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Purpose: The lack of individual socioeconomic status (SES) information in cancer registry data necessitates the use of area-based measures to investigate health disparities. Concerns about confidentiality, however, prohibit publishing patients' residential locations at the subcounty level. We developed a census tract-based composite SES index to be released in place of individual census tracts to minimize the risk of disclosure. Methods: Two SES indices based on the measures identified in the literature were constructed using factor analysis. The analyses were repeated using the data from the 2000 decennial census and 2005–2009 American Community Survey to create the indices at two time points, which were linked to 2000–2009 Surveillance, Epidemiology, and End Results registry data to estimate incidence and survival rates. Results: The two indices performed similarly in stratifying census tracts and detecting socioeconomic gradients in cancer incidence and survival. The gradient in the incidence is positive for breast and prostate, and negative for lung cancers, in all races, although the level varies. The positive gradient in survival is more salient for regionalstaged breast, colorectal, and lung cancers. Conclusions: The census tract-based SES index provides a valuable tool for monitoring the disparities in cancer burdens while avoiding potential identity disclosure. This index, divided into tertiles and quintiles, is now available to the researchers on request.

Columbus, Ohio is one of the more prosperous, well-educated, and progressive cities in the United States. However, it ranks as the second worst life expectancy at birth, has a census tract wealth gap (27-year disparity), and one of the higher infant mortality rates in the country. These data suggest that there are likely several high-risk, vulnerable neighborhoods in Columbus with residents experiencing disparate and adverse outcomes. Illustrative of this fact are studies that have examined the social processes and mechanisms through which neighborhood contexts are at the forefront, including exposures to chemical stressors such as particulate matter (PM2.5) as well as non-chemical stressors including violence, social determinants of health, zoning, and land use policies. It is documented that disparate and adverse outcomes are magnified in the vulnerable neighborhoods on the Near East Side as compared to Columbus city proper, Franklin County and/or the state of Ohio. As such, we developed a nuanced community engagement framework to identify potential environmental hazards associated with adverse pregnancy outcomes in those census tracts. The refined framework uses a blended version of traditional community-based participatory research (CBPR) models and is referred to as E6, Enhancing Environmental Endeavors via e-Equity, Education, and Empowerment.

Background Influenza hospitalizations result in substantial morbidity and mortality each year. Little is known about the association between influenza hospitalization and census tract‐based socioeconomic determinants beyond the effect of individual factors. Objective To evaluate whether census tract‐based determinants such as poverty and household crowding would contribute significantly to the risk of influenza hospitalization above and beyond individual‐level determinants. Methods We analyzed 33 515 laboratory‐confirmed influenza‐associated hospitalizations that occurred during the 2009‐2010 through 2013‐2014 influenza seasons using a population‐based surveillance system at 14 sites across the United States. Results Using a multilevel regression model, we found that individual factors were associated with influenza hospitalization with the highest adjusted odds ratio (AOR) of 9.20 (95% CI 8.72‐9.70) for those ≥65 vs 5‐17 years old. African Americans had an AOR of 1.67 (95% CI 1.60‐1.73) compared to Whites, and Hispanics had an AOR of 1.21 (95% CI 1.16‐1.26) compared to non‐Hispanics. Among census tract‐based determinants, those living in a tract with ≥20% vs <5% of persons living below poverty had an AOR of 1.31 (95% CI 1.16‐1.47), those living in a tract with ≥5% vs <5% of persons living in crowded conditions had an AOR of 1.17 (95% CI 1.11‐1.23), and those living in a tract with ≥40% vs <5% female heads of household had an AOR of 1.32 (95% CI 1.25‐1.40). Conclusion Census tract‐based determinants account for 11% of the variability in influenza hospitalization.

Background The COVID‐19 pandemic had a significant impact on cancer screening and treatment, particularly in 2020. However, no single study has comprehensively analyzed its effects on cancer incidence and disparities among groups such as race/ethnicity, socioeconomic status (SES), persistent poverty (PP), and rurality. Methods Utilizing the recent data from the United States National Cancer Institute's Surveillance, Epidemiology, and End Results Program, we calculated delay‐ and age‐adjusted incidence rates for 13 cancer sites in 2020 and 2015–2019. Percent changes (PCs) of rates in 2020 compared to 2015–2019 were measured and compared across race/ethnic, census tract‐level SES, PP, and rurality groups. Results Overall, incidence rates decreased from 2015–2019 to 2020, with varying PCs by cancer sites and population groups. Notably, NH Blacks showed significantly larger PCs than NH Whites in female lung, prostate, and colon cancers (e.g., prostate cancer: NH Blacks −7.3, 95% CI: [−9.0, −5.5]; NH Whites: −3.1, 95% CI: [−3.9, −2.2]). Significantly larger PCs were observed for the lowest versus highest SES groups (prostate cancer), PP versus non‐PP groups (prostate and female breast cancer), and all urban versus rural areas (prostate, female breast, female and male lung, colon, cervix, melanoma, liver, bladder, and kidney cancer). Conclusions The COVID‐19 pandemic coincided with reduction in incidence rates in the U.S. in 2020 and was associated with worsening disparities among groups, including race/ethnicity, SES, rurality, and PP groups, across most cancer sites. Further investigation is needed to understand the specific effects of COVID‐19 on different population groups of interest.

Background To reduce health disparities and improve the health of Americans overall, addressing community-level social and structural factors, such as social vulnerability, may help explain the higher rates of HIV diagnoses among and between race/ethnicity groups. Methods Data were obtained from CDC’s National HIV Surveillance System (NHSS) and the CDC/ATSDR social vulnerability index (SVI). NHSS data for Black, Hispanic/Latino, and White adults with HIV diagnosed in 2018 were linked to SVI data. To measure the relative disparity, rate ratios (RRs) with 95% CIs were calculated to examine the relative difference comparing census tracts with the lowest SVI scores (quartile 1, Q1) to those with the highest SVI scores (quartile 4, Q4) by sex assigned at birth for age group and region of residence. Differences in the numbers of diagnoses across the quartiles were analyzed by sex assigned at birth and transmission category. Results There were 13,807 Black, 8747 Hispanic/Latino, and 8325 White adults who received a diagnosis of HIV infection in the United States in 2018—with the highest HIV diagnosis rates among adults who lived in census tracts with the highest vulnerability (Q4). For each race/ethnicity and both sexes, the rate of HIV diagnoses increased as social vulnerability increased. The highest disparities in HIV diagnosis rates by SVI were among persons who inject drugs, and the highest within-group RRs were typically observed among older persons and persons residing in the Northeast. Conclusion To reach the goals of several national HIV initiatives, efforts are needed to address the social vulnerability factors that contribute to racial and ethnic disparities in acquiring HIV and receiving care and treatment.

The geographic distribution of nonprofit antipoverty organizations has important implications for economic development, social services, public health, and policy efforts. With counts of antipoverty nonprofits at the census tract level in Greater Hartford, Connecticut, we examine whether these organizations are located in areas with high levels of poverty with a spatial zero-inflated-Poisson model. Covariates that measure need, resources, urban structure, and demographic characteristics are incorporated into both the zero-inflation component and the Poisson component of the model. Variation not explained by the covariates is captured by the combination of a spatial random effect and an unstructured random effect. Statistical inferences are done within the Bayesian framework. Model comparison with the conditional predictive ordinate suggests that the random effects and the zero-inflation are both important components in fitting the data. All three need measures-proportion of people below the poverty line, unemployment rate, and rental occupancy-are found to have significantly positive effect on the mean of the count, providing evidence that antipoverty nonprofits tend to locate where they are needed. The dataset and R/OpenBUGS code are available in supplementary materials online.

   Background Assessing HIV diagnosis and the social vulnerability index (SVI) by themes (socioeconomic status, household composition and disability, minority status and English proficiency, and housing type and transportation) might help to identify specific social factors contributing to disparities across census tracts with high rates of diagnosed HIV infection in the USA. Methods We examined HIV rate ratios in 2019 using data from CDC’s National HIV Surveillance System (NHSS) for Black/African American, Hispanic/Latino, and White persons aged ≥ 18 years. NHSS data were linked to CDC/ATSDR SVI data to compare census tracts with the lowest SVI (Q1) and highest SVI (Q4) scores. Rates and rate ratios were calculated for 4 SVI themes by sex assigned at birth for age group, transmission category, and region of residence. Results In the socioeconomic theme analysis, we observed wide within-group disparity among White females with diagnosed HIV infection. In the household composition and disability theme, we observed high HIV diagnosis rates among Hispanic/Latino and White males who lived in the least socially vulnerable census tracts. In the minority status and English proficiency theme, we observed a high percentage of Hispanic/Latino adults with diagnosed HIV infection in the most socially vulnerable census tracts. In the housing type and transportation theme, we observed a high percentage of HIV diagnoses attributed to injection drug use in the most socially vulnerable census tracts. Conclusion The development and prioritization of interventions that address specific social factors contributing to disparities in HIV across census tracts with high diagnosis rates are critical to reducing new HIV infections in the USA.

Existing research in small-area demographic forecasting suffers from two important limitations: (1) a paucity of studies that quantify patterns of error in either total or age/sex-specific estimates and (2) limited methodological innovation aimed specifically at improving the accuracy of such forecasts. This paper attempts to fill, in part, these gaps in existing research by presenting a comparative evaluation of the accuracy of standard and spatially-weighted Hamilton-Perry forecasts for urbanized census tracts within incorporated New Mexico municipalities. These comparative forecasts are constructed for a 10-year horizon (base 1 April 2000 and target 1 April 2010), then compared to the results of the 2010 Census in an ex post facto evaluation. Results are presented for the standard Hamilton-Perry forecasts as well as two sets that incorporate two common variants of spatial weights to improve forecast accuracy. Findings are discussed in the context of what is currently known about error in small-area demographic forecasts and with an eye toward continued innovations.

Background Area-level measures are often used to approximate socioeconomic status (SES) when individual-level data are not available. However, no national studies have examined the validity of these measures in approximating individual-level SES. Methods Data came from ~ 3,471,000 participants in the Mortality Disparities in American Communities study, which links data from 2008 American Community Survey to National Death Index (through 2015). We calculated correlations, specificity, sensitivity, and odds ratios to summarize the concordance between individual-, census tract-, and county-level SES indicators (e.g., household income, college degree, unemployment). We estimated the association between each SES measure and mortality to illustrate the implications of misclassification for estimates of the SES-mortality association. Results Participants with high individual-level SES were more likely than other participants to live in high-SES areas. For example, individuals with high household incomes were more likely to live in census tracts ( r = 0.232; odds ratio [OR] = 2.284) or counties ( r = 0.157; OR = 1.325) whose median household income was above the US median. Across indicators, mortality was higher among low-SES groups (all p < .0001). Compared to county-level, census tract-level measures more closely approximated individual-level associations with mortality. Conclusions Moderate agreement emerged among binary indicators of SES across individual, census tract, and county levels, with increased precision for census tract compared to county measures when approximating individual-level values. When area level measures were used as proxies for individual SES, the SES-mortality associations were systematically underestimated. Studies using area-level SES proxies should use caution when selecting, analyzing, and interpreting associations with health outcomes.

In an experiment on Airbnb, we find that applications from guests with distinctively African American names are 16 percent less likely to be accepted relative to identical guests with distinctively white names. Discrimination occurs among landlords of all sizes, including small landlords sharing the property and larger landlords with multiple properties. It is most pronounced among hosts who have never had an African American guest, suggesting only a subset of hosts discriminate. While rental markets have achieved significant reductions in discrimination in recent decades, our results suggest that Airbnb's current design choices facilitate discrimination and raise the possibility of erasing some of these civil rights gains.