0.75-0.90). The rate among Hispanic compared with White women did not differ statistically significantly in 23/36 states but was lower in 12/36 states, with the lowest IRR in Ohio (0.57, 95%CI=0.43-0.74), and higher in Massachusetts (1.21, 95%CI=1.04-1.39). The rate among AIAN compared with White women did not differ in 5/8 states but was lower in Arizona (0.53, 95%CI=0.38-0.73) and North Carolina (0.68, 95%CI=0.46-0.96), and higher in Oklahoma (1.30, 95%CI=1.09-1.54). Conclusions: State variations in TNBC incidence rates both within and between populations are substantial, signifying the important role of potentially modifiable risk factors in determining the risk of TNBC by state and race/ethnicity. This finding highlights the need for more research to identify factors contributing to these variations to develop more effective preventive measures. Meanwhile, to mitigate the impact of the disproportionate burden of TNBC across states and racial/ethnic groups, universal access to screening modalities and timely, guideline-concordant treatments is essential. Citation Format: Hyuna Sung, Daniel Wiese, Ismail Jatoi, Ahmedin Jemal. State variation in racial and ethnic disparities in triple-negative breast cancer rates: NPCR-SEER incidence data, 2015-2019 [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P4-03-24.

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

Residential histories linked to cancer registry data provide new opportunities to examine cancer outcomes by neighborhood socioeconomic status (SES). We examined differences in regional stage colon cancer survival estimates comparing models using a single neighborhood SES at diagnosis to models using neighborhood SES from residential histories.We linked regional stage colon cancers from the New Jersey State Cancer Registry diagnosed from 2006 to 2011 to LexisNexis administrative data to obtain residential histories. We defined neighborhood SES as census tract poverty based on location at diagnosis and across the follow-up period through 31 December 2016 based on residential histories (average, time-weighted average, time-varying). Using Cox proportional hazards regression, we estimated associations between colon cancer and census tract poverty measurements (continuous and categorical), adjusted for age, sex, race/ethnicity, regional substage, and mover status.Sixty-five percent of the sample was nonmovers (one census tract); 35% (movers) changed tract at least once. Cases from tracts with20% poverty changed residential tracts more often (42%) than cases from tracts with5% poverty (32%). Hazard ratios (HRs) were generally similar in strength and direction across census tract poverty measurements. In time-varying models, cases in the highest poverty category (20%) had a 30% higher risk of regional stage colon cancer death than cases in the lowest category (5%) (95% confidence interval [CI] = 1.04, 1.63).Residential changes after regional stage colon cancer diagnosis may be associated with a higher risk of colon cancer death among cases in high-poverty areas. This has important implications for postdiagnostic access to care for treatment and follow-up surveillance. See video abstract: http://links.lww.com/EDE/B705.

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–&lt, 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.

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.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.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.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.

Background: Mapping breast cancer survival can help cancer control programs prioritize efforts with limited resources. We used Bayesian spatial models to identify whether breast cancer survival among patients in New Jersey (NJ) varies spatially after adjusting for key individual (age, stage at diagnosis, molecular subtype, race/ethnicity, marital status, and insurance) and neighborhood measures of poverty and economic inequality [index of concentration at the extremes (ICE)]. Methods: Survival time was calculated for all NJ women diagnosed with invasive breast cancer between 2010 and 2014 and followed to December 31, 2015 (N = 27,078). Nonlinear geoadditive Bayesian models were used to estimate spatial variation in hazard rates and identify geographic areas of higher risk of death from breast cancer. Results: Significant geographic differences in breast cancer survival were found in NJ. The geographic variation of hazard rates statewide ranged from 0.71 to 1.42 after adjustment for age and stage, and were attenuated after adjustment for additional individual-level factors (0.87–1.15) and neighborhood measures, including poverty (0.9–1.11) and ICE (0.92–1.09). Neighborhood measures were independently associated with breast cancer survival, but we detected slightly stronger associations between breast cancer survival, and the ICE compared to poverty. Conclusions: The spatial models indicated breast cancer survival disparities are a result of combined individual-level and neighborhood socioeconomic factors. More research is needed to understand the moderating pathways in which neighborhood socioeconomic status influences breast cancer survival. Impact: More effective health interventions aimed at improving breast cancer survival could be developed if geographic variation were examined more routinely in the context of neighborhood socioeconomic inequalities in addition to individual characteristics.

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, Highlights • Precision Public Health calls for linking surveillance data to identify fewer neighborhoods for intervention. • Sensitivity/specificity methods can measure the utility of Precision Public Health by identifying optimal data combinations. • Select combinations of linked Census and liver cancer registry data reduced neighborhood targets more than Census data alone. • Precision Public Health improves the prioritization of liver cancer prevention efforts.

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&ndash, 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.

The identification and mitigation of sampling biases is commonly overseen in species distribution modeling, even though bias can seriously compromise the validity of modeling outcomes. Here we propose methods to 1) detect and mitigate spatial and detection sampling biases in the use of machine learning methods for modeling species occurrence and 2) assess the magnitude of bias and the effectiveness of bias mitigation on modeling prediction, variable importance, and model performance. We illustrate these techniques through the calibration of boosted decision trees for the prediction of annual occurrences of Aedes albopictus, an invasive disease vector, in South-East Pennsylvania between 2001 and 2015. Methods consist of the application of spatial filters and the assignment of sampling reliability weights to observed locations. We tested the performance of spatial bias mitigation by comparing the frequency distribution obtained for predictors before and after filtering with the distribution that would be obtained under an ideal sampling design. We also tested the performance of detection bias mitigation by comparing the importance of variables representing detection bias before and after the assignment of reliability weights. Results show that spatial filtering reduced differences between the frequency distribution obtained with the unfiltered data and the distribution that would be obtained under a reference sampling design. The assignment of sampling reliability weights to observations reduced the relative influence of detection bias on fitted models. The mitigation of spatial bias had a larger effect on modeling prediction and accuracy estimates compared to detection bias mitigation. Spatial sampling bias mitigation largely tended to reduce the number of years of predicted A. albopictus occurrence while detection bias mitigation tended to increase it. Our results highlight the importance of identifying, quantifying and mitigating observation biases as a standard practice in the use of machine learning methods for species occurrence modeling because biases can compromise the reliability of modeling outcomes and interpretation.

Background: Liver cancer rates are rising, particularly in minority populations. Risk factors for liver cancer, such as alcohol/drug use, metabolic disorders, and viral hepatitis B/C (HBV/HCV), are generally modifiable through lifestyle interventions, vaccinations, and treatments. However, resources are often limited and strategies to prioritize geographic areas most in need of cancer prevention are needed. Recommendations exist to focus prevention efforts on groups with the highest rates of liver cancer, namely Hispanics, Blacks, and individuals born 1950-1959 who are at high risk for HCV. We compare the sensitivity/specificity of this approach to a dual approach that couples liver cancer disease cluster statistics with neighborhood-level demographic data to inform liver cancer prevention. Methods: Pennsylvania (PA) Liver Cancer Registry data from 2007-2014 were linked to the 2010 U.S. Census data via a geocode at the census tract level with ArcGIS software. Using the space-time scan statistic in SaTScan and relative risk estimates from BayesX, we also identified high-risk clusters or geographic areas with significantly elevated rates of incident liver cancer, adjusted for age, gender, and diagnosis year. Analyses were conducted using elliptical spatial windows and Poisson models. Census tracts in the top 80th percentile for percentage of Hispanics, Blacks or those born between 1950-1959 were also identified. The sensitivity, specificity, and positive predictive value (PPV) of a census tract being located in a high-risk cluster and/or testing positive or negative for at least one of three neighborhood variables (higher % Black, % Hispanic, 1950-1959 birth cohort) were calculated. Results: There were 9,460 cases of liver cancer diagnosed in PA. Five high-risk clusters were identified (relative risks ranged from 1.83-3.73, all p Conclusions: Coupling disease cluster statistics with neighborhood demographic data refines the identification of areas that carry a greater than expected burden of liver cancer and reduces intervention targets more than neighborhood demographics alone. However, additional analyses are needed to improve the sensitivity/specificity of this combined geospatial approach. Consideration of other patient and neighborhood level socioeconomic data is also being explored and will be presented to further inform the prioritization of liver cancer prevention efforts. This abstract is also being presented as Poster C014. Citation Format: Shannon M. Lynch, Daniel Wiese, Kristen Sorice, Minhhuyen Nguyen, Evelyn Gonzalez, Kevin Henry. Geospatial analytics and sensitivity/specificity assessments to inform liver cancer prevention [abstract]. In: Proceedings of the Eleventh AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2018 Nov 2-5; New Orleans, LA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl):Abstract nr PR13.

Aedes albopictus is a viable vector for several infectious diseases such as Zika, West Nile, Dengue viruses and others. Originating from Asia, this invasive species is rapidly expanding into North American temperate areas and urbanized places causing major concerns for public health. Previous analyses show that warm temperatures and high humidity during the mosquito season are ideal conditions for A. albopictus development, while its distribution is correlated with population density. To better understand A. albopictus expansion into urban places it is important to consider the role of both environmental and neighborhood factors. The present study aims to assess the relative importance of both environmental variables and neighborhood factors in the prediction of A. albopictus’ presence in Southeast Pennsylvania using MaxEnt (version 3.4.1) machine-learning algorithm. Three models are developed that include: (1) exclusively environmental variables, (2) exclusively neighborhood factors, and (3) a combination of environmental variables and neighborhood factors. Outcomes from the three models are compared in terms of variable importance, accuracy, and the spatial distribution of predicted A. albopictus’ presence. All three models predicted the presence of A. albopictus in urban centers, however, each to a different spatial extent. The combined model resulted in the highest accuracy (74.7%) compared to the model with only environmental variables (73.5%) and to the model with only neighborhood factors (72.1%) separately. Although the combined model does not essentially increase the accuracy in the prediction, the spatial patterns of mosquito distribution are different when compared to environmental or neighborhood factors alone. Environmental variables help to explain conditions associated with mosquitoes in suburban/rural areas, while neighborhood factors summarize the local conditions that can also impact mosquito habitats in predominantly urban places. Overall, the present study shows that MaxEnt is suitable for integrating neighborhood factors associated with mosquito presence that can complement and improve species distribution modeling.

Der vorliegende Beitrag prasentiert erstmals eine Beschreibung des Partnermarkts in Deutschland und dessen Entwicklung auf der Basis von Langsschnittdaten. Die Ergebnisse basieren auf einer Auswertung kleinraumiger Bevolkerungsdaten auf Kreisebene und auf einem Indikatorenkonzept, das mit der Altersrelevanz, Bildungsrelevanz, Konkurrenz und Verfugbarkeit die theoretisch zentralen Dimensionen des Partnermarkts adaquat berucksichtigt. Untersucht werden verschiedene Annahmen uber die Entwicklung der Partnermarktchancen im Lebensverlauf und uber diesbezugliche Unterschiede zwischen unterschiedlichen Geburtsjahrgangen. Herausgearbeitet werden die geschlechtsspezifischen Muster der Altersabhangigkeit des Partnermarkts sowie die aus der Geburtenentwicklung, dem Wandel partnerschaftlicher Lebensformen, der Bildungsexpansion und aus Migrationsprozessen resultierenden Kohortenunterschiede.

Public and scientific conceptions of identity are changing alongside advances in biotechnology, with important relevance to health and medicine. In particular, biological identity, once predominantly conceived as static (e.g., related to DNA, dental records, fingerprints) is now being recognized as dynamic or fluid, mirroring contemporary understandings of psychological and social identity. The dynamism of biological identity comes from the individual body's unique relationship with the world surrounding it, and therefore may best be described as biosocial. This paper reviews advances in scientific understandings of identity and presents a model that contrasts prior static approaches to biological identity from more recent dynamically-relational ones. This emerging viewpoint is of broad significance to health and medicine, particularly as medicine recognizes the significance of biography – i.e. the multiple, dense interactions imparted on a body across spatio-temporal dimensions – to phenotypic prediction, especially disease risk.