Assessing the association between air pollution and child development in São Paulo, Brazil.

BACKGROUND: Outdoor air pollution is increasingly recognised as a key threat to population health globally, with particularly high risks for urban residents. In this study, we assessed the association between residential nitrogen dioxide (NO2) exposure and children's cognitive and behavioural development using data from São Paulo Brazil, one of the largest urban agglomerations in the world.
METHODS: We used data from the São Paulo Western Region Birth Cohort, a longitudinal cohort study aiming to examine determinants as well as long-term implications of early childhood development. Cross-sectional data from the 72-month follow-up was analysed. Data on NO2 concentration in the study area was collected at 80 locations in 2019, and land use regression modelling was used to estimate annual NO2 concentration at children's homes. Associations between predicted NO2 exposure and children's cognitive development as well as children's behavioural problems were estimated using linear regression models adjusted for an extensive set of confounders. All results were expressed per 10 μg/m3 increase in NO2.
RESULTS: 1143 children were included in the analysis. We found no association between NO2 and children's cognitive development (beta -0.05, 95% CI [-0.20; 0.10]) or behavioural problems (beta 0.02, 95% CI [-0.80; 0.12]).
CONCLUSION: No association between child cognition or child behaviour and NO2 was found in this cross-sectional analysis. Further research will be necessary to understand the extent to which these null results reflect a true absence of association or other statistical, biological or adaptive factors not addressed in this paper.

Introduction

Outdoor air pollution is increasingly recognised as one of the most critical challenges for global public health. In particular the concern arises in urban areas, where pollution levels are generally highest, since pollutants are primarily produced by the combustion of fossil fuel [1].

The most studied pollutants affecting health are particulate matter (PM), ozone (O3), sulphur dioxide (SO2) and nitrogen dioxide (NO2). Exposure to these pollutants can trigger inflammatory processes that can cause damage to various organs such as the lungs, heart, and brain [2,3] and has also been associated with a higher risk of developing illnesses such as respiratory diseases [4], cancer [2,5] and cardiovascular problems [3,6,7].

Various studies have reported adverse health effects of air pollution for children. Prenatal and childhood exposure to NO2 has been associated with asthma [8,9], failing lung development and consequent respiratory difficulties [10] and lower birth weight [10]. Existing research also suggests a positive association between traffic related air pollutants, and delays in brain maturation [11,12], memory [13,14] and cognition [12,15] among school children. Epidemiological and animal studies [16,17] have attributed these associations to changes in the central nervous system (CNS) which can be caused by an activation of the systemic [16] or local [18] immune systems and by oxidative stress [17,19].

Given that in Brazil over 80% of the population live in dense urban settings [20], average exposure to air pollution among Brazilian citizens is high [21]. São Paulo constitutes the largest urban area in Brazil and ranks among the largest cities in the world, with an estimated metropolitan population of 22 million and continued rapid growth [22]. Despite municipal efforts to improve air quality [23], air pollution levels remain a concern.

Previous studies from São Paulo have linked air pollution to non-accidental and cardiovascular mortality [7], especially in the elderly population [24], hospitalisations for respiratory problems in children [25], low birth weight [26] and premature births [27].

Few studies have investigated the impact of air pollution on cognitive development in children, most of which focus on Europe. Given that children are likely to be more susceptible to air pollution because their central nervous and immune systems are still developing [2,3,16], the lack of evidence in this area is somewhat surprising.

This study aims to address this research gap by exploring the association between nitrogen dioxide exposure and children’s cognitive and behavioural development using prospectively collected data on child development and air pollution from São Paulo, Brazil.

Methods

Study population and study area

Data on child development was collected through the São Paulo Western Region Birth Cohort (SP-ROC) [28]. The SP-ROC cohort was set up as a longitudinal study to assess the impact of social and environmental factors on child development. All children born at the University Hospital of São Paulo between April 1, 2012 and March 31, 2014 and living in the Butantã- Jaguaré area were included in the original sample. Data was collected at birth from medical records and then during five visits: postpartum, at 6, 12, 36 and 72 months. The Butantã- Jaguaré district is about 70km2 large and situated in the Western part of São Paulo city, with an estimated population of 637’000 people in 2010 [29]. The study area is characterized by a dense network of streets of all sizes and includes a wide variety of different neighbourhoods, ranging from the university campus, residential neighbourhoods and green areas to favelas and industrial areas [29].

In this study, all cohort children still living in the Butantã-Jaguaré area with a complete 72-month follow-up and geocoded address were included, as shown in the flowchart in Fig 1. For the 72-month assessment, trained interviewers surveyed 1849 children. Due to the Covid-19 situation in Brazil, data collection for this follow-up had to be terminated in April 2020. A total of 163 records had invalid addresses for geocoding and were dropped from the analysis. Additionally, 128 children were living outside the study area, while for 57, the International Development and Early Learning Assessment (IDELA) was incomplete. Furthermore, 358 children moved between the 36-month assessment and the 72-month assessment and were excluded from the analysis because assigning appropriate NO2 exposure levels was not possible. This resulted in 1143 children being eligible for the main analysis. 65 children out of these 1143 moved before the 36-month assessment and 1078 never moved.

Fig 1

Flow chart displaying the sample size.

The data collection for the SP-ROC cohort was approved by the University of São Paulo’s Hospital das Clínicas ethics committee (CAPPESQ HC_FMUSP) under protocol N° 01604312.1.0000.0065. Written informed consent from the caregivers was obtained before data collection. The work presented in this paper was formally reviewed and approved by the Ethikkommission Nordwest- und Zentralschweiz (EKNZ) under protocol N° AO_2020–00024.

NO2 exposure

With the goal to investigate the effect of air pollution on child development, we focused on NO2 as our primary pollutant. NO2 primarily results from traffic related burning of fossil fuel and can be measured relatively easily and cheaply using passive gas samplers (80). Annual mean outdoor NO2 concentrations at the home address of the children were predicted by a land use regression (LUR) model based on NO2 measurements over 2 one-week periods at 80 locations in 2019. The first measurements were done in summer (February), reflecting the hot humid and rainy conditions, the second in winter (August) reflecting the colder and dry conditions. Temporal adjustments were made using weekly data collected at a reference monitoring site all year around to calculate adjusted annual mean NO2 concentrations. As described in more details elsewhere [30], a LUR model was developed, using supervised stepwise linear regression by including GIS predictor variables (e.g. roads, land use, altitude, green space etc.), explaining 66% of the variation in the adjusted annual mean NO2 concentrations. The predictions from the annual model were then mapped for the entire study area on a 25x25 meters grid in QGIS 3.4.4. Using the same software, the predicted NO2 values were assigned to each child’s geocoded home address.

Outcome measures

Based on the existing literature, we considered two primary outcomes: children’s cognitive ability and children’s behavioural difficulties.

Cognition

Children’s overall development was assessed using the International Development and Early Learning Assessment (IDELA) tool. The IDELA includes 22 tasks divided into the following four domains: Motor (gross and fine), Communication/Emerging Literacy, Problem solving/Emergent Numeracy, and Personal-social/Social-emotional [31]. Trained interviewers explained the tasks to each child and recorded observed answers. Raw IDELA scores were standardised to z-scores within the analysed sample.

Behavioural problems

Children’s behavioural problems were captured using the Child Behavior Checklist (CBCL) [32]. The CBCL scale contains 120 items for children aged 6 to 18 and are graded on a three-point Likert scale, where 0 means that the behaviour was not reported by the caregiver (absent), 1 means the behaviour occurs sometimes, and 2 means the behaviour occurs often. All questions ask about children’s behaviour within the six months preceding the interview. In order to facilitate interpretation, we normalised raw CBCL scores within our sample to a z-score with mean zero and standard deviation of one.

Caregiver interview and child assessments were performed during a home visit by a trained team of enumerators with previous experience in child assessments. Before assessment, caregivers signed a written consent form.

Statistical analysis

In order to describe the sample regarding demographic, socioeconomics and other variables, we firstly performed descriptive statistics. The main analysis, was a cross-sectional analyses of the outcome data collected during the 72-month follow-up. We standardised both outcomes to mean zero and standard deviation one. We executed univariate and multivariable linear regressions to check for confounders. The univariate and the multivariate models are given by:

Univariate Model

Multivariate Model Where Y is the outcome of an child i, NO2 is the estimated annual exposure, and εi is the model residuals. To address heteroscedasticity as well as potential non-linearity concerns, we used the robust sandwich estimator. For the sensitivity analysis linear regressions were performed on a sub sample. Only children living at the same address since birth were considered in the subsample. Additionally, we performed linear regressions with a categorical exposure variable to compare children exposed to NO2 concentrations in the bottom and the top decile with the rest of the sample. As a further sensitivity analysis, we repeated the main analysis by omitting the children considered being outliers. Children showing an IDELA z-score lower than -3.5 (11 children) and children showing a CBCL z-score higher than 3.75 (6 children) were considered outliers. All statistical analyses were performed using the STATA version 16.0 statistical software package [33].

Other variables

We adjusted for an extensive list of potential confounders in the regression, including child gender, child age in months, child skin-colour, birthweight, gestational length, delivery type, mother’s age at delivery, mother’s skin-colour, maternal depression, caregiver’s marital status, caregiver’s relation to the child, caregiver’s age, highest school grades of caregiver, highest school grade of household head, household size, financial support, socio-economic status (following the Brazilian economic status classifications) and home stimulation score.

Apart from gender, birthweight, gestational length and delivery type, which were collected at birth; all data were collected by the SP-ROC-Cohort 72-month follow-up questionnaire.

Results

A total of 1143 children were included in our main analysis. As shown in Table 1, 51% of children were male. The age at assessment varied between 4 to 7.5 years, with a mean age of 76 months. Most of the children were classified as white (45%) or mixed (51%). 81 children were born preterm (7%) and 6% had a birth weight < 2500 grams. Mother’s age at delivery ranged between 13 and 46 years, with a median of 26 years. 37% of children were born with a caesarean. At the 72-month follow up 65% of the caregivers lived with a partner and 68% completed a middle or higher school grade. 24% of the households received financial support. 44% of the mothers showed symptoms of mild or severe depression. Further characteristics are described in S1 Table. The comparison of the sample at birth and the sample at the 72-month follow-up is presented in S2 Table. Although we had a high rate of loss of follow-up, mostly because of the Covid-19 pandemic, the sample’s characteristics did not differ from the original sample at birth.

Table 1

Participants’ characteristics.

Variables			N (Percentage)
Female gender		558	(48.82)
Age in months*		76	± 5.20
Child’s skin-color	White	517	(45.23)
	Mixed	581	(50.83)
	Black	42	(3.67)
	Others	3	(0.26)
Low weight at birth (<2500g)		74	(6.47)
Pre-term gestation		81	(7.10)
Delivery type	Regular	545	(47.68)
	Caesarean	423	(37.01)
	Forceps	175	(15.31)
Mother’s age at delivery	≤19	172	(15.05)
	20–29	609	(53.28)
	≥30	362	(31.67)
Mother having depression		500	(43.74)
Caregiver is married or live with a partner		729	(64.63)
Caregiver’s highest grade completed	None	23	(2.02)
	Elementary	342	(30.08)
	Middle	656	(57.70)
	Upper	116	(10.20)
Households getting financial support		272	(23.80)

Based on 1143 participants. Mean and standard deviation (rather than N%)shown for age.

Mean annual NO2 concentration ranged from 31.1 μg/m3 to 128.8 μg/m3 in the study area. Estimated concentrations at the residential addresses of children ranged from 34.0 μg/m3 to 98.0 μg/m3, with a median exposure of 40.8 μg/m3 and a standard deviation of 4.9 μg/m3. All participants were exposed to NO2 concentrations higher than 10 μg/m3, the WHO air quality guideline value for annual mean NO2 [34].

IDELA and CBCL z-scores ranged between -5.98 and 1.40 (IDELA) and between -1.15 and 5.69 (CBCL).

Table 2 provides the main regressions’ results for the full sample of 1143 children, as well as for the subsample of children who never moved (N = 1078). We found no significant associations between NO2 and children’s cognition (IDELA) and behaviour (CBCL) in either sample.

Table 2

Association of NO2 exposure as continuous [μg/m3] with IDELA (z-score) and with CBCL (z-score), unadjusted and adjusted* models.

	Unadjusted		N	Adjusted		N
IDELA z-score	β (95% CI)	p-value		β (95% CI)	p-value
All **	-0.05 (-0.19;0.10)	0.52	1143	-0.05 (-0.20;0.10)	0.52	1098
Never Moved ***	-0.08 (-0.23;0.07)	0.30	1078	-0.08 (-0.24;0.07)	0.28	1033
CBCL z-score
All	0.003 (-0.09;0.10)	0.95	1142	0.02 (-0.80;0.12)	0.75	1098
Never Moved	0.02 (-0.09;0.12)	0.75	1077	0.03 (-0.08;0.14)	0.61	1033

Results are expressed per 10 μg/m3.

* Models adjusted for: child gender, child age in months, child skin-color, birthweight, gestational length, delivery type, mother’s age at delivery, mother’s skin-color, maternal depression, caregiver’s marital status, caregiver’s relation to the child, caregiver’s age, highest school grades of caregiver, highest school grade of household head, household size, financial support, socio-economic status, and home stimulation score.

**All: These children live at the same address since longer than three years before 72-moth follow-up.

***Never moved: These children never changed the address since birth. These children constitute the sensitivity analysis sample.

IDELA = International Development and Early Learning Assessment; CBCL = Child Behavior Checklist; CI = Confidence interval.

Table 3 shows results for a categorical exposure variable. No indications for decreased cognitive functions or increased behavioural problems for the highest exposed group (highest decile) was found. Fig 2 provides further details on the exposure levels in the higher and lower decile.

Table 3

Unadjusted and adjusted* association of bottom and top decile exposure to NO2 [μg/m3] with IDELA (z-score) and CBCL (z-score).

	Unadjusted		N	Adjusted		N
	β (95% CI)	p-value		β (95% CI)	p-value
IDELA z-score			1143			1098
38.9–45.7	REF		915	REF		879
<38.9	-0.01 (-0.20;0.19)	0.93	114	-0.09 (-0.28;0.10)	0.37	109
>45.7	0.03 (-0.19;0.26)	0.76	114	-0.01 (-0.24;0.23)	0.97	110
CBCL z-score			1142
38.9–45.7	REF		914	REF		1098
<38.9	0.14 (-0.06;0.35)	0.17	114	0.11 (-0.08;0.31)	0.26	109
>45.7	0.03 (-0.13;0.19)	0.70	114	0.06 (-0.10;0.22)	0.44	110

*Models adjusted for: child gender, child age in months, child skin-color, birthweight, gestational length, delivery type, mother’s age at delivery, mother’s skin-color, maternal depression, caregiver’s marital status, caregiver’s relation to the child, caregiver’s age, highest school grades of caregiver, highest school grade of household head, household size, financial support, socio economic status, and home stimulation score.

IDELA = International Development and Early Learning Assessment; CBCL = Child Behavior Checklist; CI = Confidence interval.

Fig 2

Range and frequency of predicted NO2 concentrations at children’s home addresses.

In S3 Table we show results that exclude outliers—the results are consistent with the full sample regressions presented here.

Discussion and conclusion

This study aimed to assess association between exposure to NO2 and child development. Overall exposure levels were very high in the study settings, with all participants exposed to NO2 concentrations above the WHO annual recommended level of 10 μg/m3 [34]. Despite these high levels of exposure, we found no association between exposure to NO2, and children’s cognitive development or children’s behavioural problems in our sample.

In the literature, evidence on the association between NO2 and child development remains uncertain. Freire et al. found a negative association between NO2 exposure higher than 24 μg/m3 and general cognition compared with NO2 exposures lower than 15.4 μg/m3 [35], although these differences were not statistically significant. Forns et al. looked at air pollution in different schools in Barcelona and found a significant negative association with all measured pollutants and working memory, and largest associations for NO2 [13]. Comparing children at high and low NO2 exposure Sunyer et al. found negative associations between NO2 and working memory [14]. In general, most of the literature seems to find a negative relationship between NO2 exposure and children’s cognition, although the overall evidence was classified as insufficient in literature review from 2015 [36]. The same literature review also reported insufficient evidence for the association between NO2 and child behaviour [36]. The majority of studies published so far have focused on exposure levels at school [13,14].

This project was initiated with a strong prior of finding negative associations between NO2 exposure and children’s development and behaviour. We performed NO2 measurements directly in the study area, within the personal premises of 80 SP-ROC study participants. These measurements enabled us to build a robust LUR model to predict NO2 concentrations. Another strength of our study is the SP-ROC-Cohort dataset, which contains detailed information on families and home environments, enabling us control for a large range of potential confounders. The data set also contains both direct assessments of children’s cognitive skills and detailed reports on behavioural issues, allowing us to look at both dimensions of child development.

Despite this setup, we found no evidence of negative associations between NO2 and child’s development and behaviour. There are some limitations that may have contributed to the lack of association. First, we estimated the NO2 concentration at the residential address of the children, which describes their exposure only during a part of the day. Most of the children in the study went to kindergarten or school, and we did not collected data on NO2 concentration in these locations. Second, as with all NO2 exposure models, a certain extent of misclassification of exposure levels cannot be avoided. Given that we measured exposure levels twice across 80 different measurement locations, the scope for such measurement error seems however small. From a purely statistical perspective, we were of course also limited by the very high average exposure levels with relatively limited variability. The sample contained only 2 children with NO2 exposure levels below 35 μg/m3, and only a few with NO2 exposure levels above 50 μg/m3. Even if NO2 exposure is harmful, it is possible that differences in exposure levels in our sample were too small to result in statistically detectable differences in child outcomes. It is of course also possible that the true causal effect of NO2 exposure is smaller than the current literature suggests, or that negative effects only occur in settings where other protective factors (that may have been in place here) are not present.

Further research will be needed to better understand the diverging results in the current literature.

Participants’ characteristics at 72-month follow-up.

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Participants’ characteristics at birth vs. at 72-month follow-up.

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Sensitivity analysis without outliers.

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(DOCX)

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10 Nov 2021

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Reviewer #1: This work is a useful addition to the literature. However, there are some instances where the author needs to make some improvements. Below are my comments:

1) The authors should highlight the methodology's appropriateness. Why linear regression model was used? I have doubts that this is the most appropriate statistical approach. Moreover, in Methods section, it’s important to describe how the model and metrics were applied, including their equations.

2) The influence of meteorological conditions on the air pollutant concentrations needs to be considered at the time of the study.

3) Discussion: comparisons with previous studies are absent.

Reviewer #2: The present study addressed the association between nitrogen dioxide exposure and children’s cognitive and behavioral development in São Paulo City. It is a relevant theme and the importance of the study is clearly stated in the manuscript. However, a substantial modification is required and some issues must be addressed:

I suggest to include more information regarding the NO2 when the authors introduced the air pollutants, considering it is the pollutant included in the study.

Why did the authors choose to monitor the NO2 concentration and why the concentration of other pollutants was not included?

I suggest a reorganization of the introduction section. Some paragraphs could be merged to make the text more concise.

The characterization of the study area could be more specific, including the main pollution sources nearby and a figure to illustrate.

Why did the authors choose a 2 one-week monitoring period in the summer and winter, This information could be better explained in the methods section.

Line 225 – the sentence “ This study aimed at assessing the associations between exposure to NO2 and children’s 226 development.” should be grammar checked.

To continue the revision process of this article, the authors should state clearly the contribution to the results to the literature, make a more robust discussion about their data, and demonstrate how the strengths of the study minimize the limitations observed.

The author needs to rearrange the conclusions, once the information in the conclusion is still not very informative and do not reflect the content and results of the research that has been done.

The quality of the figures should be improved and the format of the tables revised.

The manuscript must be grammar-checked.

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18 Jan 2022

Reviewer #1: This work is a useful addition to the literature. However, there are some instances where the author needs to make some improvements. Below are my comments:

1) The authors should highlight the methodology's appropriateness. Why linear regression model was used? I have doubts that this is the most appropriate statistical approach. Moreover, in Methods section, it’s important to describe how the model and metrics were applied, including their equations.

Thank you for this comment and apologies for the lack of clarity in the original manuscript. The linear model was used because we are working with a normalized outcome variable, i.e. z-score with mean 0 and SD 1. In the text we added following sentence to clarify the appropriateness of the method: “We standardised both outcomes to mean zero and standard deviation one.” Line 146-147. We also added the following equations to the text, which describe the exact model used in the regressions.

Y_i= β_0 + β_(1 ) 〖NO〗_2 + ε_i

Y_i= β_0 + β_(1 ) 〖NO〗_2 + β_(2 ) gender + β_(3 ) age_months+ β_(4 ) skincolor+ β_(5 ) birthweight+ β_(6 ) gest_length+ β_(7 ) delivery+ β_(8 ) age_mother+ β_(9 ) mother_skincolor+ β_(10 ) mother_depression+ β_(11 ) caregiver_age+ β_(12 ) marital_status+ β_(13 ) caregiver_relation+ β_(14 ) caregiver_grade+ β_(15 ) headhousehold_grade+ β_(16 ) household_size+ β_(17 ) financial_support + β_(18 ) SES + β_(19 ) MICS+ ε_i

2) The influence of meteorological conditions on the air pollutant concentrations needs to be considered at the time of the study.

Many thanks for this comment. We took differences in meteorological conditions in our air pollution modelling into account by performing the NO2 monitoring campaigns in 2 different seasons; winter (August 2019) reflecting hot, humid and rainy conditions and summer (Feb 2019) reflecting the dry conditions. At the same time we measured NO2 continuously (weekly NO2 measurements) at a regional background site inside the study area over a full year. Using this continuous record, we were able to adjust the summer and winter NO2 measurements from the 2 campaigns to an adjusted annual average NO2 concentration. The details of the monitoring campaign and the subsequent calculation of the adjusted annual average NO2 concentrations are described in Luminati et al. (2021). In the revised text we integrated this comment on lines 112-115:

“The first measurements were done in summer (February), reflecting the hot humid and rainy conditions, the second in winter (August) reflecting the colder and dry conditions. Temporal adjustments were made using weekly data collected at a reference monitoring site all year around to calculate adjusted annual mean NO2 concentrations.”

3) Discussion: comparisons with previous studies are absent.

We carefully edited the discussion.

Reviewer #2: The present study addressed the association between nitrogen dioxide exposure and children’s cognitive and behavioral development in São Paulo City. It is a relevant theme and the importance of the study is clearly stated in the manuscript. However, a substantial modification is required and some issues must be addressed:

Many thanks for these friendly comments. We did our best to address all remaining issues as outlined below.

I suggest to include more information regarding the NO2 when the authors introduced the air pollutants, considering it is the pollutant included in the study. Why did the authors choose to monitor the NO2 concentration and why the concentration of other pollutants was not included?

We focused on NO2 as this is a good proxy for traffic related air pollution and it is relatively easy and cheap to monitor. We have added this information to the text on lines 107-109 where we write:

“With the goal to investigate the effect of air pollution on child development, we focused on NO2 as our primary pollutant. NO2 primarily results from traffic related burning of fossil fuel and can be measured relatively easily and cheaply using passive gas samplers (80).“

I suggest a reorganization of the introduction section. Some paragraphs could be merged to make the text more concise.

We did some changes in the introduction in order to make the text more concise.

The characterization of the study area could be more specific, including the main pollution sources nearby and a figure to illustrate.

We added some more information in the text addressing this comment as suggested. On lines 84-87 of the revised manuscript, we write:

“The study area is characterized by a dense network of streets of all sizes and includes a wide variety of different neighbourhoods, ranging from the university campus, residential neighbourhoods and green areas to favelas and industrial areas (29).”

Why did the authors choose a 2 one-week monitoring period in the summer and winter, This information could be better explained in the methods section.

Thank you. We integrated more information in the revised text on lines 112-115, where we write: ”The first measurements were done in summer (February), reflecting the hot humid and rainy conditions, the second in winter (August) reflecting the colder and dry conditions.”

To continue the revision process of this article, the authors should state clearly the contribution to the results to the literature, make a more robust discussion about their data, and demonstrate how the strengths of the study minimize the limitations observed. The author needs to rearrange the conclusions, once the information in the conclusion is still not very informative and do not reflect the content and results of the research that has been done.

We carefully edited the discussio

Submitted filename: Response to Reviewers.docx

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10 Mar 2022

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Reviewer #2: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

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Reviewer #2: Partly

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: No

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Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #2: The authors made the modifications in the introduction, however, the discussion is still insufficient to make the result more robust and it is not well explored. The authors had an interest and complete databases, however, they could not discuss the absence of result and attributed only to limitations, as the lack of data and sample.

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Reviewer #2: No

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17 Mar 2022

Reviewer #2 Comments:

“The authors made the modifications in the introduction, however, the discussion is still insufficient to make the result more robust and it is not well explored. The authors had an interest and complete databases, however, they could not discuss the absence of result and attributed only to limitations, as the lack of data and sample.”

(Reply): Thanks for the kind review. We are happy to hear that the introduction is good shape now. We have done our best to further improve the Discussion. We agree that we have a rather nice and comprehensive database, but that unfortunately does not allow us to directly identify the reason why we do not see the expected outcome gradient. There are many potential reasons why this could be the case, and we can only speculate about them. We pasted the revised Discussion below and would be grateful for any further suggestions.

“Despite these high levels of exposure, we found no association between exposure to NO2, and children’s cognitive development or children’s behavioural problems in our sample. In the literature, evidence on the association between NO2 and child development remains uncertain. Freire et al. found a negative association between NO2 exposure higher than 24 μg/m3 and general cognition compared with NO2 exposures lower than 15.4 μg/m3 (37), although these differences were not statistically significant. Forns et al. looked at air pollution in different schools in Barcelona and found a significant negative association with all measured pollutants and working memory, and largest associations for NO2 (13). Comparing children at high and low NO2 exposure Sunyer et al. found negative associations between NO2 and working memory (14). In general, most of the literature seems to find a negative relationship between NO2 exposure and children’s cognition, although the overall evidence was classified as insufficient in literature review from 2015 (38). The same literature review also reported insufficient evidence for the association between NO2 and child behaviour (38). The majority of studies published so far have focused on exposure levels at school (13, 14, 39).

This project was initiated with a strong prior of finding negative associations between NO2 exposure and children’s development and behaviour. We performed NO2 measurements directly in the study area, within the personal premises of 80 SP-ROC study participants. These measurements enabled us to build a robust LUR model to predict NO2 concentrations. Another strength of our study is the SP-ROC-Cohort dataset, which contains detailed information on families and home environments, enabling us control for a large range of potential confounders. The data set also contains both direct assessments of children’s cognitive skills and detailed reports on behavioural issues, allowing us to look at both dimensions of child development.

Despite this setup, we found no evidence of negative associations between NO2 and child’s development and behaviour. There are some limitations that may have contributed to the lack of association. First, we estimated the NO2 concentration at the residential address of the children, which describes their exposure only during a part of the day. Most of the children in the study went to kindergarten or school, and we did not collected data on NO2 concentration in these locations. Second, as with all NO2 exposure models, a certain extent of misclassification of exposure levels cannot be avoided. Given that we measured exposure levels twice across 80 different measurement locations, the scope for such measurement error seems however small. From a purely statistical perspective, we were of course also limited by the very high average exposure levels with relatively limited variability. The sample contained only 2 children with NO2 exposure levels below 35 μg/m3, and only a few with NO2 exposure levels above 50 μg/m3. Even if NO2 exposure is harmful, it is possible that differences in exposure levels in our sample were too small to result in statistically detectable differences in child outcomes. It is of course also possible that the true causal effect of NO2 exposure is smaller than the current literature suggests, or that negative effects only occur in settings where other protective factors (that may have been in place here) are not present. Further research will be needed to better understand the diverging results in the current literature.”

Submitted filename: Response to Reviewers 16 March 2022.docx

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25 Apr 2022

Assessing the association between air pollution and child development in São Paulo, Brazil

PONE-D-21-30740R2

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: No

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: No

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Reviewer #2: (No Response)

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