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Open Access Publications from the University of California

Scholarly Works (3 results)

  • Thesis
  • Peer Reviewed
UC Berkeley Electronic Theses and Dissertations (2012)

To investigate the extent to which traffic exposure affects the lung function of children with asthma and how local neighborhood factors may modify this relation, a merge of epidemiologic, environmental health and geographic methods is necessary. People and places are linked; therefore, it is necessary to consider place-effects on health as well as environmental exposures. The Fresno Asthmatic Children's Environment Study (FACES) is a longitudinal cohort study of children with asthma in Fresno, California that followed participants from 2000-2008 to explore short-term and long-term effects of ambient air pollution on lung function (as measured by spirometry, wheeze, and asthma symptoms). With publicly available data on traffic counts in Fresno, CA from 2000-2008, I built a spatial model of traffic exposure that varies both temporally and spatially for the FACES cohort. To capture and quantify neighborhood characteristics, I constructed individual neighborhoods based on global positioning software (GPS) data and walking distances around participant homes. To evaluate neighborhood deprivation, I collected geographic information system (GIS) data on parks, grocery stores, bus stops, etc. from publicly available sources and created an index based on Item Response Theory. To assess the marginal risk difference of lung function among children with asthma exposed to high levels of traffic pollution and those exposed to lower levels of traffic pollution (as measured by traffic density), I apply semi-parametric causal inference methods and use Targeted Maximum Likelihood Estimation (TMLE). More FACES participants who live in high deprivation neighborhoods are also farther away from high traffic areas. Neighborhood deprivation, as defined by a combination of GIS variables in this study, does not track well with US Census poverty. The marginal change in lung function from exposure to high neighborhood traffic to lower neighborhood traffic, without stratification for neighborhood deprivation, is -0.233 (95% CI -0.338, -0.129). The results can be interpreted as--the average decrease of FEV1 is 0.233 L, or there is a 12% reduction in lung function. Either neighborhood deprivation does not modify the effect of traffic on lung function or there is not enough data to evaluate this type of effect modification. The findings indicate that neighborhood exposure to traffic adversely affects lung function among the FACES cohort of children with asthma.

    Cover page: The Impact of Neighborhood Traffic Density and Deprivation on Lung Function Among Children with Asthma
    • Article
    • Peer Reviewed
    UC San Francisco Previously Published Works (2018)
    Community-level approaches for pediatric asthma management rely on locally collected information derived primarily from two sources: claims records and school-based surveys. We combined claims and school-based surveillance data, and examined the asthma-related risk patterns among adolescent students. Symptom data collected from school-based asthma surveys conducted in Oakland, CA were used for case identification and determination of severity levels for students (high and low). Survey data were matched to Medicaid claims data for all asthma-related health care encounters for the year prior to the survey. We then employed recursive partitioning to develop classification trees that identified patterns of demographics and healthcare utilization associated with severity. A total of 561 students had complete matched data; 86.1% were classified as high-severity, and 13.9% as low-severity asthma. The classification tree consisted of eight subsets: three indicating high severity and five indicating low severity. The risk subsets highlighted varying combinations of non-specific demographic and socioeconomic predictors of asthma prevalence, morbidity and severity. For example, the subset with the highest class-prior probability (92.1%) predicted high-severity asthma and consisted of students without prescribed rescue medication, but with at least one in-clinic nebulizer treatment. The predictive accuracy of the tree-based model was approximately 66.7%, with an estimated 91.1% of high-severity cases and 42.3% of low-severity cases correctly predicted. Our analysis draws on the strengths of two complementary datasets to provide community-level information on children with asthma, and demonstrates the utility of recursive partitioning methods to explore a combination of features that convey asthma severity.
      Cover page: Optimizing community-level surveillance data for pediatric asthma management
      • Article
      • Peer Reviewed
      UC Berkeley Previously Published Works (2017)
      Racial and ethnic segregation has been linked to a number of deleterious health outcomes, including violence. Previous studies of segregation and violence have focused on segregation between African Americans and Whites, used homicide as a measure of violence, and employed segregation measures that fail to take into account neighborhood level processes. We examined the relationship between neighborhood diversity and violent injury in Oakland, California. Violent injuries from the Alameda County Medical Center Trauma Registry that occurred between 1998 and 2002 were geocoded. A local measure of diversity among African American, White, Hispanic, and Asian populations that captured interactions across census block group boundaries was calculated from 2000 U.S. Census data and a Geographic Information System. The relationship between violent injuries and neighborhood level of diversity, adjusted for covariates, was analyzed with zero-inflated negative binomial regression. There was a significant and inverse association between level of racial and ethnic diversity and rate of violent injury (IRR 0.30; 95% CI: 0.13-0.69). There was a similar relationship between diversity and violent injury for predominantly African American block groups (IRR 0.23; 95% CI: 0.08-0.62) and predominantly Hispanic block groups (IRR 0.08; 95% CI: 0.01-0.76). Diversity was not significantly associated with violent injury in predominantly White or Asian block groups. Block group racial and ethnic diversity is associated with lower rates of violent injury, particularly for predominantly African American and Hispanic block groups.
        Cover page: Violent Injury and Neighborhood Racial/Ethnic Diversity in Oakland, California
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