Small-Area Analyses Using Public American Community Survey Data: A Tree-Based Spatial Microsimulation Technique

Quantitative sociologists and social policymakers are increasingly interested in local context. Some city-specific studies have developed new primary data collection efforts to analyze inequality at the neighborhood level, but methods from spatial microsimulation have yet to be broadly used in sociology to take better advantage of existing public data sets. The American Community Survey (ACS) is the largest household survey in the United States and indispensable for detailed analysis of specific places and populations. The authors propose a technique, tree-based spatial microsimulation, to produce “small-area” (census-tract) estimates of any person- or household-level phenomenon that can be derived from ACS microdata variables. The approach is straightforward and computationally efficient, based only on publicly available data, and it provides more reliable estimates than do prevailing methods of microsimulation. The authors demonstrate the technique’s capabilities by producing tract-level estimates, stratified by race/ethnicity, of (1) the proportion of people in the census-tract population who have children and work in an essential occupation and (2) the proportion of people in the census-tract population living below the federal poverty threshold and in a household that spends greater than 50 percent of monthly income on rent or owner costs. These examples are relevant to understanding the sociospatial inequalities dramatized by the coronavirus disease 2019 pandemic. The authors discuss potential extensions of the technique to derive small-area estimates of variables observed in surveys other than the ACS.

The Catalan Syndrome? Revisiting the Relationship Between Income and Support for Independence in Catalonia

The surge in support for independence in Catalonia (Spain) has received much political, journalistic, as well as academic attention. A popular account of the Catalan case stresses the allegation that motives relating to fiscal selfishness are behind the independence movement. The evidence presented in support of this argument is the positive correlation between income and support for independence. Some scholars, such as Thomas Piketty, even talk about a “Catalan syndrome,” according to which support for independence can ultimately be explained by fiscal selfishness and the prospect of creating a sort of tax haven in Catalonia. As prominent as this argument is, in this article I show that it rests on weak theoretical and empirical grounds. In order to do so, I reassess the existing evidence, using a more nuanced empirical strategy that allows for non-linear relations to emerge and controls for potential confounders. Then, I also present new evidence based on recently published census-tract level fiscal data, merged with election results. Finally, I spell out the mechanisms and observable implications of the “Catalan syndrome” argument and show that fiscal selfishness is not an important driver of the Catalan independence movement.

Multilevel mediation analysis on time-to-event outcomes: Exploring racial/ethnic disparities in breast cancer survival in California

Background Third-variable effect refers to the effect from a third-variable that explains an observed relationship between an exposure and an outcome. Depending on whether there is a causal relationship from the exposure to the third variable, the third-variable is called a mediator or a confounder. The multilevel mediation analysis is used to differentiate third-variable effects from data of hierarchical structures. Data Collection and Analysis We developed a multilevel mediation analysis method to deal with time-to-event outcomes and implemented the method in the mlma R package. With the method, third-variable effects from different levels of data can be estimated. The method uses multilevel additive models that allow for transformations of variables to take into account potential nonlinear relationships among variables in the mediation analysis. We apply the proposed method to explore the racial/ethnic disparities in survival among patients diagnosed with breast cancer in California between 2006 and 2017, using both individual risk factors and census tract level environmental factors. The individual risk factors are collected by cancer registries and the census tract level factors are collected by the Public Health Alliance of Southern California in partnership with the Virginia Commonwealth University's Center on Society and Health. The National Cancer Institute work group linked variables at the census tract level with each patient and performed the analysis for this study. Results We found that the racial disparity in survival were mostly explained at the census tract level and partially explained at the individual level. The associations among variables were depicted. Conclusion: The multilevel mediation analysis method can be used to differentiate mediation/confounding effects for factors originated from different levels. The method is implemented in the R package mlma.

Pet-Friendly for Whom? An Analysis of Pet Fees in Texas Rental Housing

Previous studies have underscored the difficulty low-income pet owners often face when attempting to secure affordable rental housing. Further exacerbating this housing disparity are fees charged on top of normal monthly rent to pet owners in “pet-friendly” rental housing. In this study, we aggregated rental housing listings from the twenty most populous cities in Texas, USA from a popular online rental database. We paired the rental listings with census tract information from the American Community Survey in order to investigate economic and racial/ethnic patterns in the spatial distribution of the properties. We find that less expensive pet-friendly listings were more likely to have pet fees charged on top of rent than rental units that were more expensive. Additionally, when pet fee burden was defined as a function of average income by census tract, low-income communities and communities of color were more likely than higher income and predominantly White communities to pay disproportionately higher fees to keep pets in their homes. We also find patterns of spatial inequalities related to pet fee burden by a metric of income inequality by city. The burden of pet rental fees may contribute to both housing insecurity and companion animal relinquishment. We discuss these findings as they relate to inequalities in housing, with particular attention to marginalized and disadvantaged people with pets. We conclude with recommendations for policy and practice.

Effect of Pesticide Exposure on Non-Hodgkin Lymphoma Incidence and Survival in California

Introduction: While previous studies propose pesticide exposure to be a risk factor for non-Hodgkin lymphoma (NHL) development, results are inconclusive. In addition, the impact of pesticide exposure on NHL survival is not well-established. Therefore, we identified NHL patients from the California Cancer Registry and linked these patients with the statewide pesticide use reporting database to determine the impact of pesticide exposure on NHL-related incidence and outcomes. Methods: Using the California Cancer Registry, we identified patients with a first primary diagnosis of NHL from 2010-2016 and linked these patients with CalEnviroScreen 3.0 to obtain production agriculture pesticide exposure to 70 chemicals from the state mandated Pesticide Use Reporting (PUR) by census tract from 2012-2014. In addition, data from PUR was integrated into a geographic information system that employs land use data to estimate cumulative exposure to specific pesticides previously associated with NHL (glyphosate, organophosphorus, carbamate, phenoxyherbicide and 2,4-dimethylamine salt) between 10 years prior up to 1 year after NHL diagnosis. SEER*Stat software was used to calculate NHL subtype incidence rates by census tract pesticide use level. Multivariable cox proportional hazards regression models were used to evaluate the impact of total pesticide exposure from CalEnviroScreen 3.0 and individual pesticide exposure from geographic land use data on lymphoma-specific and overall survival. Results: Among 35,808 NHL patients identified, 44.2% were exposed to pesticide in their census tract of residence. Pesticide exposure was higher in Hispanic/Latino (46.5%) and non-Hispanic white (45.6%) then Asian/Pacific Islander (37.2%) and African American (34.9%) patients with NHL. Glyphosate, organophosphorus, carbamate, phenoxyherbicide and 2,4-dimethylamine salt exposure was reported in 34.1%, 26.0%, 10.6%, 14.0% and 12.8% of NHL patients, respectively. Pesticide exposure was not associated with increased NHL incidence by NHL subtype or subgroups defined by sociodemographic factors. Total pesticide exposure at time of diagnosis was not associated with lymphoma-specific or overall survival. In addition, no association was consistently found between glyphosate, organophosphorus, carbamate, phenoxyherbicide and 2,4 dimethylamine salt exposure and lymphoma-specific or overall survival. Conclusion: In this large population-based study of neighborhood agricultural pesticide exposure, pesticide exposure was noted to be prevalent among patients diagnosed with NHL, with high pesticide exposure particularly observed in Hispanics/Latinos and non-Hispanic whites. However, pesticide exposure was not consistently associated with increased NHL incidence or worse NHL lymphoma-specific or overall survival. Disclosures Poh: Acrotech: Honoraria; Incyte: Research Funding; Morphosys: Consultancy. Tuscano: BMS: Research Funding; Seattle Genetics: Research Funding; Takeda: Research Funding; Acrotech: Research Funding; Genentech: Research Funding; Pharmacyclics: Research Funding; Abbvie: Research Funding.

Property Risk Assessment of Sinkhole Hazard in Louisiana, U.S.A

Sinkholes (or dolines) are an often-overlooked environmental hazard. The processes that lead to their formation are slow and insidious, which encourage a lack of awareness or concern for the potential danger, until the sudden, climactic formation leads to unexpected property damage and possibly human casualties. This research identifies the risk to residential properties to the sinkhole hazard, using Louisiana, United States as a case study. Risk is defined as the product of the hazard intensity and the loss to structure and contents within the building resulting from the hazard-related disaster. Results suggest that risk is highly scale-dependent. Although the risk due to sinkholes is small on a per capita basis statewide, especially when compared to the per capita risk of other natural hazards, the property risk for census tracts or census blocks partially or completely overlying a salt dome is substantial. At finer scales, Terrebonne Parish, in coastal southeastern Louisiana, has the greatest concentration of salt domes, while Madison Parish, which is east of Monroe, has the highest percentage of area at risk for sinkhole formation, and St. Mary Parish—immediately west of Terrebonne—has the greatest risk of property loss. An Acadia Parish census tract has the maximum annual property losses in 2050 projected at $40,047 (2010$), and the highest projected annual per building ($43) and per capita ($18) property loss are in the same St. Mary Parish census tract. At the census block level, maximum annual property loss ($7,040) is projected for a census block within Cameron Parish, with maximum annual per building loss ($85 within West Baton Rouge Parish), and maximum per capita annual property loss ($120 within Plaquemines Parish). The method presented in this paper is developed generally, allowing application for risk assessment in other locations. The results generated by the methodology are important to local, state, and national emergency management efforts. Further, the general public of Louisiana, and other areas where the developed method is applied, may benefit by considering sinkhole risk when purchasing, remodeling, and insuring a property, including as a basis of comparison to the risk from other types of hazard.

Trends in Precancerous Cervical Lesions by Area-Based Measures of Poverty, Race, and Ethnicity, Connecticut, 2008-2018

Objectives Trends in the incidence of precancerous cervical lesions can be monitored to evaluate the impact of human papillomavirus (HPV) vaccination. The objective of this analysis was to determine whether declines in precancerous cervical lesions varied by area-based measures of poverty, race, and ethnicity. Methods We analyzed 11 years of incidence data (2008-2018) from a statewide active surveillance system of precancerous cervical lesions in Connecticut. We divided area-based measures of poverty, race, and ethnicity (percentage of the population in a census tract who were living below the federal poverty level, who were Black, and who were Hispanic) at the census-tract level into 4 groups (<5.0%, 5.0%-9.9%, 10.0%-19.9%, ≥20.0%) using recommended cut points from the Public Health Disparities Geocoding Project. We estimated incidence rates and average annual percentage changes (AAPCs) stratified by age and each area-based measure using Joinpoint regression software. We used total population and estimated screened population as denominators for each age group to calculate rates and AAPCs. Results During 2008-2018 in Connecticut, 18 878 women aged 21-39 were diagnosed with precancerous cervical lesions. After adjusting for screening, the largest declines occurred among women aged 21-24 (AAPC = −11.5%; 95% CI, −13.6% to −9.4%). We found significant and similar annual declines (~10%-12%) in this age group across all 4 levels of poverty, race, and ethnicity. Conclusions This analysis adds to the growing body of evidence demonstrating the positive impact of population-level HPV vaccination among young women that appears similar across area-based measures of sociodemographic characteristics. Monitoring is necessary to ensure the continuation of this progress in all communities.

2. Space Time Trends of Community Onset Staphylococcus aureus Infections in Children Living in Southeastern United States: 2002-2016

Background Staphylococcus aureus (S. aureus) remains a serious cause of infections in the United States and worldwide. Methicillin susceptible S. aureus (MSSA) is the cause of half of all health care–associated staphylococcal infections, and Methicillin Resistant S. aureus (MRSA) is the leading cause of community onset skin and soft tissue infections in the US. This study looks at a 15-year trend of community onset (CO)-MRSA and MSSA infections and determines ‘best’ to ‘worst’ infection trends. We identified distinct groups of CO-MRSA and MSSA infection rate trajectories by grouping census tracts of the 20 county Atlanta Metropolitan Statistical Area (MSA) between 2002 to 2016 with similar temporal trajectories. Methods This is a retrospective study from 2002-2016, using electronic health records of children living in Atlanta, Georgia with S. aureus infections and relevant US census data (at the census tract level). A group based trajectory model was applied to generate community onset S. aureus trajectory infection groups (low, high, very high) by census tract and were mapped using ArcGIS. Results Three CO-MSSA infection groups (low, high, very high) and two CO-MRSA infection groups (low, high) were detected among 909 census tracts in the 20 counties. We found ~74% of all the census tracts with S.aureus occurrence during this time period belonged to low infection rate groups for both MRSA and MSSA, with a higher proportion occurring in the less densely populated counties. Census tracts in DeKalb County, one of Atlanta’s most densely populated areas, had the highest proportion of the worst infection trend patterns (CO-MRSA high or very high, CO-MSSA high or very high). Trends of Community-Onset MRSA and MSSA Infection Rates Based on Group-based Trajectory Models Spatial patterns for CO-MRSA and CO-MSSA Trajectory Trends in the Atlanta Metropolitan Area Between 2002 to 2016 Conclusion Trends of S. aureus infection patterns, stratified by antibiotic resistance over geographic areas and time, identify communities with higher risks for MRSA infection compared to MSSA infection. Further investigation of the determinants of the trajectory groupings and the geographic outliers identified by this study may be a way to target prevention strategies aimed to prevent S. aureus infections. Disclosures All Authors: No reported disclosures

Census tract socioeconomic indicators and COVID-19-associated hospitalization rates—COVID-NET surveillance areas in 14 states, March 1–April 30, 2020

Objectives Some studies suggested more COVID-19-associated hospitalizations among racial and ethnic minorities. To inform public health practice, the COVID-19-associated Hospitalization Surveillance Network (COVID-NET) quantified associations between race/ethnicity, census tract socioeconomic indicators, and COVID-19-associated hospitalization rates. Methods Using data from COVID-NET population-based surveillance reported during March 1–April 30, 2020 along with socioeconomic and denominator data from the US Census Bureau, we calculated COVID-19-associated hospitalization rates by racial/ethnic and census tract-level socioeconomic strata. Results Among 16,000 COVID-19-associated hospitalizations, 34.8% occurred among non-Hispanic White (White) persons, 36.3% among non-Hispanic Black (Black) persons, and 18.2% among Hispanic or Latino (Hispanic) persons. Age-adjusted COVID-19-associated hospitalization rate were 151.6 (95% Confidence Interval (CI): 147.1–156.1) in census tracts with >15.2%–83.2% of persons living below the federal poverty level (high-poverty census tracts) and 75.5 (95% CI: 72.9–78.1) in census tracts with 0%–4.9% of persons living below the federal poverty level (low-poverty census tracts). Among White, Black, and Hispanic persons living in high-poverty census tracts, age-adjusted hospitalization rates were 120.3 (95% CI: 112.3–128.2), 252.2 (95% CI: 241.4–263.0), and 341.1 (95% CI: 317.3–365.0), respectively, compared with 58.2 (95% CI: 55.4–61.1), 304.0 (95%: 282.4–325.6), and 540.3 (95% CI: 477.0–603.6), respectively, in low-poverty census tracts. Conclusions Overall, COVID-19-associated hospitalization rates were highest in high-poverty census tracts, but rates among Black and Hispanic persons were high regardless of poverty level. Public health practitioners must ensure mitigation measures and vaccination campaigns address needs of racial/ethnic minority groups and people living in high-poverty census tracts.

Reanalysis of the association between reduction in long-term PM2.5 concentrations and improved life expectancy

Background Much of the current evidence of associations between long-term PM2.5 and health outcomes relies on national or regional analyses using exposures derived directly from regulatory monitoring data. These findings could be affected by limited spatial coverage of monitoring data, particularly for time periods before spatially extensive monitoring began in the late 1990s. For instance, Pope et al. (2009) showed that between 1980 and 2000 a 10 μg/m3 reduction in PM2.5 was associated with an average 0.61 year (standard error (SE) = 0.20) longer life expectancy. That analysis used 1979–1983 averages of PM2.5 across 51 U.S. Metropolitan Statistical Areas (MSAs) computed from about 130 monitoring sites. Our reanalysis re-examines this association using modeled PM2.5 in order to assess population- or spatially-representative exposure. We hypothesized that modeled PM2.5 with finer spatial resolution provides more accurate health effect estimates compared to limited monitoring data. Methods We used the same data for life expectancy and confounders, as well as the same analysis models, and investigated the same 211 continental U.S. counties, as Pope et al. (2009). For modeled PM2.5, we relied on a previously-developed point prediction model based on regulatory monitoring data for 1999–2015 and back-extrapolation to 1979. Using this model, we predicted annual average concentrations at centroids of all 72,271 census tracts and 12,501 25-km national grid cells covering the contiguous U.S., to represent population and space, respectively. We averaged these predictions to the county for the two time periods (1979–1983 and 1999–2000), whereas the original analysis used MSA averages given limited monitoring data. Finally, we estimated regression coefficients for PM2.5 reduction on life expectancy improvement over the two periods, adjusting for area-level confounders. Results A 10 μg/m3 decrease in modeled PM2.5 based on census tract and national grid predictions was associated with 0.69 (standard error (SE) = 0.31) and 0.81 (0.29) -year increases in life expectancy. These estimates are higher than the estimate of Pope et al. (2009); they also have larger SEs likely because of smaller variability in exposure predictions, a standard property of regression. Two sets of effect estimates, however, had overlapping confidence intervals. Conclusions Our approach for estimating population- and spatially-representative PM2.5 concentrations based on census tract and national grid predictions, respectively, provided generally consistent findings to the original findings using limited monitoring data. This finding lends additional support to the evidence that reduced fine particulate matter contributes to extended life expectancy.