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.

Inequality and Income Segregation in Brazilian cities: A Nationwide Analysis

Urban segregation has brought significant challenges to cities worldwide and has important implications for health. This study aimed to assess income segregation in the 152 largest Brazilian cities included in the SALURBAL Project and identify specific socioeconomic characteristics related to residential segregation by income. Using the Brazilian demographic census database of the year 2010, we calculated the income dissimilarity index (IDI) at census tract level for each SALURBAL city; subsequently comparing it with Gini and other local socioeconomic variables. We evaluated our results' robustness using a bootstrap correction to the IDI to examine the consequences of using different cut-offs of income that were relevant in the context of strong urban and regional inequalities. We identified a 2 minimum wages cut-off as the most appropriate and found little evidence of upward bias in the calculation of the IDI regardless of the cut-off used. Among the 10 most segregated cities, 9 are in the Northeast region, the region with the highest income inequality and poverty in Brazil. Our results indicate that the Gini index and poverty are the main variables associated with residential segregation, measured by the IDI. Social and environmental characteristics were also associated with IDI, reinforcing the notion that access to education, water, sanitation, and better residential conditions are fundamental to improving social equity.

The Unequal Availability of Rental Housing Information Across Neighborhoods

As more urban residents find their housing through online search tools, recent research has theorized the potential for online information to transform and equalize the housing search process. Yet, very little is known about what rental housing information is available online. Using a corpus of millions of geocoded Craigslist advertisements for rental housing across the 50 largest metropolitan statistical areas in the United States merged with census tract–level data from the American Community Survey, we identify and describe the types of information commonly included in listings across different types of neighborhoods. We find that in the online housing market, renters are exposed to fundamentally different types of information depending on the ethnoracial and socioeconomic makeup of the neighborhoods where they are searching.

Racial and Neighborhood-level Disparities in COVID-19 Incidence Among Patients on Hemodialysis in New York City

Background The coronavirus disease 2019 (COVID-19) pandemic has disproportionately affected socially disadvantaged populations. Whether disparities in COVID-19 incidence related to race/ethnicity and socioeconomic factors exist in the hemodialysis population is unknown. Methods Our study involved patients receiving in-center hemodialysis in New York City. We used a validated index of neighborhood social vulnerability, the Social Vulnerability Index (SVI), which comprises 15 census tract-level indicators organized into four themes: socioeconomic status, household composition and disability, minority status and language, and housing type and transportation. We examined the association of race/ethnicity and the SVI with symptomatic COVID-19 between March 1, 2020, and August 3, 2020. COVID-19 cases were ascertained using PCR testing. We performed multivariable logistic regression to adjust for demographics, individual-level social factors, dialysis-related medical history, and dialysis facility factors. Results Of the 1378 patients on hemodialysis in the study, 247 (17.9%) developed symptomatic COVID-19. In adjusted analyses, non-Hispanic Black and Hispanic patients had significantly increased odds of COVID-19 compared with non-Hispanic White patients. Census tract-level overall SVI, modeled continuously or in quintiles, was not associated with COVID-19 in unadjusted or adjusted analyses. Among non-Hispanic White patients, the socioeconomic status SVI theme, the minority status and language SVI theme, and housing crowding were significantly associated with COVID-19 in unadjusted analyses. Conclusions Among patients on hemodialysis in New York City, there were substantial racial/ethnic disparities in COVID-19 incidence not explained by neighborhood-level social vulnerability. Neighborhood-level socioeconomic status, minority status and language, and housing crowding were positively associated with acquiring COVID-19 among non-Hispanic Whites. Our findings suggest that socially vulnerable patients on dialysis face disparate COVID-19-related exposures, requiring targeted risk-mitigation strategies.

Racial Disparity and Social Determinants in Receiving Timely Surgery Among Stage I–IIIA Non-small Cell Lung Cancer Patients in a U.S. Southern State

Delayed surgery is associated with worse lung cancer outcomes. Social determinants can influence health disparities. This study aimed to examine the potential racial disparity and the effects from social determinants on receipt of timely surgery among lung cancer patients in Louisiana, a southern state in the U.S. White and black stage I–IIIA non-small cell lung cancer patients diagnosed in Louisiana between 2004 and 2016, receiving surgical lobectomy or a more extensive surgery, were selected. Diagnosis-to-surgery interval >6 weeks were considered as delayed surgery. Social determinants included marital status, insurance, census tract level poverty, and census tract level urbanicity. Multivariable logistic regression and generalized multiple mediation analysis were conducted. A total of 3,616 white (78.9%) and black (21.1%) patients were identified. The median time interval from diagnosis to surgery was 27 days in whites and 42 days in blacks (P < 0.0001). About 28.7% of white and 48.4% of black patients received delayed surgery (P < 0.0001). Black patients had almost two-fold odds of receiving delayed surgery than white patients (adjusted odds ratio: 1.91; 95% confidence interval: 1.59–2.30). Social determinants explained about 26% of the racial disparity in receiving delayed surgery. Having social support, private insurance, and living in census tracts with lower poverty level were associated with improved access to timely surgery. The census tract level poverty level a stronger effect on delayed surgery in black patients than in white patients. Tailored interventions to improve the timely treatment in NSCLC patients, especially black patients, are needed in the future.

Geographic health inequalities in Madrid City: exploring spatial patterns of respiratory disease mortality

The unequal geographic distribution of health determinants could denote situations of environmental injustice. This work aims to identify spatial patterns of respiratory disease mortality and their association with the education level and the atmospheric pollution in Madrid. To this purpose, we applied spatial analysis through statistical techniques and Geographic Information Systems at the census tract level. The analysis showed a slight but significantly higher risk of mortality in areas with more unfavourable socioeconomic and environmental conditions. This work has the potential to inform public policy and research on links among social, environmental and health inequalities in Madrid City.