Estimation of Prevalence and Factors Associated with Low Birth Weight in Aspirational Districts of India

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India is a major burden on the world with a significant part of it being in socioeconomically poor areas like Aspirational Districts. The objective of this study was to estimate prevalence of LBW and discuss how far it is related to maternal characteristics, in these districts. Methods: The present research relies on secondary analysis of the data of the National Family Health Survey-5 (NFHS-5, 2019-21), the cross-sectional survey that covered the whole country and was carried out on the national level. It was analysed on 26,042 women 15 to 49 years of age living in Aspirational Districts with most recent single institution live birth and a registered birth weight. LBW was defined as < 2500 g. Just the survey-weighted descriptive statistics, chi-square tests, and multivariate logistic regression analysis were performed using version 9.4 of the SAS taking into consideration clustering, stratification, and sampling weights. Results: Results: The prevalence of LBW was 17.3%. The odds were found to be higher in younger mothers (1524 years), women in low wealth quintile, less educated women underweight women, and anaemic women and pregnancy complications. Weighted scale: Adequate antenatal care (4 or more visits) and high maternal BMI had low odds. There was geographic difference between districts. Conclusion: Aspirational Districts still suffer LBW. Results show a correlation with maternal socioeconomic and maternal health factors. Since it was cross-sectional in nature, interpretation of the results is expected to be correlational. Enhancing maternal nutrition and fair access to antenatal care could be used to decrease LBW. Low birth weight maternal health aspirational districts NFHS-5 antenatal care maternal nutrition health disparities socioeconomic factors and Viksit Bharat Figures Figure 1 Figure 2 Introduction Low birth weight (LBW) or birth weight below 2500g regardless of gestational age, is both a sign of maternal and infant health and a significant factor in infant survival ( 1 ). It is an indicator of maternal health status, as well as the quality of care in pregnancy, and beyond because it constitutes an important population-level indicator of health system level. LBW is recognized as a significant societal issue in the world, especially in the low- and middle-income countries (LMICs), where maternal and neonatal morbidity and mortality are disproportionately high as a burden ( 2 , 3 ). It is predicted that about 20 million LBW infants are being born annually showing almost 15–20 percent of the total live births in the world ( 3 , 4 ). LBW is among the primary causes of neonatal mortality, and it has been found that LBW is the main cause of all neonatal deaths in the world, which is nearly 60–80 percent of all infant deaths ( 5 , 6 ). Low-weight babies have greater risks of inducing short-term problems including respiratory distress, hypothermia, infections and feeding problems that increase the risks of the early death considerably ( 3 , 5 ). In addition to neonatal effects, the LBW has been reported to have long-term negative outcomes, such as cognitive development, lower educational attainment, and higher risk of developing non-communicable diseases (type 2 diabetes, hypertension, and cardiovascular diseases) in adulthood ( 7 , 8 ). Such long-term outcomes demonstrate why LBW must be addressed as a clinical issue as well as a larger public health concern. This distribution of LBW burden is uneven as most of the cases are in LMICs. In South Asia, and especially in the region, the level of prevalence is among the highest in the world due to the combination of maternal malnourishment, socioeconomic inequality, and insufficient access to the quality medical services ( 2 , 9 ). However, India alone carries a large burden of the burden globally, with national estimates stating that there is a steadfast percentage of 18.2 indicating that the number of births is below the standard of low birth weight, according to the National Family Health Survey-5 (NFHS-5, 2019-21) ( 10 ). Despite improvement this has occurred, though at a slow pace, unevenly, as structural inequalities in health and nutrition have remained in place. LBW has been demonstrated to be aided by a substantial number of determinants, which include maternal, socioeconomic, and healthcare related factors. Age of the mother is also a significant biological factor with younger mothers especially adolescents who are at a greater risk because their bodies are still developing and their nutritional competition between the mother and foetus is greater ( 11 – 13 ). The socioeconomic status is usually assessed by measuring wealth index or income, and it is determined to have numerous avenues to LBW, such as the access to proper nutrition, healthcare facilities, and conditions to live ( 9 , 14 ). The poorer women have a higher likelihood of food insecurity, low dietary nutritional intake, and access to low-standard maternal health, which result in poor birth outcomes. Mother education has also been a constant relationship with LBW. Women who have gone through education will embrace proper health practices, use antenatal care services, and better nutrition practices throughout pregnancy( 14 , 15 ). Education is also a contributor to health literacy so that women would know the complications during pregnancy and be able to get immediate medical attention. Nonetheless, education tends to have an interplay with other more socioeconomic influences and therefore education is not only its own determinant but also a proxy of social advantage. The most significant birth weight determinants include maternal nutritional status. Underweight women experiencing low body mass index (BMI) during or prior to pregnancy are in risk of giving birth to low weight babies because of poor nutritional stores and poor placental functioning ( 16 , 17 ). Micronutrient deficiency especially of iron, folate and zinc also causes limited foetal growth. Maternal anaemia which continues to be one of the most prevalent in India has been linked to a decreased oxygen uptake by the unborn baby, as well as high risk of poor birth outcomes ( 18 , 19 ). Although national programs are aimed at the reduction of anaemia, its presence in the country continues to be a major challenge. The use of ANC is significant in enhancing the outcome of the pregnancy through offering a chance to detect and manage pregnancy complications in early stages, nutritional supplements, and health education. It has been demonstrated that poor ANC correlates with risks of developing LBW ( 11 , 12 ). Nevertheless, there is new evidence indicating that quality and content of ANC services can be of importance rather than the number of visits itself. Poor continuity of care, inadequate screening and suboptimal counselling may hinder the effectiveness of ANC to enhance the outcomes of birth. Another factor contributing to LBW is pregnancy complications such as hypertensive disorders, infection and gestational conditions. These may affect the placental functioning and foetal development that results in intrauterine growth retardation and preterm delivery. It is the interplay of maternal health, nutritional status, and the ability to obtain healthcare that exacerbates the mechanisms of LBW; therefore, the struggle of the issue is multifactorial. Other structural and contextual factors including caste, geographic setting and rural urban differences contribute greatly to maternal and child health in India besides individual level determinants. The disadvantages that many women in marginalized social groups must bear are usually compounded, such as low access to healthcare services, discrimination, and substandard living conditions ( 9 ). In rural communities specifically, poor health care facilities, lack of trained health care providers, and impediments to availing maternal healthcare services characterize the areas. Appreciating these differences, the Government of India initiated in 2018 the Aspirational Districts Programme (ADP), which aims to rapidly boost development in 112 poorly performing districts on the level of the most important areas, such as health and nutrition ( 20 ). These areas are set according to their poor score of development indicators and are put on the list of places of special interventions and observations. The main goal of the programme is to improve the outcomes of the work of the health care systems, both maternal and child health. Nonetheless, such districts still must struggle with the issues of high poverty rates, under-feeding and poor access to healthcare. Although the ADP is a policy important policy, the burden and determinants of the LBW in Aspirational Districts are poorly surveyed. Majority of the current literature has concentrated on national or state-level estimates and this could obliterate the trends of significant subregion differences and determinants in specific contexts. Knowledge about the patterns and correlates of LBW in Aspirational Districts is crucial to the proposed solution of creating interventions aimed at LBW and informing policymaking. Besides, although big data surveys like NFHS-5 are valuable to study the trends at a population-level, the cross-sectional characteristic of big data restricts the opportunity to determine causal associations. However, this kind of data is essential to determine high-risk groups and inform the evidence-based approaches to the community. Hence, the objective of the study will be to determine the prevalence of low birth weight and investigate its relationship with socioeconomic and health-related factors of the mother in Aspirational Districts of India based on nationally representative NFHS-5. This study aims to offer context-specific evidence to affirm current activities of enhancement of maternal and child health outcomes in India by addressing development-priority districts. Methods Study design and data source The given research is founded on the secondary analysis of data available in the National Family Health Survey-5 (NFHS-5), which took place in 2019–2021 ( 10 ). The NFHS-5 is a household survey that is conducted nationally on behalf of the Ministry of Health and Family Welfare, government of India and coordinated by the International Institute for Population Sciences (IIPS), Mumbai. The survey was done on a multistage stratified cluster sampling design to promote representativeness in national, state, and district spheres. As primary sampling units (PSUs) in rural and census enumeration block (CEBs) in urban areas, villages and blocks were used, respectively. The households were randomly selected using a systematic sampling technique within a PSU. NFHS-5 surveyed 724,115 women (age 15–49 years old) on their demographic features, socioeconomic status, maternal health, and child health and nutritional status by greater than 95 percent response rate. Study population and sample selection The current study targeted women living in districts that were affected under the Aspirational Districts Programme (ADP). Respondents in those districts were identified through a set of district identifiers that were found in the NFHS-5 dataset. The analytic sample was limited among the women between the ages 15–49 years with the most recent live birth of less than five years before the survey. The analysis was further uniquely constrained by births and institutional deliveries where the birth weight was recorded by use of written health cards or maternal recall to enhance the measurement accuracy and minimize the level of bias. They included 26,042 women in the final analysis sample after the use of these inclusion criteria. Outcome variable Low birth weight (LBW) was the main outcome measure, which was defined as a birth weight below 2,500 grams of weight in line with the World Health Organization criteria ( 1 ). Information on birth weight in NFHS-5 was reported on written health records where they were available or maternal recollection where available. The outcome variable was coded in terms of binary indicator: 1 = Low birth weight (< 2500 g) 0 = Normal birth weight (≥ 2500 g) Independent variables The independent variables were assigned on the basis of the previous testing outcomes on the factors which determine the low birth weight in India and in other low- and middle-income countries ( 11 , 12 ). The following variables were divided into three domains: Socioeconomic and demographic characteristics The age of the mothers was divided into 15–24 years, 25–34 years, and = > 35 years of age. There were the marginalized (Scheduled Castes/Scheduled Tribes) and non-marginalized (Other Backward Classes/General) types of caste. Religion was categorized as Hindu, Muslim, Christian and others. The household wealthy status was identified by using NFHS wealth index, which was grouped into low, middle, and high. Other variables were place of residence (rural/urban), employment (employed/not employed), marital status and maternal education (illiterate/literate). Service utilization variable The use of antenatal care (ANC) was sorted according to the number of visits, less than four visits and four or more visits in accordance with the WHO guidelines. Maternal health-related characteristics Body mass index (BMI) was used to determine the maternal nutritional status as; underweight ( = 30 kg/m2). Haemoglobin was used to define anaemia status as anaemic or non-anaemic. The interviewee's birth interval was divided into less than 24 months and 24 months and above. One binary variable (yes/no) was pregnancy-related complications. The recoding was done on all variables using NFHS standard coding scheme. Statistical analysis SAS version 9.4 was used to do all statistical analyses. The distribution of the maternal characteristics was summarized with the help of descriptive statistics that allowed estimating the prevalence of low birth weight. The results are given in percentage based on the survey and with a 95% confidence interval. The survey-adjusted chi-square tests were employed to determine the bivariate relationships between low weight and bivariate explanatory variables as this was a complex sampling study. The analysis took place on multivariable binary logistic regression with the help of PROC SURVEYLOGISTIC to estimate adjusted odds ratios (AORs) and 95% confidence intervals. The regression model had all the relevant socioeconomic, demographic, and maternal health variables on theoretical and empirical grounds. Key methodological specification: The analyses took into consideration all the complex survey design, sampling weights, clustering and stratification. The association estimates that were made with low birth weight were all based on models that were survey-weighted based on clustering and stratification to make the correct one at population level. The p-value of statistical significance was set at less than 0.05. Interpretation of findings Since the NFHS-5 data was cross-sectional, the findings obtained in the present study cannot be taken as causal relationships but as correlational. The analysis has found that there is a significant relationship between maternal characteristics and low birth weight; however, it does not discover the pathways of time and causation. Ethical considerations NFHS-5 data is transparent and anonymized ( 10 ). Results The number of women involved was 26,042. Most of them (54.1) were aged 25–34 years and 83.1 were in the rural areas. A good percentage of them fell into lower wealth category (61.3%). Undernutrition among the maternal population was rampant (25% underweight), and 64.7% of the female population had anaemia. About 51.64% of them had four or above antenatal care visits. The prevalence of LBW was 17.3% (95% CI: 16.8–17.8). Table 1 Socio-demographic and Maternal Health Characteristics of Respondents (Survey-Weighted), NFHS-5, 2019–21 Variable Category n Weighted % 95% CI Maternal age (years) (N = 26042) 15–24 9255 37.64 36.88–38.40 25–34 14338 54.11 53.36–54.86 ≥ 35 2449 8.24 7.83–8.65 Caste (N = 24519) Non-marginalised 13297 61.60 60.48–62.72 Marginalised 11222 38.40 37.28–39.52 Religion (N = 26042) Hindu 19819 79.65 78.58–80.72 Muslim 3993 16.08 15.05–17.11 Christian 1270 1.88 1.63–2.13 Others 960 2.39 2.13–2.65 Wealth index (N = 26042) Low 16350 61.25 60.30–62.20 Middle 4483 17.10 16.43–17.77 High 5209 21.65 20.88–22.42 Residence (N = 26042) Rural 22380 83.10 82.27–83.93 Urban 3662 16.90 16.08–17.72 Education (N = 26042) Illiterate 9014 35.25 34.37–36.13 Literate 17028 64.75 63.87–65.63 Employment status (N = 3968) not employed 2957 77.80 76.07-79.5414 employed 1011 22.19 20.45–23.92 Marital status (N = 26042) Never in union 51 0.12 0.08–0.17 married 25662 98.80 98.62–98.97 widowed 187 0.63 0.52–0.74 divorced 34 0.086 0.03–0.14 separated 108 0.36 0.27–0.46 BMI (N = 26042) Underweight 6304 25.00 24.29–25.71 Normal 16458 61.72 60.96–62.48 Overweight 2599 10.47 9.97–10.97 Obese 681 2.82 2.54–3.10 ANC visits (N = 25836) < 4 visits 11416 48.45 47.42–49.48 ≥ 4 visits 14420 51.55 50.51–52.59 Anaemia (N = 25251) Not anaemic 8868 35.31 34.49–36.13 Anaemic 16383 64.69 63.88–65.50 Birth interval (N = 17223) < 24 months 4025 26.13 25.23–27.03 ≥ 24 months 13198 73.87 72.97–74.77 Pregnancy complications (N = 26042) No 8845 33.27 32.36–34.18 Yes 17197 66.73 65.83–67.63 Low birth weight (N = 26042) No 21538 82.63 82.02–83.24 Yes 4504 17.37 16.76–17.98 Table 2 Stratified Result Table Between Maternal Characteristics and Low Birth Weight Variable Category LBW (n) % (95% CI) χ² value p-value Age (years) 15–24 1770 19.26 (18.26–20.26) 39.20 < 0.0001 25–34 2344 16.25 (15.44–17.06) ≥ 35 390 16.07 (14.26–17.87) Caste Non-marginalised 2206 16.54 (15.71–17.37) 19.52 0.0008 Marginalised 2042 18.74 (17.76–19.71) Religion Hindu 3547 17.54 (16.85–18.22) 6.68 0.1578 Muslim 655 16.46 (14.93–17.99) Christian 129 15.15 (11.95–18.35) Others 173 19.62 (16.49–22.75) Wealth-index Low 3054 18.75 (17.98–19.52) 55.59 < 0.0001 Middle 678 15.61 (14.27–16.95) High 772 14.85 (13.59–16.11) Residence Rural 3900 17.50 (16.85–18.15) 1.46 0.4202 Urban 604 16.74 (15.04–18.44) Employment status Not employed 462 16.45 (14.80–18.10) 6.14 0.0433 Employed 194 20.03 (16.84–23.21) Marital status Never in union 10 18.11 (5.62–30.60) 5.04 0.2762 Married 4441 17.41 (16.80–18.03) Widowed 35 15.52 (9.74–21.31) Divorced 5 14.00 (0.00–28.50) Separated 13 9.19 (3.50–14.87) Education Illiterate 1779 20.01 (18.96–21.07) 69.05 < 0.0001 Literate 2725 15.93 (15.21–16.65) BMI Underweight 1309 20.62 (19.33–21.90) 91.20 < 0.0001 Normal 2770 16.93 (16.18–17.67) Overweight 332 12.90 (11.32–14.47) Obese 93 14.89 (11.40–18.38) ANC visits < 4 visits 2054 17.86 (16.97–18.74) 4.92 0.0812 ≥ 4 visits 2405 16.81 (16.00–17.63) Anaemia Not anaemic 1453 16.63 (15.65–17.61) 4.07 0.0984 Anaemic 2904 17.64 (16.88–18.39) Birth interval < 24 months 706 17.09 (15.71–18.47) 3.70 0.1174 ≥ 24 months 2084 15.86 (15.05–16.67) Pregnancy complications No 1368 15.59 (14.64–16.54) 28.58 < 0.0001 Yes 3136 18.26 (17.49–19.02) The study looked at how different factors related to mothers were associated with low birth weight (LBW) in infants. It was found that the mother's age, wealth quintile, education, residence type, BMI, and pregnancy complications were significantly linked to LBW (P < 0.05). However, factors like caste, religion, employment status, marital status, ANC visits, anaemia, and birth interval did not show a significant association with LBW. A higher prevalence was observed among younger mothers aged 15–24 years, those belonging to poorer households, and illiterate women. Additionally, the prevalence was greater among women who had fewer than four antenatal care (ANC) visits. It was also found to be higher among anaemic and underweight women, as well as among those who experienced pregnancy-related complications. Furthermore, a higher prevalence was noted among women with a birth interval of less than 24 months. Table 3 Multivariable Binary Logistic Regression of Factors Associated with Low Birth Weight Variable Category AOR 95% CI p-value Age (ref: 15–24) 25–34 0.82 0.75–0.90 < 0.001 ≥ 35 0.77 0.66–0.90 0.001 caste (ref: non-marginalised) marginalised 1.074 0.97–1.19 0.1583 Religion (ref: Hindu) Muslim 0.975 0.86–1.13 0.7289 Christian 0.837 0.64–1.09 0.1916 others 1.183 0.96–1.46 0.116 Wealth (ref: Low) Middle 0.88 0.78–0.99 0.04 High 0.86 0.76–0.98 0.02 Education (ref: Illiterate) Literate 0.77 0.71–0.85 < 0.001 Residence (ref: Rural) Urban 1.15 1.00–1.32 0.049 employment status (ref: non-employed) employed 1.17 0.91–1.49 0.23 marital status (ref: never in union) married 0.994 0.44–2.27 0.99 widowed 0.851 0.33–2.18 0.74 divorced 0.595 0.12–2.89 0.52 separated 0.468 0.16–1.39 0.17 BMI (ref: Underweight) Normal 0.81 0.74–0.89 < 0.001 Overweight 0.66 0.56–0.78 < 0.001 Obese 0.76 0.56–1.03 0.07 ANC (ref: <4) ≥ 4 visits 0.99 0.91–1.08 0.82 Anaemia (ref: No) Yes 1.03 0.94–1.13 0.52 Birth interval (ref: <24) ≥ 24 months 0.95 0.84–1.06 0.34 Pregnancy complications (ref: No) Yes 1.22 1.12–1.34 < 0.001 Table 3 displays the adjusted odds ratios (AORs) of factors related with low birth weight (LBW) estimated by a survey-weighted multivariable logistic regression model that considered the possible confounding effect of sociodemographic and maternal health-related variables. The estimates have been generated by adjusting age of mother, caste, religion, wealth index, place of residence, education and body mass index as a result of previous literature review. LBW was significantly associated with the maternal age, wealth index, education, residence place, body mass index and pregnancy complication at P-value less than 0.05. Women aged 25–34 years (AOR: 0.82, 95% CI: 0.75–0.90) and those aged > 35 years (AOR: 0.77, 95% CI: 0.66–0.90) had significantly lower odds of delivering LBW infants compared to women aged 15–24 years. Women who were literate (AOR: 0.77, 95% CI: 0.71–0.85) and belong to middle (AOR: 0.88; 95% CI: 0.78–0.99) and higher wealth status (AOR: 0.86; 95% CI: 0.760.98) group had significantly lower odds of having LBW infants as compared those women who are illiterate and belong to lower wealth status. Another significant association was seen with place of residence as those women who belongs to urban area (AOR: 1.15, 95% CI: 1.00-1.32) had higher odds of having LBW infants as compared to those women who resides in rural area. Maternal nutritional status also proved as an independent determinant of LBW. Women with normal BMI (AOR: 0.81; 95% CI: 0.74–0.89) and overweight BMI (AOR: 0.66; 95% CI: 0.56–0.78) were found to have significantly lower chances of giving birth to LBW babies compared to underweight women. The association between the obese women and LBW was not significant (AOR: 0.76; 95% CI: 0.56–1.03), Pregnancy-related complications were considerably correlated with high odds of LBW (AOR: 1.22; 95% CI: 1.12–1.34), which implied that the risk was higher in the presence of pregnancy-related complications among women. Whereas some factors like caste, religion, employment status, 4 or more Anc visits, anaemia and birth interval were not significantly associated with LBW. Geographic variation Map Interpretation: The map visually represents the prevalence of low birth weight (LBW) children across India’s Aspirational Districts, using a color-coded scheme to denote varying levels of prevalence. Districts are categorized into five groups based on the percentage of LBW children: ≤ 5% (dark green) 6–10% (light green) 11–15% (yellow) 16–20% (orange) ≥ 21% (red) A noticeable spatial disparity in LBW prevalence is evident across the country. High-burden districts (colored in red, indicating ≥ 21% LBW prevalence) are prominently concentrated in central, eastern, and parts of southern India, particularly in states such as Bihar, Madhya Pradesh, Chhattisgarh, Odisha, and Maharashtra. These regions also coincide with areas characterized by socio-economic deprivation, limited access to quality healthcare, and poor maternal nutrition. In contrast, several districts in northeastern India and some parts of northern India (notably in Jammu & Kashmir and Himachal Pradesh) show relatively low prevalence (≤ 10%), suggesting regional variation in healthcare access, nutritional practices, and maternal health services. The clustering of high-prevalence districts in specific zones indicates a potential need for region-specific interventions. These may include strengthening maternal health services, improving antenatal care coverage, addressing food insecurity, and community-based awareness programs. This map underscores the critical need for targeted policy attention and resource allocation in the most affected districts. It also serves as a valuable tool for identifying priority areas where maternal and child health programs must be intensified to reduce the burden of low birth weight and associated health risks. Discussion This paper presents detailed evidence on the prevalence and determinants of low birth weight in Aspirational Districts of India on nationally representative data. The LBW (17.3) prevalence in this study is like the national estimates in NFHS-5 which points out to the fact that despite the directed policy efforts, LBW has continued to be a public health issue in the development-prioritizing districts ( 10 , 11 ). Socioeconomic inequalities The results indicate that there is a vivid socioeconomic gradient of LBW with more prevalence in poor women and less educated women. The findings are in line with other researches carried out in India and other developing nations ( 11 , 12 ). The socioeconomic disadvantage has a variety of mechanisms on the birth outcomes, among them being poor maternal nutrition, poor living conditions, and insufficient access to quality healthcare services ( 9 , 14 ). Education of the mothers, especially, turned out to be a potent preventive measure. The educated women are more likely to use the amenities of the antenatal care, make the right food choices and healthcare services, which are all leading to better birth statistics. Nonetheless, education can also serve as a surrogate of greater socioeconomic benefits, and the independent impact should be viewed with discretion of mothers and their nutritional status. One of the most influential predictive factors of LBW in this study was the maternal nutritional status. The odds of childbearing underweight women producing LBW babies were much greater, which is in line with the current research that maternal undernutrition is a risk factor of intrauterine growth restriction ( 16 , 17 ). Poor maternal nutritional reserves may impair placental function, leading to inadequate fetal development and resulting in adverse birth outcomes. Surprisingly, the common women with normal and higher BMI had reduced odds of being underweight as opposed to women who were of LBW. Although this result indicates that adequate nutritional status protects, it cannot be construed that overweight or obesity is good because there are other negative pregnancy outcomes which have been linked to higher BMI, which include gestational diabetes and hypertensive disorders. Pregnancy complications Problems with pregnancy were also highly correlated with higher odds of LBW. This observation is in line with clinical facts, with which complications like hypertension, infections, and other gestational conditions may negatively impact foetal development and expose the foetus to the risk of being born with low birth weight. These findings show the significance of antenatal care services in identifying and managing pregnancy complications in the initial stages. Antenatal care and anaemia Unlike in other studies which have been done in the past, there was no significant relationship between LBW and the use of antenatal care as well as anaemia adjusted. This finding suggests that the quality, timing, and content of antenatal care may be more critical than the number of visits alone, particularly in resource-constrained settings. Possible explanation of lack of association between anaemia and LBW is the limit of measurement, residual confounding or complex interaction between various factors that have an effect on determining the birth weight. As the level of anaemia among women in India is very high, it is a significant public health concern that should be given more attention. Urban–rural and geographic variation The randomness of the tendency of LBW to be a little higher in the urban population can be attributed to the increased weight of urban poverty, overcrowding and other environmental pressures in urbanizing central cities. This observation creates the necessity of handling intra-urban disparities in the health of the mother. The visual geographic difference in the prevalence of LBW within Aspirational Districts clearly shows the significance of interventions in particular contexts. These disparities are probably driven by differences in socioeconomic development, infrastructure of healthcare and program implementation ( 20 , 21 ). Policy implications The policy and program design implications of the findings of this study would be significant in Aspirational Districts. The intervention types should focus on the enhancement of maternal nutrition and the quality of antenatal care delivery, and the socioeconomic disparities. Specific measures aimed at high-burden districts could also be quite effective in limiting the number of cases with LBW. Interpretation Results are to be viewed as an association but not a cause because the data used was cross-sectional. The observed associations can also be caused by residual confounding and measurement limitations. Strengths The research has several strengths: 1. It uses a substantial, nationwide representative data (NFHS-5) that makes it robust in estimating the population level. 2. The relevance of findings is improved by Focus on Aspirational Districts, which is a subpopulation of interest in terms of policy implications. 3. Complex Survey design features, such as weights, clusters, or strata used in the incorporation, give rigor to methods. 4. The study narrows on those districts under the Aspirational Districts Programme, which offer policy-relevant insights on regions of development priorities, which are not usually adequately reflected in national studies. Limitation of the study There are significant limitations of the study are: 1. The cross-sectional study type does not allow making a causal conclusion; no observed relationships can draw a sense of time or a sense of direction. 2. The Recall bias caused by maternal recall data of birth weight could also be a problem with LBW prevalence estimates. 3. The exclusion of women with home deliveries and infants with unmeasured or unknown birth weight may introduce selection bias due to measurement error in home-recorded birth weight, as these groups often belong to socioeconomically disadvantaged populations at higher risk, potentially leading to an underestimation of the true burden. Conclusion Low birth weight is a long-standing social health issue in the Aspirational Districts of India that has prevalence rates equivalent to the national estimates but indicative of underlying socioeconomic and health system inequalities. As noted in the current study, maternal under-nutrition, lower socio-economic status, low levels of educational attainment and pregnancy related complications are the important factors that are linked with low birth weight in these development-priority districts. These findings highlight the need to intensify maternal nutrition interventions especially among the undernourished mothers and to treat more social causes of poor health including poverty and education. It is important to enhance the quality and efficacy of antenatal services such as early identification and treatment of pregnancy complications to increase the outcomes of births. Since prevalence of low-birthweight was observed to be geographically different, it is necessary to have specific and even localized interventions in high-burden districts. The decision to strengthen health systems, improve service delivery, and equitable access to maternal healthcare services should be the main focus in the Aspirational Districts Programme policy. Since the analysis is carried out using the cross-sectional data it should be assumed that the results should not be treated as the cause-effect relationships. However, the research offers valuable information to guide the way people should implement health strategies to reduce the low birth weight and enhance maternal and child health outcomes in India. Nationally representative data and even more longitudinal studies should be conducted to see the causal pathways better and assess the efficiency of interventions. Declarations Ethical approval: This is a secondary data analysis and does not require ethical approval for this paper. In accordance with the Declaration of Helsinki, ethics approval was waived by the Institutional Ethics Committee of the International Institute of Health Management Research, New Delhi, India. Informed consent was not obtained. Consent to participate: The current study used secondary data; therefore, informed consent of parents to participate was not required. Data availability statement: The data that support the findings of this study are available from the corresponding author upon reasonable request. Competing interests: The authors declare no competing interests. Dual-publication: This article has been submitted to this journal only and has not been submitted to other journals for publication. Authorship: Anmol Rai: Draft writing, review & validation. Saurabh Kumar: Resources, formal analysis, data curation, software and validation. Shivam Kumar Sharma: Conceptualisation, resources, formal analysis and validation. Ayush Vardhan: Formal analysis, draft writing, review & editing, and validation. Nikhil Kumar: Data visualization and validation. Sumant Swain: Conceptualisation, draft writing, review, editing, and validation. Open-access: All authors have given their consent to publish this article online, offline, or in a hybrid mode. Third-party-material: Not applicable Clinical trial number: not applicable Funding: The research study reported in this manuscript has not received any form of funding. Author Contribution Anmol Rai: Draft writing, review & validation. Saurabh Kumar: Resources, formal analysis, data curation, software and validation. Shivam Kumar Sharma: Conceptualisation, resources, formal analysis and validation. Ayush Vardhan: Formal analysis, draft writing, review & editing, and validation. Nikhil Kumar: Data visualization and validation. Sumant Swain: Conceptualisation, draft writing, review, editing, and validation. References World Health Organization. Low birth weight. Geneva: World Health Organization; 2025. Blencowe H, Krasevec J, de Onis M, Black RE, An X, Stevens GA, et al. National, regional, and worldwide estimates of low birthweight in 2015, with trends from 2000. Lancet Glob Health. 2019;7(7):e849–60. Duran R, Ozbek UV, Ciftdemir NA, Acunaş B, Süt N. The relationship between leukemoid reaction and perinatal morbidity in low birth weight infants. Int J Infect Dis. 2010;14(11):e998–1001. United Nations Children’s Fund (UNICEF). World Health Organization (WHO). Low birthweight estimates: levels and trends 2015–2020. New York: UNICEF; 2023. Ahankari AS, Myles PR, Dixit JV, Tata LJ, Fogarty AW. Risk factors for maternal anaemia and low birth weight in rural India. Public Health. 2017;151:63–73. India State-Level Disease Burden Initiative Malnutrition Collaborators. The burden of child and maternal malnutrition in India. Lancet Child Adolesc Health. 2019;3(12):855–70. Bianchi ME, Restrepo JM. Low birth weight as a risk factor for non-communicable diseases in adults. Front Med (Lausanne). 2021;8:793990. Kramer MS. Determinants of low birth weight: methodological assessment and meta-analysis. Bull World Health Organ. 1987;65(5):663–737. Balarajan Y, Selvaraj S, Subramanian SV. Health care and equity in India. Lancet. 2011;377(9764):505–15. International Institute for Population Sciences (IIPS), ICF. National Family Health Survey (NFHS-5), 2019–21: India. Mumbai: IIPS; 2021. Singh D, Manna S, Barik M, Rehman T, Kanungo S, Pati S. Prevalence and correlates of low birth weight in India: findings from NFHS-5. BMC Pregnancy Childbirth. 2023;23:456. Zaveri A, Paul P, Saha J, Barman B, Chouhan P. Maternal determinants of low birth weight among Indian children: evidence from NFHS-4. PLoS ONE. 2020;15(12):e0244562. Tessema ZT, Tamirat KS, Teshale AB, Tesema GA. Prevalence of low birth weight in Sub-Saharan Africa: a generalized linear mixed model. PLoS ONE. 2021;16(3):e0248417. Godah MW, Beydoun Z, Abdul-Khalek RA, Safieddine B, Khamis AM, Abdulrahim S. Maternal education and low birth weight in low- and middle-income countries: a systematic review and meta-analysis. Matern Child Health J. 2021;25(8):1305–15. Silvestrin S, Silva CH, Hirakata VN, Goldani AA, Silveira PP, Goldani MZ. Maternal education level and low birth weight: a meta-analysis. J Pediatr (Rio J). 2013;89(4):339–45. Bhutta ZA, Das JK, Rizvi A, Gaffey MF, Walker N, Horton S, et al. Evidence-based interventions for improvement of maternal and child nutrition. Lancet. 2013;382(9890):452–77. Woldeamanuel GG, Geta TG, Mohammed TP, Shuba MB, Bafa TA. Effect of nutritional status of pregnant women on birth weight. SAGE Open Med. 2019;7:2050312119827096. Christian P, Khatry SK, Katz J, Pradhan EK, LeClerq SC, Shrestha SR, et al. Effects of maternal micronutrient supplementation on birth weight. BMJ. 2003;326(7389):571. Jana A, Saha UR, Reshmi RS, Muhammad T. Relationship between low birth weight and infant mortality: evidence from NFHS-5. Arch Public Health. 2023;81:28. Rana MJ, Kim R, Ko S, Dwivedi LK, James KS, Sarwal R. Small area variations in low birth weight in India. Matern Child Nutr. 2022;18(3):e13369. Kundu RN, Ghosh A, Chhetri B, Saha I, Hossain MG, Bharati P. Urban–rural variation in low birth weight in India. BMC Pregnancy Childbirth. 2023;23:616. Additional Declarations No competing interests reported. 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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-9215325","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":626505086,"identity":"ce95cfe1-880b-4e8e-a1a2-264b0d741aba","order_by":0,"name":"Anmol Rai","email":"","orcid":"","institution":"Asian Development Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Anmol","middleName":"","lastName":"Rai","suffix":""},{"id":626505087,"identity":"d08aaf71-dec5-46af-8ae8-d6a504ef7c6a","order_by":1,"name":"Saurabh Kumar","email":"","orcid":"","institution":"Piramal Swasthya Management and Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Saurabh","middleName":"","lastName":"Kumar","suffix":""},{"id":626505091,"identity":"bc8f6077-c1d5-4ddb-8630-91e86cc0a9f9","order_by":2,"name":"Shivam Kumar Sharma","email":"","orcid":"","institution":"Piramal Swasthya Management and Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Shivam","middleName":"Kumar","lastName":"Sharma","suffix":""},{"id":626505094,"identity":"febf9a32-3fc6-4a21-b909-5f522ac96dce","order_by":3,"name":"Ayush Vardhan","email":"","orcid":"","institution":"International Institute of Health Management Research (IIHMR)","correspondingAuthor":false,"prefix":"","firstName":"Ayush","middleName":"","lastName":"Vardhan","suffix":""},{"id":626505095,"identity":"398f1c4f-e27c-42c0-95bb-539cec2556b1","order_by":4,"name":"Nikhil Kumar","email":"","orcid":"","institution":"Piramal Swasthya Management and Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Nikhil","middleName":"","lastName":"Kumar","suffix":""},{"id":626505097,"identity":"68c913ff-85e2-4fe5-a965-2f21dc1daa90","order_by":5,"name":"Sumant Swain","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYPACGx42CSDFw2CRwAakJcAIL0hIg2mRIFrLYYgakBYGBgLq5WdkJ34u/HFehk+6O+3DmwqJPD4G5oO3gS7Mw6XF4EbuZukZCbd52GTObp4554xEMRsDW7I10LpinFokcjdI84C0SORuZuZtk0hsY+AxkwZqSWzA6bDczb95Es5BtfwDaeH/hlcLw43cbUBbDkC1NIBtYcOrxeDM223WPGnJYC2Mc44BtTCzGVvOMcDjsPbczbd5bOzsQS5keFNjkzi/vfnhjTcVdbgdhgmYwbYTr34UjIJRMApGASYAAL/RSHySvdpKAAAAAElFTkSuQmCC","orcid":"","institution":"International Institute of Health Management Research (IIHMR)","correspondingAuthor":true,"prefix":"","firstName":"Sumant","middleName":"","lastName":"Swain","suffix":""}],"badges":[],"createdAt":"2026-03-24 18:38:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9215325/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9215325/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107393118,"identity":"024d321c-e46e-4614-a1d3-16f3bd8d61fe","added_by":"auto","created_at":"2026-04-21 05:58:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":37310,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eFlow diagram illustrating the selection of the analytic sample\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9215325/v1/bc7f9db2a0b0a2fbd01ac4b2.png"},{"id":107485904,"identity":"dabc3014-3dd6-4369-972c-bdb06a335865","added_by":"auto","created_at":"2026-04-22 02:36:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":312102,"visible":true,"origin":"","legend":"\u003cp\u003eDistrict-Level Prevalence of Low Birth Weight in Aspirational Districts (Descriptive Map)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9215325/v1/2c9e140ba693f6bbc4f797ea.png"},{"id":107487551,"identity":"fab8c7aa-2d57-43b1-9704-14c97dc3bca7","added_by":"auto","created_at":"2026-04-22 02:42:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1071275,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9215325/v1/1dcc47f8-5995-446f-b4fb-8b7de0839846.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Estimation of Prevalence and Factors Associated with Low Birth Weight in Aspirational Districts of India","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLow birth weight (LBW) or birth weight below 2500g regardless of gestational age, is both a sign of maternal and infant health and a significant factor in infant survival (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). It is an indicator of maternal health status, as well as the quality of care in pregnancy, and beyond because it constitutes an important population-level indicator of health system level. LBW is recognized as a significant societal issue in the world, especially in the low- and middle-income countries (LMICs), where maternal and neonatal morbidity and mortality are disproportionately high as a burden (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). It is predicted that about 20\u0026nbsp;million LBW infants are being born annually showing almost 15\u0026ndash;20 percent of the total live births in the world (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). LBW is among the primary causes of neonatal mortality, and it has been found that LBW is the main cause of all neonatal deaths in the world, which is nearly 60\u0026ndash;80 percent of all infant deaths (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Low-weight babies have greater risks of inducing short-term problems including respiratory distress, hypothermia, infections and feeding problems that increase the risks of the early death considerably (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In addition to neonatal effects, the LBW has been reported to have long-term negative outcomes, such as cognitive development, lower educational attainment, and higher risk of developing non-communicable diseases (type 2 diabetes, hypertension, and cardiovascular diseases) in adulthood (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Such long-term outcomes demonstrate why LBW must be addressed as a clinical issue as well as a larger public health concern. This distribution of LBW burden is uneven as most of the cases are in LMICs. In South Asia, and especially in the region, the level of prevalence is among the highest in the world due to the combination of maternal malnourishment, socioeconomic inequality, and insufficient access to the quality medical services (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). However, India alone carries a large burden of the burden globally, with national estimates stating that there is a steadfast percentage of 18.2 indicating that the number of births is below the standard of low birth weight, according to the National Family Health Survey-5 (NFHS-5, 2019-21) (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Despite improvement this has occurred, though at a slow pace, unevenly, as structural inequalities in health and nutrition have remained in place.\u003c/p\u003e \u003cp\u003eLBW has been demonstrated to be aided by a substantial number of determinants, which include maternal, socioeconomic, and healthcare related factors. Age of the mother is also a significant biological factor with younger mothers especially adolescents who are at a greater risk because their bodies are still developing and their nutritional competition between the mother and foetus is greater (\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The socioeconomic status is usually assessed by measuring wealth index or income, and it is determined to have numerous avenues to LBW, such as the access to proper nutrition, healthcare facilities, and conditions to live (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The poorer women have a higher likelihood of food insecurity, low dietary nutritional intake, and access to low-standard maternal health, which result in poor birth outcomes. Mother education has also been a constant relationship with LBW. Women who have gone through education will embrace proper health practices, use antenatal care services, and better nutrition practices throughout pregnancy(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Education is also a contributor to health literacy so that women would know the complications during pregnancy and be able to get immediate medical attention. Nonetheless, education tends to have an interplay with other more socioeconomic influences and therefore education is not only its own determinant but also a proxy of social advantage. The most significant birth weight determinants include maternal nutritional status. Underweight women experiencing low body mass index (BMI) during or prior to pregnancy are in risk of giving birth to low weight babies because of poor nutritional stores and poor placental functioning (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Micronutrient deficiency especially of iron, folate and zinc also causes limited foetal growth. Maternal anaemia which continues to be one of the most prevalent in India has been linked to a decreased oxygen uptake by the unborn baby, as well as high risk of poor birth outcomes (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Although national programs are aimed at the reduction of anaemia, its presence in the country continues to be a major challenge. The use of ANC is significant in enhancing the outcome of the pregnancy through offering a chance to detect and manage pregnancy complications in early stages, nutritional supplements, and health education. It has been demonstrated that poor ANC correlates with risks of developing LBW (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Nevertheless, there is new evidence indicating that quality and content of ANC services can be of importance rather than the number of visits itself. Poor continuity of care, inadequate screening and suboptimal counselling may hinder the effectiveness of ANC to enhance the outcomes of birth. Another factor contributing to LBW is pregnancy complications such as hypertensive disorders, infection and gestational conditions. These may affect the placental functioning and foetal development that results in intrauterine growth retardation and preterm delivery. It is the interplay of maternal health, nutritional status, and the ability to obtain healthcare that exacerbates the mechanisms of LBW; therefore, the struggle of the issue is multifactorial. Other structural and contextual factors including caste, geographic setting and rural urban differences contribute greatly to maternal and child health in India besides individual level determinants. The disadvantages that many women in marginalized social groups must bear are usually compounded, such as low access to healthcare services, discrimination, and substandard living conditions (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In rural communities specifically, poor health care facilities, lack of trained health care providers, and impediments to availing maternal healthcare services characterize the areas. Appreciating these differences, the Government of India initiated in 2018 the Aspirational Districts Programme (ADP), which aims to rapidly boost development in 112 poorly performing districts on the level of the most important areas, such as health and nutrition (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). These areas are set according to their poor score of development indicators and are put on the list of places of special interventions and observations. The main goal of the programme is to improve the outcomes of the work of the health care systems, both maternal and child health. Nonetheless, such districts still must struggle with the issues of high poverty rates, under-feeding and poor access to healthcare. Although the ADP is a policy important policy, the burden and determinants of the LBW in Aspirational Districts are poorly surveyed. Majority of the current literature has concentrated on national or state-level estimates and this could obliterate the trends of significant subregion differences and determinants in specific contexts. Knowledge about the patterns and correlates of LBW in Aspirational Districts is crucial to the proposed solution of creating interventions aimed at LBW and informing policymaking. Besides, although big data surveys like NFHS-5 are valuable to study the trends at a population-level, the cross-sectional characteristic of big data restricts the opportunity to determine causal associations. However, this kind of data is essential to determine high-risk groups and inform the evidence-based approaches to the community. Hence, the objective of the study will be to determine the prevalence of low birth weight and investigate its relationship with socioeconomic and health-related factors of the mother in Aspirational Districts of India based on nationally representative NFHS-5. This study aims to offer context-specific evidence to affirm current activities of enhancement of maternal and child health outcomes in India by addressing development-priority districts.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and data source\u003c/h2\u003e \u003cp\u003eThe given research is founded on the secondary analysis of data available in the National Family Health Survey-5 (NFHS-5), which took place in 2019\u0026ndash;2021 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The NFHS-5 is a household survey that is conducted nationally on behalf of the Ministry of Health and Family Welfare, government of India and coordinated by the International Institute for Population Sciences (IIPS), Mumbai. The survey was done on a multistage stratified cluster sampling design to promote representativeness in national, state, and district spheres. As primary sampling units (PSUs) in rural and census enumeration block (CEBs) in urban areas, villages and blocks were used, respectively. The households were randomly selected using a systematic sampling technique within a PSU. NFHS-5 surveyed 724,115 women (age 15\u0026ndash;49 years old) on their demographic features, socioeconomic status, maternal health, and child health and nutritional status by greater than 95 percent response rate.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy population and sample selection\u003c/h3\u003e\n\u003cp\u003eThe current study targeted women living in districts that were affected under the Aspirational Districts Programme (ADP). Respondents in those districts were identified through a set of district identifiers that were found in the NFHS-5 dataset. The analytic sample was limited among the women between the ages 15\u0026ndash;49 years with the most recent live birth of less than five years before the survey. The analysis was further uniquely constrained by births and institutional deliveries where the birth weight was recorded by use of written health cards or maternal recall to enhance the measurement accuracy and minimize the level of bias.\u003c/p\u003e \u003cp\u003eThey included 26,042 women in the final analysis sample after the use of these inclusion criteria.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eOutcome variable\u003c/h3\u003e\n\u003cp\u003eLow birth weight (LBW) was the main outcome measure, which was defined as a birth weight below 2,500 grams of weight in line with the World Health Organization criteria (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Information on birth weight in NFHS-5 was reported on written health records where they were available or maternal recollection where available.\u003c/p\u003e \u003cp\u003eThe outcome variable was coded in terms of binary indicator:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Low birth weight (\u0026lt;\u0026thinsp;2500 g)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e0\u0026thinsp;=\u0026thinsp;Normal birth weight (\u0026ge;\u0026thinsp;2500 g)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003eIndependent variables\u003c/h3\u003e\n\u003cp\u003eThe independent variables were assigned on the basis of the previous testing outcomes on the factors which determine the low birth weight in India and in other low- and middle-income countries (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The following variables were divided into three domains:\u003c/p\u003e \u003cp\u003e \u003cb\u003eSocioeconomic and demographic characteristics\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe age of the mothers was divided into 15\u0026ndash;24 years, 25\u0026ndash;34 years, and =\u0026thinsp;\u0026gt;\u0026thinsp;35 years of age. There were the marginalized (Scheduled Castes/Scheduled Tribes) and non-marginalized (Other Backward Classes/General) types of caste. Religion was categorized as Hindu, Muslim, Christian and others. The household wealthy status was identified by using NFHS wealth index, which was grouped into low, middle, and high. Other variables were place of residence (rural/urban), employment (employed/not employed), marital status and maternal education (illiterate/literate).\u003c/p\u003e\n\u003ch3\u003eService utilization variable\u003c/h3\u003e\n\u003cp\u003eThe use of antenatal care (ANC) was sorted according to the number of visits, less than four visits and four or more visits in accordance with the WHO guidelines.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMaternal health-related characteristics\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBody mass index (BMI) was used to determine the maternal nutritional status as; underweight (\u0026lt;\u0026thinsp;18.5 kg/m2), normal (18.5\u0026ndash;24.9 kg/m2), overweight (25-29.9 kg/m2), and obese (\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;30 kg/m2). Haemoglobin was used to define anaemia status as anaemic or non-anaemic. The interviewee's birth interval was divided into less than 24 months and 24 months and above. One binary variable (yes/no) was pregnancy-related complications. The recoding was done on all variables using NFHS standard coding scheme.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eSAS version 9.4 was used to do all statistical analyses. The distribution of the maternal characteristics was summarized with the help of descriptive statistics that allowed estimating the prevalence of low birth weight. The results are given in percentage based on the survey and with a 95% confidence interval. The survey-adjusted chi-square tests were employed to determine the bivariate relationships between low weight and bivariate explanatory variables as this was a complex sampling study. The analysis took place on multivariable binary logistic regression with the help of PROC SURVEYLOGISTIC to estimate adjusted odds ratios (AORs) and 95% confidence intervals. The regression model had all the relevant socioeconomic, demographic, and maternal health variables on theoretical and empirical grounds.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eKey methodological specification:\u003c/h3\u003e\n\u003cp\u003eThe analyses took into consideration all the complex survey design, sampling weights, clustering and stratification. The association estimates that were made with low birth weight were all based on models that were survey-weighted based on clustering and stratification to make the correct one at population level. The p-value of statistical significance was set at less than 0.05.\u003c/p\u003e\n\u003ch3\u003eInterpretation of findings\u003c/h3\u003e\n\u003cp\u003eSince the NFHS-5 data was cross-sectional, the findings obtained in the present study cannot be taken as causal relationships but as correlational. The analysis has found that there is a significant relationship between maternal characteristics and low birth weight; however, it does not discover the pathways of time and causation.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEthical considerations\u003c/h2\u003e \u003cp\u003eNFHS-5 data is transparent and anonymized (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe number of women involved was 26,042. Most of them (54.1) were aged 25\u0026ndash;34 years and 83.1 were in the rural areas. A good percentage of them fell into lower wealth category (61.3%). Undernutrition among the maternal population was rampant (25% underweight), and 64.7% of the female population had anaemia. About 51.64% of them had four or above antenatal care visits. The prevalence of LBW was 17.3% (95% CI: 16.8\u0026ndash;17.8).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-demographic and Maternal Health Characteristics of Respondents (Survey-Weighted), NFHS-5, 2019\u0026ndash;21\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eMaternal age (years) (N\u0026thinsp;=\u0026thinsp;26042)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.88\u0026ndash;38.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53.36\u0026ndash;54.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.83\u0026ndash;8.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eCaste (N\u0026thinsp;=\u0026thinsp;24519)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-marginalised\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60.48\u0026ndash;62.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarginalised\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37.28\u0026ndash;39.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eReligion (N\u0026thinsp;=\u0026thinsp;26042)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e79.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e78.58\u0026ndash;80.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.05\u0026ndash;17.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChristian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.63\u0026ndash;2.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.13\u0026ndash;2.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eWealth index (N\u0026thinsp;=\u0026thinsp;26042)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60.30\u0026ndash;62.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.43\u0026ndash;17.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.88\u0026ndash;22.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eResidence (N\u0026thinsp;=\u0026thinsp;26042)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82.27\u0026ndash;83.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.08\u0026ndash;17.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eEducation (N\u0026thinsp;=\u0026thinsp;26042)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34.37\u0026ndash;36.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63.87\u0026ndash;65.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eEmployment status (N\u0026thinsp;=\u0026thinsp;3968)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enot employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76.07-79.5414\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.45\u0026ndash;23.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eMarital status (N\u0026thinsp;=\u0026thinsp;26042)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever in union\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.08\u0026ndash;0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e98.62\u0026ndash;98.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.52\u0026ndash;0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03\u0026ndash;0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eseparated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.27\u0026ndash;0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eBMI (N\u0026thinsp;=\u0026thinsp;26042)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.29\u0026ndash;25.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60.96\u0026ndash;62.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.97\u0026ndash;10.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.54\u0026ndash;3.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eANC visits (N\u0026thinsp;=\u0026thinsp;25836)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;4 visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.42\u0026ndash;49.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4 visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50.51\u0026ndash;52.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eAnaemia (N\u0026thinsp;=\u0026thinsp;25251)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot anaemic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34.49\u0026ndash;36.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnaemic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63.88\u0026ndash;65.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eBirth interval (N\u0026thinsp;=\u0026thinsp;17223)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;24 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.23\u0026ndash;27.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;24 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e72.97\u0026ndash;74.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ePregnancy complications (N\u0026thinsp;=\u0026thinsp;26042)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.36\u0026ndash;34.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65.83\u0026ndash;67.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eLow birth weight (N\u0026thinsp;=\u0026thinsp;26042)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82.02\u0026ndash;83.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.76\u0026ndash;17.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStratified Result Table Between Maternal Characteristics and Low Birth Weight\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLBW (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e% (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2; value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.26 (18.26\u0026ndash;20.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.25 (15.44\u0026ndash;17.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.07 (14.26\u0026ndash;17.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCaste\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-marginalised\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.54 (15.71\u0026ndash;17.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarginalised\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.74 (17.76\u0026ndash;19.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eReligion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.54 (16.85\u0026ndash;18.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1578\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.46 (14.93\u0026ndash;17.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChristian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.15 (11.95\u0026ndash;18.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.62 (16.49\u0026ndash;22.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eWealth-index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.75 (17.98\u0026ndash;19.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.61 (14.27\u0026ndash;16.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.85 (13.59\u0026ndash;16.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.50 (16.85\u0026ndash;18.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.74 (15.04\u0026ndash;18.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEmployment status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.45 (14.80\u0026ndash;18.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0433\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.03 (16.84\u0026ndash;23.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever in union\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.11 (5.62\u0026ndash;30.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2762\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.41 (16.80\u0026ndash;18.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.52 (9.74\u0026ndash;21.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.00 (0.00\u0026ndash;28.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeparated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.19 (3.50\u0026ndash;14.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.01 (18.96\u0026ndash;21.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.93 (15.21\u0026ndash;16.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.62 (19.33\u0026ndash;21.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.93 (16.18\u0026ndash;17.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.90 (11.32\u0026ndash;14.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.89 (11.40\u0026ndash;18.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eANC visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;4 visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.86 (16.97\u0026ndash;18.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0812\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4 visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.81 (16.00\u0026ndash;17.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAnaemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot anaemic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.63 (15.65\u0026ndash;17.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0984\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnaemic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.64 (16.88\u0026ndash;18.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBirth interval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;24 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.09 (15.71\u0026ndash;18.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;24 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.86 (15.05\u0026ndash;16.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePregnancy complications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.59 (14.64\u0026ndash;16.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.26 (17.49\u0026ndash;19.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe study looked at how different factors related to mothers were associated with low birth weight (LBW) in infants. It was found that the mother's age, wealth quintile, education, residence type, BMI, and pregnancy complications were significantly linked to LBW (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, factors like caste, religion, employment status, marital status, ANC visits, anaemia, and birth interval did not show a significant association with LBW.\u003c/p\u003e \u003cp\u003eA higher prevalence was observed among younger mothers aged 15\u0026ndash;24 years, those belonging to poorer households, and illiterate women. Additionally, the prevalence was greater among women who had fewer than four antenatal care (ANC) visits. It was also found to be higher among anaemic and underweight women, as well as among those who experienced pregnancy-related complications. Furthermore, a higher prevalence was noted among women with a birth interval of less than 24 months.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable Binary Logistic Regression of Factors Associated with Low Birth Weight\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (ref: 15\u0026ndash;24)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.75\u0026ndash;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.66\u0026ndash;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ecaste (ref: non-marginalised)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emarginalised\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.97\u0026ndash;1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1583\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eReligion (ref: Hindu)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.86\u0026ndash;1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChristian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.64\u0026ndash;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1916\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eothers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.96\u0026ndash;1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eWealth (ref: Low)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.78\u0026ndash;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.76\u0026ndash;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation (ref: Illiterate)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.71\u0026ndash;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence (ref: Rural)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u0026ndash;1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eemployment status\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(ref: non-employed)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.91\u0026ndash;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003emarital status\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(ref: never in union)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.44\u0026ndash;2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u0026ndash;2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u0026ndash;2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eseparated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16\u0026ndash;1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eBMI (ref: Underweight)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.74\u0026ndash;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.56\u0026ndash;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.56\u0026ndash;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eANC (ref: \u0026lt;4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4 visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.91\u0026ndash;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnaemia (ref: No)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94\u0026ndash;1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBirth interval (ref: \u0026lt;24)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;24 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84\u0026ndash;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePregnancy complications (ref: No)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.12\u0026ndash;1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the adjusted odds ratios (AORs) of factors related with low birth weight (LBW) estimated by a survey-weighted multivariable logistic regression model that considered the possible confounding effect of sociodemographic and maternal health-related variables. The estimates have been generated by adjusting age of mother, caste, religion, wealth index, place of residence, education and body mass index as a result of previous literature review. LBW was significantly associated with the maternal age, wealth index, education, residence place, body mass index and pregnancy complication at P-value less than 0.05.\u003c/p\u003e \u003cp\u003eWomen aged 25\u0026ndash;34 years (AOR: 0.82, 95% CI: 0.75\u0026ndash;0.90) and those aged\u0026thinsp;\u0026gt;\u0026thinsp;35 years (AOR: 0.77, 95% CI: 0.66\u0026ndash;0.90) had significantly lower odds of delivering LBW infants compared to women aged 15\u0026ndash;24 years. Women who were literate (AOR: 0.77, 95% CI: 0.71\u0026ndash;0.85) and belong to middle (AOR: 0.88; 95% CI: 0.78\u0026ndash;0.99) and higher wealth status (AOR: 0.86; 95% CI: 0.760.98) group had significantly lower odds of having LBW infants as compared those women who are illiterate and belong to lower wealth status.\u003c/p\u003e \u003cp\u003eAnother significant association was seen with place of residence as those women who belongs to urban area (AOR: 1.15, 95% CI: 1.00-1.32) had higher odds of having LBW infants as compared to those women who resides in rural area.\u003c/p\u003e \u003cp\u003eMaternal nutritional status also proved as an independent determinant of LBW. Women with normal BMI (AOR: 0.81; 95% CI: 0.74\u0026ndash;0.89) and overweight BMI (AOR: 0.66; 95% CI: 0.56\u0026ndash;0.78) were found to have significantly lower chances of giving birth to LBW babies compared to underweight women. The association between the obese women and LBW was not significant (AOR: 0.76; 95% CI: 0.56\u0026ndash;1.03), Pregnancy-related complications were considerably correlated with high odds of LBW (AOR: 1.22; 95% CI: 1.12\u0026ndash;1.34), which implied that the risk was higher in the presence of pregnancy-related complications among women. Whereas some factors like caste, religion, employment status, 4 or more Anc visits, anaemia and birth interval were not significantly associated with LBW.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGeographic variation\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMap Interpretation:\u003c/h2\u003e \u003cp\u003eThe map visually represents the prevalence of low birth weight (LBW) children across India\u0026rsquo;s Aspirational Districts, using a color-coded scheme to denote varying levels of prevalence. Districts are categorized into five groups based on the percentage of LBW children:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e\u0026le;\u0026thinsp;5% (dark green)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e6\u0026ndash;10% (light green)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e11\u0026ndash;15% (yellow)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e16\u0026ndash;20% (orange)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e\u0026ge;\u0026thinsp;21% (red)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eA noticeable spatial disparity in LBW prevalence is evident across the country. High-burden districts (colored in red, indicating\u0026thinsp;\u0026ge;\u0026thinsp;21% LBW prevalence) are prominently concentrated in central, eastern, and parts of southern India, particularly in states such as Bihar, Madhya Pradesh, Chhattisgarh, Odisha, and Maharashtra. These regions also coincide with areas characterized by socio-economic deprivation, limited access to quality healthcare, and poor maternal nutrition.\u003c/p\u003e \u003cp\u003eIn contrast, several districts in northeastern India and some parts of northern India (notably in Jammu \u0026amp; Kashmir and Himachal Pradesh) show relatively low prevalence (\u0026le;\u0026thinsp;10%), suggesting regional variation in healthcare access, nutritional practices, and maternal health services. The clustering of high-prevalence districts in specific zones indicates a potential need for region-specific interventions. These may include strengthening maternal health services, improving antenatal care coverage, addressing food insecurity, and community-based awareness programs.\u003c/p\u003e \u003cp\u003eThis map underscores the critical need for targeted policy attention and resource allocation in the most affected districts. It also serves as a valuable tool for identifying priority areas where maternal and child health programs must be intensified to reduce the burden of low birth weight and associated health risks.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis paper presents detailed evidence on the prevalence and determinants of low birth weight in Aspirational Districts of India on nationally representative data. The LBW (17.3) prevalence in this study is like the national estimates in NFHS-5 which points out to the fact that despite the directed policy efforts, LBW has continued to be a public health issue in the development-prioritizing districts (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSocioeconomic inequalities\u003c/h2\u003e \u003cp\u003eThe results indicate that there is a vivid socioeconomic gradient of LBW with more prevalence in poor women and less educated women. The findings are in line with other researches carried out in India and other developing nations (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The socioeconomic disadvantage has a variety of mechanisms on the birth outcomes, among them being poor maternal nutrition, poor living conditions, and insufficient access to quality healthcare services (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Education of the mothers, especially, turned out to be a potent preventive measure. The educated women are more likely to use the amenities of the antenatal care, make the right food choices and healthcare services, which are all leading to better birth statistics. Nonetheless, education can also serve as a surrogate of greater socioeconomic benefits, and the independent impact should be viewed with discretion of mothers and their nutritional status. One of the most influential predictive factors of LBW in this study was the maternal nutritional status. The odds of childbearing underweight women producing LBW babies were much greater, which is in line with the current research that maternal undernutrition is a risk factor of intrauterine growth restriction (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Poor maternal nutritional reserves may impair placental function, leading to inadequate fetal development and resulting in adverse birth outcomes. Surprisingly, the common women with normal and higher BMI had reduced odds of being underweight as opposed to women who were of LBW. Although this result indicates that adequate nutritional status protects, it cannot be construed that overweight or obesity is good because there are other negative pregnancy outcomes which have been linked to higher BMI, which include gestational diabetes and hypertensive disorders.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePregnancy complications\u003c/h2\u003e \u003cp\u003eProblems with pregnancy were also highly correlated with higher odds of LBW. This observation is in line with clinical facts, with which complications like hypertension, infections, and other gestational conditions may negatively impact foetal development and expose the foetus to the risk of being born with low birth weight. These findings show the significance of antenatal care services in identifying and managing pregnancy complications in the initial stages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eAntenatal care and anaemia\u003c/h2\u003e \u003cp\u003eUnlike in other studies which have been done in the past, there was no significant relationship between LBW and the use of antenatal care as well as anaemia adjusted. This finding suggests that the quality, timing, and content of antenatal care may be more critical than the number of visits alone, particularly in resource-constrained settings. Possible explanation of lack of association between anaemia and LBW is the limit of measurement, residual confounding or complex interaction between various factors that have an effect on determining the birth weight. As the level of anaemia among women in India is very high, it is a significant public health concern that should be given more attention.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eUrban\u0026ndash;rural and geographic variation\u003c/h2\u003e \u003cp\u003eThe randomness of the tendency of LBW to be a little higher in the urban population can be attributed to the increased weight of urban poverty, overcrowding and other environmental pressures in urbanizing central cities. This observation creates the necessity of handling intra-urban disparities in the health of the mother. The visual geographic difference in the prevalence of LBW within Aspirational Districts clearly shows the significance of interventions in particular contexts. These disparities are probably driven by differences in socioeconomic development, infrastructure of healthcare and program implementation (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003ePolicy implications\u003c/h2\u003e \u003cp\u003eThe policy and program design implications of the findings of this study would be significant in Aspirational Districts. The intervention types should focus on the enhancement of maternal nutrition and the quality of antenatal care delivery, and the socioeconomic disparities. Specific measures aimed at high-burden districts could also be quite effective in limiting the number of cases with LBW.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eInterpretation\u003c/h2\u003e \u003cp\u003eResults are to be viewed as an association but not a cause because the data used was cross-sectional. The observed associations can also be caused by residual confounding and measurement limitations.\u003c/p\u003e \u003c/div\u003e \u003cp\u003e\u003cstrong\u003eStrengths\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research has several strengths:\u003c/p\u003e\n\u003cp\u003e1. It uses a substantial, nationwide representative data (NFHS-5) that makes it robust in estimating the population level.\u003c/p\u003e\n\u003cp\u003e2. The relevance of findings is improved by Focus on Aspirational Districts, which is a subpopulation of interest in terms of policy implications.\u003c/p\u003e\n\u003cp\u003e3. Complex Survey design features, such as weights, clusters, or strata used in the incorporation, give rigor to methods.\u003c/p\u003e\n\u003cp\u003e4. The study narrows on those districts under the Aspirational Districts Programme, which offer policy-relevant insights on regions of development priorities, which are not usually adequately reflected in national studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitation of the study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are significant limitations of the study are:\u003c/p\u003e\n\u003cp\u003e1. The cross-sectional study type does not allow making a causal conclusion; no observed relationships can draw a sense of time or a sense of direction.\u003c/p\u003e\n\u003cp\u003e2. The Recall bias caused by maternal recall data of birth weight could also be a problem with LBW prevalence estimates.\u003c/p\u003e\n\u003cp\u003e3. The exclusion of women with home deliveries and infants with unmeasured or unknown birth weight may introduce selection bias due to measurement error in home-recorded birth weight, as these groups often belong to socioeconomically disadvantaged populations at higher risk, potentially leading to an underestimation of the true burden.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eLow birth weight is a long-standing social health issue in the Aspirational Districts of India that has prevalence rates equivalent to the national estimates but indicative of underlying socioeconomic and health system inequalities. As noted in the current study, maternal under-nutrition, lower socio-economic status, low levels of educational attainment and pregnancy related complications are the important factors that are linked with low birth weight in these development-priority districts. These findings highlight the need to intensify maternal nutrition interventions especially among the undernourished mothers and to treat more social causes of poor health including poverty and education. It is important to enhance the quality and efficacy of antenatal services such as early identification and treatment of pregnancy complications to increase the outcomes of births. Since prevalence of low-birthweight was observed to be geographically different, it is necessary to have specific and even localized interventions in high-burden districts. The decision to strengthen health systems, improve service delivery, and equitable access to maternal healthcare services should be the main focus in the Aspirational Districts Programme policy. Since the analysis is carried out using the cross-sectional data it should be assumed that the results should not be treated as the cause-effect relationships. However, the research offers valuable information to guide the way people should implement health strategies to reduce the low birth weight and enhance maternal and child health outcomes in India. Nationally representative data and even more longitudinal studies should be conducted to see the causal pathways better and assess the efficiency of interventions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is a secondary data analysis and does not require ethical approval for this paper. In accordance with the Declaration of Helsinki, ethics approval was waived by the Institutional Ethics Committee of the International Institute of Health Management Research, New Delhi, India. Informed consent was not obtained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current study used secondary data; therefore, informed consent of parents to participate was not required.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDual-publication:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article has been submitted to this journal only and has not been submitted to other journals for publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship: \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnmol Rai: Draft writing, review \u0026amp; validation. Saurabh Kumar: Resources, formal analysis, data curation, software and validation. Shivam Kumar Sharma: Conceptualisation, resources, formal analysis and validation. Ayush Vardhan: Formal analysis, draft writing, review \u0026amp; editing, and validation. Nikhil Kumar: Data visualization and validation. Sumant Swain: Conceptualisation, draft writing, review, editing, and validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOpen-access:\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors have given their consent to publish this article online, offline, or in a hybrid mode.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThird-party-material:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003enot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research study reported in this manuscript has not received any form of funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnmol Rai: Draft writing, review \u0026amp; validation. Saurabh Kumar: Resources, formal analysis, data curation, software and validation. Shivam Kumar Sharma: Conceptualisation, resources, formal analysis and validation. Ayush Vardhan: Formal analysis, draft writing, review \u0026amp; editing, and validation. Nikhil Kumar: Data visualization and validation. Sumant Swain: Conceptualisation, draft writing, review, editing, and validation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Low birth weight. Geneva: World Health Organization; 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlencowe H, Krasevec J, de Onis M, Black RE, An X, Stevens GA, et al. National, regional, and worldwide estimates of low birthweight in 2015, with trends from 2000. Lancet Glob Health. 2019;7(7):e849\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuran R, Ozbek UV, Ciftdemir NA, Acunaş B, S\u0026uuml;t N. The relationship between leukemoid reaction and perinatal morbidity in low birth weight infants. Int J Infect Dis. 2010;14(11):e998\u0026ndash;1001.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnited Nations Children\u0026rsquo;s Fund (UNICEF). World Health Organization (WHO). Low birthweight estimates: levels and trends 2015\u0026ndash;2020. New York: UNICEF; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhankari AS, Myles PR, Dixit JV, Tata LJ, Fogarty AW. Risk factors for maternal anaemia and low birth weight in rural India. Public Health. 2017;151:63\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIndia State-Level Disease Burden Initiative Malnutrition Collaborators. The burden of child and maternal malnutrition in India. Lancet Child Adolesc Health. 2019;3(12):855\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBianchi ME, Restrepo JM. Low birth weight as a risk factor for non-communicable diseases in adults. Front Med (Lausanne). 2021;8:793990.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKramer MS. Determinants of low birth weight: methodological assessment and meta-analysis. Bull World Health Organ. 1987;65(5):663\u0026ndash;737.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalarajan Y, Selvaraj S, Subramanian SV. Health care and equity in India. Lancet. 2011;377(9764):505\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInternational Institute for Population Sciences (IIPS), ICF. National Family Health Survey (NFHS-5), 2019\u0026ndash;21: India. Mumbai: IIPS; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh D, Manna S, Barik M, Rehman T, Kanungo S, Pati S. Prevalence and correlates of low birth weight in India: findings from NFHS-5. BMC Pregnancy Childbirth. 2023;23:456.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZaveri A, Paul P, Saha J, Barman B, Chouhan P. 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Arch Public Health. 2023;81:28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRana MJ, Kim R, Ko S, Dwivedi LK, James KS, Sarwal R. Small area variations in low birth weight in India. Matern Child Nutr. 2022;18(3):e13369.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKundu RN, Ghosh A, Chhetri B, Saha I, Hossain MG, Bharati P. Urban\u0026ndash;rural variation in low birth weight in India. BMC Pregnancy Childbirth. 2023;23:616.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Low birth weight, maternal health, aspirational districts, NFHS-5, antenatal care, maternal nutrition, health disparities, socioeconomic factors and Viksit Bharat","lastPublishedDoi":"10.21203/rs.3.rs-9215325/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9215325/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eLow birth weight (LBW) is a birth weight that is below 2,500 g, which is a serious issue that affects the whole healthcare, and it is one of the top reasons of morbidity and mortality of newborns in the world. India is a major burden on the world with a significant part of it being in socioeconomically poor areas like Aspirational Districts. The objective of this study was to estimate prevalence of LBW and discuss how far it is related to maternal characteristics, in these districts.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eThe present research relies on secondary analysis of the data of the National Family Health Survey-5 (NFHS-5, 2019-21), the cross-sectional survey that covered the whole country and was carried out on the national level. It was analysed on 26,042 women 15 to 49 years of age living in Aspirational Districts with most recent single institution live birth and a registered birth weight. LBW was defined as \u0026lt;\u0026thinsp;2500 g. Just the survey-weighted descriptive statistics, chi-square tests, and multivariate logistic regression analysis were performed using version 9.4 of the SAS taking into consideration clustering, stratification, and sampling weights. Results:\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eThe prevalence of LBW was 17.3%. The odds were found to be higher in younger mothers (1524 years), women in low wealth quintile, less educated women underweight women, and anaemic women and pregnancy complications. Weighted scale: Adequate antenatal care (4 or more visits) and high maternal BMI had low odds. There was geographic difference between districts.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eAspirational Districts still suffer LBW. Results show a correlation with maternal socioeconomic and maternal health factors. Since it was cross-sectional in nature, interpretation of the results is expected to be correlational. Enhancing maternal nutrition and fair access to antenatal care could be used to decrease LBW.\u003c/p\u003e","manuscriptTitle":"Estimation of Prevalence and Factors Associated with Low Birth Weight in Aspirational Districts of India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 05:58:32","doi":"10.21203/rs.3.rs-9215325/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b8cc72fe-9a48-4821-9b65-cc23fde145fe","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T17:09:36+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 05:58:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9215325","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9215325","identity":"rs-9215325","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}