Understanding population health involves understanding connections between social and environmental determinants of health. The life course perspective places importance on how these determinants may shape and promote pediatric health disparities. Asthma and birth outcomes are two key health outcomes of critical relevance for children’s environmental health disparities. This dissertation builds upon the growing literature on how social factors (e.g. poverty, experiences of discrimination, social deprivation) in combination with other factors (e.g. age group, atopy status, air pollution) may impact these outcomes.

Data from the Study of African Americans, Asthma, Genes & Environments (SAGE II) were used for Chapters 2 and 3. In this study, African American youth with and without asthma aged 8-21 years were recruited from the San Francisco Bay Area from 2008 to 2014. Participants were part of a larger pediatric case-control study to identify genetic and socio-environmental factors in asthma prevalence and severity among minority youth in the United States. For Chapter 4, data from the California Department of Public Health’s Statistical Birth Master Files for the years 2014 and 2015 were analyzed. There were 266,127 births used in this analysis. The mother’s census tract of residence at the time of delivery was used to link person-level data to area-level data.

Chapter 2 examines the independent and joint effects of racial/ethnic discrimination and early life exposure to air pollutants on asthma in the SAGE II study population. Studies on racial/ethnic discrimination, a toxic stressor, on air pollution-related asthma risk among children have been limited. This chapter evaluates the effects of racial/ethnic discrimination and high NO2/PM10 on asthma status and whether these associations vary with age. Results suggest that racial/ethnic discrimination and exposure to air pollutants, together, increase the likelihood of asthma more than either exposure alone among African American youth.

In Chapter 3, I evaluated the association between neighborhood deprivation and asthma exacerbation among African American youth and determined if this association differs by atopy. Using principal components analysis, neighborhood deprivation scores were created for the 1,554 census tracts associated with the study participants’ residential addresses and quartiles of scores (categorized 1-4, low to high deprivation) were assigned for each participant. Generalized estimating equations were used to estimate the effects of neighborhood deprivation on asthma exacerbation. Literature relating environmental factors to asthma morbidity across different asthma phenotypes has been conflicting. Our study suggests that children, especially African American youth, with atopic asthma may be more sensitive to neighborhood-deprivation related triggers compared with those with no atopy.

In Chapter 4, I examined the relationship between social deprivation and air pollution on preterm birth risk beyond individual level effects. Utilizing hierarchical generalized linear models, we found that the population components score (PCS), a measure of social deprivation, explained the greatest preterm birth variance and was positively associated with preterm birth after accounting for PM2.5, diesel PM, and individual-level risk factors. Furthermore, we found cross-level interactions between (1) PCS and Insurance Coverage and (2) PCS and WIC. Results from our study promote the use of cumulative measures of social deprivation versus single level measures in relation to preterm birth and highlight the interplay between key area-level and individual-level risk factors. This exploratory study supports the growing literature of a place-based understanding of preterm birth risk.

These findings show that social factors matter for asthma and preterm birth. Exploring joint effects of social and environmental determinants is worth considering to more fully characterizing risk for these outcomes, especially for youth of color. We support the awareness and the development of policies that impact key social and environmental determinants of child health outcomes (e.g. racial/ethnic segregation, housing/neighborhood conditions, and educational/economic opportunities).

Millions of people are exposed to arsenic in drinking water. An ancient poison, arsenic occurs naturally in groundwater and geothermal springs. Removing arsenic from drinking water costs about $200 million every year in the United States alone. The brunt of this is borne by California and other western states, where groundwater is needed more for drinking water.

Arsenic in drinking water causes cardiovascular death, cognitive deficits in children, reproductive problems, and cancer. Surprisingly, many studies have shown that the human lung is especially susceptible to ingested arsenic. After being consumed in drinking water, arsenic accumulates in the lungs. Lung cancer is now believed to be the most common cause of death from this widespread contaminant.

Most lung carcinogens, including tobacco smoke, asbestos, and silica, also cause non-malignant respiratory effects. Evidence suggests that arsenic in drinking water follows this pattern, but nearly all data involve adults with recent exposures. The impacts of early-life arsenic exposures on nonmalignant lung disease are largely unknown. In northern Chile, the city of Antofagasta (population 390,000 in 2014) had high concentrations of arsenic in drinking water (>800 µg/L) from 1958 until 1970, when a new treatment plant was installed. This scenario, with its large population, distinct period of high exposure, and accurate data on past exposure, is virtually unprecedented in environmental epidemiology. Chapter 2 of this dissertation describes a pilot study on early-life arsenic exposure and long-term lung function. We recruited a convenience sample consisting primarily of nursing school employees in Antofagasta and Arica (population 160,000) a city with low drinking water arsenic. Lung function and respiratory symptoms in 32 adults exposed to >800 µg/L arsenic before age 10 were compared to 65 adults without high early-life exposure. Early-life arsenic exposure was associated with 11.5% lower forced expiratory volume in one second (FEV1) (p = 0.04), 12.2% lower forced vital capacity (FVC) (p = 0.04), and increased breathlessness (prevalence odds ratio = 5.94, 95% confidence interval 1.36–26.02). Exposure-response relationships between early-life arsenic concentration and adult FEV1 and FVC were also identified (p trend = 0.03). These results suggest that early-life exposure to arsenic in drinking water may have irreversible respiratory effects of a magnitude similar to smoking throughout adulthood. Given the small study size and non-random recruitment methods, further research is needed to confirm these findings.

The arsenic concentrations >800 µg/L in Chile can reveal previously unknown health outcomes, but they do not shed much light on what the drinking water standard should be. Arsenic is known to cause lung cancer at concentrations above about 200 µg/L. The effects of lower exposures are unknown. This uncertainty has created controversy over the 10 µg/L World Health Organization guideline and U.S. regulatory limit for public water supplies because arsenic is widespread in groundwater naturally and expensive to remove. In Chapter 3, I present the first lung cancer study in the largest U.S. populations with exposures between 50 and 100 µg/L. This was also the first U.S. lung cancer study with individual data on past drinking water arsenic concentrations. We enrolled 196 lung cancer cases and 359 controls, matched on age and sex, from western Nevada and Kings County, California in 2002–2005. After adjusting for age, sex, education, smoking and occupational exposures, odds ratios for arsenic concentrations ≥85 μg/L (median = 110 μg/L, mean = 173 μg/L, maximum = 1,460 μg/L) more than 40 years before enrollment were 1.39 (95% CI = 0.55–3.53) in all subjects and 1.61 (95% CI = 0.59–4.38) in smokers. Although odds ratios were greater than 1.0, these increases may have been due to chance given the small number of subjects exposed more than 40 years before enrollment. The findings suggest that concentrations near 100 μg/L are not associated with markedly high relative risks.

The California Nevada lung cancer case-control study, designed before research in Chile suggested arsenic-related cancer latencies of 40 years or more, illustrates the difficulties of identifying arsenic-related health effects in low-exposure countries with mobile populations like the U.S. Making matters worse, many wells used by participants 20–60 years ago could not be measured because they could not be located or had fallen into disrepair. A major problem for health studies of arsenic and other drinking water contaminants is a lack of measurements for unregulated water sources such as private wells, which are much more likely to have high arsenic than public supplies. For Chapter 4, I developed models to estimate arsenic in unmeasured wells in the western Nevada study area. In cross-validations with five partitions of 3138 residential wells, inverse distance weighted (IDW) averaging was the best categorizer of arsenic, averaging 71% correct assignment to the high, medium and low categories used in the lung cancer study. The next best categorizers were the random forest model (70% correct) and the median of measurements within 408 m (69%). These models categorized wells better than kriging (67%), consistent with findings in other areas. Nearest neighbors averaged 66% correct assignment. For exact arsenic concentrations, the best models were the random forest model (Arsenic = 0.95*RFM, R2 = 0.34) and kriging (Arsenic = 1.00*OK, R2 = 0.30). The RFM might be considered the best model overall, since it categorized measurements almost as well as IDW, had a higher R2 (0.34 versus 0.28), and a linear slope closer to 1 (0.95 versus 0.80). In terms of detecting high arsenic wells, RFM was also just behind IDW. These models can be substantially improved, but IDW and RFM already nearly doubled the number of unmeasured wells correctly categorized around Fallon, compared to the alternative of treating them as zeroes in effect estimates based on participants’ highest known concentrations. Given that the direction of the bias is the same whether unmeasured wells are estimated or excluded, the smaller bias using estimates is preferable.

Chapter 5 presents meta-analyses of low exposure lung cancer studies, and assesses differences in relative risks between men and women. Pooled results of six independent studies suggest that increased lung cancer risks reported for concentrations between 10 and 100 µg/L are not likely due to chance (pooled relative risk = 1.09, 95% CI = 1.02-1.20), but there was substantial heterogeneity between studies (p<0.001). The higher relative risk of 4.1 (95% CI = 1.8-9.6) in the case-control study by Ferreccio et al. (2000) might be explained by effects in ecological studies being diluted by in-migration or uncontrolled confounding. Relative risks were higher in men in Chile, in women in Taiwan, and similar for all countries combined. The gender differences in Chile and Taiwan were not likely due to chance, and may be explained by differences in study participation, case ascertainment, smoking, occupational, or household smoke exposures.

Chapter 6 concludes with a summary of recent research and ideas for future studies. New findings of health effects below 100 µg/L, including lung cancer in people with early life exposures (Steinmaus et al. 2014), need to be confirmed. However, increasing evidence supports the 2001 decision to tighten the U.S. regulatory from 50 to 10 µg/L. Detecting the increases in health risk expected below 50 µg/L (e.g., odds ratios below 1.5) will likely require very large sample sizes, but may be possible in studies with accurate data on early life exposures, inter-individual differences in arsenic metabolism, and related susceptibility factors (e.g., age, genetics, gender, diet, health status, smoking, and occupational exposures). On the environmental side, high arsenic concentrations have been predicted in many regions that lack available measurements, including much of the Amazon Basin (Amini et al. 2008), contrary to the expectations of some geologists (Ravenscroft et al. 2009). Measurements in these areas can test and improve models of arsenic in groundwater, improve exposure assessment, and help ensure the safety of millions of people.

Exposure to secondhand smoke (SHS) is harmful and hazardous to the health of the general public. A large body of research has been conducted in this topic, and great efforts have been made to prevent people from being exposed to SHS. Legislation on restricting smoking in workplaces and many public places has also been increasing. However, tobacco industries have been fighting against smoking bans in restaurants and bars with multiple strategies, which has led to the current situation that smoking bans in restaurants and bars usually lag behind other environments in many countries. As of January 2012, a total of 66 nations worldwide have enacted a 100% smoke-free law in workplaces and hospitality venues, while only 46 of the 66 include both restaurants and bars, and more than 90% of the world population can't enjoy smoke-free restaurants and bars. In addition, tobacco industries have made continuing efforts to remove existing smoking bans, and such efforts are sometimes successful. For example, as of October 2011, 15 U.S. municipalities that had adopted effective smoke-free laws subsequently repealed, weakened, or postponed them due to such efforts.

This dissertation aims to quantify SHS exposure and the attendant health risks, morbidity and mortality, among restaurant and bar servers and patrons, to provide scientific evidence on whether SHS exposure in restaurants and bars can be ignored and whether restaurants and bars should be exempted from smoking bans. The dissertation consists of seven chapters. Chapter 1 presents the general background, Chapters 2 and 3 focus on quantifying SHS exposure in restaurants and bars by workers and patrons; Chapter 4 evaluates the efficacy of different smoking policies adopted to reduce SHS exposure in restaurants and bars in Beijing China; and Chapters 5 and 6 assess the excess heath risks, morbidity and mortality, due to SHS exposure in restaurants and bars, and the last chapter summarizes the findings and conclusions from the previous five chapters.

The study in Chapter 2 applies multiple approaches to assess restaurant and bar servers' and patrons' exposure to SHS two years after the implementation of the governmental smoking restriction in Beijing, 2010. Of the 79 restaurants and bars monitored in the study, 37 (47%) nominally prohibited smoking, and 14 (18%) restricted smoking to designated sections. A total of 121 visits were made during peak-patronage times, and smoking was observed in 26 (51%) of these nominal nonsmoking venues or sections. Patrons were exposed to a median (interquartile range IQR) of 27 (4-93) µg/m³ of fine particulates derived from SHS (SHS PM) and a median (IQR) of 1.53 (0.69-3.10) µg/m³ of airborne nicotine during their visits. For servers, continuous real-time sampling of SHS PM and sequential area sampling of airborne nicotine, for more than 24 hours in two restaurants, showed obvious spikes of SHS concentrations during peak-patronage times, and SHS concentrations remained high during intervals between peak-patronage times or in evenings due to staff smoking. Servers were exposed to a median (IQR) of 2.62 (1.22-5.40) µg/m³ of airborne nicotine during their day-time working hours by one-day active personal sampling, and 1.83 (0.92-3.21) µg/m³ of airborne nicotine during a whole week by week-long passive sampling. Nonparametric Kruskal-Wallis rank tests of SHS concentrations by different nominal smoking policies showed statistically significant difference of peak-patronage-time SHS PM and airborne nicotine concentrations, while no statistically significant differences of one-day average nicotine concentration by active area or personal sampling, or of week-long average nicotine concentration by passive sampling. Comparison of results by different sampling approaches showed that both measured SHS PM and airborne nicotine concentrations were significantly related to observed active smoker activities. A slope of 17 µg/m³ of SHS PM per one µg/m³ of nicotine was observed. Time-weighted nicotine concentrations by one-hour peak-patronage time area sampling were higher than those by one-day area sampling and by week-long area sampling; and results of peak-patronage-time sampling could explain about half of the variance of the results by the latter two sampling approaches. One-hour peak-patronage-time area nicotine sampling results were very close to one-day personal nicotine sampling results. Thus, peak-time area sampling is a feasible and also a reasonably accurate way to access patrons' exposure to SHS during their short-term visits and servers' exposure during their full shifts.

Chapter 3 develops and evaluates a mass balance model to predict SHS concentrations in restaurants and bars in China during peak-patronage times. The model is based on field data from an intensive study, with field monitoring of SHS concentrations in a representative sample of Minnesota restaurants and bars during representative peak-patronage times, and field data from existing studies of Chinese restaurants and bars. The model could predict SHS PM concentrations reasonably well, but not so well for airborne nicotine concentrations. Using the model and Monte Carlo simulation, the mean (SD) of simulated SHS PM concentrations was predicted to be 135 (182) μg/m³, 90 (129) μg/m³, and 49 (79) μg/m³ in restaurants with smoking allowed everywhere, designated smoking sections of restaurants, and designated nonsmoking restaurants, respectively. Predicted SHS concentrations in bars were about two times as in restaurants with the same smoking policy. These predicted concentrations were used to assess the health risks for both servers and patrons in Chapter 6.

Chapter 4 uses field data collected in three previous studies from 2006 to 2008 and the study conducted in 2010, which is presented in Chapter 2, to evaluate the efficacy of different smoking policies adopted in Beijing restaurants and bars during this time period. There were significant overlaps of sampling venues included in each year. In 2006, all voluntary smoking bans in restaurants and bars were completely self-motivated by owners, and in 2007, they were encouraged by the government. Less than 20% of restaurants and bars prohibited or restricted smoking in 2006 or 2007. This indicates that both the self-motivated and governmental encouraged voluntary smoking bans are rarely adopted; thus, voluntary smoking bans cannot protect people from SHS exposure in restaurants and bars. When the Beijing government started to require smoking restrictions in restaurants and bars in 2008, more than 80% of venues did so as required; in these venues, the active smoking rate of patrons decreased, while no significant changes were observed in venues without any policy changes. However, some venues stopped prohibiting or restricting smoking two years later in 2010, resulting in less than 60% restaurants and bars nominally prohibited or restricted smoking, showing non-continuous enforcement by the government and decreasing compliance by venue owners. Though SHS PM concentrations in Beijing restaurants and bars decreased after the governmental smoking restriction in both 2008 and 2010, compared to those in 2006 and 2007, this happened in all the venues followed up with, regardless of the policy changes. In 2010, two years after the smoking restrictions, both SHS PM concentrations and active smoking rates in restaurants and bars were higher than in 2008, regardless of the changes in smoking policy. The similarity of SHS levels experienced by servers of restaurants and bars with different nominal smoking policies during their full shifts in 2010 also showed poor enforcement and compliance of the restrictions two years after the implementation.

Chapters 5 and 6 estimate the health risks and excess morbidity and mortality caused by SHS exposure in restaurants and bars in Minnesota, in the U.S., and in China. Intensive field monitoring of SHS exposure in a representative sample of 65 Minnesota restaurants and bars, for multiple times in each venue, showed that more than 80% of patrons were exposed to SHS concentrations above the threshold of eye and nasal irritation during more than 80% of their visits. Patrons' and servers' lifetime excess risk (LER) of lung cancer death (LCD) due to SHS exposure in restaurants and bars in both Minnesota and in China was well above the acceptable level of 1×10^-6. And this was true even for patrons who visited designated nonsmoking sections only for about 1.5 hours a week in their lifetime. The LER can be much higher for patrons who visit restaurants and bars more often, or for patrons who also visit smoking sections or venues allowing smoking everywhere. As for servers, their LER of LCD or asthma initiation (estimated for Minnesota and U.S. restaurant and bar servers only) could be higher than the significant risk of 1×10^-3, considered an unsafe level by the U.S. Occupational Safety and Health Administration (OSHA). In the population level, SHS exposure in restaurants and bars was estimated to cause three LCDs and 32 ischaemic heart disease (IHD) deaths per year among the general nonsmoking population, and 53 new asthma cases per year among nonsmoking servers in Minnesota, 214 LCDs and 3001 IHD deaths per year among the general nonsmoking population, and 1420 new asthma cases per year among nonsmoking servers in the U.S. This death toll was predicted to be 1325 LCDs and 1525 IHD deaths a year in China.

In all, restaurants and bars are major employers, and they are also important public places for the general population. This dissertation shows that both servers and patrons are exposed to high concentrations of SHS in restaurants and bars, and the attendant health risks, morbidity, and mortality are too significant to be ignored. Thus, to protect people from the health hazards of SHS exposure, restaurants and bars should not be exempted from any smoking bans. The only effective way is to create 100% smoke-free environments by comprehensive smoking bans, and just passing a smoking ban is not enough, while full enforcement and compliance is extremely important.

Introduction

Smoking is generally not regulated in restaurants or bars in China, or the restrictions are not fully implemented if there are any, while the related hazard health effects are not recognized by the majority of the Chinese population.

Objectives

This study aims to assess the excess health risks and mortality attributed to secondhand smoke (SHS) exposure in restaurants and bars for both servers and patrons to provide necessary evidence for advancing tobacco control in this microenvironment.

Methods

Two approaches were used for the assessment. One is a continuous approach based on existing field measurements and Repace and Lowrey’s dose-response model, and the other is a categorical approach based on exposure or not and epidemiological studies.

Results

Based on the continuous approach, servers were estimated to have a lifetime excess risk (LER) of lung cancer death (LCD) of 730 to 1,831×10⁻⁶ for working five days a week for 45 years in smoking restaurants and 1,862 to 8,136×10⁻⁶ in smoking bars, and patrons could have a LER of LCD of 47 to 117×10⁻⁶ due to visiting smoking restaurants for an average of 13 minutes a day for 60 years, and 119 to 522×10⁻⁶ due to visiting smoking bars. The categorical approach estimated that SHS exposure in restaurants and bars alone caused 84 LCD and 57 ischemic heart disease (IHD) deaths among nonsmoking servers and 1,2419 LCDs and 1,689 IHD deaths among the nonsmoking patron population.

Conclusions

SHS exposure in restaurants and bars alone can impose high lifetime excess risks of lung cancer death and ischemic heart disease deaths to both servers and patrons, and can cause a significant number of deaths each year in China. These health risks and deaths can be prevented by banning smoking in restaurants and bars and effectively implementing these smoking bans.

Background

Objectives

Methods

Results

Conclusions

Background

Objectives

Results

Conclusions