Poor air quality is associated with impaired visual cognition in the first two years of life: A longitudinal investigation

  1. John P Spencer  Is a corresponding author
  2. Samuel H Forbes
  3. Sophie Naylor
  4. Vinay P Singh
  5. Kiara Jackson
  6. Sean Deoni
  7. Madhuri Tiwari
  8. Aarti Kumar
  1. School of Psychology, University of East Anglia, United Kingdom
  2. Department of Psychology, Durham University, United Kingdom
  3. Community Empowerment Lab, India
  4. Department of Pediatrics, Brown University, United States

Abstract

Background:

Poor air quality has been linked to cognitive deficits in children, but this relationship has not been examined in the first year of life when brain growth is at its peak.

Methods:

We measured in-home air quality focusing on particulate matter with diameter of <2.5 μm (PM2.5) and infants’ cognition longitudinally in a sample of families from rural India.

Results:

Air quality was poorer in homes that used solid cooking materials. Infants from homes with poorer air quality showed lower visual working memory scores at 6 and 9 months of age and slower visual processing speed from 6 to 21 months when controlling for family socio-economic status.

Conclusions:

Thus, poor air quality is associated with impaired visual cognition in the first two years of life, consistent with animal studies of early brain development. We demonstrate for the first time an association between air quality and cognition in the first year of life using direct measures of in-home air quality and looking-based measures of cognition. Because indoor air quality was linked to cooking materials in the home, our findings suggest that efforts to reduce cooking emissions should be a key target for intervention.

Funding:

Bill & Melinda Gates Foundation grant OPP1164153.

Editor's evaluation

This study presents an important finding on the negative association of indoor air quality with visual cognition in the first two years of life. Key strengths include the longitudinal design, fine-grained measures of indoor air quality, and multi-modal assessment of cognitive functioning in a large sample of infants from families across diverse SES strata. The evidence provided is solid and will be of interest to researchers working in the fields of neurocognition, child development, environmental and public health.

https://doi.org/10.7554/eLife.83876.sa0

Introduction

The impact of poor air quality on neurocognitive health is a global concern. This impact was recently quantified by the Global Burden of Disease Study in India, attributing 1.67 million deaths in 2019 to air pollution-related causes with an overall national economic loss of $36.8 billion (Pandey et al., 2021). Such economic losses are compounded by evidence that poor air quality impacts neurocognitive health in childhood as economic losses accumulate over years of reduced productivity (Heckman and Mosso, 2014). Studies have reported that poor air quality and proximity to roadways is associated with reduced general cognitive functioning in childhood (Freire et al., 2010; Wang et al., 2009; Harris et al., 2015; Suglia et al., 2008) and slower growth in working memory (Sunyer et al., 2015). Exposure to poor air quality is also a risk factor for child emotional and behavioural problems which can have severe impacts on families (Midouhas et al., 2019).

But how early do these effects emerge? This is an important question as animal studies show profound impacts on the brain in early development, with inhalation of diesel exhaust and ultrafine particles resulting in elevated cytokine expression and oxidative stress in the brain (Gerlofs-Nijland et al., 2010; Bos et al., 2012), as well as altered neurogenesis (Patten et al., 2020). Very small particulate fragments (particulate matter with diameter of <2.5 μm; PM2.5) are of major concern as they can move from the respiratory tract into the circulatory system reaching the brain. The brain may also be particularly sensitive during infancy due an immature detoxification response (Grandjean and Landrigan, 2014). Several large-scale studies have looked at the effects of prenatal exposure to nitrous oxide and PM2.5 on human cognition early in development (Guxens et al., 2012; Guxens et al., 2014). Results show reduced psychomotor functioning at 1–6 years of age, but no associations with early cognition, although three studies have reported slower cognitive growth and emotional / conduct problems in children exposed to poorer indoor air quality as assessed via questionnaires (Midouhas et al., 2019; Gonzalez-Casanova et al., 2018; Vrijheid et al., 2012).

Critically, no studies have looked at the relationship between poor air quality and cognition in the first year of life when brain size doubles and may be particularly sensitive to toxins. This may reflect the challenge of assessing cognition in infancy. While standardized measures exist (e.g. Mullen Scales of Early Learning), these tools may not generalize to non-Western cultures where air quality is poorest. An alternative is to measure infant cognition using specially-designed looking-based tasks. Multiple aspects of visual cognition that are predictive of later cognitive abilities (Rose et al., 2012) can be measured reliably in the first year across cultures (Rose, 1994; Wijeakumar et al., 2019). For instance, visual processing speed measured using looking-based tasks in both infancy and toddlerhood is significantly predictive of working memory and executive function scores at 11 years (Rose et al., 2012). A second challenge is that infants spend much of the first year indoors. Consequently, data from outdoor monitoring stations may not accurately reflect air quality exposure critical for early brain development, particularly in contexts where use of, for instance, solid fuels in cooking may lead to differences between indoor and outdoor air quality. Recent advances in air quality monitoring allow for the measurement of PM2.5 directly in homes; this may be critical as PM2.5 is possibly the most neurotoxic component of residential air quality (Block and Calderón-Garcidueñas, 2009).

Here, we examined the relationship between poor air quality and cognition in infancy using looking-based measures of cognition and in-home measures of PM2.5. To assess visual cognition, we used a specially-designed, transculturally-relevant visual cognition task which assessed infants’ visual working memory and processing speed (Figure 1A and B; see Ross-Sheehy et al., 2003), capitalizing on infants’ tendency to look away from visual familiarity and toward visual novelty. This task has been examined in several prior studies (Oakes et al., 2006; Oakes et al., 2009), but this is the first study to use this task longitudinally with a sample of non-western infants. Infants visually explored two displays that blinked ‘on’ and ‘off’. On one side – the ‘no change’ side – the same colored squares were always presented; on the other side, one randomly-selected square changed color after each blink. If infants begin looking at the ‘no change’ side and they can remember the colors in working memory, they should lose interest in this display, releasing fixation to visually explore the ‘changing’ display. Here, they should detect the novel color and sustain looking to this display, leading to a strong change preference – a high proportion of looking to the changing side. This change preference is modulated by visual working memory capacity. When the number of items on the screen is small (i.e. the memory load is low), infants can detect the difference between the ‘no change’ and ‘change’ displays, and they show a higher change preference. With increasing memory load (and increasing difficulty), infants may falsely detect changes on the ‘no change side’ and have difficulty releasing fixation, leading to a lower change preference (Perone et al., 2011). Thus, the change preference score yields a quantifiable measure of infants’ visual working memory abilities. Note that if infants begin looking at the ‘change’ side, they should remain looking at this display with a consistently high change preference score (see Methods for further discussion).

Our study was conducted in Shivgarh, India, a rural community in Uttar Pradesh, one of the states in India that has been most strongly impacted by poor air quality (Pandey et al., 2021). We report data from 215 families from a range of socio-economic backgrounds (see Table 1). Infants were enrolled at 6 months (N=108) or 9 months of age (N=107) at which time they completed an in-lab assessment of visual cognition (see Figure 1A) as well as standardized psychomotor and cognitive assessments. A similar assessment was repeated a year later when the same infants were 18 or 21 months of age (see Methods). We examined these two cohorts based on evidence that there is an improvement in visual working memory capacity between 6 and 8 months. In particular, Ross-Sheehy and colleagues (Ross-Sheehy et al., 2003) showed that 6.5-month-old infants demonstrate greater-than-chance change preference scores with a memory load of one item, while 10- and 13-month-old infants showed greater-than-chance change preference scores for memory loads of two and three items. Similarly, 6.5-month-old infants could remember one spatial location, while 8- and 12.5-month-old infants showed evidence of remembering multiple locations (Oakes et al., 2011). By assessing infants in rural India on either side of this transition, we hoped to either replicate a similar transition or, alternatively, to reveal a delay in this transition across cultures.

Table 1
Proportional distribution of key demographic indices for families classified as high versus low SES based on a median split of family SES measured using the modified Kuppuswamy scale.
Electricity? Income* Cooking Fuel High SES Low SES
No Low Cow dung 0.02 0.06
Wood 0.02 0.25
LPG 0.01 0.01
Medium Cow dung 0.01 0.02
Wood 0.04 0.19
LPG 0.00 0.00
High Cow dung 0.00 0.00
Wood 0.03 0.08
LPG 0.01 0.00
Yes Low Cow dung 0.04 0.01
Wood 0.08 0.10
LPG 0.04 0.01
Medium Cow dung 0.03 0.02
Wood 0.17 0.17
LPG 0.02 0.00
High Cow dung 0.02 0.01
Wood 0.22 0.06
LPG 0.26 0.02
  1. *

    Incomes ranged from ₹8000 to ₹480,000 with tertile divisions at ₹45,000 and ₹72,720.

Methods

Participants

Infants born to mothers from Shivgarh, Uttar Pradesh, India and who were aged 6 months±15 days or 9 months±15 days were eligible for participation. Infants were initially screened as belonging to either ‘high socioeconomic status (SES)’ (both parents having >10 years of education) or ‘low SES’ families (both parents have ≤ 5 years of education; see Ahmed et al., 2018 for a similar screening approach). Infants born to parents screened with colour vision deficits (due to the nature of the VWM task), or with any congenital problems, or gestational age <26 weeks at birth, were excluded from the study.

We brought 257 families to the lab for the VWM and cognitive assessment; however, 17 families (10 6-month-old infants; 7 9-month-old infants) did not complete the assessment and were dropped from the study. The remaining 240 families were followed up for the duration of the study which spanned two years and included the following: (1) a laboratory assessment in year 1 at 6 or 9 months of age; (2) a home visit every three months thereafter for the remainder of year 1 (e.g. at 9, 12, and 15 months of age for the 6 month cohort); (3) a laboratory assessment in year 2 at 18 or 21 months of age; (4) a home visit every three months thereafter for the remainder of year 2 (e.g. at 21, 24, and 27 months of age for the 6-month cohort). Enrollment was distributed over 4 waves separated by 3 months, such that we enrolled approximately 60 infants per wave of data collection (for a full list of data collected in the study, see Supplementary Materials). The study was approved by the Community Empowerment Lab Institutional Ethics Committee (Ref. No: CEL/2018005). Participants’ caregivers provided written informed consent; where caregivers were illiterate, a witness gave signed consent accompanied by a thumb impression of the caregiver in place of a signature. At the end of each laboratory session, families received a small token of appreciation.

Air quality data were collected for a total of 219 children; of those, 215 had data that survived initial data quality checks (described below). 213 children also had data from the visual working memory task and cognitive assessment during either the first laboratory assessment (when infants were 6 or 9 months of age) or the second laboratory assessment (when infants were 18 or 21 months of age). In particular, 204 participants contributed visual working memory data in year 1 when they were 6 months of age (N=109; N girls = 50; M age = 182.4 days, SD age = 14.9 days) or 9 months of age (N=95; N girls = 51; M age = 267.2 days, SD age = 14.2 days). In year 2, 181 participants contributed visual working memory data when the infants were 18 months of age (N=90; N girls = 45; M age = 546.2 days, SD age = 19.2 days) or 21 months of age (N=91; N girls = 45; M age = 627.4 days, SD age = 27.7 days).

As part of the first laboratory visit, caregivers were interviewed to obtain demographic indices and family SES data using the modified Kuppuswamy Scale (Mohd Saleem, 2020). This scale classifies SES using occupation, education, and family income. Table 1 shows how high and low SES families (using a median split on the Kuppuswamy SES Score) were distributed along multiple demographic indices.

Materials

Visual cognition task

We used a preferential looking change detection task (Ross-Sheehy et al., 2003) with a 10 s trial duration (see Wijeakumar et al., 2019; Delgado Reyes et al., 2020). A 42-inch LCD monitor that was connected to a PC running Experiment Builder was used to display the stimuli. Looking data were collected at 500 Hz using an Eyelink 1000 Plus eye-tracker (SR Research). Where eye-tracking data were not available, looking data were collected with a webcam and hand-coded at 30 frames-per-second using Datavyu, 2014.

Infants sat on their mother’s lap 100 cm from the screen. A target sticker was placed on the infant’s forehead so the eye-tracking system could track head movement. The stimuli consisted of two side-by-side flickering displays composed of colored squares (Figure 1A). One side contained the ‘change’ display and the other contained the ‘no-change’ display. Each stimulus display area was 29.5 cm in width and 21 cm in height, with a 21 cm gap between the display on the left and right (each coloured square was approximately 5cm x 5 cm). The displays had a solid grey background. The colours of the squares presented on each display were selected from a set of nine colours: green (RGB: 0, 153, 0), brown (128, 64, 32), black (0, 0, 0), violet (128, 0, 128), cyan (128, 255, 255), yellow (255, 255, 0), blue (0, 0, 255), white (255, 255, 255), and red (255, 0, 0). The set size (number of items) was the same between the two displays and remained constant during the 10 s trials. The colors on a display were always different from each other but colors could be repeated between the displays.

Standardized assessments

We collected standardized psychomotor and cognitive assessments in year 1 using the Mullen Scales of Early Learning (MSEL). In year 2, we used the Ages and Stages Questionnaire (3rd edition; ASQ). We switched to the ASQ in year 2 because preliminary testing with the MSEL revealed many questions that were not relevant to the rural setting in Shivgarh.

Air quality monitoring

Air quality data were acquired using the Air Visual Node monitor (model B01MF6X1 YK, Atlanta Healthcare, Inc). We selected this device based on validation data comparing the device to a reference Beta Attenuation Monitor (BAM) in Beijing, China during June of 2015 (Molenar, 2016). Daily concentration estimates from the two devices correlated highly (R2 =0.96). We purchased 20 devices.

Procedure

Visual cognition task

Children were tested individually. The task began with a 5-point calibration sequence. Next, the visual cognition task began (see Figure 1A). The squares simultaneously appeared for 500ms and disappeared for 250ms during the 10 s trials. For the ‘no-change’ display, the colors of the squares remained constant throughout the trial. For the ‘change’ display, one of the squares changed color after each disappearance. The changing square was randomly selected, and its color was derived from the set of colors not currently present in that display. Consistent with prior studies, memory load was varied between 1, 2, and 3 items on each side for 6- and 9-month-old infants and 2, 4, and 6 items on each side for 18- and 21-month-old infants. Infants were presented with 36 total trials in six blocks of six trials. Each block contained three trials (one for each load) for each change side. Participants could take breaks between blocks. Participants completed on average 20.99 trials in year 1 (SD = 9.72) and 26.25 trials in year 2 (SD = 9.66).

Standardized assessments

Standardized assessments were conducted in a quiet room with trained staff. The MSEL was administered in Year 1, while the ASQ was administered in Year 2. The ASQ questionnaire for each infant was selected using the online ASQ calculator (https://agesandstages.com/free-resources/asq-calculator/). We adapted the ASQ administration to improve the reliability of the data. Specifically, a trained assessor administered the ASQ in collaboration with the parent. In cases where ASQ questions asked about behaviors that could be elicited (e.g. ‘When you ask your child to, does he go into another room to find a familiar toy or object?’), these tasks were completed ‘live’, ensuring that the child was given ample time. If a question was not amenable to live assessment, the mother’s verbal report was taken as the response.

Use of the Air Visual Node device

During each in-home assessment period, air quality data were collected across 3 days with the monitor placed at roughly head height in the room where the child typically slept or spent most of their time (see Figure 2A). The monitor was inaccessible to children. Air quality was monitored in 3-monthly intervals in-between the Year 1 and Year 2 laboratory visits for a maximum of 6 collection periods for each household. Data were collected from the AVN device every 10 s. Households had to have at least 5 hr of recorded data in any given round for that round to be included in analysis.

Data from the air quality monitors were downloaded after each 3-day period and quality checked. After 8–10 months of use, a few of the devices were returning very high or very low values. Henceforth, devices were regularly cleaned. After cleaning, multiple devices were tested in the same room to ensure they were returning the same air quality readings. Any devices returning erroneous values were retired. By the end of the 2-year study, 11 devices had been retired.

Methods of analysis

Visual cognition measures

The eye-tracking data were exported on a frame-by-frame basis using SR research Data Viewer. The area of interest around the two objects was increased to match video coded data which coded looking to the left, right, or away. Where there was no recorded eye-tracking data, the hand-coded video data were used instead. Of the 9956 total trials used to calculate the scores, 3047 trials (30.6%) were manually coded. We re-coded 17% of the data to check reliabilities. Reliabilities were very good with a mean Kappa for the 6-month cohort of 0.73 and a mean Kappa for the 9-month cohort of 0.83.

The looking data were read into R and pre-processed using the R package eyetrackingR (Dink and Ferguson, 2021). For the change preference measure, trials where more than 75% of the data was recorded as not looking at the screen were excluded. Initial analyses of these change preference scores revealed that the change preference measure was not robust longitudinally, that is, year 1 change preference scores did not predict year 2 change preference scores (note: a full correlation table for key measures used in the present study can be found in the supplementary materials; see Supplementary file 1). This was the case for the present sample as well as a longitudinal sample from urban UK infants collected as part of a complementary study (see S. Forbes, K. Jackson, K. Mee, J. McCarthy, L. Delgado-Reyes, M. Tiwari, V. Singh, A. Kumar, J. P. Spencer, A longitudinal assessment of the development of visual working memory accross cultures. manuscript in preparation for details). Additional analyses from a forthcoming paper (Forbes et al., manuscript in preparation) revealed that sorting trials based on where infants are looking at the start of the first ‘change’ display (i.e. 1000ms) yields two measure that are stable longitudinally – a ‘first-look change’ score and a ‘first-look no-change’ score. This makes sense as prior to the first change on the screen participants have no way of knowing which is the ‘no-change’ side and which is the ‘change’ side. Furthermore, the demands placed on visual cognition differ depending on the starting location. If infants start on the ‘no-change’ side, they should notice the ‘sameness’ if the number of items is within their visual working memory capacity and release fixation due to a lack of visual novelty. Thus, ‘first-look no-change’ trials might be particularly sensitive to individual differences in visual working memory capacity (for discussion, see Forbes et al., manuscript in preparation). By contrast, if infants start on the ‘change’ side, they should readily notice the novelty and remain fixated on that display. Note that sorting trials based on where infants are looking at a key moment in time is a standard approach in the infant eye-tracking literature (e.g. see Fernald et al., 1998).

Infants’ looking data were sorted into two types of trials (first-look no-change, first-look change) based on where they were looking at the onset of the first change (1000ms after trial start). Unfortunately, 1030 trials (13.1% of the total trials) had missing data within the time window from 1000 to 1100ms (e.g. due to a failure to track the eye in this window). Thus, we allowed the ‘first-look’ classification to be determined based on the first frame of non-missing eye-tracking data up to 2500ms (which spanned from the onset of the first change display [1000ms] through two display +delay periods). This allowed us to classify an additional 613 trials, yielding a total trial loss of 417 trials (5.3%) due to a failure to classify the ‘first-look’ status. The change preference analysis focused on the analysis period from 1750ms to 6750ms. We trimmed the last few seconds of data from each trial as the number of eye-tracking samples diminished as attention waned (see Forbes et al., manuscript in preparation for a detailed analysis justifying this time window).

The shift rate measure was taken from the full length of any trial. This measure counted the number of switches participants made from one side of the screen to the other divided by the number of seconds that participants were looking at the display, resulting in shifts per second.

The baseline model for each change preference measure was a linear mixed-effect model with year (1 or 2), working memory load (low, medium or high), SES (Kuppuswamy SES score), and age cohort (6 or 9 months) as independent variables. We also included the mean proportion looking to the first fixated item in the first time window (1–750ms) before any visual changes had been introduced to get an initial measure of visual dynamics for each infant (LookingWindow1). Year and age cohort were difference-coded, SES was centered, and load was input as a factor. To allow for individual differences across year and load, a random intercept for each participant was included. To arrive at a minimal baseline model, we began with a model that only included main effects. We then introduced two-way, three-way, and four-way interactions, only including interactive effects that showed evidence of improving the model fit. For the ‘first-look no-change’ measure, the final baseline model included all main effects and the following interactions: Year x SES, Year x LookingWindow1, SES x LookingWindow1, and Year x SES x LookingWindow1 (see Table 2). For the ‘first-look change’ measure, the final baseline model included all main effects (see Table 10).

Table 2
Baseline model for the change preference scores.

Model parameters for linear mixed effect model assessing the impact of year, load, SES score based on the Kuppuswamy scale, age cohort, and visual dynamics in LookingWindow1 on the ‘first-look no-change’ change preference scores (baseline change preference model).

Variable Estimate Std. Error DF t value Pr(>|t|)
(Intercept) 0.488 0.054 534.793 9.062 <0.001
Year –0.044 0.107 742.433 –0.411 0.681
Load1 0.029 0.009 857.116 3.130 0.002
Load2 –0.001 0.009 854.106 –0.114 0.909
SES –0.006 0.013 527.033 –0.417 0.677
LookingWindow1 –0.067 0.065 548.916 –1.038 0.300
Age 0.027 0.014 183.400 1.913 0.057
Year:SES 0.036 0.027 715.570 1.350 0.177
Year:LookingWindow1 0.075 0.129 731.071 0.582 0.561
SES:LookingWindow1 0.006 0.016 532.948 0.381 0.704
Year:SES:LookingWindow1 –0.054 0.033 710.951 –1.665 0.096

All models were assessed for fit based on a Q-Q plot of the residuals and the R package DHARMa (Hartig, 2020). Analyses reported in the tables use the p values calculated using Satterthwaite’s method from the package lmerTest (Kuznetsova et al., 2017) to enable the reader to glean the direction of all effects. In addition, the contribution of each significant effect to a model was assessed using type 3 Wald Chi-squared tests, with effect sizes reported in text using the effectsize package in R (Ben-Shachar et al., 2020). In all models we aimed to theoretically motivate the inclusion of all effects and limit spurious correlations by only adding effects where they were thought to contribute a priori. In the case of interactions, models were tested to see whether interactions improved model fit through formal model comparison, and interactions were not included if they did not contribute to model fit.

The baseline model for shift rate was arrived at using a similar procedure. The final baseline model included all main effects and a Year x SES interaction (see Table 3).

Table 3
Model parameters for linear mixed effect model assessing the impact of year, load, SES and age cohort on the shift rate (baseline visual processing speed model).
Variable Estimate Std. Error DF t value Pr(>|t|)
(Intercept) 0.631 0.014 196.30 45.260 <0.001
Year 0.016 0.017 893.40 0.963 0.336
Load1 0.040 0.011 812.80 3.485 <0.001
Load2 0.001 0.011 811.70 0.071 0.943
SES 0.002 0.003 207.00 0.545 0.587
Age 0.012 0.028 195.80 0.429 0.668
Year:SES –0.007 0.004 928.20 –1.553 0.121

Standardized assessments

We used non-standardized measures from the MSEL and ASQ as there are no standardized scores for rural India. From the MSEL, we use the composite standard T-score as a general measure of cognition. The baseline model for this measure was a linear model with age cohort and SES included as main effects (see Table 4). For the ASQ, we used the problem-solving measure. The baseline model was a linear model with the ASQ measure as the dependent variable and age cohort and SES as independent variables (see Table 4).

Table 4
Model parameters for linear models describing effects of age cohort and SES on standardized cognitive scores (baseline standardized cognitive models).

Measures included are MSEL composite T-score and ASQ problem solving score.

Measure Variable Estimate Std. Error t value Pr(>|t|)
Mullen Composite (Intercept) 101.299 0.960 105.562 <0.001
Age –0.345 1.920 –0.180 0.857
SES 0.769 0.232 3.308 0.001
ASQ Problem Solving (Intercept) 30.581 0.955 32.032 <0.001
Age 5.549 1.909 2.907 0.004
SES 0.923 0.231 3.998 <0.001

Data filtering and analysis of air quality data

The air quality data were collated and read into R. Data were inspected to understand the cyclical patterns across days and months. The air quality data were filtered to remove data from any defective devices (see above), and then down-sampled so that we had one observation per hour. This smoothed the data and removed temporal autocorrelation. The down-sampled data were then fit with a generalized additive mixed model (GAMM) in R using the ‘bam’ function from the package mgcv (Wood, 2011). The model included air quality as the dependent variable and a tensor smooth of time of day (in hours) and date, with a cubic regression spline of 11 dimensions for each as independent variables. The model also allowed a smoothed random effect of participant. To reduce autocorrelation, the model was fit with a Rho of 0.6. Temporal autocorrelation was assessed using the itsadug package in R (van Rij et al., 2020). The model provided an excellent fit, capturing annual fluctuations by date (see Figure 2B) and daily fluctuations by hour (see Figure 2C).

The participant-level random intercept was extracted from the model, to act as a single data measurement to represent air quality in each home. This measurement was highly correlated with the mean air quality score for each participant, t(213) = 21.348, p<0.001. This individual level score was centered around 0, where a positive score indicated a higher air quality score (i.e. poorer air quality) on average than the grand mean (i.e. 185.824). For visualization purposes, Figures 2 and 3 were adjusted by adding the grand mean to the individual score. Note that the participant-level random intercept provides a measurement that is more robust than the mean air quality value as time of day and seasonal effects are included in the model. This was particularly evident in cases where a participant contributed only a small number of samples (in which case, the sample values can be biased due to time of day).

We modelled individual estimates of air quality using a linear model with the AQI random intercept value as the dependent variable and age cohort and cooking fuel type as independent variables (see Table 5). A second model for air quality was run but using the SES score (centered) as a replacement for cooking fuel (also in Table 5). These models were comparable, showing the strong relationship between cooking fuel and SES (see also, Table 1).

Table 5
Model parameters for linear models assessing the baseline variability of SES, cooking fuel, and age cohort on the air quality reading (baseline air quality models).
Model Variable Estimate Std. Error t value Pr(>|t|)
Cooking Fuel (Intercept) 1.461 2.767 0.528 0.598
Cooking Fuel1 10.866 4.517 2.405 0.017
Cooking Fuel2 –1.519 3.119 –0.487 0.627
Age –0.548 4.213 –0.13 0.897
SES (Intercept) –0.004 2.099 –0.002 0.999
SES –1.152 0.511 –2.255 0.025
Age –1.714 4.198 –0.408 0.683

Analysis of air quality associations with cognition

To assess the association of air quality with visual cognition, we added air quality to the baseline model for each dependent variable, that is, for the ‘first-look no-change’ change preference score (Table 6), shift rate (Table 7), and the ‘first-look change’ change preference score (Table 11). An Air Quality x Year interaction was also included in the ‘first-look no-change’ change preference model as this improved the model fit, assessed using the anova function in R, Χ2 (1)=4.824, p=0.028, η2 (partial)=0.005.

Table 6
Model parameters for linear mixed-effect model assessing the impact of air quality (AQI) on the baseline change preference score model which included Year, Load, SES, Age Cohort, and LookingWindow1 as predictors (see Table 2).
Variable Estimate Std. Error DF t value Pr(>|t|)
(Intercept) 0.494 0.054 535.80 9.186 <0.001
Year –0.044 0.107 741.90 –0.411 0.681
Load1 0.029 0.009 858.10 3.135 0.002
Load2 –0.001 0.009 855.20 –0.124 0.901
SES –0.009 0.014 524.70 –0.663 0.508
LookingWindow1 –0.075 0.065 549.90 –1.155 0.249
Age 0.026 0.014 184.70 1.810 0.072
AQI 0.000 0.000 181.80 –0.654 0.514
Year:SES 0.040 0.027 720.10 1.471 0.142
Year:LookingWindow1 0.075 0.128 730.70 0.585 0.559
SES:LookingWindow1 0.010 0.016 530.80 0.607 0.544
Year:AQI 0.001 0.000 965.10 2.431 0.015
Year:SES:LookingWindow1 –0.057 0.033 714.90 –1.750 0.080
Table 7
Model parameters for linear mixed-effect model assessing the impact of air quality (AQI) on the baseline visual processing speed (shift rate) model which included Year, Load, SES, and Age Cohort as predictors (see Table 3).
Variable Estimate Std. Error DF t value Pr(>|t|)
(Intercept) 0.631 0.014 195.40 45.655 <0.001
Year 0.016 0.017 894.50 0.977 0.329
Load1 0.040 0.011 813.20 3.501 <0.001
Load2 0.001 0.011 812.10 0.045 0.964
SES 0.001 0.003 205.10 0.225 0.823
Age 0.009 0.028 195.30 0.318 0.750
AQI 0.001 0.000 192.10 2.166 0.032
Year:SES –0.006 0.004 928.60 –1.493 0.136

To assess the association of air quality with standardized cognitive scores, we added air quality to the baseline model for the MSEL scores (Table 8) and the ASQ problem-solving scores (Table 9). In addition, following findings from Guxens et al., 2014, we conducted analyses on the ASQ fine and gross motor scores to examine whether these measures were associated with air quality (see Table 9).

Table 8
Model parameters for linear model assessing the impact of air quality (AQI) on the baseline Mullen model (Composite T-Score) which included Age Cohort and SES as predictors (see Table 4).
Variable Estimate Std. Error t value Pr(>|t|)
(Intercept) 101.301 0.961 105.398 <0.001
Age –0.377 1.923 –0.196 0.845
SES 0.746 0.236 3.163 0.002
AQI –0.019 0.032 –0.596 0.552
Table 9
Model parameters for linear models assessing the impact of air quality (AQI) on the baseline ASQ model which included Age Cohort and SES as predictors (see Table 4).

For comparison with prior work, we include analyses of the ASQ Problem Solving score as well as Fine and Gross Motor scores.

Measure Variable Estimate Std. Error t value Pr(>|t|)
ASQ Problem Solving (Intercept) 30.563 0.955 32.004 <0.001
Age 5.617 1.910 2.941 0.004
SES 0.890 0.233 3.817 <0.001
AQI –0.030 0.031 –0.983 0.327
ASQ Fine Motor (Intercept) 34.224 1.039 32.944 <0.001
Age 5.655 2.078 2.721 0.007
SES 0.815 0.254 3.214 0.002
AQI –0.025 0.033 –0.748 0.455
ASQ Gross Motor (Intercept) 36.849 1.061 34.733 <0.001
Age 8.221 2.122 3.874 <0.001
SES 0.571 0.259 2.206 0.029
AQI –0.054 0.034 –1.582 0.115
Table 10
Model parameters from the baseline mixed-effects model assessing the effects of Year, Load, SES, LookingWindow1 and Age cohort on the first look change measure.
Variable Estimate Std. Error DF t value Pr(>|t|)
(Intercept) 0.592 0.054 613.50 11.009 <0.001
Year 0.030 0.013 966.10 2.353 0.019
Load1 0.000 0.009 846.40 0.024 0.981
Load2 0.002 0.009 847.40 0.175 0.861
SES 0.002 0.002 205.30 0.874 0.383
LookingWindow1 0.057 0.065 628.30 0.882 0.378
Age –0.019 0.015 188.80 –1.331 0.185
Table 11
Model parameters assessing the impact of air quality (AQI) on the baseline mixed-effects model assessing the effects of Year, Load, SES, LookingWindow1 and Age cohort on the first look change measure.
Variable Estimate Std. Error DF t value Pr(>|t|)
(Intercept) 0.589 0.054 612.30 10.944 <0.001
Year 0.030 0.013 967.10 2.395 0.017
Load1 0.000 0.009 847.40 –0.008 0.994
Load2 0.002 0.009 848.30 0.182 0.855
SES 0.002 0.002 205.30 1.105 0.270
LookingWindow1 0.062 0.065 626.90 0.952 0.341
Age –0.018 0.014 189.50 –1.275 0.204
AQI 0.000 0.000 185.80 1.263 0.208
Year:AQI –0.001 0.000 968.20 –1.402 0.161

Results

We focused on three primary measures from the preferential looking task: (1) the shift rate, that is, the number of looks back and forth (per second), (2) the change preference score for trials where infants’ ‘first look’ was to the ‘no change’ side, and (3) the change preference score for trials where infants’ ‘first look’ was to the ‘change’ side. Use of the shift rate measure was motivated by prior work showing that visual processing speed in infancy is predictive of longer-term cognitive outcomes (Rose et al., 2012). The latter two measures are an adaptation of the standard ‘change preference’ looking measure from this task. Note that because the ‘first-look no-change’ measure starts at 0 (i.e. looking to no change) and the ‘first-look change’ measure starts at 1 (i.e. looking to change), chance performance is no longer anchored at 0.5. Although this differs from prior work, the change was motivated by evidence that the standard measure is not predictive longitudinally, while the adapted measures are (see Methods for discussion). Note further that we focus on the ‘first look no-change’ measure below (referred to as the ‘change preference’ score for simplicity), as the only significant finding for the ‘first look change’ measure was a decrease in the change preference score from year 1 to year 2 (see Table 10 and Table 11).

Six-month-old infants showed lower change preference scores than 9-month-old infants (Figure 1C), Χ2 (1)=3.66, p=0.056 ηp 2 = 0.02 (for full results, see Table 2). In addition, change preference scores decreased as the memory load increased, Χ2 (2)=12.53, p=0.002 ηp 2 = 0.01 (see Figure 1D). Similar effects were observed in an analysis of infants’ rate of looking back and forth between the displays (i.e. shift rate; see Table 3). Infants’ shift rate decreased as the memory load increased, Χ2 (2)=16.52, p<0.001 ηp 2 = 0.02 (see Figure 1E). In addition, infants’ standardized cognitive scores in year 1, F(1) = 10.94, p=0.001 ηp 2 = 0.050, and year 2, F(1) = 15.99, p<0.001 ηp 2 = 0.080, were consistently lower for low SES infants (see Figure 1F and G and Table 4).

Variations in infants’ cognitive performance.

(A) A schematic of the visual cognition task. (B) An infant performing the task. (C) The 6-month-old cohort (N = 107) had lower ‘first-look no-change’ change preference scores relative to the 9-month-old cohort (N = 106). (D) Infants showed higher change preference scores in the low memory load condition (N = 210) relative to the medium (N = 208) and high loads (N = 209). (E) Infants had faster visual processing speed (higher shift rates) in the low load condition (N = 206) relative to the medium (N = 206) and high loads (N = 205). (F) Standardized composite scores from the Mullen Scales of Early Learning (MSEL) in year 1 were higher for high SES infants (N = 97) than for low SES infants (N = 112). (G) Problem-solving scores from the Ages and Stages Questionnaire (ASQ) in year 2 were higher for high SES infants (N = 84) than for low SES infants (N = 96). Note that for F and G, a continuous SES score based on the Kuppuswamy Scale (see Mohd Saleem, 2020) was used in analysis, but this was median-split for ease of visualization. Line in boxplots shows the median, lower and upper hinges show the first and third quartiles, lower and upper whiskers extend to the smallest and largest point no more than 1.5 * the interquartile range from the closest hinge respectively, and data beyond teh whiskers are outlying and are plotted individually.

We recorded in-home air quality using a laser particle sensor (Air Visual Node, Atlanta Healthcare, Inc) placed in the home (see Figure 2A) for 3 continuous days during each assessment period. Field workers were instructed to place the sensor in the room where infants slept or spent most of their time. We re-assessed air quality up to six times for each family (every three months in-between lab visits; M visits = 4, SD = 1.16). These data were modelled and a participant-level score which adjusted for season and time of day was extracted for use in the analyses (see Methods). We focused on PM2.5 concentrations expressed as US Air Quality Index (AQI) values. This index maps PM2.5 concentrations measured in μg/m3 to a more intuitive categorical scale where AQI values under 50 are good, values from 51 to 100 indicate moderate air quality, values 101–150 indicate air that is unhealthy for sensitive groups, and values higher than 151 are considered unhealthy extending up to hazardous (>301). Note that AQI values can be readily converted to μg/m3 using on-line calculators (e.g. https://www.airnow.gov/aqi/aqi-calculator/).

Variations in in-home air quality (PM2.5) by year, by day, and by type of cooking fuel.

(A) Three examples of in-home sensor placement for households of varying SES levels. (B) Variations in in-home air quality over years in the study (participants contributing data = 215). Black dots show mean air quality index over each 3-day assessment period with standard errors indicating variability over households collected on the same day. Black line shows our model fit through these data. Red line shows best-fitting curve from outdoor air quality observations recorded in Lucknow, India. (C) Daily variations in in-home air quality with peaks at meal preparation times (participants contributing data = 215). Points indicate raw data (with standard errors), the line indicates our model fit. (D) Plots showing poorer in-home air quality for households that used cow dung for cooking fuel (N = 25) relative to wood (N = 152) and liquified petroleum gas (LPG; N = 38). Boxplot details are same as in Figure 1.

As can be seen in Figure 2B, air quality in Shivgarh was quite poor, with AQI values often higher than 151, with an overall mean of 207. This is comparable to recent AQI data for the northern states in India that ranged from 186 to 267 (Pandey et al., 2021). We also compared in-home air quality to daily outdoor air quality from the nearest monitoring station in Lucknow (red line in Figure 2B; see Central Pollution Control Board in India: https://cpcb.nic.in). Annual fluctuations in in-home air quality generally mirrored fluctuations in outdoor air quality in Lucknow, although with a lower peak in the winter of 2018–2019 (for similar seasonal variations in indoor and outdoor air quality, see Rohra and Taneja, 2016). We note that this lower peak occurred during a pause in our indoor air quality data collection; thus, we cannot evaluate if this is a mismatch or simply a result of limited data during this period in the indoor air quality model.

In-home air quality (PM2.5) was strongly influenced by daily meal preparation, with peaks in poor air quality occurring during meal preparation times (see Figure 2C; see Mukhopadhyay et al., 2012 for similar meal preparation findings). Air quality was poorest in households using cow dung for fuel and best in households using LPG, F(2) = 3.23, p=0.042 ηp 2 = 0.030 (see Figure 2D; Table 5). Note that we were not able to look at the impact of cooking fuel independently from SES as most families that used LPG fuel fell into the high SES tertile (see Table 1).

Next, we explored the association between air quality (PM2.5) and cognitive measures while controlling for both age cohort and SES (see Methods). Critically, infants living in homes with poor air quality had poorer visual cognitive performance. Infants from households with poorer air quality had lower change preference scores in year 1, an effect that was attenuated in year 2, Χ2 (1)=5.91, p=0.015 ηp 2 = 0.006 (see Figure 3A and Year x Air Quality interaction in Table 6). We also found a strong negative association of air quality with visual processing speed (shift rate), Χ2 (1)=4.69, p=0.03 ηp 2 = 0.02 (see Air Quality main effect in Table 7). As can be seen in Figure 3B, infants from households with poor air quality showed slower rates of visual processing. Note that both effects shown in Figure 3 were robust in models that controlled for SES using the modified Kuppuswamy Scale which aggregates effects of family occupation, education, and income (see Mohd Saleem, 2020). Moreover, in both cases, models that included air quality captured a significant proportion of variance above and beyond the baseline cognitive models: models comparing the baseline change preference model (Table 2) to a model that included air quality (Table 6) showed that the air quality model captured a greater proportion of variance in the change preference scores, Χ2 (2)=6.35, p=0.04; similarly, comparing the baseline visual processing speed model (Table 3) to a model that included air quality (Table 7) showed that the air quality model captured a greater proportion of variance in processing speed, Χ2 (1)=4.74, p=0.03.

Poor air quality is associated with impaired visual cognition in infancy.

(A) Infants from households with better air quality (lower AQI scores) had higher visual working memory scores in year 1 (see dark purple line; N = 199) relative to effects in year 2 (pink line; N = 179). (B) Infants from households with better air quality (lower AQI scores) also had faster visual processing speeds (higher shift rates; N = 213). Dots in both panels show raw data, line indicates linear trend with the ribbon indicating the 95% confidence interval.

One concern with these findings is that change preference and shift rate are measured in the same task and are correlated. Thus, it is possible the findings above reflect some shared variance rather than separate effects. To examine this, we re-ran the change preference / air quality analysis, adding shift rate as a predictor of the change preference score. This analysis revealed a main effect of shift rate, Χ2 (1)=17.36, p<0.001 ηp 2 = 0.02, confirming the relationship with the change preference score; however, the Load and Year x Air Quality interaction remained significant (see Supplementary file 2). Thus, the effects shown in Figure 3A are statistically robust, even when shift rate is included in the change preference model. We also re-ran the shift rate / air quality model, including the change preference score as a predictor. This analysis revealed a main effect of change preference score, Χ2 (1)=23.02, p<0.001 ηp 2 = 0.02. Critically, however, the Load and Air Quality main effects remained significant even when the change preference score was included as a predictor (see Supplementary file 3).

Another key question is why the change preference measure only showed a robust association with air quality in the first year. As discussed in the Methods, we modulated the working memory load in year 2 to make the task more challenging and age-appropriate; thus, it is possible we made the task too hard for some infants, dampening our ability to detect individual differences in working memory. To explore this possibility, we re-ran the change preference / air quality analysis, only using data from load 2 trials (i.e. the ‘medium’ load in year 1 and the ‘low’ load in year 2; see Methods). In this analysis with identical task stimuli, we still found a significant Year x Air Quality interaction, F(1,340) = 6.83, p=0.009 ηp 2 = 0.02 (see Supplementary file 4).

Finally, we note that air quality did not significantly impact standardized cognitive scores in year 1 (measured using the Mullen Scales) or year 2 (measured using the ASQ; see Tables 8 and 9). This may indicate that the effects of air quality are specific to the visual cognitive system rather than impacting the more general aspects of cognition and psychomotor function assessed by these measures.

Discussion

The present findings indicate that poor air quality (PM2.5) is associated with slower visual processing speed in the first two years of life and poorer visual working memory scores in year 1. These negative impacts were evident only for looking-based measures of cognition. Our results contrast with findings from prior studies that have failed to show an association between outdoor air quality and cognition in early development. It is possible that this difference reflects the broader range of PM2.5 exposure in our sample. For instance, Guxens and colleagues (Guxens et al., 2014) reported no systematic relationships between air quality and cognitive measures across six European birth cohorts (although they did find effects on psychomotor function); however, air quality ranged from AQI values of 53–72. Our mean AQI value (207) was three to four times higher. Thus, infants in the present report were exposed to much poorer air quality which might explain the strong relationship with visual cognition.

It is also possible that indoor levels of PM2.5 are critical to cognition in infancy. Most prior studies have looked at outdoor air quality, although several studies have reported a negative impact of poor indoor air quality on cognition as assessed via questionnaires (Midouhas et al., 2019; Gonzalez-Casanova et al., 2018; Vrijheid et al., 2012; Midouhas et al., 2018). Interestingly, Vrijheid et al., 2012 found that use of a gas cooker in the home showed a stronger negative association with standardized cognitive scores after 14 months. Our findings extend this work by also showing an association between poor indoor air quality and visual cognition as early as 6 months of age.

One question raised by our findings is why visual working memory effects were isolated to year 1. We investigated one possibility – that the working memory task was too difficult in year 2. Results suggested that this was not the case: when we compared identical conditions across years 1 and 2 we still found an inverse relationship between change preference scores and air quality in year 1 but not in year 2. Another possibility is that the impact of indoor air quality on visual cognition wanes in year 2. Our findings for shift rate argue against this possibility as these findings were robust across both years. The robustness of the shift rate effect may suggest that visual processing speed is a particularly sensitive measure in infancy, consistent with other work showing that visual processing speed in infancy is predictive of schooling outcomes 11 years later (Rose et al., 2012). A third possibility for why the impact of air quality on change preference scores wanes in year 2 is that other factors which are not correlated with air quality have a stronger impact on visual working memory in year 2. For instance, we are currently examining how interactions with caregivers impact infants’ visual working memory abilities. We suspect these interactions have a strong influence on visual working memory development, particularly in year 2 after extended interactions accumulate over time. Interestingly, infant-led interactions – which have been shown to support working memory development (Landry et al., 2006; Perone and Spencer, 2013) – appear to be quite frequent in low SES families in our sample. It is possible such positive influences counter the impact of poor air quality in some families.

Another interesting result from the present study was the specificity of our findings to the visual cognition task. In particular, although results from the Mullen and Ages and Stages Questionnaire showed robust relationships with SES suggesting good sensitivity, these measures showed no associations with air quality. Thus, air quality may have specific effects in infancy, targeting early emerging cognitive systems. Future work will be needed to determine if this is the case. For instance, it would be interesting to examine if air quality has an impact on auditory and/or statistical learning processes in the first two years of life (Saffran et al., 1996).

Strengths of the current study include our use of multiple measures to assess infant cognition including looking-based measures, the longitudinal design, inclusion of a large sample, and a high density of air quality measurements for each household. We also included a continuous range of SES families from the same micro-cultural context, allowing us to tease apart influences of SES while holding culture relatively constant.

Regarding limitations, we note that effect sizes were relatively low in the present study. In addition, there was some instability in the air quality devices over time (see Methods). Note that although laser particle sensors have shown robust correlations with beta attenuation monitors in laboratory and field tests (Zhang and Srinivasan, 2020), it would be ideal in future work to use personal air quality monitors such as gravimetric devices that correct light scatter and use weighted PM2.5. This would enable a direct, localized measure of the air each child is exposed to.

Another limitation of the present study was the absence of more detailed information about cooking practices in the home. Although we identified the primary cooking fuel used in each household, it is likely some households used multiple cooking methods, and other fuels for warming, smoking, and so on (Rohra and Taneja, 2016; Mukhopadhyay et al., 2012). Future work will also be needed to carefully examine whether poor air quality has a causal influence on cognition in early development. Given recent data on the impact of poor air quality on the developing brain in animals, future work using neuroimaging tools might be particularly useful to clarify mechanistic pathways in infancy.

Our data suggest that global efforts to improve air quality could have benefits to infants’ emerging cognitive abilities. This, in turn, could have a cascade of positive impacts, including positive impacts on families as well as economic consequences as improved cognition can lead to improved economic productivity longer-term and reduce the burden on healthcare and mental health systems (Moffitt et al., 2011). Our results showing links between indoor air quality and cooking materials also suggest that efforts to reduce cooking emissions in homes should be a key target for intervention. This requires both increased availability of clean technologies and uptake of such technologies in rural households where traditional methods of meal preparation might be a barrier (see Mukhopadhyay et al., 2012). Our findings can motivate both policymakers and families to improve air quality as this should positively boost the neurocognitive health of young infants.

Data availability

All data and code are available at https://osf.io/fspzb/.

The following data sets were generated
    1. Spencer JP
    2. Forbes S
    (2023) Open Science Framework
    ID fspzb. Air quality and visual working memory.

References

    1. Rose SA
    (1994)
    Relation between physical growth and information processing in infants born in India
    Child Development 65:889–902.

Decision letter

  1. Charlotte Cecil
    Reviewing Editor; Erasmus MC, Netherlands
  2. Chris I Baker
    Senior Editor; National Institute of Mental Health, United States
  3. Zsuzsa Kaldy
    Reviewer; College of Liberal Arts, UMass Boston, United States

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Decision letter after peer review:

Thank you for submitting your article "Poor air quality is associated with impaired visual cognition in the first two years of life: a longitudinal investigation" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Chris Baker as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal their identity: Zsuzsa Kaldy (Reviewer #2).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1) Please provide more information about the following general aspects of the study methodology and variables examined:

a. A rationale for the recruitment strategy and sample selection (e.g. why the study included two cohorts of infants, aged specifically 6 and 9 months of age);

b. Adding a table or correlation plot (e.g. in supplementary materials) showing how the study variables relate to each other and over time;

c. Clarifying the strategy for model building and covariate adjustment, as certain covariates (e.g. gender) seem to be included in models unevenly ;

d. Commenting on the power to test 2-, 3- and 4-way interactions given the available sample size;

e. Expanding on findings regarding within-subject comparisons.

2) The treatment and analysis of data from the computerized tasks need further clarification, particularly in relation to the following points:

a. Can the authors clarify why they focused only on 'first-look same trials' (i.e. when infants focused on the unchanging stimulus) in their change preference analyses? As suggested by the reviewers, it would be important to provide a comparison by reporting looking time when infants had their first look at the changing stimulus;

b. Analyses were limited to a specific temporal window, and it is unclear how this choice might have influenced the results. Can the authors provide more information about this, for example using the 'time course' plot suggested by Reviewer 3;

c. The authors should comment on potential reasons why change preference scores in this study were on average lower than 0.5 (i.e. chance level) and how this might impact the interpretation of findings;

d. The rationale for using the shift rate measure needs to be expanded given the potential for inter-dependence with change preference score.

3) Re-evaluating and expanding the interpretation of findings:

a. The authors mention that the lack of significant associations between indoor air pollution and questionnaire-based measures of cognition may be due to the measures having lower validity for this type of sample. However, as pointed out by several reviewers, these measures do associate with other study variables, such as SES, as expected. Can the authors revise their conclusions accordingly and expand on alternative potential reasons for the lack of associations with indoor air pollution?

b. Conclusions about associations between indoor air pollution and performance on the visual cognition task should be qualified by stating more clearly that observed effect sizes were generally small, the relationship was not consistent over time and that overall performance on change preference was lower than expected (i.e. on average below 0.5). As suggested by Reviewer 2, the authors should also perform a post hoc analysis to test whether an association between air quality and change preference scores in Year 2 is observed when restricting data to set sizes 2 and 4 only. This will help readers evaluate whether the lack of associations in Year 2 may be due to the task being too difficult or whether other factors may be at play.

Reviewer #1 (Recommendations for the authors):

In this study, Spencer et al. examine whether indoor air quality is associated with multiple aspects of infant cognitive functioning, based on longitudinal data from a sample of families from rural India. Briefly, infants were enrolled at either 6 or 9 months of age. Laboratory assessments were performed at baseline and repeated one year later, whereas home visits to measure air quality took place after each laboratory assessment (i.e., baseline and follow-up), and were repeated every three months for a period of one year. The main effects of air quality as well as interactions with factors such as age, gender, and socio-economic status were tested. Based on these data, the authors report an association between poorer air quality and worse performance on a computerized task of visual cognition. Specifically, associations with processing speed were observed at both time points, while associations with working memory were only observed at baseline (year 1). No associations were identified with standardized questionnaire-based measures of psychomotor or cognitive function.

Generally, the study is well-written, the methods are for the most part appropriate and clearly explained, and the findings make an important contribution to the literature, by investigating for the first time associations between indoor air quality and cognition in the first two years of life. As such, the study may be of broad interest and have an impact, particularly within the fields of neurocognition, child development, and environmental and public health. At the same time, some of the study conclusions are difficult to evaluate based on the information provided. In particular, little information is presented regarding how the study variables relate to each other (and over time) in this sample, why this specific recruitment strategy was chosen, and the rationale for the models tested, particularly the uneven use of covariates. Key strengths and areas for improvement are described in more detail below.

• A key strength of the study is the assessment of indoor air quality – measured repeatedly using an objective device – which may be particularly relevant during the first year of life when children spend much time at home. The authors focused specifically on the presence of very small particles (PM2.5), given previous evidence that these can have neurotoxic and adverse health effects, particularly at high levels of exposure. Levels of indoor air quality were found to largely mirror those of outdoor air quality (based on nearby monitoring stations). This Non-Western sample showed on average exposure to 'unhealthy' levels of PM2.5, based on the US Air Quality Index. The authors found that indoor air quality was linked to meal preparation, and was poorest in homes where solid cooking materials were used. Although it is difficult to draw causal inferences based on these data and to tease apart the effect of cooking materials from broader socio-economic factors, results suggest that the type of cooking fuel used may be an important modifiable target for the improvement of indoor air quality, and potentially downstream health effects.

• Another key strength of the study is the focus on cognition as an outcome during the first two years of life, a period marked by rapid brain growth. It is, however, unclear what the specific rationale was for including children at baseline who were either 6 or 9 months old (e.g., convenience sampling or scientifically motivated), and whether any differences in associations were observed between these two age groups. It is also unclear whether the visual cognition task was developed for this particular study or has been used in previous research, and if so, whether it has been found to be predictive of later cognitive function (including aspects other than visual cognition). The strategy for covariate adjustment appeared to be inconsistent across models tested, with, for example, gender being included as a covariate in some models but not others. In their models, the authors assess two-, three- and four-way interactions in relation to their cognitive outcomes, but no calculations are made to test how powered these analyses are based on the available sample size. Finally, the authors conclude that poor air quality associates with impaired visual cognition across the first two years of age. However, the findings show that for working memory, this association holds true only at baseline, and seems to go in the opposite direction at follow-up. The authors mention that this may be due to the task being too difficult at follow-up, but this is difficult to evaluate given that longitudinal associations between performance in Year 1 and Year 2 are not presented. The authors also mention as an alternative explanation that as children grow up they may spend more time outdoors, so that outdoor air quality may become more important. This may seem unlikely given that indoor and outdoor air pollution were found to largely mirror one another. A more detailed discussion of these findings, as well as potential strategies that could be used in the future to address developmentally-dynamic associations between air quality and cognitive functioning, are warranted.

Below are some specific comments and suggestions that could help improve the manuscript:

– The authors report differences in performance between 6 and 9-month-old infants. It is unclear whether these age-related effects of change preference and memory load are also observed one year later.

– How correlated were the measures of cognition between baseline and follow-up? Generally, I would strongly recommend adding a correlation table/plot with the study variables and covariates, to show how strongly related these are across the type of variable and time point examined.

– Line 95: 'In addition, there was an interaction between SES, measured as a continuous variable based on the Kuppuswamy scales, and year on shift rate, with an increase in shift rate in year 2 of the study, but only for the low SES participants who had a lower starting shift rate in year 1'. This is quite a dense sentence and it would be useful to break it down to explain more clearly the interaction and what it means.

– When examining the association between air quality and cognitive outcomes, why is age cohort adjusted for rather than used as a variable of interest? Is it because the authors assume that associations will be independent of age (or are only interested in testing age-independent effects)?

– The choice of covariates is a bit unclear to me. In particular, gender seems to be included in some models but not in others. It would be helpful to frontload the strategy for covariate adjustment at the beginning of the analysis section.

– Line 178: 'These findings indicate that poor air quality (PM2.5) is associated with impaired visual cognition in the first two years of life'. This sentence seems to only partially reflect the results of the study, as my understanding is that associations with shift rate were only found during year 1, and actually associations show an opposite direction in year 2.

– Line 197: 'It may also indicate that in the second year of life, children spend less time indoors, with outdoor air quality becoming a more important factor'. This sentence seems at odds with the authors' finding that indoor air quality largely mirrored outdoor air quality.

– Figure 3A: it remains unclear to me why associations go in opposite directions between year 1 and year 2, it would be helpful if the authors could expand on this.

– Figure 3B: Why is there a single line here? does it represent year 1 or year 2 of the assessment?

– Table 1: Why is income specifically shown instead of the composite SES variable used in the analyses? Generally, it would be helpful to show percentages to aid the interpretation of the distribution of cooking methods across SES categories.

– Table 5: Did cooking fuel associate with air quality reading even after adjusting for SES? It seems based on this table that cooking fuel and SES are examined as predictors in separate models, so unclear to what extent cooking fuel independently associates with air quality.

Reviewer #2 (Recommendations for the authors):

This important study demonstrates that poor indoor air quality impairs cognitive development as early as 6 months of age. The evidence presented is highly compelling, with longitudinal tracking of multiple cognitive indices in a large sample of infants from families across diverse SES strata. The findings have a high public health significance as they support policy changes that will push cooking technologies toward zero emission methods.

1. The authors state that they 'focused on the 'first-look same' trials' as a key aspect of infants' looking is whether they can release fixation from the 'same' side and show a preference for looking at the 'change' side (see 22)" (Line 343-344). This step is unclear: did they only keep these trials in their change preference analyses? If yes, that would be a very unusual analytical choice. The paper that is referenced here (Perone et al., 2012) does not apply this method.

2. Average change preference scores (proportion looking to the changing side) seem to be generally below 0.5, which is puzzling, since the main prediction, based on previous studies, is that there will be an above-chance preference for the changing side. In the current study, this measure varies between chance level performance in the low-load condition to below chance level in the high-load condition (Figure 1D).

3. Air quality is negatively correlated with change preference scores in young infants (Year 1), but not 12 months later (Year 2), which is puzzling. The authors offer a potential explanation, namely that by increasing the set sizes in the task, they inadvertently made it too hard for 18-21-month-olds. This could easily be tested in an exploratory analysis of a subsample of the Year 2 data that is limited to set sizes 2 and 4 only. If the main effect of AQI is not significant, then this would make this explanation unlikely.

4. There was no significant association between air quality and parent-based measures of cognitive skills (MSEL, ASQ) at either of the two-time points. The authors argue that this would be "a possible indication of the need to use specially-designed tasks" (Line 174-175). I do not think this interpretation is warranted. SES had a highly significant relationship with both parent-based scores and age had an effect on ASQ, that is, these measures corroborate previous findings, while these were not so robustly present for the visual WM task measures.

5. The rationale presented for the second dependent variable (shift rate) in the WM study needs to be explained in more detail. If infants are able to detect the changes happening on a particular side, we would expect their gaze to stay longer on that side (which is the basis of the first dependent variable, change preference score). Thus, the two measures are not independent. A recent infant study found that longer looking times (and lower shift rates) reflected higher confidence in a particular choice (Dautriche et al., 2022, Psych Sci).

6. The analysis choice mentioned in #1 needs to be clarified and justified.

7. Conclusions about changes in working memory performance need to be qualified with the note that overall performance did not show the expected level.

8. The alternative explanation mentioned in #3 should be tested and results and interpretation need to be added.

9. The conclusion offered about the relative merits of lab-based and parent-based measures needs to be rephrased.

10. The rationale for the shift rate measure needs to be expanded.

Reviewer #3 (Recommendations for the authors):

This paper describes an extensive study testing the impact of air quality, measured in households in Shivgarh, India, on cognitive development during the first two years of life. The size of the cohort, longitudinal design, SES controls, and fine-grained measures of indoor air quality are all strengths of the study. As well, the range of air quality variation and variety of cognitive measures make for a sensitive design. Overall, a picture emerges of a significant, negative association between air quality and cognitive development. However, given the lack of robustness of some of the findings, there are opportunities for further study and discussion.

At the outset, the paper frames the impact of negative neurocognitive health outcomes in broad economic terms. It would be informative to also have a more local, person-centered context that includes individual, family, and community perspectives on the air quality itself, its causes and consequences, and its impact beyond strict economics.

Given the longitudinal design, it would have been interesting to have seen more within-subjects comparisons, with perhaps an exploration of within- versus between-subject variability, individual differences, and their relationship to potential mechanisms of susceptibility, and resilience, to the effects of air pollution.

The authors' concern that the Mullen and ASQ might not be a fully valid measure in this community seems reasonable (though this point could use further elaboration). This concern is used as an argument to explain why there was no significant relationship found between air quality and these measures. However, if I am understanding the tables correctly, both measures were, as would be generally expected, associated with SES. This would tend to actually lend them some support as valid measures in this context. This seems then an apparent discrepancy that the authors could try to reconcile or provide further insight into.

While the authors report significant results showing negative perceptual/cognitive outcomes associated with poor air quality (which is plausible and supported by previous literature and animal work) effect sizes are relatively small and the relationship is not consistent (air quality associated only with 'change preference' in year 1 and 'switch rate' only in year 2, e.g.). Further, there was some treatment of the data that felt arbitrary and made the reader crave both a fuller contextualization and fuller reporting, of the results. Most notably the choice to focus analyses only on the half of the trials where infants had their first fixation on the 'same' (unchanging) stimulus stream. Unless I missed it, it would be nice to see a comparison looking time when the infants have their first look at the changing stimulus stream. Also, analyses were limited to a particular temporal window within a trial, 1500-6500ms. How does this choice influence the results? One way to capture all the richness of the data in one visualization would be to plot 'time course' analyses, showing, ms by ms, the average proportion of time participants spent on the changing versus 'same' streams, perhaps broken down by the stream they start on.

The 'change detection' visual tests used here are common in the field. However, there is no consensus on the mechanisms that they tap into. Here they are described as tests of working memory but given their rapid pace and simple manipulation of basic visual attributes (i.e. noting flickering color changes), many researchers think they instead test earlier, 'lower-level' perceptual/attentional mechanisms. As the authors and others seek, perhaps, to connect air quality to the disruption of particular brain mechanisms and processes (and better relate those mechanisms and processes to subsequent cognitive and educational outcomes), a more precise understanding of the tests themselves will be necessary.

I think most of my comments are covered above. Mostly, I would like to see a fuller description of the results (for visual tests), and graphs could use revising for clarity.

https://doi.org/10.7554/eLife.83876.sa1

Author response

Essential revisions:

1) Please provide more information about the following general aspects of the study methodology and variables examined:

a. A rationale for the recruitment strategy and sample selection (e.g. why the study included two cohorts of infants, aged specifically 6 and 9 months of age);

This information has been added (p.3), clarifying that we were expecting a difference in visual working memory (VWM) performance between 6 and 9 months based on studies of western infants. Alternatively, a lack of difference between 6 and 9 months might suggest a developmental delay in VWM in rural India.

b. Adding a table or correlation plot (e.g. in supplementary materials) showing how the study variables relate to each other and over time;

We have added this to the supplementary materials as requested (see Figure S1).

c. Clarifying the strategy for model building and covariate adjustment, as certain covariates (e.g. gender) seem to be included in models unevenly ;

We have clarified our strategy (see p.10-11). Note that we inadvertently included gender in the previous version of the paper based on older analyses of the Mullen/ASQ data; however, we double-checked this and there is no strong statistical reason for including gender. Thus, gender has now been removed making our analysis approach consistent across models.

d. Commenting on the power to test 2-, 3- and 4-way interactions given the available sample size;

We have more carefully specified our analysis strategy (see p.11) and how we tried to construct both theoretically-motivated statistical models and streamlined models that had sufficient power. We address these points in more detail below.

e. Expanding on findings regarding within-subject comparisons.

We have added several follow-up analyses to the results (see p. 6-7) to clarify the findings. This was in response to the reviewer queries below which we discuss in the following sections.

2) The treatment and analysis of data from the computerized tasks need further clarification, particularly in relation to the following points:

a. Can the authors clarify why they focused only on 'first-look same trials' (i.e. when infants focused on the unchanging stimulus) in their change preference analyses? As suggested by the reviewers, it would be important to provide a comparison by reporting looking time when infants had their first look at the changing stimulus;

We have clarified why we focused on the ‘first-look no-change’ trials (p. 3). Briefly, a forthcoming paper that examines this sample along with a longitudinal sample of infants from the UK (over 400 infants in the combined sample) shows that the traditional measure of performance in this task – the change preference score – is not stable longitudinally (see p.11). By contrast, our new ‘first-look’ measures are stable longitudinally, that is, year 1 scores measured at 6 and 9 months predict year 2 scores measured at 18 and 21 months within-subjects. Moreover, additional analyses from the same forthcoming paper suggest that ‘first-look no-change’ scores are most indicative of VWM capacity. In this context, it is interesting that we find associations between air quality and the ‘first-look no-change’ scores only. Note that we have also added analyses of ‘first-look change’ scores in the revised manuscript (see Tables 10 and 11). There were no associations between this second measure and air quality.

b. Analyses were limited to a specific temporal window, and it is unclear how this choice might have influenced the results. Can the authors provide more information about this, for example using the 'time course' plot suggested by Reviewer 3;

We have added more rationale for the temporal window chosen and refined this window a bit more in the revised paper (see p.11).

c. The authors should comment on potential reasons why change preference scores in this study were on average lower than 0.5 (i.e. chance level) and how this might impact the interpretation of findings;

We have clarified (p.3) that the new ‘first-look no-change’ measure is not anchored at 0.5 (since, conceptually, this measure starts at 0 and moves away from 0 over the time window).

d. The rationale for using the shift rate measure needs to be expanded given the potential for inter-dependence with change preference score.

We have expanded the rationale here (p. 3). We have also added a follow-up test to show that both associations with air quality (i.e., ‘first-look no-change’ to air quality and shift rate to air quality) are robust, even when the complementary measure is added to the model. For instance, air quality continues to be associated with the ‘first-look no-change’ measure even when shift rate is added to the statistical model (see p.6).

3) Re-evaluating and expanding the interpretation of findings:

a. The authors mention that the lack of significant associations between indoor air pollution and questionnaire-based measures of cognition may be due to the measures having lower validity for this type of sample. However, as pointed out by several reviewers, these measures do associate with other study variables, such as SES, as expected. Can the authors revise their conclusions accordingly and expand on alternative potential reasons for the lack of associations with indoor air pollution?

We have revised this section and thank the reviewers for the comment. Our conclusion is that air quality has a specific impact on the visual cognitive system. This is an early-developing system that is indexed by the preferential looking task. By contrast, the questionnaire-based measures target more general aspects of development. Thus, air quality appears to impact VWM specifically rather than these more general aspects of development. Note that we cannot conclude that VWM is the only system impacted by air quality. It is possible that other early-developing systems are also impacted; this will require further study. We mention, for instance, that it would be interesting to probe if air quality impacts auditory and/or statistical learning processes (p. 8).

b. Conclusions about associations between indoor air pollution and performance on the visual cognition task should be qualified by stating more clearly that observed effect sizes were generally small, the relationship was not consistent over time and that overall performance on change preference was lower than expected (i.e. on average below 0.5). As suggested by Reviewer 2, the authors should also perform a post hoc analysis to test whether an association between air quality and change preference scores in Year 2 is observed when restricting data to set sizes 2 and 4 only. This will help readers evaluate whether the lack of associations in Year 2 may be due to the task being too difficult or whether other factors may be at play.

We have qualified our conclusions remarking on the effect sizes and the inconsistency for the VWM scores in year 2. On that front, we note that other data from our study suggest that VWM in year 2 is impacted by interactions with the caregiver; thus, it is possible that air quality is primarily impactful early in infancy when this cognitive system is initially organizing but other factors play a more central role as the system changes from year 1 to year 2 (see p. 8-9).

We did note that the change preference scores were below 0.5 and that this was expected with the new ‘first-look no-change’ measure.

Finally, we ran the suggested analysis including only the ‘load 2’ data. This is useful in that we still get a year x air quality interaction. Thus, it does not appear that the VWM task is too difficult in year 2 to detect an effect.

Reviewer #1 (Recommendations for the authors):

In this study, Spencer et al. examine whether indoor air quality is associated with multiple aspects of infant cognitive functioning, based on longitudinal data from a sample of families from rural India. Briefly, infants were enrolled at either 6 or 9 months of age. Laboratory assessments were performed at baseline and repeated one year later, whereas home visits to measure air quality took place after each laboratory assessment (i.e., baseline and follow-up), and were repeated every three months for a period of one year. The main effects of air quality as well as interactions with factors such as age, gender, and socio-economic status were tested. Based on these data, the authors report an association between poorer air quality and worse performance on a computerized task of visual cognition. Specifically, associations with processing speed were observed at both time points, while associations with working memory were only observed at baseline (year 1). No associations were identified with standardized questionnaire-based measures of psychomotor or cognitive function.

Generally, the study is well-written, the methods are for the most part appropriate and clearly explained, and the findings make an important contribution to the literature, by investigating for the first time associations between indoor air quality and cognition in the first two years of life. As such, the study may be of broad interest and have an impact, particularly within the fields of neurocognition, child development, and environmental and public health. At the same time, some of the study conclusions are difficult to evaluate based on the information provided. In particular, little information is presented regarding how the study variables relate to each other (and over time) in this sample, why this specific recruitment strategy was chosen, and the rationale for the models tested, particularly the uneven use of covariates. Key strengths and areas for improvement are described in more detail below.

• A key strength of the study is the assessment of indoor air quality – measured repeatedly using an objective device – which may be particularly relevant during the first year of life when children spend much time at home. The authors focused specifically on the presence of very small particles (PM2.5), given previous evidence that these can have neurotoxic and adverse health effects, particularly at high levels of exposure. Levels of indoor air quality were found to largely mirror those of outdoor air quality (based on nearby monitoring stations). This Non-Western sample showed on average exposure to 'unhealthy' levels of PM2.5, based on the US Air Quality Index. The authors found that indoor air quality was linked to meal preparation, and was poorest in homes where solid cooking materials were used. Although it is difficult to draw causal inferences based on these data and to tease apart the effect of cooking materials from broader socio-economic factors, results suggest that the type of cooking fuel used may be an important modifiable target for the improvement of indoor air quality, and potentially downstream health effects.

• Another key strength of the study is the focus on cognition as an outcome during the first two years of life, a period marked by rapid brain growth. It is, however, unclear what the specific rationale was for including children at baseline who were either 6 or 9 months old (e.g., convenience sampling or scientifically motivated), and whether any differences in associations were observed between these two age groups.

Thanks for the comment. We have added a more detailed rationale for these ages in the revised manuscript. Briefly, there is a change in VWM capacity between 6 and 8.5 months; thus, we divided our cohort into two groups on either side of this developmental transition. This allowed us to probe how VWM changes over ages in rural India relative to western samples. Note that we included Age as a predictor in all models and did not see any evidence that the air quality associations reported here were related to Age.

It is also unclear whether the visual cognition task was developed for this particular study or has been used in previous research, and if so, whether it has been found to be predictive of later cognitive function (including aspects other than visual cognition).

The preferential looking task has been used in prior work, but, until recently, there have not been any longitudinal assessments. We have now completed two longitudinal studies – one in the UK and one in rural India. This work motivated the particular measures used here (for discussion, see p.11).

The strategy for covariate adjustment appeared to be inconsistent across models tested, with, for example, gender being included as a covariate in some models but not others.

Apologies for the oversight regarding gender. We have now carefully re-run all analyses and found no evidence warranting the inclusion of gender. Thus, this predictor has been removed from all analyses.

In their models, the authors assess two-, three- and four-way interactions in relation to their cognitive outcomes, but no calculations are made to test how powered these analyses are based on the available sample size.

In all models, we aimed to theoretically motivate the inclusion of all effects and limit spurious correlations by only adding effects where they were thought to contribute a priori. In the case of interactions, models were tested to see whether interactions improved model fit through formal model comparison, and interactions were not included if they did not contribute to model fit. The goal was to create the most streamlined models possible to ensure there was sufficient power in all analyses.

Finally, the authors conclude that poor air quality associates with impaired visual cognition across the first two years of age. However, the findings show that for working memory, this association holds true only at baseline, and seems to go in the opposite direction at follow-up. The authors mention that this may be due to the task being too difficult at follow-up, but this is difficult to evaluate given that longitudinal associations between performance in Year 1 and Year 2 are not presented.

We have conducted longitudinal analyses in a forthcoming paper, combining the current data set with data from a longitudinal study in the UK (N > 400 total). Results revealed that the standard measure from the preferential looking task – the change preference score – was not stable longitudinally. Critically, the new ‘first look’ measures used here ARE stable longitudinally. We provide an overview of these findings on p. 11. In addition, we have added a correlation table to the supplementary materials so the reader can get a sense for how the key measures reported here are associated.

We also appreciate the comment about task difficulty. We added a new follow-up analysis to the paper comparing load 2 for year 1 and year 2. Results show that the Year x AQI interaction is robust with this dataset, ruling out the task difficulty explanation. We have updated the Discussion accordingly. Note that we suspect there is no robust link between change preference scores and air quality in year 2 because other factors which are not correlated with air quality are impacting the development of VWM. We mention one possibility (dyadic interactions with the caregiver) on p. 8.

The authors also mention as an alternative explanation that as children grow up they may spend more time outdoors, so that outdoor air quality may become more important. This may seem unlikely given that indoor and outdoor air pollution were found to largely mirror one another. A more detailed discussion of these findings, as well as potential strategies that could be used in the future to address developmentally-dynamic associations between air quality and cognitive functioning, are warranted.

This is a fair point. We have removed the comment about outdoor air quality in the revised paper and have added a more detailed discussion of our findings (p. 7-8).

Below are some specific comments and suggestions that could help improve the manuscript:

– The authors report differences in performance between 6 and 9-month-old infants. It is unclear whether these age-related effects of change preference and memory load are also observed one year later.

The age effect in Table 2 is an Age main effect; this suggests there is some common variance that extends over both years in the study. Similarly, the Load effect is a main effect. We did not find evidence that Age or Load interacted with other variables.

– How correlated were the measures of cognition between baseline and follow-up? Generally, I would strongly recommend adding a correlation table/plot with the study variables and covariates, to show how strongly related these are across the type of variable and time point examined.

We have added a correlation table to the supplementary materials as requested. We also note on p. 11 that the ‘first-look’ measures we used here are stable longitudinally (as we describe in a forthcoming paper).

– Line 95: 'In addition, there was an interaction between SES, measured as a continuous variable based on the Kuppuswamy scales, and year on shift rate, with an increase in shift rate in year 2 of the study, but only for the low SES participants who had a lower starting shift rate in year 1'. This is quite a dense sentence and it would be useful to break it down to explain more clearly the interaction and what it means.

In cleaning up our analysis script, we noticed that some missing observations had crept into this data frame. Consequently, the Year x SES interaction is no longer significant and this dense sentence has been deleted.

– When examining the association between air quality and cognitive outcomes, why is age cohort adjusted for rather than used as a variable of interest? Is it because the authors assume that associations will be independent of age (or are only interested in testing age-independent effects)?

Each model was conceived with parsimony in mind; we also sought to simplify the models based on theoretical motivations. We adjusted for age because we theorise that the older cohort might have a higher baseline cognitive score, but we had no reason to think that this difference would change over time as a function of air quality. We note that we did explore possible interactions with Age in initial models and found no robust relationships.

– The choice of covariates is a bit unclear to me. In particular, gender seems to be included in some models but not in others. It would be helpful to frontload the strategy for covariate adjustment at the beginning of the analysis section.

Apologies for this lack of consistency. As described above, this has been cleaned up. In short, we found no evidence of links between our measures and gender.

– Line 178: 'These findings indicate that poor air quality (PM2.5) is associated with impaired visual cognition in the first two years of life'. This sentence seems to only partially reflect the results of the study, as my understanding is that associations with shift rate were only found during year 1, and actually associations show an opposite direction in year 2.

We have revised this sentence: “These findings indicate that poor air quality (PM2.5) is associated with slower visual processing speed in the first two years of life and poorer visual working memory scores in year 1.”

– Line 197: 'It may also indicate that in the second year of life, children spend less time indoors, with outdoor air quality becoming a more important factor'. This sentence seems at odds with the authors' finding that indoor air quality largely mirrored outdoor air quality.

That is correct and we have edited the discussion to more carefully reflect this point.

– Figure 3A: it remains unclear to me why associations go in opposite directions between year 1 and year 2, it would be helpful if the authors could expand on this.

We have enriched our Discussion of this result on p.7.

– Figure 3B: Why is there a single line here? does it represent year 1 or year 2 of the assessment?

The analyses show a main effect of shift rate; thus, we have aggregated shift rate over years to reflect this main effect.

– Table 1: Why is income specifically shown instead of the composite SES variable used in the analyses? Generally, it would be helpful to show percentages to aid the interpretation of the distribution of cooking methods across SES categories.

Income is useful here to show the distribution of income relative to SES (which is a composite of income, education, occupation). As requested, we have converted the data to proportions.

– Table 5: Did cooking fuel associate with air quality reading even after adjusting for SES? It seems based on this table that cooking fuel and SES are examined as predictors in separate models, so unclear to what extent cooking fuel independently associates with air quality.

Thanks for this comment. SES and cooking fuel choice are highly correlated (see Table 1) and theoretically should be as well, so this violates the assumption of independence in linear models. As such, we represent them as two models here to highlight their correspondence.

Reviewer #2 (Recommendations for the authors):

This important study demonstrates that poor indoor air quality impairs cognitive development as early as 6 months of age. The evidence presented is highly compelling, with longitudinal tracking of multiple cognitive indices in a large sample of infants from families across diverse SES strata. The findings have a high public health significance as they support policy changes that will push cooking technologies toward zero emission methods.

1. The authors state that they 'focused on the 'first-look same' trials' as a key aspect of infants' looking is whether they can release fixation from the 'same' side and show a preference for looking at the 'change' side (see 22)" (Line 343-344). This step is unclear: did they only keep these trials in their change preference analyses? If yes, that would be a very unusual analytical choice. The paper that is referenced here (Perone et al., 2012) does not apply this method.

We have expanded our discussion of how we treat the change preference scores. The new additional discussion hopefully clarifies these points. Note that we cited Perone et al. here as we have used our neurocomputational model to understand the new ‘first-look’ measures described herein, but this citation has now been removed for clarity.

2. Average change preference scores (proportion looking to the changing side) seem to be generally below 0.5, which is puzzling, since the main prediction, based on previous studies, is that there will be an above-chance preference for the changing side. In the current study, this measure varies between chance level performance in the low-load condition to below chance level in the high-load condition (Figure 1D).

As described above and in the revised paper, the new ‘first-look’ measures we used are not anchored around 0.5 (because ‘first-look no-change’ starts at 0 and ‘first-look change’ starts at 1). We have clarified this in the revised paper.

3. Air quality is negatively correlated with change preference scores in young infants (Year 1), but not 12 months later (Year 2), which is puzzling. The authors offer a potential explanation, namely that by increasing the set sizes in the task, they inadvertently made it too hard for 18-21-month-olds. This could easily be tested in an exploratory analysis of a subsample of the Year 2 data that is limited to set sizes 2 and 4 only. If the main effect of AQI is not significant, then this would make this explanation unlikely.

This was an excellent suggestion. We conducted this analysis and the year X AQI interaction remains. Thus, this is not a likely explanation for the effect.

4. There was no significant association between air quality and parent-based measures of cognitive skills (MSEL, ASQ) at either of the two-time points. The authors argue that this would be "a possible indication of the need to use specially-designed tasks" (Line 174-175). I do not think this interpretation is warranted. SES had a highly significant relationship with both parent-based scores and age had an effect on ASQ, that is, these measures corroborate previous findings, while these were not so robustly present for the visual WM task measures.

Yes. Fair play. Perhaps the most direct explanation is that air quality impacts VWM but not other more general aspects of motor/cognitive/social development. That is, maybe the impact on VWM is specific and the test that assesses VWM picks up on this. We have expanded the discussion to highlight these points.

5. The rationale presented for the second dependent variable (shift rate) in the WM study needs to be explained in more detail. If infants are able to detect the changes happening on a particular side, we would expect their gaze to stay longer on that side (which is the basis of the first dependent variable, change preference score). Thus, the two measures are not independent. A recent infant study found that longer looking times (and lower shift rates) reflected higher confidence in a particular choice (Dautriche et al., 2022, Psych Sci).

This is a good point. We had a priori reasons to look at both measures. That said, we conducted two follow-up analyses to see if some shared variance among measures was driving our results. These analyses suggest that links between air quality and the visual cognition measures are robust, even when controlling for this shared variance by including both visual cognition measures in the same models.

A recent infant study found that longer looking times (and lower shift rates) reflected higher confidence in a particular choice (Dautriche et al., 2022, Psych Sci).

There is a long history of interpreting shift rates in the infant literature. While the cited paper is interesting, we opted not to include this in the revised paper, instead interpreting shift rates in line with our prior neurocomputational models (see, e.g., Perone et al., 2012).

6. The analysis choice mentioned in #1 needs to be clarified and justified.

Yes. We have expanded our discussion of the analyses. See, for instance, p. 10-11.

7. Conclusions about changes in working memory performance need to be qualified with the note that overall performance did not show the expected level.

We have clarify this point above and in the revised paper. Our new ‘first look’ measures are not expected to be anchored to 0.5.

8. The alternative explanation mentioned in #3 should be tested and results and interpretation need to be added.

Done. See above.

9. The conclusion offered about the relative merits of lab-based and parent-based measures needs to be rephrased.

This has been updated.

10. The rationale for the shift rate measure needs to be expanded.

This has been added.

Reviewer #3 (Recommendations for the authors):

This paper describes an extensive study testing the impact of air quality, measured in households in Shivgarh, India, on cognitive development during the first two years of life. The size of the cohort, longitudinal design, SES controls, and fine-grained measures of indoor air quality are all strengths of the study. As well, the range of air quality variation and variety of cognitive measures make for a sensitive design. Overall, a picture emerges of a significant, negative association between air quality and cognitive development. However, given the lack of robustness of some of the findings, there are opportunities for further study and discussion.

At the outset, the paper frames the impact of negative neurocognitive health outcomes in broad economic terms. It would be informative to also have a more local, person-centered context that includes individual, family, and community perspectives on the air quality itself, its causes and consequences, and its impact beyond strict economics.

We have added a more family-centred perspective in the Introduction and Discussion, highlighting the impact air quality can have on families, particularly via developmental impacts which can lead to emotional and behavioural problems.

Given the longitudinal design, it would have been interesting to have seen more within-subjects comparisons, with perhaps an exploration of within- versus between-subject variability, individual differences, and their relationship to potential mechanisms of susceptibility, and resilience, to the effects of air pollution.

All of our linear mixed-effects models were within-subjects longitudinally; thus, we are not sure exactly what the reviewer would like to see here. In the revised paper, we tried to clarify our analysis approach. We also note that we used measures that were longitudinally stable (see, e.g., the discussion of our ‘first-look’ measures on p. 11).

The authors' concern that the Mullen and ASQ might not be a fully valid measure in this community seems reasonable (though this point could use further elaboration). This concern is used as an argument to explain why there was no significant relationship found between air quality and these measures. However, if I am understanding the tables correctly, both measures were, as would be generally expected, associated with SES. This would tend to actually lend them some support as valid measures in this context. This seems then an apparent discrepancy that the authors could try to reconcile or provide further insight into.

This is an excellent point. We have refined our discussion of the Mullen/ASQ findings. We think our results suggest that visual cognition is specifically impacted by air quality (vs more general aspects of cognition/motor/social functioning).

While the authors report significant results showing negative perceptual/cognitive outcomes associated with poor air quality (which is plausible and supported by previous literature and animal work) effect sizes are relatively small and the relationship is not consistent (air quality associated only with 'change preference' in year 1 and 'switch rate' only in year 2, e.g.).

We have noted the relatively small effect sizes. We also note that effects of air quality on shift rate were robust across both years.

Further, there was some treatment of the data that felt arbitrary and made the reader crave both a fuller contextualization and fuller reporting, of the results. Most notably the choice to focus analyses only on the half of the trials where infants had their first fixation on the 'same' (unchanging) stimulus stream.

This is an excellent suggestion. We have added these analyses, noting no relationships between air quality and the ‘first look change’ trials.

Unless I missed it, it would be nice to see a comparison looking time when the infants have their first look at the changing stimulus stream. Also, analyses were limited to a particular temporal window within a trial, 1500-6500ms. How does this choice influence the results? One way to capture all the richness of the data in one visualization would be to plot 'time course' analyses, showing, ms by ms, the average proportion of time participants spent on the changing versus 'same' streams, perhaps broken down by the stream they start on.

This is an excellent suggestion; however, this is precisely what we do in a forthcoming paper examining the current dataset combined with a large longitudinal study in the UK (N > 400). Rather than repeat this analysis here, we refer to our forthcoming paper providing an overview of our findings.

The 'change detection' visual tests used here are common in the field. However, there is no consensus on the mechanisms that they tap into. Here they are described as tests of working memory but given their rapid pace and simple manipulation of basic visual attributes (i.e. noting flickering color changes), many researchers think they instead test earlier, 'lower-level' perceptual/attentional mechanisms. As the authors and others seek, perhaps, to connect air quality to the disruption of particular brain mechanisms and processes (and better relate those mechanisms and processes to subsequent cognitive and educational outcomes), a more precise understanding of the tests themselves will be necessary.

This is an excellent point and is something we are working on. For instance, in our forthcoming paper, we use the neurocomputational model from Perone et al. (2012) to explain how the ‘first-look’ measures are influenced by individual differences in VWM. Inclusion of such work here is beyond the scope of the present paper, but we do point to our forthcoming paper on this topic.

https://doi.org/10.7554/eLife.83876.sa2

Article and author information

Author details

  1. John P Spencer

    School of Psychology, University of East Anglia, Norwich, United Kingdom
    Contribution
    Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    j.spencer@uea.ac.uk
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7320-144X
  2. Samuel H Forbes

    Department of Psychology, Durham University, Durham, United Kingdom
    Contribution
    Resources, Data curation, Software, Formal analysis, Visualization, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1022-4676
  3. Sophie Naylor

    School of Psychology, University of East Anglia, Norwich, United Kingdom
    Contribution
    Data curation, Formal analysis
    Competing interests
    No competing interests declared
  4. Vinay P Singh

    Community Empowerment Lab, Lucknow, India
    Contribution
    Resources, Data curation, Supervision, Methodology, Project administration
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1421-8775
  5. Kiara Jackson

    School of Psychology, University of East Anglia, Norwich, United Kingdom
    Contribution
    Data curation, Formal analysis
    Competing interests
    No competing interests declared
  6. Sean Deoni

    Department of Pediatrics, Brown University, Providence, United States
    Contribution
    Conceptualization, Data curation, Supervision, Funding acquisition, Methodology, Project administration, Writing – review and editing
    Competing interests
    Sean Deoni has grants or contracts, received consulting fees from and has patents planned, issued or pending with Nestle Nutrition and has received Payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Wyeth Nutrition and Mead Johnson Nutrition. The author has no other competing interests to declare
  7. Madhuri Tiwari

    Community Empowerment Lab, Lucknow, India
    Contribution
    Data curation, Supervision, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  8. Aarti Kumar

    Community Empowerment Lab, Lucknow, India
    Contribution
    Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Methodology, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared

Funding

Bill and Melinda Gates Foundation (OPP1164153)

  • John P Spencer

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We are grateful to the community of Shivgarh, especially the mothers and babies who participated. We thank the 1000 Dreams Team at CEL – Tarawati Chachi, Hariprasad, Matadeen, Ram Kishore, Mukesh, Arjun, Ashok Kumar Pandey, Shivendra, Raj Kishore, Sunil Shukla, Satya Prakash Singh, Vineeta Maurya, Babita Singh, Saroj, Ritu Singh, Pooja Verma, Beenu Pandey, Jitendra Patel, Amit Patel, Dileep Verma, Saurabh Verma, Ankit Bajpayee, Jyoti Singh, Jay Prakash, Shreya Singhal, Dr. Ravi Keshava Lele, Rajesh Kumar, and Abhishek Singh who was the Operations Lead for the study. We also thank Ranjit Kumar for his administrative and logistical support, and Mohammad Amir, Sudhanshu Srivastav, Deepak Sahu, Vishwadeep Mukherjee, and Amit Tandon for their technological support. Our gratitude to Dr. Vishwajeet Kumar and Mr. Rakesh Pratap for their support and guidance. Outdoor air quality data from Lucknow were downloaded from https://www.kaggle.com/rohanrao/air-quality-data-in-india. We thank Dan Pope, Elisa Puzzolo, and Prerna Aneja for useful discussions. Bill & Melinda Gates Foundation grant OPP1164153 (JPS).

Ethics

The study was approved by the Community Empowerment Lab Institutional Ethics Committee (Ref. No: CEL/2018005). Participants' caregivers provided written informed consent; where caregivers were illiterate, a witness gave signed consent accompanied by a thumb impression of the caregiver in place of a signature.

Senior Editor

  1. Chris I Baker, National Institute of Mental Health, United States

Reviewing Editor

  1. Charlotte Cecil, Erasmus MC, Netherlands

Reviewer

  1. Zsuzsa Kaldy, College of Liberal Arts, UMass Boston, United States

Version history

  1. Received: September 30, 2022
  2. Preprint posted: October 17, 2022 (view preprint)
  3. Accepted: March 24, 2023
  4. Version of Record published: April 25, 2023 (version 1)

Copyright

© 2023, Spencer et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 703
    Page views
  • 96
    Downloads
  • 2
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. John P Spencer
  2. Samuel H Forbes
  3. Sophie Naylor
  4. Vinay P Singh
  5. Kiara Jackson
  6. Sean Deoni
  7. Madhuri Tiwari
  8. Aarti Kumar
(2023)
Poor air quality is associated with impaired visual cognition in the first two years of life: A longitudinal investigation
eLife 12:e83876.
https://doi.org/10.7554/eLife.83876

Share this article

https://doi.org/10.7554/eLife.83876

Further reading

    1. Epidemiology and Global Health
    Qixin He, John K Chaillet, Frédéric Labbé
    Research Article

    The establishment and spread of antimalarial drug resistance vary drastically across different biogeographic regions. Though most infections occur in sub-Saharan Africa, resistant strains often emerge in low-transmission regions. Existing models on resistance evolution lack consensus on the relationship between transmission intensity and drug resistance, possibly due to overlooking the feedback between antigenic diversity, host immunity, and selection for resistance. To address this, we developed a novel compartmental model that tracks sensitive and resistant parasite strains, as well as the host dynamics of generalized and antigen-specific immunity. Our results show a negative correlation between parasite prevalence and resistance frequency, regardless of resistance cost or efficacy. Validation using chloroquine-resistant marker data supports this trend. Post discontinuation of drugs, resistance remains high in low-diversity, low-transmission regions, while it steadily decreases in high-diversity, high-transmission regions. Our study underscores the critical role of malaria strain diversity in the biogeographic patterns of resistance evolution.

    1. Epidemiology and Global Health
    Nora Schmit, Hillary M Topazian ... Azra C Ghani
    Research Article

    Large reductions in the global malaria burden have been achieved, but plateauing funding poses a challenge for progressing towards the ultimate goal of malaria eradication. Using previously published mathematical models of Plasmodium falciparum and Plasmodium vivax transmission incorporating insecticide-treated nets (ITNs) as an illustrative intervention, we sought to identify the global funding allocation that maximized impact under defined objectives and across a range of global funding budgets. The optimal strategy for case reduction mirrored an allocation framework that prioritizes funding for high-transmission settings, resulting in total case reductions of 76% and 66% at intermediate budget levels, respectively. Allocation strategies that had the greatest impact on case reductions were associated with lesser near-term impacts on the global population at risk. The optimal funding distribution prioritized high ITN coverage in high-transmission settings endemic for P. falciparum only, while maintaining lower levels in low-transmission settings. However, at high budgets, 62% of funding was targeted to low-transmission settings co-endemic for P. falciparum and P. vivax. These results support current global strategies to prioritize funding to high-burden P. falciparum-endemic settings in sub-Saharan Africa to minimize clinical malaria burden and progress towards elimination, but highlight a trade-off with ‘shrinking the map’ through a focus on near-elimination settings and addressing the burden of P. vivax.