NONPARAMETRIC BAYESIAN INFERENCES ON PREDICTOR-DEPENDENT RESPONSE DISTRIBUTIONS

A common statistical problem in biomedical research is to characterize the relationship between a response and predictors. The heterogeneity among subjects causes the response distribution to change across the predictor space in distributional characteristics such as skewness, quantiles and residual variation. In such settings, it would be appealing to model the conditional response distributions as flexibly changing across the predictors while conducting variable selection to identify important predictors both locally (within some local regions) and globally (across the entire range of the predictor space) for the response distribution change. Nonparametric Bayes methods have been very useful for flexible modeling where nonparametric distributions are assumed unknown and assigned priors such as the Dirichlet process (DP). In recent years, there has been a growing interest in extending the DP to a prior model for predictor-dependent unknown distributions, so that the extended priors are applied to flexible conditional distribution modeling. However, for most of the proposed extensions, construction is not simple and computation can be quite difficult. In addition, literature has been focused on estimation and few attempts have been made to address related hypothesis testing problems such as variable selection. Paper 1 proposes a local Dirichlet process (lDP) as a generalization of the Dirichlet process to provide a prior distribution for a collection of random probability measures indexed by predictors. The lDP involves a simple construction, results in a marginal Dirichlet process prior for the random measure at any specifc predictor value, and leads to a straightforward posterior computation. In paper 2, we propose a more general approach not only estimating the conditional response distribution but also identifying important predictors for the response distribution change both with local regions and globally. This is achieved through the probit stick-breaking process mixture (PSBPM) of normal linear regressions where the PSBP is a newly proposed prior for dependent probability measures and particularly convenient to incorporate variable selection structure. In paper 3, we extend the paper 2 method for longitudinal outcomes which are correlated within subject. The PSBPM of linear mixed effects (LME) model is considered allowing for individual variability within a mixture component.Doctor of Philosoph

Measuring discrimination in South Korea: Underestimating the prevalence of discriminatory experiences among female and less educated workers?.

Objectives To investigate the possibility that Koreans show different patterns in reporting discriminatory experiences based on their gender and education level, we analyzed the participants who answered “Not Applicable” for the questions of discriminatory experiences that they were eligible to answer. Methods Discriminatory experiences in eight social situations were assessed using the 7th wave of Korean Labor and Income Panel Study. After restricting the study population to waged workers, a logistic regression model was constructed to predict the probability that an individual has experienced discrimination based on the observed covariates for each of eight situations, using the data of participants who answered either Yes or No. With the model fit, the predicted logit score of discrimination (PLSD) was obtained for participants who answered Not Applicable (NA), as well as for those who answered Yes or No. The mean PLSD of the NA group was compared with those of the Yes group and the No group after stratification by gender and education level using an ANOVA model. Results On the questions of discrimination in getting hired and receiving income, the PLSD of the NA group was significantly higher than that of the No group and was not different from that of Yes group for female and junior high or less educated workers, suggesting that their NA responses were more likely to mean that they have experienced discrimination. For male and college or more educated workers, the NA group had a PLSD similar to that for the No group and had a significantly higher PLSD than the Yes group, implying that their NA responses would mean they that they have not experienced discrimination. Conclusions Our findings suggest that the responses of NA on the discrimination questionnaire may need different interpretation based on the respondents\u27 gender and education level in South Korea

Measuring Discrimination in South Korea: Underestimating the Prevalence of Discriminatory Experiences among Female and Less Educated Workers?

Objectives: To investigate the possibility that Koreans show different patterns in reporting discriminatory experiences based on their gender and education level, we analyzed the participants who answered “Not Applicable” for the questions of discriminatory experiences that they were eligible to answer. Methods Discriminatory experiences in eight social situations were assessed using the 7th wave of Korean Labor and Income Panel Study. After restricting the study population to waged workers, a logistic regression model was constructed to predict the probability that an individual has experienced discrimination based on the observed covariates for each of eight situations, using the data of participants who answered either Yes or No. With the model fit, the predicted logit score of discrimination (PLSD) was obtained for participants who answered Not Applicable (NA), as well as for those who answered Yes or No. The mean PLSD of the NA group was compared with those of the Yes group and the No group after stratification by gender and education level using an ANOVA model. Results: On the questions of discrimination in getting hired and receiving income, the PLSD of the NA group was significantly higher than that of the No group and was not different from that of Yes group for female and junior high or less educated workers, suggesting that their NA responses were more likely to mean that they have experienced discrimination. For male and college or more educated workers, the NA group had a PLSD similar to that for the No group and had a significantly higher PLSD than the Yes group, implying that their NA responses would mean they that they have not experienced discrimination. Conclusions: Our findings suggest that the responses of NA on the discrimination questionnaire may need different interpretation based on the respondents' gender and education level in South Korea

Associations between Long-Term Exposure to Chemical Constituents of Fine Particulate Matter (PM2.5) and Mortality in Medicare Enrollees in the Eastern United States

Background: Several epidemiological studies have reported that long-term exposure to fine particulate matter (PM2.5) is associated with higher mortality. Evidence regarding contributions of PM2.5 constituents is inconclusive. Objectives: We assembled a data set of 12.5 million Medicare enrollees (≥ 65 years of age) to determine which PM2.5 constituents are a) associated with mortality controlling for previous-year PM2.5 total mass (main effect); and b) elevated in locations exhibiting stronger associations between previous-year PM2.5 and mortality (effect modification). Methods: For 518 PM2.5 monitoring locations (eastern United States, 2000–2006), we calculated monthly mortality rates, monthly long-term (previous 1-year average) PM2.5, and 7-year averages (2000–2006) of major PM2.5 constituents [elemental carbon (EC), organic carbon matter (OCM), sulfate (SO42–), silicon (Si), nitrate (NO3–), and sodium (Na)] and community-level variables. We applied a Bayesian hierarchical model to estimate location-specific mortality rates associated with previous-year PM2.5 (model level 1) and identify constituents that contributed to the spatial variability of mortality, and constituents that modified associations between previous-year PM2.5 and mortality (model level 2), controlling for community-level confounders. Results: One–standard deviation (SD) increases in 7-year average EC, Si, and NO3– concentrations were associated with 1.3% [95% posterior interval (PI): 0.3, 2.2], 1.4% (95% PI: 0.6, 2.4), and 1.2% (95% PI: 0.4, 2.1) increases in monthly mortality, controlling for previous-year PM2.5. Associations between previous-year PM2.5 and mortality were stronger in combination with 1-SD increases in SO42– and Na. Conclusions: Long-term exposures to PM2.5 and several constituents were associated with mortality in the elderly population of the eastern United States. Moreover, some constituents increased the association between long-term exposure to PM2.5 and mortality. These results provide new evidence that chemical composition can partly explain the differential toxicity of PM2.5. Citation: Chung Y, Dominici F, Wang Y, Coull BA, Bell ML. 2015. Associations between long-term exposure to chemical constituents of fine particulate matter (PM2.5) and mortality in Medicare enrollees in the eastern United States. Environ Health Perspect 123:467–474; http://dx.doi.org/10.1289/ehp.130754

Assessing seasonality and the role of its potential drivers in environmental epidemiology: a tutorial.

Several methods have been used to assess the seasonality of health outcomes in epidemiological studies. However, little information is available on the methods to study the changes in seasonality before and after adjusting for environmental or other known seasonally varying factors. Such investigations will help us understand the role of these factors in seasonal variation in health outcomes and further identify currently unknown or unmeasured risk factors. This tutorial illustrates a statistical procedure for examining the seasonality of health outcomes and their changes, after adjusting for potential environmental drivers by assessing and comparing shape, timings and size. We recommend a three-step procedure, each carried out and compared before and after adjustment: (i) inspecting the fitted seasonal curve to determine the broad shape of seasonality; (ii) identifying the peak and trough of seasonality to determine the timings of seasonality; and (iii) estimating the peak-to-trough ratio and attributable fraction to measure the size of seasonality. Reporting changes in these features on adjusting for potential drivers allows readers to understand their role in seasonality and the nature of any residual seasonal pattern. Furthermore, the proposed approach can be extended to other health outcomes and environmental drivers

Assessing seasonality and the role of its potential drivers in environmental epidemiology: a tutorial

Several methods have been used to assess the seasonality of health outcomes in epidemiological studies. However, little information is available on the methods to study the changes in seasonality before and after adjusting for environmental or other known seasonally varying factors. Such investigations will help us understand the role of these factors in seasonal variation in health outcomes and further identify currently unknown or unmeasured risk factors. This tutorial illustrates a statistical procedure for examining the seasonality of health outcomes and their changes, after adjusting for potential environmental drivers by assessing and comparing shape, timings and size. We recommend a three-step procedure, each carried out and compared before and after adjustment: (i) inspecting the fitted seasonal curve to determine the broad shape of seasonality; (ii) identifying the peak and trough of seasonality to determine the timings of seasonality; and (iii) estimating the peak-to-trough ratio and attributable fraction to measure the size of seasonality. Reporting changes in these features on adjusting for potential drivers allows readers to understand their role in seasonality and the nature of any residual seasonal pattern. Furthermore, the proposed approach can be extended to other health outcomes and environmental drivers

Tropical influenza and weather variability among children in an urban low-income population in Bangladesh

Background: Influenza seasonality in the tropics is poorly understood and not as well documented as in temperate regions. In addition, low-income populations are considered highly vulnerable to such acute respiratory disease, owing to limited resources and overcrowding. Nonetheless, little is known about their actual disease burden for lack of data. We therefore investigated associations between tropical influenza incidence and weather variability among children under five in a poor urban area of Dhaka, Bangladesh.Design: Acute respiratory illness data were obtained from a population-based respiratory and febrile illness surveillance dataset of Kamalapur, a low-income urban area in southeast Dhaka. Analyzed data were from January 2005 through December 2008. Nasopharyngeal wash specimens were collected from every fifth eligible surveillance participant during clinic visits to identify influenza virus infection with viral culture and reverse transcriptase?polymerase chain reaction. Time series analysis was conducted to determine associations between the number of influenza cases per week and weather factors. Zero-inflated Poisson and generalized linear Poisson models were used in the analysis for influenza A and B, respectively.Results: Influenza A had associations with minimum temperature, relative humidity (RH), sunlight duration, and rainfall, whereas only RH was associated with influenza B. Although associations of the other weather factors varied between the two subtypes, RH shared a similar positive association when humidity was approximately 50?70%.Conclusions: Our findings of a positive RH association is consistent with prior studies, and may suggest the viral response in the tropics. The characteristics of settlement areas, population demographics, and typical overcrowding of urban poverty may also contribute to different impacts of rainfall from higher economic population. Further investigations of associations between tropical influenza and weather variability for urban low-income populations are required for better understanding

Associations of chemical composition and sources of PM2.5 with lung function of severe asthmatic adults in a low air pollution environment of urban Nagasaki, Japan

Previous studies have linked ambient PM2.5 to decreased pulmonary function, but the influence of specific chemical elements and emission sources on the severe asthmatic is not well understood. We examined the mass, chemical constituents, and sources of PM2.5 for short-term associations with the pulmonary function of adults with severe asthma in a low air pollution environment in urban Nagasaki, Japan. We recruited 35 asthmatic adults and obtained the daily record of morning peak expiratory flow (PEF) in spring 2014–2016. PM2.5 filters were extracted from an air quality monitoring station (178 days) and measured for 27 chemical elements. Source apportionment was performed using Positive Matrix Factorization (PMF). We fitted generalized linear model with generalized estimating equation (GEE) method to estimate changes in PEF (from personal monthly maximum) and odds of severe respiratory deterioration (first ≥ 15% PEF reduction within a 1-week interval) associated with mass, constituents, and sources of PM2.5, with adjustment for temperature and relative humidity. Constituent sulfate (SO42−) and PM2.5 from oil combustion and traffic were associated with reduced PEF. An interquartile range (IQR) increase in SO42− (3.7 μg/m3, average lags 0–1) was associated with a decrease of 0.38% (95% confidence interval = −0.75% to −0.001%). An IQR increase in oil combustion and traffic-sourced PM2.5 (2.64 μg/m3, lag 1) was associated with a decrease of 0.33% (−0.62% to −0.002%). We found a larger PEF decrease associated with PM2.5 from dust/soil on Asian Dust days. There was no evidence linking total mass and metals to reduced pulmonary function. The ventilatory capacity of adults with severe asthma is susceptible to specific constituents/sources of PM2.5 such as sulfate and oil combustion and traffic despite active self-management of asthma and low air pollution levels in the study location

Nonlinear temperature-suicide association in Japan from 1972 to 2015: Its heterogeneity and the role of climate, demographic, and socioeconomic factors.

It has been reported that suicide is associated with ambient temperature; however, the heterogeneity in this association and its underlying factors have not been extensively investigated. Therefore, we investigated the spatial and temporal variation in the temperature-suicide association and examined climatic, demographic, and socioeconomic factors that may underlie such heterogeneity. We analyzed the daily time-series data for the suicide counts and ambient temperature, which were collected for the 47 prefectures of Japan from 1972 to 2015, using a two-stage analysis. In the first stage, the prefecture-specific temperature-suicide association was estimated by using a generalized linear model. In the second stage, the prefecture-specific associations were pooled, and key factors explaining the spatial and temporal variation were identified by using mixed effects meta-regression. Results showed that there is an inverted J-shape nonlinear association between temperature and suicide; the suicide risk increased with temperature but leveled off above 24.4 °C. The nationwide relative risk (RR) for the maximum suicide temperature versus 5th temperature percentile (2.9 °C) was estimated as 1.26 (95% CI: 1.22, 1.29). The RRs were larger for females than for males (1.32 vs. 1.22) and larger for elderly people (?65 y) than for the non-elderly (15-64 y) (1.51 vs. 1.18). The RRs were larger for rural prefectures, which are characterized by smaller population, higher proportions of females and elderly people, and lower levels of financial capability and the proportion of highly educated people. The RRs were also larger in colder and less humid prefectures. These findings may help in understanding the potential mechanism of the temperature-suicide association and projecting the future risk of suicide under climate change