Association of Prenatal COVID-19 Infection with Maternal Peripartum and Neonatal Complications in Gujarat India

Association of Prenatal COVID-19 Infection with Maternal Peripartum and Neonatal Complications in Gujarat 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 Short Report Association of Prenatal COVID-19 Infection with Maternal Peripartum and Neonatal Complications in Gujarat India Farjana Memon, Komal Shah, Modou Jobarteh, Nandan Thakkar, Fatimakhatun Memon, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9325039/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background The COVID-19 pandemic has intensified health risks across the population, particularly among vulnerable groups such as pregnant women & children. Pregnant women are considered most vulnerable due to physiological and immunological changes that may exacerbate disease severity as well as subsequent health outcomes. Empirical evidences showed possible links between prenatal COVID-19 infection and adverse maternal & neonatal outcomes. However, evidence from resource-constrained countries remains limited. Methods This study adopted a case-control design with the enrollment of a total of 593 mother-neonate pairs (191 COVID-positive and 402 COVID-negative) from two districts in Gujarat. Key variables analysed were maternal sociodemographic and clinical characteristics, pregnancy-related complications, delivery & neonatal health outcomes. Results Among the enrolled participants, 80.4% were aged ≤ 30 years, 74% resided in urban areas, and 58.2% were above the poverty line. Maternal peripartum and neonatal complications documented in 26.6% and 27.1% of cases, respectively. Prenatal COVID-19 infection was significantly associated with maternal peripartum complications in unadjusted logistic regression analysis (cOR = 1.49; 95% CI: 1.01–2.17; p = 0.042), but this was not sustained significant after adjustment (aOR = 1.25; 95% CI: 0.82–1.89; p = 0.295). Neonatal complications were significantly associated with prenatal COVID-19 infection in both unadjusted and adjusted logistic regression models (aOR = 1.87; 95% CI: 1.27–2.75; p = 0.008). Conclusion Prenatal COVID-19 infection was independently associated with neonatal complications, reinforcing the need for targeted monitoring and care during pregnancy & postpartum amid pandemics. In-utero COVID-19 exposure Neonatal Complication Maternal peripartum complications Prenatal COVID-19 infection Figures Figure 1 Figure 2 Introduction Since its emergence in December 2019, the COVID-19 pandemic has profoundly affected humanity across social, economic, political, and health domains. Pregnant women have been identified as a particularly vulnerable group owing to unique physiological and anatomical changes that impact their disease progression, pathophysiology, pregnancy outcomes. [ 1 – 3 ] Although the COVID-19 infection rates are similar between pregnant and non-pregnant women, evidence shows that pregnant women face a heightened risk of severe symptoms, often requiring intensive care, ICU admission, mechanical ventilation, or even extracorporeal membrane oxygenation support. [ 4 – 8 ] Recent studies have demonstrated that COVID-19 infection could be a risk factor for a range of pregnancy-related complications, leading to adverse outcomes for maternal, fetal, and neonatal. These complications may include hypertensive disorders during pregnancy, higher likelihood of cesarean delivery, increased rates of preterm birth, neonatal ICU admissions, and changes in breastfeeding practices. [ 2 , 9 , 10 ] Additionally, the presence of pre-existing comorbidities and socioeconomic factors, daily stressors, and limited access to adequate antenatal, Perinatal, and postpartum care can further exacerbate these adverse outcomes. [ 11 ] In response to the increased vulnerability of pregnant women to severe outcomes, the scientific community actively began gathering data to better understand COVID-19’s nature and medical impact on high-risk groups. This effort has led to the implementation of tailored policies and preventive measures like timely updates in COVID-19 vaccination and pre-delivery screening. Despite these advancements, evidence documented adverse impacts among these groups, especially in resource-constrained settings. Fully understanding COVID-19’s impact on such populations remains an evolving area of research, especially region-specific. While the acute phase of the pandemic has subsided, new robust, context-specific insights are crucial for a comprehensive understanding of these impacts. [ 1 , 2 , 12 ] This study, therefore, aims to assess the impact of prenatal COVID-19 infection on maternal peripartum & neonatal Complications in Gujarat, India. Methods Study design: This study adopted a case control design, a part of a larger ambispective longitudinal study cohort in Gujarat India settings. The study considered the pandemic waves from March to October 2020 as the first wave, February to May 2021 as the second wave, and November 2021 to March 2022 as the third wave. A total of four follow-ups were conducted at six-month intervals of from the first contact. This study presents findings from the initial enrollment phase, capturing reproductive indicators, healthcare access, COVID-19 infection status during pregnancy, and peripartum maternal and neonatal complications. Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research . Sample size & Sampling frame: We initially identified participants from public, private, and COVID-19-designated hospital databases with the key criteria of having pregnancy during the covid-19 pandemic & covid-19 infection during pregnancy. They were assessed for other eligibility per inclusion criteria and contacted for study participation through informed consent. The potential consented index participants were labelled as Covid diagnosed/positive participants and further followed for snowballing sampling to enhance the sample size. Simultaneously, a comparison group of women from the same region and the same period of pregnancy who tested negative for COVID-19 during pregnancy were identified, contacted, and recruited in a 1:2 ratio. They were labelled as COVID-19, not diagnosed/negative group of participants. The study included and assessed 593 mother-neonate pairs during the COVID-19 pandemic from selected study settings. Study Settings: This study was carried out in two geographically distinct districts within the Indian state of Gujarat: Ahmedabad, an urban area, and Sabarkantha, a rural area. Furthermore, study participants were recruited from all seven zones of Ahmedabad and all blocks of Sabarkantha district. Variables of Interest: Maternal peripartum complications and Neonatal Complications are the primary outcomes of interest. Dependent Variables: Maternal peripartum complications: This study considered the “Maternal peripartum complications” as shortly just before, during, and immediately after giving birth. we created a composite variable namely “Maternal peripartum complications” as a dichotomous dependent variable and defined as the occurrence of any of the following conditions: oligohydramnios, convulsions, sudden hike of blood pressure during labour, pre-eclampsia, eclampsia (a sudden complication of pre-eclampsia marked by seizure and required medical emergency), tachycardia during labour, pre-term labour, premature rupture of membranes (PROM), prolonged labour, lack of cervical dilation. All complications were identified through a review of maternal medical records and verified through structured verbal questionnaires, including questions related to the indication for cesarean delivery and any maternal complications experienced during labour. Neonatal Complications: We created a composite variable namely “Neonatal Complications” as a dichotomous dependent variable and defined it as the occurrence of low birth weight or required NICU admission during 28 days of birth. Independent variables The independent variables related to maternal characteristics included socio-demographic factors such as age, district, religion, caste, education, and type of ration card (The ration card, issued by the state government based on socioeconomic status, served as a proxy for the participant's economic background) and reproductive history, which includes status of gravida, place of delivery, and type of delivery. Additional independent variables comprised pregnancy-related complications during antenatal periods (gestational hypertension, gestational diabetes, mild to moderate anaemia), as well as COVID-19 infection status during pregnancy. For Neonates, it includes all maternal independent variables mentioned above, and an additional variable specific to the neonates was the sex of the child. Patient and Public Involvement: The study did not involve patients or study participants in the design, conduct, reporting, or dissemination plans of this study. Recruitment strategies and study procedures were designed solely by the research team. Future dissemination of study findings will follow standard academic channels without direct patient or public co-production. However, it will be shared with the participants on a request basis for their reading & to know the study outcomes. Statistical methods and modelling Simple descriptive statistics using mean and median for continuous data and frequency for categorical data were used to gain an initial understanding of the data. To assess the association between maternal peripartum & neonatal complications (dependent variable) and prenatal COVID-19 infection (key predictor), a chi-square test of independence was performed. Subsequently, both unadjusted and adjusted binomial logistic regression models were applied to estimate the strength and direction of the association between the dependent and independent variables while accounting for potential confounders. Many variables are record-based, so we observed missing data in our study. Observations with missing data were excluded from the logistic regression models; this represented a maximum of 4% of total observations. Statistical analyses were performed using JAMOVI (version 2.6.26). Results Table 1: Sociodemographic Profile (n=593) Characteristics N (%) Maternal Age (in years) ≤ 30 years of age 477 80.4 > 30 years of age 116 19.6 Prenatal COVID-19 infection COVID (-) 402 67.8 Covid (+) 191 32.2 District Ahmedabad 439 74 Sabarkantha 154 26 Caste General 193 32.5 OBC/SC/ST* 400 67.5 Level of education No formal Education 61 10.3 Primary to H. Secondary 398 67.1 Graduation/above graduation 134 22.6 Ration card APL** 345 58.2 BPL** 128 21.6 Occupation Housewife 520 87.7 Working women 73 12.3 Child median age at enrollment (in months) 11 [11.7 – 12.7] Gender of the child Male 307 51.8 Female 286 48.2 Note: *OBC: Other Backwards Class, SC*: Scheduled Caste, ST*: Scheduled Tribes ** APL – Above poverty line, BPL: Below poverty line Table 1 presents a detailed overview of the demographic, socioeconomic, and health-related characteristics of the study population. A significant majority of the mothers are less than or equal to 30 years of age (80.4%), recruited from urban areas (74%), and above the poverty line (58.2%). Additionally, most of the recruited women belong to the OBC/ST/SC category (67.5%), have primary to higher secondary education (67.1%), and are from the above-poverty families (58.2%) as categorized by the Indian government to distribute the subsidized food grains. Table 2: Prevalence of Maternal Peripartum & Neonatal Complications (n=593) Characteristics N Prevalence Rate [95% CI] Maternal peripartum complications Yes 158 26.6 [23.2 - 30.4] No 435 73.4 [69.6 - 76.8] Neonatal Complication Yes 161 27.1 [23.7 - 30.9] No 432 72.8 [69.1 - 76.3] Note: Refer to the "Variables" section for details of Peripartum Maternal & Neonatal complications Table 2 provides the prevalence of maternal peripartum and neonatal complications among the study population, which is 26.6 % and 27.1 %, respectively, with a 95% confidence interval (CI). As shown in the table, the Neonatal complication is slightly higher than the maternal peripartum complication. Bivariate Analysis of various predictors of interest on Maternal Peripartum & Neonatal Complications The series of chi-square tests of independence was used to assess the association between various maternal characteristics and peripartum maternal and neonatal complications. Table 3: Association of Maternal Characteristics with Maternal Peripartum and Neonatal Complications (N = 593) Characteristics Maternal Peripartum Complication Neonatal Complications No (n=435) Yes (n=158) p No (n=432) Yes (n=161) p District Ahmedabad 316 (72.6%) 123 (77.8%) 0.201 316 (73.1%) 123 (76.4%) 0.422 Sabarkantha 119 (27.4%) 35 (22.2%) 116 (26.9%) 38 (23.6%) Maternal Age ≤ 30 years 363 (83.4%) 114 (72.2%) 0.002 348 (80.6%) 129 (80.1%) 0.906 > 30 years 72 (16.6%) 44 (27.8%) 84 (19.4%) 32 (19.9%) Caste Reserved* 303 (69.7%) 97 (61.4%) 0.058 295 (68.3%) 105 (65.2%) 0.478 General 132 (30.3%) 61 (38.6%) 137 (31.7%) 56 (34.8%) Education No formal Education 36 (8.3%) 10 (6.3%) 0.233 33 (7.6 %) 13 (8.1%) 0.757 Till high school 299 (68.7%) 99 (62.7 %) 298 (69.0%) 115 (71.4%) Graduation and above 91 (20.9%) 43 (27.2%) 101 (23.4%) 33 (20.5%) Wealth index measured by availability of Ration card APL 252 (57.9%) 93 (58.9%) 0.582 244 (56.5%) 101 (62.7%) 0.373 BPL 98 (22.5%) 30 (19.0%) 96 (22.2%) 32 (19.9%) None 85 (19.5%) 35 (22.2%) 92 (21.3%) 28 (17.4%) Occupation Housewife 382 (87.8%) 138 (87.3%) 0.760 379 (87.7%) 141 (87.6%) 0.673 Working 53 (72.6%) 20 (27.4%) 53 (12.3%) 20 (12.4%) COVID-19 infection during pregnancy COVID (-) 305 (70.1%) 97 (61.4%) 0.044 310 (71.8%) 92 (57.1%) <.001 COVID (+) 130 (29.9%) 61 (38.6%) 122 (28.2%) 69 (42.9%) COVID-19 vaccination before delivery Yes 39 (12.2%) 15 (12.9%) 0.939 111 (25.7%) 46 (28.7%) 0.455 ANC visit as per guidelines No 17 (3.9%) 6 (3.8%) 0.951 15 (3.5%) 8 (5.0%) 0.401 Complications during pregnancy (antenatal period) Yes 85 (19.6%) 52 (33.1%) <.001 89 (20.6%) 48 (30.0%) 0.017 Gravida status Primigravida 155 (35.6%) 54 (34.2%) 0.743 152 (35.2%) 57 (35.4%) 0.96 Multigravida 280 (64.4%) 104 (65.8%) 280 (64.8%) 104 (64.6%) Place of delivery Private 224 (51.9%) 80 (51.3%) 0.926 242 (56.4%) 62 (39.0%) <.001 Public 208 (48.1%) 76 (48.7%) 187 (43.6%) 97 (61.0%) Type of delivery Vaginal 257 (59.1%) 68 (43.0%) <.001 236 (54.6%) 89 (55.3%) 0.888 C-section 178 (40.9%) 90 (57.0%) 196 (45.4%) 72 (44.7%) Gender - Child Male NA NA NA 232 (53.7%) 75 (46.6%) 0.123 Female NA NA 200 (46.3%) 86 (53.4%) As shown in Table 3, maternal peripartum complications were significantly associated with maternal age (χ² = 9.51, p = .002), COVID-19 infection during pregnancy (χ² = 4.07, p = .044), complications during pregnancy (χ² = 16.73, p < .001), and type of delivery (χ² = 12.48, p < .001). Post hoc standardized residuals indicated that the higher rates of complications among women with prenatal COVID-19 infection (residual = ±2.01), pregnancy-related complications during the antenatal period (±3.44), and higher maternal age (±3.07) reflect a non-random pattern, suggesting these factors may have a meaningful influence rather than representing chance variation. No significant associations were found with district, caste, education, wealth index, occupation, COVID-19 vaccination status, ANC visits, gravida status, or place of delivery. Neonatal complications were significantly associated with maternal COVID-19 infection/in utero covid-19 exposure (χ² = 14.14, p < .001), complications during pregnancy (χ² = 5.70, p = .017), and place of delivery (χ² = 14.15, p < .001). Post hoc standardized residuals showed that higher complication rates among neonates of mothers with prenatal COVID-19 infection (residual = ±3.39), pregnancy complications (±2.39), and deliveries in public health facilities (±3.75) also reflect a non-random pattern, suggesting these associations are unlikely due to chance alone. No significant associations were observed with maternal age, caste, education, wealth index, occupation, vaccination status, ANC visits, gravida, type of delivery, or child’s gender. Independent Predictors of Maternal Peripartum & Neonatal Complications: Logistic Regression To further examine the strength and direction of association between prenatal COVID-19 infection and maternal peripartum & Neonatal complications while adjusting for potential confounders, a binomial logistic regression analysis was conducted. Before performing the analysis, the validity of the logistic regression model was ensured by assessing key statistical assumptions. Model Fit: A stepwise logistic regression approach was used to assess the association between prenatal COVID-19 infection and Peripartum maternal and neonatal complications. Variables with a p-value ≤ 0.30 in bivariate analysis were included in the adjusted models to control for potential confounding. Model 1 included only the main predictor (prenatal COVID-19 infection), Model 2 added sociodemographic variables, and Model 3 further adjusted for reproductive and clinical factors. Model fit improved across stages, with a decrease in deviance and AIC, and an increase in McFadden’s R² (from 0.008 to 0.057 for maternal and from 0.012 to 0.048 for neonatal models), indicating better explanatory power in the fully adjusted models. As shown in Figure 1, the final adjusted logistic regression model examined the association between prenatal COVID-19 infection and maternal peripartum complications. The model included the following control variables based on theoretical relevance and bivariate significance (p ≤ 0.30): maternal age (reference: younger group), education level (reference: no formal education), caste (reference: General category), district of residence (reference: Ahmedabad), and presence of pregnancy-related complications during antenatal period (reference: no complications). After adjusting for all covariates, the odds of experiencing maternal peripartum complications were higher among women with prenatal COVID-19 infection; however, this association was no longer statistically significant after controlling for other maternal characteristics, indicating that the effect of prenatal COVID-19 infection was confounded with other maternal characteristics. Other covariates including education, caste, and district of residence also did not show significant associations in the fully adjusted model. Notably, pregnancy-related complications and increasing maternal age remained significant independent predictors of peripartum complications after adjusting for all other variables. As shown in Figure 2, the final adjusted logistic regression model assessed the association between prenatal COVID-19 infection and neonatal complications. Similar to the maternal peripartum complications model, variables were selected based on theoretical relevance and bivariate significance, including pregnancy-related complications, place of delivery (Private hospital) and gender of the baby (female). After adjustment, prenatal COVID-19 infection, complications during pregnancy, and delivery in public health facilities were significantly associated with higher odds of neonatal complications. In contrast, the gender of the baby did not show a significant association. Notably, prenatal COVID-19 infection and pregnancy-related complications remained independent predictors of neonatal complications after controlling for all other covariates. Discussion This community-based analysis of 593 mother–neonate pairs initially showed a borderline significant increase in the odds of maternal peripartum complications; this association weakened and lost statistical significance after adjusting for other explanatory variables. This decline could be attributed to the non-significant association between COVID-19 infection and pre-term labour, a key outcome/event included in our composite index of maternal peripartum complications. However, our study reported a non-significant association between pre-term labour; it was commonly observed to be significantly associated with COVID-19 infection in many earlier studies. [3,13–15] In contrast, many other studies reported similar or unclear results about the incidence in the pre-term. For Neonatal complications, prenatal COVID-19 infection remained a significant independent predictor. Higher odds of having neonatal complications were observed with neonates born to COVID-19-positive mothers, and this was consistent even after adjusting for other maternal characteristics. Most of the complications observed in our analysis were not isolated incidents; several previous studies have similarly reported associations between COVID-19 infection and neonatal complications. [16–22] De Medeiros et al.'s findings aligned with the higher rates of neonatal complications (higher NICU admission & Low birth weight) [23,24] , and a study done by Norman et al found that these findings were consistent even after adjusting for other maternal characteristics [25] . In contrast, Wilkinson et al. observed no statistically significant differences. [26] Additionally, neonatal complications were more likely among offspring born to mothers who experienced pregnancy complications, pre-term births, and deliveries in public healthcare facilities. The key strength of this study is that it is a community-based primary data study, and the data are mainly from the first two waves of the pandemic, where young populations were at higher risk; hence, it would represent the real-time scenario for the outcome of interest. Secondly, it contains the controlled arm, which allows it to be compared to women without COVID-19 infection. Several limitations also need to be acknowledged. Firstly, the study excluded pregnant women with SARS-CoV-2 who weren’t screened for any of the reasons, and potential asymptomatic COVID-19 in the community. Secondly, although most of the COVID-19-positive participants were mildly to moderately infected, it’s worth presenting the outcome of interest according to the severity of the infection. However, due to the limited sample size for COVID-positive, too much stratification is not possible; hence, it is missing in our findings. Thirdly, while the study provides valuable insights into COVID-19 infection status and management, it limited the scope to present data on stillbirth and neonatal mortality due to the ongoing pandemic and challenges in securing community permission. Conclusion Our study provides compelling region-specific evidence of increased odds of peripartum maternal and significant neonatal complications associated with prenatal COVID-19 infection and in-utero exposure to the infection. This consistent association underscores the potential biological and systemic impact of in-utero exposure to maternal COVID-19 infection. These findings can offer robust evidence for public health strategies, particularly in pandemics and health crises. To get better insights, further in-depth exploration of biological mechanisms and health system factors that may mediate these outcomes is recommended. Declarations Acknowledgement: We would like to express our sincere gratitude to all the enrolled study participants who generously shared their valuable support and share the required information. We are also thankful to our colleagues Dr. Monali Patel and Dr. Shailee Patil for their assistance in data collection. Our heartfelt thanks go out to the Deputy Health Officers of the respective zonal health offices, Medical Officers at the Urban health centres, and the dedicated Anganwadi workers from all the respective AWCs for providing invaluable support during the field data collection. The successful completion of this study would not have been possible without the unwavering support of the Ahmedabad Municipal Corporation and the Private and public health providers in Ahmedabad and Sabarkantha districts. We thank all these stakeholders for their crucial contributions to our research/study. Funding: The study was initiated under the Action Against Stunting Hub, funded by UKRI GCRF. However, Funder had no direct involvement in the design, conduct & dissemination of findings. Competing interests: The authors declare that they have no competing interests Data Availability statement: The datasets used/analysed in this study will be made accessible upon request from the Principal Investigator at [email protected] . Ethics approval and consent to participate: Ethics approval was granted by the institutional ethics committees of the Indian Institute of Public Health Gandhinagar (IIPHG) (Referenace no: SHSRC/2021/2185) and London School of Hygiene and Tropical Medicine (LSHTM) (reference no: 72848). Informed consent was obtained through fingerprint or signature on a pre-structured form that provided detailed information about the project and withdrawal policy. The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and relevant national and institutional guidelines. Clinical trial number: Not Applicable Consent for publication: Not Applicable Author Contribution All authors contributed equally to the development of this study. DS, KS, CF, and MJ participated in the study's conception and design. FM was responsible for data collection, analysis, and drafting the initial version of the manuscript. FKM assisted in data cleaning & writing the first draft of manuscript. DS, KS, CF, NT, AS, and TP critically reviewed the manuscript. All authors read and approved the final manuscript.Abbreviations: DS – Deepak Saxena; KS – Komal Shah; CF – Claire Heffernan; MJ – Modou Jobarteh; FM – Farjana Memon; FKM: Fatimakhatun Memon, NT – Nandan Thakkar; AS – Anish Sinha; TP – Tapasvi Puwar References Ferrara A, Hedderson MM, Zhu Y, Avalos LA, Kuzniewicz MW, Myers LC et al. 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Available from: https://www.bmj.com/content/370/bmj.m3320 de Medeiros KS, Sarmento ACA, Costa APF, Macêdo LT, de Silva A, de Freitas LAS. CL, Consequences and implications of the coronavirus disease (COVID-19) on pregnancy and newborns: A comprehensive systematic review and meta-analysis. International Journal of Gynecology & Obstetrics [Internet]. 2022 Mar 1 [cited 2024 Sep 4];156(3):394–405. Available from: https://onlinelibrary.wiley.com/doi/full/ 10.1002/ijgo.14015 Norman M, Navér L, Söderling J, Ahlberg M, Hervius Askling H, Aronsson B et al. Association of Maternal SARS-CoV-2 Infection in Pregnancy With Neonatal Outcomes. JAMA [Internet]. 2021 May 25 [cited 2024 Sep 5];325(20):2076–86. Available from: https://jamanetwork.com/journals/jama/fullarticle/2779586 Wilkinson M, Johnstone ED, Simcox LE, Myers JE. The impact of COVID-19 on pregnancy outcomes in a diverse cohort in England. Sci Rep [Internet]. 2022 Dec 1 [cited 2024 Sep 4];12(1). Available from: https://pubmed.ncbi.nlm.nih.gov/35042979/ Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 21 May, 2026 Reviewers agreed at journal 13 May, 2026 Reviews received at journal 13 May, 2026 Reviewers agreed at journal 11 May, 2026 Reviewers invited by journal 08 May, 2026 Editor invited by journal 19 Apr, 2026 Editor assigned by journal 19 Apr, 2026 Submission checks completed at journal 17 Apr, 2026 First submitted to journal 17 Apr, 2026 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. 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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-9325039","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":641395819,"identity":"6c59092e-e559-4cb0-b167-1381bd6e3bbd","order_by":0,"name":"Farjana Memon","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYBAC9gYwZcPDxsBDpBaeA2AqjXQth0FMYrWIHT664eOO8zJ80r2HXzBU3LNrIKhFOi3t5swzt3nYZM6lWTCcKU4mqMVeOsfsNm8bUItEjpkBY1tCMmGHSed/A2o5R5KWHDaglgMgLcYPgFrsiNCSZnZzZlsy0C9nzBgSziQkEKEl+dmNj2129vKze4w/fKhIsCeoBQEkGNgkgFYkNpCihfkDkCLFllEwCkbBKBghAACVMDfN8WpmqwAAAABJRU5ErkJggg==","orcid":"","institution":"Indian Institute of Public Health Gandhinagar","correspondingAuthor":true,"prefix":"","firstName":"Farjana","middleName":"","lastName":"Memon","suffix":""},{"id":641395820,"identity":"48a7b74a-5c5d-46ac-81c9-ea1aca0f0349","order_by":1,"name":"Komal Shah","email":"","orcid":"","institution":"Indian Institute of Public Health Gandhinagar","correspondingAuthor":false,"prefix":"","firstName":"Komal","middleName":"","lastName":"Shah","suffix":""},{"id":641395824,"identity":"d348eca6-5282-4376-a8bf-2c5243165b68","order_by":2,"name":"Modou Jobarteh","email":"","orcid":"","institution":"London School of Hygiene \u0026 Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Modou","middleName":"","lastName":"Jobarteh","suffix":""},{"id":641395827,"identity":"363cbe1f-2e6f-4dc8-b288-6ab87c6a3f19","order_by":3,"name":"Nandan Thakkar","email":"","orcid":"","institution":"University of North Carolina at Chapel Hill","correspondingAuthor":false,"prefix":"","firstName":"Nandan","middleName":"","lastName":"Thakkar","suffix":""},{"id":641395831,"identity":"1877df39-0b67-4ef5-98e6-24b0379b2903","order_by":4,"name":"Fatimakhatun Memon","email":"","orcid":"","institution":"Narendra Modi Medical College","correspondingAuthor":false,"prefix":"","firstName":"Fatimakhatun","middleName":"","lastName":"Memon","suffix":""},{"id":641395832,"identity":"79585015-2790-4948-ae4d-91903d7e1d38","order_by":5,"name":"Anish Sinha","email":"","orcid":"","institution":"Indian Institute of Public Health Gandhinagar","correspondingAuthor":false,"prefix":"","firstName":"Anish","middleName":"","lastName":"Sinha","suffix":""},{"id":641395837,"identity":"77810c66-1067-4080-8744-3e7b7e4d9ad2","order_by":6,"name":"Tapasvi Puwar","email":"","orcid":"","institution":"Indian Institute of Public Health Gandhinagar","correspondingAuthor":false,"prefix":"","firstName":"Tapasvi","middleName":"","lastName":"Puwar","suffix":""},{"id":641395838,"identity":"76e14d9d-853f-4df6-a7b4-4e4bb69399d5","order_by":7,"name":"Deepak Saxena","email":"","orcid":"","institution":"Indian Institute of Public Health Gandhinagar","correspondingAuthor":false,"prefix":"","firstName":"Deepak","middleName":"","lastName":"Saxena","suffix":""},{"id":641395839,"identity":"0e6e1616-d225-4154-993c-e2cff4efbbc5","order_by":8,"name":"Claire Heffernan","email":"","orcid":"","institution":"London School of Hygiene \u0026 Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Claire","middleName":"","lastName":"Heffernan","suffix":""}],"badges":[],"createdAt":"2026-04-05 08:53:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9325039/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9325039/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109759570,"identity":"00348e43-9861-40c1-a5ad-2fd207cda29f","added_by":"auto","created_at":"2026-05-22 07:27:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":97353,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdjusted logistic regression of prenatal COVID-19 infection and Maternal Peripartum complications\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9325039/v1/8cd7c3c519385b97735a8743.png"},{"id":109457384,"identity":"5a758bf0-005a-429f-8fb0-287a280cb2a9","added_by":"auto","created_at":"2026-05-18 10:18:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":94671,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdjusted logistic regression of prenatal COVID-19 infection and Neonatal Complications:\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9325039/v1/8129aff1005379cd15e2f8a0.png"},{"id":109765236,"identity":"0e7c93ca-45ef-46a7-8683-873f56198962","added_by":"auto","created_at":"2026-05-22 07:41:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":442966,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9325039/v1/9313f9da-4d3b-42bd-8918-392fef222c97.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of Prenatal COVID-19 Infection with Maternal Peripartum and Neonatal Complications in Gujarat India","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSince its emergence in December 2019, the COVID-19 pandemic has profoundly affected humanity across social, economic, political, and health domains. Pregnant women have been identified as a particularly vulnerable group owing to unique physiological and anatomical changes that impact their disease progression, pathophysiology, pregnancy outcomes.\u003csup\u003e[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e Although the COVID-19 infection rates are similar between pregnant and non-pregnant women, evidence shows that pregnant women face a heightened risk of severe symptoms, often requiring intensive care, ICU admission, mechanical ventilation, or even extracorporeal membrane oxygenation support.\u003csup\u003e[\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eRecent studies have demonstrated that COVID-19 infection could be a risk factor for a range of pregnancy-related complications, leading to adverse outcomes for maternal, fetal, and neonatal. These complications may include hypertensive disorders during pregnancy, higher likelihood of cesarean delivery, increased rates of preterm birth, neonatal ICU admissions, and changes in breastfeeding practices.\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e Additionally, the presence of pre-existing comorbidities and socioeconomic factors, daily stressors, and limited access to adequate antenatal, Perinatal, and postpartum care can further exacerbate these adverse outcomes.\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn response to the increased vulnerability of pregnant women to severe outcomes, the scientific community actively began gathering data to better understand COVID-19\u0026rsquo;s nature and medical impact on high-risk groups. This effort has led to the implementation of tailored policies and preventive measures like timely updates in COVID-19 vaccination and pre-delivery screening. Despite these advancements, evidence documented adverse impacts among these groups, especially in resource-constrained settings. Fully understanding COVID-19\u0026rsquo;s impact on such populations remains an evolving area of research, especially region-specific. While the acute phase of the pandemic has subsided, new robust, context-specific insights are crucial for a comprehensive understanding of these impacts.\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e This study, therefore, aims to assess the impact of prenatal COVID-19 infection on maternal peripartum \u0026amp; neonatal Complications in Gujarat, India.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design:\u0026nbsp;\u003c/strong\u003eThis study adopted\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ea case control design, a part of a larger ambispective longitudinal study cohort in Gujarat India settings. The study considered the pandemic waves from March to October 2020 as the first wave, February to May 2021 as the second wave, and November 2021 to March 2022 as the third wave. A total of four follow-ups were conducted at six-month intervals of from the first contact. This study presents findings from the initial enrollment phase, capturing reproductive indicators, healthcare access, COVID-19 infection status during pregnancy, and peripartum maternal and neonatal complications. Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample size \u0026amp; Sampling frame:\u003c/strong\u003e We initially identified participants from public, private, and COVID-19-designated hospital databases with the key criteria of having pregnancy during the covid-19 pandemic \u0026amp; covid-19 infection during pregnancy. They were assessed for other eligibility per inclusion criteria and contacted for study participation through informed consent. The potential consented index participants were labelled as Covid diagnosed/positive participants and further followed for snowballing sampling to enhance the sample size. Simultaneously, a comparison group of women from the same region and the same period of pregnancy who tested negative for COVID-19 during pregnancy were identified, contacted, and recruited in a 1:2 ratio. They were labelled as COVID-19, not diagnosed/negative group of participants. The study included and assessed 593 mother-neonate pairs during the COVID-19 pandemic from selected study settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Settings:\u0026nbsp;\u003c/strong\u003eThis study was carried out in two geographically distinct districts within the Indian state of Gujarat: Ahmedabad, an urban area, and Sabarkantha, a rural area. Furthermore, study participants were recruited from all seven zones of Ahmedabad and all blocks of Sabarkantha district.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariables of Interest:\u0026nbsp;\u003c/strong\u003eMaternal peripartum complications and Neonatal Complications are the primary outcomes of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDependent Variables:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaternal peripartum complications:\u003c/strong\u003e\u0026nbsp; This study considered the \u0026ldquo;Maternal peripartum complications\u0026rdquo; as shortly just before, during, and immediately after giving birth. we created a composite variable namely \u0026ldquo;Maternal peripartum complications\u0026rdquo; as a dichotomous dependent variable and defined as the occurrence of any of the following conditions: oligohydramnios, convulsions, sudden hike of blood pressure during labour, pre-eclampsia, eclampsia (a sudden complication of pre-eclampsia marked by seizure and required medical emergency), tachycardia during labour, pre-term labour, premature rupture of membranes (PROM), prolonged labour, lack of cervical dilation. All complications were identified through a review of maternal medical records and verified through structured verbal questionnaires, including questions related to the indication for cesarean delivery and any maternal complications experienced during labour.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNeonatal Complications:\u003c/strong\u003e We created a composite variable namely \u0026ldquo;Neonatal Complications\u0026rdquo; as a dichotomous dependent variable and defined it as the occurrence of low birth weight or required NICU admission during 28 days of birth.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndependent variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe independent variables related to maternal characteristics included socio-demographic factors such as age, district, religion, caste, education, and type of ration card (The ration card, issued by the state government based on socioeconomic status, served as a proxy for the participant\u0026apos;s economic background) and reproductive history, which includes status of gravida, place of delivery, and type of delivery. Additional independent variables comprised pregnancy-related complications during antenatal periods (gestational hypertension, gestational diabetes, mild to moderate anaemia), as well as COVID-19 infection status during pregnancy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor Neonates, it includes all maternal independent variables mentioned above, and an additional variable specific to the neonates was the sex of the child. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient and Public Involvement:\u0026nbsp;\u003c/strong\u003eThe study did not involve patients or study participants in the design, conduct, reporting, or dissemination plans of this study. Recruitment strategies and study procedures were designed solely by the research team. Future dissemination of study findings will follow standard academic channels without direct patient or public co-production. However, it will be shared with the participants on a request basis for their reading \u0026amp; to know the study outcomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical methods and modelling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSimple descriptive statistics using mean and median for continuous data and frequency for categorical data were used to gain an initial understanding of the data. To assess the association between maternal peripartum \u0026amp; neonatal complications (dependent variable) and prenatal COVID-19 infection (key predictor), a chi-square test of independence was performed. Subsequently, both unadjusted and adjusted binomial logistic regression\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003emodels were applied to estimate the strength and direction of the association between the dependent and independent variables while accounting for potential confounders.\u003c/p\u003e\n\u003cp\u003eMany variables are record-based, so we observed missing data in our study. Observations with missing data were excluded from the logistic regression models; this represented a maximum of 4% of total observations. Statistical analyses were performed using JAMOVI (version 2.6.26).\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eTable 1: Sociodemographic Profile (n=593)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMaternal Age (in years)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 23px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 24px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026le; 30 years of age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 23px;\"\u003e\n \u003cp\u003e477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 24px;\"\u003e\n \u003cp\u003e80.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026gt; 30 years of age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 23px;\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 24px;\"\u003e\n \u003cp\u003e19.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePrenatal COVID-19 infection\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 24px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eCOVID (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 23px;\"\u003e\n \u003cp\u003e402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 24px;\"\u003e\n \u003cp\u003e67.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eCovid (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 23px;\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 24px;\"\u003e\n \u003cp\u003e32.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eDistrict\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 23px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 24px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eAhmedabad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 23px;\"\u003e\n \u003cp\u003e439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 24px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eSabarkantha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 23px;\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 24px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCaste\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 23px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 24px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eGeneral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 23px;\"\u003e\n \u003cp\u003e193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 24px;\"\u003e\n \u003cp\u003e32.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eOBC/SC/ST*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 23px;\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 24px;\"\u003e\n \u003cp\u003e67.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eLevel of education\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 23px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 24px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eNo formal Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 23px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 24px;\"\u003e\n \u003cp\u003e10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003ePrimary to H. Secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 23px;\"\u003e\n \u003cp\u003e398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 24px;\"\u003e\n \u003cp\u003e67.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eGraduation/above graduation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 23px;\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 24px;\"\u003e\n \u003cp\u003e22.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eRation card\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 23px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 24px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eAPL**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 23px;\"\u003e\n \u003cp\u003e345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 24px;\"\u003e\n \u003cp\u003e58.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eBPL**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 23px;\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 24px;\"\u003e\n \u003cp\u003e21.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eOccupation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 23px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 24px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eHousewife\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 23px;\"\u003e\n \u003cp\u003e520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 24px;\"\u003e\n \u003cp\u003e87.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eWorking women\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 23px;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 24px;\"\u003e\n \u003cp\u003e12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eChild median age at enrollment (in months)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 48px;\"\u003e\n \u003cp\u003e11 [11.7 \u0026ndash; 12.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eGender of the child\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 23px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 24px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 23px;\"\u003e\n \u003cp\u003e307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 24px;\"\u003e\n \u003cp\u003e51.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 23px;\"\u003e\n \u003cp\u003e286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 24px;\"\u003e\n \u003cp\u003e48.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"3\" valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eNote: *OBC: Other Backwards Class, SC*: Scheduled Caste, ST*: Scheduled Tribes ** APL \u0026ndash; Above poverty line, BPL: Below poverty line\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 1 presents a detailed overview of the demographic, socioeconomic, and health-related characteristics of the study population. A significant majority of the mothers are less than or equal to 30 years of age (80.4%), recruited from urban areas (74%), and above the poverty line (58.2%). Additionally, most of the recruited women belong to the OBC/ST/SC category (67.5%), have primary to higher secondary education (67.1%), and are from the above-poverty families (58.2%) as categorized by the Indian government to distribute the subsidized food grains.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Prevalence of Maternal Peripartum \u0026amp; Neonatal Complications (n=593)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"596\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 229px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 272px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrevalence Rate [95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"3\" style=\"width: 596px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal peripartum complications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 229px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 95px;\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 272px;\"\u003e\n \u003cp\u003e26.6 [23.2 - 30.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 229px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 95px;\"\u003e\n \u003cp\u003e435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 272px;\"\u003e\n \u003cp\u003e73.4 [69.6 - 76.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 229px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeonatal Complication\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 95px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 272px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 229px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 95px;\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 272px;\"\u003e\n \u003cp\u003e27.1 [23.7 - 30.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 229px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 95px;\"\u003e\n \u003cp\u003e432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 272px;\"\u003e\n \u003cp\u003e72.8 [69.1 - 76.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"3\" style=\"width: 596px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eNote: Refer to the \u0026quot;Variables\u0026quot; section for details of Peripartum Maternal \u0026amp; Neonatal complications\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2 provides the prevalence of maternal peripartum and neonatal complications among the study population, which is 26.6 % and 27.1 %, respectively, with a 95% confidence interval (CI). As shown in the table, the Neonatal complication is slightly higher than the maternal peripartum complication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBivariate Analysis of various predictors of interest on Maternal Peripartum \u0026amp; Neonatal Complications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe series of chi-square tests of independence was used to assess the association between various maternal characteristics and peripartum maternal and neonatal complications.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Association of Maternal Characteristics with Maternal Peripartum and Neonatal Complications (N = 593)\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"116%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"3\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal Peripartum Complication\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"3\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeonatal Complications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo (n=435)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes (n=158)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo (n=432)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes (n=161)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"7\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eDistrict\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 27px;\"\u003e\n \u003cp\u003eAhmedabad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e316 (72.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e123 (77.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e316 (73.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e123 (76.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 27px;\"\u003e\n \u003cp\u003eSabarkantha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e119 (27.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e35 (22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e116 (26.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e38 (23.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMaternal Age\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026le; 30 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e363 (83.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e114 (72.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e348 (80.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e129 (80.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026gt; 30 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e72 (16.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e44 (27.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e84 (19.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e32 (19.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"7\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCaste\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 27px;\"\u003e\n \u003cp\u003eReserved*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e303 (69.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e97 (61.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e295 (68.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e105 (65.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 27px;\"\u003e\n \u003cp\u003eGeneral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e132 (30.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e61 (38.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e137 (31.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e56 (34.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"7\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eEducation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 27px;\"\u003e\n \u003cp\u003eNo formal Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e36 (8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e10 (6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e33 (7.6 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e13 (8.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.757\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 27px;\"\u003e\n \u003cp\u003eTill high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e299 (68.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e99 (62.7 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e298 (69.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e115 (71.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 27px;\"\u003e\n \u003cp\u003eGraduation and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e91 (20.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e43 (27.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e101 (23.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e33 (20.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"6\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eWealth index measured by availability of Ration card\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 27px;\"\u003e\n \u003cp\u003eAPL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e252 (57.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e93 (58.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e244 (56.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e101 (62.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 27px;\"\u003e\n \u003cp\u003eBPL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e98 (22.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e30 (19.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e96 (22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e32 (19.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 27px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e85 (19.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e35 (22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e92 (21.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e28 (17.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"5\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eOccupation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 27px;\"\u003e\n \u003cp\u003eHousewife\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e382 (87.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e138 (87.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e379 (87.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e141 (87.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.673\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 27px;\"\u003e\n \u003cp\u003eWorking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e53 (72.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e20 (27.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e53 (12.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e20 (12.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"7\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCOVID-19 infection during pregnancy\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 27px;\"\u003e\n \u003cp\u003eCOVID (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e305 (70.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e97 (61.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e310 (71.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e92 (57.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 27px;\"\u003e\n \u003cp\u003eCOVID (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e130 (29.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e61 (38.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e122 (28.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e69 (42.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"7\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCOVID-19 vaccination before delivery\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 27px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e39 (12.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e15 (12.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e111 (25.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e46 (28.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"7\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eANC visit as per guidelines\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 27px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e17 (3.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e6 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e15 (3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e8 (5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.401\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"7\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eComplications during pregnancy (antenatal period)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 27px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e85 (19.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e52 (33.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e89 (20.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e48 (30.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"7\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eGravida status\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 27px;\"\u003e\n \u003cp\u003ePrimigravida\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e155 (35.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e54 (34.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e152 (35.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e57 (35.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 27px;\"\u003e\n \u003cp\u003eMultigravida\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e280 (64.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e104 (65.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e280 (64.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e104 (64.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"7\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePlace of delivery\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 27px;\"\u003e\n \u003cp\u003ePrivate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e224 (51.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e80 (51.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e242 (56.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e62 (39.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 27px;\"\u003e\n \u003cp\u003ePublic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e208 (48.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e76 (48.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e187 (43.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e97 (61.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"7\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eType of delivery\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 27px;\"\u003e\n \u003cp\u003eVaginal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e257 (59.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e68 (43.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e236 (54.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e89 (55.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 27px;\"\u003e\n \u003cp\u003eC-section\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e178 (40.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e90 (57.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e196 (45.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e72 (44.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"7\" style=\"width: 100px;\"\u003e\n \u003cp\u003eGender - Child\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 27px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e232 (53.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e75 (46.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 27px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 13px;\"\u003e\n \u003cp\u003e200 (46.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 14px;\"\u003e\n \u003cp\u003e86 (53.4%)\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\u003eAs shown in Table 3, maternal peripartum complications were significantly associated with maternal age (\u0026chi;\u0026sup2; = 9.51, p = .002), COVID-19 infection during pregnancy (\u0026chi;\u0026sup2; = 4.07, p = .044), complications during pregnancy (\u0026chi;\u0026sup2; = 16.73, p \u0026lt; .001), and type of delivery (\u0026chi;\u0026sup2; = 12.48, p \u0026lt; .001). Post hoc standardized residuals indicated that the higher rates of complications among women with prenatal COVID-19 infection (residual = \u0026plusmn;2.01), pregnancy-related complications during the antenatal period (\u0026plusmn;3.44), and higher maternal age (\u0026plusmn;3.07) reflect a non-random pattern, suggesting these factors may have a meaningful influence rather than representing chance variation. No significant associations were found with district, caste, education, wealth index, occupation, COVID-19 vaccination status, ANC visits, gravida status, or place of delivery.\u003c/p\u003e\n\u003cp\u003eNeonatal complications were significantly associated with maternal COVID-19 infection/in utero covid-19 exposure \u0026nbsp;(\u0026chi;\u0026sup2; = 14.14, p \u0026lt; .001), complications during pregnancy (\u0026chi;\u0026sup2; = 5.70, p = .017), and place of delivery (\u0026chi;\u0026sup2; = 14.15, p \u0026lt; .001). Post hoc standardized residuals showed that higher complication rates among neonates of mothers with prenatal COVID-19 infection (residual = \u0026plusmn;3.39), pregnancy complications (\u0026plusmn;2.39), and deliveries in public health facilities (\u0026plusmn;3.75) also reflect a non-random pattern, suggesting these associations are unlikely due to chance alone. No significant associations were observed with maternal age, caste, education, wealth index, occupation, vaccination status, ANC visits, gravida, type of delivery, or child\u0026rsquo;s gender.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndependent Predictors of Maternal Peripartum \u0026amp; Neonatal Complications: Logistic Regression\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further examine the strength and direction of association between prenatal COVID-19 infection and maternal peripartum \u0026amp; Neonatal complications while adjusting for potential confounders, a binomial logistic regression analysis was conducted. Before performing the analysis, the validity of the logistic regression model was ensured by assessing key statistical assumptions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel Fit:\u003c/strong\u003e A stepwise logistic regression approach was used to assess the association between prenatal COVID-19 infection and Peripartum maternal and neonatal complications. Variables with a p-value \u0026le; 0.30 in bivariate analysis were included in the adjusted models to control for potential confounding. Model 1 included only the main predictor (prenatal COVID-19 infection), Model 2 added sociodemographic variables, and Model 3 further adjusted for reproductive and clinical factors. Model fit improved across stages, with a decrease in deviance and AIC, and an increase in McFadden\u0026rsquo;s R\u0026sup2; (from 0.008 to 0.057 for maternal and from 0.012 to 0.048 for neonatal models), indicating better explanatory power in the fully adjusted models.\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 1, the final adjusted logistic regression model examined the association between prenatal COVID-19 infection and maternal peripartum complications. The model included the following control variables based on theoretical relevance and bivariate significance (p \u0026le; 0.30): maternal age (reference: younger group), education level (reference: no formal education), caste (reference: General category), district of residence (reference: Ahmedabad), and presence of pregnancy-related complications during antenatal period (reference: no complications).\u003c/p\u003e\n\u003cp\u003eAfter adjusting for all covariates, the odds of experiencing maternal peripartum complications were higher among women with prenatal COVID-19 infection; however, this association was no longer statistically significant after controlling for other maternal characteristics, indicating that the effect of prenatal COVID-19 infection was confounded with other maternal characteristics. Other covariates including education, caste, and district of residence also did not show significant associations in the fully adjusted model.\u003c/p\u003e\n\u003cp\u003eNotably, pregnancy-related complications and increasing maternal age remained significant independent predictors of peripartum complications after adjusting for all other variables.\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 2, the final adjusted logistic regression model assessed the association between prenatal COVID-19 infection and neonatal complications. Similar to the maternal peripartum complications model, variables were selected based on theoretical relevance and bivariate significance, including pregnancy-related complications, place of delivery (Private hospital) and gender of the baby (female). After adjustment, prenatal COVID-19 infection, complications during pregnancy, and delivery in public health facilities were significantly associated with higher odds of neonatal complications. In contrast, the gender of the baby did not show a significant association. Notably, prenatal COVID-19 infection and pregnancy-related complications remained independent predictors of neonatal complications after controlling for all other covariates.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis community-based analysis of 593 mother\u0026ndash;neonate pairs initially showed a borderline significant increase in the odds of maternal peripartum complications; this association weakened and lost statistical significance after adjusting for other explanatory variables. This decline could be attributed to the non-significant association between COVID-19 infection and pre-term labour, a key outcome/event included in our composite index of maternal peripartum complications.\u0026nbsp;However, our study reported a non-significant association between pre-term labour; it was commonly observed to be significantly associated with COVID-19 infection in many earlier studies.\u003csup\u003e[3,13\u0026ndash;15]\u003c/sup\u003e In contrast, many other studies reported similar or unclear results about the incidence in the pre-term.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor Neonatal complications, prenatal COVID-19 infection remained a significant independent predictor.\u0026nbsp;Higher odds of having neonatal complications were observed with neonates born to COVID-19-positive mothers, and this was consistent even after adjusting for other maternal characteristics. Most of the complications observed in our analysis were not isolated incidents; several previous studies have similarly reported associations between COVID-19 infection and neonatal complications.\u003csup\u003e[16\u0026ndash;22]\u003c/sup\u003e De Medeiros et al.\u0026apos;s findings aligned with the higher rates of neonatal complications (higher NICU admission \u0026amp; Low birth weight) \u003csup\u003e[23,24]\u003c/sup\u003e, and a study done by Norman et al found that these findings were consistent even after adjusting for other maternal characteristics \u003csup\u003e[25]\u003c/sup\u003e. In contrast, Wilkinson et\u0026nbsp;al. observed no statistically significant differences.\u003csup\u003e[26]\u003c/sup\u003e Additionally, neonatal complications were more likely among offspring born to mothers who experienced pregnancy complications, pre-term births, and deliveries in public healthcare facilities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe key strength of this study is that it is a community-based primary data study, and the data are mainly from the first two waves of the pandemic, where young populations were at higher risk; hence, it would represent the real-time scenario for the outcome of interest. Secondly, it contains the controlled arm, which allows it to be compared to women without COVID-19 infection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSeveral limitations also need to be acknowledged. Firstly, the study excluded pregnant women with SARS-CoV-2 who weren\u0026rsquo;t screened for any of the reasons, and potential asymptomatic COVID-19 in the community. Secondly, although most of the COVID-19-positive participants were mildly to moderately infected, it\u0026rsquo;s worth presenting the outcome of interest according to the severity of the infection. However, due to the limited sample size for COVID-positive, too much stratification is not possible; hence, it is missing in our findings. Thirdly, while the study provides valuable insights into COVID-19 infection status and management, it limited the scope to present data on stillbirth and neonatal mortality due to the ongoing pandemic and challenges in securing community permission.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study provides compelling region-specific evidence of increased odds of peripartum maternal and significant neonatal complications associated with prenatal COVID-19 infection and in-utero exposure to the infection. \u0026nbsp;This consistent association underscores the potential biological and systemic impact of in-utero exposure to maternal COVID-19 infection. These findings can offer robust evidence for public health strategies, particularly in pandemics and health crises. To get better insights, further in-depth exploration of biological mechanisms and health system factors that may mediate these outcomes is recommended.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u0026nbsp;\u003c/strong\u003eWe would like to express our sincere gratitude to all the enrolled study participants who generously shared their valuable support and share the required information. We are also thankful to our colleagues Dr. Monali Patel and Dr. Shailee Patil for their assistance in data collection. Our heartfelt thanks go out to the Deputy Health Officers of the respective zonal health offices, Medical Officers at the Urban health centres, and the dedicated Anganwadi workers from all the respective AWCs for providing invaluable support during the field data collection. The successful completion of this study would not have been possible without the unwavering support of the Ahmedabad Municipal Corporation and the Private and public health providers in Ahmedabad and Sabarkantha districts. We thank all these stakeholders for their crucial contributions to our research/study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e The study was initiated under the Action Against Stunting Hub, funded by UKRI GCRF. However, Funder had no direct involvement in the design, conduct \u0026amp; dissemination of findings.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability statement:\u0026nbsp;\u003c/strong\u003eThe datasets used/analysed in this study will be made accessible upon request from the Principal Investigator at [email protected].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e Ethics approval was granted by the institutional ethics committees of the Indian Institute of Public Health Gandhinagar (IIPHG) (Referenace no: SHSRC/2021/2185) and London School of Hygiene and Tropical Medicine (LSHTM) (reference no: 72848). Informed consent was obtained through fingerprint or signature on a pre-structured form that provided detailed information about the project and withdrawal policy. The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and relevant national and institutional guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not Applicable\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed equally to the development of this study. DS, KS, CF, and MJ participated in the study's conception and design. FM was responsible for data collection, analysis, and drafting the initial version of the manuscript. FKM assisted in data cleaning \u0026amp; writing the first draft of manuscript. DS, KS, CF, NT, AS, and TP critically reviewed the manuscript. All authors read and approved the final manuscript.Abbreviations: DS \u0026ndash; Deepak Saxena; KS \u0026ndash; Komal Shah; CF \u0026ndash; Claire Heffernan; MJ \u0026ndash; Modou Jobarteh; FM \u0026ndash; Farjana Memon; FKM: Fatimakhatun Memon, NT \u0026ndash; Nandan Thakkar; AS \u0026ndash; Anish Sinha; TP \u0026ndash; Tapasvi Puwar\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFerrara A, Hedderson MM, Zhu Y, Avalos LA, Kuzniewicz MW, Myers LC et al. Perinatal Complications in Individuals in California With or Without SARS-CoV-2 Infection During Pregnancy. JAMA Intern Med [Internet]. 2022 May 1 [cited 2024 Nov 10];182(5):503\u0026ndash;12. 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Impact of SARS-CoV-2 on pregnancy and neonatal outcomes: An open prospective study of pregnant women in Brazil. Clinics (Sao Paulo) [Internet]. 2022 Jan 1 [cited 2024 Sep 5];77. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/35797767/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/35797767/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnight M, Bunch K, Vousden N, Morris E, Simpson N, Gale C et al. Characteristics and outcomes of pregnant women admitted to hospital with confirmed SARS-CoV-2 infection in UK: national population based cohort study. BMJ [Internet]. 2020 Jun 8 [cited 2024 Sep 4];369. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/35042979/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/35042979/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"In-utero COVID-19 exposure, Neonatal Complication, Maternal peripartum complications, Prenatal COVID-19 infection","lastPublishedDoi":"10.21203/rs.3.rs-9325039/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9325039/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe COVID-19 pandemic has intensified health risks across the population, particularly among vulnerable groups such as pregnant women \u0026amp; children. Pregnant women are considered most vulnerable due to physiological and immunological changes that may exacerbate disease severity as well as subsequent health outcomes. Empirical evidences showed possible links between prenatal COVID-19 infection and adverse maternal \u0026amp; neonatal outcomes. However, evidence from resource-constrained countries remains limited.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study adopted a case-control design with the enrollment of a total of 593 mother-neonate pairs (191 COVID-positive and 402 COVID-negative) from two districts in Gujarat. Key variables analysed were maternal sociodemographic and clinical characteristics, pregnancy-related complications, delivery \u0026amp; neonatal health outcomes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the enrolled participants, 80.4% were aged\u0026thinsp;\u0026le;\u0026thinsp;30 years, 74% resided in urban areas, and 58.2% were above the poverty line. Maternal peripartum and neonatal complications documented in 26.6% and 27.1% of cases, respectively. Prenatal COVID-19 infection was significantly associated with maternal peripartum complications in unadjusted logistic regression analysis (cOR\u0026thinsp;=\u0026thinsp;1.49; 95% CI: 1.01\u0026ndash;2.17; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042), but this was not sustained significant after adjustment (aOR\u0026thinsp;=\u0026thinsp;1.25; 95% CI: 0.82\u0026ndash;1.89; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.295). Neonatal complications were significantly associated with prenatal COVID-19 infection in both unadjusted and adjusted logistic regression models (aOR\u0026thinsp;=\u0026thinsp;1.87; 95% CI: 1.27\u0026ndash;2.75; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ePrenatal COVID-19 infection was independently associated with neonatal complications, reinforcing the need for targeted monitoring and care during pregnancy \u0026amp; postpartum amid pandemics.\u003c/p\u003e","manuscriptTitle":"Association of Prenatal COVID-19 Infection with Maternal Peripartum and Neonatal Complications in Gujarat India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-18 10:18:15","doi":"10.21203/rs.3.rs-9325039/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-21T22:32:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"310175148709789305845577641101276212125","date":"2026-05-13T22:11:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-13T05:04:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"175862545186536707507055094544835963098","date":"2026-05-11T06:57:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-08T06:39:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-20T02:35:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-19T22:14:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-17T12:07:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2026-04-17T10:14:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9537a472-652a-4bda-aeed-07cd17ca5b1b","owner":[],"postedDate":"May 18th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-21T22:32:18+00:00","index":71,"fulltext":""},{"type":"reviewerAgreed","content":"310175148709789305845577641101276212125","date":"2026-05-13T22:11:06+00:00","index":57,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-13T05:04:59+00:00","index":56,"fulltext":""},{"type":"reviewerAgreed","content":"175862545186536707507055094544835963098","date":"2026-05-11T06:57:22+00:00","index":55,"fulltext":""},{"type":"reviewersInvited","content":"40","date":"2026-05-08T06:39:00+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T10:18:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-18 10:18:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9325039","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9325039","identity":"rs-9325039","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}