Methodological concerns regarding the identification of neighborhood causal effects dominates research on neighborhood spatial health inequality. I contend that the myopic focus on these methodological concerns curtails the theoretical development into the nature of spatial health inequality by attempting to control away, rather than provide an account of, the process of residential mobility that differentially sorts residents by their health risk. In this paper, I use unique data from the 2004-5 Chicago Area Study that links individual-level health status with residential preferences for actual communities in a major city to study patterns of residential preferences. Combining these data with health assessments from residents already living in the queried communities using the 2002 Chicago Community Adult Health Study, I examine how much health composition of current residents influences health preferences.I argue populationlevel avoidance from stigmatized places marked by unhealthy and racial composition rather than individual-level selection structure spatial health inequality.
here we exist in space guides whom we know and with whom we build families, influences our health, and determines whether and where we move. Demographers’ history of “putting people in place,” as Barbara Entwisle convincingly argues, makes us particularly well-suited not only to examine the influence of spatial relationships on fertility, mortality and migration, but also social outcomes that occur in between those events. In this position statement I contend that two problems persist in our research on the topic that must be addressed in order to improve the state of research investigating spatial inequality: 1) determining whether contextual or compositional forces perpetuate spatial inequality and 2) the need for cost-efficient data collection tools adaptable to large surveys. Although both are typically treated as methodological, I argue below that the first is a theoretical problem for which demographers possess the necessary tools but insufficient theoretical orientation. I propose that we devote more research to developing how compositional forces perpetuate spatial inequality and briefly describe tools to address the second.
Recommendations for fruit and vegetable consumption are largely unmet. Lower socio-economic status (SES), neighbourhood poverty and poor access to retail outlets selling healthy foods are thought to predict lower consumption. The objective of the present study was to assess the interrelationships between these risk factors as predictors of fruit and vegetable consumption.Cross-sectional multilevel analyses of data on fruit and vegetable consumption, socio-demographic characteristics, neighbourhood poverty and access to healthy retail food outlets.Survey data from the 2002 and 2004 New York City Community Health Survey, linked by residential zip code to neighbourhood data.Adult survey respondents (n 15 634).Overall 9?9% of respondents reported eating $5 servings of fruits or vegetables in the day prior to the survey. The odds of eating $5 servings increased with higher income among women and with higher educational attainment among men and women. Compared with women having less than a high-school education, the OR was 1?12 (95% CI 0?82, 1?55) for high-school graduates, 1?95 (95% CI 1?43, 2?66) for those with some college education and 2?13 (95% CI 1?56, 2?91) for college graduates. The association between education and fruit and vegetable consumption was significantly stronger for women living in lower- v. higher-poverty zip codes (P for interaction,0?05). The density of healthy food outlets did not predict consumption of fruits or vegetables.Higher SES is associated with higher consumption of produce, an association that, in women, is stronger for those residing in lower-poverty neighbourhoods.
Workload, response rate, data yield, and data quality of travel diaries are interacting variables. It has long been suspected that it is impossible to maximize all variables at the same time. Still, empirical work trying to improve understanding of the trade-offs among them has been rare. Results are reported of experiments with long-distance diaries, which aim to clarify some of the possible relationships. The object of experimentation is surveys of long-distance travel behavior, which are currently of particular interest in Europe and elsewhere. The development of the tourism industry, deregulation of the long-distance modes, and infrastructure concerns require improved data about long-distance travel, both in improved inventories and in improved behavioral understanding. The experiments undertaken varied the workload of the respondents by varying the number of items to be reported about any long-distance journey, the duration of the survey period, and the temporal orientation of the survey. The results indicate that the response rate and the data yield, that is, the number of reported journeys and stages, change systematically with changes in the experimental variables (reduced response rates for prospective surveys, reduced number of reported journeys, and stages for retrospective surveys). Detailed results for these trade-offs are given. The trade-offs force the designer of such surveys to choose carefully and to invest time and effort in correcting for the potential biases resulting from this systematic behavior.
Michael D.M. Bader, Siri Warkentien Sociological Science, March 2, 2016 DOI 10.15195/v3.a8 Abstract We argue that existing studies underestimate the degree to which racial change leads to residential segregation in post-Civil Rights American neighborhoods.
Abstract The analytical procedure described here permits the determination of creatinine, the degradation product of creatine or creatine phosphate, in urine using high‐performance liquid chromatography. For that purpose, urine samples are diluted with ultrapure water at a ratio of 1:5 and then analyzed isocratically using HPLC‐UV. Aqueous standards are used for calibration.
UNSTRUCTURED Clinical epidemiology and patient-oriented health care research that incorporates neighborhood-level data is becoming increasingly common. A key step in conducting this research is converting patient address data to longitude and latitude data, a process known as geocoding. Several commonly used approaches to geocoding (eg, ggmap or the tidygeocoder R package) send patient addresses over the internet to web-based third-party geocoding services. Here, we describe how these approaches to geocoding disclose patients’ personally identifiable information (PII) and how the subsequent publication of the research findings discloses the same patients’ protected health information (PHI). We explain how these disclosures can occur and recommend strategies to maintain patient privacy when studying neighborhood effects on patient outcomes.
Ordinary kriging, a spatial interpolation technique, is commonly used in social sciences to estimate neighborhood attributes such as physical disorder. Universal kriging, developed and used in physical sciences, extends ordinary kriging by supplementing the spatial model with additional covariates. We measured physical disorder on 1,826 sampled block faces across 4 US cities (New York, Philadelphia, Detroit, and San Jose) using Google Street View imagery. We then compared leave-one-out cross-validation accuracy between universal and ordinary kriging and used random subsamples of our observed data to explore whether universal kriging could provide equal measurement accuracy with less spatially dense samples. Universal kriging did not always improve accuracy. However, a measure of housing vacancy did improve estimation accuracy in Philadelphia and Detroit (7.9 and 6.8% lower root mean square error, respectively) and allowed for equivalent estimation accuracy with half the sampled points in Philadelphia. Universal kriging may improve neighborhood measurement.
'%3e%3cpath fill='%23012169' d='M0 0v30h60V0z'/%3e%3cpath stroke='%23fff' stroke-width='6' d='m0 0 60 30m0-30L0 30'/%3e%3cpath stroke='%23C8102E' stroke-width='4' d='m0 0 60 30m0-30L0 30' clip-path='url(%23b)'/%3e%3cpath stroke='%23fff' stroke-width='10' d='M30 0v30M0 15h60'/%3e%3cpath stroke='%23C8102E' stroke-width='6' d='M30 0v30M0 15h60'/%3e%3c/g%3e%3c/svg%3e)