Environmental Protection Agency as probable carcinogens are most commonly measured in residential dust, namely, benzoanthracene, chrysene, benzofluoranthene, benzofluoranthene, benzopyrene, indenopyrene, and dibenzoanthracene . Table 4 shows summary statistics from studies which reported levels of these PAHs in residential dust. Residential dust appears to be an important source of direct exposure to PBDEs and PAHs, especially for young children due to their hand-to-mouth behavior. In fact, dust exposure may be the dominant source of PBDEs for individuals from North America and Asia . Likewise, inadvertent dust ingestion could be responsible for a large proportion of overall intake of the less volatile PAHs , including those PAHs considered to be carcinogenic . Although dust exposure appears to play a less important role in the intake of PCBs and tobacco smoke constituents, young children can still receive a substantial portion of their chemical intake via residential dust . These findings support the use of residential dust as a medium to measure chemical exposures in epidemiological studies, particularly those focusing on childhood diseases. Dust levels of PBDEs and PCBs appear to vary geographically based on historic patterns of production and use of these chemicals. Likewise, concentrations of PAHs in residential dust appear to vary across the U.S. due to geographic differences in traffic density and, possibly, the use of coal-tar for sealing pavement.
However, aside from geographic location,how to cure cannabis researchers have had only limited success in finding determinants of chemical levels in residential dust. Even when collecting comprehensive survey information about residence characteristics, investigators have failed to identify strong predictors of chemical levels in dust . Given the relative paucity of data regarding dust contaminants in California, one objective of this dissertation will be to use data from the NCCLS to compare dust concentrations of chemical contaminants measured in California homes to levels reported in other studies from around the world. Additionally, since past investigators have failed to identify strong predictors of chemical levels in residential dust, this dissertation will employ the extensive self-reported information collected by the NCCLS to further investigate possible determinants of the concentrations of nicotine, PAHs, and PCBs in residential dust. Finally, since there is limited information regarding how concentrations of nicotine, PAHs, and PCBs vary across time and space within a residence , this dissertation will characterize the variability of dust measurements within and between residences. This dissertation will conclude with a discussion of the benefits and limitations of residential dust measurements for estimating chemical exposures, and suggestions for future research. The NCCLS is an ongoing case-control study conducted in the San Francisco Bay area and California Central Valley where cases aged 0-14 years are ascertained from nine pediatric clinical centers. Controls, matched to cases on date of birth, gender, race, and Hispanic ethnicity, are selected from the California birth registry .
To date, two rounds of dust collection have been conducted to obtain information about chemical contamination in study homes. Initially, cases and controls aged 0-7 years that were living at the home they occupied at the time of diagnosis from December 1999 through November 2007 were eligible for dust collection. Among 731 subjects determined to be eligible, 629 subjects participated in this first round of dust collection from 2001-2007. Dust samples were initially collected by interviewers using the high volume surface sampler , but in some study homes, interviewers collected the contents of household vacuum cleaners during in-home visits instead. Dust sampled during the initial collection period was analyzed for PAHs, PCBs, and nicotine at the Battelle Memorial Institute laboratories in Columbus, Ohio using methods described in Chapters 3-5. Surplus dust was stored at the Battelle lab in 2g aliquots at -20oc until 2010. In 2010, subjects participated in a second round of dust collection, where they were asked to remove the contents of their household vacuum cleaner , place the contents in a sealable polyethylene bag, and ship the bag to a study center in Berkeley, CA in a prepaid package. To be eligible for this second round of dust collection, cases and controls needed to be living in the same home that they occupied during the first round of dust collection . Thus, by design, dust collection in the NCCLS was limited to stationary residents. NCCLS interviewers attempted to contact 355 subjects that were potentially eligible for repeated dust collection, of those 270 households were confirmed to be eligible, 216 households agreed to participate in repeated dust collection, and 204 households successfully mailed their vacuum cleaner dust to the NCCLS study center. Figure 5 shows a schematic representation of the NCCLS dust analysis plan.
For each residence, the initial dust sample was shipped to Dr. Rappaport’s lab on the UC Berkeley campus in 2010. Dust samples from both collection rounds were analyzed for PBDEs, PCBs, and PAHs at the California Department of Toxic Substances Control in Berkeley, California using the method described in this chapter. Analyses in Chapter 6 are based on dust samples that were analyzed at the CA DTSC lab using the method described in this chapter. Analyses in Chapters 3-5 are based on dust samples that were analyzed at the Battelle Memorial Institute using methods described in subsequent chapters. Written informed consent was obtained from all NCCLS children’s parent or legal guardian in accordance with the Institutional Review Boards’ requirements at the University of California, Berkeley and all other participating institutions. There were two goals in developing this analytical method; namely, accuracy and precision. Precision ensures that measured concentrations are representative of their true values and thereby prevents the misclassification of exposure. To ensure accuracy and precision, several quality control measures were employed. Firstly, the validity of the method was tested using the NIST SRM 2585 and found to be accurate. Subsequently, the accuracy of the results was confirmed by the use of method blanks and quality control samples. Precision was evaluated using duplicate samples and quality control samples . Both of these aspects of quality control provide estimates of the magnitude of error introduced by the analytical method – an important consideration when interpreting the final results of any health study based on these measurements. Quality control assessments revealed several limitations of the analytical method. Specifically, BDE-209 measurements appear to be relatively inaccurate and imprecise . Moreover, method blanks were prone to contamination from BDE-209. Likewise, samples were prone to contamination from volatile PAHs and as a result, measurements of these 4 PAHs were imprecise in repeated quality control testing. It is important to consider the quality of the data when reporting results and interpreting findings. One advantage of the method described above is its ability to analyze PBDEs, PCBs, and PAHs using a single extraction and clean-up procedure. Of course, the benefits of such an efficient protocol are savings in time and money. The disadvantage is that the method is not optimized for any of three chemical classes, individually. Additionally, the method cannot be used to analyze nicotine. The International Agency for Research on Cancer concluded that active tobacco smoking causes cancer of the lung, oral cavity, pharynx, nasal cavity and paranasal sinuses, larynx, esophagus, stomach, pancreas, liver, kidney, ureter, urinary bladder, uterine cervix and bone marrow . Additionally, IARC asserted that involuntary smoking also causes lung cancer . More recently, it has been suggested that involuntary smoking might contribute to the risk of childhood leukemia . Beyond active and secondhand smoking,cure cannabis individuals may be exposed to carcinogenic tobacco constituents which contaminate hair, clothing, furniture and dust particles . Young children are at particular risk from exposure to third hand tobacco smoke due to their hand-to mouth behavior . Epidemiologists generally rely on self-reported smoking histories when investigating the health effects of tobacco smoke. It is generally assumed that self reported smoking information is reliable, and, indeed, validation studies using nicotine specific cotinine biomarkers as “gold” standards have shown that only about 5% of professed non-smokers are actually smokers . However, deception rates as high as 25% have been observed when parents report their smoking habits in studies involving their children’s health .
Thus, in studies of children’s health, the use of self-reports could result in substantial misclassification of children’s true exposures to cigarette smoke and introduce bias in the exposure-response relationship . As such, investigators from the NCCLS who are investigating the potential association between childhood leukemia and parental smoking , have a particular interest in developing unbiased measures of cigarette smoke exposure. To reduce misclassification of exposure to cigarette smoke, it is beneficial to use an objective measure of exposure such as nicotine in indoor air , cotinine in urine , or nicotine in hair . Alternatively, researchers have suggested using nicotine levels in residential dust as surrogates for in-home exposures to cigarette smoke . Indeed, previous research has shown that nicotine concentrations in residential dust are highly correlated with children’s levels of urinary cotinine in smoking households . However, previous investigations of nicotine levels in residential dust involved small numbers of households and were unable to thoroughly examine the determinants of nicotine concentrations in residential dust . Parents, primarily the mother , responded to two sets of questionnaires, each with inquiries about smoking habits, as outlined in Figure 9. The initial interview ascertained the smoking status of the mother, father, and others in the household at several time points of interest. Additionally, the first interview asked the respondent for the number of cigarettes smoked per day for some but not all of the time periods. A subsequent interview at the time of dust collection ascertained the total number of cigarettes smoked per day inside the residence during the previous month. This additional question dealt specifically with smoking inside the home and was, therefore, expected to correspond to the concurrent residential-dust nicotine measurements. However, responses from the first interview were also considered as potential determinants of the concentrations of nicotine in residential dust, because nicotine is known to persist indoors where it is protected from degradation by moisture, sunlight, and microbial action. Because the distribution of nicotine in residential dust was highly skewed, the nonparametric Kruskal-Wallis one-way analysis of variance was used to compare the distribution of nicotine in residential dust between various groups throughout the analysis. The data had an approximate log-normal distribution, so the natural log of the concentration was used in all analyses involving the continuous variable. Residential-dust nicotine measurements below the limit of detection, i.e., 20 ng/g, were assigned a value of one-half the limit of detection. Pairwise correlation coefficients between the natural log-transformed residential-dust nicotine concentrations and self-reported cigarette consumption were estimated. Although Pearson correlation coefficients are reported, results were similar when using Spearman rank coefficients. Seven groups of variables were considered for inclusion in the residential-dust nicotine regression models: self-reported smoking, parental demographics, residence characteristics, child-specific variables, sampling conditions, time effects, and ethnicity . Groups of highly correlated variables were analyzed by principal components analysis to produce simpler, but meaningful, summary measures of the variables within these groups for inclusion in the final residential-dust nicotine regression models . The remaining groups of candidate variables were modeled individually using backwards elimination to identify other variables used in the final models. In addition to main effects, significant interactions between self-reported smoking variables and parental demographic variables and between self-reported smoking variables and case-control status were included. Using the variables identified in group screening, two subsequent regression analyses were performed with case and control households combined. The first analysis used all possible households regardless of the sampling method . The second analysis used only HVS3- sampled households; this analysis included size of sampling area, a variable that was only relevant in homes where HVS3 sampling was done. The analysis included 233 cases and 236 controls with residential-dust nicotine measurements. Nicotine was detected in 89% of the residences. The nicotine concentrations ranged from not detected to a maximum of 35,000 ng/g, with a median value of 265 ng/g and an interquartile range between 96 and 612 ng/g. Table 14 shows the prevalence of smoking during various time periods, the median concentration of nicotine in residential dust for smokers and non-smokers in each category, and the P-value from the Kruskal-Wallis one-way ANOVA comparing the distributions of concentrations of nicotine from residences with smokers versus those residences without smokers.