Medicine, Faculty of
Population and Public Health (SPPH), School of
DSpace
UBCV
Yu, Xing
Master of Science - MSc
University of British Columbia
Background: Noise pollution has been linked to impaired development in a variety of language comprehension activities, including speaking, reading, and memory. The underlying processes, however, are not fully understood. Residential greenness, on the other hand, has been found to reduce noise, but this modifying effect of greenness on noise has not been adequately investigated in the environmental health literature. Objectives: To characterize the association between residential exposure to noise and language development and the potential modifying effects of greenspace in Metro Vancouver, British Columbia. Methods: The study was conducted within the framework of the Born to be Wise project and used linked data to conduct descriptive statistical analyses to summarize community-level exposures, as well as individual-level health and community-level socio-demographic information. Multilevel multivariable models were built to characterize the association between residential exposure to noise and children’s language development. Stratified analyses were conducted to assess whether differences exist in effect estimates of biological sex and age. The propensity score matching method was employed as a secondary analysis to replicate the associations. Results: The study included 30,048 children for which there was data on noise exposure. The mean noise level was 63.5 dB(A), and the mean percentage of vegetation within a 250 m buffer was 31.4%. This study found the increase in residential exposure to noise independently decreased the language developmental outcomes in basic literacy (Unadjusted: -0.14, CI: -0.11~ -0.16; Adjusted: -0.09, CI: -0.06 ~ -0.11), advanced literacy (Unadjusted: -0.22, CI: -0.17 ~ -0.27, adjusted: -0.14, CI: -0.09~-0.18), and communication and general knowledge (Unadjusted: -0.36, CI: -0.32 ~0.40, adjusted: -0.08, CI: -0.11~-0.15). The greenspace found to have minimal moderation effects on the adverse effects of noise on language development. Conclusions: This study found association between higher residential exposure to noise and impaired language development, and that greenspace had minimal effect modification. Future studies can be conducted using longitudinal data to improve the evidence level. This research also demonstrates that greenspace is not remediation solution to noise pollution, the health policy can be pursued by acoustic design to attenuate noise for better children’s language development outcomes.
RESIDENTIAL EXPOSURE TO NOISE AND LANGUAGE DEVELOPMENT: EFFECT MODIFICATION BY RESIDENTIAL GREENSPACE by Xing Yu B.A. (Hons), Simon Fraser University, 2020 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Population and Public Health) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) September 2023 © Xing Yu, 2023 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Residential Exposure to Noise and Language Development: Effect Modification by Residential Greenspace submitted by Xing Yu in partial fulfilment of the requirements for the degree of Master of Science in Population and Public Health Examining Committee: Dr. Hugh Davies, Professor, Population and Public Health, UBC Co-supervisor Dr. Matilda van den Bosch, Adjunct Professor, Population and Public Heath, UBC Co-supervisor Dr. Martin Guhn, Associate Professor, Population and Public Health, UBC Supervisory Committee Member Dr. Hind Sbihi, Adjunct Professor, Population and Public Heath, UBC Supervisory Committee Member iii Abstract Background: Noise pollution has been linked to impaired development in a variety of language comprehension activities, including speaking, reading, and memory. The underlying processes, however, are not fully understood. Residential greenness, on the other hand, has been found to reduce noise, but this modifying effect of greenness on noise has not been adequately investigated in the environmental health literature. Objectives: To characterize the association between residential exposure to noise and language development and the potential modifying effects of greenspace in Metro Vancouver, British Columbia. Methods: The study was conducted within the framework of the Born to be Wise project and used linked data to conduct descriptive statistical analyses to summarize community-level exposures, as well as individual-level health and community-level socio-demographic information. Multilevel multivariable models were built to characterize the association between residential exposure to noise and children’s language development. Stratified analyses were conducted to assess whether differences exist in effect estimates of biological sex and age. The propensity score matching method was employed as a secondary analysis to replicate the associations. Results: The study included 30,048 children for which there was data on noise exposure. The mean noise level was 63.5 dB(A), and the mean percentage of vegetation within a 250 m buffer was 31.4%. This study found the increase in residential exposure to noise independently decreased the language developmental outcomes in basic literacy (Unadjusted: -0.14, CI: -0.11~ iv -0.16; Adjusted: -0.09, CI: -0.06 ~ -0.11), advanced literacy (Unadjusted: -0.22, CI: -0.17 ~ -0.27, adjusted: -0.14, CI: -0.09~-0.18), and communication and general knowledge (Unadjusted: -0.36, CI: -0.32 ~0.40, adjusted: -0.08, CI: -0.11~-0.15). The greenspace found to have minimal moderation effects on the adverse effects of noise on language development. Conclusions: This study found association between higher residential exposure to noise and impaired language development, and that greenspace had minimal effect modification. Future studies can be conducted using longitudinal data to improve the evidence level. This research also demonstrates that greenspace is not remediation solution to noise pollution, the health policy can be pursued by acoustic design to attenuate noise for better children’s language development outcomes. v Lay Summary Language development is a strong indicator of social wellbeing, with significant financial and societal well-being for the future Canadian population. Exposure to noise pollution was investigated for its association with children’s language development, as noise may directly affect factors important to language acquisition, such as speech perception, reading comprehension, and cognitive load. The modifying effect of greenspace on the impacts of noise was also examined in this thesis. This thesis used a cohort linked language outcome assessments with community-level exposure to noise and greenspace in metro Vancouver. The thesis found poorer assessed language performance was associated with increased in exposure to noise, and greenspace had minimal effect modifying it. This research may provide insight for researchers and health policymakers about the necessity of tackling noise pollution. Future research in this field is warranted to fully understand the effects of change in noise levels. vi Preface This thesis received ethical approval from the University of British Columbia (UBC) Behavioral Research Ethics Board (REB). Behavioral Research Ethics Board (certificate H18-00908). The project is considered a minimal risk due to the anonymized nature of the data. Data were drawn from the ongoing Born to be Wise (BtbW) project, and are securely stored and managed by PopDataBC. Ethics approval for this study was granted by the University of British Columbia (UBC) The work presented in this thesis was conducted under the supervision of Dr. Hugh Davies and the co-supervision of Dr. Matilda van den Bosch, with guidance from the supervisory committee: Dr. Hind Sbihi and Dr. Martin Guhn. Analyses were conducted by Xing Yu with guidance from committee members. Finally, the thesis writing was completed by Xing Yu, and reviewed by the committee members. Portions of this thesis appear as leading-authored, peer-reviewed journal articles. The work presented in this thesis was the result of exploring the data that used in the BtbW Project. My supervisor and committee helped define the research questions and aided in the interpretation of the results. Portions of this thesis will appear in a peer-reviewed journal. • Xing Yu, Ingrid Jarvis, Martin Guhn, Hind Sbihi, Hugh Davies, Matilda van den Bosch, Noise Exposure and Language Acquisition among Vancouver Children: Effect Modification by Residential Greenspace. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ..................................................................................................................................x List of Figures ............................................................................................................................... xi List of Abbreviations .................................................................................................................. xii Acknowledgements .................................................................................................................... xiii 1. Introduction and Literature review .....................................................................................1 1.1 Problem statement ................................................................................................................. 1 1.1.1 Language development .................................................................................................. 1 1.1.1 Exposure to noise ........................................................................................................... 2 1.2 Possible mechanisms for how noise affect language-related abilities .................................. 3 1.2.1 Proximal acoustic input and its relation to language acquisition ................................... 4 1.2.3 Cognitive processing ...................................................................................................... 5 1.2.4 Noise-induced deteriorating effects ............................................................................... 7 1.2.5 Empirical evidence on reading comprehension ............................................................. 8 1.2.6 Interaction of noise with other environmental attributes ............................................... 9 1.3 Knowledge gaps and Study Objectives............................................................................... 10 2. Methods .....................................................................................................................................12 2.1. Study sample ...................................................................................................................... 12 2.2. Measurement ...................................................................................................................... 14 viii a) Outcome: Early childhood development at the age of 5 ............................................... 14 b) Data linkage .................................................................................................................. 17 c) Primary exposure: Noise .............................................................................................. 17 d) Secondary exposure: greenspace .................................................................................. 18 e) Neighbourhood-level covariates ................................................................................... 19 f) Individual-level covariates ............................................................................................ 21 2.3 Statistical analyses .................................................................................................... 21 2.3.1 Descriptive analyses.................................................................................................. 22 2.3.2 Model selections ....................................................................................................... 22 2.3.3 Primary analyses ....................................................................................................... 24 2.3.3.1 Objective 1 - Examine the association between residential exposure to noise and language development outcomes, adjusting for confounders and random effects ............... 24 2.3.3.2 Objective 2 - Examine the modifying effects of greenspace ................................ 25 2.3.4 Secondary analyses ................................................................................................... 26 2.3.4.1 Robustness – Propensity Score Matching ............................................................. 26 2.3.4.2 Effect modification on sex and age ....................................................................... 27 3. Results ...................................................................................................................................29 3.1 Descriptive Statistics ........................................................................................................... 29 3.2 Primary analyses results ...................................................................................................... 31 3.3 Secondary analyses ............................................................................................................. 35 3.3.1 Robustness – Propensity Score Matching .................................................................... 35 3.3.2 Effect Modification – Age and Biological sex ............................................................ 37 4. Discussion..............................................................................................................................38 ix 4.1 Early-life residential exposure to noise may harm language development ........................ 39 4.2 Minimal modifying effects of greenspace on noise and EDI outcomes ............................. 41 4.3 Strengths and limitations..................................................................................................... 43 4.4 Implications......................................................................................................................... 45 4.5 Future research .................................................................................................................... 46 5. Conclusion ............................................................................................................................48 Bibliography .................................................................................................................................49 Appendices ....................................................................................................................................63 Appendix A Pearson correlation for collinearity test ............................................................... 63 Appendix B Retention of covariates ......................................................................................... 64 Appendix C Covariates selection to address collinearity ......................................................... 66 Appendix D Association between exposures and language development and its related outcomes ................................................................................................................................... 68 Appendix E Distribution of both exposures.............................................................................. 73 Appendix F Comparison of multilevel linear regression analysis of residential noise exposure and EDI mean score in language development related subdomains with covariate adjustment during the model selection. ....................................................................................................... 74 x List of Tables Table 1 Subdomain of the outcome of interests in this project. .................................................... 16 Table 2 List of combinational exposure groups ............................................................................ 25 Table 3 A list of the explanations by combinational exposures. .................................................. 26 Table 4 Characteristics of the BtbW cohort in Metro Vancouver, Canada .................................. 30 Table 5 The odds ratio being underdevelopment in the subdomains ............................................ 32 Table 6 The estimated change of the subdomain mean score ....................................................... 32 Table 7 The estimated change of the subdomain mean score in combinational exposures .......... 34 Table 8 The estimated change of the subdomain mean score in PSM high noise group. ............. 36 Table 9 Interaction term on age – older ........................................................................................ 37 Table 10 Interaction term on sex – male ....................................................................................... 37 xi List of Figures Figure 1 The flowchart of the data source, data management, and sample stratification.. ........... 13 Figure 2 Guiding Directed Acyclic Graph (DAG) for the relationship between noise and language development . ................................................................................................................ 19 Figure 3 Distribution of being different noise groups. Distribution of being different noise groups. ........................................................................................................................................... 36 xii List of Abbreviations BC - British Columbia BtbW – Born to be Wise CI – Confidence Interval CM - centimeter dB(A) - A-weighted decibel DOHaD - Developmental Origin of Health and Disease DF – Degree of Freedom DAG - Directed Acyclic Graph EDI - Early Developmental Instrument ESL – English as Second Language HELP - Human Early Learning Partnership ICC - Intraclass Correlation Coefficient IQR - Interquartile range Lden - Day–evening–night Noise Level LUR - Land Use Regression NDVI - Normalized Difference Vegetation Index PopData BC - Population Data BC PSM - Propensity Score Match SE – Standard Error SES - Socioeconomic Status UBC - University of British Columbia xiii Acknowledgements I would like to express my heartfelt gratitude to my supervisor, Dr. Hugh Davies, for his exceptional supervision throughout my study. His patience, genuine, and insightful guidance have been invaluable in shaping both my academic and personal growth as a student researcher. Under his expert supervision, I have been able to flourish in a supportive environment that fosters creativity and intellectual curiosity. His unwavering support and encouragement have been a constant source of motivation and inspiration, allowing me to reach my full potential as a student researcher. He has provided me with numerous opportunities to showcase my ideas and skills, and his feedback has been instrumental in my growth as a research trainee. I am truly grateful for the amazing experiences and growth opportunities that I have had under his supervision. In addition, I would like to express my gratitude to the entire BtbW team, especially Dr. Matilda van den Bosch, for their kindness, support, and encouragement. The team has provided me with an inclusive and collaborative environment that has allowed me to thrive both academically and personally. Their positive attitudes and warm atmosphere have made me feel valued and supported, and I am grateful for the wonderful relationships I have formed with my colleagues. I would like to extend my sincerest gratitude to both Dr. Hind Sbihi and Dr. Martin Guhn for their integral role in my thesis. Dr. Sbihi's expertise in data analysis has been instrumental in my success of this thesis, and her feedback on my analytical design has been incredibly helpful. Meanwhile, Dr. Guhn's significant contributions to my research through his insightful supervision meetings on EDI knowledge have greatly enhanced my understanding of the subject xiv and my approach to it. His guidance and support in my writing have been crucial in ensuring the quality and accuracy of my work. I would also like to express my gratitude to the faculty, staff, and fellow students at SPPH, who have inspired me to continue my work in health research. Their passion for their work, as well as their kindness and support, has made my time at the institution truly special. I have learned so much from my colleagues and have been able to gain a new perspective on the field of health research, thanks in large part to the supportive community at SPPH. Finally, I owe a great deal of gratitude to my parents, who have supported me both morally and financially throughout my education. Their unwavering support has given me the confidence to pursue my dreams, and I am eternally grateful for their love and guidance. This project was part of the Born to be Wise project (PI Dr. Matilda van den Bosch), funded through the Canadian Institutes of Health Research. My project also has been supported by Fraser Basin Council (the Elizabeth Henry scholarship). 1 1. Introduction and Literature review 1.1 Problem statement 1.1.1 Language development Language development is a notable children’s developmental milestone. Optimal language development would prepare children’s communicative and perceptual intelligence for social and academic participation in kindergarten1, while delayed language development puts children at increased risk for social exclusion and exposure to bullying 2. Language development thus is important in health research as it serves as a clear indicator of social wellbeing. Also, the language development among children bears significantly on health and societal/economic well-being for the future Canadian population. It is noteworthy that in British Columbia (BC), over 10% of children in 2019 were prone to language and cognitive underdevelopment before kindergarten3, which highlights the urgent need to identify factors that negatively impact language development. By understanding these factors, we can work towards creating effective interventions and support systems that promote optimal language development and improve the social and academic participation of children in kindergarten, ultimately contributing to the future health and well-being of the Canadian population. Recent advances in the Developmental Origins of Health and Disease (DOHaD) paradigm4 have provided critical insights into the influence of the early life environment on children’s development and subsequent risk of chronic conditions later in life. Addressing issues during early childhood can have far-researching implications for health throughout the lifespan, making it a critical approach to mitigate the negative effects of poor environmental conditions and promote optimal early childhood development. While extensive research has explored the 2 association between some modifiable environmental conditions and language development, such as levels of pollution and natural environments, and disadvantaged neighborhoods, the effects of physical environment on language development remain largely underexplored in the literature, despite their potential for substantial impact. Although several socioenvironmental factors, such as parental education and socioeconomic status (SES), have been implicated in developmental psychology research, the role of physical attributes of the environment, including air pollutants, noise, and greenspace, requires further investigation5. Exposure to noise pollution, especially, might be a primary focus for studies on children’s language development, as noise may directly affect factors important to language acquisition, such as speech perception6–8, reading comprehension9,10, and cognitive load11. Understanding the relationship between physical environmental factors and language development can provide valuable insights into the mechanisms underlying developmental processes and inform interventions to promote optimal language acquisition in children. 1.1.1 Exposure to noise With urbanization rapidly increasing, adverse effects on urban exposures are becoming increasingly important in public health. Noise, a ubiquitous pollutant, is a major environmental hazard stemming from transportation, industry, and social sources, which has been shown to affect long-term health outcomes12, and may be especially harmful to children’s cognitive development9. Unfortunately, there is no way to switch off our perception of noise, making it difficult to escape its consequences, particularly during the night13. As such, it is crucial to address the issue of noise pollution to improve public health outcomes within urban settings. 3 The World Health Organization (WHO) estimates that every year, around one million healthy years of life are lost due to noise-induced hearing loss globally14, which underscores the scale of the noise exposure issue. Furthermore, noise exposure has a social justice component; the unequal distribution of noise exposure among children in Vancouver highlights the importance of addressing noise pollution in health policy. For instance, Amram et al. (2011) found that noise levels in Vancouver were considerably higher at 75 meters of main roadways (70.2 ± 5.7 dBA vs. 57.9 ± 6.5 dBA; p 0.01), and schools in low-income neighbourhoods were subjected to higher levels of noise compared to high-income neighbourhoods15. This disparity emphasizes the need for targeted interventions to reduce noise exposure in disadvantaged communities, which are likely to experience more adverse health outcomes as a result of noise pollution. Despite its significant impacts of noise on population’s health in BC, noise pollution has become a significant unsolved public health problem and research is needed to inform health policy in Canada. Canada’s noise policies are based on community response to annoying sounds rather than being health-based, and substantive prospects to improve health policy would be enhanced with the support of additional scientific evidence16. Therefore, it is essential to develop evidence-based policies to reduce the burden of noise pollution and promote healthy urban living. 1.2 Possible mechanisms for how noise affect language-related abilities To understand the impact of noise on language development, it is essential to explore the potential mechanisms through which noise can affect language-related abilities. This section will elucidate these possible mechanisms, tracking the pathways from external perceptual 4 environmental to internal cognitive processes that are involved in language-related abilities. Following this exploration, I suggest the relevant negative effects that may arise from indirect pathways, such as sleep disturbance and stress. By examining both direct and indirect pathways, a more holistic understanding of the complex relationship between noise exposure and language development outcomes may be outlined. Such an approach would provide a nuanced perspective, taking into account the different layers of influence that noise may have on language development. 1.2.1 Proximal acoustic input and its relation to language acquisition Although the capacity to differentiate auditory properties (e.g., spatial, amplitude, content, etc.) is essential for speech understanding, acoustic transmission deterioration may especially comprise sound intelligibility within noise among children. In general, humans focus their attention on critical information in a loud environment based on sound properties such as strength, frequency, stress, and content (i.e., the cocktail party effect)17,18. The ability to select attention is gradually developed as a result of perceptual exposures and experiences, but children are of particular concern due to their developing abilities to capture phonetic characteristics19. Data suggest human ability to comprehend acoustic information does not reach an adult’s level until they are in their teenage years20–23. Human ability to segregate sound sources within a noisy environment might be described based on top-down and bottom-up processing. Top-down is the process of extracting the sound information from the noisy backdrop by utilizing prior knowledge, context of the language, and recognizing linguistic structure. In contrast, bottom-up is the process that parses and decodes sound information using the listening situation and peripheral factors24. As such, children are 5 vulnerable due to their insufficient experience of challenges to using either or both processes25. Accordingly, children’s ability to apprehend phonological structure from sound information is limited, hindering speech perception in noisy environments and leading to incomplete language structural knowledge26,27. As a result, the noise-induced impacts on these processes can hinder speech perception in noisy environments, leading to incomplete language structural knowledge and potentially causing delays in the intrinsic ability to recognize and comprehend acoustic information, eventually impacting language development outcomes. 1.2.3 Cognitive processing Noise not only masks speech sounds, but also draws on cognitive resources, particularly working memory and attentional skills, which are essential for speech comprehension 19–28. Therefore, understanding how noise affects these cognitive abilities is essential in assessing the impacts of noise on language development. Working memory serves as a cognitive processer to manage both the short-term moments and total storage of auditory information29, a crucial component of speech comprehension, particularly during the early stages of language acquisition. As such, the brain stores acoustic objects in working memory to complete speech comprehension, then generates a memory rehearsal for auditory modalities to be instantly encoded for semantic understanding. Finally, comprehension can be accomplished by connecting the serial order of mental objects29,30. Thus working memory is a crucial cognitive step for retaining and learning novel speech comprehension sound data during the early stages of language acquisition31. However, irrelevant sounds may reduce the processing resources available for all sound comprehension processes, such as rehearsal, encoding, and processing. According to Neath's “feature model”, when noise 6 interferes with the integrity of memory traces with similar phonetic features during the rehearsal phase, the probability of a correct match during the encoding process is reduced, resulting in lower serial recall performance during the processing32. This conclusion has been replicated in several laboratory experiments33–35. It is also possible that the phonological loop’s distraction would directly impact short-term memory, which propagate across a long-term semantic network36,37. Similarly, young children have limited working memory capacity and auditory cues for recalling information in the correct order29. In addition, Clark et al. suggested chronic noise exposure during early life may impair working memory skills9, weakening the ability to learn language. Attention skills are critical for aiding the phonological loop and rehearsal process29,38, while auditory stimuli disruption may divert attention resources away from key focused activities19,39. In addition to these laboratory studies, noise in naturalistic settings, such as road noise at school may impair children's attention and verbal recall40–42, implying that there were no adaption effects to traffic noise on attention abilities, further emphasizing the need for noise reduction in learning environments. Likewise, noise is likely to have a greater impact on children since memory rehearsal requires more mental effort than adults43, leading to more significant recall errors, further delaying the language acquisition process. The disruption of these language-related cognitive processes may also impede the intrinsic ability to recognize and comprehend acoustic information and acquire language structural knowledge, leading to significant delays in language acquisition. 7 1.2.4 Noise-induced deteriorating effects In addition to the direct effects of noise on the perceptual pathway and cognitive process, the indirect effects of noise on language development are also of significant concern. Exposure to traffic noise has been linked to sleep disruption in children, particularly road traffic noise, which has been found to increase arousal and displeasure during sleep44. Noise-induced sleep disruption may exacerbate the negative effects of noise on cognitive function by causing discomfort, daytime tiredness, and a deterioration in mood and emotional control45,46. Also, sleep deprivation can result in the loss of new phonological mappings during the rapid eye movement sleep period47, with recovery sleep having no reversal impact on its impairment48. The sleep disruption thus may further exacerbate the negative effects of noise on cognitive function, including language acquisition. Stress might exacerbate the negative effects of noise on cognitive performance by decreasing brain development9,49. Chronic stress would maintain high cortisol levels, causing malfunctions in the hippocampus, which is in charge of language and executive processes50. Evans and colleagues found that children who have been exposed to airplane noise for more than two years have greater levels of adrenaline and noradrenaline activation51,52. Also, stress may cause motivational deficits and learning helplessness in children53,54, affecting language development indirectly. In this way, noise has a cascading effect on language development, starting with disruptions in the external perceptual environmental and cognitive process and ultimately leading to a range of indirect effects on language acquisition. 8 1.2.5 Empirical evidence on reading comprehension Language development is a multi-faceted process that requires a range of skills, including sound perception, vocabulary acquisition, grammar comprehension, and reading ability. As such, reading comprehension is a common metric to assess children’s language developmental outcomes. In the case of English, a sound-based language, reading comprehension requires a child's proficiency to match speech sounds and printed words, as written words are essentially representations of sounds30,55–57. However, chronic noise exposure can impede a child's ability to perceive and process sounds, potentially leading to poor reading comprehension skills. For instance, Evans and Maxwell found that school-aged children who had poor speech perception skills due to chronic noise exposure were also poorer readers10, and several observational studies found this association was not mediated by sustained attention during the assessment10,11. For example, a pooled research study conducted in Europe discovered a correlation between airplane noise exposure and impaired reading comprehension. Despite these findings establish the association between noise and reading comprehension deficiency, it is difficult to establish a causal relationship of the association due to the cross-sectional nature of some studies. However, a German longitudinal study that used Munich airport relocation found that school-aged children with poor speech perception skills due to chronic noise exposure were also poorer readers10, and this effect was not mediated by sustained attention during the assessment10,11. This observational study provided causal evidence of adverse effects of noise on reading comprehension deficiency and cognitive performance with higher ecological validity compared to laboratory studies. 9 Although those observational studies provide valuable evidence of adverse effects of noise on reading comprehension deficiency and cognitive performance with higher ecological validity compared to laboratory studies11, their data is restricted to aircraft noise at schools. Also, confounder assessment is a possible common flaw among these observational studies because of the neglect of some critical contextual factors such as birth outcomes58. Thus, these findings may permit speculation of a causal association, but they may result from inherent biases in the included studies, particularly residual confounding. Therefore, further research is necessary to establish a more conclusive association, taking into account critical contextual factors. 1.2.6 Interaction of noise with other environmental attributes It is important to acknowledge that noise exposure does not occur in a vacuum, and that other environmental factors may influence the effects of noise on language acquisition-related tasks. The co-occurrence of factors such as greenspace and air pollution could be regarded as potentially modifying effects on the association between noise and language development. For instance, urban greenspace might also modify the non-acoustic effects of noise on language development. Neighbouring greenspaces can improve cognitive development, such as working memory, attentiveness, and school performance among primary school children59,60. Experimental data suggests viewing and walking in nature may provide “therapeutic effects” on attention deficit in children61. Recent studies also found that neighbourhood greenspace is associated sleep quality and duration62–64. Thus, greenspace may potentially reduce the distracting effects of noise on the phonological loop and attentional burden, providing a modifying effect on noise on language development. 10 1.3 Knowledge gaps and Study Objectives While potential adverse effects of noise on language development can be understood from its interference with speech perception, impaired cognitive ability, and its aggravating effects from sleep disturbance and stress. However, most of the evidence from these studies is limited to laboratory settings or airplane noise, and may not be generalizable to residential areas where children spend the majority of their time, or traffic noise, a ubiquitous source of noise pollution. The effect of residential noise exposure on children’s language development is critical in the field of early development, given that children spend the majority of their time at home, and the possible role of sleep disturbance as a mechanism affecting language acquisition. Additionally, Due to the absence of some possible crucial contextual data such as birth outcomes, confounding may be a limitation among earlier observational studies. Because the demographic data in the reviewed research were self-reported, errors in individual-level covariates might occur owing to social desirability. These limitations may possibly harm accuracy of the findings. Furthermore, rigorous community-level variables used as proxies to assess the demographic information would increase the accuracy and provide better information for policymakers to identify vulnerable communities. To address the knowledge gap, my thesis research utilized a dataset linking urban exposures, including noise and greenspace, to a birth cohort, with data on early child development, measured using the Early Development Instrument (EDI) at age five65 to characterize the impact of residential exposure to noise on language development. I hypothesized that there would be a negative association between residential noise and language developmental 11 outcomes. I hypothesized that the association is independent of demographic context. Given the potential remediation benefits of greenspace from the literature, I expected greenspace to modify the association between residential exposure to noise and language development outcome. To examine the association between exposure to residential noise and language development during early life, as well as the potential modifying effects of greenness, I used a population-based birth cohort from Early Development Instrument (EDI) in Metro Vancouver, Canada to address the following thesis objectives: 1. Examine the association between residential exposure to noise and language development, adjusting for confounding effects and random errors of teachers. 2. Examine the modifying effects of greenspace on the association between noise and language development, adjusting for confounding effects and random errors on teachers. 12 2. Methods 2.1. Study sample To address the objectives in section 1.3, the present study conducted an analysis of existing, linked data drawn from the Born to be Wise (BtbW)66 study to examine the association between residential noise exposure and children’s language developmental outcomes in kindergarten children in Vancouver. The sample is a population-based cohort of approximate 34,000 infants born in Metro Vancouver, BC, Canada, between April 1, 2000, and December 31, 2005, comprising all live, singleton births to women resident in Metro Vancouver66. The study cohort was constructed using administrative data from several sources, including the BC Ministry of Health67,90,91, the BC Vital Statistics Agency92,93, Perinatal Services BC68, and the Human Early Learning Partnership (HELP)69, accessed through Population Data BC94. To link the data on an individual level, a consolidation file provided by Population Data BC was used, which includes demographics and geocodes. A probabilistic-deterministic linking technique was utilized to link individual-level data, which included information on maternal health, residency during pregnancy, medication, and hospital records for both mother and child from pregnancy to the age of five. To be eligible for inclusion in the cohort, participants had to have attended kindergarten between the 2005-06 and 2010-11 school years65, be enrolled in the mandatory provincial health insurance, Medical Services Plan, and have resided in the study region from the time of the child's birth until the time of the outcome assessment. Children who moved out of the study region, or had gaps in their residence history were removed from the cohort. All exposures were 13 averaged (annual values) over the exposure period, including changes in addresses of participants who moved during this time using weighted averaging. The dataset excluded children who do not have data on noise as well as those with speech and hearing impairments (Figure 1). The dataset was linked as follows: EDI measures and individual-level covariates (e.g., maternal and child health were linked by Provincial Health Number / Provincial Education Number; community-level covariates and urban environment exposures were linked at the residential postal code level through a consolidation file provided by the PopDataBC, which contains postal code for the participants. Figure 1 The flowchart of the data source, data management, and sample stratification. The flowchart of the data source, data management, and sample stratification. See measures section below for the definition for the 14 detailed variables (ID=Individual, PC=postal code). The N in bracket is the available number in each dataset. The noise missingness, speech impairment, and hearing impairment are non-mutually exclusive. 2.2. Measurement a) Outcome: Early childhood development at the age of 5 The unique index in Early Development Instrument (EDI) provides one of North America's most comprehensive population-based early childhood development datasets65. The index was administered to kindergarten children in Canada public schools, to measure children's readiness for school. The data for the EDI were collected during the latter half of the kindergarten year (in February and March), which serves two important purposes. Firstly, by this point, teachers have developed a deep understanding of their students, enabling them to efficiently and accurately complete the instrument. Secondly, many children who initially faced challenges at the beginning of the school year, whether due to unfamiliarity with the school environment or language of instruction, have had the opportunity to catch up with their peers within a few months. The Early Development Instrument (EDI) is a reliable tool for assessing the holistic developmental health of children during the transition from early development to school age. It encompasses of demographic information and 103 binary and Likert-scale items, each scored from 0 (low) to 10 (high), assessing a child's ability to meet age-appropriate developmental expectations in five domains: (1)physical health and well-being, (2) social competence, (3) emotional maturity, (4) language and cognitive development, (5) communication and general knowledge65. For each of the five domains, a child receives a domain score, calculated as the mean across all domain items, ranging from 0 (lowest) to 10 (highest). EDI provides a robust and 15 reliable source of data on early childhood development, several studies have shown that the EDI is valid and reliable as a measure of children's developmental outcomes70–72. The term wave in the context of the EDI refers to different cycles of data collection. For example, wave 2 indicates data collected in the years 2004 and 2005. This project includes children from the wave 2 (2004/05), wave 3(2007/08), and wave 4(2009/10). Two of the EDI domains, language and cognitive development as well communication skills and general knowledge, were employed as the outcomes of this project. Multiple questions relevant to children's language and cognigtive skills were used to measure children's basic and advanced literacy as well communication and general knowledge in the, with higher scores indicating better developmental outcomes65. Specifically, I utilized the mean scores from the subdomains of language and cognitive development, as well the communication and general knowledge as my outcome measures domains (See Table 1). In line with Janus and Offord's (2007) recommendations, this study recoded all items on a scale ranging from 0 to 10, which enabled me to combine the items and calculate total scores for each of the subdomains. In addition, children who meet expectations for only a few or none of the subdomain items were considered to be below developmental expectations (coded as 1), which suggests that they have a level of development that is likely to hinder their success in school. Conversely, children who meet expectations for all or almost subdomain items were coded as 0. By employing this binary outcome, this project was able to assess the presence of below developmental expectations in children73. 16 Table 1 Subdomain of the outcome of interests in this project. LANGUAGE & COGNITIVE DEVELOPMENT Includes reading awareness, age-appropriate reading and writing skills, age-appropriate numeracy skills, ability to understand similarities and differences, and ability to recite back specific pieces of information from memory. Basic literacy Children who have all the basic literacy skills: know how to handle a book, can identify some letters and attach sounds to some letters, show awareness of rhyming words, know the writing directions, and are able to write their own name. Would you say that this child: • knows how to handle a book (e.g., turn a page) • is able to identify at least 10 letters of the alphabet • is able to attach sounds to letters • is showing awareness of rhyming words • is able to participate in group reading activities • is experimenting with writing tools • is aware of writing directions in English (left to right, top to bottom) • is able to write his/her own name in English Advanced literacy Children who have at least half of the advanced literacy skills: reading simple, complex words or sentences, writing voluntarily, writing simple words or sentences. Would you say that this child: • is able to read simple words • is able to read complex words • is able to read simple sentences • is able to write simple words • is able to write simple sentences • is interested in writing voluntarily (and not only under the teacher’s direction) COMMUNICATION SKILLS & GENERAL KNOWLEDGE Includes skills to communicate needs and wants in socially appropriate ways, symbolic use of language, storytelling, and age-appropriate knowledge about the life and world around. Communication and general knowledge Children who have excellent or very good communication skills; can communicate easily and effectively, can participate in story-telling or imaginative play, articulates clearly, show adequate general knowledge, and are proficient in their native language. How would you rate this child’s: • ability to listen in English • ability to tell a story • ability to take part in imaginative play • ability to communicate own needs in a way understandable to adults and peers • ability to understand on first try what is being said to him/her • ability to articulate clearly, without sound substitutions • ability to use language effectively in English Would you say that this child: • answers questions showing knowledge about the world (e.g., leaves fall in the autumn, apple is a fruit, dogs bark) 17 b) Data linkage In Canadian epidemiological research, the six-digit postal code, which corresponds to one side of a city street block, is a common geographical unit. Residential mobility (variable - moved) was characterized as any changes in the child's reported six-digit postal code during the exposure period. Exposure estimates were linked to all outcome variables and covariates at the six-digit postal code level. A time-weighted estimate of early-life residential exposure was created by averaging the exposures data (e.g., noise and greenspace) from birth to the time of EDI completion and accounting for residential address changes reported in the healthcare records. c) Primary exposure: Noise The current study estimated residential exposure to noise using a established Vancouver noise map74. Based on data from 2003, the noise map calculated the annual average 24-hour community noise levels using CADNA-A software74. Data describing roads, junctions, traffic speed and volume, trains, aeroplanes, topography, and building heights and footprints were used to construct the noise map74. The construction and evaluation of this model was described in detail elsewhere74. The noise map generated estimates of noise on a 10 X 10 m grid energetically-averaged over each six-digit postal code. Metro Vancouver is densely populated, and the majority of postal codes reflect small geographic regions with around 35 people in a typical cohort75. This study 18 employed the Day–evening–night Noise Level (Lden) metric on A-weighted equivalent continuous noise level (Lden, dBA) to characterize the varying periods of susceptibility to noise, which computes noise assessment by adding penalties of a 5 dB(A) weighting to evening hours (7 - midnight) and a 10 dB(A) to night hours (midnight – 7am). d) Secondary exposure: greenspace To estimate residential greenspace, this study employed a unique Landsat-derived greenness time series. Details of the metric have been described elsewhere76. In brief, annual Landsat satellite image composites (with a spatial resolution of 30 m) were utilized in the study dataset from 1984 to 2016 to detect three spectral features of physical materials per pixel: vegetation (i.e., green vegetation), high albedo (e.g., impermeable surfaces), and dark vegetation (e.g.,, shadows cast by objects, such as buildings)76. For each year, a sub-pixel linear spectral unmixing technique was used to calculate the yearly proportion of each spectral attribute per pixel in the research region. In this data source, greenspace was represented as an annual proportion of the vegetation characteristic per pixel for each year of the study, from birth through EDI evaluation. The mean percentage of vegetation value within a 250 m buffer surrounding participants' six-digit postal code centroid was used to assess the residential exposure to greenspace. Because I expected children to spend the majority of their time at or near their home before attending primary school, we chose a buffer size of 250 m to serve as a proxy for exposure within walking distance of home. Covariates 19 A directed-acyclic graph (DAG, Figure 2) was constructed to depict possible causal assumptions that guide the analysis by illustrating the independent association between residential noise and language development, potential confounding variables and effect modifiers drawn from literature. Figure 2 Guiding Directed Acyclic Graph (DAG) for the relationship between noise and language development The grey lines indicate the independent association of interest, while the purple lines indicate potential confounding associations. The green space age, gender are possible effect modifiers. The circled ones were the covariates used in the primary analyses. All the grey ones except for the modifiers were included in the secondary analyses. e) Neighbourhood-level covariates I used neighborhood-level demographic variables to identify community-level SES factors, such as deprivation score, low education rate, lone parent family rate, unemployment rate, perceived minority rate, and median income. (See figure 3, DAG). To enhance accuracy, a small, relatively stable geographic unit from the census data, dissemination areas, was used. However, residual confounding cannot be entirely ruled out. Participant data was geocoded at the 20 six-digit postal code of the residential address, which corresponds approximately to the centroid of one block face or an apartment building in the study area, and all linkages, with the exception of SES occurred at this spatial level. Using six-digit postal code data, Statistics Canada's (2005 census) British Columbia Perinatal Data Registry classified local urbanicity as urban, rural-urban fringe, or rural. Namely, neighborhoods were classified urban if they were located in areas with a population of at least 50,000 people in the surrounding census metropolitan area. Household size-adjusted average family income was utilized for the purpose of ranking all the DAs in question. These DAs were then divided into quintiles based on this ranking system77. This quintile system was employed in order to create a relative census variable, which could then be used to compare individuals residing within a specific geographic region. Education was categorized into three distinct levels: those without a high school diploma, those with a secondary education, and those with post-secondary education. I incorporated a census- and geographically-based index of marginalization into our study, namely the Canadian Marginalization (CAN-Marg) Index78. This index has been widely implemented in epidemiological research across Canada, and is derived from factor analysis of various census variables79,80. It has also been found to be consistent over different time periods and geographic regions81. Unlike conventional socioeconomic indicators such as income and education, the CAN-Marg Index aims to characterize disparities in marginalization in both urban and rural areas and assess inequalities in multiple aspects of health and social well-being. This project includes the material deprivation derived from the CAN-Marg Index, which was 21 categorized into five quintiles based on their distribution, with the highest quintile representing the most deprived and the lowest quintile representing the least deprived. f) Individual-level covariates The study accessed the BC Perinatal Registry and the BC Vital Statistics Agency utilising individual Provincial Health numbers to acquire information on mother and child health status. The BC Perinatal Data Registry records 99.9% of BC births68. The project accessed data on prenatal, intrapartum, and postpartum mother and baby care, birth outcomes, and maternal behaviours during pregnancy such as tobacco, alcohol, and drug use. Other biological information on children, such as biological sex, date of birth, and parental age at birth, was obtained from BC Vital Statistics. In the current investigation, biological sex and age were regarded possible impact modifiers (See DAG, Figure 3). Biological sex was categorized as a binary response (male or female). The age of participants was calculated using their month of birth, dividing them into two groups: younger (born between July and December) and older (born between January and June). This is because the cut-off for the school year is determined by age in July of each year. Respondents with missing data for any outcome, exposure, or confounder variable were excluded from the study sample. 2.3 Statistical analyses The present study adopts a cross-sectional study design and linear regression models to examine the association between residential noise and language development among children in Metro Vancouver. 22 2.3.1 Descriptive analyses Descriptive statistics were performed using the mean (and standard deviation) for the continuous variable or frequencies and proportions (in percentages) for categorical variables. 2.3.2 Model selections I started with All Possible Variables from the linkage of primary datasets, and pre-screened for overlap, gross missingness, and conceptual importance. This resulted in a smaller set of potential variables, including individual demographic variables: mover, gender, number of living children in the family, birth outcomes and parental health: maternal BMI category, maternal age, father's age, gestational age, birth head circumference, birth weight, Apgar 5-minute score, community-level covariates: quintiles of material deprivation, median income level, unemployment rate, low education rate, lone parent rate, perceived visual minority, and EDI covariates: wave, age, ESL, urbanicity, French or other immersion. Variables with significant amounts of missing data, such as maternal BMI and father's age, were then removed. For the continuous variables, multicollinearity is typically considered present when an absolute correlation coefficient surpasses 0.7 between two or more predictor variables. Consequently, based on this threshold, we opted to exclude the variables pertaining to birth length and average income level from our model (see Appendix A). The derivational score and lower education rate presented a borderline case with a correlation of 0.69. Despite their substantial correlation, I decided to retain both variables in my model selection model, given their distinct yet critical measures. In terms of categorical variables, the wave of EDI displayed a significant correlation with a host of factors, including gravida, birth head circumference, birth weight, birth length, Apgar 5-minute score, community-level median household income, and community-level perceived minority rate. Consequently, I removed the 'wave' variable from the list of covariates. 23 The final covariable list included [Individual demography] Mover, Gender, Number of living children, [birth outcome and parental health] Gestational age, Birth head circumference, Birth weight, Apgar 5 minutes, [Community-level covariates] Quintiles of material deprivation, Median household income, Unemployment rate, Low education rate, Lone parent rate, perceived visual minority, [EDI covariates] Age, ESL, Urbanicity. The process of building my model adopted a manual forward stepwise selection approach. The model started without any variables, and then proceeds by sequentially integrating the coefficient (noise), and potential confounders. The inclusion order of these potential confounders was specifically designed, starting with individual demographic variables, then transitioning to birth outcomes and parental health-related factors. Following these were community-level covariates, and finally, the model incorporated the EDI covariate (as ordered in step six in variable selection). Variables manifesting elevated Variance Inflation Factor (VIF) scores (typically above 5 or 10) or low tolerance values (below 0.1 or 0.2) were removed. The selection of potential confounders was informed by two distinct models, each encapsulating a variety of theoretically significant covariates. These span from the more specific to the broader considerations. The criteria for these selections are further detailed below. Model 1: Employing a multilevel multivariable strategy, this model retained potential confounders leading to a 10% change in noise coefficient. This model includes family income level and ESL status, reflecting the original model laid out in the thesis. Model 2: Relying purely on conceptually driven selection, this model will include all potential confounders from individual demographic variables, three birth outcome and parental health proxies, three community-level covariates, and two EDI covariate proxies (i.e., age and ESL). Variables were introduced based on the decrease the Akaike Information Criterion (AIC). Each model emphasizes distinct variable selection aspects and criteria. The optimal model was discerned through a comparative analysis encompassing criteria like AIC, predictor count, and observation number. The AIC was recalculated with each variable introduction, ensuring a comprehensive and accurate model representation. After careful evaluation, Model 1 was 24 selected due to its efficiency in terms of predictor count while maintaining comparable AIC values and observation numbers as the alternate model. A comprehensive comparison of the two multilevel linear regression models examining associations between noise and EDI mean score for language related can be found in Appendix F. 2.3.3 Primary analyses 2.3.3.1 Objective 1 - Examine the association between residential exposure to noise and language development outcomes, adjusting for confounders and random effects Bi-variate logistics and linear models were employed to characterize the association between the residential noise and each EDI outcome variable. A 10% change in main variable coefficient values was used as a criterion in the multivariable multilevel model to select confounders (as the model 1 mentioned in the 2.3.2 model selection). This method was used rather than all possible covariates to reduce computational expense for the model running. As such, the only covariates with impacts on the coefficient of noise sufficient enough (i.e., 10% cut-off) were family income level, and English as Second Language (ESL) status (See appendix A). Subsequently, multivariable logistic models were employed to characterize the association between the residential exposure to noise and being under developmental expectations (coded as 1)1 in multiple language-related outcomes, adjusting on covariates and random effects. In addition, multivariable linear models were employed to characterize the association between the residential exposure to noise and multiple language-related outcomes, 1 See a) Outcome: Outcome: Early childhood development at the age of 5 in 2.2 measurement 25 adjusting on covariates and random effects. Covariables in both models include median family income and ESL status. The data was nested, with children grouped by teachers, and our multilevel linear regression controlled for the random effects on teachers. 2.3.3.2 Objective 2 - Examine the modifying effects of greenspace To investigate the impact modification of greenspace has on an area, we conducted an analysis of combined exposures to noise and to vegetation. These exposures were classified as Poor, Green and Loud, Gray and Quiet, and Excellent, as detailed in Table 1. In order to enhance contrast, we opted to only include quartile 1 and 4 in generating these groups. It should be noted that the distribution of the two exposures was uneven, thus using quintiles may not have provided adequate contrast between the groups. Therefore, utilizing quartiles was deemed a more suitable approach (Table 2). Table 2 List of combinational exposure groups We employed this method to investigate the effect modification of both low-noise and high-greenspace in various contexts. For the effects of low-noise, the comparison between excellent and Green and Loud is comparing a change in noise in a context of high greenspace, whereas the comparison between Gray and Quiet and Poor is comparing a change in noise a context of low greenness. When it came to the effects of high-greenspace, the comparison between excellent and Gray and Quiet groups was used (with noise constantly low), while the Category Noise Vegetation Poor (n=5,939) H (Highest quartile) L (Lowest quartile) Green and Loud (n=6,082) H H Gray and Quiet (n=6,054) L L Excellent (n=8,910) L H 26 comparison between Green and Loud and Poor examined the effect of greenspace in the context of high noise background (Table 3). Table 3 A list of the explanations by combinational exposures. Effects on Comparisons Contexts low-noise Excellent vs Green and Loud high greenspace Gray and Quiet vs Poor low greenness high-greenspace Excellent vs Gray and Quiet Low noise Green and Loud vs Poor High noise 2.3.4 Secondary analyses 2.3.4.1 Robustness – Propensity Score Matching To ensure the reliability of the association between exposure to noise and language development, we implemented a propensity score matching (PSM) approach. This involves including all possible covariates that are not collinear with each other (i.e., the "kitchen sink" method) to ensure the most accurate and reliable results. For the PSM, some covariates were removed from the list because of the high missingness with low impact on the coefficient of noise, such as maternal BMI, father’s age, French immersion, and other immersion classes than French (details see Appendix B). The covariates in PSM models include children’s age, gender, maternal age, gestational age, birth head circumference, birth weight, Apgar 5 minutes, wave of EDI, English as a Second Language (ESL), urbanicity, whether is a mover, number of living children at home, community-level quintiles of material deprivation, median income level, unemployment rate, low education rate (no certificate, diploma, or degree), lone parent rate, visible minority rate. 27 The present study was conducted by matching propensity scores on high and low noise interquartile to assess the robustness of the results. The highest interquartile was defined as high noise neighbourhood groups (coded as 1), and the low noise neighbourhood group includes the lowest quantiles (coded as 0). To achieve these two comparable groups, this project first fitted a logistic regression modelling with all covariates used from the DAG to calculate the probability of both groups. All predictors were included in the multivariate models as they were identified as important in the relevant literature and theoretical frameworks, even if they were not statistically significant at p-value < 0.05. This inclusion is deemed acceptable given that the model was already parsimonious and that the study objectives are to assess a relationship rather than ascertain the strongest predictors of the outcome, while an automated variable selection method risks excluding the key independent variable. I then used 1:1 nearest neighbourhood matching without replacement to limit the individuals to two comparable groups whose variables were similar. We excluded missing data from our analysis. This approach allowed us to assess the robustness of our results and to examine the association between high noise exposure and language development outcomes. Ultimately, we used the high noise exposure level to examine its association with language development outcome. This approach will ensure the reliability and accuracy of our results, allowing us to draw valid conclusions about the relationship between noise exposure and language development. 2.3.4.2 Effect modification on sex and age Biological sex and age were considered possible effect modifiers. This project included the interaction terms in the multivariable model and assess whether or not the effect modification 28 is statistically significant. The statistically significant at p < 0.05 was considered to be effect modification. All analyses were conducted using R statistical package version 4.0.282,83. 29 3. Results 3.1 Descriptive Statistics My study analyzed language-related developmental outcomes of 30,048 children from the April 1, 2000 to December 31, 2005 in EDI waves 2-4 (2004-2007, 2007-2009, and 2009-2011) for whom we could link to noise exposure. The mean noise level was 63.5 dB(A), and the mean percentage of greenspace within a 250 m residential buffer was 31.4%. The association between noise and greenspace and language outcomes were presented in appendix D. Of the 30,048 respondents, approximately 44.1 % of children were “older” (born between January and June versus July to December) (N=13,255), 50.8% were male (N=15,269). The highest proportion of subjects were in wave 4 (2009-2011) (N=13,331, 44.4%), followed by wave 3 (2007-2009) (N=12,042, 40.1%), and wave 2 (2004-2007) (N=4,675, 15.6%). Regarding individual-level covariates, the majority of participants lived in the urban core (N=28,313, 94.9%), and the rest live in urban or rural fringe (N=1,518, 5.1%). In terms of quintiles of material deprivation (quintile 1 refers to least deprived and 5 refers to most deprived), the greatest fraction of children were in quintile 2, (N=8,688 29.5%), and followed by quintile 3 (N=7,630, 25.9%), quintile 1 (N=6,851, 23.3%), quintile 4 (N=4442, 15.1%) and quintile 5 (N=1,841, 6.3%). About 52.7 % of the children had moved at least once during their lives (N=15,829, 52.7%). The average number of children living in each household was 0.7, and the mean maternal age at birth was 31.1 years. In terms of birth outcomes, the mean gestational age was 39.0 weeks, the mean birth head circumference was 34.6 centimeters (cm), the mean birth weight was 3399.6 grams, and the mean Apgar 5-min score was 9.1. 30 Table 4 Characteristics of the BtbW cohort in Metro Vancouver, Canada Variables Total sample (30,048) Environmental exposure variables1 Noise, dB(A) 63.5 ± 4.8 Vegetation, % 31.4 ± 8.3 Individual-level covariates Older cohort (Jan to June) n, (%) 13255 (44.1) Male n, (%) 15269 (50.8) Maternal age year 31.1 ± 5.2 Gestational age weeks, Mean ± SD2 39.0 ± 1.7 Birth head circumference cm, Mean ± SD2 34.6 ± 1.7 Birth weight grams, Mean ± SD2 3399.6 ± 528.3 Apgar 5minutes Mean ± SD2 9.1 ± 0.6 Wave n, (%) 2 (2004 - 2007) 4675 (15.6) 3 (2007 - 2009) 12042 (40.1) 4 (2009 - 2011) 13331 (44.4) ESL identified2 9930 (33.3) Urbanicity n, (%)2 Urban fringe 774 (2.6) Rural fringe 744 (2.5) Urban core 28313 (94.9) Have moved n, (%) 15829 (52.7) Number of living children at home 0.7 ± 0.9 Quintiles of material deprivation 1 (least deprived) 6851 (23.3) 2 8688 (29.5) 3 7630 (25.9) 4 4442 (15.1) 5 (most deprived) 1841 (6.3) Community-level covariates1 Median income dollar2 (CAD) 25571.5 ± 7911.6 Unemployment rate2 % 5.6 ± 3.9 Low education rate2 % 18.6 ± 9.0 Lone parent rate2 % 15.4 ± 8.9 Perceived minority rate2 % 46.4 ± 26.4 1 Frequency (%) or mean ± SD is shown as appropriate unless otherwise indicated. 2Missing: Gestational age (2.1%); Birth head circumference (1.0%); Birth weight (0.1%); Apgar 5-minutes (0.2%); ESL (0.9%), Urbanicity (0.7%), Quintiles of material deprivation (2.0%), Median income (1.7%), Unemployment rate (1.7%); Low education rate (1.7%); Lone parent rate (1.7%); Perceived minority rate (1.8%). 31 With respect to community-level covariates, the median income was $ 25,571.50 CAD, and unemployment rate was 5.6%, the low education (no certificate, diploma, or degree) rate was 18.6%, the lone parent rate 15.4%, and the perceived minority rate was 46.4%. The details can be found in Table 4. 3.2 Primary analyses results Objective 1 - Examine the effect of residential exposure to noise on language developmental outcomes, adjusting for confounders and random effects The results of the unadjusted linear regression model were provided in Table 4. Data presented are β-coefficients (95% Confidence Interval (CI)) for one interquartile range (IQR) increase in exposure to noise (Lden) at child’s residence. As such, for every IQR increase in residential exposure to noise (6.2 Lden), there was a 0.14 (95% CI: 0.11, 0.16) decrease in the basic literacy score, a 0.22 (95% CI: 0.17, 0.27) decrease in the advanced literacy score, and a 0.36 (95% CI: 0.32, 0.40) decrease in communication and general knowledge. These findings were supported by the fact that none of the reported results cross the confidence interval of zero, indicating that these associations were highly consistent and robust. To address our first objective of adjusting for confounders and random effects of teachers between residential noise and language developmental outcomes, the results of the multivariable logistics regression model with adjustment on confounders (median income level, and ESL Statues) and random effects for teacher are provided in Table 5. After accounting for potential confounding factors, each interquartile range (IQR) increase in residential exposure to noise (6.2 Lden) was associated with a 17% higher likelihood (95% CI: 1.12; 1.18) of being below developmental expectations in basic literacy scores. Similarly, there was a 10% higher likelihood (95% CI: 1.05; 1.15) of being below developmental expectations in advanced literacy scores and 32 a 10% higher likelihood (95% CI: 1.06; 1.14) of being below developmental expectations in communication and general knowledge (see Table 5). Furthermore, we conducted multivariable linear regression analyses with control for confounders (median income level and ESL status) and random effects for teachers, and the results are presented in Table 6. After adjusting for potential confounders, for every IQR increase in residential exposure to noise (6.2 Lden), there was a 0.09 (95% CI: 0.06, 0.11) decrease in the basic literacy score, a 0.14 (95% CI: 0.09, 0.18) decrease in the advanced literacy score, and a 0.11 (95% CI: 0.08, 0.15) decrease in communication skills (see Table 6). Thus, these findings provided evidence for a significant association between increased residential noise exposure and an increased risk of falling below developmental expectations in these specific subdomains, even after controlling for potential confounding variables and random effects of teachers. Table 5 The odds ratio being underdevelopment in the subdomains per one IQR increase in Lden ICC, Intraclass Correlation Coefficient Table 6 The estimated change of the subdomain mean score in per one IQR increase in Lden ICC, Intraclass Correlation Coefficient Outcomes Crude Adjusted Fixed adjusted effect Adjusted ICC Basic literacy 1.25 [1.19; 1.31] 1.16 [1.11; 1.22] 1.17 [1.12; 1.18] 0.21 Advanced literacy 1.13 [1.09; 1.17] 1.09 [1.05; 1.13] 1.10 [1.05; 1.15] 0.35 communication and general knowledge 1.26 [1.22; 1.30] 1.10 [1.06; 1.14] 1.10 [1.06; 1.14] 0.34 Outcomes Crude Adjusted Fixed adjusted effect Adjusted ICC Basic literacy -0.14 [-0.11; -0.16] -0.09 [-0.06; -0.11] -0.09 [-0.06; -0.11] 0.10 Advanced literacy -0.22 [-0.17; -0.27] -0.15 [-0.10; -0.20] -0.14 [-0.09; -0.18] 0.26 communication and general knowledge -0.36 [-0.32; -0.40] -0.14 [-0.10; -0.17] -0.11 [-0.15; -0.08] 0.19 33 Objective 2 - Examine the modifying effects of residential greenspace To achieve our secondary objective of assessing the modifying effects of residential greenness on the association between noise exposure and language developmental outcomes, I presented the results from unadjusted and adjusted regression models in Table 7. The purpose of the comparison between different groups, including Green and Quiet and Green and Loud, Gray and Quiet and Poor, was to investigate the impacts of low-noise in high-greenspace by different contexts. The comparison of Green and Quiet and Green and Loud groups was used to suggest the impacts of noise in high-greenspace areas, and we can observe that low-noise was associated with higher score in both basic and advanced literacy, albeit the adjusted effect on Green and Loud is not statistically significant. Green and Quiet was associated with a much higher increase in language development score compared to Green and Loud, although it was attenuated after the adjustment. To examine the impact of noise in the context of low greenness, Gray and Quiet was compared to Poor. In both crude and adjusted models, Gray and Quiet was associated with higher increases in both basic and advanced literacy than the Green and Loud group, while the effects were reduced in adjusted models. The Green and Quiet group showed a slightly increase than the Green and Loud group in the crude model, but significantly greater after adjustment, suggesting that the low-noise was associated with better language development in low greenspace areas. Together, these findings suggested the beneficial effects of lower-noise on children’s language development. To study the effects of greenspace, Green and Loud was compared with Poor to examine the effects of vegetation in the high-noise context; as a result, the high-greenspace was 34 associated with higher language development score compared to Poor. However, this increase was lower than the other two low-noise groups (Gray and Quiet and Green and Quiet), suggesting that greenspace did not have more positive effects on language development than low-noise. The comparison of Green and Quiet and Gray and Quiet groups was used to suggest the effects of greenspace within the low-noise context; the differences were fairly modest after adjustment, indicating that the impacts of greenspace were minimal in the low-noise areas. Taken together, it was found that Green and Quiet leads to significant improvements in all three EDI outcomes in adjusted analysis. However, For the two literacy outcomes (basic and advanced) lower noise appears to be the factor associated with the improvement with similar increases seen in the grey/quiet and green/quiet exposures, suggesting green space was not associated with improved scores, and there was no interaction. For communication and general knowledge outcomes, both high-greenspace and low-noise levels were found to positively influence EDI scores, with no evidence of interaction. Overall, the results indicate that exposure to quiet environments could lead to significant improvements in children's developmental outcomes, but there was no evidence of interaction effects on greenspace. Table 7 The estimated change of the subdomain mean score in combinational exposures. Outcomes Exposures Crude Adjusted Basic literacy Ref (poor) Green and Loud 0.21 [0.10;0.32] 0.08 [-0.03;0.20] Gray and Quiet 0.34 [0.23;0.45] 0.25 [0.13;0.36] Green and Quiet 0.42 [0.33;0.50] 0.22 [0.12;0.32] Advanced literacy Ref (poor) Green and Loud 0.32 [0.09;0.54] 0.08 [-0.16;0.31] Gray and Quiet 0.38 [0.15;0.60] 0.34 [0.11;0.56] Green and Quiet 0.67 [0.50;0.85] 0.36 [0.16;0.56] 35 communication and general knowledge Ref (poor) Green and Loud 1.01 [0.83;1.19] 0.44 [0.27;0.62] Gray and Quiet 0.97 [0.79;1.15] 0.53 [0.36;0.70] Green and Quiet 1.35 [1.21;1.49] 0.43 [0.28;0.58] The adjusted model control for median income level, and ESL Statues. The random effect was adjusted on teacher ID. The grey ones were not statistically significant. All estimated effects and 95% confidence intervals are rounded. They were tested by Pillai’s test of unequal covariance. 3.3 Secondary analyses 3.3.1 Robustness – Propensity Score Matching Our secondary analyses supported our primary findings, indicating that higher residential noise exposure was associated with decreased language development outcomes. To further investigate this relationship, we conducted propensity score matching analyses on all available cases (N=13,766), using noise exposure classified by IQR. We included all available covariates in the model, including mover status, gender, number of living children, maternal age, gestational age, birth head circumference, birth weight, Apgar 5 mins, deprivation score category, median income level, unemployment rate, low education rate, lone parent rate, visible minority rate, wave, age, ESL status, and urbanicity. Each of the resulting high and low noise groups included 7,512 individuals (as shown in Figure 4). While the distribution of covariate values between the high and low noise groups may not appear similar, this could be due to the presence of extreme covariate values. However, it's important to note that the 'kitchen sink method' was used to match the groups on as many variables as possible to test for robustness, so an exact match may not be necessary. Nonetheless, 36 it is still important to assess the balance achieved and consider the implications for future studies that primarily rely on propensity score matching. Propensity score matching suggested that being in the high noise group was associated with a significant decrease of 0.28 in basic literacy score, 0.45 in advanced literacy score, and 0.75 in communication and general knowledge, as shown in Table 6. These findings provided further evidence for the association between residential noise exposure and impaired language development outcomes. Figure 3 Distribution of being different noise groups. Distribution of being different noise groups. The X-axis represent the predicted probability of being high or low group given all possible covariates, with higher scores indicating a higher likelihood of being in the group. Table 8 The estimated change of the subdomain mean score in Lden per one IQR increase in PSM high noise group. Outcome Estimate Pr(>|t|) Basic literacy -0.28 <0.01 Advanced literacy -0.45 <0.01 Communication and general knowledge -0.75 <0.01 37 3.3.2 Effect Modification – Age and Biological sex We incorporated the interaction of age and Sex on noise in the multivariable models using the covariates from the main analyses. We can see that age appeared to modify the effect of residential exposure to noise on basic literacy and communication and general knowledge, with older individuals exhibiting better scores, while no such effect was observed for advanced literacy (Table 9). There were no significant modifying effects found for sex on the relationship between noise and outcome (Table 10). Table 9 Interaction term on age – older Outcomes Estimate P-value Basic Literacy 0.09 <0.05 Advanced Literacy 0.03 0.54 Communication and general knowledge 0.08 0.04 Table 10 Interaction term on sex – male Outcomes Estimate P-value Basic Literacy -0.008 0.75 Advanced Literacy 0.07 0.18 Communication and general knowledge 0.005 0.90 38 4. Discussion The findings of this thesis demonstrated the associations between increase in early-life residential exposure to noise and poorer language developmental outcomes, and that the association were independent of demographic characteristics and from birth outcome status. In particular, for every 6.2 dB(A) increase (in the interquartile range) of noise exposure, there was a corresponding 17% (95% CI: 1.12; 1.18) higher likelihood of children scoring below developmental expectations in basic literacy, 10% (95% CI: 1.05; 1.15) in advanced literacy, and 10% (95 % CI: 1.06; 1.14) in communication and general knowledge compared to their peers who experience lower levels of noise exposure. Given that the average noise level falls within the range of 63.5 ± 4.8 dB(A), which may vary between 58.7 and 68.3dB(A), it indicated that even within this relatively moderate range, small increases in noise exposure have the potential to impact children's developmental outcomes, as indicated by the odds ratio. Considering that the prevalence of under expectational expectation in each subdomain surpasses 10% (10.1% for basic literacy, 21.5% for advanced literacy, and 51.6 % for communication and general knowledge), there is a possibility that the odds ratio may overstate the risk associated with noise exposure. Thus, this project also examines the main scores of each subdomain to provide a more precise result. In particular, there was a corresponding decrease of 0.09 (95% CI: -0.06; -0.11) in basic literacy, 0.14 (95% CI: -0.09; -0.18) in advanced literacy, and 0.11 (95% CI: -0.15; -0.08) in communication and general knowledge out of 10 in EDI score, after adjusting for family median income level and children’s ESL, and random effects on teachers. These findings were supported by the fact that none of the reported results cross the confidence interval of zero, indicating that these associations were highly consistent and robust. 39 It is pertinent to highlight that the alternative model, as illustrated in Appendix F (examining the association between noise and the EDI mean score for language-related outcomes), yielded consistent results, thereby reinforcing the robustness of these findings. It's worth noting that our results are consistent with those reported in another study conducted within the same cohort, which found a decrease of 0.17 (95 % CI: 0.04-0.29) in the entire domain of language and cognitive development associated with noise exposure84. In addition, the study explored the potential modifying effects of urban greenspace on the association between noise exposure and language developmental outcomes. The results showed that while optimal developmental outcomes were associated with low noise and high greenspace settings, the effects of greenspace were modest in high noise developmental environments. These findings supported the first hypothesis of the study, which proposed that residential exposure to noise would be independently related to poorer language developmental outcomes. However, the finding did not support the second hypothesis that greenspace would modify the association the association between residential exposure to noise and language developmental outcomes. 4.1 Early-life residential exposure to noise may harm language development The present study contributed to the growing body of research linking exposure to noise with impaired language related skills in early childhood. Previous laboratory studies suggested that decreased acoustic resolution or cognitive interference may explain this association (section 1.2.1.1 and 1.2.1.2). While relationship between exposure to residential noise and language development has been investigated in several studies, with previous research linking noise exposure to reduced language acquisition in laboratory settings, our findings provide real-world evidence that supports these experimental associations. Our results were generally consistent 40 with other observational studies in the literature10,11,51,85 , but our study had some unique strengths, including a larger and more representative sample, more precise community-level variables. My secondary analyses using the propensity matching approach provided additional support for the independent relationship between residential noise and impaired language ability. The mechanisms that might explain the relationship between noise exposure and delayed language development are multifaceted, as discussed in Chapter 1 of this thesis. Poor speech perceptual environments, diminished cognitive capability, lower learning ability due to persistent stress exposure, and brain underdevelopment are all possible contributors. This study thus provided ecological evidence to the association between noise and language development. Future research should examine how changes in noise levels over time impact language development outcomes, in order to generate stronger causal evidence. Longitudinal data from multiple time points would be required for this type of analysis. Unlike the previous two school-based studies9,11 in the literature, my thesis focused on residential noise exposure, providing robust evidence that highlights the potential detrimental effects of residential noise on children's language development. This is particularly significant considering that children spend the majority of their time at home. This shift in focus from school-based studies to residential noise exposure strengthened the evidence base, enhancing our understanding of the potential harm that noise can have on children's language skills. By recognizing the significance of residential noise exposure, we could take proactive steps to mitigate its adverse effects and promote optimal language development in children. 41 4.2 Minimal modifying effects of greenspace on noise and EDI outcomes While some studies suggested that greenspace can alleviate the negative impact of noise exposure on cognitive function56,59,61, our study only partially supported this claim. Our findings align with Jarvis et al (2021)84 conducted on the same cohort, which found no association between greenspace and EDI language and cognitive development domain. Although Jarvis et al (2021)84 assessed the whole domain of language and cognitive development, including literacy, numeracy, and memory skills, which renders our findings is not completely comparable, the results between us align, indicating no connection between greenspace and language development in this cohort. One potential explanation for this finding could be attributed to the misclassification of greenspace measurement. To elaborate, unlike comparable studies conducted with the same cohort86, my research did not take into account the various types of land use within the greenspace, which may have diluted the impacts of greenspace on language development. However, it is important to note that the inclusion of both noise and greenspace data in studies is a relatively new and emerging field, and further research is required to corroborate our findings. A promising avenue for future studies would be to combine data sources with information on land use types in order to gain a more comprehensive understanding of how different forms of greenspace (e.g., grass, tress, etc.) impact the relationship between noise exposure and language acquisition. Furthermore, our analysis indicated that greenspace had a greater impact on communication and general knowledge than on literacy skills. This difference could be due to 42 the fact that communication and general knowledge is closely tied to social interaction and interpersonal skills, while literacy skills may be more influenced by factors such as formal education. Therefore, family environment might play a greater role than physical activity in this context, as children from families who live in greener neighbourhoods tend to have better communication and general knowledge scores than those living in more grey areas, even when both are exposed to low levels of noise. However, this finding might be explained by self-selection into residential greenspace. Specifically, individuals who choose to live in areas with more greenspace tend to have better educational backgrounds and benefit from more linguistic inputs within their families. Consequently, the association between greenspace and language development might be diluted by the pre-existing preference and financial availability of chosen greenspace areas87. It is challenging to quantify these pre-existing and pertinent social factors, such as the number of children in the family, choice of school for children, availability of parks around the residential areas, and so on88. Future studies should consider including variables to assess these factors to obtain a better understanding of the potential association between greenspace and language development. Also, our study was limited by its cross-sectional design, which precludes drawing causal conclusions. As a result, further research incorporating more detailed information on land use types, self-selection bias, and longitudinal data is necessary to build a more complete understanding of the relationship between greenspace, noise exposure, and language development. 43 4.3 Strengths and limitations This study had several strengths. First, this project addressed important knowledge gaps in the literature by estimating the association between residential noise and childhood language development and the corresponding modifying effects of greenspace exposure. This represented an important step forward in our understanding of the complex interplay between residential exposure to noise and greenspace and their potential implications for child language development. Secondly, this study is distinct from previous research in that it is the only population-based cohort study that includes lifetime residential noise and greenspace exposure (from birth to kindergarten) and childhood development while also controlling for high quality covariates. This had a high degree of generalizability to the study's findings, which may be applicable to other highly urbanized metropolitan areas with characteristics similar to Metro Vancouver. Third, this project employed high-quality exposure data to estimate residential greenspace using a unique measure that has been demonstrated in earlier research to be a more sensitive measure of vegetation than frequently used greenness measures (e.g., the NDVI), particularly in urban contexts Fourth, the outcome measures used in this study have established reliability and sensitivity with previous studies. This strengthened the validity and comparability of the study's results with those of other studies in the field and contributed to the overall robustness of the study. Finally, the use of propensity score techniques in secondary analyses represented an additional strength of this study. By accounting for self-selection biases in neighborhood choice, this approach helps to strengthen the internal validity of the study's findings and enhances the credibility of its conclusions. 44 Despite the strengths, this study also had several limitations. The thesis project only had outcome data at one point in time, making it impossible to draw a causal association, but its findings can inspire future relevant longitudinal studies. Also, the research region was limited to Metro Vancouver, which might limit generalizability to other nations with differing socioeconomic or policy contexts. Nonetheless, the area had a comparably considerable variation in population-level and individual-level covariates, allowing this study to assess the consistency of findings across subgroups (e.g., different family income-level, age, and gender). Furthermore, we have also found that age may modify the impact of noise exposure on language development. This is not surprising, as language development naturally increases with age. Therefore, further research is required to fully integrate the age modification effects into our analysis. Non-differential misclassification might occur in both exposures and outcomes. For the exposure measurement, the exposures were chosen from the six-character residential postal code; as a result, the estimation may not accurately represent everyone's everyday condition. However, Land Use Regression (LUR) modelling had shown to be the most effective approach for backcasting and forecasting exposure to spatially heterogeneous pollutants within urban areas89. Although earlier research demonstrating the geographic stability of the application, the noise map did not account for error sources in predicted exposure to the local environment, such as room orientation and time spent at home89. Non-differential misclassification in the outcome measurement might have resulted from the teachers' subjectiveness in the assessment, but this was minimized by the multilevel multivariable regression account for misclassification on teacher. Also, unknown confounding effects was impossible to be eliminated in observational studies. Despite this flaw in causal 45 inference, this experiment would nevertheless yield some scientific information on the impact of noise on children's language development. Finally, there was insufficient literature to know if prolonged exposure to noise leads to increasing, constant, or decreasing effects of noise on language or its relevant development due to adaptation. 4.4 Implications The present study represented a significant contribution to the field of environmental health research by addressing important knowledge gaps regarding the impact of residential noise exposure on childhood language development. By utilizing a unique combination of data sources and a representative population, the study seeks to provide new insights into early developmental predictors and to build on the DOHaD theories. This innovative linkage between noise and language development has the potential to pave the way for further research in this area. Diverging from previous literature centered on school settings9,10, my thesis delved into the realm of residential noise exposure, unearthing compelling evidence that underscores the potential detriments it poses to children's language development. This pioneering research assumed heightened significance considering the substantial amount of time children spend in the comfort of their homes. By redirecting our attention from school-based studies to the impact of noise within residential environments, we have bolstered the foundation of evidence, thus deepening our comprehension of the deleterious consequences noise can have on children's linguistic aptitude. Acknowledging the crucial role played by residential noise exposure empowers us to proactively tackle its adverse effects and foster an environment conducive to optimal language development in children. 46 Furthermore, the study highlighted the importance of multidisciplinary collaboration in advancing public health knowledge. The successful integration of expertise from various fields, along with innovative data linkage and the support of organizations such as schools, the health ministry, and citizens, has facilitated the generation of new insights. The findings of this research underscored the importance of epidemiology as a shared benefit for society and emphasize the need for engagement among diverse stakeholders, including BC citizens, environmental scientists, and the BC government, to inform and improve noise health policy. Unlike the European Union's approach of treating environmental noise as a health problem, Canada's noise policies are based on community response to annoying sounds rather than being health-based. However, this study's findings emphasize the need to incorporate the idea of noise reduction in health policy. While increasing greenspace may not be an ideal intervention to mitigate the negative consequences of noise, noise reduction should be prioritized as the primary solution. The findings of this study could inform the BC government's efforts to launch local noise surveillance and community-based noise abatement programs to benefit local residents. 4.5 Future research Future research can also explore potential mechanisms that underlie the relationship between noise exposure and language development, such as the impact of noise on cognitive processes like attention and working memory. Longitudinal studies could also investigate how changes in noise exposure over time impact language development, which could provide valuable information for developing effective noise reduction interventions. In terms of public health interventions, this thesis suggested that noise reduction should be prioritized over increasing greenspace as a primary solution to the negative consequences of noise pollution on 47 language development. Future research could investigate the effectiveness of different noise reduction strategies, such as sound insulation, traffic management, and building design. Future research to expand this field is needed to improve literature in environmental health and language acquisition. Further studies with longitudinal data on residential exposure to noise would provide insights into its etiology and natural history. 48 5. Conclusion The present thesis had provided evidence for the independent association between early-life residential exposure to noise and impaired language developmental outcomes, highlighting the significant impact of noise pollution on children's language development and emphasizing the need for further research to expand our understanding of this relationship. While the relationship between greenspace and language development remained unclear, this study provides a strong foundation for future research to investigate how the environment impacts language acquisition and identify potential interventions to support healthy language development in children. Furthermore, this study emphasized the importance of multidisciplinary collaboration in advancing public health knowledge and the role of epidemiology as a shared benefit for society. Developing effective public health interventions to address noise pollution and support healthy language acquisition in children is crucial, this thesis calls for a more health-focused approach to noise policy and highlights the need for public health interventions aimed at mitigating the harmful effects of noise pollution on children's developmental health. 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The data linkage process. Available from: https://www.popdata.bc.ca/datalinkage/process [Accessed 22 January 2021]. 63 Appendices Appendix A Pearson correlation for collinearity test 64 Appendix B Retention of covariates Table A. The list of covariates. The coefficient displays the coefficient of noise while controlling for the selected covariates, standard error, degree of freedom, and the power lose during the model building. The variables in red were excluded during the process. Variable Coefficient SE DF power lose Noise -0.14 0.01 29523 Mover -0.13 0.01 29522 1 (0.00%) Gender -0.13 0.01 29521 1 (0.00%) Lone parent rate -0.13 0.01 28951 570 (1.93%) Number of living children -0.13 0.01 29322 199 (0.67%) Maternal BMI category -0.11 0.01 22898 6424 (21.91%) Maternal age -0.12 0.01 29321 1 (0.00%) Age of father -0.12 0.01 27325 1996 (6.81%) Gestational age -0.12 0.01 28388 933 (3.18%) Birth head circumference -0.12 0.01 28472 -84 (-0.30%) Birth weight -0.12 0.01 28454 18 (0.06%) Apgar 5_minutes -0.11 0.01 28412 42 (0.15%) Quintiles of material deprivation -0.12 0.01 27858 554 (1.95%) 65 Median income -0.10 0.01 27857 1 (0.00%) Unemployment rate -0.09 0.01 27856 1 (0.00%) Low education rate -0.09 0.01 27855 1 (0.00%) Lone parent rate -0.09 0.01 27854 1 (0.00%) Perceived visual minority -0.09 0.01 27853 1 (0.00%) Wave -0.09 0.01 27851 2 (0.01%) Age -0.09 0.01 27850 1 (0.00%) French immersion -0.09 0.01 27804 46 (0.17%) Other immersion -0.09 0.01 27589 215 (0.77%) ESL -0.08 0.01 27628 222 (0.80%) Urbanicity -0.07 0.01 27626 2 (0.01%) 66 Appendix C Covariates selection to address collinearity Table B. The list of covariates, their corresponding b1 coefficients with the change, standard error with the change, and the decision of retaining. Variables Coefficient SE Retain Noise -0.137 0.0124 Mover -0.13 (-2.75%) 0.01 (0.11%) N Gender -0.14 (-0.86%) 0.01 (0.11%) N Number of livings -0.14 (0.61%) 0.01 (0.67%) N Maternal age -0.13 (6.05%) 0.14 (0.00%) N Gestational age -0.13 (4.13%) 0.01 (2.00%) N Birth head circumference -0.13 (2.57%) 0.01 (0.96%) N Birth weight -0.13 (3.61%) 0.00 (0.07%) N Apgar 5-minutes -0.13 (1.82%) 0.01 (0.17%) N Deprivation -0.14 (-2.25%) 0.01 (1.96%) N Median Income -0.11 (21.30%) 0.01 (1.73%) Y Unemployment Rate -0.11 (1.30%) 0.01 (0.00%) N Low education -0.11 (0.65%) 0.01 (0.32%) N 67 Lone parent rate -0.10 (4.65%) 0.01 (-0.16%) N Perceived visible minority -0.10 (2.32%) 0.01 (-1.02%) N wave -0.11 (1.21%) 0.01 (0.00%) N Age -0.10 (3.44%) 0.01 (0.47%) N ESL -0.09 (21.00%) 0.01 (-0.47%) Y Urban -0.08 (3.21%) 0.01 (-0.55%) N 68 Appendix D Association between exposures and language development and its related outcomes 69 70 71 72 73 Appendix E Distribution of both exposures 74 Appendix F Comparison of multilevel linear regression analysis of residential noise exposure and EDI mean score in language development related subdomains with covariate adjustment during the model selection. Basic literacy Model 1 Model 2 Groups Variable Coeff SE Coeff SE 75 Noise -0.09 0.01 -0.08 0.01 Individual demography Moved NA NA -0.13 0.02 Gender NA NA -0.42 0.02 Number of living children NA NA -0.15 0.01 Birth outcomes and parental Health Gestational age NA NA 0.04 0.01 Birth weight NA NA 0.04 0.01 Apgar score NA NA 0.03 0.02 Community-level covariates Deprivation score NA NA 0.03 0.01 Median income level 0.08 0.01 0.02 0.01 Lower education NA NA -0.01 0.00 EDI covariates Age NA NA 0.34 0.02 ESL -0.37 0.02 -0.30 0.02 Model quality indicator: Number of observations 28787 27911 Number of Predictors 3 12 AIC 108878.8 104322.3 76 Advanced literacy Model 1 Model 2 Groups Variable Coeff SE Coeff SE Noise -0.14 0.02 -0.12 0.02 Individual demography Moved NA NA -0.19 0.04 Gender NA NA -1.08 0.04 Number of living children NA NA -0.30 0.02 Birth outcomes and parental Health Gestational age NA NA 0.09 0.02 Birth weight NA NA 0.04 0.02 Apgar score NA NA 0.06 0.03 Community-level covariates Deprivation score NA NA 0.07 0.02 Median income level 0.11 0.02 0.01 0.02 Lower education NA NA -0.03 0.00 EDI covariates Age NA NA 0.88 0.04 ESL -0.43 0.04 -0.30 0.04 Model quality indicator: Number of observations 28787 27911 Number of Predictors 3 12 AIC 141701.3 135692.7 77 Communication skills Model 1 Model 2 Groups Variable Coeff SE Coeff SE Noise -0.14 0.02 -0.11 0.02 Individual demography Moved NA NA -0.11 0.03 Gender NA NA -0.72 0.03 Number of living children NA NA -0.10 0.02 Birth outcomes and parental Health Gestational age NA NA 0.08 0.02 Birth weight NA NA 0.05 0.02 Apgar score NA NA 0.04 0.02 Community-level covariates Deprivation score NA NA 0.01 0.01 Median income level 0.11 0.02 0.03 0.02 Lower education NA NA -0.02 0.00 EDI covariates Age NA NA 0.56 0.03 ESL -0.43 0.04 -2.02 0.03 Model quality indicator: Number of observations 28787 27911 Number of Predictors 3 12 78 AIC 132154.5 126974.5
Thesis/Dissertation
eng
Population and Public Health
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Davies, Hugh, 1957-
Bosch, Matilda van den
Residential exposure to noise and language development : effect modification by residential greenspace
Text