Area Poverty Rates Research Articles

Disparities in breast cancer prevention initiatives at a comprehensive cancer center: Who are we serving?

1615 Background: Despite advances in personalized breast cancer risk assessment and screening, populations at highest risk for cancer-related mortality are often excluded. At the IU Simon Comprehensive Cancer Center, a high-risk screening program (HRSP) identifies and serves women at increased risk of breast cancer. Risk assessment using Tyrer Cuzick v.8 and Gail models is performed in mammography; women at increased risk are navigated to a clinic appointment to discuss individualized screening and prevention services including MRI, chemoprevention, and/or genetic testing. We hypothesize gaps in the demographic and socioeconomic (SES) characteristics of women seen for a HRSP appointment, compared to women receiving a cancer diagnosis at the same mammography sites. Methods: Retrospective review was performed of women who met criteria for the HRSP (age ≤65 and Gail 5 yr risk ≥3% or TC v.8 10-yr risk ≥8%, or recommended by the radiologist) and subsequently accepted, scheduled, and attended an appointment from 6/2020 to 6/2022 (n=233). This population was compared to women who were diagnosed with breast cancer at the same clinical sites over the same dates, without history of being evaluated in the HRSP. Age was restricted to ≤65 to match the HRSP population (n=685). Zip codes were used to calculate area-based SES by median household income and percent poverty. CDC social vulnerability index (SVI) was evaluated using the U.S. Census Bureau and American Community Survey (0=least vulnerable, 1=most vulnerable). Comparisons across groups by age, race, insurance status, area SES and SVI were made using the Wilcoxon or Chi-square test. Results: Cancer cases were significantly older than those attending the HRSP (median age 54 vs 49 p < 0.001). Black patients represented 19.4% of participants diagnosed with cancer, versus 10.8% of participants attending the HRSP (p=0.0032). There was also a disparity in insurance coverage, with 83.8% of women seen in the HRSP being privately insured, versus 75.6% of women diagnosed with cancer (p=0.011). There was no statistical difference in SVI (0.64 vs 0.66 p=0.11) or area poverty rate (11.2 vs 12.0 p=0.26) between the groups; cancer cases came from areas with lower household income (69,611 vs 73,165 p=0.04). Conclusions: Women seen in the HRSP were more likely to be younger, White, privately insured, and from an area with higher household income, compared to women diagnosed with cancer in the same health system at the same mammography sites. This highlights disparities in access to individualized breast cancer interception such as chemoprevention, high-risk screening, and genetic testing, which may further existing disparities in breast cancer-related mortality based on race and SES. Further studies will investigate barriers and motivators to personalized breast cancer prevention in high-risk groups, and utilize community partnerships to develop equitable interventions.

Multiple myeloma (MM) therapy within a Medicare insured patient population: Role of initial care setting and socioeconomic status.

e19057 Background: Approval of MM therapies including immunomodulatory agents (IMiDs), and proteasome inhibitors (PIs), coupled with increasing evidence supporting PI-IMiD combinations, maintenance (MT), and autologous stem cell transplant (ASCT) among fit patients, have expanded options, complexity and duration of active therapy. Prior research reported race disparities in initial therapy receipt and survival differences based on provider characteristics. It is unknown whether these effects extend to longer-term management. Methods: We selected adults with MM diagnosis (dx) between 07/07-12/15 from the SEER-Medicare linked database. We required continuous Medicare Parts A, B, and D enrollment and no HMO/PPO coverage from 12 months prior to dx through death or end of study (12/16) and MM therapy initiation w/in 12 months post dx. Outcomes including line of therapy (LOT) 1 PI-IMiD combination, LOT1 length, ASCT, MT, any LOT2, and any LOT4, were constructed using claims indicators for oral and IV drugs and receipt dates. Initial provider setting was assigned as academic &/or NCI cancer center, non-teaching hospital, or community (non-hospital) based on 1st oncologist E & M visit w/in 2 months pre- or post-MM dx. We also examined race (white, black, other), Hispanic ethnicity, Part D Low Income Subsidy (LIS) receipt and linked area poverty rate. Multivariable logistic and Cox proportional hazard regressions controlled for demographics, health status, and diagnosis year. Results: The cohort (n = 5678) was 51% male, 14% Black, and 51% age ≥75 years. Initial care setting was 50% academic/NCI, 32% non-teaching hospital, and 19% community; 22%, 53%, and 19% received LOT1 PI-IMiD, LOT 2, and LOT 4, respectively. Median LOT1 length was 165 days. Relative to academic/NCI, community care was associated with lower adjusted odds of all outcomes (e.g. LOT2, aOR: .56, 95% confidence interval (CI) .48 -.65, p < .001; LOT4, aOR: .74, 95% CI .58-.95, p = .02) except LOT1 PI-IMiD. Care at non-teaching hospitals was associated with reduced odds of LOT2. Part D LIS was associated with lower odds of LOT1 PI-IMiD, while increasing % poverty was associated with lower odds of LOT1 PI-IMiD combinations, LOT1 ASCT, LOT2 and LOT4. No associations were observed with race or ethnicity and any outcome. Conclusions: In Medicare insured adults treated for MM, initial care in a community setting was associated with shorter and less complex therapy. Socioeconomic status (Part D LIS, area poverty rates), but not race or ethnicity, was a consistent predictor of MM treatment.

The relationship between area poverty rate and site-specific cancer incidence in the United States.

BACKGROUNDThe relationship between socioeconomic status and cancer incidence in the United States has not traditionally been a focus of population-based cancer surveillance systems.METHODSNearly 3 million tumors diagnosed between 2005 and 2009 from 16 states plus Los Angeles were assigned into 1 of 4 groupings based on the poverty rate of the residential census tract at time of diagnosis. The sex-specific risk ratio of the highest-to-lowest poverty category was measured using Poisson regression, adjusting for age and race, for 39 cancer sites.RESULTSFor all sites combined, there was a negligible association between cancer incidence and poverty; however, 32 of 39 cancer sites showed a significant association with poverty (14 positively associated and 18 negatively associated). Nineteen of these sites had monotonic increases or decreases in risk across all 4 poverty categories. The sites most strongly associated with higher poverty were Kaposi sarcoma, larynx, cervix, penis, and liver; those most strongly associated with lower poverty were melanoma, thyroid, other nonepithelial skin, and testis. Sites associated with higher poverty had lower incidence and higher mortality than those associated with lower poverty.CONCLUSIONSThese findings demonstrate the importance and relevance of including a measure of socioeconomic status in national cancer surveillance. Cancer 2014;120:2191–2198. © 2014 The Authors. Cancer published by Wiley Periodicals, Inc. on behalf of American Cancer Society.A majority of cancer sites exhibit significant associations with area poverty rates. Those associated with higher poverty had lower incidence but higher mortality than those associated with lower poverty.

An Application of Bayesian Methods to Small Area Poverty Rate Estimates

Efforts to estimate various sociodemographic variables in small geographic areas are proving difficult with the replacement of the Census long-form with the American Community Survey (ACS). Researchers interested in subnational demographic processes have previously relied on Census 2000 long-form data products in order to answer research questions. ACS data products promise to begin providing up-to-date profiles of the nation’s population and economy; however, unit- and item-level nonresponse in the ACS have left researchers with gaps in subnational coverage resulting in unstable and unreliable estimates for basic demographic measures. Borrowing information from neighboring areas and across time with a spatiotemporal smoothing process based on Bayesian statistical methods, it is possible to generate more stable and accurate estimates of rates for geographic areas not represented in the ACS. This research evaluates this spatiotemporal smoothing process in its ability to derive estimates of poverty rates at the county level for the contiguous United States. These estimates are then compared to more traditional estimates produced by the US Census Bureau, and comparisons between the two methods of estimation are carried out to evaluate the practical application of this smoothing method. Our findings suggest that by using available data from the ACS only, we are able to recreate temporal and spatial patterns of poverty in US counties even in years where data are sparse. Results show that the Bayesian methodology strongly agrees with the estimates produced by the SAIPE program, even in years with little data. This methodology can be expanded to other demographic and socioeconomic data with ease.

Small Area Estimation Using a Reweighting Algorithm

SummaryThe paper describes a method of small area estimation which uses a reweighting algorithm to reweight survey data to a number of known totals (benchmarks) for small areas. The method has so far been used to estimate small area poverty rates and housing stress. The method gives poverty rates for small areas that are similar to those available from the 2006 Australian census, when the same definition of poverty was used. Various methods of validating the poverty rates have been used, including aggregating the poverty rates to a larger area and comparing them with official Australian Bureau of Statistics estimates from a survey, and applying the spatial microsimulation to larger areas and comparing with official Australian Bureau of Statistics survey results. Both these tests show that the estimates are comparable and fairly robust for most states in Australia.

Surveillance of the short-term impact of fine particle air pollution on cardiovascular disease hospitalizations in New York State

BackgroundStudies have shown that the effects of particulate matter on health vary based on factors including the vulnerability of the population, health care practices, exposure factors, and the pollutant mix.MethodsWe used time-stratified case-crossover to estimate differences in the short-term impacts of PM2.5 on cardiovascular disease hospital admissions in New York State by geographic area, year, age, gender, co-morbid conditions, and area poverty rates.ResultsPM2.5 had a stronger impact on heart failure than other cardiovascular diagnoses, with 3.1% of heart failure admissions attributable to short-term PM2.5 exposure over background levels of 5 ug/m3. Older adults were significantly more susceptible to heart failure after short-term ambient PM2.5 exposure than younger adults.ConclusionThe short-term impact of PM2.5 on cardiovascular disease admissions, and modifications of that impact, are small and difficult to measure with precision. Multi-state collaborations will be necessary to attain more precision to describe spatiotemporal differences in health impacts.

A Multilevel Analysis of State and Regional Disparities in Childhood and Adolescent Obesity in the United States

This study examines state- and regional disparities in obesity prevalence among 46,707 US children and adolescents aged 10-17 years before and after adjusting for individual socioeconomic and behavioral characteristics and area deprivation measures. The 2003 National Survey of Children's Health was used to calculate obesity prevalence in nine geographic regions and in the 50 states and the District of Columbia (DC). Logistic regression was used to estimate odds of obesity and adjusted prevalence. OLS regression was used to determine the amount of variance explained by income inequality, poverty, and violent crime rates. The prevalence of childhood obesity varied substantially across geographic areas, with the Southcentral regions of the US having the highest prevalence (> or =18%) and the Mountain region the lowest prevalence (11.4%). Children in West Virginia, Kentucky, Texas, Tennessee, and North Carolina (adjusted prevalence >18.3%) had over twice the odds of being obese than their Utah counterparts (adjusted prevalence = 10.4%). Geographic disparities in obesity were similar for male and female children. Individual characteristics such as race/ethnicity, household socioeconomic status, neighborhood social capital, television viewing, recreational computer use, and physical activity accounted for 55% of the state and 25% of the regional disparities in obesity. Area poverty rates accounted for an additional 18% of the state variance in adjusted obesity prevalence. Although individual and area level socioeconomic factors are important predictors, substantial geographic disparities in childhood and adolescent obesity remain. Prevention efforts targeting individual risk factors as well as contextual social and environmental factors may reduce geographic disparities in childhood and adolescent obesity.

Geographic Clustering of Adequate Diagnostic Follow-Up after Abnormal Screening Results for Breast Cancer among Low-income Women in Missouri

In an effort to examine a cluster of late-stage breast cancer, this study reports (1) the extent of geographic variation in adequacy of diagnostic follow-up (ADFU) after abnormal breast cancer screening results across Missouri's counties and census tracts, (2) whether various personal characteristics or area poverty account for any geographic clustering observed, and (3) the association between area poverty rate and ADFU. We used 1998-2002 Missouri Show Me Healthy Women breast and cervical cancer program data from 2580 low-income women aged 50-64 who had abnormal breast cancer screening results. ADFU was based on established guidelines. Poverty rate was from the 2000 census data. We used 3 complementary statistical approaches. Overall, 26.9% of screening results were inadequately followed up. County-level geographic variation accounted for 6.7% of the total variance in ADFU, while the census-tract-level variation was negligible. Women's sociodemographic characteristics, symptoms reported at time of screening, and screening results accounted for 25% of the county-level variation in ADFU. Statistically significant geographic variation in ADFU remained that could not be explained. Beyond 70 miles from the women's residence, the likelihood of receiving ADFU was geographically uncorrelated. We identified one large geographic cluster extending beyond the borders of counties and census tracts where women were less likely to receive ADFU (relative risk = 0.64; p = 0.01). Efforts to improve the likelihood of ADFU should be directed at examining the relative contributions of the healthcare and social environments and characteristics of the women in the area where women were less likely to receive ADFU especially in the cluster area of late-stage breast cancer rather than targeting efforts at the county or census-tract level.