Ambient air pollution exposure and child birthweight in East African countries: Identifying the sensitive periods

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The adjusted linear regression model showed that the increase in 1 µg/m 3 in O 3 exposure during the first, second and third pregnancy trimesters, respectively resulted in the reduction of the birthweight by 0.00300 kg (Standard Error (SE) of 0.00092 kg), 0.00232 kg (SE: 0.0093), 0.00164 kg (SE: 0.00096) and 0.00674 kg (SE: 0.00143). The logistic regression model revealed that a 1 µg/m 3 increase in prenatal exposure to O 3 concentration is associated with 4.7% (95% Confidence Interval (CI): 0.4487-1.4434) and 6.3% (95% CI: 0.4552-1.4808) prevalence of the underweight births during the second and third gestation trimesters, respectively. PM 2.5 and CO did not demonstrate significant impacts on birthweight, although CO seemed to impair the birthweight outcomes, especially the exposure during the last trimester of pregnancy. The findings of this study have important public health implications that aim to protect health in its early development. air pollution children growth birthweight underweight East Africa Introduction Air pollution is the highest environmental and health threat. Although air pollution harms all age groups of people; pregnant women, newborns, children, elders, and persons with underlying diseases are the most affected by air pollution. Several studies associated air pollution exposure with results, such as lung function and development (Galstyan et al. 2023 ; Korten et al. 2017 ), childhood asthma, allergic rhinitis, and eczema (Deng et al. 2016 ; Galstyan et al. 2023 ). Prenatal exposure to air pollution impairs also fetal health (Rani and Dhok 2023 ). However, the extent of these damages is still uncertain in developing countries, particularly in sub-Saharan Africa where air quality monitoring and the capacity to conduct epidemiological studies are limited. In addition, most of the existing literature has focused on household air pollution and fine particulate pollution. Although household air pollution is a foremost threat to children’s health, women, and elders, especially in developing countries where solid biomass fuels constitute the main household source of energy for cooking and heating, ambient air pollution also demonstrated an increasing concern in low-and middle-income countries (Agbo et al. 2021 ; Amegah and Agyei-Mensah 2017 ; Lubimov et al. 2016 ). This study aimed to assess the impact of prenatal exposure to ambient air pollution on the child’s birth weight in five East African countries of Burundi, Kenya, Rwanda, Tanzania, and Uganda, examining the susceptible exposure windows. Material and methods The children’s weight measurements at birth and associated household socioeconomic indicators were retrieved from the Demographic and Health Survey (DHS) waves. DHS program records the socio-economic background of children and their parents, as well as anthropometric information on children's development. The Global Positioning System (GPS) coordinates of each interviewee are recorded and reported with a random displacement of 2 km in urban areas and 5 km in rural areas to protect household confidentiality (Corsi et al. 2012 ). To make our analysis, data inclusion requirements consisted of singleton birth, having the birthweight record, and being located in a cluster with possible air pollutants matching the gestation period. Table 1 gives the DHS wave used for each country, the sample size, and relevant statistics. We traced back environmental information starting nine months before the birth date of the child up to the registered birthdate. Table 1 Demographic and Health Survey waves included in this study. Country DHS waves Initial sample size Final sample size Average birthweight* Underweight births (%) Burundi DHS-2016-2017 13 192 4717 3.164 (0.611) 7.93 Kenya DHS-2022 19 530 4324 3.208 (0.588) 7.34 Rwanda DHS-2019-2020 8 092 3517 3.305 (0.620) 5.09 Tanzania DHS-2015-2016 10 233 5356 3.260 (0.574) 4.95 Uganda DHS-2016 15 522 2782 3.364 (0.813) 8.09 Total 66 569 20696 3.249 (0.633) 6.57* *The mean birthweight is given in kilograms, and the standard deviation is in parentheses. All statistics are according to the final sample size. Despite the several environmental factors that may affect Utero and Vivo children’s health, accurate data on all potential confounders are usually not available. We fitted the multiple linear regression model with air quality data including fine particulate matter (PM 2.5 ), ground-level ozone (O 3 ) and carbon monoxide (CO) adjusted with weather variables of temperature, precipitation, and wind speed. The adjustment of the model also included the maternal education level, the household wealth index and location (rural against urban). We used a unique dataset from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) (Global Modeling and Assimilation Office (GMAO) 2015 ) for air pollutants and weather variables. All data were averaged for every three months (trimester) as well as for the whole pregnancy period for each child. Table 2 gives the summary statistics of air pollutants concerned with this study and associated meteorological variables averaged for nine months from each DHS cluster. Table 2 Statistics of prenatal air pollution and meteorological variables exposure. Indicators CO (ppb) PM 25 (µg/m 3 ) O 3 (µg/m 3 ) Temperature (K) Precipitation (mm/day) Wind (m/s) Mean 115.92 29.06 69.08 294.84 0.07 4.30 SD 29.82 9.44 3.72 2.86 0.05 1.14 Minimum 54.99 9.00 61.00 287.20 0.00 2.43 1st Quartile 91.88 20.88 66.28 292.97 0.04 3.47 2nd Quartile 120.01 30.24 68.69 294.20 0.07 3.87 3rd Quartile 138.76 36.88 71.20 296.77 0.09 4.90 Maximum 394.12 52.50 91.23 303.80 0.30 9.10 To ensure the best match of DHS and pollution records, we clipped each independent variable for each cluster buffer to assign exposure and averaged value of grid-point within each clustered buffer zone of 2 km in urban areas and 5 km in rural areas, using the Zonal Statistics Tool in ArcGIS Pro. The DHS five-digit wealth index from each wave and country has been converted into quintiles (lowest, low, middle, high, and highest), before merging datasets, to ensure their uniformity and transferability. Data analysis and modelling have been done using Python (version 3.10.9) and R (version 4.3.2) (Butler et al. 2017 ). Results and discussion Results Adjusted linear regression model results presented in Table 3 revealed that prenatal exposure to O 3 is associated with a significant negative impact on childbirth weight. According to the model, the increase in 1 µg/m 3 in O 3 exposure resulted in the reduction of the recorded child birthweight by 0.00300 kg (Standard Error (SE) of 0.00092), 0.00232 kg (SE: 0.0093), 0.00164 kg (SE: 0.00096) and 0.00674 kg (SE: 0.00143) during the first, second, third-trimester and entire pregnancy exposure period, respectively. Prenatal exposure to PM 2.5 and CO did not demonstrate a significant impact on the birthweight. Nevertheless, exposure to CO resulted in negative birthweight association during the whole digestion period and the first and the last trimester of the pregnancy. For instance, the increase in 1 ppb in CO above the mean value led to a decrease in the measured birthweight by 0.00027 kg (SE: 0.00015) for the last trimester of pregnancy exposure and 0.00025 kg (SE: 0.00031) for the entire pregnancy exposure to CO. Furthermore, PM 2.5 showed non-significant negative birthweight outcomes only for the exposure during the second trimester. The increase of 1 µg/m 3 in PM 2.5 , during the second trimester of pregnancy, suggested the reduction in birthweight by 0.00097 kg (SE: 0.00076). Other covariates were associated with child birthweight in the presumed directions. Table 3. Adjusted linear regression model estimates result of the impact of air pollution on birthweights. Pollutants Exposure Period Coefficients ( ) Standard Error PM 2.5 Entire Pregnancy 0.00035 0.00097 Trimester 1 0.00048 0.00076 Trimester 2 -0.00097 0.00076 Trimester 3 0.00085 0.00077 O 3 Entire Pregnancy -0.00674*** 0.00143 Trimester 1 -0.00300*** 0.00092 Trimester 2 -0.00232** 0.00093 Trimester 3 -0.00164* 0.00096 CO Entire Pregnancy -0.00025 0.00031 Trimester 1 -0.00033 0.00025 Trimester 2 0.00024 0.00023 Trimester 3 -0.00027* 0.00015 Note : The table reports the Adjusted linear regression model coefficient estimates of the impact of prenatal exposure to air pollution on birthweight and their standard errors. All models were adjusted for maternal education, household wealth index, household location (rural against urban), air temperature, wind speed and precipitation. ***Statistical significance at the 1% level, **Statistical significance at the 5% level, *Statistical significance at the 10% level. Using the logistic regression model, we derived the Odds ratios to determine the most influential period for the underweight birth occurrences. The Logistic regression model estimates the regression coefficient ( ) and the exponential function of the regression coefficient ( ) is the odds ratio associated with a one-unit increase in exposure (Sperandei 2014). All models were adjusted for maternal education, household wealth index, household location (rural against urban), air temperature, wind speed and precipitation. Indeed, only prenatal O 3 exposure yielded a significant positive association with underweight birth records. Following the model (Table 4), the increase in 1 µg/m 3 of O 3 , above the average values, predicted the increase in underweight occurrences by a multiple of 1.0084 (95% Confidence Interval (CI): 0.4298-1.3662) and the multiples of 1.0564 (95% CI: 0.4526-1.4659), 1.0626 (95% CI: 0.4552-1.4808) and 1.0470 (95% CI: 0.4487-1.4434) during the first-, second-, and third-trimester of pregnancy exposure, respectively. Prenatal exposure to PM 2.5 and CO did not show significant odds ratios for the children’s underweight birth incidences. Table 4. Adjusted odds ratio estimates of the impact of air pollution on underweight birth prevalence Pollutants Exposure period Odds Ratio 95% CI p-value PM 2.5 Entire Pregnancy 0.6047 0.0467-1.8244 0.7002 Trimester 1 1.1814 0.5032-1.7738 0.7019 Trimester 2 1.1934 0.5083-1.8020 0.6848 Trimester 3 1.1787 0.5021-1.7672 0.7057 O 3 Entire Pregnancy 1.0084 0.4298-1.3662 0.0486 Trimester 1 1.0564 0.4526-1.4659 0.0615 Trimester 2 1.0626 0.4552-1.4808 0.0523 Trimester 3 1.0470 0.4487-1.4434 0.0490 CO Entire Pregnancy 1.0371 0.4488-1.3964 0.7321 Trimester 1 0.8985 0.0728-1.0879 0.9335 Trimester 2 1.0340 0.4475-1.3894 0.6376 Trimester 3 1.0379 0.4491-1.3986 0.5306 Note: The table reports the logistic regression odds ratio estimates of the impact of prenatal exposure to air pollution on the children’s birth weight. All models were adjusted for maternal education, household wealth index, household location (rural against urban), air temperature, wind speed and precipitation. Discussion The impacts of air pollution exposure on pregnancy outcomes are poorly investigated in sub-Saharan Africa. Given the importance of the birthweight in the later development of the child, this study aimed to assess the impact of PM 2.5 , CO and O 3 on the birthweight in the East African region. The region is reported among regions recording lower child’s anthropometric outcomes (UNICEF et al. 2023) and is characterized by high pollution induced by open biomass burning, accelerated economic development, rapid urbanization and population growth (Baharane and Shatalov 2023 ; Kalisa et al. 2023 ; Opio et al. 2021 ). We found that prenatal O 3 exposure slightly lowers birth weight with its exposure more sensitive during the last two pregnancy trimesters. This study agreed with a recent study on the impact of O 3 on child development (Balietti et al. 2022 ; Tong et al. 2023 ; Wang et al. 2021 ). Using a large sample size from the Guangzhou megacity (China), Wang, et al. ( 2021 ) found that the most susceptible exposure period is the second pregnancy trimester (Wang et al. 2021 ) and Balietti, et al. ( 2022 ) assessed the impact of air pollutants on child development for the whole pregnancy period in India (Balietti et al. 2022 ). Although the underlying mechanisms for O 3 exposure during pregnancy and low birth weights are still unclear, all these studies suggest its association. Generally, fetal weight starts to increase from the second trimester of the gestation period and requires more oxygen and nutrients. In addition to oxygen and nutrients, the fetal-maternal dependencies increase, such as oxidative damage mediators related to O 3 exposure, which may therefore influence fetal growth (Sales et al. 2018 ). Although CO seems to act in a negative direction, this study did not find any significant birthweight impairment from ambient PM 2.5 and CO exposure during the pregnancy period. However, the negative association between PM 2.5 and CO and the birthweight was confirmed by da Silva et al. ( 2014 ) and Salam et al. ( 2005 ) (da Silva et al. 2014 ; Salam et al. 2005 ) but, in their study, Balakrishnan et al. ( 2023 ) did not find any association of CO with the lower birthweight occurrences (Balakrishnan et al. 2023 ). It was documented that high exposure to particulate matter may cause alveolar inflammation and alterations in blood viscosity, which also affect the placenta’s normal function (da Silva et al. 2014 ). Carbon monoxide generally acts by limiting the amount of oxygen in transport by the blood for both the fetal and pregnant woman thus impairing the fetal growth. Nonetheless, this study presented some limitations. The first limitation consists of our assumption of a single cluster for each woman during the gestation period. The second limitation is related to the fact that we required a gridded air pollution dataset which should be obtained only by using air quality modeling or remote sensing approaches. However, the MERRA-2 dataset used by this study has demonstrated robust results in similar studies (Balietti et al. 2022 ; Cui et al. 2022 ). Nevertheless, our study has important public health implications that aim for the protection of children's health. Conclusions The danger of air pollution exposure is not limited to the pregnant woman but also reaches the fetus. This study revealed that, in the East African region, prenatal exposure to ground-level O 3 is associated with lower birthweight records with the second and third trimester of the pregnancy being the more susceptible periods of exposure. PM 2.5 and CO did not demonstrate significant impacts, although CO seemed to act in a negative direction, especially during the third gestation period. Adjusted logistic regression estimates showed that a 1 µg/m 3 increase in O 3 exposure in the second and the third pregnancy period respectively results in underweight birth occurrence by 6.3% (95% CI: 0.4552–1.4808) and 4.7% (95% CI: 0.4487–1.4434). Our findings about the sensitive air pollution exposure window also provide a research basis for further investigating additional mechanisms of child growth impairment by prenatal exposure to ambient air pollution. Declarations Authors’ contributions Valérien Baharane : Study conceptualization, data acquisition and analysis and the first draft of the manuscript. Andrey Borisovich Shatalov and Maxim Viktorovich Larionov : Supervised, reviewed, and edited the first manuscript. All authors read and approved of the final manuscript . Availability of data and material Data on the children’s birth weight and associated household socioeconomic indicators for the concerned countries may be acquired, on request, from the DHS Program administration (https://dhsprogram.com/). Air pollution and climate variables are freely available, after registration, at https://giovanni.gsfc.nasa.gov/giovanni/. Other analysis and program codes used in this study are available on request from the corresponding author. Ethical approval and consent to participate This study was concerned with the analysis of the survey dataset available online with all identifier information removed, no ethics approvals were required. The corresponding author requested and obtained authorization to use the DHS dataset of the concerned countries from the DHS program administration and we agreed to adhere to the terms and conditions imposed by the DHS Program. Consent to publish Not applicable Competing interest The authors declare no competing interests. Funding No funding was received for conducting this study. References Agbo, K. E., Walgraeve, C., Eze, J. I., Ugwoke, P. E., Ukoha, P. O., & Van Langenhove, H. (2021). A review on ambient and indoor air pollution status in Africa. Atmospheric Pollution Research , 12 (2), 243–260. https://doi.org/10.1016/j.apr.2020.11.006 Amegah, A. K., & Agyei-Mensah, S. (2017). Urban air pollution in Sub-Saharan Africa: Time for action. Environmental pollution , 220 , 738–743. https://doi.org/10.1016/j.envpol.2016.09.042 Baharane, V., & Shatalov, A. B. (2023). Ambient air pollution level in the East African region based on satellite remote sensing of NO2, CO, and Aerosol optical depth. Web of Conferences (E3S) , 407 (02003). https://doi.org/https://doi.org/10.1051/e3sconf/202340702003 Balakrishnan, K., Steenland, K., Clasen, T., Chang, H., Johnson, M., Pillarisetti, A., et al. (2023). 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Environmental Health Perspectives , 113 (11), 1638–1644. https://doi.org/10.1289/ehp.8111 Sales, F., Peralta, O. A., Narbona, E., McCoard, S., De los Reyes, M., González-Bulnes, A., & Parraguez, V. H. (2018). Hypoxia and oxidative stress are associated with reduced fetal growth in twin and undernourished sheep pregnancies. Animals , 8 (11), 217. https://doi.org/10.3390/ani8110217 Sperandei, S. (2014). Understanding logistic regression analysis. Biochemia Medica , 24 (1), 12–18. https://doi.org/10.11613/BM.2014.003 Tong, M., Xu, H., Wang, R., Liu, H., Li, J., Li, P., et al. (2023). Estimating birthweight reduction attributable to maternal ozone exposure in low- and middle-income countries. Science advances , 9 (49), eadh4363. https://doi.org/10.1126/sciadv.adh4363 UNICEF, WHO, & World Bank Group. (2023). Levels and trends in child malnutrition: Key finding of the 2023 edition . Asia-Pacific Population Journal (Vol. 24). Wang, Q., Miao, H., Warren, J. L., Ren, M., Benmarhnia, T., Knibbs, L. D., et al. (2021). Association of maternal ozone exposure with term low birth weight and susceptible window identification. Environment International , 146 , 106208. https://doi.org/10.1016/j.envint.2020.106208 Additional Declarations No competing interests reported. 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-5735744","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":407745188,"identity":"08963f93-35a6-481e-a8b5-1170bdb132cb","order_by":0,"name":"Valérien Baharane","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYDACCQY2CIO9AUgYWJCihecASIsEKVokEiBcgsDgdvOzBz/32OXxSz6/uuFHgQQDf3t3An4td46ZG/Y8Sy6WnJ1TdrMH6DCJM2c34NUiOSPBTILnAHPihts5aTd4gFoMJHIJaUn/JvnnQH3i/ptn0m7+IUYLv0SOmTTPgcOJGyTYj90myhZ+mTNl0jIHjifOOJPDdlvGQIKHoF/YpNu3Sb45UJ3Y33782c03f2zk+Nt78WtBAjwGYJJY5SDA/oAU1aNgFIyCUTCCAABkd0fU4vPrDAAAAABJRU5ErkJggg==","orcid":"","institution":"Peoples’ Friendship University of Russia named after Patrice Lumumba","correspondingAuthor":true,"prefix":"","firstName":"Valérien","middleName":"","lastName":"Baharane","suffix":""},{"id":407745190,"identity":"425d1788-e0ed-44ec-833c-0bfb0961eb3f","order_by":1,"name":"Andrey Borisovich Shatalov","email":"","orcid":"","institution":"Peoples’ Friendship University of Russia named after Patrice Lumumba","correspondingAuthor":false,"prefix":"","firstName":"Andrey","middleName":"Borisovich","lastName":"Shatalov","suffix":""},{"id":407745191,"identity":"4d96cea9-a606-463f-9059-b3f296ace102","order_by":2,"name":"Maxim Viktorovich Larionov","email":"","orcid":"","institution":"Federal State Budgetary Educational Institution of Higher Education Russian Biotechnological University","correspondingAuthor":false,"prefix":"","firstName":"Maxim","middleName":"Viktorovich","lastName":"Larionov","suffix":""},{"id":407745192,"identity":"3d01306f-5898-4c9d-970a-820fdc2ab403","order_by":3,"name":"Anton V. 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Although air pollution harms all age groups of people; pregnant women, newborns, children, elders, and persons with underlying diseases are the most affected by air pollution. Several studies associated air pollution exposure with results, such as lung function and development (Galstyan et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Korten et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), childhood asthma, allergic rhinitis, and eczema (Deng et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Galstyan et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Prenatal exposure to air pollution impairs also fetal health (Rani and Dhok \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the extent of these damages is still uncertain in developing countries, particularly in sub-Saharan Africa where air quality monitoring and the capacity to conduct epidemiological studies are limited. In addition, most of the existing literature has focused on household air pollution and fine particulate pollution. Although household air pollution is a foremost threat to children\u0026rsquo;s health, women, and elders, especially in developing countries where solid biomass fuels constitute the main household source of energy for cooking and heating, ambient air pollution also demonstrated an increasing concern in low-and middle-income countries (Agbo et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Amegah and Agyei-Mensah \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lubimov et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This study aimed to assess the impact of prenatal exposure to ambient air pollution on the child\u0026rsquo;s birth weight in five East African countries of Burundi, Kenya, Rwanda, Tanzania, and Uganda, examining the susceptible exposure windows.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003eThe children\u0026rsquo;s weight measurements at birth and associated household socioeconomic indicators were retrieved from the Demographic and Health Survey (DHS) waves. DHS program records the socio-economic background of children and their parents, as well as anthropometric information on children's development. The Global Positioning System (GPS) coordinates of each interviewee are recorded and reported with a random displacement of 2 km in urban areas and 5 km in rural areas to protect household confidentiality (Corsi et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). To make our analysis, data inclusion requirements consisted of singleton birth, having the birthweight record, and being located in a cluster with possible air pollutants matching the gestation period. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e gives the DHS wave used for each country, the sample size, and relevant statistics. We traced back environmental information starting nine months before the birth date of the child up to the registered birthdate.\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\u003eDemographic and Health Survey waves included in this study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDHS waves\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInitial sample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFinal sample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAverage birthweight*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnderweight births (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBurundi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDHS-2016-2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.164 (0.611)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKenya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDHS-2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.208 (0.588)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRwanda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDHS-2019-2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.305 (0.620)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTanzania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDHS-2015-2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.260 (0.574)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUganda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDHS-2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.364 (0.813)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e66 569\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e20696\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.249 (0.633)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e6.57*\u003c/b\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\u003e*The mean birthweight is given in kilograms, and the standard deviation is in parentheses. All statistics are according to the final sample size.\u003c/p\u003e \u003cp\u003eDespite the several environmental factors that may affect Utero and Vivo children\u0026rsquo;s health, accurate data on all potential confounders are usually not available. We fitted the multiple linear regression model with air quality data including fine particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e), ground-level ozone (O\u003csub\u003e3\u003c/sub\u003e) and carbon monoxide (CO) adjusted with weather variables of temperature, precipitation, and wind speed. The adjustment of the model also included the maternal education level, the household wealth index and location (rural against urban). We used a unique dataset from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) (Global Modeling and Assimilation Office (GMAO) \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) for air pollutants and weather variables. All data were averaged for every three months (trimester) as well as for the whole pregnancy period for each child. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e gives the summary statistics of air pollutants concerned with this study and associated meteorological variables averaged for nine months from each DHS cluster.\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\u003eStatistics of prenatal air pollution and meteorological variables exposure.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCO (ppb)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePM\u003csub\u003e25\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003cp\u003e(K)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePrecipitation\u003c/p\u003e \u003cp\u003e(mm/day)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWind\u003c/p\u003e \u003cp\u003e(m/s)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e294.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e287.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1st Quartile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e292.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd Quartile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e294.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd Quartile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e138.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e296.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e394.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e303.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.10\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\u003eTo ensure the best match of DHS and pollution records, we clipped each independent variable for each cluster buffer to assign exposure and averaged value of grid-point within each clustered buffer zone of 2 km in urban areas and 5 km in rural areas, using the Zonal Statistics Tool in ArcGIS Pro. The DHS five-digit wealth index from each wave and country has been converted into quintiles (lowest, low, middle, high, and highest), before merging datasets, to ensure their uniformity and transferability. Data analysis and modelling have been done using Python (version 3.10.9) and R (version 4.3.2) (Butler et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cp\u003eResults\u003c/p\u003e\n\u003cp\u003eAdjusted linear regression model results presented in Table 3 revealed that prenatal exposure to O\u003csub\u003e3\u003c/sub\u003e is associated with a significant negative impact on childbirth weight. According to the model, the increase in 1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e in O\u003csub\u003e3\u003c/sub\u003e exposure resulted in the reduction of the recorded child birthweight by 0.00300 kg (Standard Error (SE) of 0.00092), 0.00232 kg (SE: 0.0093), 0.00164 kg (SE: 0.00096) and 0.00674 kg (SE: 0.00143) during the first, second, third-trimester and entire pregnancy exposure period, respectively. Prenatal exposure to PM\u003csub\u003e2.5\u003c/sub\u003e and CO did not demonstrate a significant impact on the birthweight. Nevertheless, exposure to CO resulted in negative birthweight association during the whole digestion period and the first and the last trimester of the pregnancy. For instance, the increase in 1 ppb in CO above the mean value led to a decrease in the measured birthweight by 0.00027 kg (SE: 0.00015) for the last trimester of pregnancy exposure and 0.00025 kg (SE: 0.00031) for the entire pregnancy exposure to CO. \u0026nbsp;Furthermore, PM\u003csub\u003e2.5\u003c/sub\u003e showed non-significant negative birthweight outcomes only for the exposure during the second trimester. The increase of 1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e in PM\u003csub\u003e2.5\u003c/sub\u003e, during the second trimester of pregnancy, suggested the reduction in birthweight by\u0026nbsp;0.00097 kg (SE: 0.00076). Other covariates were associated with child birthweight in the presumed directions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Adjusted linear regression model estimates result of the impact of air pollution on birthweights.\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePollutants\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExposure Period\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficients (\u003c/strong\u003e \u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eEntire Pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.00035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e0.00097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eTrimester 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.00048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e0.00076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eTrimester 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e-0.00097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e0.00076 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eTrimester 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.00085 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e0.00077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eEntire Pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e-0.00674***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e0.00143\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eTrimester 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e-0.00300***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e0.00092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eTrimester 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e-0.00232**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e0.00093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eTrimester 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e-0.00164*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e0.00096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eEntire Pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e-0.00025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e0.00031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eTrimester 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e-0.00033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e0.00025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eTrimester 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.00024 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e0.00023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eTrimester 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e-0.00027*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e0.00015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNote\u003c/em\u003e\u003c/strong\u003e: The table reports the Adjusted linear regression model coefficient estimates of the impact of prenatal exposure to air pollution on birthweight and their standard errors. All models were adjusted for maternal education, household wealth index, household location (rural against urban), air temperature, wind speed and precipitation. ***Statistical significance at the 1% level, **Statistical significance at the 5% level, *Statistical significance at the 10% level.\u003c/p\u003e\n\u003cp\u003eUsing the logistic regression model, we derived the Odds ratios to determine the most influential period for the underweight birth occurrences. The Logistic regression model estimates the regression coefficient (\u0026nbsp;) and the exponential function of the regression coefficient (\u0026nbsp;) is the odds ratio associated with a one-unit increase in exposure (Sperandei 2014). All models were adjusted for maternal education, household wealth index, household location (rural against urban), air temperature, wind speed and precipitation. Indeed, only prenatal O\u003csub\u003e3\u003c/sub\u003e exposure yielded a significant positive association with underweight birth records. Following the model (Table 4), the increase in 1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e of O\u003csub\u003e3\u003c/sub\u003e, above the average values, predicted the increase in underweight occurrences by a multiple of 1.0084 (95% Confidence Interval (CI): 0.4298-1.3662) and the multiples of 1.0564 (95% CI: 0.4526-1.4659), 1.0626 (95% CI: 0.4552-1.4808) and 1.0470 (95% CI: 0.4487-1.4434) during the first-, second-, and third-trimester of pregnancy exposure, respectively. Prenatal exposure to PM\u003csub\u003e2.5\u003c/sub\u003e and CO did not show significant odds ratios for the children\u0026rsquo;s underweight birth incidences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Adjusted odds ratio estimates of the impact of air pollution on underweight birth prevalence\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"635\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePollutants\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExposure period\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eEntire Pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e0.6047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0.0467-1.8244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.7002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eTrimester 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.1814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0.5032-1.7738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.7019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eTrimester 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.1934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0.5083-1.8020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.6848\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eTrimester 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.1787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0.5021-1.7672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.7057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eEntire Pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.0084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0.4298-1.3662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.0486\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eTrimester 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.0564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0.4526-1.4659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.0615\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eTrimester 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.0626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0.4552-1.4808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.0523\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eTrimester 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.0470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0.4487-1.4434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.0490\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eCO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eEntire Pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.0371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0.4488-1.3964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.7321\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eTrimester 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e0.8985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0.0728-1.0879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.9335\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eTrimester 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.0340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0.4475-1.3894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.6376\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eTrimester 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.0379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0.4491-1.3986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.5306\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: The table reports the logistic regression odds ratio estimates of the impact of prenatal exposure to air pollution on the children\u0026rsquo;s birth weight. All models were adjusted for maternal education, household wealth index, household location (rural against urban), air temperature, wind speed and precipitation.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe impacts of air pollution exposure on pregnancy outcomes are poorly investigated in sub-Saharan Africa. Given the importance of the birthweight in the later development of the child, this study aimed to assess the impact of PM\u003csub\u003e2.5\u003c/sub\u003e, CO and O\u003csub\u003e3\u003c/sub\u003e on the birthweight in the East African region. The region is reported among regions recording lower child\u0026rsquo;s anthropometric outcomes (UNICEF et al. 2023) and is characterized by high pollution induced by open biomass burning, accelerated economic development, rapid urbanization and population growth (Baharane and Shatalov \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kalisa et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Opio et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We found that prenatal O\u003csub\u003e3\u003c/sub\u003e exposure slightly lowers birth weight with its exposure more sensitive during the last two pregnancy trimesters. This study agreed with a recent study on the impact of O\u003csub\u003e3\u003c/sub\u003e on child development (Balietti et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tong et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Using a large sample size from the Guangzhou megacity (China), Wang, et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that the most susceptible exposure period is the second pregnancy trimester (Wang et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Balietti, et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) assessed the impact of air pollutants on child development for the whole pregnancy period in India (Balietti et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Although the underlying mechanisms for O\u003csub\u003e3\u003c/sub\u003e exposure during pregnancy and low birth weights are still unclear, all these studies suggest its association. Generally, fetal weight starts to increase from the second trimester of the gestation period and requires more oxygen and nutrients. In addition to oxygen and nutrients, the fetal-maternal dependencies increase, such as oxidative damage mediators related to O\u003csub\u003e3\u003c/sub\u003e exposure, which may therefore influence fetal growth (Sales et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Although CO seems to act in a negative direction, this study did not find any significant birthweight impairment from ambient PM\u003csub\u003e2.5\u003c/sub\u003e and CO exposure during the pregnancy period. However, the negative association between PM\u003csub\u003e2.5\u003c/sub\u003e and CO and the birthweight was confirmed by da Silva et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Salam et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) (da Silva et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Salam et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) but, in their study, Balakrishnan et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) did not find any association of CO with the lower birthweight occurrences (Balakrishnan et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It was documented that high exposure to particulate matter may cause alveolar inflammation and alterations in blood viscosity, which also affect the placenta\u0026rsquo;s normal function (da Silva et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Carbon monoxide generally acts by limiting the amount of oxygen in transport by the blood for both the fetal and pregnant woman thus impairing the fetal growth.\u003c/p\u003e \u003cp\u003eNonetheless, this study presented some limitations. The first limitation consists of our assumption of a single cluster for each woman during the gestation period. The second limitation is related to the fact that we required a gridded air pollution dataset which should be obtained only by using air quality modeling or remote sensing approaches. However, the MERRA-2 dataset used by this study has demonstrated robust results in similar studies (Balietti et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Cui et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Nevertheless, our study has important public health implications that aim for the protection of children's health.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe danger of air pollution exposure is not limited to the pregnant woman but also reaches the fetus. This study revealed that, in the East African region, prenatal exposure to ground-level O\u003csub\u003e3\u003c/sub\u003e is associated with lower birthweight records with the second and third trimester of the pregnancy being the more susceptible periods of exposure. PM\u003csub\u003e2.5\u003c/sub\u003e and CO did not demonstrate significant impacts, although CO seemed to act in a negative direction, especially during the third gestation period. Adjusted logistic regression estimates showed that a 1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e increase in O\u003csub\u003e3\u003c/sub\u003e exposure in the second and the third pregnancy period respectively results in underweight birth occurrence by 6.3% (95% CI: 0.4552\u0026ndash;1.4808) and 4.7% (95% CI: 0.4487\u0026ndash;1.4434). Our findings about the sensitive air pollution exposure window also provide a research basis for further investigating additional mechanisms of child growth impairment by prenatal exposure to ambient air pollution.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eVal\u0026eacute;rien Baharane\u003c/em\u003e: Study conceptualization, data acquisition and analysis and the first draft of the manuscript. \u0026nbsp; \u003cem\u003eAndrey Borisovich Shatalov\u003c/em\u003e and \u003cem\u003eMaxim Viktorovich Larionov\u003c/em\u003e: Supervised, reviewed, and edited the first manuscript. All authors read and approved of the final manuscript\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData on the children\u0026rsquo;s birth weight and associated household socioeconomic indicators for the concerned countries may be acquired, on request, from the DHS Program administration (https://dhsprogram.com/). Air pollution and climate variables are freely available, after registration, at https://giovanni.gsfc.nasa.gov/giovanni/. \u0026nbsp;Other analysis and program codes used in this study are available on request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cstrong\u003eand consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was concerned with the analysis of the survey dataset available online with all identifier information removed, no ethics approvals were required. The corresponding author requested and obtained authorization to use the DHS dataset of the concerned countries from the DHS program administration and we agreed to adhere to the terms and conditions imposed by the DHS Program.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for conducting this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAgbo, K. E., Walgraeve, C., Eze, J. I., Ugwoke, P. E., Ukoha, P. O., \u0026amp; Van Langenhove, H. (2021). 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Association of maternal ozone exposure with term low birth weight and susceptible window identification. \u003cem\u003eEnvironment International\u003c/em\u003e, \u003cem\u003e146\u003c/em\u003e, 106208. https://doi.org/10.1016/j.envint.2020.106208\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"air pollution, children growth, birthweight, underweight, East Africa","lastPublishedDoi":"10.21203/rs.3.rs-5735744/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5735744/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis article assessed the impacts of prenatal exposure to PM\u003csub\u003e2.5\u003c/sub\u003e, CO and O\u003csub\u003e3\u003c/sub\u003e on the child’s birth weight in five East African countries trying to identify the susceptible exposure periods. The adjusted linear regression model showed that the increase in 1 µg/m\u003csup\u003e3\u003c/sup\u003e in O\u003csub\u003e3\u003c/sub\u003e exposure during the first, second and third pregnancy trimesters, respectively resulted in the reduction of the birthweight by 0.00300 kg (Standard Error (SE) of 0.00092 kg), 0.00232 kg (SE: 0.0093), 0.00164 kg (SE: 0.00096) and 0.00674 kg (SE: 0.00143). The logistic regression model revealed that a 1 µg/m\u003csup\u003e3\u003c/sup\u003e increase in prenatal exposure to O\u003csub\u003e3\u003c/sub\u003e concentration is associated with 4.7% (95% Confidence Interval (CI): 0.4487-1.4434) and 6.3% (95% CI: 0.4552-1.4808) prevalence of the underweight births during the second and third gestation trimesters, respectively. PM\u003csub\u003e2.5\u003c/sub\u003e and CO did not demonstrate significant impacts on birthweight, although CO seemed to impair the birthweight outcomes, especially the exposure during the last trimester of pregnancy. The findings of this study have important public health implications that aim to protect health in its early development.\u003c/p\u003e","manuscriptTitle":"Ambient air pollution exposure and child birthweight in East African countries: Identifying the sensitive periods","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-04 05:11:39","doi":"10.21203/rs.3.rs-5735744/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":"afdb074a-ade1-4c2f-b67a-a0f1e5b18fb2","owner":[],"postedDate":"February 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-20T12:20:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-04 05:11:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5735744","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5735744","identity":"rs-5735744","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}