Spatial Heterogeneity in Spillover Effects of Assisted and Unassisted Rental Housing

Three new contributions are added to the literature on subsidized rental housing impacts on nearby property values: 1) A primary focus on the spatial heterogeneity of these effects which warrants caution regarding citywide results; 2) an analysis by zoning area, and 3) a comparison of impacts with unsubsidized apartments. An adjusted-interrupted time series (difference-in-difference) model is estimated with a comprehensive dataset for Seattle, WA (1987-97). Contrary to NIMBY expectations, the predominant impact is an upgrading effect of lower-value areas. However, spillover effects are very sensitive to how data are pooled across space: The citywide upgrading effects are driven by poorer pockets adjacent to affluent areas with no or small effects in more diverse low- and medium income areas. They only occur in single-family, not multi-family zones. The only negative effects were associated with vouchers in one of the affluent areas. Impacts of unsubsidized rentals are very similar to those of subsidized ones, suggesting an independent effect beyond subsidy status. These findings are explained with Seattle's dispersion and good neighbor policies, with gentrification pressures as a possible alternative explanation. Site visits confirmed the location of subsidized sites in lower-value areas and the higher maintenance quality of subsidized vis-à-vis unsubsidized units.

Does Context Matter for the Relationship between Deprivation and All-Cause Mortality? The West vs. the Rest of Scotland

Background A growing body of research emphasizes the importance of contextual factors on health outcomes. Using postcode data for Scotland (UK), this study tests the hypothesis of spatial heterogeneity in the relationship between area-level deprivation and mortality to determine if contextual differences in the West vs. the rest of Scotland influence this relationship. Research into health inequalities frequently fails to recognise spatial heterogeneity in the deprivation-health relationship, assuming that global relationships apply uniformly across geographical areas. In this study, exploratory spatial data analysis methods are used to assess local patterns in deprivation and mortality. Spatial regression models are then implemented to examine the relationship between deprivation and mortality more formally. Results The initial exploratory spatial data analysis reveals concentrations of high SMR and deprivation values (hotspots) in the West of Scotland and concentrations of low values (coldspots) for both variables in the rest of the country. The main spatial regression result is that deprivation is the only variable that is highly significantly correlated with all-cause mortality in all models. However, in contrast to the expected spatial heterogeneity in the deprivation-mortality relationship, this relation does not vary between regions in any of the models. This result is robust to a number of specifications, including weighing for population size, controlling for spatial autocorrelation and heteroskedasticity, assuming a non-linear relationship between mortality and deprivation, breaking the dependent variable into male and female SMRs, and distinguishing between West, North and Southeast regions. The rejection of the hypothesis of spatial heterogeneity in the relationship between deprivation and mortality complements prior research on the stability of the deprivation-mortality relationship over time. Conclusions The obtained homogeneity in the deprivation-mortality relationship across the regions of Scotland and the absence of a contextualized effect of region highlights the importance of taking a broader strategic policy that can combat the toxic impacts of deprivation on health. Focusing on a few specific places (e.g. 15% of the poorest areas) to concentrate resources might be a good start but the impacts of deprivation on mortality is not restricted to a few places. A comprehensive strategy that can be sustained over time might be needed to interrupt the linkages between poverty and mortality.

Improving the Multi-Dimensional Comparison of Simulation Results: A Spatial Visualization Approach

Results from simulation experiments are important in applied spatial econometrics to, for instance, assess the performance of spatial estimators and tests for finite samples. However, the traditional tabular and graphi- cal formats for displaying simulation results in the literature have several disadvantages. These include loss of results, lack of intuitive synthesis, and difficulty in comparing results across multiple dimensions. We pro- pose to address these challenges through a spatial visualization approach. This approach visualizes model precision and bias as well as the size and power of tests in map format. The advantage of this spatial approach is that these maps can display all results succinctly, enable an intuitive interpretation, and compare results efficiently across multiple dimensions of a simulation experiment. Due to the respective strengths of tables, graphs and maps, we propose this spatial approach as a supplement to traditional tabular and graphical display formats. To allow readers to generate maps such as the ones presented in this article, a package (written in Python) has been made available by the authors as free/libre software. The package includes an example as well as a short tutorial for researchers without programming experience and can be downloaded at: https://github.com/darribas/simVizMap.

Does Context Matter for the Relationship between Deprivation and All-Cause Mortality? The West vs. the Rest of Scotland

One of the assumptions that is often made in modeling the relationship between deprivation and mortality is that this relationship will remain the same across space. There is little justification presented in the literature as to why the deprivation-mortality relationship will be homogenous across space. The homogeneity of this relationship over space is an empirical question and most of the published literature does not formally test this relationship. Using postcode data for Scotland (UK), this study addresses this research gap and tests the hypothesis of spatial heterogeneity in the relationship between area-level deprivation and mortality. Research into health inequalities frequently fails to recognise spatial heterogeneity in the deprivation-health relationship, assuming that global relationships apply uniformly across geographical areas. In this study, exploratory spatial data analysis methods are used to assess local patterns in deprivation and mortality. A variety of spatial regression models are then implemented to examine the relationship between deprivation and mortality. The hypothesis of spatial heterogeneity in the relationship between deprivation and mortality is rejected. Implications of the homogeneity of the deprivation-mortality relationships for addressing health inequities are discussed in light of the inverse care law.

Does context matter for the relationship between deprivation and all-cause mortality? The West vs. the rest of Scotland

abstract: Background A growing body of research emphasizes the importance of contextual factors on health outcomes. Using postcode sector data for Scotland (UK), this study tests the hypothesis of spatial heterogeneity in the relationship between area-level deprivation and mortality to determine if contextual differences in the West vs. the rest of Scotland influence this relationship. Research into health inequalities frequently fails to recognise spatial heterogeneity in the deprivation-health relationship, assuming that global relationships apply uniformly across geographical areas. In this study, exploratory spatial data analysis methods are used to assess local patterns in deprivation and mortality. Spatial regression models are then implemented to examine the relationship between deprivation and mortality more formally. Results The initial exploratory spatial data analysis reveals concentrations of high standardized mortality ratios (SMR) and deprivation (hotspots) in the West of Scotland and concentrations of low values (coldspots) for both variables in the rest of the country. The main spatial regression result is that deprivation is the only variable that is highly significantly correlated with all-cause mortality in all models. However, in contrast to the expected spatial heterogeneity in the deprivation-mortality relationship, this relation does not vary between regions in any of the models. This result is robust to a number of specifications, including weighting for population size, controlling for spatial autocorrelation and heteroskedasticity, assuming a non-linear relationship between mortality and socio-economic deprivation, separating the dependent variable into male and female SMRs, and distinguishing between West, North and Southeast regions. The rejection of the hypothesis of spatial heterogeneity in the relationship between socio-economic deprivation and mortality complements prior research on the stability of the deprivation-mortality relationship over time. Conclusions The homogeneity we found in the deprivation-mortality relationship across the regions of Scotland and the absence of a contextualized effect of region highlights the importance of taking a broader strategic policy that can combat the toxic impacts of socio-economic deprivation on health. Focusing on a few specific places (e.g. 15% of the poorest areas) to concentrate resources might be a good start but the impact of socio-economic deprivation on mortality is not restricted to a few places. A comprehensive strategy that can be sustained over time might be needed to interrupt the linkages between poverty and mortality.The electronic version of this article is the complete one and can be found online at: https://ij-healthgeographics.biomedcentral.com/articles/10.1186/1476-072X-10-3

Uncertain Uncertainty: Spatial Variation in the Quality of American Community Survey Estimates

The U.S. Census Bureau's American Community Survey (ACS) is the foundation of social science research, much federal resource allocation and the development of public policy and private sector decisions. However, the high uncertainty associated with some of the ACS's most frequently used estimates can jeopardize the accuracy of inferences based on these data. While there is high level understanding in the research community that problems exist in the data, the sources and implications of these problems have been largely overlooked. Using 2006-2010 ACS median household income at the census tract scale as the test case (where a third of small-area estimates have higher than recommend errors), we explore the patterns in the uncertainty of ACS data. We consider various potential sources of uncertainty in the data, ranging from response level to geographic location to characteristics of the place. We find that there exist systematic patterns in the uncertainty in both the spatial and attribute dimensions. Using a regression framework, we identify the factors that are most frequently correlated with the error at national, regional and metropolitan area scales, and find these correlates are not consistent across the various locations tested. The implication is that data quality varies in different places, making cross-sectional analysis both within and across regions less reliable. We also present general advice for data users and potential solutions to the challenges identified

Spatial analysis of elderly access to primary care services

BACKGROUND: Admissions for Ambulatory Care Sensitive Conditions (ACSCs) are considered preventable admissions, because they are unlikely to occur when good preventive health care is received. Thus, high rates of admissions for ACSCs among the elderly (persons aged 65 or above who qualify for Medicare health insurance) are signals of poor preventive care utilization. The relevant geographic market to use in studying these admission rates is the primary care physician market. Our conceptual model assumes that local market conditions serving as interventions along the pathways to preventive care services utilization can impact ACSC admission rates. RESULTS: We examine the relationships between market-level supply and demand factors on market-level rates of ACSC admissions among the elderly residing in the U.S. in the late 1990s. Using 6,475 natural markets in the mainland U.S. defined by The Health Resources and Services Administration's Primary Care Service Area Project, spatial regression is used to estimate the model, controlling for disease severity using detailed information from Medicare claims files. Our evidence suggests that elderly living in impoverished rural areas or in sprawling suburban places are about equally more likely to be admitted for ACSCs. Greater availability of physicians does not seem to matter, but greater prevalence of non-physician clinicians and international medical graduates, relative to U.S. medical graduates, does seem to reduce ACSC admissions, especially in poor rural areas. CONCLUSION: The relative importance of non-physician clinicians and international medical graduates in providing primary care to the elderly in geographic areas of greatest need can inform the ongoing debate regarding whether there is an impending shortage of physicians in the United States. These findings support other authors who claim that the existing supply of physicians is perhaps adequate, however the distribution of them across the landscape may not be optimal. The finding that elderly who reside in sprawling urban areas have access impediments about equal to residents of poor rural communities is new, and demonstrates the value of conceptualizing and modelling impedance based on place and local context

Managed Care and the Diffusion of Endoscopy in Fee-for-Service Medicare

To determine whether Medicare managed care penetration impacted the diffusion of endoscopy services (sigmoidoscopy, colonoscopy) among the fee-for-service (FFS) Medicare population during 2001–2006

Lipoprotein(a) induces caspase-1 activation and IL-1 signaling in human macrophages

IntroductionLipoprotein(a) (Lp(a)) is an LDL-like particle with an additional apolipoprotein (apo)(a) covalently attached. Elevated levels of circulating Lp(a) are a risk factor for atherosclerosis. A proinflammatory role for Lp(a) has been proposed, but its molecular details are incompletely defined.Methods and resultsTo explore the effect of Lp(a) on human macrophages we performed RNA sequencing on THP-1 macrophages treated with Lp(a) or recombinant apo(a), which showed that especially Lp(a) induces potent inflammatory responses. Thus, we stimulated THP-1 macrophages with serum containing various Lp(a) levels to investigate their correlations with cytokines highlighted by the RNAseq, showing significant correlations with caspase-1 activity and secretion of IL-1β and IL-18. We further isolated both Lp(a) and LDL particles from three donors and then compared their atheroinflammatory potentials together with recombinant apo(a) in primary and THP-1 derived macrophages. Compared with LDL, Lp(a) induced a robust and dose-dependent caspase-1 activation and release of IL-1β and IL-18 in both macrophage types. Recombinant apo(a) strongly induced caspase-1 activation and IL-1β release in THP-1 macrophages but yielded weak responses in primary macrophages. Structural analysis of these particles revealed that the Lp(a) proteome was enriched in proteins associated with complement activation and coagulation, and its lipidome was relatively deficient in polyunsaturated fatty acids and had a high n-6/n-3 ratio promoting inflammation.DiscussionOur data show that Lp(a) particles induce the expression of inflammatory genes, and Lp(a) and to a lesser extent apo(a) induce caspase-1 activation and IL-1 signaling. Major differences in the molecular profiles between Lp(a) and LDL contribute to Lp(a) being more atheroinflammatory

Modeling Spatial Spillover Effects From Rental to Owner Housing: The Case of Seattle

172 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008.The sensitivity analysis suggests that, at least for the case of Seattle, the AITS-DID model is very sensitive to which years are pooled and which spatial subsets are taken. The fact that the pre-post relationship is not found to be robust in the case of Seattle is cause for concern since it is at the heart of the analysis. Spatial fixed effects did not fully control for spatially correlated sales prices as expected (controlling for spatial autocorrelation tends to primarily make a difference for impacts significant at a 0.05 level although there are important exceptions).U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD