A protocol for estimating health burden posed by early life exposure to ambient fine particulate matter and its heavy metal composition: A mother-child birth (ELitE) cohort from central India

A protocol for estimating health burden posed by early life exposure to ambient fine particulate matter and its heavy metal composition: A mother-child birth (ELitE) cohort from central India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Study protocol A protocol for estimating health burden posed by early life exposure to ambient fine particulate matter and its heavy metal composition: A mother-child birth (ELitE) cohort from central India Tanwi Trushna, Vikas Yadav, Uday Kumar Mandal, Vishal Diwan, Rajnarayan R Tiwari, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3969211/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Pregnant women and children are vulnerable to air pollution-related adverse health effects, especially those residing in low-resource and high-exposure settings like India. However, evidence regarding the effects of early-life exposure to air particulate matter (PM) on childhood growth/developmental trajectory is contradictory; evidence about specific constituents of PM like heavy metals is limited. Similarly, there are few Indian cohorts investigating PM exposure and the incidence of acute respiratory infection during infancy. This study protocol aims to fill these critical gaps in knowledge. Methods We aim to establish a mother-child birth cohort through the enrolment of 1566 pregnant women residing in two urban areas of central India. Antenatally we will collect socioeconomic, demographic, and clinical information, and details of confounding variables from these mothers, who will then be followed up till delivery to assess their exposure to air PM. Biomonitoring to assess heavy metal exposure will be limited to the top five heavy metals found in the air of their residential city. At delivery, pregnancy outcomes will be noted followed by postnatal follow-up of live-born children till the first year of life to assess their achievement of growth/development milestones and exposure to pollutants. We will also estimate the incidence of ARI during infancy. Discussion This manuscript describes the protocol for an Indian mother-child air pollution birth cohort study which aims to generate comprehensive evidence regarding the adverse effects of early-life exposure to air PM and its constituent heavy metals among Indian children. This study will provide an epidemiological basis for further understanding in this context. Finally, by reporting our carefully planned study methods/outcome measures, which are at par with published and ongoing birth cohorts, we aim to serve as the starting point for similar cohorts in the future which when considered together would generate enough evidence to facilitate context-specific policy-making and development of appropriate prevention and mitigation strategies. Acute Respiratory Infection Air Pollution Birth Cohort Child Development Growth Heavy Metals India Particulate Matter Study Protocol Figures Figure 1 Figure 2 Background Air pollution is the biggest worldwide threat to human health and life expectancy with 7 million global deaths being attributable to its exposure. [ 1 , 2 ] The United Nations Environment Programme (UNEP) reported that the vast majority of people worldwide reside in places where the concentration of particulate matter (PM) pollutants in the air exceeds the stringent permissible limits prescribed in the 2021 air quality guidelines of World Health Organization (WHO). [ 3 , 4 ] People living in India and other low- and middle-income countries, particularly pregnant women and children, are at significantly higher health risk. [ 5 – 7 ] Therefore, the WHO– United Nations Children's Fund (UNICEF)–Lancet Commission in its 2020 report entitled “A Future for the World’s Children?” has stressed evidence generation and subsequent interventions to safeguard the health of children, especially in high pollution exposure settings, to expedite the fulfilment of the 48 child-related Sustainable Development Goal (SDG) indicators. [ 8 , 9 ] Air pollution adversely affects pregnancy, foetal growth and development. [ 10 – 12 ] However, evidence regarding the effects of early-life exposure to air particulate matter on childhood growth trajectory is contradictory. Some studies demonstrate an increased relative risk of childhood stunting, wasting, and being underweight[ 13 – 15 ] while others have also reported a higher risk of childhood obesity and raised body mass index (BMI). [ 16 , 17 ] Even among infants, published evidence is contradictory. For example, a Colorado-based prospective cohort study reported higher adiposity among exposed infants at the 5th month follow-up. [ 18 ] Similar results were reported by a Chinese birth cohort. [ 19 ] In contrast, a cohort study from Ghana reported lower length-for-age (stunting), and weight-for-length (wasting) z-scores among exposed infants. [ 20 ] On the other hand, a recent randomized trial conducted in four low- and middle-income countries which substituted biomass burning with clean cooking fuel leading to a reduction in antenatal personal exposure levels reported no difference in the risk of stunting in infants. [ 21 ] Therefore, further research is needed to provide confirmatory evidence. This ambiguity in evidence might be because few studies have investigated the differential effect of chemical constituents of particulate matter, which are a heterogeneous mixture of multiple components with varying toxicity profiles[ 22 ]. Particularly, there is a paucity of longitudinal research investigating the association between early-life exposure to multiple heavy metals and aberrations in childhood growth or development. [ 23 , 24 ] Considering the magnitude of the public health burden posed by air pollution and child growth/developmental abnormalities in low- and middle-income countries such as India,[ 25 ] where annually more than 250 million under-five children fail to attain their optimum developmental potential,[ 26 ] this study protocol aims to generate comprehensive evidence in this context to complement the limited India-specific evidence published so far [ 27 – 29 ]. Another adverse effect of prenatal air pollution exposure is the alteration of immune mechanisms in children,[ 30 – 32 ] which can increase the risk of infectious diseases such as acute respiratory infections (ARIs). Evidence supporting this link is mostly based on studies conducted in high-income countries. [ 33 ] The burden of childhood ARI is huge in India, which is one of the top 15 countries globally in terms of the prevalence of ARI and subsequent childhood mortality with 0.4 million under-five children dying annually from ARI-related diseases. [ 34 ] However, Indian evidence on air pollution-induced childhood ARI is mostly limited to cross-sectional surveys or ecological retrospective data analysis. Further, these studies have used proxy measures such as questionnaire data to elicit either household exposures,[ 35 , 36 ] or ambient air pollution. [ 37 , 38 ] Thus, considering these knowledge gaps, this study protocol also focuses on identifying the association of variation in the incidence of ARI till one year of age among children exposed during early life to different levels of air PM and its heavy metal content measured through extensive personal exposure measurements. Methods Aims and Objectives We aim to establish an urban mother-child birth cohort in central India. The data collected from this cohort will enable us to understand the health burden posed by early life exposure (both pre-and post-natal) to air PM and its heavy metal composition in Indian children and the role of such environmental exposure in the multifactorial aetiology of our chosen study outcomes. The specific objectives being addressed in this research are: To investigate the variation in achievement of growth /developmental milestones during the infancy period of children attributable to different levels of early life exposure to air pollution To assess the incidence of acute respiratory infections during the infancy period of children attributable to different levels of early-life exposure to air pollution To find the association of exposure to selected heavy metals (as represented by blood concentration) among pregnant mothers /children with assessed morbidity outcomes of children. Study Design This is the protocol for a population-based prospective cohort study. During the first phase of this study which will be implemented over three years, we will establish the mother-child birth cohort and conduct regular follow-ups for exposure and clinical assessments of study participants during the antenatal and 1-year postnatal period (see Fig. 1). In the next phase, the established cohort will be followed up annually by securing further funding. Figure 1: Overview of activities to be conducted in the study (Note: Activities under the study are listed on each row and the timing of each activity is indicated by a tick mark in the cells corresponding to the column headings showing the phase of study such as enrolment, gestational follow-up, post-delivery and month-wise follow-up (FU) in the infancy period. Ambient air particulate matter/heavy metal assessment and land-use regression (LUR) modelling will be done over the entire study period and have been shown using arrows. Abbreviations: ANC: Antenatal Care, APGAR: Scoring given to child after birth based on Appearance, Pulse, Grimace, Activity, and Respiration, ARI: Acute Respiratory Infection, HC: Head Circumference, LUR: Land Use Regression, MAC: Mid-Arm Circumference, NICU- Neonatal Intensive Care Unit, PM: Particulate Matter.) Study Setting The cohort population will be enrolled from within the boundaries of urban local bodies (municipal corporations) of two selected cities (Gwalior and Ujjain) of Madhya Pradesh (MP) which is a large province located in central India (see Fig. 2). The process of how we finalised these two cities for the establishment of the cohort has been detailed in Additional File 1 of Supplementary Information. MP is a large province with a total population exceeding 72 million[ 39 ], of which around 25% reside in urban areas. [ 40 ] MP has thirty-two cities with Gwalior and Ujjain ranking as the third and fifth most populous cities in the province, respectively. [ 41 ] Child population accounts for 11.17% and 11.45% of the total population of these two cities. [ 42 , 43 ] Average literacy rate of the total population (Gwalior: 84.14% versus Ujjain: 84.43% both of which are at a higher level than the Indian average of 74.04%) and proportion of slum population (Gwalior: 28.97% versus Ujjain: 23.32% both of which are at a higher level than the Indian average of 5.41%) in these two cities are comparable highlighting that the socioeconomic scenario of these two cities are similar. [ 42 , 43 ] Figure 2: Map showing the location of study cities The image shows clockwise the map of India with the location of the central Indian province of Madhya Pradesh demarcated in green, inset of Madhya Pradesh showing the location of study cities- Gwalior and Ujjain and finally a Google map of the cities themselves. (Note: This image was developed by the research team in the QGIS software (Version 3.28.15). The India map with state and provincial boundaries and point location of Indian District Head Quarters (indicating location of cities) was retrieved from the Survey of India- Administrative Boundary Database available at https://onlinemaps.surveyofindia.gov.in/Digital_Product_Show.aspx ) Study population: Consenting pregnant women (n = 783 from each city), aged ≥ 18 years, with documented gestational age less than or equal to 16th week who have been primarily residing in the study cities for at least the past year (and have no imminent plan to shift residence for more than a continuous period of one month away from their current address during the study period) will be enrolled. We will exclude pregnant women who have a history of using any assisted reproductive technology or who have been diagnosed with a high-risk pregnancy where complications are anticipated. [ 44 ] We will also exclude pregnant women with occupation or lifestyle factors that are reported by previous studies to be sources of exposure to high levels of PM. [ 45 – 47 ] Mothers with pregnancy loss, preterm delivery; and term neonates with birth asphyxia and diagnosed congenital anomalies will be identified using relevant records and dropped from further follow-up. Details of operational definitions to be used for excluding participants at the time of enrolment and during follow-up phases have been described in Table S1 of Additional File 2 of Supplementary Information. Sample size calculation The calculated sample size is 571 mother-child pairs. Our main objective is to investigate the effect of early-life exposure to air pollution on child growth/development. Stunting, a prevalent manifestation of chronic malnutrition, is one of the main indicators for delay in the achievement of growth/developmental milestones in children. [ 48 ] Hence, we have calculated the sample size by assuming a power of 80% and, a two-sided confidence level of 95%, and by using data available from a recent publication [ 46 ] which estimated an odds ratio (OR) of 1.74 for stunting among under-five children with long-term exposure to PM 2.5 . We applied relevant continuity correction and inflation to account for participant exclusions during follow-up, and anticipated non-response / participant attrition. Thus, we will enrol 783 pregnant women in each study city (1566 participants in total) to establish the final cohort of 571 mother-child pairs. Further details of sample size calculation have been enumerated in Additional File 3 of Supplementary Information. We will continue to use the same cohort for our subsequent objectives. Sampling strategy We will obtain information about eligible pregnant women in the study area from community health workers (CHWs), who are usually the first point of contact for antenatal care (ANC) among Indian pregnant women. [ 49 , 50 ] Therefore, before the launch of the study, we will identify and enlist, with the help of relevant district-level offices, all the CHWs working in both study cities under the Integrated Child Development Scheme (ICDS) and National Health Mission (NHM). [ 51 , 52 ] These include Anganwadi workers (AWW) and Accredited Social Health Activists (ASHA), respectively. [ 49 , 50 ] We will approach all CHWs to gain their consent to cooperate and those willing will be briefed about the participant enrolment procedure of the study. We will contact pregnant women identified by CHWs at their home or at the nearest Anganwadi Centre (community maternal and child care centre in India functioning under the ICDS [ 53 ]) to describe our study, confirm their eligibility using a pre-designed checklist, and finally obtain written informed consent. After administration of written informed consent (including consent for follow-up of their child for one year), pregnant women will be enrolled for participation in the study. Data collection Data (exposure) collection from public sources in each city: Neighbourhood concentration of air PM (PM 10 and PM 2.5 ) : Hourly mean values of PM 10 and PM 2.5 will be taken from the Central Pollution Control Board (CPCB) website where open-access data is available of fixed site automatic monitors known as the continuous ambient air quality monitoring stations (CAAQMS) [ 54 – 56 ]. In addition, we will also retrieve the past five years data (2023 − 2019) of daily/annual average air PM recorded by CAAQMS and additional manual gravimetric samplers functioning under the National Air Quality Monitoring Program (NAMP) from CPCB. [ 57 – 59 ] There are only 4 monitoring sites in both Ujjain (one CAAQMS and three additional NAMP samplers) and Gwalior (four CAAQMS and two of these four locations also have NAMP samplers) as per the information listed by CPCB. [ 57 , 60 ] Finally, we will develop an exposure database at a high spatial scale using the Land Use Regression (LUR) model for both cities at a 1 km spatial scale. LUR has been widely used to predict the concentration of air pollutants (measured through distant fixed site monitors) at target locations (i.e., the residential addresses of the selected pregnant women). [ 61 – 63 ] We will develop our LUR model following the methodology validated for urban areas under the ESCAPE project. [ 64 ] LUR modelling strategy: LUR model will be developed by using multiple regression equations based upon the relationship between the measured concentration of PM 2.5 and PM 10 at the fixed monitoring stations and other relevant predictor variables computed using GIS for pre-determined zones of influence around each site. The regression equations will be constructed as follows: $${C}_{i}=a+\sum _{j=1}^{n}X\sum _{k=1}^{n}{\left(b{X}_{i}\right)}_{jk}+\epsilon$$ Where Ci is the predicted concentration of PM at the residential address of study participants “i”; constant “a” equals the regional background concentration; (b) jk weight assigned to variable j for the kth zone; (Xi) jk is the value of variable j calculated for kth zone around residential address “i”; ε is an error which reflects the variation in concentrations not explained by the model. In our LUR model, we will use data for a range of geographic, population and emission source-specific predictor variables retrieved from open access sources/local authorities such as local municipal corporations and/or collected from field-level verification, and satellites. For example, data such as the type of land use (residential, industrial, commercial, forest, agricultural, commercial), road network (road type/length/distance/traffic intensity), population and household density, elevation, and available potential emission source data such as industry count/density/distance will be used, in line with previous studies. [ 61 – 63 ] Satellite-derived aerosol optical depth and meteorological variables from reanalysis data will also be used as predictor variables. We will adopt a supervised forward addition linear regression approach for model development and will perform diagnostics on the fitted models to ensure the linear regression is maintained. Finally, we will assess the performance of the developed model by adopting a 10-fold cross-validation approach. [ 65 ] Exposure attribution to individual subjects : We will assign ambient PM 2.5 and PM 10 exposure to each subject using the LUR-based exposure data and geocodes of each subject’s residence. For estimating adverse birth outcomes, in-utero exposure for the mother will be attributed to the period from the day of conception to the date of delivery. Once the child is born, early-life exposure will be attributed to the child based on the date of birth and the follow-up sequence. Air PM heavy metal concentration : Heavy metal concentration in the ambient air of the study cities will be measured in the filter papers collected from the manual gravimetric samplers (i.e., high volume samplers - HVAS) installed in both cities. Continuous air sampling will be done two days per a week throughout the year (104 annual observations) following CPCB methodology[ 66 ] such that particles of the desired size are collected on pre-weighted filter papers. The concentration of multiple trace metals present in the particulate matter collected on a randomly chosen subset of these filter papers will then be estimated using the energy-dispersive X-ray fluorescence (EDXRF) technique. We will compile a city-wise list of heavy metals with established risk to child growth/development / ARI (identified based on previously published literature) that exceed the permissible limits in the ambient air. We will then conduct biomonitoring of study participants to estimate, in their blood samples, the concentration of five of the most relevant air PM heavy metals identified in each city. The weekly concentration of these five identified heavy metals in each city will also be measured during the study period by analysing ambient air PM collected on HVAS filter papers using the hot-acid digestion method given by the United States Environmental Protection Agency (USEPA). [ 67 ] Data (outcome, covariates, and exposure) collection from study participants: Baseline Phase For pregnant women (Baseline Visit) : In the first visit to households of eligible and consenting pregnant women, the detailed residential address (including contact details and geographic coordinates), and details of the participant and her household members will be collected at the time of enrolment. Briefly, we will collect information on age, education, occupation, lifestyle factors, medical history (including details of current/past pregnancy and prescription drug use), and relevant family history from the pregnant women. The maternal height and weight will be recorded at the baseline visit. Detailed data about previous pregnancies (if any) will be such as parity status, birth intervals between consecutive births, and history of anaemia/malaria/urinary tract infection/Other infections/any other complications in past pregnancy. In addition, the demographic details, socioeconomic characteristics, water, sanitation, hygiene, and environmental factors related to her household will be collected. Finally, maternal blood samples (approximately 5 ml) will be collected for heavy metal analysis following standard protocols used in Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES). Details are mentioned in Additional File 4 of the Supplementary Information. Follow-up Phase: For pregnant women- Antenatal (trimester-wise) follow-up (Follow-up visits 1–3) The enrolled pregnant women will be followed up once in each trimester in the remaining period of gestation to collect information regarding the occurrence of spontaneous abortion/miscarriage and the details of scheduled antenatal check-ups undergone by the pregnant women. We will note their preference for a hospital/healthcare facility where they would opt for their delivery. In addition, we will also measure the 24-hour personal exposure to air PM (PM 10 and PM 2.5 ) of each enrolled pregnant woman. Child follow-up (Follow-up visits 4–17) Early Neonatal (Follow-up visits 4) : According to the information regarding the calculated expected date of delivery obtained during the baseline visit, the pregnant women will be contacted within 1 week of the expected delivery to collect information on pregnancy/birth outcomes (date of delivery to calculate child chronological age, live birth versus stillbirth, gestational age at delivery and congenital anomalies). Study staff after getting information about the delivery will visit the hospital/house of the mother to collect anthropometric data of the baby (preferably within 72 hours of delivery) to supplement the information obtained from hospital/medical records. In cases where the mother/child cannot be contacted within 72 hours of delivery, then we will try to collect relevant data about the child as early as possible before the 1st-month follow-up visit. Monthly follow-up of the child till 1st year of age (Follow-up visits 5–17) : Follow-up of all eligible live-born children will be done at their homes/nearest Anganwadi centre. To ensure timely and smooth data collection, follow-up visits will be scheduled in a manner that these visits coincide with routine house-to-house visits done by CHWs under different ongoing national programs (such as Home-based newborn care (HBNC) and Home-Based Care for Young Child Programme (HBYC) under NHM and “Poshan Abhiyan”,[ 68 ] “Dastak Abhiyan” specific to MP[ 69 ]). Briefly, in each monthly follow-up visit, anthropometry, achievement of developmental milestones, and acute morbidity profile will be recorded. Additional information about at-birth APGAR score, weight, length, head, and mid-arm circumference will be collected in the 5th visit (child age 1 month). Blood samples for heavy metal analysis and the data on child immunization and feeding practices will be collected in the 17th visit (during the 12th month of the child’s age). Data to be collected The list of domains, variables, and timing of measurement of this study are described in subsequent paragraphs and are shown in Fig. 1. Exposure variables : The main exposure/independent variables to be investigated in this study are mean (daily/monthly/annual) concentration of particulate matter (PM 10 and PM 2.5 ) and mean levels of heavy metal in blood samples of participant mothers and children. Study outcomes : The main study outcomes are related to childhood growth and development as shown in Table 1 – 2 . In addition, we will also estimate the incidence rate of ARIs (number of episodes of ARI during infancy / total number of children under follow-up). We modified the Integrated Management of Childhood Illness definition for ARI (given by WHO/UNICEF[ 70 ]) which has been used previously [ 71 ] to create an operational definition of ARI for this study. Modification was done by incorporating the signs and symptoms mentioned in the Integrated Management of Neonatal and Childhood Illness (IMNCI), Ministry of Health & Family Welfare, Government of India. Accordingly, ARI will be defined as cough or difficulty breathing with or without any general danger signs, with or without any chest indrawing, with or without any stridor and with or without any fast breathing. Depending upon the site of inflammation determined by the paediatrician based on clinical signs and symptoms, it will be classified as ARI of the upper respiratory tract and ARI of the lower respiratory tract. Further, depending upon the severity, ARI will be classified as per IMNCI guidelines into “No Pneumonia: cough or cold”, “Pneumonia”, and “Severe Pneumonia or Very Severe Disease”. Based on previous research, we will define each episode of ARI to last for two weeks. [ 72 ] When a child who has had no symptoms for at least one week develops signs and symptoms of ARI, we will treat that as a new episode. [ 72 ] Table 1 Operational Definitions, indicators, and measurement methods to be used for child growth-related outcome measures Outcome Operational Definition Variable/Indicator Measurement method • Small for gestational age (SGA) As per the WHO definition of SGA given by the 1995 WHO expert committee, infants having a birth weight for gestational age below the 10th percentile based on a sex-specific reference population. [ 134 , 135 ] i. Proportion of children diagnosed as SGA at birth (proxy of in-utero growth of the child) ii. Based on hospital records- verified by study staff Low Birth Weight (LBW) • Infants with a birth weight of less than 2500 g, regardless of gestational age at the time of birth. [ 136 , 137 ] The proportion of children diagnosed as LBW at birth (proxy of in-utero growth of the child) Based on hospital records- verified by study staff Overweight/ obese: • Children’s body mass index (BMI - calculated as weight in kilograms divided by height in metres squared) will be plotted on WHO BMI charts and those with a BMI above the 95th percentile will be classified as obese and overweight will indicate those children whose BMI falls between the 85th and 95th percentile. [ 138 , 139 ] The proportion of overweight/obese (based on BMI for age) infants at the end of the first year of life. Monthly measurement of height/length, weight and calculation of BMI Underweight, wasted and stunted • Z-scores for growth indicators (length/height-for-age, weight-for-age, weight-for-length/height, BMI-for-age) will be estimated by comparing each child’s height/length and weight with WHO growth standards as shown in the image below (which has been adapted from the WHO child growth standards: training course on child growth assessment)[ 140 ] Based on where their calculated z-scores lie with respect to the median value of the standard reference population, children will be categorized as: ¬ -Underweight: ‘weight for age’ below minus two standard deviations (-2SD) ¬ -Severely underweight: ‘weight for age’ below minus three standard deviations (− 3SD) -Stunted: ‘height/length for age’ below minus two standard deviations (-2SD) -Severely Stunted: height/length for age’ below minus three standard deviations (-3SD) -Wasted: ‘weight for height/length’ below minus two standard deviations (-2SD) iii. Proportion of children with abnormal Z-scores for afore-mentioned growth indicators (i.e., underweight, severely underweight, stunted, severely stunted, wasted and severely wasted) at the end of the first year of life. Z-scores for growth indicators (length/height-for-age, weight-for-age, weight-for-length/height, BMI-for-age) obtained from comparison of each child’s height/length and weight with WHO growth standards Table 2 Operational Definitions, indicators, and measurement methods to be used for child development-related outcome measures Outcome Operational Definition Variable/Indicator Measurement method Development progress The developmental level of the child will be assigned a score ASQ-3.[ 141 , 142 ] ASQ-3 has been widely used in many countries as a field-based parent-completed screening tool to assess five developmental domains. [ 143 ] It has also been previously used and validated in the Indian population[ 144 , 145 ] i. Domain-specific and mean score of ASQ-3 obtained monthly Ages and stages questionnaires- third edition (ASQ-3) questionnaire [ 141 , 142 ] Developmental Quotient (DQ) is calculated by dividing Developmental Age by Chronological Age and multiplying it by 100. [ 146 , 147 ] Developmental Age (DA): Each child will be assigned a DA at each follow-up visit for each of the 5 developmental domains i.e., gross motor, fine motor, adaptive/cognitive, language and personal-social developmental domains. Study staff under the supervision of a paediatrician will estimate DA according to the achievement of domain-specific developmental milestones. Chronological Age (CA): the actual age of the child calculated from his/her date of birth ii. - Proportion of children with delayed achievement of development milestones at the end of the first year of life. iii. - Domain-specific monthly developmental level of each infant obtained through Development quotient calculationiv. Questionnaire developed by authors and pilot-tested in an ongoing built environmental child cohort [ 148 ] Table 1 : Operational Definitions, indicators, and measurement methods to be used for child growth-related outcome measures Table 2 : Operational Definitions, indicators, and measurement methods to be used for child development-related outcome measures Confounding variables/effect modifiers : We will collect information for a wide range of covariates identified from a detailed literature review. We will collect information using questionnaires and case report forms developed for this study. Wherever possible, relevant questions will be adapted from pre-existing validated questionnaires and to minimize attrition over time, we will ensure that an optimal number of questions will be asked without missing out on important information. Child-specific details will be retrieved from discussions with care providers and by consulting available hospital records. Briefly, we will record information such as age, gender, birth order, status of immunization (i.e., as per the National Immunization Schedule (NIS) of the Universal Immunization Programme (UIP) in India [ 73 ]), history of anaemia and other acute morbidities (defined as per the Integrated Disease Surveillance Project (IDSP) of the Government of India[ 74 ]), relevant medical history including at-birth details of APGAR score, length, weight, head circumference, and mid-arm circumference, neonatal intensive care unit (NICU) admission, co-morbidities and family history. We will collect family-specific information in the form of a household roster detailing the age, education, occupation, relevant medical history, and tobacco/alcohol consumption (using standard questions provided by the WHO STEPwise instrument[ 75 ]) of family members. Then the household socioeconomic status (SES) will be categorised based on scores assigned using the 2020 revision of the Kuppuswamy Scale. [ 76 ] Household water sanitation and hygiene assessment will be done using a pre-tested structured questionnaire which the study team has developed by adapting questions from the “Core questions on drinking-water and sanitation for household surveys” given by WHO/UNICEF.[ 77 ] In addition to the data collected from both parents at baseline, current pregnancy-related information will also be collected from the mother. After delivery, during monthly follow-up of the child, the mother will be asked regarding breastfeeding and complementary feeding (after the child attains 6 months of age) practices. For data collection on complementary feeding, we will use a questionnaire previously used in Indian settings. [ 78 ] Finally, information about other environmental factors such as the built environment and other indoor exposures will be collected. An exposure questionnaire will be used that has been designed by adapting relevant questions from available validated questionnaires. For example, questions about the built environmental factors such as ventilation, building material, area and construction of house and fuels used for cooking or heating were adapted from the 'household questionnaire' of the National Family Health Survey, India 2019-20 (NFHS – 5) and the ‘house-listing and housing census schedule’ of census 2011. [ 79 , 80 ] This allows for easy comparison of the questionnaire findings with national data. Data Management and Analysis Quality Control We will formulate a Data Collection and Management Committee (DCMC) consisting of the principal investigator and other senior investigators of the team. This committee will ensure that during the implementation of this research, the national guidelines for biomedical research data collection and management[ 81 ] are followed stringently to maintain the quality of data collection and analysis. For example, equipment used in the study will be routinely calibrated, and laboratory testing will be conducted by using high-quality reagents. Research staff will be adequately trained before the start of project work. We will validate the study questionnaires which have been developed by adapting questions from standardized tools and these will be pilot-tested before use. DCMC will also handle requisite communication with the ethics committee and other ethical considerations including data dissemination. During the drafting of this manuscript, details have been reported as per the STROBE checklist modified to suit requirements specific to the protocol of cohort studies (see Additional File 5 of Supplementary Information). Data management The DCMC will ensure proper management of data. Filled questionnaires will be stored in secure locked cabinets while online or computer-based information will be kept safe by using passwords accessible only to the research team. All data will be assigned unique identifiers and linkages between exposure, outcome and confounders data will be done using these code numbers. Data entry done by research staff will be supervised by the DCMC who will at random verify entered data to check for data entry mistakes. Missing data will be managed by multiple imputation. Statistical analysis and reporting The data entered into Microsoft Excel workbooks will be exported to SPSS statistics software (Version 25) and the latest versions of R packages/R studio for analysis. Data will be summarised using appropriate descriptive statistics like mean/standard deviation (SD) or median/Interquartile range. For categorical variables, summary estimates will be reported using frequency with percentages. Initial analysis will be done by considering participants as per their recruitment city i.e., comparison of outcome variables in participants recruited from cities with high versus low pollution levels. For this, Relative risk (RR) and attributable risk (AR) will be calculated through the relevant equations using the probability of outcome (incidence/proportion) among the high pollution group (exposed) divided by the probability of outcome among the low air pollution group (relatively unexposed). The next level of analysis will be based on the range of PM 2.5 /PM 10 exposure concentrations and blood heavy metal levels found in the study participants (i.e., pregnant mothers/live-born children). Using this range, we will divide the participants into quartiles based on their exposure levels and the risk of outcome variable in different quartiles will be estimated. To assess possible correlations between environmental variables- PM 2.5 /PM 10 /blood heavy metal levels and the health outcomes – growth indicator related z scores/development quotient/ARI episodes, we will use a matrix of Pearson correction coefficients. Finally, multivariable logistic regression model analyses will be used to study the relationship between environmental variables- PM 2.5 /PM 10 /blood heavy metal levels and the health outcomes in binary forms – growth/development/ARI, in detail. Briefly, multiple linear Mixed-Effects regression equations will be used to study the relationship between the levels of early-life exposure (concentration of environmental pollutants- PM 2.5 /PM 10 concentration/ heavy metals in pregnant mother and the child participant- monthly means for the former and cumulative mean- i.e., in-utero maternal exposure summed with infancy period exposure to heavy metal) and child health outcomes- mean anthropometric data (height, weight, head, and mid-arm circumference) and developmental level (obtained from developmental quotient / mean score on ASQ-3 scale) of each participant (live-born children) while adjusting for confounders. Similar regression equations will be used to analyse the association between exposure and delay in the achievement of growth and developmental milestones. Then, the PM 2.5 and PM 10 data and other environmental/sociodemographic data will be used in a logistic regression model of ARI symptoms within a distributed lag nonlinear modelling framework (DLNM) [ 82 , 83 ] to test lag associations of PM 2.5 /PM 10 exposure with binary (presence/absence) of ARI symptoms and growth z-scores in the preceding month as has been used in previous literature [ 84 – 86 ]. Analyses will be performed separately for each child's morbidity outcomes like growth/developmental parameters/domain. All analyses will adjust for different confounding variables included in the study. Details of such confounders for statistical adjustment will include gender, family size, socioeconomic status, gynaecological/obstetrics/other relevant medical history, and data from other environmental assessments of the households (built environmental attributes). We will also conduct a subgroup analysis to adjust the effect of other pre-existing childhood morbidities (such as low birth weight, and small-for-gestational age) on the studied association. Ethical considerations This study was reviewed and approved by the Institutional Ethics Committee (Human), National Institute for Research in Environmental Health (vide approval letter number: ICMR-NIREH/BPL/IEC/2023-24/1307 dated 16/02/2024). At the beginning of research work, we will obtain written informed consent from the pregnant women both for their and their child’s participation allowing both the collection of data collection and its scientific dissemination. Mother/children found morbid during the survey will be referred to a government hospital located near the study setting. We will inform the study participants and their families about the final findings of the study through meetings organised in the community with the help of the CHWs. We will also disseminate the findings scientifically through publication of peer-reviewed articles. Discussion The ELitE birth cohort study will be an important addition to the currently sparse evidence base about the long-term effects of early-life exposure to air pollutants among Indian children. Owing to physiological pulmonary adaptation during pregnancy, women inhale a higher volume of air and the pollutants in it per respiratory cycle. [ 87 ] Adverse health effects in the mother coupled with the now acknowledged potential of PM to cross the placenta cause placental malfunctioning as well as other direct and indirect environmental insults to the susceptible developing foetus. [ 88 – 92 ] Postnatally, multiple factors such as higher baseline ventilation rates, predominance of mouth-breathing bypassing nasal clearance of particles, and immature immune/pulmonary systems contribute to both higher risk of PM exposure and increased inhaled dose post similar level of exposure in children who have different deposition and clearance probabilities than adults. [ 93 ] Therefore, pregnant women and children, especially those residing in low-resource and high-exposure settings like India, are vulnerable to air pollution-related adverse health effects and the prospective collection of data longitudinally documenting such effects is essential. The adverse effects of PM exposure on child growth/developmental trajectory have been shown by epidemiological studies albeit with contradictory findings. [ 13 – 17 ] But the biological mechanisms mediating these effects are not well explored leading to perpetuating ambiguity. [ 94 ] Recently, Sinharoy et al., 2020 attempted to elucidate the pathophysiological framework underlying the exposure-response relationship between air pollution and stunting. [ 95 ] Authors posited that antenatal exposure via oxidative stress and inflammation leads to mitochondrial dysfunction, decreased DNA methylation and shortened telomere length; all of which contribute to placental dysfunction and poor foetal growth. [ 95 ] Further, authors argue that adverse effects on developing immune and pulmonary systems affecting the child’s susceptibility to infections compounded by alterations in appetite versus bodily requirements, and dietary metabolite absorptions vis-à-vis loss, particularly that of vitamin D, can contribute to post-natal growth failure leading to stunting. [ 95 ] Similar mechanisms have also been reported previously. [ 96 , 97 ] Growth failures due to chronic malnutrition during early childhood can independently translate into developmental delay. [ 98 , 99 ] On the other end of this spectrum, research has found links between PM exposure and adiposity. The association between PM and obesity is well established among older children and adults as shown by a recent review. [ 100 ] It is said to be mediated via many mechanisms ranging from oxidative stress/inflammation, and epigenetic changes to perturbations in metabolic and intestinal flora balance. [ 100 ] However, the common postulation explaining this dichotomy in the PM effect on growth trajectory reasoned that children with early-life growth restriction are at higher risk of metabolic diseases including obesity at a later phase in life as per the developmental origins of health and disease. [ 101 , 102 ] However, recent evidence challenges this mode of reasoning since obesity and higher adiposity have been noted among exposed infants. [ 18 , 19 ] Another line of thinking has evolved which contemplates that the phenotype of foetal growth restriction in an antenatally exposed mother could be immediately followed by rapid weight gain in the infancy period. [ 103 ] However, the overall paucity of longitudinal evidence precludes the drawing of any conclusions. The study presented here will help fill in the existing gap in knowledge owing to the scarcity of longitudinal evidence by establishing a birth cohort consisting of 571 mother-child pairs from two urban areas of central India. Although findings are not expected to be generalizable on a global scale, the prospective follow-up data collected in this cohort study will provide strong evidence for planning and policymaking in this important field. Methodological considerations This study has multiple advantages owing to its multi-disciplinary research team which aids comprehensive information collection and would enhance the validity of the findings of the study. Such a strength would be crucial since we are dealing with child growth and development which is a complex multidimensional concept. [ 104 , 105 ] Children are vulnerable to the effects of the complex interplay between a multitude of factors ranging from environmental, and psychosocial, to genomic insults. [ 106 ] These factors are hypothesized to begin their effect even before conception. [ 107 ] Therefore, researchers are increasingly acknowledging the concept of the “exposome”, first introduced by Dr Wild in 2005, as the entirety of all non-genomic influences from conception to death owing to the internal effects from the human bodily environment and external influences encompassing harmful exposures to chemical pollutants/infectious pathogens, health behavioural factors linked to tobacco/alcohol consumption, physical activity or diet and even societal, economic, psychological and spiritual factors. [ 108 , 109 ] However, accounting for all potential exposure and confounder variables is a humongous task demanding high-end infrastructure, cost-intensive analytic methods and high-dimensional data computational facility, all of which are limited in low-resource settings. [ 110 ] Therefore, although we have not been able to incorporate multiple exposures such as residential greenness, high-decibel community noise, water/food pollutants such as pesticides, endocrine disrupting chemicals, etc. with known effects on child growth/developmental trajectory and morbidity profile,[ 111 – 117 ] and “omic” technologies; we have planned for this longitudinal transgenerational study with follow-up at regular intervals and biological sampling to initiate the investigation in this field in India. Gradually as evidence from similar cohorts starts accruing, robust data analysis and strengthening of the evidence base can be done in the future by merging collected exposure/health outcome data with advanced analysis of stored biological samples as has been done by the EXPOsOMICS research group. [ 118 ] A limitation of this exposure-response cohort is the potential of missing personal exposure information in the first trimester. Considering operational feasibility 16th week was chosen as the cut-off for pregnant women's enrolment and the start of exposure assessment in the antenatal period. Previous research has shown that most women in India report their first antenatal care visit within a median time of 16 weeks of gestation and only 19.6% of pregnancies are detected and registered within the first trimester. [ 119 , 120 ] Hence, it is likely that we will be enrolling most of our pregnant participants in the gestational period of the 12th -16th week and our ground-data-based exposure assessment will fail to account for the complete picture of first-trimester exposure. This can be a significant limitation since it is plausible that the sensitive window for exposure in this context can be during the first trimester. Data analysed from the INMA Spanish birth cohort showed that the z-score for length at the 6th month of the child’s age reduced by 6% for every 10-µg/m 3 increase in antenatal NO 2 exposure during the first trimester. [ 121 ] Block and Calderón-Garcidueñas 2009 also stated that first-trimester exposure to organic constituents of PM such as polycyclic aromatic hydrocarbons had a maximal effect on foetal growth when compared with later exposures. [ 122 ] This limitation affects birth cohorts in low- and middle-income countries in Asia and Africa. For example, a rural Ghanaian pregnancy cohort investigating air pollution and childhood growth trajectory enrolled pregnant women at or before the 24th week of gestation. [ 123 ] Authors would have been facing similar constraints as us since the median time to first visit for antenatal care among African women has been reported to be 5 months[ 124 ]. We have attempted to overcome this limitation by incorporating past exposure data obtained from CPCB ground monitoring stations and remote-sensing-based estimates of PM 2.5 into the constructed LUR models. Previous studies have used this method. For example, the recently published Californian population-based prospective pregnancy cohort study (MADRES) enrolled pregnant women with a gestational age of less than 30 weeks and obtained air pollution exposure information using data from the United States Environmental Protection Agency Air Quality System to model daily participant exposure beginning from 2 years before pregnancy. [ 125 ] But the use of only LUR modelling-based exposure assessment suffers from ecological fallacy. [ 126 ] Final estimation is highly dependent upon the density of monitoring stations from which PM data can be used to train the models and the granularity of data for predictor variables and thus, cannot accurately differentiate between individual-specific exposure, particularly when their residential addresses are clustered close together. [ 127 , 128 ] Hence, data triangulation by adding personally measured and biomonitoring data would lead to higher confidence in the overall exposure assessment. [ 129 ] We have thus adopted a multi-pronged strategy to estimate accurate exposure information of the study participants. Finally, to ensure the highest level of internal validity of our study, we will attempt to overcome the biases inherent in adopting a cohort study design. [ 130 , 131 ] For example, to control for attrition bias, loss-to-follow-up will be minimised through better rapport building with the study participants with the support of CHWs. Published evidence acknowledges the crucial role played by CHWs who are usually residents of the locality where they work and hence, have an established rapport with their beneficiaries. [ 132 , 133 ] By seeking the active cooperation of CHWs, through appropriate incentivisation, we will be leveraging this trust built with the community and hence, we expect attrition to be minimal in the study. In addition, we will also systematically document the characteristics (including the reasons for withdrawal) of those participants who decline further follow-up. Although not completely amenable to control, other selection biases like non-response bias and healthy entrant bias will be accounted for by asking for and recording relevant information about those pregnant women who refuse to participate in the study when approached for consent. [ 130 , 131 ] Similarly, information and confounding bias will be minimised by ensuring the use of validated pilot-tested questionnaires and appropriate collection of data on exposure, outcome and confounding variables by trained research staff. [ 130 , 131 ] Conclusion This manuscript described the methodology for the establishment of an Indian mother-child air pollution birth cohort and its subsequent follow-up. This study aims to fill the gaps in published evidence regarding the adverse effects of early-life exposure to air PM and its constituent heavy metals among Indian children. Despite the inherent drawbacks of resource limitation precluding comprehensive exposure assessment and the low scope of generalizability of our findings to all population sub-groups residing in the Indian sub-continent and other low- and middle-income countries, this study with its strengths will provide an epidemiological basis to further understanding in the context. Finally, by reporting our carefully planned study methods/outcome measures, which are at par with published and ongoing birth cohorts, we aim to serve as the starting point for similar cohorts in the future which when considered together would generate enough evidence to facilitate context-specific policy-making and development of appropriate prevention and mitigation strategies. Abbreviations ANC Antenatal Care APGAR Scoring given to child after birth based on Appearance, Pulse, Grimace, Activity, and Respiration AR Attributable Risk ARI Acute Respiratory Infection ASHA Accredited Social Health Activists ASQ-3 Ages and Stages Questionnaire- Third Edition AWW Anganwadi Workers AQI Air Quality Index BMI Body Mass Index, CAAQMS:Continuous Ambient Air Quality Monitoring Stations CA Chronological Age CHW Community Health Workers CPCB Central Pollution Control Board DA Developmental Age DCMC Data Collection and Management Committee DNA Deoxyribonucleic Acid DQ Developmental Quotient DLNM Distributed Lag Non-Linear Model EDD Expected Date Of Delivery EDXRF Energy-Dispersive X-Ray Fluorescence GIS Geographic Information System HBNC Home-Based Newborn Care HBYC Home-Based Care for Young Child Programme HVAS High Volume Air Sampler ICD International Classification Of Disease ICDS Integrated Child Development Scheme ICP-OES Inductively Coupled Plasma Optical Emission Spectroscopy IDSP Integrated Disease Surveillance Project IMNCI Integrated Management of Neonatal and Childhood Illness LBW Low Birth Weight LMP Last Menstrual Period LUR Land Use Regression MP Madhya Pradesh NAAQS National Ambient Air Quality Standards NFHS National Family Health Survey NHM National Health Mission NICU Neonatal Intensive Care Unit NIS National Immunization Schedule NO 2 Nitrogen Dioxide OR Odds Ratio PM Particulate Matter PM 10 Particulate Matter With Aerodynamic Diameter < 10µm PM 2.5 Particulate Matter With Aerodynamic Diameter < 2.5µm RR Relative Risk SD Standard Deviation SDG Sustainable Development Goal SES Socio-Economic Status SGA Short For Gestational Age SO 2 Sulphur Dioxide UIP Universal Immunization Programme UNICEF United Nations Children's Fund UNEP United Nations Environment Programme USEPA United States Environmental Protection Agency WHO World Health Organisation. Measurement Units: m: metre; ml: millilitre (1ml= 10 -3 litre); µm: micrometre (1 µm = 10 -6 m); kg: kilogram; g: gram(1g=10 -3 kilogram); µg: microgram (1 µg = 10 -6 g) Declarations Availability of data and materials No data has been generated yet as data collection and participant recruitment have not commenced. However, data that is expected to be generated in this study can be provided in the future, upon request, by the corresponding author (Dr Yogesh Damodar Sabde, via [email protected] ). Acknowledgements We would like to acknowledge the then members of the scientific advisory committee of ICMR-NIREH, Bhopal and other anonymous reviewers who were involved in the detailed critique of this study during the multi-stage review and approval process undertaken by the funding agency. Their suggestions and comments helped us further refine the methodology. Funding The study, on which this protocol is based, was funded by the Indian Council of Medical Research (Grant No. F.No.5/7/12/IM/2023-RCN Dated 25/01/2024; PI: Dr Yogesh Damodar Sabde). The funding agency has not played any role in the conceptualisation of the study or the drafting of this manuscript nor will it have any role in the collection, analysis, and interpretation of data generated in this study. Author information Authors and Affiliations Department of Environmental Health and Epidemiology, ICMR-National Institute for Research in Environmental Health, Bhopal, Madhya Pradesh, India Tanwi Trushna, Vikas Yadav, Uday Kumar Mandal, Rajnarayan R Tiwari, Yogesh Damodar Sabde Department of Environmental Monitoring and Exposure Assessment (Water and Soil), National Institute for Research in Environmental Health, Bhopal, Madhya Pradesh, India Vishal Diwan Department of Environmental Biochemistry, ICMR-National Institute for Research in Environmental Health, Bhopal, Madhya Pradesh, India Rajesh Ahirwar Department of Biostatistics and Bioinformatics, ICMR-National Institute for Research in Environmental Health, Bhopal, Madhya Pradesh, India Dharma Raj, Sindhuprava Rana Department of Child Health Research, National Institute For Research In Reproductive and Child Health (ICMR-NIRRCH), Mumbai, Maharashtra, India Suchitra Vishwambhar Surve Centre for Atmospheric Sciences, Indian Institutes of Technology (IIT Delhi), New Delhi, Delhi, India Sagnik Dey Centre of Excellence for Research on Clean Air, Indian Institutes of Technology (IIT Delhi), New Delhi, India Sagnik Dey Contributions Y.D.S is the principal investigator of the study and obtained the necessary funds. Together with T.T., Y.D.S. conceptualised the study. T.T. drafted the manuscript of the study protocol which was substantively edited and proofread by Y.D.S., V.D., V.Y., U.K.M., R.A., D.R., S.P.R., S.V.S., S.D. and R.R.T. Y.D.S. and T.T. prepared figures 1-2 while V.D., V.Y., U.K.M. and S.V.S. aided in creating the tables 1-2 and S1. S.D. and R.A. contributed to drafting the methodology for exposure assessment including LUR modelling and laboratory testing for heavy metals. Y.D.S., T.T., V.D., V.Y., U.K.M., and S.V.S. discussed and planned the fieldwork and the questionnaires for data collection from human participants. These components have been incorporated in the methods section of the manuscript and in the supplementary information. All authors have substantively commented and helped in the finalisation of the study protocol. All authors have approved the submitted version of this manuscript and agree to personal accountability for the same. Corresponding author Correspondence to Yogesh Damodar Sabde (email: [email protected] ). Ethics declarations Ethics approval and consent to participate Ethics approval has been obtained for this study from the Institutional (Human) Ethics Committee, National Institute for Research in Environmental Health (Approval Letter No: ICMR-NIREH/BPL/IEC/2023-24/1307 dated 16/02/2024). Written informed consent will be obtained from all participants. For children (infants) enrolled in this study, written informed consent will be sought from the legal guardian(s). Consent for publication Not applicable as the manuscript is the protocol of a study and no personally identifiable information about the participants has been collected yet. Competing interests The authors declare that they have no competing interests. References World Health Organization(WHO). Ambient (outdoor) air pollution. 2018. https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health. 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Understanding basic concepts of developmental diagnosis in children. Transl Pediatr. 2020;9 Suppl 1:S9–22. Gupta P, Menon PSN, Ramji S, Lodha R. PG textbook of pediatrics. First edition. New Delhi: Jaypee Brothers Medical Publishers (P), Ltd; 2015. Sabde YD, Trushna T, Mandal UK, Yadav V, Sarma DK, Aher SB, et al. Evaluation of health impacts of the improved housing conditions on under-five children in the socioeconomically underprivileged families in central India: A 1-year follow-up study protocol. Front Public Health. 2022;10. Additional Declarations No competing interests reported. Supplementary Files AdditionalFile1.docx Additional file 1 File format: .doc Title of data: Selection of cities for establishment of cohort Description of data: This file provides a detailed description of how we selected the two cities for the establishment of this cohort. AdditionalFile2.docx Additional file 2 File format: .doc Title of data: Supplementary Table S1: Exclusion criteria Description of data: This table shows the details of operational definitions to be used for excluding participants at the time of enrolment and during follow-up phases. AdditionalFile3.docx Additional file 3 File format: .doc Title of data: Sample Size Calculation Description of data: This file contains the complete description of how the sample size for this study was calculated. AdditionalFile4.docx Additional file 4 File format: .doc Title of data: Laboratory Tests to be done in the study Description of data: This file contains the detailed description of the protocol to be followed during heavy metal analysis in samples. AdditionalFile5.docx Additional file 5 File format: .doc Title of data: STROBE Checklist Description of data: In this file we have reported the line and page numbers which contain the relevant sections of the STROBE checklist that we have modified to suit requirements specific to the protocol of cohort studies. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3969211","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Study protocol","associatedPublications":[],"authors":[{"id":273658869,"identity":"91db2baf-c75d-4e93-aea1-00eacc341d63","order_by":0,"name":"Tanwi Trushna","email":"","orcid":"","institution":"Department of Environmental Health and Epidemiology, ICMR-National Institute for Research in Environmental Health, Bhopal, Madhya Pradesh, 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gestational follow-up, post-delivery and month-wise follow-up (FU) in the infancy period. Ambient air particulate matter/heavy metal assessment and land-use regression (LUR) modelling will be done over the entire study period and have been shown using arrows. Abbreviations: ANC: Antenatal Care, APGAR: Scoring given to child after birth based on Appearance, Pulse, Grimace, Activity, and Respiration, ARI: Acute Respiratory Infection, HC: Head Circumference, LUR: Land Use Regression, MAC: Mid-Arm Circumference, NICU- Neonatal Intensive Care Unit, PM: Particulate Matter.)\u003c/p\u003e","description":"","filename":"Figure1StudySchedule.png","url":"https://assets-eu.researchsquare.com/files/rs-3969211/v1/e170716d7acbb6720991740c.png"},{"id":51446001,"identity":"0b363590-9cd8-43ba-ad2c-80648927428e","added_by":"auto","created_at":"2024-02-21 18:13:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":638637,"visible":true,"origin":"","legend":"\u003cp\u003eMap showing the location of study cities\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe image shows clockwise the map of India with the location of the central Indian province of Madhya Pradesh demarcated in green, inset of Madhya Pradesh showing the location of study cities- Gwalior and Ujjain and finally a Google map of the cities themselves.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(Note: This image was developed by the research team in the QGIS software (Version 3.28.15). The India map with state and provincial boundaries and point location of Indian District Head Quarters (indicating location of cities) was retrieved from the Survey of India- Administrative Boundary Database available at\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ehttps://onlinemaps.surveyofindia.gov.in/Digital_Product_Show.aspx )\u003c/p\u003e","description":"","filename":"Figure2studyarea.png","url":"https://assets-eu.researchsquare.com/files/rs-3969211/v1/90536eee60c6b61239916b7f.png"},{"id":55753200,"identity":"c43296a7-046b-4442-bbc5-552e576bf5a7","added_by":"auto","created_at":"2024-05-02 16:20:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1248053,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3969211/v1/aa751916-8331-4657-8631-a35d33040a2f.pdf"},{"id":51446002,"identity":"462582be-7358-4273-af92-2cb3c3699abe","added_by":"auto","created_at":"2024-02-21 18:13:09","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23839,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eAdditional file 1\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eFile format: .doc\u003c/p\u003e\n\u003cp\u003eTitle of data: Selection of cities for establishment of cohort\u003c/p\u003e\n\u003cp\u003eDescription of data: This file provides a detailed description of how we selected the two cities for the establishment of this cohort.\u003c/p\u003e","description":"","filename":"AdditionalFile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3969211/v1/ce72c2caaf1669bc64e74e2b.docx"},{"id":51446003,"identity":"8b7343b7-8ec6-420a-9c5f-d070ae65ead2","added_by":"auto","created_at":"2024-02-21 18:13:09","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":41076,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eAdditional file 2\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eFile format: .doc\u003c/p\u003e\n\u003cp\u003eTitle of data: Supplementary Table S1: Exclusion criteria\u003c/p\u003e\n\u003cp\u003eDescription of data: This table shows the details of operational definitions to be used for excluding participants at the time of enrolment and during follow-up phases.\u003c/p\u003e","description":"","filename":"AdditionalFile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-3969211/v1/06c993902419aa3f0995f1bd.docx"},{"id":51446004,"identity":"846a7508-95b3-471b-be54-af593565aa31","added_by":"auto","created_at":"2024-02-21 18:13:09","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":29426,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eAdditional file 3\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eFile format: .doc\u003c/p\u003e\n\u003cp\u003eTitle of data: Sample Size Calculation\u003c/p\u003e\n\u003cp\u003eDescription of data: This file contains the complete description of how the sample size for this study was calculated.\u003c/p\u003e","description":"","filename":"AdditionalFile3.docx","url":"https://assets-eu.researchsquare.com/files/rs-3969211/v1/61f1bdb38dc93be7109b5997.docx"},{"id":51446005,"identity":"fbb3f0a8-6892-4e33-95cc-f3295b9acd94","added_by":"auto","created_at":"2024-02-21 18:13:10","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":18449,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eAdditional file 4\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eFile format: .doc\u003c/p\u003e\n\u003cp\u003eTitle of data: Laboratory Tests to be done in the study\u003c/p\u003e\n\u003cp\u003eDescription of data: This file contains the detailed description of the protocol to be followed during heavy metal analysis in samples.\u003c/p\u003e","description":"","filename":"AdditionalFile4.docx","url":"https://assets-eu.researchsquare.com/files/rs-3969211/v1/3cb6ad15061fc1959e5f1f57.docx"},{"id":51446006,"identity":"93aa6c38-43bd-4594-87b3-523ae53c3cdd","added_by":"auto","created_at":"2024-02-21 18:13:10","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":24042,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eAdditional file 5\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eFile format: .doc\u003c/p\u003e\n\u003cp\u003eTitle of data: STROBE Checklist\u003c/p\u003e\n\u003cp\u003eDescription of data: In this file we have reported the line and page numbers which contain the relevant sections of the STROBE checklist that we have modified to suit requirements specific to the protocol of cohort studies.\u003c/p\u003e","description":"","filename":"AdditionalFile5.docx","url":"https://assets-eu.researchsquare.com/files/rs-3969211/v1/f120fab22183bcea0748cc4c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A protocol for estimating health burden posed by early life exposure to ambient fine particulate matter and its heavy metal composition: A mother-child birth (ELitE) cohort from central India","fulltext":[{"header":"Background","content":"\u003cp\u003eAir pollution is the biggest worldwide threat to human health and life expectancy with 7\u0026nbsp;million global deaths being attributable to its exposure. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] The United Nations Environment Programme (UNEP) reported that the vast majority of people worldwide reside in places where the concentration of particulate matter (PM) pollutants in the air exceeds the stringent permissible limits prescribed in the 2021 air quality guidelines of World Health Organization (WHO). [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] People living in India and other low- and middle-income countries, particularly pregnant women and children, are at significantly higher health risk. [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] Therefore, the WHO\u0026ndash; United Nations Children's Fund (UNICEF)\u0026ndash;Lancet Commission in its 2020 report entitled \u0026ldquo;A Future for the World\u0026rsquo;s Children?\u0026rdquo; has stressed evidence generation and subsequent interventions to safeguard the health of children, especially in high pollution exposure settings, to expedite the fulfilment of the 48 child-related Sustainable Development Goal (SDG) indicators. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAir pollution adversely affects pregnancy, foetal growth and development. [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] However, evidence regarding the effects of early-life exposure to air particulate matter on childhood growth trajectory is contradictory. Some studies demonstrate an increased relative risk of childhood stunting, wasting, and being underweight[\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] while others have also reported a higher risk of childhood obesity and raised body mass index (BMI). [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] Even among infants, published evidence is contradictory. For example, a Colorado-based prospective cohort study reported higher adiposity among exposed infants at the 5th month follow-up. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] Similar results were reported by a Chinese birth cohort. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] In contrast, a cohort study from Ghana reported lower length-for-age (stunting), and weight-for-length (wasting) z-scores among exposed infants. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] On the other hand, a recent randomized trial conducted in four low- and middle-income countries which substituted biomass burning with clean cooking fuel leading to a reduction in antenatal personal exposure levels reported no difference in the risk of stunting in infants. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] Therefore, further research is needed to provide confirmatory evidence. This ambiguity in evidence might be because few studies have investigated the differential effect of chemical constituents of particulate matter, which are a heterogeneous mixture of multiple components with varying toxicity profiles[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Particularly, there is a paucity of longitudinal research investigating the association between early-life exposure to multiple heavy metals and aberrations in childhood growth or development. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] Considering the magnitude of the public health burden posed by air pollution and child growth/developmental abnormalities in low- and middle-income countries such as India,[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] where annually more than 250\u0026nbsp;million under-five children fail to attain their optimum developmental potential,[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] this study protocol aims to generate comprehensive evidence in this context to complement the limited India-specific evidence published so far [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnother adverse effect of prenatal air pollution exposure is the alteration of immune mechanisms in children,[\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] which can increase the risk of infectious diseases such as acute respiratory infections (ARIs). Evidence supporting this link is mostly based on studies conducted in high-income countries. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] The burden of childhood ARI is huge in India, which is one of the top 15 countries globally in terms of the prevalence of ARI and subsequent childhood mortality with 0.4\u0026nbsp;million under-five children dying annually from ARI-related diseases. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] However, Indian evidence on air pollution-induced childhood ARI is mostly limited to cross-sectional surveys or ecological retrospective data analysis. Further, these studies have used proxy measures such as questionnaire data to elicit either household exposures,[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] or ambient air pollution. [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] Thus, considering these knowledge gaps, this study protocol also focuses on identifying the association of variation in the incidence of ARI till one year of age among children exposed during early life to different levels of air PM and its heavy metal content measured through extensive personal exposure measurements.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAims and Objectives\u003c/h2\u003e \u003cp\u003eWe aim to establish an urban mother-child birth cohort in central India. The data collected from this cohort will enable us to understand the health burden posed by early life exposure (both pre-and post-natal) to air PM and its heavy metal composition in Indian children and the role of such environmental exposure in the multifactorial aetiology of our chosen study outcomes. The specific objectives being addressed in this research are:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo investigate the variation in achievement of growth /developmental milestones during the infancy period of children attributable to different levels of early life exposure to air pollution\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo assess the incidence of acute respiratory infections during the infancy period of children attributable to different levels of early-life exposure to air pollution\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo find the association of exposure to selected heavy metals (as represented by blood concentration) among pregnant mothers /children with assessed morbidity outcomes of children.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis is the protocol for a population-based prospective cohort study. During the first phase of this study which will be implemented over three years, we will establish the mother-child birth cohort and conduct regular follow-ups for exposure and clinical assessments of study participants during the antenatal and 1-year postnatal period (see Fig.\u0026nbsp;1). In the next phase, the established cohort will be followed up annually by securing further funding.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1: Overview of activities to be conducted in the study\u003c/b\u003e \u003c/p\u003e \u003cp\u003e(Note: Activities under the study are listed on each row and the timing of each activity is indicated by a tick mark in the cells corresponding to the column headings showing the phase of study such as enrolment, gestational follow-up, post-delivery and month-wise follow-up (FU) in the infancy period. Ambient air particulate matter/heavy metal assessment and land-use regression (LUR) modelling will be done over the entire study period and have been shown using arrows. Abbreviations: ANC: Antenatal Care, APGAR: Scoring given to child after birth based on Appearance, Pulse, Grimace, Activity, and Respiration, ARI: Acute Respiratory Infection, HC: Head Circumference, LUR: Land Use Regression, MAC: Mid-Arm Circumference, NICU- Neonatal Intensive Care Unit, PM: Particulate Matter.)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStudy Setting\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe cohort population will be enrolled from within the boundaries of urban local bodies (municipal corporations) of two selected cities (Gwalior and Ujjain) of Madhya Pradesh (MP) which is a large province located in central India (see Fig.\u0026nbsp;2). The process of how we finalised these two cities for the establishment of the cohort has been detailed in Additional File 1 of Supplementary Information. MP is a large province with a total population exceeding 72 million[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], of which around 25% reside in urban areas. [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] MP has thirty-two cities with Gwalior and Ujjain ranking as the third and fifth most populous cities in the province, respectively. [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] Child population accounts for 11.17% and 11.45% of the total population of these two cities. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] Average literacy rate of the total population (Gwalior: 84.14% versus Ujjain: 84.43% both of which are at a higher level than the Indian average of 74.04%) and proportion of slum population (Gwalior: 28.97% versus Ujjain: 23.32% both of which are at a higher level than the Indian average of 5.41%) in these two cities are comparable highlighting that the socioeconomic scenario of these two cities are similar. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 2: Map showing the location of study cities\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe image shows clockwise the map of India with the location of the central Indian province of Madhya Pradesh demarcated in green, inset of Madhya Pradesh showing the location of study cities- Gwalior and Ujjain and finally a Google map of the cities themselves.\u003c/p\u003e \u003cp\u003e(Note: This image was developed by the research team in the QGIS software (Version 3.28.15). The India map with state and provincial boundaries and point location of Indian District Head Quarters (indicating location of cities) was retrieved from the Survey of India- Administrative Boundary Database available at\u003c/p\u003e \u003cp\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://onlinemaps.surveyofindia.gov.in/Digital_Product_Show.aspx\u003c/span\u003e\u003cspan address=\"https://onlinemaps.surveyofindia.gov.in/Digital_Product_Show.aspx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e )\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStudy population:\u003c/h2\u003e \u003cp\u003eConsenting pregnant women (n\u0026thinsp;=\u0026thinsp;783 from each city), aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years, with documented gestational age less than or equal to 16th week who have been primarily residing in the study cities for at least the past year (and have no imminent plan to shift residence for more than a continuous period of one month away from their current address during the study period) will be enrolled. We will exclude pregnant women who have a history of using any assisted reproductive technology or who have been diagnosed with a high-risk pregnancy where complications are anticipated. [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] We will also exclude pregnant women with occupation or lifestyle factors that are reported by previous studies to be sources of exposure to high levels of PM. [\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] Mothers with pregnancy loss, preterm delivery; and term neonates with birth asphyxia and diagnosed congenital anomalies will be identified using relevant records and dropped from further follow-up. Details of operational definitions to be used for excluding participants at the time of enrolment and during follow-up phases have been described in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e of Additional File 2 of Supplementary Information.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSample size calculation\u003c/h2\u003e \u003cp\u003eThe calculated sample size is 571 mother-child pairs. Our main objective is to investigate the effect of early-life exposure to air pollution on child growth/development. Stunting, a prevalent manifestation of chronic malnutrition, is one of the main indicators for delay in the achievement of growth/developmental milestones in children. [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] Hence, we have calculated the sample size by assuming a power of 80% and, a two-sided confidence level of 95%, and by using data available from a recent publication [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] which estimated an odds ratio (OR) of 1.74 for stunting among under-five children with long-term exposure to PM\u003csub\u003e2.5\u003c/sub\u003e. We applied relevant continuity correction and inflation to account for participant exclusions during follow-up, and anticipated non-response / participant attrition. Thus, we will enrol 783 pregnant women in each study city (1566 participants in total) to establish the final cohort of 571 mother-child pairs. Further details of sample size calculation have been enumerated in Additional File 3 of Supplementary Information. We will continue to use the same cohort for our subsequent objectives.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSampling strategy\u003c/h2\u003e \u003cp\u003eWe will obtain information about eligible pregnant women in the study area from community health workers (CHWs), who are usually the first point of contact for antenatal care (ANC) among Indian pregnant women. [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] Therefore, before the launch of the study, we will identify and enlist, with the help of relevant district-level offices, all the CHWs working in both study cities under the Integrated Child Development Scheme (ICDS) and National Health Mission (NHM). [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] These include Anganwadi workers (AWW) and Accredited Social Health Activists (ASHA), respectively. [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] We will approach all CHWs to gain their consent to cooperate and those willing will be briefed about the participant enrolment procedure of the study.\u003c/p\u003e \u003cp\u003eWe will contact pregnant women identified by CHWs at their home or at the nearest Anganwadi Centre (community maternal and child care centre in India functioning under the ICDS [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]) to describe our study, confirm their eligibility using a pre-designed checklist, and finally obtain written informed consent. After administration of written informed consent (including consent for follow-up of their child for one year), pregnant women will be enrolled for participation in the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eData (exposure) collection from public sources in each city:\u003c/h2\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNeighbourhood concentration of air PM (PM\u003c/span\u003e \u003csub\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e10\u003c/span\u003e \u003c/sub\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eand PM\u003c/span\u003e\u003csub\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2.5\u003c/span\u003e\u003c/sub\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e: Hourly mean values of PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e will be taken from the Central Pollution Control Board (CPCB) website where open-access data is available of fixed site automatic monitors known as the continuous ambient air quality monitoring stations (CAAQMS) [\u003cspan additionalcitationids=\"CR55\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. In addition, we will also retrieve the past five years data (2023\u0026thinsp;\u0026minus;\u0026thinsp;2019) of daily/annual average air PM recorded by CAAQMS and additional manual gravimetric samplers functioning under the National Air Quality Monitoring Program (NAMP) from CPCB. [\u003cspan additionalcitationids=\"CR58\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] There are only 4 monitoring sites in both Ujjain (one CAAQMS and three additional NAMP samplers) and Gwalior (four CAAQMS and two of these four locations also have NAMP samplers) as per the information listed by CPCB. [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] Finally, we will develop an exposure database at a high spatial scale using the Land Use Regression (LUR) model for both cities at a 1 km spatial scale. LUR has been widely used to predict the concentration of air pollutants (measured through distant fixed site monitors) at target locations (i.e., the residential addresses of the selected pregnant women). [\u003cspan additionalcitationids=\"CR62\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] We will develop our LUR model following the methodology validated for urban areas under the ESCAPE project. [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLUR modelling strategy:\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eLUR model will be developed by using multiple regression equations based upon the relationship between the measured concentration of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e at the fixed monitoring stations and other relevant predictor variables computed using GIS for pre-determined zones of influence around each site. The regression equations will be constructed as follows:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Equa\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${C}_{i}=a+\\sum _{j=1}^{n}X\\sum _{k=1}^{n}{\\left(b{X}_{i}\\right)}_{jk}+\\epsilon$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhere Ci is the predicted concentration of PM at the residential address of study participants \u0026ldquo;i\u0026rdquo;; constant \u0026ldquo;a\u0026rdquo; equals the regional background concentration; (b)\u003csub\u003ejk\u003c/sub\u003e weight assigned to variable j for the kth zone; (Xi)\u003csub\u003ejk\u003c/sub\u003e is the value of variable j calculated for kth zone around residential address \u0026ldquo;i\u0026rdquo;; ε is an error which reflects the variation in concentrations not explained by the model.\u003c/p\u003e \u003cp\u003eIn our LUR model, we will use data for a range of geographic, population and emission source-specific predictor variables retrieved from open access sources/local authorities such as local municipal corporations and/or collected from field-level verification, and satellites. For example, data such as the type of land use (residential, industrial, commercial, forest, agricultural, commercial), road network (road type/length/distance/traffic intensity), population and household density, elevation, and available potential emission source data such as industry count/density/distance will be used, in line with previous studies. [\u003cspan additionalcitationids=\"CR62\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] Satellite-derived aerosol optical depth and meteorological variables from reanalysis data will also be used as predictor variables. We will adopt a supervised forward addition linear regression approach for model development and will perform diagnostics on the fitted models to ensure the linear regression is maintained. Finally, we will assess the performance of the developed model by adopting a 10-fold cross-validation approach. [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eExposure attribution to individual subjects\u003c/span\u003e: We will assign ambient PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e exposure to each subject using the LUR-based exposure data and geocodes of each subject\u0026rsquo;s residence. For estimating adverse birth outcomes, in-utero exposure for the mother will be attributed to the period from the day of conception to the date of delivery. Once the child is born, early-life exposure will be attributed to the child based on the date of birth and the follow-up sequence.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAir PM heavy metal concentration\u003c/span\u003e: Heavy metal concentration in the ambient air of the study cities will be measured in the filter papers collected from the manual gravimetric samplers (i.e., high volume samplers - HVAS) installed in both cities. Continuous air sampling will be done two days per a week throughout the year (104 annual observations) following CPCB methodology[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] such that particles of the desired size are collected on pre-weighted filter papers. The concentration of multiple trace metals present in the particulate matter collected on a randomly chosen subset of these filter papers will then be estimated using the energy-dispersive X-ray fluorescence (EDXRF) technique. We will compile a city-wise list of heavy metals with established risk to child growth/development / ARI (identified based on previously published literature) that exceed the permissible limits in the ambient air. We will then conduct biomonitoring of study participants to estimate, in their blood samples, the concentration of five of the most relevant air PM heavy metals identified in each city. The weekly concentration of these five identified heavy metals in each city will also be measured during the study period by analysing ambient air PM collected on HVAS filter papers using the hot-acid digestion method given by the United States Environmental Protection Agency (USEPA). [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eData (outcome, covariates, and exposure) collection from study participants:\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eBaseline Phase\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFor pregnant women (Baseline Visit)\u003c/span\u003e: In the first visit to households of eligible and consenting pregnant women, the detailed residential address (including contact details and geographic coordinates), and details of the participant and her household members will be collected at the time of enrolment. Briefly, we will collect information on age, education, occupation, lifestyle factors, medical history (including details of current/past pregnancy and prescription drug use), and relevant family history from the pregnant women. The maternal height and weight will be recorded at the baseline visit. Detailed data about previous pregnancies (if any) will be such as parity status, birth intervals between consecutive births, and history of anaemia/malaria/urinary tract infection/Other infections/any other complications in past pregnancy. In addition, the demographic details, socioeconomic characteristics, water, sanitation, hygiene, and environmental factors related to her household will be collected. Finally, maternal blood samples (approximately 5 ml) will be collected for heavy metal analysis following standard protocols used in Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES). Details are mentioned in Additional File 4 of the Supplementary Information.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eFollow-up Phase:\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003eFor pregnant women- Antenatal (trimester-wise) follow-up (Follow-up visits 1\u0026ndash;3)\u003c/h2\u003e \u003cp\u003eThe enrolled pregnant women will be followed up once in each trimester in the remaining period of gestation to collect information regarding the occurrence of spontaneous abortion/miscarriage and the details of scheduled antenatal check-ups undergone by the pregnant women. We will note their preference for a hospital/healthcare facility where they would opt for their delivery. In addition, we will also measure the 24-hour personal exposure to air PM (PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e) of each enrolled pregnant woman.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eChild follow-up (Follow-up visits 4\u0026ndash;17)\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEarly Neonatal (Follow-up visits 4)\u003c/span\u003e: According to the information regarding the calculated expected date of delivery obtained during the baseline visit, the pregnant women will be contacted within 1 week of the expected delivery to collect information on pregnancy/birth outcomes (date of delivery to calculate child chronological age, live birth versus stillbirth, gestational age at delivery and congenital anomalies). Study staff after getting information about the delivery will visit the hospital/house of the mother to collect anthropometric data of the baby (preferably within 72 hours of delivery) to supplement the information obtained from hospital/medical records. In cases where the mother/child cannot be contacted within 72 hours of delivery, then we will try to collect relevant data about the child as early as possible before the 1st-month follow-up visit.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMonthly follow-up of the child till 1st year of age (Follow-up visits 5\u0026ndash;17)\u003c/span\u003e: Follow-up of all eligible live-born children will be done at their homes/nearest Anganwadi centre. To ensure timely and smooth data collection, follow-up visits will be scheduled in a manner that these visits coincide with routine house-to-house visits done by CHWs under different ongoing national programs (such as Home-based newborn care (HBNC) and Home-Based Care for Young Child Programme (HBYC) under NHM and \u0026ldquo;Poshan Abhiyan\u0026rdquo;,[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] \u0026ldquo;Dastak Abhiyan\u0026rdquo; specific to MP[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]). Briefly, in each monthly follow-up visit, anthropometry, achievement of developmental milestones, and acute morbidity profile will be recorded. Additional information about at-birth APGAR score, weight, length, head, and mid-arm circumference will be collected in the 5th visit (child age 1 month). Blood samples for heavy metal analysis and the data on child immunization and feeding practices will be collected in the 17th visit (during the 12th month of the child\u0026rsquo;s age).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eData to be collected\u003c/h2\u003e \u003cp\u003eThe list of domains, variables, and timing of measurement of this study are described in subsequent paragraphs and are shown in Fig.\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eExposure variables\u003c/span\u003e: The main exposure/independent variables to be investigated in this study are mean (daily/monthly/annual) concentration of particulate matter (PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e) and mean levels of heavy metal in blood samples of participant mothers and children.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eStudy outcomes\u003c/span\u003e: The main study outcomes are related to childhood growth and development as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In addition, we will also estimate the incidence rate of ARIs (number of episodes of ARI during infancy / total number of children under follow-up). We modified the Integrated Management of Childhood Illness definition for ARI (given by WHO/UNICEF[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]) which has been used previously [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e] to create an operational definition of ARI for this study. Modification was done by incorporating the signs and symptoms mentioned in the Integrated Management of Neonatal and Childhood Illness (IMNCI), Ministry of Health \u0026amp; Family Welfare, Government of India. Accordingly, ARI will be defined as cough or difficulty breathing with or without any general danger signs, with or without any chest indrawing, with or without any stridor and with or without any fast breathing. Depending upon the site of inflammation determined by the paediatrician based on clinical signs and symptoms, it will be classified as ARI of the upper respiratory tract and ARI of the lower respiratory tract. Further, depending upon the severity, ARI will be classified as per IMNCI guidelines into \u0026ldquo;No Pneumonia: cough or cold\u0026rdquo;, \u0026ldquo;Pneumonia\u0026rdquo;, and \u0026ldquo;Severe Pneumonia or Very Severe Disease\u0026rdquo;. Based on previous research, we will define each episode of ARI to last for two weeks. [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e] When a child who has had no symptoms for at least one week develops signs and symptoms of ARI, we will treat that as a new episode. [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOperational Definitions, indicators, and measurement methods to be used for child growth-related outcome measures\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOperational Definition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariable/Indicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMeasurement method\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026bull; Small for gestational age (SGA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAs per the WHO definition of SGA given by the 1995 WHO expert committee, infants having a birth weight for gestational age below the 10th percentile based on a sex-specific reference population. [\u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e134\u003c/span\u003e, \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e135\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ei. Proportion of children diagnosed as SGA at birth (proxy of in-utero growth of the child)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eii. Based on hospital records- verified by study staff\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow Birth Weight (LBW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; Infants with a birth weight of less than 2500 g, regardless of gestational age at the time of birth. [\u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e136\u003c/span\u003e, \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e137\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe proportion of children diagnosed as LBW at birth (proxy of in-utero growth of the child)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBased on hospital records- verified by study staff\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight/ obese:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; Children\u0026rsquo;s body mass index (BMI - calculated as weight in kilograms divided by height in metres squared) will be plotted on WHO BMI charts and those with a BMI above the 95th percentile will be classified as obese and overweight will indicate those children whose BMI falls between the 85th and 95th percentile. [\u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e138\u003c/span\u003e, \u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e139\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe proportion of overweight/obese (based on BMI for age) infants at the end of the first year of life.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMonthly measurement of height/length, weight and calculation of BMI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight, wasted and stunted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; Z-scores for growth indicators (length/height-for-age, weight-for-age, weight-for-length/height, BMI-for-age) will be estimated by comparing each child\u0026rsquo;s height/length and weight with WHO growth standards as shown in the image below (which has been adapted from the WHO child growth standards: training course on child growth assessment)[\u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e140\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eBased on where their calculated z-scores lie with respect to the median value of the standard reference population, children will be categorized as:\u003c/p\u003e \u003cp\u003e\u0026not; -Underweight: \u0026lsquo;weight for age\u0026rsquo; below minus two standard deviations (-2SD)\u003c/p\u003e \u003cp\u003e\u0026not; -Severely underweight: \u0026lsquo;weight for age\u0026rsquo; below minus three standard deviations (\u0026minus;\u0026thinsp;3SD)\u003c/p\u003e\u003cp\u003e-Stunted: \u0026lsquo;height/length for age\u0026rsquo; below minus two standard deviations (-2SD)\u003c/p\u003e \u003cp\u003e-Severely Stunted: height/length for age\u0026rsquo; below minus three standard deviations (-3SD)\u003c/p\u003e \u003cp\u003e-Wasted: \u0026lsquo;weight for height/length\u0026rsquo; below minus two standard deviations (-2SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eiii. Proportion of children with abnormal Z-scores for afore-mentioned growth indicators (i.e., underweight, severely underweight, stunted, severely stunted, wasted and severely wasted) at the end of the first year of life.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ-scores for growth indicators (length/height-for-age, weight-for-age, weight-for-length/height, BMI-for-age) obtained from comparison of each child\u0026rsquo;s height/length and weight with WHO growth standards\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOperational Definitions, indicators, and measurement methods to be used for child development-related outcome measures\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOperational Definition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariable/Indicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMeasurement method\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDevelopment progress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe developmental level of the child will be assigned a score ASQ-3.[\u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e141\u003c/span\u003e, \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e142\u003c/span\u003e] ASQ-3 has been widely used in many countries as a field-based parent-completed screening tool to assess five developmental domains. [\u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e143\u003c/span\u003e] It has also been previously used and validated in the Indian population[\u003cspan citationid=\"CR144\" class=\"CitationRef\"\u003e144\u003c/span\u003e, \u003cspan citationid=\"CR145\" class=\"CitationRef\"\u003e145\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ei. Domain-specific and mean score of ASQ-3 obtained monthly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAges and stages questionnaires- third edition (ASQ-3) questionnaire [\u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e141\u003c/span\u003e, \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e142\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDevelopmental Quotient (DQ) is calculated by dividing Developmental Age by Chronological Age and multiplying it by 100. [\u003cspan citationid=\"CR146\" class=\"CitationRef\"\u003e146\u003c/span\u003e, \u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e147\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eDevelopmental Age (DA): Each child will be assigned a DA at each follow-up visit for each of the 5 developmental domains i.e., gross motor, fine motor, adaptive/cognitive, language and personal-social developmental domains. Study staff under the supervision of a paediatrician will estimate DA according to the achievement of domain-specific developmental milestones. Chronological Age (CA): the actual age of the child calculated from his/her date of birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eii. - Proportion of children with delayed achievement of development milestones at the end of the first year of life.\u003c/p\u003e \u003cp\u003eiii. - Domain-specific monthly developmental level of each infant obtained through Development quotient calculationiv.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuestionnaire developed by authors and pilot-tested in an ongoing built environmental child cohort [\u003cspan citationid=\"CR148\" class=\"CitationRef\"\u003e148\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: \u003cb\u003eOperational Definitions, indicators, and measurement methods to be used for child growth-related outcome measures\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: \u003cb\u003eOperational Definitions, indicators, and measurement methods to be used for child development-related outcome measures\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eConfounding variables/effect modifiers\u003c/span\u003e: We will collect information for a wide range of covariates identified from a detailed literature review. We will collect information using questionnaires and case report forms developed for this study. Wherever possible, relevant questions will be adapted from pre-existing validated questionnaires and to minimize attrition over time, we will ensure that an optimal number of questions will be asked without missing out on important information.\u003c/p\u003e \u003cp\u003eChild-specific details will be retrieved from discussions with care providers and by consulting available hospital records. Briefly, we will record information such as age, gender, birth order, status of immunization (i.e., as per the National Immunization Schedule (NIS) of the Universal Immunization Programme (UIP) in India [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]), history of anaemia and other acute morbidities (defined as per the Integrated Disease Surveillance Project (IDSP) of the Government of India[\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]), relevant medical history including at-birth details of APGAR score, length, weight, head circumference, and mid-arm circumference, neonatal intensive care unit (NICU) admission, co-morbidities and family history.\u003c/p\u003e \u003cp\u003eWe will collect family-specific information in the form of a household roster detailing the age, education, occupation, relevant medical history, and tobacco/alcohol consumption (using standard questions provided by the WHO STEPwise instrument[\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]) of family members. Then the household socioeconomic status (SES) will be categorised based on scores assigned using the 2020 revision of the Kuppuswamy Scale. [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e] Household water sanitation and hygiene assessment will be done using a pre-tested structured questionnaire which the study team has developed by adapting questions from the \u0026ldquo;Core questions on drinking-water and sanitation for household surveys\u0026rdquo; given by WHO/UNICEF.[\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eIn addition to the data collected from both parents at baseline, current pregnancy-related information will also be collected from the mother. After delivery, during monthly follow-up of the child, the mother will be asked regarding breastfeeding and complementary feeding (after the child attains 6 months of age) practices. For data collection on complementary feeding, we will use a questionnaire previously used in Indian settings. [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eFinally, information about other environmental factors such as the built environment and other indoor exposures will be collected. An exposure questionnaire will be used that has been designed by adapting relevant questions from available validated questionnaires. For example, questions about the built environmental factors such as ventilation, building material, area and construction of house and fuels used for cooking or heating were adapted from the 'household questionnaire' of the National Family Health Survey, India 2019-20 (NFHS \u0026ndash; 5) and the \u0026lsquo;house-listing and housing census schedule\u0026rsquo; of census 2011. [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e] This allows for easy comparison of the questionnaire findings with national data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eData Management and Analysis\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003eQuality Control\u003c/h2\u003e \u003cp\u003eWe will formulate a Data Collection and Management Committee (DCMC) consisting of the principal investigator and other senior investigators of the team. This committee will ensure that during the implementation of this research, the national guidelines for biomedical research data collection and management[\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e] are followed stringently to maintain the quality of data collection and analysis. For example, equipment used in the study will be routinely calibrated, and laboratory testing will be conducted by using high-quality reagents. Research staff will be adequately trained before the start of project work. We will validate the study questionnaires which have been developed by adapting questions from standardized tools and these will be pilot-tested before use. DCMC will also handle requisite communication with the ethics committee and other ethical considerations including data dissemination. During the drafting of this manuscript, details have been reported as per the STROBE checklist modified to suit requirements specific to the protocol of cohort studies (see Additional File 5 of Supplementary Information).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eData management\u003c/h2\u003e \u003cp\u003eThe DCMC will ensure proper management of data. Filled questionnaires will be stored in secure locked cabinets while online or computer-based information will be kept safe by using passwords accessible only to the research team. All data will be assigned unique identifiers and linkages between exposure, outcome and confounders data will be done using these code numbers. Data entry done by research staff will be supervised by the DCMC who will at random verify entered data to check for data entry mistakes. Missing data will be managed by multiple imputation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis and reporting\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe data entered into Microsoft Excel workbooks will be exported to SPSS statistics software (Version 25) and the latest versions of R packages/R studio for analysis. Data will be summarised using appropriate descriptive statistics like mean/standard deviation (SD) or median/Interquartile range. For categorical variables, summary estimates will be reported using frequency with percentages. Initial analysis will be done by considering participants as per their recruitment city i.e., comparison of outcome variables in participants recruited from cities with high versus low pollution levels. For this, Relative risk (RR) and attributable risk (AR) will be calculated through the relevant equations using the probability of outcome (incidence/proportion) among the high pollution group (exposed) divided by the probability of outcome among the low air pollution group (relatively unexposed).\u003c/p\u003e \u003cp\u003eThe next level of analysis will be based on the range of PM\u003csub\u003e2.5\u003c/sub\u003e /PM\u003csub\u003e10\u003c/sub\u003e exposure concentrations and blood heavy metal levels found in the study participants (i.e., pregnant mothers/live-born children). Using this range, we will divide the participants into quartiles based on their exposure levels and the risk of outcome variable in different quartiles will be estimated. To assess possible correlations between environmental variables- PM\u003csub\u003e2.5\u003c/sub\u003e /PM\u003csub\u003e10\u003c/sub\u003e/blood heavy metal levels and the health outcomes \u0026ndash; growth indicator related z scores/development quotient/ARI episodes, we will use a matrix of Pearson correction coefficients.\u003c/p\u003e \u003cp\u003eFinally, multivariable logistic regression model analyses will be used to study the relationship between environmental variables- PM\u003csub\u003e2.5\u003c/sub\u003e /PM\u003csub\u003e10\u003c/sub\u003e/blood heavy metal levels and the health outcomes in binary forms \u0026ndash; growth/development/ARI, in detail. Briefly, multiple linear Mixed-Effects regression equations will be used to study the relationship between the levels of early-life exposure (concentration of environmental pollutants- PM\u003csub\u003e2.5\u003c/sub\u003e /PM\u003csub\u003e10\u003c/sub\u003e concentration/ heavy metals in pregnant mother and the child participant- monthly means for the former and cumulative mean- i.e., in-utero maternal exposure summed with infancy period exposure to heavy metal) and child health outcomes- mean anthropometric data (height, weight, head, and mid-arm circumference) and developmental level (obtained from developmental quotient / mean score on ASQ-3 scale) of each participant (live-born children) while adjusting for confounders. Similar regression equations will be used to analyse the association between exposure and delay in the achievement of growth and developmental milestones. Then, the PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e data and other environmental/sociodemographic data will be used in a logistic regression model of ARI symptoms within a distributed lag nonlinear modelling framework (DLNM) [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e] to test lag associations of PM\u003csub\u003e2.5\u003c/sub\u003e /PM\u003csub\u003e10\u003c/sub\u003e exposure with binary (presence/absence) of ARI symptoms and growth z-scores in the preceding month as has been used in previous literature [\u003cspan additionalcitationids=\"CR85\" citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnalyses will be performed separately for each child's morbidity outcomes like growth/developmental parameters/domain. All analyses will adjust for different confounding variables included in the study. Details of such confounders for statistical adjustment will include gender, family size, socioeconomic status, gynaecological/obstetrics/other relevant medical history, and data from other environmental assessments of the households (built environmental attributes). We will also conduct a subgroup analysis to adjust the effect of other pre-existing childhood morbidities (such as low birth weight, and small-for-gestational age) on the studied association.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eEthical considerations\u003c/h2\u003e \u003cp\u003eThis study was reviewed and approved by the Institutional Ethics Committee (Human), National Institute for Research in Environmental Health (vide approval letter number: ICMR-NIREH/BPL/IEC/2023-24/1307 dated 16/02/2024). At the beginning of research work, we will obtain written informed consent from the pregnant women both for their and their child\u0026rsquo;s participation allowing both the collection of data collection and its scientific dissemination. Mother/children found morbid during the survey will be referred to a government hospital located near the study setting. We will inform the study participants and their families about the final findings of the study through meetings organised in the community with the help of the CHWs. We will also disseminate the findings scientifically through publication of peer-reviewed articles.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe ELitE birth cohort study will be an important addition to the currently sparse evidence base about the long-term effects of early-life exposure to air pollutants among Indian children. Owing to physiological pulmonary adaptation during pregnancy, women inhale a higher volume of air and the pollutants in it per respiratory cycle. [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e] Adverse health effects in the mother coupled with the now acknowledged potential of PM to cross the placenta cause placental malfunctioning as well as other direct and indirect environmental insults to the susceptible developing foetus. [\u003cspan additionalcitationids=\"CR89 CR90 CR91\" citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e] Postnatally, multiple factors such as higher baseline ventilation rates, predominance of mouth-breathing bypassing nasal clearance of particles, and immature immune/pulmonary systems contribute to both higher risk of PM exposure and increased inhaled dose post similar level of exposure in children who have different deposition and clearance probabilities than adults. [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e] Therefore, pregnant women and children, especially those residing in low-resource and high-exposure settings like India, are vulnerable to air pollution-related adverse health effects and the prospective collection of data longitudinally documenting such effects is essential.\u003c/p\u003e \u003cp\u003eThe adverse effects of PM exposure on child growth/developmental trajectory have been shown by epidemiological studies albeit with contradictory findings. [\u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] But the biological mechanisms mediating these effects are not well explored leading to perpetuating ambiguity. [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e] Recently, Sinharoy et al., 2020 attempted to elucidate the pathophysiological framework underlying the exposure-response relationship between air pollution and stunting. [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e] Authors posited that antenatal exposure via oxidative stress and inflammation leads to mitochondrial dysfunction, decreased DNA methylation and shortened telomere length; all of which contribute to placental dysfunction and poor foetal growth. [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e] Further, authors argue that adverse effects on developing immune and pulmonary systems affecting the child\u0026rsquo;s susceptibility to infections compounded by alterations in appetite versus bodily requirements, and dietary metabolite absorptions vis-\u0026agrave;-vis loss, particularly that of vitamin D, can contribute to post-natal growth failure leading to stunting. [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e] Similar mechanisms have also been reported previously. [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e] Growth failures due to chronic malnutrition during early childhood can independently translate into developmental delay. [\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eOn the other end of this spectrum, research has found links between PM exposure and adiposity. The association between PM and obesity is well established among older children and adults as shown by a recent review. [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e] It is said to be mediated via many mechanisms ranging from oxidative stress/inflammation, and epigenetic changes to perturbations in metabolic and intestinal flora balance. [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e] However, the common postulation explaining this dichotomy in the PM effect on growth trajectory reasoned that children with early-life growth restriction are at higher risk of metabolic diseases including obesity at a later phase in life as per the developmental origins of health and disease. [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e] However, recent evidence challenges this mode of reasoning since obesity and higher adiposity have been noted among exposed infants. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] Another line of thinking has evolved which contemplates that the phenotype of foetal growth restriction in an antenatally exposed mother could be immediately followed by rapid weight gain in the infancy period. [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e] However, the overall paucity of longitudinal evidence precludes the drawing of any conclusions. The study presented here will help fill in the existing gap in knowledge owing to the scarcity of longitudinal evidence by establishing a birth cohort consisting of 571 mother-child pairs from two urban areas of central India. Although findings are not expected to be generalizable on a global scale, the prospective follow-up data collected in this cohort study will provide strong evidence for planning and policymaking in this important field.\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eMethodological considerations\u003c/h2\u003e \u003cp\u003eThis study has multiple advantages owing to its multi-disciplinary research team which aids comprehensive information collection and would enhance the validity of the findings of the study. Such a strength would be crucial since we are dealing with child growth and development which is a complex multidimensional concept. [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e, \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e] Children are vulnerable to the effects of the complex interplay between a multitude of factors ranging from environmental, and psychosocial, to genomic insults. [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e] These factors are hypothesized to begin their effect even before conception. [\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e] Therefore, researchers are increasingly acknowledging the concept of the \u0026ldquo;exposome\u0026rdquo;, first introduced by Dr Wild in 2005, as the entirety of all non-genomic influences from conception to death owing to the internal effects from the human bodily environment and external influences encompassing harmful exposures to chemical pollutants/infectious pathogens, health behavioural factors linked to tobacco/alcohol consumption, physical activity or diet and even societal, economic, psychological and spiritual factors. [\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e, \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e] However, accounting for all potential exposure and confounder variables is a humongous task demanding high-end infrastructure, cost-intensive analytic methods and high-dimensional data computational facility, all of which are limited in low-resource settings. [\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e] Therefore, although we have not been able to incorporate multiple exposures such as residential greenness, high-decibel community noise, water/food pollutants such as pesticides, endocrine disrupting chemicals, etc. with known effects on child growth/developmental trajectory and morbidity profile,[\u003cspan additionalcitationids=\"CR112 CR113 CR114 CR115 CR116\" citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e] and \u0026ldquo;omic\u0026rdquo; technologies; we have planned for this longitudinal transgenerational study with follow-up at regular intervals and biological sampling to initiate the investigation in this field in India. Gradually as evidence from similar cohorts starts accruing, robust data analysis and strengthening of the evidence base can be done in the future by merging collected exposure/health outcome data with advanced analysis of stored biological samples as has been done by the EXPOsOMICS research group. [\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eA limitation of this exposure-response cohort is the potential of missing personal exposure information in the first trimester. Considering operational feasibility 16th week was chosen as the cut-off for pregnant women's enrolment and the start of exposure assessment in the antenatal period. Previous research has shown that most women in India report their first antenatal care visit within a median time of 16 weeks of gestation and only 19.6% of pregnancies are detected and registered within the first trimester. [\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e, \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e] Hence, it is likely that we will be enrolling most of our pregnant participants in the gestational period of the 12th -16th week and our ground-data-based exposure assessment will fail to account for the complete picture of first-trimester exposure. This can be a significant limitation since it is plausible that the sensitive window for exposure in this context can be during the first trimester. Data analysed from the INMA Spanish birth cohort showed that the z-score for length at the 6th month of the child\u0026rsquo;s age reduced by 6% for every 10-\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e increase in antenatal NO\u003csub\u003e2\u003c/sub\u003e exposure during the first trimester. [\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e] Block and Calder\u0026oacute;n-Garcidue\u0026ntilde;as 2009 also stated that first-trimester exposure to organic constituents of PM such as polycyclic aromatic hydrocarbons had a maximal effect on foetal growth when compared with later exposures. [\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e] This limitation affects birth cohorts in low- and middle-income countries in Asia and Africa. For example, a rural Ghanaian pregnancy cohort investigating air pollution and childhood growth trajectory enrolled pregnant women at or before the 24th week of gestation. [\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e] Authors would have been facing similar constraints as us since the median time to first visit for antenatal care among African women has been reported to be 5 months[\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe have attempted to overcome this limitation by incorporating past exposure data obtained from CPCB ground monitoring stations and remote-sensing-based estimates of PM\u003csub\u003e2.5\u003c/sub\u003e into the constructed LUR models. Previous studies have used this method. For example, the recently published Californian population-based prospective pregnancy cohort study (MADRES) enrolled pregnant women with a gestational age of less than 30 weeks and obtained air pollution exposure information using data from the United States Environmental Protection Agency Air Quality System to model daily participant exposure beginning from 2 years before pregnancy. [\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e] But the use of only LUR modelling-based exposure assessment suffers from ecological fallacy. [\u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e] Final estimation is highly dependent upon the density of monitoring stations from which PM data can be used to train the models and the granularity of data for predictor variables and thus, cannot accurately differentiate between individual-specific exposure, particularly when their residential addresses are clustered close together. [\u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e127\u003c/span\u003e, \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e128\u003c/span\u003e] Hence, data triangulation by adding personally measured and biomonitoring data would lead to higher confidence in the overall exposure assessment. [\u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e129\u003c/span\u003e] We have thus adopted a multi-pronged strategy to estimate accurate exposure information of the study participants.\u003c/p\u003e \u003cp\u003eFinally, to ensure the highest level of internal validity of our study, we will attempt to overcome the biases inherent in adopting a cohort study design. [\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e, \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e] For example, to control for attrition bias, loss-to-follow-up will be minimised through better rapport building with the study participants with the support of CHWs. Published evidence acknowledges the crucial role played by CHWs who are usually residents of the locality where they work and hence, have an established rapport with their beneficiaries. [\u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e, \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e133\u003c/span\u003e] By seeking the active cooperation of CHWs, through appropriate incentivisation, we will be leveraging this trust built with the community and hence, we expect attrition to be minimal in the study. In addition, we will also systematically document the characteristics (including the reasons for withdrawal) of those participants who decline further follow-up. Although not completely amenable to control, other selection biases like non-response bias and healthy entrant bias will be accounted for by asking for and recording relevant information about those pregnant women who refuse to participate in the study when approached for consent. [\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e, \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e] Similarly, information and confounding bias will be minimised by ensuring the use of validated pilot-tested questionnaires and appropriate collection of data on exposure, outcome and confounding variables by trained research staff. [\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e, \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis manuscript described the methodology for the establishment of an Indian mother-child air pollution birth cohort and its subsequent follow-up. This study aims to fill the gaps in published evidence regarding the adverse effects of early-life exposure to air PM and its constituent heavy metals among Indian children. Despite the inherent drawbacks of resource limitation precluding comprehensive exposure assessment and the low scope of generalizability of our findings to all population sub-groups residing in the Indian sub-continent and other low- and middle-income countries, this study with its strengths will provide an epidemiological basis to further understanding in the context. Finally, by reporting our carefully planned study methods/outcome measures, which are at par with published and ongoing birth cohorts, we aim to serve as the starting point for similar cohorts in the future which when considered together would generate enough evidence to facilitate context-specific policy-making and development of appropriate prevention and mitigation strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eANC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAntenatal Care\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPGAR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eScoring given to child after birth based on Appearance, Pulse, Grimace, Activity, and Respiration\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAttributable Risk\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eARI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcute Respiratory Infection\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASHA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAccredited Social Health Activists\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASQ-3\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAges and Stages Questionnaire- Third Edition\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAWW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnganwadi Workers\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAQI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAir Quality Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index, CAAQMS:Continuous Ambient Air Quality Monitoring Stations\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronological Age\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCHW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCommunity Health Workers\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCPCB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCentral Pollution Control Board\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDevelopmental Age\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDCMC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eData Collection and Management Committee\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDeoxyribonucleic Acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDQ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDevelopmental Quotient\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDLNM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDistributed Lag Non-Linear Model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEDD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExpected Date Of Delivery\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEDXRF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEnergy-Dispersive X-Ray Fluorescence\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGIS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeographic Information System\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHBNC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHome-Based Newborn Care\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHBYC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHome-Based Care for Young Child Programme\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHVAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh Volume Air Sampler\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Classification Of Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICDS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntegrated Child Development Scheme\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICP-OES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInductively Coupled Plasma Optical Emission Spectroscopy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIDSP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntegrated Disease Surveillance Project\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIMNCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntegrated Management of Neonatal and Childhood Illness\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLBW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow Birth Weight\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLMP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLast Menstrual Period\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLUR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLand Use Regression\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMadhya Pradesh\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNAAQS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Ambient Air Quality Standards\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNFHS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Family Health Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNHM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Health Mission\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNICU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNeonatal Intensive Care Unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNIS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Immunization Schedule\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNitrogen Dioxide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eParticulate Matter\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eParticulate Matter With Aerodynamic Diameter\u0026thinsp;\u0026lt;\u0026thinsp;10\u0026micro;m\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eParticulate Matter With Aerodynamic Diameter\u0026thinsp;\u0026lt;\u0026thinsp;2.5\u0026micro;m\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRelative Risk\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSDG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSustainable Development Goal\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSocio-Economic Status\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSGA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eShort For Gestational Age\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSO\u003csub\u003e2\u003c/sub\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSulphur Dioxide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUIP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUniversal Immunization Programme\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUNICEF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnited Nations Children's Fund\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUNEP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnited Nations Environment Programme\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUSEPA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnited States Environmental Protection Agency\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organisation.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e\n\u003cp\u003eMeasurement Units:\u003c/p\u003e\n\u003cp\u003em: metre; ml: millilitre (1ml= 10\u003csup\u003e-3\u003c/sup\u003elitre); \u0026micro;m: micrometre (1 \u0026micro;m\u0026nbsp;= 10\u003csup\u003e-6\u003c/sup\u003em); kg: kilogram; g: gram(1g=10\u003csup\u003e-3\u003c/sup\u003ekilogram); \u0026micro;g: microgram (1 \u0026micro;g\u0026nbsp;= 10\u003csup\u003e-6\u003c/sup\u003eg)\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eNo data has been generated yet as data collection and participant recruitment have not commenced. However, data that is expected to be generated in this study can be provided in the future, upon request, by the corresponding author (Dr Yogesh Damodar Sabde, via [email protected]). \u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge the then members of the scientific advisory committee of ICMR-NIREH, Bhopal and other anonymous reviewers who were involved in the detailed critique of this study during the multi-stage review and approval process undertaken by the funding agency. Their suggestions and comments helped us further refine the methodology.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe study, on which this protocol is based, was funded by the Indian Council of Medical Research (Grant No. F.No.5/7/12/IM/2023-RCN Dated 25/01/2024; PI: Dr Yogesh Damodar Sabde). The funding agency has not played any role in the conceptualisation of the study or the drafting of this manuscript nor will it have any role in the collection, analysis, and interpretation of data generated in this study.\u003c/p\u003e\n\u003cp\u003eAuthor information\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAuthors and Affiliations\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Environmental Health and Epidemiology, ICMR-National Institute for Research in Environmental Health, Bhopal, Madhya Pradesh, India\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTanwi Trushna, Vikas Yadav, Uday Kumar Mandal, Rajnarayan R Tiwari, Yogesh Damodar Sabde\u003c/p\u003e\n\u003cp\u003eDepartment of Environmental Monitoring and Exposure Assessment (Water and Soil), National Institute for Research in Environmental Health, Bhopal, Madhya Pradesh, India\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVishal Diwan\u003c/p\u003e\n\u003cp\u003eDepartment of Environmental Biochemistry, ICMR-National Institute for Research in Environmental Health, Bhopal, Madhya Pradesh, India\u003c/p\u003e\n\u003cp\u003eRajesh Ahirwar\u003c/p\u003e\n\u003cp\u003eDepartment of Biostatistics and Bioinformatics, ICMR-National Institute for Research in Environmental Health, Bhopal, Madhya Pradesh, India\u003c/p\u003e\n\u003cp\u003eDharma Raj, Sindhuprava Rana\u003c/p\u003e\n\u003cp\u003eDepartment of Child Health Research, National Institute For Research In Reproductive and Child Health (ICMR-NIRRCH), Mumbai, Maharashtra, India\u003c/p\u003e\n\u003cp\u003eSuchitra Vishwambhar Surve\u003c/p\u003e\n\u003cp\u003eCentre for Atmospheric Sciences, Indian Institutes of Technology (IIT Delhi), New Delhi, Delhi, India\u003c/p\u003e\n\u003cp\u003eSagnik Dey\u003c/p\u003e\n\u003cp\u003eCentre of Excellence for Research on Clean Air, Indian Institutes of Technology (IIT Delhi), New Delhi, India\u003c/p\u003e\n\u003cp\u003eSagnik Dey\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eContributions\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eY.D.S is the principal investigator of the study and obtained the necessary funds. Together with T.T., Y.D.S. conceptualised the study. T.T. drafted the manuscript of the study protocol which was substantively edited and proofread by Y.D.S., V.D., V.Y., U.K.M., R.A., D.R., S.P.R., S.V.S., S.D. and R.R.T. Y.D.S. and T.T. prepared figures 1-2 while V.D., V.Y., U.K.M. and S.V.S. aided in creating the tables 1-2 and S1. S.D. and R.A. contributed to drafting the methodology for exposure assessment including LUR modelling and laboratory testing for heavy metals. Y.D.S., T.T., V.D., V.Y., U.K.M., and S.V.S. discussed and planned the fieldwork and the questionnaires for data collection from human participants. These components have been incorporated in the methods section of the manuscript and in the supplementary information. All authors have substantively commented and helped in the finalisation of the study protocol. All authors have approved the submitted version of this manuscript and agree to personal accountability for the same.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eCorresponding author\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Yogesh Damodar Sabde (email: [email protected]).\u003c/p\u003e\n\u003cp\u003eEthics declarations\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eEthics approval and consent to participate\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval has been obtained for this study from the Institutional (Human) Ethics Committee, National Institute for Research in Environmental Health (Approval Letter No: ICMR-NIREH/BPL/IEC/2023-24/1307 dated 16/02/2024). Written informed consent will be obtained from all participants. For children (infants) enrolled in this study, written informed consent will be sought from the legal guardian(s). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eConsent for publication\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable as the manuscript is the protocol of a study and no personally identifiable information about the participants has been collected yet.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eCompeting interests\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization(WHO). Ambient (outdoor) air pollution. 2018. https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health. Accessed 17 Jun 2020.\u003c/li\u003e\n\u003cli\u003eGreenstone M, Hasenkopf C. Air Quality Life Index (AQLI) - 2023 Annual Update. 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Ages and Stages Questionnaire as a screening tool for developmental delay in Indian children. Indian Pediatr. 2012;49:457\u0026ndash;61.\u003c/li\u003e\n\u003cli\u003eGulati S, Israni A, Squires J, Singh A, Madaan P, Kamila G, et al. Socio-cultural Adaptation and Validation of Ages and Stages Questionnaire (ASQ 3) in Indian Children Aged 2 to 24 Months. Indian Pediatr. 2023;:S097475591600555.\u003c/li\u003e\n\u003cli\u003eBrown KA, Parikh S, Patel DR. Understanding basic concepts of developmental diagnosis in children. Transl Pediatr. 2020;9 Suppl 1:S9\u0026ndash;22.\u003c/li\u003e\n\u003cli\u003eGupta P, Menon PSN, Ramji S, Lodha R. PG textbook of pediatrics. First edition. New Delhi: Jaypee Brothers Medical Publishers (P), Ltd; 2015.\u003c/li\u003e\n\u003cli\u003eSabde YD, Trushna T, Mandal UK, Yadav V, Sarma DK, Aher SB, et al. 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Front Public Health. 2022;10.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Acute Respiratory Infection, Air Pollution, Birth Cohort, Child Development, Growth, Heavy Metals, India, Particulate Matter, Study Protocol","lastPublishedDoi":"10.21203/rs.3.rs-3969211/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3969211/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePregnant women and children are vulnerable to air pollution-related adverse health effects, especially those residing in low-resource and high-exposure settings like India. However, evidence regarding the effects of early-life exposure to air particulate matter (PM) on childhood growth/developmental trajectory is contradictory; evidence about specific constituents of PM like heavy metals is limited. Similarly, there are few Indian cohorts investigating PM exposure and the incidence of acute respiratory infection during infancy. This study protocol aims to fill these critical gaps in knowledge.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe aim to establish a mother-child birth cohort through the enrolment of 1566 pregnant women residing in two urban areas of central India. Antenatally we will collect socioeconomic, demographic, and clinical information, and details of confounding variables from these mothers, who will then be followed up till delivery to assess their exposure to air PM. Biomonitoring to assess heavy metal exposure will be limited to the top five heavy metals found in the air of their residential city. At delivery, pregnancy outcomes will be noted followed by postnatal follow-up of live-born children till the first year of life to assess their achievement of growth/development milestones and exposure to pollutants. We will also estimate the incidence of ARI during infancy.\u003c/p\u003e\u003ch2\u003eDiscussion\u003c/h2\u003e \u003cp\u003eThis manuscript describes the protocol for an Indian mother-child air pollution birth cohort study which aims to generate comprehensive evidence regarding the adverse effects of early-life exposure to air PM and its constituent heavy metals among Indian children. This study will provide an epidemiological basis for further understanding in this context. Finally, by reporting our carefully planned study methods/outcome measures, which are at par with published and ongoing birth cohorts, we aim to serve as the starting point for similar cohorts in the future which when considered together would generate enough evidence to facilitate context-specific policy-making and development of appropriate prevention and mitigation strategies.\u003c/p\u003e","manuscriptTitle":"A protocol for estimating health burden posed by early life exposure to ambient fine particulate matter and its heavy metal composition: A mother-child birth (ELitE) cohort from central India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-21 18:13:04","doi":"10.21203/rs.3.rs-3969211/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"90a71a6c-e1d1-41b1-a70d-892a440ead30","owner":[],"postedDate":"February 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-02T16:11:52+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-21 18:13:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3969211","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3969211","identity":"rs-3969211","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}